Search Results (4,452)

Water quality assessment is crucial for the sustainable use and management of groundwater resources. This study was carried out in the irrigated plains of Vehari District, Punjab, Pakistan, to evaluate groundwater suitability for a managed aquifer recharge (MAR) project. Twenty groundwater samples were collected in June 2021 from an area of 1522 km² and analysed for major physicochemical parameters including electrical conductivity (EC), total dissolved solids (TDS), pH, turbidity, calcium (Ca), magnesium (Mg), chloride (Cl), alkalinity (Alk), bicarbonate (HCO₃⁻), hardness, potassium (K), sulphate (SO₄²⁻), sodium (Na), and nitrate (NO₃⁻). Water quality was assessed using WHO and PID standards, alongside derived hydrochemical indices such as sodium percentage (%Na), Kelly’s ratio (KR), sodium adsorption ratio (SAR), residual sodium carbonate (RSC), and the water quality index (WQI). The dataset was interpreted using geo-statistical, geospatial, multivariate, and correlation analyses. Cations and anion dominance followed the order Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻ > NO₃⁻. According to the WQI analysis, 35% of the water samples are classified as “poor,” half (50%) as “very poor,” and the remaining 15% as “unsuitable” for drinking purposes. However, irrigation suitability indices confirmed that groundwater is generally acceptable for agricultural use, though unfit for drinking. The outcomes of this study provide essential insights for groundwater management in the region, where the Punjab Irrigation Department (PID) has initiated a MAR project. Considering that the irrigation sector is the major groundwater consumer in the area, the compatibility of groundwater and surface water quality supports the implementation of MAR to enhance agricultural sustainability. Full article

Background: Due to chronic venous insufficiency, venous leg ulcers (VLUs) develop as chronic wounds characterized by impaired healing, persistent inflammation, and endothelial dysfunction. Nitrosative stress, mitochondrial damage, and tissue apoptosis caused by excess nitric oxide (NO) produced by iNOS in macrophages and fibroblasts are contributing factors in the chronic wound environment; therefore, pharmacological modulation of iNOS presents an attractive mechanistic target in chronic wound pathophysiology. Methods: Herein, we present the use of a structure-based computational strategy to assess the inhibition of tavaborole, a boron-based antifungal agent, against iNOS using human iNOS crystal structure (PDB ID: iNOS) by molecular docking using AutoDock 4.2, 500 ns simulation of molecular dynamics (MD), with equilibration within ~50 ns and analyses over full trajectory and binding free energy calculations through the MM-PBSA approach. Results: Docking studies showed favorable binding of tavaborole (–6.1 kcal/mol) in the catalytic domain, which stabilizes contacts with several key residues (CYS200, PRO350, PHE369, GLY371, TRP372, TYR373, and GLU377). MD trajectories for 1 ns showed stable structural configurations with negligible deviations (RMSD ≈ 0.44 ± 0.10 nm) and hydrogen bonding, and MM-PBSA analysis confirmed energetically favorable complex formation (ΔG_binding ≈ 18.38 ± 63.24 kJ/mol) similar to the control systems (L-arginine and 1400W). Conclusions: Taken together, these computational findings indicate that tavaborole can stably occupy the iNOS active site and interact with key catalytic residues, providing a mechanistic basis for further in vitro and ex vivo validation of its potential as an iNOS inhibitor to reduce nitrosative stress and restore endothelial homeostasis in venous leg ulcers, rather than direct therapeutic proof. Full article

Kiln-fired clay bricks are energy-intensive and carbon-heavy. This study develops and validates kiln-free, pressure-free, and ambient-cured geopolymer (GPM) bricks made from uncalcined clay and Class F fly ash. A two-stage experimental program screened 33 mixes (12–16 M NaOH and 396 cubes tested at 14–90 days) and then scaled six optimized mixes to 90 full-size bricks for mechanical, durability, and microstructural evaluation. Bricks with an optimal mix of 20–30% clay and 70–80% fly ash achieved a compressive strength of up to 32.5 MPa, satisfying ASTM C62 (for severe weathering) requirements. Relative to fired clay units, GPM bricks delivered +61% average compressive strength (up to +91%), +56.5% average modulus of rupture (up to +103%), 6–29% lower water absorption, and 42–84% higher UPV while their strength losses after 28-day immersion in 5% H₂SO₄ or 3.5% NaCl were only ~3–5%. SEM confirmed a dense N-A-S-H gel matrix with reduced porosity. Eco-efficiency analysis showed ~95% lower embodied CO₂ (0.26–0.31 vs. 5.5 kg eCO₂ per brick) and ~35% lower cost per MPa of strength than fired clay bricks. The findings demonstrate a practical, low-carbon brick manufactured without mechanical pressing or heat curing, delivering verified performance and durability under ambient conditions. Full article

In this work, we first establish a new connection between adjoint Bernoulli’s polynomials and gamma function as a new sequence of linear positive operators denoted by ${\mathfrak{S}}_{r,\varsigma ,\lambda }(.;.)$. Further, convergence results for these sequences of operators, i.e., ${\mathfrak{S}}_{r,\varsigma ,\lambda }(.;.)$ are derived in various functional spaces with the aid of the Korovkin theorem, the Voronovskaja-type theorem, the first order of the modulus of continuity, the second order of the modulus of continuity, Peetre’s K-functional, the Lipschitz condition, etc. In the last section, we focus our research on the bivariate extension of these sequences of operators; their uniform rate of approximation and order of approximation are investigated in different functional spaces. Full article

The behavior of a new modification of operators of the Baskakov–Schurer–Stancu variant is discussed in this study. First, we establish certain necessary moment and central moment estimates. We then demonstrate the weighted approximation result of the suggested operators using a Korovkin-type theorem in weighted spaces. We also give the rate at which these operators converge. Next, we establish theorems of pointwise convergence. Finally, we show several graphical representations to illustrate the accuracy and functionality of the operators. Full article

Climate change brings considerable danger to India’s economic progress, with the agricultural sector and farmers’ livelihoods being particularly vulnerable. Himachal Pradesh is especially susceptible owing to its reliance on climate-sensitive economic activities and limited capacity to adapt to climate variability. Strengthening adaptation strategies in Himachal Pradesh is crucial for fortifying the resilience of communities reliant on environmental resources for their sustenance and economic well-being. This study examines the extent of adoption of Climate-Smart Agricultural Practices (CSAPs), identifies the factors influencing their uptake, and assesses their impact on the livelihood vulnerability of farm households in the temperate region of Himachal Pradesh. Using a multistage random sampling framework, data were collected from 432 farm households through primary surveys and secondary sources. The analysis employs descriptive statistics, a composite livelihood vulnerability index, and Ordinal Logistic and Multiple Linear Regression models. Results show higher adoption of low-cost practices such as composting, fruit-based agroforestry, crop–livestock integration, and mulching, while capital-intensive practices like micro-irrigation were limited due to financial constraints. Adoption is positively influenced by education, extension access, farming experience, financial resources, and climate information exposure. Importantly, CSAPs adoption is found to significantly reduce livelihood vulnerability, indicating enhanced resilience and reduced exposure to climate-induced risks among farm households. The findings highlight climate-smart agriculture as an effective adaptation strategy and underscore the need for policies that strengthen extension services, improve access to credit, and promote affordable climate-smart technologies to enhance resilience in vulnerable hill regions. Full article

Parkinson’s disease is a neurodegenerative disorder with progressive impairment in patients worldwide, featuring manifestations of both motor dysfunction and various/list-specific non-motor symptoms. Early diagnosis and personalized treatment thus remain the biggest challenges in managing the disease. Artificial intelligence (AI), especially machine learning techniques, has shown immense potential for countering such challenges during the past years. This short review aims to summarize recent innovations in applying Machine Learning (ML) and Deep Learning (DL) to Parkinson’s disease, explicitly directed toward developing diagnostic tools, the prediction of progression, and personalized treatment strategies. We discuss several ML and DL approaches, including supervised and unsupervised learning models that have been applied to classify symptoms and identify biomarkers. In addition, integrating clinical and imaging data into disease models continues to advance. This indicates the emerging role of DL in bypassing the limitations of standard methods. This review of the future of AI in Parkinson’s disease research outlines its possible directions for enhancing patient care and clinical outcomes. Full article

Diabetic wounds remain chronically non-healing due to impaired angiogenesis, persistent inflammation, and defective extracellular matrix remodelling. In recent years, stem cell-derived exosomes have emerged as a potent cell-free regenerative strategy capable of recapitulating the therapeutic benefits of mesenchymal stem cells while avoiding risks associated with direct cell transplantation. This review critically evaluates the preclinical evidence supporting the use of exosomes derived from adipose tissue, bone marrow, umbilical cord, and induced pluripotent stem cells for diabetic wound repair. These exosomes deliver bioactive cargos such as microRNAs, proteins, lipids, and cytokines that modulate key signalling pathways, including Phosphatidylinositol 3-kinase/Protein kinase (PI3K/Akt), Nuclear factor kappa B (NF-κB), Mitogen-activated protein kinase (MAPK), Transforming growth factor-beta (TGF-β/Smad), and Hypoxia inducible factor-1α/Vascular endothelial growth factor (HIF-1α/VEGF), thereby promoting angiogenesis, accelerating fibroblast and keratinocyte proliferation, facilitating re-epithelialization, and restoring immune balance through M2 macrophage polarization. A central focus of this review is the recent advances in exosome-based delivery systems, including hydrogels, microneedles, 3D scaffolds, and decellularized extracellular matrix composites, which significantly enhance exosome stability, retention, and targeted release at wound sites. Comparative insights between stem cell therapy and exosome therapy highlight the superior safety, scalability, and regulatory advantages of exosome-based approaches. We also summarize progress in exosome engineering, manufacturing, quality control, and ongoing clinical investigations, along with challenges related to standardization, dosage, and translational readiness. Collectively, this review provides a comprehensive mechanistic and translational framework that positions stem cell-derived exosomes as a next-generation, cell-free regenerative strategy with the potential to overcome current therapeutic limitations and redefine clinical management of diabetic wound healing. Full article

The cement industry is at the core of global economic and infrastructure development accounts, but it also accounts for 7% to 9% of total emitting CO₂ For Pakistan, it is a major consumer of coal, emitting 8.9 Mt of CO₂ annually, resulting in nearly 49% of the country’s coal While several strategic initiatives are being adopted to lower conventional fuel consumption in the cement sector such as an increased shift towards solar energy deployment, initiating the shift from coal to alternate materials, but a well-regulated alternative fuel policy framework across cement production processes remains a clear gap in the industry’s decarbonization efforts. Given this challenge, this study conducts a scenario-informed quantitative evaluation using the Low-Emission Analysis Platform (LEAP) to explore the decarbonization potential of fuel switching in Pakistan’s cement industry, aligning it with NDC, Net-zero, and energy transition targets. The results reveal that swapping out coal and petroleum coke for cleaner alternatives would be necessary for reducing emissions by 13.5 Mt under the NDC scenario and 17.1 Mt for net-zero by 2050. However, achieving these targets requires a well-defined policy framework, regulatory support for Refuse-Derived Fuel (RDF) and Tire-Derived Fuel (TFD), building a sustainable biomass chain and quality control units, and capital investment in cleaner fuels. Full article

Saline water intrusion poses a major threat to groundwater security in arid and semi-arid regions, reducing freshwater availability and challenging sustainable water resource management. Accurate delineation of the fresh-saline water interface is therefore essential; however, conventional hydrochemical and laboratory-based assessments remain costly, invasive, and spatially limited. Resistivity methods have long been used to infer subsurface salinity, as low resistivity typically reflects clay-rich saline water and higher resistivity reflects freshwater-bearing sand or gravel. Yet, resistivity values for similar lithologies frequently overlap, causing ambiguity in distinguishing fresh and saline aquifers. To overcome this limitation, Dar–Zarrouk (D–Z) parameters are often applied to enhance hydrogeophysical discrimination, but previous studies have relied exclusively on one-dimensional (1D) D–Z derivations using vertical electrical sounding (VES), which cannot resolve the lateral complexity of alluvial aquifers. This study presents the first application of electrical resistivity tomography (ERT) to derive two- and three-dimensional D–Z parameters for detailed mapping of the fresh-saline water interface in the alluvial aquifers of Punjab, Pakistan. ERT provides non-invasive, continuous, and high-resolution subsurface imaging, enabling volumetric assessment of aquifer electrical properties and salinity structure. The resulting 2D/3D models reveal the geometry, depth, and spatial continuity of salinity transitions with far greater clarity than VES-based or purely hydrochemical methods. Physicochemical analyses from boreholes along the ERT profiles independently verify the geophysical interpretations. The findings demonstrate that ERT-derived 2D/3D D–Z modeling offers a cost-effective, scalable, and significantly more accurate framework for assessing fresh-saline water boundaries. This approach provides a transformative pathway for sustainable groundwater monitoring, improved well siting, and long-term aquifer protection in salinity-stressed alluvial regions. Full article

Background/Objectives: This study aimed to determine the antimicrobial susceptibility patterns of multidrug resistant (MDR) and extensively resistant (XDR) Salmonella enterica serovar Typhi (S. Typhi) strains among children, along with the associated behavioral and environmental risk factors across different population groups in multiple districts of Punjab, Pakistan. Methods: A cross-sectional study was conducted across 20 districts in Punjab, Pakistan. Structured questionnaires were used to assess sociodemographic and behavioral determinants. Blood cultures from febrile children were obtained for the isolation and identification of S. Typhi, followed by antimicrobial susceptibility testing and screening for the resistance genes. Results: A total of 900 blood samples were collected and 41.5% were positive for S. Typhi. The proportion of culture-positive cases were higher among children aged 6–12 years (34.8%). Sociodemographic and behavioral analysis revealed that children from low-income households (PKR < 20,000 showed significantly higher infection rate (67.1%, p < 0.001). Antimicrobial susceptibility testing revealed high resistance rates against several antibiotics: Ciprofloxacin (88.8%), Trimethoprim/sulfamethoxazole (83.7%), Ampicillin (73.8%) and Chloramphenicol (72.7%). However, all isolates remained susceptible to carbapenems and azithromycin. The prevalence of MDR and XDR S. Typhi in urban areas was 28.1% and 60.8%, respectively, while rural areas showed 22.6% MDR and 20.6% XDR. In contrast, nomadic populations exhibited a higher rate of MDR (49.3%) but a lower XDR prevalence of 18.6% with significant geographic variations in resistance patterns. Molecular analysis revealed a high prevalence of resistance genes, including sul1 (83.7%), sul2 (79.7%), followed by dfrA7 (81.3%), catA1 (64.9%) and bla_TEM (60.5%), bla_CTX-M-1 (12.5%), bla_CTX-M-15 (25.9%) and qnrS (88.8%), respectively. Conclusions: The study underscores a persistent typhoid burden and widespread antimicrobial resistance among children in Punjab. Targeted vaccination, antibiotic stewardship, public health education are urgently needed, especially among the nomadic population, where healthcare access and hygiene awareness are limited. Full article

Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a unified deep learning framework designed to accurately classify short human-like actions, specifically, “pour” and “stir” from sequential video data. The framework integrates adaptive time-distributed convolutional encoding with temporal reasoning modules to enable robust recognition under realistic robotic-interaction conditions. A balanced dataset of 2000 videos was curated and processed through a consistent spatiotemporal pipeline. Three architectures, LRCN, CNN-TD, and ConvLSTM, were systematically evaluated. CNN-TD achieved the best performance, reaching 98.68% accuracy with the lowest test loss (0.0236), outperforming the other models in convergence speed, generalization, and computational efficiency. Grad-CAM visualizations further confirm that NovAc-DL reliably attends to motion-salient regions relevant to pouring and stirring gestures. These results establish NovAc-DL as a high-precision real-time-capable solution for deployment in healthcare monitoring, industrial automation, and collaborative robotics. Full article

Tissue regeneration is essential for wound healing, organ function restoration, and overall patient recovery. Its success significantly impacts medical procedures in fields like internal medicine and orthopedics, enhancing patient quality of life. Recent advances in regenerative medicine, particularly the combination of advanced drug delivery systems (DDS) and bioengineering, have enabled customized methods to improve tissue regeneration outcomes. However, conventional tissue engineering techniques have drawbacks, often using static scaffolds that lack the dynamic properties of real tissues, leading to subpar healing outcomes. The use of 3D printing and other advanced scaffolding techniques allows for the creation of bio functional scaffolds that deliver bioactive molecules at precise locations and times. The optimal integration of biological systems with enhanced material properties for personalized treatment options remains unclear. There is a need for more research into the complex interactions between cellular biology, drug delivery, and material technology to improve tissue regeneration. Despite progress in developing bioactive scaffolds and localized drug delivery methods, the interactions among different scaffold materials, bioactive agents, and cellular behaviors within the regenerative ecosystem are not fully understood. While there is extensive research on 3D-printed scaffolds in tissue engineering, there is a lack of studies integrating bio printing with in vivo biological reactions in real time. Limited research on the dynamic integration of patient-specific parameters in regeneration methods highlights the need for customized approaches that consider individual physiological differences and the complex biological environment at injury sites. Additionally, challenges arise when translating laboratory results into effective therapeutic applications, underscoring the necessity for interdisciplinary collaboration and innovative design approaches that align advanced material properties with biological needs. Full article

Background/Objectives: Alzheimer’s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI). Methods: Four ML classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were trained on demographic and clinical features. For stage-wise classification, five DL models—CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2—were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations. Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment. Full article

Globally, Spodoptera frugiperda is a major threat to many important crops, including maize, rice, and cotton, causing significant economic damage. To control this invasive pest, environmentally friendly pest control techniques, including pheromone detection and identification of potential molecular targets to disrupt S. frugiperda mating communication, are needed. Female moths biosynthesize pheromones and emit them from the pheromone gland, which significantly depends on the intrinsic factors of the moth. Male S. frugiperda have a sophisticated olfactory circuit on their antennae that recognizes pheromone blends via olfactory receptor neurons (ORNs). With its potential to significantly modify the insect genome, CRISPR/Cas9 offers a revolutionary strategy to control this insect pest. The impairing physiological behaviors and disrupting the S. frugiperda volatile-sensing mechanism are the main potential applications of CRISPR/Ca9 explored in this review. Furthermore, the release of mutant S. frugiperda for their long-term persistence must be integral to the adoption of this technology. Looking forward, CRISPR/Cas9-based gene drive systems have the potential to synergistically target pheromone signaling pathways in S. frugiperda by disrupting pheromone receptors and key biosynthesis genes, thereby effectively blocking intraspecific communication and reproductive success. In conclusion, CRISPR/Cas9 provides an environmentally friendly and revolutionary platform for precise, targeted pest management in S. frugiperda. Full article