ethicalabs/Research-Intent-Collab huggingface dataset:
Conserving Native Vegetation on Private Land: Subsidizing Sustainable Use of Biodiversity?

Abstract                The Convention on Biological Diversity 1992 (the Biodiversity Convention) has as its primary objective the conservation of biological diversity.1 Running a close second is the objective of sustainable use of biological diversity.2 Simultaneous achievement of such objectives often runs contrary to the desires of large landowners in Australia, particularly when such landowners are engaged in primary production industries.
Investigation of laser printing for 3D printing and additive manufacturing

Additive Manufacturing (AM), popularly called “3D printing,” has\ud benefited from many two-dimensional (2D) printing technology\ud developments, but has yet to fully exploit the potential of digital\ud printing techniques.\ud The very essence of AM is accurately forming individual layers and\ud laminating them together. One of the best commercially proven\ud methods for forming complex powder layers is laser printing, which\ud has yet to be used to directly print three-dimensional (3D) objects\ud above the microscale, despite significant endeavour.\ud The core discovery of this PhD is that the electrostatic charge on\ud toner particles, which enables the digital material patterning\ud capabilities of 2D laser printing/photocopying, is disabling for\ud building defect-free 3D objects after the manner attempted to\ud date. Toner charge is not mostly neutralized with fusing as\ud previously assumed.\ud This work characterizes and substantiates the accumulation of\ud residual toner charge as a primary cause for defects arising in 3D\ud printed bodies. Next, various means are assessed to manage and\ud neutralize residual toner charge. Finally, the complementary\ud implementation of charge neutralization with electrostatic transfer\ud methods is explored.
Continuous Intelligence Delivery In HR Technology: Integrating DevOps Automation, CI/CD Pipelines, And Predictive Machine Learning Within SAP SuccessFactors Environments

Cloud based human resource platforms increasingly demand operational stability, controlled configuration governance, and analytical depth that extend beyond periodic release cycles and static reporting models. This study presents a Continuous Intelligence Delivery framework designed for SAP SuccessFactors environments that unifies DevOps automation practices, structured CI CD pipelines, and predictive machine learning techniques into a cohesive operational architecture. The framework integrates automated configuration lifecycle management, regression validation, role based security testing, and transport governance with embedded predictive models using regression, classification, and time series methods to support attrition risk assessment, workforce demand forecasting, compensation variance analysis, and absence pattern monitoring. By aligning release orchestration with statistical learning driven insight generation, the proposed model transforms HR technology platforms into adaptive systems capable of iterative improvement while preserving audit traceability and compliance integrity. Implementation pathways, governance controls, and performance validation mechanisms are articulated to demonstrate practical feasibility within enterprise SAP SuccessFactors landscapes. The study advances a structured and scalable blueprint for embedding continuous operational intelligence within HR technology ecosystems and establishes a foundation for future research in automated workforce systems engineering.
Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in LixCoO2

46 pages, 15 figures
Efficacy, safety, and cost-effectiveness analysis of Cerebrolysin in acute ischemic stroke: A rapid health technology assessment

This study conducts a rapid health technology assessment to systematically evaluate the effectiveness, safety, and cost-effectiveness of Cerebrolysin as an adjunctive therapy for acute ischemic stroke to provide evidence-based medicine for clinical decisions of Cerebrolysin. All systematic reviews/meta-analyses, pharmacoeconomic studies, and health technology assessment reports of Cerebrolysin for the treatment of acute ischemic stroke before August 17, 2023, were retrieved from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang, Weipu, Sinomed database and the official website of health technology assessment. According to the inclusion and exclusion criteria, 2 researchers independently carried out screening, data extraction, and quality evaluation and descriptively analyzed the results of the included studies. A total of 14 pieces of literature were incorporated, comprising 8 systematic reviews/meta-analyses and 6 pharmacoeconomic studies. In terms of effectiveness, compared to control groups, the use of Cerebrolysin as a treatment for acute ischemic stroke demonstrates certain advantages, including enhancement in total efficacy rate, neurological function, upper limb motor dysfunction, and facilitation of the recovery of activities of daily living. Especially in patients with moderate to severe acute ischemic stroke, Cerebrolysin has demonstrated the ability to enhance neurological function recovery and ameliorate disabilities. Regarding safety, adverse reactions were mild or comparable to those in the control group. The primary findings of economic studies reveal that advocating for the use of Cerebrolysin offers certain cost-effectiveness advantages. Cerebrolysin contributes to improved clinical efficacy and evaluation indexes while demonstrating favorable safety and economic benefits.
Sustainable development control? Zoning and land use regulations for urban form, biodiversity conservation and green design in Australia

This paper examines how Australian land use plans incorporate provisions for sustainable urban form, design, biodiversity conservation, and climate change. Despite the long-standing sustainability objectives in state policy frameworks, a survey of 291 comprehensive plans finds implementation within local instruments is far from universal. Differences in patterns of policy adoption, as well as potential explanations for these differences, including geographical location, patterns of residential growth, the socio-economic composition of local communities, political forces, and policy evolution over time, are explored. Overall, areas experiencing more intensive growth tend to have newer plans, which in turn, contain more sustainability provisions, suggesting a responsive rather than repressive relationship between development pressures and regulatory development control.
The Effect of Accountability, Transparency, and Abnormal Accrual Against Potential Corruption in Indonesia

Corruption is a serious problem that has become a concern in many countries, including Indonesia. Regional governments as entities responsible for the provision of public services and the use of public budgets have an important role in efforts to prevent and reduce corruption. Corrupt practices undermine public trust in the government, hinder economic, social and political development, and result in significant financial losses for the state and society. Corruption results in huge losses for Indonesian society and the economy. Public funds that should be used for infrastructure development, education and health services are often misused or siphoned off by corrupt parties. This impact can hinder improving the quality of life of Indonesian people and the country's economic growth. The crime of corruption is defined as a violation of people's rights, therefore the crime of corruption is included in the category of extraordinary crimes, where extraordinary methods are needed to overcome them (Butt, 2017).
Cheating for Problem Solving: A Genetic Algorithm with Social Interactions

We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, ie animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.
Gene regulatory networks: a primer in biological processes and statistical modelling

Modelling gene regulatory networks not only requires a thorough understanding of the biological system depicted but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarise the reader with the biological processes and molecular factors at play in the process of gene expression regulation.We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Amongst other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.
Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features

With increased access to digital technology, there has been a surge in the use of and interest in digital phenotyping as a tool to calculate various features from raw smart device data. However, the increased usage of digital phenotyping has created confusion. The vast number of sensors that can be utilized to collect passive data, and diverse methods utilized to convert that sensor data into features has introduced conflicting results and conclusions into the literature. Consequently, there is an identified need for standardizing how digital phenotyping data is measured and collected. This review evaluates the different sensors and methods utilized in digital phenotyping research across 112 papers, with the goal of finding the most common platforms, sensors, and methods for each behavioral measure. This should help guide future digital phenotyping research, and resolve some existing confusion in the field. Information on each study's data sensor variables were tracked and consolidated into a double-coded Codebook. Variables assessed included but were not limited to data sensors, features extracted from data sensors, statistical methods used, phone type, patient access to phones, and characteristics of patient population. This review found that most studies used Android devices (n = 67) or both Android and iPhone (n = 38) with an average duration of 14.3 weeks. The GPS sensor was also found to be the most frequently used sensor. This review underscores the need for standardization in methodological reporting, sensor utilization, and feature extraction across mental health studies.
Land Rights and Neoliberalism: \ud An Irreconcilable Conflict for Indigenous Peoples in India?\ud

Does legislation that grants land rights necessarily ensure justice? The Forest Rights Act of 2006 (FRA) in India, a landmark social justice law, aims to enhance land security for forest peoples. Increasingly displaced by development and extractive industries which intensifies impoverishment, indigenous peoples in India should with the FRA, be able to protect their land, their livelihoods and their culture. Continued government violations of forest land rights in the name of development, highlight that economically vulnerable populations lack the power to take advantage of legislation. I examine the tension of current indigenous land struggles in the context of the legal frameworks of the FRA, and the neoliberal culture of India. \ud
Galoisian Methods for Testing Irreducibility of Order Two Nonlinear Differential Equations

The aim of this article is to provide a method to prove the irreducibility of non-linear ordinary differential equations by means of the differential Galois group of their variational equations along algebraic solutions. We show that if the dimension of the Galois group of a variational equation is large enough then the equation must be irreducible. We propose a method to compute this dimension via reduced forms. As an application, we reprove the irreducibility of the second and third Painlevé equations for special values of their parameter. In the Appendix, we recast the various notions of variational equations found in the literature and prove their equivalences.
Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP

Mechanistic interpretability seeks to understand the neural mechanisms that enable specific behaviors in Large Language Models (LLMs) by leveraging causality-based methods. While these approaches have identified neural circuits that copy spans of text, capture factual knowledge, and more, they remain unusable for multimodal models since adapting these tools to the vision-language domain requires considerable architectural changes. In this work, we adapt a unimodal causal tracing tool to BLIP to enable the study of the neural mechanisms underlying image-conditioned text generation. We demonstrate our approach on a visual question answering dataset, highlighting the causal relevance of later layer representations for all tokens. Furthermore, we release our BLIP causal tracing tool as open source to enable further experimentation in vision-language mechanistic interpretability by the community. Our code is available at https://github.com/vedantpalit/Towards-Vision-Language-Mechanistic-Interpretability.
Medical and health sciences’ community attitudes towards hybrid journals, academic networks, social media and research evaluation metrics: a perspective from Greece

Purpose – The present paper attempts to identify medical and health scientists' attitude towards the use of hybrid journals, social media and academic networks and the selection factors of publication medium, including alternative metrics. Design/methodology/approach - A quantitative survey was conducted, based on a structured questionnaire. It focused on Health Sciences and Medicine, with a population sample mainly consisting of hospital healthcare professionals. Likert scale, simple multiple-choice and ranking type questions were used. Analysis: 215 completed questionnaires were gathered and various statistical parameters were correlated with the demographic data (profession, experience, gender). Findings - Most participants agreed that publishing in hybrid journals results in a significantly high cost for the authors which cannot be paid without funding support and probably affects the validity of the evaluation procedures. They also agreed that an open article published in a hybrid journal needs a shorter time for the peer review process, receives more citations and contributes essentially to the research process. Most of the participants use or would like to use ResearchGate and Google Scholar services. Social media involvement was considerably low in participants' responses. The number of publications and the prestige / credibility of the publishing media are considered to be the most important factors in research evaluation and in publishing media selection respectively. Originality/value - No survey has been reported recently that focuses on health professionals' attitude towards Open Access movement in Greece. It is valuable to explore this community’s attitude because of their extremely active publishing profile, which decisively affects their career and largely contributes to research progress and national innovation.
Reflections on uncertainty in risk assessment and risk management by the Society of Environmental Toxicology and Chemistry (SETAC) precautionary principle workgroup

Quantitative uncertainty assessments and the distribution of risk are under scrutiny and significant criticism has been made of null hypothesis testing when careful consideration of Type I (false positive) and II (false negative) error rates have not been taken into account. An alternative method, equivalence testing, is discussed yielding more transparency and potentially more precaution in the quantifiable uncertainty assessments. With thousands of chemicals needing regulation in the near future and low public trust in the regulatory process, decision models are required with transparency and learning processes to manage this task. Adaptive, iterative, and learning decision making tools and processes can help decision makers evaluate the significance of Type I or Type II errors on decision alternatives and can reduce the risk of committing Type III errors (accurate answers to the wrong questions). Simplistic cost-benefit based decision-making tools do not incorporate the complex interconnectedness characterizing environmental risks, nor do they enhance learning, participation, or include social values and ambiguity. Hence, better decision-making tools are required, and MIRA is an attempt to include some of the critical aspects.
The Impact of Parental Mediation and Social Media Use Frequency on Adolescent Mental Health in China

With the development of the new media era, people have begun to use social media, and teenagers are no exception. The complex and diverse information and content online pique their curiosity, leading them to become immersed in the online world for extended periods, unable to extricate themselves. Furthermore, inappropriate parenting styles have led to an increasing number of adolescents experiencing a range of mental health issues due to social media usage. Therefore, this study aims to analyze the impact and relationship between the frequency of social media use in China and parental mediation on adolescents’ mental health. The analysis of adolescents’ mental health will be conducted through the lens of parental mediation theory. This study used a survey method for data collection, targeting adolescents aged 10 to 19. At least 30 responses were collected for each age group, with 19 questions. Research has confirmed that the frequency of social media use is negatively correlated with the mental health of adolescents, while parental media restrictions and active mediation are positively correlated with adolescents’ mental health. Through this research, it is hoped that parents will remain attentive to their children’s physical and mental development recognize the pessimistic consequences that social media can have on their children’s overall well-being, and work together to create an environment that promotes both online health and mental well-being, allowing young people to grow in a positive and loving atmosphere.
The role of surgical simulation and the learning curve in robot-assisted surgery

New surgical teaching methods are continuously being developed to overcome the learning curves of new advanced surgical procedures. The learning curve is recognized in most minimally invasive and robot-assisted surgery. The development of complex skills-training models and simulators, although in its infancy, has started to facilitate the transfer of these skills to novice surgeons without increasing the risk to patients' safety. Robotic surgery, whether in the specialties of urology, general surgery, or cardiac surgery, has become the ideal platform to integrate simulators for teaching purposes. Its different interface requires the surgeon to acquire more advanced skills compared with conventional open or laparoscopic surgery. However, simulators can allow the naïve surgeon to develop these skills and pass the learning curve without the medico-legal implications of surgical training, limitations in trainee working hours, and ethical considerations of learning basic skills on humans.
Compressive sensing: Performance comparison of sparse recovery algorithms

Spectrum sensing is an important process in cognitive radio. A number of sensing techniques that have been proposed suffer from high processing time, hardware cost and computational complexity. To address these problems, compressive sensing has been proposed to decrease the processing time and expedite the scanning process of the radio spectrum. Selection of a suitable sparse recovery algorithm is necessary to achieve this goal. A number of sparse recovery algorithms have been proposed. This paper surveys the sparse recovery algorithms, classify them into categories, and compares their performances. For the comparison, we used several metrics such as recovery error, recovery time, covariance, and phase transition diagram. The results show that techniques under Greedy category are faster, techniques of Convex and Relaxation category perform better in term of recovery error, and Bayesian based techniques are observed to have an advantageous balance of small recovery error and a short recovery time.
Engineering and physical fundamentals for the plasma processing technology of MNUP and MOF spent nuclear fuel of fast neutron reactors

Abstract                One of the urgent tasks of the nuclear power industry today is reprocessing of spent nuclear fuel (SNF) and radioactive waste (RW), which is necessary to switch to a closed fuel cycle in order to use the reactor fuel resources to more extent by separating minor actinides for reuse of refabricated fuel. Another equally important driver for the creation and implementation of such technology is the environmental requirements aimed at reducing the disposal of radioactive waste and the scope of high-level waste transportation. It should be noted that any civil technology for SNF reprocessing must meet the requirement of non- proliferation of nuclear weapons, i.e. is obliged to prevent the release of plutonium, including by changing the operating modes of the equipment. There are promising hydrometallurgical and pyrochemical technologies developed at present day, as well as plasma processing methods. This report presents the engineering and physical fundamentals of plasma separation of SNF and RW. The potential advantages of plasma technologies for SNF or RW processing include a small amount of additional waste, the ability to adapt to different types of SNF and RW, and a possibility of implementing the technology into existing and designed material processing cycles and varying the processing scale from on-site to plant-size ones within large facilities. An important feature of plasma methods, i.e. insufficient selectivity for the separation of minor actinides from each other, shall also be mentioned. It is precisely such “crude” approach that ensures acceptable civilian processing technology, which satisfies the conditions for non-proliferation of nuclear weapons. The paper proposes an approach aimed to use the accelerating potential to overcome the energy and angular distribution of plasma ions at the entrance to the separation area and a potential well for the spatial separation of ions of different masses. It considers the physical principles of the plasma separation method and its main stages. There are provided experimental results achieved so far at a pilot facility for testing the plasma separation method. The results of calculations of ion trajectories and energy cost estimates are shown, demonstrating the prospects of the plasma method for process application. The process flow diagram of plasma processing and the steps to be taken to develop the technology are also discussed.