In this project, we use a statistical multiple regression to study the impact of eight various predictors (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) to estimate the cooling load energy efficiency of residential buildings. We try to analyze and visualize the effect of each predictor with each of the response variable using different classical statistical analysis tools used in describing linear models, in such a way so that we can find out the most strongly related predictor variables. Before starting all of this, we use the idea of model selection by stepwise regression technique and compare the AIC of these models and identified a better model between all of them. Then, we compare a classical linear regression approach by simulations on 768 diverse residential buildings show that we can predict CL with low mean absolute error. By using ANOVA we determine variation in the different residuals. Also, we use non constant variance test to verify it. Furthermore, we check leverage and influence points as well as outliers as well as determined cook distance for influential points. By taking box cox transformation and weights, we also introduce WLS technique to fit the model for better results and did all type of important analysis to understand the energy efficiency. Finally, we show 5-fold cross validation to verify our model.

Ploidy is a significant type of genetic variation, describing the number of chromosome sets per cell. Ploidy evolves in natural populations, clinical populations, and lab experiments, particularly in fungi. Despite a long history of theoretical work on this topic, predicting how ploidy will evolve has proven difficult, as it is often unclear why one ploidy state outperforms another. Here, we review what is known about contemporary ploidy evolution in diverse fungal species through the lens of population genetics. As with typical genetic variants, ploidy evolution depends on the rate that new ploidy states arise by mutation, natural selection on alternative ploidy states, and random genetic drift. However, ploidy variation also has unique impacts on evolution, with the potential to alter chromosomal stability, the rate and patterns of point mutation, and the nature of selection on all loci in the genome. We discuss how ploidy evolution depends on these general and unique factors and highlight areas where additional experimental evidence is required to comprehensively explain the ploidy transitions observed in the field and the lab.

Standard evolutionary theory, supported by mathematical modelling of outbred, dispersed populations predicts that ageing is not an adaptation. We recently argued that in clonal, viscous populations, programmed organismal death could promote fitness through social benefits and has, in some organisms (e.g. Caenorhabditis elegans), evolved to shorten lifespan. Here we review previous adaptive death theory, including consumer sacrifice, biomass sacrifice, and defensive sacrifice types of altruistic adaptive death. In addition we discuss possible adaptive death in semelparous fish, coevolution of reproductive and adaptive death, and adaptive reproductive senescence in C. elegans. We also describe findings from recent tests for the existence of adaptive death in C. elegans using computer modelling. Such models have provided new insights into how trade-offs between fitness at the individual and colony levels mean that senescent changes can be selected traits. Exploring further the relationship between adaptive death and social interactions, we consider examples where adaptive death results more from action of kin than from self-destructive mechanisms and, to describe this, introduce the term adaptive killing of kin.

Sustainable agricultural production is endangered by several ecological factors such as drought, extreme temperatures, excessive salts, parasitic ailments, and insect pest infestation. These challenging environmental factors may have adverse effects on future agriculture production in many countries. In modern agriculture, conventional crop breeding techniques alone are inadequate for achieving the increasing population’s food demand on a sustainable basis. The advancement of molecular genetics and related technologies are promising tools for the selection of new crop species. Gene pyramiding through marker assisted selection (MAS) and other techniques have accelerated the development of durable resistant/tolerant lines with high accuracy in the shortest possible period of time for agricultural sustainability. Gene stacking has not been fully utilized for biotic stress resistance development and quality improvement in most of the major cultivated crops. This review emphasizes on gene pyramiding techniques that are being successfully deployed in modern agriculture for improving crop tolerance to abiotic and biotic stresses for sustainable crop improvement.

The objective of this study was to determine the association of physiological responses and root distribution patterns on yield of the second ratoon cane and the relationships among these traits. Seventeen sugarcane genotypes were planted in a randomized complete block design with four replications. The second ratoon crop was evaluated for germination percentage, cane yield, SPAD chlorophyll meter reading (SCMR), chlorophyll fluorescence, relative water content (RWC), specific leaf area (SLA) and stomatal conductance. Root length density (RLD) was evaluated by auger method. The root samples were divided into upper soil layer and lowers soil layers to study root distribution patterns. Sugarcane genotypes were significantly different for RLD, germination percentage and cane yield. Root distribution patterns were classified into three groups based on the RLD. High RLD between plants in the upper soil layers at 90 DAH was positively correlated with high germination, whereas high RLD between rows in the lower soil layers at 90 and 270 DAH was associated with high cane yield. RWC at 90 DAH and stomatal conductance at 180 DAH were closely related to germination percentage, whereas chlorophyll fluorescence and stomatal conductance at 180 DAH were closely related to cane yield.

The microspheres constituted by proteinoids synthesized from Fox’s simulation experiments. They had peptide bond structure and weak catalysis, as well as proliferated themselves. Such microspheres were believed the models for primitive life. Due to lack of metabolism and self-reproduction, the microspheres could not meet requirements of life. Thus, how microspheres could evolved into primitive life remain unsolved mysteries. The microspheres were supposed a dissipative structure and the processes of absorption and hydrolysis could be balanced to maintain their stability by consuming proteinoid. Proteinoid molecules differed in their life spans, which were mainly determined by their multi-space structures. Consequently, molecule selection and retention could occur spontaneously in microspheres and lead to a more organized and stabilized structure of the whole microsphere with time through dissipative process. More complex chain network of chemical reactions could happen in microspheres because the proteinoid with complex, ordered multi-space structure and relatively high catalytic activity would retain. In such microspheres, nucleotides could produce and further aggregate into RNA. The synthesis of real proteins could take place with RNA as the template catalyzed by proteinoids or RNA inside microspheres. When template-based protein molecules replaced the proteinoid inside the microspheres, a protein-based self-catalyzed network of chemical reactions could take place. It is plausible if Fox’s proteinoids microspheres is to dawn on a dissipative structure, then molecule selection could occur spontaneously by “dissipative” proteinoids, and the microspheres would acquire catalytic activity due to preserved the proteinoid with a large molecular weight and relatively complex and ordered multi-space structure, and relatively high catalytic activity. Thus the microspheres would spontaneously go to self-organizing, and evolve into primitive life.

The aim of the present study is to investigate both the performance and preferences of males and females Computer Science (CS) graduates. In order to attain the above goal, a quantitative case study was conducted regarding 89 degrees, acquired from 2006 to 2012, from the Department of Computer Science and Technology, University of Peloponnese, Greece. The analysis of the data revealed that in terms of performance, no significant differences between the mean grades of males and females exist, in almost most of the courses included in the curriculum of the aforementioned CS department. Any statistically significant differences in performances were present in almost equal number of courses in favor of males and females. It seems also, that females performed better in the courses they selected more than males. Regarding preferences, in CS courses, it seems that gender differences are existent. Males preferred more than females did core programming courses and advanced topics of Software Systems, computer networks, computer engineering, robotics and mathematics, whereas females preferred more the study of algorithms and security issues, computer fractals, data management, computer architecture, and mobile communication. In addition, females preferred courses in reference with humanities and social sciences, CS terminology, and career opportunities. Yet, females did not select any of programming lab-based courses, computer engineering, computer network issues and robotics.

A ~3-kb deletion-type DNA copy-number variation (CNV, esv3587290) located at intron 7 of the VANGL1 gene (1p13.1, MIM*610132) has been proposed as a genetic factor for developing lupus nephritis (LN) in adult systemic lupus erythematosus (SLE) patients across European-descent populations, but its replication in other ethnicities has been inconsistent and its association with LN in childhood-onset SLE (cSLE) remains unknown. Here, we performed an exploratory association study in a sample of 66 unrelated cSLE Mexican patients (11 males, 55 females; ages 7.8 to 18.6 years). Two stratified groups were compared: cSLE patients with (N=39) or without (N=27) LN, as diagnosed by renal biopsy (N=17), proteinuria (N=33), urinary protein:creatinine ratio >0.2 (N=34), and erythrocyturia and/or granular casts in urinary sediment (N=16). For esv3587290 CNV genotyping, we performed an end-point PCR assay with breakpoint confirmation by Sanger sequencing. We also determined the allelic frequencies of the esv3587290 CNV in 181 deidentified ethnically-matched individuals (reference group). The obtained genotypes were tested for Hardy-Weinberg equilibrium (HWE) by c2 test. Associations between LN and esv3587290 CNV were tested by calculating the Odds Ratio (OR) and using Pearson's c² tests with a 95% confidence interval and P≤0.05. The esv3587290 CNV allele (OR 0.108, 95% CI 0.034-0.33, P=0.00003) and the heterozygous genotype (OR 0.04, 95% CI 0.119-0.9811, P=0.002) showed a significant protective effect against LN development. Finally, we characterized the precise breakpoint of the esv3587290 CNV as NG_016548.1(NM_138959.3):c.1314+1339_1315-897del (https://databases.lovd.nl/shared/variants/0000918418#00025811) in our population. This report supports the notion that a broad genetic heterogeneity underlies the susceptibility for developing LN.

Water hyacinth (Eichhornia crassipes) is a potential accumulator of water pollutants in aquatic ecosystems, and its presence in water systems can affect water quality. This study used different field measurements and laboratory tests of Lake Water to determine the impact of water hyacinth phytoremediation capacity. A total of eight sampling stations used for the two lakes; Lake Koka and Lake Ziway. Sampling stations were selected from sites infested with water hyacinth (low, medium and high) and non-water hyacinth aquatic plants during wet and dry seasons to compare the effects of plants on water quality in the two lakes. All the sampled stations had various human interventions. The water samples were tested for the selected physico-chemical properties namely phosphate, nitrate, pH, electrical conductivity (EC), Biological oxygen demand (BOD5), temperature, and heavy metals (Chromium (Cr), Lead (Pb), Cadmium (Cd), Zinc (Zn), and Copper (Cu). These water quality variables were compared by means of ANOVA. Despite the COD of Lake Ziway, this study found no significant (p > 0.05) variation in the concentrations of Cu, EC, pH and temperature between wet and dry seasons in either lake. Variations in Zn concentration and other physico-chemical parameters (EC, BOD, COD, nitrate, phosphate) between low, medium and high levels of water hyacinth were significant in both lakes (p<0.05). Water hyacinth has shown significant phytoremediation nature during wet and dry seasons. The lowest average heavy metal, phosphate, and nitrate concentrations; and significant pH and temperature variations were observed in Lakes Koka and Lake Ziway, among water hyacinth and other grass-infested sites.

In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.

Soil CO2 efflux (FCO2) plays a dominant role in the terrestrial carbon (C) cycle but interpreting constraints on local observations is impeded by challenges in disentangling belowground CO2 sources. Trees contribute most C to forest soils, so linking aboveground properties to FCO2 could open new avenues to study plant-soil feedbacks and facilitate scaling; furthermore, FCO2 responds dynamically to meteorological conditions, complicating predictions of total FCO2 and forest C balance. We tested for proximity effects of individual Acer saccharum Marsh. trees on FCO2, comparing FCO2 within 1 m of mature stems to background fluxes before and after an intense rainfall event. Wetting significantly increased background FCO2 (6.4±0.3 vs. 8.6±0.6 s.e. μmol CO2 m-2s-1), with a much larger enhancement near tree stems (6.3±0.3 vs. 10.8±0.4 μmol CO2 m-2s-1). FCO2 varied significantly among individual trees and post-rain values increased with tree diameter (with a slope of 0.058 μmol CO2 m-2s-1 cm-1). Post-wetting amplification of FCO2 (the ‘Birch effect’) in root zones often results from the improved mobility of labile carbohydrates and further metabolization of recalcitrant organic matter, which may both occur at higher densities near larger trees. Our results indicate that plant-soil feedbacks change through tree ontogeny and provide evidence for a novel link between whole-system carbon fluxes and forest structure.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

Predicting the growth response of seedlings from the environmental responses of photosynthesis and metabolism may be improved by considering the dynamics of non-structural carbohydrate, NSC, over a diurnal cycle. Attenuation of growth metabolism when NSC content is low could explain why some NSC is conserved through the night. A dynamic model, incorporating diurnal variation in NSC, was developed to simulate growth of seedlings hour-by-hour. I compared predictions of this model to published growth and NSC data for seedlings that varied according to temperature, light, day length, or CO₂. Prolonged-darkness experiments showed a temperature dependent upper limit on the respiration capacity. Respiration was attenuated as NSC was depleted. Furthermore, when NSC was high at dawn, inhibition of photosynthesis could attenuate the accumulation of NSC under low temperature, or high light, or high CO₂. These concepts were used to simulate plant metabolism and growth rates and diurnal variation of NSC in tomato seedlings under two light levels and various temperatures. Comparison of other results using the same model parameters showed the dynamic model could predict results for starch and starch-less plants, and when growth was affected by CO₂ enrichment and day length.

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction, while also enhancing security through a secret sharing mechanism. Experimental results indicate that the generated steganographic text effectively preserves the sentiment and semantic information of the cover text.

This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of credit worthiness.

The neural system has limited resources to interact with the environment, and this is where attention comes into play. Attention is a complex process that provides numerous benefits, but there has been a longstanding debate about the mechanisms involved in attentional orientation. Two widely proposed mechanisms are top-down and bottom-up, which relate to the organism's current behavioral goal and the salience of environmental stimuli, respectively. However, distinguishing between these mechanisms can be challenging in both behavioral and neural studies. This review introduces a new conceptualization in which both top-down and bottom-up mechanisms are viewed as aspects of the same process. This unified approach aims to prepare the organism to face stimuli and events based on its prior experiences, immediate (the current task that he/she is aware of), and innate (to be alive) demands.

: Based on fairness perspective, this paper combines the social vulnerability assessment of natural hazards with multi-objective site selection model to propose a new site selection planning model for central refuges. To begin with, the social vulnerability assessment system of regional natural disasters is founded through literature analysis, and the social vulnerability index of natural disasters is constructed; and based on the exponential differentiation of index to depict regional refuge demand, design vulnerability coverage function quantitive site selection fairness; then taking the shortest weighted evacuation distance, maximum vulnerability coverage and construction cost, the coverage population and refuge quantity as constraint conditions to construct multi-objective optimization model; finally, taking Zigong City, Sichuan Province as the object of empirical research, adapting NSGA-II algorithm to get the Pareto optimal solution set, and using TOPSIS method to get the optimal location scheme. The research results can provide a new method for the location layout of central refuge.

Based on the theory of behavioral economics, this study deeply analyzes the principles of behavioral economics involved in the development of mass sports, including psychological expectation, selection deviation and incentive mechanism, rationalize the current situation of group sports development, discusses how to effectively promote the development of mass sports, and puts forward a series of concrete countermeasures. In view of the problem of behavioral expectations, it is suggested to establish a comprehensive publicity and education system to improve the public's awareness and understanding of sports, and to stimulate individuals' active participation through self-setting goals and planning methods. In view of the selection bias, it is suggested that the government and relevant institutions provide diversified and attractive sports activity options, and make full use of technological means to increase the visibility and participation of activities.In addition, in view of the incentive mechanism problem, it is advocated to adopt differentiated incentive measures to meet the needs of different groups. The government can provide some preferential policies and establish incentive mechanisms to provide support for the organization of mass sports activities. It is expected that this research can stimulate the enthusiasm of the masses to participate in sports and promote the healthy development of mass sports.

This study explores the complexities of sport manager selection, investigating the impact of affirmative action, recruitment strategies, organizational culture, diversity initiatives in board selection, and career advancement insights from coaches' perspectives. To conduct this investigation, a purposive sampling technique was employed, selecting 65 prominent sports managers and coaches from Romania. Participants, aged between 35 to 58 years old, boasted an average of 10 to 20 years of sports experience and were chosen based on specific criteria, including academic qualifications. Data collection occurred over an extended period, spanning from fall 2022 to spring 2024, through semi-structured interviews with sport managers and coaches. The findings unveiled persistent challenges in effectively implementing affirmative action measures, as sport managers grapple with balancing the promotion of deserving candidates while maintaining continuity in board culture. Achieving meaningful diversity necessitates nuanced approaches that consider both candidate quality and cultural compatibility. Moreover, the study sheds light on coaches' perspectives when considering executive council positions, highlighting various pathways to career advancement. Recruitment practices prioritized informal methods, emphasizing board cohesion. Despite the recognized importance of diversity, challenges persist in selecting candidates solely based on demographic representation. The study provides valuable insights into promoting diversity and inclusivity within sports organizations' executive councils. Future research directions may include exploring longitudinal trends and delving into the qualitative experiences of underrepresented groups, with the aim of advancing understanding and promoting equity within sports organizations.

This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, particularly susceptible to instability. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By contrasting empirical correlations with those derived from random matrices, we uncover non-random properties and underlying relationships within financial data. We then utilize this methodology to construct the correlation network dependence structure used in portfolio optimization. The empirical analysis presented in this paper validates the effectiveness of RMT in enhancing portfolio diversification and risk management strategies. This research contributes by offering investors and portfolio managers with methodological insights to construct portfolios that are more stable, robust, and diversified. At the same time, it advances our comprehension of the intricate statistical principles underlying multivariate financial data.

Garlic, as an important economic crop in China, still has room for improvement in terms of identification using remote sensing technology, Among them, high-precision classification of garlic has become an important subject. The Erhai Lake is an important freshwater lake in China. Under the influence of technology and policies, significant changes have occurred in the cultivation of garlic crops. this study constructed multidimensional features for crop classification suitable for Google Earth Engine, and Propose a method for identifying garlic crops using sample and feature datasets under limited conditions. The results indicate that: 1) In the land-use classification of the Erhai Lake Basin, the importance ranking of characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. 2) The Random Forest method based on feature selection demonstrates high classification accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reaches 95.79%, with a Kappa coefficient of 0.9481. 3) The expansion direction of garlic cultivation in the Erhai Lake Basin initially strengthened and then weakened from 1999 to 2023. The vertical development of garlic cultivation reached saturation, showing a slow trend towards horizontal expansion between 2005 and 2018. The planting distribution in various townships in the Erhai Lake Basin gradually shifted from a relatively uniform distribution to an upstream development in the basin. This study utilizes the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Simultaneously, it accurately, rapidly, and efficiently extracts planting information, demonstrating significant potential for practical applications.

In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. Experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems.

Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

Among the plethora of financial management tools available, stock investments stand out as the most recognizable and widely accepted option. However, investors often make buying and selling decisions based on unverified information provided by stock analysts. Since the credibility of this information remains uncertain, it frequently results in investment losses. Ordinary investors relying solely on rumors often miss out on prime opportunities for buying and selling stocks. In the current volatile investment landscape, various investment strategies cater to different situations. If individuals can effectively identify opportunities within this complex environment and dynamically adapt suitable investment strategies at any given time, substantial returns can be achieved. The ability of investors to promptly detect shifts in the current stock market environment and make accurate decisions regarding stock selection, timing, and capital allocation strategies is of utmost importance. This study presents a dynamic framework for investment decision support, which utilizes evolutionary strategies to determine the optimal timing for key strategic tools such as stock selection, timing, and capital allocation. By leveraging historical data for training and testing, this approach aims to assist investors in adapting to the ever-changing stock market environment. If investors can successfully identify and implement an appropriate investment strategy to aid in making accurate decisions, it is possible to achieve significant excess returns.

Milk MIR spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test-day records, and reference FA concentrations of 155 first-parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and five feature selection methods were evaluated. The results indicated that the Competitive Adaptive Reweighted Sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables [days in milk at the test day, age at the test day, pregnancy stage (in days), number of days open, number of inseminations, and somatic cell count] yielded the best FA profile predictions based on Partial Least Square regression. In particular, ten FAs (C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium-chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA) presented accuracies based on the determination coefficient (R2cv) ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. By running CARS 1,000 times in internal validations, we obtained the frequency of selected milk MIR wavenumber for 35 FAs. The most related wavenumbers to FAs were found within 1,003 to 1,145 cm-1, while other discrete areas were between 1,651 to 1,797 and 2,834 to 2,954 cm-1. These biomarkers may give insights into the relationship between MIR spectra and FA phenotypes. In conclusion, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available.

Random Forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones. In response, we introduced the Bayesian Random Forest (BRF) method specifically designed for high-dimensional datasets with a sparse covariate structure. Our research demonstrates that BRF possesses the oracle property, which means it achieves strong selection consistency without compromising efficiency or bias.

Canistel (Pouteria campechiana (Kunth) Baehni) (yellow sapote, canistel). Originally from Mexico, the fruit is edible, and the tree is used as an ornamental and medicinal plant. Descriptive studies were carried out with the objective of morphologically characterizing the fruits and leaves of trees located in different regions of Mexico to select outstanding specimens and propose their massive reproduction. The trees were selected in three zones. In zone 2 from flower anthesis to harvest 11 months, fruits with 3 seeds, greater weight (180 to 330 g) and quantity of pulp (198.88 g), subglobose shape, total soluble solids 33 %. The fruits of zone 1 at harvest 8 months and zone 3 took 9 months to be harvested, in the rest of the characteristics evaluated were exceeded by the fruits of zone 2. To characterize the canistel (P. campechiana) leaf and identify accessions or cultivars, the three main variables, according to the principal component analysis were: leaf area, perimeter, and minor diameter. The three zones where canistel fruits and leaves were characterized are important and can be recommended for propagation in zones with favorable climatic conditions for fruiting in tropical and subtropical regions of Mexico.

Machine learning methods have been a standard approach to select features that are associated with an outcome and build a prediction model when the number of candidate features is large. LASSO has been one of the most popular approaches to this end. LASSO approach selects features with large regression estimates, rather than based on statistical significance, associating the outcome, by imposing L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model loses the prediction accuracy. In this paper, we propose to use the standard regression methods (without any penalizing approach) with stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation based method selects very small number of features while maintaining a high prediction power, while LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high dimensional data even without advanced bioinformatics knowledge.

As most prominent plant-associated disease, photosensitization in large herbivores provides a substantial data base to evaluate the conditions under which animals are concerned. The purpose of this meta study was to investigate whether the level of feed plant diversity and feed choice influence the incidence of photosensitization. Case reports from 1900 to 2022 served as database. 113 publications described 178 cases of altogether 12 animal species, most of them being farm animals. 73.6% of the cases originated from three continents: South America, Australia and North-America. Of the 40 phototoxic agents, herbs represented the majority (63.6%). Brachiaria, Froelichia and other four genus were associated in almost 50% of the cases. Usually, animals received feed both of normal quality and in fresh state. Secondary photosensitization was most frequent only when associated with poor feed quality. If the animals had had access to high-diversity feed instead of low-diversity feed, the incidence was 27.5% smaller. If the animals had the choice between various kind of feed, the incidence was even reduced by 56.1%. Horses could select the least, however, suffered mainly from primary photosensitization. We conclude that farmers may prevent photosensitization in husbandry animals by allowing both more feed choice and feed diversity.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

The aim of this study was to isolate and characterize autochthonous Saccharomyces cerevisiae yeasts from Cabernet Sauvignon grape must and to analyze how they impacted the final characteristics of the wines obtained from the same must type. This work was carried out in a “Pago” winery located in Requena, Spain. Twenty-two isolates were obtained from grape must. After molecular identification and typing, 11 different strains were determined. Growth-related parameters and metabolic characteristics (glucose and fructose consumption, and ethanol, glycerol and acetic acid production) were determined by laboratory-scale fermentations (50 mL) of Cabernet Sauvignon must. From 2 L fermentation batches, residual sugar, density, ethanol concentration, titratable and volatile acidities, pH, and polyphenolic and volatile compounds were determined, as well as the sensory attributes of the produced wines. Differences between strains in the growth and metabolic parameters were observed. Eleven S. cerevisiae strain profiles were characterized to establish which strain or strains of the selected yeasts would produce the highest polyphenolic and aromatic concentration of the red Cabernet wines made in the “Pago winery” to then multiply them for their use to make the red wines of this winery.

With the growing popularity of smartphones, user identification has become an essential component of maintaining security and privacy. This study investigates how smartphone accelerometer data can be used to identify users, and it makes recommendations for the ideal application parts. Accelerometer data from the HMOG public dataset was used to train deep learning, conventional classifiers, and voting classifiers, which were then utilized to identify users. To enhance performance, feature selection and pre-processing techniques were researched. The results show that RFE feature selection outperforms other approaches and that LSTM followed by XGBoost has the best identification performance as indicated by a relatively large number of machine learning performance measures. The proposed identification system nevertheless performed well and outperformed existing methods, which were principally created and tested on the same HMOG public smartphone dataset, even with a larger number of users. Further work would be necessary for such an application to reach its full potential, though.

Nowadays, the selection of web services with uncertain quality of service (QoS) is gaining a lot of attention in the service-oriented computing paradigm (soc). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-Complete. The search time is mainly dependent on the number of the requested tasks, the size the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the tasks’ services and a bat algorithm metaheuristic for selecting the final near optimal compositions. The fitness used by the metaheuristic aims to fulfill all the global constraints of the user. The experimental study shows that the ranking heuristics, termed “fuzzy pareto dominance” and "Zero-order stochastic dominance", are highly effective than the other heuristics and most of the existing state-of-the-art methods.

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

In the era of big data, feature engineering has proved its efficiency and importance in dimensionality reduction and useful information extraction from original features. Feature engineering can be expressed as dimensionality reduction and is divided into two types of methods such as feature selection and feature extraction. Each method has its pros and cons. There are a lot of studies to combine these methods. Sparse autoencoder (SAE) is a representative deep feature learning method that combines feature selection with feature extraction. However, existing SAEs do not consider the feature importance during training. It causes extracting irrelevant information. In this paper, we propose a parallel guiding sparse autoencoder (PGSAE) to guide the information by two parallel guiding layers and sparsity constraints. The parallel guiding layers keep the main distribution using Wasserstein distance which is a metric of distribution difference, and it suppresses the leverage of guiding features to prevent overfitting. We perform our experiments using four datasets that have different dimensionality and number of samples. The proposed PGSAE method produces a better classification performance compared to other dimensionality reduction methods.

In the chemical processing industries, sensors for pumps are among the most commonly used machinery. Condition-based maintenance (CBM) and prognosis health management (PHM) determine the most cost-effective time to overhaul pumps. In order to determine the status of the pump, a signal-emitting accelerometer is employed. Stationarity-based feature extraction from amplitude signals is used to process the signal. Utilizing the time-domain function, multiple statistical results were produced. Eight fault codes were classified using support vector machine method. The enormous amount of data points necessitated the use of feature selection. In terms of accuracy, precision, recall, and F1 score, the Chi-square feature selection method exceeds other approaches.

Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

As urban development increases in density, the space to grow urban trees becomes more constrained. In heavily developed areas, small stature trees can be planted to reduce both above- and below-ground conflicts with infrastructure elements. However, even these species have their limits when placed in extremely confining conditions. In this study, we build on past work to determine the minimum planting widths of small stature urban trees. We found that species, stem diameter, and the height at which stem diameter measurements occurred were all strong predictors of trunk flare diameter (adjusted R² of 0.843). Additionally, we modelled the relationship between planting space and the presence or absence of hardscape conflicts – using the predictions derived from this effort to project the potential cost savings in two United States cities. Study results provide a guideline to create sufficient space for urban trees and minimize infrastructure damage and associated cost savings.

Plant breeding primarily focuses on improving conventional agronomic traits, e.g. yield, quality, and resistance to biotic and abiotic stress; however, genetic improvement methods are being rapidly enhanced through genomics and phenomics. In the Genomics-Phenomics-Agronomics (GPA) paradigm, diverse research approaches have been conducted to bridge any two of these elements, and recently, all of them together. This review first highlights the progress to link i) genomics to agronomics; ii) genomics to phenomics; and iii) phenomics to agronomics. Secondly, the GPA domain is dissected into different layers, each addressing the three elements simultaneously. These dissected layers include genetic dissection through gene mapping using genome-wide association studies and genomic selection using Best Linear Unbiased Prediction, Bayesian approaches, and machine learning. The objective of the review is to help readers to grasp the core developments among the exponentially growing literature in each of these fields. Through this review, the connections among the three elements of the GPA paradigm are coherently integrated toward the prospect of sustainable development of agronomic traits through both genomics and phenomics.

Whole genome pooled sequence data of 12 Pakistani Teddy goats is analyzed for positive selection signatures as their breed defining characteristics. Selection imprints left in the Teddy genome are unveiled by genomic differentiation after the successful paired-end alignment of 635,357,043 reads with (ARS1) reference genome assembly. Pooled-heterozygosity ( ) and Tajima’s D (TD) are applied for validation and getting better hits of selection signals, while pairwise F_ST statistics is conducted on Teddy vs. Bezoar (wild goat ancestor) for genomic differentiation. Annotation of regions under positive selection reveals 59 genes underlying production and adaptive traits. score ≥ 5 detected six windows having highest scores on Chr. 29, 9, 25, 15 and 14 that harbor HRASLS5, LACE1 and AXIN1 genes which are candidate for embryonic development, lactation and body height. Secondly, TD value of ≤ -2.2 showed 4 windows with very strong hits on Chr.5 & 9 harbor STIM1 and ADM genes related to body mass and weight. Lastly, F_ST analysis generated three strong signals with threshold ≤ 0.42 on Chr.12 & 5 harbor ITGB1 gene associated with milk production & lactation traits. Other significant selection signatures encompass genes associated with wool production, prolificacy, immunity and coat colors. In brief, this study identified the genes under selection in this Pakistani goat breed that will be helpful to refining future breeding policies and converging required productive traits within and across other goat breeds and to explore full genetic potential of this valued livestock species.

Epigenetic aging has been found associated with a number of phenotypes and diseases. Few studies investigated its effect on lung function in relatively older people. However, this effect has not been explored in younger population. This study examines whether lung function at adolescent can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (Forced Expiratory Volume in one second) and FVC (Forced Vital Capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over life span can be beneficial to assess the lung health in adolescence.

MicroRNAs are used as biomarkers for classification of cancer subtypes since certain miRNAs are differentially expressed in normal and patient samples. Moreover, miRNAs target mRNAs and can heavily influence Gene Expressions. Thus, deregulation of miRNAs is linked to various disorders. Thus, miRNAs can be used for prognosis and developing personalized health solutions for patients. Given the importance of miRNAs, there has been substantial work done in the field. In this paper, recent works in the field of using miRNAs expressions of patients were considered. A total of 20 papers were surveyed which utilized feature selection ensembles, fuzzy logic as well as deep learning. 10 papers have been reported which offer insight into how miRNAs can be utilized for subtype-specific or generalized cancer diagnosis.

Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized R-R intervals and features relative to the width of the QRS complex are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats respectively. In comparison with other state of the art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.

A regularization approach to model selection, within a generalized HJM framework, is introduced which learns the closest arbitrage-free model to a prespecified factor model. This optimization problem is represented as the limit of a one-parameter family of computationally tractable penalized model selection tasks. General theoretical results are derived and then specialized to affine term-structure models where new types of arbitrage-free machine learning models for the forward-rate curve are estimated numerically and compared to classical short-rate and the dynamic Nelson-Siegel factor models.

The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.

Model selection and model averaging have been the popular approaches in handling modelling uncertainties. Fan and Li(2006) laid out a unified frame work for variable selection via penalized likelihood. The tuning parameter selection is vital in the optimization problem for the penalized estimators in achieving consistent selection and optimal estimation. Since the OLSpost-LASSO estimator by Belloni and Chernozhukov (2013), few studies have focused on the finite sample performances of the class of OLS post-penalty estimators with the tuning parameter choice determined by different tuning parameter selection approaches. We aim to supplement the existing model selection literature by studying such a class of OLS post-selection estimators. Inspired by the Shrinkage Averaging Estimator (SAE) by Schomaker(2012) and the Mallows Model Averaging (MMA) criterion by Hansen (2007), we further propose a Shrinkage Mallows Model Averaging (SMMA) estimator for averaging high dimensional sparse models. Based on the Monte Carlo design by Wang et al. (2009) which features an expanding sparse parameter space as the sample size increases, our Monte Carlo design further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of model selection and model averaging estimators. From our data examples, we find that the OLS post-SCAD(BIC) estimator in finite sample outperforms most of the current penalized least squares estimators as long as the number of parameters does not exceed the sample size. In addition, the SMMA performs better given sparser models. This supports the use of the SMMA estimator when averaging high dimensional sparse models.

The demographic history of anatomically modern humans (AMH) involves multiple migration events, population extinctions and genetic adaptations. As genome-wide data from complete genome sequencing becomes increasingly abundant and available even from extinct hominins, new insights of the evolutionary history of our species are discovered. It is currently known that AMH introgressed with archaic hominins once they left the African continent. Current out of African human genomes carry fragments of archaic origin. This review focuses on the fitness consequences of archaic interbreeding in current human populations. We discuss new insights and challenges that researchers face when interpreting the potential impact of introgression on fitness and testing hypotheses about the role of selection within the context of health and disease.

In wild habitats, fruit dehiscence is a critical strategy for seed dispersal; however, in cultivated crops it is one of the major sources of yield loss. Therefore, indehiscence of fruits, pods, etc., was likely to be one of the first traits strongly selected in crop domestication. Even with the historical selection against dehiscence in early domesticates, it is a trait still targeted in many breeding programs, particularly in minor or underutilized crops. Here, we review of this trait in pulse (grain legume) crops, which are of growing importance as a source of protein in human and livestock diets, and which have received less attention than cereal crops and the model plant Arabidopsis thaliana. We specifically focus on the i) history of indehiscence in domestication across legumes, ii) structures and the mechanisms involved in shattering, iii) the molecular pathways underlying this important trait, iv) an overview of the extent of crop losses due to shattering, and the effects of environmental factors on shattering, and, v) efforts to reduce shattering in crops. While our focus is mainly pulse crops, we also included comparisons to crucifers and cereals because there is extensive research on shattering in these taxa.

A basic problem in all postulated pathways of prebiotic chemistry is the low concentration which generally is expected for interesting reactants in fluid environments. Even though compounds like nucleobases, sugars or peptides principally may form spontaneously under environmental conditions, they will always be rapidly diluted in an aqueous environment. In addition, any such reaction leads to side products which often exceed the desired compound and generally hamper the first steps of a subsequent molecular evolution. Therefore, a mechanism of selection and accumulation of relevant prebiotic compounds seems to be crucial for molecular evolution. A very efficient environment for selection and accumulation can be found in the fluid continuum circulating in tectonic fault zones. Vesicles which form spontaneously at a depth of approximately 1 km present a selective trap for amphiphilic molecules, especially for peptides composed of hydrophilic and hydrophobic amino acids in a suitable sequence. The accumulation effect is shown in a numeric simulation on a simplified model. Further, possible mechanisms of a molecular evolution in vesicle membranes are discussed. Altogether, the proposed scenario can be seen as an ideal environment for constant, undisturbed molecular evolution in and on cell-like compartments, the latter offering preferential starting conditions for a subsequent RNA world.

A few empirical examples document fixed alternative male mating strategies in animals. Here we focus on the polymorphism of male mating strategies in the ruff (Calidris pugnax, Aves Charadriiformes). In ruffs, three fixed alternative male mating strategies coexist and are signaled by extreme plumage polymorphism. We first present relevant data on the biology of the species. Then we review the available knowledge of the behavioral ecology of ruffs during the breeding season and we detail the characteristics of each of the three known fixed male mating strategies. We next turn to the exceptional quality results accumulated on both the structural and functional genomics of the ruff over the past few years. We show how much these genomic data can shed a new, mechanistic light on the evolution and maintenance of the three fixed alternative male mating strategies. We then look if there are sufficient indication to support frequency-dependent selection as key mechanism in maintaining these three strategies. Specifically, we search for evidences of equal fitness among individuals using each of the three strategies. Finally, we propose three lines of research avenues that will help to understand the eco-evolutionary dynamics of phenotypic differences within natural populations of this iconic model species.

This paper presents a thorough investigation into the shear strength capacity of reinforced concrete deep beams, with a focus on improving predictive accuracy beyond existing code provisions. Analyzing 198 deep beams from 15 investigations, the study considers parameters such as concrete compressive strength (fc’), shear span to effective depth ratio (av/d), and reinforcement ratios (ρs, ρv, ρh ). Introducing a novel predictive model (Equation 7), the study rigorously evaluates it using nonlinear regression analysis and statistical metrics (MAE, RMSE, R2). The proposed model demonstrates a significant reduction in the coefficient of variation (COV) to 27.08%, surpassing existing codes' limitations. Comparative analyses highlight the model's robustness, revealing improved convergence of data points and minimal sensitivity to variations in key parameters. The findings suggest that the proposed model offers enhanced predictive accuracy across diverse scenarios, making it a valuable tool for structural engineers. This research contributes to advancing the understanding of shear strength in reinforced concrete deep beams, offering a reliable and versatile predictive model with implications for refining design methodologies and enhancing the safety and efficiency of structural systems.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

The vacuum preloading method effectively strengthens the soft soil foundations with vertical drainage, which will produce a smear effect when laying sand drains. Meanwhile, the seepage of pore water and soil deformation during consolidation exhibits nonlinear characteristics. Therefore, based on Gibson's 1D large strain consolidation theory, this paper developed a more generalized large strain radical consolidation model of sand-drained soft foundations under free strain assumption. In this system, the double logarithmic compression permeability relationships for soft soils with large strain properties, the variation of the radical permeability coefficient in the smear zone, and the effect of the non-Darcy flow were all included. Then, the partial differential control equations were numerically solved by the finite difference method and validated with existing radical consolidation test results and derived analytical solutions. Finally, the influences of relevant model parameters on consolidation are discussed. The analysis shows that the greater the maximum dimensionless vacuum negative pressure P0, the faster the consolidation rate of sand-drained foundations. Meanwhile, the decrease in the negative pressure transfer coefficient k1 will result in a decreasing final settlement amount. Moreover, the consolidation rate of sand-drained foundations is slower considering the non-Darcy flow, but the final settlement is unaffected.

The South-to-North Water Transfer Jiangsu Water Supply Area (JWSA) is a mega inter-basin water transfer area (water source) that provides water resources from JiangHuai, combines drainage and flooding management, and regulates nearby rivers and lakes. Analyzing the spatiotemporal soil moisture dynamics in the area will inform agricultural drought and flood disaster assessment and early warning studies. Therefore, we evaluated the quality of European Space Agency Climate Change Initiative Soil moisture (ESA CCI_SM) data in the South-North Water Transfer JWSA. Then, we used ensemble empirical modal decomposition, Mann-Kendall tests, and regression analysis to study the spatiotemporal variation in soil moisture for the past 29 years. The CCI _SM data showed a high correlation with local soil measurements at nine sites. We then analyzed the CCI_SM data from three pumping stations (the Gaogang, Hongze, and Liushan stations) in the South-North Water Transfer JWSA. These stations had similar periodic characteristics of soil moisture, with significant periodic fluctuations around 3.1 d. The overall soil moisture at the three typical pumping stations showed an increasing trend. We then investigated whether there were abrupt soil moisture changes at each station. The spatial distribution of soil moisture in the South-North Water Transfer JWSA was characterized by “dry north and wet south”, with higher soil moisture in winter, followed by autumn, and low soil moisture in spring and summer. Although the linear trend of soil moisture in the South-North Water Transfer JWSA varied in significance, the overall soil moisture in the JWSA has increased over the past 29 years. The areas with significantly enhanced soil moisture are mainly distributed in the Yangzhou and Huai'an areas in the southeastern part of the study area. The areas with significantly decreased soil moisture are small in size and mainly located in northern Xuzhou.

We study in this paper the time evolution of stock markets using a statistical physics approach. We consider an ensemble of agents who sell or buy a good according to several factors acting on them: the majority of the neighbors, the market ambiance, the variation of the price and some specific measure applied at a given time. Each agent is represented by a spin having a number of discrete states q or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter T which plays the role of the temperature in physics: low T corresponds to a calm market, high T to a turbulent one. We show that there is a critical value of T, say T_c, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. The specific measure, by the government or by economic organisms, is parameterized by $H$ applied on the market at the time t₁ and removed at the time t₂. We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. In particular we show that the price strongly fluctuates near T_c and there exists a critical value H_c above which the boosting effect remains after H is removed. Our model replicates the stylized facts in finance (time-independent price variation), volatility clustering (time-dependent dynamics) and persistent effect of a temporary shock. The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time. Results are shown and discussed.

Ground-level ozone (O3) increases in the northeastern part of Eurasia, where larch species are dominant trees and have been planted intensively. The Japanese larch (Larix kaempferi; hereafter larch) is classified as the tree species most of its sensitive to O3 based on data from long-shoot needles of seedlings. This criterion should be reconsidered based on O3 uptake, which is strongly depended on stomatal conductance (Gs) of adult trees through the difference in relevant needle traits. Because Gs is closely correlated with photosynthetic activity, we measured the in situ seasonal and yearly change in photosynthetic function, needle mass per area (LMA) and nitrogen (N) content of both short- and long-shoot needles of the canopy of larch trees over successive 3 years. No difference was observed in the in light saturated photosynthetic rate at ambient CO2 (Psat) between needles of hetero types of shoot in the latter part of the growing season, but clear differences were found within a specific year, indicating that seasonal changes in climatic factors determine Psat in the long term. The Psat-N relations differed significantly between the needle types. However, Psat at saturated CO2 (Pmax)-N was quite similar, implying that the CO2 diffusion difference between short- and long-shoots is responsible for the differing Psat -N. In conclusion, seasonal and yearly variations in photosynthetic capacity are mainly determined mainly by climatic variations, whereas shoot type determined the traits of photosynthetic N utilization as well as Gs regulation.

In this paper we analyse spatial variation in Japanese dialectal lexicon by assembling a set of methodologies using theories in variationist linguistics and GIScience, and tools used in historical GIS. Based on historical dialect atlas data, we calculate a linguistic distance matrix across survey localities. The linguistic variation expressed through this distance is contrasted with several measurements, based on spatial distance, utilised to estimate language contact potential across Japan, historically and at present. Further, administrative boundaries are tested for their separation effect. Measuring aggregate association within linguistic variation can contrast previous notions of dialect area formation by detecting continua. Depending on local geographies in spatial subsets, great circle distance, travel distance and travel times explain a similar proportion of the variance in linguistic distance despite the limitations of the latter two. While they explain the majority, two further measurements estimating contact have lower explanatory power: least cost paths modelling contact before the industrial revolution, based on DEM and seafaring, and a linguistic influence index based on settlement hierarchy. Historical domain boundaries and present day prefecture boundaries are found to have a statistically significant effect on dialectal variation. However, the interplay of boundaries and distance is yet to be identified. We claim that a similar methodology can address spatial variation in other digital humanities, given a similar spatial and attribute granularity.

The maritime environment is the setting for a variety of economic activities, such as offshore wind energy, aquaculture, salt extraction, and oil and gas platforms. While some of these activities have a long-term presence, others require decarbonisation as they head towards their demise. In this context, the aim of this study is to develop a methodology to replace the electrical energy from high-emission industrial processes offshore with clean electricity generated by offshore wind energy. The proposal is structured in three phases: Initiation, which involves the collection of quantitative, technical and geospatial information of the study area; Indicators, where the main indicators are calculated and the best alternative is selected using multi-criteria evaluation methods; and finally, short, medium and long-term scenarios are proposed. The methodology is evaluated in Spain, the best alternative with a nominal power of 225 MW, is capable of avoiding up to 1.44 MtCO₂ by 2050.

Machine learning methods used for classification can face challenges due to class imbalance, where a certain class is underrepresented. Over/under-sampling of minority/majority class observations, or model selection for ensemble methods alone, may not be effective if the class imbalance ratio is very high. To address this issue, this paper proposes a method called enhance tree ensemble (ETE) based on generating synthetic data for minority class observations in conjunction with tree selection based on their performance on the training data. The proposed method first generates minority class instances to balance the training data and then uses the idea of tree selection by leveraging out-of-bag (〖ETE〗_OOB) and sub-samples (〖ETE〗_SS) observations, respectively. The efficacy of the proposed method is assessed using twenty benchmark problems for binary classification with moderate to extreme class imbalance, comparing it against other well-known methods such as optimal tree ensemble (OTE), SMOTE random forest (〖RF〗_SMOTE), over-sampling random forest (〖RF〗_OS), under-sampling random forest (〖RF〗_US), k-nearest-neighbor (k-NN), support vector machine (SVM), tree, and artificial neural network (ANN). Performance metrics such as classification error rate and precision are used for evaluation purposes. The analyses of the study revealed that the proposed method, based on data balancing and model selection, yielded better results than the other methods.

This qualitative study explores supplier selection criteria from the perspectives of procurement professionals across diverse industries. Supplier selection is crucial for organizational efficiency and performance, influenced by factors beyond traditional cost considerations. The research aims to uncover nuanced criteria such as quality assurance, supplier reliability, innovation capability, sustainability practices, strategic alignment, relational dynamics, and risk management strategies. These criteria reflect a shift towards holistic evaluation frameworks that integrate economic, social, and environmental dimensions in procurement decision-making. Methodologically, the study employed semi-structured interviews with 30 procurement professionals, ensuring depth and diversity in perspectives. Thematic analysis was used to identify recurring themes and patterns, illuminating the complexities and strategic importance of supplier selection processes. Findings highlight the strategic role of suppliers in driving innovation, enhancing supply chain resilience, and supporting organizational goals. Moreover, the study underscores the significance of sustainable sourcing practices, ethical considerations, and effective supplier relationships in fostering long-term partnerships and mitigating operational risks. Practical implications suggest that organizations should adopt integrated approaches to supplier selection, leveraging data-driven insights and technological advancements to optimize decision-making. By prioritizing strategic alignment, fostering collaborative partnerships, and implementing robust risk management strategies, organizations can navigate uncertainties, capitalize on market opportunities, and sustain competitive advantage.

Although evolutionary theory has yet to furnish a comprehensive rationale for the underlying mechanism of natural selection, it is pertinent to note that the phenomenon transcends the confines of biological systems, exhibiting analogous patterns in physical systems as well. Specifically, isolated and closed systems tend to evolve towards states of progressively increasing entropy. In scenarios where multiple paths for entropy increase are accessible, such systems tend to favor combinations of paths that exhibit the most rapid entropy increase rates. Among the various processes in nature, life activities constitute a salient means of achieving entropy increase. Genetic variation within organisms generates individuals that vary in their rates of entropy increase. As these organisms interact, compete and combine, they form diverse combinations of entropy-increasing pathways. Nature, in turn, selects among these combinations, favoring those that culminate in the fastest entropy increase, thereby propelling the evolution of life. Essentially, the evolution of life is an ongoing exploration of diverse combinations of pathways that maximize entropy increase across various energy pools. Amidst continuous genetic variation and the selective pressure imposed by nature for maximum entropy production rates, information accumulates, leading to a corresponding acceleration in the rate of entropy increase. This natural selection, which favors maximizing the entropy at the fastest possible rate, serves as the ultimate driving force for the origin and evolution of life.

Breeding for low hydrogen cyanide (HCN) varieties is a major objective of programmes targeting boiled cassava food product. To enhance breeding for low HCN, knowledge on genetic variation and trait heritability is essential. In this study, 64 cassava clones established across four locations and evaluated for HCN using three HCN assessment methods; 1 to 9 scale, 0 ppm to 800 ppm scale and a quantitative assay based on spectrophotometer readings (HCN_Spec). Data were also collected on the weather variables precipitation, relative humidity, and temperature. Significant differences were observed among clones (P < 0.001) and locations (p < 0.001). There were also significant (p < 0. 001) clone by environment interactions. Locations Arua and Serere posted higher HCN scores among clones and were associated with significantly higher (p < 0.001) mean daily temperature (K) and lower relative humidity (%) across 12-hour and 18-hour intervals. Within locations HCN broad sense heritability estimates ranged 0.22 – 0.64 while combined location heritability estimates ranged 0.14 to 0.32. Relationships between the methods were positive and strong (R2 = 0.75 – 0.92). It is expected that the information herein will accelerate efforts towards breeding for low HCN cassava varieties.

Sexual selection involving female choice or female preference (‘inter-sexual’ selection) or male-male competition (‘intra-sexual’ selection) is one of the key mechanisms for evolutionary diversification and speciation. In particular, sexual selection is recently suggested to be an important mode to drive the evolution of “novel” phenotype (i. e. “evolutionary novelty”). Despite extensive studies performed on sexually selected traits or male-specific ornaments (or weapon-like structures) with respect to their evolutionary origin and history and fitness benefits, relatively little is known about the molecular genetic mechanisms underlying their developmental process. However, with advances of genomic technologies (including whole transcriptome analysis using Next Generation Sequencing [NGS] techniques; RNA-Seq), progresses have been made to unveil the genetic background underpinning diverse sexually selected traits in different animal taxa. In the present review, we compile empirical data on the genes, genetic mechanisms or regulatory pathways underlying various sexually selected traits to explore whether the “common” genetic architectures shape the development and evolution of those traits across evolutionarily distant animal lineages. We show that the recruitment of pre-existing genetic network for a new purpose (i.e. gene network “co-option”) is rather widespread in the development and evolution of sexually selected traits, indicating that particular genes or gene-sets are repeatedly involved in different sexually selected traits. Information of genes or genetic mechanisms regulating the development of sexually selected traits would be an essential piece to complete a whole picture of the origin and evolution of sexually selected traits.

The effects of four cycles of recurrent selection on simple sequence repeat (SSR) marker allele frequencies and population structure were examined in the Maksimir 3 Synthetic (M3S) maize population (Zea mays L.). Genotyping of 32 plants from each cycle of selection at 38 SSR loci revealed that the mean number of alleles per locus and the mean expected heterozygosity were preserved over cycles of selection, indicating maintenance of sufficient genetic variability in the population needed for future genetic gain. Waples test of selective neutrality revealed that genetic drift was the main force in changing allele frequencies in the population. The proportion of selectively nonneutral loci in single cycles of selection varied between 16% and 37%. Some nonneutral loci shared the same genomic locations with previously published QTLs controlling important agronomic traits. Between 5% and 29% of loci were found to be in significant Hardy-Weinberg (HW) disequilibrium with the majority showing an excess of homozygosity. Excess of homozygosity at several loci was highly consistent across cycle populations suggesting positive assortative mating as the possible cause of the observed HW disequilibrium. Linkage disequilibrium (LD) tests revealed that the M3S population was essentially in linkage equilibrium. The proportion of pairs of loci in significant LD varied across cycles of selection between 0.1% and 1.8% probably due to the effects of genetic drift and epistatic selection.

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.

Recently, Electric Two-Wheelers ETW are changing the face of the global automotive market. This study focused on selecting proper material and mechanical isolation gap to design a protective enclosure for the battery pack of ETW. The integration of the Failure, Modes, Mechanism and Effect Analysis (FMMEA) method is utilized to develop the interface matrix and the severity index of different components of the enclosure. By analyzing different forces from the road conditions, dynamics during turn, acceleration, and deceleration with the enclosure, it becomes a crucial load-bearing element. Employing Finite Element Modeling (FEM), structural strength using materials like AL6061, Q235, C22000, DC 01 and Teflon are assessed under varying static, dynamic and thermal conditions. Modal analysis is conducted to observe the excitation frequencies where the maximum deformation for the metal enclosure is observed beyond 500Hz. AL6061 material that can withstand the stresses and deformations that are under allowable stress limits with negligible deformation is most preferable material based on the results. Minimal of 2.5 mm gap to be provided in case of metal casing and 10mm in case of Teflon is proven.

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and quantitative trait loci (QTLs) for grain length, co-detected by all GAPIT models on (Chr) 1–3, 5, 7, and 11), were qGL1-1BFSG (AX-95918134, Chr1: 3820526 bp) explains 65.2%–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5623288 bp) for grain width explains 15.5%–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18023142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: AX-154787777 bp) of the observed PVE. For t length-to-width ratio, the qLWR2BFSG (AX-274833045, Chr2: 10000097 bp) explains nearly 15.2%–32% of PVE for LWR. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28484619 bp) and explains 32.8%–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8%–54% of PVE. Putative Candidate genes pooled from major QTLs for each grain traits have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

The most frequent, noticeable, and frequent natural calamity in the karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram highway, particularly during the monsoon, causing a major loss of life and property. Therefore, it was necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper's major goal is to provide new integrative models for assessing landslide susceptibility in a prone area of north of Pakistan. To do this, the training of an artificial neural network (ANN) is supervised using metaheuristic and Bayesian techniques: particle swarm optimization algorithm (PSO), Genetic algorithm (GA), Bayesian optimization Gaussian process (BO_GP), and Bayesian optimization Gaussian process (BO_TPE). 304 previous landslides and the eight most prevalent conditioning elements combine to form a geographical database. The models are hyper-parameter optimized, and the best ones are employed to generate the susceptibility maps. The area under the receiving operating characteristic curve (AUROC) accuracy index found demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying artificial neural networks (ANNs) for landslide mapping, susceptibility analysis, and forecasting are studied in this research it’s observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE are relatively small, ranging from 0.3166% to 1.8399%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it's important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the KKH. The algorithms considered include Information Gain, Gain Ratio, OneR Classifier, Subset Evaluators, Principal Components, Relief Attribute Evaluator, Correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping.

Large terrestrial carnivores play a crucial role in top-down control within terrestrial ecosystems, maintaining ecosystem stability and biodiversity. However, intense interspecific competition often arises among sympatric large carnivores, leading to population reductions or even extinctions. Spatial partitioning through divergent habitat selection helps mitigate such competition. In Xinlong County, Sichuan Province, we used 293 infrared cameras for monitoring from September to May 2016 and March to October 2022. By employing the Generalized Linear Model (GLM) and the Maximum Entropy Model (MaxEnt), we developed an ensemble model predicting the suitable habitat distribution of leopards (Panthera pardus) and wolves (Canis lupus). We analyzed the main environmental factors influencing habitat selection and the fragmentation of suitable habitats. We found that suitable habitat distribution differed significantly between them. Both species preferred areas with gentle slopes close to settlements. While leopards' habitat selection primarily depended on the distance from settlements, the slope was predominant for wolves. Suitable habitats displayed aggregation, yet wolves exhibited higher fragmentation and more complex patch shapes, indicating greater susceptibility to human activities. These results suggest that sympatric large carnivores, such as leopards and wolves, can reduce spatial competition intensity and promote spatial partitioning by selecting divergent suitable habitats, thereby facilitating species coexistence.

Waste management is an unavoidable technological operation in the process of raw material extraction. The main characteristic of this technological operation is the handling of large quantities of waste material, which can amount to several hundred million cubic metres. Working with this amount of material usually requires high-capacity systems for excavation and loading, a large fleet of trucks for haulage, construction, and maintenance of a complex roads network, use of a significant area of land in order to achieve the required capacities, etc. At the same time, this operation must comply with all administrative and environmental standards. Therefore, optimising waste rock management (particularly haulage and dumping) has the potential to significantly improve the overall value of the project. This paper presents a hybrid model for the optimisation of waste dump design and site selection. The model is based on different mathematical methods (genetic algorithm, analytic hierarchy process and heuristic methods) adapted to different aspects of the problem. The main objective of the model is to provide a solution (in analytical and graphical form) for the draft waste dump design, on the basis of which the final waste dump design can be defined.

Forest vegetation dynamics (succession and dominance) is an ecological phenomenon that is still difficult to characterize and integrate into management practices. In this study, the understanding of forest dynamics is explored based on Grime’s theoretical triangular model of plant adaptive strategies using the example of Lithuania’s forest ecosystems. The idea behind this is the hypothesis that forest dynamics is linked to natural selection as an evolutionary process that exhibits differential species responses to competition, stress, and disturbance. The aim of this study is to explore the adaptive relationships in hemi-boreal forests. Grime’s and Pierce’s secondary CSR strategies, which describe various equilibria between competitiveness (C), stress tolerance (S), and ruderalism (R), were considered to reflect four establishment and development adaptive specialization characteristics of forest tree species. As a result of the study, four types of tree functional groups were identified: stress-resistant ruderals, competitive stress-sensitive ruderals, ruderal stress-sensitive competitors, and stress-resistant competitors. Based on this, we propose that reforestation move away from single species regeneration by implementing the maintenance of these four types of functional groups. In conclusion, forest management must consider the existence of the established equilibria between plant competitiveness, stress tolerance, and ruderalism. The formal concepts presented in this article can serve as a guide for future relevant research and development of appropriate methods for studying real forests. This study is unique in that no previous work has linked forest dynamics and natural selection in the context of Lithuania’s forest ecosystems.

Marker-assisted selection (MAS) and genomic selection (GS) have been used to select individuals with desirable traits. MAS used a few markers associated with a specific trait to select individuals with desirable traits, which are determined after a Genome-wide association studies (GWAS). On the contrary, GS uses a large number of markers distributed across the genome to predict the genomic breeding values for a further selection of the individuals. In general, MAS has shown a high prediction accuracy but is not suitable for traits that are controlled for multiple genes, and has another constraint, it is required the phenotypic data; on the contrary, GS has not shown the highest prediction accuracy as MAS but it takes into account the effect of multiple genes controlling a target trait and it can be used without phenotypic data. Including GWAS-selected markers in GS can enhance the reduced prediction accuracy that GS shows in comparison with MAS. Thus, the objective of this study was to compare the prediction accuracy of MAS, and some models of genomic prediction (gBLUP, gBLUP including GWAs-selected markers, and some Bayesian models such as Bayes A, Bayes B, Bayes LASSO and Bayesian Ridge Regression) with GWAS-selected markers incorporated in gBLUP in order to confirm if the incorporation of GWAS in GS increases the prediction accuracy of GS. As a model for this study, it was used data from Sorghum which has shown population structure, to evaluate if the incorporation of GWAs-selected markers into GS improves prediciton accuracy. It was used a sample of 6000 SNPs out of the 265.487 reported in the study conducted by Morris et al (2013), and also it was considered some parameters that affect the efficiency of the selection such as the size of the training population, the heritability, and the number of QTNs. The GWAS-selected SNPs were identified after using the model BLINK. The results showed that the incorporation of GWAS-selected markers enhanced the performance of the genomic selection with similar prediction accuracy as MAS, the number of QTNs and size of the training population affected the accuracy, with higher accuracy with a bigger size of the training population and with a lower number of QTNs, but it seems that the heritability does not have any impact in the model where GWAS-selected SNPs were included in gBLUP.

As a new distributed machine learning (ML) approach, federated learning (FL) shows the great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning assisted FL framework. Then the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a deep Q-netwok (DQN) based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but has stronger robustness to resist malicious nodes.

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maxi-mally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7,560 proteins related to GBM disease and 3,860 proteins associated with signaling pathways involved in GBM. By integrating the results of the aforementioned analyses and performing centrality analysis again, eleven key genes involved in GBM disease were identi-fied. ProteomicsDB or Gliovis databases were used for determining the gene expression in normal or tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of iden-tified genes, miRNAs, and metabolites associated with GBM disease was done using ExpressAna-lyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was done through the pathway, joint pathway, and network analyses. The results of this study identified 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in the GBM disease development. To establish early diagnostic methods and plan personalized GBM treatment strategies, the identified top genes-miRNAs and metabolite signatures can be targeted.

Disruptive selection arises when extreme phenotypes have a fitness advantage compared to more intermediate phenotypes. Theory and evidence suggest that intraspecific resource competition is a key driver of disruptive selection. However, while competition can be indirect (exploitative) or direct (interference), the role of interference competition in disruptive selection has not been tested, and most models of disruptive selection assume exploitative competition. We experimentally investigated whether the type of competition affects the outcome of competitive interactions using a system where disruptive selection is common: Mexican spadefoot toads (Spea multiplicata). Spea tadpoles develop into alternative resource-use phenotypes: carnivores, which consume fairy shrimp and other tadpoles, and omnivores, which feed on algae and detritus. Tadpoles intermediate in phenotype have low fitness when competition is intense, as they are outcompeted by the specialized tadpoles. Our experiments revealed that the presence of carnivores significantly decreased foraging behavior in intermediate tadpoles, and that intermediate tadpoles had significantly lower growth rates in interference competition treatments with carnivores but not with omnivores. Interference competition may therefore be important in driving disruptive selection. As carnivore tadpoles are also cannibalistic, the ‘fear’ effect may have a greater impact on intermediate tadpoles than exploitative competition alone, similarly to non-consumptive effects in predator-prey or intraguild relationships.

The article presents research results of the γ-ray and fast neutrons impact on various rice species, in order to obtain mutant forms resistant to salinity (NaCl) and drought factors (sorbitol. They are going to be used as initial forms in synthetic selection when creating varieties, adapted to the stressful conditions of, both, soil and climate in the Kazakhstan Aral Sea. The average lethal doses (LD50) of γ-rays and fast neutrons, as well as the average NaCl and sorbitol lethal concentrations were established. Such environment is best suited for clear manifestation of the mutagenic effect of ionizing radiation and the resistance of rice plants to salinity and drought. A distinct dependence in the effect of ionizing radiation and stress factors on the number of induced resistant mutant forms has been related to the initial rice sort. The largest number of mutant forms was obtained from the local variety Syr Suluy, followed by Leader and AyKerim varieties. From all varieties, the number of mutants obtained from exposure to γ-rays was 43 pieces out of 4500 grains, and from the impact of fast neutrons - 115 pieces out of 2700 grains. M1 mutant plants significantly differ from the initial forms in terms of morphological features – plant height, panicle length, grain size Most plants are characterized by short growth and even dwarfism (˂80 cm). They are lodging tolerant, have short and highly sterile panicles, indicating that they are mutants and resistant to salinity, drought, or both.

Extensive use of petrochemical plastic packaging leads to the greenhouse gas emission and con-tamination to soil and oceans, posing major threats to the ecosystem. The packaging needs, hence, are shifting to bioplastics with natural biodegradability. Lignocellulose, the biomass from forest and agriculture, can produce cellulose nanofibrils (CNF), a biodegradable material with ac-ceptable functional properties, that can make packaging among other products. CNF extracted from lignocellulosic wastes can lower the packaging carbon footprint and the production cost. Most of these low value feedstocks go to alternative applications, that make their use in CNF packaging competitive. To transfer the waste materials from current practices to the packaging production, it is imperative to assess their sustainability, encompassing environmental and eco-nomic impacts along with the feedstock physical and chemical properties. A consolidated over-view of these criteria is absent in the literature. This study performs an iterative literature review and presents thirteen attributes, delineating sustainability of lignocellulosic wastes for commer-cial CNF packaging production. These criteria data are gathered for the UK waste streams, and transformed into a quantitative matrix, evaluating the waste feedstock sustainability for CNF packaging production. The presented approach can be adopted to decision scenarios in bioplas-tics packaging conversion and waste management.

The need for Artificial Intelligence (AI) and Machine Learning (ML) technologies is increasingly being leveraged for optimizing Data centers’ (DCs’) operations as the volume of operations management data increase tremendously. These strategies can assist operators in better understanding their DC operations and making informed decisions up front to preserve service reliability and availability. Aiming at creating models that optimize energy efficiency, identify inefficient resource utilization and scheduling policies, and predict outages. Apart from model hyperparameter tuning, feature selection is a crucial step to identify relevant features with the objective of providing insight into the data, improving performance, and reducing computational expenses. Although several feature selection methods have been discussed in various domains, none have been discussed in the context of the data center. This paper introduces SHapley Additive exPlanation (SHAP), a class of additive feature attribution values-based feature selection that is rarely discussed in literature. We compared the effectiveness of SHAP method with several widely used methods. We used a real DC dataset obtained from the ENEA CRESCO6 cluster with 2,0832 cores to evaluate the methods. To demonstrate the comparison of the methods, we picked the top 10 most important features from each method, the predictions were retrained, and their performance was evaluated using MAE, RMSE, and MPAE. The results show that the SHAP-assisted feature selection performed best and align with human intuition.

Ramsey theory influences the dynamics of mechanical systems, which may be described as abstract complete graphs. We address a mechanical system which is completely interconnected with the two kinds of ideal Hookean springs. The suggested system mechanically corresponds to the cyclic molecules, in which functional groups are interconnected with two kinds of chemical bonds, represented mechanically with two springs k1 and k2. In this paper, we consider a Cyclic system (molecule) built of six equal masses m and two kinds of springs. We pose the following question: what is the minimal number of masses in the such a system in which three masses are constrained to be connected with spring k1 or three masses to be connected with spring k2? The answer to this question is supplied by the Ramsey theory, and it is formally stated as follows: what is the minimal number R3,3? The result emerging from the Ramsey theory is R3,3=6. Thus, in the aforementioned interconnected mechanical system will be necessarily present the triangles (at least one triangle), built of masses and springs. This prediction constitutes the vibrational spectrum of the system. Thus, the Ramsey Theory supplies the selection rules for the vibrational spectra of the cyclic molecules. Symmetrical system built of six vibrating entities is addressed. The Ramsey approach works for 2D and 3D molecules, which may be described as abstract complete graphs. The extension of the proposed Ramsey approach to the systems, partially connected by ideal springs, viscoelastic systems and systems, in which elasticity is of an entropy nature is discussed. “Multi-color systems” built of three kinds of ideal springs are addressed. The notion of the inverse Ramsey network is introduced and analyzed.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

The current and projected climate change that is represented by increasing temperatures, humidity levels and irregular rainfall patterns, promotes the occurrence of preharvest sprouting (PHS) in wheat. PHS results in significant economic losses, globally, which necessitates the need for high-yielding cultivars with increased PHS tolerance, hence this study was conducted. The current study evaluated two doubled-haploid (DH) wheat populations of Tugela-Dn × Elands and Elands × Flamink across six environments in the Free State Province of South Africa to select genotypes with increased PHS tolerance and further map the underlying loci. Significant effects of DH lines (194) and environments (6) were observed for PHS tolerance. The results of this study validate previous findings that PHS is only expressed when environmental conditions are conducive. Quantitative trait loci (QTL) mapping using single nucleotide polymorphism (SNP) and silicoDArT markers revealed three additive QTL with major effects on chromosomes 5B and 7B, and these QTL were detected more than once, when conditions were favourable. These QTL explained a phenotypic variation (PVE) varying between 10.08% and 20.30% (LOD = 2.73 – 3.11). About 16.50% of DH lines performed to the level of Elands (the PHS-tolerant parent) and are recommended for further selection in a pre-breeding or breeding programme. The findings of the study are expected to facilitate the on-going breeding efforts for PHS tolerance in winter wheat.

In the era of sustainable development, green supplier selection has become a key component of supply chain management, as it considers criteria such as carbon footprint, water usage, energy usage and recycling capacity. Since the green supplier selection problem involves subjective criteria and uncertainty in preferences, it is well suited to using multi-criteria decision-making methods (MCDM). Although few researchers have investigated MCDM for green supplier selection under uncertainty using intuitionistic and Pythagorean fuzzy sets. Still, the newly developed fermatean fuzzy can handle greater data uncertainty than intuitionistic and Pythagorean fuzzy sets. Since interval extension of fuzzy theory provides more accurate modeling, in this paper, we propose an interval extension of fermatean fuzzy set and discuss its fundamental set operations, arithmetic operations and related properties. We propose the Hamming distance function and the score function of the interval-valued fermatean fuzzy numbers. The recently developed Multi-attribute Border Approximation Area Comparison (MABAC) method is also considered due to its stable and simple computation compared to other conventional MCDM methods. Therefore, an interval-valued fermatean fuzzy MABAC method is proposed and solved to select a green supplier based on multiple criteria. The proposed method's comparative analysis and sensitivity analysis validate the proposed method's effectiveness and robustness.

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skewed Exponential Power Distribution (also called Skewed Generalized Error Distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing $k$-means approach. The usefulness of the proposal is showed by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips, and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW), and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

Abstract. Background: This paper investigates the propagation of behaviorally transmitted traits with negative effect on host fitness. Methods: We analyze equilibrium between genetically transmitted and behaviorally transmitted competing propagators and consider whether a behavioral propagator is linked to reproduction (e.g. vertical culture transmission), or not. We employ combined genetic and behavior-induced fitness components for hosts, while behavioral propagators have replication factors to distinguish from what’s good for the host (fitness). Results: A trait which spreads faster than its marginal host fitness contribution reduces population will establish itself. The often transient nature of laterally transmitted traits may be a defense against accumulation of deleterious traits. Laterally transmitted traits with high spreading rate often do not equalize with genetic traits, spreading outside natural selection of the hosts. Vertical transmission reduces replication rate and allows group selection against deleterious behaviorally transmitted traits. Competing mutually exclusive propagators contribute to inequality and altruism, but compete through adverse fitness since exclusivity assumes low conversion. Conclusion: Behaviorally transmitted traits, in some cases a tremendous advantage, may also be a significant problem in the development of societies.

Groundnut (Arachis hypogaea L.) is a major food and cash crop in Burkina Faso. Due to growing demand for raw oilseeds, there is an increasing interest in groundnut production from traditional rain-fed areas to irrigated environments. However, despite implementation of many initiatives in the past to increase groundnut productivity and production, the groundnut industry still struggles to prosper, due to several constraints including minimal development research and fluctuating markets. Yield penalty due to drought and biotic stresses continue to be a major drawback for groundnut production. This review traces progress in the groundnut breeding that started in Burkina Faso before the country’s political independence in 1960 through to present times. Up to the 1980s, groundnut improvement was led by international research institutions such as IRHO (Institute of Oils and Oleaginous Research) and ICRISAT (International Crops Research Institute for the Semi-Arid Tropics). However, international breeding initiatives were not sufficient to establish a robust domestic groundnut breeding programme. This review also provides essential information about opportunities and challenges of groundnut research in Burkina Faso, emphasising the need for institutional attention to genetic improvement of the crop.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are amongst the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this case study. Actual locations of dams constructed in the area were used to verify the results of both methods.

The present work shows a novel physical-mathematical model to estimate the average corrosion resistance index from the crystallographic texture in API 5L steels. The crystallographic texture of the studied steels was measured by means of the X-ray diffraction technique. The model, based on the symmetric spherical surface harmonics for a BCC structure, is capable of describing the anisotropy surface of anodic dissolution resistance of the crystal and establishing a straightforward relationship between crystallographic texture, surface roughness, and metal corrosion behavior. The predictions of the average corrosion resistance index made from the crystallographic texture were in good agreement with those obtained from potentiodynamic polarization curves for the investigated steels. This agreement validates the capacity of this model and opens the possibility of applying it as a novel criterion for the material selection and design stages in order to combat metal corrosion problems.

This paper models the factors explaining households members economic well-being in Burkina Faso, with a focus on the relative influence of gender inequality in literacy status. It does so, using data from the 2014 survey on household living conditions and a semi-parametric bi-variate sample selection modeling approach. This approach compared to the classic Heckman two-step estimator is methodologically innovative because it deals simultaneously with non-random sample selection using conventional systems of two equations, non-linear covariates' effects using spline approach, and the non-normal bivariate distribution using copula functions. The graphical results from the Lorenz curves combined with the numerical Atkinson and Gini coefficients suggest that inequality in overall per-capita consumption spending among households headed by literates is higher than that of their illiterate counterparts in 2009 and 2014. However, independently of the head of household’s literacy status, the level of inequality in total economic well-being decreased from 2009 to 2014. Using the poverty indices of Watts, Sen, Foster ( alpha= 1)) we found that poverty among households headed by literates is lower than that of their illiterates counterparts for both years, although overall poverty decreased nationally between 2009 and 2014. The results also show that although the gender inequality in literacy status does not translate into inequalities in non-food wellness, it does however for food-wellness as female headed households have 38.9% less per-capita food consumption spending than their men counterparts. Combining both food and non-food consumption spending, total economic well-being also seems to exhibit gender inequality as female headed households now have relatively 26.7% less combined per-capita consumption spending.

As a procedure deriving UH (unit hydrograph), the root selection method necessitates only storm runoff data. However, this method must deal with the uncertainty related to the noise fluctuation of runoff ordinates and derive one optimal UH from many storms. This study proposes a procedure that applies the Savitzky-Golay filter to smooth the noise fluctuation of the runoff ordinates and uses the linear combination of UHs from individual storms to derive an optimal UH. The proposed method is applied to the storms of the Nenagh River basin in Ireland. The applicability of the Savitzky-Golay filter for smoothing the noise fluctuation of storm runoffs is examined by means of the Nash-Sutcliffe efficiency index. Furthermore, the root selection method is extended to also estimate IUHs. The results show that the adoption of the Savitzky-Golay filter improves the applicability of the root selection method and that the optimal UH predicts accurately the time-to-peak and peak discharge.

Multi-sensor data fusion of E-tongue and E-nose can provide a more comprehensive and more accurate analysis results. However, it also brings some redundant information, it is a hot issue to reduce the feature dimension for pattern recognition. In this paper, the taste-olfactory data fusion based on E-tongue and E-nose combined with Support Vector Machine (SVM) was used to classify five different beers. First, the taste and olfactory feature information were obtained based on E-tongue and E-nose. Second, the original feature data of single system were fused, then Principal Component Analysis (PCA) was applied to extract principal components, Genetic Algorithm-Partial Least Squares (GA-PLS) was used to select the characteristic variables, 20 subsets were generated with those variables based on the best Variable Importance of Projection (VIP) score. Finally, the classification models based on SVM were established, also c and g of SVM were calculated by Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the classification results of all subsets were obtained. The results showed that the classification accuracy using data fusion was much higher over single E-tongue and single E-nose, and the variable selection method by VIP had the best classification performance in #12 subset coupled with GA-SVM.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Most of the mid-high permeability sandstone oilfields in China have entered the ultra-high water cut stage, accompanied with the highly-dispersed residual oil, and the control effect of mi-cro-heterogeneity on the residual oil is increasingly prominent. Understanding the microscopic characteristics of pore structural and its evolution during the long-term waterflooding is critical for enhancing the accuracy and efficiency of oil field exploitation. While the previous insights paid more attention to the geometrical and morphological features of pore structure and ignores the relation of pore structure with the fluid flow capacity. In this study, we propose a microscopic pore structure classification method considering pore size and local permeable capacity, which realizes the physical classification and quantitative evaluation of pore structure characteristics for different permeability rocks. The micro-pore structure is classified into 6 types based on the key evaluation parameters of pore diameter and flow flux area. By taking six mid-high permea-bility sandstone core samples from Daqing oilfield in China, a series of long-term waterflooding experiments are carried out and the interior pore structure was captured using high precision CT scanning technique. The results reveals that high-permeability reservoirs exhibit concentrated pores diameters and finer throats, whereas mid-permeability reservoirs exhibit smaller pores and throats in greater quantity. With the increase of water injection volumes, the permeable ca-pacity and connectivity of the high-connectivity pore space in ultra-high- and high-permeability reservoirs are enhanced, and in mid-permeability reservoirs, the micro-heterogeneity intensifies the increase in low-connectivity small pores. These findings provide new insights for the micro-scopic heterogeneity and a solid theoretical basis for investigating the occurrence of microscopic residual oil and optimising enhanced oil recovery strategies at the late stage of ultra-high water cut.

In urban areas like Chicago, daily life extends above ground level due to prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between April 8 and May 7, 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 meters away. Results show that outdoor PM2.5 concentrations peak at 14 meters height, then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 AM under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portable and non radiation. The system is capable of reconstruction of electrical conductivity tomographic images at a rate of 30.624 frames per second and about 5 minutes for imaging. The current source employs 0.5 mA, adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. The article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs, through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians for to obtain images of respiratory diseases. The reconstruction method (GREIT) was evaluated with experimental data and It was compared with the Gauss-Newton and Total Variation methods, where GREIT proved to be superior.

This work investigates how to balance the electricity supply and demand in a carbon-neutral northern Europe. Applying a cost-minimizing electricity system model including options to invest in eleven different flexibility measures, cost-efficient combinations of strategies to manage varia-tions are identified. The results of the model are post-processed using a novel method to map the net load before and after flexibility measures are applied to reveal the contribution of each flexi-bility measure. The net load is mapped in the space spanned by the amplitude, duration, and number of occur-rences. The mapping shows that, depending on cost structure, flexibility measures contribute to reduce the net load in three different ways; 1) by reducing variations of long duration but low amplitude, 2) by reducing variations of high amplitude but short duration and low occurrence or 3) by reducing variations of high amplitude, short duration and high occurrence. It is found that cost-efficient variation management is achieved by combining wind and solar power and by combining strategies (1-3) to manage variations. The cost-efficient combination of strategies de-pends on electricity system context where electricity trade, flexible hydrogen and heat production (1) manage the majority of variations in regions with good conditions for wind power while sta-tionary batteries (3) are the main contributors in regions with good conditions for solar power.

Let g be a strictly increasing function on $\left(a,b\right),$ having a continuous derivative g′ on $\left(a,b\right).$ For the Lebesgue integrable function $f:\left(a,b\right)\to \mathbb{C}$ , we define the k-g-left-sided fractional integral of f by $S_{k,g,a+}f\left(x\right)={\int }_a^{x}k\left(g\left(x\right)-g\left(t\right)\right)g^{\prime }\left(t\right)f\left(t\right)dt,x\in \left(a,b\right]$ and the k-g-right-sided fractional integral of f by $S_{k,g,b-}f\left(x\right)={\int }_x^{b}k\left(g\left(t\right)-g\left(x\right)\right)g^{\prime }\left(t\right)f\left(t\right)dt,x\in [a,b),$ where the kernel k is defined either on $\left(0,\infty \right)$ or on $\left[0,\infty \right)$ with complex values and integrable on any finite subinterval. In this paper we establish some Ostrowski and trapezoid type inequalities for the k-g-fractional integrals of functions of bounded variation. Applications for mid-point and trapezoid inequalities are provided as well. Some examples for a general exponential fractional integral are also given.

Rice blast is the most harmful disease caused by Magnaporthe oryzae, leading to yield loss. The aim of the work is to improve resistance of rice to blast by introducing and pyramiding genes using conventional breeding technique and modern MAS analysis. Pyramiding is the combination of several resistance genes in one genotype, in this case it improves the resistance of rice to pathogens. MAS analysis revealed that 3 out of 4 studied domestic high-yielding rice cultivars (Bakanasski, Aisaule and Aru) contain 1 blast resistance gene (Pi-2). It was discovered 7 pyramidal lines with the introduction of a combination of 2 blast resistance genes (Pi-1 and Pi-ta) into 6 hybrids and 3 genes (Pi-1, Pi-33 and Pi-ta) into 1 line. Phytopathological test of local cultivars and pyramidal lines of rice showed that 3 out of 7 pyramidal lines have moderate resistance: 2 lines (F2 Bakanasski/7667 (var. vulgaris Koern.) and F2 Aisaule/7664 (var. italica Alef.)), containing 2 resistance genes (Pi-1 and Pi-ta) and 1 line – F2 Aisaule/7689 (var. zeravschanica Brsch.), carrying 3 genes (Pi-1, Pi-33 and Pi-ta), while the local cultivars Bakanasski, Aisaule and Aru are susceptible. As a result, the blast resistance of rice was improved by creating pyramidal lines. Pyramided lines can be used for germplasm exchange and in rice breeding programs to improve blast resistance in rice.

Wireless mesh networks (WMNs) are gaining popularity due to their versatility and cost-effectiveness. However, selecting appropriate gateways to connect these networks to external networks remains a key challenge. This paper presents a comprehensive review of gateway selection methods in WMNs, classifying them into network-centric, user-centric, and hybrid approaches. Key factors such as network topology, traffic volume, quality of service, and available resources are considered in evaluating these methods. The effectiveness of different strategies is assessed using metrics like throughput, latency, energy usage, and fairness. The impact of mobility, node heterogeneity, and security concerns on gateway selection is also explored. In addition to reviewing existing methods, the paper identifies research gaps and proposes future directions. Advanced methods that can adapt to changing network environments and support large-scale deployments are highlighted. This survey offers valuable insights for researchers and network designers to improve the efficiency and reliability of WMNs in real-world applications.