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The accelerated development of Machine Learning (ML) tools, combined with broader access to frameworks and infrastructures, has driven the rapid adoption of ML-based solutions in industry. However, their integration into software systems introduces unique challenges, particularly for managing technical debt (TD). While existing frameworks/standards such as Cross-Industry Standard Process for Data Mining (CRISP-DM) and ISO/IEC 5338 provide guidance for ML development, they fail to address the complex interplay of technical and nontechnical factors contributing to TD.

This dataset collects production data of G12 and M10 monocrystalline silicon photovoltaic cells at various stages including metal silicon smelting, polycrystalline silicon purification, monocrystalline silicon growth, silicon wafer cutting, and monocrystalline silicon solar cell manufacturing, covering different implementation processes. It aims to conduct in-depth analysis of the production process and provide a basis for optimizing the process and improving the performance of the cells.

This dataset is sourced from an integrated energy system in a region of northern China, encompassing a wide range of energy data including wind power, photovoltaic power, electricity load, thermal load, and cooling load. The dataset features a time resolution of one hour, with a 24-hour timescale for each selected day. A total of five typical days have been chosen, representing different seasonal and weather conditions, to capture the variations in energy production and consumption patterns.

The data was collected by a tester holding a Xiaomi 13 smartphone while walking and collecting data in an underground parking lot covering a 16x70m area. The data includes 5G radio features and geomagnetic field information.

Collection Time: From 09:58 AM to 10:34 AM on July 13, 2024.

Total Samples: 12,800

Training Set (including validation set): 10,240

Test Set: 2,560

Ionic wind has shown promising applications in many fields, but it still encounters the challenges of low wind velocity and high discharge voltage. Here we propose a method of enhancing the velocity of ionic wind at given discharge voltages by Joule-heating the discharge electrode in a wire-grid corona discharge scheme. Ionic wind velocity is found to increase with the temperature of the discharge electrode with an enhancement by a magnitude of more than one order at low discharge voltages.

Microsoft contains a productive tool known as MS Office but the inclusion of VBA Macros inside the MS Office for automation purposes makes it a way for attackers to perform malicious activities. To get an up-to-date dataset, the research regarding VBA macros is still working to find efficient ways to detect it. To perform analysis, the dataset is required which is publically harder to find. To overcome this issue, a dataset is created from VirusTotal, VirusShare, Zenodo, Malware Bazaar, Github and InQuest Labs.

A craniometry study was undertaken to obtain anthropometric measurements of three hundred and five (305) medical staff within Trinidad & Tobago which is a twin island republic situated in the Caribbean. A non-contact measurement method was used involving 3D scanning equipment to record the geometry of each subject’s head as a digital file. The digital files were then processed using CAD software to obtain measurements for twenty-two (22) facial points of interest. In addition, the gender of each staff member was recorded.

Client-facing services are latency-sensitive and thus require consistently low response times to attract and retain users. We identify that these applications usually have varying object sizes, dynamic workloads, and complex query-processing functions. Switch-based hotspot offloading is a trendy solution for latency-sensitive applications to achieve high system throughput with an acceptable P99 query response latency.

This study proposes and validates a data mining-based interface development method (DaMIM) to optimize menu-driven user interfaces. DaMIM comprises six steps—target task definition, user selection, data collection, application of data mining techniques, analysis and evaluation, and determination of alternatives. Cluster analysis is used as the data mining technique. The proposed method is validated using a program developed to measure the reaction times of menu selection.

The rise of social media platforms such as Twitter has resulted in a significant increase in spam tweets, which may negatively impact both individual and platform providers. In this study, we propose an automated spam detection on Arabian Gulf Dialect Using Machine Learning Techniques to classify tweets as spam or legitimate. This research presents a machine learning-based technique for detecting spam on Twitter in the Arabian Gulf dialect.