In this research, assume a SuperHyperGraph. Then a “SuperHyperForcing” Z(NSHG) for a neutrosophic SuperHyperGraph NSHG : (V,E) is the minimum cardinality of a SuperHyperSet S of black SuperHyperVertices (whereas SuperHyperVertices in V (G) \ S are colored white) such that V (G) is turned black after finitely many applications of “the color-change rule”: a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white SuperHyperNeighbor of a black SuperHyperVertex; a “neutrosophic SuperHyperForcing” Zn(NSHG) for a neutrosophic SuperHyperGraph NSHG : (V,E) is the minimum neutrosophic cardinality of a SuperHyperSet S of black SuperHyperVertices (whereas SuperHyperVertices in V (G) \ S are colored white) such that V (G) is turned black after finitely many applications of “the color-change rule”: a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white SuperHyperNeighbor of a black SuperHyperVertex. Assume a SuperHyperGraph. Then a “SuperHyperForcing” Z(NSHG) for a neutrosophic SuperHyperGraph NSHG : (V,E) is the minimum cardinality of a SuperHyperSet S of black SuperHyperVertices (whereas SuperHyperVertices in V (G) \ S are colored white) such that V (G) is turned black after finitely many applications of “the color-change rule”: a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white SuperHyperNeighbor of a black SuperHyperVertex; a “neutrosophic SuperHyperForcing” Zn(NSHG) for a neutrosophic SuperHyperGraph NSHG : (V,E) is the minimum neutrosophic cardinality of a SuperHyperSet S of black SuperHyperVertices (whereas SuperHyperVertices in V (G) \ S are colored white) such that V (G) is turned black after finitely many applications of “the color-change rule”: a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white SuperHyperNeighbor of a black SuperHyperVertex. Assume a SuperHyperGraph. Then an “δ−SuperHyperForcing” is a minimal SuperHyperForcing of SuperHyperVertices with minimum cardinality such that either of the following expressions hold for the (neutrosophic) cardinalities of SuperHyperNeighbors of s ∈ S : |S∩N(s)|>|S∩(V \N(s))|+δ, |S∩N(s)|<|S∩(V \N(s))|+δ.Thefirst Expression, holds if S is an “δ−SuperHyperOffensive”. And the second Expression, holds if S is an “δ−SuperHyperDefensive”; a“neutrosophic δ−SuperHyperForcing” is a minimal neutrosophic SuperHyperForcing of SuperHyperVertices with minimum neutrosophic cardinality such that either of the following expressions hold for the neutrosophic cardinalities of SuperHyperNeighbors of s ∈ S : |S ∩ N(s)|neutrosophic > |S ∩ (V \ N (s))|neutrosophic + δ, |S ∩ N (s)|neutrosophic < |S ∩ (V \ N (s))|neutrosophic + δ. The first Expression, holds if S is a “neutrosophic δ−SuperHyperOffensive”. And the second Expression, holds if S is a “neutrosophic δ−SuperHyperDefensive”. It’s useful to define “neutrosophic” version of SuperHyperForcing. Since there’s more ways to get type-results to make SuperHyperForcing more understandable. For the sake of having neutrosophic SuperHyperForcing, there’s a need to “redefine” the notion of “SuperHyperForcing”. The SuperHyperVertices and the SuperHyperEdges are assigned by the labels from the letters of the alphabets. In this procedure, there’s the usage of the position of labels to assign to the values. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

A single water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and suitable in industrial use. Magnetic chitosan polymers offer the advantage of easy separation from the dye solution. In this work, a Chitosan-Fe polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water. The polymer was characterized in terms of surface morphology (SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), X-ray powder diffraction (XRD) and density functional theory (DFT). The reported results hint that 1 g and pH 7.0 were the best conditions to carry out both kinetic and isotherm models. To understand the adsorption behavior of Direct Red 83:1 onto the polymers, adsorption rate and maximum adsorption capacity were calculated using kinetic tests and isotherm curves respectively. The kinetic data and mechanism of the adsorption process were analyzed by three models and the equilibrium data by three adsorption isotherms. The results indicated that the adsorption process follows a pseudo-second-order kinetics (R2= 0.999) and fits the Temkin isotherm (R2= 0.946), indicating that the adsorption occurred on heterogeneous surfaces. The newly synthesized Chitosan-Fe polymer exhibited good adsorption properties and easy separation of cleaned water.

Climate change is unpredictable and can change rapidly or over time; anthropogenic stressors work synergistically to strengthen their impact on vulnerable ecosystems and the fisheries sector. This study documented the past occurrence of extreme climate change impacts, such as supertyphoon Pablo (ST: Bopha), which severely affected Baganga, Davao Oriental, about ten years ago. A mixed-methods data collection was used, combining semi-structured interviews with small-scale fishers (N=120) in four fishing villages and focus group discussions (N=4, participated by about 4 focus groups) on assessing the impacts and their adaptation after climatic events occurred. Our findings indicate that the four selected fishing villages were exposed to common factors of climate change events that destroyed many lives and their livelihood. Including a decrease in catch per trip and the fishing operations disruption (98%). However, due to the impact of the supertyphoon, small-scale fishers in Baganga developed psychological distress (emotional 44% and physical 24%) due to the extreme event or disaster and economic losses (32%). In order to survive, their adaptation strategies relied on government and non-government-provided provisions and projects, e.g., planting trees, constructing a sea wall with wave breakers, cash for work, and tourism as an alternative livelihood. Despite this, our study revealed that the community had limited knowledge on the impacts of climate change, lack clear management goals especially for fishers, and the widespread use of illegal fishing gears. The local government must strongly implement rules, policies, and adaptation measures that will contribute to better and more resilient communities. This can help them to withstand future shocks and provide sustainable fisheries resources that will benefit all the fishers. The study will be useful to policymakers, disaster management group and organization that will help assist affected communities highly affected by climatic events.

Empirical Orthogonal Functions (EOF) analysis between the anomalies of Lake Titicaca water levels (WLs) and the Pacific Interdecadal Oscillation (IPO) reveals a correlation coefficient of 0.71 between the WLs and the principal component (PC1). These findings demonstrate the high accuracy of the IPO in representing the positive and negative phases of the water levels. By utilizing wavelet functions, we identified multi-decadal variability with periods ranging between 20 and 30 years. The statistical techniques employed indicate a relationship between IPO and ENSO; however, they operate on different timescales of climate variability. The IPO exhibits longer-term variability, typically spanning 20-30 years, while ENSO events occur on average every 2-7 years. Nevertheless, the IPO has the ability to modulate the strength and frequency of El Niño and La Niña events, and vice versa. During the positive phase of the IPO, there is a tendency for more frequent and intense El Niño events, accompanied by fewer La Niña events. This can be attributed to warmer sea surface temperatures in the central and eastern Pacific, which create favorable conditions for El Niño development. Conversely, during the negative phase of the IPO, there is a tendency for more frequent and intense La Niña events, with fewer occurrences of El Niño events, due to cooler sea surface temperatures in the central and eastern Pacific.

This research is focused on two key areas. The first is mapping the 2022 flood in the Red River of the North near Grafton, North Dakota, US, and the second is evaluating the scour potential of the Grafton Bridge. Local scour of bridge piers can cause hydraulic structures such as bridge piers and abutments to fail during floods, making it a crucial area of investigation. To collect bathymetry and discharge data during low and high flow conditions, including a flood event with a 16.5-year return period in 2022, an Autonomous Surface Vehicle (ASV) incorporated with LiDAR DEM (Digital Elevation Model) data obtained from the US Geological Survey (USGS) National Map was used. Flood mapping and evaluation of local scour around the bridge pier were conducted using the HEC-RAS 6.0.0 software, which utilizes the Colorado State University method as a default equation. This research demonstrates the potential of ASVs in collecting critical data and LiDAR DEM data is an efficient method for flood mapping and determining scour potential, as it integrates bathymetry, flow velocity, and flood prediction.

We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and temperature extreme (difference between maximum and minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration after the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, temperature extreme was significantly and positively correlated with mortality in the low-and middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed effect in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggest that warm climate may reduce the mortality rate in high-income economies but in low and middle-income countries temperature extreme may increase the mortality risk.

This paper performs non-parametric Mann Kendall (MK) trend analysis of historical hydroclimatic data (1961-2016), an ensemble climate model validation and a computation of 16 Expert Team on Climate Change Detection and Indices (ETCCDI) temperature and rainfall extremes indices. The climate indices are evaluated using MK test and annual trend analysis for two Representative Concentration Pathways (RCP4.5 & RCP8.5) future scenarios from 2020 to 2045 over Mono River Basin (MRB) in Togo. The annual and seasonal trend analyses are assessed on historical potential evapotranspiration, mean temperature, rainfall and discharge data. Results show positive and negative trends of hydroclimatic data over MRB from1961 to 2016. Mean temperatures increase significantly in most of the stations while a negative non-significant trend is noticed for rainfall. Meanwhile, the discharge presents a significant seasonal and annual trend for three gauge stations (Corrokope, Nangbéto and Athiémé). Validation of the ensemble climate models reveals that the model under-estimates observations at Sokode, Atkakpamé and Tabligbo stations, however linear regression and spatial correlation coefficients are higher than 0.6. Moreover, the percentage of bias between climate model and observations are less than 15% at most of the stations. Finally, the computation of extreme climatic indices under RCP4.5 and RCP8.5 scenarios shows a significant annual trend of some extreme climatic indices of rainfall and temperature at selected stations between 2020 and 2045 in the MRB. Therefore, relevant governmental politics are needed to elaborate strategies and measures to cope with projected climate changes impacts in the country.

Soil pH, either low (acidity) or high (alkalinity) is one of the major constraints that affect many biochemical and biological processes within the cell. The present study was carried out to understand the oxidative damage and antioxidant defense in wheat (Triticum aestivum L. cv. BARI Gom-25) grown under different pH regimes. Eight-day-old seedlings were exposed to growing media with different pH levels (4.0, 5.5, 7.0 and 8.5). Seedlings grown in pH 4.0 and in pH 8.5 showed reductions in biomass, water, and chlorophyll contents; whereas plants grown at pH 7.0 (neutral) exhibited better performance. Extremely acidic (pH 4.0) and/or strongly alkaline (pH 8.5)-stress also increased oxidative damages in wheat by excess reactive oxygen species (ROS) generation and methylglyoxal (MG) production, which increased lipid peroxidation and disrupted the redox state. In contrary, the lowest oxidative damage was observed at neutral condition followed by strong acidic condition (pH 5.5), which was attributed mainly due to better performance of the antioxidant defense and glyoxalase systems. Interestingly, seedlings grown at pH 5.5 showed a significant increase in morphophysiological attributes compared with extreme acidic (pH 4.0)- and strong alkaline (pH 8.5)-stress treatments, which indicates the tolerance of wheat to the acidic condition.

In this research, new setting is introduced for new SuperHyperNotions, namely, SuperHyperDefensive SuperHyperAlliances and Neutrosophic SuperHyperDefensive SuperHyperAlliances. Two different types of SuperHyperDefinitions are debut for them but the research goes further and the SuperHyperNotion, SuperHyperUniform, and SuperHyperClass based on that are well-defined and well-reviewed. The literature review is implemented in the whole of this research. For shining the elegancy and the significancy of this research, the comparison between this SuperHyperNotion with other SuperHyperNotions and fundamental SuperHyperNumbers are featured. The definitions are followed by the examples and the instances thus the clarifications are driven with different tools. The applications are figured out to make sense about the theoretical aspect of this ongoing research. The “Cancer’s Recognitions” are the under research to figure out the challenges make sense about ongoing and upcoming research. The special case is up. The cells are viewed in the deemed ways. There are different types of them. Some of them are individuals and some of them are well-modeled by the group of cells. These types are all officially called “SuperHyperVertex” but the relations amid them all officially called “SuperHyperEdge”. The frameworks “SuperHyperGraph” and “neutrosophic SuperHyperGraph” are chosen and elected to research about “Cancer’s Recognitions”. Thus these complex and dense SuperHyperModels open up some avenues to research on theoretical segments and “Cancer’s Recognitions”. Some avenues are posed to pursue this research. It’s also officially collected in the form of some questions and some problems. Assume a SuperHyperGraph. An “SuperHyperAlliance” is a minimal SuperHyperSet of SuperHyperVertices with minimum cardinality such that either of the following expressions hold for the cardinalities of SuperHyperNeighbors of s∈S:,|S∩N(s)|>|S∩(V \N(s))|,and|S∩N(s)|<|S∩(V \N(s))|.Thefirst Expression, holds if S is SuperHyperOffensive. And the second Expression, holds if S is “SuperHyperDefensive”. It’s useful to define “neutrosophic” version of SuperHyperDefensive SuperHyperAlliances. Since there’s more ways to get type-results to make SuperHyperDefensive SuperHyperAlliances more understandable. For the sake of having neutrosophic SuperHyperDefensive SuperHyperAlliances, there’s a need to “redefine” the notion of “SuperHyperDefensive SuperHyperAlliances”. The SuperHyperVertices and the SuperHyperEdges are assigned by the labels from the letters of the alphabets. In this procedure, there’s the usage of the position of labels to assign to the values. Assume a SuperHyperAlliance. It’s redefined neutrosophic SuperHyperAlliance if the mentioned Table holds, concerning, “The Values of Vertices, SuperVertices, Edges, HyperEdges, and SuperHyperEdges Belong to The Neutrosophic SuperHyperGraph” with the key points, “The Values of The Vertices & The Number of Position in Alphabet”, “The Values of The SuperVertices&The Minimum Values of Its Vertices”, “The Values of The Edges&The Minimum Values of Its Vertices”, “The Values of The HyperEdges&The Minimum Values of Its Vertices”, “The Values of The SuperHyperEdges&The Minimum Values of Its Endpoints”. To get structural examples and instances, I’m going to introduce the next SuperHyperClass of SuperHyperGraph based on SuperHyperDefensive SuperHyperAlliances. It’s the main. It’ll be disciplinary to have the foundation of previous definition in the kind of SuperHyperClass. If there’s a need to have all SuperHyperConnectivities until the SuperHyperDefensive SuperHyperAlliances, then it’s officially called “SuperHyperDefensive SuperHyperAlliances” but otherwise, it isn’t SuperHyperDefensive SuperHyperAlliances. There are some instances about the clarifications for the main definition titled “SuperHyperDefensive SuperHyperAlliances”. These two examples get more scrutiny and discernment since there are characterized in the disciplinary ways of the SuperHyperClass based on SuperHyperDefensive SuperHyperAlliances. For the sake of having neutrosophic SuperHyperDefensive SuperHyperAlliances, there’s a need to “redefine” the notion of “neutrosophic SuperHyperDefensive SuperHyperAlliances” and “neutrosophic SuperHyperDefensive SuperHyperAlliances”. The SuperHyperVertices and the SuperHyperEdges are assigned by the labels from the letters of the alphabets. In this procedure, there’s the usage of the position of labels to assign to the values. Assume a neutrosophic SuperHyperGraph. It’s redefined “neutrosophic SuperHyperGraph” if the intended Table holds. And SuperHyperDefensive SuperHyperAlliances are redefined “neutrosophic SuperHyperDefensive SuperHyperAlliances” if the intended Table holds. It’s useful to define “neutrosophic” version of SuperHyperClasses. Since there’s more ways to get neutrosophic type-results to make neutrosophic SuperHyperDefensive SuperHyperAlliances more understandable. Assume a neutrosophic SuperHyperGraph. There are some neutrosophic SuperHyperClasses if the intended Table holds. Thus SuperHyperPath, SuperHyperCycle, SuperHyperStar, SuperHyperBipartite, SuperHyperMultiPartite, and SuperHyperWheel, are “neutrosophic SuperHyperPath”, “neutrosophic SuperHyperCycle”, “neutrosophic SuperHyperStar”, “neutrosophic SuperHyperBipartite”, “neutrosophic SuperHyperMultiPartite”, and “neutrosophic SuperHyperWheel” if the intended Table holds. A SuperHyperGraph has “neutrosophic SuperHyperDefensive SuperHyperAlliances” where it’s the strongest [the maximum neutrosophic value from all SuperHyperDefensive SuperHyperAlliances amid the maximum value amid all SuperHyperVertices from a SuperHyperDefensive SuperHyperAlliances.] SuperHyperDefensive SuperHyperAlliances. A graph is SuperHyperUniform if it’s SuperHyperGraph and the number of elements of SuperHyperEdges are the same. Assume a neutrosophic SuperHyperGraph. There are some SuperHyperClasses as follows. It’s SuperHyperPath if it’s only one SuperVertex as intersection amid two given SuperHyperEdges with two exceptions; it’s SuperHyperCycle if it’s only one SuperVertex as intersection amid two given SuperHyperEdges; it’s SuperHyperStar it’s only one SuperVertex as intersection amid all SuperHyperEdges; it’s SuperHyperBipartite it’s only one SuperVertex as intersection amid two given SuperHyperEdges and these SuperVertices, forming two separate sets, has no SuperHyperEdge in common; it’s SuperHyperMultiPartite it’s only one SuperVertex as intersection amid two given SuperHyperEdges and these SuperVertices, forming multi separate sets, has no SuperHyperEdge in common; it’s SuperHyperWheel if it’s only one SuperVertex as intersection amid two given SuperHyperEdges and one SuperVertex has one SuperHyperEdge with any common SuperVertex. The SuperHyperModel proposes the specific designs and the specific architectures. The SuperHyperModel is officially called “SuperHyperGraph” and “Neutrosophic SuperHyperGraph”. In this SuperHyperModel, The “specific” cells and “specific group” of cells are SuperHyperModeled as “SuperHyperVertices” and the common and intended properties between “specific” cells and “specific group” of cells are SuperHyperModeled as “SuperHyperEdges”. Sometimes, it’s useful to have some degrees of determinacy, indeterminacy, and neutrality to have more precise SuperHyperModel which in this case the SuperHyperModel is called “neutrosophic”. In the future research, the foundation will be based on the “Cancer’s Recognitions” and the results and the definitions will be introduced in redeemed ways. The recognition of the cancer in the long-term function. The specific region has been assigned by the model [it’s called SuperHyperGraph] and the long cycle of the move from the cancer is identified by this research. Sometimes the move of the cancer hasn’t be easily identified since there are some determinacy, indeterminacy and neutrality about the moves and the effects of the cancer on that region; this event leads us to choose another model [it’s said to be neutrosophic SuperHyperGraph] to have convenient perception on what’s happened and what’s done. There are some specific models, which are well-known and they’ve got the names, and some SuperHyperGeneral SuperHyperModels. The moves and the traces of the cancer on the complex tracks and between complicated groups of cells could be fantasized by a neutrosophic SuperHyperPath(-/SuperHyperCycle, SuperHyperStar, SuperHyperBipartite, SuperHyperMultipartite, SuperHyperWheel). The aim is to find either the longest SuperHyperDefensive SuperHyperAlliances or the strongest SuperHyperDefensive SuperHyperAlliances in those neutrosophic SuperHyperModels. For the longest SuperHyperDefensive SuperHyperAlliances, called SuperHyperDefensive SuperHyperAlliances, and the strongest SuperHyperCycle, called neutrosophic SuperHyperDefensive SuperHyperAlliances, some general results are introduced. Beyond that in SuperHyperStar, all possible SuperHyperPaths have only two SuperHyperEdges but it’s not enough since it’s essential to have at least three SuperHyperEdges to form any style of a SuperHyperCycle. There isn’t any formation of any SuperHyperCycle but literarily, it’s the deformation of any SuperHyperCycle. It, literarily, deforms and it doesn’t form. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

The critical hypoxia in COVID-19 patients during this pandemic, has taken away many lives all around the globe. The mechanism has been poorly understood and initially, word got around that it was a SARS (Severe Acute Respiratory Syndrome) pneumonia. The atypical images in lung computerized axial tomography (CAT) scans were alarming. This immediately alerted everyone including poor countries to purchase lacking sophisticated ventilator equipment. However, in some countries, even 88% of the patients on ventilators lost their lives. New observations and pathological findings are gradually clarifying the disease. What seems evident is that it is not only one disease but several, with different responses in different countries and different altitudes. The critical hypoxia and «gasping» present in some patients are not totally understood. It was mentioned that it could be like a High-Altitude Pulmonary Edema (HAPE). Hereby, as high-altitude medicine and hypoxia physiology specialists, we compare the pathophysiology with that of high-altitude exposure in order to understand the mechanisms involved. Some differences in lung radiological images along with transmission and viral attack mechanisms are discussed. The oxygen transport triad used at high-altitude can be applied on this pathology in order to propose even the use of erythropoietin (EPO) early in the treatment. The immune system is the most important long-term survival tool, so we suggest a short-term strategy: the use of special Earth open-circuit astronaut-resembling suits with effective outside air filtering re-breathing mechanisms in order to return to work and daily activities, without contamination risk. Thereby, the curve can be flattened without quarantine and the economy could recover.

Portugal Mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be protected from natural coastal hazard, namely extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard in the Continental Atlantic coast of Portugal Mainland was evaluated for 2025, 2050 and 2100 in the whole coastal extension with different sea level scenarios for different extreme event return periods and due to SLR. A coastal flooding probabilistic map was produced based on the developed methodology using Geographic Information Systems (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on flood probabilistic bases through five probability intervals of 20% of amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).

The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, important explanatory variables have not been found for the GEV shape parameter, despite its critical significance, which stems from the fact that it determines the behaviour of the upper tail of the distribution. Here we examine the nature of the shape parameter by revealing its relationships with basin attributes. We use a dataset that comprises information about daily streamflow and forcing, climatic indices, topographic, land cover, soil and geological characteristics of 591 basins with minimal human influence in the contiguous United States. We propose a framework that uses random forests and linear models to find (a) important predictor variables of the shape parameter and (b) an interpretable model with high predictive performance. The process of study comprises of assessing the predictive performance of the models, selecting a parsimonious predicting model and interpreting the results in an ad-hoc manner. The findings suggest that the shape parameter mostly depends on climatic indices, while the selected prediction model results in more than 20% higher accuracy in terms of RMSE compared to a naïve approach. The implications are important, since incorporating the regression model into regional flood frequency analysis frameworks can considerably reduce the predictive uncertainties.

I introduce now for the first time the neutrosophic modal logic. The Neutrosophic Modal Logic includes the neutrosophic operators that express the modalities. It is an extension of neutrosophic predicate logic, and of neutrosophic propositional logic. In order for the paper to be self-contained, I also recall the etymology and definition of neutrosophy and of neutrosophic logic. Several examples are presented as well.

The Kashmir valley is prone to flooding due to its peculiar geomorphic setup compounded by the rapid anthropogenic land system changes and climate change. The study assesses the impact of land use and land cover (LULC) changes between 1980 and 2020 and extreme rainfall on peak discharge and sediment yield in the Upper Jhelum Basin (UJB), Kashmir Himalaya, India using KINEROS2 model. Analysis of LULC change revealed a notable shift from natural LULC to more intensive human-modified LULC, including a decrease in vegetative cover, deforestation, urbanization, and improper farming practices. The findings revealed a strong influence of the LULC changes on peak discharge, and sediment yield relative to the 2014 timeframe, which coincided with the catastrophic September 2014 flood event. The model predicted a peak discharge of 115101 cubic feet per second (cfs) and a sediment yield of 56.59 tons/ha during the September 2014 flooding, which is very close to the observed peak discharge of 115218 cfs indicating that the model is reliable for discharge prediction. The model predicted a peak discharge of 98965 cfs and a sediment yield of 49.11 tons/ha in 1980, which increased to 118366 cfs and, 58.92 tons/ha respectively in 2020, showing an increase in basin’s flood risk over time. In the future, it is anticipated that the ongoing LULC changes will make flood vulnerability worse, which could lead to another major flooding in the event of an extreme rainfall as predicted under climate change and, in turn compromise achievement of sustainable development goals (SDG). Therefore, regulating LULC in order to modulate various hydrological and land surface processes would ensure stability of runoff and reduction in sediment yield in the UJB, which is critical for achieving many SDGs.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

The target area of the present study is the entrance to the Sulina channel in the Black Sea, the zero-kilometer of the Danube River. This represents the southern gate of the seventh Pan-European transport corridor and is subjected to high navigation traffic. The coastal environment at the Danube’s mouth is very often subjected to strong environmental conditions inducing high risks for navigation hazards. From this perspective, the objective of the present work is to provide a more comprehensive picture concerning the past and future expected dynamics of the environmental matrix in this area, including especially wind and wave conditions. An analysis of some in situ measurements performed at the zero kilometer of the Danube is first carried out for the 15 years 2009-2023. A second analysis is based on data provided by regional climate wind models. Two 30-year periods are considered, the recent past (1976-2005), when also comparisons with ERA5 reanalysis data were performed, and the near future (2041-2070), when two different models and three climate scenarios were considered. Finally, using as a forcing factor each of the wind fields before analyzed, simulations with a spectral wave model were carried out and the nearshore wave conditions were evaluated. The results show that both extreme wind and wave conditions are expected to be slightly enhanced in the future. Strong wind fields are characteristic in this area, with wind gusts exceeding on average with more than 70% the maximum hourly averaged wind speed. As regards the waves, due to the complex nearshore phenomena, considerable enhancements in terms of significant wave heights are induced. Furthermore, there is also a high risk of rogue wave’s occurrence.

This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindness’s meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.

Three significant changes have occurred in the winter climate in Europe recently: increased UK flooding; Iberian drought; and warmer temperatures north of the Alps. The literature links all three to a persistent, significant increase in sea level pressure over Southern Europe, the Mediterranean, Iberia and the Eastern Atlantic (SEMIEA) which changes the atmospheric circulation system: forcing cold fronts to the north away from Iberia; and creating a south westerly flow around the northern perimeter of the high-pressure region bringing warmer, moist air from the subtropical Atlantic to the UK and Europe which increases precipitation in the UK and raises the temperature in Europe. I use the Last Millennium Ensemble, MERRA-2 and Terra-NCEP data to demonstrate that the extreme, anthropogenic, West African aerosol Plume (WAP) which only exists from December to April perturbs the northern, regional Hadley Circulation creating the high pressure in the SEMIEA. I also show that the anthropogenic WAP has only existed in its extreme form in recent decades as the two major sources of the WAP aerosols: biomass burning; and gas flaring have both increased significantly since 1950 due to: a four-fold increase in population; and gas flaring rising from zero to 7.4 billion m3/annum and note that this time span coincides with the changes in the three elements of the winter climate of Europe. I also suggest that it may be possible to eliminate the WAP and return the winter climate of Europe to its natural state after the crucial first step of recognising the cause of the changes is taken.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

This study investigates the importance of convective systems for extreme rainfall along the northern coast of the Gulf of Guinea (GG) and their relationship with atmospheric and oceanic conditions. Convective system data (MCSs), daily precipitation, sea surface temperature (SST) and moisture flux anomalies are used from June to September 2007-2016. The results show that 2/3 of MCSs crossing Abidjan are produced in June, which is the core of the major rainy season. Likewise, 2/3 of MCSs originate from continental areas, while 1/3 comes from the ocean. Oceanic MCSs are mostly initiated close to the coast, which also corresponds to the Marine Heat Waves region. Continental MCSs are mostly initiated inland. The results also highlight the moisture flux contribution of three zones which have an impact on the onset and the sustaining of MCSs: (i) the seasonal migration of ITCZ, (ii) the GG across the northern coastline, (iii) and finally the continent. These contributions of moisture fluxes coincide with oceanic warming off the Northeast Brazil and the northern coast of the GG both two days before and the day of extreme rainfall events. The ocean contributes to moisten the atmosphere, and therefore to supply and sustain the MCSs during their lifecycle.

Keywords: Geobacillus thermocatenolatus; lipases; ethanolysis; ionic liquids; kinetic resolution; mandelic acid.

New setting is introduced to study quasi-degree and quasi-co-degree arising from co-neighborhood. quasi-degree and quasi-co-degree is about a vertex which are applied into the setting of neutrosophic graphs. . The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs and star-neutrosophic graphs, complete-bipartite-neutrosophic graphs and complete-multipartite-neutrosophic graphs are investigated in the terms of a vertex which is called either quasi-degree or quasi-co-degree. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form quasi-degree and quasi-co-degree. Quasi-degree is a value of a vertex which is maximum amid all values of vertices which are neighbors to a fixed vertex. Quasi-co-degree is a value of an edge which is maximum amid all values of edges which are neighbors to a fixed vertex but corresponded vertex is representative for this notion. Using different values which are related to a vertex inspire us to focus on edge and vertices which are corresponded to a fixed vertex. The notion of neighborhood is used to collect either vertices are titled neighbors or edges are incident to fixed vertex. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definitions are provided. Using fixed vertex has key role to have these notions in the form of vertex or edge. The value of an edge has eligibility to call quasi-co-degree but the value of a vertex has eligibility to call quasi-degree. Some results get more frameworks and perspective about these definitions. The way in that, two vertices have connection together, open the way to define neighborhood and co-neighborhood. The maximum values in neighborhood and co-neighborhood introduces quasi-degree and quasi-co-degree, respectively. New name is chosen from degree. Since amid all vertices with different degrees, one vertex is chosen. In other words, one vertex is fixed and its degree turns out quasi-degree where two degrees could be assigned to a vertex. Degree of edges and degree of vertices. The number of edges which are incident to the vertex and the number of vertices which are neighbors to the vertex. Degree and co-degree are the notions which are transformed to use in quasi-style. Two neutrosophic values introduce two neutrosophic vertices separately in each settings. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to purse this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Many various using of this new-born fuzzy model for solving real-world problems and urgent requirements involve introducing new concept for analyzing the situations which leads to solve them by proper, quick and ecient method based on statistical data. This gap between the model and its solution cause that we introduce nikfar domination in neutrosophic graphs as creative and eective tool for studying a few selective vertices of this model instead of all ones by using special edges. Being special selection of these edges aect to achieve quick and proper solution to these problems. Domination hasn't ever been introduced. So we don't have any comparison with another denitions. The most used graphs which have properties of being complete, empty, bipartite, tree and like stu and they also achieve the names for themselves, are studied as fuzzy models for getting nikfar dominating set or at least becoming so close to it. We also get the relations between this special edge which plays main role in doing dominating with other special types of edges of graph like bridges. Finally, the relation between this number with other special numbers and characteristic of graph like order are discussed.

Objectives to assess COVID-19 mortality rates per country population. To determine what if any independent country-specific variables from 9 different databases were correlated. Design population based retrospective cohort study. Setting analysis of global COVID-19 treatment and containment strategies using data from 9 worldwide websites. Participants 108 countries worldwide. Interventions none. Main Outcome Measures were COVID-19 death rates per country population analyzed by univariate and multivariate analysis. The main outcome parameters were to determine if there are any correlations between the percentage of countrywide COVID-19 deaths/population by the countries’ percent vaccinated. Secondary outcome measures include the effect of other independent variables on COVID-19 death rates per country population including: health expenditures per capita, annual income per capita, COVID-19 tests per 1000 people, stringency index (a measure of each countries containment strategies), hydroxychloroquine score (a measure of each countries use), ivermectin score (a measure of each countries use), hypertension, obesity, diabetes, and specific countries and geographic locations. Results COVID-19 vaccination rates ranged from 0-99% in 108 countries. Univariate analysis demonstrates the following independent variables to correlate with COVID-19 deaths/population (correlation coefficient, p value): countrywide COVID-19 vaccination rates (+0.2936, p=0.002); healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); stringency index (+0.3098, p=0.0011); hydroxychloroquine index (-0.1337, p=0.0678); and ivermectin index (-0.1383, p=0.1535). Conclusions Increasing rates of COVID-19 vaccination are associated with increase COVID-19 death rates per country population (p=0.002). Other variables associated include healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); and stringency index (+0.3098, p=0.0011).

In this research article, the notions of SuperHyperDominating and SuperHyperResolving are defined in the setting of neutrosophic SuperHyperGraphs. Some ideas are introduced on both notions of SuperHyperDominating and SuperHyperResolving, simultaneously and as the same with each other. Some neutrosophic SuperHyperClasses are defined based on the notion, SuperHyperResolving. The terms of duality, totality, perfectness, connectedness, and stable, are added to basic framework and initial notions, SuperHyperDominating and SuperHyperResolving but the concentration is on the “perfectness” to figure out what’s going on when for all targeted SuperHyperVertices, there’s only one SuperHyperVertex in the intended set. There are some instances and some clarifications to make sense about what’s happened and what’s done in the starting definitions. The key point is about the minimum sets. There are some questions and some problems to be taken as some avenues to pursue this study and this research. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

Abstract: (1) Background: tuberculosis presents an epidemiological trend towards inequality, especially among people in social exclusion and situations of vulnerability. To analyze territories where there is a concentration of people diagnosed with tuberculosis, in a street situation, and who make chronic use of alcohol, tobacco, and illicit drugs. We will also analyze trends in this health condition in southern Brazil; (2) Methods: Ecological study, developed in the 399 municipalities of Paraná, Southern Brazil, with all tuberculosis cases in the homeless population registered in the Information System of Notifiable Diseases between 2014 to 2018. For data analysis, we used descriptive statistics, for the time series, the Prais-Winsten autoregression method, whereas for spatial analysis, we used the Getis-Ord Gi technique*; (3) Results: in total, 560 cases were reported. We found a predominance of alcohol, smoking, and illicit drugs users, with an increasing trend in the state and clusters of spatial risk in the East health macro-region; (4) Conclusions: We observed territories with critical levels of highly vulnerable people who use psychoactive substances and are in a street situation. The results highlight the importance of incorporating public policies of social protection to these individuals and resolutive health services that receive and assist in eradicating TB.

Various authors have highlighted the importance of marriage as a social marker that alter the so-cial categorization of individuals and their relationships from youth to adulthood according to the cultural construction of the life course in Arab countries. This article aims to analyze the in-teraction between the socio-political framework (structure) and the capacity for individual action (agency) in the context of biographical experiences to achieve emancipation in Morocco. This per-spective responds to the demand of different authors to include the subjective approaches of young people in the process of analysis. This analysis will be guided by some questions: Which is the capacity of young Arabs to decide the orientation of their life trajectories? What factors (cultural, family, socioeconomic, educational, etc.) create and shape the expectations of young people re-garding their transition to adult life? Is there a specific model of emancipation in the Arab Medi-terranean countries?

New setting is introduced to study chromatic number. Different types of chromatic numbers and neutrosophic chromatic number are proposed in this way, some results are obtained. Classes of neutrosophic graphs are used to obtains these numbers and the representatives of the colors. Using colors to assign to the vertices of neutrosophic graphs is applied. Some questions and problems are posed concerning ways to do further studies on this topic. Using different types of edges from connectedness in same neutrosophic graphs and in modified neutrosophic graphs to define the relation amid vertices which implies having different colors amid them and as consequences, choosing one vertex as a representative of each color to use them in a set of representatives and finally, using neutrosophic cardinality of this set to compute types of chromatic numbers. This specific relation amid edges is necessary to compute both types of chromatic number concerning the number of representative in the set of representatives and types of neutrosophic chromatic number concerning neutrosophic cardinality of set of representatives. If two vertices have no intended edge, then they can be assigned to same color even they’ve common edge. Basic familiarities with neutrosophic graph theory and graph theory are proposed for this article.

New setting is introduced to study chromatic number. vital chromatic number and n-vital chromatic number are proposed in this way, some results are obtained. Classes of neutrosophic graphs are used to obtains these numbers and the representatives of the colors. Using colors to assign to the vertices of neutrosophic graphs is applied. Some questions and problems are posed concerning ways to do further studies on this topic. Using vital edge from connectedness to define the relation amid vertices which implies having different colors amid them and as consequences, choosing one vertex as a representative of each color to use them in a set of representatives and finally, using neutrosophic cardinality of this set to compute vital chromatic number. This specific relation amid edges is necessary to compute both vital chromatic number concerning the number of representative in the set of representatives and n-vital chromatic number concerning neutrosophic cardinality of set of representatives. If two vertices have no vital edge, then they can be assigned to same color even they’ve common edge. Basic familiarities with neutrosophic graph theory and graph theory are proposed for this article.

New setting is introduced to study chromatic number. Neutrosophic chromatic number and chromatic number are proposed in this way, some results are obtained. Classes of neutrosophic graphs are used to obtains these numbers and the representatives of the colors. Using colors to assigns to the vertices of neutrosophic graphs is applied. Some questions and problems are posed concerning ways to do further studies on this topic. Using strong edge to define the relation amid vertices which implies having different colors amid them and as consequences, choosing one vertex as a representative of each color to use them in a set of representatives and finally, using neutrosophic cardinality of this set to compute neutrosophic chromatic number. This specific relation amid edges is necessary to compute both chromatic number concerning the number of representative in the set of representatives and neutrosophic chromatic number concerning neutrosophic cardinality of set of representatives. If two vertices have no strong edge, then they can be assigned to same color even they’ve common edge. Basic familiarities with neutrosophic graph theory and graph theory are proposed for this article.

New setting is introduced to study co-neighborhood, neutrosophic t-neighborhood, neutrosophic quasi-vertex set, neutrosophic quasi-order, neutrosophic neighborhood, neutrosophic co-t-neighborhood, neutrosophic quasi-edge set, neutrosophic quasi-size, Neutrosophic number, neutrosophic co-neighborhood, co-neutrosophic number, quasi-number and quasi-co-number. Some classes of neutrosophic graphs are investigated.

New setting is introduced to study the global powerful alliance. Global powerful alliance is about a set of vertices which are applied into the setting of neutrosophic graphs. Neighborhood has the key role to define this notion. Also, neighborhood is defined based on strong edges. Strong edge gets a framework as neighborhood and after that, too close vertices have key role to define global powerful alliance based on strong edges. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs excluding empty, path, star, and wheel and containing complete, cycle and r-regular-strong are investigated in the terms of set, minimal set, number, and neutrosophic number. Neutrosophic number is used in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number. It’s called “modified neutrosophic number”. Summation of three values of vertex makes one number and applying it to a set makes neutrosophic number of set. This approach facilitates identifying minimal set and optimal set which forms minimal-global-powerful-alliance number and minimal-global-powerful-alliance-neutrosophic number. Two different types of sets namely global-powerful alliance and minimal-global-powerful alliance are defined. Global-powerful alliance identifies the sets in general vision but minimal-global-powerful alliance takes focus on the sets which deleting a vertex is impossible. Minimal-global-powerful-alliance number is about minimum cardinality amid the cardinalities of all minimal-global-powerful alliances in a given neutrosophic graph. New notions are applied in the settings both individual and family. Family of neutrosophic graphs has an open avenue, in the way that, the family only contains same classes of neutrosophic graphs. The results are about minimal-global-powerful alliance, minimal-global-powerful-alliance number and its corresponded sets, minimal-global-powerful-alliance-neutrosophic number and its corresponded sets, and characterizing all minimal-global-powerful alliances, minimal-t-powerful alliance, minimal-t-powerful-alliance number and its corresponded sets, minimal-t-powerful-alliance-neutrosophic number and its corresponded sets, and characterizing all minimal-t-powerful alliances. The connections amid t-powerful-alliances are obtained. The number of connected components has some relations with this new concept and it gets some results. Some classes of neutrosophic graphs behave differently when the parity of vertices are different and in this case, cycle, and complete illustrate these behaviors. Two applications concerning complete model as individual and family, under the titles of time table and scheduling conclude the results and they give more clarifications and closing remarks. In this study, there’s an open way to extend these results into the family of these classes of neutrosophic graphs. The family of neutrosophic graphs aren’t study deeply and with more results but it seems that analogous results are determined. Slight progress is obtained in the family of these models but there are open avenues to study family of other models as same models and different models. There’s a question. How can be related to each other, two sets partitioning the vertex set of a graph? The ideas of neighborhood and neighbors based on strong edges illustrate open way to get results. A set is global powerful alliance when two sets partitioning vertex set have uniform structure. All members of set have more amount of neighbors in the set than out of set and reversely for non-members of set with less members in the way that the set is simultaneously t-offensive and(t-2)-defensive. A set is global if t=0. It leads us to the notion of global powerful alliance. Different edges make different neighborhoods but it’s used one style edge titled strong edge. These notions are applied into neutrosophic graphs as individuals and family of them. Independent set as an alliance is a special set which has no neighbor inside and it implies some drawbacks for these notions. Finding special sets which are well-known, is an open way to purse this study. Special set which its members have only one neighbor inside, characterize the connected components where the cardinality of its complement is the number of connected components. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

New setting is introduced to study the global offensive alliance. Global offensive alliance is about a set of vertices which are applied into the setting of neutrosophic graphs. Neighborhood has the key role to define this notion. Also, neighborhood is defined based on strong edges. Strong edge gets a framework as neighborhood and after that, too close vertices have key role to define global offensive alliance based on strong edges. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs containing complete, empty, path, cycle, star, and wheel are investigated in the terms of set, minimal set, number, and neutrosophic number. Neutrosophic number is defined in new way. It’s first time to define this type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number. It’s called “modified neutrosophic number”. Summation of three values of vertex makes one number and applying it to a set makes neutrosophic number of set. This approach facilitates identifying minimal set and optimal set which forms minimal-global-offensive-alliance number and minimal-global-offensive-alliance-neutrosophic number. Two different types of sets namely global-offensive alliance and minimal-global-offensive alliance are defined. Global-offensive alliance identifies the sets in general vision but minimal-global-offensive alliance takes focus on the sets which deleting a vertex is impossible. Minimal-global-offensive-alliance number is about minimum cardinality amid the cardinalities of all minimal-global-offensive alliances in a given neutrosophic graph. New notions are applied in the settings both individual and family. Family of neutrosophic graphs is studied in the way that, the family only contains same classes of neutrosophic graphs. Three types of family of neutrosophic graphs including m-family of neutrosophic stars with common neutrosophic vertex set, m-family of odd complete graphs with common neutrosophic vertex set, and m-family of odd complete graphs with common neutrosophic vertex set are studied. The results are about minimal-global-offensive alliance, minimal-global-offensive-alliance number and its corresponded sets, minimal-global-offensive-alliance-neutrosophic number and its corresponded sets, and characterizing all minimal-global-offensive alliances. The connection of global-offensive-alliances with dominating set and chromatic number are obtained. The number of connected components has some relations with this new concept and it gets some results. Some classes of neutrosophic graphs behave differently when the parity of vertices are different and in this case, path, cycle, and complete illustrate these behaviors. Two applications concerning complete model as individual and family, under the titles of time table and scheduling conclude the results and they give more clarifications. In this study, there’s an open way to extend these results into the family of these classes of neutrosophic graphs. The family of neutrosophic graphs aren’t study deeply and with more results but it seems that analogous results are determined. Slight progress is obtained in the family of these models but there are open avenues to study family of other models as same models and different models. There’s a question. How can be related to each other, two sets partitioning the vertex set of a graph? The ideas of neighborhood and neighbors based on strong edges illustrate open way to get results. A set is global offensive alliance when two sets partitioning vertex set have uniform structure. All members of set have more amount of neighbors in the set than out of set. It leads us to the notion of global offensive alliance. Different edges make different neighborhoods but it’s used one style edge titled strong edge. These notions are applied into neutrosophic graphs as individuals and family of them. Independent set as an alliance is a special set which has no neighbor inside and it implies some drawbacks for these notions. Finding special sets which are well-known, is an open way to purse this study. Special set which its members have only one neighbor inside, characterize the connected components where the cardinality of its complement is the number of connected components. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

New notion of dimension as set, as two optimal numbers including metric number, dimension number and as optimal set are introduced in individual framework and in formation of family. Behaviors of twin and antipodal are explored in fuzzy(neutrosophic) graphs. Fuzzy(neutrosophic) graphs, under conditions, fixed-edges, fixed-vertex and strong fixed-vertex are studied. Some classes as path, cycle, complete, strong, t-partite, bipartite, star and wheel in the formation of individual case and in the case, they form a family are studied in the term of dimension. Fuzzification(neutrosofication) of twin vertices but using crisp concept of antipodal vertices are another approaches of this study. Thus defining two notions concerning vertices which one of them is fuzzy(neutrosophic) titled twin and another is crisp titled antipodal to study the behaviors of cycles which are partitioned into even and odd, are concluded. Classes of cycles according to antipodal vertices are divided into two classes as even and odd. Parity of the number of edges in cycle causes to have two subsections under the section is entitled to antipodal vertices. In this study, the term dimension is introduced on fuzzy(neutrosophic) graphs. The locations of objects by a set of some junctions which have distinct distance from any couple of objects out of the set, are determined. Thus it’s possible to have the locations of objects outside of this set by assigning partial number to any objects. The classes of these specific graphs are chosen to obtain some results based on dimension. The types of crisp notions and fuzzy(neutrosophic) notions are used to make sense about the material of this study and the outline of this study uses some new notions which are crisp and fuzzy(neutrosophic). Some questions and problems are posed concerning ways to do further studies on this topic. Basic familiarities with fuzzy(neutrosophic) graph theory and graph theory are proposed for this article.

This article presents a new method of segmenting images with gray levels. The method is based on determining several thresholds for separation of gray levels. The determination of these thresholds is done using the certainty of the neutrosophic information. The concept of this method can be stated simply: to choose the local maximums for the neutrosophic certainty.

In this paper, a geometric model is introduced for Neutrosophic data problem for the first time. This model is based on neutrosophic sets and neutrosophic relations. Neutrosophic control points are defined according to these points, resulting in neutrosophic Bézier curves.

In this paper, we present demographic, clinical, anamnestic, cognitive, and functional data, as well as haematological, haematochemical, immunological and genetic parameters of an exceptional individual: Antonino Turturici, a semi-supercentenarian who held the title of the oldest living Italian centenarian from December 28, 2020, to September 23, 2021. The purpose of this study is to offer fresh insights into extreme phenotypes, with a particular focus on immune-inflammatory parameters. To the best of our knowledge, this study represents the first phenotypic investigation of a semi-supercentenarian, illustrating both indicators of age-related immune phenotype (ARIP) recognized as significant gauges of biological ageing and INFLA-score, a metric designed to assess the cumulative impact of inflammatory markers. The aim of this study was, indeed, to advance our understanding of the role of immune-inflammatory responses in achieving extreme longevity. The results bolster the idea that the immune system can play a role in promoting extreme longevity. However, this does not exclude the involvement of other body systems or organs in achieving extreme longevity.

New setting is introduced to study resolving number and chromatic number alongside dominating number. Different types of procedures including set, optimal set, and optimal number alongside study on the family of neutrosophic hypergraphs are proposed in this way, some results are obtained. General classes of neutrosophic hypergraphs are used to obtains these numbers and the representatives of the colors, dominating sets and resolving sets. Using colors to assign to the vertices of neutrosophic hypergraphs and characterizing resolving sets and dominating sets are applied. Some questions and problems are posed concerning ways to do further studies on this topic. Using different ways of study on neutrosophic hypergraphs to get new results about numbers and sets in the way that some numbers get understandable perspective. Family of neutrosophic hypergraphs are studied to investigate about the notions, dimension and coloring alongside domination in neutrosophic hypergraphs. In this way, sets of representatives of colors, resolving sets and dominating sets have key role. Optimal sets and optimal numbers have key points to get new results but in some cases, there are usages of sets and numbers instead of optimal ones. Simultaneously, three notions are applied into neutrosophic hypergraphs to get sensible results about their structures. Basic familiarities with neutrosophic hypergraphs theory and hypergraph theory are proposed for this article.

This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.

New setting is introduced to study types of coloring numbers, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Different types of procedures including neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are proposed in this way, some results are obtained. General classes of neutrosophic hypergraphs are used to obtain chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Using colors to assign to the vertices of neutrosophic hypergraphs and characterizing representatives of the colors are applied in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. Some questions and problems are posed concerning ways to do further studies on this topic. Using different ways of study on neutrosophic hypergraphs to get new results about number, degree and co-degree in the way that some number, degree and co-degree get understandable perspective. Neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are studied to investigate about the notions, coloring, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. In this way, sets of representatives of colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges have key points to get new results but in some cases, there are usages of sets and numbers instead of optimal ones. Simultaneously, notions chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges are applied into neutrosophic hypergraphs, especially, neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs to get sensible results about their structures. Basic familiarities with neutrosophic hypergraphs theory and hypergraph theory are proposed for this article.

Neutrosophic cubic set is the hybridization of the concept of neutrosophic set and interval neutrosophic set. Neutrosophic cubic set has the capacity to express the hybrid information of both the interval neutrosophic set and the single valued neutrosophic set simultaneously. As newly defined, little research on the operations and applications of neutrosophic cubic sets appear in the current literature. In the present paper we propose the score, accuracy functions for neutrosophic cubic sets and prove their basic properties. We firstly develop TODIM method to solve multi attribute group decision making in neutrosophic cubic set environment, which we call NC-TODIM. Also, we solve a MAGDM problem using the proposed NC-TODIM method to show the applicability and effectiveness of the developed method. We also conduct sensitivity analysis to show the impact of ranking order of the alternatives for different values of attenuation factor of losses for multi-attribute group decision making problem.

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real- time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Purpose: There is limited research on the reasons behind startup failure, and none of the available studies use a competency approach. In this study we applied Spencer’s competency model to identify the competencies in startups which, according to their CEOs, contributed to failure. Methodology: Three coders analyzed the stories of 50 startup failures published online using modified Critical Incident Technique. Findings: Two salient competencies were identified playing a fundamental role in startup failures if missing: Information seeking and Customer service orientation. A network pattern of 9 more prevalent competency deficits was created: Technical/professional/managerial expertise, Analytical thinking, Flexibility, Self-control, Concern for order, quality and accuracy, Interpersonal understanding, Self-confidence, Team leadership and Teamwork and cooperation. Besides startup-specific behavior descriptions were added to Spencer’s competencies. Research implications: Competency approach and Critical Incident Technique method proved to be feasible to identify competency deficits in failed startups. Practical implications: The identified competency deficits offer relevant focus areas for the assessment and development of startup teams. Originality: The study provided research evidence to describe the competency deficits of startup teams that are connected to their failure.

In a recent paper, we extended previous studies on solar-terrestrial relationships by investigating the origin of the March 2012 heatwave in northeast USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), the Interplanetary Space (ACE satellite), the Earth’s magnetosphere (Earth based measurements, NOAA-19 satellite), the top of the clouds (TERRA and AQUA satellites) and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, on the same day as the heatwave starting in USA, (ii) during the March 2012 winter-like event in Thrace (6-15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (> 500 MeV), (iii) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8 - 10 and 12-13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (iv) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~-2000V/m - ~1800V/m on 8 March, (v) the winter-like 8-10 March 2012 weather occurred after the detection of the main SEP event related with a coronal mass ejection released in the interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8-10 March weather was related with a deep drop of ~630C in the cloud top temperature measured by MODIS/TERRA, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~410N) and we found, for the first time sudden voltage changes of ~3.5kV in the electric grid in Greece, during the decay phase of the March 2012 superstorm. We discuss the winter-like March 2012 event in Thrace in terms of solar cosmic ray influence on the low troposphere mediated by positive NAO. Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research.

Neutrosophic cubic set (NCS) is one of the important family members of neutrosophic hybrid sets. Neutrosophic cubic set has more strength than other family members of neutrosophic hybrid sets to express incomplete information due to the presence of interval valued neutrosophic set (IVNS) and single valued neutrosophic set (SVNS) in its structure. Cross entropy measure is one of the best way to calculate the divergence of any variable from the priori one variable. In this paper we first define a new cross entropy measure under NCSs environment which we call NC- cross entropy measure. We investigate the basic properties of NC-cross entropy. We also propose weighted NC-cross entropy and investigate its basic properties. We develop a novel multi attribute decision making (MADM) strategy based on weighted NC-cross entropy. To show the feasibility and applicability, we solve a MADM problem using the proposed strategy.

New setting is introduced to study the alliances. Alliances are about a set of vertices which are applied into the setting of neutrosophic graphs. Neighborhood has the key role to define these notions. Also, neighborhood is defined based on the edges, strong edges and some edges which are coming from connectedness. These three types of edges get a framework as neighborhood and after that, too close vertices have key role to define offensive alliance, defensive alliance, t-offensive alliance, and t-defensive alliance based on three types of edges, common edges, strong edges and some edges which are coming from connectedness. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs containing complete, empty, path, cycle, bipartite, t-partite, star and wheel are investigated in the terms of set, minimal set, number, and neutrosophic number. In this study, there’s an open way to extend these results into the family of these classes of neutrosophic graphs. The family of neutrosophic graphs aren’t study but it seems that analogous results are determined. There’s a question. How can be related to each other, two sets partitioning the vertex set of a graph? The ideas of neighborhood and neighbors based on different edges illustrate open way to get results. A set is alliance when two sets partitioning vertex set have uniform structure. All members of set have different amount of neighbors in the set and out of set. It leads us to the notion of offensive and defensive. New ideas, offensive alliance, defensive alliance, t-offensive alliance, t-defensive alliance, strong offensive alliance, strong defensive alliance, strong t-offensive alliance, strong t-defensive alliance, connected offensive alliance, connected defensive alliance, connected t-offensive alliance, and connected t-defensive alliance are introduced. Two numbers concerning cardinality and neutrosophic cardinality of alliances are introduced. A set is alliance when its complement make a relation in the terms of neighborhood. Different edges make different neighborhoods. Three types of edges are applied to define three styles of neighborhoods. General edges, strong edges and connected edges are used where connected edges are the edges arising from connectedness amid two endpoints of the edges. These notions are applied into neutrosophic graphs as individuals and family of them. Independent set as an alliance is a special set which has no neighbor inside and it implies some drawbacks for this notions. Finding special sets which are well-known, is an open way to purse this study. Special set which its members have only one neighbor inside, characterize the connected components where the cardinality of its complement is the number of connected components. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Single valued neutrosophic set has king power to express uncertainty characterized by indeterminacy, inconsistency and incompleteness. Most of the existing single valued neutrosophic cross entropy bears an asymmetrical behavior and produce an undefined phenomenon in some situations. In order to deal with these disadvantages, we propose a new cross entropy measure under single valued neutrosophic set (SVNS) environment namely SN- cross entropy and prove its basic properties. Also we define weighted SN-cross entropy measure and investigate its basic properties. We develop a new multi attribute group decision making (MAGDM) strategy for ranking of the alternatives based on the proposed weighted SN-cross entropy measure between each alternative and the ideal alternative. Finally, a numerical example of MAGDM problem of investment potential is solved to show the validity and efficiency of proposed decision making strategy. We also present comparative anslysis of the obtained result with the results obtained form the existing solution strategies in the solution.

Myostatin inhibition therapy has held much promise for the treatment of muscle wasting disorders. This is particularly true for the fatal myopathy, Duchenne Muscular Dystrophy (DMD). Following on from promising pre-clinical data in dystrophin-deficient mice and dogs, several clinical trials were initiated in DMD patients using different modality myostatin inhibition therapies. All failed to show modification of disease course as dictated by the primary and secondary outcomes measures selected: the myostatin inhibition story thus far, is a failed clinical story. These trials have recently been extensively reviewed and reasons why pre-clinical data collected in animal models has failed to translate into clinical benefit to patients has been purported. However, the biological mechanisms underlying translational failure need to be examined to ensure future myostatin inhibitor development endeavors do not meet with the same fate. Here, we explore the biology which could explain the failed translation of myostatin inhibitors in the treatment of DMD.

Despite all scientific efforts and many protracted and expensive clinical trials, no new drug has been approved by FDA for treatment of Alzheimer disease (AD) since 2003. Indeed, more than 200 investigational programs have failed or have been abandoned in the last decade. The most probable explanations for failures of disease-modifying treatments (DMTs) for AD may include late initiation of treatments during the course of AD development, inappropriate drug dosages, erroneous selection of treatment targets, and mainly an inadequate understanding of the complex pathophysiology of AD, which may necessitate combination treatments rather than monotherapy. Clinical trials’ methodological issues have also been criticized. Current drug-development research for AD is aimed to overcome these drawbacks. Preclinical and prodromal AD populations, as well as traditionally investigated populations representing all the clinical stages of AD, are included in recent trials. Systematic use of biomarkers in staging preclinical and prodromal AD and of a single primary outcome in trials of prodromal AD are regularly integrated. The application of amyloid, tau, and neurodegeneration biomarkers, including new biomarkers—such as Tau positron emission tomography, neurofilament light chain (blood and CSF biomarker of axonal degeneration) and neurogranin (CSF biomarker of synaptic functioning)—to clinical trials allows more precise staging of AD. Additionally, use of the Bayesian statistics, modifiable clinical trial designs, and clinical trial simulators enrich the trial methodology. Besides, combination therapy regimens are currently assessed in clinical trials. The abovementioned diagnostic and statistical advances, which have been recently integrated in clinical trials, are consequential to the recent failures of studies of disease-modifying treatments. Their experiential rather than theoretical origins may better equip potentially successful drug-development strategies.

The concept of neutrosophic number is a significant mathematical tool to deal with real scientific problems because it can tackle indeterminate and incomplete information which exists generally in real problems. In this article, we use neutrosophic numbers (a + bI), where a and bI denote determinate component and indeterminate component respectively. We explore the situations in which the input information is needed to express in terms of neutrosophic numbers. We define score functions and accuracy functions for ranking neutrosophic numbers. We then define a cosine function to determine unknown criteria weights. We define neutrosophic number harmonic mean operators and proved their basic properties. Then, we develop two novel MCGDM strategies using the proposed aggregation operators. We solve a numerical example to demonstrate the feasibility and effectiveness of the proposed two strategies. Sensitivity analysis with variation of “I” on neutrosophic numbers is performed to demonstrate how the preference ranking order of alternatives is sensitive to the change of “I”. The efficiency of the developed strategies is ascertained by comparing the obtained results from the proposed strategies with the existing strategies in the literature.

Extracorporeal membrane oxygenation (ECMO) can be a life-saving intervention in cases of potentially reversible refractory respiratory failure. One such indication can be bleomycin-induced lung injury. However, in some cases, the injury can be so severe that it becomes irreversible and creates complex medical decisions regarding life support and the continuation of care when no additional therapeutic options are feasible, particularly in cases of patients who were young and fully functional prior to an acute illness. In cases of full pulmonary replacement with mechanical support and the degree of functionality that can be attained utilizing modalities such as ECMO can obscure the true severity of illness and make end-of-life decisions significantly harder for families and caregivers.

Background: The coronavirus disease-19 (COVID-19) has spread globally with more than 80,000 people infected, and nearly 3000 patients died. Currently, we are in an urgent need for effective treatment strategy to control the clinical deterioration of COVID-19 patients. Methods: The clinical data of 10 COVID-19 patients receiving short-term moderate-dose corticosteroid (160mg/d) plus immunoglobulin (20g/d) were studied in the North Yard of The First Hospital of Changsha, Hunan from January 17^th to February 27^th, 2020. Epidemiological, clinical, laboratory, radiological findings were analyzed. Results: After treatment with combination of low-dose corticosteroid (40-80mg/d) and immunoglobulin (10g/d), patients’ lymphocyte count (0.88±0.34 vs 0.59±0.18, P<0.05), oxygenation index including SPO₂ (94.90±2.51 vs 90.50±5.91, P<0.05) and PaO₂/FiO₂ (321.36±136.91 vs 129.30±64.97, P<0.05) were significantly lower than pre-treatment, and CT showed that the pulmonary lesion deteriorated in all patients. While after treatment of short-term moderate-dose corticosteroid plus immunoglobulin, patients’ APACHE Ⅱ score (9.10±6.15 vs 5.50±9.01, P<0.05), body temperature (37.59±1.16 vs 36.46±0.25, P<0.05), lymphocyte count (0.59±0.18 vs 1.36±0.51, P<0.05), Lactate dehydrogenase (419.24±251.31 vs 257.40±177.88, P<0.05), and C-reactive protein (49.94±26.21 vs 14.58±15.25, P<0.05) significantly improved compared with post-treatment with low-dose therapy. In addition, oxygenation index including SPO₂ (90.50±5.91 vs 97.50±1.18, P<0.05), PaO₂ (60.47±14.53 vs 99.07±34.31, P<0.05), and PaO₂/FiO₂ (129.30±64.97 vs 340.86±146.72, P<0.05) significant improved. Furthermore, CT showed that pulmonary lesions obviously improved in 7 patients. After systematic therapy, 4 out of 10 COVID-19 patients recovered and discharged. Conclusions: Short-term moderate-dose corticosteroid plus immunoglobulin is effective for reversing the continued deterioration of COVID-19 patients who failed to respond to the low-dose therapy. Funding: This work was supported by the Innovative Major Emergency Project Funding against the New Coronavirus Pneumonia in Hunan Province (Dr. Ji-Yang Liu, number 2020SK3014; Dr. Yuan-Lin Xie, number 2020SK3013).

The paper investigates some similarity measures in interval bipolar neutrosophic environment for multi-attribute decision making problems. At first, we define Hamming and Euclidean distances measures between interval bipolar neutrosophic sets and establish their basic properties. We also propose two similarity measures based on the Hamming and Euclidean distance functions. Using maximum and minimum operators, we define new similarity measures and prove their basic properties. Using the proposed similarity measures, we propose a novel multi attribute decision making strategy in interval bipolar neutrosophic set environment. Lastly, we solve an illustrative example of multi attribute decision making and present comparison analysis to show the feasibility, applicability and effectiveness of the proposed strategy.

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

With the rapid development of big data, artificial intelligence, and Internet technologies, the human-human contact and human-machine interaction have produced an explosive growth of voice data. Rapidly identifying the speaker's identity and retrieving and managing his or her speech data in the massive amount of speech data has become a major challenge for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video, and speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of speaker's voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for information security field and achieves 89% recall rate in massive database, which has practical application value for intelligent application field.

A large part of our understanding of the biological substrates of sex-recognition and mate-recognition is derived by studying animal models. In performing those tasks, rodents rely mostly of pheromones and other olfactory cues, whereas humans rely mostly on visual cues. That may hinder the translation of rodents’ biology to humans’ biology, especially at the neural-networks level, where those cues traverse different networks in humans and rodents brains. That may be called the “pheromonal-visual gap”. A theoretical model presented here addresses those issues. The model merges observations from humans and model-animals, as reported in specific scientific reports, and general biological principles that are accepted by the scientific community. The model suggests that the voices of men and women are the innate cues based on which humans learn to use visual cues in sex-recognition and mate-recognition. Children learn the two tasks in associative learning mechanisms, by being immersed in their community, and observing adult role-models in innocuous, non-sexual scenarios. The model proposes that the human medial-geniculate-nucleus (MGN) is the analog of the rodents’ accessory-olfactory-bulb (AOB) and the main-olfactory-bulb (MOB), and that the human MASH pathway (MGN, amygdala, bnST, hypothalamus) is the analog of the rodents’ VNOP (Vomeronasal-organ-pathway). Considering the differences in the pathways should facilitate the translation from rodents’ brain nuclei and tracks to humans’. Also, the model hypothesizes that innate direct and indirect connections between auditory centers, e.g., MGN, and sex-control centers, e.g., hypothalamus, vary across three groups of children, and those variations determine the individual’s mate-recognition that emerges at puberty.

In this research, new setting is introduced for new notions, namely, SuperHyperDegree and Co-SuperHyperDegree. Two different types of definitions are debut for them but the research goes further and the SuperHyperNotion, SuperHyperUniform, and SuperHyperClass based on that are well-defined and well-reviewed. The literature review is implemented in the whole of this research. For shining the elegancy and the significancy of this research, the comparison between this SuperHyperNotion with other SuperHyperNotions and fundamental SuperHyperNumbers are featured. The definitions are followed by the examples and the instances thus the clarifications are driven with different tools. The applications are figured out to make sense about the theoretical aspect of this ongoing research. The cancer’s treatments are the under research to figure out the challenges make sense about ongoing and upcoming research. The special case is up. The cells are viewed in the deemed ways. There are different types of them. Some of them are individuals and some of them are well-modeled by the group of cells. These types are all officially called “SuperHyperVertex” but the relations amid them all officially called “SuperHyperEdge”. The frameworks “SuperHyperGraph” and “neutrosophic SuperHyperGraph” are chosen and elected to research about “cancer’s treatments”. Thus these complex and dense SuperHyperModels open up some avenues to research on theoretical segments and “cancer’s treatments”. Some avenues are posed to pursue this research. It’s also officially collected in the form of some questions and some problems. If there’s a SuperHyperEdge between two SuperHyperVertices, then these two SuperHyperVertices are called SuperHyperNeighbors. The number of SuperHyperNeighbors for a given SuperHyperVertex is called SuperHyperDegree. The number of common SuperHyperNeighbors for some SuperHyperVertices is called Co-SuperHyperDegree for them and used SuperHyperVertices are called Co-SuperHyperNeighbors. A graph is SuperHyperUniform if it’s SuperHyperGraph and the number of elements of SuperHyperEdges are the same. Assume a neutrosophic SuperHyperGraph. There are some SuperHyperClasses as follows. It’s SuperHyperPath if it’s only one SuperVertex as intersection amid two given SuperHyperEdges with two exceptions; it’s SuperHyperCycle if it’s only one SuperVertex as intersection amid two given SuperHyperEdges; it’s SuperHyperStar it’s only one SuperVertex as intersection amid all SuperHyperEdges; it’s SuperHyperBipartite it’s only one SuperVertex as intersection amid two given SuperHyperEdges and these SuperVertices, forming two separate sets, has no SuperHyperEdge in common; it’s SuperHyperMultiPartite it’s only one SuperVertex as intersection amid two given SuperHyperEdges and these SuperVertices, forming multi separate sets, has no SuperHyperEdge in common; it’s SuperHyperWheel if it’s only one SuperVertex as intersection amid two given SuperHyperEdges and one SuperVertex has one SuperHyperEdge with any common SuperVertex. The number of SuperHyperEdges for a given SuperHyperVertex is called SuperHyperDegree. The number of common SuperHyperEdges for some SuperHyperVertices is called Co-SuperHyperDegree for them. The number of SuperHyperVertices for a given SuperHyperEdge is called SuperHyperDegree. The number of common SuperHyperVertices for some SuperHyperEdges is called Co-SuperHyperDegree for them. The model proposes the specific designs. The model is officially called “SuperHyperGraph” and “Neutrosophic SuperHyperGraph”. In this model, The “specific” cells and “specific group” of cells are modeled as “SuperHyperVertices” and the common and intended properties between “specific” cells and “specific group” of cells are modeled as “SuperHyperEdges”. Sometimes, it’s useful to have some degrees of determinacy, indeterminacy, and neutrality to have more precise model which in this case the model is called “neutrosophic”. In the future research, the foundation will be based on the caner’s treatment and the results and the definitions will be introduced in redeemed ways. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

Human Action Recognition is widely used in multiple fields to recognize activities and extract spatial and temporal information. This paper focuses on analyzing various methods as well as provides extensive knowledge of the foundational concepts of the HAR. A dataset is also very crucial for any research study so we discussed the popular dataset and its features in this paper.

This article introduces a novel approach, termed the Asymmetric Probabilistic Tsetlin (APT) Machine, which incorporates the Stochastic Point Location (SPL) algorithm with the Asymmetric Steps technique into the Tsetlin Machine (TM). APT introduces stochasticity into the state transitions of Tsetlin Automata (TA) by leveraging the SPL algorithm, thereby enhancing pattern recognition capabilities. To enhance random search processes, we introduced a decaying normal distribution into the procedure. Meanwhile, the Asymmetric Steps approach biases state transition probabilities towards specific input patterns, further elevating operational efficiency. The efficacy of the proposed approach is assessed across diverse benchmark datasets for classification tasks. The performance of APT is compared with traditional machine learning algorithms and other Tsetlin Machine models, including the Asymmetric Tsetlin (AT) Machine, characterized by deterministic rules for Asymmetric transitions, and the Classical Tsetlin (CT) Machine, employing deterministic rules for symmetric transitions. Strikingly, the introduced APT methodology demonstrates highly competitive outcomes compared to established machine learning methods. Notably, both APT and AT exhibit state-of-the-art performance, surpassing the Classical Tsetlin Machine, emphasizing the efficacy of asymmetric models for achieving superior outcomes. Remarkably, APT exhibits even better performance than AT, particularly in handling complex datasets.

Hand gestures are an essential part of human-to-human communication and interaction, and therefore for technical applications, the aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware such as special gloves to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, in our approach convolutional neural network (CNN) will be explored in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the field of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase of the model accuracy was achieved. On public benchmarks the ten gestures have been classified almost perfectly with a testing accuracy of 96.5%

In this paper we use quantum machine learning to detect and classify ocular diseases across age related macular degradation, cataract, diabetic, glaucoma, hypertension, and patological myopia categories versus a control group. We analyze fundus imagery from 1000 patients. Early findings indicate there may be benefit in terms of accuracy and loss function minimization of 2.07% and 1.979x respectively compared to a similar method implemented using traditional computers.

While the desire to uncover the neural correlates of consciousness has taken numerous directions, self-face recognition has been a constant in attempts to isolate aspects of self-awareness. The neuroimaging revolution of the 1990’s bought about systematic attempts to isolate the underlying neural basis self-face recognition. These studies, including some of the first fMRI (functional Magnetic Resonance Imaging) studies, revealed a right hemisphere bias for self-face recognition in a diverse set of regions including the insula, the Dorsal Frontal Lobe, the Temporal Parietal Junction and Medial Temporal Cortex. Confirmation of these data (which are correlational) was provided by TMS (Transcranial Magnetic Stimulation) and patients in which direct inhibition or ablation of right hemisphere regions leads to a disruption or absence of self-face recognition. These data are consistent with a number of theories including a right hemisphere dominance for self-awareness and/or a right hemisphere specialization for identifying significant social relationships including to oneself.

The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

The port environments have some structures that still need regular control. However, the nature of these environments presents a number of challenges in determining an accurate vehicle position and consequently performing successful image similarity detection. These scenarios, however, can have poor visibility conditions and are highly dynamic, making place recognition difficult because the appearance of a place can change over time. Under these operational conditions, imaging sonars are a promising solution. There are some factors that affect the quality of the captured images, but the fact that they do not suffer from haze is a plus. Therefore, in this paper, a purely acoustic similarity detection method based on forward-looking sonar (FLS) data is proposed to overcome the inherent perceptual problems in a port scenario. Considering the lack of available data in this context and to facilitate the variation of environmental parameters and sensor configurations, a port scenario was simulated using the Stonefish simulator. Experiments were conducted on predefined trajectories near structures. The place recognition approach performs better compared to the results obtained with optical imagery. In general, it provides a good compromise in terms of distinctiveness and achieves a performance of 87.5% when appropriate constraints and assumptions are made.

Recognizing highly occluded objects is believed to arises from the interaction between the brain's vision and cognition controlling areas, although supporting neuroimaging data is currently limited. To explore the neural mechanism during this activity, we conducted an occlusion object recognition experiment using functional magnetic resonance imaging (fMRI). During magnet resonance examinations, 66 subjects engaged in object recognition tasks with three different occlusion degrees. Generalized linear model (GLM) analysis showed that the activation degree of occipital lobe (inferior occipital gyrus, middle occipital gyrus, and occipital fusiform gyrus) and dorsal anterior cingulate cortex (dACC) was related to the occlusion degree of the objects. Multivariate pattern analysis (MVPA) further unearthed a considerable surge in classification precision when dACC activation was incorporated as a feature. This suggested the combined role of dACC and occipital lobe in occluded object recognition tasks. Moreover, psychophysiological interaction (PPI) analysis disclosed that functional connectivity (FC) between the dACC and the occipital lobe was enhanced with increased occlusion, highlighting the necessity of FC between these two brain regions in effectively identifying exceedingly occluded objects. In conclusion, these findings contribute to understanding the neural mechanisms of highly occluded objects recognition, augmenting our appreciation of how the brain manages incomplete visual data.

This paper addresses the issue of the automatic identification of river reaches and their planform type, given the (observed) set of geomorphic elements and units. It introduces further advances with respect to the original proposal by Nardini and Brierley. And it explores explicitly the ability of the algorithm and associated tools to work properly on significantly different rivers while adopting a given same parametrization. This was indeed an envisaged ability speculated as a challenging conclusion of the previous work. The Duqueco, Laja and Biobío rivers (Chile) are analyzed for this purpose. The conclusion is definitely positive, what opens future promising application horizons.

This paper presents a new algorithm for semi-supervised action recognition using adaptive feature analysis. We model the samples of the same action as the same manifold and those of different actions as different manifolds, and integrate feature correlation mining and visual similarity mining into a joint framework. By maximizing the separability between different classes based on the labeled data, the proposed algorithm learns multiple features from labeled data and utilizes the intrinsic geometric structure of the data distribution between labeled and unlabeled data to boost the recognition. We introduce Projected Barzilai-Borwein (PBB) to train a subspace projection matrix as classifier. Extensive experiments on three benchmark datasets demonstrate that our algorithm outperforms the existing semi-supervised action recognition algorithms when there are a few labeled training samples.

When ground-penetrating radar is used to detect targets within concrete, the location of the targets, the identification of different shapes, properties and less obvious echoes all greatly increase the interpretation time of the staff and can easily cause misjudgment of the echo images. In this paper, the ground-penetrating radar echo images (B-scan) after processing are mean filtered to eliminate the direct waves that interfere greatly with the echoes. The RFB-s structure is added to the YOLOv3-SPP network structure, while the Anchor value is optimized and the EIOU loss function is introduced. For four types of data with different shapes and properties at random target locations, three models, YOLOv3, YOLOv3-SPP and the improved YOLOv3-SPP, are used for classification and identification, and the proposed algorithm models are comprehensively evaluated using model evaluation metrics. The experimental results show that the algorithm models proposed in this paper have good recognition effect in ground-penetrating radar echo image target detection.

The article proposes to use a radio holography for the detection and identification of unmanned aerial vehicles (UAVs).

Data augmentation techniques recently gained more adoption in speech processing, including speech emotion recognition. Although more data tends to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition. The experiments are conducted on the Japanese Twitter-based emotional speech corpus. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentation and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific application.

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules of regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which in turn affects vehicle idling time at intersections, fuel consumption, CO_2 emissions and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable and inexpensive way to identify the type of intersection control (e.g. traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicles crossing intersections is proposed. A modification of a well-known clustering algorithm for detecting stopping and decelerating events is presented. These detected events are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per junction arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the junction arms according to their traffic control type dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single junction arm are used (one-arm model) and another where features also from neighbouring junction arms of the same junction are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 94\% to 97\%.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in the animal welfare research, the animal affective computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in nascent stage. By capitalizing on the immense potential of biometric sensors, the artificial intelligence enabled big data methods substantially offers advancement of animal welfare standards and meet the urgent need of caretakers to respond effectively to maintain the wellbeing of their animals. Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are no scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time consuming, interrupt farming processes and involve subjective judgments. Biometric sensors data enabled by Artificial Intelligence are an emerging smart solution to unobtrusively monitoring livestock, but their potential for quantifying affective states and groundbreaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and be-havior in real time are a near-term possibility.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. 400 images of horses were collected and labeled to train both the detector and the model while 80 were used to validate the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through head, neck, ear, muzzle and eye position. The system showed an accuracy of between 69% and 74% on the validation set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

Classification of human actions from uni-modal and multi-modal datasets is an ongoing research problem in computer vision. This review is aimed to scope current literature on data-fusion and action-recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The rise in number of action recognition datasets intersects with advances in deep-learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality - such as RGB, depth, skeleton, and infrared - has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this article we will focus solely on areas such as data fusion and recognition techniques in the context of vision with a uni-modal and multi-modal perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.

The paper concludes the first research on mouth-based Emotion Recognition (ER), adopting a Transfer Learning (TL) approach. Transfer Learning results paramount for mouth-based emotion ER, because a few data sets are available, and most of them include emotional expressions simulated by actors, instead of adopting a real-world categorization. Using TL we can use fewer training data than training a whole network from scratch, thus more efficiently fine-tuning the network with emotional data and improving the convolutional neural network accuracy in the desired domain. The proposed approach aims at improving the Emotion Recognition dynamically, taking into account not only new scenarios but also modified situations with respect to the initial training phase, because the image of the mouth can be available even when the whole face is visible only in an unfavourable perspective. Typical applications include automated supervision of bedridden critical patients in an healthcare management environment, or portable applications supporting disabled users having difficulties in seeing or recognizing facial emotions. This work takes advantage from previous preliminary works on mouth-based emotion recognition using CNN deep-learning, and has the further benefit of testing and comparing a set of networks on large data sets for face-based emotion recognition well known in literature. The final result is not directly comparable with works on full-face ER, but valorizes the significance of mouth in emotion recognition, obtaining consistent performances on the visual emotion recognition domain.

Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.

This paper serves a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

Bipolar neutrosophic set is an important extension of bipolar fuzzy set. This set is a hybridization of bipolar fuzzy set and neutrosophic set. Every element of a bipolar neutrosophic set consists of three independent positive membership functions and three independent negative membership functions. In this paper, we develop cross entropy measures of bipolar neutrosophic sets and prove its properties. We also define cross entropy measures of interval bipolar neutrosophic sets and prove its properties. Thereafter, we develop two novel multi-attribute decision making methods based on the proposed cross entropy measures. In the decision making framework, we calculate the weighted cross entropy measures between each alternative and the ideal alternative to rank the alternatives and choose the best one. We solve two illustrative examples of multi-attribute decision making problems and compare the obtained result with the results of other existing methods to show the applicability and effectiveness of the developed method. In the end, the main conclusion and future scope of research are summarized.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

The research on the distribution of rural buildings is one of the fundamental works of urban-rural development in Vietnam. Adopting Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2,492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural buildings distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban-rural relations in Southeast Asia and the construction of a basic database on villages.

This study introduces an original comprehensive system centered on identifying specific terms that indicate a user's position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.

Human violence recognition is an area of great interest in the scientific community, given its broad spectrum of applications, especially in video surveillance systems, since detecting violence in real-time could prevent criminal acts and save lives. Despite the number of existing proposals and research, most focus on the precision of results, leaving aside efficiency and its practical implementation. Thus, this work proposes a model that is effective and efficient in recognizing human violence in real-time. The proposed model consists of three modules: a first module called Spatial Motion Extractor (SME), in charge of extracting regions of interest from a frame; a second module called Short Temporal Extractor (STC), whose function is to extract temporal characteristics of rapid movements, finally the Global Temporal Extractor (GET) module, responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated regarding efficiency, effectiveness, and ability to operate in real-time. The results obtained on Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to other alternatives. A VioPeru dataset was created to validate real-time applicability with violent and non-violent videos captured by real video surveillance cameras in Peru. The effectiveness results in this dataset outperformed the best existing proposal. Therefore, our proposal has contributions in efficiency, effectiveness, and real-time.

Convolution, recurrent and attention-based deep learning techniques have produced the most recent state-of-the-art results in multiple sensor-based human activity recognition (HAR) datasets. However, these techniques have high computing costs, restricting their use in low-powered devices. Different methods have been employed to increase the efficiency of these techniques; however, this often results in worse performance. Recently, pure MLP architectures have demonstrated competitive performance in vision-based tasks with lower computation costs than other deep-learning techniques. The MLP-Mixer is a pioneering pure MLP architecture that produces competitive results with state-of-the-art models in computer vision tasks. This paper shows the viability of the MLP-Mixer in sensor-based HAR. Furthermore, experiments are performed to gain insight into the Mixer modules essential for HAR, and a visual analysis of the Mixer’s weights is provided, validating the Mixer’s learning capabilities. As a result, the Mixer achieves an F1 score of 97%, 84.2%, 91.2% and 90% on the PAMAP2, Daphnet Gait, Opportunity Gestures and Opportunity Locomotion datasets, respectively, outperforming state-of-the-art models in all datasets except Opportunity Gestures.

Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has a large number of hyperparameters that are difficult to be determined, which makes the model easy to fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithm are used to solve the above problems. However, these algorithms have poor performance compared with the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the accuracy of DBO-CNN has increased by 0.6%~4.8%, and reached 98.3%.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

Micro-expression (ME) is one of the key psychological stress reactions. It is a modest, spontaneous facial mechanism. ME has significant applicability in a variety of psychologically-related sectors because to its precision and unpredictability with regard to psychological manifestations. Nevertheless, the current Micro-expression recognition (MER) algorithms have poor accuracy and a limited quantity of ME data, and this study issue has not been thoroughly investigated. Therefore, we present an approach for deep learning based on a Spatio-temporal capsule network (STCP-Net). STCP-Net has four components: a jitter reduction module, a differential feature extraction module, an STCP module, and a fully linked layer. The first two modules are aimed to extract diversifying differential features more precisely and to limit the influence of head jitter. The STCP module is used to extract Spatio-temporal features layer by layer, taking the temporal and geographical connection between features into account. This research runs sufficient trials using the Leave One Subject Out (LOSO) methodology for cross-validation using the CASMEII dataset. The conclusion and analysis demonstrate that the algorithm is innovative and efficient.

Here we present a blockchain-backed token recognition system to reward the contributions that academics make to the scientific ecosystem. Recognition is important in science but current methods, systems and incentives are limited. Specifically, the traditional focus on narrow publication metrics means diverse contributions are not captured, while bias toward senior, established scientists is common. To tackle this challenge, we explore the potential of harnessing blockchain’s collaborative, decentralised and trust-brokering properties to develop a token reward system for use by research funders. Academics would be awarded tokens for undertaking common but vital tasks such as peer review, sitting on funding committees and submitting reports. These tokens would not be tradable or specifically monetisable but would serve as a validated record of scientific contribution. They would have value in professional recruitment and job placement, support grant and award applications, and inform performance appraisals and file reviews. Coordination and cooperation across multiple funding agencies in developing the platform would provide an opportunity to aggregate and standardise recognition, given academics often work with several funders. This system’s goals are to expand recognition metrics, promote efficiencies, improve the robustness of professional assessments and enable cross-funder collaboration, thereby optimising research processes and practices in a decentralised and democratised manner.

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

In this paper, we propose a robust video understanding model for activity recognition by learning the actor’s pair-wise correlations and relational reasoning, exploiting spatial and temporal information. In order to measure the similarity between the pair appearances and construct an actor relations map, the Zero Mean Normalized Cross-Correlation (ZNCC) and the Zero Mean Sum of Absolute Differences(ZSAD) is proposed to allow the Graph Convolution Network (GCN) to learn how to distinguish group actions. We recommend that MNASNet be used as the backbone to retrieve features. Experiments show a 38.50% and 23.7% reduction in training time in the 2-stage training process along with a 1.52% improvement in accuracy against traditional methods.