Search Results (2,744)

Three-dimensional (3D) printing, also known as additive manufacturing of cementitious materials, appears to be a promising way to build in a way that is more time-efficient, cost-effective and, under certain conditions, environmentally friendly. This technology continues to exhibit significant inhomogeneity, which is frequently caused by the interlayer area. The presented research aims to clarify the influence of the interlayer surface area and delay time on the bond strength. This study involved reference cast and printed samples with different delay times and cast samples with different interlayer surface areas. Different interlayer surface areas were accomplished through the utilisation of a teeth shaper before casting the second layer. Research has shown that the interlayer surface area has a significant impact on layer bond strength; up to a 70% increase in bond strength can be achieved while increasing the area by 20%. The results show that the increase in strength due to a larger surface area remained constant in terms of percentage, across delay times, with a linear dependency on a specific range of conditions. After the threshold of the surface area increased, the bond strength could be compromised and lowered. This threshold is above a 120% increase in surface area for the used teeth geometry and material. The proposed technology of ejecting teeth to alter the interlayer surface area has the potential to reduce the heterogeneity of mechanical properties in 3D-printed objects, caused by the different delay time between layers, because of the print strategy or material shortage. Full article

To address the long-term operational challenges of space environment monitoring buoys under extreme Arctic conditions, this paper proposes an energy management optimization method based on deep reinforcement learning (DRL). By constructing a buoy system model that integrates renewable energy sources, a primary lithium battery power supply, and a battery energy storage unit, combined with an Arctic environmental model incorporating low-temperature efficiency degradation, a reward function was designed to minimize power supply deficits while ensuring system reliability. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was employed to optimize energy scheduling strategies. Simulation results based on real Arctic data (August 2024–January 2025) demonstrate that integrating wind turbines significantly reduces reliance on primary lithium batteries. Specifically, the required lithium battery capacity was reduced by 87.5% (from 61.44 kWh to 7.685 kWh), and procurement costs were lowered by approximately $68,830 compared to non-rechargeable schemes1. This method significantly enhances the buoy’s endurance and scheduling intelligence, offering valid insights into energy management in intelligent polar observation equipment. Full article

A state of terminal stagnation is often reached by software projects despite the presence of advanced tools, and these occurrences are defined within this study as software engineering deadpoints, where the cost of system recovery is frequently found to be higher than the actual value of the software. While many factors are seen to lead toward project failure, it is suggested by the evidence that technical debts are the main cause of such failures. A significant number (23.5%) of these fatal issues is created during the early architectural phases of development, and it is noted that these problems often remain hidden until they become unrecoverable. The data collected during this research show that projects facing technical obstacles (Recovery Score: 4.24) are much harder to save than those suffering with process obstacles (Recovery Score: 5.38). It was also observed that a steady reluctance to refactor old logic and an excessive number of code revisions are seen as the most reliable signs that a project is approaching a point of no return. Because these warning signs are often overlooked by management, the eventual failure of the system is often viewed as an unexpected event rather than a predictable outcome of poor early choices. By defining these terminal states, this work provides those in leadership roles with a method to differentiate between minor delays and total failure, thereby assisting teams in avoiding the heavy economic losses associated with unproductive development paths. Full article

Background/Objectives: Chronic lower-limb ulcers of mixed etiology are characterized by impaired microcirculation and persistent inflammation, leading to delayed healing, frequent hospitalizations, and a high risk of limb loss. While infrared thermography reflects local perfusion status and systemic inflammatory markers capture whole-body immune activation, these dimensions are usually assessed separately. The objective of this study was to develop and internally evaluate a composite Thermal–Inflammatory Index (TI) integrating wound-bed thermography with systemic inflammatory markers to stratify disease severity and prognosis in patients with chronic lower-limb ulcers. Methods: In this prospective observational study, 82 adults with chronic lower-limb ulcers underwent baseline infrared thermographic assessment of wound-bed temperature using a standardized protocol. Concurrently, neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were measured. The Thermal–Inflammatory Index was constructed as a standardized composite of inverted wound-bed temperature, NLR, and CRP. A simplified TI score (0–3) was derived using predefined clinical thresholds. The primary endpoint was a composite adverse outcome defined as amputation or failure to achieve complete wound healing within 12 weeks. Secondary outcomes included a prolonged hospital stay (>7 days). Discriminative performance was assessed using receiver operating characteristic analysis, and associations were examined using correlation and logistic regression models. Results: Higher TI values were associated with colder wound beds, elevated systemic inflammatory markers, and increased disease burden. The TI demonstrated moderate discrimination for the composite adverse outcome (AUC 0.75) and prolonged hospitalization (AUC 0.71), performing comparably to the strongest single component (−T_bed, AUC 0.77) while integrating local and systemic information. Each one-standard-deviation increase in TI was independently associated with higher odds of the composite adverse outcome and a prolonged hospital stay. The simplified TI score showed clear stepwise gradients in adverse outcomes and length of hospitalization. Conclusions: The Thermal–Inflammatory Index integrates thermographic and inflammatory signals into a single, clinically interpretable biomarker of severity and prognosis in chronic lower-limb ulcers. TI and the simplified TI score may support early risk stratification using low-cost, bedside-accessible data. Full article

Elastic cloud systems are increasingly employing machine learning (ML) to automate resource scaling in response to variable workloads and stringent service-level objectives. However, current ML-based autoscalers are fragmented across different platforms, objectives, and evaluation frameworks. This survey examines 60 primary studies conducted between 2015 and 2025, categorising them according to a five-dimensional taxonomy that includes goal, decision logic, scaling mode, control scope, and deployment. This study classifies supervised, unsupervised, and reinforcement learning approaches and analyzes their integration into practical frameworks, including Kubernetes-based controllers and cloud provider services. This paper summarizes the application of machine learning to workload prediction, proactive and hybrid horizontal–vertical scaling, and adaptive policy optimization. Additionally, it synthesises common evaluation practices, encompassing workloads, metrics, and benchmarks. The analysis identifies ongoing challenges: actuation delays and telemetry lag, the intricacies of hybrid scaling, coordination across multi-service and edge-cloud deployments, and the constrained joint consideration of cost, SLO, and energy objectives. The identified gaps necessitate additional research on unified machine learning-driven orchestration, multi-agent and federated control, standardised benchmarks, and sustainability-aware autoscaling. Full article

Agriculture in Bulgaria faces increasing pressure to balance profitability with environmental sustainability under the evolving framework of the Common Agricultural Policy (CAP) and the European Green Deal. This study analyses the relationship between sustainability-oriented investment support, production cost structure, and farm profitability using farm-level data from the Farm Accountancy Data Network (FADN). The analysis integrates investment-related subsidies, input intensity, productivity indicators, and structural characteristics into an econometric framework to examine their associations with economic performance. Results show that environmental payments, when aligned with efficient management, enhance profitability, whereas conventional investment and rural development support display limited or delayed effects. Higher crop protection expenditure is associated with lower profitability, suggesting cost inefficiencies in chemically intensive production systems. In contrast, fertiliser expenditure shows no significant association, while energy-related spending exhibits a positive but statistically insignificant relationship, likely reflecting mechanisation and technological modernisation effects. Structural factors, particularly farm size and land productivity, remain key determinants of profitability for balancing economic and environmental goals. Overall, the findings suggest that sustainable profitability in Bulgarian agriculture is achievable but unevenly distributed, shaped by structural conditions, managerial capacity, and the design of support instruments. The study offers empirical evidence for aligning sustainable investment incentives with farm-level competitiveness and supports the transition toward integrated economic-environmental monitoring within the forthcoming Farm Sustainability Data Network (FSDN). Full article

In the Internet of Vehicles (IoV), connected vehicles generate high-load perception tasks with large-scale and multimodal sensitive data, imposing strict requirements on latency, computing, and privacy. Existing solutions still suffer from high task service latency and privacy risks. To address these issues, this paper proposes an integrated framework that jointly considers multi-flow task offloading, adaptive privacy preservation, and latency-aware resource incentive mechanism. Specifically, we propose a Location-Aware and Trust-based (LA-Trust) dual-node task offloading algorithm based on deep reinforcement learning (DRL), which treats pre-partitioned subtasks as multiple parallel flows and enables flow-level collaborative offloading optimization across neighboring nodes, allows subtask data uploading and processing to proceed concurrently, and incorporates node security into decision making. To further enhance privacy protection, a Distribution-Aware Local Differential Privacy (DA-LDP) algorithm is designed to adaptively inject artificial noise according to data heterogeneity, balancing privacy protection and task execution accuracy. In addition, a Delay-Cost Reverse Auction (DC-RA) algorithm is proposed to further reduce latency by introducing wireless channel modeling between idle vehicles and edge nodes into the incentive mechanism. Experimental results show that the proposed framework improves task execution accuracy by 38% and reduces offloading cost, delay, incentive cost, and auction communication latency by 64.41%, 64.64%, 19%, and 44%, respectively, while more than 60% of tasks are offloaded to high-trust nodes. Full article

Efficient scheduling of co-rail waste cranes is critical for ensuring continuous incinerator operation and reducing energy costs in waste-to-energy plants. Existing scheduling methods fail to address the unique characteristics of waste crane operations like task heterogeneity and dynamic spatial interference. To address this, a mixed-integer linear programming model is established to minimize the total crane traveling distance and task delays. A two-stage Discrete Ivy-Simulated Annealing (DIVY-SA) algorithm is proposed: the Ivy algorithm (IVYA) is discretized to generate high-quality task sequences, which are then refined by Simulated Annealing (SA) via a fine-grained local search. A heuristic task assignment scheme and a discrete-event simulation module are designed to evaluate task sequences accurately. Experiments using real-world operational data from a waste incineration plant cover task scales of 25 to 200, representing scheduling horizons of 15 min to 2 h. The algorithm’s runtime (15.04–652.81 s) demonstrates computational feasibility for near-real-time scheduling via a rolling horizon strategy. Results show that DIVY-SA outperforms representative metaheuristic algorithms and reduces the average total traveling distance by 22.19% compared with manual scheduling. This work provides technical support for the intelligent upgrading of waste incineration plants, effectively cutting energy consumption and improving operational efficiency. Full article

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies—local development, simulated cloud testing, and limited cloud usage for benchmark capture—enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows. Full article

With the high penetration of distributed energy resources (DERs), which are characterized by stochasticity and intermittency, traditional centralized optimization methods face challenges such as communication packet loss, low reliability, and poor scalability in large-scale DC microgrids. Therefore, distributed optimization methods have attracted attention due to their robustness and scalability. This paper extends our previous conference work by proposing a convex-relaxation-based distributed control strategy for DC microgrids with constant power loads (CPLs) and maximum power point tracking (MPPT)-controlled distributed generations (MPPT-DGs). Furthermore, a control strategy based on distributed observers is designed to achieve global optimal control under sparse communication networks. First, an exact convex relaxation method is applied to transform the original non-convex optimal power flow (OPF) problem into a convex problem, with theoretical guarantees of exactness. Then, the Karush–Kuhn–Tucker (KKT) conditions are equivalently transformed into a consensus-based optimality condition and integrated into the distributed control framework. Next, small-signal stability analysis is performed to verify the system’s robustness. To reduce communication costs, a distributed observer-based control strategy is proposed, which can achieve optimal control under sparse communication networks. The impact of communication delays on system stability is also investigated. Finally, the simulation results verify the accuracy of convex relaxation, the effectiveness of the proposed control strategy, and its performance under communication delay. Full article

Out-of-hospital cardiac arrest (OHCA) remains a leading cause of sudden death worldwide, with wide variation in reported incidence and outcomes driven by heterogeneity in registries, emergency medical services (EMS) organization, and case definitions. Despite substantial advances in resuscitation systems, survival after EMS-treated OHCA generally remains below 10%, and outcomes are critically time dependent. Delays in emergency call activation, bystander cardiopulmonary resuscitation (CPR), and—most importantly—early defibrillation are associated with a rapid decline in return of spontaneous circulation and favorable neurological recovery. This narrative review synthesizes current evidence and implementation strategies aimed at reducing “time-to-CPR” and “time-to-shock,” with a specific focus on public-access defibrillation (PAD) as a tool to mitigate avoidable delay. Randomized trials and large registry studies consistently demonstrate that automated external defibrillator (AED) use before EMS arrival is a key determinant of survival in patients with shockable rhythms. However, the real-world effectiveness of PAD remains limited by suboptimal AED placement, restricted 24/7 accessibility, low public awareness, and underutilization driven by fear and lack of confidence. We compare different PAD delivery models—including EMS-based, police and first-responder-based, and fully integrated community systems—and summarize evidence supporting targeted, high-yield AED deployment and cost-effectiveness. In addition, we review emerging strategies to reduce avoidable delay and strengthen the early links of the chain of survival, such as school-based training programs, smartphone- and SMS-based citizen-responder networks, improved dispatch recognition of cardiac arrest (including artificial intelligence–supported tools), and drone-enabled AED delivery. Across these approaches, patient benefit critically depends on system integration, alert performance, and true AED accessibility. Finally, we describe the Italian “Progetto Vita” experience as a community-integrated model explicitly designed to minimize avoidable delay through widespread AED deployment, lay responder training, and real-time integration with EMS. We conclude by outlining future priorities, including the development of robust national OHCA registries and scalable solutions for the high burden of cardiac arrests occurring at home, such as population-level deployment of low-cost, ultra-portable AEDs. Full article

Fresh market blueberry (Vaccinium spp.) fruits are fragile and experience numerous impacts during harvest, packing, and shipping. Mechanical harvest of southern highbush blueberries (SHB) is being increasingly implemented due to rising costs and limited availability of labor. As new commercial cultivars become available, questions arise among growers as to their suitability for mechanical harvest. Early spring harvests in growing areas in the southeastern U.S. routinely occur when ambient temperatures exceed 30 °C. A series of experiments was conducted over a decade to determine the effects of mechanical impacts on fruit quality. These experiments employed a 60 cm drop height to induce bruising under three scenarios encountered during commercial harvest and handling. (1) Harvest interval: Nonimpacted ‘Star’ and ‘Sweetcrisp’ fruits had higher soluble solids content to titratable acidity ratios (SSC:TA) after a 7-day interval (Harvest 2) as compared with those from the initial Harvest 1. Impacted ‘Star’ blueberries from Harvest 2 were 70–100% softer during 14-d storage at 1 °C/85% relative humidity than those from Harvest 1, whereas ‘Sweetcrisp’ fruits were less affected by the harvest delay (30–40% increase in soft fruit). (2) Pulp temperature at impact: There were no differences in bruise severity for ‘Meadowlark’, ‘Colossus’, or ‘Sentinel’ due to pulp temperature at impact. Overall, impacted fruits consistently exhibited greater weight loss (3% to 9%), were softer, and had more severe bruising compared with nonimpacted controls. (3) Delays between harvest and impact: Delay-to-impact (5 or 24 h) did not affect weight loss for ‘Meadowlark’ (0.57% to 0.62%) during 4 d of storage at 5 °C. ‘Colossus’ and ‘Sentinel’, held overnight at 22 °C, lost approximately 35% to 45% more fresh weight after the 24 h delay to impact compared with those fruits with the 5 h delay to impact. Impacted blueberries exhibited significantly more severe bruising (38.5% to 84.4%) than control fruits (1.0% to 8.3%). ‘Sentinel’ was softer at harvest than the other cultivars and had the highest amount of severe bruising (82.7%), followed by ‘Meadowlark’ (52.67%) and ‘Colossus’ (42.57%). Flavor profiles varied by cultivar, with SSC:TA ratios ranging from 18 (‘Colossus’) to 21 (‘Meadowlark’) to 44 (‘Sentinel’). Immediately after impact at 15 °C, 20 °C, or 30 °C, the respiration rate (RR) for ‘Meadowlark’ increased as compared with the control fruit. RR for fruits at 5 °C or 10 °C remained fairly constant during the 8 h measurement period. These findings highlight the interactions of harvest interval, pulp temperature, and delay to impact on the postharvest quality of several commercially grown, SHB cultivars over this extended period of time. These three factors must be considered in order to develop effective strategies for mechanical harvest under the warm spring conditions encountered in the subtropical growing conditions in the southeastern U.S.A. Full article

This study presents a site-specific calibration of the ratio between S-wave and P-wave peak ground acceleration (PGA) for use in low-cost, on-site earthquake early warning (EEWS) systems in Iași, Romania. A dataset of 25 intermediate-depth Vrancea earthquakes (Mw 4.1–5.7; epicentral distances 150–210 km) was analyzed. PGA values were extracted for the P- and S-wave windows on both horizontal components and combined using geometric means. The resulting S/P amplitude ratios yield a median value of k_S/P = 6.19 and a logarithmic standard deviation of σlog₁₀ = 0.31, corresponding to a multiplicative uncertainty factor of approximately ×2. These results indicate that S-wave amplitudes are typically six times larger than P-wave amplitudes at this site, consistent with soft-soil amplification observed in comparable stations in Japan and Italy. The calibrated ratio can be used as a site-specific input for future MEMS-based on-site EEW implementations to estimate the expected S-wave PGA immediately after P-wave detection, with the observed S–P delays in Iași indicating a typical available warning window of 20–22 s. Full article

The advent of 5G/6G technologies and the pervasive deployment of IoT devices are driving the emergence of demanding applications that necessitate ultra-low latency, high bandwidth, and significant computational power. Traditional cloud computing models fall short in meeting these stringent requirements. To address this, Software-Defined Edge Networks (SDENs) have emerged as a promising architecture, yet efficiently managing their heterogeneous and geographically distributed resources poses substantial challenges for optimal application provisioning. In response, this paper proposes a novel framework for intelligent task offloading, which reframes the intricate multi-component application task offloading problem as a Virtual Network Embedding (VNE) challenge within a SDEN environment. We introduce a comprehensive model where complex applications are represented as Virtual Network Requests (VNRs). In this model, each VNR consists of virtual nodes that demand specific computing and storage resources, as well as virtual links that demand specific bandwidth and must adhere to maximum tolerable delay constraints. To dynamically solve this NP-hard VNE problem in the face of stochastic VNR arrivals and dynamic network conditions, we leverage Deep Reinforcement Learning (DRL). Specifically, a Soft Actor-Critic (SAC) agent is employed at the SDN controller. This agent learns a sequential decision-making policy for mapping virtual nodes to physical edge servers and virtual links to network paths. To guide the agent towards efficient resource utilization, we define the reward for each successful embedding as the long-term revenue-to-cost ratio. By learning to maximize this reward, the agent is naturally driven to find economically viable allocation strategies. Comprehensive simulation experiments demonstrate that our SAC-based VNE approach significantly outperforms other baselines across key metrics, affirming its efficacy in dynamic SDEN environments. Full article

Background: Postoperative bleeding is a frequent complication in otolaryngology and head and neck surgery, often leading to readmissions and increased healthcare costs. Objectives: This systematic review evaluates the clinical efficacy, safety, and impact of RADA16, a synthetic self-assembling peptide hydrogel, as a topical haemostatic adjunct in this surgical field. Methods: In adherence with PRISMA 2020 guidelines, a systematic search of PubMed, Scopus, and Web of Science was conducted through December 2025. Eligible studies included adult patients undergoing otolaryngological or head and neck surgical procedures where RADA16 (CAS 289042-25-7, PuraBond^®/PuraStat^®/PuraGel^®, ^®, 3-D Matrix SAS; Caluire et Cuire, Lyon, France) was applied intraoperatively. Exclusion criteria included non-English publications, reviews, and studies without clinical outcome data. Risk of bias was assessed using the Cochrane Risk of Bias tool for RCTs and the Newcastle-Ottawa Scale for observational studies. A narrative synthesis was performed due to heterogeneity in outcome reporting. Results: Eight studies involving 1761 patients were included. In oropharyngeal surgery, RADA16 significantly reduced postoperative haemorrhage (6.3% vs. 16.7%, p = 0.016) and was associated with faster resumption of normal diet and lower pain scores (p = 0.016). In nasal surgery, it significantly lowered epistaxis rates (0.4% vs. 2.2%, adjusted OR 0.027, p = 0.026) and reduced the need for nasal packing. In cervical endocrine surgery, the rate of hematoma requiring revision was low (0.84%), with no delayed bleeding beyond 24 h. Surgeons consistently reported high satisfaction and ease of application. No serious device-related adverse events were reported. Discussion: Current evidence suggests RADA16 is a safe and effective haemostatic adjunct that can improve postoperative recovery and reduce readmission rates in specific surgical contexts. Limitations include heterogeneity in study designs, small sample sizes in some domains, and a lack of long-term follow-up. Further large-scale randomized controlled trials are needed to quantify its economic impact and formalize its role in surgical pathways. Funding: This study was funded by 3-D Matrix Medical Technology for article processing charges. The funder had no role in study design, data collection, analysis, interpretation, or writing. Registration: This review was not registered in a systematic review registry. Full article