Search Results (107)

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

While guide dogs remain the primary aid for visually impaired individuals, robotic guides continue to be an important area of research. This study introduces an indoor guide robot designed to physically assist a blind person by holding their hand with a robotic arm and guiding them to a specified destination. To enable hand-holding, we employed a camera combined with object detection to identify the human hand and a closed-loop control system to manage the robotic arm’s movements. For path planning, we implemented a Dueling Double Deep Q Network (D3QN) enhanced with a genetic algorithm. To address dynamic obstacles, the robot utilizes a depth camera alongside fuzzy logic to control its wheels and navigate around them. A 3D point cloud map is generated to determine the start and end points accurately. The D3QN algorithm, supplemented by variables defined using the genetic algorithm, is then used to plan the robot’s path. As a result, the robot can safely guide blind individuals to their destinations without collisions. Full article

With the rise of smart agriculture and the expansion of pig farming, pig aggressive behavior recognition is crucial for maintaining herd health and improving farming efficiency. The differences in background and light variation in different barns can lead to the missed detection and false detection of pig aggressive behaviors. Therefore, we propose a deep learning-based pig aggressive behavior recognition model, in order to improve the adaptability of the model in complex pig environments. This model, combined with MobileNetV2 and Autoformer, can effectively extract local detail features of pig aggression and temporal correlation information of video frame sequences. Both Convolutional Block Attention Module (CBAM) and Advanced Filtering Feature Fusion Pyramid Network (HS-FPN) are integrated into the lightweight convolutional network MobileNetV2, which can more accurately capture key visual features of pig aggression and enhance the ability to detect small targets. We extract temporal correlation information between consecutive frames by the improved Autoformer. The Gate Attention Unit (GAU) is embedded into the Autoformer encoder in order to focus on important features of pig aggression while reducing computational latency. Experimental validation was implemented on public datasets, and the results showed that the classification recall, precision, accuracy, and F1-score of the model proposed in this paper reach 98.08%, 94.44%, 96.23%, and 96.23%, and the parameter quantity is optimized to 10.41 M. Compared with MobileNetV2-LSTM and MobileNetV2-GRU, the accuracy has been improved by 3.5% and 3.0%, respectively. Therefore, this model achieves a balance between recognition accuracy and computational complexity and is more suitable for automatic pig aggression recognition in practical farming scenarios, providing data support for scientific feeding and management strategies in pig farming. Full article

This study applies path planning and obstacle avoidance to drone control for conducting riverbank inspections. Given that the river’s surrounding environments are often windy and filled with overgrown branches and unknown obstacles, this study improves path planning and obstacle avoidance to enable drones to complete inspection tasks using the planned optimal route. Multiple drones are used for larger-scale areas to reduce time consumption and increase efficiency. Regarding path planning, the A* algorithm is improved using a grid-based approach. For obstacle avoidance, depth cameras are installed on the drones, and the obtained images are processed by reinforcement Q-learning with a genetic algorithm to navigate around obstacles. Since maintaining formation is necessary during task execution, the leader–follower method of formation flight ensures that multiple drones can complete inspection tasks while maintaining formation. Full article

In Mediterranean ecosystems, a thorough understanding of seedling regeneration dynamics as well as a good predictive ability of the process is essential for sustainable forest management. Leveraging the predictive capacity of the multilayer perceptron (MLP) as recognized as artificial intelligence methodology, the authors analyzed a real case study with a dataset encompassing environmental, ecological, and forestry variables. The study focused on the cork oak (Quercus suber, L.) seedling regeneration dynamic, which is a critical process for maintaining ecosystem resilience. A set of 10 MLP with a block from 5 to 50 neurons with hyperbolic tangent (TanH), linear (LIN), and Gaussian (GAUS) activation function were tested and their performance for predictive purposes was compared with traditional quantitative approaches. The MLP configured with 40–50 neurons per activation function (TanH, LIN, GAUS) demonstrated outstanding predictive performance, achieving an area under the curve (AUC) of the receiver operating characteristic and precision-recall scores above 0.80. These models made few prediction errors, effectively explaining the majority of the data variance, as indicated by a high generalized R² and a low mislearning ratio. This approach outperformed traditional statistical models in predicting seedling regeneration. Tree density, stand density index, and acorn number played an important role, influencing the cork oak seedling prediction. In conclusion, the results of this research determined the importance of an AI classification modeling technique in the prediction of cork oak regeneration, providing practical references for future forest management strategy decisions. Full article

Objective: Temporal lobe epilepsy (TLE) constitutes the most common drug-refractory epilepsy syndrome. Tailored approaches are required, as TLE originates from extrahippocampal lesions in about one-quarter of surgical candidates. Despite high success rates in seizure control, concern persists regarding postoperative memory decline after lesionectomy. We investigated the associations between structural connectivity and postoperative memory performance in extrahippocampal TLE surgery. Methods: In total, 55 patients (25 females, 30 males; mean age 29.8 ± 14.5 years; epilepsy duration 7.9 ± 10.5 years, 31 left, 24 right TLE) with extrahippocampal TLE undergoing hippocampal-sparing surgery were evaluated with standardized pre- and postoperative neuropsychological testing. Lesion volumes intersected with Human Connectome Project-derived tractography data were employed to assess the structural connectivity integrity via voxel-based and connectome-informed lesion–symptom mapping to identify cortical and white matter structures associated with cognitive outcomes. Results: Post-surgery, the widespread structural disconnection of several major white matter pathways was found, correlating with verbal memory and delayed recall. Additionally, the structural disconnection of the ipsilateral temporal lobe white matter was further associated with hippocampal atrophy. Conclusions: Our study highlights the role of structural connectivity alterations in postoperative memory decline in extrahippocampal TLE surgery. These findings expand the traditional understanding of hippocampal integrity in memory function towards the importance of broader structural networks. Individualized, connectome-informed surgical approaches might protect neurocognitive function. Full article

As career success increasingly prioritizes personal satisfaction over traditional metrics, authentic leadership has emerged as a key driver of subjective career success. This study examines the mediating roles of psychological safety and mindfulness in this relationship within a sustainable work environment. Drawing on self-determination theory, emotional contagion theory, and conservation of resource theory, a mediation model is proposed. In this model, authentic leadership enhances psychological safety, which fosters mindfulness and ultimately leads to subjective career success. A rival mediation model further positions mindfulness as a predictor of psychological safety, offering a novel perspective on their interplay. A cross-sectional survey of 287 employees from diverse industries tested these models using correlation, multiple regression, and serial mediation analyses. The results confirm that authentic leadership significantly predicts subjective career success through psychological safety and mindfulness. The rival model suggests a bidirectional relationship, where mindfulness also contributes to psychological safety. These findings highlight the importance of fostering psychological safety and mindfulness to promote a sustainable work environment. This study contributes to both theoretical understanding and practical applications of authentic leadership in human resource management. Organizations aiming to enhance employee well-being and career satisfaction should implement leadership development and mindfulness training programs. Aligning these initiatives with the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-being; SDG 8: Decent Work and Economic Growth) can strengthen sustainable business practices. Future research should explore the long-term impact of authentic leadership and mindfulness on career success through longitudinal studies. Full article

In the argillic alteration zone of the SinAbad area of the Urumieh–Dokhtar magmatic belt (Iran), Mg-rich, Fe-poor chlorites, which crystallised at temperatures between 160 °C and 260 °C, were affected by extensive alteration to smectite mixed-layering at the micro- and nano-scales during the retrograde evolution of the hydrothermal system. Chlorites retain their usual optical aspect and properties, except for the index of refraction perpendicular to the (001) layers, which becomes lower than those parallel to the layers, producing an increase in birefringence and change in the optic and elongation signs, in comparison to the ordinary ones for Mg chlorites. Scanning electron microscopy (SEM) maps and compositions, and electron microprobe (EMP) analyses indicate minor but ubiquitous Ca (and K) content. X-ray diffraction (XRD) of chloritic concentrates allowed the identification of chlorite and tosudite. High-resolution transmission electron microscopy (HRTEM) images show major 14 Å (chlorite), with the frequent presence of 24 Å (contracted tosudite) individual layers and small packets up to five layers thick. Lateral change from 14 Å to 24 Å individual layers has been visualised. High-resolution chemical maps obtained in high-angle annular dark-field (HAADF) mode confirm the existence of areas preferentially dominated by chlorite or tosudite. The overall chemical compositions obtained by SEM, EMP, and transmission electron microscopy (TEM) align from the chlorite to the tosudite end-members, whose pure compositions could be determined from extreme analytical electron microscopy (AEM) analyses. The described intergrowths and interlayers, under the optical resolution, could provide a clue to explain changes in the normal optic properties of chlorite, which are mentioned, but not explained, in the literature. Full article

Glyoxalase I (GLYI) is a key enzyme that detoxifies methylglyoxal, a toxic byproduct of glycolysis, and is essential for plant pollination. However, the genome-wide identification and functional analysis of GLYI in Brassica rapa L. (B. rapa) remain limited. This study identified 17 BrGLYI genes (BrGLYI1–BrGLYI17) from the B. rapa genome. The self-compatible line 039-1 and the self-incompatible line GAU-28-5 were used as experimental materials, and Real-Time Quantitative Reverse Transcription PCR (RT-qPCR) was performed to examine the effect of BrGLYI genes on self-compatibility in winter B. rapa. Preliminary results showed that BrGLYI13 exhibited significant tissue specificity, with higher expression in the flowers of 039-1 compared to GAU-28-5. The open reading frame of BrGLYI13 (852 bp) was cloned from both 039-1 and GAU-28-5 cDNA, with no base mutations observed between the two lines. RT-qPCR revealed higher BrGLYI13 expression in the stigma of 039-1 compared to GAU-28-5. Based on the functional conservation and sequence homology, BrGLYI13 is speculated to play a similar role to that of AtGLYI3 in methylglyoxal detoxification and stress response. Furthermore, the knockout of AtGLYI3 resulted in reduced silique lengths and seed numbers. These findings suggest that BrGLYI13 is involved in the self-compatibility response in B. rapa and promotes the silique length and seed number in the Arabidopsis mutant, providing a basis for further research on the mechanisms of self-compatibility in B. rapa. Full article

Single-image super-resolution (SISR) methods based on convolutional neural networks (CNNs) have achieved breakthrough progress in reconstruction quality. However, their high computational costs and model complexity have limited their applications in resource-constrained devices. To address this, we propose the MSWSR (multi-scale wavelet super-resolution) method, a lightweight multi-scale feature selection network that exploits both symmetric and asymmetric feature patterns. MSWSR achieves efficient feature extraction and fusion through modular design. The core modules include a mixed feature module (MFM) and a gated attention unit (GAU). The MFM employs a symmetric multi-branch structure to efficiently integrate multi-scale features and enhance low-frequency information modeling. The GAU combines the spatial attention mechanism with the gating mechanism to further optimize symmetric feature representation capability. Moreover, a lightweight spatial selection module (SSA) adaptively assigns weights to key regions while maintaining structural symmetry in feature space. This significantly improves reconstruction quality in complex scenes. In 4× super-resolution tasks, compared to SPAN, MSWSR improves PSNR by 0.22 dB on Urban100 and 0.26 dB on Manga109 datasets. The model contains only 316K parameters, which is substantially lower than existing approaches. Extensive experiments demonstrate that MSWSR significantly reduces computational overhead while maintaining reconstruction quality, providing an effective solution for resource-constrained applications. Full article

Background: Solid organ transplant (SOT) recipients have an elevated risk of diabetes mellitus (DM). This study investigated the risk of posttransplant DM (PTDM) in a retrospective cohort study. Methods: We analyzed patients aged over 18 years who received an SOT between 2002 and 2013. Each patient was matched with four control individuals by age, sex, insured salary, urbanization level, Charlson’s comorbidity index (CCI), and year of inclusion in the study. After matching, the study comprised 6874 patients who underwent an SOT and 27,496 matched general patients as the comparison. The risk of DM among the SOT recipients was assessed using a Cox proportional hazards model after adjustment for all relevant variables. Results: The SOT cohort had a significantly higher risk of DM than general patients (adjusted hazard ratio [aHR], 1.61; 95% confidence interval [CI], 1.51–1.72). Kidney and liver recipients, respectively, had DM incidence rates 1.57 (95% CI, 1.46–1.70) and 1.73 (95% CI, 1.53–1.94) times that of the general patients. Conclusions: SOT recipients had an elevated risk of DM. Among various organ recipients, liver recipients had the highest PTDM risk. Kidney and liver recipients demonstrated the highest DM risk at 6 months after their SOT. The risk of PTDM following an SOT may result in long-term consequences. Hence, we advise the critical need for proper management to mitigate related complications after transplantation. Full article

Developing more effective gold–support synergy is essential for enhancing the catalytic performance of supported gold nanoparticles (AuNPs) in the gas-phase oxidation of ethanol to acetaldehyde (AC) at lower temperatures. This study demonstrates a significantly improved Au–support synergy achieved by copper doping in LaMO₃ (M = Mn, Fe, Co, Ni) perovskites. Among the various Au/LaMCuO₃ catalysts, Au/LaMnCuO₃ exhibited exceptional catalytic activity, achieving an AC yield of up to 91% and the highest space-time yield of 764 g_AC g_Au⁻¹ h⁻¹ at 225 °C. Notably, this catalyst showed excellent hydrothermal stability, maintaining performance for at least 100 h without significant deactivation when fed with 50% aqueous ethanol. Comprehensive characterization reveals that Cu doping facilitates the formation of surface oxygen vacancies on the Au/LaMCuO₃ catalysts and enhances Au–support interactions. The LaMnCuO₃ perovskite stabilizes the crucial Cu⁺ species, resulting in a stable Au-Mn-Cu synergy within the Au/LaMnCuO₃ catalyst, which facilitates the activation of O₂ and ethanol at lower temperatures. The optimization of the reaction conditions further improves AC productivity. Kinetic studies indicate that the cleavages of both the O-H bond and the α-C-H bond of ethanol are the rate-controlling steps. Full article

Common wheat production is significantly influenced by abiotic stresses. Identifying the genetic loci for seedling root traits and developing the available molecular markers are crucial for breeding high yielding and stable varieties. In this study, five wheat seedling root traits, including root length (RL), root surface area (RA), root volume (RV), number of root tips (RT), and root dry weight (RW), were measured in the Wp-072/Wp-119 recombinant inbred line (RIL) population. Genotyping was conducted for the RIL population and their parents using the wheat 90K single-nucleotide polymorphism (SNP) chip. In total, three quantitative trait loci (QTLs) for RL (QRL.gau-1DS, QRL.gau-1DL and QRL.gau-4AL), two QTLs for RA (QRA.gau-1D and QRA.gau-2DL), one locus for RV (QRV.gau-6AS), two loci for RW (QRW.gau-2DL and QRW.gau-2AS), and two loci for RT (QRT.gau-3AS and QRT.gau-6DL) were identified, with each explaining 4.5–8.4% of the phenotypic variances, respectively. Among these, QRT.gau-3AS, QRL.gau-4AL, and QRV.gau-6AS overlapped with the previous reports, whereas the other seven QTLs were novel. The favorable alleles of QRL.gau-1DS, QRL.gau-1DL, QRL.gau-4AL, QRA.gau-1D, QRW.gau-2AS, QRV.gau-6AS, QRT.gau-3AS, and QRT.gau-6DL were contributed by Wp-072, whereas the other two loci originated from Wp-119. Additionally, five kompetitive allele-specific PCR (KASP) markers, KASP-RL-1DL for RL, KASP-RA-1D and KASP-RA-2DL for RA, KASP-RW-2AS and KASP-RW-2DL for RW, were developed and validated successfully in 149 wheat accessions. Furthermore, seven candidate genes mainly for plant hormones were selected and validated by quantitative real-time PCR (qRT-PCR). This study provides new loci, new candidate genes, available KASP markers, and varieties for optimizing wheat root system architecture. Full article

This research applies a model-based teaching approach aimed at scrutinizing trends in the leisure, tourism, hospitality, recreation, and sport (LTHRS) field by integrating artificial intelligence (AI) along with ChatGPT, project-based learning (PBL), systems thinking, and industrial analysis tools to foster trend recognition skills. The study employs a quasi-experimental design to compare the efficacy of two instructional approaches (exploratory vs. confirmatory) concerning AI literacy and learning outcomes. Notably, the exploratory group exhibits marked improvements in AI knowledge, while the confirmatory group demonstrates enhanced trend recognition ability. Additionally, the research delves into the application effects of AI-based text mining and ChatGPT (AITM) as content analysis tools through four distinct projects (5G’s impact on tourism industries, travel trends caused by metaverse, daylily tour in Huatan Township, and Taiwanese elements in spectator sports), underscoring the substantial efficacy of AITM in capturing diverse themes, albeit with challenges in discerning subtle and subjective labels. These findings highlight the effectiveness of the model-based teaching approach and the multifaceted utility of AI and automated text mining in augmenting trend recognition skills. Full article

Zanthoxylum species have long been utilized in traditional medicine; among their various properties, they provide an analgesic effect. Central to this medicinal application are alkamides, a class of alkaloids characterized by their unsaturated fatty acid chains. These compounds are particularly noted for their distinctive alleviation of tingling and numbing effects, which are beneficial in dental pain management and local anesthesia. This review synthesizes the existing phytochemical research on alkamides derived from 11 Z. species, focusing on their chemical properties, pharmacodynamics and clinical implications. The analysis includes an examination of the structure–activity relationships (SARs), pharmacokinetics and mechanisms by which these compounds modulate sensations such as pungency and numbness, contributing to their analgesic and local anesthetic efficacy. This systemic review identifies significant research gaps, including the need for comprehensive evaluations of alkamide efficacy, detailed explorations of their pharmacological mechanisms and expanded clinical applications. These areas represent key opportunities for future investigations to enhance the understanding and utilization of alkamides in medical treatments. Full article