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Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov–Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data. [ABSTRACT FROM AUTHOR]

[Objective] With a view to providing reference for the management of phosphorus (P) and potassium (K) and environmental assessment in the orchard, the nutrient balance and the environmental risk of P and K in mango orchards under optimized and reduced application of chemical fertilizer were examined.[Method] Seven fertilization treatments were conducted: (i) No fertilization (control, NF), (ii) Sole application of basal organic fertilizer (control, CF), (iii) Constant furrow application of chemical fertilizer (A), (iv) Combined furrow application of organic-inorganic fertilizer (OS), (v) Constant furrow application of integrated water fertilizer (IWF), (vi) 30% Reduced furrow application of water fertilizer (RA), (vii) Combined application of organic-inorganic water fertilizer (OWF) . The surface runoff test was performed on the basis of rainfall simulation experiments, and estimations were made grounded on local meteorological data; The environmental risk assessment test of P and K was carried out in virtue of laboratory tests under simulated conditions to determine the threshold of environmental sensitivity of P and K, thereby dividing the scale; Nutrient balance was utilized to analyze the surplus and deficit of P and K.[Result] The surplus rate of P earned the highest ranking, up to 63.36% (67.04 kg/hhm²), in the constant furrow application of chemical fertilizer (A) ; Maximum K surplus was recorded in the furrow application of chemical fertilizer (A), with a rate of 49.49% (142.96 kg/hhm²) ; The runoff losses of P and K in mango orchards attained to 10.74 kg/hhm² and 8.86 kg/hhm² respectively. The P output and corresponding proportion was ranged as follows: Pruned branches and leaves > surface runoff > litter > harvested fruits, while the sequence of K output and corresponding proportion was: Harvested fruits > prune branches and leaves > litter > surface runoff. The risk threshold of P for a single input was 45.99 kg/hhm², and that of K reached 20.29 kg/hhm². The environmental risk of P in soil was calculated to be 103.78 mg/kg, and that of K arrived at 143.98 mg/kg. The available phosphorus contents of such treatments as A, OS, IWF and OWF exceeded the environmental risk threshold, signaling the phosphorus losses. Similarly, the potassium losses should be noted in that the available potassium contents in OWF and IWF topped the environmental risk threshold. The available potassium content in OS approached the environmental risk threshold, which suggested the potential risk of potassium losses. [Conclusion] (i) Remarkable improvements in the availability of soil potassium and the absorption of potassium by fruit trees can be achieved in virtue of combined application of organic fertilizer. (ii) Measures should be adopted to conserve soil and water in light of the large proportion of phosphorus runoff losses to the phosphorus input in orchards. (iii) Compared with other fertilization treatments, 30% reduced furrow application of water fertilizer (RA) embodies the most balanced input of P and K in general, as well as pockets of improvement. Hence a certain proportion of organic fertilizer instead of chemical fertilizer input is recommended. (iv) It is advisable to keep a single input of P no more than 45.99 kg/hhm² considering the lower demand of mango trees for phosphorus and make K input less than or equal to 20.29 kg/hhm² each time despite the great demand for potassium, and nutrients can be supplied with low amounts and high frequency. [ABSTRACT FROM AUTHOR]

Remote sensing object detection, with large differences in object size, arbitrary orientation and tight arrangement, leads to difficulties in object recognition and localization. Therefore, a remote sensing image object Detector (BAIDet) based on Background and Angle Information is proposed in this paper. Firstly, a large convolutional kernel global attention module is designed to fully utilize the global information of remote sensing images by expanding the receptive field. And obtain the edge information of ground objects through deformable convolution. Secondly, an angle-sensitive probabilistic intersection-over-union loss function (AS-ProbIoU Loss) is developed for bounding box regression for oriented object detection. Finally, experimental results on four remote sensing image datasets, DOTA, HRSC 2016, UCAS-AOD, and DIOR-R, demonstrated the effectiveness of this method. [ABSTRACT FROM AUTHOR]

To address the challenges in establishing the state transfer matrix and the complexity of eigenvalue calculation in determining the multi-parameter stability boundaries of high-order nonlinear Vienna rectifiers, a novel numerical computation method is proposed in this paper. This method leverages a numerical stability criterion and a grid variable step search to efficiently calculate these stability boundaries. The small-signal model of the Vienna rectifier is derived by constructing the time-varying state transfer matrix using the periodic solution of the harmonic balance method. Eigenvalues are rapidly calculated via the periodic numerical solution of the state transfer matrix. The proposed parameter sensitivity-based grid variable step search method ensures a fast and accurate determination of stability boundaries. A hardware experimental setup is established to validate the stability boundaries of the Vienna rectifier under various parameter variations, including load, component, and control changes. The experimental results closely match the simulations, confirming the correctness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Simple Summary: Monitoring pollinator populations is crucial for understanding biodiversity trends and ensuring the health of ecosystems, especially in agricultural landscapes. This study introduces the "Knautia Pollinator Walk" as a new method for tracking pollinator diversity and abundance. By observing pollinators visiting the inflorescence of Knautia arvensis, we found significant correlations between pollinators and land use, and there were significant differences in pollinator communities between regions. Our findings highlight the importance of habitat type in influencing pollinator populations, offering a valuable tool for conservation efforts. Declining populations of native pollinators, especially wild bees, underline the urgent need for effective monitoring within agricultural ecosystems. This study aims to (i) establish the 'Knautia Pollinator Walk' as an innovative pollinator monitoring method, (ii) examine the link between pollinator richness/density and land cover, and (iii) assess if specialist solitary bees indicate pollinator abundance and morphogroup richness. The approach involves surveying 500 Knautia arvensis inflorescences per site thrice per season. Observations of 11,567 pollinators across 203 taxa showed significant correlations between pollinator diversity and land use. Pollinator populations fluctuated with land cover type, increasing in open areas but decreasing or stabilising in forested and shrubby regions. Noteworthy differences in pollinator types were seen between Russia (solitary bees, small Diptera, Lepidoptera) and Sweden (bumblebees, beetles, furry Diptera). The "Knautia Pollinator Walk" shows promising signs of being an effective tool for monitoring spatiotemporal biodiversity trends. The method offers a scalable approach to pollinator monitoring, which is essential for developing conservation strategies and supporting pollinator populations. [ABSTRACT FROM AUTHOR]

Motivated by the notion of isotropic α -stable Lévy processes confined, by reflections, to a bounded open Lipschitz set D ⊂ R d , we study some related analytical objects. Thus, we construct the corresponding transition semigroup, identify its generator and prove exponential speed of convergence of the semigroup to a unique stationary distribution for large time. [ABSTRACT FROM AUTHOR]

The escalating computational demands of deep learning and large-scale models have led to a significant increase in energy consumption, highlighting the urgent need for more energy-efficient hardware designs. This study presents a novel weight approximation strategy specifically designed for quantized neural networks (NNs), resulting in the development of an efficient approximate multiplier leveraging most significant one (MSO) shifting. Compared to both energy-efficient logarithmic approximate multipliers and accuracy-prioritized non-logarithmic approximate multipliers, our proposed logarithmic-like design achieves an unparalleled balance between accuracy and hardware costs. When compared with the baseline exact multiplier, our innovative design exhibits remarkable reductions, encompassing a decrease of up to 28.31% in area, a notable 57.84% reduction in power consumption, and a diminution of 11.86% in delay. Experimental outcomes reveal that the proposed multiplier, when applied in neural networks, can conserve approximately 60% of energy without compromising task accuracy. Concurrently, experiments focused on the transformer accelerator and image processing illustrate the substantial energy savings that can be obtained for Large Language Models (LLMs) and image processing tasks through the implementation of our proposed design, further validating its efficacy and practicality. [ABSTRACT FROM AUTHOR]

The current active earth pressure calculation theory cannot take into account the influence of the foundation inverse slope. For the translational mode retaining wall in this paper, taking cohesive backfill as the research object, the clay within the crack depth range is equivalent to uniform overload, using the upper limit theory, establishing the energy conservation equation of the velocity field and force, and the analytical formula of the tensile crack depth and the slip surface of the cohesive backfill being obtained. By taking into account the soil arching effect, setting up the static equilibrium equation by horizontal layer analysis, deriving the active earth pressure calculation expression of cohesive backfill, it is possible to consider the influence of the basement inverse slope. In the case of cohesionless backfill, the formula can be reduced to the theoretical solution. By comparing the calculated results with the experimental values and relevant theories, this can verify the applicability of the formula. The result of research shows that, with the increase of the basement inverse slope angle, the development of the tensile crack will be promoted, the dip angle of the slip surface changes little, and the resultant force and overturning moment will decrease, which is conducive to the antisliding stability and antioverturning stability of the wall, reducing the project cost. The active earth pressure distribution of cohesionless backfill is independent of whether there is an inverse slope in the basement. The theoretical formula obtained in this paper can be used as reference for the design of the retaining wall. [ABSTRACT FROM AUTHOR]

Global climate change and the decreasing availability of high-quality water lead to an increase in the salinization of agricultural lands. This rising salinity represents a significant abiotic stressor that detrimentally influences plant physiology and gene expression. Consequently, critical processes such as seed germination, growth, development, and yield are adversely affected. Salinity severely impacts crop yields, given that many crop plants are sensitive to salt stress. Plant growth-promoting microorganisms (PGPMs) in the rhizosphere or the rhizoplane of plants are considered the "second genome" of plants as they contribute significantly to improving the plant growth and fitness of plants under normal conditions and when plants are under stress such as salinity. PGPMs are crucial in assisting plants to navigate the harsh conditions imposed by salt stress. By enhancing water and nutrient absorption, which is often hampered by high salinity, these microorganisms significantly improve plant resilience. They bolster the plant's defenses by increasing the production of osmoprotectants and antioxidants, mitigating salt-induced damage. Furthermore, PGPMs supply growth-promoting hormones like auxins and gibberellins and reduce levels of the stress hormone ethylene, fostering healthier plant growth. Importantly, they activate genes responsible for maintaining ion balance, a vital aspect of plant survival in saline environments. This review underscores the multifaceted roles of PGPMs in supporting plant life under salt stress, highlighting their value for agriculture in salt-affected areas and their potential impact on global food security. [ABSTRACT FROM AUTHOR]

Recently, quasisymmetric functions have been widely studied due to their big connection to enumerative combinatorics, combinatorial Hopf algebra and number theory. The Bayer filter mosaic, named due to Bryce Bayer (1929-2012), is a color filter array used to arrange RGB color filters on a square grid of photosensors. It is the most common pattern of filters, and almost all professional digital cameras are applications of this filter. We use this filter to introduce the Bayer Noise quasisymmetric functions, and we study some combinatorial algebraic and coalgebraic structures on Quasi-Bayer Noise modules and on Quasi-Bayer GB-Noise modules. We explicitly describe the primitive basis elements for each comultiplication defined on Quasi-Bayer Noise modules, and we calculate different kinds of comultiplications defined on Quasi-Bayer Noises module over a fixed commutative ring k. [ABSTRACT FROM AUTHOR]

The diversification of species for bioenergy silviculture in Amazonia will help several local and/or regional industrial segments. Thus, the expansion of knowledge on silvicultural practices of Tachigali vulgaris will support future commercial-scale plantations in Amazonia. This study aimed to evaluate possible differences in diameter, height, and wood volume of T. vulgaris trees grown under different nutritional doses, on two soil texture classes. In addition, we sought to determine the appropriate nutritional dose for the studied types of soil texture aiming at the production of energy wood. The study was conducted in two experimental areas of 1.6 ha each, located in the municipality of Almeirim – Pará, Brazil. Independent analyses were performed in both experimental areas at each age. The experiments were set up in a randomized block design with four blocks. A factorial scheme considering four phosphorus and three potassium doses were adopted, totaling 12 treatments. The diameter at 1.30 m (DBH), equivalent diameter, total height of the trees, and the commercial wood volume with bark per hectare (Vwb ha−1) were evaluated. A significant effect of nutrient doses was reported on DBH at the age of 2 years for the soil with a sandy loam texture. The Vwb ha−1 of the trees at 4, 5, and 6 years old was significantly affected by fertilization in clayey soils. Under the conditions of the present study, the nutritional doses of 0 kg ha−1 of triple superphosphate and 172 kg ha−1 of potassium chloride are recommended for the area with Oxisol of sandy loam texture and 195 kg ha−1 of triple superphosphate and 343 kg ha−1 of potassium chloride are recommended for the area with Oxisol of clayey texture. [ABSTRACT FROM AUTHOR]

In two-player games on graphs, the simplest possible strategies are those that can be implemented without any memory. These are called positional strategies. In this paper, we characterize objectives recognizable by deterministic Büchi automata (a subclass of ω-regular objectives) that are half-positional, that is, for which the protagonist can always play optimally using positional strategies (both over finite and infinite graphs). Our characterization consists of three natural conditions linked to the language-theoretic notion of right congruence. Furthermore, this characterization yields a polynomial-time algorithm to decide half-positionality of an objective recognized by a given deterministic Büchi automaton. [ABSTRACT FROM AUTHOR]

Copyright of School Administrators is the property of School Administration Research Association, R.O.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

C-H carboxylation of furoic acid (FA) with CO2 is an atom-efficient strategy to produce 2,5-furandicarboxylic acid (2,5-FDCA) from lignocellulose. The existing carbonate-promoted CO2 carboxylation processes rely on the use of large amounts of expensive Cs2CO3 as a deprotonating reagent and molten salt. Substitution of Cs with other cheap and abundant alkali ions (such as K and Na) can reduce the use of Cs, but it faces the problem of a low yield of 2,5-FDCA. This study found that the addition of catalytic amounts of ZnCl2 as a Lewis acid can increase the yield of 2,5-FDCA in the CO2 carboxylation reaction of Na/K-FA in a molten salt reaction system. 1H NMR analysis and DFT calculations confirmed that ZnCl2 coordinates with the furan ring through electron transfer from the conjugated furan ring to Zn2+, thereby activating the H at the C5 position of Na/K-FA. This coordination lengthened the C5-H bond and lowered its heterolytic dissociation energy, making it more susceptible to being deprotonated by CO32- and subsequently carboxylated by CO2. The developed Lewis acid coordination strategy provides a new idea for the efficient construction of C-C bonds between CO2 and aromatics through carbonate-promoted C-H carboxylation. [ABSTRACT FROM AUTHOR]

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents. [ABSTRACT FROM AUTHOR]

In this work, the hypothesis that the universe is made up of 4-dimensional spheres of space, whose diameter is the. Planck length, allows us to calculate most of the constants used in current physical theories. Calculated constants include: elementary charge, fine structure constant, electron mass, Planck constant, gravitational constant, electrical constant, Boltzmann constant, mass and charge of up and down quarks, mass of second generation quarks: strange and charm, muon mass, Z boson mass and Higgs boson mass. All of them depending on the speed of light and the Planck length, which shows that all the constants are related and not due to chance. [ABSTRACT FROM AUTHOR]

Copyright of International Journal on Humanities & Social Sciences is the property of International Journal on Humanities & Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the mechanism of harmful algal bloom formation is vital for effectively preventing algal bloom outbreaks in coastal environments. Karenia spp. blooms in the East China Sea show a significant correlation with nutrient regimes. However, the impact of key components of nutrients, especially dissolved organic nitrogen (DON), on the blooms of Karenia spp. is not clear. Quantitative research is still lacking. In this study, the cruise observations, field mesocosm-flask culture experiments, and a multinitrogen-tri-phytoplankton-detritus model (NTPD) are combined to reveal the quantitative influence of nutrient regimes on the shift of Prorocentrum donghaiense and Karenia spp. in the East China Sea. It has a synchronism rhythm of diatom-P. donghaiense-Karenia spp.-diatom loop in the field culture experiment, which is consistent with the results of the cruise observation. The results showed that the processes of terrigenous DON (TeDON) and dissolved inorganic nitrogen (DIN: NO3−-N, NH4+-N) absorption promoted P. donghaiense to become the dominant algae in the community; whereas the processes of DON from P. donghaiense absorption promoted Karenia spp. to become the dominant algae in ambient DIN exhaustion. In addition, the three-dimensional fluorescence components of humus C, tyrosine and fulvic acid can indicate the processes of growth and extinction of P. donghaiense and Karenia spp., respectively. This study infers that P. donghaiense and Karenia spp. regime shift mechanism associated with the nutrient regime in coastal waters, which provides a scientific basis for the environmental management of coastal ecosystem health. [ABSTRACT FROM AUTHOR]

Holocene quartz-rich sands such as in the Orinoco River of Venezuela and Columbia, develop under a unique set of conditions including tectonic stability, intense weathering and long-term storage of sediment prior to burial. This review paper proposes that a similar set of conditions existed on the Mesoarchaean Earth by 3.0 Ga. Compositionally and texturally mature quartz arenites are present in the Mesoarchaean Hospital Hill Subgroup and correlative lower Mozaan Group, in the cover rocks of the Beitbridge Complex in the Limpopo Belt and at Buhwa in southern Zimbabwe as well as the Neoarchaean Manjeri Formation of Zimbabwe. These quartz arenites are interpreted as first-cycle and developed following the formation of stable cratons under conditions of slow subsidence (<10 cm/1 000 years), intense weathering in a hostile chemical environment related to the ambient carbon dioxide-rich atmosphere, long-distance intrabasinal transport by tides and/or waves and microbial processes that influenced the local chemical environment. Similar examples of Mesoarchaean quartz arenites are documented from the Yilgarn Block in Western Australia, the Slave and Superior provinces in Canada and the Dharwar and Singhbhum cratons in India. On a global scale, cratonisation was diachronous with the Kaapvaal Craton stabilised on a regional scale by 3.0 Ga whereas microcontinents existed over the remainder of Earth. [ABSTRACT FROM AUTHOR]

We present a new geminal product wave function Ansatz where the geminals are not constrained to be strongly orthogonal or to be of seniority-zero. Instead, we introduce weaker orthogonality constraints between geminals that significantly lower the computational effort without sacrificing the indistinguishability of the electrons. That is to say, the electron pairs corresponding to the geminals are not fully distinguishable, and their product has yet to be antisymmetrized according to the Pauli principle to form a bona fide electronic wave function. Our geometrical constraints translate into simple equations involving the traces of products of our geminal matrices. In the simplest non-trivial model, a set of solutions is given by block-diagonal matrices where each block is 2 × 2 and consists of either a Pauli matrix or a normalized diagonal matrix multiplied by a complex parameter to be optimized. With this simplified Ansatz for geminals, the number of terms in the calculation of the matrix elements of quantum observables is considerably reduced. A proof of principle is reported and confirms that the Ansatz is more accurate than strongly orthogonal geminal products while remaining computationally affordable. [ABSTRACT FROM AUTHOR]

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane. [ABSTRACT FROM AUTHOR]

The concept of k -core, which indicates the largest induced subgraph where each node has k or more neighbors, plays a significant role in measuring the cohesiveness and engagement of a network, and it is exploited in diverse applications, e.g., network analysis, anomaly detection, community detection, etc. However, recent studies have demonstrated the vulnerability of k -core under malicious perturbations which focus on removing the minimal number of edges to make k -core structures collapse. Despite this, to the best of our knowledge, no existing research has yet concentrated on the minimal number of edges that must be removed to collapse a specific node in the k -core. To address this issue, in this paper, we make the first attempt to study the robustness of individual nodes in k -core and propose the Targeted k -node Collapse Problem (TNCP) with three novel contributions. Firstly, we offer a general definition of TNCP problem with a proof of its NP-hardness. Secondly, in order to cover the TNCP problem, we propose a heuristic algorithm named TNC and its improved version named ATNC for implementations on large-scale networks. Finally, experiments on 20 real-world networks across various domains verify the superiority of our proposed algorithms over 6 baseline methods with detailed comparisons and analyses. Resource related to our study is publicly available at https://github.com/Yocenly/TNCP. [ABSTRACT FROM AUTHOR]

Background: Colistin is a last-resort antibiotic used in extreme cases of multi-drug resistant (MDR) Gram-negative bacterial infections. Colistin resistance has increased in recent years and often goes undetected due to the inefficiency of predominantly used standard antibiotic susceptibility tests (AST). To address this challenge, we aimed to detect the prevalence of colistin resistance strains through both Vitek®2 and broth micro-dilution. We investigated 1748 blood, tracheal aspirate, and pleural fluid samples from the Intensive Care Unit (ICU), Neonatal Intensive Care Unit (NICU), and Tuberculosis and Respiratory Disease centre (TBRD) in an India hospital. Whole-genome sequencing (WGS) of extremely drug-resitant (XDR) and pan-drug resistant (PDR) strains revealed the resistance mechanisms through the Resistance Gene Identifier (RGI.v6.0.0) and Snippy.v4.6.0. Abricate.v1.0.1, PlasmidFinder.v2.1, MobileElementFinder.v1.0.3 etc. detected virulence factors, and mobile genetic elements associated to uncover the pathogenecity and the role of horizontal gene transfer (HGT). Results: This study reveals compelling insights into colistin resistance among global high-risk clinical isolates: Klebsiella pneumoniae ST147 (16/20), Pseudomonas aeruginosa ST235 (3/20), and ST357 (1/20). Vitek®2 found 6 colistin-resistant strains (minimum inhibitory concentrations, MIC = 4 μg/mL), while broth microdilution identified 48 (MIC = 32–128 μg/mL), adhering to CLSI guidelines. Despite the absence of mobile colistin resistance (mcr) genes, mechanisms underlying colistin resistance included mgrB deletion, phosphoethanolamine transferases arnT, eptB, ompA, and mutations in pmrB (T246A, R256G) and eptA (V50L, A135P, I138V, C27F) in K. pneumoniae. P. aeruginosa harbored phosphoethanolamine transferases basS/pmrb, basR, arnA, cprR, cprS, alongside pmrB (G362S), and parS (H398R) mutations. Both strains carried diverse clinically relevant antimicrobial resistance genes (ARGs), including plasmid-mediated blaNDM-5 (K. pneumoniae ST147) and chromosomally mediated blaNDM-1 (P. aeruginosa ST357). Conclusion: The global surge in MDR, XDR and PDR bacteria necessitates last-resort antibiotics such as colistin. However, escalating resistance, particularly to colistin, presents a critical challenge. Inefficient colistin resistance detection methods, including Vitek2, alongside limited surveillance resources, accentuate the need for improved strategies. Whole-genome sequencing revealed alarming colistin resistance among K. pneumoniae and P. aeruginosa in an Indian hospital. The identification of XDR and PDR strains underscores urgency for enhanced surveillance and infection control. SNP analysis elucidated resistance mechanisms, highlighting the complexity of combatting resistance. [ABSTRACT FROM AUTHOR]

Social representation theory is a branch of social psychology that aims to identify the framework of concepts, ideas, opinions, beliefs, or feelings shared by the individuals within a social group, regarding a social object. Two main problems arise in this theory. The first concerns the identification of the content of the representation, which is the set of cognitive elements shared by the group; the second concerns its structure, which is the way these elements are organized and related among themselves. It is desirable that the methods to address these problems be simple, in regards to the feasibility of the data collection, and reliable, in the sense that they should provide a clear picture of the content and the structure of the representation. No single method proposed in the literature until now fully satisfies these features at the same time. Here we propose the use of HodgeRank, a global ranking method based on the Hodge combinatorial theory, as a new tool to explore the structure of a social representation. In this proposal, the input data is the same as those required for the hierarchical word associations, which is the main method in the field of social representations. However, the HodgeRank provides richer results when compared to the usual approach to analysing this kind of data, based on the Vergés' double-entry table. The main outcome of the HodgeRank is a graph, analogous to an electric circuit, from which some structural elements of the representation can already be identified. Moreover, the HodgeRank technique identifies the sources of inconsistencies between the global ranking and the aggregated answers within the social group. We interpret such inconsistencies in terms of the stability of the representation and use them to raise conjectures about the potential dynamics of the representation. We illustrate the application of this method in the study of a social representation of COVID-19 within a group of students and also within a group of faculty members from higher education institutions in Brazil. [ABSTRACT FROM AUTHOR]

In this paper, we present connections between three models used in different research fields: weighted finite automata (WFA) from formal languages and linguistics, recurrent neural networks used in machine learning, and tensor networks which encompasses a set of optimization techniques for high-order tensors used in quantum physics and numerical analysis. We first present an intrinsic relation between WFA and the tensor train decomposition, a particular form of tensor network. This relation allows us to exhibit a novel low rank structure of the Hankel matrix of a function computed by a WFA and to design an efficient spectral learning algorithm leveraging this structure to scale the algorithm up to very large Hankel matrices. We then unravel a fundamental connection between WFA and second-order recurrent neural networks (2-RNN): in the case of sequences of discrete symbols, WFA and 2-RNN with linear activation functions are expressively equivalent. Leveraging this equivalence result combined with the classical spectral learning algorithm for weighted automata, we introduce the first provable learning algorithm for linear 2-RNN defined over sequences of continuous input vectors. This algorithm relies on estimating low rank sub-blocks of the Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed learning algorithm are assessed in a simulation study on both synthetic and real-world data. [ABSTRACT FROM AUTHOR]

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students' physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923. [ABSTRACT FROM AUTHOR]

By constructing the consumers’ utility function, this article calculates the optimal order quantity and optimal expected profit of a fresh retailer under the centralized and decentralized inventory strategy in an omni-channel supply chain. In addition, the impact of consumers’ channel preference, consumers’ demand transfer behavior, and the "buy-online-pickup-instore" (BOPS) service on retailers’ inventory strategy are analyzed. The results show that opening a BOPS service is not always beneficial to retailers. However, it is always beneficial for retailers to eliminate the differences between channels. Under the centralized inventory strategy, the optimal order quantity and expected profit are only affected by the freshness-keeping cost, while under the decentralized inventory strategy, they are affected by various consumer behaviors. A reasonable inventory management strategy should be adopted according to the product type, inventory cost, consumer preferences, and order quantity. [ABSTRACT FROM AUTHOR]

Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km 2 region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors. [ABSTRACT FROM AUTHOR]

Valleytronic materials can provide new degrees of freedom to future electronic devices. In this work, the concepts of the ferrovalley metal (FVM) and valley gapless semiconductor (VGS) are proposed, which can be achieved in valleytronic bilayer systems by electric field engineering. In valleytronic bilayer systems, the interaction between out-of-plane ferroelectricity and A-type antiferromagnetism can induce layer-polarized anomalous valley Hall (LP-AVH) effect. The K and −K valleys of FVM are both metallic, and electron and hole carriers simultaneously exist. In the extreme case, the FVM can become VGS by analogizing spin gapless semiconductor (SGS). Moreover, it is proposed that the valley splitting enhancement and valley polarization reversal can be achieved by electric field engineering in valleytronic bilayer systems. Taking the bilayer RuBr2 as an example, our proposal is confirmed by the first-principle calculations. The FVM and VGS can be achieved in bilayer RuBr2 by applying electric field. With appropriate electric field range, increasing electric field can enhance valley splitting, and the valley polarization can be reversed by flipping electric field direction. To effectively tune valley properties by electric field in bilayer systems, the parent monolayer should possess out-of-plane magnetization, and have large valley splitting. Our results shed light on the possible role of electric field in tuning valleytronic bilayer systems, and provide a way to design the ferrovalley-related material by electric field. [ABSTRACT FROM AUTHOR]

An indirect-type forced convection solar dryer implementing a phase-changing material (PCM) as the energy-storing medium was designed, fabricated, and investigated in this study. The effects of changing the mass flow rate on the valuable energy and thermal efficiencies were studied. The experimental results showed that the instantaneous and daily efficiencies of the indirect solar dryer (ISD) increased with the initial increase in mass flow rate, beyond which the change is not prominent both with and without using the PCM. The system consisted of a solar energy accumulator (solar air collector with a PCM cavity), a drying compartment, and a blower. The charging and discharging characteristics of the thermal energy storage unit were evaluated experimentally. It was found that after using PCM, drying air temperature was higher than ambient air temperature by 9–12 ℃ after sunset for 4 h. Using PCM accelerated the process by which Cymbopogon citratus was effectively dried between 42 and 59 °C of drying air. Energy and exergy analysis of the drying process was performed. The daily energy efficiency of the solar energy accumulator reached 35.8%, while the daily exergy efficiency reached 13.84%. The exergy efficiency of the drying chamber was in the range of 47–97%. A free energy source, a large reduction in drying time, a higher drying capacity, a decrease in mass losses, and improved product quality all contributed to the proposed solar dryer's high potential. [ABSTRACT FROM AUTHOR]

This paper proposed a novel distribution parameterized by four parameters. This is achieved by compounding the potentials properties of the Type I half logistic topp-leone generalized distribution family with the properties of the inverse lomax distribution to form the novel Type I half logistic topp-leone inverse lomax distribution. The novel distribution is potentially capable of extending classical inverse lomax distribution. The potentiality of the shape of the probability density function of the novel distribution is worth recognizing since it produces right skewed, approximately normal, left skewed and a reverted J-shaped. Decreasing life failure shape is also observed. Distinctive features of the novel distribution such as moments, entropy, moment generating function, reliability and hazard function were derived. The estimation method explored in this study is maximum likelihood estimation. It is adopted to estimate the novel distribution unknown parameters. Real life data set was adopted to investigate the potentiality and applicability of the novel model. The type I half logistic topp-leone inverse lomax distribution outperform the recent models. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the mineral-associated organic carbon (MOC) and its relationship to clay minerals under different temperatures and precipitation. We selected three typical grassland transects in China: Titanium Plate (TP), Mongolian Plate (MP), and Loess Plate (LP) with natural temperature gradients. Along the transect, there is a gradient in the precipitation between the various types of grasslands. The surface soil (0–10 cm) was sampled to determine the MOC. Clay minerals were charactered by X-ray diffraction (XRD). According to the findings, the MOC content increased with decreasing temperature (5.41–14.89 g/kg). MOC had a positive correlation (r = 0.67) with the amount of clay mineral content. In the large-scale study of transects, precipitation masks the effect of temperature change on the MOC to a certain extent. It indirectly affected the MOC content by affecting the mixed-layer illite/smectite (I/Sme) content, and this effect was strongest at the lowest temperature TP (r = −0.73). Except for precipitation, CaO in the soil can affect soil organic carbon (SOC) stability by influencing the pH and I/Sme. The amount of bacteria increased as a result of I/Sme, and the influence of bacteria on the MOC was surpassed only by the soil pH. Climate and clay mineral composition characteristics affected the MOC to a certain extent. Among them, the effect of precipitation change on the MOC is higher than temperature, but it has little effect on soil with a higher weathering degree and CaO content. [ABSTRACT FROM AUTHOR]

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The growing global market of dairy products has led to the need for alternative approaches regarding whey valorization, which is the primary by-product of cheese and strained yogurt production. In this context, prebiotic galactooligosaccharides can be produced enzymatically from whey using commercially available β-galactosidases. A comparative study was conducted to assess the production of galactooligosaccharides from sweet and acid whey, thereby employing two commercial β-galactosidases from Aspergillus oryzae and Kluyveromyces lactis. The study considered the initial lactose content and enzyme load as variables. The maximum yields of galactooligosaccharides in concentrated sweet whey (15% w/v initial lactose) and raw acid whey (3.1% w/v initial lactose) reached 34.4 and 14.7% with lactase from Kluyveromyces lactis (0.13 U/mL), respectively. The corresponding galactooligosaccharide yields for lactase from Aspergillus oryzae were equal to 27.4 and 24.8% in the most concentrated sweet and acid whey, respectively, using enzyme loads of 2 U/mL in sweet whey and 1 U/mL in acid whey. Concerning the profile of the produced galactooligosaccharides, the Kluyveromyces lactis lactase hydrolyzed lactose more rapidly and resulted in higher levels of allolactose and lower levels of 6-galactosyl-lactose, compared to the lactase from Aspergillus oryzae, and achieved in both cases a polymerization degree of up to six. [ABSTRACT FROM AUTHOR]

Infections with drug-resistant bacteria have become one of the greatest public health challenges, and K. pneumoniae is among the top six drug-resistant bacteria. K. pneumoniae often causes nosocomial infections, leading to illnesses such as pneumonia, liver abscesses, soft tissue infections, urinary tract infections, bacteremia, and in some cases death. As the pathogen continues to evolve and its multidrug resistance increases, K. pneumoniae poses a direct threat to humans. Drug resistance in K. pneumoniae may occur due to the formation of biofilms, efflux pumps, and the production of β-lactamases. In many cases, resistance is further enhanced by enzymatic modification and loss of porins. Drug resistance to K. pneumoniae has led to a decline in the effectiveness of conventional therapies against this pathogen. Therefore, there is an urgent need to accelerate the development of new antibiotics and explore new therapeutic approaches such as antimicrobial peptides, phages, traditional Chinese medicine, immunotherapy, Antimicrobial nanoparticle technology, antisense oligonucleotides and gene editing technologies. In this review, we discuss the mechanisms of drug resistance in K. pneumoniae and compare several new potential therapeutic strategies to overcome drug resistance in the treatment of K. pneumoniae infections. [ABSTRACT FROM AUTHOR]

Doping with hetero-atoms is an effective way to tune the properties of graphene quantum dots (GQDs). Here, potassium-doped GQDs (K-GQDs) are synthesized by a one-pot hydrothermal treatment of sucrose and potassium hydroxide solution. Optical properties of the GQDs are altered as a result of K-doping. The absorption peaks exhibit a blue shift. Multiple photoluminescence (PL) peaks are observed as the excitation wavelength is varied from 380 nm to 620 nm. New energy levels are introduced into the K-GQDs and provide alternative electron transition pathways. The maximum PL intensity of the K-GQDs is obtained at an excitation wavelength of 480 nm which is distinct from the undoped GQDs (375 nm). The strong PL of the K-GQDs at the longer emission wavelengths is expected to make K-GQDs more suitable for bioimaging and optoelectronic applications. [ABSTRACT FROM AUTHOR]

This work investigates a robust container vessel sequencing (RCVS) problem in a service route. As weekly demands vary dramatically and cannot be forecasted accurately, shipping companies need to develop a robust sequence of vessels with different capacities to maximally meet demands. As export heavily depends on economy, demands may share the same pattern in adjacent years, which motivates us to study the problem in a cyclic fashion. To refine the literature, we adopt a robust optimisation model to minimise the worst-case total cost, including container tardy and outsourcing cost, due to reliability guarantee. To accommodate human decision-making, we focus on an associated K-adaptability problem, which pre-selects a number of candidate vessel sequences and implements the best one when the uncertain demands have been observed. A branch-and-bound solution approach is explored. Numerical experiments demonstrate the performance of our approach. [ABSTRACT FROM AUTHOR]

Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person v in the network has a certain threshold t (v) for activation , i.e. adoption of the product or idea. If v has at least t (v) activated neighbors, then v will also become activated. If Alice wants to make k new friends in the network, and thereby activate the most number of people, how should she choose these friends? We study the problem of choosing the k people in the network to befriend, who will in turn activate the maximum number of people. This Maximum Influence with Links Problem has applications in viral marketing and the study of epidemics. We show that the solution can be quite different from the related and widely studied influence maximization problem where the objective is to choose a seed or target set with maximum influence. We prove that the Maximum Influence with Links problem is NP-complete even for bipartite graphs in which all nodes have threshold 1 or 2. In contrast, we give polynomial time algorithms that find optimal solutions for the problem for trees, paths, cycles, and cliques. [ABSTRACT FROM AUTHOR]

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size B, and the step size or learning rate η. For small B and large η, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the "temperature" T ≡ η/B. Yet this description is observed to break down for sufficiently large batches B ≥ B*, or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the B-η plane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch size B* separating regimes (i) and (ii) scale with the size P of the training set, with an exponent that characterizes the hardness of the classification problem. [ABSTRACT FROM AUTHOR]

The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m2 and an aggregate pulse density of 556 pulses/m2. We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas. [ABSTRACT FROM AUTHOR]