Immunohistochemistry is a powerful technique, widely used in biomedical research and clinics, that allows to determine the expression levels of some proteins of interest in tissue samples by means of the intensity of color due to the expression of biomarkers using specific antibodies. As such, immunohistochemical images are complex and problematic to be quantified. Recently we proposed a novel method including a first separation stage based on nonnegative matrix factorization (NMF) that achieved good results. However, that method was highly dependent on sparseness and non-negativity parameter choice and on algorithm initialization. Furthermore, the previously proposed method needed a reference image as starting point for the NMF algorithm. In the present work, we propose a new, simpler and robust method for the automated unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest). The new proposed method represents a simpler approach that, in one side, avoids the use of NMF for the separation stage, and in the other side, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest by means of principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing the finding of color vectors by considering the peaks of density of points. The method also develops a new scoring stage by avoiding, again, reference images, making the procedure more robust and less dependent on the parameters. Experiments for the semi-quantitative scoring of images in five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

The asymmetric synthesis of polyunsaturated triene C18:3 n-3 and C18:3 n-6 methoxylated ether lipids (MEL) of the 1-O-alkyl-sn-glycerol type is described as possible structural candidates for a triene C18:3 MEL of an unknown identity found in a mixture of shark and dogfish liver oil. Their C18:3 hydrocarbon chains constitute an all-cis methylene skipped n-3 or n-6 triene framework, along with a methoxyl group at the 2'-position and R-configuration of the resulting stereogenic centre. The methoxylated polyenes are attached by an ether linkage to the pro-S hydroxymethyl group of the glycerol backbone. The syntheses were based on the polyacetylene approach that involves a semi-hydrogenation of the resulting triynes. Both syntheses were started from our previously described enantio- and diastereomerically pure isopropylidene-protected glyceryl glycidyl ether, a double-C3 building block that was designed as a head group synthon for synthesis of various types of MELs.

Multibeam echo-sounders provide ideal data for semi-automated seabed feature extraction and accurate morphometric measurements. However, these methods have not been tested in morphologically highly complex environments. In this study, bathymetric and raw backscatter data were initially used to manually map the complex reef morphology found in the semi-enclosed Gera Gulf, in the northern Aegean Sea (Greece). A large number of reefs (more than 7000) were detected, making manual mapping extremely time-consuming. Benthic Terrain Modeller (BTM) was selected as a semi-automated method to map the reefs. The BTM did not function properly in this irregular relief and the results were discouraging. Trying to improve the BTM functionality, both bathymetry and slope were modified (enhanced); this time, the BTM outcome was unexpectedly improved, producing accurate results that appeared to exceed the accuracy of manual mapping. To compare the final maps with the distribution of the reefs, mapcurves were created to estimate the Goodness-of-Fit (GOF), while Precision, Recall, and F1 Score were also calculated. GOF values suggest more than 50% overlap between the resulting polygons while, Precision, Recall and F1 score resulted in values higher than 0.78 suggesting good detection accuracy for the semi-automated method. It becomes apparent that BTM provided more efficient results in comparison to the time-consuming manual mapping. This study asserts that semi-automated mapping stands as an effective method for delineating the geomorphometry of intricate relief and serves as a powerful tool for habitat mapping and decision-making.

Wheat yield plays an important role in the global economy. Changing climate conditions and increased drought negatively affect grain yield. Currently, breeding programs are aimed at improving wheat productivity. To accelerate the selection process of hybrids and parental forms for crosses, it is essential to pay attention to plants' phenotypic and genetic potential. We studied the genes of the "green revolution" group Rht and coarse-grained TaGW and TaGS to increase yields by selecting optimal parameters for wheat. We focused on short-stemmed and coarse-grained characteristics in a dry climate and conducted a correlation analysis between the genetic potential of wheat varieties and hybrids and their phenotypic manifestation in drought conditions. Rht-D1b significantly reduced the height of plants by 51.67% and reduced the coleoptile length by 30.45%. At the same time, TaGW8-B1a increased the grain width by 13.37%. However, in the presence of Rht-D1b and Rht-B1b in the genome, the 1000-kernal weight was 35.06 g (p<0.05), the Rht-B1b gene enhanced it by 9% (p<0.05), while both Rht-D1a and Rht-B1a alleles gave a 1000-kernal weight of 37.96 g (p<0.05). The Rht-D1b and Rht-B1b alleles gave 18.5 seeds per ear, while the Rht-D1a and Rht-B1a alleles gave the value of grains per ear -24.1 pcs. The data obtained indicated the relationship of phenotypic and genetic parameters in wheat between the coarse-grained and short-stemmed genes, which makes it possible to select based on the genetic parameters of varieties and hybrids, thereby speeding up the selection process.

Minimum housing standards aim to safeguard housing rights and enhance residential conditions. Governments develop housing policies, including provision, preservation, and support for welfare programs, guided by the status of households below these standards. Growing nations commonly utilize this approach to decrease the proportion of households not meeting these criteria. In South Korea, the ratio of households below these standards was 4.5% in 2021, down from 16.6% in 2006, as indicated by the initial Korea Housing Survey. However, this downward trend has stalled over the past decade. With the 2004 and 2011 revisions, the standards have been effective for 12 years, yet no longer mirror current housing realities due to ongoing improvements. This study aimed to propose enhancements to Korea’s minimum housing standards. Through analyzing laws, prior research, present household statuses, international cases, and expert insights, recommendations emerged. Categorizing households by size, we developed precise standards covering area, facility, and location aspects. These new standards led to an 8.4% non-compliance rate in 2021. This research’s findings anticipate aiding the revision of minimum housing standards, formulating pragmatic policies for enhancing residential conditions in line with present situations.

The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows considerable increase of the brain surface. In premature childrens, these alterations take place outside the womb that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic GUI platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians, for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches, pre-processing tools, and is friendly designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach’s effectiveness.

Recently, a paper was written to reformulate and strengthen the theory of semi-structured complex numbers H (a new number set invented to enable division by zero). Whilst the paper had profound results, the application of this number set in many areas of engineering and science has not been fully explored. Consequently, the aim of this paper was to establish the number set H as a useful mathematical tool by providing an amalgam of results arising from its application in several areas of engineering and science. In the process, this paper makes four major contributions: these are (1) determining the product of the gradients of a horizontal and vertical line; (2) developing new Euler formulas relating exponential, trigonometric and hyperbolic functions in the 3D semi-structured complex number Euclidean space; (3) establishing semi-structured complex numbers as a better extension of the complex number set than quaternions; (4) resolving singularities that may arise in engineering and science equations (because of division by zero) to develop reasonable conclusions in the absence of experimental data. These results and their applications provide a firm foundation to advance the number set H as a useful mathematical tool.

In this paper, in order to provide proper parameters for the preparation of semi-solid billets, the semi-solid annealing of hot-rolled 2A14 Al alloy was investigated. The microstructure was characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) with X-ray energy dispersive spectrometer (EDS) and electron backscattered diffraction (EBSD), scanning transmission electron microscope (STEM). XRD results show that with the increase of temperature, equilibrium θ-Al2Cu gradually dissolved in the matrix. The EDS results of SEM and STEM show that there were coarse θ-Al2Cu phase, ultra-fine precipitate Al(MnFeSi) or (Mn, Fe)Al6 phase and atomic clusters in the microstructure. EBSD results show that the recrystallization mechanism was dominated by continuous static recrystallization (CSRX), the homogeneous nucleation occurred when the sample was heated to near solidus temperature, and CSRX happened in the semi-solid temperature. In the process of recrystallization, the micro-texture changed from preferred orientation to random orientation. A variety of experimental results show that static recrystallization (SRX) occurred at semi-solid temperature due to the blocking effect of atomic clusters on dislocation slip and the Zener drag effect of fine precipitates on low angle grain boundaries (LAGBs) disappeared with melting at semi-solid temperature.

The study aimed to develop standards for a welfare assessment protocol, by validating potential categorisation thresholds for assessment of beef farms in various beef cow-calf production systems in Namibia. Forty measures combined from a New Zealand-based protocol plus Namibia-specific measures, were applied on 55 beef farms (17 commercial farms, 20 semi-commercial and 18 communal village farms) during pregnancy testing, and a questionnaire guided interview. The categorised measures on a 3-point welfare score of 0: good 1: marginal and 2: poor/unacceptable welfare were subsequently compared with derivation of thresholds based upon the poorest 15% and best 50% of herds for each measure. Overall combined thresholds of continuous measures across the 3 farm types, showed 10/22 measures that posed welfare compromise across Namibia, where commercial farms had 4/22 measures and semi-commercial and communal village farms had 12/22 and 11/22 respectively with high thresholds. Most measures-imposed thresholds were retained because of significant importance to welfare of animals and preventiveness of the traits, while leniency was given to adjust good feeding and mortality measures to signify periods of drought. Handling measures (fearful, falling/lying) and abrasions thresholds were adjusted to reflect the temporary stress caused by infrequent cattle handling, and faulty yard designs/design and possible cattle breed influence on handling. Hence, the country needs prioritised investigation of underlying contributing factors and remediation to reduce the high thresholds.

To determine the photon emission or absorption probability for a diatomic system in the context of the semiclassical approximation it is necessary to calculate the characteristic canonical oscillatory integral which has one or more saddle points. Integrals like that appear in a whole range of physical problems, e.g. the atom-atom and atom-surface scattering and various optical phenomena. A uniform approximation of the integral, based on the stationary phase method is proposed, where the integral with several saddle points is replaced by a sum of integrals each having only one or at most two real saddle points and is easily soluble. In this way we formally reduce the codimension in canonical integrals of "elementary catastrophes" with codimensions greater than 1. The validity of the proposed method was tested on examples of integrals with three saddle points ("cusp" catastrophe) and four saddle points ("swallow-tail" catastrophe).

The aim of this article is to extend the local as well as the semi-local convergence analysis of multi-point iterative methods using center Lipschitz conditions in combination with our idea, of the restricted convergence region. It turns out that this way a finer convergence analysis for these methods is obtained than in earlier works and without additional hypotheses. Numerical examples favoring our technique over earlier ones completes this article.

To reduce solar power production invariance, it is critical to study varying patterns of power production in the concerned region. Analyzing the patterns of past power production trends can help simulate power production scenarios for future. The current study area is around Amsterdam, located in Netherlands. PVoutput.org website is used to mine 6 months of solar power production data for 120 stations around Amsterdam city. FME Workbench software is used to actively fetch the data from the mentioned website and manage in a MySQL database. Solar attenuation maps created using ArcGIS, helped to graphically visualize the variations in solar power production at different times and locations. Further, spatial autocorrelation is checked between the stations using semi-variograms in geostatistical tool of ArcMap. This feature allows to check whether the stations located close to each other are more correlated to each other rather than stations which are far apart. The statistical data analysis of power production can aid solar power production companies to better interpolate and predict solar power in advance for the concerned study region.

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still signicant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certied and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certication is costly and time consuming. The study specically analyses Selective Laser Melting (SLM) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as `faulty' or `acceptable'. A fully supervised approach identied faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certication data. The results show that semi-supervised learning is a promising approach for the automatic certication of AM builds that can be implemented at a fraction of the cost currently required.

Carbon nanotubes (CNTs) are effective supports for nano metals and together they represent hybrids that combine unique properties of both. A microwave induced reaction was used to deposit nano palladium on carboxylated and octadecylamine functionalized multiwall CNTs, which were used to carry out C-C coupling reactions in DMF and toluene. These hybrids showed excellent catalytic activity with yield as high as 99.8% while its enhancement with respect to commercially available Pd/C catalyst reached as high as 109%, and the reactions times were significantly lower. Polarity of the functionalized form was found to be a significant factor with the polar carboxylated CNT showing better activity in DMF while the relatively nonpolar octadecyl amine was better in toluene. The results suggest the possibility of tailor making functionalized CNT when used as catalyst supports

Polymeric composites such as Poly-ether-ether-ketone (PEEK)/carbon fiber (CF) have been widely utilized due to outstanding performances such as high specific strength and specific modulus. The PEEK/CF components via powder-bed fusion additive manufacturing usually show brittle fracture behaviors induced by their poor interfacial affinity and inner voids. These defects are strongly associated with powder packing quality upon deposition. The particle dynamic model has been widely employed to study the interactions of particle motions. Powder property, bulk material property, and interfacial features of composite powders are key factors in the particle dynamic model. In this work, an efficient and systematic material evaluation is developed for composite powders to investigate their deposition mechanism. The discrete element method is utilized to simulate the dynamic behaviors of PEEK/CF composite powders. The powder properties, bulk material properties, and interfacial features of powders are calibrated and justified by experimental measurement, numerical simulation, and design of experiments. The particle dynamic model can well explain the powder flow behaviors and interactions. It reveals that the addition of short CF particles can assist the flow of PEEK powders and improve the packing quality of the composite powders.

We propose a promising model of discrete spacetime based on nonassociative geometry and complex networks. Our approach treats space as a simplicial 3-complex (or complex network), built from "atoms" of spacetime and entangled states forming n-dimensional simplices (n=1,2,3). At large scales, a highly connected network is a coarse, discrete representation of a smooth spacetime. We show that for high temperatures, the network describes disconnected discrete space. At the Planck temperature, the system experiences the phase transition, and for low temperatures, the space becomes a triangulated discrete space. We show that the cosmological constant depends on the universe’s topology. The "foamy" structure, analogous to Wheeler’s "spacetime foam," significantly contributes to the effective cosmological constant, which is determined by the Euler characteristic of the universe.

Hydraulic fracturing for oil-gas and geothermal reservoir stimulation is closely related to the generation and propagation of Mode I crack. Nonlinear deformation due to rock heterogeneity occurs at such crack tip, which causes the fracture process zone (FPZ) to form before the crack propagates unsteadily. However, the relationship between the FPZ characteristics and rock heterogeneity still remain elusive. We used three rock types common in reservoir for experimental investigation, and each of them includes two subtypes with different heterogeneity due to grain size or microstructural characteristics. Drawing on the experiment results, we calculated the FPZ size in each cracked chevron notched Brazilian disk, and we reproduced the formation process of the FPZ in marble by discrete element method. We showed that strong heterogeneity is favorable to large FPZ size, can enhance the ability of crack generation and complicate crack morphology. Coupling the Weibull distribution with fracture mechanics, the dependence of the FPZ size on heterogeneity degree can be theoretically explained, which suggests that inherent heterogeneity of rocks set physical foundation for formation of FPZ. These findings can improve our recognition to formation mechanism of the Mode I crack and provide useful guidelines for evaluating reservoir fracability.

It has been known that the performance of the High-Pressure Grinding Rolls (HPGR) varies as a function of method used to confine laterally the rolls, their diameter/length (aspect) ratio as well as their condition, if new or worn. However, quantifying these effects through direct experimentation in machines with reasonably large dimensions is not straightforward given the challenge, among others, of guaranteeing that the feed material remains unchanged. The present work couples the discrete element method (DEM) to multibody dynamics (MBD) and a novel particle replacement model (PRM) to simulate the performance of pilot-scale HPGRs grinding pellet feed. It shows that rotating side plates, in particular when fitted with studs, allow reaching more uniform forces along the bed, which also translates in a more constant product size along the rolls as well as higher throughput. It also shows that the edge effect is relatively constant with roll length, leading to substantially larger proportional edge regions for high-aspect ratio rolls. On the other hand, the product from the center region of such rolls was found to be finer when pressed at identical specific forces. Finally, rolls were found to have higher throughput, but generate a coarser product when worn following the commonly observed trapezoidal profile. The approach used in industry to compensate for roller wear by increasing the specific force and roll speed has then been demonstrated to be effective to maintain and potentially even increase product fineness and throughput, as long as the minimum safety gap is not reached.

This paper addresses the study of the stress field in composites continua with the multiscale approach of the DECM (Discrete Element modeling with the Cell Method). The analysis focuses on composites consisting of a matrix with inclusions of various shapes, to investigate whether and how the shape of the inclusions changes the stress field. The purpose is to provide a numerical explanation for some of the main failure mechanisms of concrete, which is precisely a composite consisting of a cement-based matrix and aggregates of various shapes. Actually, while extensive experimental campaigns detailed the shape effect of concrete aggregates in the past, so far it has not been possible to model the stress field within the inclusions and on the interfaces accurately. The reason for this lies in the limits of the differential formulation, which is the basis of the most commonly used numerical methods. The Cell Method (CM), on the contrary, is an algebraic method that provides descriptions up to the micro-scale, independently of the presence of rheological discontinuities or concentrated sources. This makes the CM useful for describing the shape effect of the inclusions, on the micro-scale. When used together with a multiscale approach, it also models the macro-scale behavior of periodic composite continua, without losing accuracy on the micro-scale. The DECM uses discrete elements precisely to provide the CM with a multiscale approach.

Protein-DNA interactions are critical for the successful functioning of all natural systems. The key role in these interactions is played by processes of protein search for specific sites on DNA. Although it has been studied for many years, only recently microscopic aspects of these processes became more clear. In this work, we present a review on current theoretical understanding of the molecular mechanisms of the protein target search. A comprehensive discrete-state stochastic method to explain the dynamics of the protein search phenomena is introduced and explained. Our theoretical approach utilizes a first-passage analysis and it takes into account the most relevant physical-chemical processes. It is able to describe many fascinating features of the protein search, including unusually high effective association rates, high selectivity and specificity, and the robustness in the presence of crowders and sequence heterogeneity.

This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment Problem (QAP) implemented on the OpenCL platform. Our work was motivated by results of time efficiency tests performed for single-swarm algorithm implementation that showed clearly that the benefits of a parallel execution platform can be fully exploited provided the processed population is large. The described algorithm can be executed in two modes: with independent swarms or with migration. We discuss the algorithm construction as well as we report results of tests performed on several problem instances from the QAPLIB library. During the experiments the algorithm was configured to process large populations. This allowed us to collect statistical data related to values of goal function reached by individual particles. We use them to demonstrate on two test cases that although single particles seem to behave chaotically during the optimization process, when the whole population is analyzed, the probability that a particle will select a near-optimal solution grows.

Alkali Silica Reaction (ASR) is known to be a serious problem for concrete worldwide, especially in high humidity and high temperature regions. ASR is a slow process that develops over years to decades and it is influenced by changes in environmental and loading conditions of the structure. The problem becomes even more complicated if one recognizes that other phenomena like creep and shrinkage are coupled with ASR. This results in synergistic mechanisms that can not be easily understood without a comprehensive computational model. In this paper, coupling between creep, shrinkage and ASR is modeled within the Lattice Discrete Particle Model (LDPM) framework. In order to achieve this, a multi-physics formulation is used to compute the evolution of temperature, humidity, cement hydration, and ASR in both space and time, which is then used within physics-based formulations of cracking, creep and shrinkage. The overall model is calibrated and validated on the basis of experimental data available in the literature. Results show that even during free expansions (zero macroscopic stress), a significant degree of coupling exists because ASR induced expansions are relaxed by meso-scale creep driven by self-equilibriated stresses at the meso-scale. This explains and highlights the importance of considering ASR and other time dependent aging and deterioration phenomena at an appropriate length scale in coupled modeling approaches.

This paper studies the duration patterns of unemployment spells for immigrants and the determinants of unemployment’s completion into one of a number of studied labour market states in Finland. We estimate a duration model for unemployment with competing risks of its terminating into employment, labour market training or economic inactivity. Taking into account the wide period of observation and opportunities to analyse processes of labour market integration during various periods of economic development in Finland, in combination with the individualistic character of the labour careers of immigrants, this research is beneficial owing to the many various findings concerning labour market integration of immigrants. The approach undertaken in this research has a dualistic “descriptive-dynamic” character under which integration is understood as a never-ending process, which is conditioned by a time period of long-term existence and a context of solitary action. We find that transitions out of unemployment spells have a cyclical character; after every new “cycle” in unemployment, the probability of terminating unemployment decreases further. We also find that ascriptive factors make sense in the process of job-placement of immigrants from unemployment. Therefore, the gender, education and age of immigrants, as well as the effect of the period in which first unemployment occurred, potentially predict transitions out of unemployment and further labour market integration of immigrants.

Facial acne is a prevalent dermatological condition regularly observed in the general population. This study introduces a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrates improvement in performance, offering a novel direction for future acne analysis.

How hands-on gardening impacts behaviors including healthy eating and physical activity during early childhood can be of critical importance for preventing early onsets of obesity. This research investigates how participating in hands-on gardening impacts preschoolers’ (3-5 years old) physical activity (measured by accelerometers) in childcare centers in the Semi-Arid Climate Zone. The research was conducted in eight licensed childcare centers located in West Texas with 149 children (n=149). Four childcare centers in the experimental group received hands-on garden interventions; the other four in the control group did not. In both experimental (intervention) and control (non-intervention) centers, children wore Actigraph GT3X+ accelerometers continuously for 5 days before and for 5 days after intervention (a total of 10 days). Results show that the duration of sedentary behavior of children in the experimental (intervention) group significantly decreased compared to children in the control (non-intervention) group. The finding suggests that the positive effects of childcare hands-on gardening on physical activity extend to semi-arid climate zones where gardening is challenging due to high temperatures and lack of annual rainfall. The research emphasizes the critical need to incorporate hands-on gardening in childcare centers as an obesity prevention strategy nationally in the U.S. and beyond.

Towards better understanding of factors controlling carbon (C) exchange between inland waters and atmosphere, we addressed inorganic carbon cycle in semi-arid lakes of Central Eurasia, subjected to strong impact of on-going climate change. For this, we assessed the hydrochemical vari-ability and quantified its control on formation of authigenic carbonate minerals, occurring within the upper layer of sediments in 43 semiarid lakes located in the southwest of Western Siberia (Central Eurasia). Based on measurements of pH, total dissolved solids (TDS), cationic and anionic composition, dissolved organic and inorganic C, as well as textural and mineralogical characterization of bottom sediments using X-ray diffraction and scanning electron microscopy, we demonstrate that lake water pH and TDS are primarily controlled by both lithological and climatic context of the lake watershed. We have not revealed any direct relationships between lake morphology and water chemistry. The most common authigenic carbonates scavenging atmospheric CO2 in the form of insoluble minerals in lake sediments were calcite, aragonite, Mg-calcite, dolomite and hydro-magnesite. The calcite was the most common component, aragonite mainly appears in lakes with sediments enriched in gastropod shells or artemia cysts, while hydromagnesite was most common in lakes with high Mg/Ca molar ratios, as well at high DIC concentrations. The relationships between mineral formation and water chemistry established in this study can be generalized to a wide suite of arid and semi-arid lakes in order to characterize current status of inorganic C cycle and predict its possible modification under on-going climate warming such as water temperature rise and change of hydrological connectivity, primary productivity and nutrient regime.

Keywords: glycerin; Ni-based catalyst; lanthanum; semi-industrial scale; hydrogen production; biorefinery; monitoring.

The positional deviation of the Automatic Emergency Call System (AECS) under collision conditions brings difficulties for post-rescue operations. Currently, there is a lack of analysis on the system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on the data deviation of the simulated GNSS system. We also propose environmental limits applicable to comprehensive AECS tests, which can objec-tively evaluate the reliability of AECS and provide data support for AECS bench test. Through the satellite signal-based semi-physical simulation, AECS is subjected to bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibra-tion and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the AECS test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature dif-ference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of satellite signal simulation, nec-essary environmental conditions for AECS test are obtained, including the temperature consistent with the maximum operating temperature of the vector generator and the vibration Power Spectral Density (PSD) lower than 1.2gRMS.

The paper proves a theorem about the structure of the distribution function on which the extremum of fractionally linear functional is reached in the presence of an uncountable number of linear constraints. The problem of finding an extremal distribution function arises when determining the optimal control strategy in a class of Markov homogeneous randomized control strategies. The structure of extremal functions is described by a finite number of parameters, hence the problem is greatly simplified since it is reduced to the search for an extremum of some function.

Semi-structured complex numbers H was a number set developed to enable division by zero in ordinary algebraic equations. Its utility has been shown in mathematics and engineering. However, very little has been done to show its usefulness in computer science. Consequently, the aim of this paper was to show the utility of semi-structured complex numbers in computer science by developing a division by zero calculator. First two computer programs were written, one for a standard (STD) calculator and the other for a division by zero (DBZ) calculator. The programs were fed 20000 randomly generated arithmetic equations of varying lengths and the space and time complexity associated with processing these equations were measured and compared to determine the efficiency of each calculator. In the process, three major contributions were made: (1) A representation for semi-structured complex numbers that enables it to be easily used by a computer was developed; (2) It was demonstrated that the DBZ calculator outperforms the STD calculator in terms of efficiency; and, (3) It was shown that the number set H reduced the amount of error handling required to run a computer program. These results provide a firm foundation to advance the number set as a useful tool in computer science.

A new set of alkoxy alcohols were synthesised by reaction of eugenol oxirane with aliphatic and aromatic alcohols. These eugenol derivatives were evaluated against their effect upon the viability of the insect cell line Sf9 (Spodoptera frugiperda).The most promising compounds, 4-(3-(tert-butoxy)-2-hydroxypropyl)-2-methoxyphenol and 4-(2-((4-fluorobenzyl)oxy)-3-hydroxypropyl)-2-methoxyphenol were submitted to in silico assays to predict possible targets. Thought an Inverted Virtual Screening approach, 23 common pesticide targets were screened and the top 2 targets predicted were further evaluated through molecular dynamics simulations and free energy calculations. In addition, these to eugenol derivatives were subjected to encapsulation and release assays using liposome-based nanosystems of egg phosphatidylcholine/cholesterol (7:3), with encapsulation efficiencies higher than 90% and release profiles well described by both Korsmeyer-Peppas and Weibull models.

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Let $G$ be a group with identity $e$ and $R$ be a commutative $G$-graded ring with nonzero unity $1$. Graded semi-primary and graded $1$-absorbing primary ideals have been investigated and examined by several authors as generalizations of graded primary ideals. However, these three concepts are different. In this article, we characterize graded rings over which every graded semi-primary ideal is graded $1$-absorbing primary and graded rings over which every graded $1$-absorbing primary ideal is graded primary.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

Recently, there has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used across India for the improvement of local water security. The Central Groundwater Board of India is en-couraging the construction of such interventions for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at basin scales to maximise their effectiveness. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling exercises. A bespoke version of the GWAVA model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the estimated average annual simulated streamflow at the outlets of the two sub-catchments and the basin whereas tanks and check dams have a more significant effect. In-terventions generally were found to increase evaporation losses across the catchment. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

Increased attention has been paid to the influence of coal mining subsidence on ecological environment. Restoration of ecosystem in damaged mining area is critical for restoring disturbed environment. The comparing of plant communities and microbial communities in the artificial restoration and natural restoration areas provides an effective method for evaluating the restoration effects. However, such studies are limited in coal mining subsidence restoration areas. Subsidence area in Shendong mining area, located in the semi-arid region of Western China, was restored from 2003 with 5 ecological restoration plant species. In July 2017, the comparison and analysis of plant and microbial communities were conducted at the artificial restoration areas (AR) and the natural remediation areas (NR). The results showed that the artificial ecological restoration in Shendong mining area has achieved some success, but it has not recovered to a similar ecosystem before the destruction. A higher plant species, coverage and bacterial community diversity were observed in AR. However, these features have lower similarity compared with those in NR sites. Potential soil factors, such as pH, moisture content, total carbon content, organic matter, nitrogen and bulk density, have a greater impact on soil bacterial community structure and diversity. In the ecological restoration of the mining area, attention should be paid to the restoration of soil properties in the mining area. This study can provide theoretical guidance for more scientific ecological restoration in the damaged mining area.

The ecological background condition of the semi-arid steppe region (SASR) is extremely fragile. It is recognized that the development of coal and electricity power is a kind of strong human interference behavior for regional landscape ecology. Landscape ecological classification (LEC) is the premise of landscape ecology research of the mining area. The current research on the SASR and grassland LEC of coal-power base is relatively less, but still remains uncertainty concerning how to stratify and classify urban mining landscapes into units of ecological significance at spatial scales appropriate for management. This study is based on hierarchy theory, scale theory, landscape process, the patch-corridor-matrix model, the network, the theory of multiple planning integration and the principle of remote sensing. According to the comprehensive principle, principles of the combining of structure and function, principle of the combining human-ominated and natural landscape, principle of emphasis, and principle of combining qualitative analysis with quantitative research of LEC in large-scale coal-power base(LSCPB). On the basis of occurrence method land classification, fully consider the ecological attributes of the land, integration pattern, processes and function theory of the landscape ecology, the LEC system of the LSCPB in the SASR has been constructed by using top-down decomposition classification method. Empirical research of the Victory and Mindong No.1 mining areas of Shenhua Group shows that the classification system constructed in this paper can meet the requirements of LEC and fully reflect the status of landscape ecology of LSCPB in SASR. This study can provide theoretical guidance for the landscape ecology of LSCPB, while also supporting a theoretical reference for the LEC research.

The objective of this research is to develop an experimental-theoretical analysis about the influence of the cooling medium and the geometry of the welding bead profile in fatigue life and associated parameters with structural integrity of welded joints. A welded joint with cruciform geometry is considered using SMAW, plates in structural steel ASTM A36 HR of 8 mm of thickness and E6013 electrode input. A three-dimensional computational model of the cruciform joint was created using the finite element method. For this model, the surface undulation of the cord and differentiation in the mechanical properties of the fusion zone were considered, the heat-affected zone (HAZ) and base material, respectively. In addition, an initial residual stress field which was established experimentally was considered. The results were a set of analytical expressions for the weld magnification factor Mk. It was found that values for the latter decrease markedly in function of the intensity of the cooling medium used in the post welding cooling phase, mainly due to the effect of the residual compressive stresses. The obtained models of behavior of the weld magnification factor are compared with the results from other researchers with some small differences, mainly due to the inclusion of the cooling effect of the post weld and the variation of the leg of the weld bead. The obtained analytical equations in the present research for Mk can be used in management models of life and structural integrity for this type of welded joint.

Semi-solid feedstock of AZ80 magnesium alloy modified by minor rare-earth Y element (0, 0.2, 0.4, 0.8 wt.%) were fabricated by strain induces melting activated (SIMA) in the form of extrusion and partial remelting. The effect of Y addition on the microstructure evolution of extruded and isothermal treated alloy was observed by optical microscope (OM), scanning electron microscope (SEM), X-ray diffraction (XRD) and quantitative analysis. The results show that the Y addition can refine the microstructure and make the β-Mg17Al12 phases agglomerating. During the subsequent isothermal treatment at 570℃, the average solid grain size, shape factor and liquid fractions increased with prolonged soaking time. Smaller spheroidal solid grains and the larger shape factor were obtained due to Y addition. The coalescence and Ostwald ripening of solid grains operated the coarsening process simultaneously. The coarsening rate constants of AZ80M1 (0.2 wt.% Y addition) of 164.22 μm3s-1 was approximately four times less than the un-modified AZ80 alloy of 689.44 μm3s-1. In contrast, the desirable semi-solid structure featured by fine, well globular solid grains and appropriate liquid fractions and shape factor was achieved in AZ80M1 alloy treated at 570℃ for 20-30 min.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

The paper delves into the critical issue of damage detection within the guy cable of a truss steel mast, a pivotal component in structural integrity. It introduces a model wherein damage manifests as a localized cross-section reduction in a single element of the cable. Employing Discrete Wavelet Transform (DWT), a pioneering methodology, the study scrutinizes the behavior of the affected elements through static structural analyses. Signal decomposition via the Mallat pyramid algorithm facilitates comprehensive examination. Static displacements at the connection point between the cable and the truss mast serve as the measured variables. Through systematic investigation, the paper evaluates the impact of damage size and location, external loading force, and cable tension force on the efficacy of the proposed approach. Utilizing data derived from Finite Element Method (FEM) computations for wavelet analysis the authors substantiate the findings with numerical examples, thus offering valuable insights into damage detection strategies for structural health monitoring and engineering applications.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.

Magnetorheological fluids (MRFs) are smart materials consisting of micro-scale magnetizable particles suspended in a carrier fluid. The rheological properties of a MRF can be changed from a fluid-state to a solid-state upon the application of an external magnetic field. This study reports the development of a particle-level simulation code for magnetic solid spheres moving through an incompressible Newtonian carrier fluid. The numerical algorithm is implemented within an open-source finite-volume solver coupled with an immersed boundary method (FVM-IBM) to perform fully-resolved simulations. The particulate phase of the MRF is modeled using the discrete element method (DEM). The resultant force acting on the particles due to the external magnetic field (i.e., magnetostatic polarization force) is computed based on the Clausius-Mossotti relationship. The fixed and mutual dipole magnetic models are then used to account for the magnetic (MAG) interactions between particles. Several benchmark flows were simulated using the newly-developed FVM-IBM-DEM-MAG algorithm to assess the accuracy and robustness of the calculations. First, the sedimentation of two spheres in a rectangular duct containing a Newtonian fluid is computed without the presence of an external magnetic field, mimicking the so-called drafting-kissing-tumbling (DKT) phenomenon. The numerical results obtained for the DKT case study are verified against published data from the scientific literature. Second, we activate both the magnetostatic polarization and the dipole-dipole forces and resultant torques between the spheres for the DKT case study. Next, we study the robustness of the FVM-IBM-DEM-MAG solver by computing multi-particle chaining (i.e., particle assembly) in a two-dimensional (2D) domain for area volume fractions of 20% (260 particles) and 30% (390 particles) under vertical and horizontal magnetic fields. Finally, the fourth computational experiment describes the multi-particle chaining in a three-dimensional (3D) domain allowing to study fully-resolved MRF simulations of 580 magnetic particles under vertical and horizontal magnetic fields.

In this paper the D 3 dihedral logistic map of fractional order is introduced. The map 1 presents a dihedral symmetry D 3 . It is numerically shown that the construction and interpretation 2 of the bifurcation diagram versus the fractional order require special attention. The system stability 3 is determined and the problem of hidden attractors is analyzed. Also, analytical and numerical 4 results show that the chaotic attractor of integer order, with D 3 symmetries, looses its symmetry 5 in the fractional-order variant.

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modelling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modelling frameworks have proved useful for explaining ecosystem response to perturbations, while requiring fewer information and providing more general predictions. However, current qualitative methods have some shortcomings which may limit their utility for specific issues. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modelling framework based on "if-then" rules which generates many alternative event sequences (trajectories). Based on expert knowledge, observations and literature, we use this framework to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups had highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modelling.

Most existing flexible count regression models allow only approximate inference. Balanced discretization is a simple method to produce a mean-parametrizable flexible count distribution starting from a continuous probability distribution. This makes easy the definition of flexible count regression models allowing exact inference under various types of dispersion (equi-, under- and overdispersion). This study describes maximum likelihood (ML) estimation and inference in count regression based on balanced discrete gamma (BDG) distribution and introduces a likelihood ratio based latent equidispersion (LE) test to identify the parsimonious dispersion model for a particular dataset. A series of Monte Carlo experiments were carried out to assess the performance of ML estimates and the LE test in the BDG regression model, as compared to the popular Conway-Maxwell-Poisson model (CMP). The results show that the two evaluated models recover population effects even under misspecification of dispersion related covariates, with coverage rates of asymptotic 95% confidence interval approaching the nominal level as the sample size increases. The BDG regression approach, nevertheless, outperforms CMP regression in very small samples (n = 15 − 30), mostly in overdispersed data. The LE test proves appropriate to detect latent equidispersion, with rejection rates converging to the nominal level as the sample size increases. Two applications on real data are given to illustrate the use of the proposed approach to count regression analysis.

State legitimacy and effectiveness could be seen by the way to deliver welfare to citizens to mitigate social grievances, that could eventually lead to conflicts (Kivimäki, 2021). Social security systems in Maghreb countries are quite similar in their architecture and aims to provide social insurance to all the workers in the labor market. However, they suffer from the same main problem: the low rate of enrollment of workers. Many workers (employees and self-employed) work informally without any social security coverage. The issue of whether informal jobs are chosen voluntarily by workers or as a strategy of last resort is controversial. Many authors recognize that the informal sector is heterogeneous and it is made up of workers who voluntary choose it and others who are pushed inside because of entry barriers to the formal sector (Günther & Launov, 2012). Using the SAHWA survey and discrete choice models, this article confirms the heterogeneity of the informal labor market in three Maghreb countries: Algeria, Morocco, and Tunisia. Furthermore, this article highlights the profiles of workers who voluntarily choose informality, which is missing from previous studies. Finally, this article proposes policy recommendations in order to extend social security to informal workers and to include them in the formal labour market.

This paper presents a new numerical method for multiscale modeling of composite materials. The new numerical model, called DECM, consists in a DEM (Discrete Element Method) approach of the Cell Method (CM) and combines the main features of both the DEM and the CM. In particular, it offers the same degree of detail as the CM, on the microscale, and manages the discrete elements individually such as the DEM—allowing finite displacements and rotations—on the macroscale. Moreover, the DECM is able to activate crack propagation until complete detachment and automatically recognizes new contacts. Unlike other DEM approaches for modeling failure mechanisms in continuous media, the DECM does not require prior knowledge of the failure position. Furthermore, the DECM solves the problems in the space domain directly. Therefore, it does not require any dynamic relaxation techniques to obtain the static solution. For the sake of example, the paper shows the results offered by the DECM for axial and shear loading of a composite two-dimensional domain with periodic round inclusions. The paper also offers some insights into how the inclusions modify the stress field in composite continua.

This study introduces a new discrete distribution which is a weighted version of Poisson-Lindley distribution. The weighted distribution is obtained using the negative binomial weight function and can be fitted to count data with over-dispersion. The p.m.f., p.g.f. and simulation procedure of the new weighted distribution, namely weighted negative binomial Poisson-Lindley (WNBPL), are provided. The maximum likelihood method for parameter estimation is also presented. The WNBPL distribution is fitted to several insurance datasets, and is compared to the Poisson and negative binomial distributions in terms of several statistical tests.

Soil water content (SWC) and soil organic carbon (SOC) are two important factors to consider when revegetation in degraded land of arid and semiarid areas. Knowing the response of SWC and SOC to vegetation restoration as well as their coupling interaction is important for sustainability of vegetation restoration. In this study, three common plantations of the same recovery years were selected on China’s Loess Plateau, which included Pinus sylvestris, Pinus tabuliformis and Populus simonii, to evaluate the response of SWC and SOC to vegetation restoration as well as their interactions, and then revealed the influencing factors of SWC and SOC variations. Results showed that compared to the grassland, three plantations all exacerbated the deficit of deep soil water, but Populus simonii increased the SOC, while the rest all decreased the SOC. And tradeoff of Populus simonii had the lowest root mean squared error (RMSD). Therefore, Populus simonii was identified as representing a suitable revegetation technique for this region. There was strong interaction between SWC and SOC for shallow soil layers. Land use type (LUT) has an important effect on deep SOC variations, while SOC and soil texture for deep SWC variations. The results were helpful to the sustainable development of artificial forests in this region as well as other similar arid and semiarid areas.

The objective of this research was to study the dynamics of moisture and nutrition at the root zone and to quantify the variables linked to efficient water management in avocado orchards of mid and high-lands of Michoacán, México, in typical Andisols of low fertility with (A)C(w2) and C(w2) climates, respectively. Using the FDR (Frequency Domain Reflectometry) technology, the dynamics of soil moisture and salinity were recorded daily at four depths: 0-15, 15-30, 30-45, and 45-60 cm. The soil moisture balance model was used to determine actual evapotranspiration (ETr), crop coefficient (Kc), and water use efficiency (WUE). Results indicated that ETr is 66-73% of the total water at 0-30 cm depth; the fertilizer absorption is limited to 0-30 cm, being negligible at deeper layers. The water use efficiency is 4.4 to 5.6 kg of fruit m-3 of applied water, and the ratio of the water use index (ETr/irrigation sheet applied, ISA) is 0.65-0.70; while the annual avocado Kc is 0.72 in Cutzato and 0.78 in Tiamba. We concluded that avocado has a low water requirement, it does not cause leaching of nutrients and/or water, the absorption of water and nutrients mostly occurs at 30 cm depth, and from a hydrological point of view, the crop is environmentally friendly.

This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the inference functional were devised. Furthermore, numerical experiments are illustrated. The complex proofs in our paper are original results which emphasizes the credibility of the class of Rényi Generalized Entropies as measures of information, and that the field is open to extend an enhanced methodology regarding queuing networks with heavy tails.

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

In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the Language Learning Model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model's accuracy. Interestingly, there's an optimal limit to how much synthetic self-explanation data can bene-fit the system. Exceeding this limit doesn't further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.

Sesquiterpene lactones (STLs) are a large group of terpenoids most commonly found in plants of the Asteraceae family, e. g. in chicory plants, displaying a wide range of interesting biological activities. However, further studies on the biological potential of chicory derived STLs and analogues are challenging as only four of these molecules are commercially available (as analytical standards), and to date there are no published or patented simple extraction-purification processes capable of large scale STLs isolation. In this work we describe a novel three-step large scale extraction and purification method for the simultaneous purification of 11,13-dihydrolactucin (DHLc) and lactucin (Lc) starting from a chicory genotype rich in these STLs and in the corresponding glycosyl and oxalyl conjugated forms. After a small-scale screening on 100 mg of freeze-dried chicory root powder, the best results were achieved with a 17 h water maceration at 30°C. With these conditions we managed to increase the content of DHLc and Lc, at the same time favouring the hydrolysis of their conjugated forms. On a larger scale, the extraction of 750 g of freeze-dried chicory root powder, followed by a liquid-liquid extraction step and a reversed-phase chromatography, allowed the recovery of 642.3 ± 76.3 mg of DHLc and 175.3 ± 32.9 mg of Lc. The two pure STLs were subsequently used in the context of semi-synthesis to generate analogues for biological evaluation as antibacterial agents. In addition, other described chicory STLs that are not commercially available were also synthesized or extracted to serve as analytical standards for the study. In particular, lactucin-oxalate and 11,13-dihydrolactucin-oxalate were synthesized in two steps starting from Lc and DHLc, respectively. On the other hand, 11β,13-dihydrolactucin-glycoside was obtained after a MeOH/H₂O (70/30) extraction, followed by a liquid-liquid extraction step and a reversed-phase chromatography. Together, this work will help facilitate the evaluation of the biological potential of chicory derived STLs and their semisynthetic analogues.

This study focuses on land cover and land management changes in relation to food security and environmental services in a semi-arid area of East Nusa Tenggara (ENT) – Indonesia. The study was conducted in Central Sumba District of ENT Province. Classification and regression tree (CART) for land cover classification has been analyzed using machine learning techniques using Google Earth Engine. A survey with Focus Group Discussion (FGD) and followed with in-depth interviews were conducted for primary data collection involving a total of 871 respondents. The Socio-economic data analyzed statistics descriptively and non-parametric tests. The study showed that: 1). There has been a substantial land use change during the devo-lution era that has both positive and negative implications for food security and envi-ronmental services. 2). There has been population pressure in the fertile or agricultur-al land as the direct impact of the development of city infrastructures; and 3). Nation-al intervention through the Food Estate program has fostered and shaped land use change and land management in Central Sumba District. The study highlights the importance that the devolution spirit should help to well manage the limited ara-ble/agricultural land in dominated semi-arid to ensure food security and environment services.

Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labelled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student-teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both unlabeled data with the pseudo boxes and labelled data as ground truth for training. It learns representations of furniture items by combining labelled and unlabeled data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labelled data, respectively. Our experiment affirms the efficiency of the proposed approach as it outperforms the fully supervised counterpart using only 10% of the labels.

Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR-camera fusion and semi-supervised learning implemented in a fully-convolutional neural network. Our method is tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all-weather scenarios, featuring night and rainy weather. In this work, it is shown that LiDAR-camera fusion with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm is measured in terms of intersection over union, precision, recall and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.

This paper presents a steady-state and transient heat conduction analysis framework using the polygonal scaled boundary finite element method (PSBFEM) with polygon/quadtree meshes. The PSBFEM is implemented with commercial finite element code Abaqus by the User Element Subroutine (UEL) feature. The detailed implementation of the framework, defining the UEL element, and solving the stiffness/mass matrix by the eigenvalue decomposition are presented. Several benchmark problems from heat conduction are solved to validate the proposed implementation. Results show that the PSBFEM is reliable and accurate for solving heat conduction problems. Not only can the proposed implementation help engineering practitioners analyze the heat conduction problem using polygonal mesh in Abaqus, but it also provides a reference for developing the UEL to solve other problems using the scaled boundary finite element method.

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

Marine vessels operating on the Arctic Sea route are constantly prone to collisions and friction with ice. This study discusses the wear of the hull plate caused by the collision of ice against vessels operating in Arctic Sea routes. The abrasive wear of the hull due to ice impact was numerically assessed based on both the incident behavior of ice particles interacting with the flow around the hull and the wear loss of the hull surface caused by the contact force of ice particles. A multi-phase approach was adopted to account for the behavior of ice particles continuously affected by the fluid force around the hull. The fluid force acting on the ice floe was evaluated using computational fluid dynamics (CFD) and the dynamic motion of the drift ice was evaluated using the discrete element method (DEM). The motion of the floating ice particles was updated in real time by iteratively coupling the fluid force and the motion of the ice floe at each time step of the numerical simulation. The results of the wear simulation models were presented in terms of the shape change of the hull surface due to wear. At first, the wear was evaluated for cases in which only the surface paint of the hull was damaged. Thereafter, a computation model considering the shape change of the hull surface experiencing long-term friction of ice particles was introduced. Finally, the numerical procedures to predict the abrasive wear of the hull surface by ice impact were discussed.

Gauss quadrature integral approximation is extended to include integrals with a measure consisting of a continuous as well as a discrete component. That is, we give an approximation for the integral of a function plus its sum over a discrete weighted set.

With growing city tourism, there is an increasing need for urban travel demand models to consider traffic generated by visitors. Existing research has concentrated on socio-demographic and journey-related factors to determine what influences the mode choice of tourists. In contrast, revealed preference data, like travel time, is almost never considered. In this article, we present the results of discrete choice modeling of city tourists’ mode choice based on revealed preference data from a survey we conducted in Kassel, Germany. We used multinomial logit models and determined the model parameters using maximum likelihood estimations. Surprisingly, travel time played a smaller role in mode choice than understood from previously established knowledge about everyday mobility. In the final model, travel time was only significant for the alternative walking. Also, most other sociodemographic and journey-related variables showed no significant influence. The final model reproduced the mode choice, but the goodness of fit was lower than expected from other research. We conclude that modeling the travel behavior of tourists is more complex than everyday mobility. An alternative approach that we suggest would be to model trip chains rather than single trips.

The high-power Asymmetric half-bridge Converter (AHBC) LED constant current driver controlled by digital current mode is a fourth-order system. Static operating point, parasitic resistance, load characteristics, sampling effect, modulation mode and loop delay will have great influence on its dynamic performance. In this paper, the small-signal pulse transfer function of the driver is established by the discrete-time modeling method for the two operating points corresponding to the three modulation modes of the trailing edge, leading edge and double edge. And, the effects of parasitic parameters, delay effect, sampling effect and load effect are fully considered in modeling. For a large number of complex exponential matrix operations, the first order Taylor formula is used for approximate calculation after the coefficient matrix is obtained by substituting the data. Then, Matlab software is used to compare and analyze the discrete-time model and the discrete-average model. The results show that the proposed discrete-time model can more accurately characterize the resonant peak and high-frequency dynamic characteristics, and is very suitable for the design of high frequency digital controller.

Investigation into geological storage of CO2 is underway at the Technology Development Plant (TDP) at Hontomín (Burgos, Spain), the only current onshore injection site in the European Union. The storage reservoir is a deep saline aquifer located within Low Jurassic Formations (Lias and Dogger), formed by fractured carbonates with low matrix permeability. Understanding the processes involved in CO2 migration within this kind of low-primary permeability carbonates influenced by fractures and faults is key to ensure safe operation and reliable plume prediction. During the hydraulic characterization tests, 2300 tons of liquid CO2 and 14000 m3 of synthetic brine were co-injected on site in various sequences to characterize the pressure response of the seal-storage pair [de Dios et al, 2017] The injection tests were analyzed with a compositional dual media model which accounts for both temperature effects (as the CO2 is liquid at the bottom of the wellbore) and multiphase flow hysteresis (to effectively simulate the alternating brine and CO2 injection tests that were performed). The pressure and temperature responses of the storage formation were history-matched mainly through the petrophysical characteristics of the fracture network [Le Gallo et al, 2017]. The dynamic characterization of the fracture network dominates the CO2 migration while the matrix does not appear to significantly contribute to the storage capacity. This initial modeling approach was improved using the characterization workflow developed within the European FP7 CO2ReMove project for sandstone fractured reservoirs [Ringrose et al., 2011; Deflandre et al., 2011]. Fractured reservoirs are challenging to handle because of their high level of heterogeneity that conditions the reservoir behaviour during the injection. In particular, natural fractures have a significant impact on well performance [Ray et al, 2012]. Furthermore, the understanding of the processes involved in CO2 migration within relatively low-permeability storage influenced by fractures and faults is essential for enabling safe storage operation [Iding and Ringrose, 2010]. As part of the European H2020 ENOS project, the site geological model is updated by integration of the recently acquired data such as the image log interpretations from injection and observation wells. The geological model is generated through the analysis and integration of data including borehole images and well test data. Following a methodology developed for naturally fractured hydrocarbon reservoirs [Ray et al., 2012], the image log analysis identified two sets of diffuse fractures. A Discrete Fracture Network [Bourbiaux et al., 2005] was built around both wells which encompass the caprock, storage and underburden formations. The fracture characteristics of the two sets of diffuse fractures, such as orientations, densities and conductivities, are calibrated upon the interpretation of the injection tests [Le Gallo et al, 2017]. For each facies, the DFN characteristics were then upscaled and propagated to the full-field reservoir simulation model as 3D fracture properties (fracture porosity, fracture permeability and equivalent block size).

Experimental recordings of the collective activity of interacting spiking neurons exhibit random behavior and memory effects, thus the stochastic process modeling the spiking activity is expected to show some degree of time irreversibility. We use the thermodynamic formalism to build a framework, in the context of spike train statistics, to quantify the degree of irreversibility of any parametric maximum entropy measure under arbitrary constraints, and provide an explicit formula for the information entropy production of the inferred Markov maximum entropy process. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

Human number understanding is thought to rely on the analogue number system (ANS), working according to Weber’s law. We propose an alternative account, suggesting that symbolic mathematical knowledge is based on a discrete semantic system (DSS), a representation that stores values in a semantic network, similar to the mental lexicon or to a conceptual network. Here, focusing on the phenomena of numerical distance and size effects in comparison tasks, first we discuss how a DSS model could explain these numerical effects. Second, we demonstrate that DSS model can give quantitatively as appropriate a description of the effects as the ANS model. Finally, we show that symbolic numerical size effect is mainly influenced by the frequency of the symbols, and not by the ratios of their values. This last result suggests that numerical distance and size effects cannot be caused by the ANS, while the DSS model might be the alternative approach that can explain the frequency-based size effect.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

Due to the difficult metallurgical conditions during production, advanced processing techniques are required to produce functionally graded metal matrix composites. In this study, we explored a novel approach by using a combination of two different methods to produce Functionally Graded 7075 Al / SiCp composites. First process was direct semi-solid stirring that was used to prevent particle agglomeration, brittle reaction products, floating or settling of the reinforcements and poor wettability for the production of SiCp (wt. 5-20%) reinforced aluminum matrix composites. In the direct semi-solid mixing process, the matrix material remains in a semi-solid state until the reinforcement addition process is completed. The second novel process was sequential squeeze casting to provide liquid diffusion between two composite layers that were used to produce functionally graded aluminum matrix composite. The microstructure and interlayer zones were characterized by using optical microscopy and scanning electron microscopy. The resulting functionally graded material was subjected to spectrometer analyses, density measurements, and metallographic examinations in order to determine the characteristics of its layers and interfacial zones, as well as to assess the formation of the graded structure. The results obtained from this research provide the potential to use this new combined manufacturing method, which is relatively inexpensive, efficient and easily applicable, in order to produce functionally graded SiCp reinforced aluminum composites.

Text annotation is an essential element of the natural language processing approaches. The manual annotation process performed by humans has several drawbacks, such as subjectivity, slowness, fatigue, and possibly carelessness. In addition, annotators may annotate ambiguous data. So, we developed the concept of automated annotation to get the best annotations using several machine-learning approaches. The proposed approach is based on an ensemble algorithm of meta-learners and meta-vectorizer techniques. The approach employs a semi-supervised learning technique for automated annotation, aimed at detecting hate speech. This involves leveraging various machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Naive Bayes (NB), in conjunction with Word2Vec and TF-IDF text extraction methods. The annotation process is performed using 13,169 Indonesian YouTube comments data. The proposed model used a Stemming approach using data from Sastrawi and also new data of 2,245 words. Semi-supervised learning uses 5%, 10%, and 20% of labeled data as compared to performing labeling based on 80% of the datasets. In semi-supervised learning, the model learns from the labeled data, which provides explicit information, and the unlabeled data, which offers implicit insights. This hybrid approach enables the model to generalize and make informed predictions even when limited labeled data is available, ultimately enhancing its ability to handle real-world scenarios with scarce annotated information. In addition, the proposed method uses a variety of thresholds for matching words labeled with hate speech ranging from 0.6, 0.7, 0.8, and 0.9. The experiment showed that the KNN-Word2ec model has the best accuracy value of 96.9% with a scenario of 5%:80%:0.9. However, several other methods have also accuracy above 90%, such as SVM and DT based on both text extraction methods in several test scenarios.

Recently, a paper was written to establish semi-structured complex numbers ℍ (a new number set invented to enable division by zero) as a useful mathematical tool providing novel results from its application in engineering and science. Whilst the paper was a milestone, the application of this number set to quantum and classical physics had not been fully explored. Consequently, the aim of this research was to use semi-structured complex numbers to develop a new mathematical model for the mass of an object and in course unite classical and quantum physics in a simple yet effective manner. As its major contributions this paper: (1) develops a new mathematical model for the mass of an object called the wave-mass function using De Broglie’s mass and frequency relation and semi-structured complex numbers; (2) uses the new wave-mass function to determine what happens to an object traveling at the speed of light; (3) derives a relation between Schrödinger’s wave function and the new wave-mass function; (4) derives a relation between Einstein’s Field Equation and the new wave-mass function; (5) uses the wave-mass function to combine Einstein’s Field Equation with Schrödinger’s wave function to form a new quantum gravity equation. These results and their applications provide a firm foundation to advance the number set ℍ as a useful mathematical tool.

Tantalum oxide thin films are deposited by DC reactive magnetron sputtering from a tantalum metallic target and oxygen as reactive gas. The reactive gas pulsing process (RGPP) is implemented to produce TaOx compounds with tunable chemical compositions. The argon mass flow rate is maintained constant whereas that of the oxygen gas is pulsed during the deposition. A constant pulsing period T = 10 s is used and the introduction time of the oxygen gas, namely the tON injection time, is systematically changed from 0 to 100 % of the pulsing period T. As the tON injection time increases, the chemical composition of as-deposited TaOx films is continuously changed from pure metallic tantalum to the over-stoichiometric Ta2O5 material. Analysis of the crystallographic structure by X-ray diffraction shows that the films adopt the body-centered cubic structure (metallic Ta) for the lowest tON injection time values (oxygen stoichiometry x &lt; 1.0) and become amorphous for the longest tON injection times. It is shown that the tON injection time is a key parameter to produce either homogeneous tantalum oxides, or periodic Ta/TaOx multilayers with alternations close to 3 nm. Similarly, optical transmittance spectra of the film/glass substrate system are recorded in the visible region and the electrical conductivity of the films is measured vs. temperature. Both exhibit a gradual evolution from metallic (300K = 8.17105 S m-1 with a null optical transmittance in the visible range) to semi-conducting (300K = 1.97103 S m-1 with a semi-transparent behavior) and finally to dielectric properties (300K &lt; 10-5 S m-1 for interferential films) as a function of the oxygen concentration in the films.

Fluorine represents a privileged building block in pharmaceutical chemistry. Diethylaminosulfur-trifluoride (DAST) is a reagent commonly used for replacement of alcoholic hydroxyl groups with fluorine and is also known to catalyze water elimination and cyclic Beckmann-rearrangement type reactions. In this work we aimed to use DAST for diversity-oriented semisynthetic transformation of natural products bearing multiple hydroxyl groups to prepare new bioactive compounds. Four ecdysteroids, including a new constituent of Cyanotis arachnoidea, were selected as starting materials for DAST-catalyzed transformations. The newly prepared compounds represented combinations of various structural changes DAST was known to catalyze, and a unique cyclopropane ring closure that was found for the first time. Several compounds demonstrated in vitro antitumor properties. A new 17-N-acetylecdysteroid (13) exerted potent antiproliferative activity and no cytotoxicity on drug susceptible and multi-drug resistant mouse T-cell lymphoma cells. Further, compound 13 acted in significant synergism with doxorubicin without detectable direct ABCB1 inhibition. Our results demonstrate that DAST is a versatile tool for diversity-oriented synthesis to expand chemical space towards new bioactive compounds.

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

This article introduces the Theory of Inverse Discrete Dynamical Systems (TIDDS), a novel methodology for modeling and analyzing discrete dynamical systems via inverse algebraic models. Key concepts such as inverse modeling, structural analysis of inverse algebraic trees, and the establishment of topological equivalences for property transfer between a system and its inverse are elucidated. Central theorems on homeomorphic invariance and topological transport validate the transfer of cardinal attributes between dynamic representations, offering a fresh perspective on complex system analysis. A significant application presented is an alternative proof of the Collatz Conjecture, achieved by constructing an associated inverse model and leveraging analytical property transfers within the inverted tree structure. This work not only demonstrates the theory's capability to address and solve open problems in discrete dynamics but also suggests vast implications for expanding our understanding of such systems

This paper aims to understand better how attitudes towards the environment could influence preferences and willingness to pay for the development of sustainable tourism products on the Spanish island of Gran Canaria. A hybrid choice model is estimated to analyse how different latent constructs related to environmental concerns affect individuals’ preferences for a set of sustainable tourism activities. The data used in the analysis is obtained from a discrete choice experiment where different scenarios with nature-based tourism packages are created. A set of measurement indicators allowed us to gain insight into the underlying latent structure regarding the individuals’ attitudes towards the environment. The analysis consists of integrating these attitudes into a choice model, focusing on a market segment primarily comprised of potential customers who are young residents and non-residents. Results reveal significant heterogeneity in preferences and willingness to pay for the various activities under study when attitudinal latent factors are incorporated into the model. Our findings provide valuable insights for researchers, policymakers, and practitioners promoting sustainable tourism products.

This paper presents three types of tensor Conjugate-Gradient methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatical determination strategy of a suitable regularization parameter is proposed for the tensor conjugate gradient (tCG) method. A truncated version and a preprocessed verion of the tCG method are further presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples are given to show the effectiveness of the proposed tCG methods in image and video restoration.

Numerous reservoirs that play a significant role in worldwide petroleum production and reserves contain fractures. Typically, the fractures must form a connected network for a reservoir to be classified as naturally fractured. Characterizing the reservoir with a focus on its fracture network is crucial for modeling and predicting production performance. To simplify the solution, dual continuum modeling techniques are commonly employed. However, to use continuum-scale approaches, properties such as average aperture, permeability, and matrix fracture interaction parameters must be assigned, making it necessary to improve fracture depiction and modeling methods. This study investigates a fractured reservoir with a low matrix permeability and a well-connected fracture network. The focus is on the impact of the hyrachical fracture network on the production performance of gas-based enhanced oil recovery methods. The Discrete Fracture Model (DFN) was utilized to create comprehensive two-dimensional models for three processes: Gas Injection (GI), Water Alternating Gas (WAG), and Foam Assisted Water Alternating Gas (FAWAG). Moreover, dimensionless numbers were employed to establish connections between properties across the entire fracture hierarchy, spanning from minor to major fractures and encompassing the fracture intensity. The results indicate that the FAWAG is more sensitive to fracture types and networks than the WAG and GI processes. Hence, the sensitivity of the individual EOR method to the fracture network requires a respective depth of description of the fracture network. However, other factors, such as reservoir fluids properties and fracture properties, might influence the recovery when the minor fracture networks are excluded. This study has determined that, among the enhanced oil recovery (EOR) techniques examined, the significance of the hyrachical depth of fracture networks diminishes as the ratio of major (primary fracture) aperture to the aperture of medium and minor fractures increases. Additionally, the impact of the assisted-gravity drainage method was greater with increased reservoir height; however, as the intensity ratio increased, the relative importance of medium and minor fracture networks decreased.

Image compression is a top priority today due to the need for faster encoding and decoding. To achieve this, the present study has proposed the use of Canonical Huffman Coding (CHC) as an entropy coder, which has a lower decoding time complexity than binary Huffman codes. For image compression, combining of the Discrete Wavelet Transform (DWT) and CHC with Principal Component Analysis (PCA) has been recommended. The lossy method has been introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed image has been found to have a better peak signal-to-noise ratio (PSNR) value. This study has developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for efficient use of storage space. The proposed technique has achieved up to 60% compression while maintaining high visual quality. This method has also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach has been tested on various color images, including Peppers 512×512 and Couple 256×256, showing improvement by 17 dB and 22 dB, respectively, while reducing bpp by 0.56 and 0.10, respectively. For grayscale images, i.e., Lena 512×512 and Boat 256×256, the proposed method has shown an improvement by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

The age of information (AoI) metric was proposed to measure the freshness of messages obtained at the terminal node of a status updating system. In this paper, the AoI of discrete time status updating system with probabilistic packet preemption is investigated by analyzing the steady state of a three-dimensional discrete stochastic process. Assuming the queue used in system is Ber/Geo/1/2*/η, which represents that the system size is 2 and the packet in buffer can be preempted by fresher packet with probability η. Instead of considering system’s AoI separately, we use a three-dimensional state vector (n,m,l) to simultaneously track the real time changes of the AoI, the age of packet in server, and the age of packet waiting in buffer. We give the explicit expression of system’s average AoI, and show that the average AoI of system without packet preemption is obtained by letting η=0. When η is set to 1, the mean of AoI of system having Ber/Geo/1/2* queue is obtained as well. Combining the results we have obtained and comparing them with corresponding average continuous AoIs, we propose a possible relationship between average discrete AoI with Ber/Geo/1/c queue and the average continuous AoI with M/M/1/c queue. For each of two extreme cases where η=0 and η=1, we also determine the stationary distribution of AoI using the probability generation function (PGF) method. The relations between average AoI and the packet preemption probability η, as well as AoI’s distribution curves in two extreme cases are illustrated by numerical simulations. Notice that the probabilistic packet preemption may occur, for example, in an energy harvest (EH) node of wireless sensor network, where the packet in buffer can be replaced only when the node collects enough energy. In particular, to exhibit the usefulness of our idea and methods and highlight the merits of considering discrete time systems, in this paper we give much more explanations showing that how the results about continuous AoI is derived by analyzing the corresponding discrete time system, and how the discrete age analysis is generalized to the system with multiple sources. In terms of packet service process, we also propose our idea to analyze system’s AoI when the service time distribution is relaxed to be arbitrary.

Ultrasonic measurements are used in civil engineering for structural health monitoring of concrete infrastructures. The late portion of the ultrasonic wavefield, the coda, is sensitive to small changes in the elastic moduli of the material. Coda Wave Interferometry (CWI) correlates these small changes in the coda with the wavefield recorded in intact, or unperturbed, concrete specimen to reveal the amount of velocity change that occurred. CWI has the potential to detect localised damages and global velocity reductions alike. In this study, the sensitivity of CWI to different types of concrete mesostructures and their damage levels is investigated numerically. Realistic numerical concrete models of concrete specimen are generated and damage evolution is simulated using the discrete element method. In the virtual concrete lab, the simulated ultrasonic wavefield is propagated from one transducer using a realistic source signal and recorded at a second transducer. Different damage scenarios reveal a different slope in the decorrelation of waveforms with the observed reduction in velocities in the material. Finally, the impact and possible generalizations of the findings are discussed and recommendations are given for a potential application of CWI in concrete at structural scale.

Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities like subgrade damages. The reason of the intensive void development lies in the mechanics of the sleeper and ballast bed interaction. The particularity of the interaction is a dynamic impact that occur due to void closure. Additionally, void zones cause inhomogeneous ballast pressure distribution between the void zone and fully supported neighbour zones. The present paper is devoted studying the mechanism of the sleeper-ballast dynamic impact in the void zone. The results of experimental in-situ measurements of rail deflections showed the significant impact accelerations in the zone even for light-weight slow vehicles. A simple 3-beam numerical model of track and rolling stock interaction has shown the similar to the experimental measurements dynamic interaction. Moreover, the model shows that the sleeper accelerations are more than 3 times higher than the corresponding wheel accelerations and the impact point appear before the wheel enters the impact point. The analysis of ballast loadings shows the specific impact behaviour in combination with the quasistatic part that is different for void and neighbour zones, which are characterised with high ballast pre-stressed conditions. The analysis of void sizes influence demonstrate that the impact loadings, wheel and sleeper maximal accelerations appear at certain void depth after which the values decrease. The ballast quasistatic loading analysis indicates more than twice increase of the ballast loading in neighbour zones for long voids and almost full quasistatic unloading for short length voids. However, the used imitation model cannot explain the nature of the dynamic impact. The mechanism of the void impact is clearly explained by the analytic solution using a simple clamped beam. A simplified analytical expression of the void impact velocity shows that it is linearly related to the wheel speed and loading. The comparison to the numerically simulated impact velocities shows a good agreement and the existence of the void depth with the maximal impact. An estimation of the long-term influences for the cases of normal sleeper loading, high ballast pre-stress and quasistatic loading in the neighbour zones and high impact inside the void are performed.

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback--Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

Based on a local causal model of the dynamics of curved discrete spacetime, a causal model of quantum field theory in curved discrete spacetime is described. On the elementary level, space(-time) is assumed to consists of interconnected space points. Each space point is connected to a small discrete set of neighboring space points. Density distribution of the space points and the lengths of the space point connections depend on the distance from the gravitational sources. This leads to curved spacetime in accordance with general relativity. Dynamics of spacetime (i.e., the emergence of space and the propagation of space changes) dynamically assigns "in-connections" and "out-connections" to the affected space points. Emergence and propagation of quantum fields (including particles) are mapped to the emergence and propagation of space changes by utilizing identical paths of in/out-connections. Compatibility with standard quantum field theory (QFT) requests the adjustment of the QFT techniques (e.g., Feynman diagrams, Feynman rules, creation/annihilation operators), which typically apply to three in/out connections, to n > 3 in/out connections. In addition, QFT computation in position space has to be adapted to a curved discrete space-time.

Based on a local causal model of the dynamics of curved discrete spacetime, a causal model of quantum field theory in curved discrete spacetime is described. At the elementary level, space(-time) is assumed to consists of interconnected space points. Each space point is connected to a small discrete set of neighbor space points. Density distribution of the space points and the lengths of the space point connections depend on the distance from the gravitational sources. This leads to curved spacetime in accordance with general relativity. Dynamics of spacetime (i.e., the emergence of space and the propagation of space changes) dynamically assigns "in-connections" and "out-connections" to the affected space points. Emergence and propagation of quantum fields (including particles) are mapped to the emergence and propagation of space changes by utilizing identical paths of in/out-connections. Compatibility with standard quantum field theory (QFT) requests the adjustment of the QFT techniques (e.g., Feynman diagrams, Feynman rules, creation/annihilation operators), which typically apply to three in/out connections, to n > 3 in/out connections. In addition, QFT computation in position space has to be adapted to a curved discrete space-time.

Inverse Sumudu transform multiple shifting properties are used to design methodology for solving ordinary differential equations. Then algorithm applied to solve Whittaker and Zettl equations to get their new exact solutions and profiles which shown through Maple complex graphicals. Table of inverse Sumudu transforms for elementary functions given for supporting the differential equations solving using inverse Sumudu transform.

The German health authorities' guidelines for medical devices in 2012 highlighted the importance of cleaning ultrasound probes, emphasizing their validation and reliability. In addition to automated and validated options, alternative manual methods such as wipe disinfection have gained traction due to their independence from additional hardware. The study examines the effectiveness of manual disinfection methods, specifically the cleaning process, addressing concerns raised by the Robert Koch Institute regarding the lack of validation for wipe disinfection of semi-critical devices. The EQUINOS colored wipe disinfection kit identified wetting gaps in all cleanings across four probes tested. The results indicate significant challenges in ensuring complete surface wetting, particularly in complex body parts such as clip-on areas and shapes for additional biopsy attachments, suggesting that manual methods alone may not adequately mitigate the risk of infection transmission (p value .001). The study concludes that while manual disinfection methods are a viable alternative to automated reprocessing, there is a critical need for enhanced training and potentially the development of more effective manual disinfection techniques or colored wipes to ensure patient safety and compliance with healthcare hygiene standards.

Environmental degradation leads to a totally unsustainable food system. In addition to this is-sue, the consumption of foods that improve people's health and well-being is recommend-ed. These guidelines create new challenges and the demand for alternatives to meet these needs in the short term. One of the alternatives is undoubtedly the use of by-products of winemaking, namely in the form of grape pomace flour (GPF), which is obtained in a totally sustainable way, requires no further water consumption or agricultural land, and is an ingredient that boasts all the characteristics that meet the circular economy and has colossal potential in promoting nutrition and healthy eating. To verify the benefits of using the Touriga Nacional and Arinto (Vitis vinifera L.) flour varieties, analytical determinations were made to identify and quantify different components, such as nitrogen and extractable phosphorus, ash, moisture, proteins, fats, dietary fibers, total sugars, and phenolic content. GPF could become a valuable ingredient due to its nutritional quality and high content in various polyphenols namely flavonoids and pyranoanthocyanins.

The assessment of existing bridges constitutes a serious challenge for the researchers since their safety evaluation is a complex task due to several sources of uncertainty affecting the parameters identified in the knowledge process. In particular, a high level of uncertainties may arise in the case of prestressing systems requiring special investigation strategies and experimental technologies. The paper aims to investigate how the knowledge uncertainties influence the safety assessment of existing post-tensioned bridges. The semi-probabilistic approach is used, and uncertainties related to the knowledge process are described considering the number of tests, the confidence level assumed for the estimation of the problem parameters, and the measurement errors of in-situ tests. Results concerning a typical post-tensioned bridge are reported and the variability of the outcomes is discussed and compared with a reference case of “perfect knowledge”. The main results of the study confirm the general robustness of the semi-probabilistic Eurocodes safety format and provide an overview of dispersion due to different choices about the number of tests and confidence level. The measurement errors may lead to significant under-estimation of the computed structural capacity with respect to the reference one.

A significantly reduced stress concentration effect and a stable deformation behavior are exhibited by the arc-like S-shaped auxetic structure. Analytical works beyond the bar and hinge method to capture structural effects, such as bending, shearing, etc., in the S-structure are scarce in the literature. The deformation pattern of the S-structure is dominated by bending and shearing within the linear elastic region. In this work, Timoshenko beam theory is used to derive closed-form expressions for overall elastic modulus and Poisson’s ratio. This agrees well with the results of finite element simulations. An adjusted R-square value of over 0.99 and almost 0.82 is obtained for elastic modulus and Poisson’s ratio, respectively. From parametric studies, it’s established that under quasi-static transverse load, strut thickness and the angle `α’ are the most important parameters for controlling elastic modulus, specific energy absorption, negative Poisson’s ratio (NPR) effect, and relative density of the entire auxetic structure. Also, it’s found that energy absorption and elastic modulus increase together. Interestingly, the elastic modulus of the structure under transverse load lies in the range from 3 MPa to 250 MPa, which means this structure is compliant enough and can be used in cushioning, packaging, soft robotics applications, etc.

Reservoir in the Central Structural Zone of the Xihu Sag is the Huagang Formation, dominated by natural gas reservoirs, and the reservoir in the Western Slope Zone is the Pinghu Formation, dominated by light oil and wet gas. In this paper, the Gas Drive Water Displacement- Magnetic Resonance Imaging (GWD-MRI) experiments are used to simulate the charging characteristics of the sandstone migration layer. The centrifugal- Magnetic Resonance (Cen-NMR) experiments simulate the short-term fast trap charging process, and the Semi-Permeable Baffle (SPB) charging experiment simulates the slow trap accumulation process. Studies have shown that there is a start-up pressure for the migration layer charging, and the start-up pressure of the core with a permeability of 0.3mD is about 0.6MPa. Experimental simulations have confirmed that there is a front zone with changed water saturation, with a width of about 1-1.5cm. The migration layer charging mainly has two actions, drive effect and carry effect. The drive effect reduces the water saturation to 70%-80%, and the carry effect can still reduce the water saturation by 5%-10%. The water saturation of rapid charging is mainly affected by the petro-physical. The porosity is high, the water saturation is low. The water saturation decreased significantly with the increase of centrifugal force when the centrifugal force is smaller. If the centrifugal force is greater than 0.8 MPa, the water saturation decreases slowly. The water saturation of the trap slowly charging is basically maintained at 40%-50%, which matches the water saturation of the airtight coring from 40%-55%, the petro-physical do not affect the final water saturation.

The combination of organic matter, iron oxide, and clay minerals is of great significance for the adsorption of copper ions (Cu). The purpose of this study is to explore the characteristics of Cu adsorption and laws governing Cu complexation to organic–inorganic, organic–clay mineral, and iron-oxide–clay mineral complexes in the sediments in the estuary of plateau fault and sinking lake——Dianchi Lake. In this study, Cu adsorption tests were performed on the three complexes, in order to study the kinetic behavior of adsorption, Langmuir and Freundlich isotherm models were used. The samples before and after adsorption were characterized via scanning electron microscope (SEM), Fourier infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Our results show that, the Freundlich isotherm models model was able to describe adsorbent behavior in comparison to the Langmuir models. During the Cu adsorption process, the iron-oxide–clay mineral complex is able to adsorb Cu, via coordination exchange, through the –OH contained therein. Organic-matter–clay mineral complexes bonded to the surfaces of clay minerals by replacing the hydroxyl groups with functional groups (carboxyl groups or phenolic hydroxyl groups) contained in the organic matter. Organic–inorganic composites then adsorbed Cu through the coordination exchange of –OH in the polar functional groups of alcohols, phenols, and carboxylic acids. The adsorption capacity of Cu in these three sediment complexes was observed to have the following order: organic–inorganic complex &gt; organic-matter–clay mineral complex &gt; iron-oxide–clay mineral complex. The semi-quantitative analysis results of Fourier Infrared Spectroscopy show that the organic matter (changes in the peak area of functional groups such as carboxyl groups) in the organic-inorganic composite material has an important effect on the amount of copper ions adsorbed by clay minerals.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

We report cross-shore profile evolution at Palanga, eastern Baltic Sea where short period waves dominate. Cross-shore profile studies began directly after a significant coastal erosion caused by storm “Anatoly” in December of 1999 and continued for a year. Further measurements were undertaken sixteen years later. Cross-shore profile ∆V(x) changes were described, and cross-shore transport rates Q(x) were calculated. A K-means clustering technique was applied to determine sections of the profile with the same development tendencies. Profile evolution was strongly influenced by the depth of closure which is constrained by a moraine layer and the presence of a groyne. The method used divided the profile into four clusters: the 1st cluster in the deepest water represents profile evolution limited by the depth of closure, and the 2nd and 3rd mostly are affected by processes induced by wind, wave and sea-water level changes. The most intensive sediment volume changes were observed directly after the coastal erosion. The largest sand accumulation was in the 4th profile cluster, which includes the upper beach and dunes. Seaward extension of the dune system caused a narrowing of the visible beach which has led to an increased sand volume (accretion) being misinterpreted as erosion

The military areas in Sardinia are around 234 km2, which constitutes 59.97% of the national surface affected by military easements. This situation is due to its geographic centrality in the Mediterranean. This contribution evaluates the performance of the Local Coastline Plan (LCP) and the Site Management Plan of Community Interest (SCI) in conditions of military constraint. The case study is the Municipality of Villaputzu where an important coastal military easement and the use of the coast for recreational tourism purposes coexist together through specific planning, a consequence of institutional agreements between the Municipal Administration of Villaputzu and the Ministry of Defense. The evaluation of the congruence of the specific objectives of the LCP and the SCI shows how their combined action favors the environmental enhancement of Sardinia, contributing to the formation of ecosystem services, even in particular conditions arising from military easements. These are sites that pass from the status of "anti-commons" to "semi-commons". In fact, the military release process in Sardinia, together with the promiscuous military and civil use, activates unique governance policies of their kind that find a significant field of application in Sardinia to guarantee a sustainable renewal of economic development of the ‘semi-commons’ awaiting to become ‘commons’

We mainly describe the influence factors of captured power by huge wave energy harvesters, which the vertical motion of buoy can transform ocean’s potential energy into piezoelectric energy power by waves. By means of semi-analytical solutions and theoretical analysis, related environmental coefficients are analyzed firstly and find the most appropriate wave frequency and geometric structure as reference. Secondly, the numerical results also discuss the impact trend of hydrodynamic parameters and geometric volume on motion, voltage and power with qualitative agreement. The simulation data confirm that structure parameters of the modified model could markedly deliver sufficient power to ocean high electrical equipment with long-time stability.

Both climate change and anthropogenic activities contribute to the deterioration of terrestrial water resources and ecosystems worldwide. Central Asian endorheic basins are among the most affected regions through both climate and human impacts. Here, we used a digital elevation model, digitized bathymetry maps and Landsat images to estimate the areal water cover extent and volumetric storage changes in small terminal lakes in Burabay National Nature Park (BNNP), located in Northern Central Asia (CA), for the period of 1986 to 2016. Based on the analysis of long-term climatic data from meteorological stations, short-term hydrometeorological network observations, gridded climate datasets (CRU) and global atmospheric reanalysis (ERA Interim), we have evaluated the impacts of historical climatic conditions on the water balance of BNNP lake catchments. We also discuss the future based on regional climate model projections. We attribute the overall decline of BNNP lakes to long-term deficit of water balance with lake evaporation loss exceeding precipitation inputs. Direct anthropogenic water abstraction has a minor importance in water balance. However, the changes in watersheds caused by the expansion of human settlements and roads disrupting water drainage may play a more significant role in lake water storage decline. More precise water resources assessment at the local scale will be facilitated by further development of freely available higher spatial resolution remote sensing products. In addition, the results of this work can be used for the development of lake/reservoir evaporation models driven by remote sensing and atmospheric reanalysis data without the direct use of ground observations.

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction.The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. As a result, there is an increasing requirement to monitor the state of these components in order to avoid such issues from occurring. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (LSTM) model. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the Autoencoder LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. Furthermore, the study used a thorough global metrics evaluation methodology to further test the model’s dependability and efficacy, consequently substantiating the proposed approach’s validity.