Search Results (1,315)

This work analyses what the systematic effect of public awareness on domestic water consumption is. In some parts of the world, the availability of water is continually decreasing, mainly due to reduced rainfall, so it is of paramount importance to raise awareness among the population. We conducted an experiment on a large sample of participating units located in urban areas of Italy, mainly in the central portion of the country. Approximately 750 people participated, belonging to 250 buildings, mainly domestic residences, but also professional offices, small companies, and student residences. In the first phase, lasting three weeks, normal per capita water consumption was quantified. Subsequently, instructions were given on how to save water during various uses in the household (showers, cleaning hands, use of water in toilets and in the kitchen, watering small green areas, use of water in the kitchen, and so on), and small visual messages conveyed through stickers were posted on water dispensers to remind users to behave properly. Finally, household consumption was assessed again during a further 3-week period. An average water-saving (WS) rate of +17.20% was found, in line with results obtained from a previous similar experiment involving a much smaller sample. Higher WS rates were recorded for buildings with less inhabitants. This experiment enabled us to quantify the significant effect of the awareness-raising action on the reduction in water consumption, without the use of any structural action (e.g., replacement of dispensers, improvement of the water system, realization of recycling systems). Moreover, the simplicity of the proposed methodology makes it suitable for implementation in other regions worldwide, thus promoting a step forward towards more sustainable use of water. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article

This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10⁻⁷, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity. Full article

Background/Objectives: This study is part of an exploratory mixed-methods project investigating how companies and their employees in Germany dealt with adapted working conditions during the COVID-19 pandemic. Here, we identify predictive factors for employees’ attitudes towards the suitability of work-related technical, organisational, and personal SARS-CoV-2 infection control measures. Methods: In July 2021, when there was little evidence to suggest that the risk of work-related exposure to SARS-CoV-2 differed between occupations and workplaces, a standardised online and an optional paper-and-pencil survey were distributed across seven companies in southern Germany. Multivariate linear regression was used for analysis. Results: A total of 821 employees participated (average response rate: 24.5%). Most of the respondents (93%) worked in large companies, in the production industry (82%), with most of them having office jobs (82%). Around 29% reported doing most of their office work remotely during the pandemic. The perceived suitability of workplace infection control measures was rated quite high, with an overall mean score of 4.11 (SD 0.60) out of a possible 5. Workplace characteristics related to the COVID-19 pandemic as well as individual perception of SARS-CoV2 and COVID-19 in general were the most prominent predictors of attitudes towards the suitability of work-related SARS-CoV-2 infection control. For example, a higher COVID-19-specific reactance was negatively associated with attitudes towards technical (ß = −0.16), organisational (ß = −0.14), and personal (ß = −0.17) infection control measures (all p-values < 0.001). Furthermore, a higher rating of the employer’s commitment to occupational safety and health related to SARS-CoV-2, a higher individual disease perception, and a higher individual COVID-19-specific resilience had a positive association with attitudes towards the suitability of infection control measures. Finally, professional activity as well as company affiliation had statistically significant associations with employees’ attitudes towards the suitability of infection control measures. Conclusions: The results provide insight into factors relevant to pandemic prevention and control. In particular, our findings highlight the potential to implement organisational measures alongside compulsory technical occupational health measures. This could inform the development of pandemic preparedness strategies that prioritise adherence to established occupational infection control measures. Full article

This narrative literature review aims to explore the risk and protective factors influencing the mental well-being of white-collar workers in the post-pandemic era. It investigates how factors vary across different phases, including pre-pandemic traditional work models, work-from-home or hybrid models during the pandemic, and the recovery phase of returning to the office in the post-pandemic era. This review highlights the diverse nature of related factors, examining constructs including stress, depression, burnout, thriving, work engagement, workaholism, motivation, workplace civility, and resilience. The Job Demands-Resources model, a recognized theoretical tool for analyzing and understanding the interactions between psychological constructs and their effects on employee well-being and turnover intention, is proposed as a useful framework to consider the relationships between the factors. By synthesizing existing research findings, this review contributes to our understanding of the complex interplay between work-related factors and employee well-being in the evolving landscape of the post-pandemic world. Understanding these dynamics is crucial for developing effective strategies to support white-collar workers’ mental well-being and productivity in the post-pandemic era. Full article

Open-plan offices have established themselves as economically efficient working environments and promote communication. Zoned lighting concepts have proven to be particularly energy-efficient and are determined by the respective occupancy profile. Due to their size, open-plan offices usually have very different levels of daylight availability depending on their position in the room, which affects the light doses per workstation. It is unclear what influence the distribution of users in the room has on the respective target values. This study therefore examines the effects of a variation in the spatial distribution of users in a real open-plan office regarding the three target values of communication distances, daily light doses, and artificial light energy requirements. Statistical methods are used to examine how a user distribution optimized for one target variable affects the other target variables. Since optimizing user distribution is an NP-hard combinatorial problem, heuristic methods are used. The results show that optimized user distribution improves only one target variable. There are no consistently strong correlations between the optimization of communication distances, energy savings, and achievable daily light doses. The work thus contributes to the holistic design of sustainable, user-centered working environments. This research is an example of a living lab case study with optimization-based modeling, emphasizing its exploratory nature rather than controlled experimental inference. Full article

Indoor environmental factors such as CO₂ concentration, temperature, and humidity can significantly influence individuals’ emotional states and productivity. This study continuously collected environmental data using wireless sensors and emotional data from wearable devices in an office-like remote-work setting. Machine learning models, including Random Forest and Gradient Boosting Decision Tree, were developed and interpreted using SHAP (Shapley Additive Explanations). The proposed models achieved estimation accuracies above 85%. SHAP analysis revealed that CO₂ concentration, temperature, and humidity were influential factors in predicting pleasant or unpleasant states. These findings demonstrate the feasibility of real-time, data-driven emotion estimation and provide insights into the design of indoor environments that foster comfort and mental well-being. Full article

Background: Seafarers are highly susceptible to work-related injuries, which can result in serious consequences or permanent disabilities. Understanding the frequency and characteristics of occupational injuries is crucial for developing effective prevention strategies and identifying their underlying patterns and causes. This study aimed to determine the frequency and characteristics of telemedicine-assisted work-related injuries among seafarers on board Italian-flagged vessels. Methods: A retrospective descriptive study was conducted to analyze occupational injuries using medical data recorded in the Centro Internazionale Radio Medico (C.I.R.M.) database from 1 January 2010 to 31 December 2022. Injuries in the database were coded according to the 10th revision of the International Classification of Diseases (ICD-10) by the World Health Organization (WHO). Variables extracted from the database included injury type, seafarers’ age, rank, nationality, worksite, gender, date of injury, affected body region, clinical outcomes, and other demographic and occupational characteristics. Injury frequency and characteristics (e.g., location, type, and cause) were analyzed and stratified by seafarers’ rank and worksite groups. Results: The analysis included 793 seafarers who sustained injuries. Their average age was 39.15 ± 10.49 years (range: 21 to 70 years). Deck ratings and engine officers accounted for 27.9% and 20% of those who claimed injuries, respectively. 39.2% of injured seafarers were aged between 30 and 40 years. In terms of affected body parts, the most reported injuries were to the hand/wrist (33.3%), followed by the knee/lower legs (21%), and the head/eye (19%). Open wounds (38%) and burns/abrasions (14%) were the most common types of injury. Slips/falls (32%), burns/explosions (16.6%), and overexertion while lifting or carrying (14.8%) were the leading causes of injury during the study period. Nearly 35% of injuries affected workers on the deck and were due mainly to slips/falls, 19% in the engine room were due to being caught in machinery or equipment, and 32.5% in the catering department were due to burns/explosions. Conclusions: One-third of seafarers who suffered work-related injuries sustained hand and/or wrist injuries, with slips/falls being a significant cause. The results of this study emphasize the need for preventative measures in the marine sector, particularly to reduce risks associated with slips and falls, overexertion, and other injury-causing factors. Campaigns for the larger use of protective equipment are desirable to reduce occupational accidents at sea and provide better health protection for seafarers. Full article

The Internet of Things (IoT) has revolutionized industries by enabling seamless data exchange between billions of connected devices. However, the rapid proliferation of IoT devices has introduced significant security challenges, as many of these devices lack robust protection against cyber threats such as data breaches and denial-of-service attacks. Addressing these vulnerabilities is critical to maintaining the integrity and trust of IoT ecosystems. Traditional cybersecurity solutions often fail in dynamic, heterogeneous IoT environments due to device diversity, limited computational resources, and inconsistent communication protocols, which hinder the deployment of uniform and scalable security mechanisms. Moreover, there is a notable lack of realistic, high-quality datasets for training and evaluating machine learning (ML) models for IoT security, limiting their effectiveness in detecting complex and evolving threats. This paper presents the development and implementation of a novel physical smart office/home testbed designed to evaluate ML algorithms for detecting and mitigating IoT security vulnerabilities. The testbed replicates a real-world office environment, integrating a variety of IoT devices, such as different types of sensors, cameras, smart plugs, and workstations, within a network generating authentic traffic patterns. By simulating diverse attack scenarios including unauthorized access and network intrusions, the testbed provides a controlled platform to train, test, and validate ML-based anomaly detection systems. Experimental results show that the XGBoost model achieved a balanced accuracy of up to 99.977% on testbed-generated data, comparable to 99.985% on the benchmark IoT-23 dataset. Notably, the SVM model achieved up to 96.71% accuracy using our testbed data, outperforming its results on IoT-23, which peaked at 94.572%. The findings demonstrate the testbed’s effectiveness in enabling realistic security evaluations and ability to generate real-world datasets, highlighting its potential as a valuable tool for advancing IoT security research. This work contributes to the development of more resilient and adaptive security frameworks, offering valuable insights for safeguarding critical IoT infrastructures against evolving threats. Full article

Background: Blood pressure is associated with overt thyroid disorders, but the role of subclinical diseases is not clear, particularly when blood pressure is assessed at the clinical office. Ambulatory blood pressure monitoring over 24 h provides additional clinical information, which correlates with many cardiovascular endpoints. The aim of our work is to examine whether thyroid function is related to systolic and diastolic blood pressure assessed by ambulatory blood pressure monitoring. Methods: We enrolled 3277 subjects from the SardiNIA project. Thyroid function and ambulatory blood pressure monitoring were assessed in all the participants. Results: TSH was associated with average 24 h and daytime DBP in males but not in females, after adjusting for confounders (respectively, Coef −0.192 p = 0.025, and Coef. −0.021, p = 0.018). We found no association between TSH and DBP or SBP during nighttime. Conclusions: Low TSH in males is positively associated with high DBP. Further studies of underlying mechanisms will need to explore our findings. Full article

This work quantifies downtime caused by cyberattacks for eight critical urban services in Milan by coupling sectoral Continuous-Time Markov Chains (CTMCs) with an approximately equal-area H3 hexagonal grid of the city. The pipeline ingests OpenStreetMap infrastructure, simulates coupled failure/repair dynamics across sectors (power, telecom, hospitals, ambulance stations, banks, ATMs, surveillance, and government offices), and reports availability, outage burden (area under the infected/down curve, or AUC), and multi-sector distress probabilities. Cross-sector dependencies (e.g., power→telecom) are modeled via a joint CTMC on sector up/down states; uncertainty is quantified with nested bootstraps (inner bands for stochastic variability, and outer bands for parameter uncertainty). Economic impacts use sector-specific cost priors with sensitivity analysis (PRCC). Spatial drivers are probed via hotspot mapping (Getis–Ord $G_i^{*}$, local Moran’s I) and spatial regression on interpretable covariates. In a baseline short decaying attack, healthcare remains the most available tier, while power and banks bear a higher burden; coupling increases P(≥$k\mathrm{sectors}\mathrm{down})$ and per-sector AUC relative to an independent counterfactual, with paired-bootstrap significance at $\alpha =0.05$ for ATMs, banks, hospitals, and ambulance stations. Government offices are borderline, and telecom shows the same direction of effect but is not significant at $\alpha =0.05$. Under a persistent/adaptive attacker, citywide downtime and P(≥2) rise substantially. Costs are dominated by telecom/bank/power under literature-informed penalties, and uncertainty in those unit costs explains most of the variance in total loss. Spatial analysis reveals statistically significant hotspots where exposure and dependency pressure are high, while a diversified local service mix appears protective. All code and plots are fully reproducible with open data. Full article

Ship waterway tunnels are a new and special type of navigation facility that has emerged in the construction of complex hubs in high mountain valleys and rivers, and they have demonstrated broad applications worldwide. Due to their characteristics of long length, a dim visual background, and enclosed space, waterway tunnels are prone to causing tension and cognitive fatigue in ship officers on watch, affecting their decision-making and control abilities. This study constructs the visual navigation environment of a typical waterway tunnel in China using a ship maneuvering simulator. By monitoring the physiological data of ship officers, such as through electroencephalograms (EEGs) and electrocardiograms (ECGs), the temporal and spatial patterns of their physiological and psychological characteristics are analyzed systematically. Based on this, a quantitative model of the cognitive load of a ship officer working in a waterway tunnel is constructed. At the same time, the navigation risk of waterway tunnels of different lengths is quantized based on the entropy weight TOPSIS method, and finally, high-risk sections in waterway tunnels are identified and visualized, providing theoretical support for the management of safety in waterway tunnels. Full article

Prolonged sitting and the adoption of unhealthy sitting postures have been a common issue generally seen among many adults and the working population in recent years. This alone has contributed to the alarming rise of various health issues, such as musculoskeletal disorders and a range of long-term health conditions. Hence, this study proposes the development of a novel smart-sensing chair system designed to analyze and provide actionable insights to help encourage better postural habits and promote well-being. The proposed system was equipped with two 32 × 32 pressure sensor mats, which were integrated into an office chair to facilitate the collection of postural data. Unlike traditional approaches that rely on generalized datasets collected from multiple healthy participants to train machine learning models, this study adopts a user-tailored methodology—collecting data from a single individual to account for their unique physiological characteristics and musculoskeletal conditions. The dataset was trained using five different machine learning models—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN)—to classify 19 distinct sitting postures. Overall, CNN achieved the highest accuracy, with 98.29%. To facilitate user engagement and support long-term behavior change, we developed SitWell—an intelligent postural feedback platform comprising both mobile and web applications. The platform’s core features include sitting posture classification, posture duration analytics, and sitting quality assessment. Additionally, the platform integrates OpenAI’s GPT-4o Large Language Model (LLM) to deliver personalized insights and recommendations based on users’ historical posture data. Full article

Purpose: This study investigates the relationship between flexible working arrangements (FWAs), employee experiences (EEs), and perceived productivity (PP) in the context of New Zealand employees. The study aims to understand how opportunities and challenges within FWAs impact employee productivity, utilising the Job Demands–Resources (JD-R) model as a theoretical framework. Design/methodology/approach: A survey was conducted with 176 employees who transitioned from traditional office settings to FWAs. Data were collected using a structured questionnaire measuring work demand, autonomy, employee experiences, and perceived productivity. The analysis involved correlational and moderated regression techniques to assess the relationships between the variables. Findings: The study found that positive employee experiences (expressed as opportunities) are significantly associated with higher perceived productivity (r = 0.610, p < 0.001), while negative experiences (expressed as challenges) are associated with lower perceived productivity (r = 0.515, p < 0.001). Moreover, management strategies were found to moderate these relationships, further influencing perceived productivity. Originality: This research contributes to the understanding of how FWAs, when effectively managed, can enhance employee productivity by fostering positive experiences. It also highlights the importance of addressing challenges to mitigate negative impacts on productivity. The use of the JD-R model offers a novel approach to exploring these dynamics in the context of FWAs. Practical and social implications: Organisations can enhance productivity by focusing on management strategies that amplify positive employee experiences and reduce challenges within FWAs. Effective FWAs can improve work–life balance, employee wellbeing, and organisational commitment, contributing to a more satisfied and productive workforce. Full article

Background: In low-resource health systems, official mHealth platforms often face usability and infrastructure barriers. In India, Community Health Workers (CHWs) and their supervisors have pragmatically turned to WhatsApp as an informal digital infrastructure. While widely adopted, its dual role as both a support system and a source of burden remains underexplored. Aim: To examine the patterns and purposes of WhatsApp use among CHWs and block-level supervisors; to identify perceived benefits, barriers, and risks; and to assess its influence on workflow, power relations, digital equity, and program outcomes in an Indian Aspirational District. Methods: We conducted a qualitative study between June and December 2023 in Muzaffarpur, Bihar, India. Data comprised 32 in-depth interviews and six focus group discussions with CHWs (Anganwadi Workers, ASHAs, ANMs) and block-level public health officers (total participants n = 81). We used reflexive thematic analysis following Braun and Clarke’s approach; reporting adhered to the COREQ guideline. Results: WhatsApp emerged as a de facto digital backbone for real-time communication, peer support, and program supervision, often perceived as more usable than official applications. Its informal adoption also created a triple burden: digital fatigue from information overload and blurred work–life boundaries; duplication of reporting across WhatsApp and official portals; and systemic inequities that disadvantaged older or less digitally literate CHWs, with risks of surveillance creep and data privacy breaches. Conclusion: WhatsApp simultaneously enables coordination and imposes workload and equity costs on India’s frontline workforce. Without formal policy and governance, this user-driven adaptation risks widening digital divides and accelerating burnout. We recommend clear protocols on purpose-limited use, investments in equitable digital capability and devices, and safeguards that protect worker well-being and data privacy. Full article