Search Results (3,717)

The integration of smart buildings (SBs) into smart cities (SCs) is critical to urban development, with the potential to improve SCs’ performance. Artificial intelligence (AI) applications have emerged as a promising tool to enhance SB and SC development. The authors apply an AI-based methodology, particularly Large Language Models of OpenAI ChatGPT-3 and Google Bard as AI experts, to uniquely evaluate 26 criteria that represent SB services across five SC infrastructure domains (energy, mobility, water, waste management, and security), emphasizing their contributions to the integration of SB into SC and quantifying their impact on the efficiency, resilience, and environmental sustainability of SC. The framework was then validated through two rounds of the Delphi method, leveraging human expert knowledge and an iterative consensus-building process. The framework’s efficiency in analyzing complicated information and generating important insights is demonstrated via five case studies. These findings contribute to a deeper understanding of the effects of SB services on SC infrastructure domains, highlighting the intricate nature of SC, as well as revealing areas that require further integration to realize the SC performance objectives. Full article

As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine’s transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management. Full article

As the organizational environment becomes more volatile, uncertain, complex, and ambiguous, and the economy becomes increasingly knowledge-based, organizational knowledge management is key for companies’ success. This is especially important as organizational ties are weaker and job-hopping becomes a more prevalent phenomenon. As human resource mobility increases, companies must ensure that knowledge remains within the company despite employee exit. In this context, the current study sought to understand how leaders’ actions can facilitate employee knowledge sharing, focusing on paradoxical leadership. Besides examining the impact of paradoxical leadership on employees’ propensity to adopt knowledge-sharing behaviors, this study also explored the effects of one potential intervening variable (i.e., promotive voice behavior) and one potential boundary condition (i.e., trust in the leader) on this relationship. A two-wave time-lagged correlational study was conducted with a sample of 154 workers from various sectors. The results of moderated mediation analysis suggest that paradoxical leaders indirectly promote greater knowledge-sharing among subordinates by fostering their promotive-voice behaviors, but only for those with high levels of trust in the leader. The implications of these findings for current organizational challenges regarding knowledge management are discussed. Full article

Background: This systematic literature review aims to explore the impact of rehabilitation in post-stroke patients, particularly highlighting the roles of virtual reality (VR) technology and functional electrical stimulation (FES). Methods: To ensure all relevant studies were included, a thorough search was conducted in PubMed and Web of Science databases using keywords such as ‘post-stroke’, ‘FES’, ‘functional electrical stimulation’, ‘virtual reality’, and ‘VR’. Studies on rehabilitating upper limb function through VR and FES in post-stroke patients were included, regardless of publication year. Studies had to compare this combination therapy with conventional methods and report outcomes related to upper limb coordination, functional mobility, and daily activities. Studies not meeting these criteria were excluded. The selection process involved screening titles, abstracts, and full texts by four independent reviewers. The quality and risk of bias of the included studies were assessed using the PEDro scale and Robvis tool. Results: The review included four studies involving 135 post-stroke patients. Two articles examined the effectiveness of an approach involving virtual reality, robotic therapy, and functional electrical stimulation in rehabilitating upper limbs in post-stroke patients, showing significant improvements in motor function and quality of life. The other two studies explored the effects of rehabilitation therapy using virtual reality combined with functional electrical stimulation on upper limb function in stroke patients, finding that combined therapy (FES with VR) was superior to functional electrical stimulation or robotic therapy. Discussion: The review was limited by the small number of studies and participants, which may affect the generalizability of the results. Variations in intervention protocols and outcome measures across studies posed challenges in synthesis. Integrating these technologies brings benefits and increases the potential for personalizing and optimizing the rehabilitation process, enhancing patient engagement and satisfaction, and promoting a holistic approach to post-stroke management. Future research should focus on larger, more standardized trials to confirm these findings and optimize intervention protocols. Full article

Background: Posterior sternoclavicular joint (SCJ) dislocations are rare events that can evolve into real emergencies due to the vital structures in the mediastinum. When closed reduction maneuvers fail, open SCJ reconstruction becomes mandatory, with literature proposing several stabilization techniques that either preserve or remove the SCJ’s mobility. This study is a narrative review of the most recent literature regarding posterior trauma to the SCJ along with a single institution’s experience of this pathology, managed either conservatively or surgically, with a figure-of-eight autologous semitendinosus graft in case of closed reduction failure. Methods: This article provides an overview of posterior traumatic SCJ dislocation, and it describes five cases of patients managed for traumatic posterior SCJ dislocation treated either conservatively or surgically with a figure-of-eight semitendinosus tendon autograft reinforced with high-strength suture tape. A comparison with the most recent literature is performed, focusing on biomechanics. Results: The demographics, the mechanism of injury, the management algorithm and the surgical strategy align with the most recent literature. Despite the final treatment, at one year of follow-up, the ROM was restored with full strength throughout the range of motion of the shoulder with no neurological deficits. The reduced joint successfully healed in imaging, and patients returned to their daily lives. The surgical site wounds and donor harvest sites were perfectly healed. Conclusions: Although recent recommendations for treating posterior traumatic SCJ dislocation have advanced, no universally accepted method of stabilization exists, and the surgical strategy is generally entrusted to the surgeon’s experience. The literature still increasingly supports figure-of-eight ligament reconstruction with a biological or synthetic graft. This work further implements the literature by reporting good outcomes at follow-up. Full article

The aim of this research has been to enable the management of trustworthy relationships between stakeholders, service providers, and physical assets, which are required in critical energy and mobility cyber–physical systems (CPS) applications. The achieved novel contribution is the concept of trustworthy communities with respective experimental solutions, which are developed by relying on verifiable credentials, smart contracts, trust over IP, and an Ethereum-based distributed ledger. The provided trustworthy community solutions are validated by executing them in two practical use cases, which are called energy flexibility and hunting safety. The energy flexibility case validation considered the execution of the solutions with one simulated and two real buildings with the energy flexibility aggregation platform, which was able to trade the flexibilities in an energy flexibility marketplace. The provided solutions were executed with a hunting safety smartphone application for a hunter and the smartwatch of a person moving around in the forest. The evaluations indicate that conceptual solutions for trustworthy communities fulfill the purpose and contribute toward making energy flexibility trading and hunting safety possible and trustworthy enough for participants. A trustworthy community solution is required to make value sharing and usage of critical energy resources and their flexibilities feasible and secure enough for their owners as part of the energy flexibility community. Sharing the presence and location in mobile conditions requires a trustworthy community solution because of security and privacy reasons, but it can also save lives in real-life elk hunting cases. During the evaluations, the need for further studies related to performance, scalability, community applications, verifiable credentials with wallets, sharing of values and incentives, authorized trust networks, dynamic trust situations, time-sensitive behavior, autonomous operations with smart contracts through security assessment, and applicability have been detected. Full article

Background: Neck pain (NP) is a prevalent musculoskeletal disorder, especially among individuals with sedentary occupations. The interplay between cervical and thoracic spine mobility is hypothesized to contribute significantly to NP severity, yet this relationship requires further exploration. Methods: This cross-sectional study involved 179 young white-collar workers with NP lasting for at least six weeks. Participants were stratified into mild (n = 78) and moderate (n = 101) pain groups based on their scores on the Northwick Park Neck Pain Questionnaire (NPQ). Cervical and thoracic range of motion (ROM) in the sagittal plane was measured using inclinometers. NP severity was further assessed using the NPQ and the Neck Disability Index (NDI). Correlation, regression, and mediation analyses were conducted to investigate the relationship between cervical and thoracic ROM and NP severity. Results: Thoracic ROM was higher in the mild pain group (median: 47.35, IQR: 10.13) than in the moderate pain group (median: 42.10, IQR: 13.60; p < 0.001). The NDI had a negative correlation with thoracic ROM (r = −0.65; p < 0.05) and a positive correlation with cervical ROM (r = 0.84; p < 0.01). Additionally, thoracic ROM mediated the effect of cervical ROM on NP, particularly influencing NDI scores (p < 0.01). Conclusions: This study found a significant association between reduced thoracic ROM and increased NP severity, highlighting the role of thoracic spine mobility in NP among young white-collar workers. Targeted interventions for thoracic dysfunction may reduce compensatory cervical strain and improve NP management, suggesting that thoracic spine assessments should be integrated into routine clinical evaluations. Full article

In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addresses the Truck–Drone Delivery Problem (TDDP), wherein a truck collaborates with a drone, acting as a mobile charging and storage unit. Although the Traveling Salesman Problem (TSP) can represent the TDDP, it becomes computationally burdensome when nodes are dynamically altered. Motivated by this limitation, our study’s primary objective is to devise a model that ensures swift execution without compromising the solution quality. We introduce two meta-heuristics: the Strawberry Plant, which refines the initial truck schedule, and Genetic Algorithms, which optimize the combined truck–drone schedule. Using “Dataset 1” and comparing with the Multi-Start Tabu Search (MSTS) algorithm, our model targeted costs to remain within 10% of the optimum and aimed for a 73% reduction in the execution time. Of the 45 evaluations, 37 met these cost parameters, with our model surpassing MSTS in eight scenarios. In contrast, using “Dataset 2” against the CPLEX solver, our model optimally addressed all 810 experiments, while CPLEX managed only 90 within the prescribed time. For 20-customer scenarios and more, CPLEX encountered memory limitations. Notably, when both methods achieved optimal outcomes, our model’s computational efficiency exceeded CPLEX by a significant margin. As the customer count increased, so did computational challenges, indicating the importance of refining our model’s strategies. Overall, these findings underscore our model’s superiority over established solvers like CPLEX and the economic advantages of drone-assisted delivery systems. Full article

Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO₂ emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT. Full article

The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO₂ emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs’ traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector’s commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. Full article

Objective: With advancements in treatment, the increasing number of women with breast cancer has led to a growing focus on enhancing their well-being by understanding health-related quality of life (HRQoL). This study aimed to investigate the association between comorbidities and HRQoL in middle-aged and older community-dwelling Korean women with breast cancer. Methods: Data from the Sixth, Seventh, and Eighth Korea National Health and Nutrition Examination Surveys between 2014 and 2020 were used to analyze 12,218 women aged ≥50 years (244 women with breast cancer vs. 11,974 women without breast cancer). HRQoL was assessed using the EQ-5D-3L tool and the EQ-5D index. Associations between comorbidities (arthritis, depression, hypertension, diabetes, and cardiovascular disease) and HRQoL were examined. Results: Among women with breast cancer, arthritis was associated with problems in mobility (OR, 3.24; 95% CI, 1.39–7.53) and pain/discomfort (OR, 7.30; 95% CI, 3.62–14.73). Depression was associated with problems in self-care (OR, 7.02; 95% CI, 1.97–25.01), usual activities (OR, 5.73; 95% CI, 1.52–21.59), pain/discomfort (OR, 5.58; 95% CI, 1.49–20.87), and anxiety/depression (OR, 3.81; 95% CI, 1.14–12.72). Arthritis and depression were also considerably associated with overall HRQoL, as measured by the EQ-5D index. Hypertension, diabetes, and cardiovascular disease were not independently associated with HRQoL. Conclusions: Arthritis and depression were markedly associated with HRQoL in middle-aged and older women in the community who suffer from breast cancer. Public health interventions that focus on managing these comorbidities can enhance the well-being of women with breast cancer. Full article

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach. Full article

Agroecology refers to the greening of agrosystems with the mobilization of ecosystem services in order to limit exogenous inputs, enhance biodiversity and moderate the exploitation of natural resources. Agroecological practices offer pathways for transformation and transition not only of agricultural systems but of entire food systems. Through its objectives, agroecology aims at both sustainable land management and the strengthening of the livelihoods of producers and rural people and thus contributes to the fight against desertification. Currently, there is little scientific literature on the characteristics of agroecology in the Maghreb region. Several studies provide important information but they do not allow drawing up a global panorama of agroecology in the region. The proposed article highlights general characteristics of agroecology in North Africa from a review of 88 sustainable agriculture projects, which it analyzes, through an inventory of agroecological practices supported by these projects, from the frameworks of the High-Level Panel of Experts on Food Security and Nutrition, 2019, on the principles of agroecology and the transition levels approach developed by Gliessman and fellows since 2007. The results show (i) differences in the observed practices depending on the agrosystems and (ii) predominant common practices across these diverse agrosystems; (iii) significant evolution in these practices over time. The majority of the agroecological innovations identified are at the plot and farm scales, with the exception of those found in oasis and mountain agrosystems, where practices integrate the scales of the territory and value chains in a more complete way. Full article

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure. Full article

The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off. Full article