Socialization of filling in SINTA data: Improving lecturers' research and publication track records by H. Hardani, Reny Amalia Permata, S. Syaidatussalihah, Alpiana Hidayatulloh, Dwi Monika Ningrum, Alpi Zaidah

“…This community service activity aims to provide socialization and training to lecturers regarding SINTA data filling following applicable guidelines. The methods used include presentation sessions, simulations, and direct assistance as well as giving pre-test and post-tests to participants. …”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points.…”
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Multilingual Word Embedding and Linguistic Linked Open Data for Tracing Semantic Change by Florentina Armaselu, Barbara McGillivray, Chaya Liebeskind, Paola Marongiu, Giedrė Valūnaitė Oleškevičienė, Elena-Simona Apostol, Ciprian-Octavian Truică

“…We argue that by joining corpus-based and lexicographical evidence and modelling the results in an interoperable format can provide more solid ground for drawing conclusions and possibilities of re-use by other applications. We define a basic schema for a resource aggregator and a model called LLODIA (Linguistic Linked Open Data for Diachronic Analysis). …”
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SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics by Chun Gong, Shengkang Li, Leying Wang, Fuxiang Zhao, Shuangsang Fang, Dong Yuan, Zijian Zhao, Qiqi He, Mei Li, Weiqing Liu, Zhaoxun Li, Hongqing Xie, Sha Liao, Ao Chen, Yong Zhang, Yuxiang Li, Xun Xu

“…These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. …”
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Data‐Driven Design of Mechanically Hard Soft Magnetic High‐Entropy Alloys by Mian Dai, Yixuan Zhang, Xiaoqing Li, Stephan Schönecker, Liuliu Han, Ruiwen Xie, Chen Shen, Hongbin Zhang

“…Abstract The design and optimization of mechanically hard soft magnetic materials, which combine high hardness with magnetically soft properties, represent a critical frontier in materials science for advanced technological applications. To address this challenge, a data‐driven framework is presented for exploring the vast compositional space of high‐entropy alloys (HEAs) and identifying candidates optimized for multifunctionality. …”
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Towards a Conceptual Modeling of Trustworthiness in AI-Based Big Data Analysis by Sebastian Bruchhaus, Alexander Kreibich, Thoralf Reis, Marco X. Bornschlegl, Matthias L. Hemmje

“…This paper introduces a conceptual mathematical framework for evaluating trustworthiness in AI-based big data analysis, emphasizing the critical role trust plays in the adoption and effectiveness of AI systems. …”
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EEG dataset for natural image recognition through visual stimuliMendeley Data by Nandan Tiwari, Shamama Anwar, Vandana Bhattacharjee

“…EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. …”
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Enablers and barriers of Adopting BI-tool for data analytics in Large Enterprises of Bangladesh by Khanom Konika, Nahid Mehzabul Hoque, Rabbani Golam Mustafa Md. Nurullah

“…The objective of this research is to examine the factors that facilitate or hinder the incorporation of Business Intelligence (BI) tools for data analysis and decision-making in enterprises in Bangladesh, using an adapted TOE framework. …”
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From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI by Ilenia Ficili, Maurizio Giacobbe, Giuseppe Tricomi, Antonio Puliafito

“…This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. …”
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Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption by Maulyanda Maulyanda, Rini Deviani, Afdhaluzzikri Afdhaluzzikri

“…Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. …”
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Power-Aware Data Transmission for Real-Time Communication in Multimedia Sensor Networks by Soyeon Rhee, Hyon-Young Choi, Hyun-Jung Lee, Myong-Soon Park

“…The availability of smart sensors equipped CMOS cameras made it possible to capture and transmit multimedia data ubiquitously. The real-time performance is essential in many applications including surveillance and healthcare monitoring in multimedia sensor networks (MSNs). …”
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Retabit: A Data-Driven Platform for Urban Renewal and Sustainable Building Renovation by Leandro Madrazo, Álvaro Sicilia, Adirane Calvo, Jordi Pascual, Enric Mont, Angelos Mylonas, Nadia Soledad Ibañez Iralde

“…The Retabit platform is a data-driven tool designed to bridge the gap between building rehabilitation and urban regeneration by integrating energy, economic, and social dimensions into a single framework. …”
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Predicting and Mitigating Delays in Cross-Dock Operations: A Data-Driven Approach by Amna Altaf, Adeel Mehmood, Adnen El Amraoui, François Delmotte, Christophe Lecoutre

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A Novel Data Classification and Scheduling Scheme in the Virtualization of Wireless Sensor Networks by Md. Motaharul Islam, Eui-Nam Huh

“…We develop a tiny virtual machine called VSNware for health care applications, which facilitates QoS-aware forwarding of data packets, maintaining the reliability, delay guarantee, and speed. …”
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An Efficiency Multiplexing Scheme and Improved Sampling Method for Multichannel Data Acquisition System by Chengqun Chu, Yongfeng Ren, Xin Liu, Yongqiu Zheng, Wei Fang

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Utility of Low-Cost Multichannel Data Acquisition System for Photoacoustic Computed Tomography by Mohsin Zafar, Rayyan Manwar, Seyed Mohsen Ranjbaran, Kamran Avanaki

“…While these systems produce high-quality 3D PA images, they require complex, multi-channel data acquisition (DAQ) systems to acquire data from all transducers. …”
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Statistical inference and data analysis of the record-based transmuted Burr X model by Alrweili Hleil

“…Finally, the practical applicability of the developed model is demonstrated by analyzing three data sets, comparing its performance with several well-known distributions. …”
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Blockchain-Based Secured Data Sharing in Healthcare: A Systematic Literature Review by Umair Ullah Tariq, Fariza Sabrina, Md Mamunur Rashid, Steven Gordon, Yufeng Lin, Zhenglin Wang, Salahuddin Azad

“…Blockchain technology is increasingly recognised for its potential to enhance the security, reliability, and resilience of distributed systems. By ensuring data integrity and protecting against unauthorised access, blockchain creates immutable and transparent records, making it an appealing option for various applications. …”
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DESIGN OF A VIRTUAL TEMPERATURE SENSOR WITH DATA DISPLAY IN A WEB INTERFACE by Paul NISTOR, Ioan ORHA

“…The project aims to develop a sensor network for temperature monitoring in various applications. Also, by implementing this system, I want to obtain a web interface that allows viewing and managing the measured temperature data.…”
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Modal Regression Estimation by Local Linear Approach in High-Dimensional Data Case by Fatimah A. Almulhim, Mohammed B. Alamari, Ali Laksaci, Zoulikha Kaid

“…Moreover, the expression of the convergence rate retains the usual form of the stochastic convergence rate in functional statistics. Simulations and real-data applications demonstrate the algorithm’s effectiveness, showing its advantage over existing methods in high-dimensional prediction tasks.…”
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