The impact of mass media on society is undeniable. Its effective influence on reporting an issue in hand can make or break the story. Free media is not an extravagance. It is the foundation of impartial development in society. Media can monitor government actions and so serve as a check on public policy. Similarly, corruption can be exposed by the media. It provides a forum for a range of viewpoints on reform and governance and aids in fostering a consensus among the populace in favour of change. These media improve the efficiency of markets. This study examines the role of mass media in Pakistan's economic development. To meet the aim of the study, this research uses the interview method. The findings of the study suggest that the media has the power to play its part in handling Pakistan's economic crisis with objective and responsible coverage. However, corporate media in Pakistan is lacking in representing actual issues or possible reasons for economic decline in the country. There is a need for more responsible reporting by mass media in Pakistan.

The 1980s proved critical not merely for contemporary Afghan history but also for the women’s struggle in Afghanistan. Showcasing Afghan women’s struggles, this article argues that the 1980s proved crucial in shaping feminist debates in Afghanistan. Arguably, Afghan women’s lived experience from the 1980s drove home the conclusion that foreign occupation does not bring women freedoms, while religious tapestries to sugarcoat secular women’s demands reinforce patriarchy. They learnt in the 1980s that their struggles antecede and should succeed foreign interventions. It was, therefore, understandable that the first demonstration against the Taliban, when they re-entered Kabul in August 2021, was held by women.

Objective: To assess deionized water's aging effect on the hardness of four direct tooth colored dental filling materials i.e., Glass Ionomer Cement, Resin Modified Glass Ionomer, Giomer and Compomer.Study Design: An in vitro experimental study performed in triplicates.Place and Duration of Study: The study was carried out at PG Laboratory, Department of Science of Dental Materials, Army Medical College, Rawalpindi, Pakistan from February 2023 to August 2023.Methods: Three disc-shaped samples (n=3) of 10 mm diameter and 1 mm thickness of each restorative material were prepared using stainless steel molds per manufacturer's instructions. These disc-shaped samples were then suspended in a 15 mL conical centrifuge tube containing 10 mL deionized water, followed by incubation in a Forced Convection Laboratory Oven at 37 C where these were allowed to age over a period of 1-2 years. Vickers Hardness of each sample was then checked at two-time intervals, i.e., after 24 h setting and after 2.5 years of ageing through the Micro Vickers Hardness Tester calibrated at 1Kgf (9.80 N) with dwell time of 10 seconds and light intensity 10. Results: After one day (24 h) aging in deionized water, maximum mean Vickers hardness number (VHN) was exhibited by Compomer (539.83±58.08 VHN) and least by GIC (175.75±24.47 VHN). After aging the sample for 912 days, maximum mean VHN was observed for GIC (420.67±99.66 VHN) and least by Compomer (354.33±9.22 VHN). Conclusion: RMGIC sustains its hardness on aging, hardness of GIC increases while that of Giomer and Compomer decreases upon aging.
 How to cite this: Gul H, Nayyer M, Junaid Y, Pasha M, Ejaz T, Liaqat U, Hassan SH. Effect of Aging on Hardness of Glass Ionomer, ResinModified Glass Ionomer, Giomer and Compomer Dental Restorative Materials. Life and Science. 2023; 4(4): 438-444. doi: http://doi.org/10.37185/LnS.1.1.437

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management.

PurposeThe banking industry has always been vulnerable to cyberattacks. In recent years, Pakistan’s banking sector experienced the most intense cyberattack in its over 70-year history. Due to these attacks, a large number of debit card accounts of major banks were negotiated. This study aims to examine the impact of cyberattack awareness and customers’ commitment levels after these cyberattacks.Design/methodology/approachThe study integrated the commitment–trust theory framework for the relationship of trust and commitment to the usage of online banking services. The partial least square structural equation modeling is being used to explore the relationship between customer’s trust, which is an outcome of continuous usage, and customer perception of affirmative cybersecurity measures the bank.FindingsThe findings revealed that customer trust in online banking is positively associated with customer commitment, but customers’ cyberattack awareness negatively impacts customer trust and commitment to online banking.Practical implicationsThe study highlights the importance of proactive communication, transparency and robust incident response that helps organizations establish themselves as trustworthy entities while prioritizing customer information and transaction protection.Originality/valueThe authors report on how cyberattacks on the banking sector influence the trust and commitment of the customers in the sector. The variable of cyberattack awareness used in this study is novel in online banking literature.

State of charge (SoC) estimation is critical for the safe and efficient operation of electric vehicles (EVs). This work proposes a hybrid multi-layer deep neural network (HMDNN)-based approach for SoC estimation in EVs. This HMDNN uses Mountain Gazelle Optimizer (MGO) as a training algorithm for the deep neural network. Our method leverages the intrinsic relationship between the SoC and the voltage/current measurements of the EV battery to accurately estimate the SoC in real time. We evaluate our approach on a large dataset of real-world EV charging data and demonstrate its effectiveness in comparison to traditional SoC estimation methods. Four diverse Li-ion battery datasets of electric vehicles are employed which are the dynamic stress test (DST), Beijing dynamic stress test (BJDST), federal urban driving schedule (FUDS), and highway driving schedule (US06) with different temperatures of 0oC,25oC,45oC. The comparison is made with Mayfly Optimization Algorithm based DNN, Particle Swarm Optimization based DNN and Back-Propagation based DNN. The evaluation indices used are normalized mean square error (NMSE), root mean square error (RMSE), mean absolute error (MAE), and relative error (RE). The proposed algorithm achieves 0.1% NMSE and 0.3% RMSE on average on all datasets, which validates the effective performance of the proposed model. The results show that the proposed neural network-based approach can achieve higher accuracy and faster convergence than existing methods. This can enable more efficient EV operation and improved battery life.

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This research uses a survey as a data gathering instrument to look into the "Impact of Pakistani Geo and Hum TV Dramas on Language and Dressing of Women”. This study is quantitative by nature. It targeted 400 men and women between the ages of 20 and 45 watching Geo and Hum programs on Television. The findings suggested that Geo and Hum TV dramas impacted women's language and attire. The hypothesis was put to the test using the chi-square statistical test. The percentage and frequency of answers were calculated using SPSS Software. The impact of Geo and Hum TV programs on women was studied using the Cultivation Theory. Results of the study show that mass media, particularly Television, has the most substantial effects on its viewers' habits. The influence of Geo and Hum TV dramas on women has impacted a lot since the arrival of cable and satellite communications. Private channels are a source of change in women’s lifestyles.

Most research has focused on determining how popularity and peer pressure impact behaviours, rather than identifying a key feature such as dental aesthetics and studying its impacts in relation to popularity and peer pressure. A cross sectional study was conducted on a sample of 527 children from four schools located in Lahore, Pakistan. A 14-point questionnaire was developed, using existing measures of peer pressure, and popularity. The selected questions were modified to investigate the issues of dental aesthetics and integrated into the WHO oral health questionnaire for children. More than 50 % of the participants indicated popularity issues regarding dental aesthetics. 63.5 % of the responses indicated an influence of relatives and friends, whereas 38 % responses reported of harassment and bullying at schools. Regression analysis shows that the females were 1.99 times more likely to get comments from relative or friends about their teeth and 2.17 times more likely to be bullied or harassed at school due to their teethwhen compared to the males. Fathers with a higher education brought about higher popularity and peer pressure issues. Mothers with a higher education were lesslikely to cause popularity and peer pressure issues thanmothers with a lower education. Popularity and peer pressure were both significantly related to higher dental visitation. Popularity and peer pressure have a direct link to dental aesthetics in an individual and are impacted by gender, family relatives and parental influences. The area of popularity and peer pressure related to dental aesthetics canbe targeted in health education programs to empower children to adopt better oral health behaviours.