Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
Accurate agricultural commodity price prediction remains a challenging task due to nonlinear market dynamics, regional variability, and frequent price fluctuations. While traditional machine learning models provide reasonable performance, they often struggle to balance feature representation and classification robustness. Building upon the optimal feature subset obtained through hybrid metaheuristic feature selection in earlier phases, this study proposes a novel MLP–SVM hybrid classification framework for agricultural commodity price prediction. In the proposed model, a Multi-Layer Perceptr
This paper explores the relationship between financial transparency and Governance Mechanisms as determinants of growth in early-stage venture capital-backed corporations. It seeks to test the direct and the moderating impact of the governance structures on the relationship between transparency and firm growth. The quantitative, cross-sectional research design was chosen. The survey that was used to collect data was structured and covered early-stage venture capital-backed firms. Multi-item Likert scales were used to measure the constructs. The proposed hypotheses were tested using regression
The global nonwoven market is experiencing sustained growth, driven by demand in filtration, hygiene, healthcare, and packaging sectors. This boom requires faster product improvement cycles, tighter fee control, and stronger product stop space, primarily basis weight (g/m2), which directly controls mechanical and functional performance This paper design for predicting line-base weight tops of 100% nonwoven polyesters version product3. the development of the artificial intelligence (AI) selection assistant done, considers key method variables as input: area of layer (stacking), draw ratio, and
This study numerically investigates the effect of an aluminum (AA6063) heat sink on the thermal and electrical performance of a solar photovoltaic (PV) panel using a finite-volume-based software. Heat sinks with 2, 4, and 23-finned configurations were analyzed under natural convection and validated against experimental data. Results show strong agreement between simulation and experiment. Increasing the number of fins significantly reduced surface temperature and improved power output. The 23-fin (F23) configuration achieved the best performance, lowering temperature and increasing peak power
The article presents the development of an intelligent wearable system for continuous monitoring of physiological parameters and early detection of stress conditions using modern microcontrollers and machine learning algorithms. The system implements a multisensor approach that includes the registration of electrodermal activity (EDA), heart rate (HR), body temperature, and accelerometer data. For automatic stress level classification, several machine learning models were compared using the main metrics (accuracy, precision, recall, F1-score). The best accuracy was achieved using the Random Fo