Search Results (287)

Asymmetric functional-order (variable-order) fractional diffusion–wave equations (FO-FDWEs) introduce considerable computational challenges, as the fractional order of the derivatives can vary spatially or temporally. To overcome these challenges, a novel spectral method employing generalized fractional-order Chelyshkov wavelets (FO-CWs) is developed to efficiently solve such equations. In this approach, the Riemann–Liouville fractional integral operator of variable order is evaluated in closed form via a regularized incomplete Beta function, enabling the transformation of the governing equation into a system of algebraic equations. This wavelet-based spectral scheme attains extremely high accuracy, yielding significantly lower errors than existing numerical techniques. In particular, numerical results show that the proposed method achieves notably improved accuracy compared to existing methods under the same number of basis functions. Its strong convergence properties allow high precision to be achieved with relatively few wavelet basis functions, leading to efficient computations. The method’s accuracy and efficiency are demonstrated on several practical diffusion–wave examples, indicating its suitability for real-world applications. Furthermore, it readily applies to a wide class of fractional partial differential equations (FPDEs) with spatially or temporally varying order, demonstrating versatility for diverse applications. Full article

Asymmetric synthesis of chiral hydroxysulfones, key pharmaceutical intermediates, is challenging. We report an efficient synthesis from readily available materials via a one-pot photo-biocatalytic cascade reaction in aqueous conditions, utilizing visible light as an energy source. This sustainable process achieves up to 84% yields and 99% ee. Engineered ketoreductase produces R-configured products with high conversion and enantioselectivity across diverse substrates. Molecular dynamics (MD) simulations explored enzyme–substrate interactions and their influence on reaction activity and stereoselectivity. Full article

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

In recent decades, fishery stock enhancement has been increasingly utilized as a restoration tool to mitigate population declines and enhance the resilience of marine fisheries. Nevertheless, persistent enhancement efforts risk eroding the evolutionary potential of wild populations via genetic homogenization and maladaptive gene flow. Using long-term monitoring data (2017–2023), we quantified the effects of large-scale Penaeus penicillatus stock enhancement (~10⁸ juveniles/yr) on wild population dynamics and genetic integrity in the Beibu Gulf ecosystem. Temporal genetic changes were assessed using eight highly polymorphic microsatellite loci, comparing founder (2017) and enhanced (2024) populations to quantify stocking impacts. Insignificantly lower expected heterozygosity was observed in the stocked population (He = 0.60, 2024) relative to natural populations (He = 0.62–0.66; p > 0.1), indicating genetic dilution effects from enhancement activities. No significant erosion of genetic diversity was detected post-enhancement, suggesting current stocking practices maintain short-term population genetic integrity. Despite conserved heterozygosity, pairwise Fst analysis detected significant genetic shifts between temporal cohorts (pre-enhancement—2017 vs. post-enhancement—2024; Fst = 0.25, p < 0.05), demonstrating stocking-induced population restructuring. Genetic connectivity analysis revealed that while the enhanced Beihai population (A-BH) maintained predominant self-recruitment (>90%), it experienced substantial stocking-derived gene flow (17% SW → A-BH). The post-stocking period showed both reduced genetic exchange with adjacent populations and increased asymmetric dispersal from A-BH (e.g., 5% to YJ), indicating that hatchery releases simultaneously enhanced population isolation while altering regional genetic structure. Our findings revealed the paradoxical dual effects of stock enhancement and allelic diversity while disrupting natural genetic architecture. This underscores the need for evolutionary-impact assessments in marine resource management. Full article

Background: Carbohydrate-derived chiral ligands are promising tools in asymmetric catalysis due to their structural diversity, chirality, and availability. However, ligands based on galactose or sorbose have been scarcely explored in the enantioselective addition of dialkylzinc reagents to aldehydes. Methods: A series of chiral diols and β-amino alcohols was synthesized from methyl D-glucopyranoside, methyl D-galactopyranoside, and D-fructose. These ligands were tested in the titanium tetraisopropoxide-promoted enantioselective addition of diethylzinc to aromatic and aliphatic aldehydes. Results: Several ligands, particularly those with a D-fructopyranose backbone, exhibited excellent catalytic activity, with conversion rates up to 100% and enantioselectivities up to 96% ee. Notably, this study reports for the first time the use of β-amino alcohols derived from fructose and sorbose in this transformation. Conclusions: Carbohydrate-based ligands represent effective, inexpensive, and structurally versatile scaffolds for developing highly enantioselective catalysts, expanding the utility of sugars in asymmetric organometallic reactions. Full article

In this paper, we provide a novel extended mixed differential model that is appealing to users because of its numerous free parameters. The motivation of this research arises from the opportunity for a general investigation of some outstanding classical and novel dynamical models. The higher energy levels known in the literature can be governed by appropriately added correction factors. Furthermore, the different applications of the considered model can be achieved only after a proper parameter calibration. All these necessitate the use of diverse optimization and approximation techniques. The proposed extended model is especially useful in the important field of decision making, namely the antenna array theory. This is due to the possibility of generating high-order Melnikov polynomials. The work is a natural continuation of the authors’ previous research on the topic of chaos generation via the term ${x\left|x\right|}^{a-1}$. Some specialized modules for investigating the dynamics of the proposed oscillators are provided. Last but not least, the so-defined dynamical model can be of interest for scientists and practitioners in the area of antenna array theory, which is an important part of the decision-making field. The stochastic control of oscillations is also the subject of our consideration. The underlying distributions we use may be symmetric, asymmetric or strongly asymmetric. The same is true for the mass in the tails, too. As a result, the stochastic control of the oscillations we purpose may exhibit a variety of possible behaviors. In the final section, we raise some important issues related to the methodology of teaching Master’s and PhD students. Full article

Background: Current cosmological fits typically assume a direct relation between cosmic time (t) and the scale factor ($a\left(t\right)$), yet this ansatz remains largely untested across diverse observations. Objectives: We (i) test whether a single power-law scaling ($a\left(t\right)\propto t^{\alpha }$) can reproduce late- and early-time cosmological data and (ii) explore whether a dynamically evolving ($\alpha \left(t\right)$), modeled as a scalar–tensor field, naturally induces directional asymmetry in cosmic evolution. Methods: We fit a constant-$\alpha$ model to four independent datasets: 1701 Pantheon+SH0ES supernovae, 162 gamma-ray bursts, 32 cosmic chronometers, and the Planck 2018 TT spectrum (2507 points). The CMB angular spectrum is mapped onto a logarithmic distance-like scale ($\mu ={log}_{10}{D}_{\ell }$), allowing for unified likelihood analysis. Each dataset yields slightly different preferred values for $H_0$ and $\alpha$; therefore, we also perform a global combined fit. For scalar–tensor dynamics, we integrate $\alpha \left(t\right)$ under three potentials—quadratic, cosine, and parity breaking (${\alpha }^{3}sin\alpha$)—and quantify directionality via forward/backward evolution and Lyapunov exponents. Results: (1) The constant-$\alpha$ model achieves good fits across all datasets. In combined analysis, it yields $H_0\simeq 70\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$ and $\alpha \simeq 1.06$, outperforming $\Lambda$CDM globally ($\Delta \mathrm{AIC}\simeq 401254$), though $\Lambda$CDM remains favored for some low-redshift chronometer data. High-redshift GRB and CMB data drive the improved fit. Numerical likelihood evaluations are approximately three times faster than for $\Lambda$CDM. (2) Dynamical $\alpha \left(t\right)$ models exhibit time-directional behavior: under asymmetric potentials, forward evolution displays finite Lyapunov exponents (${\lambda }_L\sim {10}^{-3}$), while backward trajectories remain confined (${\lambda }_L<0$), realizing classical arrow-of-time emergence without entropy or quantum input. Limitations: This study addresses only homogeneous background evolution; perturbations and physical derivations of potentials remain open questions. Conclusions: The time-scaling approach offers a computationally efficient control scenario in cosmological model testing. Scalar–tensor extensions naturally introduce classical time asymmetry that is numerically accessible and observationally testable within current datasets. Code and full data are available. Full article

During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet’s Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article

The growth of Internet of Things (IoT) systems has brought serious security concerns, especially in asymmetrical environments where device capabilities and communication flows vary widely. Many machine-learning-based intrusion detection systems struggle to address noise, uncertainty, and class imbalance. For that reason, intensive data preprocessing procedures were required. These challenges are in real-world data. In this work, we introduce a semi-supervised learning approach that uses entropy-based uncertainty filtering to improve intrusion detection in IoT environments. By dynamically identifying uncertain predictions from tree-based classifiers, we retain only high-confidence results during training. Later, confident samples from the uncertain set are used to retrain the model through a self-training loop. We evaluate this method using three diverse and benchmark datasets named RT-IoT2022, CICIoT2023, and CICIoMT2024, which include up to 34 different attack types. The experimental results reveal that XGBoost and Random Forest outperformed other tree-based models while maintaining their robustness when predicting attacks in the IoT environment. In addition, our proposed model was compared with other models proposed by researchers in the field, and the findings confirmed that our model presented promising results. Full article

This paper introduces a flexible distribution called the asymmetric double normal distribution, specifically designed to model univariate data exhibiting asymmetry and either unimodal or bimodal characteristics. This distribution is highly flexible, capable of capturing a wide range of data behaviors, from smooth densities to those with thinner tails. It generalizes the skew-normal distribution as a special case and provides a simpler alternative to mixture models by avoiding issues related to parameter identifiability. This study explores the structural and theoretical properties of the asymmetric double normal distribution, and parameter estimation is carried out using the maximum likelihood method. Simulation experiments assess the performance of the estimators, while applications in regression and real-life data fitting illustrate the practical relevance of this model. This proposed distribution proves to be a powerful tool for modeling asymmetric and bimodal data, offering significant advantages for statistical analysis in diverse applications. Full article

The Jumonji C (JMJ-C) domain-containing gene family regulates epigenetic and developmental processes in plants. We identified 55 JMJ-C genes in Populus alba × Populus glandulosa using HMM and BLASTp analyses. Chromosomal mapping revealed an asymmetric distribution with conserved synteny. Phylogenetic reconstruction revealed that PagJMJ genes segregate into five evolutionarily conserved subfamilies, exhibiting classification patterns identical to those of Arabidopsis thaliana and Populus trichocarpa. Synteny analysis indicated a closer relationship with P. trichocarpa than with A. thaliana. Motif and promoter analyses highlighted subfamily-specific features and diverse cis-elements, particularly light-responsive motifs. Expression profiling revealed tissue-specific patterns, with key genes enriched in roots, vascular tissues, and leaves. Developmental analysis in cambium and xylem identified four expression clusters related to wood formation. Co-expression analysis identified six key PagJMJ genes (PagJMJ6, 29, 34, 39, 53, and 55) strongly associated with wood formation-related transcription factors. ChIP-qPCR analysis revealed that key genes co-expressed with PagJMJ genes were marked by H3K4me3 and H3K9me2 modifications. These findings provide insights into the evolutionary and functional roles of PagJMJ genes in poplar vascular development and wood formation. Full article

Chiral ansa-η⁵-complexes of transition metals have shown remarkable efficacy in organometallic synthesis and catalysis. Additionally, enantiomerically pure ansa-complexes hold promise for the development of novel chiral materials and pharmaceuticals. The discovery and synthesis of a diverse range of group IVB and IIIB metal complexes represents a significant milestone in the advancement of stereoselective catalytic methods for constructing metal-C, C-C, C-H, and C-heteroatom bonds. The synthesis of enantiomerically pure metallocenes can be accomplished through several strategies: utilizing optically active precursors of η⁵-ligands, separation of diastereomers of complexes with enantiomerically pure agents, and synthesis via the stereocontrolled reactions of enantiomerically pure σ-complexes with prochiral anions of η⁵-ligands. This review focuses on the analysis of various nuances of the synthesis of enantiomerically pure ansa-η⁵-complexes of titanium and lanthanum families. Their applicability as effective catalysts in asymmetric carbomagnesiation, carbo- and cycloalumination, oligo- and polymerization, Diels–Alder cycloaddition, reactions of zirconaaziridines, cyclization, hydrosilylation, hydrogenation, hydroamination, and other processes are highlighted as well. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article