ethicalabs/Research-Intent-Collab huggingface dataset:
Advancing projections of phytoplankton responses to climate change through ensemble modelling

A global trend of increasing health hazards associated with proliferation of toxin-producing cyanobacteria makes the ability to project phytoplankton dynamics of paramount importance. Whilst ensemble (multi-)modelling approaches have been used for a number of years to improve the robustness of weather forecasts this approach has until now never been adopted for ecosystem modelling. We show that the average simulated phytoplankton biomass derived from three different aquatic ecosystem models is generally superior to any of the three individual models in describing observed phytoplankton biomass in a typical temperate lake ecosystem, and we simulate a series of climate change projections. While this is the first multi-model ensemble approach applied for some of the most complex aquatic ecosystem models available, we consider it sets a precedent for what will become commonplace methodology in the future, as it enables increased robustness of model projections, and scenario uncertainty estimation due to differences in model structures. (C) 2014 Elsevier Ltd. All rights reserved.
Large Scale Marine Protected Areas for Biodiversity Conservation Along a Linear Gradient: Cooperation, Strategic Behavior or Conservation Autarky?

In this paper we investigate effects of overlap in species between ecosystems along a linear gradient on the location of marine protected areas (MPAs) under full cooperation, strategic behavior and conservation autarky. Compared to the full cooperation outcome, both strategic behavior and conservation autarky lead to under-investment in biodiversity protection. Under strategic behavior, however, we observe the additional problem of “location leakage” i.e. countries invest less in species protected by others. Conservation autarky eliminates location leakage; in ecosystems with partly overlapping species compositions at country borders it even induces MPAs that are too large from a global perspective. We also find that, in our setting of a linear gradient without migrating species, countries focus their conservation efforts on species unique to their own country and that these species are relatively well protected compared to common species.
Role of Data Analytics in Blended Learning Models of Educational Technology

Blended models the new buzz word” blended” is making its impact on the society, even though not introduced in recent times their existence are dated from age old days. With several models in use their role based for learners and teachers vary according to their accessibilities. This paper discusses the models and need for study on blended models with a role based difference, its benefit’s (from teacher’s and student’s perception) and challenges. It also emphasises the need for the introduction to data analytics of models and the impact on learners.
What Makes a System Complex? - An Approach to Self Organization and Emergence

The fast changing reality in technical and natural domains perceived by always more accurate observations has drawn attention on new and very broad class of systems with specific behaviour represented under the common wording complexity. From elementary system graph representation with components as nodes and interactions as vertices, systems are shown to belong to only three states : simple, complicated, and complex, the main properties of which are discussed. The first two states have been studied at length over past centuries, and last one finds its origin in the elementary fact that when system performance is pushed up, there exists a threshold above which interaction between components overtake outside interaction. Then system self-organizes and filters corresponding outer action, making it more robust to outer effect, with emergence of new behaviour not predictable from only components study. Examples in Physics and Biology are given. Three main classes of complexity behaviour are distinguished corresponding to different grades of difficulty to handle their dynamics. The interest of using complex state properties in man-made systems is stressed. Important issues concentrating on the difficult balance between relative system isolation when becoming complex and required delegation of corresponding new capability from (outside) operator are discussed. This implies giving the system some intelligence in an adequate frame between new augmented system state and supervising operator, with consequences on canonical system triplet {effector-sensor-controller} to be reorganized in this new setting. Moreover, entering complexity state opens the possibility for function to feedback onto structure, ie to mimic at technical level the invention of Nature over Her very long history.
Explainable AI: Developing Interpretable Deep Learning Models for Medical Diagnosis

Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated remarkable potential in enhancing medical diagnosis across various specialties. However, the inherent complexity and opacity of these models pose significant challenges in clinical adoption, particularly due to the critical nature of healthcare decisions. This research paper explores the development of interpretable deep learning models for medical diagnosis, focusing on the integration of Explainable AI (XAI) techniques to enhance transparency, accountability, and trust in AI-assisted medical decision-making. We investigate various XAI methodologies, their application in different medical domains, and their impact on diagnostic accuracy and clinical interpretability. Through a comprehensive analysis of case studies, we demonstrate how explainable models can not only maintain high diagnostic performance but also provide valuable insights into their decision-making processes, potentially revolutionizing the synergy between AI and human expertise in healthcare.
Linear Programming for Influence Maximization Problems in Social Networks Based on Diffusion Models

Influence maximization is a challenging problem that combines optimization and the study of social sciences. The purpose is to find the most influential individual within a society with a limited number of initial nodes. With the increasing use of social networks these years, Understanding and using forceful diffusion will spread social events or innovations far more widely than the conventional perspective of communication. Many studies have focused on providing algorithmic methods, ignoring the study of finding the optimal point. Although it is still difficult for computer community to settle massive influence maximization issues computationally, it presents a significant opportunity to build operations research-based models, approaches, and analyses at this interface. In the current research, the influence maximization problem is investigated as a discrete optimization issue on social networks. It focuses on the creation of precise techniques for resolving massive cases near the optimal and will provide two classical diffusion models describing the propagation of influence in networks. Finally, given the additional constraints considered, an extension of the trigger model with effective analysis is proposed.
The public perception of food and non-food related risks of infection and trust in the risk communication during COVID-19 crisis: A study on selected countries from the Arab region

The coronavirus pandemic caused a state of panic worldwide. Mixed messages were given about its risk and how to contain it, when trust in authorities and reliable scientific information are essential to reduce unnecessary scares and inappropriate risk perceptions. We know little about public concerns and opinions in health authorities in the Arab world. Thus, an attempt was made to generate such information through a web-based survey. A total of 1074 subjects from Lebanon, Jordan, and Tunisia were recruited to explore their perception of food and non-food risks of infection and the influence of the source of information, trust, and attitudes towards the local authorities' communication of risk. Seventy percent of the respondents were concerned that COVID-19 may be transmitted through food. The perception of risk from touching contaminated surfaces and food packaging and being exposed to infected people during food shopping was even higher. For only less than half of the respondents, the information from local authorities was considered trustworthy and the associated risk communication and response to false rumors were timely, effective, and clear. But the satisfaction level among the Jordanians was remarkably stronger than for the Lebanese and Tunisian respondents. The demographic factors, trust in information, and attitudes towards authorities' performance in risk communication did not influence risk perceptions. Respondents' knowledge was limited based on their chief sources of information, such as social media, local news media broadcasts, and announcements by the World Health Organization. Our conclusion is that unnecessary fear increases among the public when risks that impact heath are unknown. More research in the Arab region is needed to understand the determinants of risk perceptions considering psychological factors on the risk to health. Unfortunately, it is difficult to restrict or contain misleading information from various forms of social media. We recommend that for reducing fear and building confidence with the public for appropriate action during the pandemic, local authorities should enhance the quality and level of details of the information that they share during such crises.
A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment

Cloud Computing is a new trend emerging in IT environment with huge requirements of infrastructure and resources. Load Balancing is an important aspect of cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by provisioning of resources to cloud users on demand basis in pay as you say manner. Load Balancing may even support prioritizing users by applying appropriate scheduling criteria. This paper presents various load balancing schemes in different cloud environment based on requirements specified in Service Level Agreement (SLA).
Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

Environmentally friendly shared transit systems have become ubiquitous at present. As a result, analyzing the ranges and tracts of human activities and gatherings based on bike share data is scientifically useful. This paper investigates the spatial and temporal travel characteristics of citizens based on real-time-extracted electric bikes (e-bikes) Global Positioning System (GPS) data from May to July in 2018 in the central area of Tengzhou City, Shandong Province, China. The research is conducive for the exploration of citizens’ changes in mobility behaviors, for the analysis of relationships between mobility changes and environmental or other possible factors, and for advancing policy proposals. The main conclusions of the study are as follows. First, in general, citizens’ travelling is featured by rides that are less than 10 min, shorter than 5 km, and with a speed between 5 km/h and 20 km/h. Second, in terms of temporal characteristics, monthly e-bike usage and citizens’ mobility are positively correlated with temperature in May and negatively correlated with temperature in July; an overall negative correlation is also manifested between the e-bike usage (mobility) and air quality index; daily usage reaches a trough on Tuesday and a peak on Friday, indicating the extent of mobility on respective days; e-bike usage and human outdoor behaviors are significantly lowered in rainy weather than in sunny weather; hourly rides reach a peak at 18:00 (more human activities) and a trough at 2:00 (less activities), and average hourly riding speed maximizes at 5:00 and minimizes around 8:00 and 17:00. Third, for spatial characteristics, destinations (D points) during morning rush hour and regions where e-bikes are densely employed are concentrated mainly in mid-north and middle parts of the central area (major human gatherings), and the rides have a diffusing pattern; e-bike origin–destination (O–D) trajectories radiate mostly towards the mid-north and the east during evening rush hour. In addition, 9.4% of the total trips to work areas during morning rush hour represent spillover commuting, indicating that separations between jobs and residential are not severe in the central area of Tengzhou City and commuting is relatively convenient.
Understanding biological networks Systems Biology approaches to study Cell Cycle and Metabolism

This thesis is written in publication format and comprises the work done in two main research lines: Study of the Cell Cycle regulation (Chapters 1-3) and The Integration of Metabolism and immunity (Chapters 4-7). Chapter 1 is a review article that collects relevant information on regulatory structures present in biological networks, using a Systems and Synthetic Biology approach. Chapter 2 analyses a minimal cell cycle network using the System Design Space (SDS) methodology, establishing under which conditions (parameter values) the network can exhibit bistability and oscillations. Chapter 3 uses the SDS approach to characterise the cell cycle cyclin waves in budding yeast mutant stains. Using mutant budding yeast frequency in population data provided by collaborators at the Imperial College London, it was possible to analyse how the deletion of key cell cycle genes affects the capacity of the cell to exhibit sustained oscillations and the shape of the cyclin waves. Chapter 4 is an editorial article reflecting on the importance of the balance of T cell phenotypes on health and disease, serving as the transition to the Metabolism and Immunity part of the thesis. Chapter 5 shows the use of Flux Balance Analysis (FBA) simulations to understand the response towards an experimental HIV vaccine. Starting from untargeted metabolomic data from patient serum samples, provided by collaborators at the University of Rochester it was possible to find metabolic pathways involved in the early reaction to the vaccine. Chapter 6 shows the analysis of the Priority Index Database for gene targets in autoimmune disorders, using the ranking of target genes as an input to establish similarities among autoimmune disorders and finding opportunities for drug repurposing. Finally, Chapter 7 is a perspective article that discusses the importance of addressing the metabolism when studying immune disorders, particularly the link between metabolism inflammation and ageing.
Home-based teleworking and preventive occupational safety and health measures in European workplaces: evidence from ESENER-3

This report identifies the main characteristics of home-based teleworkers and companies with teleworking in place, and looks at some of the most common prevention measures being implemented to counteract the associated physical and psychosocial risks to worker health and well-being. The report is based on data from EU-OSHA’s ESENER survey, the European Union Labour Force Survey and the Eurofound’s European Working Conditions Survey 2015. The latest, pre-pandemic survey data are analysed to provide facts and figures valuable to those responsible for planning and implementing teleworking agreements post pandemic, with a particular focus on preventing musculoskeletal disorders.
Synthesis of Mechanism for single- and hybrid-tasks using Differential Evolution

The optimal dimensional synthesis for planar mechanisms using differential evolution (DE) is demonstrated. Four examples are included: in the first case, the synthesis of a mechanism for hybrid-tasks, considering path generation, function generation, and motion generation, is carried out. The second and third cases pertain to path generation, with and without prescribed timing. Finally, the synthesis of an Ackerman mechanism is reported. Order defect problem is solved by manipulating individuals instead of penalizing or discretizing the search space for the parameters. A technique that consists in applying a transformation in order to satisfy the Grashof and crank conditions to generate an initial elitist population is introduced. As a result, the evolutionary algorithm increases its efficiency.
Root Cause Analysis in Support of Event Investigation

Reliability based methods are widely used for the safety assessment of plant system, structures and components. These methods provide a quantitative estimation of system reliability but do not provide insight into the failure mechanisms. Understanding the failure mechanisms is a must to avoid the recurrence of the events and enhancement of the system reliability. Root Cause Analysis (RCA) provides a tool for gaining detailed insights into the causes of failure of a component with particular attention to the identification of fault in component design, operation, surveillance, maintenance, training, procedures and policies which must be improved to prevent repetition of events. Dhruva is a 100 MWth research reactor located at Bhabha Atomic Research Centre, Mumbai. In this research reactor, each failure/malfunction in SSCs or human error, termed as an ‘Event’ is reported by plant Operations and discussed in plant safety committee. Depending upon the importance of the events to the safety of the plant, RCA team of experts in various disciplines analyzes the events. This paper discusses the methodologies adopted for performing RCA of different events in Dhruva reactor.
Conditional path sampling of stochastic differential equations by drift relaxation

Minor corrections, 17 pages, 2 color figures
Natural Language Processing Techniques for Electronic Health Record Analysis

The analysis of EHRs has become a cornerstone of modern healthcare, offering unprecedented opportunities to enhance patient care, improve treatment outcomes, and optimize clinical workflows. NLP techniques play a critical role in extracting meaningful insights from unstructured clinical data, enabling the automated understanding of complex medical narratives. This book chapter explores the fundamental NLP methodologies applied to EHR analysis, with a particular focus on text preprocessing, named entity recognition (NER), and medical coding automation. The chapter delves into advanced topics such as event and relation extraction, clinical text mining, and the extraction of data-driven insights for precision medicine. By leveraging NLP and machine learning models, healthcare practitioners can achieve more efficient and accurate clinical decision making. The chapter highlights the challenges and future directions in the field, emphasizing the need for continued advancements in NLP technologies to address the complexities of clinical data. Ultimately, the integration of NLP techniques in EHR analysis has the potential to revolutionize healthcare by improving the quality of care, fostering personalized medicine, and enhancing the overall patient experience.
Dilemma and Optimization Path of the World Trade Organization Trade Dispute Settlement Mechanism

The paralysis of the World Trade Organization (WTO) Dispute Settlement Mechanism (DSM) has become a systemic crisis for the multilateral trading system. This paper uses literature review and case analysis to analyze the issue. It concludes that while the 2019 suspension of the Appellate Body was directly caused by US obstruction of appointments, its deeper roots lie in the conflict between the mechanism's inherent flaws and the restructuring of global economic power. Firstly, procedural delays result in an average case processing time far exceeding the 90-day limit stipulated in the Understanding on Rules and Procedures Governing the Settlement of Disputes (DSU). Secondly, the AB has had to deal with the issue of 'overreach in rulings,' including encroaching on the mandates of other WTO bodies and exceeding its authority in reviewing factual issues. Thirdly, the mechanism struggles to reconcile the shifting balance between unilateralism and multilateralism. To address the DSM's predicament, this paper proposes a dual-track optimization path: in the short term, establishing a "Multi-party Interim Appeal Arbitration Arrangement" (MPIA) in accordance with Article 25 of the DSU as a temporary substitute for the paralyzed AB; and in the long term, undertaking systemic restructuring of the mechanism. This includes introducing qualified majority voting to break the deadlock in judgement appointments, reasonably extending time limits to suitcase complexity, and drawing on the Regional Comprehensive Economic Partnership (RCEP) model to curb judicial overreach. B The paper aims to analyze the viability of this optimization path as an interim solution.
The Impact of Implementing the HRIS (Human Resource Information System) System on the Efficiency of Human Resource Management in Indonesian Companies

The implementation of the Human Resources Information System (HRIS) has brought significant changes in human resource (HR) management, especially in Indonesian companies. HRIS enables the integration of data and HR management processes digitally, thereby increasing operational efficiency and management transparency. This study aims to analyze the impact of HRIS implementation on HR management efficiency in Indonesian companies. The research method used is library research by analyzing relevant primary and secondary sources, including journal articles, books, and official documents published in the last five years. The results show that HRIS provides great benefits, including automation of administrative processes, data-driven decision-making, and increased employee productivity. However, there are implementation challenges such as high initial costs, resistance to change, and lack of digital literacy among the workforce. This study recommends strengthening technology training for employees and infrastructure development to optimize the benefits of HRIS.
Variance-based variable selection in sensor calibration with strong interferents -- application to air pollution monitoring with a carbon nanotube sensor array

Air and water pollution are major threats to public health, highlighting the need for reliable environmental monitoring. Low-cost multisensor systems are promising but suffer from limited selectivity, because their responses are influenced by non-target variables (interferents) such as temperature and humidity. This complicates pollutant detection, especially in data-driven models with noisy, correlated inputs. We propose a method for selecting the most relevant interferents for sensor calibration, balancing performance and cost. Including too many variables can lead to overfitting, while omitting key variables reduces accuracy. Our approach evaluates numerous models using a bias-variance trade-off and variance analysis. The method is first validated on simulated data to assess strengths and limitations, then applied to a carbon nanotube-based sensor array deployed outdoors to characterize its sensitivity to air pollutants.