ethicalabs/Research-Intent-Collab huggingface dataset:
Diffusion Limits for Measure-Valued Queueing Models Without State Space Collapse, With Application to General Random Order of Service Queues

Currently, there is no general theory for deriving diffusion approximations of queueing systems with high- or infinite-dimensional state descriptors. In this paper, we address this by developing a novel framework for deriving diffusion limit equations of queueing models. The method hinges on a martingale decomposition of dynamics driven by time-changed renewal processes, which are a common feature of many queueing models. We then prove a central limit theorem for models decomposed in this way, which gives the form of stochastic differential equations (SDEs) that will be satisfied in the diffusion limit of such a system. Unlike existing approaches, our framework does not require state space collapse or tractable workload representations, enabling its application to systems with features such as reneging. We demonstrate the approach on a multiclass, multi-server random order of service queue with reneging and generally distributed interarrival, service, and patience times. In this setting, the state descriptor is measure-valued and the workload is nonlinear in the fluid limit. We prove tightness and derive SDEs satisfied by the diffusion limit. Our results offer a broadly applicable method for diffusion approximations in complex queueing systems without relying on dimension reductions or model-specific structure.
Active Materials for Aqueous Zinc Ion Batteries: Synthesis, Crystal Structure, Morphology, and Electrochemistry

Aqueous zinc ion batteries (ZIBs) are truly promising contenders for the future large-scale electrical energy storage applications due to their cost-effectiveness, environmental friendliness, intrinsic safety, and competitive gravimetric energy density. In light of this, massive research efforts have been devoted to the design and development of high-performance aqueous ZIBs; however, there are still obstacles to overcome before realizing their full potentials. Here, the current advances, existing limitations, along with the possible solutions in the pursuit of cathode materials with high voltage, fast kinetics, and long cycling stability are comprehensively covered and evaluated, together with an analysis of their structures, electrochemical performance, and zinc ion storage mechanisms. Key issues and research directions related to the design of highly reversible zinc anodes, the exploration of electrolytes satisfying both low cost and good performance, as well as the selection of compatible current collectors are also discussed, to guide the future design of aqueous ZIBs with a combination of high gravimetric energy density, good reversibility, and a long cycle life.
Music in Peace Building and Conflict Resolution: The Case of Fairouz

Bugün Ortadoğu’nun birçok ülkesinde Lübnanlı şarkıcı Fairouz kültürel ve politik bir ikon, aynı zamanda da barış sembolü ve Lübnanlılık ruhunun somutlaşmış hali olarak kabul edilir. Fairouz’un güçlü ve etkileyici sesiyle biçimlenen imgesinin 1975 yılında Lübnan’da patlak veren 15 yıllık iç savaşın ilerlemesini durduracak derecede etkili olduğuna inanılır. Fairouz bir müzisyen olarak Ortadoğu’nun hem kültürel hem de dini çeşitliliğini Lübnanlı milliyetçilik duygularıyla birlikte yansıtarak Lübnan’ın özgürlük davasıyla eşdeğer noktaya ulaşmıştır. Bu makale, hem sesi hem de sessizliğiyle Fairouz’un şiddetli toplumsal çatışmaları, birlikte yaşam, uzlaşma ve barışla ikame etme konusunda müziğin gizli gücünü ortaya koyma biçimini keşfedip tartışmayı amaçlamaktadır
Structure-Related Electrochemistry of Sulfur-Poly(Acrylonitrile) Composite Cathode Materials for Rechargeable Lithium Batteries

Abstract not Available.
Genomic signatures for predicting survival and adjuvant chemotherapy benefit in patients with non-small-cell lung cancer

Improved methods are needed for predicting prognosis and the benefit of delivering adjuvant chemotherapy (ACT) in patients with non-small-cell lung cancer (NSCLC).A novel prognostic algorithm was identified using genomic profiles from 332 stage I-III adenocarcinomas and independently validated on a separate series of 264 patients with stage I-II tumors, compiled from five previous studies. The prognostic algorithm was used to interrogate genomic data from a series of patients treated with adjuvant chemotherapy. Those genes associated with outcome in the adjuvant treatment setting, independent to prognosis were used to train an algorithm able to classify a patient as either a responder or non-responder to ACT. The performance of this signature was independently validated on a separate series of genomic profiles from patients enrolled in a randomized controlled trial of cisplatin/vinorelbine vs. observation alone (JBR.10).NSCLC patients exhibiting the high-risk, poor-prognosis form of the 160-gene prognosis signature experienced a 2.80-times higher rate of 5-year disease specific death (log rank P < 0.0001) compared to those with the low-risk, good prognosis profile, adjusted for covariates. The prognosis signature was found to especially accurate at identifying early stage patients at risk of disease specific death within 24 months of diagnosis when compared to traditional methods of outcome prediction.Separately, NSCLC patients with the 37-gene ACT-response signature (n = 70, 64 %), benefited significantly from cisplatin/vinorelbine (adjusted HR: 0.23, P = 0.0032). For those patients predicted to be responders, receiving this form of ACT conferred a 25 % improvement in the probability of 5-year-survival, compared to observation alone and adjusted for covariates. Conversely, in those patients predicted to be non-responders, ACT was observed to offer no significant survival benefit (adjusted HR: 0.55, P = 0.32).The two gene signatures overlap by one gene only SPSB3, which interacts with the oncogene MET. In this study, higher levels of SPSB3 which were associated with favorable prognosis and benefit from ACT.These complimentary prognostic and predictive gene signatures may assist physicians in their management and treatment of patients with early stage lung cancer.
Riemannian geometry of optimal driving and thermodynamic length and its application to chemical reaction networks

It is known that the trajectory of an endoreversibly driven system with minimal dissipation is a geodesic on the equilibrium state space. Thereby, the state space is equipped with the Riemannian metric given by the Hessian of the free energy function, known as Fisher information metric. However, the derivations given until now require both the system and the driving reservoir to be in local equilibrium. In the present work, we rederive the framework for chemical reaction networks and thereby enhance its scope of applicability to the nonequilibrium situation. Moreover, because our results are derived without restrictive assumptions, we are able to discuss phenomena that could not been seen previously. We introduce a suitable weighted Fisher information metric on the space of chemical concentrations and show that it characterizes the dissipation caused by diffusive driving, with arbitrary diffusion rate constants. This allows us to consider driving far from equilibrium. As the main result, we show that the isometric embedding of a steady state manifold into the concentration space yields a lower bound for the dissipation when the system is driven along the manifold. We give an analytic expression for this bound and for the corresponding geodesic, and thereby are able to dissect the contributions from the driving kinetics and from thermodynamics. Finally, we discuss in detail the application to quasi-thermostatic steady states.
Coastal vulnerability, resilience and adaptation to climate change

Prof. Dr. Horst Sterr, Prof. Dr. Ir. Pier Vellinga
Global Cybersecurity Resilience: Advanced Strategies and Emerging Technologies for Protecting Critical Digital Infrastructure

Ensuring the protection of those strategic systems and networks from constantly emerging threats in the contemporary context of globalization is crucial to maintaining states’ security and economies. This paper focuses on how organizational cybersecurity readiness can be improved and ways of putting up good defense mechanisms. These are Threat Intelligence Systems including AI &amp; ML Systems for detecting advanced threats as well as Endpoint Solutions including Endpoint Security and Networks that are fortified through Firewalls, VPN &amp; Zero Trust Architecture. Pivotal to these activities are information protection measures such as encryption and Data Loss Prevention (DLP), to safeguard the confidentiality of the data. IAM solutions that incorporate MFA and RBAC also help reduce threats of unauthorized access by controlling user account privileges. That is why adherence to the national and international cybersecurity standards with the help of governmental directions and legal laws such as NIST or GDPR makes organizational security positions more stable. Currently, new technology solutions like Artificial Intelligence, Quantum computing, and Blockchain provide better threat analytical systems and enhanced encryption methods which are necessary for safeguarding main digital assets in today’s environment.
Strategic training and exercises for critical infrastructure protection and resilience: A transition from lessons learned to effective curricula

Abstract   Research objective  To identify issues which make strategic trainings and exercises for critical infrastructure protection and resilience specific and to use them to formulate relevant curricula guidelines.    Material and methods  Experts organizing international and national strategic trainings and exercises for critical infrastructure protection and resilience were involved. Their practical knowledge and experiences were collected by qualitative survey. Experts characterized the trainings and exercises due to particular elements of decision-making process as organizational strengths, weaknesses, opportunities and threats. This allowed to classify particular lessons learned and make them operational due to formulate the reference training curricula guidelines. Effective trainings and exercises should base on underlining the strengths and the opportunities and limitation of the weaknesses and the threats.    Results  Specification of the trainings and exercises is expressed by relevant strengths, weaknesses, opportunities and threats, grouped in five areas (trainees, trainers, decision making process, materials, equipment) to be operational in terms of effective curricula formulation.    Discussion and conclusion  Strategic trainings and exercises are primarily dedicated for experienced practitioners who face complex problems for critical infrastructure protection and resilience. Use of decision information systems and decision support systems may make the trainings and exercises more practical and rational. Strong support of experienced trainers is required to effectively implement curricula and reduce organizational risks (trainees-, materials- and equipment-related). Media coverage is desired to increase reality of decision making.
Genome-Wide Analysis Identified Three-Locus DNA Methylation Signature As Biomarkers with Superior Prediction Performance for Gastric Cancer Survival

Background: The morbidity and mortality of gastric cancer (GC) is in the fourth and third place among all malignant tumors in the world. The prognosis is poor due to the lack of biomarkers for treatment selection.    Methods: To select the methylation biomarkers related to overall survival of GC patients and improve the prognosis, we analyzed the methylation sequences of 388 GC patients from the Cancer Genome Atlas (TCGA) database. These patients were divided into training and testing datasets.    Results: A total of 233 patients were randomly allocated to training dataset and 155 were allocated to testing dataset. The survival analysis showed that the three-DNA methylation signature was significantly correlated with overall survival of GC patients and could differentiate between high-risk and low-risk patients in training and testing datasets. Moreover, the three-DNA methylation signature could predict prognosis of GC patients with high sensitivity and specificity using receiver operating characteristic (ROC) analysis. The three-DNA methylation signature, independent of age, gender, radiation-therapy and histologic differentiation, could predict the prognosis of GC. It is noteworthy that the three-DNA methylation signature has higher prediction accuracy than the known biomarkers.    Interpretation: Our results suggest that the three-DNA methylation signature was a useful prognostic indicator of GC and may have translational implications in GC clinical treatment.    Funding Statement: This study was supported in part by grants from the National Natural Science Foundation of China (No. 31671311 and 81371033), the National first-class discipline program of Light Industry Technology and Engineering (LITE2018-14), the “Six Talent Peak” Plan of Jiangsu Province (No. SWYY-127), the Innovative and Entrepreneurial Talents of Jiangsu Province, the Program for High-Level Entrepreneurial and Innovative Talents Introduction of Jiangsu Province, Guangdong High-level Personnel of Special Support Program and Yangfan Plan of Talents Recruitment Grant, and Fundamental Research Funds for the Central Universities (JUSRP51712B and JUSRP1901XNC).     Declaration of Interests: The authors declare that they have no competing interests.    Ethics Approval Statement: Authors stated: "Not applicable."
Supplementary Figure S3 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Figure S3. Internal validation and clinical utility of the t.TRPC-based prognostic model.&lt;/p&gt;
From Total Quality Control to Lean Six Sigma

The main purpose and contribution of this book lies in the concurrent analysis and classification, by the means of a literature review, of the results and critical implementation factors of the six systems.
pynucastro: an interface to nuclear reaction rates and code generator for reaction network equations

pynucastro addresses two needs in the field of nuclear astrophysics: visual exploration of nuclear reaction rates or networks and automated code generation for integrating reaction network ODEs. pynucastro accomplishes this by interfacing with nuclear reaction rate parameterizations published by the JINA Reaclib project (Cyburt et al. 2010).
Legally Binding of the World Trade Organization Dispute Settlemet Body’s Decision

Dispute settlement system of the WTO DSB can be categorized as a judicial dispute settlement system. Decision (rulings and recommendations) in a dispute settlement made by the WTO DSB is binding and should be performed. In some cases, decisions made by the WTO DSB were not performed, and there is no sanction against the non-compliance with the decisions. The objective of this study is to analyze the legally binding character of the WTO DSB’s decision as a decision of a judicial organ. From the data analysis, it can be concluded that the WTO does not provide adequate sanctions against the non-compliance with the DSB’s decision. It leads to the interpretation of the DSB’s decision is international soft law norm which is not legally binding. Moreover, it can hamper the enforcement of the WTO Agreement and the achievement of the WTO’s goals. The WTO judicial system should be strengthened and improved by creating WTO independent court or tribunal, which has authority to make legally binding decision as international hard law.
Study On The Effect Of Edge Computing On Latency In Iot Applications

This article examines the effect of implementing edge computing on latency in Internet of Things (IoT) applications. Edge computing moves data processing closer to the data source to reduce latency and improve efficiency. This research compares the performance of IoT applications with and without edge computing. The results show a significant reduction in latency and an increase in application response speed.
Implementation of robust statistics in the calibration, verification and validation step of model evaluation to better reflect processes concerning total phosphorus load occurring in the catchment

Abstract   Obtaining a high quality of environmental modeling in the calibration, verification and validation processes causes problems for modelers all over the world. Uncontrolled variability of environmental conditions (diversity of natural phenomena, meteorological and climatic conditions occurring within the drainage basin), inaccuracy of measurement equipment, averaging the data obtained and the errors of human work vitiates data population to a greater or lesser extent. These factors impede the evaluation of modeling, described as the “goodness of fit” of modeling results to that of observational data.  Therefore, from the point of view of the correct interpretation of the results of calibration processes, and verification and validation of the model, it is essential to find a statistical tool which would allow limiting the influence of strongly outlying data on the final result of the correspondence of the model to the actual conditions. Such a tool may be constituted by robust statistics as presented in the article, or the L-estimators to be more precise, which appeared to be effective in the case of an environmental data set, which are characterized by certain measurement unreliability, and in addition, a limited number of result populations. In the research Marcomodel DNS was used, with a SWAT module, for two catchments located in Northern and Central Poland. The evaluation of modeling quality was conducted by the use of classical statistics and robust statistics. The results of the classical statistics were based on three statistical measurements: coefficient of determination (R2), were compared with percent bias (PBIAS) and Nash Sutcliffe efficiency (NSE), and with the results of these measurements after the process of winsorization. In the robust estimates calculation the strength of outliers was decreased. It may be a beneficial phenomenon worth considering in the evaluation of quality modeling of surface waters.
States as bystanders of legal change: Alternative paths for the human rights to water and sanitation in international law

AbstractThis article argues for international legal change in human rights as a consequence of a states-as-bystander effect: When states do neither actively drive nor block change processes, and alternative state-empowered authorities exist in a legal field, states’ position at the sidelines opens a path for non-state actors to enact substantive change. In human rights law, this is a process they route through General Comments, a powerful instrument of the human rights treaty bodies to set, expand, and redefine standards for global human rights. This article bears its core argument of a states-as-bystander effect by taking a single norm, the necessity of water for human life, and tracing its change process from non-existent in human rights law, to a non-right, to a condition for other rights, and, finally, to the recognition of water and sanitation as independent rights at the international level. Ultimately, the analysis shows that non-actors can enact change to law, and do so, on the heels of states’ relegation to the periphery of the human rights system. This opened the door for certain actors – transnational coalitions of expert body members, human rights advocates and issue professionals – to use General Comments in a way that not only impacts international legal change but can also withstand state opposition.
Deconstructing Postmodernism and the Mainstream Developmental Discourse of Women's Empowerment in the (South) Asian Context

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Sexual and reproductive health and rights through the lens of belonging : intersectional perspectives on disability, gender, and adolescence in Tanzania

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Can Air Quality Gas Sensors Be Used for Emission Monitoring of Small-Scale Local Air Pollution Sources? Pilot Test Evaluation

In recent years we have seen a growing number of applications that use various sensory measurements of physicochemical features. Within the research project “Research on the identification of combustion of unsuitable fuels and systems of self-diagnostics of boilers combustion solid fuels for domestic heating”, the authors tested and evaluated the possible use of an air quality monitoring sensor unit for the measurement of operating parameters of solid fuel burning boilers and stoves. In the Czech Republic, programs to support citizens in the replacement of domestic boilers of poor combustion quality have been subsidized for several years. Unfortunately, no assessment of the impact of subsidies on air quality has been carried out. However, the increased pollutant emissions due to improper use of domestic boilers are supposed to be one of the greatest problems with domestic boilers. Hence, providing users with real-time feedback may lead to changes in combustion conditions and consequently to a reduction in air pollution. We focus on sensory measurements of CO, CO2, NO, and VOCs as compounds that correspond to the operating conditions of the combustion process. The research included sampling, construction of the flue gas dilution duct, and the influence of direct measurement on the service life of the sensors.
Extraction of Social Determinants of Health From Electronic Health Records Using Natural Language Processing

PURPOSE             Social Determinants of Health (SDoH) have a significant effect on health outcomes and inequalities. SDoH can be extracted from electronic health records (EHR) to aid policy development and research to improve population health. Automated extraction using artificial intelligence (AI) can improve efficiency and cost-effectiveness. The focus of this study was to autonomously extract comprehensive SDoH details from EHR using a natural language processing (NLP)–based AI pipeline.                                   MATERIALS AND METHODS             A curated set of 1,000 BC Cancer clinical documents with concentrated SDoH information served as the reference standard for training and evaluating NLP models. Two pipelines were used: an open-source pipeline trained on the annotated medical documents and an industrial pretrained solution used as a benchmark. Three experiments optimized the first pipeline's performance, assessing the effect of including subtype word positions during training. The superior open-source pipeline was then used to extract SDoH information from 13,258 oncology documents.                                   RESULTS             The open-source pipeline achieved an average F1 score accuracy of 0.88 on the validation data set for extracting 13 SDoH factors, surpassing the benchmark by 5%. It excelled in detailed subtype extraction, while the benchmark performed better in identifying rarely annotated SDoH information in BC Cancer data set. Overall, 60,717 SDoH factors and associated details were extracted from BC Cancer EHR oncology documents. The most frequently extracted SDoH factors included tobacco use, employment status, marital status, alcohol consumption, and living status, occurring between 8k to 12k times.                                   CONCLUSION             This study demonstrates the potential of an NLP pipeline to extract SDoH factors from clinical notes, with strong performance on limited data, although data set–specific adjustments are needed for broader application across institutions.