NED University of Engineering and Technology

The arrival of Artificial Intelligence (AI) and the Internet of Things (IoT) has led to the development of programming-based environments for domestic and commercial applications of automation. This work is distributed into three sections: virtual assistant (VA), domestic robotics, and advanced user interface demonstration. A dissimilar public library is utilised for a virtual assistant and information to identify them, concluding the communication recognition procedure and then replying by viewing all the effects on the display screen. Domestic automation includes monitoring and changing applications. IoT-based methods were used to control the brightness of the bulb, and an IoT-based technique was used to control the speed of the DC fan. Finally, the output was displayed using a Graphical User Interface (GUI) constructed using the QT-designer tool and PyQT library. The primary value of this work is the ability to combine an AI-based virtual assistant, home automation, and a real-time graphical user interface into easy use system. Operator instructions are examined to deliver optimum solutions. The proposed scheme has better functional coverage, real-time visualisation, and better user interaction in comparison with the existing benchmark systems, which mostly concentrate on voice-based helpers and distributed IoT control systems. This scheme is recommended for use as an intellectual virtual assistant, which understands human speech and answers through created voices. This work is new because it is the first work where an AI virtual assistant, IoT based automatization, and real-time smart display are combined to make the interaction and constant situational awareness happen within one platform.

Background: Major Depressive Disorder (MDD) is a leading cause of disability worldwide, characterized by heterogeneous treatment outcomes. Predicting antidepressant response remains a persistent clinical challenge, often resulting in prolonged trial-and-error approaches. Advances in artificial intelligence (AI) and biomarker research now offer opportunities to forecast treatment outcomes more accurately through the integration of multimodal data. Objective: To develop and evaluate an AI model capable of forecasting antidepressant treatment response in patients with MDD using combined clinical, neuroimaging, genetic, and digital biomarker datasets. Methods: A descriptive study was conducted over four months at a tertiary psychiatric center in Lahore, including 68 participants aged 18–55 years diagnosed with MDD according to DSM-5 criteria. Clinical severity was assessed using the Hamilton Depression Rating Scale (HDRS-17) and the Montgomery–Åsberg Depression Rating Scale (MADRS) at baseline and after eight weeks of antidepressant therapy. Neuroimaging, genetic, and digital biomarkers were collected and analyzed. Data integration and model training employed random forest and support vector machine algorithms, with 10-fold cross-validation to ensure reliability. Results: Significant reductions were observed in HDRS-17 and MADRS scores post-treatment (p < 0.001). Responders demonstrated higher cortical thickness, greater gray matter volume, and stronger functional connectivity. The random forest model achieved superior predictive accuracy (86.8%) compared with the support vector machine (83.2%), with an AUC-ROC of 0.91. These findings indicated that integrating multimodal biomarkers improved the precision of treatment response prediction in MDD. Conclusion: The study demonstrated that AI-based multimodal modeling can accurately forecast antidepressant response, supporting the potential of precision psychiatry in optimizing individualized treatment strategies for MDD.

Abstract Lead-free tin (Sn)-based perovskite solar cells (PSCs) have emerged as a promising alternative to conventional lead counterparts due to their lower toxicity and biocompatibility. This study presents a detailed investigation of CH₃NH₃SnI₃-based PSCs, focusing on interfacial energy-band alignment to improve charge extraction and limit carrier recombination. A comparative evaluation of oxide-based electron transport layers (TiO₂, ZnO, SnO₂, CeO₂, and Nb₂O₅) was carried out using SCAPS-1D to determine the most suitable configuration. The optimized device, Ag/FTO/TiO₂/CH₃NH₃SnI₃/N-doped-TiO₂/Au, integrates an undoped TiO₂ as the electron transport layer and N-doped TiO₂ as a hole transport layer. With an absorber thickness of 900 nm, an acceptor concentration of 1 × 1015 cm-3, and a defect density of 1 × 1012 cm-3, the device achieves a power conversion efficiency of 31.7% with an open-circuit voltage of 1.05 V, a short-circuit current density of 34.67 mA cm⁻², and a fill factor of 87%. The favorable interfacial band structure and reduced charge-trapping losses contribute to the improved photovoltaic performance. The proposed configuration underscores the potential of tin-based perovskites for achieving high-efficiency, low-cost, and sustainable photovoltaic technologies.

Karachi, Pakistan’s largest metropolitan hub, produces more than 12,000 tons of municipal solid waste (MSW) daily, presenting substantial environmental and operational challenges. This study focuses on District East, one of the most populous and urbanized districts by examining its MSW characteristics, daily operational trends, and material recovery potential. The research involved field-level waste sampling at GTS Imtiaz and three supplementary sites (Purani Sabzi Mandi, NIPA, and Bahadurabad), applying ASTM D5231-92 protocols for manual sorting and ASTM D3174-07 for proximate analysis. Furthermore, elemental analysis was conducted to determine carbon, nitrogen, and sulfur composition. The study recorded daily waste flows at GTS Imtiaz throughout January 2023, accounting for 8289 trips and 44,532 tons of waste. Proximate analysis revealed that organic waste consistently exhibited high moisture content (> 50%) while heterogeneous waste showed significant volatile solids. Elemental analysis highlighted a carbon content of up to 55.1% in wood waste and nitrogen content exceeding 1.4% in organic waste, suggesting its suitability for composting and bioenergy applications. Heterogeneous waste fractions demonstrated calorific values of up to 4,232 kcal/kg, reinforcing their energy recovery potential. These insights emphasize the value of integrating localized composting initiatives and selective RDF (Refuse-Derived Fuel) strategies to alleviate landfill dependency. The findings aim to support Karachi’s waste authorities and urban planners in developing decentralized and sustainable MSWM models that are responsive to local spatial, demographic, and waste stream variations.

Vehicular ad hoc networks (VANETs) must simultaneously satisfy stringent reliability, latency, and sustainability targets under highly dynamic urban and highway mobility. Existing solutions typically optimise one or two dimensions (link stability, clustering, or energy) but lack an integrated, adaptive mechanism that fuses heterogeneous metrics while remaining lightweight and deployable. This paper introduces a VANET routing protocol named SYMPHONY (Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield) that operates in three coordinated layers: (i) a compact neighbourhood filtering stage that reduces forwarding scope and eliminates transient relays, (ii) a cluster layer that elects resilient cluster heads using fuzzy energy-aware metrics and backup leadership, and (iii) a global inter-cluster optimizer that blends a GA-reseeded swarm metaheuristic with a stability-aware pheromone scheme to produce multi-objective routes. Crucially, SYMPHONY employs an ultra-lightweight online weight-adaptation module (contextual linear bandit) to tune metric fusion weights in response to observed rewards (packet delivery ratio, end-to-end delay, and Green Performance Index). We evaluated the proposed routing protocol SYMPHONY versus strong modern baselines across urban and highway scenarios with varying density and resource constraints. The results demonstrate that SYMPHONY improves packet delivery ratio by up to 12-18%, reduces latency by 20-35%, and increases the Green Performance Index by 22-45% relative to the best baseline, while keeping control overhead and per-node computation within practical bounds.