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Context. Many invasive animals are typically active across large areas, making monitoring and control programs expensive. To be efficacious, monitoring devices and control tools need to be strategically located to maximise the probability of encounter. This requires an understanding of how the target species uses the landscape, through identifying key habitat or landscape features that are preferred and used disproportionately more frequently by the species. Spatial analysis of animal movements can help identify high use areas. Aims. The variability introduced by different range calculation methods can lead to uncertainty in subsequent habitat analyses. We aimed to determine which method is superior for accurate delineation of core areas for feral cats. Methods. We analysed spatial data from 35 collared feral cats across four Australian study sites between 2016 and 2019, and compared the core areas generated using seven commonly used home range estimation methods. Key results. We found that the a-hull method provided a higher precision of polygon placement, resulting in lower Type I and II errors and higher conformity to landscape features than other methods. The a-hull used a single default parameter and required no subjective input, making it a more objective, superior method. Conclusions. We recommend that the a-hull method be used to define core activity areas for feral cats, enabling more robust habitat analysis, and identification of key habitat and landscape features to strategically target for monitoring and control programs. Implications. This strategic approach could significantly improve cost efficiencies, particularly where existing management is widely dispersed, and core activity areas are clumped. [ABSTRACT FROM AUTHOR]

A subset K of vertices of digraph D = (V(D), A(D)) is a kernel if the following two conditions are fulfilled: (i) no two vertices of are connected by an arc in any direction and (ii) every vertex not in has an ingoing arc from some vertex in K. A quasi-kernel of D is a subset Q of vertices satisfying condition (i) and furthermore every vertex can be reached in at most two steps from Q. A vertex is source-free if it has at least one ingoing arc. In 1976, P.L. Erdös and L.A. Székely conjectured that every source-free digraph D has a quasi-kernel of size at most |V(D)| /2Recently, this conjecture has been shown to be true by Allan van Hulst for digraphs having kernels. In this note, we provide a short and simple proof of van Hulst's result. We additionally characterize all source-free digraphs D having kernels with smallest quasi-kernels of size |V (D)| /2. [ABSTRACT FROM AUTHOR]

Quantitative analysis of diffusion‐weighted magnetic resonance imaging (DW‐MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW‐MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel‐wise fitting are often unreliable. In this study, we propose a kernelized total difference‐based curve‐fitting method to estimate the IVIM parameters. Simulated DW‐MRI data at five signal‐to‐noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW‐MRI data acquired on a 1.5‐T MRI scanner with nine b‐values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion‐encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust‐region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root‐mean‐square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference‐based curve‐fitting method has the potential to improve the reliability of IVIM parametric imaging. [ABSTRACT FROM AUTHOR]

The current study envisioned to assess the physical traits of sorghum grain for selected sorghum genotypes. Seed for the genotypes was sourced from the Lupane State University Gene Bank and grown during the 2022/23 agricultural season at Lupane State University Farm experimental plots. At maturity, laboratory tests on kernel/grain hardness, 100 kernel weight, bulk density, kernel diameter, colour and determination of presence of tannins through qualitative tests were done for all the 24 sorghum genotypes. Results from analysis of variance demonstrated highly significant differences (P<0.001) on kernel weight, kernel diameter, kernel hardness and grain hardness showing a great diversity of physical traits among all the 22 genotypes and 2 commercial varieties of sorghum. Mean 100 kernel weight was 2.59g, kernel diameter was 3.49mm, bulk density was 1.23g/cm3 and kernel hardness was 28.9%. Visual assessment was done on grain colour and seed was classified under red, cream, white and brown sorghums, and mixed colours. A chi-square test found a significant relationship between grain colour and presence of tannins. Genotypes NPGRC3124, IS9405 showed moderate levels of tannins while IS13996, IS29925, NPGRC1699, NPGRC1156 and NPGRC1478 had high levels. A highly significant strong positive correlation was shown for sorghum genotype between kernel diameter and kernel weight (r=0.81 at p=0.05). Highly significant positive correlation was also observed between bulk density and kernel weight (r=0.4173 at p=0.001). Kernel hardness has a strong positive correlation with bulk density (r=0.6242). Quantification of tannins is recommended to prevent negative effects on human and livestock health. [ABSTRACT FROM AUTHOR]

Wheat is used for making many food products due to its diverse quality profile among different wheat classes. Since laboratory analysis of these end-use quality traits is costly and time-consuming, genetic dissection of the traits is preferential. This study used a genome-wide association study (GWAS) of ten end-use quality traits, including kernel protein, flour protein, flour yield, softness equivalence, solvent's retention capacity, cookie diameter, and top-grain, in soft red winter wheat (SRWW) adapted to US southeast. The GWAS included 266 SRWW genotypes that were evaluated in two locations over two years (2020–2022). A total of 27,466 single nucleotide markers were used, and a total of 80 significant marker-trait associations were identified. There were 13 major-effect quantitative trait loci (QTLs) explaining >10% phenotypic variance, out of which, 12 were considered to be novel. Five of the major-effect QTLs were found to be stably expressed across multiple datasets, and four showed associations with multiple traits. Candidate genes were identified for eight of the major-effect QTLs, including genes associated with starch biosynthesis and nutritional homeostasis in plants. These findings increase genetic comprehension of these end-use quality traits and could potentially be used for improving the quality of SRWW. [ABSTRACT FROM AUTHOR]

The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels that are then used to compare their performances of classification on various datasets. We also provide the Python codes for the reproducibility of results and, thanks to the symmetry of kernels, we can reduce the computational costs of the Gram matrices. [ABSTRACT FROM AUTHOR]

Gaussian processes are popular in spatial statistics, data mining and machine learning because of their versatility in quantifying spatial variability and in propagating uncertainty. Although there has been a prolific research activity about Gaussian processes over Euclidean domains, only recently this research has extended to non-Euclidean manifolds. This paper digs into vector-valued Gaussian processes defined over the product of a hypersphere and a Euclidean space of arbitrary dimension, which are of interest in various disciplines of the natural sciences and engineering. Under mild regularity conditions, we establish a surprising one-to-one correspondence between matrix-valued kernels associated with vector Gaussian processes over the product space, and what we term partial ultraspherical and Fourier transforms that are taken over either the sphere or the Euclidean subspace. The properties of our approach are illustrated in terms of new parametric classes of matrix-valued kernels for product spaces of a hypersphere crossed with a Euclidean space. We also provide two algorithms that allow for fast simulation of approximately Gaussian (in the sense of the central limit theorem) processes in such product spaces. [ABSTRACT FROM AUTHOR]

The variability of food resources considerably affects the habitat preferences of animals. In mast years, the availability of highly nutritive food increases significantly. We tested whether changes in the distribution of the areas of wisents, Bison bonasus L. concentration, in the Bieszczady Mountains, Poland, were connected with the availability of beechnuts. In the two beech masting years of 2013 and 2022, we considered the months with the highest availability of beechnuts, namely September and October. The beechnut crop varied significantly between as little as 2.4 g dry matter/m2, recorded within the Baligród herd's home range in 2013, and up to 238.8 g dry matter/m2 within the Tworylne herd's range, also in 2013. The analysis of the spatial distribution of beech stands within various parts of the wisents' home range showed that within the 95% kernel area, their share was mostly high, varying between 25.7% and 42.8%. Meanwhile, within the 50% kernel area, it was generally much lower, except for the year 2022 in the case of the Baligród herd. The densities of wisents varied significantly between the kernel areas of 95% and 50%, ranging between 0.04 and 0.08 animals/ha and 0.17 and 0.48 animals/ha, respectively. However, there was no statistical difference between the figures for all plots tested within the home range of the wisent population and plots dominated by beech. Based on the data obtained in this study, habitat selection patterns of wisents in the Bieszczady Mountains cannot be explained by the availability of beech stands and the phenomenon of mast years. [ABSTRACT FROM AUTHOR]

Digitalization and automation in the agricultural sector enable the enhancement of production processes, leading to increased yields. Specifically, the medications administration or complementary treatments in animals often prove to be a demanding task for human operators. This letter introduces an embedded system prototype that facilitates monitoring the level of capsules coverage in troughs through image processing. The suggested system enables an innovative antiparasitic treatment using biological control agents. The prototype utilizes a Raspberry Pi 3B as the platform to execute the developed image processing algorithm. The obtained results successfully demonstrate the algorithm’s accurate functionality estimating capsules coverage within the troughs. [ABSTRACT FROM AUTHOR]

This paper considers the problem of kernel regression and classification with possibly unobservable response variables in the data, where the mechanism that causes the absence of information can depend on both predictors and the response variables. Our proposed approach involves two steps: First we construct a family of models (possibly infinite dimensional) indexed by the unknown parameter of the missing probability mechanism. In the second step, a search is carried out to find the empirically optimal member of an appropriate cover (or subclass) of the underlying family in the sense of minimizing the mean squared prediction error. The main focus of the paper is to look into some of the theoretical properties of these estimators. The issue of identifiability is also addressed. Our methods use a data-splitting approach which is quite easy to implement. We also derive exponential bounds on the performance of the resulting estimators in terms of their deviations from the true regression curve in general L p norms, where we allow the size of the cover or subclass to diverge as the sample size n increases. These bounds immediately yield various strong convergence results for the proposed estimators. As an application of our findings, we consider the problem of statistical classification based on the proposed regression estimators and also look into their rates of convergence under different settings. Although this work is mainly stated for kernel-type estimators, it can also be extended to other popular local-averaging methods such as nearest-neighbor and histogram estimators. [ABSTRACT FROM AUTHOR]

Bandwidth selection is key for kernel quantile estimators (KQEs), which estimate quantiles by averaging all of the order statistics with appropriate kernel-weighting functions. This paper provides a new data-driven bandwidth selection method for KQEs, named the exact bootstrap-based bandwidth selection (EBS) rule. By relying on the exact analytic expressions for the bootstrap mean and variance of KQEs, the error due to bootstrap resampling is eliminated, and thus the optimal bandwidth can be obtained by minimizing the mean squared error (MSE) estimate. The effectiveness of this EBS rule is confirmed by numerical experiments. First, the bandwidth selection performance of the EBS method is compared to that of a benchmark approach. Simulation studies show that the EBS method performs well, especially in selecting bandwidths for extreme quantiles and when applied to small sample sizes with skewed distributions and relatively large variances. Second, KQEs with a bandwidth determined by our EBS rule is compared with five other state-of-the-art quantile estimators over six typical distributions. The results further validate the efficiency of the EBS method. Third, the results of simulations of controlling actual type-I error rates that occur when two independent groups are compared through quantiles further demonstrate the precision of our EBS-based KQEs. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

In this paper we consider the semiclassical version of pseudo-differential operators on the lattice space ħ Z n . The current work is an extension of the previous work (Botchway et al. in J Funct Anal 278(11):108473, 33, 2020) and agrees with it in the limit of the parameter ħ → 1 . The various representations of the operators will be studied as well as the composition, transpose, adjoint and the link between ellipticity and parametrix of operators. We also give the conditions for the ℓ p , weighted ℓ 2 boundedness and ℓ p compactness of operators. We investigate the relation between the classical and semi-classical quantization in the spirit of Ruzhansky and Turunen (Pseudo-differential operators and symmetries. Pseudo-differential operators, vol 2. Theory and Applications, Birkhäuser, Basel, 2010; J Fourier Anal Appl 16(6):943–982, 2010) RTspsJFAA and employ its applications to Schatten–von Neumann classes on ℓ 2 (ħ Z n) . We establish Gårding and sharp Gårding inequalities, with an application to the well-posedness of parabolic equations on the lattice ħ Z n . Finally we verify that in the limiting case where ħ → 0 the semi-classical calculus of pseudo-differential operators recovers the classical Euclidean calculus, but with a twist. [ABSTRACT FROM AUTHOR]

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

Cocoa (Theobroma cacao L.), indigenous to the tropical forests of the Americas, is renowned not only as the primary raw material for chocolate and its derivatives (cocoa liquor and butter) but also as a rich source of phytonutrients with beneficial health effects. Current research has elucidated that within the post-harvest process, fermentation stands as the critical stage for the formation of the principal biochemical quality markers in cocoa, known as polyphenols. These compounds contribute to the bitterness and astringency that constitute the complex flavor profile of chocolate; however, their excessive presence can be organoleptically undesirable. A high phenolic content (>10%) is associated with insufficient fermentation and certain varieties of ordinary cocoa, thereby serving as a discriminatory parameter between fine-flavor cocoa (Nacional) and bulk cocoa (CCN-51). Beyond their technological significance, these components have garnered substantial scientific interest, as polyphenol consumption is associated with potential protective effects against the development of non-communicable chronic diseases (including diabetes, cancer, and atherosclerosis), attributable to their potent antioxidant properties. In this context, the objective of this study was to evaluate the impact of fermentation time on the antioxidant capacity (AC) and total polyphenol content (TPC) in the principal Ecuadorian cocoa varieties (i.e., CCN-51 clone and Nacional). Pilot-scale fermentation experiments demonstrated significant variations in antioxidant capacity (CCN-51 clone: 785.61 to 1852.78 and Nacional: 564.32 to 1428.60 µmol TE/g) and total polyphenol content (CCN-51 clone: 52.92 to 162.82; Nacional: 40.55 to 157.50 mg gallic acid/g). Both parameters decreased markedly throughout the process, with the CCN-51 clone exhibiting greater retention.

旨在寻找油茶果最佳采后处理方式，为其后续研究奠定基础，采用液相色谱-质谱联用法（LC-MS/MS）对油茶果采后不同处理〔不处理（F8）、堆沤处理（FC）、去壳摊晒（NS）、带壳摊晒（WS）〕10、30 d种仁代谢物进行测定，并用相关软件对代谢产物进行分析。结果表明：鉴定出1 107种特征代谢物，其中正离子模式和负离子模式下鉴定的代谢物数量分别为763种和344种，代谢物主要为脂类和类脂分子（277种），苯丙烷类和聚酮类（193种），有机酸及其衍生物（172种），类苯（97种），有机杂环化合物（94种），有机氧化合物（84种），核苷、核苷酸和类似物（33种）；通过比较差异代谢物不饱和脂肪酸发现，去壳摊晒处理30 d（NS2） vs 10 d（NS1）比较组中检测到亚油酸、顺式-9-十六碳烯酸、8（9）-环氧-5Z，11Z，14Z-二十碳三烯酸、亚麻酸、蓖麻油酸、9,10-环氧十八碳烯酸共6种不饱和脂肪酸，且都表达上调；对差异代谢物进行富集分析发现，主要参与的代谢途径有苯丙氨酸代谢、类黄酮生物合成、甘油磷脂代谢、甘油酯代谢、玉米素生物合成、植物次生代谢产物的生物合成、谷胱甘肽代谢、氨基酸的生物合成、次生代谢产物的生物合成。综上，油茶果采后最佳处理方式为去壳摊晒30 d。In order to find the optimal postharvest treatment of Camellia oleifera fruit, and lay the foundation for subsequent research, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to determine the metabolites of the kernels of different postharvest treatments of Camellia oleifera fruit 〔without treatment （F8）, composting treatment（FC） , sun dried without shell （NS）, and sun dried with shell（WS）〕 for 10 d and 30 d, and relevant software was used to analyze the metabolites. The results showed that a total of 1 107 characteristic metabolites were identified, with 763 and 344 metabolites identified in the positive and negative ion modes, respectively. The main metabolites were lipids and lipid-like molecules( 277 kinds), phenylpropanoids and polykettides (193 kinds), organic acids and derivatives(172 kinds), benzenoids (97 kinds), organoheterocyclic compounds (94 kinds), organic oxygen compounds (84 kinds), nucleosides, nucleotides and analogues(33 kinds). By comparison of the unsaturated fatty acid of differential metabolites, six unsaturated fatty acids, namely linoleic acid, cis-9-hexadecenoic acid, 8 (9) - epoxy-5Z, 11Z, 14Z-eicosatrienoic acid, linolenic acid, ricinolic acid, 9,10-epoxy-octadecenoic acid, were detected in 30 d (NS2) and 10 d (NS1) of the sun dried without shell treatment, and their expressions were up-regulated. The enrichment analysis of differential metabolites results showed that the main metabolic pathways included phenylalanine metabolism, flavonoid biosynthesis, glycerol phospholipid metabolism, glycerol ester metabolism, zeatin biosynthesis, plant secondary metabolites biosynthesis, glutathione metabolism, amino acid biosynthesis, and secondary metabolites biosynthesis. Overall, the best postharvest treatment of Camellia oleifera fruit is sun dried without shell treatment for 30 d.

In this article, we propose three non parametric kernel estimators for the mean residual life function when the data are selected proportionally to their length. We evaluate the mean squared error of the three estimators and investigate the consistency for all three of them. [ABSTRACT FROM AUTHOR]

This study uses the triple-phase lag model to investigate how higher-order fractional order and memory-dependent derivatives affect reflection at the free surface of an orthotropic piezo-thermoelastic medium. The performance of both kinds of derivatives is studied using the normal mode analysis technique. Four different types of coupled reflected plane waves are identified and explore the impact of various parameters, fractional order parameter, kernel function, the higher-order time differential fractional, and memory-dependent heat conduction parameters on energy distribution with respect to the angle of incidence. The results are presented graphically, providing numerical data for reflected waves, amplitude, and energy ratios. [ABSTRACT FROM AUTHOR]

With an explosive increase of data produced annually, researchers have been attempting to develop solutions for systems that can effectively handle large amounts of data. Single-operating-system (OS) non-uniform memory access (NUMA) abstraction technology is an important technology that ensures the compatibility of single-node programming interfaces across multiple nodes owing to its higher cost efficiency compared with scale-up systems. However, existing technologies have not been successful in optimizing user performance. In this paper, we introduce a single-OS NUMA abstraction technology that ensures full compatibility with the existing OS while improving the performance at both hypervisor and guest levels. Benchmark results show that the proposed technique can improve performance by up to 4.74X on average in terms of execution time compared with the existing state-of-the-art opensource technology.

Cocoa (Theobroma cacao L.), indigenous to the tropical forests of the Americas, is renowned not only as the primary raw material for chocolate and its derivatives (cocoa liquor and butter) but also as a rich source of phytonutrients with beneficial health effects. Current research has elucidated that within the post-harvest process, fermentation stands as the critical stage for the formation of the principal biochemical quality markers in cocoa, known as polyphenols. These compounds contribute to the bitterness and astringency that constitute the complex flavor profile of chocolate; however, their excessive presence can be organoleptically undesirable. A high phenolic content (>10%) is associated with insufficient fermentation and certain varieties of ordinary cocoa, thereby serving as a discriminatory parameter between fine-flavor cocoa (Nacional) and bulk cocoa (CCN-51). Beyond their technological significance, these components have garnered substantial scientific interest, as polyphenol consumption is associated with potential protective effects against the development of non-communicable chronic diseases (including diabetes, cancer, and atherosclerosis), attributable to their potent antioxidant properties. In this context, the objective of this study was to evaluate the impact of fermentation time on the antioxidant capacity (AC) and total polyphenol content (TPC) in the principal Ecuadorian cocoa varieties (i.e., CCN-51 clone and Nacional). Pilot-scale fermentation experiments demonstrated significant variations in antioxidant capacity (CCN-51 clone: 785.61 to 1852.78 and Nacional: 564.32 to 1428.60 µmol TE/g) and total polyphenol content (CCN-51 clone: 52.92 to 162.82; Nacional: 40.55 to 157.50 mg gallic acid/g). Both parameters decreased markedly throughout the process, with the CCN-51 clone exhibiting greater retention. [ABSTRACT FROM AUTHOR]

In the framework of Clifford analysis, we study higher order Dirac operators constructed with k-vectors. We find a necessary and sufficient condition to determine whether a function cancels them. [ABSTRACT FROM AUTHOR]

Yield in many crops is affected by abscission during the early stages of fruitlet development. The reasons for fruitlet abscission are often unclear but they may include genetic factors because, in some crops, self-pollinated fruitlets are more likely to abscise than cross-pollinated fruitlets. Pollen parentage can also affect final fruit size and fruit quality. Here, we aimed to understand the effects of pollen parentage on fruitlet retention and nut quality in orchards of macadamia (Macadamia integrifolia Maiden & Betche). We identified the pollen parent of macadamia 'cultivar '816' embryos by analysing single nucleotide polymorphisms (SNPs) in their DNA using customised MassARRAY and Single Allele Base Extension Reaction (SABER) methods. This allowed us to determine the proportions of self-fertilised and cross-fertilised progeny during premature fruit drop at 6 weeks and 10 weeks after peak anthesis, as well as at nut maturity. We determined how pollen parentage affected nut-in-shell (NIS) mass, kernel mass, kernel recovery, and oil concentration. Macadamia trees retained cross-fertilised fruitlets rather than self-fertilised fruitlets. The percentage of progeny that were cross-fertilised increased from 6% at 6 weeks after peak anthesis to 97% at nut maturity, with each tree producing on average 22 self-fertilised nuts and 881 cross-fertilised nuts. Three of the four cross-pollen parents provided fruit with significantly higher NIS mass, kernel mass, or kernel recovery than the few remaining self-fertilised fruit. Fruit that were cross-fertilised by '842', 'A4', or 'A203' had 16–29% higher NIS mass and 24–44% higher kernel mass than self-fertilised fruit. Nuts that were cross-fertilised by 'A4' or 'A203' also had 5% or 6% higher kernel recovery, worth approximately $US460–540 more per ton for growers than self-fertilised nuts. The highly selective abscission of self-fertilised fruitlets and the lower nut quality of self-fertilised fruit highlight the critical importance of cross-pollination for macadamia productivity. [ABSTRACT FROM AUTHOR]

Kernel weight is a critical agronomic trait in maize production. Many genes are related to kernel weight but only a few of them have been applied to maize breeding and cultivation. Here, we identify a novel function of maize mitogen-activated protein kinase 6 (ZmMPK6) in the regulation of maize kernel weight. Kernel weight was reduced in zmmpk6 mutants and increased in ZmMPK6 -overexpressing lines. In addition, starch granules, starch content, protein content, and grain-filling characteristics were also affected by the ZmMPK6 expression level. ZmMPK6 is mainly localized in the nucleus and cytoplasm, widely distributed across various tissues, and is expressed during kernel development, which is consistent with its role in kernel weight. Thus, these results provide new insights into the role of ZmMPK6, a mitogen-activated protein kinase, in maize kernel weight, and could be applied to further molecular breeding for kernel quality and yield in maize. [ABSTRACT FROM AUTHOR]

Background and objectives: In many African countries, especially Nigeria, the fight against malnutrition and undernourishment continues to be a primary goal of development. In this regard, various strategies including the consumption of plants and vegetables are being applied. The kernel of Bush mango, which is widely consumed in Nigeria, has been broadly investigated in the literatures, while there is little information about the nutritional composition of the pulp. This study examined the nutritional composition of both kernel and pulp of the Irvingia gabonesis. Materials and methods: The Bush mango (Irvingia gabonensis) fruits were purchased from Ogbadibo, local government of Benue state, Nigeria, followed by sorting and washing to remove any dirt. The pulp was separated from the kernel, sun-dried for seven days, and oven-dried at 350 °C for 1 h to eliminate the moisture. The dried samples were milled separately with an electric food blender, and packed in a separate clean polythene bag before analysis. The phytochemicals, antioxidants, proximate composition, and amino acids were determined in the samples according to the standard analytical techniques. Results and conclusion: Amounts of saponins, alkaloids, flavonoids, tannins, cyanide, and phytate were higher in the kernel, while oxalate and total phenols were higher in the pulp. The results of antioxidants composition revealed that carotenoids were the highest antioxidant in the kernel (4.12 g/100 g) and pulp (1.73 g/100 g) of Irvingia gabonesis fruit. Study of proximate composition revealed that carbohydrate was the most abundant macronutrient in the kernel (56.96 %) and pulp (61.02 %) of Bush mango. Fatty acids and energy were accounted as 19.19% and 1977.16 kJ/100 g of the kernel, and 16.29% and 1855.80 kJ/100 g of the pulp. Amino acid profile of the samples revealed that leucine (5.49 and 3.52 g/100 g of crude protein of kernel and pulp, respectively) and tryptophan (0.58 and 0.39 g/100 g of crude protein of kernel and pulp, respectively) were the most abundant and the lowest concentrated essential amino acids. In conclusion, the pulp and kernel of Bush mango contained low concentrations of anti-nutrients, and were found to be a good source of fat, carbohydrates, and antioxidants. [ABSTRACT FROM AUTHOR]

Objectives: This study evaluated the occurrence of Schistosoma mansoni and soil‐transmitted helminths in an endemic area in the Eastern Brazilian Amazon, analysing prevalence and spatial distribution. Methods: The study was conducted in four localities of Primavera Municipality, in Pará state. Data was obtained from the Decit 40/2012 project and the participants were divided into five age range categories for evaluation: children, adolescents, young adults, adults and elderly individuals. For the diagnostic tests, Kato–Katz slides were prepared to detect S. mansoni and soil‐transmitted helminths eggs. The spatial distribution map and the Kernel Density Estimation were performed to assess the presence and location of infections. Results: Stool samples revealed the presence of hookworms, S. mansoni, Ascaris lumbricoides and Trichuris trichiura eggs. Mono‐, bi‐ and poly‐parasitic infections were observed, with a significant prevalence of hookworm monoparasitism. Conclusions: The high frequency of children infected with soil‐transmitted helminths confirms their significance as an ongoing public health problem in the poorest municipalities of Brazil. The Geographic Information System plays a crucial role in environmental surveillance and in the control of epidemics and endemic diseases, enabling accurate assessment and informed decision‐making for their control. [ABSTRACT FROM AUTHOR]

The aim of this paper is to show how rapidly decaying RBF Lagrange functions on the sphere can be used to create a numerically feasible, stable finite difference method based on radial basis functions (an RBF-FD-like method). For certain classes of PDEs this approach leads to rigorous convergence estimates for stencils which grow moderately with increasing discretization fineness. [ABSTRACT FROM AUTHOR]

Aim of the present paper is to find suitable model for bimodal data. We have modelled mixture of two Weibull distributions in the presence of competing risks and also used Epanechnikov kernel to estimate hazard and survival functions. We considered prostate cancer data for application of the mixture model and kernel. We used maximum likelihood estimation (MLE) to estimate parameters of the mixture model, as the equations have no closed form, so we considered expectation--maximization (EM) algorithm. The mixture model and kernel gave good fit to the bimodal data. The prostate cancer data consists of three causes, we have estimated hazard function for these three causes using mixture model and kernel. The asymptotic confidence interval for the parameters of mixture model to all three causes were estimated. Also compared survival curve of mixture model with kernel and Kaplan-Meier survival curves for all the three causes. [ABSTRACT FROM AUTHOR]

This work is devoted to establish the existence of multiple positive radial solutions to the following equations associated with certain boundary conditions. The Six Functionals Fixed Point Theorem serves as the basis for the technique. To illustrate the key findings, an application is given. [ABSTRACT FROM AUTHOR]

With an explosive increase of data produced annually, researchers have been attempting to develop solutions for systems that can effectively handle large amounts of data. Single‐operating‐system (OS) non‐uniform memory access (NUMA) abstraction technology is an important technology that ensures the compatibility of single‐node programming interfaces across multiple nodes owing to its higher cost efficiency compared with scale‐up systems. However, existing technologies have not been successful in optimizing user performance. In this paper, we introduce a single‐OS NUMA abstraction technology that ensures full compatibility with the existing OS while improving the performance at both hypervisor and guest levels. Benchmark results show that the proposed technique can improve performance by up to 4.74 × on average in terms of execution time compared with the existing state‐of‐the‐art open‐source technology. [ABSTRACT FROM AUTHOR]

Maize ear related traits are directly related to yield, and the analysis of their genetic basis is of great significance for guiding maize genetic improvement. In this study, the phenotypic characteristics of eight traits were identified in 168 high generation backcross recombinant inbred lines (AB-RILs) in six environments over three years. QTLs for eight traits were mapped with 11,407 SNP markers generated by 10 K liquid chip in maize. A total of 32 QTL related to eight ear traits were identified in this study, including five environmentally consistent QTLs and three pleiotropic QTL. Further, we used the genotypic and phenotypic data of 507 maize inbred lines to analyze the candidate regions of major QTL and identified 19 candidate genes that might be related to ear shape. We finally speculated four genes as candidate genes based on the analysis of evolution and expression of the genes. These results provide the important marker information for the genetic improvement of ear traits in maize breeding and offered guidance for the cloning of genes related to ear traits. [ABSTRACT FROM AUTHOR]

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Seed quality of hybrid maize is affected by different factors, including maturity time and environmental conditions of plant growth. In this study, the effect of four maturity times of hybridized maize from male and female plants was investigated in terms of grain moisture content (30, 35, 40 and 45%) with silicon foliar spraying in a field with saline soil in Kermanshah, Iran. The experiments were on maize seed vigor using the standard germination and simulated tests of cold, Hiltner brick gravel and germination test under drought stress conditions. The results showed that highest percentage and rate of germination were obtained from the treatment with 30% moisture content at maturity with silicon spraying (99% and 0.037 germination per hour). Brick gravel and drought stress tests reduced germination percentage compared to the standard test. Based on Guasnr model, the nonlinear model of germination response curve was fitting to the percentage of moisture contents at maturity time and silicon application for various tests. The activity of Catalase, Ascorbate peroxidase and Malone Dialdehyde enzymes in different tests increased with 35% seed moisture content at the time of harvest and silicon foliar spraying. On the other hand, the maximum activities of the Antioxidant enzymes were found in the germination test under drought stress conditions in the amount of 36.6 µmolmin−1mgprotein Catalase and 2829 nmolmin−1mg Ascorbate peroxidase. In general, according to the decrease in the slope of the germination curve, 30–35% moisture content at maturity time along with silicon use was found to be suitable for producing high quality maize seeds in simulated tests of environmental stresses. [ABSTRACT FROM AUTHOR]

This work is devoted to establish the existence of multiple positive radial solutions to the following equations associated with certain boundary conditions. The Six Functionals Fixed Point Theorem serves as the basis for the technique. To illustrate the key findings, an application is given. [ABSTRACT FROM AUTHOR]

We consider the inverse problem of finding the kernel of the integral term in an integro-differential equation. The problem of finding the memory kernel in the wave process is reduced to a nonlinear Volterra integral equation of the first kind of convolution type, which is in turn reduced under some assumptions to a Volterra integral equation of the second kind. Using the method of contraction mappings, we prove the unique solvability of the problem in the space of continuous functions with weighted norms and obtain an estimate of the conditional stability of the solution. [ABSTRACT FROM AUTHOR]

Field-programmable gate array (FPGA) has become an excellent hardware accelerator solution for convolutional neural networks (CNNs). Meanwhile, optimizing methods, such as model compression, have been proposed. As most CNN accelerators focus on dense neural networks, to solve the problem of difficult hardware deployment due to irregular networks, we propose a method for sparse neural networks in our work. The storage and coding format of sparse data obtained by the block pruning method is designed to make it friendly to implement on FPGA. Besides, we also propose an efficient and simple data flow by the planarization of the whole convolution calculation process. The experimental result demonstrates that our implementation can achieve clock frequency of 190 MHz, power consumption of 13.32 W and inferencing speed of 16.37 ms. Compared with some typical Mobilenet implementation schemes, our method has been proven to achieve a better balance between frequency, accuracy, power consumption, and speed. [ABSTRACT FROM AUTHOR]

Segmentation of different brain tissues such as white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM) form magnetic resonance image (MRI) is a challenging task as the various tissue regions are complex because of their overlapping, vague and indiscernible and nonlinearly separable nature. Additionally, MRI often suffers from noise and outliers. Clustering based segmentation performance can be improved significantly by providing some amount of label pixels in the clustering process by employing semi-supervision. To cope with the above mentioned challenges, for the segmentation of brain MRI a novel semi-supervised technique called Kernel Induced Semi-supervised Spatial clustering (KISS) is proposed. The method is a judicious integration of (i) kernel trick to enhance the chances of linear separability of different regions of brain boundaries, (ii) spatial contextual information to handle the noise and outliers, (iii) fuzzy set to deal with overlapping and uncertainty of different tissue boundaries, (iv) rough set to cope with the indiscernibility and vagueness of various tissue boundaries and (v) semi-supervision to direct the process of clustering in a better direction by supplying some labeled pixels with constraint seeded policy. Several benchmark true and synthetic MRI datasets with and without added noise are examined. The efficacy of the proposed technique is validated with three unsupervised and two semi-supervised techniques and evaluated using different validity indices. Improvements achieved by the proposed technique in accuracy against the closest competitive techniques are 1.125%, 0.864%, 0.729% and 1.130% for normal BrainWeb datasets and 3.53%, 2.57%, 2.90% and 4.62% for the same datasets considering 6% added "salt & pepper" noise whereas 0.475%, 0.553%, 0.585% and 0.496% for normal IBSR datasets. Improvements considering "rician" noise are 1.62% and 1.42% for IBSR datasets 144 and 150 respectively whereas, 3.73% and 0.61% for BrainWeb datasets 85 and 100 respectively. The paired t-test results statistically signify the better results in support of the proposed technique. The confidence interval test also confirms the superiority of the proposed method over other counter part techniques. The proposed technique turned out to be effective for brain MRI segmentation. Therefore, it may be utilized for other medical image segmentations in future. [ABSTRACT FROM AUTHOR]

The Schwarz problem for -analytic functions in an arbitrary ellipse is considered. The matrix is assumed to be two-dimensional with distinct eigenvalues lying above the real axis. An example of a nonconstant solution of the homogeneous Schwarz problem in the form of a vector polynomial of degree three is given. A numerical parameter of the matrix , expressed via its eigenvectors, is introduced. After that, one relation derived earlier by the present author is analyzed. Based on this analysis, a method for computing the dimension and structure of the kernel of the Schwarz problem in an arbitrary ellipse is obtained. Sufficient conditions for the triviality of the kernel expressed via the ellipse parameters, the eigenvalues of the matrix , and the parameter are obtained. Examples of one-dimensional and trivial kernels are given. [ABSTRACT FROM AUTHOR]

This paper considers paired operators in the context of the Lebesgue Hilbert space on the unit circle and its subspace, the Hardy space H 2 . The kernels of such operators, together with their analytic projections, which are generalizations of Toeplitz kernels, are studied. Results on near-invariance properties, representations, and inclusion relations for these kernels are obtained. The existence of a minimal Toeplitz kernel containing any projected paired kernel and, more generally, any nearly S ∗ -invariant subspace of H 2 , is derived. The results are applied to describing the kernels of finite-rank asymmetric truncated Toeplitz operators. [ABSTRACT FROM AUTHOR]

Detecting license plates has never been easy, particularly with the proliferation of sophisticated radars on highways and roads. By 2021, the gendarmerie and National Security Road control agents will have access to more than 1 billion smart traffic radars worldwide. This research presents a revolutionary technique for detecting and recognizing Arabic and Latin license plates. After assembling the gathered images to create a novel dataset, we utilized YOLO v7 to locate and identify the number plate in the image as the first step of the suggested procedure. Before the dataset was fed to the detection system, it was manually labeled. Afterward, we improved the recognized license plate using machine learning methods. To do this, we used kernel methods as well as thresholding to get rid of the extra vertical lines on the plate. After that, we employed Arabic OCR along with Easy OCR methods to decipher the Latin and Arabic characters on the number plate. Eventually, the proposed method achieved an F1 score of 98%,with a precision and recall of 97% and 98%, respectively. We also obtained an accuracy of 99% for image segmentation. The segmentation and detection results from the suggested strategy have shown satisfactory results. [ABSTRACT FROM AUTHOR]

Objective: The worldwide economies are built on agriculture, and plans for food security, resource allocation, and agricultural practices are all heavily influenced by accurate crop production predictions. Predictive models are becoming indispensable tools for predicting crop prospects due to the development of technology based on data. Limitation: A significant disadvantage of the ER-ETR for Hybrid Crop Yield Prediction System can involve overfitting, particularly in cases when the dataset is small or the model complexity is not well managed. Inaccurate forecasts based on unreported data and decreased generalization can result from approach. Method: Initially, the dataset is collected from the GitHub and preprocessed using the Standardscaler method. 70 % of the preprocessed data is used as the training set, and the remaining 30 % is used as the testing set. Kernel Principal Component Analysis (KPCA) is employed to extract the feature. The Least Absolute Shrinkage and Selection Operator (LESSO) Regression is used to feature selection.A reliable method for predicting hybrid crop productivity is provided by the suggested ensemble regression that makes use of feature ensemble regression using Extra Tree Regressor (ER-ETR). Result: A simple internet-based programme for immediate forecasting is created using the Python web framework, and the model that has been trained may be used to predict the resulting profitability. Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were the testing metrics utilized to assess the classification model. With a 95 % accuracy rate, the suggested model is superior to existing models in terms of accuracy in crop production forecasting while still preserving the data's original distribution.Because of the intuitive online interface, stakeholders can forecast immediately and make well-informed decisions on the best use of resources from agriculture. Conclusion: The study creates a hybrid crop yield prediction system using the ER-ETR approach. Agricultural forecasting benefits greatly from its capacity to integrate several models and take advantage of each one's advantages, which improves prediction accuracy and dependability.

Plasmodium falciparum lactate dehydrogenase (pLDH), a protein receptor with Protein Data Bank (PDB) code 1CET, was used as a molecular target for docking studies with 11 sets of piperidine-based derivatives. Modelling and geometry optimisation using density functional theory (DFT) were performed on these sets of molecules to predict and calculate the molecular descriptors and properties responsible for the bioactivity of the molecules during interaction with the protein receptor. The values obtained for the descriptors were in accordance with Lipinski's rule. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies result in orbital energies (band gaps) with more stable complex formation when reacted with the protein receptor. To predict the biological activities of the formed complexes, quantitative structural activity relationship (QSAR) models were developed using linear regression methods: multiple linear regression (MLR) and robust linear regression (RLM), and nonlinear regression methods: kernel regression (KRM) and spline regression (SRM). The nonlinear models provided a better fit than the linear models did. The KRM outperformed the SRM because of its better efficiency at a lower bandwidth (h = 0.6), although both models seemed to have better fits as the number of bandwidths increased. In addition, docking and scoring results of the compounds outperformed the standard drug (chloroquine) with binding affinity ranged from -7.5 to -8.5 kcal/mol (cf -5.8 kcal/mol for chloroquine).