This dataset proposes a new energy vehicle procurement decision-making framework that integrates data mining and social network group decision-making in an intuitive fuzzy environment. We use web crawler technology and social media public opinion analysis to capture and analyze text information about the performance of NEVs. 87,673 online blog posts about NEVs from December 1 to 31, 2024, were crawled from the API provided by the Sina Weibo Open Platform (https://weibo.com).
The Influence of the Pusdiklatkar Website Implementation on the Performance of Firefighters in DKI Jakarta This study aims to analyze the impact of implementing the Pusat Pendidikan dan Pelatihan Kebakaran (Pusdiklatkar) website on the performance of firefighters in DKI Jakarta. In the digital era, the utilization of information technology in training and personnel development has become increasingly crucial to enhancing work effectiveness and efficiency.
Data centers are increasingly adopting renewable energy sources to mitigate environmental impact and reduce operational costs. However, effectively optimizing energy costs remains challenging due to unpredictable workloads and fluctuating renewable energy availability. This paper introduces LOECM, a Lyapunov-driven online scheduling algorithm designed to minimize energy cost without relying on future information.
This project aims to optimize the routing of large-scale Automated Guided Vehicles (AGVs) in a large-scale logistics warehouse in Japan using Quantum Annealing. The project will generate real-world operational problems and use them for performance evaluation. It is designed to be compatible with various solvers, including classical solvers and quantum annealing solvers.
Recently, pulse transformers have been widely used in pulsed power systems. However, the magnetic saturation phenomenon of the pulse transformer with closed magnetic cores will change its output waveform parameters, which makes it difficult to predict the output waveforms of the pulse transformer accurately, and a more accurate model considering the nonlinear magnetization process of the pulse transformer needs to be established.
This work introduces a breakthrough two-axis electromagnetic scanning micromirror system that resolves the fundamental aperture-frequency trade-off in MEMS-based LiDAR, achieving 20 mm optical aperture with 610 Hz resonant frequency. The design seamlessly integrates a voice coil motor (VCM) for vertical actuation with a resonant horizontal scanner.
The subject of the conducted project was research and development work aimed at developing a product innovation in the form of a robotic hospital ward. The task of such a ward was to minimize the participation of medical personnel in processes requiring contact with the patient, and thus the risk of infections and further spread of viruses.
A high-efficiency energy storage battery technology development project was officially launched, attracting seven experts from different institutions to participate and form a high-efficiency energy storage battery R&D collaboration network. Based on the participants' previous project collaboration experience, three overlapping subgroups naturally formed in the network. After the research and development are completed, the nine members must jointly evaluate the most suitable TT path for the energy storage battery technology.
DALHOUSIE NIMS LAB ATTACK IOT DATASET 2025-1 dataset comprises of four prevalent types attacks, namely Portscan, Slowloris, Synflood, and Vulnerability Scan, on nine distinct Internet of Things (IoT) devices. These attacks are very common on the IoT eco-systems because they often serve as precursors to more sophisticated attack vectors. By analyzing attack vector traffic characteristics and IoT device responses, our dataset will aid to shed light on IoT eco-system vulnerabilities.
