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Inland depression irrigation districts in the arid regions of Xinjiang, owing to the absence of natural drainage conditions, exhibit unique groundwater-salt dynamics and face prominent risks of soil salinization, thus necessitating clarification of their water-salt transport mechanisms to ensure sustainable agricultural development. This study takes the Karamay Agricultural Comprehensive Development Zone as the research subject. The study examines the distribution characteristics of soil salinity, groundwater depth, and Total Dissolved Solids (TDS) of groundwater across diverse soil textures, elucidates the correlative relationships between groundwater dynamics and soil salinity, and forecasts the evolutionary trajectory of groundwater levels within the irrigation district. The findings reveal that groundwater depth in silty soil regions (3.24–3.11 m) substantially exceeds that in silty clay regions (2.43–2.61 m), whereas TDS of groundwater demonstrates marginally elevated concentrations in silty clay areas (19.05–16.78 g L⁻¹) compared to silty soil zones (18.18–16.29 g L⁻¹). Soil salinity exhibits pronounced surface accumulation phenomena and considerable inter-annual seasonal variations: manifesting a “spring-peak, summer-trough” pattern in 2023, which inversely transitioned to a “summer-peak, spring-trough” configuration in 2024, with salinity hotspots predominantly concentrated in silty clay distribution zones. A significant sigmoid functional relationship emerges between soil salinity and groundwater depth (R² = 0.73–0.77), establishing critical depth thresholds of 2.44 m for silty soil and 2.72 m for silty clay, beneath which the risk of secondary salinization escalates dramatically. The XGBoost model demonstrates robust predictive capability for groundwater levels (R² = 0.8545, MAE = 0.4428, RMSE = 0.5174), with feature importance analysis identifying agricultural irrigation as the predominant influencing factor. Model projections indicate that mean groundwater depths across the irrigation district will decline to 2.91 m, 2.76 m, 2.62 m, and 2.36 m over the ensuing 1, 3, 5, and 10 years, respectively. Within a decade, 73.33% of silty soil regions and 92.31% of silty clay regions will experience groundwater levels below critical thresholds, subjecting the irrigation district to severe secondary salinization threats. Consequently, comprehensive mitigation strategies encompassing precision irrigation management and enhanced drainage infrastructure are imperative. Full article

Existing 3D point cloud enhancement methods typically rely on artificially designed geometric transformations or local blending strategies, which are prone to introducing illogical deformations, struggle to preserve global structure, and exhibit insufficient adaptability to diverse degradation patterns. To address these limitations, this paper proposes SFD-ADNet—an adaptive deformation framework based on a dual spatial–frequency domain. It achieves 3D point cloud augmentation by explicitly learning deformation parameters rather than applying predefined perturbations. By jointly modeling spatial structural dependencies and spectral features, SFD-ADNet generates augmented samples that are both structurally aware and task-relevant. In the spatial domain, a hierarchical sequence encoder coupled with a bidirectional Mamba-based deformation predictor captures long-range geometric dependencies and local structural variations, enabling adaptive position-aware deformation control. In the frequency domain, a multi-scale dual-channel mechanism based on adaptive Chebyshev polynomials separates low-frequency structural components from high-frequency details, allowing the model to suppress noise-sensitive distortions while preserving the global geometric skeleton. The two deformation predictions dynamically fuse to balance structural fidelity and sample diversity. Extensive experiments conducted on ModelNet40-C and ScanObjectNN-C involved synthetic CAD models and real-world scanned point clouds under diverse perturbation conditions. SFD-ADNet, as a universal augmentation module, reduces the mCE metrics of PointNet++ and different backbone networks by over 20%. Experiments demonstrate that SFD-ADNet achieves state-of-the-art robustness while preserving critical geometric structures. Furthermore, models enhanced by SFD-ADNet demonstrate consistently improved robustness against diverse point cloud attacks, validating the efficacy of adaptive space-frequency deformation in robust point cloud learning. Full article

Constrained by observation conditions and high inter-class similarity, effective feature extraction and classification of synthetic aperture radar (SAR) targets in few-shot scenarios remains a persistent challenge. To address this issue, this article proposes a few-shot fine-grained SAR target classification method based on multi-scale embedding network and meta-contrastive learning (MSMC). Specifically, the MSMC integrates two complementary training pipelines; the first employs metric-based meta-learning to facilitate few-shot classification, while the second adopts an auxiliary training strategy to enhance feature diversity through contrastive learning. Furthermore, a shared multi-scale embedding network (MSEN) is designed to extract discriminative multi-scale features via adaptive candidate region generation and joint multi-scale embedding. The experimental results on the MSTAR dataset demonstrate that the proposed method achieves superior few-shot fine-grained classification performance compared to existing methods. Full article

Next-generation wireless communication 6G systems are expected to operate under diverse channel conditions and structures, requiring flexible and data-driven communication schemes. As traditional techniques face limitations in complex and dynamic environments, trained communication architectures have emerged as promising alternatives. In this paper, we present a thorough study on deep learning trained physical layer components, focusing on autoencoder-based transceivers and neural network modules that enhance the receiver’s intelligence. We further investigate two essential deep learning capabilities for modern receivers—modulation classification using neural architectures and generative data synthesis for channel estimation training. Moreover, the proposed models and simulation framework provide insight into how deep learning can be systematically integrated into the physical layer to improve adaptability, robustness, and efficiency. Full article

Objective: Given the global trend of ecological protection and sustainable development, Global Geoparks have become an essential platform for resource conservation and regional growth. Changzhi City in Shanxi Province, China, is actively applying for Global Geopark status, relying on its rich geoheritage sites, cultural history, and natural landscapes. This paper presents a systematic evaluation of the city’s geodiversity and relic value, analyzes the feasibility of establishing a Global Geopark in Changzhi City, and provides scientific support for Changzhi City’s Global Geopark application. Methods: Geodiversity data were collected by region using a 1:25,000 grid for sampling. Four methods were adopted for evaluation, namely, the Shannon diversity index, Simpson diversity index, entropy weight method (EWM), and Pielou evenness index. Upon comprehensive comparison of the four approaches, the most suitable approach was selected to produce the final results. For the value evaluation of the geoheritage, a combination of the analytic hierarchy process and the entropy weight method was employed. Results: (1) According to the results of all four methods, the geodiversity of Changzhi City is higher in the eastern and western regions and lower in the central area. (2) The geoheritage sites are mainly distributed in the eastern part of the city and have relatively high relic value. (3) Changzhi City contains abundant natural reserves and cultural resources, meeting the fundamental requirements for Global Geopark construction. Specifically, 38 townships across eight counties were identified as potential geopark areas, encompassing 54 geoheritage sites, 76 provincial-level or higher cultural-relic protection sites, and 15 provincial-level or higher natural protected areas, with a total area of 4458.51 km². Conclusions: Our results suggest that the Shannon diversity index is an effective tool for evaluating geodiversity in Changzhi City. Based on the region’s geological and natural conditions, the delineated geopark area is feasible. In summary, our findings provide essential references for the protection and sustainable development of geoheritage sites, geodiversity, and geoparks and offer strong theoretical and data support for Changzhi City’s Global Geopark application. Full article

Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies. Full article

Background/Objectives: Along the Tyrrhenian coast of central Italy, multilayered caves have yielded significant Neanderthal-era human remains. Recent excavations at Guattari Cave uncovered hominin fossils dated to approximately 66–65 ka, revealing a population with notable morpho-anatomical variability exhibiting both plesiomorphic (primitive) and autapomorphic (derived) traits. Methods: Here we present detailed morphometric and comparative analyses of cranial, dental, and postcranial remains, demonstrating affinities with Homo erectus (sensu stricto [s.s.] and lato [s.l.]), Proto-Neanderthals, classical Neanderthals, and Homo sapiens. Results: These findings indicate notable morpho-anatomical variability among the Guattari Cave hominin remains, with affinities to multiple hominin lineages during the Middle and Late Pleistocene. Pleistocene. Conclusions: The Guattari Cave assemblage thus contributes to our understanding of Eurasian hominin diversity and evolutionary dynamics, highlighting the Mediterranean as a region of interest for studying the phyletic continuity and diversity preceding modern humans. Full article

The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature convergence and an imbalance between exploration and exploitation when applied to complex engineering optimization problems. To overcome these limitations, this study proposes a modified Aquila Optimizer (m-AO) incorporating three enhancement strategies: an adaptive chaotic reverse learning mechanism to improve population diversity, an elite alternative pooling strategy to balance global exploration and local exploitation, and a shifted distribution estimation strategy to accelerate convergence toward promising regions of the search space. The performance of the proposed m-AO is evaluated using 23 classical benchmark functions, IEEE CEC 2022 benchmark problems, and a practical plate–fin heat exchanger (PFHE) design optimization problem. Numerical simulations demonstrate that m-AO achieves faster convergence, higher solution accuracy, and improved robustness compared with the original AO and several state-of-the-art metaheuristic algorithms. In the PFHE application, the proposed method yields a significant improvement in thermal performance, accompanied by a reduction in entropy generation and pressure drop under prescribed design constraints. Statistical analyses further confirm the superiority and stability of the proposed approach. These results indicate that the modified Aquila Optimizer is an effective and reliable tool for solving complex thermal system design optimization problems. Full article

This study documents the ethnomedicinal knowledge of Konak (Malatya, Eastern Anatolia, Türkiye), a region with rich plant diversity but no prior comprehensive research. The aim of the study is to systematically document and analyze the ethnomedicinal practices of Konak village, focusing on plant taxa (species, subspecies and varieties) used, preparation methods, and therapeutic applications. Data were collected through semi-structured interviews with 68 local informants. Quantitative analysis was performed using Informant Consensus Factor (F_IC) and Use Value (UV) indices. Plant specimens were collected, identified, and deposited in the herbarium. The study documented 86 plant taxa from 35 families used in 230 therapeutic applications. Lamiaceae, Asteraceae, and Rosaceae were the most represented families. High F_IC values were recorded for colds (F_IC = 0.95), stomach pain (F_IC = 0.92), and inflammation (F_IC = 0.90), indicating strong community consensus. The most frequently cited species were Origanum vulgare subsp. gracile, Mentha spp., and Rosa canina. There are novel or locally specific uses, with 13 taxa having no previously recorded ethnomedicinal applications in the reviewed literature. The findings reveal Konak as a significant repository of ethnomedicinal knowledge. High-F_IC taxa represent prime candidates for phytochemical and pharmacological research to validate traditional uses and support evidence-based phytotherapy. This study enriches regional ethnopharmacological data and highlights candidate taxa for pharmacological validation. Full article

This study investigates how board gender diversity affects firms’ long-term investment behavior in Korea, focusing on capital expenditures and R&D spending from 2011 to 2021. Using firm fixed-effects regressions and robustness tests with alternative measures of gender diversity, the results show that independent female directors are positively associated with long-term investment. However, this effect is significant only in non-Chaebol firms, where board independence is stronger, and gender diversity reflects genuine governance engagement. In Chaebol-affiliated firms, where female directors are often appointed to meet regulatory requirements, the relationship is insignificant, suggesting that diversity driven by formal compliance fails to enhance strategic decision-making. These findings highlight that the effectiveness of gender diversity depends on institutional authenticity rather than numerical representation. The study contributes to the corporate governance literature by showing how ownership structure and board independence condition the real impact of gender-diverse boards and offers policy implications for promoting substantive rather than symbolic diversity reforms. Full article

This study systematically characterized functional compartmentalization along the intestinal tract of New Zealand rabbits by analyzing mucosal tissue and luminal contents from distinct segments, including the duodenum, jejunum, ileum, cecum, and colon, using RNA-seq and 16S rRNA sequencing. Transcriptomic analysis revealed that differentially expressed genes identified between the small and large intestines were mainly enriched in digestion, absorption, and immune functions. Genes associated with the transport of amino acids, sugars, vitamins, and bile salts showed significantly higher expression in the small intestine, whereas genes related to water absorption, short-chain fatty acids (SCFAs), nucleotides, and metal ion transport were preferentially expressed in the large intestine. From an immunological perspective, genes involved in fungal responses were enriched in the small intestine, while bacterial response pathways and pattern recognition receptor (PRR) signaling genes were upregulated in the large intestine. Microbiota analysis demonstrated significantly greater diversity and abundance in the large intestine compared with the small intestine. Specifically, Proteobacteria and Actinobacteria were enriched in the small intestine, whereas Firmicutes, Verrucomicrobia, and Bacteroidetes dominated the large intestine. Correlation analysis further identified significant associations between gut microbiota composition and host genes involved in nutrient digestion and absorption. Together, these findings provide transcriptome-based evidence for regional specialization of nutrient transport, immune responses, and microbial ecology along the rabbit intestine. Full article

The integration of complementary metal–oxide–semiconductor (CMOS) and micro-electromechanical systems (MEMSs) technologies for miniaturized biosensor fabrication enables unprecedented spatiotemporal resolution in monitoring the bioelectrical activity of the nervous system. Wafer-level CMOS technology incurs high costs, but multi-project wafer (MPW) runs mitigate this by allowing multiple users to share a single wafer. Still, monolithic CMOS biosensors require specialized surface materials or device geometries incompatible with standard CMOS processes. Performing MEMS post-processing on the few square millimeters available in MPW dies remains a significant challenge. In this paper, we present a MEMS post-processing workflow tailored for CMOS dies that supports both surface material modification and layout shaping for intracortical biosensing applications. To address lithographic limitations on small substrates, we optimized spray-coating photolithography methods that suppress edge effects and enable reliable patterning and lift-off of diverse materials. We fabricated a needle-like, 512-channel simultaneous neural recording active pixel sensor (SiNAPS) technology based neural probe designed for integration with optical fibers for optogenetic studies. To mitigate photoelectric effects induced by light stimulation, we incorporated a photoelectric shield through simple modifications to the photolithography mask. Optical bench testing demonstrated >96% light-shielding effectiveness at 3 mW of light power applied directly to the probe electrodes. In vivo experiments confirmed the probe’s capability for high-resolution electrophysiological measurements. Full article

Social organizations increasingly rely on Natural Language Processing (NLP) to analyze large-scale textual data for high-stakes decisions, including university admissions, financial aid allocation, and job hiring. Current methods primarily employ topic modeling and sentiment analysis, but they fail to capture the complex ways emotions and cultural contexts shape meaning in text, potentially perpetuating bias and undermining equitable decision-making. To address this gap, we introduce the Behavioral and Emotional Theme Detection (BET) framework, a novel approach that integrates emotional, cultural, and sociological dimensions into topic detection and emotion analysis. By applying BET to English and Hebrew datasets, we showcase its multilingual adaptability and its potential to reveal rich thematic content and emotional resonance in biographical texts. Our results demonstrate that BET not only enhances the granularity and diversity of detected themes but also tracks shifts in emotional framing over time, offering deeper insights into how individuals deploy linguistic resources to position their identities, enabling more equitable assessment practices. Full article

Background: Chronic diabetic wounds, particularly diabetic foot ulcers (DFUs), are prone to recurrent infection and delayed healing, resulting in substantial morbidity, mortality, and economic burden. Multifunctional wound dressings that combine antibacterial, antioxidant, conductive, and self-healing properties may help to address the complex microenvironment of chronic diabetic wounds. Methods: In this study, ε-poly-L-lysine and amino-terminated polyethylene glycol were grafted onto carboxylated single-walled carbon nanotubes (SWCNTs) via amide coupling to obtain ε-PL-CNT-PEG. Aminated chondroitin sulfate (CS-ADH) and a catechol–metal coordination complex of protocatechualdehyde and Fe³⁺ (PA@Fe) were then used to construct a dynamic covalently cross-linked hydrogel network through Schiff-base chemistry. The obtained hydrogels (Gel0–3, Gel4) were characterized for photothermal performance, rheological behavior, microstructure, swelling/degradation, adhesiveness, antioxidant capacity, electrical conductivity, cytocompatibility, hemocompatibility, and antibacterial activity in the presence and absence of near-infrared (NIR, 808 nm) irradiation. Results: ε-PL-CNT-PEG showed good aqueous dispersibility, NIR-induced photothermal conversion, and improved cytocompatibility after surface modification. Incorporation of ε-PL-CNT-PEG into the PA@Fe/CS-ADH network yielded conductive hydrogels with porous microstructures and storage modulus (G′) higher than loss modulus (G′′) over the tested frequency range, indicating stable gel-like behavior. The hydrogels exhibited self-healing under alternating strain and macroscopic rejoining after cutting. Swelling and degradation studies demonstrated pH-dependent degradation, with faster degradation in mildly acidic conditions (pH 5.0), mimicking infected chronic diabetic wounds. The hydrogels adhered to diverse substrates and tolerated joint movements. Gel4 showed notable DPPH• and H₂O₂ scavenging (≈65% and ≈60%, respectively, within several hours). The electrical conductivity was 0.19 ± 0.0X mS/cm for Gel0–3 and 0.21 ± 0.0Y mS/cm for Gel4 (mean ± SD, n = 3), falling within the range reported for human skin. In vitro, NIH3T3 cells maintained >90% viability in the presence of hydrogel extracts, and hemolysis ratios remained below 5%. Hydrogels containing ε-PL-CNT-PEG displayed enhanced antibacterial effects against Escherichia coli and Staphylococcus aureus, and NIR irradiation further reduced bacterial survival, with some formulations achieving near-complete inhibition under low-power (0.2–0.3 W/cm²) 808 nm irradiation. Conclusions: A dynamic, conductive hydrogel based on PA@Fe, CS-ADH, and ε-PL-CNT-PEG was successfully developed. The hydrogel combines photothermal antibacterial activity, antioxidant capacity, electrical conductivity, self-healing behavior, adhesiveness, cytocompatibility, and hemocompatibility. These properties suggest potential for application as a wound dressing for chronic diabetic wounds, including diabetic foot ulcers, although further in vivo studies are required to validate therapeutic efficacy. Full article

Control measures for pine wilt disease (PWD) are widely implemented, yet multi-year field comparisons that track biodiversity trajectories across contrasting management regimes remain limited. We conducted a 3-year (2023–2025) replicated study across nine pine-forest sites in Gyeongsangnam-do, Republic of Korea, comparing three management regimes (Clear-cut, Fumigation/Aerial, Unmanaged) to evaluate regime-associated patterns in ground-active beetle diversity, activity density, and community composition while considering understory vegetation cover. Regime-associated differences were consistent but dynamic: Unmanaged stands generally supported higher richness and Shannon diversity (H′), Clear-cut stands showed the lowest diversity immediately after harvest, and Fumigation/Aerial stands maintained the highest activity density. Assemblage composition separated strongly among regimes within each year, and indicator taxa highlighted regime-associated assemblage states, notably Pheropsophus jessoensis (Fumigation/Aerial), Carabus tuberculosus (Clear-cut), and Blindus strigosus (Unmanaged). Because regimes were assigned at the site level and were partially confounded by geographic region, we interpreted these outcomes as region-structured, regime-associated patterns rather than strictly causal effects. We recommend integrating PWD management with retention forestry (e.g., partial canopy and deadwood retention) and routine biodiversity monitoring to reconcile effective disease suppression with the long-term conservation of forest biodiversity. Full article