This study aims to analyze the management of health facilities and infrastructure, identify supporting and inhibiting factors, and examine the influence of facility and infrastructure management on service quality at the Anugerah Medical Center Metro Lampung Maternity and Child Hospital (RSIA). The study uses a qualitative approach with descriptive methods. Data collection was conducted through in-depth interviews, direct observation, and documentation studies with purposively selected informants, including elements of management, health workers, administrative staff, and patients or patient families. Data validity was guaranteed through source and technical triangulation. The results of the study indicate that the management of facilities and infrastructure at the Anugerah Medical Center Metro Lampung Maternity and Child Hospital has been implemented systematically through the stages of planning, procurement, maintenance, and supervision and evaluation, although not yet fully optimal. The main supporting factors include the hospital management's commitment to improving service quality and patient safety, as well as internal policy support. Meanwhile, inhibiting factors are more technical and managerial in nature, such as the time-consuming procurement administration process, limited human resources specifically for facility maintenance, and suboptimal cross-unit coordination. The management of facilities and infrastructure has been shown to significantly impact service quality, particularly in terms of service effectiveness, patient comfort, and healthcare worker performance. The conclusion of this study indicates that the management of facilities and infrastructure is a crucial component in supporting the quality of healthcare services at the Anugerah Medical Center Metro Lampung Hospital. Therefore, it is recommended that the hospital strengthen short-term and long-term planning, optimize the integrated electronic inventory system, improve the competency of human resource facility managers, and strengthen cross-unit coordination and supervision to ensure the sustainability of the quality of maternal and child healthcare services.

The rapid growth in global population necessitates efficient energy management solutions for sustainable living. Smart Building Energy Management Systems (SBEMS) play a crucial role in achieving this goal by leveraging automation and advanced analytics. This study proposes a novel Deep Learning and IoT-based SBEMS approach to predict energy consumption, classify buildings into energy-demand clusters, and optimize the monitoring and operation of electrical equipment. While traditional statistical methods have been widely used for load forecasting, recent advancements in deep learning provide robust alternatives to address the inherent complexity of nonlinear energy consumption patterns. This research employs regression analysis and state-of-the-art neural network architectures, including Single-Step and Multi-Step Dense Models, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance prediction accuracy. Additionally, K-means clustering is introduced to segment buildings into distinct energy-demand categories, ensuring optimal energy utilization. Unlike prior studies that often lack a comprehensive approach, this work integrates all critical features under a unified framework. By applying these advanced methodologies to a unique dataset, the proposed system demonstrates improved accuracy in energy load forecasting and clustering, providing a significant contribution to the field of smart building energy management. The experimental results demonstrate that CNN and LSTM models significantly outperform conventional statistical approaches in capturing nonlinear energy consumption patterns. These outcomes support proactive energy scheduling, peak-demand mitigation, and scalable smart building energy management, offering practical value for facility managers, utility operators, and policymakers.

In recent years, Predictive Process Monitoring (PPM) has evolved at the intersection of process mining, machine learning, and data science, as organizations seek to anticipate the future course of ongoing processes. Traditional PPM mainly relies on structured event log data, but many real-world scenarios generate richer information, including text, images, audio, and video. Multimodal Predictive Process Monitoring (MM-PPM) addresses this rich data scenario by integrating complementary knowledge from heterogeneous modalities through modality-specific representations and information fusion techniques. The growing digitization of healthcare systems, combined with advances in Artificial Intelligence (AI), has accelerated AI-based PPM for analyzing sequences of clinical events, supporting decision-making, enabling personalized care, and improving clinical facility management. Given these characteristics, clinical pathways represent an ideal domain for experimenting with MM-PPM, as they may naturally involve diverse modalities such as structured records, free-text notes, or medical images. To handle multimodal information available with clinical pathways, we introduce MEDUSA , an MM-PPM approach for outcome prediction, which jointly processes medical image information coupled with the storytelling of structural records and text notes collected during the clinical pathway of a patient until the acquisition of the considered image. The evaluation of MEDUSA is done in a COVID-19 case study, to assess the performance of the proposed approach and explain how specific information within each modality influences the decisions of the predictive model.

ABSTRACT Community sport is the foundation of sport systems, yet it faces a contemporary challenge: climate hazards and a diverse set of impacts associated with climate change. Sport policy research gives little guidance to policymakers about climate hazards or how to adapt to their impacts. The aim of this study is to examine managers’ perceptions of climate impacts on sport at a community level, how managers at this level interpret and respond to such impacts, and implications for sport policy. To address this research aim, a mixed‑methods research design was used. Qualitative data were collected through 23 in-depth interviews with managers from clubs and state sporting organisations for the sports of cricket, football, tennis and triathlon in Australia, and from local government managers of community sport facilities. Data themes and sub-themes were developed using reflexive thematic analysis. Quantitative data were collected using an online survey. From the qualitative data, five themes were developed: (1) a range of climate hazards disrupted sport; (2) perceived future climate risks; (3) multifaceted climate impacts on sport; (4) participant interpretations of climate hazards and climate change; (5) climate adaptation was incremental, not transformative. From the quantitative data, only incremental climate change adaptation was evident. A policy void was apparent. There is a role for sport policy to encourage community-level managers to become transformative climate change adaptation actors. One-size-fits-all sport policy appears inadequate for climate change adaptation in climatically diverse nations like Australia. The findings indicate a new frontier for sport policy and sport policy research.

Improving the quality of health services remains a significant challenge in Indonesia, particularly due to variations in workforce capacity, uneven adoption of digital health systems, and limited stakeholder engagement. Understanding how these factors influence service quality from the perspectives of key stakeholders is essential to inform effective and sustainable improvement strategies. This study employed a qualitative descriptive design across multiple healthcare settings in Indonesia, including primary health centers, clinics, and district hospitals. Data were collected through in-depth, semi-structured interviews with healthcare providers, facility managers, policymakers, and community representatives. Participants were selected purposively based on their involvement in service delivery and quality improvement activities. Interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. An inductive coding approach was applied to identify recurring patterns and themes. Analytical rigor was enhanced through peer debriefing, source triangulation, and member checking. The analysis identified several interconnected themes influencing health service quality. Workforce capacity emerged as a central challenge, characterized by limited access to continuous training, high workloads, and gaps in clinical and managerial competencies. Digital health implementation varied widely across facilities, with barriers including low digital literacy, poor infrastructure, and limited system integration. Participants also highlighted gaps in policy implementation at the facility level, often related to bureaucratic constraints and insufficient organizational support. Community engagement was generally limited, with few structured mechanisms for capturing and responding to patient feedback. Participants emphasized that effective quality improvement requires coordinated strategies addressing human resources, digital readiness, governance, and community participation. Health service quality improvement in Indonesia is shaped by complex interactions between workforce capacity, digital health readiness, organizational support, and community engagement. Sustainable improvement requires integrated, context-sensitive strategies that strengthen human resources, support effective use of digital systems, enhance local policy implementation, and promote patient-centered care. These findings provide practical insights for policymakers and health system leaders seeking to strengthen service quality in resource-constrained settings.

Occupational health and safety are major concerns for health workers worldwide. Nurses working in resource-constrained areas are at higher risk of occupational hazards. The purpose of the study was to assess the knowledge, attitudes, and practices of nurses in a regional hospital in a resource-constrained area towards occupational health and safety. A descriptive cross-sectional survey was conducted using self-administered questionnaires among 136 nurses at a regional hospital in Ghana. The nurses were recruited from five hospital units using simple random sampling. The data were analysed using SPSS, where descriptive and inferential statistics were applied. Knowledge, attitude, and practice items were measured on a four-point Likert scale. Mean scores were computed for all the items in each domain, with higher mean scores indicating a favourable response towards occupational health and safety measures. A one-way analysis of variance (ANOVA) was conducted to compare mean Knowledge, attitude, and practice scores across demographic variables. A Post hoc analysis was conducted for variables with statistically significant scores. Additionally, Pearson correlation analysis was conducted to examine the relationships between knowledge, attitude, and practice (KAP) scores. All statistical tests were two-tailed, and the level of significance was set at p < .05. The study findings show that the participant had a high level of knowledge (94.1%) and a positive attitude (80.9%) towards occupational health and safety measures. However, practice was very low, with only 30.9% of the nurses demonstrating good occupational health and safety practices. The correlation analysis revealed a statistically significant positive relationship between knowledge and attitude (r = .507, p < .01), knowledge and practice (r = .433, p < .01), and attitude and practice (r = .656, p < .01). Additionally, a between-subject one-way ANOVA showed a statistically significant difference in knowledge [F(3,132) = 4.05, p = .009], attitude [F(3,132) = 7.18, p < .001], and practice [F(3,132) = 3.91, p = .010] based on level of education. However, there were no significant differences in knowledge, attitudes, and practices regarding occupational health and safety measures across demographic characteristics. The study's findings indicate poor practices towards occupational health and safety. The management of health facilities and health regulatory authorities should institute measures, such as refresher training and monitoring systems, to promote occupational health and safety practices. Safety protocols should also be developed, and nurses should be encouraged to follow them.

The Role of Early Handling Experiences on the Welfare of Beef Stocker Cattle.

The burden of waste: Environmental justice, health risks, and socioeconomic disparities near waste management facilities.

Occupational stress, ethical conflicts, and job satisfaction in animal research environments in Korea.

Accreditation is an incentive to improve the capacity of Health Care Organisations to provide quality of care. The National Accreditation Board for Hospitals and Health care Providers (NABH) provide third-party accreditation to health care Organisations in India. Quality health care is a fundamental patient right, yet disparities persist due to inconsistent service delivery and weak regulatory oversight. This study comprehensively maps the Non-Compliances (NCs) reported during NABH assessments of hospitals in Ahmedabad, Gujarat, undergoing entry level and full accreditation. A retrospective record review study of NABH accreditation assessment reports was conducted for hospitals, two seeking full accreditation and four entry-level accreditations, between June and July 2024. NCs were categorized into Policy, Documentation, Practice, Regulation, Patient Care, and Facility Management, and further stratified by risk severity (low, medium, high). Descriptive statistics and qualitative analyses were used to assess NC patterns. Entry-level hospitals reported 15 NCs, primarily related to documentation and safety, with most categorized as medium-risk. Full-accredited hospitals had 94 NCs, with documentation (27.6%), process (25.5%), and policy (23.4%) being the most affected domains. Safety-related NCs, although fewer, were frequently high-risk, including lapses in infection control and patient monitoring. The findings underscore systemic gaps in documentation, process standardization, and safety practices. Entry-level hospitals struggle with foundational systems, while full-accreditation hospitals face challenges in implementing complex quality processes. Addressing these NCs requires proactive leadership, staff engagement, and integration of accreditation standards into daily operations. Accreditation should be viewed not as a one-time compliance effort but as a pathway to sustained quality and patient safety.

Feeding and Eating Disorders (FED) require integrated, recovery-oriented care models that extend beyond clinical treatment and incorporate supportive environments capable of enhancing psychosocial well-being. Within this perspective, nature-based and socio-agricultural practices represent promising yet underexplored therapeutic resources, particularly when integrated into spatial planning frameworks. This study develops and tests a Geographic Information Systems (GIS)-based Decision Support System (DSS) that matches the specific therapeutic needs of individuals undergoing treatment for FED with the spatial distribution and characteristics of green and agricultural environments. The research is based on a case study involving the FED care center “Il Pellicano” in Perugia, Italy. Supply-side data were collected from 65 facilities, including 58 social farms, 6 community gardens, and the center’s private garden. Demand-side data were obtained through a questionnaire administered to patients by healthcare professionals, while supply-side attributes were collected through structured interviews with facility managers. The spatial matching process was implemented in a GIS environment using a non-compensatory multi-criteria approach that integrated thematic activities, spatial and/or organizational accessibility, confidentiality, spatial capacity, and environmental settings. The results reveal a substantial mismatch between demand and supply, with the current territorial system satisfying only 37.67% of expressed therapeutic needs. Sensitivity analysis indicates that the main constraints relate to the limited availability of medium-sized, low-attendance, and freely accessible environments. Beyond the local case study, the proposed DSS provides a transferable planning-support tool for designing personalized therapeutic pathways and strengthening the integration between green infrastructure, social farming, and healthcare systems. The study highlights the strategic role of spatial planning in promoting health equity, social inclusion, and community well-being.

This study aims to examine the effect of learning facilities management on students’ academic achievement. The problem addressed in this study is based on the importance of managing learning facilities to support the teaching and learning process, which, however, does not necessarily produce optimal learning outcomes. This research employs a quantitative approach using a survey method. The sample consisted of 36 respondents, and the data were analyzed using statistical tests, including descriptive analysis, normality test, linearity test, simple linear regression, and hypothesis testing.The results indicate that Learning facilities management has a significant effect on students’ academic achievement, with a significance value of 0.036 (&lt; 0.05). However, the contribution is relatively low, at 12.3%, suggesting that other factors play a more dominant role in influencing academic achievement. In addition, the negative direction of the relationship indicates a mismatch between the management of facilities and their implementation in the learning process. Therefore, more integrated management of learning facilities is required to optimally support the improvement of students’ academic achievement.

Purpose Chronic absenteeism has surged since the COVID-19 pandemic, yet the role of school building conditions as correlates of attendance remains under-specified at the system level. This study links chronic absenteeism rates for New York public school buildings with standardized, component-level data from the 2024 New York State Building Condition Survey. Design/methodology/approach Using a Bonferroni-adjusted multi-method statistical framework aligned with variable types (group means comparisons, correlations, and multiple linear regression with Socioeconomic Status (SES) control), we identify specific systems, materials, and practices associated with student absence. Findings Socioeconomic context accounted for a substantial proportion of the variance in chronic absenteeism (R2 = 0.40), and over half of the building features significant in bivariate analysis lost significance after controlling for nine neighborhood-level SES variables. Among building features, indoor air quality factors showed the strongest independent associations: indoor pesticide use was associated with an approximately 8.1 percentage-point increase in absenteeism (b = 8.09, p < 0.001), mold-susceptible ceiling materials with a 6.6 percentage-point increase (b = 6.57, p < 0.001), and buildings tested for radon below 4 pCi/L with a 4.1 percentage-point decrease (b = −4.14, p = 0.04). Recent renovations were also associated with a 5.5 percentage-point reduction in absenteeism (b = −5.54, p < 0.001). In contrast, aggregate facility ratings and perceived conditions (e.g. cleanliness, lighting) showed no independent association. Research limitations/implications The study is cross-sectional and observational, identifying associations rather than causal effects, and relies on inspector-assessed building conditions. Reduced sample sizes in some categories and small subgroups for certain features limit statistical power and reliability. Residual confounders not captured by the nine SES controls (e.g. district policies, transportation access) may remain. The restriction to New York State public school buildings limits cross-jurisdictional generalizability. Practical implications Facility managers and district leaders can use the findings to prioritize component-level actions that are more directly linked to attendance-relevant exposures, including reviewing indoor pesticide practices, scheduling replacement of mold-susceptible ceiling materials during renovation cycles, maintaining systematic radon testing, and using SES-adjusted evidence when ranking capital projects. Originality/value This study provides one of the first component-level, system-specific assessments linking school building conditions to chronic absenteeism across a large sample of New York State public school buildings using standardized engineer/architect survey data. It moves beyond composite “facility quality” scores to identify specific building features, including maintenance status, indoor pesticide use, moisture/mold-prone materials, radon practices, and water piping types that are independently associated with absenteeism and align with plausible health pathways. The findings offer a prioritization view for infrastructure management and operation, targeting building components strongly associated with attendance disparities.

The assessment of the Building Information Modelling (BIM) capabilities is significant for the digital transformation of the Architecture, Engineering, Construction, and Facilities Management (AEC/FM) life cycle. While existing BIM assessment models have transitioned from manual assessments to web-based platforms, many remain proprietary, lacking methodological transparency and architectural extensibility. To address these limitations, this study presents BIM Capability Assessment Application (BIM-CAP), an open-source, low-cost tool that operationalises the research-driven, process-based BIM Capability Assessment Reference Model (BIM-CAREM). BIM-CAP decouples assessment logic from the user interface through a Model-View-Template (MVT) architecture. The tool integrates BIM life-cycle-specific questions (i.e., conceptual planning, design, construction, and facilities management) with automated scoring and visual analytics, while its self-contained deployment ensures secure, localised data management. A pilot case study with the Estate Service of a UK-based University demonstrates BIM-CAP’s ability to provide transparent and process-level assessment, thereby allowing organisations to implement targeted BIM improvement strategies. Moreover, BIM-CAP’s modular architecture allows organisations to adapt the tool to their specific needs such as incorporating new BIM use-cases (e.g., 6D sustainability tracking and 7D facility management) and updating assessment questions without requiring software modification. It fosters an openBIM research environment that supports researchers in extending it for emerging technologies such as Artificial Intelligence or Digital Twin readiness.

Assessing Effectiveness of Facilities Management (FM) Providers through Critical Success Factors: Evidence from Malaysian Private Urban Housing (PUH)

Nudging towards sustainability: A systematic review of digital strategies for achieving ESG goals in corporate real estate

Implementing, Maintaining, and Evaluating Water Management Programs in Health Care: The Multidisciplinary Effort to Prevent Infections from Opportunistic Premise Plumbing Pathogens.

Global BioImaging (GBI) and its regional and national partners, such as Canada BioImaging (CBI), BioImaging North America (BINA), Latin America BioImaging (LABI), the African BioImaging Consortium (ABIC) and Microscopy Australia (MA), have been working to create training opportunities for imaging scientists across the globe. During the COVID-19 pandemic, GBI and its partners developed a virtual training platform for independent learning (GBI Virtual Training Platform), with self-guided training modules spanning from microscope operation to facility management. More recently, in-person train-the-trainer (TtT) frameworks, including the Montreal Light Microscopy Course (MLMC), have been designed to deliver a multiplying effect by providing imaging scientists with the training and resources they need to run microscopy courses in their facilities, institutions, countries, and regions. Major goals of this initiative included empowering the next generation of imaging scientists by sharing resources and expertise, providing training in course logistics and organisation and building an international collaborative network of expert trainers to accelerate access to fundamental and advanced technologies. As of April 2026, the TtT project has consisted of three TtT courses, at least 11 second-generation microscopy fundamentals courses and impacted more than 400 course faculty, corporate partners and trainees from 25 countries. LAY DESCRIPTION: This manuscript demonstrates that global train-the-trainer (TtT) programmes in bioimaging create a powerful multiplier effect, rapidly expanding microscopy training capacity. By equipping imaging scientists with course materials, training, and access to a global trainer's network, it enabled second-generation courses across multiple regions, impacting over 400 individuals in 25 countries. It empowered the next generation and strengthened international collaboration to help democratise access to advanced technologies and expertise to accelerate scientific discovery and innovation. With sufficient resources, TtT is scalable across technology areas and research fields.

The growth in urbanisation and human population has resulted in significant rise in generation of municipal solid waste (MSW) globally, with bio-waste (i.e., biodegradable waste) comprising around 40-60% of the total waste generated in many developing nations. But poor source segregation, mixed composition and variable moisture content pose major challenges to the efficiency of traditional, large-scale waste management facilities. This makes it difficult to convert bio-waste into valuable products such as bioenergy, biochar, compost and bio-based chemicals. The paper provides an overview of existing technologies and emerging strategies for sustainable and circular bio-waste management, focusing on decentralized processing technologies. The review compares biological treatment technologies (e.g., composting and anaerobic digestion) and thermochemical conversion processes (e.g., pyrolysis and gasification) in treating unsorted bio-waste. The review also examines the use of digital technologies, such as artificial intelligence to enhance waste characterization, process optimization and efficiency of the system. Furthermore, the paper explores decentralised waste management systems as a sustainable alternative to largescale facilities, with benefits such as lower transportation impacts, localised resource re-use and community engagement. Also, the review identifies important challenges and future research needs, such as waste variability, lack of real-world data, technology integration, and the need for techno-economic and life-cycle analysis. In conclusion, smart and decentralized bio-waste management approaches have great potential in facilitating circular bioeconomy, sustainable urban waste management and resource recovery, especially in densely populated areas.

Using the EU farm sustainability data network to inform GHG and ammonia national inventory accounts