Search Results (103)

An increasing number of scholars are raising concerns about the sustainability of digital health, calling for action to prevent its harmful effects on the environment. At this point, however, the comprehensive appraisal of emerging technology in the health sector remains theoretically challenging, and highly difficult to implement in practice and in ecological design. Indeed, background factors such as the rapid evolution of technology or effectiveness–efficiency tradeoffs complicate the task of distinguishing the benefits of digital health from its drawbacks, rendering early Health Technology Sustainability Assessment (HTSA) extremely complex. Within this context, the aim of this article is to draw attention to the pragmatism that should be adopted when anticipating the sustainability of technological innovation in the medical field, while simultaneously proposing an assessment framework grounded in a structural and conceptual dissection of the fundamental purpose of smart technologies and the Internet of Medical Things (IoMT). Building on this, we demonstrate how our framework can be strategically applied through a rapid back-of-the-envelope assessment of the economic and ecological balance when introducing IoMT prototypes for treating a specific condition, based on a preliminary simulation of a defined clinical outcome. In this manner, the article presents evidence that challenges two primary hypotheses, and also encourages reflection on the central role of information and its interpretation when addressing key barriers in the HTSA of digital health. Thereby, it contributes to advancing cost–benefit and cost-effectiveness evaluation tools that support eco design strategies and guide informed decision-making regarding the integration of sustainable IoMT systems into healthcare. Full article

Aims: Cue exposure therapy (CET) is a promising treatment approach for cocaine substance use disorder (SUD). CET specifically targets the psychological and physiological responses elicited by drug-related cues, aiming to reduce their motivational impact. To advance understanding of CET for cocaine treatment, this systematic review aims to categorise the range of cocaine cues used in research. Methods: A systematic review of the existing literature with searches conducted on PubMed and Web of Science bibliographic databases with no time constraints in August 2024 (PROSPERO: CRD42024554361). Three reviewers were independently involved in the screening, review and data extraction process, in line with PRISMA guidelines. Data extracted included participant demographics, study design, data on the cocaine cue task, and examples (if provided). Each study was appraised and received a quality score. The secondary outcome was to summarise examples for each category type identified. The data are presented as a narrative synthesis. Results: 3600 articles were identified and screened. 235 articles were included in the analysis. Cues identified included images, paraphernalia, drug-related words, cocaine smell, auditory stimuli presented via audiotapes, video recordings, scripts, and virtual reality environments, often combining multiple modalities. Included studies recruited cocaine-dependent individuals, recreational users, polydrug users, and non-cocaine-using controls. The sample sizes of the studies ranged from a single case study to a study including 1974 participants. Conclusions: This review found that studies employed a wide range of cue categories, but detailed examples were often lacking, limiting replication. The number and combination of cues varied: some studies used only cocaine-related images, while others included images, videos, physical items, and audiotapes. The level of immersion and personalisation also differed considerably. All studies used cocaine-specific cues, most commonly images or representations of cocaine substance, cocaine use or drug paraphernalia, drug preparation items, or conversations of cocaine use and its effects. The overall quality of the included studies was deemed good, with all adhering to standard research norms. While this review highlights the breath of cue types used in the literature, further research should focus on enhancing cue exposure techniques by incorporating more immersive and personalised stimuli, and by providing clearer documentation of cue characteristics to support replication and clinical translation. Full article

Background: Bilateral movement training (BMT) and interlimb coupling have emerged as promising neurophysiologically-based rehabilitation approaches for stroke survivors. However, the underlying mechanisms and optimal implementation strategies remain incompletely understood. This systematic review explored the neurophysiological principles underlying BMT and interlimb coupling interventions that led to positive clinical post-stroke rehabilitation outcomes, focusing on identifying the most effective bilateral and interlimb movement strategies. Methods: A 10-year literature search (2014–2024) following PRISMA guidelines was conducted across PubMed, Cochrane, and Google Scholar databases using keywords including stroke rehabilitation, bilateral movement training, cross-education, interlimb coupling, and interlimb transfer. Studies were included if they involved human subjects, clinical trials, stroke survivors, and described bilateral training protocols. Data extraction focused on neurophysiological mechanisms, intervention characteristics, and clinical outcomes. Quality assessment was performed using validated methodological appraisal tools, including the Newcastle-Ottawa Scale and Cochrane RoB 2.0. Results: Of 199 initially identified studies, 28 met inclusion criteria for detailed analysis. BMT demonstrated effectiveness in enhancing motor recovery by engaging neurophysiological mechanisms, including central pattern generators, interhemispheric coupling, and cortical disinhibition. High-intensity BMT provided significant gains for individuals with moderate to severe impairments, while low-intensity training benefited early recovery stages. Interventions incorporating task-specific exercises, robotic assistance, sensory enhancement, and virtual reality showed particular promise for addressing motor recovery complexities. However, significant research gaps were identified, including limited understanding of individualized responses to BMT, insufficient research on combined upper and lower limb training, and minimal integration of advanced technologies. Conclusions: BMT and interlimb coupling play critical roles in post-stroke rehabilitation by facilitating neural plasticity and interlimb coordination. Integrating robotic assistance, sensory enhancement, and virtual reality with BMT offers a robust framework for maximizing rehabilitation outcomes. Future research should prioritize longitudinal studies, personalized rehabilitation approaches, technology integration, and stratified interventions tailored to individual needs to optimize neuroplasticity and enhance quality of life for stroke survivors. Full article

Landing requires precise coordination among lower limb joints to absorb impact forces and maintain dynamic stability. Coordination and its variability during landing are influenced by factors such as injury status, training, sex, age, fatigue, and task complexity. Altered coordination patterns may compromise impact absorption and increase injury risk, highlighting the importance of understanding these movement strategies across populations and conditions. This scoping review aimed to map and synthesize the existing literature on lower limb joint coordination and coordination variability during landing tasks across different populations and task conditions. A comprehensive search was conducted across four databases (PubMed, Web of Science, Scopus, SPORTDiscus) through November 2024, with additional articles identified through reference screening. Peer-reviewed studies were included if they assessed joint or segmental coordination and/or coordination variability using time-series analyses (such as vector coding, continuous relative phase, and discrete relative phase) during landing tasks in human participants. Formal critical appraisal was not performed, consistent with PRISMA-ScR guidelines. Eighteen studies were thematically grouped into five focus areas: injured/at-risk individuals, training/fatigue interventions, gender differences, age differences, and healthy populations under varied landing conditions. Injured individuals exhibited altered coordination patterns, often showing either rigid or erratic strategies with excessive or reduced variability. Training interventions generally improved coordination stability, whereas fatigue increased variability and disrupted control. Females displayed more constrained patterns and lower coordination variability compared to males, particularly at the knee joint. Children demonstrated greater variability and less refined coordination than adults. Healthy individuals typically showed symmetric adaptable variability. Lower limb joint coordination and its variability during landing are shaped by injury status, fatigue, training, sex, age, and task complexity. These findings highlight the need for consistent methodologies and suggest that coordination analysis can inform injury prevention, rehabilitation, and targeted training strategies to optimize landing performance and safety. Full article

Background: The aim of this systematic review was to collect, appraise, and synthesize the available information related to the cardiovascular and metabolic demands of commonly performed firefighting tasks while wearing personal protective equipment (PPE) inclusive of self-contained breathing apparatus (SCBA). Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, academic databases (PubMed, Embase, and SPORTDiscus databases) were searched for relevant records which were subjected to dedicated eligibility criteria with included articles quality appraised using the Critical Appraisal Skills Programme (CASP) checklist. Results: Of an initial 1463 identified records, 20 studies with a mean CASP of 8.26/11 informed the review. A myriad of varying field tests have been employed to determine physical preparedness and assess the metabolic demand of firefighting. Conclusions: The volume of evidence suggests that PPE and SCBA must be incorporated when assessing the demands of firefighting as they clearly increase the metabolic cost of combined simulated firefighting tasks. Although real-world scenarios are made up of a combination of individual firefighting tasks, there remains a clear need to determine the metabolic cost of isolated firefighting tasks such as forcible entry, hose drag, victim rescue, ladder raise, and stair climbing with and without PPE and SCBA. The quantification of the metabolic demand of these tasks may assist tactical trainers when designing simulated scenarios and training programs for firefighters. Full article

Decision-making, defined as a cognitive process involving the selection of a course of action among several alternatives, is pivotal in personal and professional life and is founded on responsiveness to the context of decisional strategies—in terms of pieces of contextual features collected, evaluated, and integrated. This study explored the behavioral and electrophysiological (EEG) correlates of individual tendencies to rely on three distinct decisional strategies: Information (I-ds), Situation (S-ds), or Task (T-ds). A total of 51 individuals performed a decision-making task that required participants to face real-life decision-making situations, during which an unexpected event prompted them to appraise the situation and rely on different sources of contextual features to make the best decision and manage the problem. The behavioral data and EEG frequency bands (delta, theta, alpha, beta, and gamma) were collected during the decision-making task. The results evidenced a general predisposition to adopt a T-ds. In addition, EEG findings reported a higher increase in theta band power in the right frontal area (AF8) compared to the left temporoparietal site (TP9). Moreover, for the gamma band, higher activity was found in the T-ds compared to the I-ds in AF8. Overall, responsiveness to the context was closely linked to the assignment’s requirements. Additionally, adopting a T-ds requires high levels of multilevel attention control systems and a significant workload on human performance. Nevertheless, the T-ds remain the most employed type of responsiveness to the context approach, when compared to situational and contextual aspects. Full article

The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability. Full article

Mindfulness practice has been recognized as an effective method for reducing stress, enhancing self-awareness, and improving overall well-being, influencing various cognitive processes such as attention and self-control. While extensive research has focused on long-term mindfulness programs, recent years have seen the emergence of brief mindfulness practices. This study aims to investigate the effects of brief mindfulness on electrophysiological responses, and performance in a self-competitive context. A total of 52 participants were divided into two groups for the experiment. They were randomly assigned to one of two experimental conditions: a brief mindfulness practice (n = 29) or a control intervention (n = 23), each lasting 10 min. Initially, participants completed a cognitive task (Tower of Hanoi—ToH), followed by their respective intervention. Participants then performed the ToH task again, with encouragement to exceed their previous performance. Measures were taken for performance metrics and psychophysiological responses—including electrodermal response (EDA) and cardiovascular parameters. The results indicated that a brief mindfulness significantly enhanced performance (p < 0.05), increased self-efficacy (including self-confidence and perceived task importance) prior to the ToH task, and reduced the perceived difficulty of the task upon completion. Moreover, an increase in EDA was found in the brief mindfulness group. However, no significant changes in the measured cardiovascular responses were observed. These findings suggest that brief mindfulness can effectively improve cognitive task performance by enhancing self-efficacy and reshaping task perception. Full article

The current study highlights the potential of multimodal large language models (MLLMs) to transform higher education by identifying key factors influencing their acceptance and effectiveness. Aligning technology features with educational needs can enhance student engagement and learning outcomes. The study examined the role of MLLMs in enhancing performance benefits among higher education students, using the task–technology fit (T-TF) theory and the artificial intelligence device use acceptance (AIDUA) model. A structured questionnaire was used to assess the perceptions of 550 Saudi university students from various academic disciplines. The data were analyzed via structural equation modeling (SEM) using SmartPLS 3.0. The findings revealed that social influence negatively affected effort expectancy regarding MLLMs and that hedonic motivation was also negatively related to effort expectancy. The findings revealed that social influence and hedonic motivation negatively affected effort expectancy for MLLMs. Effort expectancy was also negatively associated with T-TF in the learning context. In contrast, task and technology characteristics significantly influenced T-TF, which positively impacted both performance benefits and the willingness to accept the use of MLLMs. A strong relationship was found between adoption willingness and improved performance benefits. The findings empower educators to strategically enhance MLLMs adoption strategically, driving transformative learning outcomes. Full article

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing. Full article

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations. Full article

Recent advances in brain mapping tools have enabled the study of brain activity during functional tasks, revealing neuroplasticity after early brain injuries and resulting from rehabilitation. Understanding the neural correlates of mobility limitations is crucial for treating individuals with cerebral palsy (CP). The aim is to summarize the neural correlates of mobility in children with CP and to describe the brain mapping methods that have been utilized in the existing literature. This systematic review was conducted based on PRISMA guidelines and was registered on PROSPERO (n° CRD42021240296). The literature search was conducted in the PubMed and Embase databases. Observational studies involving participants with CP, with a mean age of up to 18 years, that utilized brain mapping techniques and correlated these with mobility outcomes were included. The results were analyzed in terms of sample characteristics, brain mapping methods, mobility measures, and main results. The risk of bias was evaluated using a checklist previously created by our research group, based on STROBE guidelines, the Cochrane Handbook, and the Critical Appraisal Skills Programme (CASP). A total of 15 studies comprising 313 children with CP and 229 with typical development using both static and mobile techniques met the inclusion criteria. The studies indicate that children”with’CP have increased cerebral activity and higher variability in brain reorganization during mobility activities, such as gait, quiet standing, cycling, and gross motor tasks when compared with children with typical development. Altered brain activity and reorganization underline the importance of conducting more studies to investigate the neural correlates during mobility activities in children with CP. Such information could guide neurorehabilitation strategies targeting brain neuroplasticity for functional gains. Full article

Background/Objectives: The increase in digital tools in early childhood education highlights the need for evidence-based assessments that support cognitive development and align with educational requirements and technological advances. This study contributes to the evaluation of the Bilingual English Language Learner Assessment (BELLA), designed to enhance early learning through curriculum-aligned tasks in preschool-aged children. Methods: Data were collected from 17 schools, including 506 preschool children, using a mixed-model approach to assess BELLA’s capacity to appraise early numeracy, literacy, science, and social/emotional development. Analyses included a three-way ANOVA to examine the effects of sex, age, and sub-domain on pass rates and mixed-effects models to evaluate interactions between age and domain. Results: The results indicated a significant effect of age on performance across all domains, with older children demonstrating higher pass rates (p < 0.0001). No significant gender bias was detected. The interaction between age and domain was also significant (p < 0.0001), suggesting domain-specific age-related performance trends, which aligns with internal validity requirements. Conclusion: These findings position BELLA within the growing body of literature on digital media use in early childhood assessment and education, highlighting its potential as a curriculum-compliant digital assessment tool that evaluates and supports cognitive development without a gender bias. This study contributes to the field by providing empirical evidence of BELLA’s effectiveness and suggesting future research directions, including the exploration of its bilingual (and potentially multilingual) applications and external validation against existing evidence-based assessments. Full article

Olive leaves (OLLs) are an exceptional bioresource of natural polyphenols with proven antioxidant activity, yet the applicability of OLL extracts is constrained by the relatively high polarity of the major polyphenols, which occur as glycosides. To overcome this limitation, OLLs were subjected to both hydrothermal and ethanol organosolv treatments, fostered by acid catalysis to solicit in parallel increased polyphenol recovery and polyphenol modification into simpler, lower-polarity substances. After an initial screening of natural organic acids, oxalic acid (OxAc) was found to be the highest-performing catalyst. The extraction behavior using OxAc-catalyzed hydrothermal and ethanol organosolv treatments was appraised using kinetics, while treatment optimization was accomplished by deploying response-surface methodology. The comparative assessment of the composition extracts produced under optimal conditions of residence time and temperature was performed with liquid chromatography–tandem mass spectrometry and revealed that OLLs treated with 50% ethanol/1.5% HCl suffered extensive oleuropein and flavone glycoside hydrolysis, affording almost 23.4 mg hydroxytyrosol and 2 mg luteolin per g dry weight. On the other hand, hydrothermal treatment with 5% OxAc provided 20.2 and 0.12 mg of hydroxytyrosol and luteolin, respectively. Apigenin was in all cases a minor extract constituent. The study presented herein demonstrated for the first time the usefulness of using a natural, food-grade organic acid to perform such a task, yet further investigation is needed to maximize the desired effect. Full article

Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations in the future. This study examined the influence of AGVs in automated container terminals and the synergistic scheduling of UCTs on port operations. Comparative experiments were meticulously designed to evaluate the feasibility of integrated scheduling schemes. Through the development of optimization models that consider conflict-free paths for both AGVs and UCTs, as well as strategies for conflict resolution, a thorough analysis was performed. Advanced genetic algorithms were engineered to address task-dispatching models. In contrast, the A* optimization search algorithm was adapted to devise conflict-free and conflict-resolution paths for the two vehicle types. A range of scaled scenarios was utilized to assess the impact of AGVs and UCTs on the joint-scheduling process across various configuration ratios. The effectiveness of the strategies was appraised by comparing the resultant path outcomes. Additionally, comparative algorithmic experiments were executed to substantiate the adaptability, efficacy, and computational efficiency of the algorithms in relation to the models. The experimental results highlight the viability of tackling the joint-scheduling challenge presented by AGVs and UCTs in automated container terminals. When juxtaposed with alternative scheduling paradigms that operate independently, this integrated approach exhibits superior performance in optimizing the total operational costs. Consequently, it provides significant insights into enhancing port scheduling practices. Full article