Search Results (1,095)

Context: Many patients at the end of life receive antibiotics to alleviate symptoms and improve quality of life; however, clear guidelines supporting decision making about the use of antibiotics are still lacking. Objectives: This study aimed to evaluate the benefits and harms of antibiotic use among patients under a palliative care community support team in Portugal. Methods: An observational, cross-sectional, retrospective study was conducted on 249 patients who died over a two-year period, having been followed for at least 30 days prior to their death. Data included patient demographics, clinical diagnoses, antibiotic prescriptions, and symptomatic outcomes. The effects of commonly prescribed antibiotics—amoxicillin + clavulanic acid, cefixime, ciprofloxacin, and levofloxacin—were compared using statistical analyses to assess survival, symptom intensity, and functional scales. Results: Adverse events, primarily infections and secretions, occurred in 57.8% of cases, with 33.7% receiving antibiotics. No significant difference in survival was observed across the antibiotic groups (p = 0.990). Symptom intensity significantly reduced after 72 h of treatment (p < 0.05), with ciprofloxacin demonstrating the greatest symptom control. The Palliative Outcome Scale decreased uniformly, with higher scores associated with amoxicillin + clavulanic acid (p = 0.004). The Palliative Performance Scale declined post-treatment, with significant changes noted for cefixime and ciprofloxacin (p < 0.05). Conclusions: Antibiotics may improve symptom control and quality of life in the end-of-life stage. While second-line antibiotics may offer additional benefits, the heterogeneity of the sample and limited adverse effect data underscore the need for further research to guide appropriate prescription practices in palliative care. Full article

The problem of protecting 7075 Al alloy trekking poles from corrosion in complex outdoor environments was addressed using a composite conversion coating system. This system comprised Na₂MoO₄, NaF, CoSO₄·7H₂O, ethylenediaminetetraacetic acid-2Na, and H₂(TiF₆). The influences of this system on the properties of the coating layer were systematically studied by adjusting the pH of the coating solution. The conversion temperature and pH were the pivotal parameters influencing the formation of the conversion coating. The pH substantially influenced the compactness of the coating layer, acting as a regulatory agent of the coating kinetics. When the conversion temperature and pH were set to 40 °C and 3.8, respectively, the prepared coating layer displayed optimal performance in terms of compactness and protective properties. Therefore, this parameter combination favours the synthesis of high-performance conversion coatings. Microscopy confirmed the formation of a continuous, dense composite oxide film structure under these conditions, effectively blocking erosion in corrosive media. Furthermore, the optimised process led to substantial enhancements in the environmental adaptabilities and service lives of the components of trekking poles, thus establishing a theoretical foundation and technical reference for use in the surface protection of outdoor equipment. Full article

Public–private partnership (PPP) logistics infrastructure projects have become increasingly prevalent globally. Consequently, the effective management of these projects as their concession periods expire presents a crucial challenge for governments, vital for the sustainable management of PPP logistics infrastructure. This study addresses this challenge by focusing on the pivotal post-concession decision: whether the government should self-operate the mature logistics infrastructure or re-delegate its management to a private entity. Our theoretical model, built on a principal–agent framework, first establishes a social welfare baseline under government self-operation and then analyzes delegated operation under symmetric information, identifying efficiency frontiers. Under symmetric information, we find that government self-operation is more advantageous when its own operational efficiency is sufficiently high, irrespective of the private enterprise’s efficiency; conversely, delegating to an efficient private enterprise is optimal only when government operational efficiency is low. We also demonstrate that if the government can directly specify the demand quantity and service level and delegates operation via a fixed fee, the enterprise can be incentivized to align with the social optimum. However, under asymmetric information, potential welfare gains from delegation are inevitably offset by informational rent and output distortion. We further uncover non-monotonic impacts of parameters like the proportion of low-cost firms on social welfare loss and demonstrate how information asymmetry can indirectly compromise the long-term resilience of the infrastructure. Ultimately, our work asserts that delegation is only superior if its potential efficiency gains sufficiently offset the inherent losses stemming from information asymmetry. Full article

This study presents a DIKWP-driven artificial consciousness framework for IoT-enabled smart healthcare, integrating a Data–Information–Knowledge–Wisdom–Purpose (DIKWP) cognitive architecture with a software-defined IoT infrastructure. The proposed system deploys DIKWP agents at edge and cloud nodes to transform raw sensor data into high-level knowledge and purpose-driven actions. This is achieved through a structured DIKWP pipeline—from data acquisition and information processing to knowledge extraction, wisdom inference, and purpose-driven decision-making—that enables semantic reasoning, adaptive goal-driven responses, and privacy-preserving decision-making in healthcare environments. The architecture integrates wearable sensors, edge computing nodes, and cloud services to enable dynamic task orchestration and secure data fusion. For evaluation, a smart healthcare scenario for early anomaly detection (e.g., arrhythmia and fever) was implemented using wearable devices with coordinated edge–cloud analytics. Simulated experiments on synthetic vital sign datasets achieved approximately 98% anomaly detection accuracy and up to 90% reduction in communication overhead compared to cloud-centric solutions. Results also demonstrate enhanced explainability via traceable decisions across DIKWP layers and robust performance under intermittent connectivity. These findings indicate that the DIKWP-driven approach can significantly advance IoT-based healthcare by providing secure, explainable, and adaptive services aligned with clinical objectives and patient-centric care. Full article

Power system infrastructure is exposed to a range of threats, including both naturally occurring events and intentional attacks. Traditional vulnerability assessment models, typically based on the N-1 criterion, do not account for the intentionality of disruptive agents. This paper presents a game-theoretic approach to protecting power system infrastructure against deliberate attacks, taking into account the effects of demand response. The interaction between the disruptive agent and the system operator is modeled as a leader–follower Stackelberg game. The leader, positioned in the upper-level optimization problem, must decide which elements to render out of service, anticipating the reaction of the follower (the system operator), who occupies the lower-level problem. The Stackelberg game is reformulated as a bilevel optimization model and solved using a metaheuristic approach. To evaluate the applicability of the proposed method, a 24-bus test system was employed. The results demonstrate that integrating demand response significantly enhances system resilience, compelling the disruptive agent to adopt alternative attack strategies that lead to lower overall disruption. The proposed model serves as a valuable decision-support tool for system operators and planners seeking to improve the robustness and security of electrical networks against disruptive agents. Full article

With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods. Full article

Background: Perioperative care is an integral part of the procedure of a surgical operation, with strictly defined rules. The need to upgrade and improve some individual long-term processes aims at optimal patient care and the provision of high-level health services. Therefore, preoperative care is drawn up with new data resulting from the evolution of technology to upgrade the procedures that need improvement. According to the international literature, a factor considered to be of major importance is high preoperative anxiety and its effects on the patient’s postoperative course. High preoperative anxiety is postoperatively responsible for prolonged hospital stays, increased postoperative pain, decreased effect of anesthetic agents, increased amounts of analgesics, delayed healing of surgical wounds, and increased risk of infections. The use of Virtual Reality technology appears as a new method of managing preoperative anxiety. Objective: This study investigates the effect and effectiveness of Virtual Reality (VR) technology in managing preoperative anxiety in adult patients. Methods: A literature review was performed on 193 articles, published between 2017 and 2024, sourced from the scientific databases PubMed and Cochrane, as well as the trial registry ClinicalTrials, with a screening and exclusion process to meet the criterion of investigating VR technology’s effectiveness in managing preoperative anxiety in adult patients. This systematic review was conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Results: Out of the 193 articles, 29 were selected. All articles examined the efficacy of VR in adult patients (≥18) undergoing various types of surgery. The studies represent a total of 2.354 participants from 15 countries. There are two types of VR applications: distraction therapy and patient education. From the studies, 14 (48%) used the distraction VR intervention, 14 (48%) used the training VR intervention, and 1 (4%) used both VR interventions, using a range of validated anxiety scales such as the STAI, VAS-A, APAIS, and HADS. Among the 29 studies reviewed, 25 (86%) demonstrated statistically significant reductions in preoperative anxiety levels following the implementation of VR interventions. VR technology appears to manage preoperative anxiety effectively. It is a non-invasive and non-pharmacological intervention with minimal side effects. Conclusions: Based on the review, the management of preoperative anxiety with VR technology shows good levels of effectiveness. Further investigation of the efficacy by more studies and randomized controlled trials, with a larger patient population, is recommended to establish and universally apply VR technology in the preoperative care process as an effective method of managing preoperative anxiety. Full article

Asphalt pavement cracking is an important factor affecting its service life. Under certain conditions, the self-healing behavior of asphalt itself can repair pavement cracks. However, the self-healing ability of asphalt itself is limited. In order to strengthen the self-healing ability of asphalt, the microcapsule wrapped with a repair agent is pre-mixed into the asphalt mixture. When the crack occurs and spreads to the surface of the microcapsule, the microcapsule ruptures and the healing agent flows out to realize the self-healing of the crack. Current microcapsules are mostly prepared with healing agents and bio-oil as core materials, and their high-temperature resistance to rutting is poor. While the epoxy resin contains a three-membered cyclic ether, it can undergo ring-opening polymerization to bond and repair the asphalt matrix. In addition, research on microcapsules mainly focuses on the self-healing properties of microcapsule-modified asphalt. In fact, before adding microcapsules to asphalt to improve its self-healing performance, it is necessary to ensure that the asphalt has a good road performance. On this basis, the self-healing performance of asphalt is improved, thereby extending the service life of asphalt pavement. Therefore, two-component epoxy self-healing microcapsules (E-mic and G-mic) were first prepared in this paper. Then, a temperature scanning test, rheological test of bending beams, and linear amplitude scanning test were, respectively, conducted for the microcapsule/asphalt to evaluate its road performance, including the high-temperature performance, low-temperature crack resistance, and fatigue performance. Finally, the self-healing performance of microcapsules/asphalt was tested. The results showed that the self-developed epoxy self-healing microcapsules were well encapsulated and presented as spherical micron-sized particles. The average particle size of the E-mic was approximately 23.582 μm, while the average particle size of the G-mic was approximately 22.440 μm, exhibiting a good normal distribution. In addition, they can remain intact and unbroken under high-temperature conditions. The results of road performance tests indicated that the microcapsule/asphalt mixture exhibits an excellent high-temperature resistance to permanent deformation, low-temperature crack resistance, and fatigue resistance. The self-healing test demonstrated that the microcapsule/asphalt exhibited an excellent self-healing performance. When the microcapsule content was 4%, the self-healing rate reached its optimal level of 67.8%, which was 149.2% higher than that of the base asphalt. Full article

Background: Among all ambulatory antibiotic prescriptions, about 20% are non-visit-based (ordered outside of an in-person clinical encounter), and about 30% are not associated with an infection-related diagnosis code. Objective/Methods: To identify the rationale for ambulatory antibiotic prescribing, we queried the electronic health record (EHR) of a single, large health system in the Midwest United States to identify all oral antibiotics prescribed from November 2018 to February 2019 and examined visit, procedure, lab, department, and diagnosis codes. For the remaining antibiotic prescriptions—mostly non-visit-based, no-infectious-diagnosis-documented—we randomly selected and manually reviewed the EHR to identify a prescribing rationale and, if none was present, surveyed prescribers for their rationale. Results: During the study period, there were 47,619 antibiotic prescriptions from 1177 clinicians to 41,935 patients, of which 2608 (6%) were eligible non-visit-based, no-infectious-diagnosis-documented. We randomly selected 2298. There was a documented rationale for 2116 (92%) prescriptions. The most common documented reasons—not mutually exclusive—were patient-reported symptoms (71%), persistence of symptoms after initial management (18%), travel (17%), and responding to lab or imaging results (11%). We contacted 160 clinicians who did not document any prescribing rationale in the EHR and received responses from 62 (39%). Clinicians’ stated reasons included upcoming or current patient travel (19%), the antibiotic was for the prescriber’s own family member (19%), or the clinician made a diagnosis but did not document it in the EHR (18%). Conclusions: Non-visit-based, no-infectious-diagnosis-documented antibiotic prescriptions were most often in response to patient-reported symptoms, though they also occur for a variety of other reasons, some problematic, like in the absence of documentation or for a family member. Full article

This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders. Full article

The new generation of robots (Industry 5.0 and beyond) is expected to be accompanied by the massive introduction of autonomous and cooperative agents in our society, both in the industrial and service sectors. Cooperation with humans will be simplified by humanoid robots with a similar kinematic outline and a similar kinematic redundancy, which is required by the diversity of tasks that will be performed. A bio-inspired approach is proposed for coordinating the redundant DOFs of such agents. This approach is based on the ideomotor theory of action, combined with the passive motion paradigm, to implicitly address the degrees of freedom problem, without any kinematic inversion, while producing coordinated motor patterns structured according to the typical features of biological motion. At the same time, since the approach is force-field-based, it allows us to integrate the computational loop parallel modules that exploit the redundancy of the system for satisfying geometric or kinematic constraints implemented by appropriate repulsive force fields. Moreover, the model is expanded to include dynamic constraints associated with the Lagrangian dynamics of the humanoid robot to improve the energetic efficiency of the generated actions. Full article

Face-to-face interaction is one of the most natural forms of human communication. Unsurprisingly, Video Conferencing (VC) Applications have experienced a significant rise in demand over the past decade. With the widespread availability of cellular devices equipped with high-resolution cameras, Instant Messaging Video Call Applications (IMVCAs) now constitute a substantial portion of VC communications. Given the multitude of IMVCA options, maintaining a high Quality of Experience (QoE) is critical. While content providers can measure QoE directly through end-to-end connections, Internet Service Providers (ISPs) must infer QoE indirectly from network traffic—a non-trivial task, especially when most traffic is encrypted. In this paper, we analyze a large dataset collected from WhatsApp IMVCA, comprising over 25,000 s of VC sessions. We apply four Machine Learning (ML) algorithms and a Large Multimodal Model (LMM)-based agent, achieving mean errors of 4.61%, 5.36%, and 13.24% for three popular QoE metrics: BRISQUE, PIQE, and FPS, respectively. Full article

In recent years, UAV (unmanned aerial vehicle)-UGV (unmanned ground vehicle) collaborative systems have played a crucial role in emergency disaster rescue. To improve rescue efficiency, heterogeneous network and task chain methods are introduced to cooperatively develop rescue sequences within a short time for collaborative systems. However, current methods also overlook resource overload for heterogeneous units and limit planning to a single task chain in cross-platform rescue scenarios, resulting in low robustness and limited flexibility. To this end, this paper proposes Gemini, a cascaded dual-agent deep reinforcement learning (DRL) framework based on the Heterogeneous Service Network (HSN) for multiple task chains planning in UAV-UGV collaboration. Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. For each mission, a well-trained Gemini can not only allocate resources in load balancing but also plan multiple task chains simultaneously, which enhances the robustness in cross-platform rescue. Simulation results show that Gemini can increase rescue effectiveness by approximately 60% and improve load balancing by approximately 80%, compared to the baseline algorithm. Additionally, Gemini’s performance is stable and better than the baseline in various disaster scenarios, which verifies its generalization. Full article

Geopolymer, as a sustainable, low-carbon gel binder, is regarded as a potential alternative to cement. Freeze–thaw (F-T) resistance, which has a profound influence on the service life of structures, is a crucial indicator for assessing the durability of geopolymer composites (GCs). Consequently, comprehending the F-T resistance of GCs is of the utmost significance for their practical implementation. In this article, a comprehensive and in-depth review of the F-T resistance of GCs is conducted. This review systematically synthesizes several frequently employed theories regarding F-T damage, with the aim of elucidating the underlying mechanisms of F-T damage in geopolymers. The factors influencing the F-T resistance of GCs, including raw materials, curing conditions, and modified materials, are meticulously elaborated upon. The results indicate that the F-T resistance of GCs can be significantly enhanced through using high-calcium-content precursors, mixed alkali activators, and rubber aggregates. Moreover, appropriately increasing the curing temperature has been shown to improve the F-T resistance of GCs, especially for those fabricated with low-calcium-content precursors. Among modified materials, the addition of most fibers and nano-materials remarkably improves the F-T resistance of GCs. Conversely, the effect of air-entraining agents on the F-T resistance of GCs seems to be negligible. Furthermore, evaluation and prediction models for the F-T damage of GCs are summarized, including empirical models and machine learning models. In comparison with empirical models, the models established by machine learning algorithms exhibit higher predictive accuracy. This review promotes a more profound understanding of the factors affecting the F-T resistance of GCs and their mechanisms, providing a basis for engineering and academic research. Full article

Background: Helicobacter pylori infection is widely prevalent across the globe and is a major etiological agent of various gastric pathologies. This bacterium colonizes the human stomach, where it induces a range of mucosal abnormalities observable upon clinical examination. Accordingly, the present study aimed to assess the prevalence and clinical implications of H. pylori infection among patients undergoing endoscopic evaluation. Method: A cross-sectional study was conducted from January to May 2019 at endoscopy service-providing health institutions. Sociodemographic and clinical data were collected. Gastric biopsies were collected during endoscopic procedures and immediately inoculated into brain–heart infusion broth and plated out. Then, phenotypic bacterial identification was done. The collected data have been analyzed using SPSS version 23. A logistic regression model was used for association determination. Result: Among the 135 individuals enrolled in the study, 59.3% are male, and 40.7% are female, with a mean age of 45 years. H. pylori is isolated in 17.8% of participants (24/135). Notably, the majority of these isolates 71% (17/24) are from male participants, while 29% (7/24) are from females. A statistically significant association is identified between H. pylori infection and both high salt intake [AOR = 3.3; 95% CI: 1.5–10.8; p = 0.04] and the presence of duodenal ulcers [AOR = 3.8; 95% CI: 1.2–11.9; p = 0.02]. The highest prevalence of H. pylori is observed among patients diagnosed with atrophic pangastritis. Conclusions: The prevalence of H. pylori among the study participants is comparatively low. However, a significant association was observed between H. pylori infection and both high dietary salt intake and the presence of duodenal ulcers. Full article