Search Results (1,969)

Artificial intelligence (AI) increasingly permeates organizational work, yet research on AI–human collaboration remains fragmented and lacks a unified structure. This study provides a comprehensive bibliometric mapping of AI–human collaboration by examining its intellectual foundations and emerging research fronts across multiple disciplines. Using document co-citation and bibliographic coupling analysis, the study examines how research on AI–human collaboration has evolved and where it is heading. Data were collected from the Scopus database. A total of 2178 primary documents and 15,078 secondary documents were retrieved and analyzed using VOSviewer (1.6.20) software to visualize the thematic interconnectedness. Results from document co-citation revealed five significant research clusters underlying AI–human collaboration research, including psychological and social foundations of AI; organizational applications of AI in higher education; ethical–cognitive foundations of generative AI; AI literacy and educational transformation; and behavioral foundations of AI adoption. The bibliometric coupling results identified four active research fronts: AI governance, ethics, and humanization; AI–customer relationship management (CRM) adoption, capabilities, and organizational performance; anthropomorphic AI and consumer emotional response; and AI conversational agents and consumer experience dynamics. These findings suggest a thematic shift from technology-centered automation toward collaborative and human-centered integration. The study contributes theoretically by synthesizing insights across organizational behavior, psychology, and information systems to clarify the intellectual structure of this emerging domain. It also outlines implications for leaders designing AI-enabled workplaces that prioritize collaboration, ethical alignment, and adaptive capacity. Full article

Introduction: Obstructive sleep apnea (OSA) is highly prevalent, affecting up to 50% of individuals over 65 years. Elderly patients often present with atypical, fewer and less severe symptoms, suggesting age-specific phenotypes. However, comprehensive clinical phenotyping that incorporates cognitive outcomes remains limited. This study aimed to characterize OSA phenotypes through cluster analysis and evaluate their association with cognitive impairment, independently of age. Materials and Methods: Between 2020 and 2024, 409 adults with moderate-to-severe OSA were enrolled and stratified into three age groups (<65, 65–74, ≥75 years). All underwent home sleep apnea testing (HSAT), comprehensive symptom assessment, Epworth Sleepiness Scale (ESS), and Montreal Cognitive Assessment (MoCA, pathological ≤ 25 pts). Hierarchical cluster analysis (Ward’s method) used AHI, T90, BMI, and ESS. Logistic regression identified independent predictors of cognitive impairment. Results: Older groups showed lower BMI, higher comorbidity burden, fewer symptoms, and greater cognitive impairment prevalence (4.5% vs. 9.7% vs. 45.9%; p < 0.001), despite comparable polysomnographic severity across age groups. Cluster analysis identified three phenotypes: Cluster 1 (classical OSA: high AHI, BMI, T90, ESS); Cluster 2 (geriatric phenotype: low AHI, BMI, T90, ESS, highest cognitive impairment rate: 27.7%); Cluster 3 (hypersymptomatic: low AHI and T90, high sleepiness and asthenia, prevalent depression). On multivariate regression, age (OR 1.155; p < 0.001), male sex (OR 2.223; p = 0.034), and Cluster 2 (OR 3.131; p < 0.001) were independent predictors of cognitive impairment. Conclusions: Three clinically distinct OSA phenotypes were identified regardless of age and severity. The geriatric phenotype was associated with three-fold increased risk of cognitive impairment, supporting routine cognitive screening and age-adapted diagnostic strategies in elderly OSA patients. Full article

To identify suitable silage maize varieties and optimal sowing dates for Aral in southern Xinjiang, 10 silage maize varieties were evaluated under three sowing date treatments (April 22, April 28, and May 6) from 2024 to 2025. Agronomic traits, yield components, and nutritional quality indices were systematically determined. Multivariate statistical methods were employed for comprehensive evaluation. The results indicated that sowing date, variety, and their interaction exerted highly significant effects on most key agronomic traits, yields, and nutritional quality indicators of silage maize (p < 0.01). The sowing date had markedly different regulatory effects on the traits studied. Sowing on April 22 was conducive to improving the yield and fiber quality of silage maize. Sowing on April 28 optimized agronomic traits, including the uppermost ear leaf area and stem diameter. Sowing on May 6 significantly increased the crude protein and starch contents of silage maize. Cluster analyses combined with membership function analysis identified Dajingjiu 26 and Yu Qingzhu 23 as varieties with consistently excellent comprehensive traits and strong adaptability to regional ecological conditions across all sowing dates. These two varieties are recommended for priority deployment in local silage maize production, combined with their corresponding optimal sowing dates, to achieve the simultaneous optimization of yield and quality. The findings provide theoretical support and practical reference for silage maize variety selection and sowing date optimization in similar climate regions. Full article

Lentils constitute a strategically important crop within sustainable agricultural systems, particularly in the context of rising global demand for plant-based protein sources. In Switzerland, approximately 95% of lentil seeds are imported, underscoring the untapped potential for domestic production. This study systematically evaluated the performance of multiple lentil genotypes, alongside optimal seeding densities and growing seasons, through a series of field experiments conducted over five years. In addition, a sensory evaluation was performed on 12 selected genotypes to assess consumer-relevant quality traits. The findings revealed substantial variability in yield among genotypes, ranging from 0.9 to 1.6 t/ha; however, interannual variation exerted a more pronounced influence, with yields fluctuating between 0.1 and 2.0 t/ha. Notably, autumn-sown lentils achieved yields of up to 2.7 t/ha in three out of four growing seasons, even among genotypes lacking full winter-hardiness, indicating significant production potential under appropriate management conditions. Optimal plant densities were identified within the range of 180–240 plants/m². From an economic standpoint, higher seeding densities appear justifiable, as the increased seed costs are offset by corresponding gains in yield. Since intercropping of lentils with oats did not negatively affect grain yield nor the thousand kernel weight, the benefits of this cropping system are highlighted. Sensory analysis demonstrated statistically significant differences in attributes such as mealiness and juiciness, leading to the classification of genotypes into three distinct sensory clusters. Despite these differences, overall sensory variation was relatively limited, suggesting that genotype selection may be guided primarily by agronomic performance, climatic adaptability, and winter-hardiness, as well as by market preferences for seed colour and size. Collectively, these results highlight the potential of autumn sowing as a viable strategy to enhance lentil production and reduce the risk of crop failure in Swiss agricultural systems. Full article

Background: Acetification is a complex process driven by acetic acid bacteria (AAB), in which high ethanol and acidity levels require strong microbial metabolic adaptation. Although the microbiota involved in vinegar production has been described, the functional mechanisms that enable these bacteria to maintain metabolic activity remain poorly understood. In this study, the functional dynamics of AAB during Verdejo vinegar acetification were analyzed using a quantitative metaproteomic approach. Methods: Acetification was performed in submerged culture under semi-continuous conditions, and samples were collected at four stages of the cycle (S1–S4). Results: LC-MS/MS analysis led to the identification of 1626 proteins, of which 1409 were assigned to the Acetobacteraceae family. Komagataeibacter europaeus was the dominant species (73.7%). Hierarchical clustering revealed four protein abundance patterns, and differential analysis identified 350 proteins with increased abundance and 169 with decreased abundance, with the greatest changes observed between S1 and S4. Functional annotation and protein–protein interaction analyses indicated that the main metabolic adaptations involve pathways related to energy metabolism, amino acid biosynthesis, membrane-associated functions, cellular homeostasis, and acid stress response. Conclusions: Overall, the results show that K. europaeus concentrates most of the metabolic activity during acetification and that proteome reorganization reflects key molecular strategies for adaptation and survival under high-acidity conditions. Full article

Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each architectural tradition exhibits substantial intra-class variation. To address this bottleneck, we propose CTSMatch, a label-efficient semi-supervised framework that combines an ImageNet-pretrained EfficientNetV2 backbone with SoftMatch-based adaptive pseudo-label weighting so that ambiguous but informative unlabeled samples can still contribute to training, thereby reducing reliance on costly expert annotation. We also construct SemiCTS, an extension of the original CTS dataset that adds 4360 unlabeled images. Using only 545 labeled samples, CTSMatch achieves 96.93% accuracy on SemiCTS, outperforming the strongest fully supervised baseline (Dense-TL-Aug) by 2.73 percentage points and two standard semi-supervised baselines (FixMatch and FreeMatch) by 3.06 percentage points. Beyond classification, we further analyze the feature space to examine stylistic lineage through intra-style heterogeneity, inter-style transitions, and outlier detection. The results reveal two broad regional groupings, a northern cluster (Jing, Jin, Su) and a southern cluster (Chuan, Min, Wan), connected by gradual transitions rather than rigid boundaries. Approximately 15% of the samples are identified as atypical cases, including 8.7% comprising regional variants and 6.3% comprising hybrid forms. These findings show that CTSMatch provides a practical label-efficient framework for architectural heritage classification while supporting the interpretable analysis of stylistic diversification and convergence in Chinese traditional settlements. Full article

Transcriptome profiling has been widely used to dissect the molecular mechanisms underlying plant responses to environmental stresses, yet the extent to which RNA changes reflect functional protein levels remains unclear. Here, we performed an integrated multi-omics analysis of the transcriptome, proteome, phosphoproteome, and acetylome in rice during a drought–rewatering cycle. We first identified 5449 differentially expressed genes (DEGs) and 525 differentially expressed proteins (DEPs) under drought stress, followed by 4340 DEGs and 328 DEPs upon rewatering, which underpinned an extensive remodeling of photosynthetic and metabolic pathways. Temporal clustering of transcriptomic and proteomic data then delineated five distinct expression patterns for both transcripts and proteins, uncovering transcriptional and translational strategies ranging from rapid reversal to persistent stress adaptation. Despite the observed coherence in some expression clusters, we nonetheless uncovered widespread transcriptome–proteome discordance, with a substantial fraction of gene–protein pairs exhibiting uncorrelated abundance changes. Remarkably, the observed discordance is quantitatively associated with the dynamic nature of post-translational modifications, including phosphorylation and acetylation, which act as key post-transcriptional tuners to independently regulate protein abundance—particularly for components of photosynthesis and energy metabolism—enabling plants to dynamically balance stress tolerance with the maintenance of core physiological functions. Our research delves into the intricate and often distinct regulatory networks that span transcriptional, translational, and post-translational levels, extending beyond a singular transcriptional focus. Full article

This study considers the digital transformation of Kazakhstan’s agro-industrial complex, which has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. This study proposes a hybrid decision-support framework integrating a modified NSGA-III algorithm with machine learning techniques for optimizing digital marketing strategies in the agro-industrial complex of Kazakhstan. The model considers three objectives: maximizing channel efficiency and audience reach while minimizing marketing costs. Experimental results based on a dataset of N = 1200 observations demonstrate that the proposed approach improves the composite performance indicator by 12.4% compared to baseline single-objective optimization methods. Pareto front analysis reveals three distinct clusters of strategies, corresponding to (1) high-impact integrated digital TV strategies, (2) cost-efficient traditional channel strategies, and (3) high-risk high-return allocations. The clustering validity is confirmed by a silhouette score of 0.624, indicating strong separation between strategy groups. The results highlight the practical significance of adaptive budget allocation and demonstrate the effectiveness of combining evolutionary optimization with machine learning for decision support in complex marketing environments. Full article

As digital consumption increasingly unfolds in hybrid phygital environments, algorithmic systems play a growing role in shaping user choices, perceptions of fairness, and sustainability-related behaviour. Prior research has examined sustainable consumption, digital nudging, platform trust, and consumer behaviour in digital settings, but has rarely integrated perceived algorithmic fairness, digital resilience, and algorithmic responsibility perception within a single user-centered framework. Addressing this gap, this study develops and tests a multidimensional model of sustainable platform behavior (SPB). Using a triangulated design that combines bibliometric support analysis, PLS-SEM modelling, multi-group analysis, and cluster-based user segmentation, the study identifies three distinct user types and examines the relationships among the focal constructs. The results show that perceived fairness significantly predicts ARP (β = 0.493, p < 0.001), while both ARP (β = 0.427, p < 0.001) and digital resilience (β = 0.263, p < 0.001) independently contribute to SPB. The findings indicate that sustainable platform behavior is shaped not only by intention, but also by fairness perceptions, adaptive user capacity, and responsibility-based evaluations of platform systems. The study offers a user-centered framework with practical implications for designing more responsible, transparent, and sustainability-oriented digital platforms. Full article

Myrtus communis L. (common myrtle) is an economically valuable Mediterranean shrub with diverse applications in food, pharmaceutical, and ornamental sectors. However, the biochemical diversity of myrtle genotypes from Mediterranean environments remains insufficiently characterized, particularly regarding the relationship between primary and secondary metabolism and stress adaptation. This study investigated the biochemical and aroma profiles of six myrtle genotypes selected from natural populations in Antalya, Turkey, to identify chemotypic diversity and elucidate metabolic diversity observed in Mediterranean genotypes. Volatile compounds were analyzed using HS-SPME/GC-MS, while sugars and organic acids were quantified by HPLC. Multivariate statistical analyses (PCA, hierarchical clustering) were employed to evaluate metabolic relationships and genotype classification. Descriptive analysis suggested three potential chemotypic patterns: (i) 1,8-cineole-type (G34, G36) with G29 showing a transitional profile, (ii) α-Pinene-type (G15, G37), and (iii) Ester-aldehyde type (G9). These groupings are based on single volatile measurements and should be considered preliminary patterns pending validation through replicate analyses. Significant genotypic variation was observed for primary metabolites (sugars and organic acids) (p < 0.001, η² > 0.90), as evaluated by ANOVA with triplicate biological replicates. Volatile compound differences were evaluated as descriptive exploratory patterns only. Hierarchical clustering revealed three metabolic strategies: balanced metabolism integrating diverse volatile and primary metabolite profiles (Cluster 1: G9, G15, G37), terpene-rich volatile defense with enhanced organic acid metabolism (Cluster 2: G29, G36), and specialized 1,8-cineole-dominant biosynthesis (Cluster 3: G34). These findings highlight substantial metabolic diversity and provide a basis for germplasm evaluation and selection and potential applications. Full article

Helminth infections remain a major constraint to livestock productivity, particularly in regions where domestic animals and wildlife share grazing habitats. This study investigated the molecular diversity and transmission dynamics of helminth communities in sheep (Ovis aries) and saiga antelope (Saiga tatarica) in West Kazakhstan. A total of 35 animals (20 sheep and 15 saiga) were examined, and helminths were identified using polymerase chain reaction targeting the ITS1 region of ribosomal DNA for nematodes and the mitochondrial cox1 gene for cestodes. Of the 20 analyzed samples, 80% were successfully identified at the molecular level. Detected species included Haemonchus contortus, Trichuris ovis, Chabertia ovina, Moniezia expansa, and Avitellina centripunctata. Phylogenetic analysis revealed that Chabertia ovina isolates from both hosts clustered within a single monophyletic clade, indicating high genetic similarity and supporting potential cross-species transmission. Mitochondrial markers provided higher resolution for cestode differentiation, whereas ITS1 was effective for nematode identification. The predominance of Chabertia ovina in saiga suggests ecological adaptation and efficient transmission within wild populations. These findings highlight the epidemiological significance of shared grazing ecosystems and underscore the need for integrated parasite control strategies that consider both livestock and wildlife reservoirs. Full article

The security of heterogeneous unmanned systems (HUSs) operating in open environments has become a key concern. Therefore, this paper focuses on the fractional-order adaptive resilient clustering synchronization control for a class of networked HUSs composed of multiple unmanned surface vehicles and unmanned aerial vehicles subject to deception attacks and denial-of-service (DoS) attacks. First, a distributed cluster trajectory generator is designed for each vehicle in a networked HUS to estimate the output trajectory of the leader in their respective clusters in the presence of DoS attacks on the communication layer. Then, by combining backstepping control and fractional calculus, and immersion and invariance (I&I) theory, a fractional-order adaptive synchronization tracking controller is developed to form the desired cluster formation configuration under disturbances and actuator attacks. Among them, the I&I adaptive strategy is designed to estimate the lumped uncertainty caused by attacks and disturbances. Finally, stability analysis and simulation experiments demonstrate the effectiveness of the proposed control scheme. Full article

Recently, a strain of Streptococcus agalactiae serotype Ia sequence type 7 clonal complex 1 (SaIa ST7 CC1) has emerged in Latin American tilapia aquaculture as an international threat. This study evaluated outbreaks of acute streptococcosis occurring between 2021 and 2025 on commercial Nile tilapia (Oreochromis niloticus) farms in six Latin American countries, aiming to integrate molecular, clinical, pathological, and environmental data. In total, 360 moribund or recently dead fish at various production stages (larvae/fry, pre-grow-out, and grow-out) were examined, and 25 S. agalactiae isolates were serotyped and subjected to real-time PCR analysis, multilocus sequence typing (MLST), virulence and antimicrobial resistance gene profiling, and antimicrobial susceptibility testing. All isolates belonged to SaIa and shared the same ST7 CC1 MLST profile, forming a highly homogeneous cluster with reference SaIa ST7 CC1 strains previously isolated from tilapia farms in Asia. These results are consistent with the regional spread of a single clonal line. At the larval and fry stages, SaIa ST7 CC1 was associated with hyperacute septicemia, gastrointestinal hemorrhage, and frequent intestinal intussusception, whereas in pre-grow-out and grow-out fish, neurological signs were more prominent, followed by ocular signs, systemic hemorrhages, and coelomic lesions. Histopathological examination showed profuse colonization of the brain, spleen, liver, and intestine by Gram-positive cocci, accompanied by marked acute circulatory and inflammatory lesions and few chronic granulomatous responses, consistent with a rapidly progressing, highly aggressive infectious process. All outbreaks occurred during extended periods of warm water (>32 °C), with large day–night thermal gradients and reduced dissolved oxygen, suggesting that thermal stress may exacerbate disease expression in affected systems. All SaIa ST7 CC1 strains exhibited phenotypic susceptibility to florfenicol and amoxicillin, whereas 84% (21/25) and 100% (25/25) exhibited intermediate susceptibility to oxytetracycline and enrofloxacin, respectively. In total, 5 of the 21 isolates (23.8%) with intermediate susceptibility to oxytetracycline carried tetracycline resistance genes (tetM, tetO). These findings identify SaIa ST7 CC1 as a clinically significant emerging threat associated with thermally facilitated and geographically expanding streptococcosis in tilapia production in Latin America. Immediate priorities include screening imported broodstock using MLST or whole-genome sequencing (WGS), harmonized regional molecular surveillance, climate-adaptive farm management practices, prudent antimicrobial use, and serotype-matched vaccination and breeding strategies that improve both disease and heat resilience. Full article

Scientifically assessing ecosystem health and optimizing ecological source areas (ESAs) are essential for effective environmental management, particularly in ecologically strategic mountain barrier regions. However, existing studies face challenges in identifying and optimizing ESAs. To address these limitations, this study integrated the contribution–vigor–organization–resilience (CVOR)-based ecosystem health framework, a genetic algorithm (GA), and circuit theory to assess ecosystem health, optimize ESAs, and identify ecological corridors (EC) and restoration priorities in the Daiyun Mountain Rim. The results demonstrate the following: (1) a significant ecosystem health decline from 2012 to 2022, evidenced by a 38.97% to 21.09% reduction in high-priority ecological zones accompanied by increased landscape fragmentation; (2) delineation of 90 GA-optimized ESA and 248 EC (2164.71 km), forming an interconnected ecological network; (3) enhanced connectivity metrics through GA optimization, showing α-index improvements of 0.15–0.23 and β-index gains of 0.05–0.08 compared to the traditional large-patch and morphological spatial pattern analysis (MSPA)-based ESA selection methods; (4) development of a tiered spatial strategy featuring primary/secondary restoration clusters and a “three-belt–one area–multiple clusters” framework for adaptive landscape governance. Although uncertainties remain due to the selected study period, parameter settings, and lack of field-based validation, this framework provides a useful reference for ecological planning, restoration prioritization, and ecosystem management in similar mountainous ecological barrier regions. Full article

Industry 4.0 requires IoT ontologies that are interoperable, scalable, and adaptive in non-stationary industrial environments. This study combines methodological ontology optimization with a hybrid elastic framework for dynamic semantic updates and feedback-driven refinement. The methodological component systematizes literature and industrial practices to identify structural gaps and derive practical requirements. The engineering component integrates truth-table-based data structuring, vector–matrix automata for real-time classification and clustering, and in-memory event processing for low-latency operation. Experimental evaluation across no-drift, abrupt-drift, gradual-drift, and cyclic-drift scenarios shows a trade-off between semantic proximity and operational robustness: the rule-based approach reaches lower semantic distance in drift regimes, while the hybrid approach delivers higher stability and fewer false alarms in cyclic dynamics. All tested configurations preserve sub-millisecond processing latency, supporting edge/fog deployment. The results indicate that combining methodological analysis with elastic architecture is a practical pathway from static to adaptive IoT ontologies and a relevant step toward human-centric Industry 5.0 systems. Full article