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To accurately characterize the damage of rock-like materials under simultaneous or alternating tensile and compressive loading, a dynamic damage constitutive model for rock-like materials based on elastic tensile strain is developed by integrating the classical compressive plastic damage model and the tensile elastic damage model. The model is based on the Holmquist–Johnson–Cook (HJC) and Kuszmaul (KUS) models, categorizing the element stress state into tensile and compressive states through positive and negative elastic volumetric strain. It utilizes elastic tensile strain to enhance the calculation method for tensile cracks, determining the tensile strength of the principal direction based on the contribution rate of tensile principal stress for uniaxial/multiaxial loading. Additionally, it establishes a maximum elastic tensile strain rate function to rectify the model’s effect on the tensile strain rate. Through the LS-DYNA subroutine development, the model proficiently delineates the distribution of ring-shaped cracks on the frontal side and strip-shaped cracks on the rear side of the reinforced concrete slab subjected to impact loading. Numerical simulations demonstrate that the model provides more accurate damage prediction results for stress conditions involving simultaneous or alternating compression and tension, offering valuable insights for damage analysis in engineering blasting or impact penetration.

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers’ planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of “high in winter and low in summer”; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products.

In recent years, the in situ resource utilization of CO2 in the Martian atmosphere by low-temperature plasma technology has garnered significant attention. However, numerical simulation is extremely time-consuming for modeling the complex CO2 plasma, involving tens of species and hundreds of reactions, especially under Martian pressure. In this study, a deep neural network (DNN) with multiple hidden layers is introduced to investigate the CO2 conversion in radio-frequency (RF) discharges at a given power density under Martian pressure in almost real time. After training on the dataset obtained from the fluid model or experimental measurements, the DNN shows the ability to accurately and efficiently predict the various discharge characteristics and plasma chemistry of RF CO2 discharge even in seconds. Compared with conventional fluid models, the computational efficiency of the DNN is improved by nearly 106 times; thus, a real-time calculation of RF CO2 discharge can almost be achieved. The DNN can provide an enormous amount of data to enhance the simulation results due to the very high computational efficiency. The numerical data also suggest that the CO2 conversion increases with driving frequency at a fixed power density. This study shows the ability of the DNN-based approach to investigate CO2 conversion in RF discharges for various applications, providing a promising tool for the modeling of complex non-thermal plasmas.

In this study, an AM-based continuous processing reactor system was designed, manufactured, and assembled on a laboratory scale for the generation of pharmaceutical substances with an improved process control. The developed AM-based (additively manufactured) continuous pharmaceutical reactor system for the synthesis of metronidazole derivatives aimed to optimize both the physical and the chemical processes with time savings. Using AM, we were able to build reactor subcomponents with complex designs and precise dimensions, which facilitated the precise control of the reaction parameters and reduced the amount of chemicals required compared to macroscale reactors. The assembly of the whole reactor system consisted of main reactor bodies, mixers, valves, heat exchangers, electrical motors, and a microcontroller system. The assembled reactor system revealed a continuous flow of reagents and ensured uniform mixing and reaction conditions, thereby increasing the process efficiency and product quality. Five metronidazole derivatives were synthesized via two continuous processes, involving metronidazole reduction and its subsequent reactions with terephthalic aldehyde and anthracen-9(10H)-one to form Schiff bases. The optimal conditions were determined as follows: compound A (72% yield, 120 min, 55 °C), compounds B and C (63% and 68% yield, respectively, 8 h, 65 °C), and compounds D and E (74% and 85% yield, respectively, 8 h, 45 °C).

Computer vision has advanced much in recent years. Several tasks, such as image recognition, classification, or image restoration, are regularly solved with applications using artificial intelligence techniques. Image restoration comprises different use cases such as style transferring, improvement of quality resolution, or completing missing parts. The latter is also known as image inpainting, virtual image inpainting in this case, which consists of reconstructing missing regions or elements. This paper explores how to evaluate the performance of a deep learning method to do virtual image inpainting to reconstruct missing architectonical elements in images of ruined Greek temples to measure the performance of different activation functions. Unlike a previous study related to this work, a direct reconstruction process without segmented images was used. Then, two evaluation methods are presented: the objective one (mathematical metrics) and an expert (visual perception) evaluation to measure the performance of the different approaches. Results conclude that ReLU outperforms other activation functions, while Mish and Leaky ReLU perform poorly, and Swish’s professional evaluations highlight a gap between mathematical metrics and human visual perception.

Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists’ materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used—ten illuminated manuscripts created by artists stylistically grouped under the notname “Master of the Cypresses” (15th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts.

Chronic atrophic gastritis is a serious health issue beyond the stomach health problems that affect normal life. This study aimed to explore the influencing factors related to chronic atrophic gastritis (CAG) using non-invasive indicators and establish an optimal prediction model to aid in the clinical diagnosis of CAG. Electronic medical record data from 20,615 patients with CAG were analyzed, including routine blood tests, liver function tests, and coagulation tests. The logistic regression algorithm revealed that age, hematocrit, and platelet distribution width were significant influences suggesting chronic atrophic gastritis in the Chongqing population (p < 0.05), with an area under the curve (AUC) of 0.879. The predictive model constructed based on the Random Forest algorithm exhibited an accuracy of 83.15%, precision of 97.38%, recall of 77.36%, and an F1-score of 70.86%, outperforming the models constructed using XGBoost, KNN, and SVC algorithms in a comprehensive comparison. The prediction model derived from this study serves as a valuable tool for future studies and can aid in the prediction and screening of chronic atrophic gastritis.

This study investigated the utilization of by-products from Platycodon grandiflorus and the role of genetic diversity in their anti-melanogenic properties, focusing on the purple-flower (PF) and white-flower (WF) varieties. Our results show that the WF variety exhibited significantly higher anti-melanogenic activity, attributed to higher concentrations of key saponins such as platycodin D3 and platycodin D. These saponins demonstrated strong tyrosinase inhibitory effects as confirmed by molecular docking analysis. Further, the WF variety showed increased expression of genes involved in saponin biosynthesis, highlighting the role of genetic diversity in determining phytochemical composition and pharmacological efficacy. The superior anti-melanogenic activity of WF suggests its potential as a valuable ingredient in the cosmetic industry for skin-whitening products. Our findings emphasize the importance of utilizing by-products and selecting specific genotypes to enhance the quality and efficacy of plant-derived products. Future research should explore the full spectrum of bioactive compounds in P. grandiflorus, investigate sustainable extraction methods, and conduct clinical trials to validate the safety and effectiveness of these compounds in cosmetic and therapeutic applications.

In this study, we thoroughly analyzed the linearity and repeatability of force-sensing resistor (FSR) sensors through static load tests to ensure their reliability. The novelty of this research lies in its comprehensive evaluation and direct comparison of two widely used FSR sensors, i.e., Flexiforce A201-1 and Interlink FSR-402, under various loading conditions by employing a robust calibration methodology. This study provides detailed insights into the sensors’ performances, offering practical calibration equations that enhance measurement precision and reliability, which have not been extensively documented in previous studies. Our results demonstrate that the linearity of thin film FSR sensors is highly accurate, closely resembling a straight line. We employed M1 Class weights, applying loads ranging from 20 g to 300 g. The resistance of the FSR sensors, which varies with the applied load, was measured using a voltage divider circuit and an analog-to-digital converter of a microcontroller. MATLAB was used to calculate the average output voltage for each applied load and fixed resistance. Additionally, we examined the relationships among load, FSR sensor resistance, and conductivity. Our research indicates that with precise calibration, thin film FSR sensors can be highly reliable for force measurement applications.

The aim of this systematic review was twofold: to identify the main trends and issues that are being addressed by researchers in the context of physical fitness and sports performance in deaf and hard-of-hearing (D/HH) athletes and to indicate the needs and future directions that should be implemented in the training process of athletes with hearing impairments. The methodology of this systematic review was planned according to PRISMA guidelines. A search of electronic databases (PubMed, EBSCO, Scopus) was conducted to identify all studies on physical fitness, sports performance and participation, and D/HH athletes from 2003 to 2024. In total, 87 full-text articles were assessed to determine eligibility, while 34 studies met the inclusion criteria and were subjected to detailed analysis and assessment of their methodological quality. The presented systematic review indicates evidence that D/HH athletes are characterized by a similar or higher level in selected motor abilities compared to hearing athletes. Moreover, it seems that hearing impairment is not a barrier in the development of an athlete’s physical fitness, including aerobic capacity, muscular strength and power or speed of reaction. Furthermore, inclusion in sports participation and specific tools (i.e., communication aids) appear to be crucial factors for performance enhancement.

This research aims to address the limited consideration given to non-motorized transport facilities in current studies on shared bike travel demand forecasting. This study is the first to propose a method that applies complete citywide non-motorized facility data to predict bike-sharing demand. This study employs a multiscale geographically weighted regression (MGWR) model to examine the effects of non-motorized transport facility conditions, quantity of intersections, and land use per unit area on riding demand at various spatial scales. The results of comparison experiments reveal that riding demand is substantially affected by non-motorized transport facilities and the quantity of intersections.

The use of speech-based recognition technologies in human–computer interactions is increasing daily. Age and gender recognition, one of these technologies, is a popular research topic used directly or indirectly in many applications. In this research, a new age and gender recognition approach based on the ensemble of different machine learning algorithms is proposed. In the study, five different classifiers, namely KNN, SVM, LR, RF, and E-TREE, are used as base-level classifiers and the majority voting and stacking methods are used to create the ensemble models. First, using MFCC features, five base-level classifiers are created and the performance of each model is evaluated. Then, starting from the one with the highest performance, these classifiers are combined and ensemble models are created. In the study, eight different ensemble models are created and the performances of each are examined separately. The experiments conducted with the Turkish subsection of the Mozilla Common Voice dataset show that the ensemble models increase the recognition accuracy, and the highest accuracy of 97.41% is achieved with the ensemble model created by stacking five classifiers (SVM, E-TREE, RF, KNN, and LR). According to this result, the proposed ensemble model achieves superior accuracy compared to similar studies in recognizing age and gender from speech signals.

Nail biting (NB) is a repetitive and uncontrolled parafunctional activity that can affect oral health by altering tooth shape and intraoral position. Nowadays, there is not enough scientific evidence about the impact of NB on teeth; therefore, this study aimed to evaluate the effects of NB on the length, width and inclination of upper central incisors. This retrospective study involved 76 patients, 40 males and 36 females, with a mean age of 10.6 ± 0.3 years. Digital scans of the maxillary arch of each patient were recorded. Next, the length, width and inclination of upper central incisors used and not used for NB were measured. Finally, data were analysed with a paired t-test. Statistical analysis showed statistically significant differences in the length and inclination of upper central incisors used for NB compared with those of upper central incisors not used for NB, while the width did not show a significant difference. There were relevant changes in upper central incisors subjected to NB, demonstrating that NB impairs the shape, morphology and inclination of teeth. Therefore, because of the potentially negative consequences of NB, it is recommended that NB not be underestimated.

To solve the problem that the existing planetary gear train fault diagnosis methods have, namely their low diagnostic accuracy under low signal-to-noise ratio (SNR), a fault diagnosis method based on a double channel-deep residual shrinkage network (DC-DRSN) is proposed. The short-time Fourier transform (STFT) is used to convert the original vibration signal into a two-dimensional time-frequency graph, which effectively enhances the ability to express information. A DC-DRSN model is constructed, and the optimal number of residual shrinkage modules is determined by combining the diagnostic characteristics with different noises, which effectively improves the accuracy and anti-noise ability of fault diagnosis. The results of bearing and planetary gear train crack fault diagnosis show that the diagnosis method based on DC-DRSN has higher diagnostic accuracy while realizing fault diagnosis, which is better than other deep learning diagnosis methods. At the same time, the method can adapt to fault diagnosis in different noise environments, and has good expression ability and generalization ability.

This study investigates electromyography activation and force development differences in key lower limb muscles between female football players with previous anterior cruciate ligament injuries compared with healthy players. Twenty-two semi-professional players were divided into ACL-injured (n = 11) and non-injured groups (n = 11). Participants underwent maximal voluntary isometric contractions while electromyography activation, peak and average, and peak torque of force were measured. Results indicated significant differences in electromyography activation patterns between anterior cruciate ligament players and non-injured players, particularly in biceps femoris and gluteus maximus muscles. These differences were also evident when comparing between limbs within anterior cruciate ligament players. Interestingly, both groups exhibited similar peak torque of force during maximal contractions, suggesting a compensatory neuromuscular strategy that supports a return to sport based on kinetic and kinematic factors. However, these findings underscore persistent muscle integration imbalances potentially contributing to the high rate of anterior cruciate ligament reinjury. In conclusion, this study highlights the importance of evaluating electromyography activation alongside force development in understanding neuromuscular adaptations post anterior cruciate ligament injury. These insights emphasize the need for comprehensive rehabilitation strategies that address muscle imbalance to mitigate the risk of recurrent anterior cruciate ligament injuries in female football players.

When the electric locomotive pantograph is dropping, the interruption of pantograph catenary contact causes electromagnetic oscillation and arcing. The frequent arc burning that occurs due to charge accumulation results in the amplitude of overvoltage increasing gradually, posing a threat to locomotive high-voltage equipment. However, the physical mechanisms and characteristics of overvoltage are still unclear. This paper proposes a simulation model of operating overvoltage due to a dropping pantograph based on the pantograph–catenary arc and variable capacitance. Distributed RLC electromagnetic oscillation is considered, which allows the real-time calculation of arc resistance and capacitance. Under the same working conditions, the error between the simulation and test results is less than 4.0%, which proves the credibility of the model. The variation law of overvoltage under different dropping speeds or catenary phases was investigated, which shows the max amplitude is 298.20 kV and steepness is 2096.80 kV/μs at 0.30 m/s speed. The waveform shows the characteristics of high amplitude and high steepness, similar to very fast transient overvoltage (VFTO). There is a sinusoidal relationship between the catenary phase and overvoltage amplitude. The closer the catenary phase to 90°, the higher the overvoltage amplitude. The research has important guiding significance for the overvoltage formation mechanism of a traction power supply system and the insulation coordination design of high-voltage equipment.

Spent Coffee Ground (SCG) is the main coffee industry by-product, rich in dietary fibers and polyphenols. The extractable material of SCG was fractionated, and the phenolic compounds were identified and quantified. Chlorogenic and neochlorogenic acids were identified as the main phenolic components, and the Total Phenolic Content (TPC) of SCG was determined to be 2.16% (dry SCG basis). Furthermore, SCG was characterized in terms of Total Dietary Fiber content, which amounted to 66%. The SCG was valorized for the development of a bakery product (cookie) enhanced with fiber and bioactive polyphenols. Cookies were produced with the addition of 4% and 7% dry and defatted SCG (baked cookie basis). The produced cookie prototypes presented TPC and dietary fiber dependent on the addition level of SCG. TPC values were determined at 588 and 1017 ppm, while dietary fiber values were at 2.7 and 4.6%, respectively. The shelf life of the cookies was monitored over 143 days at three different temperatures (25 °C, 35 °C, and 45 °C) in terms of texture (hardness), color, Peroxide Value (PV), and TPC. It was observed that the PV value significantly increased in samples with incorporated SCG, stored at 45 °C, while in those stored at 25 °C and 35 °C, PV remained at low levels. The TPC of the SCG-enriched samples remained practically constant during the shelf life analysis, while color and hardness increased (mathematically modeled). SCG-added cookies were characterized by increased darkness, increased hardness, and a mild (desirable) coffee flavor. The overall sensory impression scores for 0%, 4%, and 7% SCG-added cookies were 7.5, 8.0, and 8.2, respectively. Based on sensory evaluation test results, the shelf lives of 0%, 4%, and 7% SCG at 25 °C were 359, 435, and 471 days, respectively. Overall, SCG is a potentially valuable ingredient that can be used to develop innovative food (baked) products with enhanced nutritional value and increased shelf life.

As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods.

Many situations can induce dizziness in healthy participants, be it when riding a carrousel or when making head movements while wearing a head-mounted display. Everybody—maybe with the exception of vestibular loss patients—is prone to dizziness, albeit to widely varying degrees. Some people get dizzy after a single rotation around the body axis, while others can perform multiple pirouettes without the slightest symptoms. We have developed a form of vestibular habituation training with the purpose of reducing proneness to dizziness. The training consists of a short (8 min) exercise routine which is moderate enough that it can easily be integrated into a daily routine. Twenty volunteers performed the training over the course of two weeks. We measured subjective dizziness before and after each daily session. We also performed several vestibular tests before (pre-test) and after (post-test) the two-week training period. They included exposure to a rotating and pitching visual environment while standing upright, as well as a physical rotation that was abruptly stopped. The results show that the dizziness induced during a given daily session decreased over the course of the two weeks. The dizziness induced by the rotating visual stimulus was significantly less after completion of the training period compared with the initial pre-test. Also, postural stability and post-rotatory spinning sensations had improved when comparing the post-test with the pre-test. We conclude that a short regular vestibular training can significantly improve proneness to dizziness.

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method.

Sharing cyber threat intelligence (CTI) can significantly improve the security of information technology (IT) in organizations. However, stakeholders and practitioners are not keen on sharing CTI data due to the risk of exposing their private data and possibly losing value as an organization on the market. We present a model for CTI data sharing that maintains trust and confidentiality and incentivizes the sharing process. The novelty of the proposed model is that it combines two incentive mechanisms: money and reputation. The reputation incentive is important for ensuring trust in the shared CTI data. The monetary incentive is important for motivating the sharing and consumption of CTI data. The incentives are based on a subscription fee and a reward score for activities performed by a user. User activities are considered in the following three fields: producing CTI data, consuming CTI data, and reviewing CTI data. Each instance of user activity is rewarded with a score, and this score generates some value for reputation. An algorithm is proposed for assigning reward scores and for recording the accumulated reputation of the user. This model is implemented on the Hyperledger Fabric blockchain and the Interplanetary File System for storing data off-chain. The implemented prototype demonstrates the feasibility of the proposed model. The provided simulation shows that the selected values and the proposed algorithm used to calculate the reward scores are in accordance with economic laws.

In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive models is challenging due to privacy and data security concerns in integrating data from multiple emergency rooms. To address this, our work applies federated learning (FL) techniques, maintaining privacy without centralizing data. Medical data, which are often non-independent and identically distributed (non-IID), pose challenges for existing FL, where random client selection can impact overall FL performance. Therefore, we introduce a new client selection mechanism based on local model evaluation (LMECS), enhancing performance and practicality. This approach shows that the proposed FL model can achieve comparable performance to centralized models and maintain data privacy. The execution time was reduced by up to 27% compared to the existing FL algorithm. In addition, compared to the average performance of local models without FL, our LMECS improved the AUC by 2% and achieved up to 23% performance improvement compared to the existing FL algorithm. This work presents the potential for effective patient severity management in multi-institutional emergency rooms using FL without data movement, offering an innovative approach that satisfies both medical data privacy and efficient utilization.

Identifying land use mix (LUM) in urban areas is challenging, often requiring extensive human intervention and fieldwork. Accurate classification of LUM is crucial for various disciplines, including urban planning, urban economics, and public health. This study addresses this need by employing Voronoi triangulation and an entropy-based LUM formula using point-based geospatial data collected from publicly available sources. The methodology was tested in two distinct urban settings: Ankara and Kadıköy. Ankara, the capital city, provides a large and diverse urban environment, while Kadıköy, a district in Istanbul known for its dynamic urban life, offers a contrasting scenario. Results were analyzed concerning local spatial autocorrelation and point of interest (POI) intensity. The comparative analysis demonstrated that the approach performs well across different urban contexts, with improved results observed in Kadıköy due to its higher density of mixed-use development. Specifically, we managed to identify mixed land use areas with an accuracy of up to 78% and an F1-score of 83% in urban regions. These findings highlight the robustness and applicability of our approach in diverse urban environments, providing valuable insights for city planners and policymakers in optimizing the allocation of urban resources and enhancing land use efficiency.

The operational state of a rotor system directly affects its working efficiency, and the axis trajectory can accurately characterize this state. Therefore, a method for extracting axis motion trajectory characteristics based on distance sequence representation is established. First, the axis trajectory sample signal is constructed from the original vibration displacement signal. Singular value decomposition (SVD) is performed on the sample signal to obtain effective components, resulting in a purified and denoised axis motion trajectory signal. Next, the axis motion trajectory signal is centralized and normalized. Feature extraction is then performed on the axis motion trajectory signal. Based on the different curvatures of various regions in the axis motion trajectory graph, data points are adaptively selected. The distances between the selected data points and a unique fixed point are calculated in the two-dimensional plane, resulting in a feature signal that characterizes the axis motion trajectory graph. This completes the extraction of the axis motion trajectory characteristics. Different rotational speeds, additional weights, and changes in rotor arrangement types are applied to a multi-disk rotor test rig to obtain measured data for various unbalanced states, validating this method. The results show that this method effectively characterizes the axis motion trajectory with strong generality.

Truncated singular value decomposition (TSVD) is a popular recovery algorithm for multimode fiber single-pixel imaging (MMF-SPI), and it uses truncation thresholds to suppress noise influences. However, due to the sensitivity of MMF relative to stochastic disturbances, the threshold requires frequent re-determination as noise levels dynamically fluctuate. In response, we design an adaptive truncation threshold determination (ATTD) method for TSVD-based MMF-SPI in disturbed environments. Simulations and experiments reveal that ATTD approaches the performance of ideal clairvoyant benchmarks, and it corresponds to the best possible image recovery under certain noise levels and surpasses both traditional truncation threshold determination methods with less computation—fixed threshold and Stein’s unbiased risk estimator (SURE)—specifically under high noise levels. Moreover, target insensitivity is demonstrated via numerical simulations, and the robustness of the self-contained parameters is explored. Finally, we also compare and discuss the performance of TSVD-based MMF-SPI, which uses ATTD, and machine learning-based MMF-SPI, which uses diffusion models, to provide a comprehensive understanding of ATTD.

Adhesive joints are some of the oldest inseparable connections, and were used much earlier than other non-separable connections (e.g., welded, soldered). Adhesives are widely used in the manufacture of vehicles, household appliances, aircraft, and medicine. One disadvantage of adhesive joints is their long bonding time (amounting, for example, to 72 h for polyurethane adhesives used in bus roof bonding), and another is their production of harmful waste. Tapes that are adhesive coated on both sides are increasingly being used to join parts during production. Such tapes have lower strength than traditional adhesives, but their bonding time is much shorter. In addition, the amount of waste remaining after production is minimized. Tapes, like adhesives, dampen vibrations well and seal the materials being joined. The purpose of this study was to evaluate the influence of selected factors on the quality of tape–steel sheet joints and to assess the possibility of testing acrylic tape–steel sheet joints using ultrasonic methods. It was found that the preparation of a surface for bonding has a significant effect on the quality of the joint, and it was confirmed that non-destructive evaluation of the quality of the tested joints by the ultrasonic method is possible. The decibel drop in the height of the first and fifth pulses obtained on the screen of the ultrasonic defectoscope was proposed as an ultrasonic measure. The highest-quality joints were characterized by a measure in the range of 12 dB, lower-quality areas of about 8 dB, and tape-free areas of about 5 dB. At the same time, it was noted that in the case of proper surface preparation, there was cohesive failure of the joint during breakage.

Essential oils (EOs) are highly concentrated and volatile blends of nonpolar substances that are derived from aromatic plant components and comprise terpenes, terpenoids, and phenylpropanoids, exhibiting diverse biological and pharmacological properties. The burgeoning pet industry is interested in EOs as a potential solution for common health issues in domestic animals, particularly in addressing antimicrobial resistance. The present literature review summarizes the composition, properties, benefits, safety considerations, and effects of EOs on domestic animals. The applications of EOs range from antimicrobial effects to antioxidant, anti-inflammatory, and anticancer activities, etc. The chemical constituents of EOs, exemplified by eucalyptus EO and rosemary EO, highlight their distinct aromatic profiles and potential benefits. Nevertheless, understanding the chemical makeup of EOs is fundamental to assessing their potential impacts on biological systems. The gut microbiota plays a crucial role in regulating various metabolic processes in the host, including energy homeostasis, glucose metabolism, and lipid metabolism. Safety considerations, including potential toxicity risk awareness, are essential when incorporating EOs into animal care routines. The feed additives incorporating EOs have shown promise in influencing gut microbiota balance, reducing inflammation, and acting as antioxidants. However, considering the potential risks associated with high doses or multiple administrations, cautious application is paramount. Preliminary studies suggest low toxicity levels, but further research is required to evaluate the safety of EOs. Though studies have reported the beneficial effects of EOs on pets and animals, further research is needed to validate the findings in real-world conditions. The paper also discussed the regulatory considerations and future perspectives on applying EOs in veterinary medicine.

The purpose of this research article was to study the physicochemical characteristics of semi-wild Jara honey grown in Western Georgia. Jara honey is produced in the alpine and sub-alpine forest zone of high mountain Adjara, which is distinguished by its variety of honey plants. The physicochemical characteristics were examined concerning the Alimemtarius Code and EU regulations: moisture content, total carbohydrates, free acidity, pH, electrical conductivity, microelements (Li, Na, K, Mg, Ca), color, total phenols, total phenolic acids, total flavonoids, proline, diastase activity, proteins, and microscopic study of pollens. Using the UPLC-MSB method, grayanotoxin-III was identified in the semi-wild Jara honey samples. The findings demonstrated that the honey has significant concentrations of phenols, phenolic acids, and flavonoids. A directly proportional relationship was established between the quantitative content of phenolic compounds and the antioxidant activity of honey. This article is the first study of the characteristics of Jara honey produced in Western Georgia.

This paper deals with the investigation of factors influencing the movement patterns of a reactivated slow-moving landslide situated in the eastern Liguria region (NW Italy) through the analysis of extensive ground-based hydrological and geotechnical monitoring data. Subsurface horizontal displacement and pore water pressure data were acquired simultaneously by means of automatic sensors positioned at pre-existing and localized failure zones. The joint examination of field measurements enabled us to explore the connections between rain, pore water pressure, and displacements. The results of continuous displacement monitoring showed that the landslide kinematics involved phases of extremely slow movements alternated with periods of relative inactivity. Both stages occurred prevalently at seasonal scale displaying similar durations. The slow-motion phases took place at relatively constant pore water pressure and were ascribed to mechanisms of viscous shear displacements along failure surfaces. Inactive phases entailed no significant deformations, mostly corresponding to prolonged dry periods. The two motion patterns were interrupted by episodic sharp deformations triggered by delayed (preparation periods from 4 to 11 days) rainfall-induced pore water pressure peaks, which were ascribed to sliding mechanisms taking place through rigid-plastic frictional behaviour. During these deformation events, hysteresis relationships between pore water pressure and displacement were found, revealing far more complex hydro-mechanical behaviour.

In conventional gasoline automobiles, the engine powers the air conditioning system and engine noise can somewhat mask the noise and vibration of the air conditioning system. In pure electric vehicles, however, the absence of an engine makes the air conditioning system’s noise more noticeable, concentrated in a limited frequency range at constant speeds. As a result, aerodynamic noise from the air conditioning system is a primary noise source in electric vehicles. Pipeline silencers are the main method for reducing this noise. The current silencer design uses plane wave acoustic theory but when cavity modal resonance occurs, the transmission loss error is relatively high. This article addresses the issue of non-planar wave cavity resonance, studying the cavity modal of a muffler using the finite element method to reveal the transmission loss under cavity mode resonance. A dual cavity expansion structure of an impedance composite muffler is proposed, with sound-absorbing materials placed in the cavity to enhance acoustic performance. The analysis of the transmission loss characteristics of the impedance composite muffler provides a theoretical basis for noise control in pure electric vehicle air conditioning systems.

The production of biogas is achieved during anaerobic digestion (AD) using organic matter as a substrate. In Mediterranean countries, a promising substrate is lignocellulose biomass of perennial grass Miscanthus x giganteus, due to its potentially high biogas yields, which could be comparable to maize silage. During AD, bacteria convert biomass into more minor compounds, which are further converted to methane by methanogenic archaea. The selection of appropriate microbes for the degradation of the substrate is crucial, and the enhancement of this step lies in the immobilization of microbes on biocarriers. Described here, a microbial consortium, de novo isolated and conditioned to degrade the Mischantus biomass, was immobilized onto several natural biocarriers: natural zeolitized tuff, ZeoSand® (Velebit Agro, Zagreb, Croatia), perlite, and corncob. There was no statistically significant difference in the number of immobilized bacteria across the different materials. Therefore, all proved to be suitable for the immobilization of the consortium. In the consortium, five bacterial species with different shares in the consortium were identified: Enterobacter cloacae, Klebsiella pneumoniae, Enterobacter asburiae, Leclercia adecarboxylata, and Exiguobacterium indicum. After immobilization on each carrier, the share of each species changed when compared to starting conditions, and the most dominant species was E. cloacae (71–90%), while the share for other species ranged from 2 to 23%. The share of E. indicum was 14% at the start. However, it diminished to less than 1% because it was overgrown during the competition with other bacterial species, not due to an inability to immobilize.

Sweet whey (SW) and yogurt acid whey (YAW) are dairy by-products of the cheese-making process and Greek-style yogurt production, respectively. Both of them are considered pollutants with huge volumes of SW and YAW produced due to the growing demand for dairy products worldwide. Moreover, whey-derived peptides, resulting from fermentation as well as from further hydrolysis during digestion, have been associated with various biological activities. In the present study, the angiotensin-converting enzyme (ACE)-inhibitory activity of 48 SW samples and 33 YAW samples from bovine, ovine, caprine, and ovine/caprine milk obtained were evaluated. Additionally, the SW and YAW digestates and two of their fractions (smaller than 10 kDa, SW-D-P10 and YAW-D-P10, and smaller than 3 kDa, SW-D-P3 and YAW-D-P3), which were obtained after in vitro digestion and subsequent ultrafiltration, were also subjected to evaluation. Our data indicated that the D-P10 and D-P3 fractions exhibited higher ACE-inhibitory activity compared to the corresponding values before digestion. The ACE-inhibitory capacity after in vitro digestion was higher for the ovine SW samples compared to their bovine and caprine counterparts. The effect of the D-P3 fraction on the inhibition of nitric oxide (NO) production and the expression of a selected panel of immune-response-related genes in LPS-stimulated RAW 264.7 macrophages was also evaluated. Fractions from both dairy by-products inhibited NO production in LPS-stimulated RAW 264.7 cells. Especially, ovine SW-D-P3 showed a strong NO inhibitory activity and suppressed inducible nitric oxide synthase (Nos2) mRNA levels. However, YAW-D-P3 could not trigger neither the gene expression of inflammatory macrophage mediators Nos2 and cyclooxygenase-2 (Ptgs2) nor tumor necrosis factor-α (Tnf) and interleukin 6 (Il6) in LPS-stimulated murine macrophages regardless of animal origin. These findings suggest that in vitro digestion could enhance the production of ACE-inhibitory peptides in both dairy by-products, while SW from ovine origin displays higher potential as an anti-inflammatory agent, effectively preventing excessive NO production.

Modern aquaculture utilizes computer vision technology to analyze underwater images of fish, contributing to optimized water quality and improved production efficiency. The purpose of this study is to efficiently perform underwater fish detection and tracking using multi-object tracking (MOT) technology. To achieve this, the FairMOT model was employed to simultaneously implement pixel-level object detection and re-identification (Re-ID) functions, comparing two backbone models: FairMOT+YOLOv5s and FairMOT+DLA-34. The study constructed a dataset targeting the popular black porgy in Korean aquaculture, using underwater video data from five different environments collected from the internet. During the training process, the FairMOT+YOLOv5s model rapidly reduced train loss and demonstrated stable performance. The FairMOT+DLA-34 model showed better results in ID tracking performance, with an accuracy of 44.1%, an IDF1 of 11.0%, an MOTP of 0.393, and an IDSW of 1. In contrast, the FairMOT+YOLOv5s model recorded an accuracy of 43.8%, an IDF1 of 14.6%, an MOTP of 0.400, and an IDSW of 10. The results of this study indicate that the FairMOT+YOLOv5s model demonstrated higher IDF1 and MOTP scores compared to the FairMOT+DLA-34 model, while the FairMOT+DLA-34 model showed superior performance in ID tracking accuracy and had fewer ID switches.

Aiming at solving the issues that fire detection is prone to be affected by environmental factors, and the accuracy of flame and smoke detection remains relatively low at the incipient stage of fire, a fire detection algorithm based on GCM-YOLO is put forward. Firstly, GhostNet is introduced to optimize the backbone network, enabling the model to be lightweight without sacrificing model accuracy. Secondly, the upsampling module is reorganized with content-aware features to enhance the detail capture and information fusion effect of the model. Finally, by incorporating the mixed local channel attention mechanism in the neck, the model can enhance the processing capability of complex scenes. The experimental results reveal that, compared with the baseline model YOLOv8n, the GCM-YOLO model in fire detection increases the mAP@0.5 by 1.2%, and the number of parameters and model size decrease by 38.3% and 34.9%, respectively. The GCM-YOLO model can raise the accuracy of fire detection while reducing the computational burden and is suitable for deployment in practical application scenarios such as mobile terminals.

Structural monitoring is crucial for assessing structural health, and high-precision deformation prediction can provide early warnings for safety monitoring. To address the issue of low prediction accuracy caused by the non-stationary and nonlinear characteristics of deformation sequences, this paper proposes a similarity clustering (SC) deformation prediction model based on GNSS/accelerometer time-frequency analysis. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the original monitoring data, and the time-frequency characteristic correlations of the deformation data are established. Then, similarity clustering is conducted for the monitoring sub-sequences based on their frequency domain characteristics, and clustered sequences are combined subsequently. Finally, the Long Short-Term Memory (LSTM) model is used to separately predict GNSS displacement and acceleration with clustered time series, and the overall deformation displacement is reconstructed based on the predicted GNSS displacement and acceleration-derived displacement. A shake table simulation experiment was conducted to validate the feasibility and performance of the proposed CEEMDAN-SC-LSTM model. A duration of 5 s displacement prediction is analyzed after 153 s of monitoring data training. The results demonstrate that the root mean square error (RMSE) of predicted displacement is 0.011 m with the proposed model, which achieves an improvement of 64.45% and 61.51% in comparison to the CEEMDAN-LSTM and LSTM models, respectively. The acceleration predictions also show an improvement of 96.49% and 95.58%, respectively, the RMSE of the predicted acceleration-reconstructed displacement is less than 1 mm, with a reconstruction similarity of over 99%. The overall displacement reconstruction similarity can reach over 95%.

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

In the dynamic and uncertain stock market, precise forecasting and decision-making are crucial for profitability. Traditional deep neural networks (DNN) often struggle with capturing long-term dependencies and multi-scale features in complex financial time series data. To address these challenges, we introduce CMR-DQN, an innovative framework that integrates discrete wavelet transform (DWT) for multi-scale data analysis, temporal convolutional network (TCN) for extracting deep temporal features, and a GRU–LSTM–Attention mechanism to enhance the model’s focus and memory. Additionally, CMR-DQN employs the Rainbow DQN reinforcement learning strategy to learn optimal trading strategies in a simulated environment. CMR-DQN significantly improved the total return rate on six selected stocks, with increases ranging from 20.37% to 55.32%. It also demonstrated substantial improvements over the baseline model in terms of Sharpe ratio and maximum drawdown, indicating increased excess returns per unit of total risk and reduced investment risk. These results underscore the efficiency and effectiveness of CMR-DQN in handling multi-scale time series data and optimizing stock market decisions.

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong compound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the electromagnetic interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, Common Mode Rejection Ratio, gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain–Computer Interfaces and neurofeedback training systems.

Noise reduction (NR) algorithms are employed in nearly all commercially available hearing aids to attenuate background noise. However, NR processing also involves undesirable speech distortions, leading to variability in hearing outcomes among individuals with different noise tolerance. Leveraging 30 participants with normal hearing engaged in speech-in-noise tasks, the present study examined whether the cortical measure of neural signal-to-noise ratio (SNR)—the amplitude ratio of auditory evoked responses to target speech onset and noise onset—could predict individual variability in NR outcomes with varying strength, thus serving as a reliable indicator of individual noise tolerance. In addition, we also measured subjective ratings of noise tolerance to see if these measures could capture different perspectives on individual noise tolerance. Results indicated a significant correlation between neural SNR and NR outcomes that intensified with increasing strength of NR processing. While subjective ratings of noise tolerance were not correlated with the neural SNR, noise-tolerance ratings could predict outcomes with stronger NR processing and account for additional variance in the regression model, although the effect was limited. Our findings underscore the importance of accurately assessing an individual’s noise tolerance characteristics in predicting perceptual benefits from various NR processing methods and suggest the advantage of incorporating both cortical and subjective measures in the relevant methodologies.

The majority of fatalities in building fires are attributed to asphyxiation caused by toxic gases. Hydrogen cyanide (HCN) is one of the toxic gases that can be released during a fire, posing a lethal risk to humans even at low concentrations. However, analysis of the risk posed by HCN in fire risk assessments using fire simulations is relatively rare. This study conducted fire simulations to examine the potential risks of HCN to occupants during a fire. The simulations considered various fire conditions in residential buildings by varying fuel types, fire growth rates, and HCN yields. The relative risk score (RRS) was derived based on the time to reach the threshold values of parameters considered critical for life safety. The results of the fire simulations indicated that the RRS for HCN was approximately 20–40 points higher than that of O2, CO, and CO2, reaching a maximum of 92 points. However, the risk posed by HCN was found to be limited in comparison to the risks associated with temperature and visibility. Nevertheless, considering that the primary cause of fatalities in fires is asphyxiation due to toxic gases, HCN must be regarded as a critical factor in fire risk assessments. Additionally, since HCN yield values can increase up to nine times depending on temperature and ventilation conditions, the risk posed by HCN could be significantly higher.

The analysis of slope failure modes is essential for understanding slope stability. This study investigated the failure modes and triggering factors of a rock slope using the limit equilibrium method, finite differences method, and exploratory factor analysis. First, the limit equilibrium method was used to identify potential sliding surfaces. Then, the finite differences method was employed to study deformation and failure features in a slope. Stability factors were calculated considering specific conditions such as rainfall, prestressing loss, and earthquakes using the strength reduction method. Finally, exploratory factor analysis was utilized to identify the triggering factors of each failure mode. The results revealed that failure modes were categorized into two types based on the positions of the sliding surface. The main triggering factors for Failure Mode 1 were rainfall and prestress loss, while for Failure Mode 2 they were earthquake loading and prestress loss. This study offers a comprehensive exploration of potential failure modes and their triggering factors from mechanical and statistical perspectives, enriching our understanding of potential failure modes in rock slopes.

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.

A cross-sectional study was conducted to biomechanically characterize Isabel’s workout (30 snatch repetitions with 61 kg fixed weight), focusing on eventual changes in knee, hip and shoulder angles. A three-dimensional markerless motion capture system was used to collect data from 11 highly trained male crossfitters along the Isabel workout performed at maximal effort. The routine was analyzed globally and in initial, middle and final phases (10, 20 and 30 repetitions, respectively). Lift total time increased (1.51 ± 0.18 vs. 1.97 ± 0.20 s) and maximal lift velocity (2.64 ± 0.12 vs. 2.32 ± 0.13 m/s) and maximal lift power (15.58 ± 2.34 vs. 13.80 ± 2.49 W/kg) decreased from the initial to final phases, while the time from lift until the bar crossed the hip and shoulder (34.20 ± 4.00 vs. 27.50 ± 5.10 and 39.70 ± 16.80 vs. 30.90 ± 13.90%) decreased along the Isabel workout. In addition, a decrease in hip flexion was observed during the last two phases when the bar crosses the knee (62.62 ± 24.80 vs. 53.60 ± 19.99°). Data evidence a decrease in the power profile and a change in hip flexion throughout the Isabel workout, without compromising the other joints.

Purpose: Physical activity induces numerous modifications in the morphological, rheological, and biochemical properties of blood. The purpose of this study was to evaluate changes in blood rheological and biochemical indicators among runners. Also, we assessed how the rheological and biochemical properties of blood in people who practised running characterised the range and direction of exercise modifications and allowed for the diagnosis of transient adaptive effects. Methods: This study included 12 athletes who regularly trained in middle- and long-distance running (6–8 times a week) and presented a high sports level (national and international class). The athletes performed a 30 min warm-up consisting of 15 min of jogging and exercises. After a 10 min rest, they completed a 3 km run with submaximal effort. Blood samples were collected at baseline and after the effort. Results: No statistically significant changes were revealed in erythrocyte, leukocyte, platelet, iron, ferritin, transferrin, erythropoietin, or C-reactive protein concentrations in the examined runners. The same applied to the elongation index at a shear stress within the range of 0.30–60.00 Pa, amplitude and total extent of aggregation, aggregation half-life, and aggregation index. A significant increase (within standard limits) was only observed in fibrinogen concentration after running. Conclusions: The lack of post-exercise changes in blood rheological and biochemical indicators in the investigated runners points at an efficient haemorheological system. This, in turn, reflects well-executed training and remarkably well-trained adaptive systems responsible for regeneration.

This study examined the thermal reflective properties of composite inorganic coatings applied to various wall types. It evaluated the influence of these coatings on the thermal insulation and reflectivity of the walls. The findings indicate that coating application markedly enhances a wall’s thermal insulation capabilities and increases its heat flux reflection ratio. These results offer crucial theoretical backing and practical guidance for the future use of coating technologies in construction. By refining the coating formulations and application processes, the thermal insulation properties of walls can be further enhanced, thus making a significant contribution to building energy efficiency and environmental protection. Consequently, this research provides essential references and insights for advancing coating technology in the construction industry.

The present study focused on the use of pouting (Trisopterus luscus), an underutilized gadoid fish species, as a fresh product of potential commercial interest. Accordingly, non-degutted pouting specimens (145–195 g and 15–22 cm) were stored under chilling conditions (0 °C) for microbial, chemical and sensory analyses to evaluate their commercial quality and shelf life. A progressive quality loss (p < 0.05) was detected for this lean species (5.58 g lipids·kg−1 muscle) as the storage time increased, as determined through microbial (aerobes, psychrotrophs and Enterobacteriaceae counts), lipid hydrolysis (free fatty acid value), lipid oxidation (conjugated diene and triene, thiobarbituric acid reactive substance, and fluorescence values) and sensory acceptance assessment. A detailed comparison to related lean fish species revealed that the pouting exhibited a fast quality breakdown under refrigeration conditions. Thus, after 9 d of chilled storage, the psychrotroph counts exceeded the acceptable limits (8.54 log CFU·g−1), and the fish specimens were found to be rejectable, with the sensory panel, external odor and eye appearance being the limiting factors. In contrast, the pouting specimens exhibited high quality after 3 d of storage, with the quality being still acceptable after 6 d. According to the current search for novel, underutilized species, pouting is proposed as a promising source.

The pursuit of durable and sustainable construction has driven interest in innovative materials, with superabsorbent polymers (SAPs) emerging as a promising solution, especially for the concrete industry. SAPs offer significant benefits to the durability of concrete structures, including mitigation of autogenous shrinkage, enhanced freeze–thaw resistance, crack sealing, and stimulation of autogenous healing. This study focuses on the impact of internal curing with SAPs on crack formation and corrosion initiation in large-scale reinforced concrete walls (14 m × 2.75 m × 0.8 m). Both commercial SAPs based on acrylic acid chemistry and in-house-developed SAPs based on alginates were evaluated. Key findings reveal that the reference wall exhibited visible cracking just five days after casting, while the SAP-treated wall remained crack-free throughout a 24-month monitoring period. Moreover, the reference wall showed corrosion initiation at two locations near the cracks within six months, whereas the SAP-treated wall exhibited no signs of corrosion potential. Laboratory tests further demonstrated a slight reduction in chloride penetration and carbonation in SAP-treated specimens compared to the reference. These results highlight the efficacy of SAPs in enhancing the durability and longevity of reinforced concrete structures.

Traditionally, users have perceived that only manual laborers or those in repetitive jobs would be subject to technological substitution. However, with the emergence of technologies like Midjourney, ChatGPT, and Notion AI, known as Artificial Intelligence-Generated Content (AIGC), we have come to realize that cognitive laborers, particularly creative designers, also face similar professional challenges. Yet, there has been relatively little research analyzing the acceptance and trust of artificial intelligence from the perspective of designers. This study integrates the TAM/TPB behavioral measurement model, incorporating intrinsic characteristics of designers, to delineate their perceived risks of AIGC into functional and emotional dimensions. It explores how these perceived characteristics, risks, and trust influence designers’ behavioral intentions, employing structural equation modeling for validation. The findings reveal the following: (1) designer trust is the primary factor influencing their behavioral choices; (2) different dimensions of perceived risks have varying degrees of impact on trust, with functional risks significantly positively affecting trust compared to emotional risks; (3) only by enhancing the transparency and credibility of Artificial Intelligence-Generated Content (AIGC) can the perceived characteristics of designers be elevated; and (4) only by effectively safeguarding designers’ legitimate rights and interests can perceived risks be significantly reduced, thereby enhancing trust and subsequently prompting actual behavioral intentions. This study not only enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading.

User reviews such as SNS feeds and blog writings have been widely used to extract opinions, complains, and requirements about a given place or product from users’ perspective. However, during the process of collecting them, a lot of reviews that are irrelevant to a given search keyword can be included in the results. Such irrelevant reviews may lead to distorted results in data analysis. In this paper, we discuss a method to detect irrelevant user reviews efficiently by combining various oversampling and machine learning algorithms. About 35,000 user reviews collected from 25 restaurants and 33 tourist attractions in Ulsan Metropolitan City, South Korea, were used for learning, where the ratio of irrelevant reviews in the two kinds of data sets was 53.7% and 71.6%, respectively. To deal with skewness in the collected reviews, oversampling algorithms such as SMOTE, Borderline-SMOTE, and ADASYN were used. To build a model for the detection of irrelevant reviews, RNN, LSTM, GRU, and BERT were adopted and compared, as they are known to provide high accuracy in text processing. The performance of the detection models was examined through experiments, and the results showed that the BERT model presented the best performance, with an F1 score of 0.965.

Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are recognized as devices that can effectively manage problems such as voltage segments, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the disadvantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular-structured memory filter. The proposed circular-structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system were built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.