Search Results (383)

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

Nanomaterials have emerged as a transformative technology in agricultural science, offering innovative solutions to improve plant–microbe interactions and crop productivity. The unique properties, such as high surface area, tunability, and reactivity, of nanomaterials, including nanoparticles, carbon-based materials, and electrospun fibers, render them ideal for applications such as nutrient delivery systems, microbial inoculants, and environmental monitoring. This review explores various types of nanomaterials employed in agriculture, focusing on their role in enhancing microbial colonization and soil health and optimizing plant growth. Key nanofabrication techniques, including top-down and bottom-up manufacturing, electrospinning, and nanoparticle synthesis, are discussed in relation to controlled release systems and microbial inoculants. Additionally, the influence of surface properties such as charge, porosity, and hydrophobicity on microbial adhesion and colonization is examined. Moreover, the potential of nanocoatings and electrospun fibers to enhance seed protection and promote beneficial microbial interactions is investigated. Furthermore, the integration of nanosensors for detecting pH, reactive oxygen species, and metabolites offers real-time insights into the biochemical dynamics of plant–microbe systems, applicable to precision farming. Finally, the environmental and safety considerations regarding the use of nanomaterials, including biodegradability, nanotoxicity, and regulatory concerns, are addressed. This review emphasizes the potential of nanomaterials to revolutionize sustainable agricultural practices by improving crop health, nutrient efficiency, and environmental resilience. Full article

This study analyzes the family of one of the most essential fractional differential equations due to its wide applications in physics and engineering: the multidimensional fractional linear and nonlinear diffusion equations. The Caputo fractional derivative operator is used to treat the time-fractional derivative. To complete the analysis and generate more stable and highly accurate approximations of the proposed models, three extremely effective techniques, known as the direct Tantawy technique, the new iterative transform technique (NITM), and the homotopy perturbation transform method (HPTM), which combine the Elzaki transform (ET) with the new iterative method (NIM), and the homotopy perturbation method (HPM), are employed. These reliable approaches produce more stable and highly accurate analytical approximations in series form, which converge to the exact solutions after a few iterations. As the number of terms/iterations in the problems series solution rises, it is found that the derived approximations are closely related to each problem’s exact solutions. The two- and three-dimensional graphical representations are considered to understand the mechanism and dynamics of the nonlinear phenomena described by the derived approximations. Moreover, both the absolute and residual errors for all generated approximations are estimated to demonstrate the high accuracy of all derived approximations. The obtained results are encouraging and appropriate for investigating diffusion problems. The primary benefit lies in the fact that our proposed plan does not necessitate any presumptions or limitations on variables that might affect the real problems. One of the most essential features of the proposed methods is the low computational cost and fast computations, especially for the Tantawy technique. The findings of the present study will be valuable as a tool for handling fractional partial differential equation solutions. These approaches are essential in solving the problem and moving beyond the restrictions on variables that could make modeling the problem challenging. 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

Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising concerns about security and regulatory compliance; (2) a lack of interpretability, where opaque AI models provide limited transparency, making it difficult for operators to trust and act on failure predictions; and (3) adaptability issues, as many existing solutions struggle to maintain a consistent performance across diverse industrial environments. Addressing these challenges requires a privacy-preserving, interpretable, and adaptive Artificial Intelligence (AI) model that ensures secure, reliable, and transparent PdM while meeting industry standards and regulatory requirements. Methods: Explainable AI (XAI) plays a crucial role in enhancing transparency and trust in PdM models by providing interpretable insights into failure predictions. Meanwhile, Federated Learning (FL) ensures privacy-preserving, decentralized model training, allowing multiple industrial sites to collaborate without sharing sensitive operational data. This proposed research developed a sustainable privacy-preserving Explainable FL (XFL) model that integrates XAI techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) into an FL structure to improve PdM’s security and interpretability capabilities. Results: The proposed XFL model enables industrial operators to interpret, validate, and refine AI-driven maintenance strategies while ensuring data privacy, accuracy, and regulatory compliance. Conclusions: This model significantly improves failure prediction, reduces unplanned downtime, and strengthens trust in AI-driven decision-making. The simulation results confirm its high reliability, achieving 98.15% accuracy with a minimal 1.85% miss rate, demonstrating its effectiveness as a scalable, secure, and interpretable solution for PdM in Industry 4.0. Full article

Accurate evaluation of the prevailing traffic operations plays an important part in developing sustainable transport systems. This research examines the suitability of the level of service (LOS) criteria developed by the Indian and United States (US) Highway Capacity Manuals (HCM) for heterogeneous and undisciplined traffic streams and proposes new criteria using a data-driven approach. Traffic data were collected from a selected major arterial in Karachi, and fundamental diagrams were developed using these data. These fundamental diagrams and field-collected data were analyzed using the K-mean clustering approach to examine the actual traffic states at various LOS bands used in practice. Associating the field-measured volume-to-capacity ratio with the speed bands used for LOS analysis gives insights into actual traffic conditions at various LOS categories. The research shows that the volume-to-capacity ratio corresponding to the speed range for LOS A is about 0.45, which implies that the heterogeneous traffic moves with comparatively higher speeds despite an increase in traffic volume. The criteria for LOS were developed using the K-mean cluster analysis technique. The proposed values of LOS criteria for speed percentages are significantly higher than those reported in both the HCMs. This research highlights the need to develop separate LOS criteria for heterogeneous and undisciplined traffic for all transportation facilities. The development of such new criteria can provide researchers and engineers with a schematic for the effective and realistic evaluation of local traffic regimes. Full article

A Digital twin (DT) enables the acquisition and subsequent analysis of large amounts of process data. Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. However, there is very little understanding of the relative merit of these methods. This paper proposes a DT framework in the context of predictive maintenance in smart manufacturing to compare the prediction efficacy of prevalent ML models. Data-driven models were developed using machine learning algorithms to predict surface roughness and power consumption during a CNC turning operation. Three process parameters, namely cutting velocity, feed rate, and depth of cut, and two dependent parameters, surface roughness and power consumption, were selected for model development. Seven ML algorithms were tested for each response parameter: Linear Regression, XGB Regressor, Random Forest Regressor, Average Ensemble, AdaBoost Regressor, SVR, and MLP. The results of the comparative analysis of the ML algorithms showed that the Random Forest Regressor is the best prediction model for surface roughness, with the highest R² (94.2% ± 2.4%), lowest MAE (0.011 ± 0.002), lowest MAPE (15.6% ± 4.0%), and lowest RMSE (0.017 ± 0.003), while the XGB Regressor demonstrated the best performance for power consumption prediction, with the highest R² (98.9% ± 0.5%), lowest MAE (22.513 ± 4.424), lowest MAPE (3.0% ± 0.5%), and lowest RMSE (42.650 ± 8.933). The best-performing machine learning algorithm was subsequently utilized in the data-driven models, helping to achieve an improved surface finish. This enables predictive maintenance, reducing energy usage for more sustainable production. Full article

Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water depth (SD) maps are generated by tracing snow occurrence from MODIS-based images of the snow-cover area, evaluating the heating degree days (HDDs) from MODIS-derived images of the land surface temperature, computing the solar radiation, and then assimilating all the previous data in the form of the snowmelt model and ground measurements of the snowmelt water release (SWE). The results show that the average snow-cover area in the Astore river basin, in the upper indus basin, ranges from 94% in winter to 20% in summer. The maps reveal that the annual average values of the SWE range from 150 mm to 535 mm, and the SD values range from 600 mm to 2135 mm, for the snowmelt period (April–September) over the years 2010–2020. The areas linked with vegetation experience low SWE accumulation because of the low slopes in the elevated regions. The meteorological parameters and basin characteristics affect the SWE and can determine the SD values. Full article

Energy and environmental challenges are driving researchers to explore cost-effective and eco-friendly nanomaterial fabrication methods. In this study, Atmospheric Pressure Microplasma (AMP) was used to synthesize iron oxide nanoparticles at varying molar concentrations of ferrous sulfate (0.5 M, 1 M, and 1.5 M) under a 15 kV discharge voltage for 90 min. The X-ray diffraction (XRD) results confirmed the formation of mixed cubic and hexagonal phases of magnetite and hematite nanoparticles. The particle size, calculated using the Debye–Scherrer formula, ranged from 9 to 11 nm, depending on the precursor concentration. Scanning electron microscopy (SEM) images revealed spherical nanoparticles at 0.5 M, while agglomeration occurred at 1.5 M. The energy-dispersive X-ray spectroscopy (EDS) analysis confirmed the presence of iron and oxygen in the synthesized nanoparticles. Fourier-transform infrared (FTIR) and UV spectroscopy showed characteristic absorption bands of iron oxide. The impact of the particle size and lattice strain on the optical properties of the nanoparticles was also studied. Smaller nanoparticles exhibited an excellent specific capacitance (627) and a strong charge–discharge performance in a 3 M KOH solution, with a high energy density (67.72) and power density (2227). As photocatalysts, the nanoparticles demonstrated a 97.5% and 96.8% degradation efficiency against methylene blue (MB) and methyl orange (MO), respectively, with high rate constants. These results surpass previous reports. The enhanced electrochemical performance and photocatalytic activity are attributed to the high-quality iron oxide nanoparticles, showing an excellent cyclic stability, making them promising for supercapacitors and environmental remediation. Full article

The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties. Financial stability, customer trust, and compliance are increasingly threatened as centralized Artificial Intelligence (AI) models fail to adapt, leading to inefficiencies, false positives, and undetected detection. These limitations necessitate advanced AI solutions for banks to adapt properly to emerging fraud patterns. While AI enhances fraud detection, its black-box nature limits transparency, making it difficult for analysts to trust, validate, and refine decisions, posing challenges for compliance, fraud explanation, and adversarial defense. Effective fraud detection requires models that balance high accuracy and adaptability to emerging fraud patterns. Federated Learning (FL) enables distributed training for fraud detection while preserving data privacy and ensuring legal compliance. However, traditional FL approaches operate as black-box systems, limiting the analysts to trust, verify, or even improve the decisions made by AI in fraud detection. Explainable AI (XAI) enhances fraud analysis by improving interpretability, fostering trust, refining classifications, and ensuring compliance. The integration of XAI and FL forms a privacy-preserving and explainable model that enhances security and decision-making. This research proposes an Explainable FL (XFL) model for financial fraud detection, addressing both FL’s security and XAI’s interpretability. With the help of Shapley Additive Explanations (SHAP) and LIME, analysts can explain and improve fraud classification while maintaining privacy, accuracy, and compliance. The proposed model is trained on a financial fraud detection dataset, and the results highlight the efficiency of detection and successful elimination of false positives and contribute to the improvement of the existing models as the proposed model attained 99.95% accuracy and a miss rate of 0.05%, paving the way for a more effective and comprehensive AI-based system to detect potential fraudulence in banking. Full article

The development of numerical or analytical solutions for fractional mathematical models describing specific phenomena is an important subject in physics, mathematics, and engineering. This paper’s main objective is to investigate the approximation of the fractional order Caudrey–Dodd–Gibbon ($CDG$) nonlinear equation, which appears in the fields of laser optics and plasma physics. The physical issue is modeled using the Caputo derivative. Adomian and homotopy polynomials facilitate the handling of the nonlinear term. The main innovation in this paper is how the recurrence relation, which generates the series solutions after just a few iterations, is handled. We examined the assumed model in fractional form in order to demonstrate and verify the efficacy of the new methods. Moreover, the numerical simulation is used to show how the physical behavior of the suggested method’s solution has been represented in plots and tables for various fractional orders. We provide three problems of each equation to check the validity of the offered schemes. It is discovered that the outcomes derived are close to the accurate result of the problems illustrated. Additionally, we compare our results with the Laplace residual power series method (LRPSM), the natural transform decomposition method (NTDM), and the homotopy analysis shehu transform method (HASTM). From the comparison, our methods have been demonstrated to be more accurate than alternative approaches. The results demonstrate the significant benefit of the established methodologies in achieving both approximate and accurate solutions to the problems. The results show that the technique is extremely methodical, accurate, and very effective for examining the nature of nonlinear differential equations of arbitrary order that have arisen in related scientific fields. Full article

Background/Objectives: Pediatric migraine is a prevalent neurological disorder that significantly impacts children’s quality of life, academic performance, and social interactions. Unlike migraines in adults, pediatric migraines often present differently and involve unique underlying mechanisms, making diagnosis and treatment more complex. Methods: This review discusses the clinical phases of pediatric migraine, key trigger factors, sex- and age-related differences, and the role of childhood maltreatment in migraine development. We also discuss episodic syndromes such as cyclic vomiting syndrome, abdominal migraine, benign paroxysmal vertigo, and benign paroxysmal torticollis, along with comorbidities such as psychiatric disorders, sleep disturbances, and epilepsy. Results: The underlying pathophysiological mechanisms for pediatric migraines, including genetic predispositions, neuroinflammation, and gut microbiota dysbiosis, are summarized. Current therapeutic strategies, including conventional and emerging pharmacological treatments, nutraceuticals, and non-pharmacological approaches, are evaluated. Non-pharmacological strategies, particularly evidence-based lifestyle interventions such as stress management, diet, hydration, sleep, exercise, screen time moderation, and cognitive behavioral therapy, are highlighted as key components of migraine prevention and management. The long-term prognosis and follow-up of pediatric migraine patients are reviewed, emphasizing the importance of early diagnosis, and tailored multidisciplinary care to prevent chronic progression. Conclusions: Future research should focus on novel therapeutic targets and integrating gut–brain axis modulation, with a need for longitudinal studies to better understand the long-term course of pediatric migraine. Full article

Background/Objectives: Hydrocolloid dressings are commonly used in the treatment of chronic wounds by forming a gel-like protective layer upon the dispersion of water, absorbing exudate, and creating a moist environment that promotes healing. However, the use of hydrocolloids has expanded outside of wound care, and this review summarizes the evidence for their use within dermatology. Methods: To perform this narrative review, several databases were searched for manuscripts that described the use of hydrocolloid dressings within dermatology. Results: The hydrophilic and colloidal dispersion properties of hydrocolloid dressings facilitate the formation of an absorptive, hydrating, and protective layer. In addition, the viscous layer supports innate immunity by activating immune cells such as granulocytes and monocytes, making them effective in wound care. Hydrocolloid dressings appear to be an effective treatment in acute wounds, with the potential of reduced healing time and easier application compared to traditional dressings. The majority of the related research suggests that hydrocolloid dressings and standard dressings have similar efficacy in healing pressure ulcers, and the prevention of hypertrophic and keloid scars. Early reports suggest that hydrocolloid dressings have a role in the treatment of facial dermatitis and acne vulgaris. Conclusions: Hydrocolloid dressings have been studied most extensively for chronic wounds and then for use in acute wounds. There have been a few studies on their use for treating acne, facial atopic dermatitis, and hypertrophic scarring. While more clinical studies are needed, there appears to be early evidence of hydrocolloid dressing use within dermatology. Full article

Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR). Various machine learning classification techniques have been utilized to distinguish cognitive states. However, conventional machine learning methods, although simpler to implement, undergo a complex pre-processing phase before network training and demonstrate reduced accuracy due to inadequate data preprocessing. Additionally, previous research in cognitive load assessment using fNIRS has predominantly focused on differentiating between two levels of mental workload. These studies mainly aim to classify low and high levels of cognitive load or distinguish between easy and difficult tasks. To address these limitations associated with conventional methods, this paper conducts a comprehensive exploration of the impact of Long Short-Term Memory (LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs) within deep learning models. This is to address the issues related to spatial feature overfitting and the lack of temporal dependencies in CNNs discussed in the previous studies. By integrating LSTM layers, the model can capture temporal dependencies in the fNIRS data, allowing for a more comprehensive understanding of cognitive states. The primary objective is to assess how incorporating LSTM layers enhances the performance of CNNs. The experimental results presented in this paper demonstrate that the integration of LSTM layers with convolutional layers results in an increase in the accuracy of deep learning models from 97.40% to 97.92%. Full article

Autophagy is a critical cellular process that maintains homeostasis by recycling damaged or aberrant components. This process is orchestrated by a network of proteins that form autophagosomes, which engulf and degrade intracellular material. In cancer, autophagy plays a dual role: it suppresses tumor initiation in the early stages but supports tumor growth and survival in advanced stages. Chronic myeloid leukemia (CML), a hematological malignancy, is characterized by the Philadelphia chromosome, a chromosomal abnormality resulting from a translocation between chromosomes 9 and 22. Autophagy has emerged as a key factor in CML pathogenesis, promoting cancer cell survival and contributing to resistance against tyrosine kinase inhibitors (TKIs), the primary treatment for CML. Targeting autophagic pathways is being actively explored as a therapeutic approach to overcome drug resistance and enhance cancer cell death. Recent research highlights the intricate interplay between autophagy and CML progression, underscoring its role in disease biology and treatment outcomes. This review aims to provide a comprehensive analysis of the molecular and cellular mechanisms underlying CML, with a focus on the therapeutic potential of targeting autophagy. Full article