Search Results (286)

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

Streaming data is becoming more and more common in the field of big data and incremental frameworks can address its complexity. The BDFCOM algorithm achieves good results on common form datasets by introducing the ordering mechanism of beta distribution weighting. In this paper, based on the BDFCOM algorithm, two incremental beta distribution weighted fuzzy C-ordered means clustering algorithms, SPBDFCOM and OBDFCOM, are proposed by combining the two incremental frameworks of Single-Pass and Online, respectively. In order to validate the performance of SPBDFCOM and OBDFCOM, this paper selects seven real datasets for experiments and compares their performance with six other incremental clustering algorithms using six evaluation metrics. The results show that the two proposed incremental algorithms perform significantly better compared to other algorithms. Full article

Background: Blackberry seed oil (BSO), obtained from Rubus spp. Xavante cultivar via supercritical CO₂ extraction, contains bioactive lipids and antioxidants, but its cosmetic application is limited by poor solubility and stability. Nanoencapsulation with poly(ε-caprolactone) (PCL) can overcome these limitations. Methods: BSO was characterized by Ultra-High-Performance Liquid Chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry and incorporated into PCL nanocapsules (NCBSO) using the preformed polymer deposition method. Physicochemical properties, stability (at 4 °C, room temperature, and 37 °C for 90 days), cytotoxicity, and collagen production were assessed in human fibroblasts. Additionally, a predictive in silico analysis using PASS Online, Molinspiration, and SEA platforms was performed to identify the bioactivities of major BSO compounds related to collagen synthesis, antioxidant potential, and anti-aging effects. Results: NCBSO showed a nanometric size of ~267 nm, low polydispersity (PDI < 0.2), negative zeta potential (−28 mV), and spherical morphology confirmed by FE-SEM. The dispersion remained stable across all tested temperatures, preserving pH and colloidal properties. In particular, BSO and NCBSO at 100 µg.mL⁻¹ significantly enhanced in vitro collagen production by 170% and 200%, respectively, compared to untreated cells (p < 0.01). Superior bioactivity was observed for NCBSO. The in silico results support the role of key compounds in promoting collagen biosynthesis and protecting skin structure. No cytotoxic effects were achieved. Conclusions: The nanoencapsulation of BSO into PCL nanocapsules ensured formulation stability and potentiated collagen production. These findings support the potential of NCBSO as a promising candidate for future development as a collagen-boosting cosmeceutical. Full article

Background: Brazilian Jiu-Jitsu (BJJ) is a physically demanding sport associated with a notable risk of musculoskeletal injuries. Understanding injury patterns, rehabilitation approaches, and psychological readiness to return to sport (RTS) is essential for prevention and management strategies. This study aimed to investigate injury characteristics among Italian BJJ athletes, assess their rehabilitation processes and psychological recovery, and identify key risk factors such as belt level, body mass index (BMI), and training load. Methods: A cross-sectional survey was conducted among members of the Italian BJJ community, including amateur and competitive athletes. A total of 360 participants completed a 36-item online questionnaire. Data collected included injury history, rehabilitation strategies, RTS timelines, and responses to the Injury-Psychological Readiness to Return to Sport (I-PRRS) scale. A Random Forest machine learning algorithm was used to identify and rank potential injury risk factors. Results: Of the 360 respondents, 331 (92%) reported at least one injury, predominantly occurring during training sessions. The knee was the most frequently injured joint, and the action “attempting to pass guard” was the most reported mechanism. Most athletes (65%) returned to training within one month. BMI and age emerged as the most significant predictors of injury risk. Psychological readiness scores indicated moderate confidence, with the lowest levels associated with playing without pain. Conclusions: Injuries in BJJ are common, particularly affecting the knee. Psychological readiness, especially confidence in training without pain, plays a critical role in RTS outcomes. Machine learning models may aid in identifying individual risk factors and guiding injury prevention strategies. Full article

Foeniculum vulgare Mill. (Apiaceae) is an aromatic medicinal plant known for its anti-inflammatory, antispasmodic, antiseptic, carminative, diuretic, and analgesic properties. This study aimed to investigate the effects of F. vulgare essential oil (FVEO; 25, 150, and 300 μL/L) on the cognitive performance and brain oxidative stress in a scopolamine (SCOP; 100 μM)-induced zebrafish model of cognitive impairment. Additionally, the pharmacokinetic properties and bioactivity profiles of the main FVEO constituents were predicted to be used in silico tools, including SwissADME, pkCSM, PASS online, and ADMETlab 2.0. Behavioral assays, novel tank diving test (NTT), Y-maze, and novel object recognition (NOR) test, were used to evaluate anxiety-like behavior, spatial memory, and recognition memory, respectively. Biochemical assessments of acetylcholinesterase (AChE) activity and oxidative stress biomarkers were also conducted. The results demonstrated that FVEO significantly improved cognitive performance in SCOP-treated zebrafish, normalized AChE activity, and reduced oxidative stress in the brain. These findings suggest the therapeutic potential of FVEO in ameliorating memory impairment and oxidative damage associated with neurodegenerative disorders such as Alzheimer’s disease (AD). Full article

We present a Forney-style factor graph representation for the class of multivariate autoregressive models with exogenous inputs, and we propose an online Bayesian parameter-identification procedure based on message passing within this graph. We derive message-update rules for (1) a custom factor node that represents the multivariate autoregressive likelihood function and (2) the matrix normal Wishart distribution over the parameters. The flow of messages reveals how parameter uncertainty propagates into predictive uncertainty over the system outputs and how individual factor nodes and edges contribute to the overall model evidence. We evaluate the message-passing-based procedure on (i) a simulated autoregressive system, demonstrating convergence, and (ii) on a benchmark task, demonstrating strong predictive performance. Full article

Positioning servo systems utilizing permanent magnet synchronous linear motors (PMSLMs) are conventionally governed by cascaded P-PI controllers, which, despite their simplicity and robustness, suffer from limited tracking and anti-disturbance performance due to their single-degree-of-freedom (1-DOF) structure. This paper introduces a novel two-degree-of-freedom (2-DOF) control algorithm that integrates explicit model predictive control (EMPC) with a differential-compensated extended state observer (DCESO). The EMPC framework leverages position and velocity as state variables, eliminating the need for integral terms and thereby enhancing dynamic response. By employing an offline optimization approach, a control law is explicitly formulated to handle system constraints while minimizing online computational overhead. Additionally, a velocity feedforward term derived from the MPC framework is incorporated to further reduce tracking errors. To bolster disturbance rejection, the proposed DCESO introduces a differential compensator that mitigates the low-pass effects inherent in traditional ESOs, thereby improving estimation dynamics. Experimental results demonstrate that the proposed method significantly outperforms the conventional P-PI controller, increasing the position loop bandwidth from 147 Hz to 208 Hz and markedly enhancing anti-disturbance performance. The algorithm’s low online computational demand makes it highly suitable for industrial applications. Full article

Background/Objectives: Previous research has shown that listeners may use acoustic cues for speech processing that are perceived during brief segments in the noise when there is an optimal signal-to-noise ratio (SNR). This “glimpsing” effect requires higher cognitive skills than the speech tasks used in typical audiometric evaluations. Purpose: The aim of this study was to investigate the use of an online test of speech processing in noise in listeners with typical hearing sensitivity (TH, defined as thresholds ≤ 25 dB HL) who were asked to determine the gender of the subject in sentences that were presented in increasing levels of continuous and interrupted noise. Methods: This was a repeated-measures design with three factors (SNR, noise type, and syntactic complexity). Study Sample: Participants with self-reported TH (N = 153, ages 18–39 years, mean age = 20.7 years) who passed an online hearing screening were invited to complete an online questionnaire. Data Collection and Analysis: Participants completed a sentence recognition task under four SNRs (−6, −9, −12, and −15 dB), two syntactic complexity settings (subjective-relative and objective-relative center-embedded), and two noise types (interrupted and continuous). They were asked to listen to 64 sentences through their own headphones/earphones that were presented in an online format at a user-selected comfortable listening level. Their task was to identify the gender of the person performing the action in each sentence. Results: Significant main effects of all three factors as well as the SNR by noise-type two-way interaction were identified (p < 0.05). This interaction indicated that the effect of SNR on sentence comprehension was more pronounced in the continuous noise compared to the interrupted noise condition. Conclusions: Listeners with self-reported TH benefited from the glimpsing effect in the interrupted noise even under low SNRs (i.e., −15 dB). The evaluation of glimpsing may be a sensitive measure of auditory processing beyond the traditional word recognition used in clinical evaluations in persons who report hearing challenges and may hold promise for the development of auditory training programs. Full article

Introduction: The food environment is an important determinant of children’s eating behavior. Improving the environment to encourage healthier choices is crucial to prevent obesity, especially in restaurants where the majority of kids’ menus are unhealthy. This study explored the perceptions, attitudes, motivations, influencing factors, and opportunities of restaurant owners, managers, and chefs for implementing healthier kids’ menus in Dutch restaurants. Method: We used a mixed methods design in two consecutive study parts. Part I consisted of an online unstandardized questionnaire that was completed by 44 restaurant owners, 26 chefs, 18 managers, and 6 other restaurant employees (n = 94). This was followed by semi-structured interviews with 3 restaurant owners, 2 chefs, and 1 manager, to gather exploratory information in Part II (n = 6). The quantitative data were categorized into three groups: restaurants without kids’ menus (n = 18), restaurants with unhealthy kids’ menus (n = 24), and restaurants with (partially) healthy kids’ menus (n = 52). Group differences were assessed using the Kruskal–Wallis test. We used thematic analysis for the interviews. Results: Parts I and II showed that the restaurant sector is aware of the need, and willing and motivated to offer healthier kids’ menus. Nevertheless, the concerns about food waste, the unhealthy demand from children and parents, and seeing eating out as a free pass to consume unhealthy meals by children and parents were important factors limiting the implementation of healthier kids’ menus. Discussion: We discussed potential solutions to enhance demand and acceptance of healthier kids’ menus, such as attractive names, storytelling, offering children’s portions based on adult menus, and using participatory approaches in which parents, children, and chefs co-create meal composition. Full article

Real-time detection of welding defects in resistance spot welding is a complex challenge. Dynamic resistance (DR) reflects nugget growth and varies with defect types, serving as a key indicator. This study presents an online quality evaluation and defect classification method for Q235 low-carbon steel welding. Welding current and voltage were collected in real-time, and DR signals were processed employing a second-order Butterworth low-pass filter featuring zero-phase processing to enhance accuracy. Empirical mode decomposition (EMD) decomposed these signals into intrinsic mode functions (IMFs) and residuals, which were classified by a support vector machine (SVM). Experiments showed the EMD-SVM method outperforms traditional approaches, including backpropagation (BP) neural networks, SVM, wavelet packet decomposition (WPD)-BP, WPD-SVM, and EMD-BP, in identifying four welding states: normal, spatter, false, and edge welding. This method provides an efficient, robust solution for online defect detection in resistance spot welding. Full article

Gastrointestinal parasites have been reported to have negative effects on chickens reared in rural areas in African countries. Hence, smallholder farmers use ethnoveterinary remedies to control these parasites in village chickens. This study used online databases (ScienceDirect, PubMed, and Google Scholar) to search, gather, and review articles published between January 1990 and June 2024 where ethnoveterinary intervention was considered to control gastrointestinal parasites, following PRISMA guidelines. A total of 540 articles were generated, and only 17 articles passed the screening process and were considered for further analysis. The findings of this review indicate that ethnoveterinary remedies are used by smallholder farmers to control gastrointestinal parasites such as Ascaridia galli, Capillaria spp., Choanotaenia infundibulum, Eimeria spp., Heterakis gallinarum, and Raillietina cesticillus in village chickens. Aloe spp., Carica papaya, Musa paradisiaca, and Venonia amygdalina were reported as the most-preferred medicines, and farmers utilized different plant parts ranging from leaves (48.8%), bark and roots (12.2%), tubers (7.3%), and seeds (4.9%). These remedies are administered per os, topically, and dermally. The current review successfully summarized ethnoveterinary intervention used by smallholder farmers to control gastrointestinal parasites in chickens found in African countries. Therefore, there is a need to investigate the efficacy of these identified ethnoveterinary medicines against gastrointestinal parasites in chickens. Full article

The United States’ nursing shortage attracted internationally educated nurses (IENs) to take the National Council Licensure Examination–Registered Nurses (NCLEX-RN), which is required to practice nursing in the U.S. Philippine-educated nurses (PENs) comprised more than half of IENs in the U.S. nursing workforce. From 2002 to 2021, only 45.8% of 177,730 PENs passed the exam. Published studies investigating IEN NCLEX-RN performance are limited. This study addresses this gap in the literature. This study determined the association between academic, initial nursing licensure, and employment factors on PEN NCLEX-RN pass rates. A retrospective correlation research design was used to determine the association among the research variables. Participants were recruited through online nursing groups. Descriptive statistics compared characteristics of PENs who passed or failed the NCLEX on the first attempt. Chi-squared and Fisher’s exact tests were used to determine the association between the research variables. Initial nursing licensure and nursing workplace were significantly associated with PENs passing the NCLEX-RN. Identifying unique PENs’ contextual characteristics is critical in preparing them to pass the NCLEX-RN. Findings provide input to educational and regulatory bodies to improve the NCLEX-RN individual outcomes and Philippine NCLEX-RN pass rates. Full article

Based on the theory of uniform finite-length incoherent line source radiation and real vehicle online test data of Shanghai Maglev trains, a prediction model for environmental noise is established using an equivalent segmented line sound source approach. The noise produced by Shanghai high-speed Maglev trains running at speeds of 235, 300, and 430 km/h is tested and analyzed using microphones. The test data are combined with computational fluid dynamics simulations to divide the train’s sound sources equally into five sections. Theoretical calculations are carried out on the noise test data collected as the train passes by, and the source strength of each individual sub-sound source during the train operation is determined using the least-squares method. As a result, a prediction model for the environmental noise of high-speed Maglev trains, represented as a combination of multiple sources, is developed. The predicted results are compared with the measured values to validate the accuracy of the model. The proposed model can be used for environmental assessments before new train lines are launched, allowing for appropriate mitigation measures to be taken in advance to reduce the impact of Maglev noise on the surrounding residential and ecological environments. Full article

Background/Objectives: Pediatric rhabdomyosarcoma is a rare and poorly understood disease. Patients and parents can have varying experiences including barriers to care, time to treatment, and treatments offered. Here, we report on patient experiences regarding their care of pediatric rhabdomyosarcoma. Methods: Two separate online parent support groups were invited to participate in a survey. The survey included questions that sought to collect patient demographics, history of rhabdomyosarcoma, treatment timelines, and barriers to care. Results: A total of 215 surveys were completed. The average time from diagnosis to treatment was 12 days (SD = 14). Only 26% were offered fertility preservation prior to treatment. For patients with recurrence, an average of 75 days passed between detection of recurrence and treatment re-initiation. Patients traveled to centers with a dedicated sarcoma program in 52% of the cases. A total of 42% of parents sought a second opinion. Of those, the majority had to wait between one week and one month to be seen by another expert. Conclusions: The data collected from the survey suggests there are several opportunities to improve care among patients with pediatric rhabdomyosarcoma. Many patients may benefit from more efficient rhabdomyosarcoma referral networks, delivering patients to experts who can quickly begin multidisciplinary treatment. Full article

Governments are focusing on citizen participation in the energy transition, e.g., with dynamic electricity tariffs, which pass part of the wholesale price volatility to end users. While often the cheapest alternative, these tariffs require micromanagement for optimization. In this research, an automated system capable of supplying electricity for home use at minimal cost called Smart Relays and Controller (SRC) is presented. SRC scrapes prices online, charges a battery system during the cheapest time slots and supplies electricity to the home energy system from the cheapest source, either the battery or the grid, while optimizing battery life. To validate the system, a comparison is made between SRC, a programmable scheduler and PVPC (Spain’s dynamic tariff) using twenty-eight months of hourly historical data. SRC is shown to be superior to both the scheduler and PVPC, with the scheduler performing worse than SRC but better than PVPC (T.T., p < 0.001). SRC achieves a 36.16% discount over PVPC, 13.89% when factoring in battery life. The savings are 44.24% higher with SRC than with a scheduler. Neither inflation nor incentives to reduce costs are considered. While we studied Spain’s tariff, SRC would work in any country offering dynamic electricity tariffs, with benefit margins dependent on their particularities. Full article