ethicalabs/Research-Intent-Collab huggingface dataset:
Evaluation of Unknown Groundwater Contaminant Sources Characterization Efficiency under Hydrogeologic Uncertainty in an Experimental Aquifer Site by Utilizing Surrogate Models

Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models. These capabilities lead to development of an efficient methodology for source characterization based on utilizing the trained and tested surrogate models in an inverse mode. The obtained results are satisfactory and show the potential applicability of the SOM and GPR-based surrogate models for unknown groundwater contaminant source characterization in an inverse mode.
Inventory and internal logistics management as critical factors affecting supply chain performance

This paper focuses on the inventory and internal logistics management problem within a specific Supply Chain (SC) node (a Distribution Centre (DC)). The objective is twofold: to monitor the performance of different inventory control policies under distinct operative scenarios and to reduce the Internal Logistic Costs (ILCs) by investigating the effect of some critical parameters (i.e., the number of incoming/outgoing trucks from suppliers/to retailers, the number of forklifts and lift trucks, etc.) for increasing the service level provided to final retailers and allocating internal resources efficiently. To this end, a simulation model of a real DC is implemented.
Case Study of GAI for Generating Novel Images for Real-World Embroidery

In this paper, we present a case study exploring the potential use of Generative Artificial Intelligence (GAI) to address the real-world need of making the design of embroiderable art patterns more accessible. Through an auto-ethnographic case study by a disabled-led team, we examine the application of GAI as an assistive technology in generating embroidery patterns, addressing the complexity involved in designing culturally-relevant patterns as well as those that meet specific needs regarding detail and color. We detail the iterative process of prompt engineering custom GPTs tailored for producing specific visual outputs, emphasizing the nuances of achieving desirable results that align with real-world embroidery requirements. Our findings underscore the mixed outcomes of employing GAI for producing embroiderable images, from facilitating creativity and inclusion to navigating the unpredictability of AI-generated designs. Future work aims to refine GAI tools we explored for generating embroiderable images to make them more performant and accessible, with the goal of fostering more inclusion in the domains of creativity and making.
Development of analytical thinking ability and attitudes towards science learning of grade-11 students through science technology engineering and mathematics (STEM education) in the study of stoichiometry

The purpose of this study was to investigate the analytical thinking abilities and attitudes towards science learning of grade-11 students through science, technology, engineering, and mathematics (STEM) education integrated with a problem-based learning in the study of stoichiometry. The research tools consisted of a pre- and post-analytical thinking ability test, a science learning attitude test, classroom observations, student reflective journals, and semi-structured interviews. The findings indicated that STEM learning activities based on problem-based learning successfully developed analytical thinking abilities and attitudes towards science learning. Consequently, the students realized how important theories are, and were able to integrate their knowledge from various fields to solve problems and to create new innovations. About 80% of the students showed higher analytical thinking ability scores above the prescribed criterion of 70% of the full score. After learning, the scores of the students were higher than those before learning at a confidence level of 0.01. The attitudes towards science learning were higher than those before learning at a confidence level of 0.01. The successful activities of STEM started with offering knowledge to students through an inquiry-based process until they could construct the knowledge on their own. After that, the teacher initiated a problem situation and allowed each group of students to create a useful product adopted from the experimental results                     via                     integrating STEM knowledge to modify their creative works.
A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, &amp; Safer Presentation for Investigating Child Sexual Abuse Material (CSAM)

Abstract   For those investigating cases of Child Sexual Abuse Material (CSAM), there is the potential harm of experiencing trauma after illicit content exposure over a period of time. Research has shown that those working on such cases can experience psychological distress. As a result, there has been a greater effort to create and implement technologies that reduce exposure to CSAM. However, not much work has explored gathering insight regarding the functionality, effectiveness, accuracy, and importance of digital forensic tools and data science technologies from practitioners who use them. This study focused specifically on examining the value practitioners give to the tools and technologies they utilize to investigate CSAM cases. General findings indicated that implementing filtering technologies is more important than safe-viewing technologies; false positives are a greater concern than false negatives; resources such as time, personnel, and money continue to be a concern; and an improved workflow is highly desirable. Results also showed that practitioners are not well-versed in data science and Artificial Intelligence (AI), which is alarming given that tools already implement these techniques and that practitioners face large amounts of data during investigations. Finally, the data exemplified that practitioners are generally not taking advantage of tools that implement data science techniques, and that the biggest need for them is in automated child nudity detection, age estimation and skin tone detection.
The Rule of Law, Judicial Independence, and Due Process

Abstract                Chapter 5 examines the continuation of the legal-judicial reform and its achievements and limitations under the Beijing government (1912–1927) and the Guomindang (GMD, or Nationalist Party) government (1927–1949). The Beijing government tried to implement an ambitious reform plan but failed to materialize it completely due to a lack of resources, among other problems. The GMD continued the reform but also instituted practices particular to its ideology of ruling the country through the party, including the invention of political offenses and their punishments through special laws and special courts. The durability of positive reform outcomes in those years is shown in the way the Chinese judiciary functioned in the Japanese-occupied territories during the Second Sino-Japanese War (1937–1945).
« From Corporate Social Responsability to Corporate Sustainability Due Diligence and Reporting. Observations on the CSDDD (proposal) and the CSRD »,

Oana ȘTEFAN, Associate Professor, King's College London (UK) Soft law, un instrument de predilecție în timp de criză sau anti-law? Gabriela OANȚĂ, Associate Professor, Universidade da Coruña (Spania) Europenizarea securității interne, securizarea Uniunii Europene Mihaela BARNES, Cercetător, Graduate Institute of International and Development Studies, Geneva (Elveția) Sustenabilitate, securitatea națională și dreptul internațional Valentina DIPSE, Associate Professor, Universidad de Cantabria (Spania) Uniunea Europeană și distribuția veniturilor și a bogăției Smaranda MIRON, Senior Dispute and Energy Lawyer, Energy Community (Austria) Solidaritate, reziliență, diversificare. Este necesară o schimbare fundamentală a piețelor de energie europene pentru a face față noilor realități politice?
Sampling‐based model order reduction for stochastic differential equations driven by fractional Brownian motion

AbstractIn this paper, we study large‐scale linear fractional stochastic systems representing, e.g., spatially discretized stochastic partial differential equations (SPDEs) driven by fractional Brownian motion (fBm) with Hurst parameter H &gt; ½. Such equations are more realistic in modeling real‐world phenomena in comparison to frameworks not capturing memory effects. To the best of our knowledge, dimension reduction schemes for such settings have not been studied so far.In this work, we investigate empirical reduced order methods that are either based on snapshots (e.g. proper orthogonal decomposition) or on approximated Gramians. In each case, dominant subspaces are learned from data. Such model reduction techniques are introduced and analyzed for stochastic systems with fractional noise and later applied to spatially discretized SPDEs driven by fBm in order to reduce the computational cost arising from both the high dimension of the considered stochastic system and a large number of required Monte Carlo runs.We validate our proposed techniques with numerical experiments for some large‐scale stochastic differential equations driven by fBm.
On some aspects of necessity of scientific and methodical provision of legal regulation of issues of protection of minors’ rights

The article is devoted to the justification of the wide use of scientific approaches within the legislative activity, particularly in the area of protection of minors’ rights. The article defines the scheme of Scientific and methodical provision of legal regulation in this area as well as the essential directions of its legal regulation.
<i>Genuineness Matters in Tourism</i>: A Phenomenology of Filipino Travel Management Students’ Emotional Experiences of Tour Guides’ Verbalized Hospitality during Destination Immersion

As the popularity of guided tours has become evident in the aspect of tourism activities, the expanding yet ambiguous role of tour guides in the tourism industry gained the attention of scholars gl...
SEM modeling with singular moment matrices: Part II: ML-Estimation of sampled stochastic differential equations

Linear stochastic di erential equations (SDE) are expressed as an ex- act discrete model (EDM) and estimated with structural equation models (SEM) and the Kalman  lter (KF) algorithm.  The SEM likelihood is well de ned even for the times series case and the SEM and KF approach yield the  same  likelihood.   The  oversampling  approach  is  introduced  in  order to  formulate  the  EDM  on  a  time  grid  which  is   ner  than  the  sampling intervals.  This leads to a simple computation of the nonlinear parameter functionals of the EDM. For small discretization intervals, the functionals can  be  linearized  and  software  permitting  only  linear  parameter  restric- tions can be used.  However, in this case the SEM approach must handle large  matrices  leading  to  degraded  performance  and  possible  numerical problems.  The methods are compared using coupled linear random oscil- lators with time varying parameters and irregular sampling times.
Root cause analysis (RCA) as a preliminary tool into the investigation of identity theft

Identity theft has been known for some centuries whereby falsified identity documents were misused as well as offences such as impersonating others were common in the society. However, the advent of technology changed the method used for conducting this crime, whereby through the use of the Internet, personal information is can be stolen and misused by criminals. The crime has its causes originating from human error and judgement to failure of computing and networking systems that allow unauthorized access to personal information. In order to provide a better tool of investigating this crime, there is the need to explore the causes of the crime thereby providing a better framework for investigating Identity theft crimes. This study uses Root Cause Analysis (RCA) as a preliminary tool that serves to provide a depicted identification of the causes of Identity theft paving the way into investigating the crime and creating incident response plans.
THE PANOPTIC SORT: RECONSTRUCTING THE RIGHT TO PRIVACY IN THE AGE OF ALGORITHMIC SURVEILLANCE

The right to privacy, once conceived as the fundamental right to be let alone, faces an existential crisis in the digital age. The pervasive extraction, analysis, and commodification of personal data by state and corporate actors have transformed privacy from a question of secrecy to one of power and autonomy. This article undertakes a socio-legal analysis of this transformation, examining how jurisprudence is evolving often belatedly to address challenges posed by surveillance capitalism, algorithmic decision-making, and the dissolution of public/private boundaries. It argues that traditional individualistic frameworks are inadequate to protect collective social identities and autonomy. By analyzing landmark case law from 2024 and 2025 across multiple jurisdictions including the Supreme Court of Canada’s rulings, the Supreme People’s Court of China’s data rights cases, and significant American jury verdicts this paper charts the emergence of new legal theories. It explores concepts of privacy as trust, group rights to privacy, and statutory torts as mechanisms to recalibrate the balance of power. The article concludes that safeguarding privacy in the contemporary moment requires a paradigm shift: moving from protecting individual secrets to regulating the technological and commercial infrastructures that enable mass surveillance, thereby preserving the conditions necessary for democratic participation and individual autonomy. Keywords: Right to Privacy, Digital Age, Surveillance Capitalism, Algorithmic Governance, Data Protection, Socio-Legal Studies, Autonomy
The fragility of human rights in times of rule of law erosion. The case of judicial independence in America and Europe

The article highlights the existence of a direct and existential relationship between human rights and the rule of law, insofar as one cannot exist without the other, and vice versa. To this end, it first reviews the historical evolution of the protection of human rights and the rule of law in both the United States and the European Union, emphasizing the international legal norms and documents in which they have been enshrined. It then takes judicial independence as a case study. The article examines the human rights that become unprotected when states undermine judicial independence, focusing on pardons and amnesties, as well as on the harassment of judges and the non-compliance with judicial decisions, in both the United States and the European Union.  Received: 19 January 2026 Accepted: 9 February 2026
Architectural Consistency Checking in Plugin-Based Software Systems

Manually ensuring that the implementation of a software system is consistent with the software architecture is a laborious and error-prone task. Thus, a variety of approaches towards automated consistency checking have been developed to counteract architecture erosion. However, these approaches lack means to define and check architectural restrictions concerning plugin dependencies, which is required for plugin-based software systems. In this paper, we propose a domain-specific language called Dependency Constraint Language (DepCoL) to facilitate the definition of constraints concerning plugin dependencies. Using DepCoL, it is possible to define constraints affecting groups of plugins, reducing the required specification effort, to formulate constraints for specific plugins only and to refine constraints. Moreover, we provide an Eclipse plugin, which checks whether the software system under development is consistent with the modeled constraints. This enables a seamless integration into the development process to effortless check consistency during development of the software system. In this way, developers are informed about dependency violations immediately and this supports developers in counteracting architecture erosion.
One Health Surveillance System in Gujarat, India: A Health Policy and Systems Research Protocol for Exploring the Cross-Sectoral Collaborations to Detect Emerging Threats at the Human-Animal–Environment Interface

The close interaction between humans, animals and the ecosystem has been a reason for the emergence and re-emergence of zoonotic diseases worldwide. Zoonoses are estimated to be responsible for 2.5 billion human illnesses and 2.7 million deaths worldwide. Gujarat is a western state in India with more than 65 million people and 26 million livestock, and includes surveillance systems for humans and animals; however, more evidence is needed on joint collaborative activities and their effect on the early warning response for zoonoses. Thus, this study aims to investigate sectoral collaborations for early warning and response systems for emerging and re-emerging zoonoses, aiming to develop a One Health surveillance (OHS) system in Gujarat, India. This case study uses policy content analysis followed by qualitative and quantitative data collection among state- and district-level surveillance actors to provide insight into the current cross-sectoral collaborations among surveillance actors. It helps identify triggers and documents factors helpful in strengthening cross-sectoral collaborations among these systems and facilitates the establishment of an OHS system in Gujarat, India.
Applications of strategies to address selection bias, measurement bias, and confounding in health economics research using primary and secondary observational data

Background: While digital healthcare platforms enable access to diverse observational data, such data can be limited by quality and accuracy issues, introducing selection bias, measurement bias, and confounding. Statistical, econometric, and data techniques can strengthen validity of observational findings. This dissertation presents three illustrative studies to demonstrate the manifestation and mitigation of biases in observational health research.Methods: Study one utilized prospective survey data to measure regional variation in health-related quality-of-life (HRQoL) during COVID-19. Study two combined the survey with COVID-19 state policy data to estimate the impact of policy on HRQoL. Study three linked electronic health records (EHR), pharmacy dispensing, and medication reconciliation (MR) data to measure drug utilization in a tele-pharmacy intervention.Results: Study one employed multiple imputation to mitigate selection bias from nonresponse, generating plausible values accounting for uncertainty, and survey weighting to mitigate selection bias from sampling undercoverage, improving generalizability without inflating variation. Study two used inverse propensity weighting to address selection bias from nonrandom attrition and validated control covariates of current and lagged statewide COVID-19 case rates in outcome models to capture variation from unobserved local pandemic signals, strengthening exogeneity. In study three, addition of MR data to EHR and dispensing data enabled intermittent tracking of non-dispensed prescriptions, while assumptions for their duration improved data capture and continuity, validated in sensitivity analysis. Conclusions: Bias in observational health research is inevitable but can be managed with creative thinking and appropriate techniques, strengthening the potential of observational data to provide valuable insights.
Big data, computational social science, and other recent innovations in social network analysis

AbstractWhile sociologists have studied social networks for about one hundred years, recent developments in data, technology, and methods of analysis provide opportunities for social network analysis (SNA) to play a prominent role in the new research world of big data and computational social science (CSS). In our review, we focus on four broad topics: (1) Collecting Social Network Data from the Web, (2) Non‐traditional and Bipartite/Multi‐mode Networks, including Discourse and Semantic Networks, and Social‐Ecological Networks, (3) Recent Developments in Statistical Inference for Networks, and (4) Ethics in Computational Network Research.
Improving Quality of End-of-Life Care Through the K-HOPE Consultative Palliative Care Model: A Prospective Study in a Tertiary Hospital

As population aging accelerates, the demand for high-quality end-of-life (EOL) care continues to rise. However, a substantial proportion of patients with terminal cancer still experience death in acute-care hospitals without adequate palliative care. Consultative palliative care (CPC) represents a feasible model for delivering palliative care without requiring dedicated inpatient units, yet evidence evaluating its clinical impact remains limited. In this study, we developed a structured hospital-based CPC model tailored to the Korean healthcare system, the Korea Holistic Optimized Palliative care for End-of-life (K-HOPE) model, and prospectively evaluated its clinical impact. K-HOPE was delivered by an interdisciplinary CPC team in a tertiary hospital. Unmet needs were assessed using the Integrated Palliative care Outcome Scale (IPOS), and longitudinal changes were analyzed using mixed-effects models for repeated measures. Among patients who died during hospitalization, quality of death was evaluated using the Good Death Scale (GDS). A total of 84 patients with terminal cancer received K-HOPE. The total IPOS score significantly decreased over time (β = −10.4, 95% CI −12.8 to −8.0; p &lt; 0.001), indicating reduced overall burden and unmet needs. Significant improvements were observed in psychological distress (p = 0.010) and communication and information needs (p &lt; 0.001), whereas changes in physical symptoms and practical concerns were not statistically significant. Among 22 patients who died during hospitalization, 59.1% achieved a good quality of death (GDS ≥ 12). Longer duration of CPC involvement was significantly associated with higher quality of death and remained an independent predictor in multivariable analysis. These findings suggest that the K-HOPE CPC model improves communication and overall EOL care experiences among hospitalized patients with terminal cancer, indicating that meaningful improvements in EOL care can occur even during short periods of CPC involvement. Structured CPC integrated into routine oncology practice represents a feasible strategy for improving EOL care in tertiary hospitals, and a standardized CPC framework may enhance the consistency and reproducibility of care delivery within the Korean healthcare system.