Search Results (474)

In precision manufacturing, selecting the most economically viable process is essential for low-volume, high-complexity applications. This study compares the machining process (MP), conventional investment casting (CIC), and rapid prototyping (RP) through a mathematical cost model based on the activity-based costing (ABC) approach. The model captures detailed cost drivers across design, logistics, production, and environmental dimensions. Results show that MP incurs the highest production cost (94.45%) but minimal logistics (3.43%). CIC bears the highest total cost and significant production overhead (93.2%), while RIC achieves the lowest total cost, driven by major savings in production (84.6%) and labor. Although RIC has slightly higher logistics than MP, it demonstrates superior economic efficiency for small-batch, high-accuracy production. This study provides a unified quantitative framework for cost comparison and offers valuable guidance for manufacturers aiming to enhance efficiency, sustainability, and profitability across diverse fabrication strategies. Full article

Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts. Full article

Background/Objectives: Knee osteoarthritis (KOA) is a prevalent disorder affecting both older adults and younger individuals, leading to compromised joint function and mobility. Early and accurate detection is critical for effective intervention, as treatment options become increasingly limited as the disease progresses. Traditional diagnostic methods rely heavily on the expertise of physicians and are susceptible to errors. The demand for utilizing deep learning models in order to automate and improve the accuracy of KOA image classification has been increasing. In this research, a unique deep learning model is presented that employs autoencoders as the primary mechanism for feature extraction, providing a robust solution for KOA classification. Methods: The proposed model differentiates between KOA-positive and KOA-negative images and categorizes the disease into its primary severity levels. Levels of severity range from “healthy knees” (0) to “severe KOA” (4). Symptoms range from typical joint structures to significant joint damage, such as bone spur growth, joint space narrowing, and bone deformation. Two experiments were conducted using different datasets to validate the efficacy of the proposed model. Results: The first experiment used the autoencoder for feature extraction and classification, which reported an accuracy of 96.68%. Another experiment using autoencoders for feature extraction and Extreme Learning Machines for actual classification resulted in an even higher accuracy value of 98.6%. To test the generalizability of the Knee-DNS system, we utilized the Butterfly iQ+ IoT device for image acquisition and Google Colab’s cloud computing services for data processing. Conclusions: This work represents a pioneering application of autoencoder-based deep learning models in the domain of KOA classification, achieving remarkable accuracy and robustness. Full article

Postharvest spoilage of horticultural produce is a significant challenge, contributing to substantial food waste and economic losses. Traditional preservation methods, such as chemical preservatives and fungicides, are increasingly being replaced by sustainable, chemical-free alternatives. Microbial interventions using beneficial bacteria, fungi, and yeasts have emerged as effective solutions to enhance the postharvest quality and extend shelf life. Advancements in omics technologies, such as metabolomics, transcriptomics, and microbiomics, have provided deeper insights into plant–microbe interactions, facilitating more targeted and effective microbial treatments. The integration of artificial intelligence (AI) and machine learning further supports the selection of optimal microbial strains tailored to specific crops and storage conditions, further enhancing the treatment efficacy. Additionally, the integration of smart cold storage systems and real-time microbial monitoring through sensor technologies offers innovative approaches to optimize microbial interventions during storage and transport. This review examines the mechanisms through which microbes enhance the postharvest quality, the role of omics technologies in improving microbial treatments, and the challenges associated with variability and regulatory approval. Amid growing consumer demand for organic and sustainable solutions, microbial-based postharvest preservation offers a promising, eco-friendly alternative to conventional chemical treatments, ensuring safer, longer-lasting produce while reducing food waste and environmental impact. Full article

Plant microbiomes, composed of a diverse array of microorganisms such as bacteria, fungi, archaea, and microalgae, are critical to plant health and resilience, playing key roles in nutrient cycling, stress mitigation, and disease resistance. Climate change is expected to intensify various abiotic stressors, such as drought, salinity, temperature extremes, nutrient deficiencies, and heavy metal toxicity. Plant-associated microbiomes have emerged as a promising natural solution to help mitigate these stresses and enhance agricultural resilience. However, translating laboratory findings into real-world agricultural benefits remains a significant challenge due to the complexity of plant–microbe interactions under field conditions. We explore the roles of plant microbiomes in combating abiotic stress and discuss advances in microbiome engineering strategies, including synthetic biology, microbial consortia design, metagenomics, and CRISPR-Cas, with a focus on enhancing their practical application in agriculture. Integrating microbiome-based solutions into climate-smart agricultural practices may contribute to long-term sustainability. Finally, we underscore the importance of interdisciplinary collaboration in overcoming existing challenges. Microbiome-based solutions hold promise for improving global food security and promoting sustainable agricultural practices in the face of climate change. Full article

Background: Intravenous magnesium sulfate (IV MgSO₄) may serve as an effective adjunct therapy to improve clinical outcomes in patients experiencing acute exacerbations of chronic obstructive pulmonary disease (AECOPDs). Methods: A comprehensive search was conducted on PubMed, Cochrane, and EMBASE from inception to April 2025 to find eligible studies comparing IV MgSO₄ plus standard treatment versus standard treatment alone. A random-effects meta-analysis was conducted using RevMan. Results: Nine studies (seven RCTs and two observational studies) met the inclusion criteria. Pooled analysis demonstrated that adjunctive IV MgSO₄ significantly improved peak expiratory flow rate at 45 min (MD = 18.50, 95% CI = 6.36 to 30.65) and significantly reduced hospital admission rates from the emergency department (OR = 0.45, 95% CI = 0.23 to 0.88). No significant differences were observed in the length of hospital stay (MD = −0.83, 95% CI = −2.99 to 1.33) and adverse events (OR = 0.79, 95% CI = 0.20 to 3.13; p = 0.73, I² = 25%) between the two groups. Conclusions: Adjunct MgSO₄ in AECOPD is associated with significant improvement in peak expiratory flow rate at 45 min and reduced hospitalization rates. Additional large-scale, multicenter randomized controlled trials are needed to validate and strengthen these findings. Full article

Forests harbor most of the world’s terrestrial biodiversity; however, traditional conservation frameworks prioritize species richness over evolutionary diversity. Phylogenetic diversity (PD) reflects the complete evolutionary history contained within a community, offering a more comprehensive understanding of biodiversity. This review examines the theoretical foundations of PD, highlights methodological advancements in its assessment, and discusses its conservation applications in forest ecosystems. We discuss key metrics, including Faith’s PD, mean pairwise distance (MPD), mean nearest taxon distance (MNTD), and indices, including the net relatedness index (NRI) and nearest taxon index (NTI), as well as analytical tools (Picante, Phylocom, Biodiverse) and frameworks like the categorical analysis of neo- and paleo-endemism (CANAPE) and the evolutionarily distinct and globally endangered (EDGE) index, evaluating their effectiveness in identifying evolutionarily significant conservation areas. We examine global and regional forest PD patterns, including elevational and latitudinal gradients, using case studies from the Pan-Himalayan region, Tibetan Plateau, and northern Pakistan, along with the environmental and anthropogenic drivers, e.g., soil pH, precipitation, land-use change, and invasive species, and historical biogeographic forces that shape lineage diversification. We emphasize the need for data standardization, regional research expansion, and the inclusion of PD in national biodiversity strategies and global policy frameworks. This review highlights the transformative potential of shifting from species-centric to evolutionarily informed conservation, and provides a critical framework for enhancing the long-term resilience and adaptive capacity of forest ecosystems. Full article

Chemotaxonomic profiling based on secondary metabolites offers a reliable approach for identifying and authenticating medicinal plants, addressing limitations associated with traditional morphological and genetic methods. Recent advances in microfluidics and nanoengineered technologies—including lab-on-a-chip systems as well as nano-enabled optical and electrochemical sensors—enable the rapid, accurate, and portable detection of key metabolites, such as alkaloids, flavonoids, terpenoids, and phenolics. Integrating artificial intelligence and machine learning techniques further enhances the analytical capabilities of these technologies, enabling automated, precise plant identification in field-based applications. Therefore, this review aims to highlight the potential applications of micro- and nanoengineered devices in herbal medicine markets, medicinal plant authentication, and biodiversity conservation. We discuss strategies to address current challenges, such as biocompatibility and material toxicity, technical limitations in device miniaturization, and regulatory and standardization requirements. Furthermore, we outline future trends and innovations necessary to fully realize the transformative potential of these technologies in real-world chemotaxonomic applications. Full article

Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model’s decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings. Full article

This study was conducted to investigate the effects of dietary addition of blueberry (Vaccinium corymbosum) powder on the growth performance, meat quality, oxidative stability and cold storage quality of Japanese quails (Coturnix coturnix japonica). In this research, 480 quail chicks were divided into four experimental groups, and each experimental group was composed of four replicates, each containing 30 quail chicks. Commercial feed was used in the study, but BBP was added to the feed at levels of 0%, 1%, 2% and 4%. Results showed that dietary addition of blueberry powder did not affect body weight gain, feed consumption and feed conversion ratio (p > 0.05). No significant difference was observed between hot and cold carcass weights and carcass yield among carcass parameters (p > 0.05). However, significant differences were found among the blueberry-supplemented groups in terms of thigh, back and neck ratios (p < 0.05). In this study, it was determined that thiobarbituric acid (TBA), pH and peroxide values in breast meat samples kept at +4 °C for 1, 3, 5 and 7 days were lower in the blueberry-supplemented groups compared to the control group and these values decreased linearly as the supplement level increased (p < 0.05). The addition of blueberries to the quail diets resulted in similar L, a and b values in breast and thigh meat and skin among the groups (p > 0.05) except for the b value in thigh meat (p < 0.05). The findings obtained in this study revealed that although adding blueberries to the quail diet did not have a significant effect on performance, the antioxidant activity and phenolic substance content of the plant had a significant effect on increasing the shelf life of meat. It was concluded that blueberry could be used as a natural additive that may replace synthetic antioxidants. Full article

The structural integrity of next-generation offshore wind turbines is highly sensitive to inflow variability, yet current standards often simplify wind conditions without capturing their combined effects on dynamic loads. To address this, we analyzed the NREL IEA 15 MW offshore wind turbine using 27 simulation cases strategically selected through Latin Hypercube Sampling (LHS) from a design space of over 14 million combinations. Four key environmental variables—Extreme Wind Speed (30–40 m/s), turbulence intensity (12–16%), Shear Exponent (0.1–0.3), and Flow Inclination Angle (−8° to +8°)—were varied to assess their influence on structural response using BLADED simulations. Results showed that the combined structural moment (Mxyz) ranged from 159,502.5 kNm (minimum) to 189,829.2 kNm (maximum), indicating a 19% increase due to inflow conditions. Maximum-moment case exhibited a 2.6× higher drag coefficient, a 13% rise in pitch bearing moment, and dominant frequency content near 0.175 Hz, closely matching the first tower side-side natural mode (0.17593 Hz), confirming potential resonance. These findings highlight the importance of multidimensional inflow modeling for identifying worst-case load scenarios and establishing a foundation for future load prediction models and support structure optimization. Full article

Effective flood management requires a comprehensive understanding of interactions between multiple flooding sources. This study investigates compound flooding in the Brisbane River Estuary (BRE), Australia, using the MIKE 21 hydrodynamic model to assess the combined effects of tidal and riverine processes on flood extent and water levels. Unlike conventional studies that evaluate these factors separately, this research quantifies the impact of boundary condition variations at the Moreton Bay entrance on flood modelling accuracy. The model was calibrated by adjusting Manning’s n, achieving a Nash–Sutcliffe efficiency (E_ns) ranging from 0.84 to 0.95. Validation results show a 90% agreement between the simulated and observed 2011 flood extent. The findings highlight the critical role of tidal boundary conditions, as their exclusion led to a 0.62 m and 0.12 m reduction in flood levels at Jindalee and Brisbane City gauges, respectively. This study provides valuable insights for improving flood risk assessment, model accuracy, and decision-making in estuarine flood management. Full article

Metal–organic frameworks (MOFs) or coordination polymers have gained enormous interest in recent years due to their extraordinary properties, including their high surface area, tunable pore size, and ability to form nanocomposites with various functional materials. MOF materials possess redox-active properties that are beneficial for electrochemical sensing applications. Furthermore, the tunable pore size and high surface area improve the adsorption or immobilization of enzymes, which can enhance the sensitivity and selectivity for specific analytes. Additionally, MOF-derived metal sulfides, phosphides, and nitrides demonstrate superior electrical conductivity and structural stability, ideal for electrochemical sensing. Moreover, the functionalization of MOFs further increases sensitivity by enhancing electrode–analyte interactions. The inclusion of carbon materials within MOFs enhances their electrical conductivity and reduces background current through optimized loading, preventing agglomeration and ensuring uniform distribution. Noble metals immobilized on MOFs offer improved stability and catalytic performance, providing larger surface areas and uniform nanoparticle dispersion. This review focuses on recent developments in MOF-based biosensors specifically for glucose, dopamine, H₂O₂, ascorbic acid, and uric acid sensing. Full article

Climate change and global warming have increased the frequency and intensity of natural hazards such as floods, landslides, and avalanches. These hazards not only have significant individual impacts but are also interconnected, often amplifying their destructive effects. Therefore, it is crucial to manage their consequences and ensure that communities and infrastructure are resilient enough to withstand these challenges. Given the limited research assessing the collective impact of natural hazards, particularly in Pakistan, this study investigates the effects of floods and landslides in the Kohistan District of northern Pakistan, an area which is highly vulnerable to such hazards yet minimally studied. Machine learning techniques, including the Analytical Hierarchy Process (AHP) and weighted overlay, along with geographic information systems (GISs) and remote sensing (RS), were employed to analyze the causative factors of these hazards. The resulting flood risk and landslide risk maps were then superimposed to produce an integrated dual-hazard risk assessment. The research findings serve as a foundation for policy-making, offering strategies to reduce risks for all stakeholders, implement adaptive measures for communities, and ensure that future developments are both resilient and sustainable. Full article