Search Results (32)

Southeast Asia relies heavily on hydropower from dams and reservoir projects, but this dependence comes at the cost of ecological damage and increased vulnerability to extreme events. This dilemma necessitates a choice between continued dam development and adopting alternative renewable options. Concerns over these environmental impacts have already led to halts in dam construction across the region. This study assesses the potential of run-of-river hydropower plants (RHPs) across 199 hydrometric stations in Mainland Southeast Asia (MSEA). The assessment utilizes power duration curves for the historical period and projections from the HBV hydrological model, which is driven by an ensemble of 31 climate models for future scenarios. Energy production was analyzed at four levels (minimum, maximum, balanced, and optimal) for both historical and future periods under varying Shared Socioeconomic Pathways (SSPs). To promote sustainable development, environmental flow constraints and carbon dioxide (CO₂) emissions were evaluated for both historical and projected periods. The results indicate that the aggregate energy production potential during the historical period ranges from 111.15 to 229.62 MW (Malaysia), 582.78 to 3615.36 MW (Myanmar), 555.47 to 3142.46 MW (Thailand), 1067.05 to 6401.25 MW (Laos), 28.07 to 189.77 MW (Vietnam), and 566.13 to 2803.75 MW (Cambodia). The impact of climate change on power production varies significantly across countries, depending on the level and scenarios. At the optimal level, an average production change of −9.2–5.9% is projected for the near future, increasing to 15.3–19% in the far future. Additionally, RHP development in MSEA is estimated to avoid 32.5 Mt of CO₂ emissions at the optimal level. The analysis further shows avoidance change of 8.3–25.3% and −8.6–25.3% under SSP245 and SSP585, respectively. Full article

The problem of the feature extraction and fusion classification of optical and SAR data remains challenging due to the differences in optical and synthetic aperture radar (SAR) imaging mechanisms. To this end, a statistical prediction interval-guided feature fusion network, SPIFFNet, is proposed for optical and SAR image classification. It consists of two modules, the feature propagation module (FPM) and the feature fusion module (FFM). Specifically, FPM imposes restrictions on the scale factor of the batch normalization (BN) layer by means of statistical prediction interval, and features exceeding the scale factor of the interval are considered redundant and are replaced by features from other modalities to improve the classification accuracy and enhance the information interaction. In the feature fusion stage, we combine channel attention (CA), spatial attention (SA), and multiscale squeeze enhanced axial attention (MSEA) to propose FFM to improve and fuse cross-modal features in a multiscale cross-learning manner. To counteract category imbalance, we also implement a weighted cross-entropy loss function. Extensive experiments on three optical–SAR datasets show that SPIFFNet exhibits excellent performance. Full article

The intensification of economic globalization and the growing scarcity of global land resources have magnified the complexity of future land use and land cover (LULC) changes. In Mainland Southeast Asia (MSEA), these transformations are particularly pronounced, yet comprehensive, targeted, and systematic reviews are scant. This research employs bibliometrics and critical literature review methodologies to scrutinize 1956 relevant publications spanning from 1990–2023, revealing key insights into the contributors to land use studies in MSEA, which include not only local researchers from countries like Thailand and Vietnam but also international scholars from the United States, China, Japan, and France. Despite this, the potential for global collaboration has not been fully tapped. This study also notes a significant evolution in data analysis methods, transitioning from reliance on single data sources to employing sophisticated multi-source data fusion, from manual feature extraction to leveraging automated deep learning processes, and from simple temporal change detection to comprehensive time series analysis using tools like Google Earth Engine (GEE). This shift encompasses the progression from small-scale case studies to extensive multi-scale system analyses employing advanced spatial statistical models and integrated technologies. Moreover, the thematic emphasis of research has evolved markedly, transitioning from traditional practices like slash-and-burn agriculture and deforestation logging to the dynamic monitoring of specialized tree species such as rubber plantations and mangroves. Throughout this period, there has been a growing focus on the broad environmental impacts of land cover change, encompassing soil degradation, carbon storage, climate change responses, ecosystem services, and biodiversity. This research not only offers a comprehensive understanding of the LULC research landscape in MSEA but also provides critical scientific references that can inform future policy-making and land management strategies. Full article

Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment. Full article

Developing stable and effective catalysts for the hydrogen evolution reaction (HER) has been a long-standing pursuit. In this work, we propose a series of single-atom catalysts (SACs) by importing transition-metal atoms into the carbon and vanadium vacancies of tetragonal V₂C₂ and V₃C₃ slabs, where the transition-metal atoms refer to Ti, V, Cr, Mn, Fe, Co, Ni, and Cu. By means of first-principles computations, the possibility of applying these SACs in HER catalysis was investigated. All the SACs are conductive, which is favorable to charge transfer during HER. The Gibbs free energy change (ΔG_H*) during hydrogen adsorption was adopted to assess their catalytic ability. For the V₂C₂-based SACs with V, Cr, Mn, Fe, Ni, and Cu located at the carbon vacancy, excellent HER catalytic performance was achieved, with a |ΔG_H*| smaller than 0.2 eV. Among the V₃C₃-based SACs, apart from the SAC with Mn located at the carbon vacancy, all the SACs can act as outstanding HER catalysts. According to the ΔG_H*, these excellent V₂C₂- and V₃C₃-based SACs are comparable to the best-known Pt-based HER catalysts. However, it should be noted that the V₂C₂ and V₃C₃ slabs have not been successfully synthesized in the laboratory, leading to a pure investigation without practical application in this work. Full article

Background/Objectives: Thus far, no studies have examined the relationship between fruit and vegetable (F and V) intake, urinary metabolite quantities, and weight change. Therefore, the aim of the current study was to explore changes in urinary metabolomic profiles during and after a 10-week weight loss intervention where participants were prescribed a high F and V diet (7 servings daily). Methods: Adults with overweight and obesity (n = 34) received medical nutrition therapy counselling to increase their F and V intakes to national targets (7 servings a day). Data collection included weight, dietary intake, and urine samples at baseline at week 2 and week 10. Urinary metabolite profiles were quantified using ¹H NMR spectroscopy. Machine learning statistical approaches were employed to identify novel urine-based metabolite biomarkers associated with high F and V diet patterns at weeks 2 and 10. Metabolic changes appearing in urine in response to diet were quantified using Metabolite Set Enrichment Analysis (MSEA). Results: Energy intake was significantly lower (p = 0.02) at week 10 compared with baseline. Total F and V intake was significantly higher at week 2 and week 10 (p < 0.05). In total, 123 urinary metabolites were quantified. At week 10, 21 metabolites showed significant changes relative to baseline. Of these, 11 metabolites also significantly changed at week 2. These overlapping metabolites were acetic acid, dimethylamine, choline, fumaric acid, glutamic acid, L-tyrosine, histidine, succinic acid, uracil, histamine, and 2-hydroxyglutarate. Ridge Classifier and Linear Discriminant Analysis provided best prediction accuracy values of 0.96 when metabolite level of baseline was compared to week 10. Conclusions: Urinary metabolites quantified represent potential candidate biomarkers of high F and V intake, associated with a reduction in energy intake. Further studies are needed to validate these findings in larger population studies. Full article

Developing highly efficient and cost-competitive electrocatalysts for the hydrogen evolution reaction (HER), which can be applied to hydrogen production by water splitting, is of great significance in the future of the zero-carbon economy. Here, by means of first-principles calculations, we have scrutinized the HER catalytic capacity of single-atom catalysts (SACs) by embedding transition-metal atoms in the C and Mo vacancies of a tetragonal Mo₃C₂ slab, where the transition-metal atoms refer to Ti, V, Cr, Mn, Fe, Co, Ni and Cu. All the Mo₃C₂-based SACs exhibit excellent electrical conductivity, which is favorable to charge transfer during HER. An effective descriptor, Gibbs free energy difference (ΔG_H*) of hydrogen adsorption, is adopted to evaluate catalytic ability. Apart from SACs with Cr, Mn and Fe located at C vacancies, all the other SACs can act as excellent catalysts for HER. Full article

Background: The incidence of many diseases increases with age and leads to multimorbidity, characterized by the presence of multiple diseases in old age. This phenomenon is closely related to systemic metabolic changes; the most suitable way to study it is through metabolomics. The use of accumulated metabolomic data to characterize this phenomenon at the system level may provide additional insight into the nature and strength of aging–disease relationships. Methods: For this purpose, metabolic changes associated with human aging and metabolic alterations under different pathological conditions were compared. To do this, the published results of metabolomic studies on human aging were compared with data on metabolite alterations collected in the human metabolome database through metabolite set enrichment analysis (MSEA) and combinatorial analysis. Results: It was found that human aging and pathological conditions involve the set of the same metabolic pathways with a probability of 99.96%. These data show the high identity of the aging process and the development of diseases at the metabolic level and allow to identify the set of metabolic pathways reflecting age-related changes closely associated with health. Based on these pathways, a metapathway was compiled, changes in which are simultaneously associated with health and age. Conclusions: The knowledge about the strength of the convergence of aging and pathological conditions has been supplemented by the rigor evidence at the metabolome level, which also made it possible to outline the age and health-relevant place in the human metabolism. Full article

Evaluation ecosystem service value (ESV) is critical, as “lucid waters and lush mountains are invaluable assets”. To assess the incremental effects of ecological assets on soil and water conservation in subtropical mountains, we developed a remote-sensing-driven mountainous equivalent factor (RS-MEF) method to estimate the ESV of Changting County, China. This method is a hybrid of a conventional equivalent factor framework and remote sensing techniques for mountains, achieving several advancements, including spatial adjustment using vegetation activity merged with productivity, improved spatial resolution, and the removal of topographic effects. Using the RS-MEF method, we estimated that the ESV of Changting County was approximately CNY 15.80 billion in 2010 and CNY 34.83 billion in 2022. Specifically, the ESV per unit area of the major soil erosion area (MSEA) in the county was less than that of the non-major soil erosion area (n-MSEA); however, the ESV growth rate of the MSEA from 2010 to 2022 was faster than that of the n-MSEA. Therefore, the ESV gap between the two areas was reduced from 28.99% in 2010 to 15.83% in 2022. Topographic gradient analysis illustrates that areas with elevations of 385 to 658 m and steep slopes achieved a high ESV, while high-elevation areas with gentle slopes will be a focus of control in the next phase. Our study demonstrates that significant achievements have been made in ecological restoration from an ESV perspective, with a notable reduction in low-ESV areas in the MSEA; the insights gained into ESV growth and its underlying factors are valuable and instructive for future soil and water conservation efforts. Full article

A new two-dimensional (2D) non-MXene transition metal carbide, Mo₃C₂, was found using the USPEX code. Comprehensive first-principles calculations show that the Mo₃C₂ monolayer exhibits thermal, dynamic, and mechanical stability, which can ensure excellent durability in practical applications. The optimized structures of Li_x@(3×3)-Mo₃C₂ (x = 1–36) and Na_x@(3×3)-Mo₃C₂ (x = 1–32) were identified as prospective anode materials. The metallic Mo₃C₂ sheet exhibits low diffusion barriers of 0.190 eV for Li and 0.118 eV for Na and low average open circuit voltages of 0.31–0.55 V for Li and 0.18–0.48 V for Na. When adsorbing two layers of adatoms, the theoretical energy capacities are 344 and 306 mA h g⁻¹ for Li and Na, respectively, which are comparable to that of commercial graphite. Moreover, the Mo₃C₂ substrate can maintain structural integrity during the lithiation or sodiation process at high temperature. Considering these features, our proposed Mo₃C₂ slab is a potential candidate as an anode material for future Li- and Na-ion batteries. Full article

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network’s advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated. Full article

COVID-19 patients suffer from the detrimental effects of cytokine storm and not much success has been achieved to overcome this issue. We sought to test the ability of selenium to reduce the impact of two important cytokine storm players: IL-6 and TNF-α. The effects of four selenium compounds on the secretion of these cytokines from THP-1 macrophages were evaluated in vitro following an LPS challenge. Also, the potential impact of methylseleninic acid (MSeA) on Nrf2 and IκBα was determined after a short treatment of THP-1 macrophages. MSeA was found to be the most potent selenium form among the four selenium compounds tested that reduced the levels of IL-6 and TNF-α secreted by THP-1 macrophages. In addition, an increase in Nrf2 and decrease in pIκBα in human macrophages was observed following MSeA treatment. Our data indicate that COVID-19 patients might benefit from the addition of MSeA to the standard therapy due to its ability to suppress the key players in the cytokine storm. Full article

“Knowledge transfer” is achieved through sharing or disseminating knowledge, and providing inputs to problem solving; it is commonly associated with attending a series of classroom lectures and maintaining students’ engagement with the taught subject. This paper examines how a specific radio frequency identification (RFID) based information system (IS) solution could be utilized to help monitor and increase engagement of university students during lecturing activities. This IS solution relies on student attendance as the main method to measure their engagement. Initially, the main stakeholders were identified: students, lecturers, administration team and the Student Loans Company (source of funding). A value proposition canvas was subsequently created, and potential system requirements were identified. A design of the proposed RFID based system was created based on these requirements and then compared with a real-life (already existing) system at Henley Business School. By comparing these two systems, the authors determined related benefits/drawbacks of the proposed system in monitoring student engagement. Potential benefits consisted of allowing all parties to easily capture attendance (with very minimal involvement of the university’s staff) and increased efficiency in analyzing student attendance data. Its main limitation was inaccurately capturing the exact time a student leaves a session. Building a working prototype for detailed evaluation and further fine-tuning/improvements must be part of future work. Full article

Past chemopreventive human trials on dietary selenium supplements produced controversial outcomes. They largely employed selenomethionine (SeM)-based diets. SeM was less toxic than selenite or methylseleninic acid (MSeA) to lung cancer cells. We thus investigated the toxic action of these Se agents in two non-small cell lung cancer (NSCLC) cell lines and ex vivo organotypic cultures (OTC) of NSCLC patient lung tissues. Stable isotope-resolved metabolomics (SIRM) using ¹³C₆-glucose and ¹³C_5,¹⁵N₂-glutamine tracers with gene knockdowns were employed to examine metabolic dysregulations associated with cell type- and treatment-dependent phenotypic changes. Inhibition of key anaplerotic processes, pyruvate carboxylation (PyC) and glutaminolysis were elicited by exposure to MSeA and selenite but not by SeM. They were accompanied by distinct anabolic dysregulation and reflected cell type-dependent changes in proliferation/death/cell cycle arrest. NSCLC OTC showed similar responses of PyC and/or glutaminolysis to the three agents, which correlated with tissue damages. Altogether, we found differential perturbations in anaplerosis-fueled anabolic pathways to underlie the distinct anti-cancer actions of the three Se agents, which could also explain the failure of SeM-based chemoprevention trials. Full article

Yams are economic and medicinal crops with a long growth cycle, spanning between 9–11 months due to their prolonged tuber dormancy. Tuber dormancy has constituted a major constraint in yam production and genetic improvement. In this study, we performed non-targeted comparative metabolomic profiling of tubers of two white yam genotypes, (Obiaoturugo and TDr1100873), to identify metabolites and associated pathways that regulate yam tuber dormancy using gas chromatography–mass spectrometry (GC–MS). Yam tubers were sampled between 42 days after physiological maturity (DAPM) till tuber sprouting. The sampling points include 42-DAPM, 56-DAPM, 87DAPM, 101-DAPM, 115-DAPM, and 143-DAPM. A total of 949 metabolites were annotated, 559 in TDr1100873 and 390 in Obiaoturugo. A total of 39 differentially accumulated metabolites (DAMs) were identified across the studied tuber dormancy stages in the two genotypes. A total of 27 DAMs were conserved between the two genotypes, whereas 5 DAMs were unique in the tubers of TDr1100873 and 7 DAMs were in the tubers of Obiaoturugo. The differentially accumulated metabolites (DAMs) spread across 14 major functional chemical groups. Amines and biogenic polyamines, amino acids and derivatives, alcohols, flavonoids, alkaloids, phenols, esters, coumarins, and phytohormone positively regulated yam tuber dormancy induction and maintenance, whereas fatty acids, lipids, nucleotides, carboxylic acids, sugars, terpenoids, benzoquinones, and benzene derivatives positively regulated dormancy breaking and sprouting in tubers of both yam genotypes. Metabolite set enrichment analysis (MSEA) revealed that 12 metabolisms were significantly enriched during yam tuber dormancy stages. Metabolic pathway topology analysis further revealed that six metabolic pathways (linoleic acid metabolic pathway, phenylalanine metabolic pathway, galactose metabolic pathway, starch and sucrose metabolic pathway, alanine-aspartate-glutamine metabolic pathways, and purine metabolic pathway) exerted significant impact on yam tuber dormancy regulation. This result provides vital insights into molecular mechanisms regulating yam tuber dormancy. Full article