Search Results (212)

Background and objectives: Trismus is a frequent and debilitating complication in people with head and neck cancer (HNC) which leads to significant functional limitations and reduced quality of life. Rehabilitation interventions are commonly recommended to manage or prevent trismus. However, in many randomized controlled trials (RCTs), the theoretical justification for these interventions is poorly articulated, and the underlying biological or physiological mechanisms are not described in detail, limiting our understanding of why certain treatments may (or may not) work. This review aimed to identify and analyze how RCTs report the rationale for rehabilitation interventions and the explanations used to manage this population. Materials and Methods: A scoping review was conducted in accordance with the PRISMA-ScR guidelines. Five databases (PubMed, PEDro, Web of Science, Scopus, and EMBASE) were searched up to May 2025 for RCTs evaluating rehabilitation interventions for the management or prevention of treatment-induced trismus in patients with HNC. Data were extracted and synthesized narratively, focusing on the type of intervention, the rationale for its use, and the proposed mechanisms of action. Results: Of 2215 records identified, 24 RCTs met the inclusion criteria. Thirteen studies focused on preventive interventions—primarily exercise therapy—while the remainder addressed established trismus using exercise, manual therapy, electrotherapy, or combined treatment modalities. The rationales provided for intervention selection were heterogeneous and often lacked depth, with most studies justifying interventions based on their potential to improve mouth opening or reduce fibrosis but rarely grounding these claims in detailed pathophysiological models. Only half of the studies provided any mechanistic explanation for the intervention’s effects, and these were typically generic or speculative. Conclusions: RCTs investigating rehabilitation interventions for treatment-induced trismus in patients with HNC frequently lack comprehensive rationales and mechanistic explanations for their interventions. This gap limits the ability to refine and optimize treatment approaches, as the underlying processes driving clinical improvements remain poorly understood. Future research should be guided by theoretical models and include objective outcomes to better elucidate the mechanisms of action of interventions to inform clinical practice. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

Background: Borderline personality disorder (BPD) is characterised by significant clinical heterogeneity. Classifying subtypes of BPD may offer deeper insights into the disorder’s complexity and inform more tailored therapeutic strategies. The exploration of data-driven subtyping using cluster-analytic approaches represents a promising avenue for capturing variability in symptomatology and comorbidity profiles. Aim: This systematic review aims to synthesise and critically evaluate the empirical studies that have applied cluster-analytic methods to identify subtypes of BPD in adult populations. It further assesses the consistency of findings and their alignment with theoretical models of the disorder. Methods: A comprehensive search of PubMed, Scopus, and PsycNet was conducted in accordance with the PRISMA guidelines. Eligible studies employed either traditional or probabilistic clustering techniques to classify adult individuals diagnosed with BPD based on the DSM criteria. A total of 29 studies, encompassing 24,345 participants, met the inclusion criteria. The study quality and risk of bias were assessed using the AXIS tool. Results: Most studies identified clinically meaningful BPD subtypes based on dimensions such as affective regulation, effortful control, interpersonal style, and impulsivity or aggression. Several findings supported the existence of internalizing and externalizing profiles, which converge with long-standing theoretical conceptualisations of BPD. However, substantial heterogeneity was observed in subtyping bases, sample characteristics, and analytic procedures. Discussion: Although this review highlights the recurring subtype patterns, the methodological inconsistencies and a lack of longitudinal and treatment-outcome data limit the strength of the conclusions. The future research should prioritise standardised subtyping frameworks and explore the prognostic and therapeutic utility of BPD subtypes in clinical settings. Full article

Background: China’s rapidly aging population has increased the need for effective community-based eldercare services. However, the lack of standardized, culturally relevant evaluation frameworks hinders consistent service quality assessment and improvement. Objective: This systematic review aims to identify, synthesize, and critically evaluate the existing quality indicators (QIs) currently utilized for home-based community elderly care HCEC in China. It also aims to highlight gaps to inform the development of a more comprehensive and context-appropriate quality framework. Methods: Following PRISMA guidelines, systematic searches were conducted across Web of Science, PubMed, Wiley, and CNKI databases for studies published in English and Chinese from 2008 onward. Extracted QIs from eligible studies were categorized using Donabedian’s structure–process–outcome (SPO) model. Results: Fifteen studies met the inclusion criteria, with QI sets ranging from 5 to 64 indicators. Most studies emphasized structural and procedural aspects, while outcome measures were limited. Key gaps include inconsistent terminology, insufficient medical care integration, narrow stakeholder engagement, and limited cultural adaptation of Western theoretical frameworks. Furthermore, subjective weighting methods predominated, impacting indicator reliability. Conclusions: Currently, there is no formal quality framework to guide service providers in HCEC, and therefore, quality indicators can be described as fragmented and lack cultural specificity, medical integration, and methodological robustness. Future research should prioritize developing culturally anchored and medically comprehensive QI frameworks, standardize indicator terminology, actively involve diverse stakeholders through participatory methods, and adopt hybrid methodological approaches combining subjective expert insights and objective, data-driven techniques. Alignment with established international standards, such as the OECD long-term care quality indicators, is essential to enhance eldercare quality and support evidence-based policymaking. Full article

The translation of motion lift, as an important performance metric of a reversing six-link loader working device, is influenced by multiple factors, such as the mechanical structure, system components, and operational experience. To ensure that the loader’s motion lift performance is optimized, this paper takes the fork trajectory and the horizontal angle between the bucket cylinder and the ground as the main optimization objectives. Kinematic modeling and multi-objective optimization are conducted to reduce the influence of external factors on the motion lift process. Firstly, a parametric model of the reversing six-link mechanism is established based on its geometric and symmetric characteristics, and the expressions for the fork’s motion trajectory and the cylinder–ground angle are derived. Then, an optimization model is constructed with the aim of minimizing both the translational error during fork lifting and the horizontal angle of the bucket cylinder. An improved multi-objective genetic algorithm is employed for the global search and optimization. Inspired by the principle of symmetry, the algorithm incorporates a structured search strategy that enhances convergence efficiency and solution balance. A multi-criteria decision function is further applied to identify the optimal solution from the Pareto front. Finally, a real-vehicle experiment validates the optimization results. The findings confirm that the proposed method significantly improves the translational performance of the fork and effectively controls the horizontal angle of the cylinder while also enhancing the driver’s visibility and coordination of the entire system. These results provide a theoretical and engineering basis for the symmetry-informed multi-objective performance optimization of loader working devices. Full article

A conflict is a situation where multiple stakeholders have different evaluations over possible scenarios or states. Conflict analysis is an essential tool for understanding and resolving complex conflicts, especially in scenarios involving multiple stakeholders and uncertainties. Confrontation analysis (ConAna) and graph model for conflict resolution (GMCR) have been integrated for dilemma reasoning and conflict resolution in region crisis analysis. This paper discusses the application of decision-theoretic rough sets (DTRS) to three-way decisions (3WD) in dilemma reasoning and conflict resolution. Three-way decisions are a strategy for making decisions under uncertain conditions, which compensates for the shortcomings of traditional two-way decisions (such as accept or reject) by introducing a “delayed decision” option. In terms of dilemma reasoning, we try to address incomplete or conflicting information and provide a more reasonable decision path for decision-makers through comprehensive evaluation of multi-criteria. In terms of conflict resolution, the DTRS model seeks a compromising solution that is acceptable to all parties by analyzing the game relationship between different stakeholders. The DTRS model combines decision-making theory and rough set theory to determine the balanced decision region by constructing a game between multiple criteria. This dynamic integration is of great significance for the study of complex international conflicts, providing a cross-disciplinary perspective for related research. In this paper, we demonstrate the application of DTRS in 3WD and discuss the relationship between DTRS and probabilistic rough sets. The research shows that the DTRS model has significant advantages in dealing with complex decision problems and can effectively deal with the conflicts and uncertainties in multi-criteria decision-making. Full article

The COVID-19 pandemic has radically transformed behavioral science research. While many disciplines have been shown increasing attention in the existing literature, behavioral science uniquely revisits old questions to develop new theoretical perspectives for the post-COVID-19 era. Our systematic search of the literature allowed us to map 505 records that met our criteria, found across 102 papers; from these, we chose 11 articles published between 2021 and 2024. The focus of this review is on examining old questions while providing fresh insights into social, psychological, cognitive, healthcare, and human behavior. The findings emphasize the relevance of the TPB, the HBM, SCT, and the COM-B model, which effectively provide new theoretical insights into post-COVID-19 research. This study shows that theory-informed practices have been integrated into behavioral science research since the COVID-19 pandemic. Practical applications depend on these insights, which can inform evidence-based practice of planned behavior in healthcare policy, academic research, and community practice. Full article

Business problems today are complicated and involve considering numerous dimensions to be weighed against each other, leading to opposing goals that must be compromised on to discover the best solution. Multi-Criteria Decision Making or MCDM plays an essential role in this situation here. MCDM techniques and procedures analyze, score, and select between options that have various conflicting criteria. This systematic review investigates applications of MCDM methods within Management Information Systems (MIS) based on evidence from 40 peer-reviewed articles selected from the Scopus database. Key methods discussed are Analytic Hierarchy Process (AHP), TOPSIS, fuzzy logic-based methods, and Analytic Network Process (ANP). These methods were applied across MIS strategic planning, re-source assignment, risk assessment, and technology selection. The review contributes further by categorizing MCDM application into thematic decision domains, evaluating methodological directions, and mapping the strengths of each method against specific MIS problems. Theoretical guidelines are suggested to align the type of decision with an appropriate MCDM strategy. The study demonstrates how the addition of MCDM enhances MIS capability with data-driven, transparent decision-making power. Implications and directions for future research are presented to guide scholars and practitioners. Full article

This study addresses threat scenario identification and quantitative evaluation in the context of the information security risk assessment process for airborne networks. A method integrating an improved system-theoretic process analysis (STPA) and the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. A safety control interaction structure is first established based on the system-level loss definition to identify unsafe control actions and derive STPA-Sec threat conditions. Then, the opinion aggregation method based on the weakest t-norm is introduced to address the uncertainty from insufficient expert consensus and opinion deviation. To address the intrinsic correlations among evaluation indicators, the criteria importance through intercriteria correlation (CRITIC) method is applied to determine indicator weights. These weights are subsequently incorporated into the TOPSIS framework to assess the identified threat scenarios. Comparative analysis confirms the effectiveness of the proposed approach. The results show that the improved STPA-TOPSIS method enables the reliable identification of security threats in airborne networks and supports the prioritization of threat scenarios by severity, facilitating the implementation of targeted mitigation strategies. Full article

The landscape visual sensitivity (LVS) assessment is recognized as a critical tool for identifying areas most sensitive to landscape changes and for informing multi-resource optimization and allocation strategies. However, conventional large-scale LVS assessment criteria and methodologies developed for natural landscapes do not satisfy the precision-oriented assessment requirements of streetscape visual sensitivity (SVS) in historic districts, nor do they facilitate the operational linkage between assessment outcomes and planning applications. This study proposes an innovative SVS–PAP assessment methodology, which is a systematic integration of the SVS assessment and public esthetic perception (PAP) evaluation. The SVS assessment criteria framework was first improved through the integration of enriched multi-modal datasets. Subjective weights were obtained via the analytic hierarchy process (AHP), incorporating expert and public judgments, while objective weights were derived through the entropy weight method (EWM) based on data information entropy. The integration of both approaches enhances the methodological rigor and scientific validity of SVS weight determination. An SVS–PAP analytical matrix was subsequently constructed through integration of SVS assessments and PAP-based scenic beauty estimation (SBE), enabling the derivation of planning strategies. An empirical validation conducted in Anshandao Historic District yielded four key findings: (1) The SVS–PAP methodology, which integrates subjective–objective evaluation factors and incorporates broad public participation, demonstrates strong scientific validity and reliability, establishing a novel paradigm for SVS assessment and strategic planning; (2) The technical framework—leveraging multi-modal data and GIS spatial analysis techniques—improves assessment precision, operability, and replicability; (3) The planning and management strategies formulated by the SVS–PAP analytical matrix were verified as reasonable, demonstrating effective planning-transition capability; (4) Notably, historical and cultural influences showed significantly higher weighting coefficients across assessment criteria compared to non-historic streetscape assessments. Overall, these research results address the persistent undervaluation of the esthetic and spiritual values of historic landscapes in multi-resource value trade-off and decision-making processes, demonstrating both theoretical and practical significance through a systematic methodological advancement. Full article

A PROM is a measure of patient needs and therapeutic progress. This paper outlines the validation of the CARe Burn Scale: Parent/Caregiver Form, a PROM that measures quality of life in parents/caregivers supporting a child with a burn injury. A literature review and interviews with sixteen parents and six burns health professionals informed the development of the PROM conceptual framework/draft form. Cognitive debriefing interviews with five parents and seven burns-specialist health professionals provided feedback to ascertain content validity, and two-hundred and four parents/caregivers took part in the field testing. Rasch measurement theory (RMT) analyses and internal consistency tests were conducted to create a shortened version and for psychometric validation. The final conceptual framework included eight domains/individual scales: Physical Well-being, Confidence with Managing Burn Wound/Scar Treatments, Social Situations, Partner Relationship, Self-worth, Negative Mood, Parent Concerns about the Appearance of their Child’s Burn Wounds/Scars, and Positive Growth. Seven scales had solutions from RMT analyses and passed internal consistency criteria. Confidence with Managing Burn Wound/Scar Treatments did not fit the Rasch model but was retained as a checklist based on theoretical insight. The CARe Burn Scale: Parent/Caregiver Form is the first and only burn-specific PROM that assesses parents’ own health needs when caring for a child with a burn. Full article

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria. Full article

This paper develops tests for hypotheses concerning subvectors of parameters in models defined by moment conditions. It is well known that conventional tests such as Wald, Likelihood-ratio and Score tests tend to over-reject when the identification is weak. To prevent uncontrolled size distortion and introduce refined finite-sample performance, we extend the projection-based test to a modified version of the score test using implied probabilities obtained by information theoretic criteria. Our test is performed in two steps, where the first step reduces the space of parameter candidates, while the second one involves the modified score test mentioned earlier. We derive the asymptotic properties of this procedure for the entire class of Generalized Empirical Likelihood implied probabilities. Simulations show that the test has very good finite-sample size and power. Finally, we apply our approach to the veteran earnings and find a negative impact of the veteran status. Full article

Objective: To provide a state of the art on the use, role, and function of music during psychedelic-assisted therapy (PAT) with ayahuasca. Introduction: Ayahuasca is a medicinal brew with psychoactive qualities used by indigenous communities throughout the Amazon region, and music is deemed crucial during ayahuasca experiences. In PAT, music forms part of the set and setting, but it lacks systematization and is poorly explored in the scientific literature. Inclusion criteria: Published literature in English, Spanish, and Portuguese, focusing on conceptualizing, defining, or describing the use, role, and/or function of music in PAT sessions with ayahuasca. Methods: This review follows the JBI methodology for scoping reviews. We will search Web of Science, Scopus, Google Scholar, and PubMed databases without year restrictions, and a hand search of articles will be performed. Two reviewers will assess titles/abstracts, followed by independent reviews of included full texts. An Excel data extraction sheet will be used to tabulate the information. The findings will be presented narratively, including respective tables or figures. If feasible, a potential theoretical framework for the use, role, and function of music during PAT with ayahuasca will be outlined, including implications for future research and clinical practice. Full article

To systematically address the intricate multiple criteria decision-making (MCDM) challenges to practical situations where uncertain and hesitant information plays a critical role in guiding optimal choices. In this article, we introduce the concept of $\mathfrak{m}$-polar Q-hesitant fuzzy ($\mathcal{MPQHF}$) $\mathfrak{B}\mathrm{C}\mathbb{K}/\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras, combining $\mathfrak{m}$-PFS theory with Q-hesitant fuzzy set theory in the framework of $\mathfrak{B}\mathrm{C}\mathbb{K}$/$\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras. This innovative approach enhances the attitudes of uncertainty, vagueness, and hesitance of data in decision-making processes. We investigate the features and actions of this proposed hybrid approach to fuzzy sets and hesitant fuzzy sets, focusing on $\mathcal{MPQHF}$ subalgebras, and explore the characteristics of several kinds of ideals under $\mathfrak{B}\mathrm{C}\mathbb{K}$/$\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras. It also showed that it can better represent complex levels of uncertainty than regular sets. The proposed method’s theoretical framework offers a better way to show uncertain data in areas like engineering, computer science, and computational mathematics. By linking theoretical advancements of $\mathcal{MPQHF}$ sets with practical applications, we highlight the benefits and challenges of this approach. Demonstrating the practical uses of the $\mathcal{MPQHF}$ sets aims to encourage broader adoption. Symmetry plays a vital role in algebraic structure and is used in various fields like decision-making, encryption, pattern recognition problems, and automata theory. Furthermore, this work enhances the understanding of algebraic structures and offers a robust tool for career exploration and development through improved decision-making methodologies. Full article