The effects of ELICE16INDURES, a well-known plant conditioner developed by the Research Institute for Medicinal Plants and Herbs Ltd. Budakalasz, Hungary, were studied in a soybean population. The active ingredients of the compound have been selected to help elicit general immunity in plants without pathogenic damage, thereby roborizing the healthy plant population and preparing it for possible future biotic stressors. Here we have analyzed changes in the expression levels of genes encoding enzymes involved in the catalysis of metabolic pathways that induce and regulate PAMP-triggered immunity (PTI) at two different time points and treatments. Twenty-three different enzymes were analyzed that catalyze different metabolic pathways, such as the biosyntheses of jasmonic acid, salicylic acid, ethylene, phenylpropanoid, flavonoid, and phytoalexin biosynthesis and cellular detoxification processes. Bioinformatical softwares werw used to analyze the results. It has been found that some of the primary defense mechanisms (e.g., Mitogen-Activated-Protein Kinase (MAPK) cascade, jasmonic acid biosynthesis, flavonoid and phytoalexin biosynthesis, etc.) that intensify following the attack of pathogens can be activated without the intrusion of the actual pathogen by an immunochemical. Thus, we proved that plant resistance can be artificially conditioned.

The larvae of some species of the subgenus Orthocladius s. str. (Diptera, Chironomidae) are here described for the first time with corrections and additions to the descriptions of adult males and pupal exuviae. The identification of larvae is generally not possible without association with their pupal exuviae and/or adult males, so the descriptions here are based only on reared material or on pupae with the associated larval exuviae. Usually, Chironomidae larvae can be separated on the basis of morphometric characters, and the most discriminant characters ones are: 1- the ratio between the width of median tooth of mentum (Dm) and the width of the first lateral tooth (Dl) = mental ratio (DmDl), 2- the ratio between the length of the first antennal segment (A1) and the combined length of segments 2-5 (A(2-5)2-5) = antennal ratio (AR). The shape of mandible, maxilla, and other body parts are almost identical in all the species considered in this study. The larva of Orthocladius (Symposiocladius) lignicola is very characteristic and can be separated by the shape of mentum and the larvae of all the known species of Symposiocladius are characterized by the presence of large Lauterborn organs on antennae and of tufts of setae on abdominal segments. The larvae of Orthocladius (Orthocladius) oblidens and Orthocladius (Orthocladius) rhyacobius can be distinguished from other species basing on their large Dm and to each other by AR. A principal component analysis was carried out using 5 characters: 1- Dm, 2- Dl, 3- length of A1, 4- width of A1 (A1W), 5- combined length of segments 2-5 (A2-5). The most discriminant characters were Dm and A1, confirming that DmDl and AR can be used to separate species at larval stage, but the large superposition of morphometric characters in different species confirms that association with pupal exuviae is in any case needed to identify larvae. In future perspective, the development of reference DNA barcodes from specimens identified by specialists is recommended since possibly the best tool for larvae identification, but association of barcodes with morphotypes is in any case fundamental.

In this short note we present a technique using which one attributes frequency and wavevector to (almost) arbitrary scalar fields. Our proposed definition is then applied to the classical wave equation to yield a novel nonlinear PDE.

Formation in soccer is among the most important tactical choices for a successful match.Herein, the simulations of 420000 match-plays have been performed varying the formation, the number of opponents that are actively pressing the team, the speed of the opponents in attempting a pass interception. Dribbling has been neglected. The match-play ends either with a successful series of passes from a central back to the line of the strikers or with the opponents that steal the ball. In this work, I demonstrate that 3-4-3 formation, which is among the most employed formations, relates to the highest probability of success.

Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

Glioma accounts for 80% of all malignant brain tumors and is the most common adult primary brain tumor. Age is an important factor affecting the development of cancer as somatic mutations accumulate with age. In this study, we aimed to analyze the significance of age-related non-silent somatic mutations in glioma prognosis. Histological tumor grade depends on age at diagnosis in patients with IDH1, TP53, ATRX, and EGFR mutations. The hierarchical clustering of patients was dominantly separated by IDH1 and EGFR mutations. Furthermore, patients with IDH1 mutation were dominantly separated by TP53 and ATRX double mutation and its double wildtype counterpart. Patients with the double mutation showed poorer prognosis than those with the double wild type genotype. In conclusion, among the many somatic mutations, those in IDH1, TP53, ATRX, and EGFR are important for glioma classification based on histological grade. Patients with EGFR mutation had the poorest prognosis, whereas those with only IDH1 mutation showed the best prognosis.

During composting process, soils undergo many changes in their physical, chemical and biological properties. Composting has been widely known as an aerobic process during which organic matter is decomposed to humus like substances broken into many organic materials or compounds. The project aims to compare and analyze different combination of composting that yield different properties and nature of soils, and testing their soil physical properties. Dug three compost pits each 1.5 m in depth, labeled compost pit A consisting of potato peels – the only kitchen waste most common around the college hostels, compost pit B consisting of leaf litter and other garden trimmings and compost pit C consisting of cow dung and the other as a controlled experiment with no composting practices.

The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of ​​the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1 - considering the flexibility, modularity and portability - and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e. the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. 32 years' continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

This paper provides a framework for the assessment of household-level risk, incorporating both a individual social risk perspective and a location-based perspective. We use this framework as a heuristic to explore the effect of social reintegration choices individuals face, which are not be addressed by current policies. For example, we explore how integrating extended family households during COVID-19 without social distancing may affect household and community risk. The goal is to aid individual decision makers, who are seeking to maintain quality-of-life while navigating local policy, with nuance relating to location-specific behavior and disease prevalence.

Sentiment analysis to characterize properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy transform to capture stylized facts and mutual connections between time series having different nature. Six years of daily Bitcoin Prices and Google Trends are analyzed in order to establish new perspectives in the management of their dynamics.

To evaluate the efficacy of fish oil for protection against coronary heart disease (CHD), we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating the use of fish oil for protection against CHD. We retrieved relevant articles published from January 1966 to January 2020 by searching the PubMed, EMBASE, Cochrane CENTRAL, and Web of Science databases. RCTs of fish oil in preventing CHD were selected. The study quality was evaluated using the Cochrane Risk of Bias tool with RevMan 5.3 software. The first selection involved 350 citations. After screening and evaluation of suitability, 19 RCTs adjusted for clustering were included in the meta-analysis. All selected manuscripts considered that fish oil was effective in preventing CHD, secondary outcome measures included angina, sepsis and death. Compared with the control group, fish oil may confer significant protection against CHD (odds ratio = 0.84; 95% confidence interval: 0.72–0.98). There was no significant difference in the incidence of secondary outcomes between the observation group and the control group (P > 0.05). The above results show that fish oil plays an important role in reducing CHD and cardiovascular events. However, because of the suboptimal quality of the studies included into the meta-analysis, these results do not justify adding fish oils systematically to the heavy pharmaceutical assortment already recommended in CHD patients.

Randomized clinical trials represent the gold standard in therapeutic research. Nevertheless, observational cohorts of patients treated for multidrug-resistant (MDR) or rifampin-resistant (RR) tuberculosis (TB) also play an important role in generating evidence to guide MDR/RR TB Generally, summary exposure classifications (e.g., ‘ever versus never’, ‘exposed at baseline’) have been used to characterize drug exposure, in the absence of detailed longitudinal data on MDR-TB regimen These summary classifications, along with an absence of data on covariates that change throughout the course of treatment, constrain researchers’ ability to answer the most relevant questions while accounting for known This paper highlights the importance of regimen changes in improving inference from observational studies of longer MDR-TB treatment regimens and offers an overview of the data and analytic strategies required to do

This preprint is focused in the presence of plastics and microplastics in food. We will discuss how many we eat, and how they arrive to the food, and why. We will treat many other things, such as the waste treatment in Europe and in Spain, with updated data; how much plastic waste is generated; what are microplastics and how they are analyzed, I will tell about the experience we have at the University of Alicante (UA); how they can be removed and we will estimate how many we eat over the course of a year.

More than 1.1 billion people lack access to electricity in the world, among them 600 million are in sub-Saharan African countries. The socio-economic life of the rural communities is highly affected by electricity supply. The demanding Interest for growth and development in developing countries led to a renewed interest in solar lights. Liter of the night light is a solar-powered light that is constructed from waste plastic bottles which light up the rural community. This paper is about experimental analysis of liter of night light for potential use in rural electrification. The technology uses environmentally friendly and locally available materials. The system is provided with battery storage, which allows it to operate for 13.5 hours continuously at full charge. Solar radiation is converted by PV solar panel to electric current. Circuit board is developed and fabricated to regulate and deliver a consumable amount of voltage by the rechargeable lead-acid battery. After testing five different solutions, the study found out that vinegar solution as the optimal solutions for the technology that can deliver 27lux of light to the surrounding. Simultaneously the study found out, that the light that is emitted from the bottle bulb is a function of volume, depth and solution type. And for this relationship curves are developed. And, the technology can be used for rural communities as it is.

Recently the increased cost-effectiveness of high-throughput technologies has made available a large number of RNA sequencing datasets to identify circular RNAs (circRNAs). However, despite many computational tools were developed to predict circRNAs, a limited number of workflows exists to predict and to characterize circRNAs. Moreover, to the best of our knowledge, these available workflows do not ensure computational reproducibility and require advanced bash scripting skills to be correctly installed and used. To cope with these critical aspects we present Docker4Circ, a new computational framework designed for a comprehensive analysis of circRNAs composed of: circRNAs prediction, classification and annotation using public databases, the back-splicing sequence reconstruction; the internal alternative splicing of circularizing exons; the alignment-free circRNAs quantification from RNA-Seq reads, and, finally, their differential expression analysis. Docker4Circ was specifically designed for making easier and more accessible circRNAs analysis thanks to the following features: (i) its R interface; (ii) the encapsulation of its computational tasks into a docker image; (iii) an available user-friendly Java GUI Interface. Furthermore, Docker4Circ ensures a reproducible analysis because all its tasks were embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project (RBP, http://reproducible-bioinformatics.org/). The effectiveness of Docker4Circ was demonstrated on a real case study whose goal is to characterize the circRNAs predicted in colorectal cancer cell lines and quantified in public RNA-Seq experiments performed on primary tumor tissues. In conclusion, we propose Docker4Circ as a framework for reproducible and comprehensive analyses of circRNAs to efficiently exploit their biological role.

Systematic traders employ algorithmic strategies to manage their investments. As a result of the deterministic nature of such strategies, it is possible to determine their exact responses to any conceivable set of market conditions. Consequently, sensitivity analysis can be conducted to systematically uncover undesirable strategy behavior and enhance strategy robustness by adding controls to reduce exposure during periods of poor performance / unfavorable market conditions or increase exposure during periods of strong performance / favorable market conditions. In this study, we formulate both a simple systematic trend-following strategy (i.e., trading model) to simulate investment decisions, and a market model to simulate the evolution of instrument prices. We then map the relationship between market model parameters under various conditions and strategy performance. We focus, in particular, on identifying the performance impact of changes in both serial dependence in price variability and changes in the trend. The long-range serial dependence of the true range worsens performance of the simple classic trend-following strategy. During periods of strong performance, the dispersion of trading outcomes increases significantly as long-range serial dependence increases.

Aims of the paper are the results of a research on a wooden box that holds an important historical document, which is a hand Bible handwritten in the thirteenth century. The tradition connect this Bible to the name of Marco Polo (Venice, 1254 - Venice, 1324), who would be the owner and that it would accompany him on his travels (1262 and 1271) in China. The Bible, of fine workmanship, written on thin parchment, and its container, along with a yellow silk cloth, is preserved in the ancient and prestigious Laurentian Library in Florence. The manuscript was in very poor condition and in the course of the study (2011) was being restored. Aims of survey were to determine the place and period of realization of the box, or rather if it be contemporary or later than the manuscript it contains and whether it was made in the East or in Europe.

The Bay of Espirito Santo is located in the Espírito Santo State, in the eastern part of Brazil. It is surrounded by the city of Vitoria on one side and by the Atlantic Ocean on the other. Superficial sediments of Espirito Santo Bay were analyzed at 12 (western shallow silt sediments) + 8 (eastern sandy sediments and relatively deep sampling stations) = 20 uniformly distributed sampling points where geochemical analysis was performed. Nineteen elements were analyzed: Mo, Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, U, Th, Sr, Cd, Sb, Bi, V, Cr, Ba, and Al. This selection was made based on the most representative heavy metals present in this area and according to the results obtained from the geochemical analysis. Their concentrations were compared with metal contamination benchmarks like Screening Quick Reference Tables (SQuiRTs), Effects Range-Low (ERLs) and Effects Range-Median (ERMs), TELs (Threshold Effects Levels), PELs (Probable Effects Levels), ERM (Effects range median), and AETs (Apparent Effects Thresholds). Results indicate that there is no particular pollution condition able to alter the condition of any part of this water body. The Authors conclude that the Espirito Santo Bay is only moderately polluted and some elements are virtually absent.

The Bay of Espirito Santo is located in the Espírito Santo State, in the eastern part of Brazil. It is surrounded by the city of Vitoria on one side and by the Atlantic Ocean on the other. Superficial sediments of Espirito Santo Bay were analyzed at 12 (western shallow silt sediments) + 8 (eastern sandy sediments and relatively deep sampling stations) = 20 uniformly distributed sampling points where geochemical analysis was performed. Nineteen elements were analyzed: Mo, Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, U, Th, Sr, Cd, Sb, Bi, V, Cr, Ba, and Al. This selection was made based on the most representative heavy metals present in this area and according to the results obtained from the geochemical analysis. Their concentrations were compared with metal contamination benchmarks like Screening Quick Reference Tables (SQuiRTs), Effects Range-Low (ERLs) and Effects Range-Median (ERMs), TELs (Threshold Effects Levels), PELs (Probable Effects Levels), ERM (Effects range median), and AETs (Apparent Effects Thresholds). Results indicate that there is no particular pollution condition able to alter the condition of any part of this water body. The Authors conclude that the Espirito Santo Bay is only moderately polluted and some elements are virtually absent.

The optimum conditions for the fermentation of African locust bean (Parkia biglobosa) into a vegetable protein based condiment (Iru) were developed using Levenberg-Marquardt (or Powell) method (using PSI software) with three (3) variables namely; inoculum concentration (bacillus subtilis), temperature and the fermentation duration. African locust bean seeds were fermented at various temperature of 40 - 70 °С for five days (120 hours) with different concentrations of Inoculum. The proximate analysis shows that fermentation increased the percentage protein. Protein had the highest composition with about 51 % after 72 hours at the lowest fermentation temperature of 40°С.

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and

CO2 injection into geological formations is considered one way of mitigating the increasing levels of carbon dioxide concentrations in the atmosphere and its effect on and global warming. In regard to sequestering carbon underground, different countries have conducted projects at commercial scale or pilot scale and some have plans to develop potential formations for carbon dioxide storage. In this study, pure CO2 injection is examined on a model with the properties of bunter sandstone and then sensitivity analyses were conducted for some of the important parameters. The results of this study show that the extent to which CO2 has been convected in the porous media in the reservoir plays a vital role in improving the CO2 dissolution in brine and safety of its long term storage. We conclude that heterogeneity plays a crucial role on the saturation distribution and can increase or decrease the amount of dissolved CO2 in water. Furthermore, the value of absolute permeability controls the effect of the Kv/Kh ratio on the CO2 dissolution in brine. In other words, as the value of vertical and horizontal permeability decreases (i.e. tight reservoirs) the impact of Kv/Kh ratio on the dissolved CO2 in brine becomes more prominent. Additionally, reservoir engineering parameters, such as well location, injection rate and scenarios, also have a high impact on the amount of dissolved CO2.

The effects of buckwheat intake on cardiovascular diseases (CVD) have not been systematically investigated. The aim of the present study was to comprehensively summarise studies in humans and animals evaluating the impact of buckwheat consumption on CVD risk markers and to conduct a meta-analysis of relevant data. Thirteen randomised, controlled human studies, two cross-sectional human studies and twenty-one animal studies were identified. Using random effects models, the weighted mean difference of post-intervention concentrations of blood glucose, total cholesterol and triglycerides were significantly decreased following buckwheat intervention compared with controls [differences in blood glucose: -0.85 mmol/L (95% CI: -1.31, -0.39), total cholesterol: 0.50 mmol/L (95% CI: -0.80, -0.20) and triglycerides: 0.25 mmol/L (95% CI: -0.49, -0.02)]. Responses of a similar magnitude were seen in two cross-sectional studies. For animal studies, nineteen of twenty-one studies showed a significant reduction in total cholesterol of between 12 and 54%, and fourteen of twenty studies showed a significant reduction in triglycerides of between 2 and 74%. All exhibited high unexplained heterogeneity. There was inconsistency in HDL cholesterol outcomes in both human and animal studies. It remains unclear whether increased buckwheat intake significantly benefits other markers of CVD risk, such as weight, blood pressure, insulin, and LDL-cholesterol, and underlying mechanisms responsible for any effects are unclear.

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behavior, but we do not yet understand how their interaction increases the risk for suicidal behavior. A new approach called network analysis can help us better understand this process as it allows to visualize and quantify complex association between many different symptoms or risk factors. A network analysis of data contain information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess these damages in such non-accessible areas, and automated image processing techniques would be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Citrus species essential oils (EOs) are significant sources of bioactive compounds and demonstrate diverse therapeutic effects. However, limited documentation exists regarding the phytochemicals associated with their biological activities. This study aims to assess the antioxidant activity, plant inhibitory properties, and brine shrimp cytotoxic effects of EOs derived from three Citrus species: C. sinensis, C. limon, and C. hystrix. Utilizing chemometric analysis and gas chromatography-mass spectrometry (GC-MS) fingerprints, the volatile components contributing to antioxidant activity were elucidated. The peels of the Citrus samples were hydro-distillated to obtain EOs, and subsequently subjected to antioxidant, plant inhibitory, and brine shrimp cytotoxic assays. The results indicated that C. limon EO exhibited the highest antioxidant activity, while C. hystrix and C. sinensis EOs demonstrated pronounced inhibitory effects against Aetrmia salina and Lactuca sativa, respectively. GC-MS analysis facilitated the identification of key compounds in each EO. Principal component analysis (PCA) and hierarchy cluster analysis (HCA) effectively categorized Citrus EOs based on their antioxidant properties, highlighting the proximity of C. limon and C. sinensis. Among the identified compounds, D-limonene, α-Terpineol, Caryophyllene, (+)-3-Carene, β-Pinene, (-)-Spathulenol, trans-p-Mentha-1(7),8-dien-2-ol, and trans-Verbenol were the most discriminating compounds affected the antioxidant activity of C.limon and C. sinensis EOs.

A set of polymer composite bolted T-joints with novel configuration consisting of internal skeleton and external skin was fabricated using a prepreg-RTM co-curing molding process. Experiments were conducted to study their mechanical properties under bending load. A finite element model with a polymer resin area between the skin and skeleton was established and verified by the experimental results. Then, the damage propagation process and failure mechanism of the joint and the influence of three factors related to the layer characteristics of the skin and skeleton were investigated by the validated models. The results show that the bending stiffness and the yield limit load of the novel composite T-joint are 0.81 times and 1.65 times that of the 2A12 aluminum T-joint respectively, while at only 55.4% of its weight. The damage of the joint initiates within the resin area and leads to a degradation of the joint bending performances. The preferred stacking sequence of the skeleton is [0/+45/90/-45]ns when primarily subjected to bending loads. The decrease in bending performances is within 5% with the inclining angle of the skeleton less than 12 degrees. The more layers of 90° in the skin, the better bending performances of the joints, while the more layer of 0°, the poorer bending performances.

Qualtra, an innovative 10 MW geothermal power plant proposal, employs a closed-loop design to mitigate emissions, ensuring no direct release into the atmosphere. A thorough assessment utilizing energy and exergy analysis, Life Cycle Assessment (LCA), Exergo-economic and Exergo Environmental Analysis (EevA) was conducted. The LCA results, utilizing the ReCiPe 2016 midpoint methodology, encompass all the spectrum of environmental indicators provided. The technology implemented makes it possible to totally avoid direct atmospheric emissions from the Qualtra plant, so the environmental impact is mainly due to indirect emissions over the life cycle. The results obtained for the Global Warming Potential indicator is about 6.6 g CO2/kWh, notably lower compared to other conventional systems. Contribution analysis reveals that the construction phase dominates, accounting for over 90% of the impact in all LCA MidPoint categories. At the component level, a single score calculation incorporating normalization and weighting was executed. The resulting single score is then used into Exergo-Environmental Analysis (EEvA), highlighting the well system as the most impactful contributor, constituting approximately 45% of the total impact. Other substantial contributions to the environmental impact include the condenser (21%), the turbine (17%), and the HEGeo (14%). The Exergo-Economic analysis assesses cost distribution across major plant components, projecting an electricity cost of about 8.3 c€/kWh.

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis of responses where participants indicated their biggest concern related to COVID-19 showed that the average subjectivity in responses by the age group of 41-50 decreased from April 2020 to June 2020, the average subjectivity in responses by the age group of 71-80 drastically increased from May 2020, and the age group of 11-20 indicated the least level of subjectivity in their responses between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, and 81-90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11-20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21-30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31-40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41-50, 51-60, 61-70, 71-80, and 81-90. Finally, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The deep learning model used the responses to multiple questions in the context of fear, preparedness, and response related to COVID-19 from the dataset and achieved an overall performance accuracy of 91.62% after 500 epochs.

N-acyl hydrazone (NAH) has been remarked as a promising scaffold in drug design, given its versatility, ease of synthesis, and appealing biological activities (i.e., antimicrobial, antitumoral, analgesic, and anti-inflammatory properties). In the global context of increasing resistance of pathogenic bacteria to antibiotics, NAHs represent potential solutions for developing improved treatment alternatives. Thus, this study presents 6 new derivatives of (EZ)-N'-benzylidene-(2RS)-2-(6-chloro-9H-carbazol-2-yl)propanohydrazide that were obtained through a microwave-assisted synthesis method. In more detail, we have joined two pharmacophore fragments in a single molecule, represented by an NSAID-type carprofen structure and a hydrazone-type structure, obtaining a new series of NSAID-N-acyl hydrazone derivatives that were further characterized spectrally using FT-IR, NMR, and HRMS investigations. Moreover, the compounds have been evaluated for their tuberculostatic activity, testing their effects on four M. tuberculosis strains (two of them susceptible to rifampicin (RIF) and isoniazid (INH), one susceptible to RIF and resistant to INH and one resistant to both RIF and INH). The results of our research highlight the potential of the prepared compounds in fighting against antibiotic-resistant M. tuberculosis strains.

The ultimate analysis parameters including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass was rarely found to be predicted by nondestructive tests until to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near infrared (NIR) raw spectra of non-wood and wood samples from fast growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5−range, and MP 3−range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples was non-wood samples of 65-67% and wood samples was 33-35%, and the model performance were evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation values (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA−PLSR, yielding R2P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR−5 range method with R2P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR−3 range method, resulting in R2P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of H model provided R2P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N, exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for robust model but also they must provide their trend line characteristics in the scatter plot i.e. correlation coefficient (R), slope and intercept (same slope and slope approached to 1 and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope and intercept to obtain the better optimized model were studied. The results show that the different species affected model performance of each parameter prediction in a different manner and by scatter plot analysis which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time of the effect is studied by the principle of scatter plot.

membrane separation technology shows promise, particularly gas separation membranes, including ceramic membrane. Ceramic membranes are increasingly gaining attention for their acid and alkali stability, corrosion resistance, high-temperature tolerance, and mechanical strength. This study focuses on alumina support ceramic membrane for CO2 capture. The membranes were characterized using contact angle measurements, scanning electron microscopy (SEM), and Fourier Transform Infrared spectroscopy (FTIR) analysis. The contact angle measurements confirmed the hydrophilic, while SEM analysis showed even particle distribution in the alumina ceramic membranes respectively. EDAX analysis revealed the elemental composition in the alumina support matrix. FTIR analysis demonstrated chemical interactions of the support membrane. Single gas permeation experiments were conducted. It can be observed that Nitrogen gas permeated faster with increase in feed pressure through the unmodified ceramic inorganic hydrophobic membrane more than the other two single gases, O2, and CO2). As a result, only high permeance separation membranes with realistic size and pressure conditions may be considered of as a practical alternative for CO2 capture. However, the presence of non-selective defects limited the improvement in selectivity of hydrophilic ceramic membrane. In this case, there is need to modify the ceramic membrane used to increase the flux, permeance of CO2, and selectivity of CO2.

Abstract: membrane separation technology shows promise, particularly gas separation membranes, including ceramic membrane. Ceramic membranes are increasingly gaining attention for their acid and alkali stability, corrosion resistance, high-temperature tolerance, and mechanical strength. This study focuses on alumina support ceramic membrane for CO2 capture. The membranes were characterized using contact angle measurements, scanning electron microscopy (SEM), and Fourier Transform Infrared spectroscopy (FTIR) analysis. The contact angle measurements confirmed the hydrophilic, while SEM analysis showed even particle distribution in the alumina ceramic membranes respectively. EDAX analysis revealed the elemental composition in the alumina support matrix. FTIR analysis demonstrated chemical interactions of the support membrane. Single gas permeation experiments were conducted. It can be observed that Nitrogen gas permeated faster with increase in feed pressure through the unmodified ceramic inorganic membrane more than the other two single gases, O2, and CO2). As a result, only high permeance separation membranes with realistic size and pressure conditions may be considered of as a practical alternative for CO2 capture. However, the presence of non-selective defects limited the improvement in selectivity of hydrophilic ceramic membrane. In this case, there is need to modify the ceramic membrane used to increase the flux, permeance of CO2, and selectivity of CO2.

Greek consumers perceive an increased risk of pesticide residues in food. This study examined Greek consumers' perceptions on the safety of Greek plant-based food compared to those originating from other EU countries. Half of Greek consumers believe that Greek food is as safe as other European foods, while the other half disagree. According to a principal components analysis and a bivariate logistic regression, several factors, such as perceived safety of plant foods, education, age, gender, traceability, perceived benefits and risks of pesticides, actual pesticide use, and authoritative information sources, influence this attitude. Authoritative knowledge in this field can reduce risk perception and improve Greek consumers’ attitudes towards food safety. Latent class analysis identified two categories of consumers. The first class receives limited information about pesticides, leading to a lower perceived pesticide benefits, higher mistrust of traceability and concerns about pesticide residues. In contrast, the second class actively searches for information from credible sources, endorses the Greek plant foods safety, acknowledges the pesticides benefits, and trust traceability. Official information is associated to reduced risk perception. Regulators should consider the impact of socio-demographic and other intrinsic characteristics on individuals' risk perceptions and prioritise transparency in risk communication strategies.

As a consequence of applying advanced maintenance practices, the theoretical probability of failures is relatively low. However, observation of low market intelligence and maintenance management has been reported. The experimental investigation is supported by findings from a survey targeting asset-intensive companies applying hydraulic power systems. Next, the study incorporates multidimensional data analysis using CA-AHC (Correspondance Analysis with Agglomerative Hierarchical Clustering) approach. The non-parametric machine learning models are used from generated feature subspace to extract features affecting maintenance performance indicators. The results support empirical evidence that equipment age increases the probability of failures. However, the novel findings show that number of maintenance personnel, equipment size measured by nominal working energy consumption, and activities dedicated to maintaining fluid cleanliness impact regression results of companies utilising hydraulic applications.

As a result of the growing demand for foods with reduced animal protein content, many new alternative diets are now emerging. Nevertheless, recent studies have shown that the Western population is unprepared for drastic changes and is disinclined to accept foods based on alternative proteins. However, hybrid products might become a good transitional offer. This study used a desirability-based mixture design to model hybrid spreadable cheese analogues (SCAs). The design combined the dairy protein (MPC), Tenebrio molitor (IF) and faba bean (FBP) flours. Nine SCAs with different MPC/FBP/IF ratios were formulated, representing 0, 50 and 100% MPC replacement (7.1% of the formula). Incorporating the IF negatively impacted the desirable texture properties. The FBP flour improved the texture (achieving increased firmness and stickiness and decreased spreadability), but only when combined with MPC. Changing the MPC/FBP/IF ratio affected the colour of SCAs. Sensory analysis showed that hybrid SCAs (≤ 50% MPC) had a more characteristic cheesy flavour than the commercial plant-based reference, and sample C2 had a texture profile similar to the dairy reference. Samples containing IF showed a better flavour profile than the products without IF. The SCAs had higher protein and lower saturated fat, starch and sugar con-tent than commercial analogues.This study demonstrates that the inclusion of alternative proteins can be effective as a strategy to reduce dairy protein content in hybrid product formulations.

Drinking water quality is a major concern, especially in African countries. This manuscript aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the chemical elements' composition and origin. Therefore, chemometrics techniques such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while au-tocorrelation analysis (AA) supplemented by simple spectral density analysis (SDA) is used for TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, three groups were controlled by TDS and EC. G1 reveals a close association between SO42−, K+, Ca2+, and TDS; G2 reveals a close association between Na+, Cl−, Mg2+, and EC; G3 shows the dissociation of bicarbonates HCO3− and NO3− from other chemical elements. AA shows a linear interrelationship of EC, Mg2+, Na+, K+, Cl−, and SO42−. However, NO3− and HCO3− indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg2+, Na+, K+, Cl−, and SO42− have a similar trend with EC. None-theless, pH, Ca2+, HCO3− and NO3− exhibit multiple peaks related to the presence of several dis-tinct cyclic mechanisms. The methods enabled the authors to conclude that the geochemical processes influencing the chemical composition are: (i) dissolution of evaporated mineral depos-its, (ii) water-rock interaction, and (iii) evaporation process. In addition, Groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca+ and another recorded only with negative charges on HCO3− and NO3−. On the other hand, SO42-, K+, Ca2+, and TDS are the major predominant elements in the groundwater’s chemical composition. The major participation of salts and chlorides is in the electrical conductivity of water. The dominance of the lithological factor in the overall mineralization of the Plio-Quaternary surface aquifer waters. The origins of HCO3− and NO3− are different. Indeed, carbonated for HCO3- has a carbonate origin, whereas NO3– has an anthropogenic origin. The salinity was affected by Mg2+, SO42-, Cl-, Na+, K+, and EC. Ca2+, HCO3− and NO3− are resulted from human activity fertilizers, the carbonate facies outcrops, and domestic sewage.

In this work, we have proposed two new methods for the synthesis of [TcO2L4]+ (where L = im-idazole (Im), methylimidazole (MeIm)) complexes using thiourea (Tu) and Sn(II) as reducing agents. The main and by-products of the reactions were determined, and possible reaction mechanisms were proposed. We have shown that the reduction of Tc(VII) with thiourea is ac-companied by the formation of the Tc(III) intermediate and further oxidation to Tc(V). The reac-tion conditions changing can lead to the formation of Tc(VII) and Tc(IV) salts. Seven new crystal structures are described in this work: Tc(V) complexes, salts with Tc(VII) and Tc(IV) anions. For halide salts of Tu the cell parameters were determined. In all obtained compounds, except for [TcO2(MeIm)4]TcO4, there are π-stacking interactions between aromatic rings. An increase in the anion size lead to weaken intermolecular interactions. Halogen bonds and anion-π interactions have also been found in hexahalide-containing compounds. The Hirshfeld surface analysis showed that the main contribution to the crystal packing is made by van der Waals interactions of the H···H type (42.5–55.1%), H···C/C ···H (17.7–21.3%) and hydrogen bonds, which contribute 15.7–25.3% in total.

Research investigating the neural substrates underpinning parental behaviour has recently gained momentum. Particularly, the hypothalamic medial preoptic area (MPOA) has been identified as a crucial region for parenting. The current study conducted a scientometric analysis of publications from 01 January 1972 to 19 January 2021 using CiteSpace software to determine trends in the scientific literature exploring the relationship between MPOA and parental behaviour. In total, 677 scientific papers were analysed, producing a network of 1509 nodes and 5498 links. Four major clusters were identified: "C-Fos Expression'', "Lactating Rat'', "Medial Preoptic Area Interaction'' and "Parental Behavior''. Their content suggests an initial trend in which the properties of the MPOA in response to parental behavior were studied, followed by a growing attention towards the presence of a brain network, including the reward circuits, regulating such behavior. Furthermore, while attention was initially directed uniquely to maternal behavior, it has recently been extended to the understanding of paternal behaviors as well. Finally, although the majority of the studies were conducted on rodents, recent publications broaden the implications of previous documents to human parental behavior, giving insight into the mechanisms underlying postpartum depression. Potential directions in future works were also discussed.

The copper mining industry is increasingly using artificial intelligence methods to improve cop-per production processes. Recent studies reveal the use of algorithms such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry, as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real. Finally, the models obtained show the following mean values: acc=94.32, p=88.47, r=99.59, and mcc=2.31. These values are highly competitive as compared with those obtained in similar studies using other approaches in the context.

Product Design is getting nowadays new challenges on developing new products, since the industry is in a rush to introduce products into the marketplace, where customers demand products faster, cheaper, and free of failures. In the meantime, global companies are always trying to improve their Product Design process to get advantages over their competitors using proven tools like FEMA and mixing methodologies like Fuzzy theories with FMEA. Even today using all this tools and combination of methodologies there is a gap to address and it is required a robust risk analysis solving current issues in the electronic industry. This document aims to reveal a novel integrated method, where Failure Mode and Effect Analysis, Pythagorean Fuzzy Sets and Dimensional Analysis are cohesive into a model that minimize the uncertainty of the ranking and prioritization over the Failure Mode and Effect Analysis execution, helping to identify risks within an accurate grade over possible failures in the Product Design process. A real practical example is used to show the proposed method, where it is identified a robust methodology integration and solid and results using a sensitivity analysis.

Nowadays, the fingerprinting methodologies of olive oils are dominated. They consider the entire analytical signal, which is acquired and recorded by the analytical instrument, directly from olive oil or isoleted fraction, i,e chromatogram. The shape and intensity of the recorded signal the instrumental fingerprint from the whole olive oil adulteration. Therefore, the methodolygy is based on the chemical composition (Fatty acids and Triglycerides compositions). However, Fatty acids composition as an indicator of purity suggests that linolenic acid content could be used as a parameter for the detection of extra virgin olive oil fraud with 5% of soybean oil. The adulteration could also be detected by the increase of the trans-fatty acid contents with 3% of soybean oil, 2% of corn oil and 4% of sunflower oil. The use of the ∆ECN42 proved to be effective in the Chemlali extra-virgin olive oil adulteration even at low levels: 1% of sunflower oil, 3% of soybean oil and 3% of corn oil. Therefore, compared to classical methods PCA and new approach of using LDA application could represent an alternative and innovative tool for faster and cheaper evaluation of extra-virgin olive oil adulteration.

To build a representation of what we see, the human brain recruits regions throughout the visual cortex in cascading sequence. Recently, an approach was proposed to evaluate the dynamics of visual perception in high spatiotemporal resolution at the scale of the whole brain. This method combined functional magnetic resonance imaging (fMRI) data with magnetoencephalography (MEG) data using representational similarity analysis and revealed a hierarchical progression from primary visual cortex through the dorsal and ventral streams. To assess the replicability of this method, here we present results of a visual recognition neuro-imaging fusion experiment, and compare them within and across experimental settings. We evaluated the reliability of this method by assessing the consistency of the results under similar test conditions, showing high agreement within participants. We then generalized these results to a separate group of individuals and visual input by comparing them to the fMRI-MEG fusion data of Cichy et al. (2016), revealing a highly similar temporal progression recruiting both the dorsal and ventral streams. Together these results are a testament to the reproducibility of the fMRI-MEG fusion approach and allows for the interpretation of these spatiotemporal dynamic in a broader context.

A groundbreaking integrated predictive model, termed Cyber Forensics Behavioral Analysis (CFBA), has been developed in an environment characterized by rapidly evolving and increasingly sophisticated cyber threats. This model merges Cyber Behavioral Sciences with Digital Forensics to enhance the prediction accuracy and effectiveness of cyber threats originating from specific Autonomous System Numbers (ASNs). It has been observed that traditional cybersecurity strategies, predominantly focused on technical aspects, must be improved in addressing the complex landscape of cyber threats. Consequently, a novel approach has been proposed in this research, combining technical expertise with insights into the behavior of cybercriminals, thereby addressing a significant gap in existing methodologies. A mixed-methods approach has been employed in the study, integrating quantitative and qualitative research methods. This approach has created a comprehensive framework incorporating digital forensics, cybersecurity, computer science, forensic psychology, and the cyber behavioral sciences which incorporates disciplines scuch as cyberpsychology and forensic cyberpsychology. The study is anchored around four key concepts: (1) Forensic Cyberpsychology, which focuses on understanding psychological aspects of cybercriminal behavior; (2) Digital Forensics, involving the collection and analysis of digital evidence from cyber incidents; (3) Predictive Modeling, which utilizes historical data and patterns to anticipate potential cyber threats; and (4) the Cyber Behavioral Analysis Metric (CBAM) and Cyber Behavioral Score (CBS), tools designed for evaluating and scoring ASNs based on their behavior in terms of cybersecurity threat risks. Challenges encountered in the development of this integration have been significant. Initial setbacks included the misalignment of traditional cyber defense methods with the evolving nature of cyber threats and the absence of a holistic approach in pre-existing models. Through the narrative of these challenges and the subsequent development of the CFBA model, the research highlights the necessity for an interdisciplinary approach to cybersecurity. The outcomes of this study are not merely theoretical but provide practical, actionable tools and frameworks that markedly improve the precision of predicting cyber threats, especially from ASNs. The CFBA model represents a significant shift in cybersecurity strategies, highlighting the importance of integrating behavioral insights with technical knowledge. The study emphasizes the need for ongoing research and collaboration in cybersecurity, advocating for an approach that is as comprehensive and multifaceted as the cyber threats it seeks to mitigate. This research has been conducted to contribute significantly to cybersecurity, offering new perspectives and methodologies in a rapidly changing domain.

The central promise of personalized medicine is individualized treatments that target molecular mechanisms underlying the physiological changes and symptoms arising from disease. We demonstrate a bioinformatics analysis pipeline as a proof-of-principle to test the feasibility and practicality of comparative transcriptomics to classify two of the most popular in vivo diet-induced models of coronary atherosclerosis, apolipoprotein E null mice and New Zealand White rabbits. Transcriptomics analyses indicate the two models extensively share dysregulated genes albeit with some unique pathways. For instance, while both models have alterations in the mitochondrion, the biochemical pathway analysis revealed, Complex IV in the electron transfer chain is higher in mice, whereas the rest of the electron transfer chain components are higher in the rabbits. Several fatty acids anabolic pathways are expressed higher in mice, whereas fatty acids and lipids degradation pathways are higher in rabbits. This reflects the differences between two translational models of atherosclerosis. This study validates transcriptome analysis as a potential method to precisely identify altered cellular and molecular pathways in atherosclerotic disease, which can be used to individualize treatment even in the absence of genetic data.

Platelet-activating factor (PAF) is a phospholipid-derived inflammatory mediator, triggering various inflammatory conditions, including eosinophil activation and recruitment. Despite the unclear mechanism underlying the formation and maintenance of nasal polyp in chronic rhinosinusitis with nasal polyps (CRSwNP), there is strongly indication of its association with type 2 inflammation. This study aimed to quantify PAF-associated gene expression, examining enzymes involved in PAF synthesis (lysophosphatidylcholine acyltransferase 1 (LPCAT1), LPCAT2, LPCAT3, and LPCAT4), and PAF degradation (PAF acetylhydrolases 1 B2 (PAFAH1B2), PAFAH1B3, and PAF acetylhydrolases 2 (PAFAH2)), and PAF receptor (PTAFR). Transcriptomic analysis encompassed CRSwNP, including eosinophilic CRS (ECRS) (n=9), nonECRS (n=8), and a subgroup of ECRS with aspirin exacerbated respiratory disease (Asp) (n=3) based on the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) criteria. Gene expression profiles from nasal polyps in CRSwNP patients and the uncinate process in normal subjects (controls) (n=6) were analyzed using bulk RNA barcoding and sequencing (BRB-seq). Hierarchal analysis, utilizing the average linkage method with iDEP1.1, clarified the relationship between Differentially Expressed Genes (DEG) expressions and cluster formation. Two clusters (cluster 1 (n=11) and cluster 2 (n=9)) of CRSwNP divided by hierarchal analysis showed distinct gene expression profiles in the specific pathway of “cytokine-cytokine receptor interaction” in KEGG pathway and upregulated type 2 inflammatory gene expression in cluster 2. In CRSwNP subclassification of ECRS, nonECRS, and Ctrl, PTAFR was only upregulated in ECRS and nonECRS. In cluster analysis, cluster 1 showed significant downregulation of LPCAT2 and upregulation of PTAFR expression, while cluster 2 showed significant upregulation of LPCAT1, PAFAH1B2, and PTAFR and downregulation of PAFAH2 expression. These findings suggest that cluster 2, characterized by severe type 2 inflammatory gene expression, manifest a prominent PAF-associated pathophysiology. Understanding this high PAF-associated pathophysiology in cluster 2 could provide valuable insights into the treatment and management of CRSwNP.

This study explores the significance of active suspension systems for vehicles with lower chassis compared to conventional ones, aiming at the development of future automobiles. Conventional linear MR (Magneto-Rheological) dampers were found inadequate in ensuring sufficient vibration control because the vehicle's chassis becomes lowered in the unmanned vehicles or purposed based vehicles. As an alternative, a rotary type of MR damper is proposed in this work. The proposed damper is designed based on pre-specified design parameters through mathematical modeling and magnetic field analyses. Subsequently, a prototype of the rotary MR damper identical to the design is fabricated, and effectiveness is shown through experimental investigations. In configuring the experiments, a proportional-integral (PI) controller is employed for current control to reduce the response time of the damper. The results presented in this work provides useful guidelines to develop a new type of MR damper applicable to various types of future vehicles’ suspension systems with low distance from the tire to the body floor.

The EU’s energy targets are to achieve at least 32% renewables in the energy mix by 2030. Part of the solution is strengthening consumer rights by empowering individuals to generate their own electricity. The study aimed to identify the most suitable energy system for electricity generation of up to 50 kW in the EU residential sector. The Multi-Criteria Decision Analysis was used to compare four systems: proton exchange membrane fuel cell with photovoltaic panels, photovoltaic panels, biomass powered Stirling engine, and solar powered Stirling engine. Based on the results, the most beneficial system for household electricity generation is the Biomass-fueled Stirling engine system due to its affordability, reliability, and low environmental impact. Governments and businesses can use these findings to improve information for the residential sector and enable the transition to renewable energy.

The Apetala2/ethylene response factor superfamily refers to a group of transcription factors that share a conserved AP2 DNA binding domain. These factors have been found to have different roles in plant responses to both biotic and abiotic stresses. Samples of hexaploid wheat, tetraploid pasta (or durum wheat), and diploid wheat progenitors were selected. The 29 dehydration-responsive element binding transcription factors in these samples were downloaded from NCBI GenBank for six different countries: Iran, China, Italy, France, Afghanistan, and Azerbaijan. The AP2 domain sequences were identified from the dehydration-responsive element binding transcription factors using PROSITE, ProDom, and SMART software. Next, all sequences were aligned using Multalin and Jalview software. The aligned sequences were then analyzed to identify amino acid locations, types, and frequencies. The multiple alignments showed that approximately 76% of the amino acid residues in the AP2 domains are conserved. According to the amino acid analysis, alanine, serine, and glutamic acid are the most abundant amino acids found in three motifs. The performed phylogenetic analysis illustrates the role of geographical effects on the transcription factor sequences of bread wheat in the Middle East. Significant differences were found between Iranian and Chinese transcription factor sequences. Moreover, genetic variation was observed in the transcription factors of Italian sequences found in pasta and wheat progenitors. Motif structures play a critical role in the domain organization of wheat proteins to enhance the characteristics of assorted metabolic pathways. The structure of the AP2 domain was analyzed by several programs, I-TASSER for instance, to identify the α-helix, β-sheets, and the regions of some significant amino acids in the 3-D model.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~ 40% of protein-coding genes in the model plant A. thaliana are still not categorized in the Gene Ontology Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus), the number of BP term annotation is far fewer, ~22%. In the current study we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and performed expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust high quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which, two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTL or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. In all, we have implemented a valuable strategy to better describe genes within specific biological processes.

The wavelet spectral characteristics of three respiratory muscle signals (Scalenus (SC), Parasternal intercostal (IC) and Rectus abdominis (RA)) and one locomotor muscle Brachioradialis (BR) were analyzed in the time-frequency (T-F) domain during breath-holding (BH). Study was performed for an end-expiratory BH maneuver on twelve healthy, physically active, naive apneists (6 professional athletes; 6 recreational athletes, two in the post-COVID-19 period) using surface electromyography (sEMG). We observe individual effects dependent on muscle oxygenation and person’s fitness, consistent with the mechanism of motor unit (MU) recruitment and the transition of slow-twitch oxidative (type 1) to fast-twitch (oxidative) glycolytic (type 2) muscle fibers. Professional athletes had longer BH duration (BHD) and strong hypercapnic response on expiratory RA, which is activated abruptly at higher BHDs in a person-specific range below 250 Hz and dependent on BHD. This is in contrast with recreational athletes, who had strong hypoxic response on inspiratory IC, which is activated faster and gradually in the frequency range of 250-450 Hz (independent of the apneist and BHD). This pilot study preliminarily indicates that it is possible to non-invasively assess the physiological characteristics of skeletal muscles, especially oxygenation, and improve physical fitness tests by determining T-F features of elevated myoelectric IC and RA activity during BH.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

Principal component analysis (PCA) is used to reduce the dimensionalities of high dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods, and their applications for investigating protein dynamics.

The epidemiological situation generated by COVID-19 has highlighted the importance of applying non-pharmacological measures. Among these, mass screening of the asymptomatic general population has been established as a priority strategy by carrying out diagnostic tests to limit the spread of the virus. In this article, we aim to evaluate the economic impact of mass COVID-19 screenings of an asymptomatic population through a Cost-Benefit Analysis based on the estimated total costs of mass screening versus health gains and associated health costs avoided. Excluding the value of monetized health, the Benefit-Cost ratio was estimated at approximately 0.45. However, if monetized health is included in the calculation, the ratio is close to 1.20. The monetization of health is the critical element that tips the scales in favour of the desirability of screening. Screenings with the highest return are those that maximize the percentage of positives detected.

The COVID-19 infections in Pakistan are spreading at an exponential rate and a point may soon be reached where rigorous prevention measures would need to be adopted. Mathematical models can help define the scale of an epidemic and the rate at which an infection can spread in a community. I used ARIMA Model, Diffusion Model, SIRD Model and Prophet Model to forecast the magnitude of the COVID-19 pandemic in Pakistan and compared the numbers with the reported cases on the national database. Results depicts that Pakistan could hit peak number of infectious cases between June 2020 and July, 2020.

Obesity is a global public health problem and the environment as its major determinant. To identify interventions an evidence base is warranted. To this aim we investigate the relationship between the consumption of foods and eating locations (like home, school/work and others) in British adolescents, using data from the UK National Diet and Nutrition Survey Rolling Program (2008–2012 and 2013-2016). Cross-sectional analysis of 62,523 food diary entries from this nationally representative sample then focused on foods contributing up to 80% total energy to the daily adolescent´s diet. Correspondence Analysis (CA) was first used to generate food-location relationship hypotheses and Logistic Regression (LR) to quantify the evidence in terms of odds ratios and formally test those hypotheses. The less-healthy foods that emerged from CA were chips, soft drinks, chocolate and meat pies. Adjusted Odds Ratios (99% CI) for consuming specific foods at a location “Other” than home (H) or school/work (S) in the 2008-12 survey sample were: for soft drinks 2.8 (2.1 to 3.8) vs. H and 2.0 (1.4 to 2.8) vs. S; for chips 2.8 (2.2 to 3.7) vs. H and 3.4 (2.1 to 5.5) vs. S; for chocolates 2.6 (1.9 to 3.5) vs. H and 1.9 (1.2 to 2.9) vs. S; and for meat pies 2.7 (1.5 to 5.1) vs. H and 1.3 (0.5 to 3.1) vs. S. These trends were confirmed in the 2013-16 survey sample. Interactions between location and BMI were not significant in either sample. In conclusion, our study showed that adolescents are more likely to consume specific less-healthy foods at locations away from home and school/work, irrespective of BMI. Such locations include leisure places, food outlets and “on the go”, hence public health policies to discourage less-healthy food choices in these locations is warranted for all adolescents.

This paper proposes the impact of plug-in electric vehicles integrated into power distribution system based on voltage dependent control. The plug-in electric vehicles was modeled as the static load model in power distribution systems under balanced load condition. The power flow analysis is determined by using the basic parameters of the electrical network. The main point of this study are compare with voltage magnitude profiles, load voltage deviation, and total power losses of the electrical power system. There are investigating the affected from constant power load, constant current load, constant impedance load and plug-in electric vehicles load, respectively. The IEEE 33 bus test system is used to test the proposed method by assigning each load type to a balanced load in steady state and applied the solving methodology based on the bus injection to branch injection matric, branch current to bus voltage matrix, and current injection matrix to solve the power flow problem. The simulation results showed that the plug-in electric vehicles load had the lowest impact compared to other loads. The lowest plug-in values for the electric vehicle loads were 0.062, 119.67 kW and 79.31 kVar for the load voltage deviation, total active power loss and total reactive power loss, respectively. Therefore, this study can be verified that the plug-in electric vehicles load were affected to the lowest of the electrical power system in condition to same sizing and position. So that, in condition to the plug-in electric vehicles load added into the electrical power system with the conventional load type or complex load type could be considered that the affected from the plug-in electric vehicles load in next study.

In this research, univariate and bivariate statistical methods were applied to rainfall, river and piezometric level datasets belonging to 24 years long time series (1986-2009). These methods, that often are used to understand the effects of precipitation on rivers and karstic springs discharge, have been used to assess, piezometric level response to rainfall and river level fluctuations in a porous aquifer. A rain gauge, a river level gauge and three wells, located in Central Italy along the lower Pescara river valley in correspondence of its important alluvial aquifer, provided the data. The statistical analysis has been used within a known hydrogeological framework, which has been refined by mean of a photo-interpretation and a GPS survey. Water-groundwater relationships were identified following the autocorrelation and cross-correlation analyses; the spectral analysis and mono-fractal features of time series were assessed, in order to provide information on multy-year variability, data distributions, their fractal dimension and the distribution return time within the historical time series. The statistical-mathematical results were interpreted through field work that identified distinct groundwater flowpaths within the aquifer and enabled the implementation of a conceptual model, improving the knowledge on water resources management tools.

The monosodium glutamate (MSG) that elicits umami taste, one of the five primary tastes, has been shown to be a helpful tool in reducing salt intake and improving eating patterns. This study aimed to investigate how MSG taste is perceived and evaluated in a sample of adult participants living in Austria, taking into account their sex, education, and travel habits. A Text Semantic-mining Analysis (TSA) was conducted using data from 300 adults (152 males and 148 females, mean age = 26.71 years). They also performed an MSG and NaCl (salt) discrimination test and were further interviewed about taste preferences, travel habits, and type of residence. The results indicated that the taste sensation elicited by the MSG solution was complex, unfamiliar and had negative valence. The descriptor most commonly used for MSG was "sour," followed by "bitter" and "salty" for the whole group, but these differed by gender. Both males and females preferred the salt solution over MSG, with no significant difference between the two groups. Additionally, there were apparent differences in primary/spicy taste preferences between genders, as males tended to prefer spicy flavors while females favored sweet and salty tastes. This finding underscores the unexplored significance of umami taste, suggesting the importance of establishing an accurate notion in European culture and promoting its usage in food choices for better health.

Background: With the rapid improvement of the healthcare technologies, security and privacy of the most sensitive data, health data is at risk. Protecting patient privacy has many components, even though the data are in electronic format. Although patient privacy is thoroughly discussed in the literature, there is no study presenting all components of patient privacy protection. Methods: In this study, a complete evaluation framework is presented, as an evaluation tool, an inventory is developed, reliability and validity of the inventory is examined. Study is conducted in three phases: Conceptual framework development, inventory development and an evaluation case study. In the evaluation, fuzzy conjoint analysis is employed to handle the subjectivity and am-biguity. As the result of the evaluation, the institution is given a patient privacy protection ma-turity grade, between 1 and 5, where 1 is the worst and 5 is the best grade. Results: In the case study, XXX’s biggest hospital, employing 800 nurses, is evaluated. Half of the nurses, 400, have partici-pated in the study. Literature tells healthcare institutions do not invest enough to protect patient privacy, and the study results support this finding. Institution’s maturity grade resulted as level 2, which is bad. Conclusion: The study measures he privacy maturity with many evaluation com-ponents. Result of the evaluation explains to the patients and the public if their data is safe or not. With this maturity grade, patients have an idea about which institution to choose, public can conclude the institutions’ reliability in terms of patient privacy.

While there are software tools available for assisting to conduct the Life Cycle Assessment (LCA), such as OpenLCA, these tools lack integration with process design, simulation and optimization software. As LCA has critical role in sustainable product design, it is presented in this work a platform called EMSO_OLCA, which integrates the LCA provided by OpenLCA into the Environment for Modeling, Simulation, and Optimization (EMSO). EMSO_OLCA incorporates a database of environmental impact assessment methodologies from OpenLCA and aligns to the principles of LCA outlined in ISO 14040 and ISO 14044. Validation tests were conducted to compare the results obtained by the LCA of sugarcane ethanol using OpenLCA and EMSO_OLCA, revealing a high level of agreement. The average relative error was 0.045%, indicating a negligible discrepancy between the tools. Besides, it took only 0.3 seconds for calculation, which is desirable for using with process systems engineering tools. A second case study was applied to a steam and bioelectricity production from the combustion of sugarcane bagasse and straw in a combined heat and power system. The results show the integration of LCA with simulation and sensitivity analysis tools, thus contributing for supporting sustainable decision-making processes. EMSO_OLCA bridges the gap between LCA and process engineering, enabling a holistic approach to sustainability, design and implementation of environmentally friendly solutions.

Users of escalators and moving walkways with sufficient space to accommodate two lanes of users often follow an implied etiquette for the two lanes: one for walking and one for standing (in the US, China, and many countries, the convention is ``walk left, stand right"). When there is high volume, e.g., when exiting a subway or train station or at the conclusion of an athletic event, the escalators often experience bottleneck congestion that constitutes the primary source of delay. It has been suggested that during such high-congestion periods it would be more efficient if everyone just stood in both lanes, with empirical evidence used to support this counterintuitive finding. Simple deterministic queueing models are used to show under what conditions such results hold and also to provide further insights regarding tradeoffs between performance metrics as a function of the distribution of walkers and standers, which could inform practical implementation policies to increase efficiency.

Background: Malnutrition commonly affects patients with oral squamous cell carcinoma (OSCC), which contributes to worsening prognosis. Moreover, specific strategies for diagnosing and managing malnutrition in OSCC are lacking. Methods: This retrospective study included 52 consecutive patients with OSCC who underwent surgery at the University Hospital of Palermo (I) from Jan 2020 to May 2023. In-hospital nutritional support was provided by a dedicated team. The patients were compared with a historical control group of 11 patients. The nutritional treatment consisted of standard oral (ON) or artificial nutrition (AN). Traditional measurements, including bioimpedance-derived body composition and volumetric reconstruction of the posterior paraspinal muscles at the C3 level based on magnetic resonance imaging (MRI-C3), were obtained in 16 patients to investigate sarcopenia. Results: The patients were categorized according to TNM stage: groups A (TNM stages I-III) and B (IVa-IVc). The prevalence of group A patients was 59.6% in the case group and 90.9% in the control group (P

The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.

This review examines the quality of capital investment. It explores the essential components and analysis involved in financially assessing an engineering project, including an assessment of various associated risks. Additionally, the review delves into the historical progression of current capital budgeting practices and surveys academic research in the field. The evaluation of investments has been a recognized challenge for the past thirty years, but it has recently gained renewed attention from both management and scholars. Investments constitute a significant and growing portion of organizations’ capital expenditures. However, assessing the impact of investments on pursued goals is challenging. Consequently, there is a strong demand for methods and techniques that can aid in evaluating investments during the proposal stage. This paper explores several investment strategies and Examines the criteria for investment.

In the last two decades, the application of composite structures in many industrial sectors has been made progressively. For the current major scientific and technological challenge, the energy transition with sustainable and eco-friendly initiatives, the polymer matrix laminated composites have been revealed as an excellent alternative for the renewable energy sector. In this context, the knowledge about mechanical behavior of multiphase materials is required. Then, researches related to macro, mesomechanical and multiscale approaches are very important to improve accuracy of the models to predict effective mechanical properties and to develop a better understanding about complex phenomena such as progressive damage, creep and fatigue. Due to theses needs, this research is a synthesis of fundamentals and advanced topics concerning to global modeling of laminated composite structures, layerwise analyzes and micro-macromechanical models, in which mainly fiber, matrix, interface and damage characteristics are studied and used in the macroscale approach. At the end of this study, some final remarks and a trend analysis are proposed based on literature consulted, in other words, expectations for the future works are made by the authors.

Understanding gene expression changes in chicks after vaccination against Newcastle Disease (ND) can reveal vaccine biomarkers. There is limited data on chicks’ early immune response after ND vaccination. Two trials aimed into this knowledge gap. In experiment one, forty-two 13 days-old specific pathogen free (SPF) chicks were used. Harderian glands (Hg) and tracheas (Tc) from five birds per group were sampled at 12, 24, and 48 hours post-vaccination (hpv) to evaluate the gene transcription levels by RNA sequencing (RNA-seq) and RT-qPCR. Results of RNA-seq were compared by glmFTest, while results of RT-qPCR were compared by t-test. With RNA-seq, significant up-regulation of interferon related genes along with JAK-STAT signaling pathways regulation was observed in the Hg at 24- hpv. None of the DEGs identified by RNA-seq were positive for RT-qPCR. Experiment 2 used one-hundred and twelve SPF and commercial chickens at 1 day-old and 14 days-old. Only the commercial birds had maternal antibodies for NDV. By RNA-seq, twenty core DEGs associated with innate immunity and viral genome replication inhibition were identified. Genes previously unlinked to NDV response, such as USP41, were identified. This research present genes with potential as immunity biomarkers for vaccines, yet further investigation is needed to correlate the core gene expression with viral shedding post-vaccination.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

One of the most crucial elements in deeply understanding humans on a psychological level is manifested through facial expressions. The analysis of a human behavior can be informed by their facial expressions, making it essential to employ indicators such as expression (Expr), valence-arousal (VA), and action units (AU). In this paper, we introduce the method proposed in the Challenge of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) at CVPR 2024. Our proposed method utilizes the multi-modal Aff-wild2 dataset, which is splitted into spatial and audio modalities. For the spatial data, we extract features using a SimMiM model that was pre-trained on a diverse set of facial expression data. For the audio data, we extract features using a WAV2VEC model. To fusion the extracted spatial and audio features, we employed the cascaded cross-attention mechanism of a transformer.

Abstract: The promotion and development of healthy cities are vital for enhancing human habitats and fostering sustainable economic growth. Based on the core database of Web of Science and the knowledge graph software, this paper makes a quantitative analysis of the literature related to attention recovery abroad. It is found that in recent years, the research on attention recovery has developed rapidly, the number of related literature has been increasing, and the research content presents the characteristics of interdisciplinary integration. By further analyzing the characteristics of research literature, research context, and knowledge basis, this paper summarizes the empirical research based on the existing quantitative analysis, reviews the research field based on the mechanism of attention recovery, and analyzes the development process and trend based on the research basis of attention recovery. Due to the change of modern life style, human health problems are becoming more and more prominent. Attention restoration design provides a new research idea and method to balance the relationship between humans and the urban environment.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Agroforestry systems (AFS) are beneficial ecosystems that increase cocoa productivity by providing shade and controlling pests. Despite deforestation, there is a transition towards more sustainable practices that combine diverse species with cocoa, generating economic and environmental benefits. This study evaluates AFS with cocoa in the Amazon region. Of the 72 farms evaluated, species diversity was observed, highlighting Cordia alliadora and Calycophyllum spruceanum with an im-portance value index (IVI) of 18.58% and 17.66%, respectively. Fruit trees such as Citrus limetta, Cocos nucifera, Mangifera indica, and Persea americana are common. The designed AFSs proved more profitable, generating revenue from the fourth year, while the conventional ones took longer. Indicators such as Net Present Value (NPV) and Internal Rate of Return (IRR) supported the effi-ciency of the designed AFS. Six optimal species were identified with specific distances and planting systems, providing up to 45.83% total shade in flat topography and up to 2405 annual shade hours on a 7° slope. These results highlight the economic and agronomic viability of AFSs designed for regional cocoa production.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, assure accuracy and reliability for auditing, and adhere to tax compliance. The design involves data selection from Web of Science Core Collection (WoS) and Scopus, published between 2007 - 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers' interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data was retrieved, 9-11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.

Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.

Reactions of Co(OAc)24H2O, N‚N'-bis(3-pyridylmethyl)oxalamide (L) and 4,4’-sulfonyldibenzoic acid (H2SDA) afforded four coordination polymers with the same mixed ligands, {[Co(L)(SDA)(H2O)2]·H2O·CH3OH}n, 1, {[Co(L)0.5(SDA)]·2H2O·0.5L}n, 2, {[Co(L)1.5(SDA)(H2O)]·H2O}n, 3, and {[Co2(L)1.5(SDA)2(H2O)2]·4H2O}n, 4, which have been structurally characterized by using single crystal X-ray crystallography. Complexes 1 – 4 are 2D layers, revealing the topologies of sql, 2,6L1, (4,4)Ia and 6L12, respectively, and demonstrating that the metal to ligand ratio, solvent system and reaction temperature are important in determining the structural diversity. Immersion of these complexes into various solvents show that the structural types govern the chemical stabilities of 1 – 4. Reversible structural transformation can be shown for complexes 1 and 2 upon solvent removal and adsorption, while those of 3 and 4 are irreversible.

It has been shown that Human papillomavirus (HPV) infection is associated with several forms of cancer. Additionally, although there are more than 100 types of HPV, more than ten types of it, including types 16 and 18, are considered high-risk. The two proteins that make up the viral capsid of HPV 16 are L1, the major capsid protein, and L2, the minor capsid protein. FDA (The Food and Drug Administration) approved HPV vaccines mostly target L1 and L2 capsid proteins, which facilitate intracellular entry during infection. In this study, it was set out to analyze the creation of phylogenetic relations of L1 nucleic acid sequences of viral isolates. This study also analyzed sequences of three key elements within the amino acid sequences of HPV 16 L1: loops critical for structural stability, nuclear localization signal sequences, and residues implicated in viral attachment. As highlighted by the literature, these elements represent pivotal aspects of viral function. Furthermore, the report highlighted the particular inter-sequence variability within the HPV 16 L1 protein sequences. Overall analysis revealed clustering of sequences primarily due to geographical and characterized by more phylogenetic relatedness within location-specific clusters.

This study investigated the accuracy and precision of a commercially available PNT solution that uses DGNSS-SBAS technology. Time and position data were sampled at a frequency of 20Hz during both short and long trajectory of a simulated controlled dry-land slalom, as well as during a real-world on-water slalom exercise. The primary objective was to assess the positional accuracy, availability, integrity, and service continuity of the PNT solution while evaluating its ability to differentiate between trajectories. Additionally, the simulated results were compared with an on-water real-world slalom test to validate the findings. The results of the controlled dry-land slalom test indicate that the PNT solution provided accurate measurements with an overall mean±SD Hrms of 0.20±0.02m. The integrity measures, HDOD and PDOP were found to be ideal to excellent, with values of 0.68±0.03, and 1.36±0.07, respectively. The PNT solution utilised an average of 20±1 satellites from the constellation, resulting in an accuracy of

B4GALNT2 is a vital candidate gene that affects the growth traits litter size in and growth traits of sheep, but whether it has the same function in goats remains to be further investigated. In this study, we utilized 348 Nanjiang Yellow goats as experimental subjects, conducted a whole exon, and the conserved the non-coding region SNP scanning of the B4GALNT2 gene. Our results revealed the presence of a synonymous mutation, rs672215506, within the exon of the B4GALNT2 gene in the Nanjiang Yellow goat population. This mutation was found to be associated with reduced mRNA stability of the B4GALNT2 gene. The results of SNPs detection of conserved non-coding region of B4GALNT2 gene showed that there were five potential regulatory SNPs in the Nanjiang Yellow goat population, except for rs66095343, the ~500 bp fragments of four SNPs (rs649127714, rs649573228, rs652899012 and rs639183528) could significantly increase luciferase activity both in goat skeletal muscle cells and 293T cells . Genetic diversity indexes indicated low or intermediate levels for all six SNPs analyzed, each conforming to Hardy-Weinberg equilibrium regarding genotypic frequencies. Association analysis indicated that rs660965343, rs649127714 and rs649573228 were significantly correlated with growth traits in the later stage of growth and development of Nanjiang Yellow goat. The haplotype combinations of H2H3 and H2H2 had higher body weight and greater body size; moreover, H2H2 haplotype combinations were significantly correlated with the litter size of Nanjiang Yellow goat. The results of our study demonstrate the potential utility of the B4GALNT2 gene as a functional genetic marker in breeding programs for Nanjiang Yellow goats.

Cannabis sativa is one of the oldest plants utilized by humans for both economic and medical purposes. Although the use of cannabis started millennia ago in the Eastern part of the hemisphere, its use has moved and flourished in the Western Nations in more recent centuries. C. sativa is the source of psychoactive cannabinoids that are consumed as recreational drugs worldwide. The C21 aromatic hydrocarbons are restricted in their natural occurrence to cannabis (with a few exceptions). Delta-9-tetrahydrocannabinol (Δ9-THC) is the main psychoactive component in cannabis with many pharmacological effects and various approved medical applications. However, a wide range of side effects are associated with the use of Δ9-THC, limiting its medical use. In 1966, another psychoactive cannabinoid, Delta-8-tetrahydrocannabinol (Δ8-THC) was isolated from marijuana grown in Maryland but in very low yield. Δ8-THC is gaining increased popularity due to its better stability and easier synthetic manufacturing procedures compared to Δ9-THC. Passing the U.S. Farm Bill in 2018 led to an increase in the sale of Δ8-THC in the United States. The marketed products contain Δ8-THC from synthetic sources. In this review, methods of extraction, purification, and structure elucidation of Δ8-THC will be presented. The issue of whether Δ8-THC is a natural compound or an artifact will be discussed, and the different strategies for its chemical synthesis will be presented. Δ8-THC of synthetic origin is expected to contain some impurities due to residual amounts of starting materials and reagents as well as side products of the reactions. The various methods of analysis and detection of impurities present in the marketed products will be discussed. The pharmacological effects of Δ8-THC, including its interaction with CB1 and CB2 cannabinoid receptors in comparison with Δ9-THC, will be reviewed.

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash processes. Some of these systems have very limited flexibility due to their rigid structures and require considerable investment in hardware. As such, there is a need for an autonomous low-cost system (~€500) that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents a sensitivity analysis of time-lapse intervals for two low-cost cameras located in a remote coastal area, overlooking a dissipative beach-dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for intervals ranging between 1 second and 30 seconds (i.e., RMSDmax for Camera 1 = 2.8 m and Camera 2 = 1.3 m). The increase of the sampling interval from 1 s to 30 s is shown to have no significant adverse effect on the precision of shoreline detection. The research found that the limitations of battery life and memory storage substantially decrease when sampling intervals increase without an effect on the data quality. This also has a positive effect for low-cost systems and removes the requirement for external battery and memory storage. On the systems used, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s., As such, the current standard of producing Timex images from 1 s sampling intervals over 10 minutes can be changed depending on the application and requirements needed for analysis.

Analyzing horse behavior is crucial for assessing training quality, particularly in accurately identifying actions like standing, walking, and running. Research has been conducted both domestically and internationally in this regard; however, challenges persist, including reliance on single sensors, poor real-time performance, and low identification accuracy. In view of these challenges, this study investigated real-time identification of horse behavior based on wearable devices. The system, centered around a microcontroller and utilizing a 4G network as a carrier, employs multi-axis IMU sensors as input sources to perceive horse posture. The study proposed a behavior classification method based on analysis of acceleration thresholds. The method consists primarily of two sequential stages: first, the resultant acceleration interval counting method, which employs a nonlinear segmentation approach for initial behavior classification; and second, the statistical analysis of variance parameters between segments, which when coupled with multi-level threshold processing, achieves a refined classification. Experimental results indicated that the interval counting method alone achieved an accuracy of 87.55%, while for the variance analysis method alone the accuracy was 90.87%. The proposed method, comprising the two stages, reached a classification accuracy of 91.57%, underpinning its usefulness in supporting future equine research.

The primary objective of this research was to assess the energy consumption of the gas treatment units at Bonny NLNG Refinery, the first Gas Refinery in Rivers State, Nigeria, while utilizing semi-lean amine. To achieve this goal, a simulation of the units was conducted using the advanced software package Aspen Hysys (V.8.3). The simulation was designed to accurately represent the dynamic behavior of the refinery's gas treatment units, allowing for a comprehensive analysis of their energy usage. The research aimed to quantify the energy consumption of the gas treatment units and identify opportunities for energy optimization. By utilizing semi-lean amine, which is known to improve energy efficiency, the study sought to evaluate the potential energy savings that could be achieved in the refinery's operations. The simulation model incorporated the specific design and operational parameters of the gas treatment units at Bonny NLNG Refinery, including the gas flow rate, lean amine concentration, absorber pressure, stripping temperature, amine circulation rate, and acid gas removal efficiency. By considering these parameters, the simulation accurately represented the dynamic behavior of the gas treatment units, enabling a detailed analysis of their energy consumption. Through the simulation, various scenarios and operational conditions were evaluated to determine the optimal set of parameters that minimized energy consumption. The research also examined the trade-offs between energy consumption, acid gas removal efficiency, and other performance indicators, such as amine circulation rate and regeneration efficiency. The findings of this research have significant implications for the energy efficiency and sustainability of gas treatment operations at Bonny NLNG Refinery. By identifying opportunities for energy optimization and providing recommendations for the utilization of semi-lean amine, the study contributes to the development of more efficient and environmentally friendly gas treatment processes. Overall, this research combines advanced simulation techniques with a comprehensive analysis of energy consumption to provide valuable insights into the energy efficiency of gas treatment units at Bonny NLNG Refinery, enabling informed decision-making and potential improvements in energy utilization. By utilizing the percentage-based unit simulation approach, a detailed examination of the energy consumption patterns was obtained. This analysis provides valuable insights into the operational efficiency and determination of potential energy-saving opportunities within the gas treatment units. This study specifically focuses on the integration of an absorption column split stream (stream flow) and a flash unit as a potential means to reduce the energy consumption of gas treatment devices. The integration of these units aims to optimize the overall energy efficiency of the gas treatment process by recovering and utilizing waste heat and reducing energy losses. The absorption column split stream allows for the diversion of a portion of the gas stream to a flash unit before entering the absorber. The flash unit operates at a lower pressure, which facilitates the release of entrained hydrocarbons and reduces the overall energy requirements for gas treatment. By separating and recovering the hydrocarbons in the flash unit, energy losses associated with their absorption and subsequent regeneration are minimized. Through the simulation, the study analyzes the energy consumption patterns of the gas treatment units with and without the integration of the absorption column split stream and flash unit. Comparative assessments are conducted to evaluate the energy savings and overall operational efficiency achieved through this integration. The findings of this research will provide valuable insights into the potential energy-saving opportunities offered by the integration of the absorption column split stream and flash unit in gas treatment devices. It will help refine the design and operation of gas treatment units, enabling more energy-efficient processes in the gas refining industry. By optimizing energy consumption in gas treatment units, the study contributes to the industry's goals of reducing greenhouse gas emissions and improving sustainability. The integration of the absorption column split stream and flash unit offers a promising approach to enhance energy efficiency, reduce operational costs, and minimize environmental impact. Overall, this research underscores the importance of exploring innovative solutions, such as the integration of different process units, to achieve energy savings in gas treatment operations. Through a comprehensive analysis of energy consumption patterns, this study aims to provide practical recommendations for optimizing energy efficiency in gas treatment units, fostering a more sustainable and efficient gas refining industry.The research specifically explores the impact of integrating the absorption column split stream and flash unit when dealing with sour gas streams containing carbon dioxide concentrations of less than mole ℅, while utilizing MDEA fluid as the solvent. Through comprehensive analysis and simulation using advanced software tools, the study demonstrates that by incorporating the absorption column split stream and flash unit, a significant reduction in device energy consumption of up to 10% can be achieved. The integration of the absorption column split stream and flash unit offers promising opportunities to enhance the energy efficiency of gas treatment devices. By diverting a portion of the gas stream to the flash unit, the separation of hydrocarbons and the subsequent release of entrained hydrocarbons at lower pressure significantly reduce the overall energy requirements for gas treatment. The comprehensive analysis and simulation conducted in the research provide concrete evidence of the energy-saving potential of this integration. By quantifying the energy consumption patterns and comparing scenarios with and without integration, the study establishes the effectiveness of the approach and its impact on operational efficiency. The findings of the research highlight the importance of considering the specific gas composition and utilizing appropriate solvents, such as MDEA, for optimizing energy efficiency. The integration of the absorption column split stream and flash unit not only reduces energy consumption but also offers the potential for cost savings and environmental benefits. Overall, the research demonstrates that by incorporating the absorption column split stream

Bulbophyllum is one of the largest genera and presents some of the most intricate taxonomic problems in the family Orchidaceae, including species of ornamental and medical importance. The lack of knowledge regarding the characterization of Bulbophyllum chloroplast (cp) genomes has imposed current limitations on our study. Here, we reported the complete cp genomes of seven Bulbophyllum species, including B. ambrosia, B. crassipes, B. farreri, B. hamatum, B. shanicum, B. triste and B. violaceolabellum, and compared with related taxa to provide a better understanding of their genomic information on taxonomy and phylogeny. A total of 28 Bulbophyllum cp genomes exhibit typical quadripartite structures with lengths ranging from 145,092 bp to 165,812 bp and GC content of 36.60% to 38.04%. Each genome contained 125–132 genes, encompassing 74–86 protein-coding genes, 38 tRNA genes, and eight rRNA genes. The genome arrangements, gene contents and length were similar with differences observed in ndh gene composition. A total of 18–49 long repeats and 38–80 simple sequence repeats (SSRs) were detected and the single-nucleotide (A/T) was dominant in Bulbophyllum cp genomes, with an obvious A/T preference. An analysis of relative synonymous codon usage (RSCU) revealed Leucine (Leu) was the most frequently used codon, while cysteine (Cys) was the least used. Six highly variable regions including ndhF-trnLUAG, trnTUGU-trnLUAA, trnFGAA-ndhJ, rps15-trnNGUU, rbcL-accD and psbI-trnSGCU were identified based on the ranking of the Pi values, had the potential to serve as DNA markers for species identification and phylogeny of the genus Bulbophyllum. Phylogenetic analysis based on the complete cp genome sequences and 68 protein-coding genes strongly supported 28 Bulbophyllum species can be divided into four branches and sects. Brachyantha, Cirrhopetalum, Leopardinae defined by morphology were non-monophyly. Our results enriched the genetic resources of Bulbophyllum species, providing valuable information to illustrate the complicated taxonomy, phylogeny and evolution process of the genus.

The study also found that older age was a protective factor in prospective studies. The consistency of results across different study designs and sensitivity analyses further supported the robustness of the findings. It is important to note that the existing literature on revictimization in women facing intimate partner violence is limited and exhibits significant heterogeneity in terms of methodology and conceptual frameworks.

Contemporary advanced models for aspect-targeted opinion word extraction (ATOWE), which predominantly utilize BERT-based encoders at a word level, have shown limited advancements when integrated with graph convolutional networks (GCNs) for syntactic tree assimilation. Recognizing the prowess of BERT subwords in encapsulating rare or context-poor words, this study pivots from syntactic trees to BERT subwords, omitting GCNs from the structural framework. Our approach, named Aspect-Enhanced Wordpiece Extraction Model (AEWEM), focuses on refining aspect representation during encoding. We propose an input format of paired sentence-aspect, diverging from traditional single-sentence inputs. AEWEM demonstrates superior performance on benchmark datasets, establishing a robust foundation for future explorations in this domain.

Mineral extraction is one of the most important for the economies of different countries, and Brazil is one of the world's primary iron ore producers. Unfortunately, the dams are still the main problem, mainly in Minas Gerais, especially after the Fundão Dam rupture happened in 2015. Additionally, there is still a massive presence of buildings produced on earth throughout the Minas Gerais mining region, built from the 18th century to today. Investigating the potential of IOT to be incorporated into traditional earthen construction techniques in regions affected by dam ruptures presents a relevant and innovative research approach. In addition, the local reuse of these sediments should be the priority. Thus, the main objective of this work was to collect, characterize, and analyze the possibilities of application of these tailings to produce RE. XRD, XRF, granulometry, porosity, specific density, toxicity, and MEV/EDS were performed on the samples collected at three points. To analyze the soil-IOT compatibility, representative mixtures of RE were produced, and the specific mass, compaction, and compressive strength were performed. It was observed that the IoT samples have a high silica content and that the mixtures of IOT-soil, even without cement, reached compressive strength values above the current international standards.

The urban planning transformations modify the morphological shape of the territory changing land uses and permeability of the areas involved in the evolution processes, thus also altering the hydrologic responses to climatic events, namely the urban drainage runoffs that impact on the environmental risk of modern cities. Using the watershed hydrologic concept, in its dynamic evolution, it is possible to consider the morphological layout together with the transformations implemented by the human activities and any different territorial portions could be analyzed in connection with the other areas related by the dynamics of the surface runoff, thus highlighting the reciprocal influences between the different areas. By examining the morphological shapes impacting surface outflows, the hydrological roles of upper-mountain, intermediate-hillside, and lower-valley areas reveal urban and non-urban connections. This elucidates hydromorphic complexities in urban transformations and assesses climate change adaptability. The proposed approach has been applied to the urban district of “Sasso Caveoso”, belonging to the city of Matera and consents to quantitatively synthesize the response of the context analyzed, referring to different scenarios and providing useful decision support tools.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

PIM1is involved in critical cellular processes, playing a key role in the regulation of the cell cycle, apoptosis, transcriptional activation, and broader signal transduction pathways . The exploration of PIM1inhibitors, such as small molecules or antibodies, represents a promising avenue in cancer research for developing novel treatment strategiess. Its significance in oncogenic signaling has led to extensive research on PIM1 as a target in cancer studies. Docking studies have revealed a robust interaction between hypericin and PIM1, suggesting a strong binding affinity (-12.5 kcal/mol). This research aims to explore the molecular dynamics of the hypericin-PIM1 interaction, identifying potential binding sites and elucidating the impact on PIM1-mediated cellular processes, such as cell cycle regulation and apoptosis. The findings may contribute valuable insights into developing hypericin-based therapies targeting PIM1, paving the way for novel and effective strategies in cancer research.

This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.

Leucobryum is a moss genus that exhibits various bioactivities. However, the identification of Leucobryum species with morphology alone remains difficult. Nowadays, chemical profile analysis provides additional tools for plant classification, especially in medicinal plants. This method uses chemical similarities to identify the differences among some plants, especially for the different varieties of the same species. The objective of this study was to obtain the chemical profiles and use chemometrics to identify selected Leucobryum species found in Thailand. Lipophilic extracts from 18 samples of five taxa were analyzed with TLC and HPLC. Cluster analysis and PCA were used to investigate the similarities of the chemical profiles. PERMANOVA was performed to determine the difference among taxa. The results showed that the TLC profile could separate only the genus of the sample, while HPLC chromatograms showed separation among the Leucobryum taxa and consistent patterns within the same species. In addition, the chemical profiles of Leucobryum species were separated at the species level in PCA. The PERMANOVA showed significantly different profiles among the species (P = 0.002). Therefore, this work illustrated the potential of chemometrics as supporting evidence for species delimitation in the moss genus Leucobryum.

Elongate loach (Leptobotia elongata) is an endemic fish in China. Previous studies have provided some insights into the mitochondrial genome composition, and the phylogenetic relationships of L. elongata inferred using protein-coding genes (PCGs). However, the detailed information about is limited. Therefore, in this study, we sequenced the complete mitochondrial genome of L. elongata and analyzed its structural characteristics. The PCGs and mitochondrial genome were used for selective stress analysis and genomic comparative analysis respectively. The complete mitochondrial genome of the L. elongata, together with those of 36 Cyprinidae species, was used to infer the phylogenetic relationships of the Cobitidae family through maximum likelihood (ML) reconstruction. The results showed that the genome sequence has a full length of 16,591 bp, which includes 13 PCGs, 22 transfer RNA genes (tRNA), two ribosomal RNA genes (rRNA), and two non-coding regions (CR D-loop and light chain sub-chain replication origin OL). Overall, L. elongata shared the same gene arrangement and composition of the mitochondrial genes with other teleost fishes. The Ka/Ks ratios of all mitochondrial PCGs were less than 1, indicating that all the PCGs were evolving under purifying selection. Genome comparison analyses showed a significant sequence homology of species of Leptobotia. A significant identity between L. elongata and the other 5 Leptobotia species was observed in the visualization result, except for L. mantschurica, which lacked the tRNA-Arg gene and had a shorter tRNA-Asp gene. The phylogenetic tree revealed that the Cobitidae species examined here can be grouped into two clades, with L. elongata forming a sister relationship with L. microphthalma. This study could provide additional inferences for a better understanding of the phylogenetic relationships among Cobititdae species.

This study investigates the magnetization of Cobalt (Co) nanoparticles with two different crystal structures: a centered cubic (fcc) and an hexagonal close packed (hcp), under the magnetic field of the Earth. For that reason, the hysteresis loops of the two material samples are exported and compared with the corresponding value by experimental procedure in the laboratory under the Earth’s magnetization. For both samples, the nanoparticle number variation and the nearest neighbor distance effect, in the Co hysteresis loop are investigated, structural information that is useful for the sample preparation. For the simulations the Finite Element Method (FEM) was used, while experiments are conducted using a measurement set-up based on fluxgate magnetometers. From the simulation, it is proven that, for both samples, the nanoparticle number effect does not change the hysteresis loop. In contrast, when the distance between the nanoparticles increases, the magnetization obtains smaller value. This study aims to build a novel, simple and economical measurement arrangement enabling in situ magnetic measurements for ferromagnetic nano-material characterization. As the need for continuous knowledge of the condition and the phase of ferromagnetic nanoparticles that is crucial in many applications, like catalytic processes, this work aims to introduce an alternative approach that will add another dimension to conventional chemical reaction engineering this of the magnetic state of the material resolved in time and space and to contribute in the field of the monitoring and the early fault diagnosis of nanoparticle materials.

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection across all studied breeds. Both of these genes are associated with muscular development in pigs and other species. Furthermore, in the Large White breed a number of genes were detected by the two methods, such as ATP1A1, CASQ2, CD2, IGSF3, MAB21L3, NHLH2, SLC22A15, VANGL1. In Duroc breed a different set of genes was detected, such as ARSB, BHMT, BHMT2, DMGDH, JMY. The function of these genes was related to body weight, production efficiency and meat quality, average daily gain and other similar traits. Overall, our results have identified a number of genomic regionsthat are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

Introduction: EEGLAB is one of the most famous software for processing, analyzing, and researching experiments that have Electroencephalography (EEG) datasets. Due to the numerous and famous add-ins along with global, widespread communications and online free training, its popularity increased every year. Method: To address this phenomenon from a bibliographic perspective, we found 20,464 citations in Google Scholar for the main EEGLAB reference since 8/27/2023. Then, only the Web of Science (WOS) articles were 12,700 that they were extracted. The results were analyzed with Bibliometrix package from CRAN R software. Results: The time span of these articles is from 2004 to 2023 with 12,700 documents in 1,125 sources (journals, books, etc.), 29,125 authors, 19,062 author’s keywords, 13,707 keywords PLUS, 279,617 references. The annual growth rate is 28.12 %, international Co-authorship is 37.27 % and Co-authors per document is s 4.89 and the average citations per document is 22.51. The most relevant sources are Neuroimage, Frontiers in Human Neurosciences, Scientific Reports, Psychophysiology, and PLOS One with 780, 526, 446,425, and 371 articles, respectively. The most cited countries are the USA, Germany, and the United Kingdom with 93,093, 32,621, and 20,748 total citations, respectively. The ERPLAB, ADJUST, and ICLabel add-ins have the local to global citation ratios equal to 85.4%, 65.1%, and 78.2% respectively. Other bibliometric analyses such as co-occurrence networks and thematic maps of abstracts, titles, and keywords are estimated and presented. Conclusions: EEGLAB is among the most cited MATLAB toolboxes in computational neuroscience. Many developed and developing countries use it in their research publications.

Cervical cancer (CC) continues to be a significant global public health concern, even with preventive measures in place. In women with early-stage CC, the status of lymph nodes is of paramount importance, not only for final prognosis but also for determining the best therapeutic strategy. According to main international guidelines, pelvic full lymphadenectomy (PLND) is recommended for lymph node staging. However, in these early stages of CC, Sentinel lymph node biopsy (SLNB) has emerged as a precise technique for evaluating lymph node involvement, improving its morbidity profile. We performed a literature review through PubMed articles about progress on the application of SLNB in women with early-stage CC focusing on the comparison with PET/CT and PLND in terms of oncological outcomes and diagnostic accuracy. While the superiority of SLNB is clear compared to radiologic modalities, it demonstrates no clear oncologic inferiority over PLND, given the higher detection rate of positive lymph nodes and predominance of no lymph node recurrences. However, due to a lack of prospective evidence, particularly concerning long-term oncological safety, SLNB is not the current gold standard. With careful patient selection and adherence to straightforward protocols, a low false negative rate can be ensured. The aim of the ongoing prospective trials is to address these issues.

Perceptual and statistical evidence has underlined voice characteristics of individuals affected by genetic syndromes different from that of normophonic subjects. In this paper we propose a procedure for the systematic collection of such pathological voices and the development of AI-based automatic tools to support differential diagnosis. Guidelines are provided concerning most suitable recording devices, vocal tasks and acoustical parameters, in order to simplify, speed up and make the whole procedure homogenous and replicable. The proposed procedure was applied to a set of 56 subjects, affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS) and Smith-Magenis syndrome (SMS). The whole database has been divided into three groups, respectively called: paediatric subjects (PS, individuals < 12 years of age), female adults (FA) and male adults (MA) subjects. In line with literature results, the Kruskal-Wallis test and post-hoc analysis with Dunn-Bonferroni test highlighted several significant differences in acoustical features not only between healthy subjects and patients, but also across syndromes within PS, FA and MA groups. Machine learning provided for the PS group a k-nearest neighbour classifier with 75% accuracy, for the FA group a support vector machine (SVM) model with 84% accuracy and for the MA group a SVM model with 97% accuracy. These preliminary results suggest that the proposed procedure, based on acoustical analysis and AI, might be helpful for an effective non-invasive automatic characterization of genetic syndromes.