ethicalabs/Research-Intent-Collab huggingface dataset:
Matheurisc approaches for solving transport optimization problems in urban logistics. (Approches matheuristiques pour la résolution de problèmes d'optimisation des transports en logistique urbaine).

In this thesis, we are interested in the problems raised by the optimization of vehicle routing for delivery in urban areas. We pay close attention to the problem of synchronized resources. Indeed, the urban context imposes hard constraints on the usable space. In the theoretical models developed, this translates into resource constraints common to all vehicles. The first problem we have developed deals with the delivery of parcels with time windows while considering multiple delivery options for each order. The synchronization of resources between vehicles comes from the consideration of the global quality of service according to the customers' preferences as well as the capacity of shared delivery locations, such as lockers. The second problem addresses the optimization of the collection and delivery of parcels via a two-echelons logistics system. The synchronized resources between all vehicles are then the limited storage capacity of intermediate warehouses, called satellites. To solve these problems, we have developed large neighborhood search methods based on small destructions. We also studied their hybridization with MIP models and dynamic programming. Thus, our methods are categorized as matheuristics. ; Dans cette thèse, nous nous intéressons aux problématiques soulevées par l'optimisation des tournées de véhicules pour la livraison en milieu urbain. Nous nous sommes attachés à la problématique des ressources synchronisées. Le contexte urbain impose des contraintes sur l'espace utilisable. Dans les modèles théoriques développés, cela se traduit par des contraintes de ressources communes à tous les véhicules. Le premier problème que nous avons développé traite de la livraison de colis avec fenêtre horaire en considérant de multiples options de livraison pour chaque commande. La synchronisation de ressources entre les livreurs vient de la prise en compte d'un niveau de service global ainsi que de la capacité de lieux de livraison partagé, tels que des consignes. Le second problème traité vise à optimiser ...
Beyond Neural Incompatibility: Easing Cross-Scale Knowledge Transfer in Large Language Models through Latent Semantic Alignment

an early-stage version
Optimal and fast detection of spatial clusters with scan statistics

We consider the detection of multivariate spatial clusters in the Bernoulli model with $N$ locations, where the design distribution has weakly dependent marginals. The locations are scanned with a rectangular window with sides parallel to the axes and with varying sizes and aspect ratios. Multivariate scan statistics pose a statistical problem due to the multiple testing over many scan windows, as well as a computational problem because statistics have to be evaluated on many windows. This paper introduces methodology that leads to both statistically optimal inference and computationally efficient algorithms. The main difference to the traditional calibration of scan statistics is the concept of grouping scan windows according to their sizes, and then applying different critical values to different groups. It is shown that this calibration of the scan statistic results in optimal inference for spatial clusters on both small scales and on large scales, as well as in the case where the cluster lives on one of the marginals. Methodology is introduced that allows for an efficient approximation of the set of all rectangles while still guaranteeing the statistical optimality results described above. It is shown that the resulting scan statistic has a computational complexity that is almost linear in $N$.
Equity in water and sanitation: Developing an index to measure progressive realization of the human right

We developed an index to measure progressive realization for the human right to water and sanitation. While in this study we demonstrate its application to the non-discrimination and equality component for water, the conceptual approach of the index can be used for all the different components of the human right. The index was composed of one structural, one process, and two outcome indicators and is bound between -1 and 1, where negative values indicate regression and positive values indicate progressive realization. For individual structural and process indicators, only discrete values such as -1, -0.5, 0, 0.5, and 1 were allowed. For the outcome indicators, any value between -1 and 1 was possible, and a State's progress was evaluated using rates of change. To create an index that would allow for fair comparisons between States and across time, these rates of change were compared to benchmarked rates, which reflect the maximum rates a State can achieve. Using this approach, we calculated the index score for 56 States in 2010 for which adequate data were available and demonstrated that these index scores were not dependent on factors such as achieved level of coverage or gross national income. The proposed index differs from existing measures of inequality as it measures rate of change and not level of achievement, and thus addresses the principle of progressive realization that is fundamental to human rights.
Development of an implementation intervention to promote adoption of the COMFORT clinical practice guideline for peripartum pain management: a qualitative study

Abstract                 Background                 Pain management after childbirth is widely variable, increasing risk of untreated pain, opioid harms, and inequitable experiences of care. The Creating Optimal Pain Management FOR Tailoring Care (COMFORT) clinical practice guideline (CPG) seeks to promote evidence-based, equitable acute peripartum pain management in the United States. We aimed to identify contextual conditions (i.e., barriers and facilitators) and discrete implementation strategies (i.e., theory-based actions taken to routinize a clinical practice) likely to influence COMFORT CPG uptake and specify corresponding multi-component implementation interventions at the perinatal quality collaborative- and unit-level.                                Methods                 We conducted a qualitative study involving virtual individual interviews and focus groups. Interviews included individuals undergoing childbirth from 2018–2023, (recruited through two online registries), and actively practicing maternity clinicians and surgeons, (recruited via snowball sampling with the eDelphi panel creating the COMFORT CPG), caring for pregnant people in the United States. Focus groups included physicians, midwives, nurses, and unit-based quality improvement (QI) staff working at Michigan hospitals within the Obstetrics Initiative, a statewide perinatal quality collaborative funded by Blue Cross Blue Shield of Michigan and Blue Care Network. The Consolidated Framework for Implementation Research, Expert Recommendations for Implementing Change taxonomy, and Replicating Effective Programs framework informed data collection and analysis. Qualitative content analysis characterized influential contextual conditions, which were linked to implementation strategies and tools using principles of implementation mapping. We then specified multi-component implementation interventions for use by quality collaboratives and unit-based teams.                                Results                 From May–September 2023, we completed 57 semi-structured individual interviews (31 patients, 26 clinicians) and six focus groups (44 QI champions). Participants identified 10 key conditions influential for COMFORT CPG adoption. Findings enabled identification of five collaborative-level implementation strategies, 27 unit-level implementation strategies, and 12 associated tools to promote COMFORT CPG adoption including the specification of each strategy’s hypothesized mechanism of action and each tool’s goal and potential uses.                                Conclusions                 This work identifies contextual conditions and implementation strategies and tools at the perinatal quality collaborative and unit levels to promote COMFORT CPG adoption on maternity units. These findings may foster more rapid CPG implementation and thereby promote more equitable and evidence-based perinatal pain management care.
Light Baryon Spectroscopy at BESIII

The BESIII collaboration has collected large data samples in the center-of-mass energy range from 1.84 to 4.95 GeV. These data provide a low-background environment and suitable phase space for studying the spectroscopy of light baryons. In this article, we review the achievements of baryon spectroscopy studies by the BESIII collaboration. Most of the results are obtained through the partial wave analysis (PWA) method, with spin and parity well determined.
Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities

Cancer continues to be a leading cause of mortality worldwide, presenting substantial challenges to public health systems. The traditional approaches to cancer diagnosis and prognosis prediction exhibit certain limitations with respect to accuracy, comprehensiveness, dynamic monitoring, and personalization. With the advancement of artificial intelligence (AI) technologies, novel diagnostic and predictive methods are increasingly addressing these shortcomings. This review provides a comprehensive overview of the primary AI algorithms applied in oncology, including machine learning, deep learning, and large language models. It further examines the distinctive characteristics and appropriate use cases of AI algorithms, highlighting their specific roles in cancer screening, diagnostic accuracy, and outcome forecasting. Additionally, the review discusses emerging trends and persistent challenges, aiming to provide actionable insights that support clinical decision-making and advance scientific innovation in this rapidly evolving field. In conclusion, this review systematically outlines recent advances in AI applications for cancer diagnosis and prognostic prediction, with the objective of facilitating a transformative shift in oncology from experience-based practices toward data-driven precision medicine.
CORE COMPETENCY IN RESEARCH OF TEXTS CALLING FOR UNDERMINING THE CONSTITUTIONAL ORDER, TERRITORIAL INTEGRITY AND SOVEREIGNTY OF UKRAINE

In a situation where our state has faced a significant number of threats from the Russian Federation, among which threats of information terrorism and extremism represent a significant part of the existing risks, the number of criminal investigations into the facts of committing such crimes has increased. The investigation of almost each of them is associated with the need to use particular knowledge to ensure completeness, comprehensiveness, objectivity and at the same time compliance with reasonable deadlines for pre-trial investigation. The peculiarity of a significant number of modern expert studies conducted in criminal proceedings on extremist crimes is that their objects are products of speech activity which find their expression in oral or written speech and are stored on a storage medium in graphic or digital forms. Both data storage materials and the speech product stored on them are subject to expert research. The aim of research is to analyse the practice of using the capabilities of forensic examination in order to study texts during the pre-trial investigation of crimes related to public calls for committing illegal actions aimed at undermining the constitutional order, territorial integrity and sovereignty of Ukraine; to reveal the potentials of linguistic expert examination and to justify the need for comprehensive psychological and linguistic studies of texts calling for the perpetration of illegal actions, as well as denial of the legitimacy of resolving legal issues during such examinations. The study substantiates that the most important tool for identifying, investigating and proving facts of extremist activities aimed at undermining the constitutional order, territorial integrity and sovereignty of Ukraine are forensic examination and expert research. Manifestations of speech extremism become frequent objects of study in forensic linguistic examination. When translating the signs of such extremist crimes into a meta-language of linguistic semantics, two groups of features are distinguished – semantic and pragmatic. By virtue of introduction of the methodology for conducting forensic linguistic, psychological and linguistic examination of materials in criminal proceedings related to the investigation of the facts of calls for extremism and terrorism, modern experts are armed with a system of linguistic categories and methods that must be used in the study of statements that have fallen into the legal sphere. Forensic examinations in such criminal proceedings are also a means of remedial action, since the correctness of its expert evaluation depends on an objective, methodically correct expert assessment of the disputed material. Due to the verbal nature of the objects of expert research, bodies of pre-trial investigation most often appoint forensic linguistic, psychological, psycho-linguistic, and other examinations, as well as various options for complex research.
The heliospheric modulation of cosmic ray protons during increased solar activity: effects of the position of the solar wind termination shock and of the heliopause

Abstract. The effects on the modulation of cosmic ray protons of different positions for the solar wind termination shock and for the heliopause are illustrated for moderate solar maximum conditions. This is done with a numerical model which includes diffusive termination shock acceleration, a heliosheath and drifts. The modulation is computed for the heliospheric equatorial plane and at 35° heliolatitude and for both magnetic polarity cycles of the Sun. It was found that the differences between the modulation for the two solar polarity cycles are less significant at a heliolatitude of 35° than in the equatorial plane. The modulation for the different heliopause positions are qualitatively similar, although there are clear quantitative differences which should be observable with the two Voyager spacecraft in the outer heliosphere. It is illustrated that the motion of the termination shock from 90 AU to 100 AU, with the heliopause fixed at 120 AU, is not crucially important to global modulation. What is of primary importance is the location of the heliopause. It can also be concluded from the results that significant asymmetric modulation is to be expected between the up-wind and down-wind directions of the heliosphere but this may become measureable only when spacecraft move beyond the termination shock into the heliosheath. Keywords. Interplanetary physics (Cosmic rays; Heliopause and solar wind termination) – Space plasma physics (Transport processes)
Dashboard Design to Monitor the Number of Students of the Faculty of Information Technology, Tarumanagara University

Students in a study program in the faculty as one of the stakeholders have an important role in higher education. The continuity and smoothness of teaching and learning activities depend on the number of students in a study program. Faculty of Information Technology Tarumanagara University currently has two study programs: Informatics Engineering and Information Systems. Tarumanagara University already has a system that is used to manage student data, but the existing system needs to be developed further to face the tight competition with other universities, especially in study programs related to information technology.  So far, the existing system has not been able to provide a concise overview of student data. The information available is only limited to the number of students this year more or less than the previous year. For this reason, a monitoring system is needed that can provide more accurate and systematic information. This monitoring is visualized in the form of a dashboard that will provide information about trends in the number of students, school origin, gender, and address / region of origin. The dashboard design method uses the software development life cycle (SDLC) waterfall model. The dashboard is developed using PowerBI Desktop.
Factors influencing implementation success of guideline-based clinical decision support systems: A systematic review and gaps analysis

To provide an integrated and differentiated understanding of factors influencing guideline-based CDSS implementation and illustrate the gaps in the current literature.A systematic literature review and in-depth exploration of factors impeding or facilitating successful implementation of guideline-based CDSS supporting physicians in clinical decision-making was performed. Factors were identified thematically by textual analysis of the included publications and were individually mapped to the human, organization and technology-fit (HOT-fit) framework for evaluating implementations of health information systems.A total of 421 factors were found in 35 included publications from a total of 3676 publications. The mapping of factors concerning CDSS implementation on the HOT-fit framework revealed gaps in each domain of the framework and showed that research has mainly focused on human and technology factors and less on organizational factors.Future research within the field of guideline-based CDSS should focus on evaluating implementations through the use of socio-technical models to study guideline-based CDSS system implementations from a multidimensional view. Furthermore, research is needed to explore whether use of these models during the planning phases of a CDSS project is useful in anticipating and preventing implementation barriers from occurring and exploiting facilitators to a successful implementation of the system.
The Analysis on the monopolization and Intellectual property rights in patent and copyrights

This research paper explores the rising trend of monopolization that is taking place within the intellectual property rights domain. The study particularly focuses on copyrights and patents, which are the two most essential components of intellectual property. Besides, the paper extensively explores the multifaceted implications this monopolization has for several vital domains, including innovation, economic equity, and access to knowledge. Though it is quite well known that copyrights are there to provide protection for creative works, and patents are there to protect novel inventions, the serious misuse of such protections by giant corporations has, unfortunately, led to a marked increase in market concentration. Market concentration has grave implications because, apart from choking competition among the entities, it also prevents access to important information. Ultimately, this leads to the prevention of the creativity that drives society forward and contributes to its development. It engages in an intensive comparative study involving copyrights and patents, in which it attempts to explore the relative roles played by these rights, either as inducers of or obstacles to innovation and economic development. It further seeks to highlight the significant broader socio-economic effects that such rights have on the society, from the impacts that manifest as a result of monopolistic practice across diverse sectors. Furthermore, it proffers several policy reforms intended at achieving the desired balance between protecting intellectual property while ensuring equitable access and innovation promotion within the economy.
Research on federated learning approach based on local differential privacy

As a type of collaborative machine learning framework, federated learning is capable of preserving private data from participants while training the data into useful models.Nevertheless, from a viewpoint of information theory, it is still vulnerable for a curious server to infer private information from the shared models uploaded by participants.To solve the inference attack problem in federated learning training, a local differential privacy federated learning (LDP-FL) approach was proposed.Firstly, to ensure the federated model training process was protected from inference attacks, a local differential privacy mechanism was designed for transmission of parameters in federated learning.Secondly, a performance loss constraint mechanism for federated learning was proposed and designed to reduce the performance loss of local differential privacy federated model by optimizing the constraint range of the loss function.Finally, the effectiveness of proposed LDP-FL approach was verified by comparative experiments on MNIST and Fashion MNIST datasets.
Ethical Challenges in Artificial Intelligence: Fairness, Transparency, and Accountability

This article examines the ethical challenges of artificial intelligence (AI), focusing on the principles of fairness, transparency, and accountability. It analyzes the sources of algorithmic bias, explores methods for explainability, and evaluates accountability frameworks for AI-driven harms. Drawing on case studies in predictive policing, medical diagnostics, finance, recruitment, and autonomous vehicles, the article highlights both risks and corrective practices.  Special emphasis is placed on developing economies, where limited resources, reliance on imported systems, and data deficits exacerbate ethical risks. The study synthesizes theoretical, practical, and global perspectives, contributing to debates on aligning AI with justice, inclusivity, and human rights.
The Ripple Effect: Critical Pragmatism, Conflict Resolution and Peace Building through Sport in Deeply Divided Societies

The idea for this chapter comes from a simple question that was asked to me by Dominic Malcolm, fellow Sport Sociologist, during a staff and graduate seminar that I gave at the University of Loughborough in 2006. I had given a presentation about Football for Peace, a cross-community, co-existence programme involving Jewish and Arab towns and villages in Israel that I was directing. When I had finished speaking I was asked a familiar array of questions, mainly concerned with logistical and methodological issues. Then Dominic raised his hand and asked, ‘What difference does the fact that you are a sociologist make to your leadership and development of a project like this?’
AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements

AbstractBreakthrough achievements in protein structure prediction have occurred recently, mostly due to the advent of sophisticated machine learning methods and significant advancements in algorithmic approaches. The most recent version of the AlphaFold model, known as “AlphaFold-latest,” which expands the functionalities of the groundbreaking AlphaFold2, is the subject of this article. The goal of this novel model is to predict the three-dimensional structures of various biomolecules, such as ions, proteins, nucleic acids, small molecules, and non-standard residues. We demonstrate notable gains in precision, surpassing specialized tools across multiple domains, including protein–ligand interactions, protein–nucleic acid interactions, and antibody–antigen predictions. In conclusion, this AlphaFold framework has the ability to yield atomically-accurate structural predictions for a variety of biomolecular interactions, hence facilitating advancements in drug discovery.