Empirical Validation Research Articles

Needs assessment of people living with cognitive impairment/dementia, a requirement of comprehensive psychogeriatric assessment and person-centered care. Empirical validation of the model in a community study

IntroductionCognitive impairment and dementia are part of a continuum that progressively leads to functional impairment and dependency. Dementia is a paradigmatic example of chronic and complex psychogeriatric diseases, requiring a comprehensive assessment. The authors underline the importance of implementing a formal assessment of needs (whether met or unmet) as an essential element of comprehensive assessment. The aim of this paper is to empirically validate this model of approach towards dementia, the needs assessment, demonstrating the relationship between needs and functionality/dependency in people with cognitive impairment/dementia in the community.Material and methodsCommunity-based, cross-sectional, descriptive epidemiological study based on the reanalysis of data from a two-phase community epidemiological study conducted in Santiago de Compostela, Spain, of 800 people over 65 years of age. The present study reanalyzes a subsample of 368 people, including those with dementia/cognitive impairment. The comprehensive assessment of the sample included sociodemographic variables, the presence of chronic diseases, health self-perception, assessment of affective, cognitive and functional state, as well as the needs assessment. The main instruments used were the MMSE, the Geriatric Depression Scale (GDS), the Barthel and Katz Indices, the Lawton Scale, and, for the needs assessment, the Camberwell Assessment of Need for the Elderly (CANE). For the clinical diagnosis of dementia, the ICD-10 criteria were followed. Statistical analysis: A study of the association between variables was carried out through hypothesis testing and a multivariate study was performed using regression models to analyze the relationship between the different variables defining disability/dependency, other health conditions and sociodemographic variables and the MMSE score as an expression of the cognitive impairment/dementia continuum.ResultsPeople with cognitive impairment/dementia had a higher number of needs compared to the healthy population. The severity of cognitive impairment is a significant predictor of dependency in Basic Activities of Daily Living (BADL) and is also a predictor of a greater number of needs (both met and unmet).DiscussionThe present study provides empirical evidence of the importance of implementing scales to assess the needs of people with cognitive impairment, as part of the process of comprehensive biopsychosocial assessment and person-centered care for dementia.

Preserving Fairness in AI under Domain Shift

Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an AI model is trained on an annotated training dataset with sensitive attributes and then fielded for utilization. This training strategy is effective in problems with stationary distributions, where both the training and testing data are drawn from the same distribution. However, it is vulnerable with respect to distributional shifts in the input space that may occur after the initial training phase. As a result, the time-dependent nature of data can introduce biases and performance degradation into the model predictions, even if the model is initially fair. Model retraining from scratch using a new annotated dataset is a naive solution that is expensive and time-consuming. We develop an algorithm to adapt a fair model to remain fair and generalizable under domain shift using solely new unannotated data points. We recast this learning setting as an unsupervised domain adaptation (UDA) problem. Our algorithm is based on updating the model such that the internal representation of data remains unbiased despite distributional shifts in the input space. We provide empirical validation on three common fairness datasets to show that the challenge exists in practical setting and to demonstrate the effectiveness of our algorithm.

Effect of Brand Image and Brand Trust on Purchase Intention of Fast-Moving Consumer Goods

An extensive analysis is performed in this research regarding how consumer relationships with brands influence brand trust and brand image, which represents a strong form of brand support rooted in consumer connections. This study examines how two consumer-brand relationship elements, specifically brand trust and brand image, influence buying intentions. Based on a review of the literature pertaining to consumer-brand dynamics, a theoretical framework is formulated that addresses the constructs of brand trust and brand image in their association with purchase intentions. In the current investigation, a quantitative approach is employed to rigorously assess the impact of brand trust and brand image on consumer purchasing intentions concerning fast-moving consumer goods. The data collected from a cohort of 167 individuals residing in the city of Madurai served as a basis for the empirical validation of the research hypothesis, which was established through the administration of a meticulously designed questionnaire. It was discovered that there exists a direct and significant correlation between brand trust, brand image, and purchase intention. This finding indicates that the connection between brand trust and brand image positively influences purchase intentions. Furthermore, it was uncovered that brand trust exerts a stronger effect on purchase intention than brand image alone. In summary, the findings highlight the substantial impact of consumer-brand relationships on purchasing intentions. The research recognizes specific constraints and provides suggestions for subsequent investigations within this field. As consumers increasingly exhibit intense loyalty and disdain towards brands, marketers must comprehend the importance of brands in the contemporary consumption landscape. This research indicates that marketers can enhance consumer purchase intentions and transform potential customers into actual buyers. Keywords: Brand trust, Consumer-brand relationships, Purchase intention , Brand image, Brand equity.

Auditory Temporal Processing Can Predict Reading Measurements in Neurotypical Adults: Evidence for Cascading Effects of Low-Level Auditory Processing on High-Level Cognitive Ability

ABSTRACT Accurate processing of auditory timing information has been proposed as crucial for the development of various complex cognitive abilities, such as literacy. However, empirical validation of this hypothesis has primarily involved using isochronous stimuli, which do not capture the nonlinearity inherent in the perception of timing in speech and music. Additionally, most work has focused on clinical/developmental populations and assessed timing at slower time scales, limiting our knowledge of this relationship. The current study introduces a perturbation stimulus design aimed at overcoming these limitations. We examined the association between reading abilities and behavioral entrainment to perturbation stimuli and applied a novel curve-fitting technique to analyze participants’ entrainment in response to perturbations. We demonstrate that behavioral entrainment during the post-perturbation time window yields superior predictive value across a wide array of reading measurements compared to entrainment to isochronous stimuli (before the perturbation) in a neurotypical adult population. Our perturbation paradigm effectively reveals individual differences in auditory temporal processing among neurotypical adults, offering a novel approach to investigating the link between auditory temporal processing and literacy skills beyond clinical/developmental contexts. Our novel curve-fitting analysis effectively captures the nonlinear aspects of participants’ entrainment when recovering from tempo perturbations.

Integrated Dynamic Analysis of Thin-Walled Beams: Coupled Bidirectional Bending, Torsion, and Axial Vibrations Under Axial Loads

This paper proposes a beam model integrating the Timoshenko beam theory with Vlasov beam theory to capture the coupled behavior of bidirectional bending, torsion, and axial vibration in thin-walled beams subjected to axial loads. Our model incorporates the effects of shear deformation, rotational inertia, and axial loads, offering a comprehensive approach to complex dynamic behaviors. By utilizing Hamilton’s principle, we derived a complete set of coupled dynamic equations and boundary conditions. The highlight of this model is its capacity to accurately predict the dynamic response of thin-walled beams under multifaceted loading conditions, surpassing traditional models by integrating coupled axial vibrations. This research significantly advances the understanding of the dynamic behavior of thin-walled beams, providing a precise analytical tool for structural design and safety assessment. The robustness and accuracy of the proposed model were validated through extensive theoretical analysis and empirical validation, equipping engineers with critical insights to optimize the design of engineering structures subjected to complex dynamic loads.

Accelerating Agile Quality Assurance with AI-Powered Testing Strategies

The infusion of Artificial Intelligence (AI) into Agile software development is revolutionizing the domain of software testing, reshaping conventional methodologies to meet the demands of today’s complex and accelerated development cycles. Agile frameworks, renowned for their iterative workflows and adaptability, often encounter limitations in scaling to the velocity and intricacy of modern projects. AI emerges as a game-changer, introducing sophisticated capabilities such as hyper-automation, predictive defect analytics, and context-aware decision-making, thereby addressing these limitations with precision and scalability. This paper investigates the transformative influence of AI on Agile testing methodologies, with a focus on specific use cases, the operational efficiencies gained through AI-augmented workflows, and the seamless collaboration between human testers and intelligent systems. A comprehensive, architecture-driven framework for embedding AI into Agile testing cycles is presented, with empirical validation through case studies that demonstrate tangible improvements in accuracy, productivity, and sprint adaptability. Keywords: Agile Methodology, Artificial Intelligence, Quality Assurance, Predictive Analytics, Test-Driven Development (TDD), Behavior-Driven Development (BDD), Natural Language Processing

Cultivating Consciousness: A Meta-Abilities Model for 21st Century Academic Leadership Development

This investigation integrates consciousness expansion into UK academic leadership training to address complex educational environments. Utilizing an integrative review and Grounded Theory elements, it presents the Conceptual Model of Consciousness Development for Academic Leaders (CMDAL) encompassing seven components: Self-awareness, Self-regulation, Cognitive Development, Interpersonal domain, ethical development, spiritual development, and Change capacity (adaptability). Significant and complex challenges that cannot be resolved with traditional ways have evidenced the gap between them and current competencies-based leadership training; Models that use a holistic approach are more appropriate for developing academic leaders; Consciousness expansion is the pillar of these holistic models. The CMDAL proposed model, focusing on seven dimensions highlights the importance of complementing current holistic leadership development with elements of consciousness expansion to develop higher-order abilities or Meta-abilities that can enhance leadership effectiveness as they foster psychological maturity. Future research should address the empirical validation of the model. Limitations include more analysis for context-specific adaptations and longitudinal research/testing to establish efficacy.

Sustainable and slow cities: the quest for conviviality

The concept of conviviality, introduced by Ivan Illich in his book Tools for Conviviality (1973), offers a solution to the counterproductive effects of fast transport. It is now widely recognized that fast transport does not save time for individuals and communities but rather increases the feeling of time famine and ultimately leads to greater energy consumption to travel longer distances. In today’s context of urban time famine and ecological crisis, I show that Illich’s system of thought finds empirical validity. An inductive approach in nine “Cittàslow” labeled European cities demonstrates that a slow pace of life and mobility contributes to the maintenance of friendly or convivial relations. Incorporating slow mobility policies into the broader framework of convivial cities could become a key instrument in the transition to a more sustainable path, while also offering prospects for future lifestyles that people find desirable. The article concludes by arguing that the pursuit of conviviality in cities can help promote slow mobility and sustainable living, ultimately contributing to a more environmentally conscious and socially connected society.

Autumn diet of Little Owls (Athene noctua) in southern Tunisia

Abstract The Little Owl (Athene noctua) is a widespread nocturnal raptor whose diet has extensively been studied. In the Mediterranean, a tendency towards increased ingestion of insects has been highlighted, but this tendency still needs more empirical validation. Here, we provide data on the autumn diet of Little Owls in an arid area in southern Tunisia. We analyzed 621 pellets from 30 different territories and identified 4,267 prey items, with insects being by far the most ingested prey. However, based on biomass, the diet was more evenly distributed across the identified prey categories. Vertebrates accounted for 63% of the biomass ingested, while the remaining part of prey composition was almost equally shared between insects and arachnids. The importance of arachnids looks like a characteristic dietary trait of Little Owls in south Mediterranean deserts, where arachnids represent a main component of the community of potential prey. In summary, our findings highlight the great food opportunism of the Little Owl due to its capability of adapting its diet to the availability of potential prey.

Impact of rotor geometry and fluid injection on screw compressor performance

Abstract Screw compressors are widely used in industrial and commercial applications due to their high efficiency, reliability, and durability. However, there is still room for improvement in terms of energy efficiency and performance. To address this, the current study investigates the impact of screw rotor geometrical parameters: wrap angle, relative length, and built-in volume ratio and fluid injection methods (oil, and water injection) on the performance of screw compressors. The research offers a detailed analysis of these parameters, with a focus on the empirical validation of water injection’s influence through experimental testing on an industrial screw compressor. The findings reveal that water injection reduces specific power consumption by 4.78 % compared to oil injection, highlighting its potential for improving compressor efficiency. This work contributes valuable insights into optimizing screw compressor design for enhanced performance and energy efficiency.

Artificial intelligence in digital marketing automation: Enhancing personalization, predictive analytics, and ethical integration

Artificial Intelligence (AI) is revolutionizing digital marketing automation by enhancing efficiency, personalization, and predictive capabilities. This study examines the role of AI in transforming marketing practices, focusing on its applications, benefits, ethical considerations, and future directions. By leveraging AI tools such as predictive analytics, NLP, and chatbots, businesses can achieve improved customer segmentation, content personalization, and campaign optimization in marketing strategies. Secondary data from journals, articles, and conference papers were synthesized to provide insights into AI's impact on digital marketing automation. A systematic literature review utilizing the PRISMA methodology initially identified 2,850 records from database searches. Following the removal of duplicates and non-relevant studies, 1,035 records were screened for eligibility based on defined criteria, resulting in the inclusion of 150 relevant studies and 25 high-quality reports for detailed analysis. This robust approach ensured the inclusion of high-quality research, minimizing biases. The findings reveal that AI enhances digital marketing by streamlining processes, automating repetitive tasks, and delivering hyper-personalized customer experiences. Predictive analytics helps anticipate consumer behavior, while chatbots improve real-time customer engagement. However, challenges such as data privacy, algorithmic bias, and the high costs of AI adoption persist. AI adoption allows businesses to make data-driven decisions, improve customer retention, and maximize return on investment. Ethical AI practices, such as transparency and algorithm fairness, are essential for maintaining consumer trust. The study primarily focuses on existing literature, with limited empirical validation. Future research should explore long-term effects of AI-driven marketing on consumer behavior and investigate its integration with emerging technologies like the Internet of Things (IoT) and blockchain. Additionally, tailored AI solutions for SMEs and under-researched areas, such as B2B marketing, are critical for inclusive growth.

An explicit discrete-time dynamic vehicle model with assured numerical stability

Numerical stability is of great significance for discrete-time dynamic vehicle model. Among the unstable factors, low-speed singularity stands out as one of the most challenging issues, which arises from that the denominator of tire side angle term only contains the vehicle longitudinal speed. Consequently, for the common low-speed and stop-start driving scenarios, the calculated tire slip angle will approach infinity, which will further lead to the numerical explosion of other vehicle states. In response to this critical challenge, we propose a discrete-time dynamic vehicle model that effectively mitigates the low-speed singularity issue, ensuring numerical stability and maintaining the explicit form – highly favoured by model-based control algorithms. To validate the numerical stability of our model, we conduct a rigorous theoretical analysis, establishing sufficient conditions for stability, and conduct extensive empirical validation tests across a wide spectrum of speeds. Subsequent to the validation process, we conduct comprehensive simulations comparing our proposed model with both kinematic models and existing dynamic models discretised through the forward Euler method. The results demonstrate that our proposed model shows better comprehensive performance in terms of both the accuracy and numerical stability. Finally, the real vehicle experiments are carried out to support that our proposed model can closely aligns to the real vehicle trajectories showcasing its practicality and ease of use. Notably, our work stands as the pioneering endeavour in introducing an explicit discrete-time dynamic vehicle model suitable for common urban driving scenarios including low-speed and stop-start.

Toward measuring R&D knowledge management capability: scale development and empirical validation

Purpose This study aims to perceive research and development (R&D) knowledge management from a capability-based view, develops a theoretically grounded construct and empirically validates it within R&D organizational settings. Design/methodology/approach The paper presents a five-step multi-method research design consisting of a literature review, expert interviews and surveys to develop and validate the R&D knowledge management capability scale within R&D organizations. Findings R&D knowledge management capability construct emerges as a three-dimensional construct with dimensions – knowledge creation, communication and commercialization which can be measured through a parsimonious set of items, designed at team levels within R&D organizations. Research limitations/implications R&D managers can use this R&D knowledge management capability scale to measure, compare and develop the knowledge management capabilities of their R&D teams. Originality/value The three dimensions of R&D knowledge management capabilities along with their measurement items are the unique contributions of this study to the knowledge-based view of the firm.

Mathematical analysis and empirical validation of Thermal denaturation of Glucokinase

Mathematical analysis and empirical validation of Thermal denaturation of Glucokinase

Person re-identification transformer with patch attention and pruning

Person re-identification (Re-ID), which is widely used in surveillance and tracking systems, aims to search individuals as they move between different camera views by maintaining identity across various camera views. In the realm of person re-identification (Re-ID), recent advancements have introduced convolutional neural networks (CNNs) and vision transformers (ViTs) as promising solutions. While CNN-based methods excel in local feature extraction, ViTs have emerged as effective alternatives to CNN-based person Re-ID, offering the ability to capture long-range dependencies through multi-head self-attention without relying on convolution and downsampling. However, it still faces challenges such as changes in illumination, viewpoint, pose, low resolutions, and partial occlusions. To address the limitations of widely used person Re-ID datasets and improve the generalization, we present a novel person Re-ID method that enhances global and local information interactions using self-attention modules within a ViT network. It leverages dynamic pruning to extract and prioritize essential image patches effectively. The designed patch selection and pruning for person Re-ID model resulted in a robust feature extractor even in scenarios with partial occlusion, background clutter, and illumination variations. Empirical validation demonstrates its superior performance compared to previous approaches and its adaptability across various domains.

Creation of a Diagnostic Test Instrument with Four Tier Multiple Choice (FTMC) to Determine Students' Misconceptions About Chemical Bonding Material

As part of chemistry learning, students are faced with the need to master various chemical concepts that have been previously learned and be able to connect these concepts with the material they are learning to achieve a comprehensive understanding. Students who have difficulty in understanding concepts correctly will cause concept errors or misconceptions. Finding these misconceptions is the first step in helping students overcome them. A diagnostic test is one technique for identifying misconceptions. The purpose of this study is to develop a four-tier multiple-choice (FTMC) diagnostic test that may be used to determine students' misconceptions about chemical bonding materials in terms of validity, practicality and efficacy. Without the implementation stage, this kind of research is research and development (R&D) utilizing the ADDIE development model (Analyze, Design, Development, Implementation, Evaluation). The limited trial was conducted on 30 students of SHS 1 Dukun Gresik. Based on content validity and construct validity, the findings demonstrated that the four-tier multiple-choice (FTMC) diagnostic test instrument had a median score of 5, indicating that it was deemed valid. According to the students' questionnaire responses, the diagnostic test instrument's practicality was 86.67%, with a very practical category. Reliability and empirical validity tests of the test items are used to determine effectiveness. Six test items fell into the invalid category and fourteen test items fell into the valid category, according to the test item validity results. The test instrument was deemed reliable after the reliability test yielded a reliability rating of 0.780.

Correlation times of velocity and kinetic helicity fluctuations in non-helical hydrodynamic turbulence

Non-helical turbulence within a linear shear flow has demonstrated efficient amplification of large-scale magnetic fields in numerical simulations, but its precise mechanism remains elusive. The incoherent $\alpha$ mechanism proposes that a zero-mean fluctuating transport coefficient $\alpha$ (linked to kinetic helicity) in the shear flow is a candidate driver. Previous renovating-flow models have proposed that the correlation time of helicity fluctuations must be sufficiently extended to overcome turbulent magnetic diffusivity, yet only empirical validation of this concept has been obtained. In this study, we conduct direct numerical simulations of weakly compressible non-helical hydrodynamic turbulence. We scrutinize the correlation times of velocity and kinetic helicity fluctuations in distinct flow configurations, including rotation, shearing and Keplerian flows, as well as the shearing burgulence counterpart. Our findings indicate that rotation contributes to a prolonged correlation time of helicity compared with velocity, particularly notable in auto-correlations of both volume-averaged quantities and individual Fourier modes due to the formation of large-scale vortices. In contrast, moderate shear strength does not exhibit significant scale separation, with shear flows elongating vortices in the shear direction. Shearing burgulence, characterized by shorter helicity correlation times, appears less conducive to hosting the incoherent $\alpha$ effect. Notably, at modest shear rates, only Keplerian flows exhibit sufficiently coherent helicity fluctuations, in contrast to shearing flows. However, the relative strength of helicity fluctuations compared with turbulent diffusivity is significantly lower, raising doubts about the viability of the incoherent $\alpha$ effect as a potential dynamo driver in the subsonic flows examined in this study.

Feasibility and Acceptability of Internet-Based Emotion-Focused Cognitive Behavioral Therapy in Women with Suspected Fetal Anomalies: A Quasi-Experimental Study

Background: There is a notable lack of empirical validation regarding the use of internet-based emotion-focused cognitive behavioral therapy (IECBT) for counseling pregnant women. Objectives: This study examines the feasibility, acceptability, and effectiveness of IECBT in counseling women with suspected fetal anomalies. Methods: In a quasi-experimental study, 130 pregnant women diagnosed with anxiety disorders were randomly assigned to either IECBT with spousal participation (65 participants) or IECBT alone (65 participants). All participants completed three pregnancy-specific stress questionnaires—the NuPDQ, Spielberger State Anxiety Inventory, and Edinburgh Depression Scale—both before and immediately after the intervention. Results: Within-group analyses indicated that both counseling methods, with and without spousal involvement, led to significant reductions in pregnancy-specific stress, depression, and anxiety symptoms by the end of the intervention (P < 0.001). Between-group comparisons revealed that IECBT with spousal participation significantly reduced pregnancy-specific stress (effect size = 0.055, P = 0.007) and depressive symptoms (effect size = 0.480, P < 0.001) compared to IECBT without spousal involvement. However, neither intervention produced a significant reduction in anxiety symptoms (effect size = 0.001, P = 0.787). Conclusions: Both IECBT approaches (with and without spousal participation) are feasible, reliable, and effective methods for alleviating depression and stress symptoms in pregnant women with suspected fetal anomalies.

A hybrid approach for modelling safety hazards in China subways: multi-stakeholders’ perspective

PurposeUnlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.Design/methodology/approachBy administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.FindingsThe optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.Research limitations/implicationsWhile our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.Originality/valueThe established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Natural and Artificial Intelligence Interactions in Digital Networking: A Multilayer Network Model for Economic Value Creation

This study investigates the integration of natural intelligence (NI) and artificial intelligence (AI) within traditional firms linked through a multilayer network framework. The research explores this central question: How can the integration of NI and AI, facilitated by copula nodes, drive economic value creation in digitized firms? The paper combines theoretical and empirical results of the observed increments in cost-benefit marginality linked with AI adoption by checking out testing in various domains such as manufacturing, retailing, finance, etc. The performance of the model shows enhanced efficiency and decision-making, as well as savings in costs. Copula nodes that connect multilayer networks blend NI with AI, boosting overall value. Unlike existing studies, this framework operationalizes copula nodes to capture real-time dependencies between human-driven and AI-driven processes, offering a comprehensive view of economic value creation across digitized firms. A methodology section outlines the empirical validation process conducted across multiple industries (manufacturing, retail, and finance), providing key insights into efficiency, decision-making, and cost optimization. The practical implications offer a strategic pathway for firms to enhance profitability and competitiveness in the digital age. The results underscore the importance of strategically integrating AI with human intelligence to maximize economic outcomes. Received: 18 August 2024 | Revised: 22 October 2024 | Accepted: 10 November 2024 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Roberto Moro Visconti: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing -Review & Editing, Visualization, Supervision, Project administration, Funding acquisition.