This research aims to evaluate the long-term changing trends of climate over Gilgit-Baltistan (GB) from 1955 to 2018, over 64 years of observation data from six major stations in GB. For this purpose, Mann-Kendall and Sen’s Slope Estimator tests were employed to examine the seasonal and annual trends in key climatic parameters, temperature, and precipitation. Results signify an overall increasing trend in annual temperature at most stations, with statistically significant warming trends at Skardu and Astore at the same rate of 0.02 oC/year; Bunji showed a significant declining trend. Seasonal analysis revealed a consistent rise in winter temperatures at most stations, while summer temperatures declined, except at Astore, where slight increasing trend (0.01 oC/year) was detected. Precipitation trends revealed spatial variability, with a significant increasing trend in Gupis, mostly during Spring, Summer, and Winter, with the highest increasing rate of 1.24 mm/year. Whereas Astore and Chilas stations showed non-significant decreasing trends in annual precipitation, -0.80 mm/year and -0.13 mm/year, respectively. The findings of this research revealed a shifting climatic regime in GB, characterized by increased seasonal precipitation and warmer winters, which has prime implications for glacier mass balance and downstream water flow. These findings specify critical scientific evidence for climate adaptation strategies and water resource management planning in the Upper Indus Basin (UIB).

Traditional inventory management systems are often constrained by manual inefficiencies, inaccurate forecasting, and poor scalability, particularly in dynamic retail environments. This paper introduces a novel, intelligent inventory management solution that integrates a high performance PackMLP model for time series sales forecasting with a modified A-LIQ (Advanced Laser Inertial QR) code scanning framework for real time inventory tracking. The scanning module leverages inertial correction and visual calibration to enhance recognition robustness, achieving 99.2% accuracy with an average latency of 0.9 seconds, even under suboptimal scanning conditions. For forecasting, we propose PackMLP, a lightweight, attention free deep learning model that combines multi-scale patch embedding, trend residual decomposition, and cross channel MLP blocks to capture temporal and inter-variable dependencies. Evaluated on the M5 Walmart dataset, PackMLP outperforms leading state-of-the-art (SOTA) models, including Transformer, Informer, PatchTST, PatchMLP, and TSMixer, achieving the lowest NWRMSLE (Normalized Weighted Root Mean Squared Logarithmic Error) of 0.0811, MAE (Mean Absolute Error) of 0.358, and MAPE (Mean Absolute Percentage Error) of 14.6%, while also offering the smallest model size (39 MB) and fastest inference time (12.2 ms/sample). A one-month deployment in a real retail setting demonstrated a 4.4% improvement in inventory accuracy, a 25% reduction in stockouts, and a 15% decrease in holding costs compared to manual systems. With its modular architecture, web-based dashboard, and real time low stock alerts, the system offers a cost effective, scalable alternative to complex enterprise platforms. These results confirm PackMLP’s potential as a practical and high accuracy forecasting engine for next generation inventory management systems.

The presence of low nutrient content in the lignocellulosic biomass, such as canola straw (CS), limits its anaerobic biodegradability. Therefore, the main focus of the study is to enhance the nutrient content during anaerobic digestion (AD) of CS. For this purpose, three different inoculum sources such as anaerobically digested manure (ADM), acclimatized sludge (AS), and mixed inoculum (MI), and different concentrations of laterite powder (2, 4, 6, 8, and 10%) were added to enhance the batch AD of CS. Results of the study revealed that the reactor inoculated with ADM performed better, and 15.23% and 10.31% higher biogas yields were observed from the reactors inoculated with AS and MI. Moreover, the 2% addition of laterite powder and ADM as inoculum produced the highest biogas yield of 602 NmL/gVS with a 76.4% VS reduction due to the synergistic effect. In addition, reactors showed better buffering capacity and stability in terms of volatile fatty acids to alkalinity ratios due to the addition of laterite powder during AD of CS. Furthermore, characterization of laterite revealed the presence of an iron source, which created the favourable environment for methanogenesis due to direct interspecies electron transfer (DIET). Biokinetic parameters revealed that the modified Gompertz model (MGM) reproduced the biogas production data. In conclusion, AD of CS may be enhanced by using good-quality inoculum and the addition of lateritic mineral. The addition of microbially active inoculums and laterite powder as a nutrient source may also enhance the anaerobic digestate quality and may be used as fertilizer.

Current imbalance in freight share (96% roads vs. 4% railways) in Pakistan, is impacting the performance of highways and is causing under-utilization of existing railway network. Expected increased freight demand due to China-Pakistan Economic Corridor (CPEC), necessitates a strategic shift towards multimodal freight transportation. With this background, this research provides a multidimensional analysis of strategies to enhance Pakistan Railways (PR) freight capacity by shifting calculated freight load from highways under CPEC scenario and examines their effects on the sustainability of transportation infrastructure owing to reduction in highways maintenance and rehabilitation (M&R) expenditure. Addressing the crucial issue of over-reliance on roads for freight transportation, this research employs economic indicators in statistical modeling to forecast freight demand. Three strategic railway freight capacity enhancement options have been proposed for 2025 and 2030. This study highlights that PR would be able to accommodate up to 24.07% of total freight by 2025 without compromising railway passenger load. Additionally, this study reveals that if the current freight share distribution remains unchanged, then highways M&R expenditure is expected to be increased by over 84%, by the year 2025, compared to the year 2015. However, highways M&R expenditure is expected to be reduced by 78.77% and 60.56% by the year 2025 and 2030, respectively, basing on the proposed freight load distribution strategies. This study would be highly beneficial for transportation agencies in optimal freight transportation management while ensuring efficient sustainability of transportation infrastructure, especially for the countries that have a high dependency on roads for freight transportation.

This study presents an analytical investigation of nonlinear heat transfer in viscoelastic materials processed through a single screw extruder. The model is based on a viscoelastic fluid framework, which captures both elastic and viscous effects relevant to polymer melt behaviour. A perturbation technique is applied to solve the governing nonlinear differential equations for temperature fields in the barrel, screw, and fluid domains. This study investigates nonlinear temperature effects, offering new insights into the thermal behaviour under screw-driven extrusion. A thorough investigation is conducted on the influence of key parameters, including the pressure gradient, Weissenberg number (Wi), and Brinkman number (Br), on temperature and heat flux profiles. The results show that increasing Br from 1 to 5 significantly raises the peak temperature and flow characteristics, and are interpreted in terms of the underlying physical mechanisms.

With the exponential upward thrust in residential energy consumption, effective Demand-Side Management (DSM) has come to be vital to ensure grid balance and user satisfaction. This paper proposes a holistic and customer-centric method for DSM via the improvement of the Automated Load Scheduling (ALS) algorithm. Unlike conventional strategies that neglect user preferences, ALS permits dynamic equipment categorization, priority-based scheduling, and real-time consumption comments, ensuring active consumer participation. The ALS set of rules employs dynamic programming to evenly distribute appliance hundreds across a 24-hour cycle, minimizing Peak-to-Average Ratio (PAR), electricity cost, and consumer inconvenience. A flexible power level selection mechanism, quadratic pricing version, and iterative optimization framework further enhance adaptability to numerous household patterns. Empirical results from weekday and weekend situations exhibit huge reductions in PAR—over 32% on weekdays and 46% on weekends—along with improved energy efficiency and cost savings. The proposed ALS framework offers a sturdy, scalable solution for smart home strength control systems in smart grid environments.

Designing multi-terminal HVDC systems presents significant challenges, particularly in control, modeling, and protection. One of the most critical concerns is the protection of these systems from DC short circuits. DC circuit breakers (DCCBs) have traditionally been a key solution for handling fault conditions. However, as these systems scale up to meet increasing power demands, the associated cost and technical complexity of deploying DCCBs across large-scale systems make them less practical and economically viable. This has driven the exploration of alternative or supplementary strategies to address short circuits more cost-effectively. In this regard, fault current limiter (FCL) circuits combined with DC breakers have been proposed to reduce their demands. This paper presents an improved non-superconducting FCL circuit paired with a hybrid DC circuit breaker to enhance system performance. The proposed topology integrates power electronic (PE) bidirectional switches, current-limiting inductors, and a discharging resistor. Simulations have been conducted using PSCAD/EMTDC software. An equivalent circuit model based on the Zhoushan HVDC project is used for simulation analysis to study behavior under the influence of this breaker. In contrast to current approaches that utilize continuous current-limiting components, the proposed topology minimizes power loss during normal operation. The simulation results show robust current-limiting performance, quicker fault isolation, and reduced energy absorption by the breaker. With the proposed circuit, the fault clearing time is improved by 23%, and energy absorption performance is enhanced by 98%. Compared to the existing topology in the literature the proposed design demonstrates superior current limiting performance. The proposed FCL reduces the fault current to 2 kA whereas the literature reported topology limits it to 2.5 kA. Further, it achieves significantly lower energy absorption, measured at 0.12 MJ compared to 0.4 MJ in the literature. A comparative analysis with the existing methods from the literature demonstrates that the proposed FCL offers superior current-limiting capabilities, faster fault response, and lower power losses.

The main purpose of the present study is to examine the key role of Cu and Fe3O4 nanoparticles that are submerged in human blood in the existence of magnetohydrodynamics (MHD) flow through the stenosis artery. The reason behind the selection of Cu and Fe3O4 nanoparticles is that they show high potential usefulness in drug delivery and imaging properties. Moreover, the governing partial differential equations (PDEs) that define the flow and the heat transfer characteristics of blood-based hybrid nanofluid (HNF) are converted to non-dimensional form of ordinary differential equations (ODEs) using suitable similarity transformation. The shooting method is applied to solve the equations through Maple software to observe the effects of specified nanoparticles volume fractions and used physical parameters in stenotic arteries. The results show that the velocity of human blood gradually decreases with an increase in the size of the nanoparticles but temperature increases in both cases of the , either stretching or shrinking. Moreover, an increase in magnetic, suction/injection, and nanoparticle volume fractions decrease the velocity of the blood-based hybrid nanofluid flow through the stenotic artery. While, an increase in thermal radiation, magnetic, flow parameters, and nanoparticle volume fraction increases the temperature of the blood during flow through the stenotic artery. On the other hand, the Prandtl number and suction/injection parameter decrease the temperature of the blood during flow through the stenotic artery. The present research has the potential that be proven highly advantageous for an effective drug delivery in human blood arteries.

The industrial manipulator uses complex control systems and requires specialist equipment for object detection and sorting. These are expensive and complicated. Installing and maintaining these manipulators require professional personnel and equipment. The manipulator design in this project is fully modular, easier to automate, and inexpensive, which is designed to enable industrial automation in developing countries. Using Topology in conjunction with Fusion 360 and ANSYS material consumption is reduced by up to 30% while keeping the material strength and stress constant. 3D printing parts help in reducing manufacturing complexity, time, and material consumption by reducing the overall manufacturing cost. With a Workspace of two feet and an operating temperature range from 10-60 °C, it is designed to deliver 3 DOF. To prevent the system from overheating, an effective cooling system for the controller and driver, is also developed using CFD analysis. The manipulator is linked with several sensors and a camera, to provide full autonomous control. As a result, it can be utilized in various industrial applications for precise and quick output. In this project, a manipulator is designed to sort objects as they come off the conveyor and pick and place them at the design location. A conveyor is intended for item movement, both manipulator and conveyor are controlled by a microcontroller and a PC via Computer vision. Initially sorting of objects is performed through both color sensors. Then, using computer vision, an algorithm is trained to identify any defective item among the bunch using a camera and place it at the designated location. This computer vision algorithm can detect an object at a range of 3 feet and provides proper control with an accuracy of 90-95%, depending on the amount of information about the object collected and analyzed. This algorithm is also adaptable, as it can identify any object once it has been trained.

An antimicrobial barrier may accelerate rehabilitation by facilitating the healing of living tissues affected by injuries caused by burning. Fibrous mats based on nanotechnology have been extensively researched for their potential as drug delivery systems. The fabrication of electrospun polymeric nanofibrous mats containing non-steroidal anti-inflammatory medications (NSAIDs) and antibacterial agents has been discussed in this article. Electrospinning has been employed to create nanofibrous mats from pure zein and pure gelatin, and their combined use with Ibuprofen, an NSAID. Due to the ability of these electrospun nanofibrous mats to control exudation, they keep the site dry and shield it from microbiological activity, which makes them a good option for wound healing. In addition to providing an antibacterial layer that promotes wound healing, the manufactured mats can also function as medicine transporters. This research article extensively addresses the drug release profile from the carrier nanofibrous mats and the characteristics of fiber mats using standard characterization techniques like Fourier-transform infrared spectroscopy (FTIR) and Scanning electron microscope (SEM). The resultant fiber mats' drug release kinematics are compared to the standard mathematical models (Korsmeyer-pappas and Higuchi. The cumulative drug percentage released from these mats consistently validated Higuchi’s model, which exhibited diffusion-controlled super case-II transport (n>1). The results indicate that the Ibuprofen is efficiently loaded onto the nanofibers, with a uniform distribution of the drug throughout the fiber matrix and ensures that the drug is released in a controlled and sustained manner, promoting effective wound healing.