Republic Polytechnic

BackgroundCounty-level governments (CLG) are the basic organizational units of China's administrative power. The collaborative management paths (CMP) for public health at the CLG carry a variety of pressures and are responsible for coordinating the allocation of resources, and need to be well developed in terms of their capacity structure.MethodThis study defines the CMP of CLG in public health as six variables of policy resource: configuration capability, perception capability, insight capability, integration capability, learning capability, and innovation capability. This study incorporates the fsQCA algorithm to explore the non-linear relationship between the collaborative management capabilities of the CMP of CLG in public health and policy resources.ResultsA configuration of the CMP of CLG for public health was identified (solution coverage 36.67%, solution consistency 98.24%). The CLG's CMP has full-time-phase characteristics, i.e., the diversion management time-phase is characterized by conventional and non-conventional management time-phase groupings, but the non-conventional management time-phase does not have a bottleneck level. CLG's CMP has 3 core elements (Integration, Learning, and Perception Capabilities) and 2 supporting elements (Innovation and Insight Capabilities). The bottleneck level analysis of CLG's CMP resulted in a 10% level of perceived capacity being required to achieve a 60% level of configured capacity. The sensitivity test of the CMP for CLG suggests that the pathway is robust.ConclusionThis study presents a framework for observing/interpreting the results of CLG as a managerial behavior (policy resource management) at the grassroots level of government from the perspective of CMP.

Abstract A large number of studies have examined the economic benefits in hosting mega sports events. The aim of this study was to examine the non-economic benefits arising from hosting a regional sports event, the Southeast Asian Games, hosted by Singapore in 2015. In this study, 805 students from a tertiary educational institution in Singapore were surveyed. The results showed that while there was an increase in national pride as measured by the General National Pride Scale, it was not statistically significant. This was contrary to earlier studies and suggests that any increase in national pride in hosting sports events may be diminished when the event is of a smaller scale. Using the short form of the International Physical Activity Questionnaire (IPAQ), the results also showed that there was an increase in the intention to participate at higher levels of physical activity. This suggests that there may be non-economic benefits in hosting regional sports events such as the SEA Games.

Rapid technological progress and increasingly pressing needs for energy efficiency, safety, and personalised comfort have driven the development of intelligent systems for residential automation. This paper presents the design and implementation of a modular IoT smart-home system based on a microcontroller architecture with real-time data processing. The developed prototype integrates sensor modules for detecting temperature, humidity, air quality, illuminance, vibration, precipitation, and flame, as well as actuators for automated control of windows, doors, lighting, ventilation, and alarm mechanisms. The system is connected to a mobile application that enables monitoring and interactive control in real time, and users can define scenarios such as “night mode” or “away mode”. Special emphasis in the design is placed on the system’s modularity, its energy optimisation, and the ability to adapt behaviour based on historical data and user habits. The system’s functionality was tested on a physical model and in real conditions, establishing that it reacts within a time window of 1–3 seconds from the moment a change in environmental parameters is detected. The obtained results indicate significant potential for integrating microcontrollers, an IoT platform, and adaptive control algorithms in the domain of smart buildings and future concepts of urban automation. The paper also opens up avenues for further development with integrated machine-learning and artificial-intelligence algorithms aimed at achieving fully autonomous control of the residential environment. This iteration includes a fully functional physical prototype and application, while the predictive AI part is evaluated offline via simulation/emulation based on recorded logs, without on-device inference.

A Transcritptome dataset of whole blood RNA-seq data from type 1 diabetic (T1D) patients and healthy volunteers was deposited on GEO dataset in 2020 (GSE123658). Because of recurrent citations to the transimmunom dataset, read counts at gene level are revised and putative up-regulated biomarkers for T1D diganosis and targeted therapy are presented. We presente TRHDE, GOLGA8I, HBZ, BOLA2B, SPATA22, SEMA6B and FOXQ1 genes as putative biomakers for diagnosis (log2FC ≥1.5 and a padj ≤ 0.05). All together, our result points to GSE123658 dataset as source of biomarkers and therapy target genes.

The world has eagerly embraced 5G technology, which makes significant strides in mobile communications. However, the full benefits of $\mathbf{5 G}$ hardware, particularly in massive sensor communications and very low latency applications, have not yet been fully realized. Also the current costs of new 5 G hardware are quite high, requiring users to optimally configure their systems. To help users navigate these challenges we propose SymbIoTic5G, a novel hardware-based $\mathbf{5 G}$ industrial experimentation testbed. Our novelty stems from using commercially available 5 G infrastructure as far as possible, using programmable network switches to emulate unavailable $\mathbf{5 G}$ features. It enables realistic, scalable testing without the cost of full physical deployments. Our main contributions include i) AIdriven communication resource allocation tested on two strategies to optimize quality of service, and ii) scalable sensor simulation framework that realistically preserves physical and statistical sensor properties. In combining AI, sensor emulation, and real-world 5G, SymbIoTic5G bridges the gap between theory and deployment, offering a cost-effective platform for next-generation industrial network research.

This study develops a multi-robot system for factory environments, focusing on autonomous navigation, self-localization, and object manipulation. The approach integrates AMCL for localization, Dijkstra's algorithm for path planning, and YOLOv5 for object detection. Key objectives include achieving autonomous locomotion, object recognition, and manipulation, evaluated in the Gazebo simulation environment using ROS. The paper discusses locomotion mechanisms, manipulation techniques, collaborative behaviors, and empirical findings. This research enhances industrial collaborative robotics by improving efficiency and adaptability in complex scenarios, paving the way for future exploration.

Accurate medical image retrieval plays a pivotal role in clinical decision-making, yet it faces two challenges: the scarcity of annotated data and the inherent inconsistency in medical image styles. To address these issues, we propose a novel Siamese network-based retrieval method that leverages 3D model information to enhance the model’s performance. The proposed approach incorporates CrossNorm normalization, improving robustness to noise and unseen corruptions. In addition, the proposed method leverages 3D information to facilitate more effective image comparisons, resulting in improved accuracy in similarity measurements. Experiments on the USenhance2023 and ISIC2019 datasets demonstrate that our method consistently outperforms traditional L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> normalization, with mAP gains typically exceeding 1%.

A feature of 5G communications is high capacity conveyance of sensor information, paramount for big data applications. However the inconsistent roll-out of 5G features hinders the development of ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC) and dynamic network slicing. This paper introduces a hardware-based 5G test-bed that emulates dynamic slicing and large-scale sensor data processing using AI-powered resource optimization. Taking advantage of current 5G infrastructure and surrogate sensor data generation, our system enables realistic big data analytics in industrial environments. An AI-based decision tree model dynamically assigns network slices, optimizing throughput costs. Our surrogate data provides good quality supplemental sensor data, preserving key statistical and spectral properties enhancing sensor-driven big data applications. Our experiments show improved data transfers, adaptive network slicing and scalable big data processing. This offers a cost-effective solution for small/medium-sized enterprises (SMEs) and research/development (R&D) teams. This work bridges the gap between theoretical 5G slicing and real-world deployment for 5G-based big data applications. It provides a repeatable framework for industrial IoT and smart manufacturing, preparing for full commercial availability of 5G features<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Ministry of Education Singapore, grant 2021MOE-IF-024.

PurposePeople engagement is critical to continuous improvement programmes’ performance and success. This study proposes a combined ISO standard-Grey system approach to assess the level of adoption of people engagement practices in continuous improvement programmes. To exemplify the use of this approach, this research analyses the level of adoption of these practices in Brazilian organizations in the manufacturing sector.Design/methodology/approachThe combined ISO standard-grey system approach is based on the ISO 10018:2022 standard and two grey system techniques (grey target and grey fixed weight clustering). The exemplification used industry experts from Brazil.FindingsThe approach developed presents ten people engagement practices in continuous improvement programmes to be assessed. To perform this assessment, a five-linguistic-terms scale is proposed. The exemplification of the approach application for Brazilian reality indicated that the adoption of the analysed practices is at an intermediate level in the country, with possibilities for improvement.Originality/valueThis research presents in detail an interesting method for assessing the adoption level of people engagement practices in continuous improvement programmes and contributes to debates regarding how these programmes can achieve success, primarily through company employees’ participation and engagement.