Interconnected components of holistic development, such as being thankful, addressing basic psychological needs, and acting effectively toward others, should be a priority for college athletes. Athletes at the College level need all-encompassing support systems to ensure their health, happiness, and success because of the special difficulties they have juggling their academic, athletic, and personal schedules. Problems with work-life balance, stress, and performance expectations all impede College Student Athletes’ holistic development. A thorough plan that considers all of the social, emotional, and psychological aspects impacting athlete development is necessary to overcome these obstacles. An Integrated Holistic Development Program for College Athletes (IHDP-CA) is suggested in this paper as a method that incorporates various aspects of positive psychology, mindfulness, resilience training, and the enhancement of interpersonal skills. Athletes at the College level can benefit from this all-encompassing program’s emphasis on helping others, developing an attitude of gratitude, and meeting basic psychological requirements. Sports counseling services, schools, and College athletic teams can all benefit from the IHDP-CA. A more positive and supportive sporting environment can be achieved when the program takes a more holistic approach to athletes’ needs, improving their mental health, social connections, and overall performance. The possible effect of the IHDP-CA on the holistic development outcomes of College Student-Athletes will be predicted through simulation analysis. To gain a better understanding of the program’s long-term viability, efficacy, and scalability, this analysis will run simulations of different situations and tweak program settings.

Using the Resource Advantage Theory approach, this research aims to examine the gap between entrepreneurial opportunities and marketing performance, with market-based innovation capability acting as a mediating variable. The data collection method used non-probability sampling with a purposive sampling technique. The data that was eligible to be processed were 250 respondents. Hypothesis testing was used using the AMOS application. The research results show that market-based innovation capability can improve marketing performance as a mediating variable. In addition, market penetration strength can also improve marketing performance. As a strategic variable, market-based innovation capability (MBIC) converts entrepreneurial opportunities into competitive advantages relevant to market needs. In addition, business actors become more adaptive and responsive to market dynamics, increasing competitiveness sustainably. MBIC, rooted in the Resource Advantage Theory of competition, contributes to developing market-based innovation strategies in the UMKM sector.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

Among carbon nanoparticles, fullerene has been observed as a unique zero-dimensional hollow molecule. Fullerene has a high surface area and exceptional structural and physical features (optical, electronic, heat, mechanical, and others). Advancements in fullerene have been observed in the form of nanocomposites. Application of fullerene nanocomposites has been found in the membrane sector. This cutting-edge review article basically describes the potential of fullerene nanocomposite membranes for water remediation. Adding fullerene nanoparticles has been found to amend the microstructure and physical features of the nanocomposite membranes in addition to membrane porosity, selectivity, permeation, water flux, desalination, and other significant properties for water remediation. Variations in the designs of fullerene nanocomposites have resulted in greater separations between salts, desired metals, toxic metal ions, microorganisms, etc. Future investigations on ground-breaking fullerene-based membrane materials may overcome several design and performance challenges for advanced applications.