Nuzhat Khan

Optimizing wirelength involves predicting the total length of wires needed to connect different components within a chip during cell placement. It is a fundamental challenge in very-large-scale integration (VLSI) of integrated circuit (IC) design, as it directly impacts the overall performance and manufacturability of chips. Accurate wire-length estimation in the early stages of the design process is critical for guiding subsequent optimization tasks. This paper proposes a novel hybrid linear regression wirelength (hybrid-LRWL) method that combines the strengths of existing methods rectilinear Steiner minimal tree (RSMT) for low-degree nets and a statistical learning-based approach for high-degree nets. Additionally, it compares the performance of three well-established wirelength estimation techniques: half-perimeter wirelength (HPWL), rectilinear minimum spanning tree (RMST), and RSMT. The methods were evaluated using the International Symposium on Physical Design (ISPD) 2011 benchmark suite, considering accuracy and computational efficiency. The experimental results demonstrated that the proposed hybrid method achieves superior accuracy, with a mean error of less than 0.05% in total wirelength, closely approximating RSMT results. The proposed method reduces computational time up to 3.6 times faster than traditional RSM-based methods. The results establish a strong framework for accurate and efficient wirelength estimation in VLSI design for modern, high-performance ICs.

Climate-smart agriculture (CSA) is an integrated approach to farming that focuses on increasing agricultural productivity, enhancing resilience to climate change, and reducing greenhouse gas (GHG) emissions while ensuring sustainability and food security. This systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines explores the key role of CSA in addressing the challenges of climate change within the context of global food production. It investigates the impacts of climate change on agriculture, emphasizing disruptions to weather patterns, extreme events, and the consequent threats to food security. The paper discusses the core principles of CSA, highlighting sustainable intensification, climate resilience, and reduced greenhouse gas emissions after an in-depth analysis of 20 articles selected based on predefined inclusion criteria. Moreover, a range of CSA practices and technologies is evaluated with a specific emphasis on research conducted over the past five years. In addition, insights into the potential of CSA practices are provided to enhance agricultural productivity while maintaining environmental sustainability. Case studies from diverse regions demonstrating the practical benefits of CSA are also included in the review. Furthermore, the paper addresses policy support and existing challenges in promoting CSA, as reported during 2018-2023. Finally, it emphasizes the need for future advancements and interdisciplinary collaboration to mitigate the negative impact of climate change on global food security. This comprehensive study lays the foundation for implementing a sustainable and resilient framework by transforming conventional agriculture into CSA.

Due to the intermittent behaviour of the sun, accurate prediction of solar photovoltaic (PV) power is crucial for efficient and reliable operation of solar power plants. This paper presents state of the art approach for PV panels power prediction using machine learning (ML) method. Two ML models, namely Random Forest (RF) and Support Vector Machine (SVM) are trained and tested using input data of solar irradiance, ambient temperature, wind speed, humidity, precipitation and PV output power. The case study is presented for the grid tied PV system installed at University Tun Hussein Onn campus Batu Pahat Malaysia. The results indicated regression predictions reasonably fit the actual data, proving good potential of ML for PV power prediction. Besides, the predictive performance of RF and SVM was compared based on three evaluation metrics: coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE). Both ML models showed comparable predictive power with RF performing slightly better than SVM. The R2 value for RF was 0.850 compared to 0.832 for SVM, indicating that RF was able to explain more of the variability in the data. Additionally, RF had lower values for both RMSE and MAE, indicating that it was better able to predict values of the solar PV power output. The conclusion from this study imparts the importance of ML methods to predict PV power which could be useful for optimizing the efficiency and reliability of solar energy systems.

Dear authors, please improve the manuscript by considering the following aspects. Please replace the term “infectious leaves” with an appropriate one, such as “infected leaves. Similarly, try to use field-specific standard words like "pre...

* The abstract of the research article outlines a study focused on addressing the challenge of ensuring reliable performance for electric vehicle (EV) charging ports within charging infrastructure. However, it lacks to provide specific quantitative...

Given the significant impact of the UN 2015 agenda on sustainable development, the search for eco-friendly and efficient energy solutions has intensified. The renewable based hybrid systems have emerged as feasible solution in this context. This paper explores the potential of utilizing a hybrid energy system (HES) from a distinctive perspective — addressing the challenges of load shedding at the distribution level. The HES configuration in this investigation includes photovoltaic (PV) array, wind turbines (WT), battery storage unit (BSU) and diesel generator system. The research involves simulations, optimization, and sensitivity analysis for a residential community in the southwestern part of Pakistan, which frequently experiences load shedding. Grasshopper optimization algorithm (GOA) is applied to simultaneously minimize the levelized electricity cost (LEC), payback period (PBP), and loss of power supply probability (LPSP). Simulations compare different HES configurations based on the estimation of local renewable energy potential and the load shedding schedule of the utility company. The optimal solution, with $N_{PV}=110$ , $N_{WT}=2$ , and $N_{BSU}=16$ , was selected, resulting in a minimum LEC of 6.64 cents/kWh and a PBP of 7.4 years. These results are validated using the particle swarm optimization algorithm (PSO). The performance of HES is benchmarked against conventional solutions, such as standalone diesel generators, battery-based uninterruptible power supplies (UPS), and combinations of generators and UPS. The findings demonstrate the significant superiority of HES over conventional solutions in terms of reduced LEC, shorter PBP and reduced carbon emissions. Finally, the sensitivity analysis examines the impact of varying component prices, feed-in tariff rates, meteorological conditions, and load demand variations on the LEC and PBP of the HES.