Search Results (185)

This study focuses on Shanghai, a pioneer city in China’s community renewal practices. In recent years, community renewal driven by civic participation has become a prominent research topic, leading to the emergence of numerous exemplary cases in Shanghai. However, field investigations revealed that many projects have experienced varying degrees of physical deterioration and a decline in spatial vitality due to insufficient maintenance, reflecting unsustainable outcomes. In response, this study examines a bottom-up community renewal project led by the research team, aiming to explore how broad civic participation can promote sustainable community renewal. A multidisciplinary approach incorporating perspectives from ecology, the humanities, economics, and sociology was used to guide citizen participation, while participatory observation methods recorded emotional shifts and maintenance behavior throughout the process. The results showed that civic participatory actions under the guidance of sustainability principles effectively enhanced citizens’ sense of community identity and responsibility, thereby facilitating the sustainable upkeep and operation of community spaces. However, the study also found that bottom-up efforts alone are insufficient. Sustainable community renewal also requires top-down policy support and institutional safeguards. At the end, the paper concludes by summarizing the practical outcomes and proposing strategies and mechanisms for broader application, aiming to provide a reference for related practices and research. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

LLMs are trained using many thousands of GPUs in well-known conventional models. It is necessary to address numerous issues in the training process, such as manual data collection organization, data parallel, model parallel, evaluation, testing, deployment, transferring large data streams, detecting errors, ongoing maintenance, and project management. A team of dozens of engineers is required to handle system problems in the training process. Therefore, it is time-consuming and expensive to build an efficient and fault-tolerant system based on Kubernetes. This paper develops SimpleScale for building LLMs based on FSDP and Slurm, which is a simple and efficient training system that includes the training agent, the efficient parallel strategy, the optimal step of checkpoint, and so on. Using the proposed system enables us to significantly accelerate the process of building the LLM without incurring substantial time spent on various manual issues. The proposed 1024-GPU cluster was tested on TinyLlama, which has 1.1 billion parameters and 300 billion tokens. For example, utilizing a 16-H100 GPU cluster accelerated the traditional TinyLlama training time costs from 89.05 days to 39.05 days. In the future, the project team plans to integrate Flash-Attention3, aiming for an MFU of more than 60% while maintaining the acceleration achieved in the present work. Full article

Despite recent advances in the treatment of obesity, lifestyle medicine remains foundational to the treatment of individuals with obesity, regardless of the modality chosen by the patient with the guidance of the clinician they are working with, including in conjunction with, as appropriate, anti-obesity medications and metabolic surgery. Lifestyle medicine involves the use of diet, exercise, sleep, stress, and other lifestyle modalities in the treatment of disease. Clinicians and health systems should, after a patient-centered discussion with the patient, do their best to ensure access to lifestyle treatments. Gold standard guidelines recommend intensive, multicomponent lifestyle change programs for obesity treatments with evidence-based diet and exercise counseling and established, theoretically driven behavior change components. Clinicians treating obesity should be aware of their own biases, make efforts to reduce stigmatizing experiences in their practice, and address weight stigma in their treatment plans as needed. A variety of dietary patterns can be used to support patients with obesity, and clinicians should make evidence-based but patient-centered recommendations that aim to maximize adherence. Diet and exercise can play an important role in reducing the side effects of treatment and optimizing outcomes in weight loss, attenuating the effects of metabolic adaptation, and weight maintenance. Exercise should be increased gradually to reduce injury with a goal of 200–300 min (approximately 3.3–5 h) of moderate to vigorous intensity exercise per week to maximize weight maintenance effects with exercise prescriptions customized to patients risks. A variety of practice models can be leveraged along with the use of an interdisciplinary team to provide lifestyle medicine care for those with obesity. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

Background/Objectives: To identify factors associated with the referral by a multiprofessional team to dental services for children and adolescents with rare genetic diseases. Methods: A cross-sectional study was developed with 87 children/adolescents with mucopolysaccharidosis (n = 26) and osteogenesis imperfecta (n = 61) and their caregivers. Recruitment took place at reference centers for rare genetic conditions in five Brazilian states. The caregivers answered a questionnaire on the children. They were examined for malocclusion, dental anomalies, caries experience, and gingivitis. Bivariate and multivariate analyses of the data were performed, considering a 95% confidence level. Results: The average age of children/adolescents was 10.4 years (±5.6) and 17.3% had never gone to a dentist. Among those with past dental experience, the reason for most appointments was oral prophylaxis/preventive maintenance (62.1%). With regard to referrals to a dentist by the multidisciplinary team, 29.9% had never received a referral. The likelihood of having been referred to a dentist by the multiprofessional team was 2.67 times greater for female patients (95% CI: 0.96–7.42) and 7.74 times greater for children/adolescents with a history of toothache (95% CI: 1.61–37.14). Conclusions: Female children/adolescents with mucopolysaccharidosis and osteogenesis imperfecta and those with a history of dental pain were more likely to have been advised by the multiprofessional team to seek dental treatment. Full article

Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management. Full article

Historical cross-sectional approaches examining cognitive aging consistently reveal a pattern of steady decline on nonverbal problem-solving, speeded tasks, and maintenance on verbal tasks. However, as measures developed and broadened the factor structure to align with Cattell–Horn–Carroll (CHC) theory, and age ranges were extended from 75 to 90 years, a more nuanced approach to cognitive aging emerged. The present study, using the Wechsler Adult Intelligence Scale, Fifth Edition (WAIS-5), examined the cognitive aging process through a cross-sectional approach. WAIS-5 normative sample data (aligned with the 2022 U.S. census) were obtained from the test publisher. The sample included adult participants aged 20–24 through 85–90 (n = 1660), which were mapped into 11 age groups. Using post-stratification weighting to control for educational attainment, cognitive decline was observed throughout aging; verbal skills were maintained longer than other abilities, while processing speed declined steadily and rapidly from young adulthood to old age. Working memory was vulnerable to the aging process but demonstrated slower patterns of decline than the other vulnerable abilities. Fluid reasoning and visual spatial skills (although aligning with separate CHC broad abilities theoretically) were strikingly similar in their pattern of decline across a person’s lifespan. Results are highly consistent with the large body of cross-sectional research conducted during the previous generation by Salthouse and his colleagues, as well as other teams of researchers. Full article

Enhancing the seismic resilience of urban water distribution networks (WDNs) requires the improvement of both earthquake resistance and rapid recovery capabilities within the system. This paper proposes a multi-objective method to enhance the seismic resilience of the WDNs, focusing on system restoration capabilities while comprehensively considering the hydraulic recovery index, maintenance time, and maintenance cost. The method utilizes a random simulation approach to generate various damage scenarios for the WDN, considering pipe leakage, pipe bursts, and variations in node flow resulting from changes in water pressure. It characterizes the functions of the WDN through hydraulic service satisfaction and quantifies system resilience using a performance response function. Additionally, it determines the optimal dispatch strategy for emergency repair teams and the optimal emergency repair sequence for earthquake-damaged networks using a genetic algorithm. Furthermore, a comprehensive computational platform has been developed to systematically analyze and optimize seismic resilience strategies for WDNs. The feasibility of the proposed method is demonstrated through an example involving the WDN in Xi’an City. The results indicate that the single-objective seismic resilience improvement method based on the hydraulic recovery index is the most effective for enhancing the seismic resilience of the WDN. In contrast, the multi-objective method proposed in this article reduces repair time by 17.9% and repair costs by 3.4%, while only resulting in a 0.2% decrease in the seismic resilience of the WDN. This method demonstrates the most favorable comprehensive restoration effect, and the success of our method in achieving a symmetrically balanced restoration outcome demonstrates its value. The proposed methodology and software can provide both theoretical frameworks and technical support for urban WDN administrators. Full article

Urban green infrastructure (UGI) projects rely on collaboration and involve a diverse team of professionals, including constructors, designers, green builders, and maintenance staff. This socially oriented case study focuses on the relational dynamics among UGI professionals, their roles in landscape construction processes, and how these relationships can influence the project’s success and its capacity to implement carbon-smart solutions. “Carbon-smart solutions” refers here to practices aimed at maximising carbon sequestration and storage while minimising carbon emissions. Data for this study were collected through semi-structured in-depth interviews and analysed using deductive qualitative analysis. A coding framework, investigator triangulation, and a representative sample of various professionals were employed to confirm the data’s validity. This study identified several relational factors that either challenge or drive the project’s success and carbon smartness. At the interpersonal level, the determinant drivers and challengers in UGI professionals’ relations were linked to the definition of working roles, power dynamics, the building of mutual trust through open communication, and the possession of the necessary sustainability skills. At the institutional level, relations concerning the shared principles and rationales of the project, as well as the project design process and diverse working cultures, presented both constraints and advances in project success and carbon-smart solutions. Full article

Background/Objectives: The objective of this review is to map nurses’ responsibilities in addressing individuals who are victims of violence, specifically in the preservation of forensic evidence in both intra- and extra-hospital contexts. The research question is as follows: What are a nurse’s responsibilities regarding the care of victims of violence for the preservation of forensic evidence in in-hospital and out-of-hospital emergencies? Methods: The mnemonic PCC (P-Population, C-Concept, and C-Context) method was followed according to the recommendations of the Joanna Brigs Institute (2024). The population is defined as “individuals who are victims of violence”, the concept (phenomenon of interest) as “nursing interventions determining responsibilities in forensic evidence preservation”, and the context as “intra- and extra-hospital emergencies”. A search was conducted in the following databases: CINAHL Complete via EBSCO Host, Medline Complete via EBSCO Host, PubMed, Web of Science, and RCAAP via B-ON. From the total articles retrieved, 18 were selected for analysis. Results: The 18 articles emphasized the critical role of nurses in preserving forensic evidence in both intra- and extra-hospital contexts. These findings were grouped into six domains to clarify the topic: (1) knowledge and training of nurses on forensic evidence preservation and the implementation of standardized protocols; (2) collection of biological and non-biological samples to preserve evidence on the victim’s body; (3) collection of biological and non-biological samples to preserve evidence on the victim’s objects and belongings; (4) documentation of forensic traces and evidence; (5) maintenance of the chain of custody; and (6) ongoing interprofessional collaboration between nurses, law enforcement, and judicial authorities. Conclusions: Intra- and extra-hospital emergencies, teams must be proficient in recognizing victims of violence and in conducting appropriate evidence preservation to ensure their legal admissibility. Full article

Natural language interfaces can transform the construction industry by enhancing accessibility and reducing administrative workload in the day-to-day operations of project teams. This paper introduces the Voice-Integrated Scheduling Assistant for 4D BIM (VISA4D) tool that integrates speech recognition and Natural Language Processing (NLP) capabilities with Building Information Modeling (BIM) to streamline construction schedule updating and maintenance processes. It accepts voice and text inputs for schedule updates, facilitating real-time integration with Autodesk Navisworks, and eliminates the need for direct access to or advanced knowledge of BIM tools. It also provides visual progress tracking abilities through colour-coded elements within the 4D BIM model for communicating task status updates within the project teams. To demonstrate its capability to enhance schedule updating and maintenance efficiency, the VISA4D tool is implemented in an office building project in Canada and user testing is performed. An overall accuracy of 89% was observed in successfully classifying 71 out of 80 tested construction-specific commands, while the user surveys indicated high usability, with 92% of participants finding VISA4D easy to use and reporting consistent command recognition accuracy. This study advances the existing work on AI-enhanced construction management tools by tackling the challenges associated with their practical implementation in field operations. Full article

Introduction: The occurrence of a second primary lung cancer after head and neck cancer is a challenge for multidisciplinary teams, since the development of a second lung cancer negatively affects the survival rate of patients with head and neck cancer. Case Presentation: This article presents the case of a patient with a double location of cancer: inoperable stage III nasopharyngeal carcinoma, biopsied in December 2017 (non-keratinizing nasopharyngeal carcinoma), treated by means of radiotherapy and chemotherapy (2018–2021), and stage IV lung cancer (squamous carcinoma) with lung metastases, diagnosed in December 2021, treated using polychemotherapy, subsequent maintenance monochemotherapy, radiotherapy of the thorax, and subsequent maintenance monochemotherapy with a favorable result. The patient was still under treatment as of February 2025, the date of the preparation of the current article. Discussion and Literature Review: Regarding the location of the second metachronous cancer, studies show that the most frequent locations are the lungs and the esophagus, with the main causes being alcohol consumption and smoking. Therefore, these patients should be monitored by screening the respiratory and digestive tracts, especially in men, in order to identify a second cancer, either synchronous or metachronous, in an early stage. Conclusions: Educating the patient with head and neck cancer regarding quitting smoking and cutting out alcohol, as well as conducting a follow-up survey, may reduce the incidence of multiple primaries. Moreover, the multidisciplinary management of second primary lung malignancies in patients with head and neck cancer may lead to long-term disease monitoring. Full article

Against the backdrop of the increasingly salient constraints of resource scarcity and environmental pressures on global economic development, sustainable innovation emerges as an imperative strategic pathway for corporations to secure a competitive edge in the international marketplace. Corporate innovation capability serves as the critical factor for both the advancement of sustainable innovation and the maintenance of the corporate competitive edge. While the extant literature has extensively explored how internal and external governance mechanism forces shape corporate investment decision-making, the critical role of non-CEO executives in the process of corporate innovation investment decision-making remains conspicuously underexplored. This study examines the effect of bottom–up governance mechanisms within executive teams on corporate innovation investment from the perspective of non-CEO executive independence. We used a sample of A-listed companies on the Shanghai and Shenzhen stock exchanges from 2007 to 2021 for empirical tests. We found a U-shaped relation between non-CEO executive independence and corporate innovation investment, and this finding still held after addressing endogeneity issues and conducting a series of robustness tests. Mechanism analysis revealed that both non-CEO executives’ decision horizon and firm agency costs positively moderate this U-shaped relationship. This U-shaped effect is pronounced in firms with lower CEO power, lower levels of corporate governance, and non-state-owned firms. Our findings provide an important basis for clarifying the internal governance mechanism of the executive teams while offering new insights for optimizing the allocation of corporate resources and promoting corporate innovation from the perspective of improving corporate governance. Full article

Background: Work engagement is defined as a positive and fulfilling work-related state of mind, characterized by vigor, dedication, and absorption. High levels of engagement are associated with improved organizational functioning and a stronger sense of belonging among employees. Objective: This study, conducted in collaboration with a large metalworking company in central Italy, aimed to explore the relationship between work engagement and organizational cynicism among Maintenance Team Leaders. Specifically, the goals were to assess the levels of engagement and emotional involvement and to examine how these dimensions vary according to socio-demographic factors such as gender, age, educational background, and seniority. The ultimate aim was to support occupational physicians and workplace safety officers in identifying the best practices for preventing psychosocial risks, work-related stress, and burnout. Methods: A total of 99 Maintenance Leaders participated in the study. The Italian version of the Utrecht Work Engagement Scale (UWES) was used to assess the three core dimensions of engagement: vigor, dedication, and absorption. Additionally, the Cynicism scale development by Naus, Van Iterson, and Roe was administered. Results: While the sample size limits generalizability, the findings offer preliminary insight into engagement levels within this population. The results emphasize the need to expand the sample and to conduct comparative analyses across different teams within the company better understand engagement patterns and inform targeted interventions. Conclusions: Although work engagement has been extensively studied in the healthcare sector, especially during the COVID-19 pandemic, limited research has addressed its role in the industrial context, and even less within the metalworking sector. This study contributes to filling that gap by providing an initial profile of engagement among maintenance leaders and by highlighting the interplay between engagement, cynicism, and individual characteristics in a high-demand industrial environment. Full article