The environment and the economy are negatively impacted by conventional energy sources, such as coal, gasoline, and other fossil fuels. Pakistan’s reliance on these resources has resulted in a catastrophic energy crisis. This has driven the government to make critical decisions such as early retail closures, power outages for the industrial sector, and an increase to two days a week vacations. Wind energy, accessible and affordable, will become a viable option for meeting Pakistan’s present and future energy demands. Approximately 3% of Pakistan’s land can produce nearly 132 GW of power with an installed capacity of 5 MW per km². In this study, four zones (Karachi, Thatta, Badin, and Jamshoro) in Sindh province are assessed for the feasibility of wind energy generation. The installed capacity, generator types, and detailed specifications are provided for each zone. Moreover, the wind mapping of Pakistan is presented considering the four potential zones. The zones are analyzed using annual wind speed and power output considering wind data measured at 50 m height over one year. The higher mean speed is recorded at Jamshoro compared to other zones. The analysis indicates that all four sites are suitable for large-scale wind power generation due to their energy potential.

Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women`s at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies.

The surging electricity demand in Pakistan has led to frequent blackouts, prompting government initiatives to expand power plant capacities and improve the national grid. The government prioritizes integrating large‐scale renewable energy sources, such as wind and solar power, to reduce dependence on conventional power plants. However, the intermittency of renewables leads to forecasting errors, requiring extra power reserves from conventional units, thereby escalating operational costs and CO₂ emissions. The country currently utilizes a manual mechanism for power balancing operations, overlooking critical grid constraints of the transmission line loadings. In such conditions, injecting large‐scale power from renewables can lead to significant fluctuations in line power flows, risking transmission line loadings and compromising the system’s secure operation. Hence, this paper has developed an automatic generation control (AGC) model for the highly wind‐integrated power system to alleviate line congestions in the network and enhance the economic operation of the system. The study utilizes the Pakistan power system as a case study to simulate the proposed model. The developed real‐time power dispatch strategy for the AGC system considers the constraints of the transmission line to avoid congestion. It integrates wind energy as operating reserves to enhance the economic operation of the system. When managing line congestion, it identifies overloaded bus lines and adjusts power regulation accordingly while compensating for shortfalls by augmenting transmitted power from regional grid stations. However, it maintains a constant dispatch ratio without line overloads, aligned with generation capacities. Additionally, the strategy integrates reserve power from the wind power plant and traditional generating units to further improve economic operations. Simulations have been conducted using PowerFactory software, employing the eight‐bus and five‐machine models to replicate the characteristics of the Pakistan power system. The results demonstrate the effectiveness of the proposed AGC design in mitigating transmission line congestion of power systems that are heavily integrated with wind energy sources while simultaneously ensuring the economic operation of generating units.