The selection of a suitable place for an activity is an important decision made for a project, which requires assessing it from different points of view. Educational use is one of the most complicated and substantial uses in urban space that requires precise and logical attention to its location and neighborhood with similar and consistent uses. Faculties of universities are educational spaces that should be protected against physical and moral damage to create a healthy educational environment. To do this, it is necessary to find and assess the factors affecting the location of educational spaces. The extant study aimed at finding and assessing the factors affecting the location of educational spaces to locate art and architecture schools or faculties in 4 important universities. The present study is applied developmental research in terms of nature and descriptive-analytical in terms of method. This study used the AHP (Analytical Hierarchy Process) weighing and controlled the prioritization through the TOPSIS (Technique for Order Preference by Similarity) technique in the methodology phase. Since there was no criterion and metric for these centers, six were chosen as the primary metrics after reviewing the relevant theoretical foundations, early investigations, and collecting effective data. Finally, the results indicated the most important factors of vehicular or roadway access, pedestrian access, slope, parking, adjacency, neighborhood, and area. Among the mentioned factors, pedestrian access (w: 0.4231) had the highest weight and was the priority in the location of architecture faculty in studied campuses and areas inside the universities.

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.

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.

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.

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.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.