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.

This study aimed to assess the influence of awareness and health habituation techniques, student management activities, the role of stakeholders, and the character of healthy living on health independence. The method used in this study is quantitative with descriptive test analysis techniques, partial t statistics and F test. This research was conducted in elementary schools in East Java Province, consisting of 92 elementary schools in 5 regions at East Java. Samples were taken using purposive techniques, and the number of samples was 348 people, consisting of principals, teachers and students. The results found that awareness and health habituation techniques have a significant influence on the character of healthy life of students, student management activities have a significant influence on the character of healthy life, the role of stakeholders has a significant influence on the character of healthy life, awareness and health habituation technique have a significant influence on health independence, student management activities have a significant influence on health independence, the role of stakeholders has a significant influence on health independence, the character of healthy living has a significant effect on health independence, and student management activities and the role of stakeholders have a significant effect on the character of healthy life, and have a significant impact on health independence.

The purpose of this study is to explore new financial product’s impact on the behaviour of individual investors. To analyze investors’ risk and return expectations, this article investigates trading volumes before and after the introduction of financial product innovation. An event research technique was used to gather data from the National Stock Exchange. Data was analyzed using descriptive statistics and the Sharpe ratio approach, which were provided by different investors. The research results highlight that individual investors’ overreaction behaviour is brought out by financial product innovation. Furthermore, the study’s results imply that rising trading volumes are not entirely explained by updated risk-adjusted returns and that new financial products lead to excessive trading by investors and lowering returns. Higher trading volumes are not explained by better risk-adjusted returns. Young investors often respond irrationally to information offered by financial advisors, resulting in short-term gains at the expense of long-term gains. The study demonstrates that the development of innovative financial products does not always result in investors’ long-term prosperity. Worse outcomes and excessive trading could follow from it. The paper concludes by providing various real-world implications that the benefits and drawbacks of innovative financial products should be spelled out in detail by financial institutions and representatives. his research contributes to the implementation of individual investors’ overreaction behaviour that is brought out by financial product innovation. It highlights that higher trading volumes are not explained by better risk-adjusted returns.

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

Himalayan ‘Ecotone’ temperate conifer forest is the cradle of life for human survival and wildlife existence. Human intervention and climate change are rapidly degrading and declining this transitional zone. This study aimed to quantify the floristic structure, important value index (IVI), topographic and edaphic variables between 2019 and 2020 utilizing circular quadrant method (10m × 10m). The upper-storey layer consisted of 17 tree species from 12 families and 9 orders. Middle-storey shrubs comprise 23 species representing 14 families and 12 orders. A total of 43 species of herbs, grasses, and ferns were identified from the ground-storey layer, representing 25 families and 21 orders. Upper-storey vegetation structure was dominated by Pinus roxburghii (22.45%), while middle-storey vegetation structure was dominated by Dodonaea viscosa (7.69%). However, the ground layer vegetation was diverse in species composition and distribution. By using Ward’s agglomerative clustering technique, the floral vegetation structure was divided into three floral communities. Ailanthus altissima, Pinus wallichiana, and P. roxburghii had the highest IVI values in Piro–Aial (Group 2), Piwa–Quin (Group 3) and Aial–Qugal (Group 2). The IVI values for Aesculus indica, Celtis australis, and Quercus incana in Aial-Qugal (Group 2) were not determined. Nevertheless, eleven of these species had 0 IVI values in Piro–Aial (Group 2) and Piwa–Quin (Group 3). Based on the CCA ordination biplot, significant differences were observed in floral characteristics and distribution depending on temperature, rainfall, soil pH, altitude, and topographic features. Based on Ward’s agglomerative clustering, it was found that Himalayan ‘Ecotone’ temperate conifer forests exhibit a rich and diverse floristic structure.