Standard Framework Research Articles

Elevating the human experience through service standards: insights from the global refugee crisis

PurposeThis article proposes a set of three service standards for serving humanity; develops the Agency, Dignity and Diversity (ADD) Service Standards Framework; integrates these standards with human experience; and then applies this framework to refugee service experiences.Design/methodology/approachBuilding on Transformative Service Research (TSR), we propose service standards for humanity and connect these standards to elevating the human experience. Subsequently, the ADD Service Standards Framework for serving humanity is presented and applied to the human experiences of refugees.FindingsThree service standards for serving humanity are proposed: empowering human agency, respecting human dignity and honoring human diversity. Further, we apply these three standards to offer a set of standard-specific practical actions adapted to the plight of refugees.Practical implicationsThe practical implications affect all aspects of humanity’s service systems. Service standards should be universal, responsive to various needs of individuals and groups and adaptable to changing needs.Originality/valueThis paper contributes service standards for guiding service systems of any kind in serving humanity and provides a comprehensive framework for designing and implementing service standards that can elevate the human experience.

Ukrainian Popular Music in Times of War: National Identity, Transnational Projections and the Musician as Grassroots Ambassador

This article investigates the ways in which Ukrainian independent popular music after February 2022 has contributed to domestic nation-building and international nation-branding. Taking as case studies six artists who have extensively toured abroad with their musical projects, the article traces the evolution of the internal imaginings and external projections of Ukrainian identity enabled via musical activity. The article shows that the war has generated a reconfiguration of the relationship between the artist and their social responsibility, in which music has become a fundamental weapon to raise transnational awareness of the Ukrainian struggle and to materially influence Ukraine’s defence. This reconfiguration has prompted the performers to take the role of grassroots cultural ambassadors and create DIY networks of solidarity, largely outside of state initiatives. In this process, ideas about Ukraine did not adhere to a standardized framework for the purpose of articulating unity, but rather reflected Ukraine’s unity in cultural diversity.

Data Hub for Life Cycle Assessment of Climate Change Solutions—Hydrogen Case Study

Life cycle assessment, which evaluates the complete life cycle of a product, is considered the standard methodological framework to evaluate the environmental performance of climate change solutions. However, significant challenges exist related to datasets used to quantify these environmental indicators. Although extensive research and commercial data on climate change technologies, pathways, and facilities exist, they are not readily available to practitioners of life cycle assessment in the right format and structure using an open platform. In this study, we propose a new open data hub platform for life cycle assessment, considering a hierarchical data flow starting with raw data collected on climate change technologies at laboratory, pilot, demonstration, or commercial scales to provide the information required for policy and decision-making. This platform makes data accessible at multiple levels for practitioners of life cycle assessment, while making data interoperable across platforms. The proposed data hub platform and workflow are explained through the polymer electrolyte membrane electrolysis hydrogen production as a case study. The climate change environment impact of 1.17 ± 0.03 kg CO2 eq./kg H2 was calculated for the case study. The current data hub platform is limited to evaluating environmental impacts; however, future additions of economic and social aspects are envisaged.

Navigating Challenges in Online Cybersecurity Education: Insights from Postgraduate Students and Prospects for a Standardized Framework

This study focuses on the challenges encountered in online cybersecurity education. It adopts an exploratory research design using a mixed-methods approach to investigate the perceptions and experiences of postgraduate students enrolled in an online cybersecurity program. The collection of data is structured into two distinct phases. In the initial phase, qualitative insights are gathered through workshops with students and industry experts, followed by administering a questionnaire to delve deeper into students’ perceptions and challenges. Thematic analysis of the responses reveals significant interest in online cybersecurity programs but also highlights issues such as ineffective communication and poor engagement. The findings highlight the necessity for a standardized framework to improve communication, engagement, and overall effectiveness in virtual learning environments. By empowering instructors to design more interactive courses and leveraging technology efficiently, the framework aims to improve student motivation, satisfaction, and learning outcomes. Moreover, it serves as a valuable resource for institutions, fostering collaboration and innovation in cybersecurity education. Implementing this framework will create a more conducive learning environment, directly enhancing student preparation for success in the dynamic field of cybersecurity.

Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles

Abstract Exergy-based control strategies for ground hybrid electric vehicles (HEVs) enable to pursue unconventional optimization goals that are inaccessible when standard energy-based modeling frameworks based on fuel consumption minimization are used. In this work, we formulate and solve offline and online exergy-based optimization strategies for military HEVs aimed at the minimization of genset exergy destruction and thermal emissions to increase vehicle efficiency and minimize the risk of thermal imaging detection, respectively. We refer to the offline version of these strategies as exergy minimization strategies (ExMSs). Adaptive ExMSs (A-ExMSs) are then formulated for online implementation. Moreover, charge increasing (CI) ExMSs and A-ExMSs are developed to charge the battery as much as possible during a driving mission that is followed by a silent watch phase. To assess the performance of the proposed strategies, the results obtained by the ExMSs and A-ExMSs are compared to the benchmark solutions obtained by Dynamic Programming.

The NIH Pain Common Data Elements: A Great Start but a Long Way to the Finish Line.

The NIH Pain Common Data Elements (CDEs) provide a standardized framework for pain research, but their implementation and interpretation present challenges. To review the NIH CDE Program's selected pain domains, provide best practices for implementing required questions, and offer a checklist for appropriate CDE use in clinical trials and secondary data analysis. This work analyzed the ten core pain research domains selected by the NIH CDE Program and discuss their limitations and considerations for use. The manuscript provides an overview of the ten core pain research domains, including pain intensity, interference, physical function, sleep, catastrophizing, depression, anxiety, global impression of change, substance use screening, and quality of life. It offers sample scenarios for implementing required questions and presents a checklist to guide researchers in using pain CDEs effectively for clinical trials and secondary data analysis. Key challenges identified include contextual variability, lack of validation across all pain conditions and populations, and potential misuse or misinterpretation of measures. This work proposes solutions such as supplementary measures, context-specific guidance, comprehensive training programs, and ongoing refinement of the CDE framework. While NIH Pain CDEs are valuable tools for standardizing pain assessment in research, addressing challenges in their implementation and interpretation is crucial for improving the consistency, validity, and interpretability of pain research data, ultimately advancing the field and enhancing patient care.

Rapid estimation of carbon footprints in agrochemical development: correlation of process mass intensity with CO2 emissions.

The agricultural sector faces a challenge in balancing increasing food demand while minimizing environmental impacts. Crop protection products are crucial for achieving high crop yields and ensuring food security. However, life cycle assessment (LCA), the standard framework for evaluating environmental impact, is time-consuming and costly, especially during early product development. To address this, a novel tool correlating process mass intensity (PMI) with greenhouse gas (GHG) emissions has been developed as a streamlined alternative. A strong linear correlation (R2 = 0.95) was identified between PMI and product GHG emissions, enabling rapid carbon footprint estimation using simplified PMI data. The model was validated using 13 small molecule active ingredients (AIs), showing a mean absolute error (MAE) of 55 g CO₂/kg AI and a root mean square error (RMSE) of 64 kg CO₂/kg AI. Residual analysis demonstrated random distribution, suggesting reliable predictions. The PMI-based tool provides rapid, accurate estimates of product carbon footprint (PCF), supporting early-stage decision-making in research and development for agrochemical processes. Its simplicity makes it applicable across various chemical sectors and valuable for sustainability efforts. © 2024 Society of Chemical Industry.

Comparative assessment of ESG ratings methodology and results based on XBRL

This study provides a comparative analysis of Environmental, Social, and Governance (ESG) ratings methodologies and explores the potential of eXtensible Business Reporting Language (XBRL) to enhance transparency and comparability in ESG reporting. Evaluating ratings from different agencies, the research identifies significant methodological inconsistencies that lead to conflicting information for investors and stakeholders. Statistical tests and adjusted rating scales confirm substantial divergence in ESG scores, primarily due to differing data categories and indicators used by rating firms. Using a sample of 265 European companies, the study demonstrates that individual ESG agencies report markedly different ratings for the same firms, which can mislead stakeholders. It proposes that XBRL based reporting can mitigate these inconsistencies by providing a standardized framework for data collection and reporting. XBRL enables accurate and efficient data collection, reducing human error and enhancing the transparency of ESG reports. The findings advocate for integrating XBRL in ESG reporting to achieve higher levels of comparability and reliability. The study calls for greater regulatory oversight and the adoption of standardized taxonomies in ESG reporting to ensure consistent and comparable data across sectors and jurisdictions. Despite challenges like the lack of a standardized taxonomy and inconsistent adoption, the research contends that XBRL can significantly improve the reliability of ESG ratings. In conclusion, this study suggests that standardizing ESG data through XBRL could provide a viable solution to the unreliability of current ESG rating scales, supporting sustainable business practices and informed decision making by investors.

Benchmark dose modeling for epidemiological dose-response assessment using case-control studies.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Study of the Framework and Attributes of Ethical Standards in Journalism

It has always been difficult to create fair evaluation criteria for journalists, especially when it comes to judging morality and ethical conduct in news reporting. These guidelines function as yardsticks for gauging the ethics of news topics and the objectivity of journalistic techniques. These criteria are becoming more and more difficult as journalism changes, especially in the digital age. Fostering ethical journalism requires an understanding of its categorization and internal structure. This analysis employs a conceptual framework to explore the evaluation standards of news ethics. The method involves: Literature Review: Examining existing academic literature, ethical codes, and industry guidelines to outline the prevailing evaluation standards in journalism. Thematic Analysis: Identifying and categorizing the components of evaluation standards into normative and quality standards, assessing their relevance to different aspects of journalistic practice. Case Studies: Analyzing specific instances where journalistic ethics were put to the test, allowing for a practical understanding of how these standards function in real-world scenarios. The primary objective of this study is to clarify the internal structure of journalistic ethics evaluation standards and highlight the distinctions between normative and quality standards. By doing so, the analysis aims to provide insights into how these standards can be effectively applied to evaluate news behaviors and moral character in journalism. What is deemed ethical in one context may not hold the same weight in another. This relativity underscores the importance of understanding the specific circumstances surrounding journalistic practices. Historical Context: The evolution of journalistic ethics is tied to historical events and societal changes. Standards that once sufficed may need reevaluation to address contemporary challenges, particularly with the rise of digital media and misinformation. Complex Internal Structure: The internal structure of evaluation standards can be multi-layered, with different levels of criteria applied depending on the context and specific ethical dilemmas faced by journalists. This complexity requires continuous dialogue and adjustment as new ethical challenges arise. Practical Implications: Journalists and media organizations must engage with these standards actively, incorporating them into training and policy development. By fostering an understanding of both normative and quality standards, the industry can promote a more ethical and accountable journalism practice.

How do ML practitioners perceive explainability? an interview study of practices and challenges

Explainable artificial intelligence (XAI) is a field of study that focuses on the development process of AI-based systems while making their decision-making processes understandable and transparent for users. Research already identified explainability as an emerging requirement for AI-based systems that use machine learning (ML) techniques. However, there is a notable absence of studies investigating how ML practitioners perceive the concept of explainability, the challenges they encounter, and the potential trade-offs with other quality attributes. In this study, we want to discover how practitioners define explainability for AI-based systems and what challenges they encounter in making them explainable. Furthermore, we explore how explainability interacts with other quality attributes. To this end, we conducted semi-structured interviews with 14 ML practitioners from 11 companies. Our study reveals diverse viewpoints on explainability and applied practices. Results suggest that the importance of explainability lies in enhancing transparency, refining models, and mitigating bias. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) are frequently used by ML practitioners to understand how models work, while tailored approaches are typically adopted to meet the specific requirements of stakeholders. Moreover, we have discerned emerging challenges in eight categories. Issues such as effective communication with non-technical stakeholders and the absence of standardized approaches are frequently stated as recurring hurdles. We contextualize these findings in terms of requirements engineering and conclude that industry currently lacks a standardized framework to address arising explainability needs.

Nist Aal Adaptive Security Framework

In our digitally evolving security world, adaptive and risk - based authentication methods are the key countermeasures responding to the current rising cyber risks. The National Institute of Standards and Technology (NIST) Authenticator Assurance Levels (AAL) framework offers a specific guidance on the implementation of adaptive security measures. The study is designed to put the "NIST AAL Adaptive Security Framework" into practice by using technologies such as Ping Federate and Ping Davinci systems for the timely risk analysis of IT environments and advanced MFA approaches against emerging threats. AAL model can define authentication procedures customized as per the level of risk, requirements and compliance regulations of the organization. It is essential to classify authentication into separate assurance level (AAL1, AAL2 and AAL3) as it helps to make modifications according to contextual factors like perceived risk and user's behaviour. This is vital as it enhances security while it optimizes user experience. This study examines the proposed framework’s efficacy in reducing cyber - threats and strengthening security posture of organizations.

Hydrodynamization and resummed viscous hydrodynamics

In this paper, I review our current understanding of the applicability of hydrodynamics to modeling the quark–gluon plasma (QGP), focusing on the question of hydrodynamization/thermalization of the QGP and the anisotropic hydrodynamics (aHydro) far-from-equilibrium hydrodynamic framework. I discuss the existence of far-from-equilibrium hydrodynamic attractors and methods for determining attractors within different hydrodynamical frameworks. I also discuss the determination of attractors from exact solutions to the Boltzmann equation in relaxation time approximation and effective kinetic field theory applied to quantum chromodynamics. I then present comparisons of the kinetic attractors with the attractors obtained in standard second-viscous hydrodynamics frameworks and aHydro. I demonstrate that, due to the resummation of terms to all orders in the inverse Reynolds number, the aHydro framework can describe both the weak- and strong-interaction limits. I then review the phenomenological application of aHydro to relativistic heavy-ion collisions using both quasiparticle aHydro and second-order viscous aHydro. The phenomenological results indicate that aHydro provides a controlled extension of dissipative relativistic hydrodynamics to the early-time far-from-equilibrium stage of heavy-ion collisions. This allows one to better describe the data and to extract the temperature dependence of transport coefficients at much higher temperatures than linearized second-order viscous hydrodynamics.

Determinants and patterns of self-medication: A comprehensive literature review

Background- Self-medication is a major public health concern practiced worldwide. Research indicates that improper self-medication leads to adverse drug reactions, masks underlying diseases, results in incorrect diagnoses, escalates morbidity, contributes to drug interactions, fosters antibiotic resistance, and strains resources within the healthcare system. Many studies on self-treatment have been published for certain countries or diseases, in addition to specific demographics, including teenagers, senior citizens, and medical pupils. The issue of self-treatment and related antibiotic resistance poses a serious threat to global public health, as there has been a reported rise in self-medication practices over the past ten years. To obtain a thorough yet equivalent awareness of related issues and to make more definitive and practical conclusions for future action planning than those derived from reviews on the topic, it may be beneficial to integrate the current global self-medication data. Therefore, we set out to undertake a study to fully understand the prevalence, sample demographics, medications consumed, target ailment, and reasons for self-medication. Methods-Using the terms "self-medication, “Factors associated with it," and "non-prescription," a systematic review of the literature on self-medication practices was conducted using the databases PubMed, Medline, and Web of Science. The search was limited to cross-sectional study articles published between 2018 and 2022. Results- This analysis comprised 23 studies comprising 8,445 individuals in total. Headache, pain, and fever were shown to be among the most prevalent indications for self-treatment, followed by cold and cough. A summary of 20 studies inferred that Analgesics, antipyretics, and NSAIDs were the most self-practiced medications. Conclusion- Self-medication is widely practiced worldwide. It is critical to control and oversee appropriate self-medication practices through the enactment of strict laws and the involvement of legislators and medical experts. Large-scale public health awareness efforts on a variety of platforms, efficient medication distribution regulation, and legal proceedings against medical malpractice should all receive a great deal of attention. More cross-sectional studies employing a standard methodological framework are needed to have a deeper understanding of the incidence and practices of self-medication worldwide.

"Best" Iterative Coupled-Cluster Triples Model? More Evidence for 3CC.

To follow up on the unexpectedly good performance of several coupled-cluster models with approximate inclusion of 3-body clusters [Rishi, V.; Valeev, E. F. J. Chem. Phys. 2019, 151, 064102.] we performed a more complete assessment of the 3CC method [Feller, D. . J. Chem. Phys. 2008, 129, 204105.] for accurate computational thermochemistry in the standard HEAT framework. New spin-integrated implementation of the 3CC method applicable to closed- and open-shell systems utilizes a new automated toolchain for derivation, optimization, and evaluation of operator algebra in many-body electronic structure. We found that with a double-ζ basis set the 3CC correlation energies and their atomization energy contributions are almost always more accurate (with respect to the CCSDTQ reference) than the CCSDT model as well as the standard CCSD(T) model. The mean absolute errors in cc-pVDZ {3CC, CCSDT, and CCSD(T)} electronic (per valence electron) and atomization energies relative to the CCSDTQ reference for the HEAT data set [Tajti, A. . J. Chem. Phys. 2004, 121, 11599-11613.], were {24, 70, 122} μEh/e and {0.46, 2.00, 2.58} kJ/mol, respectively. The mean absolute errors in the complete-basis-set limit {3CC, CCSDT, and CCSD(T)} atomization energies relative to the HEAT model reference, were {0.52, 2.00, and 1.07} kJ/mol, The significant and systematic reduction of the error by the 3CC method and its lower cost than CCSDT suggests it as a viable candidate for post-CCSD(T) thermochemistry applications, as well as the preferred alternative to CCSDT in general.

Engineering and Technological Advancements in Repetitive Transcranial Magnetic Stimulation (rTMS): A Five-Year Review

In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS’s clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS’s sustainable impact.

Research on the construction of a service quality evaluation system for university student dormitories based on the Delphi method

Student dormitories play a pivotal role in the daily lives of university students in China, significantly influencing their academic, residential, and developmental experiences. However, there is a lack of a standardized framework to evaluate the service quality of these dormitories. This study aimed to develop a customized service quality evaluation index system specifically for Chinese university dormitories. The Delphi method and Analytic Hierarchy Process (AHP) were employed, involving 15 experts from Chinese universities. Two rounds of expert deliberation were conducted, starting with 28 initial indicators. The expert consultations resulted in the identification of six primary and 23 secondary indicators that form the service quality evaluation system. This framework is tailored to the specific needs of Chinese university dormitories and provides a solid technical foundation for future evaluations. It is expected to contribute to the improvement of dormitory management practices across China.

Mixed Cardiogenic Shock: A Proposal for Standardized Classification, a Hemodynamic Definition, and Framework for Management.

The classification of cardiogenic shock (CS) has evolved from a singular cold-and wet-hemodynamic profile. Data from registries and clinical trials have contributed to a broader recognition that although all patients with CS have insufficient cardiac output leading to end organ hypoperfusion, there is considerable variability in CS acuity, underlying etiologies, volume status, and systemic vascular resistance. Mixed CS can be broadly categorized as CS with at least 1 additional shock state. Mixed CS states are now the second leading cause of shock in contemporary coronary intensive care units, but there is little high-quality evidence to guide routine care, and there are no standardized classification frameworks or well-established hemodynamic definitions. This primer summarizes the current epidemiology and proposes a classification framework and invasive hemodynamic parameters to guide categorization that could be applied to help better phenotype patients captured in registries and trials, as well as guide management of mixed CS states.

Rule of law, economic structure and development

AbstractIf rule of law encourages relationship‐specific investments, then industries that use intermediates which require relationship‐specific investments should have a disproportionately low share of output or labour in countries where rule of law is weak. We find robust support for this prediction using data on industry composition for 189 countries. Using a standard preference framework to construct model‐implied income values from the estimated coefficients, we find that the interaction between relationship specificity and rule of law may be an economically significant determinant of aggregate outcomes through its influence on productivity and economic structure.

Accelerating spin Hall conductivity predictions via machine learning

AbstractAccurately predicting the spin Hall conductivity (SHC) is crucial for designing novel spintronic devices that leverage the spin Hall effect. First‐principles calculations of SHCs are computationally intensive and unsuitable for quick high‐throughput screening. Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework. We found that Res‐CGCNN surpasses CGCNN, achieving a mean absolute error of 115.4 (ℏ/e) (S/cm) for regression and an area under the receiver operating characteristic curve of 0.86 for classification. Additionally, we utilized Res‐CGCNN to conduct high‐throughput screenings of materials in the Materials Project database that were absent in the training set. This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000 (ℏ/e) (S/cm), which were validated through first‐principles calculations. This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition, serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs.