Joy Learning: Smartphone Application For Children With Parkinson Disease

Parkinson's is a Neurologic disorder that not only affects the human body but also their social and personal life. Especially children having the Parkinson's disease come up with infinite difficulties in different areas of life mostly in social interaction, communication, connectedness, and other skills such as thinking, reasoning, learning, remembering. This study gives the solution to learning social skills by using smartphone applications. The children having Parkinson's disease (juvenile) can learn to solve social and common problems by observing real-life situations that cannot be explained properly by instructors. The result shows that the application will enhance their involvement in learning and solving a complex problem

Corporate Risk Tolerance and Acceptability towards Sustainable Energy Transition

The omnipresence of risk prevails in almost every aspect of human life. Individuals and societal factors are pivotal in the decision-making process to judge acceptability and tolerability of risk. Tolerability of risk (ToR) is characterized by dynamism pinned in the process of decision making that helps to gauge the society and individual’s risk. The energy transition implies switching the energy system from fossil fuels or any traditional mechanism to modern renewable sources that are sustainable. The energy transition is paramount important in the current global energy system to attain sustainable goals for organizations. This study used the positivism research paradigm to address the research questions. The quantitative approach helps to examine the cause-and-effect relationship. It also helps to collect systematic information to meet the objectives of the research. A total sample of 300 was selected for the data collection from renewable energy companies. The study used positivism research philosophy applied deductive approach. The data is analyzed through PLS-SEM. It is summarized that the scale of risk acceptability and tolerability in Pakistan is moderate which encourages companies to work progressively and increases sociocultural activities to make society a partner of this new shift in energy transition that will ultimately increase the level of risk acceptability. Nevertheless, as a society, people are neither high-risk takers nor risk avoiders due to income constraints, macroeconomic uncertainty, and political instability.publishedVersio

Short-term global horizontal irradiance forecasting using weather classified categorical boosting

Accurate short-term solar irradiance (SI) forecasting is crucial for renewable energy integration to ensure unit commitment and economic load dispatch. However, hourly SI prediction is very challenging due to atmospheric conditions and weather fluctuations. This study proposes a hybrid approach using weather classification and boosting algorithms for short-term global horizontal irradiance (GHI) forecasting. In data pre-processing steps, we employ random forest for feature selection and K-means clustering for weather classification. The weather-based clustered data is used for the model training using categorical boosting (CatBoost). The proposed weather-classified categorical boosting (WC-CB) scheme is compared with benchmarks in literature like adaptive boosting (AdaBoost), bi-directional long short-term memory (BiLSTM) and gated recurrent unit (GRU) using datasets from two distinct geographical locations obtained from the National Solar Radiation Database (NSRDB). The results show that the proposed WC-CB hybrid approach has lower forecast errors compared to conventional CatBoost modelling. The error reduction of 16% and 39% in root mean square error and 6% and 9% in mean absolute error is recorded for the two datasets, respectively. These findings demonstrate the importance of weather classification in improving forecasting accuracy with potential implications for broader renewable energy applications

A hybrid approach for forecasting occupancy of building’s multiple space types

The occupancy datasets are useful for planning important buildings’ related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants’ count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants’ count in each room. The models’ performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively

Library Professionals Learning Behaviour with the Level of Expertise: A Survey from Pakistan

Purpose: This study is designed to examine the relationship of learning behavior with the level of expertise among library professionals working in Pakistan. It aims to compare the level of professional skills for different library areas. The study also explores the correlation of demographic factors with the level of expertise. Research Methodology: A quantitative research design is used to carry out the current study. A structured questionnaire is developed to seek responses from respondents. Some hypotheses are developed to check these relationships. Library professionals working as practitioners in different library sectors or faculty members teaching at library schools are the population of the study. A convenient sampling technique is used to collect data. The Link to the online questionnaire was shared on social media and LISTSERVS for recruitment of data. Both descriptive and inferential statistics were used to analyze data. Practical implications: This study will show the perspectives and dynamics of learning among library professionals working in different capacities in Pakistan. Results will help in measuring the level of expertise among library professionals in different library specializations. Organizational and personal issues that were instrumental and detrimental to their learning will help planning future professional development programs. It will also serve as a guideline for improvement in expertise among library professionals and a baseline for future longitudinal studies across different disciplines

Employee Perception, Barriers towards Career Development and HRM Strategies tenacity Employee Career Development

Purpose: The aim of this study is to examine the influence of Employee Perception, Barriers towards career development and HRM Strategies on Employee Career Development in the telecommunication sector of Pakistan. Design/Methodology/Approach: This study adopted quantitative approach using questionnaires. The data was collected from 203 employees working in Telecommunication companies of Pakistan. The selection criterion of the respondents was based on convenient random sampling. Statistical analysis was performed using Structural Equation Modeling – Partial Least Squares (SEM-PLS). Findings: The findings revealed that Employee Perception and HRM Strategies significantly impact Employee Career Development. At the same time, Barriers towards career development also have insignificant impact on Employee Career Development. Implications/Originality/Value: This study shall significantly contribute in developing the fair Human Resource Management (HRM) strategies, positive employee perception and that can improve the performance of employees and help them develop the skills they need to establish a human resource sector

Impact of Online Freelancing on Economic Growth of Developing Countries Like Pakistan

In the digital age, freelancing has had a huge impact and as an alternate job option, has grown more popular. In this field, studies have been scarce. The link between freelancing and economic growth is investigated in this study. A total of 100 freelancers were interviewed using a Google form. The current administration has set aside and given special attention to assisting freelancers, with the goal of allowing them to play an important role in their families as a whole. Anyone with particular skills can work as a freelancer without getting a graduate degree. However, in Pakistan, a freelancer's life is already difficult due to poor working conditions, a lack of affordable internet, and the exorbitant cost of technical peripherals. Furthermore, the social components are more difficult because many people here are discouraged from seeking demanding work rather than a government job. Hence, because of developing countries, online freelancing can create employment