Focusing on high-endization, intelligentization, and greenization development in manufacturing, this study constructs a comprehensive evaluation index system for the integrated development of Chinese manufacturing’s “three transformations”. It measures the integrated development level of these transformations across 30 provinces in China using the CRITIC-entropy weight method and the coupling coordination degree model, and it explores the regional differences and dynamic evolution of this integrated development level by means of approaches such as kernel density estimation and spatial Markov chains. The findings reveal that (1) the integrated development level of the “three transformations” in regional manufacturing shows an overall upward trend across all regions, with green development significantly outpacing both intelligent and high-end development, and (2) there are notable disparities in the convergence levels across regions. The central and northeastern regions show a trend of multipolarization, while the internal differences in the eastern and western regions have expanded but no bipolarization has emerged. (3) Finally, the convergence of the “three transformations” exhibits club convergence characteristics, with a low probability of state transitions but significant spatial spillover effects. Based on these findings, policy recommendations are proposed to promote the integrated development of the “three transformations” in China’s manufacturing sector, providing insights for high-quality development.

Due to Internet, mobile phone, and side discussion distractions, students lose interest in class quickly. This may be a consequence of the present teaching approach, which is predicated on slides. Numerous researchers are incorporating novel teaching strategies that aim to enhance the dynamic nature of classes, including task-based learning, flipped classrooms, and activities. Most educational games (EGs) are modified or expired versions of previous games. This is especially relevant when the instructor has little time to build the game or little experience. Software Product Line (SPL) technique combines systems with comparable capabilities to create similar products. This research demonstrates a literature review to evaluate the benefits, characteristics, time, and effort of building an SPL for EG development. Narrative, feedback, learning progression, educational activities, and reinforcement were among the essential attributes of the SPL that were identified during the course of the investigation. Additionally, the motivation for development illustrated that this activity could decrease the time and cost necessary to create these games, while simultaneously reducing the learning curve of students by utilizing games with comparable structures, images, and narratives. Lastly, the instruments that can facilitate the development of SPL and the efforts necessary to develop them are addressed.

With the development of 3D printing, weapons are easily printed without any restriction from the original providers. Therefore, anti-weapon detection is necessary issue in safe 3D printing to prevent the printing of 3D weapon models. In this paper, we would like to propose an anti-weapon detection algorithm to prevent the printing of 3D weapon models for safe 3D printing based on the D2 shape distribution and the convolutional neural networks (CNNs). The purpose of the proposed algorithm is to detect anti-3D weapon models when they are used in 3D printing. The D2 shape distribution is computed from random points on the surface of a 3D weapon model and their geometric features in order to construct a D2 vector. The D2 vector is then trained by a CNNs. The CNNs is used to detect anti-3D weapon models for safe 3D printing by training D2 vectors which have been constructed from the D2 shape distribution of 3D weapon models. Experimental results show that the histogram of the D2 shape distribution of 3D weapon models in the same class is similar. Training and testing results also show that the accuracy of the proposed algorithm is higher than the conventional works. The proposed algorithm is applied in small application, and it could detect anti-3D weapon models for safe 3D printing.

O presente estudo traz algumas explicações sobre estoques em um hospital localizado na Região Metropolitana de Natal/RN. No Método de Pesquisa, foi realizado um estudo bibliográfico para a obtenção de conceitos de autores sobre o assunto estudado. Com isso, foram realizadas visitas ao hospital filantrópico com entrevistas semiestruturadas ditadas ao gestor de estoque do hospital. Os resultados deste artigo apresentam soluções dos problemas relacionados a grandes compras de materiais e ausências de alguns produtos, utilizando métodos como a Curva ABC, Giro e Cobertura de Estoques, ajudando assim, a administrar o estoque existente. A importância deste trabalho se reflete em atingir estes objetivos tornando este trabalho executável para a gerência do hospital, para auxiliar a utilizar os métodos propostos dentro dos estoques a fim de controlar as saídas e entradas dos materiais médicos hospitalares e dos medicamentos utilizados neste hospital.

This study is intended to suggest best model to predict students’ academic performance at university level. For this purpose, primary data is collected from 400 undergraduate and graduate students of eight departments of Mirpur University of Science and Technology (MUST), which were selected through stratified random sampling. CGPA is used as the indicator of students’ academic performance. Stepwise linear regression is used to select the best model to predict students’ academic performance at tertiary level. The final model is selected through stepwise regression included six variables: Student’s IQ, ownership of AC, gender, geographic location, self-study hours and ownership of fridge as the significant predictors of students’ academic performance at tertiary level. IQ, ownership of assets (AC and Fridge) and self-study hours are found to have positive effect on student’s CGPA while being male and household’s distance to nearest market (measure of geographic location) are found to have negative effect on student’s CGPA. The results suggest that the adoption of policies which encourage assets accumulation by households and which encourage students to devote more time to their self-study.

In this study, drought analysis is used to calculate missing meteorological data. The study makes extensive use of Artificial Neural Network (ANN) methods like Feed Forward Back Propagation (FFBPNN) and Generalized Regression Neural Networks (GRNN). The Seyhan Basin, which is an area of arable land in Turkey, was chosen to supply precipitation and flow data as input data for these models. The method achieving the best correlation result was used for the subsequent predictive model. Hydrological drought analysis was calculated using different methods, including the Standardized Precipitation Index (SPI), the Standardized Runoff Index (SRI) and the De Martonne Index (DMI). From the results obtained by these methods, present and prospective drought graphs were created using a Multiple Nonlinear Regression (MNLR) method. Consequently, an increase in possible drought and extreme drought values were generally observed and it is thought that this increase will continue in the future. If drought values do increase in future years, it is vital to use water resources effectively in these conditions.

In this paper, an efficient multiple-input multiple-output (MIMO) detection scheme with low complexity is proposed. The proposed scheme has a feature of combined a QRD-M with a complex lattice reduction (LR)-aided detection scheme. For the first T stages, the QRD-M detection is executed. And then, the complex LR-aided detection is executed for last Nt-T stages. Simulation results show that the proposed detection scheme provides error performance comparable to the QRD-M detection. And this scheme can significantly reduce the computational complexity compared with the QRD-M, because the complexity for the QRD-M is limited by the newly adopted parameter T. The value of T is determined by required system performance.

In this paper, a selective QR-based detection scheme using channel condition is proposed in MIMO-OFDM system. In the proposed scheme, a channel state is estimated by using channel condition number. And then, either QRD-M detection scheme or QR-PIC detection scheme is selected according to the channel condition number. The proposed scheme selects QRD-M detection scheme in case of bad channel condition and QR-PIC detection scheme in case of good channel condition. Compared with the QRD-M detection scheme, the proposed detection scheme can achieve a similar performance with significant reduction in computational complexity.