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Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

Medial knee osteoarthritis (OA) is the most common OA subtype, and its progression has been strongly linked to increased knee adduction moment (KAM). Among various gait modification strategies, medial knee-thrust gait has emerged to be promising to attenuate KAM. This study aimed to clarify the biomechanical mechanism through which knee-thrust gait reduces KAM. Fourteen healthy young adults were recruited as a first step to understanding the mechanism. KAM was reduced by 21% in the first peak and 36% in the second peak during the knee-thrust gait compared to the normal gait. Stepwise multiple regression indicated that the moment arm (MA) was the primary contributor to both KAM peaks (adj. R 2  = 0.9, respectively). Furthermore, the reduction in MA was caused by the lateral deviation of the center of pressure relative to the knee joint center (C P) which outweighed the increase in medial tilting of the ground reaction force observed in knee-thrust gait (adj. R 2  > 0.80). These findings suggest that the reduction in KAM peaks is predominantly due to a shortened MA, affected by increased C P accompanied by wider step width. Understanding this mechanism may offer insights into enhancing gait modification strategies to reduce medial knee loading and potentially slow the progression of medial knee OA. However, since this study was conducted on healthy participants, caution is warranted when generalizing the findings to patients with knee OA.

The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acceleration and tri-axial angular velocity, along with video recordings, were collected from 78 beef calves across two farms. The LightGBM algorithm was employed for behavior classification, and model performance was evaluated using a confusion matrix, the area under the receiver operating characteristic curve (AUC-ROC), and Pearson’s correlation coefficient (r). Model 1 achieved a high performance in recognizing natural suckling (accuracy: 99.10%; F1 score: 96.88%; AUC-ROC: 0.999; r: 0.997), rumination (accuracy: 97.36%; F1 score: 95.07%; AUC-ROC: 0.995; r: 0.990), and feeding (accuracy: 95.76%; F1 score: 91.89%; AUC-ROC: 0.990; r: 0.987). Model 2 exhibited an excellent classification of lying (accuracy: 97.98%; F1 score: 98.45%; AUC-ROC: 0.989; r: 0.982) and standing (accuracy: 97.98%; F1 score: 97.11%; AUC-ROC: 0.989; r: 0.983). Model 3 achieved a reasonable performance in recognizing coughing events (accuracy: 88.88%; F1 score: 78.61%; AUC-ROC: 0.942; r: 0.969). This study demonstrates the potential of machine learning and collar-mounted sensors for monitoring multiple behaviors in calves, providing a valuable tool for optimizing production management and early disease detection in the CCC system

Peroxisome proliferator-activated receptor-gamma (PPARγ) is a transcription factor involved in adipogenesis, and its transcriptional activity depends on its ligands. Thiazolidinediones (TZDs), well-known PPARγ agonists, are drugs that improve insulin resistance in type 2 diabetes. However, TZDs are associated with severe adverse effects. As current therapies are not well designed, novel PPARγ agonists have been investigated in adipocytes. (E)-2-methoxy-4-(3-(4-methoxyphenyl) prop-1-en-1-yl) phenol (MMPP) is known to have anti-arthritic, anti-inflammatory, and anti-cancer effects. In this study, we demonstrated the adipogenic effects of MMPP on the regulation of PPARγ transcriptional activity during adipocyte differentiation in vitro . MMPP treatment increased PPARγ transcriptional activity, and molecular docking studies revealed that MMPP binds directly to the PPARγ ligand binding domain. MMPP and rosiglitazone showed similar binding affinities to the PPARγ. MMPP significantly promoted lipid accumulation in adipocyte cells and increased the expression of C/EBPβ and the levels of p-AKT, p-GSK3, and p-AMPKα at an early stage. MMPP enhanced the expression of adipogenic markers such as PPARγ, C/EBPα, FAS, ACC, GLUT4, FABP4 and adiponectin in the late stage. MMPP also improved insulin sensitivity by increasing glucose uptake. Thus, MMPP, as a PPARγ agonist, may be a potential drug for type 2 diabetes and metabolic disorders, which may help increase adipogenesis and insulin sensitivity.

The effects of operating parameters such as reaction temperature, space velocity, and feed gas composition on the performance of the methane dry-reforming reaction (DRM) over the Ni/Al2O3 catalyst are systemically investigated. The Ni/Al2O3 catalyst, which is synthesized by conventional wet impregnation, showed well-developed mesoporosity with well-dispersed Ni nanoparticles. CH4 and CO2 conversions over the Ni/Al2O3 catalyst are dramatically increased as both the reaction temperature is increased, and space velocity is decreased. The feed gas composition, especially the CO2/CH4 ratio, significantly influences the DRM performance, catalyst deactivation and the reaction behavior of side reactions. When the CO2-rich gas composition (CO2/CH4 > 1) was used, a reverse water gas shift (RWGS) reaction significantly occurred, leading to the consumption of hydrogen produced from DRM. The CH4-rich gas composition (CO2/CH4 < 1) induces severe carbon depositions followed by a reverse Boudouard reaction, resulting in catalytic activity drastically decreasing at the beginning followed by a stable conversion. The catalyst after the DRM reaction with a different feed ratio was analyzed to investigate the amount and structure of carbon deposited on the catalyst. In this study, we suggested that the optimal DRM reaction conditions can achieve stable performances in terms of conversion, hydrogen production and long-term stability.

This study compared the effects of two C3 forages (Italian ryegrass [RG], Timothy grass [TG]) and two C4 forages (Klein grass [KG], Bermuda grass [BG]) on growth performance, nutrient digestibility, and nitrogen (N) balance in Korean crossbred black goats to evaluate C4 warm-season forages as alternatives under changing climate conditions. Sixteen castrated goats (10 months old) were allocated to four treatments using a randomized complete block design. Diets contained 40% treatment-specific forage and 60% commercial concentrate. After adaptation periods, a 5-day metabolism trial measured performance and digestibility parameters. No significant differences occurred among treatments for growth performance or digestibility of dry matter, crude protein, neutral detergent fiber, ash, and non-fiber carbohydrate. C4 grasses showed significantly lower acid detergent fiber and ether extract digestibility than C3 grasses, with KG having the lowest ether extract digestibility. The KG group had higher N intake and absorption than the TG group, while BG showed lower urinary and total N excretion than KG. No differences existed in retained N, utilization efficiency, or biological value among groups. Both C3 and C4 forages supported comparable goat growth performance, providing a reference for utilizing different photosynthetic pathway forages under changing climatic conditions.

The proliferation of distributed energy resources has increased the complexity of power system analysis and operation. To address the complexity, various algorithms have been studied on classical computers, but their performance was constrained by hardware limitations of classical computers. As a new computing paradigm, quantum computing has recently been applied to power system operations to enhance computational efficiency, and early studies on quantum computing application have demonstrated the computational efficiency. Although there remains the limited scalability in current quantum devices, various algorithms have been developed by combining classical and quantum computers to exploit quantum computing fully. Therefore, with the brief introduction of quantum computing and its computing systems, this paper reviews and discusses the recent studies particularly on power flow and optimal power flow calculations using various quantum algorithms ranging from pure quantum computing algorithms to hybrid quantum-classical algorithms. In addition, this paper suggests new research subjects in power flow calculations for future studies.

This study aimed to characterize behavioral differences between true estrus (TE) and false estrus (FE) in cows using neck-mounted six-axis inertial measurement unit sensors to reduce false positives in automated detection systems. A retrospective analysis was conducted on 1464 validated estrus alerts from 414 Hanwoo cows across 13 commercial farms in South Korea. Alerts were classified as TE (625 alerts) or FE (839 alerts) based on comprehensive validation criteria, including standing heat observation, artificial insemination records, ovulation confirmation, and pregnancy outcomes. Mounting activity, rumination time, and lying time were analyzed. True estrus exhibited significantly higher (p < 0.0001) total number of mounts and maximum mounting duration compared to FE over the entire observation period. Notably, the maximum number of mounts per hour was higher (p < 0.0001) in FE before alert generation but higher (p < 0.0001) in TE afterward, with FE declining rapidly. Coefficients of variation for rumination and lying time were significantly higher (p < 0.0001) in TE than in FE, indicating greater behavioral disruption. These findings revealed that secondary behavioral signs exhibit distinct quantitative and temporal patterns between TE and FE, suggesting potential criteria that could be integrated into automated detection algorithms to reduce false-positive rates.

(E)-2-methoxy-4-[3-(4-methoxyphenyl) prop-1-en-1-yl] phenol (MMPP), a novel synthetic analog of (E)-2,4-bis(p-hydroxyphenyl)-2-butenal (BHPB), exerts anti-inflammatory and anticancer effects by downregulating the STAT3 pathway. It has also been recently reported that MMPP can act as a PPAR agonist which enhances glucose uptake and increases insulin sensitivity. However, it has not yet been elucidated whether MMPP can act as an antagonist of MD2 and inhibit MD2-dependent pathways. In this study, we evaluated the underlying modulatory effect of MMPP on inflammatory responses in LPS-stimulated THP-1 monocytes. MMPP inhibited the LPS-induced expression of inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, as well as the inflammatory mediator COX-2. MMPP also alleviated the IKKαβ/IκBα and JNK pathways and the nuclear translocation of NF-κB p50 and c-Jun in LPS-stimulated THP-1 monocytes. In addition, the molecular docking analyses and in vitro binding assay revealed that MMPP can directly bind to CD14 and MD2, which are expressed in the plasma membrane, to recognize LPS first. Collectively, MMPP was directly bound to CD14 and MD2 and inhibited the activation of the NF-κB and JNK/AP-1 pathways, which then exerted anti-inflammatory activity. Accordingly, MMPP may be a candidate MD2 inhibitor targeting TLR4, which exerts anti-inflammatory effects.

Objective: The purpose of this study was to identify through infrared thermal imaging technology the facial surface temperature (FST) of laying hens in response to the variations in their thermal environment, and to identify the regional differences in FST to determine the most stable and reliable facial regions for monitoring of thermoregulatory status in chickens. Methods: Thirty Hy-Line Brown hens (25-week-old) were sequentially exposed to three different thermal conditions; optimal (OT, 22°C±2°C), low (LT, 10°C±4°C), and high temperature (HT, 30°C±2°C). The mean values of FST in five facial regions including around the eyes, earlobes, wattles, beak and nose, and comb were recorded through infrared thermography. The maximum FST (MFST) was also identified among the five face-selective regions, and its relationship with temperature-humidity index (THI) was established to identify the range of MFST in response to the variations in their thermal environment. Results: Hens exposed to OT condition at 15:00 displayed a higher temperature at wattles and around the eyes compared to other regions (p<0.001). However, under LT condition at 05:00 to 08:00, around the eyes surface temperature showed the highest value (p<0.01). In HT, wattles temperature tended to show the highest temperature over almost time intervals. Main distribution regions of MFST were wattles (63.3%) and around the eyes (16.7%) in OT, around the eyes (50%) in LT, and wattles (62.2%) and comb (18.3%) in HT. The regression equation between MFST and THI was estimated as MFST = 35.37+ 0.2383×THI (R2 = 0.44; p<0.001). Conclusion: The FST and the frequency of MFST in each facial region of laying hens responded sensitively to the variations in the thermal environment. The findings of this experiment provide useful information about the effect of the thermal conditions on the specific facial regions, thus offering an opportunity to stress and welfare assessment in poultry research and industry.