ethicalabs/Research-Intent-Collab huggingface dataset:
Optimization of Cold Chain Transportation Path of Pharmaceutical Logistics Based on Roulette Wheel Algorithm

This paper explores the optimization of cold chain transportation paths for pharmaceutical logistics to reduce carbon emissions and energy consumption. By analyzing the impact of logistics path selection, transportation mode, and vehicle configuration, a multi-objective optimization model is constructed, considering logistics cost, transportation time, carbon emissions, and energy consumption. The roulette algorithm within a genetic algorithm is employed to solve the model by reducing unnecessary driving distances. A case study verifies the model’s effectiveness in achieving a green and efficient pharmaceutical logistics system, contributing to the green transformation of the pharmaceutical industry.
High-resolution projections of climate change over the Iberian Peninsula using a mesoscale model

Tesis Univ. Granada. Departamento de Física Aplicada. Leída el 7 de octubre de 2011
Halo Properties in Cosmological Simulations of Self‐interacting Cold Dark Matter

We present a comparison of halo properties in cosmological simulations of collisionless cold dark matter (CDM) and self-interacting dark matter (SIDM) for a range of dark matter cross sections. We find, in agreement with various authors, that CDM yields cuspy halos that are too centrally concentrated as compared to observations. Conversely, SIDM simulations using a Monte Carlo N-body technique produce halos with significantly reduced central densities and flatter cores with increasing cross section. We introduce a concentration parameter based on enclosed mass that we expect will be straightforward to determine observationally, unlike that of Navarro, Frenk &amp; White, and provide predictions for SIDM and CDM. SIDM also produces more spherical halos than CDM, providing possibly the strongest observational test of SIDM. We discuss our findings in relation to various relevant observations as well as SIDM simulations of other groups. Taking proper account of simulation limitations, we find that a dark matter cross section per unit mass of sigma_DM ~= 10^{-23}-10^{-24} cm^2/GeV is consistent with all current observational constraints.
On Understanding the Relation of Knowledge and Confidence to Requirements Quality

Context and Motivation: Software requirements are affected by the knowledge and confidence of software engineers. Analyzing the interrelated impact of these factors is difficult because of the challenges of assessing knowledge and confidence.   Question/Problem: This research aims to draw attention to the need for considering the interrelated effects of confidence and knowledge on requirements quality, which has not been addressed by previous publications.   Principal ideas/results: For this purpose, the following steps have been taken: 1) requirements quality was defined based on the instructions provided by the ISO29148:2011 standard, 2) we selected the symptoms of low qualified requirements based on ISO29148:2011, 3) we analyzed five Software Requirements Specification (SRS) documents to find these symptoms, 3) people who have prepared the documents were categorized in four classes to specify the more/less knowledge and confidence they have regarding the symptoms, and 4) finally, the relation of lack of enough knowledge and confidence to symptoms of low quality was investigated. The results revealed that the simultaneous deficiency of confidence and knowledge has more negative effects in comparison with a deficiency of knowledge or confidence.   Contribution: In brief, this study has achieved these results: 1) the realization that a combined lack of knowledge and confidence has a larger effect on requirements quality than only one of the two factors, 2) the relation between low qualified requirements and requirements engineers' needs for knowledge and confidence, and 3) variety of requirements engineers' needs for knowledge based on their abilities to make discriminative and consistent decisions.
Unveiling Diplomatic Narratives: Analyzing United Nations Security Council Debates Through Metaphorical Cognition.

The United Nations Security Council (UNSC) is entrusted with the responsibility of safeguarding global peace and security. Prominent global security concerns will be deliberated upon, and viewpoints will be presented within the UNSC. Analyzing the cognitive patterns from UNSC debates helps scholars gain insights into the intricacies of international relations and diplomatic discourse. In this study, our focus lies in the cognitive analysis of debates held within the UNSC. We employ metaphors and their associated concept mappings as a methodological tool to dissect the cognitive nuances present in the debates, spanning from January 1995 to December 2020. To undertake this extensive analysis from a large volume of documents, we leverage MetaPro, a state-of-the-art computational metaphor processing system to obtain the concept mappings of metaphors. We analyze cognitive variations by temporal and geographical variables. We also demonstrate the correlation between metaphor-reflected cognition and diplomatic behavior, and their recursive influence, based on large sample research. Our major finding highlights the mutual impacts of metaphorical cognition and voting behavior at the UN.
Elaborating Justice for Victims at the International Criminal Court: Beyond Rhetoric and The Hague

Justice for victims has often been invoked as the raison d’etre of international criminal justice, by punishing perpetrators of international crimes. This article attempts to provide a more holistic account of justice for victims by examining victims’ needs, interests, and rights. The International Criminal Court itself includes participation, protection and reparation for victims, indicating they are important stakeholders. This article also suggests that victims are integral to the purpose of the ICC in ending impunity by ensuring transparency of proceedings. However, there are limits to the resources and capacity of the ICC, which can only investigate and prosecute selected crimes. To overcome this justice gap, this article directs the debate towards a victim-orientated agenda to complementarity, where state parties and the Assembly of State Parties should play a greater role in implementing justice for victims domestically. This victim-orientated complementarity approach can be achieved through new ASP guidelines on complementarity, expanding universal jurisdiction, or seeking enforcement and cooperation through regional and international bodies and courts, such as Universal Periodic Review or the African Court’s International Criminal Law Section. In the end, if we are serious about delivering justice for victims we need to move beyond the rhetoric, with realistic expectations of what the ICC can achieve, and concentrate our attention to what states should be doing to end impunity.
Ecological and forestry features of broad leaved forest and their role in the conservation of biodiversity in the Novgorod region

Abstract                The article provides information on the distribution and condition of broad leaved forest in the Novgorod region. A semi-landscape distribution of broad leaved forest sections is presented. Based on the data of 143 sample plots, the biological diversity of broad leaved forest was estimated.
Systematic Analysis of Art Project Management Risks: an  Exploration of Assessment Methods and Control Strategies

This study is dedicated to exploring risk assessment and control strategies in arts project management. Through a systematic theoretical framework, combining quantitative and qualitative research methods, this paper develops and validates a risk assessment model adapted  to the characteristics of art projects. The empirical study involves three different types of art projects, and the results show that the model can  effectively identify key risks and guide the implementation of risk control strategies. The study highlights the importance of systematic risk  management in enhancing the success of arts projects and provides practical strategies and tools for arts project managers to facilitate further  integration of theory and practice.
Artificial bee colonies for continuous optimization: Experimental analysis and improvements

The artificial bee colony (ABC) algorithm is a recent class of swarm intelligence algorithms that is loosely inspired by the foraging behavior of honeybee swarms. It was introduced in 2005 using continuous optimization problems as an example application. Similar to what has happened with other swarm intelligence techniques, after the initial proposal, several researchers have studied variants of the original algorithm. Unfortunately, often these variants have been tested under different experimental conditions and different fine-tuning efforts for the algorithm parameters. In this article, we review various variants of the original ABC algorithm and experimentally study nine ABC algorithms under two settings: either using the original parameter settings as proposed by the authors, or using an automatic algorithm configuration tool using a same tuning effort for each algorithm. We also study the effect of adding local search to the ABC algorithms. Our experimental results show that local search can improve considerably the performance of several ABC variants and that it reduces strongly the performance differences between the studied ABC variants. We also show that the best ABC variants are competitive with recent state-of-the-art algorithms on the benchmark set we used, which establishes ABC algorithms as serious competitors in continuous optimization.
Development of mRNA manufacturing for vaccines and therapeutics: mRNA platform requirements and development of a scalable production process to support early phase clinical trials

The remarkable success of SARS CoV-2 mRNA-based vaccines and the ensuing interest in mRNA vaccines and therapeutics have highlighted the need for a scalable clinical-enabling manufacturing process to produce such products, and robust analytical methods to demonstrate safety, potency, and purity. To date, production processes have either not been disclosed or are bench-scale in nature and cannot be readily adapted to clinical and commercial scale production. To address these needs, we have advanced an aqueous-based scalable process that is readily adaptable to GMP-compliant manufacturing, and developed the required analytical methods for product characterization, quality control release, and stability testing. We also have demonstrated the products produced at manufacturing scale under such approaches display good potency and protection in relevant animal models with mRNA products encoding both vaccine immunogens and antibodies. Finally, we discuss continued challenges in raw material identification, sourcing and supply, and the cold chain requirements for mRNA therapeutic and vaccine products. While ultimate solutions have yet to be elucidated, we discuss approaches that can be taken that are aligned with regulatory guidance.
Chiral Phase Transition in Linear Sigma Model with Nonextensive Statistical Mechanics

From the nonextensive statistical mechanics, we investigate the chiral phase transition at finite temperature T and baryon chemical potential μB in the framework of the linear sigma model. The corresponding nonextensive distribution, based on Tsallis’ statistics, is characterized by a dimensionless nonextensive parameter, q, and the results in the usual Boltzmann-Gibbs case are recovered when q→1. The thermodynamics of the linear sigma model and its corresponding phase diagram are analysed. At high temperature region, the critical temperature Tc is shown to decrease with increasing q from the phase diagram in the (T,μ) plane. However, larger values of q cause the rise of Tc at low temperature but high chemical potential. Moreover, it is found that μ different from zero corresponds to a first-order phase transition while μ=0 to a crossover one. The critical endpoint (CEP) carries higher chemical potential but lower temperature with q increasing due to the nonextensive effects.
Exploring Large Language Models for Ontology Alignment

This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.
Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation

Hybrid quantum-classical machine learning offers a path to leverage noisy intermediate-scale quantum (NISQ) devices for drug discovery, but optimal model architectures remain unclear. We systematically optimize the quantum-classical bridge architecture of generative adversarial networks (GANs) for molecule discovery using multi-objective Bayesian optimization. Our optimized model (BO-QGAN) significantly improves performance, achieving a 2.27-fold higher Drug Candidate Score (DCS) than prior quantum-hybrid benchmarks and 2.21-fold higher than the classical baseline, while reducing parameter count by more than 60%. Key findings favor layering multiple (3-4) shallow (4-8 qubit) quantum circuits sequentially, while classical architecture shows less sensitivity above a minimum capacity. This work provides the first empirically-grounded architectural guidelines for hybrid models, enabling more effective integration of current quantum computers into pharmaceutical research pipelines.
Power spectrum constraints from spectral distortions in the cosmic microwave background

%The content of this replacement paper is identical to the original. %We have attempted to fix the postscript so that it will print out on %a larger number of printers. Using recent experimental limits on $μ$ distortions from COBE FIRAS, and the large lever-arm spanning the damping of sub-Jeans scale fluctuations to the scale of the COBE DMR fluctuations, we set a constraint on the slope of the primordial power spectrum $n$. It is possible to analytically calculate the contribution over the full range of scales and redshifts, correctly taking into account fluctuation growth and damping as well as thermalization processes. We find that the 95\% upper limit is weakly dependent on cosmological parameters, e.g. $n&lt;1.54 (h=0.5)$ and $n&lt;1.56 (h=1.0)$ for $Ω_0=1$ with marginally weaker constraints for $Ω_0&lt;1$ in a flat $Ω_0 +Ω_Λ=1$ universe.
National-patriotic education of youth in the context of defending the sovereignty and territorial integrity of the state

The article deals with approaches to understanding the national-patriotic upbringing of modern youth as a system of measures aimed at the formation of a citizen-patriot of Ukraine, acting on the basis of national and European values. It is shown that patriotic education is a social activity that forms a person’s attitude towards himself, his people and the Motherland. This attitude is manifested in the corresponding feelings, beliefs, ideas. It is proved that patriotic education should be the core of all educational work, since we must educate a person who has a sense of pride for his state, conscientiously fulfills public duties, is aware of the social needs of theMotherland and the Ukrainian people, loves close and dear people.
Computer Vision to Detect Foreign Objects

The detection of foreign objects and contaminants in food is a critical safety task.  With the level of automation at every level of food production and the rates at which food is  being produced, it is becoming increasingly more important to have systems that can  automatically detect foreign matter along the way.    Plastic is common in the food processing industry and is used in liners, containers, disposable  gloves, food testing instruments, hearing protection, identity badges, conveyor parts, and guides  along conveyors. Plastic conveyor parts are a particular risk because they are hard and small  pieces can be created if a conveyor becomes jammed or if parts are improperly inspected.  Regular inspection of conveyor and accounting for production parts is common practice, but  pieces worn plastic parts may not be discovered until after the product has left a facility.    This paper describes a color vision system designed to find non-metallic, foreign objects larger  than 1.5mm in a processed meat stream. Many processing operations have already standardized on blue color for non-metallic objects that are present in production areas to make  those objects easier for human inspectors to see. The same blue color scheme could make an  computer vision system very practical for foreign object detection and removal. The system to  screen for blue plastic parts can be integrated into the kick-off devices used with preinstalled  metal detectors, so the system could be inexpensive to implement.
Abstract 14276: Using Artificial Intelligence and Natural Language Processing on Electronic Health Records of Heart Failure Patients to Examine the Impact of Estimated Glomerular Filtration Rate Trend on Mortality

Introduction and Hypothesis:             Heart failure (HF) often leads to an imbalance between heart and kidney function, with a poor prognosis in HF patients linked to dynamic changes in renal function as well as chronic kidney disease (CKD). This study aimed to determine the incidence of reduced renal function upon HF diagnosis and to explore the potential for dynamic changes in kidney function to predict short- and long-term outcomes in a real-world cohort of HF patients.                                   Methods:             Clinical data of patients with a diagnosis of HF (index event) were gathered retrospectively using natural language processing. Patients were classified as having CKD based on their eGFR (&lt;60 mL/min/1.73m2) and a history of CKD and/or dialysis. eGFR trajectories were analyzed using linear mixed effects models. Cox proportional hazard models were used to analyze the relationship between baseline variables and mortality. Joint modeling was employed to assess the relationship between the eGFR trend and mortality.                                   Results:             There were 1986 patients in the study population, with a mean (SD) age of 74.8 ± 11.7 years and 58% of males. At the index event, 58% (n = 1156) had CKD, and 17% (n = 339) had HF with preserved ejection fraction (HFpEF). Median follow-up was 3.16 years. After 2 years, 10% (n = 198) of the patients had passed away. The proportion of CKD at index was considerably lower in the 2-year survivors group (55% vs 76%, p &lt; 0.001) in this group. The calculated mean annual eGFR reduction was 4.2 mL/min/1.73m             2             . Age, CVA, diastolic blood pressure, hemoglobin, HDL, potassium, and NT-proBNP levels in blood serum were found to be the primary predictors of mortality in multivariate Cox PH model. Joint modeling revealed that a current eGFR value that was 10 mL/min/1.73m             2             lower between patients corresponded with a higher mortality hazard of 1.22 ± 0.05 (p &lt; 0.001). In addition, a drop in eGFR of 10 mL/min/1.73m             2             over the previous year showed an the elevated mortality hazard of 1.97 ± 0.16 (p &lt; 0.001).                                   Conclusions:             We show that the prevalence of CKD is significant in a real-world population of HF patients, and CKD is a standalone predictor of mortality. Current eGFR value and the eGFR slope from the previous year have a potential to be used for individual mortality hazard assessment in the clinical follow-up of HF patients.
Testing Platform-Independent Quantum Error Mitigation on Noisy Quantum Computers

We apply quantum error mitigation techniques to a variety of benchmark problems and quantum computers to evaluate the performance of quantum error mitigation in practice. To do so, we define an empirically motivated, resource-normalized metric of the improvement of error mitigation which we call the improvement factor, and calculate this metric for each experiment we perform. The experiments we perform consist of zero-noise extrapolation and probabilistic error cancellation applied to two benchmark problems run on IBM, IonQ, and Rigetti quantum computers, as well as noisy quantum computer simulators. Our results show that error mitigation is on average more beneficial than no error mitigation - even when normalized by the additional resources used - but also emphasize that the performance of quantum error mitigation depends on the underlying computer.
GEM*: A Molecular Electronic Density-Based Force Field for Molecular Dynamics Simulations

GEM*, a force field that combines Coulomb and Exchange terms calculated with Hermite Gaussians with the polarization, bonded, and modified van der Waals terms from AMOEBA is presented. GEM* is tested on an initial water model fitted at the same level as AMOEBA. The integrals required for the evaluation of the intermolecular Coulomb interactions are efficiently evaluated by means of reciprocal space methods. The GEM* water model is tested by comparing energies and forces for a series of water oligomers and MD simulations. Timings for GEM* compared to AMOEBA are presented and discussed.
Corporate Social Responsibility Reporting in the Cruise IndustryAn analysis of the development in corporate social responsibility reporting in the cruise industry,using a longitudinal mixed-methods approach

Master´s thesis in Business Administration (BE501)
Thermodynamics of concentration vs flux control in chemical reaction networks

We investigate the thermodynamic implications of two control mechanisms of open chemical reaction networks. The first controls the concentrations of the species that are exchanged with the surroundings, while the other controls the exchange fluxes. We show that the two mechanisms can be mapped one into the other and that the thermodynamic theories usually developed in the framework of concentration control can be applied to flux control as well. This implies that the thermodynamic potential and the fundamental forces driving chemical reaction networks out of equilibrium can be identified in the same way for both mechanisms. By analyzing the dynamics and thermodynamics of a simple enzymatic model, we also show that while the two mechanisms are equivalent at steady state, the flux control may lead to fundamentally different regimes where systems achieve stationary growth.
Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning

Abstract                 Background                 Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical.                                Methods                 Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach.                                Results                 We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail.                                Conclusions                 Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.