University of Swat

Recently proliferation of Pharmaceutical Pollution (PPs) in aquatic ecosystems has emerged as a pressing environmental concern. Understanding PPs, ecological impact, and remediation is vital to protecting ecosystems and community people. This thorough review article explores the complex landscape of PPs, covering their diversity, origins, effects, and methods of mitigation. The escalating presence and importance of PPs highlight the need for thorough categorization into distinct types, discussing the impact, various sources, and causes of entry into the environment through domestic and industrial water wastes, agricultural runoff, and other pathways. The rise in PPs is due to heightened pharmaceutical usage, insufficient regulations, and limited public awareness. The adverse impacts of PPs are on ecosystems, human health, and the development of antimicrobial resistance. A comprehensive investigation was conducted to evaluate the impact of pharmaceutical products (PPs) on the activation of sewage sludge in water treatment systems. Diverse treatment approaches spanning chemical (e.g., coagulation), physical (e.g., adsorption, filtration), biological (e.g., activated sludge), and physicochemical processes were systematically analyzed for their efficacy in treating pharmaceutical-laden wastewater. However, these methods possess several merits and demerits, such as higher operation and installation costs. The sludge activation process stands out for its ability to produce high-quality effluent with lower installation costs, effectively treating wastewater containing drugs like antibiotics, depressants, and hormones. The sludge activation process employs removal mechanisms like physisorption, biodegradation, and chemical transformation to effectively mitigate PPs using a variety of efficient adsorbents derived from activated sludge. Removal mechanisms include adsorption using silanol and carbonyl functional groups, ion exchange, and dissociative adsorption. To tackle pharmaceuticals products pollution it is important to continue research, enforce stronger regulations, raise public awareness and adopt eco-friendly drug manufacturing to protect the ecosystem and public health. The idea is to provide a comprehensive view of PPs while stressing the need to include intrigue undertakings to mitigate the negative effects of PPs on the natural environment and people’s health. Graphical Abstract

This work investigates the dynamical behavior of soliton solutions for a (3+1)-dimensional Boussinesq model, a key equation in describing nonlinear wave phenomena in multi dimensional settings. Two distinctive analytical techniques, the Chamani method (CHAM) and Kudryashov’s auxiliary equation method, are employed to derive exact soliton solutions. The Chamani method utilizes a systematic framework to construct analytical solutions by reducing the governing equation to a simpler form, while Kudryashov’s auxiliary method incorporates a polynomial form of solutions to extract explicit solitonic structures. Comparison of these methods reveals intricate features of the soliton solutions, including their propagation, interaction dynamics, and stability properties. The study provides valuable insights into the rich nonlinear dynamics of higher-dimensional systems and demonstrates the efficacy of these methods in addressing complex models in mathematical physics. Additionally, by selecting various constant values, we create 2D, 3D and related contour plots to be aware of the physical interpretations of these solutions. Therefore, we obtain superior physical behaviors from these solutions.

Globally, tickborne orthonairoviruses are regarded as a danger to public health. The new infectious virus known as Yezo virus, which is spread by tick bites, produces a condition marked by fever and a decrease in leucocytes and blood platelets. We suggest a multiepitope vaccination design that makes use of immunoinformatics technologies to combat this new danger. Sequences from Yezo virus proteins were gathered, and they allowed us to identify T-cell and linear B-cell epitopes. The vaccine design showed good physical and chemical characteristics as well as allergenicity and antigenicity. Simulations of molecular docking revealed robust contact with toll-like receptor 4. The HDOCK server generated the docking scores for protein interactions i.e. -295.74 kcal/mol for the epitopes in combined form: -281.98 kcal/mol by the epitopes obtained from nucleoprotein, and epitopes obtained from the glycoprotein shows − 262.67 kcal/mol in response to TLR4. The dynamic analysis of vaccine binding with these receptors was conducted with regards to interaction energetics and complex stability. Results showed that vaccine construct was stable throughout the simulation time intervals with strong hydrogen bonds interactions with TLR4 receptor residues. Lastly, we hypothesize that the vaccination sequence described here has a great chance of eliciting particular and protective immune responses, pending assessment of further experimental investigation. Graphical abstract

The selection of optimal carbon capture technologies is paramount in enhancing the efficiency of carbon emission mitigation efforts. Due to the multifaceted nature of the influencing factors, a robust and systematic approach is essential for identifying the most effective solution. The present research introduces an innovative fuzzy multi-criteria decision-making framework developed to address these types of challenges. In the first phase, we introduce novel operational laws for p,q-quasirung orthopair fuzzy (p,q-QOF) sets, thoroughly exploring their fundamental properties. Based on these operational laws, a set of advanced aggregation operators is developed, including the p,q-Quasirung Orthopair Fuzzy Weighted Exponential Averaging (p,q-QOFWEA) operator and its dual counterpart, the ${\text{D}}_{(p,q)}$ QOFWEA operator, which significantly enhance decision-making capabilities. In the second phase, we extend the traditional entropy method to the p,q-QOF context for the determination of criteria weights and provide a comprehensive outline of the decision-making process. The effectiveness of the proposed approach is demonstrated through its application to a real-world case study focused on the selection of suitable carbon capture technologies. Numerical results highlight the superiority of the proposed method, yielding a prioritized list of carbon capture technologies with practical relevance to modern applications. This work offers a novel contribution by introducing the p,q-QOF framework and showcasing its potential for addressing complex decision-making problems in environmental technology selection.

This study presents an advanced mathematical perspective of a generalized diabetes model, emphasizing the critical complications associated with this disease, such as cardiovascular disease, kidney failure, nerve damage, vision problems, and weakened immunity conditions, which can escalate into life-threatening conditions such as heart attacks, strokes, and blindness. Blood glucose, an essential energy source for the human body, is regulated by hormones such as insulin and glucagon. Diabetes emerges either due to the body’s resistance to insulin or the autoimmune destruction of insulin-producing cells in the pancreas. Focusing on these physiological insights, we reformulate the blood glucose-insulin (MBGI) model by incorporating some novel parameters, introducing a dietary intake compartment, and employing a new fractional operator in the sense of a fractal-fractional derivative to better capture the complex dynamics of the disease. This study investigates the existence, uniqueness, and Hyers-Ulam stability of solutions via fixed-point approaches, particularly Leray-Schauder techniques. Furthermore, a numerical scheme based on Newton’s polynomial interpolation is developed to visualize the behavior of the model. The attained results show that increasing both the fractal dimension and fractional order leads to a crucial reduction in glucose concentration, offering valuable insights for the effective management and control of diabetes.

This study proposes a novel sex-structured syphilis transmission model that incorporates key biological features such as congenital infection and disability due to long-term complications. While previous works have explored syphilis dynamics using fractional calculus, we extend this framework by introducing two additional compartments: congenitally infected newborns ( C ) and individuals with syphilis-induced disabilities ( D ), allowing for a more realistic representation of vertical transmission and chronic disease outcomes. The model is formulated using the Atangana-Baleanu-Caputo (ABC) fractional derivative to capture memory effects and non-local dynamics often observed in infectious disease spread. We rigorously analyze the positivity and boundedness of solutions, and establish the existence and uniqueness using Lipschitz continuity and Banach space theory. The basic reproduction number R 0 is derived to characterize disease dynamics, and both local and global stability of the disease-free equilibrium are proven. A detailed sensitivity analysis identifies the most influential parameters affecting R 0 , such as transmission probabilities, treatment rates, and the vertical transmission factor. Numerical simulations based on the Adams-Bashforth and Newton polynomial schemes validate the theoretical findings and provide insights into the potential effectiveness of various intervention strategies. Overall, this work contributes an enhanced modeling framework that integrates mathematical rigor with public health relevance, offering valuable guidance for targeted strategies to control syphilis and reduce its congenital and long-term burden.

This study investigates the analytical solutions of optical solitons governed by the a time-dependent Paraxial Equation incorporating Kerr law nonlinearity. A rigorous analytical method is developed to obtain these solutions, offering insights into the behavior of optical solitons in nonlinear media. Here, we have applied the eMETEM approach for the first time to create robust solutions to the time-dependent Paraxial Equation. Building an efficient plan to solve the governing model has been our main goal. The Kerr law nonlinearity, which describes the intensity-dependent refractive index, profoundly affects the propagation characteristics of optical pulses. Through systematic analysis, key properties such as soliton formation, stability, and evolution are elucidated. The derived analytical solutions provide a valuable framework for understanding the intricate dynamics of optical solitons in nonlinear media, facilitating advancements in various applications including optical communication and signal processing.

This study introduces a novel distance measure (DM) for (p,q,r)−spherical fuzzy sets ((p,q,r)−SFSs) to improve decision-making in complex and uncertain environments. Many existing distance measures either fail to satisfy essential axiomatic properties or produce unintuitive outcomes. To address these limitations, we propose a new three-dimensional divergence-based DM that ensures mathematical consistency, enhances the discrimination of information, and adheres to the axiomatic framework of distance theory. Building on this foundation, we construct a multi-criteria decision-making (MCDM) model that utilizes the proposed DM to evaluate and rank alternatives effectively. The applicability and robustness of the model are validated through a practical case study, demonstrating that it leads to more rational, consistent, and reliable decision outcomes compared to existing approaches.

The current monkeypox virus outbreak is probably linked with the novel substitutions in the G9R protein of the replication complex. The G9R‐E4R interface act as a potential druggable site for the inhibition of monkeypox replication. Therefore, we employed computational methods to explore potential natural products to inhibit the G9R interface. Our results revealed that among the 5230 compounds only four compounds (top hit 1‐4) reported excellent docking scores of ‐8.96 kcal/mole, ‐8.33 kcal/mol, ‐7.98 kcal/mol and ‐7.85 kcal/mol respectively. However, the reported KD value were ‐7.68 kcal/mol, ‐7.05 kcal/mol, ‐7.53 kcal/mol, and ‐7.77 kcal/mol respectively. The selected lead compounds exhibited consistent dynamics in all‐atoms simulations, reflects their robustness and suitability for engaging effectively with the interface residues. Furthermore, the binding free energy was calculated to be ‐47.86 kcal/mol for the top hit 1 complex, ‐45.51 kcal/mol for the top hit 2 complex, ‐41.63 kcal/mol for the top hit 3 complex, and ‐43.81 kcal/mol for the top hit 4 complex. These compounds also showed favorable pharmacokinetic properties with minimal toxicity risks. The results indicate that compounds top hit 1‐4 demonstrate a higher pharmacological potential against the G9R protein of monkypox virus, suggesting immediate experimental testing to assess their clinical viability.

The skin is the largest and most sensitive part of the body and acts as an external defense against danger. There are one billion bacteria per square centimeter in the skin. In addition to helping improve the environment, it produces healthy microbiome metabolites that are necessary to maintain skin moisture, elasticity, and strength. Changing the habitat of bacteria on the skin, either orally or through the use of specific probiotics, can help restore the microbiome of healthy individuals. Cosmetics are products used to beautify the human body, enhance beauty, or change appearance. Cosmetics are products that combine cosmetics and medicine. Environmentally friendly methods have become important in many industries, including cosmetics. People’s demand for environmental products continues to grow, and companies need to conduct life cycle assessments (LCAs) to assess the impact of their products on the environment throughout their lifespan. Sustainable development goals (SDGs) will balance environmental, economical, and social issues. Sustainable development will help in building inclusive, resilient earth for animals and humans. SDGs will make development for future generation without compromising the needs of the present generation. To ensure sustainability, cosmetics must be environmentally friendly and purposeful. The cosmetic industry is utilizing new ingredients such as gold nanoparticles to improve these effects. The aim of all research is to use life cycle analysis to estimate the ecological impact of beauty cream production during its life cycle. This includes everything from raw materials to production, packaging, transportation, and postuse disposal.

The rapid urbanization in the Kabul River Basin has increased the demand for water for both drinking and commercial purposes, leading to domestic and industrial water insecurity. Assessing the groundwater potential of the Kabul River Basin is highly crucial for effective water management. The aim of this paper is to identify potential zones for groundwater by employing a Geographic Information System and an Analytical Hierarchy Process approach to formulate a cumulative score based on seven thematic images—rainfall, geology, lineament density, drainage density, land use/land cover, soil type, and slope—within the Kabul River, with assigned weightages of 32%, 27%, 12%, 10%, 8%, 6%, and 5%, respectively, with a consistency ratio of 0.053 (5%), demonstrating the reliability of the results. The study shows that the first three factors contribute more to the percentages of Groundwater Potential Zones. The identified groundwater potential is classified into very good, good, medium, poor, and very poor zones, covering 35.45% (19,989 km²), 37.2% (20,978 km²), 23.16% (13,063 km²), 4.13% (2332 km²), and 0.06% (19 km²), respectively. Groundwater potential in the basin is predominantly classified as good to medium; however, there are notable variations across sub-basins. The Swat sub-basin and western parts of the Kabul River Basin, encompassing the Panjshir and Parwan districts, exhibit exceptionally high groundwater potential. In contrast, the Panjkora sub-basin (Dir district) and southwestern areas of the Kabul River Basin, covering parts of the Ghazni and Wardak districts, have very limited groundwater potential.

Water pollution is a significant threat for human health, particularly in developed countries. This study advances the mathematical understanding of WP transmission dynamics by developing a fractional–fractal derivative framework with non-singular kernels and the Mittage–Leffler function, which successfully preserves the non-local behavior of pollutants. The fractional–fractal derivatives in sense of the Atangana–Baleanu–Caputo formulation inherently captures the non-local and memory-dependent behavior of pollutant diffusion, addressing limitations of classical differential operators. A novel parameter, γ, is introduced to represent the recovery rate of water systems through treatment processes, explicitly modeling the bridge between natural purification mechanisms and engineered remediation efforts. Furthermore, this study establishes stability analysis, and the existence and uniqueness of the solution are established through fixed-point theory to ensure the mathematical stability of the system. Moreover, a numerical scheme based on the Newton polynomial is formulated, by obtaining significant simulations of pollution dynamics under various conditions. Graphical results show the effect of important parameters on pollutant evolution, providing useful information about the behavior of the system.

Advanced and rapid digitalization brings complex challenges in managing massive digital collections, necessitating well-defined attributes to uniquely identify and efficiently access, preserve, and retrieve digital objects. Metadata plays a vital role, providing structure, accessibility, and transformation potential alongside content. This study aims to create a comprehensive metadata set capturing all essential attributes of digital objects. In particular, many critical metadata elements required for effective news article management are not readily available within the sources. This study introduces a “Digital News Stories Preservation (DNSP)” framework with twenty-eight metadata elements: sixteen explicit and twelve implicit. These elements are categorized as optional, repeatable, explicit, or implicit and support robust news preservation. Implicit metadata is extracted directly from the content, enabling advanced search and linkage between related news articles within the “Digital News Stories Archive (DNSA).” The experimental results indicate that 55% of explicit metadata items and 65% of implicit metadata items were present across selected news sources. The study compares the proposed metadata against five established standards, highlighting unique elements and offering insights for enhanced news management and retrieval. The study explores metadata adoption for educational digital resources, detailing mappings to educational objectives and evaluating each metadata element’s educational effectiveness. By embedding these elements in digital platforms, news sources can improve content management, accessibility, and educational applications, supporting both media and academic environments.

Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been proposed to address this issue. The proposed protocol deploys a deep reinforcement learning agent with fixed hyperparameters to optimize the duty cycling of the nodes. The reinforcement learning agent monitors essential parameters such as energy level, packet drop rate, neighboring nodes’ status, and preamble sampling. The agent stores the information as a representative state and adjusts the duty cycling of all nodes. The performance of RL-BMAC is compared to that of conventional B-MAC through extensive simulations. The results obtained from the simulations indicate that RL-BMAC outperforms B-MAC in terms of throughput by 58.5%, packet drop rate by 44.8%, energy efficiency by 35%, and latency by 26.93%

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges.

In this paper, we investigate the properties of ion-acoustic waves (IAWs) with finite orbital angular momentum (OAM) modes in a collisionless, unmagnetized, and homogeneous non-relativistic plasma comprised of degenerate electron species and inertial ions. Utilizing a fluid model, we derive the linear paraxial equation for ion density perturbations, where the electric field is decomposed into cylindrical components to reveal a helical, non-planar wave structure. The electrostatic potential is expressed in a Laguerre–Gaussian (LG) profile and analyzed numerically. We calculate the angular momentum density, demonstrating linear dependence on the angular (quantum) mode number. Our results show that the angular mode number, radial mode number, degenerate electron density, beam waist, and azimuthal angle have a considerable effect on the LG potential in a non-relativistic partially degenerate plasma system. Specifically, we emphasize the impact of electron number density (typically that of semiconductor quantum well environments) on the profile of the LG-type electrostatic potential. This research provides new insights into the behavior of IAWs with OAM in partially degenerate plasmas, with potential implications for advanced plasma applications and technologies.

Background: Hypoxia plays a key role in cancer progression, mainly by stabilizing and activating hypoxia-inducible factor-1 (HIF-1). For HIF-1 to function under low oxygen conditions, it must interact with the transcriptional coactivator p300, a critical step for promoting cancer cell survival and adaptation in hypoxic environments. Methods: Consequently, we used drug design and molecular simulation techniques to screen phytochemical databases, including traditional Chinese and African medicine sources, for compounds that could disrupt the p300/HIF-1 interaction. Results: In this study, we identified potential compounds with high docking scores such as EA-176920 (−8.719), EA-46881231 (−8.642), SA-31161 (−9.580), SA-5280863 (−8.179), NE-5280362 (−10.287), NE-72276 (−9.017), NA-11210533 (−10.366), NA-11336960 (−7.818), TCM-5281792 (−12.648), and TCM-6441280 (−9.470 kcal/mol) as lead compounds. Furthermore, the compound with the highest docking score from each database (EA-176920, SA-31161, NE-5280362, NA-11210533, and TCM-5281792) was subjected to further analysis. The stable binding affinity of these compounds with p300 was confirmed by Post-simulation binding free energy (−22.0020 kcal/mol, −25.4499 kcal/mol, −32.4530 kcal/mol, −33.9918 kcal/mol, and −57.7755 kcal/mol, respectively) and KD analysis. Moreover, the selected compounds followed the Lipinski rules with favorable ADMET properties like efficient intestinal absorption, high water solubility, and no toxicity. Conclusions: Our findings highlight the potential of natural compounds to target key protein–protein interactions in cancer and lay the groundwork for future in vitro and in vivo studies to explore their therapeutic potential. Specifically, disrupting the p300/HIF-1 interaction could interfere with hypoxia-driven pathways that promote tumor growth, angiogenesis, and metastasis, offering a promising strategy to suppress cancer progression at the molecular level.

This study investigates the interplay of climatic drivers and human activities on vegetation dynamics in the ecologically sensitive Western Himalayas, encompassing Khyber Pakhtunkhwa (KPK), Azad Jammu and Kashmir (AJK), and Gilgit-Baltistan (GB). Using 24 years (2000–2023) of NDVI data from Landsat imagery, advanced techniques, such as the Hurst exponent for long-term persistence and residual trend analysis for isolating anthropogenic impacts, were employed to assess vegetation trends and predict their sustainability. Significant positive trends in NDVI were observed across 20.75% of the area, attributed to afforestation initiatives and conservation efforts, while 56.76% of the region showed no significant change, indicating ecological stability. KPK recorded the highest annual NDVI increase (0.001), driven by afforestation efforts, whereas arid conditions constrained GB’s limited growth (0.00037 annually). Temperature emerged as the dominant climatic driver, influencing 61.53% of the area, with precipitation positively correlating with NDVI in 42.54% of the region. Notably, human activities, including afforestation, contributed to a 28.94% increase in residual NDVI trends, underscoring their pivotal role in vegetation recovery. These findings offer actionable insights for climate-resilient land management, emphasizing the integration of conservation strategies to mitigate the dual impacts of climate change and human activities in mountainous ecosystems.