Search Results (1,859)

Background and Objectives: Orthodontic treatment, whether fixed or removable, offers several benefits, including improved aesthetics, enhanced oral function, and increased self-confidence. However, it may also cause discomfort and pain, particularly following adjustment visits. This study aimed to assess pain characteristics (latency and continuity), food impairment, weight loss, and analgesic use in relation to treatment duration and appliance type. Methods: This observational study included 160 orthodontic patients who completed a structured questionnaire comprising 13 single-choice items. The questionnaire assessed age, gender, residential environment, educational status, type and duration of orthodontic treatment, pain characteristics (duration, latency, continuity), food impairment, and analgesic use. Inclusion criteria specified patients with moderate anterior crowding undergoing fixed orthodontic treatment or treatment with clear aligners on both arches, for at least one month. All fixed appliance cases involved 0.022-inch-slot Roth prescription brackets. Results: Patients undergoing fixed orthodontic treatment reported a higher frequency of pain (91.4%), greater need for analgesics (95.2%), and more food impairment compared to those with clear aligners. Patients treated for less than 6 months more frequently reported pain lasting 1 week (57.1%), while those treated for 1–2 years more commonly reported pain lasting several days (43.8%). Conclusions: Fixed orthodontic appliances are associated with greater discomfort, longer pain latency, more frequent analgesic use, and higher dietary impact compared to clear aligners. These findings emphasize the importance of personalized patient counseling and proactive pain management to improve compliance, enhance quality of life, and support informed decision-making in orthodontic care. Full article

Structural health monitoring in resource-constrained environments demands crack segmentation models that match the accuracy of heavyweight convolutional networks while conforming to the power, memory, and latency limits of watt-level edge devices. This study presents a lightweight dual-attention network, which is a four-stage U-Net compressed to one-quarter of the channel depth and augmented—exclusively at the deepest layer—with a compact dual-attention block that couples channel excitation with spatial self-attention. The added mechanism increases computation by only 19%, limits the weight budget to 7.4 MB, and remains fully compatible with post-training INT8 quantization. On a pixel-labelled concrete crack benchmark, the proposed network achieves an intersection over union of 0.827 and an F1 score of 0.905, thus outperforming CrackTree, Hybrid 2020, MobileNetV3, and ESPNetv2. While refined weight initialization and Dice-augmented loss provide slight improvements, ablation experiments show that the dual-attention module is the main factor influencing accuracy. With 110 frames per second on a 10 W Jetson Nano and 220 frames per second on a 5 W Coral TPU achieved without observable accuracy loss, hardware-in-the-loop tests validate real-time viability. Thus, the proposed network offers cutting-edge crack segmentation at the kiloflop scale, thus facilitating ongoing, on-device civil infrastructure inspection. Full article

Background: Constipation is a common gastrointestinal disorder with a significant impact on quality of life. Methods: Constipation was induced in male ICR mice via 25% cotrimoxazole gavage (20 mL/kg/day for 7 days). Mice were divided into prevention (pre-MBSFL), treatment (MBSFL), and control groups. MBSFL was prepared by fermenting mung bean starch with Lactobacillus plantarum (1:3 w/v ratio, 37 °C for 48 h), and administered via daily oral gavage (250 mg/kg bw) for 14 days. Fecal parameters (water content and first black stool latency), gastrointestinal motility (gastric emptying and small intestinal propulsion), serum biomarkers (NO, VIP, SP, and 5-HT), and intestinal gene expression (5HTR₄, SERT, and MAO_A) were analyzed. Results: MBSFL intervention restored fecal water content by 38%, reduced first black stool latency from 6.2 h to 3.1 h, and improved small intestinal propulsion by 64%. Additionally, it downregulated serum NO (25%) and VIP (32%) while upregulating SP (49%) and 5-HT (78%) levels. Intestinal 5HTR4 and SERT expression increased by 78% and 71%, respectively, with MAOA suppression (25%). Microbial analysis revealed a 140% increase in Dubosiella and 49% in Lactobacillus abundance, alongside a 62% reduction in Mucispirillum. MBSFL contained polysaccharides (12.3% w/w) and organic acids, including hydroxy butyric acid (4.2 mg/mL). Conclusions: MBSFL alleviates constipation through dual mechanisms: modulating 5-HT pathway activity and restoring gut microbiota homeostasis. Full article

This paper presents new fast algorithms for the fourth type discrete Hartley transform (DHT-IV) for input data sequences of lengths from 3 to 8. Fast algorithms for small-sized trigonometric transforms can be used as building blocks for synthesizing algorithms for large-sized transforms. Additionally, they can be utilized to process small data blocks in various digital signal processing applications, thereby reducing overall system latency and computational complexity. The existing polynomial algebraic approach and the approach based on decomposing the transform matrix into cyclic convolution submatrices involve rather complicated housekeeping and a large number of additions. To avoid the noted drawback, this paper uses a structural approach to synthesize new algorithms. The starting point for constructing fast algorithms was to represent DHT-IV as a matrix–vector product. The next step was to bring the block structure of the DHT-IV matrix to one of the matrix patterns following the structural approach. In this case, if the block structure of the DHT-IV matrix did not match one of the existing patterns, its rows and columns were reordered, and, if necessary, the signs of some entries were changed. If this did not help, the DHT-IV matrix was represented as the sum of two or more matrices, and then each matrix was analyzed separately, if necessary, subjecting the matrices obtained by decomposition to the above transformations. As a result, the factorizations of matrix components were obtained, which led to a reduction in the arithmetic complexity of the developed algorithms. To illustrate the space–time structures of computational processes described by the developed algorithms, their data flow graphs are presented, which, if necessary, can be directly mapped onto the VLSI structure. The obtained DHT-IV algorithms can reduce the number of multiplications by an average of 75% compared with the direct calculation of matrix–vector products. However, the number of additions has increased by an average of 4%. Full article

In Next-Generation Low-Earth-Orbit (LEO) satellite networks, securing inter-satellite communication links (ISLs) through robust authentication is critical due to the dynamic and distributed nature of non-terrestrial environments. Traditional authentication frameworks often fall short under these conditions, prompting the adoption of Zero-Trust Security (ZTS) models. However, existing ZTS protocols incur significant computational overhead, especially as the number of satellite nodes increases, thereby affecting both communication network efficiency and security. To address this, a novel bio-inspired intelligent ZTS approach, i.e., Manta Ray Foraging Cost-Optimized Zero-Trust Security (MRFCO-ZTS), has been developed to leverage bio-inspired data-enabled learning principles to enhance secure satellite communication. The model ingests high-density satellite network data and continuously verifies access requests by formulating a cost function that balances the risk level, attack likelihood, and computational delay in an effective manner. The Manta Ray Foraging Optimization (MRFO) algorithm is applied to minimize this cost function and to enable efficient classification of nodes as detector or attacker based on historical authentication as well as nodes dynamic behaviors. MRFCO-ZTS enables precise identification of attacker behavior while ensuring secure data transmission among verified satellites. The developed MRFCO-ZTS framework is evaluated using a series of numerical simulations under varying satellite user loads, with performance assessed in terms of security accuracy, latency, and operational efficiency. Full article

The increase in garbage generated in modern societies demands the implementation of a more sustainable model as well as new methods for efficient waste management. This article describes the development and implementation of a prototype of a smart bin that automatically sorts waste using a multi-agent system and blockchain integration. The proposed system has sensors that identify the type of waste (organic, plastic, paper, etc.) and uses collaborative intelligent agents to make instant sorting decisions. Blockchain has been implemented as a technology for the immutable and transparent control of waste registration, favoring traceability during the classification process, providing sustainability to the process, and making the audit of data in smart urban environments transparent. For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. The YOLOv12 version was selected due to its overall performance, which is superior to others with mAP@50 values of 86.2%, an overall accuracy of 84.6%, and an average F1 score of 80.1%. Latency was kept below 9 ms per image with YOLOv12, ensuring smooth and lag-free processing, even for utilitarian embedded systems. This allows for efficient deployment in near-real-time applications where speed and immediate response are crucial. These results confirm the viability of the system in both accuracy and computational efficiency. This work provides an innovative solution in the field of ambient intelligence, characterized by low equipment cost and high scalability, laying the foundations for the development of smart waste management infrastructures in sustainable cities. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

As digital transformation in manufacturing accelerates, process bottleneck prediction has emerged as a central task in industrial automation. To streamline manufacturing processes, where diverse tasks interact in complex ways, it is essential to identify in advance both the location and timing of bottleneck occurrences. However, manufacturing environments often lack high-performance computing resources and must rely on cost-effective, resource-constrained embedded devices, making fast and accurate prediction challenging. We present MicroForest, a lightweight decision tree-based model designed to predict multiple process bottlenecks simultaneously under such resource-constrained environments. MicroForest reassembles the high-information-gain nodes from dozens of large random forests into compact forests. Evaluated on a simulation containing up to 150 production tasks, MicroForest achieves 34%p higher recall scores compared to original random forests while shrinking model size by two orders of magnitude and accelerating inference latency by up to $7.2\times$. Compared with other recent work, MicroForest outperforms them with the highest prediction accuracy (F1 = 0.74) and shows a much gentler increase in latency as process complexity grows. Full article

Background: Thymus numidicus Poiret. (Lamiaceae) is an endemic plant with well-known antibacterial properties. It has been largely used in traditional Algerian medicine. This study aimed to compare the chemical composition of essential oils (EOs) extracted from leaves and flowers using the gas chromatography–mass spectrometry method, as well as to investigate its analgesic and anti-inflammatory activities. Results: The EOs were rich in monoterpenes and classified as a thymol chemotype. In vivo experiments revealed that acute treatment with T. numidicus EO (20 and 80 mg/kg) significantly increased the thermal threshold on the hot-plate at all tested hours compared to the control animals (p < 0.001, respectively), while only the higher dose had a similar effect to the metamizole group at 2 and 3 h. In the mechanical stimulus test, both doses of the EO led to a late analgesic effect presented with increased paw withdrawal threshold only during the third hour compared to the control group (p < 0.05, respectively). In the plethysmometer test both doses of the EO dose-dependently reduced paw volume with nearly 10% and 15% compared to the control animals at all tested hours (p < 0.001, respectively), with a more pronounced volume reduction in the higher dose. In a neuropathic pain model, the EO (20 mg/kg and 80 mg/kg) dose-dependently increased the withdrawal latency time towards thermal stimuli and enhanced the paw withdrawal threshold in response to mechanical pressure at all tested hours compared to the CCI-group (p < 0.001, respectively). These findings demonstrate the potent analgesic and anti-inflammatory effects of T. numidicus EO in models of acute and neuropathic pain. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

Preterm birth and the neonatal pathological sequelae that follow spontaneous preterm labor are closely associated with maternal and fetal inflammatory activation. Previous studies have indicated a role for the complement system in this inflammatory response. Utilizing an LPS inflammation-induced model of preterm birth, we investigated various delivery outcomes and their correlation with complement activation products within cervical, uterine, and fetal brain tissue after administration of LPS. We provide further evidence that complement-mediated inflammation within cervical and uterine tissue contributes to aberrant cellular changes and an increase in preterm delivery. We additionally show that a targeted complement inhibitor that specifically targets to sites of complement activation (CR2-Crry) mitigates the effects of LPS-induced pathology and preterm birth. Complement inhibition increased latency to delivery, mean gestational age at delivery, and average number of viable pups. Furthermore, the improved delivery outcomes seen with CR2-Crry treatment correlated with a reduced inflammatory response in maternal tissue and in fetal brain tissue in terms of reduced complement activation, reduced pro-inflammatory cytokines, and reduced macrophage recruitment. These data indicate that complement inhibition represents a potential therapeutic strategy for preventing preterm birth. The localization of complement inhibition by a site-targeting approach reduces the possibility of unwanted off-target effects. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article

The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosystem tailored for aviation maintenance education. The system integrates three core digital twin models: the learner digital twin, which continuously reflects individual trainee competence; the ideal competence twin, which encodes regulatory skill benchmarks; and the learning ecosystem twin, a stratified repository of instructional resources. These components are orchestrated through a real-time adaptive engine that performs multi-dimensional competence gap analysis and dynamically matches learners with appropriate training content based on gap severity, Bloom’s taxonomy level, and content fidelity. The system architecture uses a cloud–edge hybrid model to ensure scalable, secure, and latency-sensitive delivery of training assets, ranging from computer-based training modules to high-fidelity operational simulations. Simulation results confirm the system’s ability to personalize instruction, accelerate competence development, and support continuous regulatory readiness by enabling closed-loop, adaptive, and evidence-based training pathways in digitally enriched environments. Full article

Background: Prevention and clinical management of musculoskeletal injuries have historically focused on the assessment and training of modifiable physical factors, but perceptual decision-making has only recently been recognized as a potentially important capability. Immersive virtual reality (VR) systems can measure the speed, accuracy, and consistency of body movements corresponding to stimulus–response instructions for the completion of a forced-choice task. Methods: A cohort of 26 female college soccer players (age 19.5 ± 1.3 years) included 10 players who participated in a baseline assessment, 10 perceptual-response training (PRT) sessions, a post-training assessment that preceded the first soccer practice, and a post-season assessment. The remaining 16 players completed an assessment prior to the team’s first pre-season practice session, and a post-season assessment. The assessments and training sessions involved left- or right-directed neck rotation, arm reach, and step-lunge reactions to 40 presentations of different types of horizontally moving visual stimuli. The PRT program included 4 levels of difficulty created by changes in initial stimulus location, addition of distractor stimuli, and increased movement speed, with ≥90% response accuracy used as the criterion for training progression. Perceptual latency (PL) was defined as the time elapsed from stimulus appearance to initiation of neck rotation toward a peripheral virtual target. The speed–accuracy tradeoff was represented by Rate Correct per Second (RCS) of PL, and inconsistency across trials derived from their standard deviation for PL was represented by intra-individual variability (IIV). Perceptual Decision Efficiency (PDE) represented the ratio of RCS to IIV, which provided a single value representing speed, accuracy, and consistency. Statistical procedures included the bivariate correlation between RCS and IIV, dependent t-test comparisons of pre- and post-training metrics, repeated measures analysis of variance for group X session pre- to post-season comparisons, receiver operating characteristic analysis, and Kaplan–Meier time to injury event analysis. Results: Statistically significant (p < 0.05) results were found for pre- to post-training change, and pre-season to post-season group differences, for RCS, IIV, and PDE. An inverse logarithmic relationship was found between RCS and IIV (Spearman’s Rho = −0.795). The best discriminator between injured and non-injured statuses was PDE ≤ 21.6 (93% Sensitivity; 42% Specificity; OR = 9.29). Conclusions: The 10-session PRT program produced significant improvement in perceptual decision-making that appears to provide a transfer benefit, as the PDE metric provided good prospective prediction of musculoskeletal injury. Full article

Background: Sleep is a fundamental component of health, and deprivation has been linked to numerous adverse outcomes, including reduced academic and occupational performance, greater risk of accidents, and increased susceptibility to chronic diseases and premature mortality. Dietary and lifestyle behaviors are increasingly recognized as key determinants of sleep quality. Women are particularly susceptible to sleep disturbances due to hormonal fluctuations and psychosocial factors. However, women remain underrepresented in sleep research. This study aims to examine the associations between sleep quality, nutrition, and lifestyle in a large cohort of Spanish women. Methods: A cross-sectional study was conducted with 785 women aged 18–64. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and the NutSo-HH questionnaire on dietary and lifestyle behaviors. Descriptive analyses, correlation matrices, Gaussian Graphical Models, and Principal Component Analyses were used to assess relationships between variables. Results: More than half of the participants rated their sleep quality as good or very good, although over 30% experienced frequent nighttime awakenings. Poor sleep quality was significantly associated with higher alcohol consumption, lower vegetable and white fish intake, and lower levels of physical activity. Diets rich in ultra-processed foods correlated moderately with subjective poor sleep and daytime dysfunction. However, no strong associations were found between stimulant consumption, late meals, or dietary patterns (e.g., Mediterranean diet) and sleep. Self-perceived health emerged as a protective factor, while nocturnal lifestyles were linked to longer sleep latency and fragmented sleep. Conclusions: In adult women, better sleep quality is linked to healthy dietary choices, regular physical activity, and a positive perception of general health. In contrast, alcohol use and irregular lifestyles are associated with poor sleep. Individual variability and cultural adaptation may moderate the impact of some traditionally harmful behaviors. Personalized, multidimensional interventions are recommended for promoting sleep health in women. Full article