Search Results (106)

Hybridization in vehicle powertrains extends beyond the aggregate system level and can target individual components to enhance engine performance. While prior studies have highlighted the performance benefits of electrified turbochargers, this work focuses on mitigating engine-out emissions for a medium- to heavy-duty diesel engine with an electrified airpath. Unlike conventional engines and actuators, the alternative engine architecture with an electrified airpath provided superior airpath control. This is critical for fuel-led diesel engines, where the initial combustion cycles during the tip-in phase of a transient operate at a rich equivalence ratio. In this work, a 3.2 L two-cylinder opposed piston two-stroke (OP2S) engine equipped with an Electrically Assisted Turbocharger (EAT) and an electrically operated EGR pump was experimentally tested in a Hardware in the Loop (HIL) setup under transient conditions. Actuator positions were varied to identify strategies that mitigate soot and NO_x without compromising transient response. The experiments are discussed case-wise, where the effects of each airpath actuator, including fuel rate shaping, are analyzed, showing to what extent each strategy mitigates emissions. At the end, an optimized case is presented to the readers for their perusal. The electrified airpath, along with fuel rate shaping, demonstrated cumulative soot reduction up to 92% and NO_x emissions by 77% for a transient load step between 3 and 13 bar BMEP at a mid-engine speed of 1250 rpm. Full article

Distracted driving is one of the primary causes of road traffic accidents. Behavior recognition technology based on machine vision has emerged as a research hotspot due to its non-contact and high-efficiency nature. To address the challenges of complex lighting conditions in the driver’s cabin, low detection accuracy for small-scale keypoints, and the difficulty in effectively characterizing behavioral features, this paper proposes a distracted driving behavior recognition method based on an improved YOLOv8n-Pose model and multi-feature fusion. First, the original YOLOv8n-Pose model is optimized. A P2 detection layer is added to enhance the feature extraction capabilities for small-scale human keypoints, and the SE attention module is incorporated to improve the model’s robustness under complex lighting conditions. In addition, the loss function is replaced with focal loss to tackle the class imbalance problem, thus forming the YOLOv8n-PSF-Pose keypoint detection network. Subsequently, based on the coordinates of 12 human keypoints extracted by this network, a multi-dimensional feature vector is constructed, which takes joint angles as the core and integrates the relative distances between keypoints and the number of valid keypoints. Finally, a BP neural network is adopted to classify the constructed feature vectors, enabling the accurate recognition of six typical distracted driving behaviors (normal driving, drinking or eating, making phone calls, using mobile phones, operating vehicle infotainment systems, and turning around to fetch items). The experimental results show that the improved YOLOv8n-PSF-Pose model achieves an mAP50 of 93.8% in keypoint detection, which is 6.7 percentage points higher than the original model; the BP classification model based on multi-feature fusion achieves an F1-score of 97.7% in the behavior recognition task, which is significantly better than traditional classifiers such as SVM and random forest, and the image processing speed on the NVIDIA RTX 3090TI reaches a high throughput of 45 FPS. This proves that the proposed method achieves an excellent balance between accuracy and speed. This study provides an effective solution for the real-time and accurate recognition of distracted driving behaviors. Full article

Background: Specific health checkups in Japan aim to prevent and detect non-communicable diseases (NCDs). Lifestyle information and non-invasive measurements obtained during these checkups are valuable for population health monitoring. This study aimed to develop a predictive model for type 2 diabetes mellitus (T2DM) using only non-invasive measurements and to identify key predictors. Methods: A retrospective observational study was conducted using linked health checkup records and medical claims from a city in Japan. Logistic regression was performed to predict a T2DM diagnosis. Results: A total of 409 of the 1363 participants were diagnosed with T2DM, including 285 of the 950 participants aged 40–74 years and 124 of the 413 participants aged ≥75 years. The model achieved an area under the receiver operating characteristic curve of 0.680 for those aged 40–74 years and 0.665 for those aged ≥75 years, indicating moderate discrimination. Key predictors included male sex, use of antihypertensive drugs, walking speed, and eating habits within 2 h before bedtime. In particular, male sex, having a slower walking speed, and not eating within 2 h before bedtime were positively associated with T2DM diagnosis. Conversely, the absence of antihypertensive or lipid-lowering medications was negatively associated with T2DM diagnosis. Conclusion: A model based solely on non-invasive measurements moderately identified individuals at risk for T2DM in this community-based Japanese population. Routinely collected health checkup data may support early identification and targeted preventive strategies. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is often accompanied by metabolic syndrome (MetS), forming a high-risk phenotype with elevated cardiometabolic burden. The contribution of lifestyle behaviors—particularly eating mechanics and psychological eating cues—to disease severity remains unclear. This study examined independent associations of dietary behaviors and physical activity (PA) with OSA severity, sleep quality, and metabolic health. Methods: Forty-four OSA patients (mean age 38.3 ± 9.1 years; 89% male) underwent attended polysomnography, dual-energy X-ray absorptiometry, and metabolic profiling. Validated questionnaires assessed dietary behaviors, PA, and sleep quality. Hierarchical logistic regression identified predictors of MetS, severe OSA, and poor sleep quality. Results: The prevalence of MetS was 45%. Compared with those with OSA alone, participants with MetS demonstrated significantly greater central adiposity and more severe nocturnal hypoxemia, despite similar apnea–hypopnea indexes. In multivariable models, MetS was independently associated with higher body mass index (adjusted odds ratio [aOR] = 1.64; p = 0.008) and reward eating (aOR = 3.34; p = 0.041), whereas higher total PA was associated with reduced odds (aOR = 0.96; p = 0.026). Poor subjective sleep quality was significantly associated with younger age (aOR = 0.91; p = 0.037). For severe OSA, slow chewing was associated with significantly reduced odds (aOR = 0.24; p = 0.038), while emotional eating was associated with increased odds (aOR = 2.40; p = 0.048). Conclusions: This hypothesis-generating study identifies a high-risk OSA phenotype marked by metabolic dysfunction and hypoxemia. Eating speed (a proxy for mindful eating), emotional and reward-driven eating, and PA independently shape metabolic and respiratory outcomes. These findings support incorporating behavioral nutrition into multidisciplinary OSA management. Full article

Background/Objective: Mexico has experienced an increase in the prevalence of overweight and obesity among schoolchildren, predisposing them to type 2 diabetes mellitus. In addition, rapid eating has been increasingly implicated in the dysregulation of appetite control, greater energy intake, and adverse metabolic outcomes in children. Prior evidence indicates that a faster eating pace is associated with excess adiposity and lipid metabolism. This study aimed to compare cardiovascular risk factors (waist circumference, waist-to-height ratio, body mass index (BMI), and lipid profile) among school-aged Mexican children according to self-reported eating speed. Design: Cross-sectional observational study. Setting: Public elementary schools in Mexico. Participants: Ninety school-aged children (52.2% female) aged 6–12 years old. Eating speed was assessed using an adapted and validated self-administered questionnaire. Intervention: No intervention was applied; participants were classified into slow-, normal-, or fast-eating groups according to their usual eating speed as reported in the instrument, which includes questions regarding self-perception and family perception. Main Outcome Measure: The primary outcomes included anthropometric parameters (BMI, waist circumference, and waist-to-height ratio), blood pressure (systolic and diastolic), and biochemical markers of lipid metabolism (triglycerides, total cholesterol, and HDL cholesterol). Analysis: Descriptive statistics were computed, and comparisons across eating speed groups were performed using one-way ANOVA for continuous variables and chi-square tests for categorical data. Statistical significance was set at p < 0.05. Results: Among the 90 children evaluated, 17.7% were classified as fast eaters. Although gender differences in eating speed were not statistically significant (χ²= 4.607, p = 0.100), a higher proportion of boys were classified as fast eaters. Children in the fast-eating group exhibited significantly higher BMI (1.4 kg/m²), waist circumference (4 cm greater), and modest elevations in triglyceride and total cholesterol levels, alongside lower HDL cholesterol, relative to their slow-eating peers (all p < 0.05). Among all variables, only diastolic blood pressure differed significantly across groups (F = 3.92, p = 0.022), with fast eaters showing the highest values. Nevertheless BMI, waist circumference, triglyceride levels, and total cholesterol were not statistically significant in the logistic regression, and HDL cholesterol demonstrated an association close to 95% [0.051 (0.011–0.226)] to a protective factor against cardiometabolic events, estimating an effect size of 1.64 using Cohen’s d, which is considered a large effect, when compared to their slower-eating peers. Conclusions and Implications: Faster eating speed was consistently associated with unfavorable anthropometric and lipid profile indicators, aligning with previous evidence linking rapid eating to early cardiometabolic alterations. These findings emphasize the relevance of including eating behavior assessments in pediatric cardiovascular risk screenings and prevention strategies. Full article

The effects of administering 10 mL of maternal bovine appeasing substance (mBAS) or water (control; CT) at weaning (day 0) before transport on feedlot adaptation and efficiency were evaluated in twenty-two Angus-influenced heifers (n = 11/treatment) over 28 days. Body weight (BW), salivary cortisol, blood for complete blood cell count, rectal temperature, chute score and exit speed were collected on days 0, 14 and 27. Intake, feeding duration, frequency and rate, as well as activity and rumination were monitored daily using automated systems. Average daily gain (ADG) and gain-to-feed ratio (G:F) were calculated for each 14-day interval as well as for the entire feeding period. Treated heifers spent less time eating (p ≤ 0.06) on weeks 1 and 2, with greater feeding rate and activity (p < 0.01) in week 1, followed by reduced activity (p ≤ 0.05) in weeks 2, 3 and 4. Rumination was longer (p < 0.05) in weeks 3 and 4, coinciding with greater (p ≤ 0.05) final BW, ADG_0–27, ADG_14–27, and G:F_0–27, G:F_14–27. Lymphocyte and hematocrit were lower (p < 0.05) on days 14 and 27, respectively, and platelets tended to be greater (p = 0.08) than CT for the entire period. Treated heifers achieved numerically greater profit margins than CT. Overall, mBAS enhanced feedlot adaptability post-weaning, improving production efficiency, which may translate into potential profitability; however, this interpretation should be viewed cautiously considering some design limitations. Full article

Cow behavior recognition constitutes a fundamental element of effective cow health monitoring and intelligent farming systems. Within large-scale cow farming environments, several critical challenges persist, including the difficulty in accurately capturing behavioral feature information, substantial variations in multi-scale features, and high inter-class similarity among different cow behaviors. To address these limitations, this study introduces an enhanced target detection algorithm for cow behavior recognition, termed RFR-YOLO, which is developed upon the YOLOv11n framework. A well-structured dataset encompassing nine distinct cow behaviors—namely, lying, standing, walking, eating, drinking, licking, grooming, estrus, and limping—is constructed, comprising a total of 13,224 labeled samples. The proposed algorithm incorporates three major technical improvements: First, an Inverted Dilated Convolution module (Region Semantic Inverted Convolution, RsiConv) is designed and seamlessly integrated with the C3K2 module to form the C3K2_Rsi module, which effectively reduces computational overhead while enhancing feature representation. Second, a Four-branch Multi-scale Dilated Attention mechanism (Four Multi-Scale Dilated Attention, FMSDA) is incorporated into the network architecture, enabling the scale-specific features to align with the corresponding receptive fields, thereby improving the model’s capacity to capture multi-scale characteristics. Third, a Reparameterized Generalized Residual Feature Pyramid Network (Reparameterized Generalized Residual-FPN, RepGRFPN) is introduced as the Neck component, allowing for the features to propagate through differentiated pathways and enabling flexible control over multi-scale feature expression, thereby facilitating efficient feature fusion and mitigating the impact of behavioral similarity. The experimental results demonstrate that RFR-YOLO achieves precision, recall, mAP50, and mAP50:95 values of 95.9%, 91.2%, 94.9%, and 85.2%, respectively, representing performance gains of 5.5%, 5%, 5.6%, and 3.5% over the baseline model. Despite a marginal increase in computational complexity of 1.4G, the algorithm retains a high detection speed of 147.6 frames per second. The proposed RFR-YOLO algorithm significantly improves the accuracy and robustness of target detection in group cow farming scenarios. Full article

Background: This study aimed to determine whether mental health status contributes to speed eating in adolescents and young adults. Methods: This study enrolled 106 subjects (53 males and 53 females), ranging in age from 12 to 24 years. After a self-administered lifestyle questionnaire and the 12-item General Health Questionnaire (GHQ-12) were administered, a swallowing threshold test was performed. The swallowing threshold was determined based on the concentration of dissolved glucose obtained from the gummy jellies. Low swallowing threshold was characterized by glucose levels falling within the bottom 20th percentile. GHQ-12 was categorized into poor (score 4–12) and normal (score 0–3). Following the univariate analysis, a multivariate binary logistic regression analysis was conducted to determine the factors linked to a low swallowing threshold. Results: Binomial logistic regression analysis revealed that the factors associated with a low swallowing threshold included poor mental health (odds ratio [OR] = 8.47, p = 0.007, confidence interval [CI] = 2.437–32.934) and no physical activity (OR = 5.604, p = 0.008, CI = 1.562–22.675). Conclusions: Speed eating is closely associated with risk behaviors for poor mental health in adolescents and young adults. Full article

This study explores the effects of electrically assisted turbochargers (EAT) on the performance of diesel engines by incorporating an electrical motor/generator into a conventional turbocharged model. The engine simulations were conducted at three different power levels of 2, 2.5, and 3 kW to assess the impact of electrical assistance. The results demonstrated that EAT significantly boosts engine performance, with an increase in boost pressure of up to 58.9% at 1000 rpm and an average increase of 30.9% across the low engine speed range (1000–2200 rpm). Additionally, the maximum turbocharger speed was achieved at lower engine speeds, dropping from 2400 rpm to as low as 1600 rpm with 3 kW assistance. Engine torque improved by up to 28.2% at 1000 rpm, and brake-specific fuel consumption (BSFC) was reduced by as much as 8.1%. Transient simulations showed notable improvements in response times, with turbo lag reduced by up to 53% under acceleration conditions. Overall, EAT technology provides significant enhancements in engine efficiency, torque output, fuel economy, and transient response, positioning it as a promising solution for improving diesel engine performance, particularly in addressing turbo lag and low-speed inefficiencies. Full article

Accurate behavioral monitoring of silkworms (Bombyx mori) during the fourth instar development is crucial for enhancing productivity and welfare in sericulture operations. Current manual observation paradigms face critical limitations in temporal resolution, inter-observer variability, and scalability. This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. Methodologically, Res2Net blocks are first integrated into the backbone network to enable hierarchical residual connections, expanding receptive fields and improving multi-scale feature representation. Second, standard convolutional layers are replaced with distribution shifting convolution (DSConv), leveraging dynamic sparsity and quantization mechanisms to reduce computational complexity. Additionally, the minimum point distance intersection over union (MPDIoU) loss function is proposed to enhance bounding box regression efficiency, mitigating challenges posed by overlapping targets and positional deviations. Experimental results demonstrate that RDM-YOLO achieves 99% mAP@0.5 accuracy and 150 FPS inference speed on the datasets, significantly outperforming baseline YOLOv5s while reducing the model parameters by 24%. Specifically designed for deployment on resource-constrained devices, the model ensures real-time monitoring capabilities in practical sericulture environments. Full article

This study investigated the phytochemical content of melon sprouts obtained from by-product seeds of fruit processing and the elicitation effect obtained by the application of salinity to the growing substrate. Seeds from two melon Cultivars (Thales and SV9424ML) were sprouted at 0, 12.5, 25, and 50 mM NaCl concentrations (Salt). Due to intra-lot seed variability in germination speed, sprouts were harvested at 1 and 2 weeks after sowing (WAS), included as an experimental factor (Harvest), collecting, at each harvest, only those that had reached the ready-to-eat stage. Seed germination, shoot and root lengths, fresh and dry weights, and their content in phenolic compounds were determined. Cultivar, Harvest, and Cultivar × Harvest interaction affected sprout phenolic compound content more than Salt. In general, Thales exhibited a significantly greater phenolic compound content (+67.9%, on average). Harvest influenced phytochemicals, with sprouts at 2WAS exhibiting lower flavonoid and hydroxybenzoic acid levels (−31.3% and −73.0%, respectively), yet higher hydroxycinnamic acid content (+298.6%). This was a consequence of variations in p-coumaric and ferulic acids at 2WAS and in flavonoids at 1WAS. Moreover, Salt had an appreciable effect only on Thales, at moderate levels (25 mM NaCl). Our results suggest that the sprouting of by-product seeds of vegetables should be finely modulated based on the seed intra-lot variability in germination speed and on cultivar responsiveness to salinity for phytochemical elicitation. Full article

Melon is a valuable crop that generates significant by-products during consumption and processing. Among these, seeds are rich in phenolic compounds and might be used to produce sprouts with increased content of these bioactive substances. This study evaluated phenolic compounds (PhCs) in sprouts of two melon cultivars, Thales and SV9424ML, obtained from seeds having different germination speeds, thus harvested at 6 and 14 days after sowing (DAS). A factorial combination of cultivar and harvest time was tested in a completely randomized design with four replicates. Thales produced more ready-to-eat sprouts at 6 DAS than SV9424ML (64.0% vs. 46.7%). Sprouting significantly increased total PhCs content, particularly flavonoids, with Thales showing higher values than SV9424ML (50.2 vs. 32.6 mg kg⁻¹ DW). Phenolic profiles significantly varied among cultivars and harvests. Sprouts at 6 DAS had more total hydroxybenzoic acids and flavonoids, while 14 DAS sprouts were richer in hydroxycinnamic acids. Significant differences between harvest dates were observed in the concentrations of protocatechuic, vanillic (VanA), p-coumaric (p-CouA), ferulic (FerA) acids, and orientin (Ori) for Thales, and of VanA, p-CouA, FerA, and Ori for SV9424ML. Results are encouraging, but future investigations are essential to understand whether these sprouts can be suitable for fresh consumption, food supplements, or phytochemical extraction. Full article

This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms. Full article

Background/Objectives: Obesity is a major contributor to cardiovascular disease, yet traditional risk assessment methods may overlook behavioral and circadian influences that modulate metabolic health. Chronotype, physical activity, sleep quality, eating speed, and breakfast habits have been increasingly associated with cardiometabolic outcomes. This study aims to evaluate the associations between these behavioral factors and both anthropometric and biochemical markers of cardiovascular risk among obese candidates for bariatric surgery. Methods: A cross-sectional study was conducted in a sample of 286 obese adults (78.3% females, mean 44.3 years, SD = 10.8, mean BMI = 42.5 kg/m², SD = 6.2) followed at a central Portuguese hospital. Chronotype (reduced Morningness–Eveningness Questionnaire), sleep quality (Pittsburgh Sleep Quality Index), physical activity (Godin–Shephard Questionnaire), eating speed, and breakfast skipping were assessed. Cardiovascular risk markers included waist-to-hip ratio (WHR), waist-to-height ratio, A Body Shape Index (ABSI), Body Roundness Index, atherogenic index of plasma (AIP), triglyceride–glucose index (TyG), and homeostatic model assessment for insulin resistance (HOMA-IR). Results: Men exhibited significantly higher WHR, ABSI, HOMA-IR, TyG, and AIP. Eveningness was associated with higher insulin (r = −0.168, p = 0.006) and HOMA-IR (r = −0.156, p = 0.011). Poor sleep quality was associated with higher body fat mass (r = 0.151, p = 0.013), total cholesterol (r = 0.169, p = 0.005) and LDL cholesterol (r = 0.132, p = 0.030). Faster eating speed was associated with a higher waist circumference (r = 0.123, p = 0.038) and skeletal muscle mass (r = 0.160, p = 0.009). Conclusions: Male sex, evening chronotype, and poor sleep quality were associated with more adverse cardiometabolic profiles in individuals with severe obesity. These findings support the integration of behavioral and circadian factors into cardiovascular risk assessment strategies. Full article

Supposing that polydextrose molecules could improve the hard texture of cooked rice based on intermolecular interactions and forming a hydrogel-like network structure, this study added polydextrose (moisture content 1%) at 0%, 3%, 5%, 7%, and 10% concentrations to rice (cv. Super Qianhao, SQ) milled from a 3-year-stored paddy and compared their cooking properties, their cooked rice texture, the pasting and thermal properties of their flours, the thermo-mechanical characteristics of their flour dough, and the microstructure of their cooked rice grains with a newly harvested japonica rice cv. Nanjing 5 (NJ5). With an increase in polydextrose addition, a General Linear Model (GLM) analysis showed that the cooking times of two japonica rice varieties was significantly (p < 0.05) reduced, and their gruel solid loss increased. Adding polydextrose significantly reduced the hardness, springiness, gumminess, and chewiness of cooked rice and increased the cohesiveness and resilience. By increasing polydextrose addition in rice flours, the peak, breakdown, and setback viscosities of pasting were significantly decreased, but the pasting temperature and peak time increased. Adding polydextrose reduced the gelatinization enthalpy and increased gelatinization peak temperature of the rice flour and significantly decreased the ageing of the retrograded rice flour paste stored at 4 °C when measured at 21 days. A Mixolab test showed that the stability time of the rice flour dough increased, and the protein weakening, gelatinization peak torque, and starch breakdown, as well as the starch setback and the speeds of heating, gelatinization, and enzymatic degradation all decreased. The addition of 5–10% polydextrose significantly reduced the amorphous and crystalline regions of starch and relative percent of β-sheet in cooked rice grains, with an increase in the relative percent of α-helix, random coil, and β-turn. Observing the microstructure, we confirmed that polydextrose addition facilitated the formation of a soft and evenly swollen honeycomb structure of the cooked rice. These results suggest that polydextrose might decrease the cooked rice hardness and improve the eating quality of sea rice through intermolecular interactions. Full article