Balanced Framework Research Articles

Exploring and Analyzing ESG Activity Evaluation Factors of Sports Brands using Delphi and AHP Techniques

PURPOSE This study aimed to identify and establish ESG activity assessment factors tailored for sports brands, offering a foundational framework for fostering sustainability within the sports industry.METHODS A Delphi survey was conducted between December 1, 2022, and January 31, 2023, involving university professors, sports brand executives, ESG researchers, sports marketing majors , a nd sports brand influencers. Data were analyzed using SPSS WIN 24.0 and MS Excel, encompassing research subject classification, consistency verification, and empirical analysis.RESULTS The assessment of ESG activity evaluation factors of sports brands revealed that governance, social, and environmental dimensions ranked in descending order of relative importance. Among specific evaluation criteria, the adoption of employee ethics regulations was deemed paramount in the governance evaluation area, while support for stakeholder welfare and social well-being took precedence in the social evaluation category. In the environmental sector, the production and delivery of eco-friendly, durable products held the highest significance. Further analysis, considering the weighted priorities of all evaluation factors, emphasized the importance of factors, such as the adoption of employee ethics regulations, support for stakeholder welfare, social well-being, and the production and delivery of eco-friendly, durable products. Conversely, disclosure of company information and issues, support for the underprivileged, and general shareholders' meeting or agenda for the protection of shareholders' rights and interests ranked lower.CONCLUSIONS In summary, this study validates the role of governance (G) aspects within ESG activities for sports brands, alongside environmental and social dimensions. It highlights the importance of a balanced ESG framework, particularly in an era marked by heightened social responsibility awareness among consumers. Prioritizing governance in marketing strategies not only distinguishes sports brands but also necessitates impactful promotion and advertising efforts.

Lived Experiences and Daily Life Fatigue among Non-Medical Prescribed Opioid Users in Pakistan: A Thematic Analysis

The misuse of opioids for non-medical purposes is a persistent public health concern worldwide, particularly in developing countries like Pakistan, where a considerable number of individuals experiences the deleterious physiological and psychological consequences stemming from this behavior. The primary aim of the study was to assess the personal experiences and fatigue levels of individuals engaging in non-medical opioid use. The data were collected from addiction, and rehabilitation centers and the sample consisted of 12 individuals both males and females, who aged from 18 to 60 years (M=27.64, SD=9.10). Additionally, individuals who were actively involved in non-medical opioid usage were included in the study. These individuals were assessed through the utilization of a semi-structured interview approach, providing a balanced framework that allowed for both predetermined questions and open-ended inquiries to gather comprehensive information. The finding reveals that various factors, including physical and mental health issues, family dynamics, and peer influence, contribute to opioid consumption. The study identifies a wide range of symptoms, from psychological and physical effects to dependency and isolation. Moreover, rehabilitation efforts may be affected by factors such as individual motivation and familial support, while the lack of awareness about addiction treatability serves as an inhibitory factor. Although the use of opioids can initially cause feelings of pleasure, relaxation, and heightened energy akin to manic symptoms, it frequently leads to detrimental psychological outcomes. These adverse effects include impulsive actions, guilt, and disruption in daily routines.

Performance indicators framework for assessment of national libraries using the analytic hierarchy process

The National Library and Archives of Iran is the most important library in the country, taking on tasks such as providing and protecting the written and unwritten national heritage and organizing the services of other libraries in Iran. An assessment of the National Library and Archives of Iran is critical to understanding how the organization contributes to organizational and strategic goals. This article aims to show the rankings and priorities of performance indicators based on the four perspectives of the balanced scorecard framework (resources, access and infrastructure; use; efficiency; and potentials and development) and with use of the analytic hierarchy process. The perspectives of resources, use, efficiency, and potentials and development were rated. The top performance indicators included: ‘Level of user satisfaction with searching the National Library and Archives of Iran’s website’, ‘Percentage of acquired books in any medium and multimedia resources entered by legal deposit’ and ‘Qualitative evaluation using case studies of use and value of the collection and services’. Pervious literature shows various applications of a joint balanced scorecard and analytic hierarchy process. This study applies this integrated framework throughout an organization in a new service field.

Overcoming Barriers to Text and Data Mining in the Era of ChatGPT: The Proposed Data Act as a Game-Changer

Abstract Article 35 of the proposed Data Act intends to free databases comprised of machine-generated data from the constraints of sui generis database protection. The provision may provide some remedy for researchers facing challenges under the text and data mining (TDM) exceptions of the Directive on Copyright in the Digital Single Market. However, the wording of Art. 35 may also raise questions and create legal uncertainties for scholars, as scientific research is not the main aim of the proposed legislation. This article argues that, even after the proposed Art. 35 of the Data Act, some databases comprised of machine-generated data may still fall within the scope of the sui generis database protection under certain circumstances. The discussion revolves, inter alia, around the concept of recorded data in the context of the ‘obtaining-creating dichotomy’, the use of mixed databases and the notion of derived or inferred data, and the issue of researchers’ access to machine-generated data. The findings of this article are intended to offer guidance to researchers using Artificial Intelligence tools, such as ChatGPT, to mine databases on how they may effectively avoid a potential infringement of the sui generis database right. The paper hopes to encourage new changes in the EU regulation on TDM that could create a more balanced and research-friendly framework.

Differential evolution with stage stratification method and dual balanced mutation strategy for real-parameter numerical optimization

Traditional differential evolution (DE) algorithms have functional limitations in effectively addressing increasingly intricate numerical optimization problems. The key to responding to this challenge is to strike a suitable balance between exploration and exploitation. Exploration is used to find the global optimal solution, and exploitation is used to improve the accuracy of the global optimal solution. Therefore, this study introduces a novel differential evolution algorithm with a stage stratification method and a dual balanced mutation strategy framework, named SbmDE. To enhance the balance between convergence and diversity, the population is stratified into exploration and exploitation layers based on the size of the fitness value and evolutionary period. In the exploitation layer, a hybridization mutation is applied as the first mutation. A novel dual improved mutation operation is proposed and applied to the exploration layer. For the first mutation, the hybridization mutation is used in the same manner as for the exploitation layer. For the secondary mutation, two improved mutation strategies are proposed to alleviate premature convergence at the early stage of evolution and to enhance the local neighborhood search at a later stage, named DE/ranking-to-rand/1 and DE/best-current-dev/1. Experiments were conducted on the CEC2017 benchmark suite, which contains 29 single-objective real-parameter numerical optimization problems, to evaluate the performance of the proposed algorithm. Compared with 11 state-of-the-art algorithms, the results demonstrate the superiority of the proposed algorithm, which does not increase the time complexity. Additionally, based on the four engineering design problems, the proposed algorithm is fully competent in solving practical constrained optimization problems.

From Homeostasis to Pathology: Decoding the Multifaceted Impact of Aquaporins in the Central Nervous System.

Aquaporins (AQPs), integral membrane proteins facilitating selective water and solute transport across cell membranes, have been the focus of extensive research over the past few decades. Particularly noteworthy is their role in maintaining cellular homeostasis and fluid balance in neural compartments, as dysregulated AQP expression is implicated in various degenerative and acute brain pathologies. This article provides an exhaustive review on the evolutionary history, molecular classification, and physiological relevance of aquaporins, emphasizing their significance in the central nervous system (CNS). The paper journeys through the early studies of water transport to the groundbreaking discovery of Aquaporin 1, charting the molecular intricacies that make AQPs unique. It delves into AQP distribution in mammalian systems, detailing their selective permeability through permeability assays. The article provides an in-depth exploration of AQP4 and AQP1 in the brain, examining their contribution to fluid homeostasis. Furthermore, it elucidates the interplay between AQPs and the glymphatic system, a critical framework for waste clearance and fluid balance in the brain. The dysregulation of AQP-mediated processes in this system hints at a strong association with neurodegenerative disorders such as Parkinson's Disease, idiopathic normal pressure hydrocephalus, and Alzheimer's Disease. This relationship is further explored in the context of acute cerebral events such as stroke and autoimmune conditions such as neuromyelitis optica (NMO). Moreover, the article scrutinizes AQPs at the intersection of oncology and neurology, exploring their role in tumorigenesis, cell migration, invasiveness, and angiogenesis. Lastly, the article outlines emerging aquaporin-targeted therapies, offering a glimpse into future directions in combatting CNS malignancies and neurodegenerative diseases.

Transformation of Two-Dimensional Roesser Model Into Two-Dimensional Decoupled Model: Application to Model Reduction via Balanced Framework

Two transformations are presented in this research; the first approach takes a generic two-dimensional (2-D) framework and transforms it into a diagonalized 2-D framework without resorting to minimal rank-decomposition criteria. Afterward, it converts the diagonalized system into two subsystems (i.e., one-dimensional (1-D) cascaded systems). Moreover, the proposed transformation retains the symmetrical structure of the two decomposed subsystems. Likewise, the second transformation converts 1-D submodels to the balanced structure used for model reduction operations. The proposed model reduction approach efficiently performs the model reduction task on a given 2-D diagonalized model by utilizing optimal Hankel norm approximation. The numerical results demonstrate that the proposed transformations efficiently convert the standard 2-D framework to a diagonalized model form and develop a stable reduced-order model.

SFoG-RPI: A Secured QoS Aware and Load Balancing Framework for FoG Computing in Healthcare Paradigm

SFoG-RPI: A Secured QoS Aware and Load Balancing Framework for FoG Computing in Healthcare Paradigm

Integrated planning for a global pharmaceutical supply chain: an ambidexterity perspective

AbstractBased on the concept of ambidexterity, we develop a multi-objective, multi-product, and multi-period model to integrate planning for research and development (R&D) and production and distribution (P&D) in a global pharmaceutical supply chain (PSC) considering delays in the entire supply chain. We also propose robust possibilistic programming (RPP) approach to deal with the epistemic uncertainty of some critical input parameters. Applying the ambidexterity approach that emphasizes optimizing a balanced framework based on the R&D and P&D planning, our study reconciles the explorative and exploitative supply chain strategies in the context of global PSCs. The proposed integrated model can manage the inherent delays and uncertainties in the R&D processes and P&D systems via a novel, credibility-based, robust possibilistic programming model. We illustrate the application of our model using a real-world case study of one of the largest and most reputable pharmaceutical companies in Iran. The results affirm the credibility and feasibility of the proposed model when juxtaposed with a non-integrated model. Our study suggests the use of ambidexterity approach in resource allocation planning, risk management, and enhancement of performance in sophisticated settings such as global PSCs.

A Comparison of Islamic Law and the Algerian Family Code within the Framework of Unfair Divorce Provisions

Unfair divorce remains a significant issue faced by contemporary societies. Addressing this problem requires effective legal and legitimate interventions. The Algerian Family Code derives its fundamental principles from Islamic Sharia and is influenced by Islamic law. However, over time, the Algerian Family Code has undergone various changes and adjustments to align with modern legal and social needs. The implementation of unfair divorce under Islamic law and the Algerian Family Code has witnessed several differences and contradictions. This study examines the provisions related to unfair divorce in Islamic law and the Algerian Family Code, highlighting their similarities and differences and the extent to which the Algerian Family Code is influenced by Islamic law. The study focuses on the extent of harmony and divergence between the legal and jurisprudential bases concerning unfair divorce in Islamic Sharia and the Algerian Family Code. Considering that Islamic Sharia serves as the primary source of the Algerian Family Code, the study investigates the challenges and disagreements encountered in their application. The significance of this research lies in shedding light on legal and jurisprudential matters related to unfair divorce in Algeria, providing crucial references to legal, religious, and academic institutions to establish a balanced legal framework in cases of unfair divorce, and encouraging further research to find comprehensive and harmonious legal solutions for Algerian society. The study adopts a comparative and analytical approach by collecting data and legal sources related to unfair divorce in Islamic and Algerian law, including legal texts on family and legislation in Algeria, fatwas, and judicial decisions concerning divorce cases. The obtained information is meticulously analyzed and compared to identify the similarities and differences between the two legal systems regarding the concept and conditions of unfair divorce. Moreover, the study investigates the laws and provisions aimed at preventing unfair divorce in both Islamic law and the Algerian Family Code. The analysis and comparison reveal significant disparities in the concept and procedures of unfair divorce between Islamic law and the Algerian Family Code. While Islamic law refers to such divorces as "bid'i divorce," the Algerian legislator designates them as "unfair divorce." Furthermore, the instances qualifying as unfair divorce differ between the two systems. Under Islamic law, unfair divorce occurs during the menstruation period, during the "clean" period when the husband touches the wife, through three pronouncements in a single session, or during a terminal illness. In contrast, the Algerian legislator limits unfair divorce to three situations: when the husband intends to harm his wife through divorce, when the husband seeks a minor benefit at the expense of his wife, or when the husband's purpose in divorce is to obtain an unlawful advantage. Lastly, concerning unfair divorce compensation, Islamic law grants the divorced woman the right to "mut'a" to deter the husband and compensate for her losses. The Algerian legislator, upon proving unfair divorce, awards the divorced spouse both the "mut'a" stipulated by Sharia and compensation for the damages incurred.

Bentonite homogenization with technological voids upon hydration

During the construction of the nuclear waste geological repository, the inevitable creation of technological gaps will lead to the redistribution of bentonite material along the wetting path upon water intake, thus impacting its localized hydraulic and mechanical properties. It is therefore of the utmost importance to be able to understand and predict the dry density inhomogeneous distribution throughout the buffer zone. This work proposes a microscopic dynamic force balance framework, incorporating the influence of van der Waals force, diffuse double layer force, and boundary friction force, to explore the underlying mechanism of the bentonite homogenization process upon hydration. The microscopic assumption postulates that bentonite materials are an assembly of numerous parallel thin sheets with other dimensions substantially exceeding the thickness, establishing an association between the aforementioned force and the material’s volume fraction. Particular attention is directed toward exploring the mechanism of boundary friction's effects on the inhomogeneous distribution of dry density with hydration time. The application of the framework to examine the impact of engineered voids with 10% and 20% of the total volume yields a satisfactory reproduction in terms of the spatial and temporal evolution of dry density, validating the robust prediction capacity of the established model. The investigation indicates that compacted blocks in proximity to technological voids experience a rapid reduction in dry density upon hydration, leading to the homogenization process characterized by elevated values in the initial void-affected region and diminished values in the innermost area. Whereas full homogenization is yet to be achieved within the observation period of up to 2160 h, and the greater the variability of the dry density within the specimens containing larger initial voids. Further analysis reveals that as the hydration process advances, the friction force within the portion featuring the initial void exhibits a gradual increase, while emergence gradually decreases in the inner swelling zone. Notably, the high friction factor at the bentonite and steel interface allows for the maintenance of large stress gradients and the associated density gradients.

Unbalanced Powers Compensation by Distributed Batteries in Microgrid: A Data-Driven Approach

Unbalanced active powers, caused by uneven allocation and high penetration of single-phase rooftop PVs and loads in four-wire microgrid, can affect power quality and system reliability. This paper proposes a deep reinforcement learning (DRL) based strategy to compensate the unbalanced active powers at the point of common connection (PCC) by employing distributed single-phase battery systems in the microgrid and subsidized by a utility. An active power balancing framework based on Markov decision processes (MDPs) is proposed. To access the phase active powers at the PCC, a phase selection strategy based on labelled data is developed so that each local battery system can match the phase active power at the PCC with its own phase using a unidirectional communication link. Also, a state preparation strategy for obtaining an observation state used by the DRL algorithm is proposed. Then, the active power balancing framework is solved by employing a deep deterministic policy gradient (DDPG) algorithm. Real data of single-phase rooftop PVs and loads is utilized for training and testing on modified IEEE-13-bus feeder. The verification results show that the distributed battery systems can compensate the unbalanced active powers. Thus, neutral current and voltage unbalance are minimized while the batteries' active powers and state of charges are maintained within predefined limits.

The Protection of Performers' Rights in Copyright Law in India: A Comparative Analysis

This comparative research paper examines the protection of performers' rights in copyright law, focusing on the right to be identified as the performer and the right to object to derogatory treatment of performances. Through an analysis of legal provisions, treaties, and case law, this study provides a comprehensive understanding of the scope, limitations, and enforcement of these rights in various jurisdictions. The research explores historical development, international standards, and challenges in implementing and enforcing rights. The findings contribute valuable insights for policymakers, practitioners, and performers seeking to enhance the protection of performers' rights within copyright law. The study aims to foster a balanced framework that preserves performers' creative contributions and maintains a harmonized copyright ecosystem. Key Words: Right, Performer, Artistic, Copyright, Protection

Hierarchical and two-stage framework for the paced mixed-model assembly line balancing and sequencing problem considering ergonomic risk

Paced mixed-model assembly lines are popular with various manufacturing enterprises. However, they face the weakness that they are at risk of line stoppages owing to the occurrence of workstation work overload situations. Moreover, the consideration of workers' health and ergonomic risk on manual assembly lines is a necessity required by legislation. Therefore, this article addresses mixed-model assembly line balancing and sequencing taking the problem of ergonomic risk into consideration and manages work overloads using a side-by-side policy with utility workers. To solve these problems, this article proposes an hierarchical and two-stage framework. The optimization goals are to minimize the number of workstations and utility workers. Furthermore, this article integrates an iterated greedy algorithm into a genetics algorithm to obtain global exploration and local exploitation ability. Finally, a divide-and-conquer strategy is proposed to meet the challenge of solving a large-scale problem. Experimental results show the effectiveness of the mechanism proposed in this article.

Comprehensive biofuel policy analysis framework: A novel approach evaluating the policy influences

The use of biofuel as an alternative to petroleum-based fuels was started in the 1970s in response to the oil crisis. In last two decades, there has been a massive rise in global utilization of biofuels which represents its growing importance as an alternative fuel source. This rise is attributed to the supportive biofuel policies which stimulate the growth in biofuel production and utilization. The biofuel policy mechanism of major producing countries got specific attention in this study. Australia, Brazil, Canada, China, European Union, India, Japan, Malaysia and the USA jointly controlled 99% of the global biofuel production and utilization. Several policy measures from creating and sustaining biofuel demand to establishing supportive economic and regulatory frameworks are already in place in the aforementioned countries. The study performs compositional policy analysis by developing a framework termed as ‘Comprehensive Biofuel Policy Analysis Framework’. A policy score named ‘Total Policy Measure Support’ is the outcome of the analysis. It reveals that countries with higher scores have better-performing biofuel sectors than the countries with low scores. Countries with moderate scores have inconsistency between their value and biofuel sector performance. It is linked with the absence or less than required focus on single or multiple policy components resulting in an imbalance in the policy framework. This underlines the importance of a balanced policy framework. If there is a lesser or no balance between the different policy components, biofuel production and utilization cannot grow despite high Total Policy Measure Support scores.

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

Why defuzzification matters: An empirical study of fresh fruit supply chain management

The measurement validity of analytic hierarchy process (AHP) questionnaires used for multi-criteria decision-making (MCDM) and their fuzzification-based extensions have not yet been definitively investigated. In this context, we investigated the effect of a fuzzification procedure on the variable measurement validity and statistical results of an AHP model. Based on market data regarding factors influencing the production and marketing stages of the Taiwanese litchi export chain, we verified whether the estimated variables exhibit unbiased validity after applying a fuzzification procedure using the balanced scorecard framework and MCDM. We also measured the effectiveness of two defuzzification methods—centre of gravity (COG) and extent analysis method (EAM)—and compared their estimation performances. Further, we successfully established fresh fruit supply chain indicators and assigned weights to them corresponding to their importance. The results revealed that global priority values determined using COG were similar to those estimated using the fuzzification-free AHP, which is critical for simple hierarchical structures. The EAM accurately identified the crucial indicators and variables affecting the complex supply system of fresh fruit, highlighting the benefits of fuzzy AHP data application corresponding to extensive hierarchies. The proposed method is expected to aid decision makers in improving the quality of their decision-making by evaluating the effect of data fuzzification with respect to information scale and applying appropriate defuzzification methods to reduce option screening.

W-doped niobate-based perovskites: Synthesis by ultrasonic spray pyrolysis and structural characterization

Nanostructured hollow spheres composed of K0·5Na0.5(WxNb1-x)O3 were synthesized by ultrasonic spray pyrolysis. Correlations between the W-doping content and properties of the [NbO6] octahedra, including morphology, texture, crystalline structure, and polarization were found. Scanning and transmission electron microscopy analysis indicate the present samples are nanostructured hollow spheres. Textural analysis indicates the samples are composed of a balanced framework including micropores (40–50%) and mesopores (50–60%). Structural refinement by the Rietveld method indicated that non-isovalent W6+ cation only occupies the Nb5+ site in the host structure. Several space groups including Amm2, Pbcm, and Pm were used to refine the W-doped K0·5Na0·5NbO3 structure and the best adjustment was reached assuming the co-existence of two distinct symmetries. The FTIR bands associated with a corner-shared NbO6 octahedron suggested an increase in Nb–O bond length in agreement with the Rietveld refinement. In general, ultrasonic spray pyrolysis is an effective methodology for the one-step synthesis of nanostructured crystallites of W-doped niobate-based perovskites with a tailored structure and morphology.

Errata for “A framework for covariate balance using Bregman distances”

Errata for “A framework for covariate balance using Bregman distances”

Artificial Intelligence Empowered Traffic Control for Internet of Things with Mobile Edge Computing

Mobile edge computing (MEC) is one of the efficient technologies to provide satisfying quality of experience (QoE) for emerging computation-intensive applications in internet of things (IoT). However, some new challenges will be encountered when MEC is applied in a large-scale IoT with massive access devices or heavy traffic loads such as load balancing and traffic offloading. Aiming at the solution of these problems, this paper proposes a learning-based traffic control architecture for IoT with MEC. Moreover, a deep-learning-based load balancing framework is developed to control user association in IoT. The user association is determined at each IoT access points (IAP) by the deep neural network (DNN), which is the duplication of the well-trained DNN with the global network information. In addition, we propose a reinforcement-learning-based partial traffic offloading scheme to reduce the traffic origination. The IoT devices are able to independently decide its offloading radio according to the channel quality information, service requirement, and workload of the IAP. Simulation results indicate that the proposed deep-learning-based load balancing scheme is able to achieve uniform traffic distribution, and meanwhile our partial offloading policy can significantly reduce the network traffic.