Search Results (1,608)

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

Maize–soybean intercropping is widely practised to improve land use efficiency, but shading from maize often limits soybean growth and productivity. Melatonin, a plant signaling molecule with antioxidant and growth-regulating properties, has shown potential in mitigating various abiotic stresses, including low light. This study investigated the efficacy of applying foliar melatonin (MT) to enhance shade tolerance and yield performance of soybean under intercropping. Four melatonin concentrations (0, 50, 100, and 150 µM) were applied to soybean grown under mono- and intercropping systems. The results showed that intercropping significantly reduced growth, photosynthetic activity, and yield-related traits. However, the MT application, particularly at 100 µM (MT₁₀₀), effectively mitigated these declines. MT₁₀₀ improved plant height (by up to 32%), leaf area (8%), internode length (up to 41%), grain yield (32%), and biomass dry matter (30%) compared to untreated intercropped plants. It also enhanced SPAD chlorophyll values, photosynthetic rate, stomatal conductance, chlorophyll fluorescence parameters such as Photosystem II efficiency (ɸPSII), maximum PSII quantum yield (Fv/Fm), photochemical quenching (qp), electron transport rate (ETR), Rubisco activity, and soluble protein content. These findings suggest that foliar application of melatonin, especially at 100 µM, can improve shade resilience in soybean by enhancing physiological and biochemical performance, offering a practical strategy for optimizing productivity in intercropping systems. Full article

In sustainable development studies, a key question is how the abundance of natural resources influences long-run economic growth. However, there is no consensus on this issue. Some literature suggests a negative impact, while other studies find no effect at all, and other research indicates a positive impact. This study aims to examine the relationship between natural resource rents, renewable energy, and economic growth in the Gulf Cooperation Council (GCC) countries over the period from 1990 to 2023. The study utilizes the Method of Moments Quantile Regression (MMQR) to provide reliable findings across different quantiles. We also incorporate a series of control variables, including capital, labor force participation, non-renewable energy, and trade openness. The findings indicate that natural resources rent enhances economic growth in GCC countries, supporting the Rostow hypothesis. Although renewable energy has a positive impact on economic growth, it does not have an effect on natural resource rents. Additionally, capital, labor force participation, non-renewable energy, and trade openness play a critical role in raising economic growth in these countries. Based on the empirical results, this study provides several valuable recommendations for policymakers to enhance the management of natural resources in GCC countries. Full article

Three-dimensional semantic segmentation is a fundamental problem in computer vision with a wide range of applications in autonomous driving, robotics, and urban scene understanding. The task involves assigning semantic labels to each point in a 3D point cloud, a data representation that is inherently unstructured, irregular, and spatially sparse. In recent years, deep learning has become the dominant framework for addressing this task, leading to a broad variety of models and techniques designed to tackle the unique challenges posed by 3D data. This survey presents a comprehensive overview of deep learning methods for 3D semantic segmentation. We organize the literature into a taxonomy that distinguishes between supervised and unsupervised approaches. Supervised methods are further classified into point-based, projection-based, voxel-based, and hybrid architectures, while unsupervised methods include self-supervised learning strategies, generative models, and implicit representation techniques. In addition to presenting and categorizing these approaches, we provide a comparative analysis of their performance on widely used benchmark datasets, discuss key challenges such as generalization, model transferability, and computational efficiency, and examine the limitations of current datasets. The survey concludes by identifying potential directions for future research in this rapidly evolving field. Full article

Background: Fowl typhoid (FT), a septicemic infection caused by Salmonella Gallinarum (SG), and H9N2 avian influenza are two economically important diseases that significantly affect the global poultry industry. Methods: We exploited the live attenuated Salmonella Gallinarum (SG) mutant JOL3062 (SG: ∆lon ∆pagL ∆asd) as a delivery system for H9N2 antigens to induce an immunoprotective response against both H9N2 and FT. To enhance immune protection against H9N2, a prokaryotic and eukaryotic dual expression plasmid, pJHL270, was employed. The hemagglutinin (HA) consensus sequence from South Korean avian influenza A virus (AIV) was cloned under the Ptrc promoter for prokaryotic expression, and the B cell epitope of neuraminidase (NA) linked with matrix protein 2 (M2e) was placed for eukaryotic expression. In vitro and in vivo expressions of the H9N2 antigens were validated by qRT-PCR and Western blot, respectively. Results: Oral immunization with JOL3121 induced a significant increase in SG and H9N2-specific serum IgY and cloacal swab IgA antibodies, confirming humoral and mucosal immune responses. Furthermore, FACS analysis showed increased CD4+ and CD8+ T cell populations. On day 28 post-immunization, there was a substantial rise in the hemagglutination inhibition titer in the immunized birds, demonstrating neutralization capabilities of immunization. Both IFN-γ and IL-4 demonstrated a significant increase, indicating a balance of Th1 and Th2 responses. Intranasal challenge with the H9N2 Y280 strain resulted in minimal to no clinical signs with significantly lower lung viral titer in the JOL3121 group. Upon SG wildtype challenge, the immunized birds in the JOL3121 group yielded 20% mortality, while 80% mortality was recorded in the PBS control group. Additionally, bacterial load in the spleen and liver was significantly lower in the immunized birds. Conclusions: The current vaccine model, designed with a host-specific pathogen, SG, delivers a robust immune boost that could enhance dual protection against FT and H9N2 infection, both being significant diseases in poultry, as well as ensure public health. Full article

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation. Full article

The use of aluminum honeycomb structures is fast expanding in advanced sectors such as the aeronautics, aerospace, marine, and automotive industries. However, processing these structures represents a major challenge for producing parts that meet the strict standards. To address this issue, an innovative manufacturing method using longitudinal ultrasonic vibration-assisted cutting, combined with a CDZ10 hybrid cutting tool, was developed to optimize the efficiency of traditional machining processes. To this end, a 3D numerical model was developed using the finite element method and Abaqus/Explicit 2017 software to simulate the complex interactions among the cutting tool and the thin walls of the structures. This model was validated by experimental tests, allowing the study of the influence of milling conditions such as feed rate, cutting angle, and vibration amplitude. The numerical results revealed that the hybrid technology significantly reduces the cutting force components, with a decrease ranging from 10% to 42%. In addition, it improves cutting quality by reducing plastic deformation and cell wall tearing, which prevents the formation of chips clumps on the tool edges, thus avoiding early wear of the tool. These outcomes offer new insights into optimizing industrial processes, particularly in fields with stringent precision and performance demands, like the aerospace sector. Full article

A graph with $q(a+t)$ vertices is known as a q-broom-like graph $K_q\sqcap B(a;t)$, which is produced by the hierarchical product of the complete graph $K_q$ by the rooted broom $B(a;t)$, where $q\ge 3,a\ge 1$ and $t\ge 1$. A numerical quantity associated with graph structure is called a topological index. The inverse sum indeg index (shortened to $ISI$ index) is a topological index defined as $ISI\left(G\right)={\displaystyle \sum _{v_i{v}_j\in E\left(G\right)}}{\displaystyle \frac{d_{v_i}{d}_{v_j}}{d_{v_i}+d_{v_j}}}$, where $d_{v_i}$ is the degree of the vertex $v_i$. In this paper, we take into consideration the $ISI$ index for q-broom-like graphs and perform a thorough analysis of it. We find the $ISI$ spectrum of q-broom-like graphs and derive the closed formulas for their $ISI$ index and $ISI$ energy. We also characterize extremal graphs and arrange them according to their $ISI$ index and $ISI$ energy, respectively. Further, a quantitative structure–property relationship is used to predict six physicochemical properties of sixteen alkaloid structures using $ISI$ index and $ISI$ energy. Both graph invariants have significant correlation values, indicating the accuracy and utility of the findings. The conclusions made in this article can help chemists and pharmacists research alkaloids’ structures for applications in industry, pharmacy, agriculture, and daily life. Full article

Background: Methotrexate (MTX) remains the most commonly prescribed drug used to treat rheumatoid arthritis (RA). Polymorphisms in solute carrier organic anion transporter family member 1B3 (SLCO1B3) and SLCO1B1 may play a critical role in MTX pharmacokinetics and patient outcomes. However, research on these polymorphisms in Saudi Arabia remains limited. We evaluated the association of SLCO1B3 (rs4149117, rs7311358) and SLCO1B1 (rs2306283, rs4149056) polymorphisms with MTX efficacy and safety in Saudi patients with RA. Methods: This multicenter, case-control study included patients diagnosed with RA in Jeddah and Taif. Demographic and clinical data were collected and analyzed. Genotyping of SLCO1B3 (rs4149117, rs7311358) and SLCO1B1 (rs2306283, rs4149056) polymorphisms was performed using Sanger sequencing. Statistical analyses, including logistic regression and haplotype analysis, were conducted to evaluate associations between these polymorphisms, MTX efficacy, and toxicity. Results: The study cohort comprised 100 patients with RA, with 46 showing a good response to MTX and 54 showing a poor response. Clinical predictors of MTX response were significantly higher in patients with poor response. Both SLCO1B3 polymorphisms (rs4149117, rs7311358) were significantly associated with anemia. Significant associations were found between SLCO1B1 (rs2306283) and gastrointestinal disturbances and anemia. The GAAT haplotype was significantly more prevalent among good responders, while the TGGT haplotype was significantly associated with poor responders. Conclusions: These results highlight the importance of genetic testing in predicting MTX treatment outcomes and tailoring personalized treatment plans for patients with RA to improve efficacy and minimize adverse effects. Full article

Madagascar harbors a rich marine biodiversity, yet detailed knowledge of its fish species remains limited. Of the 1689 species listed in 2018, only 22% had accessible cytochrome oxidase I (COI) sequences in public databases. In response to growing pressure on fishery resources, this study aims to strengthen biodiversity monitoring tools. Its objectives were to enrich the COI database for Malagasy marine fishes, create the first 12S reference library, and evaluate the taxonomic resolution of different 12S metabarcodes for eDNA analysis, namely MiFish, Teleo1, AcMDB, Ac12S, and 12SF1/R1. An integrated approach combining morphological, molecular, and phylogenetic analyses was applied for specimen identification of fish captured using various types of fishing gear in Toliara and Ranobe Bays from 2018 to 2023. The Malagasy COI database now includes 2146 sequences grouped into 502 Barcode Index Numbers (BINs) from 82 families, with 14 BINs newly added to BOLD (The Barcode of Life Data Systems), and 133 cryptic species. The 12S library comprises 524 sequences representing 446 species from 78 families. Together, the genetic datasets cover 514 species from 84 families, with the most diverse being Labridae, Apogonidae, Gobiidae, Pomacentridae, and Carangidae. However, the two markers show variable taxonomic resolution: 67 species belonging to 35 families were represented solely in the COI dataset, while 10 species from nine families were identified exclusively in the 12S dataset. For 319 species with complete 12S gene sequences associated with COI BINs (Barcode Index Numbers), 12S primer sets were used to evaluate the taxonomic resolution of five 12S metabarcodes. The MiFish marker proved to be the most effective, with an optimal similarity threshold of 98.5%. This study represents a major step forward in documenting and monitoring Madagascar’s marine biodiversity and provides a valuable genetic reference for future environmental DNA (eDNA) applications. Full article

Model-Based Systems Engineering (MBSE) has gained significant attention from both industry and academia as an effective approach to managing product complexity. Despite its progress, current MBSE concepts, tools, languages, and methodologies face notable challenges in industrial applications, particularly in addressing design variability, ensuring model consistency, and enhancing operational efficiency. Based on the authors’ industry observations and literature analysis, this paper identifies the primary limitations of traditional MBSE, and introduces MBSE 2.0, a next-generation evolution characterized by comprehensive, integrated, and intelligent features. Key enabling technologies, such as model governance, integrated design methods, and AI-enhanced system design, are explored in detail. Additionally, several preliminary explorations were introduced under the guidance of the MBSE 2.0 philosophy. This study introduces the MBSE 2.0 concept to stimulate discussion and guide future efforts in academia and industry, emphasizing key advancements and highlighting several key and pressing perspectives to alleviate current limitations in industrial practice. Full article

Fiber-reinforced composites contain microscale features such as variations in local fiber volume fraction, fiber clusters, and resin-rich regions, which may impact mechanical properties. Microscale models need to be large enough to capture these features while maintaining high fidelity to capture the localized fiber-to-fiber interactions. This makes it difficult to efficiently model regions with equivalent fiber morphologies to as-manufactured scans and to perform large statistical studies to examine how these features drive mechanical performance. This study uses a novel microstructure generator and an efficient micromechanical model along with a characterization method that measures the geometry of these features to simulate a wide range of microstructures for strength and stiffness. After understanding how the mechanical properties are affected by morphology through correlation matrices, equivalent microstructures were generated to regions of an as-manufactured composite. The generation of microstructures based on different morphological descriptors allows for an understanding of which features are valuable when modeling these materials. In comparing microstructures with different equivalent descriptors to the case with all six descriptors, it was found that only using local fiber volume fraction median resulted in over predictions of strength and stiffness. Once two descriptors or more were introduced, such as local fiber volume fraction median and inter-quartile range, there was no significant difference in strength and stiffness. This suggests that at least two descriptors should be considered when generating equivalent microstructures for mechanical properties. Full article

Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics—recall, area under the curve (AUC), and accuracy—were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 ± 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74–0.82), accuracy = 0.81 (0.69–0.89), and recall = 0.90 (0.70–0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68–0.82), accuracy = 0.73 (0.59–0.84), and recall = 0.87 (0.50–0.98)); valve-related dysfunction (AUC = 0.73 (0.62–0.84), accuracy = 0.79 (0.57–0.91), and recall = 0.54 (0.26–0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67–0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets. Full article

Peripheral arterial disease (PAD) is a macrovascular diabetic complication, characterized by atherosclerotic plaque formation due to hyperglycemia and dyslipidemia. The molecular mechanisms involved in PAD-T2DM pathogenesis will help in understanding and early prognosis; therefore, we aim to evaluate FABP4 levels and Nrf2 single-nucleotide polymorphisms (SNPs) among PAD-T2DM patients. In a case-control study, 123 samples (healthy control HC, T2DM, and PAD-T2DM; n = 41 each) were collected from the diabetic foot clinic at Mayo Hospital, Lahore. Baseline and biochemical data were collected. PAD diagnosis was established by measuring the ankle-brachial index with color Doppler ultrasound. Serum FABP4 levels were measured using an ELISA. Nrf2 SNP rs35652124 analysis was performed by restriction fragment length polymorphism. PAD-T2DM prevalence was higher among male subjects (61.1%). Fasting plasma glucose levels (p = 0.02), total cholesterol (p < 0.0001), and LDL-cholesterol (p = 0.01) were significantly higher in PAD-T2DM as compared to T2DM. SNP association analysis showed that homozygous genotype TT (OR: 3.85, 95% (CI): 1.22–12.11, p = 0.02) and T-allele (OR: 1.31, 95% (CI): 1.31–4.67, p = 0.005) were significantly associated with PAD-T2DM. FABP4 levels were higher in the PAD-T2DM group as compared to T2DM (p < 0.0001) and were significantly associated with Nrf2 SNP genotype TT (p < 0.001) and CT (p = 0.01) in PAD-T2DM. Our results showed, for the first time, that the Nrf2 SNP is significantly associated with PAD-T2DM and FABP4 levels compared to T2DM. Full article

Carbon dioxide (CO₂) geosequestration is a critical technology for reducing greenhouse gas emissions, with shale caprocks, such as Opalinus Clay (OPA), serving as essential seals to prevent CO₂ leakage. This study employs computational fluid dynamics and finite element analysis to investigate the hydromechanical behavior of OPA during CO₂ injection, integrating qualitative and quantitative insights. Validated numerical models indicate that capillary forces are the most critical factor in determining the material’s reaction, with an entry capillary pressure of 2–6 MPa serving as a significant threshold for CO₂ breakthrough. The numbers show that increasing the stress loading from 5 to 30 MPa lowers permeability by 0.3–0.45% for every 5 MPa increase. Porosity, on the other hand, drops by 9.2–9.4% under the same conditions. The OPA is compacted, and axial displacements confirm numerical models with an error margin of less than 10%. Saturation analysis demonstrates that CO₂ penetration becomes stronger at higher injection pressures (8–12 MPa), although capillary barriers slow migration until critical pressures are reached. These results demonstrate how OPA’s geomechanical stability and fluid dynamics interact, indicating that it may be utilized as a caprock for CO₂ storage. The study provides valuable insights for enhancing injection techniques and assessing the safety of long-term storage. Full article