Collaborative Model Research Articles

Advancing justice: The impact of Brazil’s convict genetic profile identification project after 5 years

This comprehensive study analyzed the implementation and impacts of the Convict Genetic Profile Identification Project in Brazil. By examining semiannual reports from the Integrated Network of DNA Databases (RIBPG) alongside a literature review covering the period from January 2018 to February 2024, the study evaluated the project’s relevance. The project’s primary aim was to enhance DNA databases, thereby fortifying crime prevention and resolution efforts. Notably, the results revealed a substantial increase in genetic profiles of convicted offenders included into the Brazilian National DNA Database (from 2,008 in 2017 to 54,657 in 2019) − a development that aligns with legislative goals. Despite legal and ethical debates, the project demonstrated a positive impact on criminal investigations, with a notable rise in database matches (from 376 in 2017 to 1,418 in 2019). Scientific literature had highlighted the project’s effectiveness in resolving crimes. The project has matured into a well-established process, characterized by continuous insertions and advancements, emphasizing a comprehensive operational framework. Beyond quantitative milestones, it has made qualitative contributions to justice. This collaborative model serves as an example for impactful public policies under Brazil’s Ministry of Justice and Public Security.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

The Ambulatory Diabetes Outreach Program (ADOP): Rigorous Evaluation of a Pharmacist and Nurse-Led Care Model.

Although several systematic reviews found that ambulatory diabetes mellitus (DM) interventions involving pharmacists generally yielded better outcomes than the ones that did not, existing studies have limitations in rigor and study design. To examine the intention-to-treat effects of the Ambulatory Diabetes Outreach Program (ADOP) on participants' A1c values and healthcare utilization over a 52-month follow-up period. Difference-in-differences with staggered adoption. Specifically, we employed the Callaway and Sant'Anna's "group-time average treatment effect" estimator using not-yet treated as controls adjusting for patient's age, BMI, sex, race, comorbidity, payor, and socio-economic status. All patients with at least one ADOP treatment encounter from July 2017 to October 2021, regardless of program completion or length of exposure to the program. ADOP, a collaborative population health program led by pharmacists and nurse specialists to provide individualized type 2 DM management and education within a large and diverse health system. Patients' A1c values and healthcare utilization, including inpatient admission, inpatient days, and numbers of visits to the emergency department, urgent care, and primary care in recent 6months. ADOP participation was associated with an overall average reduction of 1.04 percentage points (95%CI - 1.12, - 0.95) in A1c level. Similar A1c reductions were also observed in the subgroups by sex and race/ethnicity. An average of 2months were required to reach the overall average effect, which persisted over 4years. Compared to the respective utilization levels pre-intervention, participants also had average reductions in inpatient admissions by 32.4%, inpatient days by 81.6%, visits to the emergency department by 21.6%, and primary care by 17.9%. The results suggest that a collaborative model of pharmacist and nurse-led type 2 DM intervention was effective in improving A1c outcomes and reducing healthcare utilization in the long term.

Fusion innovation: Multi-scale dilated collaborative model of ConvNeXt and MSDA for fault diagnosis

Bearings and gears are critical components in modern industry, and cross-domain diagnosis of these elements is of great significance. However, in practical applications, challenges such as insufficient training data and variability between equipment arise. To address this issue, this study proposes an innovative neural network structure, ConvNeXt, and a Multi-Scale Dilated Attention (MSDA) mechanism to improve the accuracy problem caused by inadequate feature extraction. ConvNeXt improves upon traditional convolutional neural networks by introducing a multi-scale attention mechanism to enhance the model's performance and expressiveness. Through parallel multi-channel convolution operations, ConvNeXt can capture dependencies between different channels and reduce the number of parameters. Meanwhile, the MSDA mechanism allows signals to interact and exchange information at different scales, effectively extracting complex features in one-dimensional signals. Experimental results demonstrate a significant performance improvement in one-dimensional signal processing using ConvNeXt and MSDA, better capturing relationships between global and local features in one-dimensional signals and enhancing model accuracy. The joint application of ConvNeXt and MSDA brings new solutions to one-dimensional signal processing, offering potential opportunities for effective monitoring of critical components in rotating machinery. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with an average accuracy of 94.28%, providing reliable support for bearing fault diagnosis.

TRANSFORMING CREATIVE INDUSTRIES: A COLLABORATIVE GOVERNANCE APPROACH FOR DIGITALIZATION IN WEST JAVA

This research aims to analyze the application of Collaborative Governance to advance the creative industry in West Java. Case studies were conducted in Bandung City and Garut Regency. This research uses a qualitative approach, revealing findings that the collaborative governance process in advancing the creative industry in Bandung has been effective and involves elements of the government, private sector, universities, media, and society. However, more optimal arrangements are still needed in the Garut Regency. The ideal Collaborative Governance Model involves the 'Penta helix'—government, society, academia, entrepreneurs, and the media (ABCGM). This cross-sector collaboration is expected to build togetherness in developing the creative industry. This research presents an overview of successful Collaborative Governance practices that formulate policy recommendations and collaborative models. This model guides the government and stakeholders in supporting the digitalization of creative industries in West Java. The contribution of this research lies in the strategic direction to achieve sustainable digital transformation in regional creative industries.

Association Between Antepartum Admission and Postpartum Depressive Symptoms: Short Title: Antepartum Hospital Admission and Postpartum Depressive Symptoms

BackgroundPostpartum depression affects 14% of pregnant individuals and is a leading cause of preventable maternal mortality. Complications of pregnancy, such as preterm labor or pre-eclampsia, may require hospitalization for close monitoring and management. The impact of an antenatal hospitalization during pregnancy on postnatal depression remains understudied. ObjectiveTo evaluate whether hospital admission during pregnancy was associated with postpartum depressive symptoms in individuals who were enrolled in a collaborative care model and to evaluate whether enrollment in the collaborative care model during pregnancy mitigated this association. Study DesignThis secondary analysis of a prospective cohort study included perinatal people enrolled in a collaborative care model at a quaternary academic center between 2017-2021. The primary outcome was presence of moderately severe or severe postpartum depressive symptoms at 6 weeks postpartum defined as a score of 15 or greater on a PHQ-9. The prevalence of symptoms of this severity was compared between those who experienced an antepartum hospitalization and those who did not using bivariable and multivariable analyses. A Breslow Day test was used to evaluate whether any observed association between antepartum hospitalization and postpartum depressive symptoms differed based on timing of enrollment in the collaborative care model. ResultsDuring the study period, 1897 individuals met inclusion criteria. Of these, 162 (8.5%) were admitted to the hospital during pregnancy. Of those with an antepartum hospitalization, 20 (12.4%) developed moderately severe to severe postpartum depressive symptoms compared to 136 (7.8%) of those who were not hospitalized (p=0.046). After adjustment for confounders identified through use of a directed acyclic graph, this difference did not persist in multivariable analysis (aOR 1.55, 95% CI [0.87-2.75]). A Breslow Day test demonstrated heterogeneity across enrollment timing, so subgroup analyses were performed for those enrolled during pregnancy (n=930) vs postpartum (n=967). Hospital admission was associated with higher rates of moderately severe to severe postpartum depressive symptoms in those enrolled in the collaborative care model postpartum (19.7% vs 10.6%, p=0.015, aOR 2.25, 95% CI [1.07-4.71]), but not those enrolled antenatally (5.8% vs 5.0%, p=0.735, aOR 1.09, 95% CI [0.38-3.19]). ConclusionsAntepartum hospital admission was associated with higher rates of moderately severe to severe depressive symptoms. This association did not exist among individuals enrolled in collaborative care model during pregnancy, suggesting a potential protective effect afforded by engagement in a mental health support programming.

Predictors of symptom change in the mental health of refugees and asylum seekers (MEHIRA) study examining the effects of a stepped and collaborative care model – A multicentered rater-blinded randomized controlled trial

BackgroundPredictors of symptom improvement are an essential starting point for tailoring psychological treatments to each individual and, in turn, increasing treatment efficacy overall. However, such research regarding refugees/asylum seekers from Arabic-/Farsi-speaking countries is lacking. The current study aimed to characterize predictors for symptom improvement in the Mental Health in Refugees and Asylum Seekers (MEHIRA) study, one of the most extensive multicentered controlled trials on a nationwide stepped and collaborative care model compared to routine German mental health care. MethodsVariables characterizing symptom change were chosen using backward elimination and inserted into logistic regression models for two depression endpoints, namely the Patient Health Questionnaire-9 (PHQ-9) and the Montgomery Asberg Depression Rating Scale (MADRS). ResultsSix variables were found to be at least marginally significantly associated with symptom decrease in both outcomes: baseline depressive symptom load, comorbid post-traumatic stress disorder, identifying as a refugee, years of schooling, physical health, and post-migration social status difference. Additionally, psychological health and resilience were marginally significant for one of the models. LimitationsSome predictor constructs - such as social support - were not adequately measured to replicate previous findings. Additionally, the study was underpowered for symptom change prediction of individual treatments beyond the group intervention. ConclusionsThese outcomes indicate that trauma-related elements as well as content on refugee identity and post-migration social status changes should be included in depression interventions for refugees. Further, recommendations for future outcome prediction studies are made.

ADCMDES: Design of an augmented cross-domain collaborative recommendation model using novel distance metric and ensemble stratification

Collaborative recommendation involves leveraging information from one entity to suggest attributes of other entities through pattern analysis. These models are invaluable for understanding data behaviour’s impact on related entities. Researchers propose various pattern recognition and correlation models, employing distance metrics like Jaccard, Cosine, etc., to estimate correlations between user queries and recommendation datasets. However, these models become less efficient as dataset size increases due to exponential correlation estimation delays. To enhance scalability while retaining recommendation quality, a new approach is introduced: ADCMDES. This novel augmented cross-domain collaborative model employs a hybrid distance metric and ensemble stratification for dataset pruning. It operates semi-supervisedly, requiring information about the entities being collaborated upon. This data is used to cluster similar collaborative entities, producing a condensed cluster with greater relevancy to user queries. Utilizing word2vec, records are transformed into features for an ensemble classification engine. The resultant model categorizes user input, directing it to the most relevant cluster. Entries within this cluster are ranked using a hybrid metric amalgamating 18 distance measures, enhancing correlation between input queries and recommendations. In testing, ADCMDES exhibited 15% better accuracy, 8% better precision, 9% better recall, and 3% lower RMSE compared to standard models across datasets. While some delay is introduced due to ensemble classification and augmented feature pooling, this doesn't considerably affect long-term recommendation performance and can be mitigated through parallel processing and redundancy reduction techniques.

Performance evaluation of vibration-damping bucking bar in human-robot collaboration

Abstract The riveting process in aircraft manufacturing generates large vibrations, and if it is exposed to vibration for a long period of time, riveting workers may suffer from Hand-Arm Vibration Syndrome causing irreversible damage to the robot. In this study, firstly, the vibration mechanical model of human-robot collaboration was established, and then the vibration characteristics of human-robot collaboration under squeezing force were deduced. Finally, an experiment was performed to evaluate the vibration damping performance of vibration-damping bucking bars by evaluating the mean peak squeezing force with different rivet guns and input pneumatic pressure. Compared with the conventional stainless steel bucking bar, the PU-cylinder bucking bar has a lower mean peak squeezing force. The results show that the utilization of the PU-cylinder bucking bar in human-robot collaborative riveting operations can considerably reduce the vibration transmitted from the rivet gun to the robot and the operator.

Simulation study on collaborative shopping formation from the perspective of group structure and two-dimensional opinion

Group communication and collaborative shopping models are favoured by consumers, so research on group opinion has attracted the attention of businesspeople. Based on the dual-process model (DW model), interaction rules for developing consumer opinions are constructed, and the influence of factors such as group structure, sponsor characteristics, and sub-opinions on collaborative shopping is investigated via numerical simulations. The results show that the group structure of consumers is closely related to the success of the joint purchase. The sponsor influences the development of the sub-options of the group, which can split the original convergent and consistent opinions, and the sub-options of the group can split within a specific range. The conclusions can provide theoretical support for businesspeople to successfully carry out collaborative shopping and promote the vigorous development of social commerce.

Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management

Strategic behavior is common in multi-criteria sorting, where manipulated alternatives are allocated to the target category to achieve the intended goal. Engaging in such strategic behavior in sorting consensus often comes at a cost, which is closely tied to the degree of preference adjustments and the level of trust relationships among decision makers. This study investigates strategic behavior using piecewise cost-based consensus mechanism in social trust network. Firstly, the trust relationship dependent piecewise cost is formally defined and formulated by considering the impact of trust relationship on the preference adjustments willingness to achieve sorting consensus. Then, based on the trust relationship dependent piecewise cost, a preference strategic manipulation model is proposed to achieve strategic behavior in sorting consensus, which is implemented by minimizing the cost of strategically guiding decision makers in adjusting their preferences. Considering that social trust network affects strategic manipulation, a preference-network collaborative strategic manipulation model is further proposed, which is designed to strategically guide decision makers in adjusting both their preferences and social trust networks. Moreover, the practicality of proposed strategic manipulation models is exemplified through a case study on supply chain risk management and their performance is validated via simulation analysis.

Effectiveness of a task-sharing collaborative care model for the detection and management of depression among adults receiving antiretroviral therapy in primary care facilities in South Africa: A pragmatic cluster randomised controlled trial

BackgroundHIV is characterised by high rates of comorbidity with mental health conditions including depression, as such, the detection and treatment of comorbid depression is critical to achieve viral load suppression. This study evaluated the effectiveness of a collaborative care intervention for depression among adults with comorbid depression symptoms receiving ART in primary health care (PHC) facilities. MethodsWe conducted a pragmatic cluster-randomised trial in 40 clinics in the North West province of South Africa. PHC clinics were stratified by sub-district and randomised in a 1:1 ratio. Participants were ≥ 18 years, receiving ART, and had depression symptoms indicated by Patient Health Questionnaire-9 (PHQ-9) score ≥ 9. Intervention clinics received: i) supplementary mental health training and clinical communication skills for PHC nurses; ii) workshops for PHC doctors on treating depression; and iii) lay counselling services. Using mixed effects regression models, we assessed co-primary outcomes of PHQ-9 response at 6 months (≥50 % reduction in baseline PHQ-9 score) and viral load suppression at 12 months (viral load<1000 copies/mL). ResultsThe intervention had no effect in PHQ-9 response (49 % vs 57 %, risk difference (RD) = −0.08, 95 % CI = -0.19; 0.03, p = 0.184) or viral load suppression (85 % vs 84 %, RD = 0.02, 95 % CI = −0.01; 0.04, p = 0.125). Nurses referred 4298 clinic patients to counsellors, however, only 66/1008 (7 %) of intervention arm participants were referred to counsellors at any point during the study. LimitationsThe highly pragmatic approach of this trial limited exposure to the counselling component of the intervention and referral to doctors for initiation of antidepressant treatment was extremely low. ConclusionThe trial showed no effect of a district-based intervention to strengthen collaborative care for depression. The trial revealed the extent of the treatment gap in the context of scaling up mental health services. Trial registrationClinicalTrials.gov (NCT02407691); Pan African Clinical Trials Registry (201504001078347).

Improving goal-concordant care via a uniquely collaborative and multi-site PFAC model.

265 Background: Studies reveal a mismatch between the goals of care (GOC) of cancer patients and actual care received. The 10 members of the Alliance of Dedicated Cancer Centers (ADCC) convened the Improving Goal Concordant Care (IGCC) initiative to address system gaps. IGCC was led by palliative care and oncology experts, with guidance from patient and family advisors. Uniquely, the advisors were identified from patient family advisory councils (PFAC) at each center, forming a collaborative Advisory Group (AG) with the lived experience of each center and the knowledge of advocating for the patient/family voice locally. Methods: PFAC members were recruited from each site to serve on the AG. The 19 member AG met quarterly, with minutes shared with the IGCC oversight group, the Implementation Workgroup (IWG). In September 2023, all AG members were surveyed about perceptions of PFAC engagement and local impact of IGCC. In May 2024, 20 IWG members were surveyed about implementation successes and barriers, including items related to PFAC engagement. Results: AG evaluation and recommendations altered IGCC planned core implementation and evaluation actions meaningfully; for example in 1) selection of structured fields in electronic health record (EHR) and universally accessible GOC documentation and in 2) centering patients in the GOC measure set, including incorporating “Heard &amp; Understood” survey into IGCC evaluation. Other AG guidance around direct patient engagement readiness for GOC conversations and implementing patient GOC surveys was left to individual sites to consider with variable adoption. AG Survey: 63% response rate. 80% noted that the GOC program was discussed with their local PFAC and 92% experienced changed routine patient care after GOC implementation. IWG Survey: 100% response rate. 68% agreed that their center sought guidance from their PFAC during GOC implementation and that leadership presented to their PFAC about their GOC implementation. 26% indicated reporting data to their PFAC; of those that did, 80% agreed that this positively impacted their GOC implementation. Conclusions: While PFACs are occasionally included in quality interventions, this model centered the patient/family voice by supporting a PFAC stakeholder feedback loop working collaboratively and locally to support implementation and evaluation of IGCC objectives. While not all AG recommendations were formally integrated into IGCC, all were considered and many adopted at individual centers. Despite ongoing IGCC-level involvement, center-specific engagement of PFACs varied. Future efforts can track this during process evaluation as local PFAC engagement was highly valued. Consensus building for national best practice implementation and evaluation by recruiting local experts including PFAC members into national collaborative efforts is a successful model for local change.

Validating performance status and activities of daily living assessment tools for Chinese palliative care in a cancer setting: A cross-cultural psychometric study

ObjectivesNational approaches to the routine assessment of palliative care patients improve patient outcomes. However, validated tools and a national methodology for this are lacking in Mainland China. The Australian Palliative Care Outcome Collaboration (PCOC) model is a well-established national program aimed at improving the quality of palliative care based on point-of-care outcomes assessment. This study aimed to culturally adapt and validate two measures used in PCOC (Australia-modified Karnofsky Performance Status (AKPS), Resource Utilization Groups - Activities of Daily Living (RUG-ADL)) in the Chinese context. MethodsA cross-cultural adaptation and validation study involving forward and backward translation methods, cognitive interviewing, and psychometric testing. ResultsTwo minor adjustments were made to the scoring instructions for the RUG-ADL, and the AKPS remained unchanged. Twenty-two clinicians participated in psychometric testing, completing 363 paired assessments on 135 inpatients. The correlations between AKPS and the Barthel index (BI) for activities of daily living (r ​= ​0.77, P ​< ​0.001), AKPS and RUG-ADL (r ​= ​−0.82, P ​< ​0.001), RUG-ADL and BI (r ​= ​−0.67 to −0.76) demonstrated good concurrent validity for both the AKPS and the RUG-ADL. The inter-rater reliability for AKPS (k ​= ​0.63) and RUG-ADL were substantial and moderate (k ​= ​0.51–0.56), respectively. The RUG-ADL also showed good internal consistency (Cronbach's alpha ​= ​0.92). Both tools were able to detect patients' urgent needs. ConclusionsThe Chinese version of AKPS and RUG-ADL can be systematically used to assess performance status and dependency among palliative care patients. However, observational assessments and enhanced communication between clinicians and patients/caregivers is also recommended for optimal clinical utility.

Collaborative operation of active distribution network considering PV-storage-charging-compensation

Abstract With the conjunction of clean energy generation and electric cars, the power distribution network is gradually shifting from a traditional one-way radiative network to an active network. Coordinated control of these controllable elements of “source-grid-load-storage” can improve the operating voltage level and economic benefits, and reduce the load peak and off-peak difference. A collaborative dispatch model for active power distribution networks including PV-storage-charging-compensation is constructed in this paper, with the goal of minimizing network losses and PV curtailment costs. The cone transformation is used to handle non-linear constraints of power flow. Compared with existing ordered charging control methods, the constructed model can maximize the control efficiency of the distribution grid, enhance the PV absorption capacity, and effectively slow down the upgrading and renovation of the distribution network.

How to enable human-centric collaboration in social product development paradigm: A practical and theoretical exploration

Since 2012, social product development (SPD) has emerged as a potential paradigm for new product development (NPD). However, the emerging SPD practices are difficult to remain and promote after their early success. The lack of sustainable human-centric collaborative settings will hinder the release of crowd-based collective intelligence in NPD. Currently, the core mechanisms of human-centric collaboration (HCC) in SPD are not clear. To narrow the gap between theory and practice, a two-way empirical study is conducted through an in-deep industrial practice investigation and theory-oriented research based on cooperative inquiry. An extreme case of a famous automotive company is sampled as the object of the cooperative inquiry. The proposed theoretical models are verified by two real SPD pilot projects. Inspired by the discipline of Collaborative Engineering, this study explores the HCC issues and it core mechanisms in the context of SPD. Two novel foundation theoretical models are developed to address the focused scientific problem and engineering challenge. A bottom-up model is developed to structurally elicit the HCC requirements and develop reasonable information modules for specific HCC work practices. A top-down model is developed to improve the manageability and reusability of the learnable experience from historical HCC work practices for better business-technology-system alignment. This study put forward novel theoretical models for designing and deploying collaborative work practices for HCC in SPD. A classification-based Descriptive Model of Collaboration (DMoC) is proposed, which is domain-agnostic and customized for SPD requirements. Our model inherits and outperforms in modelling capability than five other proven modelling approaches.

Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware

To achieve optimization operation between the energy hub operator and the load aggregators, this paper proposes a collaborative model for the energy hub and load aggregator considering the carbon intensity-driven and uncertainty. Firstly, a time-coupled carbon emission flow model for energy storage is proposed to cope with the change in carbon intensity at the injection node. Meanwhile, a carbon intensity-based incentive price is developed for interruptible loads with diverse carbon intensity. Secondly, the Wasserstein metric-based distributionally robust optimization method is leveraged to height against the energy hub uncertainty, in which the infinite-dimensional distributionally robust optimization model is reformulated into a tractable linear programming. Thirdly, considering the multi-entity characteristics of energy hub and load aggregators, a collaborative optimization method based on the alternating direction method of multipliers algorithm is constructed. Finally, to meet the convergence requirements of the alternating direction method of multipliers algorithm, the iterative convexification algorithm is applied to transform the nonconvex bi-level energy hub-users model into an iterative convex model by avoiding binary variables and big-M parameters. Consequently, the energy hub and load aggregator can operate cooperatively and protect privacy in uncertain conditions. Simulation results verify the effectiveness of the proposed model and method. Simulation results demonstrate the effectiveness of the proposed model and method. Specifically, the iterative convexification algorithm achieved a profit of ¥1653.82 for the load aggregator, which is 24.4 % higher than the profit obtained by CONOPT (¥1329.73) and 20.3 % higher than that obtained by IPOPT (¥1374.69), thus providing substantial advantages over traditional nonlinear solvers.

Analysis of the Effectiveness of a Fall Prevention Program Incorporating an Interprofessional Team Collaboration Model on Reducing Fall Risk in Elderly Living in Long-term Care Facilities

Concurrent with population ageing, falls have become a significantly more challenging public health issue among older adults. Three years of data collected recently from a nursing home in northern Taiwan reveals an increasing trend in fall density that is primarily associated with aging, physiological decline, chronic diseases, polypharmacy, osteoporosis, and lack of exercise. The percentage of nursing home residents at high risk of falls is currently at 12.6%, and the fall rate has been reported as reaching as high as 30% annually. A fall prevention program was implemented to reduce the fall incidence rate to 18%, with secondary goals of improving fall prevention awareness, behavior, self-efficacy, lower limb muscle strength, balance, and gait by 10% on average, respectively, between pre-test and post-test. From September 30, 2023 to February 29, 2024, a health promotion activity and fall prevention exercise course were implemented using an interdisciplinary team collaboration model over a six-week period, providing individualized exercise for the participants. The study included 20 older adults with an average age of 88 years. Most (90%; n = 18) had chronic diseases, 25% (n = 5) were on more than nine medications, 70% (n = 14) had reduced bone mass, and 40% (n = 8) were at high risk of falls, with a fall incidence rate of 30% during the immediately preceding year. Post-intervention, the fall incidence rate dropped to 5%, fall prevention awareness, behavior, and self-efficacy increased by 18.3%, and lower limb muscle strength, balance, and gait improved by 11.7%. The post-test results in fall prevention awareness, behavioral changes, self-efficacy, and lower limb strength, balance, and gait were all significantly better than pre-test results, with all results achieving statistical significance. The project results support the positive effects of the developed intervention effectively on elderly physical fitness and fall risk, providing valuable insights for the implementation of fall prevention strategies in nursing homes.

Recent trend in the care of chronic kidney disease patients: A shift from paternalistic to collaborative approach

The global rise in chronic kidney disease (CKD) necessitates a re-evaluation of healthcare practices. This review explores the shift from paternalistic to collaborative model, focusing on patient empowerment. This article aims to elucidate the foundational concept of patient empowerment, describe the goals of patient-centered care (PCC), identify barriers to PCC implementation, and propose strategies to enhance PCC. The ultimate aim is to empower CKD patients in self-care. The article explores multifaceted strategies of PCC, highlights the pivotal role of shared decision-making, and underscores the necessity of a collaborative clinician-patient partnership. It emphasizes the significance of patient-centered education, benefits of PCC in CKD care, and the role of artificial intelligence in improving healthcare outcomes. This review concludes by summarizing insights into the transformative shift toward patient-centered approaches in CKD management. It emphasizes the positive impact of this paradigm shift on both the healthcare system and CKD patients. The findings highlight the positive effects of PCC on both the health-care system and CKD patients.

Failure analysis of photovoltaic strings by constructing a digital multi-twin integrating theory, features, and vision

Timely and accurate failure analysis of photovoltaic (PV) systems is crucial forensuring the stable operation of power grids. However, existing failure analysis and diagnosis algorithms based on deep neural networks excessively rely on high-quality failure state data collected by sensors. This is extremely difficult to achieve in real photovoltaic power plants that are commonly equipped with self-protection mechanisms. To address this issue, we propose a Digital Multi-Twin integrating Theory, Features, and Vision (TFV-DMT) for failure analysis of PV strings in PV systems. This method first constructs theoretical simulation twins, feature twins, and visual twins based on the concept of digital twins, specifically tailored for actual PV systems, and designs a multi-twin collaborative model for model updating and failure diagnosis. Secondly, to better construct the visual twin, we introduce a Two-Dimensional Gram Angle Field Transformation Algorithm (TDGAF) to achieve targeted two-dimensional mapping of PV feature data, facilitating a more direct expression of failure characteristics. Furthermore, by constructing a Swish-activated Deep Convolutional Generative Adversarial Network (SDCGAN) to achieve balanced augmentation of mapping data, the model bias of theoretical simulation twins can be reduced. Finally, we propose a Swin-LT network that incorporates a Lightweight Dual-Channel Attention Module (LDAM) to better analyze the features of the visual twin, enabling more precise fault diagnosis. The algorithm has been validated on a real 250 kW grid-connected PV system, with results indicating that the proposed digital twin model is effective, achieving a diagnosis accuracy rate of 98.8 % for string failures.