Search Results (14,783)

Digital technologies make it possible to combine multiple technical functionalities within applications that address practical and organizational needs. This paper presents Cozzmo, an Android mobile prototype for supporting communication and coordination in shared households. The system combines chat, polls, chores, shopping support, photo albums, presence awareness, mood indicators, and location-based alerts in one application. The prototype was implemented in native Java for Android using Firebase services and an MVVM architecture with LiveData. Its real-time behavior was evaluated on two physical Android devices under mixed connectivity conditions, including mobile data, hotspot use, and temporary connection loss. The evaluation examined end-to-end propagation delay, recovery after reconnection, and state convergence during concurrent user actions. In the reported test sessions, the prototype preserved update order in baseline scenarios, recovered queued messages after short interruptions, and reached a consistent final state in the concurrent voting and task-update tests. The time needed for updates to appear in the interface was less than the propagation delay, suggesting that the measured response path was shaped mainly by network and backend propagation. These findings indicate that the prototype is technically viable and can serve as a basis for further work on mobile systems for household collaboration. Full article

To address the challenge of stable tracking of moving maritime targets by unmanned aerial vehicle(UAV) swarm in environments with threat zones and platform failure risks, this paper proposes a cooperative tracking and guidance strategy integrating Distributed Model Predictive Control (DMPC) with Sequential Quadratic Programming (SQP). A cooperative tracking model is developed incorporating UAV kinematics, environmental threats, stereo-vision positioning, and field-of-view constraints. Two original strategies are introduced within the DMPC framework: an altitude-cooperative target recapture strategy reduces target total loss duration by approximately 7 s compared to fixed-altitude baselines, while a distributed formation reconfiguration strategy restores stable tracking within 10 s after member failure and ensures safe inter-UAV separation. A multi-constraint trajectory tracking controller based on DMPC-SQP achieves real-time co-optimization of threat avoidance, formation maintenance, and tracking accuracy. Simulation results in dense threat environments demonstrate a 93.4% Quadratic Programming feasibility rate, with mean tracking error reduced by 25.4% over fixed-altitude DMPC and 48.7% over methods based on the Linear Quadratic Regulator (LQR), while maintaining robust performance under 300 ms communication delay, sensor noise, and moderate wind disturbance. Full article

Understanding how information spreads in complex networks is essential for analyzing social influence, opinion formation, and the emergence of collective behavior. In many real-world systems, interactions are not instantaneous but involve delays due to communication, cognition, and response times. Motivated by this observation, the present paper investigates a delayed network model of information spreading, focusing on how time delay and interaction strength shape the system’s dynamical behavior. The novelty of the proposed approach lies in the formulation of a discrete-time network model that explicitly incorporates delayed interactions within a nonlinear dynamical framework. Using delay difference equations, the model captures both local coupling effects and memory-driven feedback, allowing for a systematic study of their combined impact on stability and complexity. Analytical results establish the existence of steady states and provide conditions for their local stability, revealing critical thresholds at which the system undergoes qualitative transitions. These findings are complemented by extensive numerical simulations. In particular, bifurcation analysis and the computation of the largest Lyapunov exponent demonstrate a progression from stable equilibria to oscillatory behavior, and further to chaotic dynamics as the delay and coupling strength increase. Our results highlight the fundamental role of delay as a mechanism that enhances nonlinear complexity and promotes unpredictable dynamics in networked systems. These insights contribute to a deeper understanding of information propagation processes, and may inform the design and control of spreading phenomena in social and technological networks. Full article

In the context of rapid technological evolution and increasing market uncertainty, technoparks have emerged as critical ecosystems for bridging scientific research and high-tech industrial production; however, their effectiveness is often constrained by limited flexibility, fragmented control mechanisms, and delayed decision-making processes. Motivated by these challenges, this article investigates the automation of control processes in research-driven and flexible manufacturing environments within technopark infrastructures, positioning automation as a strategic lever for enhancing operational adaptability and innovation throughput. The study conceptualizes control process automation as a multi-stage framework encompassing data acquisition, processing, intelligent analysis, and real-time decision execution and examines the role of enabling technologies such as artificial intelligence, the Internet of Things (IoT), and cyber-physical systems in supporting this paradigm. The analysis demonstrates that the integration of these technologies significantly improves production flexibility, resource optimization, and responsiveness to dynamic conditions, while simultaneously accelerating the transformation of scientific and research outputs into measurable economic value. By combining theoretical foundations with illustrative practical applications, the article substantiates the effectiveness of automated control systems and highlights their strategic relevance for increasing the competitiveness of technoparks, fostering sustainable technological innovation, and shaping resilient long-term development strategies. Full article

Collagen V is a minor fibrillar collagen that regulates type I collagen fibrillogenesis; however, its time-dependent role during the stages of adult tendon healing remains unclear. We investigated the stage-specific effects of inducible Col5a1 knockdown during tendon repair in a murine injury model. Collagen V expression was transiently suppressed during either the late inflammatory phase (tamoxifen-induced knockdown at 5 days post-injury; TM5) or early remodeling phase (tamoxifen-induced knockdown at 21 days post-injury; TM21), with outcomes assessed using gene expression, ultrastructural, and mechanical analyses. Early knockdown at 5 days post-injury, TM5, was associated with increased fibril diameter, greater fibril heterogeneity, and reduced structural mechanical properties, including decreased stiffness and maximum load. In contrast, delayed knockdown at 21 days post-injury, TM21, imposed after initial fibril organization, resulted in minimal changes in quasi-static mechanics but altered viscoelastic behavior and late-stage gene expression. An allele dose-dependent response was observed, with complete Col5a1 knockdown producing greater structural disorganization and mechanical deficits. Transcriptional changes suggested time-dependent effects on extracellular matrix regulation, including proteoglycans, remodeling enzymes, and tenogenic markers. Collectively, these findings indicate that collagen V may function as a time- and dose-sensitive contributor to tendon healing, with a critical role during early fibril organization. Disruption during this time window has lasting effects on tendon structure and mechanics, informing stage-specific therapeutic strategies for tendon repair. Full article

Digital games have been increasingly recognized as promising tools for supporting meaningful learning in school contexts. This study examined the effects of Alien Health, an embodied video game designed to promote nutrition and healthy eating learning among fifth-grade students in Chile. A quasi-experimental repeated-measures design was conducted with 327 students from 10 public, subsidized-private, and private schools in the Province of Concepción. Using pre-existing fifth-grade classes, participants were organized into a control group, who received traditional teacher-led instruction and an experimental group, who engaged with the video game without teacher mediation. Learning outcomes were assessed using the Nutrition and Healthy Eating (NHE) test, a 10-item curriculum-aligned instrument developed for this study and reviewed by expert teachers. The test was administered at pretest, posttest, and delayed retest. Data were analyzed through repeated-measures ANOVA, considering total scores, curricular sub-objectives, and Bloom-based cognitive categories. Results showed a differentiated pattern over time. Control students tended to achieve higher scores at the immediate posttest, particularly in tasks associated with factual recall, whereas experimental students showed more stable performance at delayed retest and stronger outcomes in some higher-order tasks, especially food classification and integrative diet design. Differences in school type and gender were also observed, suggesting that the effects of the intervention varied across educational contexts. Overall, the findings indicate that embodied video games may serve as a valuable complement to traditional instruction by supporting longer-term knowledge retention and conceptual integration in primary education. The study contributes empirical evidence from a Latin American context to the literature on embodied learning, game-based education, and nutrition teaching. Full article

Background/Objectives: Radiotherapy is a cornerstone of head and neck cancer treatment, but it can adversely affect periodontal tissues by impairing vascularity, cellularity, and healing capacity. The present study aimed to histologically and immunohistochemically evaluate radiotherapy-induced changes in periodontal tissues in a rat model of experimental periodontitis, with particular focus on periodontal ligament width (PerioW) and the RANK/RANKL/OPG axis at different healing stages. Methods: Seventy-two male Sprague–Dawley rats were allocated to three groups: irradiation only (Rt), ligature-induced periodontitis only (Pt), and ligature-induced periodontitis followed by irradiation (PtRt). Experimental periodontitis was induced by placing ligatures around the maxillary first molars for two weeks. On the day of ligature removal, the irradiated groups received a single 20 Gy dose to the head and neck region. Animals were euthanized on days 1, 15, and 30 after ligature removal (n = 8/group/time point). Histomorphometric analysis of PerioW was performed on H&E-stained sections, and immunohistochemical staining was used to quantify RANK, RANKL, and OPG expression. Results: On day 1, PerioW did not differ significantly among groups, although the PtRt group had the highest mean. A significant intergroup difference was observed for RANKL, with higher expression in PtRt than in Pt. On day 15, PerioW differed significantly among groups, with the lowest value in Rt and the highest in PtRt; the Rt–PtRt comparison was significant. At this time point, RANK, RANKL, OPG, and the RANKL/OPG ratio showed no significant intergroup differences. On day 30, no significant intergroup differences were found for PerioW or immunohistochemical parameters; however, PtRt continued to show the highest PerioW and OPG values and the lowest RANKL/OPG ratio. Conclusions: Radiotherapy superimposed on periodontitis enhanced early pro-resorptive signaling and delayed structural normalization of periodontal tissues. Although late increases in OPG suggested a compensatory response, this appeared insufficient to fully reverse radiation-associated periodontal alterations. These findings support the importance of controlling periodontal inflammation before radiotherapy to reduce subsequent periodontal tissue damage. Full article

Edge computing on unmanned aerial vehicles (UAVs) enables low-latency wildfire monitoring by performing visual inference onboard; however, practical deployment is constrained by limited labeled data and resource budgets that often preclude reliance on large GPU servers. This work investigates transfer learning (TL) for UAV-based wildfire smoke and flame detection and evaluates its impact on both detection accuracy and edge deployment performance. We introduce the Aerial Fire and Smoke Essential (AFSE) dataset (282 aerial-view images; classes—smoke and fire), compiled from publicly available wildfire footage and FLAME2. Lightweight YOLO models are fine-tuned using heterogeneous (MS COCO) and homogeneous (FASDD) source pretraining and are assessed using mAP@0.5 together with frames per second (FPS), average inference power, energy consumption, and the normalized energy–delay product (EDP) on an edge computing platform. Results show that TL substantially improves detection accuracy on AFSE, achieving up to 79.2% mAP@0.5, while reducing training time, and improving cross-validation stability. On the tested edge platform, TL does not materially change inference speed or energy use, indicating that accuracy gains from TL do not automatically translate to improved efficiency without additional optimization. Among the evaluated lightweight detectors, YOLOv5n achieves the best mAP@0.5 while maintaining the highest edge device throughput, processing images nearly twice as fast as YOLO11n without hardware acceleration. More broadly, the measured throughput and energy differences among lightweight YOLO variants show that edge model selection should be guided by application-specific accuracy, latency, and energy constraints. Full article

Background: Postoperative ileus (POI) remains a leading driver of delayed recovery and prolonged length of stay (LOS) after colorectal surgery. Although ERAS is well established, less is known about how pathway adherence and implementation fidelity relate to bowel recovery in pragmatic minimally invasive practice. Objectives: To evaluate whether a structured ERAS pathway, delivered in routine care, was associated with lower POI and improved early recovery compared with contemporaneous standard care after elective minimally invasive colorectal surgery. Methods: In a prospective, non-randomized pragmatic comparative study conducted from January 2022 to September 2024, 123 adults undergoing elective laparoscopic colorectal resection were managed with either an ERAS pathway (n = 62) or standard care (n = 61). POI was operationalized prospectively using predefined clinical criteria and daily team assessment. Primary outcome was POI. Secondary outcomes included time to flatus, LOS, 48 h opioid use (morphine milligram equivalents, MME), complications (Clavien–Dindo), 30-day readmission, and Quality of Recovery (QoR-15). Multivariable logistic regression and propensity score–adjusted sensitivity analyses were performed to address baseline imbalance. Results: POI occurred in 7/62 (11.3%) in ERAS vs. 22/61 (36.1%) in standard care (p = 0.002). ERAS patients had earlier flatus (38.6 ± 15.2 h vs. 60.0 ± 20.1 h, p < 0.001), shorter LOS (4.2 [3.4–5.0] vs. 5.4 [4.5–6.8] days, p < 0.001), lower 48 h opioids (35.4 [25.2–47.8] vs. 61.1 [41.5–88.6] MME, p < 0.001), and higher QoR-15 at POD2 (113.9 ± 14.9 vs. 104.8 ± 15.5, p = 0.001). In the primary multivariable model, ERAS was independently associated with lower POI odds (adjusted OR 0.2; 95% CI 0.1–0.7; p = 0.013); the association remained directionally similar in propensity-adjusted sensitivity analysis (adjusted OR 0.31; 95% CI 0.12–0.79; p = 0.015). Higher adherence was associated with lower POI and lower opioid exposure. Conclusions: In this prospective cohort, ERAS implementation was associated with lower POI incidence and faster early recovery; however, findings should be interpreted as observational and hypothesis-generating rather than causal. Full article

The efficient scheduling of airport logistics vehicles is crucial for ensuring timely and cost-effective ground operations, particularly under dynamic disturbances such as flight delays, cancellations, and new task arrivals. With the increasing deployment of Internet of Things (IoT) technologies in airport environments, real-time data from sensors and connected devices enables efficient and adaptive scheduling. This paper considers a dynamic Airport Logistics Vehicle Scheduling (ALVS) problem that aims to minimize both vehicle usage and total task waiting time while satisfying task precedence and time window constraints. To address this problem, we propose a hybrid optimization framework, termed Periodic and Event-Driven Rolling Horizon Optimization (PERHO), which integrates periodic updates with event-driven rescheduling to adapt to real-time task variations in airport ground operations. Within PERHO, an Order-aware Adaptive Strategy Selection (OASS) algorithm is developed to dynamically select the most appropriate task sequencing heuristic from a candidate set based on recent performance and order relationships. Extensive experiments across various instance scales and dynamic scenarios demonstrate the effectiveness of the proposed PERHO-OASS approach. In experiments considering dynamic events, PERHO-OASS reduces vehicle usage and task waiting time by an average of 23.55% and 61.95%, respectively, over fixed heuristic algorithms, and by an average of 3.77% and 17.30% over adaptive selection methods, demonstrating strong robustness under uncertainty. The proposed approach can support airport operators in improving the efficiency and reliability of ground logistics operations. Full article

Flow-induced vibration of high-speed pantographs becomes increasingly important as train speed approaches and exceeds 400 km/h. This paper develops an efficient computational framework that couples component-resolved unsteady aerodynamic loads from improved delayed detached eddy simulation (IDDES) with a dynamic-stiffness-method (DSM) flexible model of a high-speed pantograph. Two operating orientations, namely, knuckle-downstream and knuckle-upstream, are compared at 400 km/h, and the more unfavorable knuckle-upstream orientation is further investigated over 400km/h–600km/h. The DSM model contains 49 beam elements and 42 nodes and shows good agreement with a refined three-dimensional solid-element finite element model in the low-order frequency range. For a 2 s transient analysis, the proposed model predicts the panhead displacement response with peak errors below 5% relative to the finite element model while reducing the computational time from 53 min 22 s to 35 s on the same platform. The results show that vertical vibration dominates the structural response, with the panhead peak vertical displacement reaching about 20 mm in the studied 400 km/h open-line case. Frequency-domain inspection of the panhead aerodynamic lift and vertical displacement shows that broadband aerodynamic excitation mainly activates the low-order structural modes, with a low-frequency aerodynamic component around 3Hz–4Hz and additional energy mainly over the 20Hz–30Hz range. The knuckle-upstream orientation increases the standard deviation of the equivalent contact-force response by 46% compared with the knuckle-downstream orientation at 400 km/h. For the knuckle-upstream orientation, increasing speed from 400 km/h to 600 km/h raises the standard deviation by 189%. The proposed framework provides an efficient tool for rapid comparative evaluation of pantograph flow-induced vibration under multiple operating conditions. Full article

Background: Pulmonary actinomycosis is a rare chronic infection that frequently mimics lung malignancy, often leading to delayed diagnosis due to its non-specific clinical and radiological presentation. Given the diagnostic challenges associated with this condition, the aim of this study was to evaluate the clinical presentation, diagnostic pathways, treatment strategies, and outcomes of patients diagnosed with pulmonary actinomycosis in a single center. Methods: We retrospectively reviewed patients diagnosed with pulmonary actinomycosis at our institution between January 2014 and December 2022. Diagnosis was established based on compatible clinical and radiological findings together with microbiological identification of Actinomyces by culture or polymerase chain reaction. Results: Twenty-two patients were included in the final analysis. The median age was 61.5 years and males were more frequently affected (59%). The median time from initial hospitalization to definitive diagnosis was 70 days. Actinomyces odontolyticus was the most frequently identified species. All patients received antibiotic therapy, with a median treatment duration of 45.5 days. Thirteen patients underwent surgical intervention, performed either for diagnostic purposes or for treatment of complications. Complete disease eradication through surgical management was achieved in six cases. During follow-up (median 24 months), overall survival at three years was 78%, with one death directly related to pulmonary actinomycosis. Conclusions: Pulmonary actinomycosis remains a diagnostic challenge due to its non-specific clinical presentation and low microbiological yield. Early clinical suspicion and a combined diagnostic approach including bronchoscopy and microbiological testing are essential for timely diagnosis. Surgical intervention may play an important diagnostic and therapeutic role in selected patients. Full article

Operational shutdowns following hazardous liquid pipeline incidents are critical but poorly understood events that impact the U.S. energy supply. Although prior research has investigated the causes and outcomes of pipeline failures, limited work has explained what drives both the likelihood of a shutdown and the duration once it begins. The goal of this study is to address this gap by developing a hurdle regression model to examine the two-stage shutdown mechanism in pipeline incidents, using the Pipeline and Hazardous Materials Safety Administration (PHMSA) incident dataset from 2010 to 2025. The hurdle model consists of a logistic regression restricted to pre-decision predictors to model the probability of shutdown, and a lognormal regression to model the duration of those leading to shutdown. The results revealed that distinct factors are associated with each outcome. Shutdown probability is associated with pre-decision operational and contextual indicators, including operating pressure at the time of incident, accident type, location, monitoring presence, and response delay. In contrast, shutdown duration is associated with logistical complexity and post-incident severity, including incidents at pipeline crossings, pressures exceeding 110% of the maximum operating pressure, and reported property damage. These findings, while exploratory in nature given the use of public incident data, offer practical reference points for operators and regulators who aim to shorten recovery time and strengthen the resilience of energy infrastructure. Full article

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km²) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km²); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13. Full article

Background/Objectives: The use of artificial intelligence (AI) in imaging departments is increasing in Europe. This study assesses the clinical value of an AI fracture algorithm by assessing ease of use, clinicians’ trust, and perceived barriers and benefits of this decision support tool in daily practice across two emergency departments (EDs) in Denmark. Methods: An online survey was distributed over four weeks (June–July 2025) to healthcare professionals interpreting radiographs in the ED at Lillebaelt Hospital. The survey included open-ended, closed-ended, and free-text questions addressing AI use. Additionally, an observational study was conducted, including workflow observations and time tracking of patient progression through the ED. Historical injury conference records from February 2023 to 2025 were reviewed to assess changes in patient management before and after AI implementation. Results: A total of 56 responses were obtained (24 male, 32 female). Most respondents reported a positive attitude toward the algorithm. Ease of use was rated satisfactory by 51 out of 56 participants, and 48 were satisfied with AI as a clinical decision support tool. Overall trust was high, with more than two thirds (n = 38) “agreeing” or “strongly agreeing” that the algorithm reliably detects fractures. However, an asymmetry in clinical trust was observed, whereby clinicians expressed greater confidence in their own assessments when the algorithm indicated the presence of a fracture than when it did not. Value stream analyses showed a delay of 6–23 min between radiograph acquisition and availability of the AI report. No differences were observed in the number of patients with treatment changes before, during, or after full implementation of the algorithm. Conclusions: In our limited study population, the AI fracture detection tool was overall well received by clinicians, although some observations indicate that implementation and workflow integration still require improvement. Larger studies are needed to validate the reported barriers and benefits of the AI fracture detection tool. Full article