ethicalabs/Research-Intent-Collab huggingface dataset:
Generative                     <scp>AI</scp>                     for Art Therapy Informed Visual Emotional Expression

ABSTRACT                   Technology‐supported emotional wellness interventions have gained significant traction in recent years, yet many fail to address the unique needs of marginalized populations whose identities and experiences often go unrecognized in standardized approaches. Drawing inspiration from art therapy's established success in utilizing creative expression for emotional processing, this research investigates how AI‐generated content can serve as a bridge between traditional therapeutic approaches and culturally responsive care. Through a pilot study involving three participants, we identified key challenges and opportunities in leveraging AI‐assisted creative tools for therapeutic and expressive purposes. Our findings reveal both technical limitations and promising pathways for developing more inclusive and culturally sensitive AI‐powered visual expression platforms.
Application of Learning Analytics Technology in Architectural Education Informatics

Learning analytics technology, a crucial component of educational informatics, offers educators profound insights into students’ learning behaviors and outcomes. This study investigates the application of learning analytics technology in architectural education informatics, using the “Fundamentals of Architectural Design” course at Huazhong University of Science and Technology as a case study. It explores the application of learning analytics technology in architectural education, including learning behavior analysis, learning outcome prediction, and learning intervention strategies. The findings reveal that learning analytics technology can significantly enhance teaching efficiency, optimize learning experiences, and promote educational equity. The study concludes that learning analytics technology holds great potential for application in architectural education and provides strong support for the informatization reform of architectural education.
Modeling metabolic networks in systems biology with artificial intelligence tools

This paper discusses the experience of applying artificial intelligence methods for modeling metabolic networks in systems biology. Based on data from the KEGG database, a network of interactions between key metabolites and enzymes of human metabolism was constructed and analyzed using the Python NetworkX library. Topological properties of the network nodes and edges – indices of degree, intermediate and centrality of proximity – were calculated, and the role of essential and non-essential metabolites in the network was quantified. Statistical analysis confirmed the biological validity of the model and showed that essential metabolites play the role of central nodes. This study provides effective methods for a comprehensive and systematic study of metabolic processes at the interface of artificial intelligence and systems biology and opens up the possibility of predicting dynamic changes in in metabolism using graph neural networks.
Investigation of Sterilizers Causing Wet Loads Through Root Cause Analysis

Belimed Incorporated sterilizers are used in the sterile processing department (SPD) to decontaminate operating room instruments and tools used during surgery. There are 3 phases to the sterilization process: conditioning, exposure, and the exhaust phases. During the conditioning phase, air is removed from the chamber using a vacuum system; otherwise, it would inhibit steam contact with the instruments being cleaned. Steam is then released to fill the space in the chamber during the exposure phase, increasing the pressure and temperature inside to a desired level. This sterilizes the items inside over a fixed period based on the predetermined settings. Finally, during the exhaust phase, the drain is opened to release steam, which lowers the pressure inside the vessel and allows the items to dry.                               1                             The quality of sterilization steam is vital for operating room (OR) surgeries to proceed as planned. During the month of July, our New England–based teaching hospital witnessed multiple wet loads, which resulted in the cancellation of several surgeries and delaying pivotal patient care. These wet loads were caused by condensation of equipment after being sterilized as A/C units are placed too close to the sterilizer units. Also contributing to wet loads was the temperature of incoming cold-water lines being out of specification by 4 degrees. Warm water causes inefficient vacuum pumps, which prevents the air in the system from being removed during the conditioning phase. Additional investigation found procedures and policies were occasionally neglected resulting in contaminated trays. A root cause analysis (RCA) was conducted by our Risk Management (RM) department as these issues affected patient care. The root/proximate causes were identified, and an action plan was formulated with the help of Clinical Engineering (CE), Belimed, and the facilities staff to prevent wet load recurrence.
Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language

Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.
AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.
Surgical Simulation Robot with Haptics and Friction Compensation

Haptic feedback brings a surgical simulator closer to real surgery. However, friction in surgical simulator's hardware affects its performance significantly. We introduce a surgical simulation robot with roller mechanism for laparoscopic surgical simulation. Roller mechanism is implemented in a constrained space to reduce the friction. Motion based friction cancellation method is also applied to further mitigate the friction effects. Comparing with the same surgical simulation robot without roller mechanism, the one with roller mechanism reduces friction by 32.86i?ź% and 38.87i?ź% on two motion directions, and the motion based friction cancellation method can mitigate the friction effect by 49.46i?ź% and 62.08i?ź% on the two motion directions.
Emerging Technologies, Extreme Uncertainty, and the Principle of Rational Precautionary Reasoning

(2012). Emerging Technologies, Extreme Uncertainty, and the Principle of Rational Precautionary Reasoning. Law, Innovation and Technology: Vol. 4, No. 1, pp. 35-65.
Transformer Abnormal State Identification Based on TCN-Transformer Model in Multiphysics

Transformers are critical components in power systems, and their operational stability plays a decisive role in ensuring the safety and reliability of the power grid. To address the challenges of accurately assessing transformer health due to the influence of load and environmental factors during actual operation, this paper analyzes the electrical, thermal, and vibrational characteristics of transformers. A k-means++ algorithm is employed to classify operating conditions based on three key parameters: transformer load current, ambient temperature, and operating voltage. A fusion model based on the Temporal Convolutional Network-Transformer (TCN-Transformer) is proposed to identify abnormal operating states of transformers. Experiments were conducted using a 500 kV transformer as an example. The results demonstrate that the proposed TCN-Transformer model significantly outperforms comparative algorithms in terms of prediction accuracy. The model effectively captures critical information within the data, achieving superior multivariate feature sequence prediction for transformers. These findings validate the reasonableness and accuracy of the proposed method for identifying abnormal transformer operating states.
A Human-Centered Design Methodology to Enhance the Usability, Human Factors, and User Experience of Connected Health Systems: A Three-Phase Methodology

Design processes such as human-centered design, which involve the end user throughout the product development and testing process, can be crucial in ensuring that the product meets the needs and capabilities of the user, particularly in terms of safety and user experience. The structured and iterative nature of human-centered design can often present a challenge when design teams are faced with the necessary, rapid, product development life cycles associated with the competitive connected health industry. We wanted to derive a structured methodology that followed the principles of human-centered design that would allow designers and developers to ensure that the needs of the user are taken into account throughout the design process, while maintaining a rapid pace of development. In this paper, we present the methodology and its rationale before outlining how it was applied to assess and enhance the usability, human factors, and user experience of a connected health system known as the Wireless Insole for Independent and Safe Elderly Living (WIISEL) system, a system designed to continuously assess fall risk by measuring gait and balance parameters associated with fall risk. We derived a three-phase methodology. In Phase 1 we emphasized the construction of a use case document. This document can be used to detail the context of use of the system by utilizing storyboarding, paper prototypes, and mock-ups in conjunction with user interviews to gather insightful user feedback on different proposed concepts. In Phase 2 we emphasized the use of expert usability inspections such as heuristic evaluations and cognitive walkthroughs with small multidisciplinary groups to review the prototypes born out of the Phase 1 feedback. Finally, in Phase 3 we emphasized classical user testing with target end users, using various metrics to measure the user experience and improve the final prototypes. We report a successful implementation of the methodology for the design and development of a system for detecting and predicting falls in older adults. We describe in detail what testing and evaluation activities we carried out to effectively test the system and overcome usability and human factors problems. We feel this methodology can be applied to a wide variety of connected health devices and systems. We consider this a methodology that can be scaled to different-sized projects accordingly.
Interpretable Deep Learning for Brain Tumor Diagnosis: Occlusion Sensitivity-Driven Explainability in MRI Classification

Magnetic resonance imaging (MRI) serves as a crucial diagnostic tool, particularly for brain tumors where early detection significantly improves patient prognosis. The growing use of deep learning in medical imaging has led to substantial progress, yet the opaque nature of these models creates barriers to clinical acceptance, especially for critical applications such as tumor diagnosis. Our research applies explainable AI (XAI) techniques to improve the transparency of CNN-based brain tumor detection using MRI data. Working with a dataset containing 7,022 images spanning four tumor categories, our model attains 80\% accuracy while employing occlusion sensitivity analysis to produce visual interpretations. These heatmaps identify the most influential regions for predictions, giving clinicians insight into the model's decision process. This XAI integration enhances both understanding and accountability in healthcare AI systems, facilitating more reliable diagnostic tools.Precise early identification of brain tumors through MRI dramatically affects survival outcomes, though human interpretation remains time-consuming and variable. While CNNs show impressive classification results, their unclear reasoning limits clinical implementation. Our study introduces an XAI approach that pairs an accurate CNN classifier (80% on 7,024 multi-class scans) with occlusion analysis to create intuitive visual explanations. By methodically altering image areas and measuring prediction variations, we generate heatmaps that accurately pinpoint tumor-distinguishing features, matching radiological assessment. Comparative results demonstrate occlusion analysis's superiority over gradient methods like Grad-CAM in spatial precision for tumor classification (meningioma, glioma, pituitary). This research progresses clinically useful AI by connecting model effectiveness with interpretability in brain tumor imaging.
Failure Analysis and Reliability Optimization Approaches for Particulate Filter of Diesel Engine after-Treatment System

Article Failure Analysis and Reliability Optimization Approaches for Particulate Filter of Diesel Engine after-Treatment System  Dongsheng Zhang 1,2, Minglong Li 2, Liguang Li 1,*, Jun Deng 1, Ye Li 3, Rongfang Zhou 3, and Long Ma 1   1 School of Automotive Studies, Tongji University, Shanghai 201804, China 2 Perkins Small Engines (Wuxi) Co., Ltd., Wuxi 214001, China 3 Perkins Power Systems Technology (Wuxi) Co., Ltd., Wuxi 214001, China * Correspondence: liguang@tongji.edu.cn   Received: 20 September 2024; Revised: 10 December 2024; Accepted: 14 January 2025; Published: 14 February 2025  Abstract: Diesel particulate filter (DPF) clogging and high temperature failures are predominant issues affecting the reliability of diesel engines in the market applications. These failures, which include substrate crack and melting, can lead to a significant increment of tailpipe particulate matter (PM) emissions, even exceeding the acceptable limits. Such DPF events not only diminish the vehicle productivity but also escalate the maintenance costs. The DPF, situated downstream in the diesel engine exhaust system, is directly influenced by the health state of the upstream engine and diesel oxidation catalyst (DOC). Addressing the risks of DPF system failures is a complex systems engineering challenge. This paper employs a fault tree analysis (FTA) to identify the root causes of these failures, considering the DPF after-treatment functions, all elements affecting system performance, and key interconnections among these elements. Then the DPF reliability optimization strategies are discussed from a system optimization perspective, focusing on reducing the engine-out PM, ensuring the appropriate substrate volume and precious metal coating content for DPF clogging, improving the virtual DPF soot loading sensor accuracy, lowering the extremely uneven flow or DPF soot loading and adopting the conservative regeneration control for high temperature failures. These measures are crucial to mitigate the failure risks and ensure the reliable DPF operation. To achieve the tighter PN requirement of future regulation, additional DPF optimizations would be required. Adopting the new Cordierite material with a higher porosity, further smaller mean pore size and uniform pore size distribution are one of current developing tendencies from existing studies. The Cordierite material with membrane design would be a new developing direction for further improving of filtration efficiency and better hysteresis of DPF pressure drop, plus lower porosity and thicker wall design would get better robustness and DPF pressure drop.
Projecting the Impact of Climate Change on the Black Sea: A High-Resolution Regional Modeling Study

Global climate models struggle to accurately represent the dynamics of the Black Sea because it's so isolated from the open ocean, connected only by the narrow Turkish Straits. To overcome this limitation and study how the Black Sea will be affected by future climate change, we need to use high-resolution regional models. We've developed a new 5km resolution regional model of the Black Sea using the NEMO 4.2.1 ocean circulation model. To validate our new model, we compared it against a well-established 2.5-km resolution model from the Copernicus Marine Service model setup. Both models share the same numerical core, vertical layers, horizontal and vertical parameterizations. In our first comparison, we used historical ERA5 atmospheric data from 1980 to 2015. The new 5km model performed very well, with biases that were remarkably similar to those of the higher-resolution 2.5-km model. While the 2.5-km model revealed finer details like the Batumi Gyre and specific features near the Crimea Peninsula, both models provided comparable results for deep-ocean dynamics. This confirms that our 5-km model is reliable enough for long-term climate simulations.Next, we simulated three different future climate scenarios—RCP2.6, RCP4.5, and RCP8.5—using atmospheric data from the 0.01 degree CNRM-ALADIN model in the CMIP5 CORDEX-Europe project. All three scenarios show that the Black Sea surface will get warmer and saltier. In the model simulations, the surface temperature increases 1.5, 2.0, 3.5 degrees between different RCP scenarios respectively, and also gets saltier up to 3 psu. Notably, the Cold Intermediate Layer (CIL), a distinct oceanographic feature which is characterized by a layer of relatively cold water is projected to disappear after 2040 in the RCP8.5 scenario. The warming isn't limited to the surface; it penetrates deep, reaching down to 700 meters. Likewise, the Black Sea is becoming saltier, with this salinification also extending to 700 meters. To increase the robustness of our findings, we also performed an additional RCP8.5 simulation using a different atmospheric model, the SMHI-RCA4 from the CMIP5 CORDEX-Europe project. This ensemble approach helps us account for model uncertainties and strengthens our confidence in the projected climate changes for the Black Sea.These findings suggest that under a high-emission scenario like RCP8.5, the Black Sea's temperature and salinity profiles will change dramatically. These changes could significantly alter its circulation patterns, stratification, and overall ecosystem dynamics.
The Effects of Social Media Use, Mental Health Problems, and Tobacco Advertising on Adolescent Anxiety

This study examined the relationship between digital media consumption, peer influence, tobacco advertising exposure, and anxiety levels among adolescents in Jakarta, Indonesia. Using a mixed methods approach, data were collected from 1,278 junior and senior high school students through multi-stage cluster sampling surveys and focus group discussions. Quantitative results showed that 72.8% of respondents experienced anxiety, social media usage (p = 0.000), social media use at late night (OR = 2.153; 95% CI: 1.643–2.822; p = 0.000), and peer influence (OR = 2.434; 95% CI: 1.772–3.342; p = 0.000) were significantly associated with anxiety. Peer influence was identified as the strongest predictor of anxiety, nearly doubling the likelihood of experiencing anxiety. Exposure to tobacco advertising on social media showed no statistically significant association with anxiety (p = 0.162). Qualitative findings highlighted the role of peer relationships in providing emotional support and influencing smoking behaviors as coping mechanisms for anxiety. These findings emphasize the need to address social dynamics and digital media habits in adolescent mental health interventions. Policymakers and educators should prioritize strategies promoting moderated social media use and supportive peer environments to mitigate anxiety in youth within Indonesia’s evolving digital landscape.
The Emergency Security Trap: Structural Negligence in Surveillance Governance — A Zero Leap Theory Analysis of Privacy Rights Under Emergency Powers

Emergency security powers—mass surveillance programs, expanded police authorities, and intelligence-gathering infrastructure enacted during crises—exhibit a predictable failure mode: measures declared as temporary become permanent, structurally altering the relationship between citizen and state without restoration of privacy consent mechanisms. This paper applies Zero Leap Theory (ZLT) to demonstrate that privacy consent operates as a physical constraint: a necessary permeability condition for legitimate security intervention. When this gate closes through mass surveillance without particularized suspicion, secret programs without oversight, or emergency bypass of judicial review—no increase in security intensity can substitute for its absence without generating structural resistance, behavioral chilling, and systemic distrust.  We analyze the constitutional and international principles governing privacy and surveillance (Fourth Amendment, ECHR Article 8, ICCPR Article 17) through ZLT's formal framework, demonstrating that these principles encode thermodynamically coherent constraints. We introduce the concept of structural negligence per se: a failure mode where emergency surveillance measures persist beyond their justifying conditions without sunset provisions, threat-based exit criteria, or independent oversight.  Historical analysis of emergency security measures—from the USA PATRIOT Act (2001), the Five Eyes global surveillance network, and the UK's mass CCTV deployment—validates ZLT's predictions: emergency security powers without structural safeguards drift toward permanence with mathematical predictability. We provide an operational audit protocol (IAS-SEC) and demonstrate via the Exit Coercion Index (ECI) that legislative approval does not constitute viable thermodynamic consent in high-coercion environments.