bluuebunny/crossref_metadata_2025 huggingface dataset:
Description of Meteorological Indices Presented Based on Long-Term Yields of Winter Wheat in Southern Germany
Authors: Heil Kurt , Klöpfer Christian , Hülsbergen Kurt-Jürgen , Schmidhalter Urs
Published: September 2023

This study had three main objectives. First, weather indices were listed and their derivations were described to show which weather parameters could be used to describe the influence on agricultural yields. Second, farmers and agricultural scientists should be given the opportunity to evaluate the weather of the observation years in the study region. Furthermore, significant fluctuations in winter wheat yields were compared with weather events. As weather variables, 45 meteorological indices were used, such as precipitation-, temperature-, precipitation-temperature-, growing-period-, and radiation-related indices. In the case of winter wheat, heat waves and dry periods were the most important factors that affected the yields. For the past 20 years, in particular, there have been recurrent spring and summer months with low precipitation and, in some cases, significantly too warm periods, such as in 2003 and 2018 (April to October 2003: +16% °C, 2018: +27% °C, 2003: −38% mm, 2018: −12% mm in relation to 1978 to 2020), which were associated with particularly high yield losses. The qualitative assessments illustrate that in the observation period, years with reduced yield compared with the multiannual trend were frequently well explainable by extreme weather events.
The Growth Effect and Its Influencing Factors: Empirical Evidence Regarding China’s Fruit and Vegetable Exports to RCEP Countries
Authors: Tong Guangji , Hong Rui , Shi Lei
Published: September 2023

Fruit and vegetable products, integral to human nutrition, play a vital role in dietary patterns. Moreover, the Regional Comprehensive Economic Partnership (RCEP) region, a critical market for Chinese fruit and vegetable exports, has observed the growing presence of these Chinese produce groups. The ratification of the RCEP bolsters the liberalization of fruit and vegetable commerce in the region, consequently fostering opportunities for its development. Nonetheless, existing studies have insufficiently addressed fruit and vegetable commerce in the region and its consequent effect on trade expansion. In this context, it is crucial to analyze the trade pattern associated with the swift export growth of fruit and vegetables. This study employs binary marginal analysis and the stochastic frontier gravity model. This study’s findings reveal that, with respect to the growth effect, the expansion of China’s fruit and vegetable exports to RCEP countries in recent years primarily stems from the contribution of the extensive margin. Considering the factors influencing trade, metrics like free trade agreements (FTAs), the extent of trade liberalization, political expenditure levels, government transparency, and liner transport connectivity significantly impact China’s fruit and vegetable exports. Regarding trade efficiency, the current efficiency value of China’s fruit and vegetable exports to RCEP countries is relatively low, characterized by substantial country-specific variations and immense future trade potential. The insights gleaned from this research can offer decision-making support for the collaboration on fruit and vegetable trade between China and the RCEP region.
Factors Influencing Farmers’ Vertical Collaboration in the Agri-Chain Guided by Leading Enterprises: A Study of the Table Grape Industry in China
Authors: Li Wen , Liu Chenying , Yang Qizhi , You Yulan , Zhuo Zhihang , Zuo Xiaolin
Published: September 2023

Leading agricultural enterprises attracting farmers to participate in vertical collaboration within the industry chain can expedite the process of agricultural industrialization and help achieve rural revitalization. This study focuses on table grape growers in the Yangtze River Delta region of China as the research subjects. Instead of examining the impact of specific elements individually on farmers’ involvement in vertical collaboration within the industry chain, this study emphasizes the combination of multiple factors influencing farmers’ engagement. Employing a fuzzy-set qualitative comparative analysis and adopting a configurational perspective, this research investigates how six factors—growers’ age, ratio of sales income to household income, production scale, market distance, financial support, and technical support—combine to influence farmers’ participation in vertical collaboration within the industry chain. This study reveals that leading enterprises can indeed promote farmers’ participation in vertical cooperation within the agricultural chain, and four pathways are identified. Based on these findings, three configurations are summarized: risk-averse, resource-constrained, and burden-alleviating. Specific strategies and recommendations for targeting each of these configurations are proposed based on the findings, along with policy suggestions for regulating the six factors, both by the enterprises themselves and by the government.
Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm
Authors: Siriani Allan Lincoln Rodrigues , Miranda Isabelly Beatriz de Carvalho , Mehdizadeh Saman Abdanan , Pereira Danilo Florentino
Published: September 2023

The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.
Effect of Plastic Membrane and Geotextile Cloth Mulching on Soil Moisture and Spring Maize Growth in the Loess–Hilly Region of Yan’an, China
Authors: Jia Zhifeng , Wu Bobo , Wei Wei , Chang Yingjie , Lei Rui , Hu Weiwei , Jiang Jun
Published: September 2023

In order to study the effect of a plastic membrane and geotextile cloth mulching on soil moisture and crop growth in the loess–hilly region, a one-year continuous field monitoring experiment was carried out in Ansai District, City of Yan’an, Shaanxi Province, China. The experimentation included three treatments: plastic membrane and geotextile cloth mulching on the ridge (MB), geotextile cloth mulching on the ridge (DB), and bare soil ridge (CK). Soil moisture and water potential sensors were installed to monitor the changes in soil moisture content and water potential at 5, 15, and 30 cm below the furrow surface and meteorological data above the soil surface, and the growth traits, yield, and quality of maize were analyzed. The results showed the following: (1) The soil water-storage capacity of the three treatments dropped to a minimum in the filling stage and gradually recovered in the mature stage. The average water-storage capacity for the MB treatment was 35.5% higher than that for the DB treatment and 85.1% higher than that for the CK treatment, significant throughout the whole growth period. (2) For four types of rainfall events, namely, light, medium, heavy, and storm rainfall, significant responses were observed at 5 cm below the ground for three treatments, and the fastest response was in MB due to its best rain-collection effect. A significant response was also observed at 15 and 30 cm below the surface of the furrow during medium, heavy, and storm rainfall, while no significant difference in response time was found between the three treatments due to the restriction of the soil infiltration capacity. (3) The differences between the three treatments in the agronomic traits of maize, except for plant height and stem thickness, were insignificant (p < 0.05). The seed moisture content and yield for the MB treatment were the highest, with values of 40.33% and 8366 kg/hm2, respectively, followed closely by the DB treatment, with values of 38.61% and 7780 kg/hm2, respectively, and the smallest values were observed in the CK treatment, with values of 35.80% and 6897 kg/hm2, respectively. Compared with those for the CK treatment, the average starch content and the average lipid content for the mulching treatments (MB, DB) decreased by 13.40% and 17.11%, respectively, while the average protein content of maize increased by 7.86%. Overall, a plastic membrane and geotextile cloth mulching could significantly increase soil moisture and spring maize yield due to their better rain-collection effect.
Differences in Mother–Infant Bond and Social Behavior of African Elephant Calves Living In Situ and Ex Situ
Authors: Hoerner Franziska , Rendle-Worthington Jake , Lawrenz Arne , Oerke Ann-Kathrin , Damerau Karsten , Borragán Santos Santiago , Hard Therese , Preisfeld Gela
Published: September 2023

African zoo elephants live in safe environments with sufficient resources, are protected from threats, and have their health and body conditions cared for. Calves ex situ undergo the same developmental stages as in situ and are raised by the whole family unit. However, due to environmental differences, there might be behavioral modifications between calves in situ and ex situ. We hypothesize that these differences increase with ongoing generations. This ethological study compares social and general behavior and the distance calves kept to their mothers’ between calves of the first (F1) and second (F2) zoo generation and the wild. Using ethological methods, data were collected for ~90 in situ calves and 16 ex situ (8 F1, 8 F2) between the ages of 0.5 to 4 years (120 observation hours per group). Results showed that in situ calves spent significantly more time close to mothers than the F1 and the F2 zoo generations (F1/in situ: p = <0.001; F2/in situ: p = 0.007). The behaviors of eating, drinking, trunk movement, washing, and affiliative behaviors showed significant differences between in situ and ex situ calves. The amount and distribution of affiliative and agonistic behavior initiated and received by calves was displayed with a greater variety ex situ. Ex situ calves not only performed affiliative but, in contrast to the in situ, also agonistic behavior (F1/in situ: initiated p = 0.002, received p = 0.010; F2/in situ: initiated p = 0.050, received p = 0.037). The comparison of zoo generations suggests that differences did not increase with the generation. The more casual binding between mothers and offspring in zoos and the age-dependent improvement of social behavior of zoo-born calves are seen as a result of elephants’ adaptation to secure zoo conditions. The results of this study agree with the faster development of ex situ African elephants, like earlier puberty and more frequent breeding patterns, as known from the literature.
Novel Y RNA-Derived Fragments Can Differentiate Canine Hepatocellular Carcinoma from Hepatocellular Adenoma
Authors: Ushio Norio , Hasan Md Nazmul , Arif Mohammad , Miura Naoki
Published: September 2023

Hepatocellular carcinomas (HCC) are common tumors, whereas hepatocellular adenomas (HCA) are rare, benign tumors in dogs. The aberrant expression of noncoding RNAs (ncRNAs) plays a pivotal role in HCC tumorigenesis and progression. Among ncRNAs, micro RNAs have been widely researched in human HCC, but much less widely in canine HCC. However, Y RNA-derived fragments have yet to be investigated in canine HCC and HCA. This study targeted canine HCC and HCA patients. We used qRT-PCR to determine Y RNA expression in clinical tissues, plasma, and plasma extracellular vesicles, and two HCC cell lines (95-1044 and AZACH). Y RNA was significantly decreased in tissue, plasma, and plasma extracellular vesicles for canine HCC versus canine HCA and healthy controls. Y RNA was decreased in 95-1044 and AZACH cells versus normal liver tissue and in AZACH versus 95-1044 cells. In plasma samples, Y RNA levels were decreased in HCC versus HCA and Healthy controls and increased in HCA versus Healthy controls. Receiver operating characteristic analysis showed that Y RNA could be a promising biomarker for distinguishing HCC from HCA and healthy controls. Overall, the dysregulated expression of Y RNA can distinguish canine HCC from HCA. However, further research is necessary to elucidate the underlying Y RNA-related molecular mechanisms in hepatocellular neoplastic diseases. To the best of our knowledge, this is the first report on the relative expression of Y RNA in canine HCC and HCA.
Comparison of Multiple-Locus Variable-Number Tandem Repeat Analysis Profiles of Enteropathogenic Yersinia spp. Obtained from Humans, Domestic Pigs, Wild Boars, Rodents, Pork and Dog Food
Authors: Sannö Axel , Rosendal Thomas , Aspán Anna , Backhans Annette , Jacobson Magdalena
Published: September 2023

The enteropathogenic Yersinia genus is commonly detected in wildlife including wild boars. Difficulties in its cultivation may hamper subsequent epidemiological studies and outbreak investigations. Multiple-locus variable-number tandem repeat analysis (MLVA) of Yersinia (Y.) enterocolitica and Y. pseudotuberculosis has proven useful in source attribution and epidemiological studies but has hitherto relied on the analysis of isolates. In the present study, MLVA profiles generated from 254 isolates of Y. enterocolitica indicated similarities between human, pig and rodent isolates. Further, MLVA analyses of 13 Y. pseudotuberculosis pure-cultured isolates were compared to MLVA analyses performed directly on the 14 PCR-positive enrichment broths from which the isolates originated, which showed matching MLVA profiles. This indicates that MLVA analysis performed directly on enrichment broths could be a useful method for molecular epidemiological investigations. In addition, 10 out of 32 samples of wild boar minced meat obtained from private hunters and from approved wild-game-handling establishments were PCR-positive for the presence of Y. enterocolitica and may indicate a risk for public health.
A Scoping Review on Tyrosine Kinase Inhibitors in Cats: Current Evidence and Future Directions
Authors: Žagar Žiga , Schmidt Jarno M.
Published: September 2023

Tyrosine kinase inhibitors (TKIs) have become invaluable in the treatment of human and canine malignancies, but their role in feline oncology is less defined. While toceranib phosphate and masitinib mesylate are licensed for use in dogs, no TKI is yet approved for cats. This review systematically maps the research conducted on the expression of tyrosine kinases in neoplastic and non-neoplastic domestic feline tissues, as well as the in vitro/in vivo use of TKIs in domestic cats. We identify and discuss knowledge gaps and speculate on the further research and potential indications for TKI use in cats. A comprehensive search of three electronic databases and relevant paper reference lists identified 139 studies meeting the inclusion criteria. The most commonly identified tumors were mast cell tumors (MCTs), mammary and squamous cell carcinomas and injection-site sarcomas. Based on the current literature, toceranib phosphate appears to be the most efficacious TKI in cats, especially against MCTs. Exploring the clinical use of TKIs in mammary carcinomas holds promise. Despite the progress, currently, the evidence falls short, underscoring the need for further research to discover new indications in feline oncology and to bridge the knowledge gaps between human and feline medicine.
High Performance Liquid Chromatography Analysis and Description of Purine Content of Diets Suitable for Dogs with Leishmania Infection during Allopurinol Treatment—A Pilot Trial
Authors: Kaempfle Melanie , Bergmann Michèle , Koelle Petra , Hartmann Katrin
Published: September 2023

Reducing the alimentary purine intake contributes to the prevention of purine (especially xanthine) urolith formation, a common adverse effect of allopurinol treatment in dogs with Leishmania infections. Analyses of the purine content are not required in order to advertise a diet as low in purine. Due to different analytical methods, data provided on purine content are barely comparable. The aim of this study was to investigate the total purine content of 12 different dog diets. For this, the purine bases adenine, guanine, xanthine, and hypoxanthine were determined by standardised high performance liquid chromatography in commercially available urinary diets (n = 4), kidney diets (n = 2), low protein diets (n = 3), 1 vegan diet, 1 regular diet for healthy adult dogs, and 1 homemade low purine diet. Total purine amounts ranged between 10.2 and 90.9 mg/100 g of dry matter. The daily purine intake calculated for a 20 kg standard dog with the analysed diets ranged between 21.9 and 174.7 mg. The lowest daily purine intakes were achieved by 2 urinary urate diets, followed by the homemade diet. Differences in the purine content of commercially available diets need to be considered. Awareness has to be raised when selecting diets for dogs with Leishmania infections during allopurinol treatment in order to minimise the risk of urolith formation.
Stunning Compliance in Halal Slaughter: A Review of Current Scientific Knowledge
Authors: Sazili Awis Qurni , Kumar Pavan , Hayat Muhammad Nizam
Published: September 2023

Muslim scholars are not unanimous on the issue of the application of stunning in the halal slaughtering of animals. Appropriate stunning makes animals unconscious instantaneously, thus avoiding unnecessary pain and stress during the slaughtering of animals. The present review comprehensively summarizes the available scientific literature on stunning methods in view of their halal compliance during the slaughter of animals. The issue of maximum blood loss, reversibility of consciousness, and animals remaining alive during the halal cut are the key determinants of approval of stunning in the halal slaughter. Further, missed stuns due to poor maintenance of equipment, improper applications, and poor restraining necessitates additional stunning attempts, which further aggravates pain and stress in animals. Scientific findings suggest that halal-compliant stunning technologies are reversible, do not kill animals prior to the halal cut, and do not obstruct blood loss. There is a need to carry out further research on the refinement of available stunning technologies and their application, proper restraints, proper identification of the death status of animals, and assurance of animal welfare in commercial halal meat production.
Effects of Silage Type and Feeding Intensity on Carcass Traits and Meat Quality of Finishing Holstein–Friesian Bulls
Authors: Nogalski Zenon , Modzelewska-Kapituła Monika , Tkacz Katarzyna
Published: September 2023

The study aimed at evaluating the influence of silage type (grass silage—GS, and maize silage—MS) and energy level in the feed portion on animals’ performance, carcass value and the quality of Holstein–Friesian bull meat. The animals were reared using intensive (I) feeding, 1.02 feed units for meat production (UFV)/kg dry matter (DM), and semi-intensive (SI) feeding, 0.94 UFV/kg DM. Thirty-two HF bulls with an average live weight of 530 kg were assigned to four feeding groups. The proportions (g/kg DM) of feed in the diets were as follows: group GS-I, GS 500, concentrate 500; group GS-SI, GS 700, concentrate 300; group MS-I, MS 500, concentrate 500; and group MS-SI, MS 700, concentrate 300. After 4 months of the finishing phase, bulls were slaughtered and samples of the longissimus lumborum muscle were collected. The silage type did not affect performance, carcass value or meat quality. However, in the intensive feeding, a 33% increase (p < 0.01) in daily weight gain and a reduction in the feed conversion ratio compared to semi-intensive feeding were noted. The carcasses of bulls fed intensively received higher scores for conformation and fatness than the carcasses of bulls fed semi-intensively. The meat of I group bulls had a higher intramuscular fat content and received higher juiciness, tenderness, taste and overall acceptability scores.
Application of Extracellular Polymeric Substances during Cultivation of Microalgae Biomass
Authors: Rusanowska Paulina , Zieliński Marcin , Dudek Magda , Dębowski Marcin
Published: September 2023

Extracellular polymeric substances (EPS) produced by microorganisms contain polymers that are used for the bioflocculation of microalgae; however, these polymers are also organic compounds that might be used as carbon sources. The study analyzed two strategies for the introduction of EPS for Tetraselmis subcordiformis, Chlorella sp., and Arthrospira platensis biomass harvesting. In the first variant, EPS in the dose of 100 mg TOC/g were added to the photobioreactor every other day from the beginning of the cultivation, while in the second variant, EPS in the two doses of 100 mg TOC/g and 300 mg TOC/g were only added at the end of cultivation. In the first variant, the results proved that microalgae/cyanobacteria can use the EPS as external carbon sources. The cultures were characterized by a faster increase in biomass concentration, which contained less chlorophyll. However, the EPS content did not change. In the second variant, the addition of EPS did not affect the EPS content and the sedimentation of the Chlorella sp. biomass. The biomass of T. subcordiformis was characterized by a much better sedimentation coefficient. The greatest differences were observed in the A. platensis culture: the biomass concentration increased from 1.2 ± 0.2 g/L to 1.9 ± 0.2 g/L, EPS content increased by 16%, and sedimentation efficiency increased to 72%.
A Real-Time Dynamic Gesture Variability Recognition Method Based on Convolutional Neural Networks
Authors: Amangeldy Nurzada , Milosz Marek , Kudubayeva Saule , Kassymova Akmaral , Kalakova Gulsim , Zhetkenbay Lena
Published: September 2023

Among the many problems in machine learning, the most critical ones involve improving the categorical response prediction rate based on extracted features. In spite of this, it is noted that most of the time from the entire cycle of multi-class machine modeling for sign language recognition tasks is spent on data preparation, including collection, filtering, analysis, and visualization of data. To find the optimal solution for the above-mentioned problem, this paper proposes a methodology for automatically collecting the spatiotemporal features of gestures by calculating the coordinates of the found area of the pose and hand, normalizing them, and constructing an optimal multilayer perceptron for multiclass classification. By extracting and analyzing spatiotemporal data, the proposed method makes it possible to identify not only static features, but also the spatial (for gestures that touch the face and head) and dynamic features of gestures, which leads to an increase in the accuracy of gesture recognition. This classification was also carried out according to the form of the gesture demonstration to optimally extract the characteristics of gestures (display ability of all connection points), which also led to an increase in the accuracy of gesture recognition for certain classes to the value of 0.96. This method was tested using the well-known Ankara University Turkish Sign Language Dataset and the Dataset for Argentinian Sign Language to validate the experiment, which proved effective with a recognition accuracy of 0.98.
Research on the Water Entry of the Fuselage Cylindrical Structure Based on the Improved SPH Model
Authors: Wang Lu , Yang Yang , Yang Qiuzu
Published: September 2023

During aircraft landing on water, the intense impact load may lead to significant local deformation of the fuselage skin. Ensuring the aircraft’s integrity and reliability is of paramount importance. This paper investigates the fuselage skin’s dynamic response during water entry. In the simulation of complex water entry problems, the smoothed particle hydrodynamics (SPH) method can fully leverage the advantages of the particle method. However, the traditional SPH method still suffers from the drawbacks of tensile instability, significantly affecting the computational accuracy. Therefore, this paper first introduces the improved SPH model addressing fluid and solid tensile instability issues. Furthermore, the Riemann-based contact algorithm at the fluid–solid interface is also demonstrated. Based on the above improved SPH model, the simulation of water entry of the elastic cylinder is performed to validate the efficacy of the improved SPH model. Then, the dynamic response characteristics of elastic fuselage skin and the skin–stringer–floor–column structure when it enters the water are analyzed, including the deformation features and slamming force. Lastly, based on the presented damage model, a study is conducted on the water entry of the metallic elastic–plastic skin–stringer–floor–column structure, analyzing the locations of failure and providing guidance for the structural safety design of engineering.
Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
Authors: Han Xianquan , Chen Xijiang , Deng Hui , Wan Peng , Li Jianzhou
Published: September 2023

Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point cloud, which affects the accuracy of point cloud classification and segmentation. To address this issue, this paper proposes a local domain multi-level feature fusion point cloud deep learning network. First, dynamic graph convolutional operation is utilized to obtain the local neighborhood feature of the point cloud. Then, relation-shape convolution is used to extract a deeper-level edge feature of the point cloud, and max pooling is adopted to aggregate the edge features. Finally, point cloud classification and segmentation are realized based on global features and local features. We use the ModelNet40 and ShapeNet datasets to conduct the comparison experiment, which is a large-scale 3D CAD model dataset and a richly annotated, large-scale dataset of 3D shapes. For ModelNet40, the overall accuracy (OA) of the proposed method is similar to DGCNN, RS-CNN, PointConv and GAPNet, all exceeding 92%. Compared to PointNet, PointNet++, SO-Net and MSHANet, the OA of the proposed method is improved by 5%, 2%, 3% and 2.6%, respectively. For the ShapeNet dataset, the mean Intersection over Union (mIoU) of the part segmentation achieved by the proposed method is 86.3%, which is 2.9%, 1.4%, 1.7%, 1.7%, 1.2%, 0.1% and 1.0% higher than PointNet, RS-Net, SCN, SPLATNet, DGCNN, RS-CNN and LRC-NET, respectively.
Study on the Frontal Collision Safety of Trains Based on Collision Dynamics
Authors: Li Zongzhi , Zhu Tao , Xiao Shoune
Published: September 2023

The high-speed collision issue brought on by railway vehicles’ intensive and high-speed operation has become a critical concern for operational safety. Therefore, it is necessary to research the high-speed collision problem of trains. Firstly, a train collision dynamic model suitable for higher collision velocities is established. This model includes a 38-degree-of-freedom (DOF) three-dimensional vehicle model and a three-layered vibrating fixed-track model. Corresponding mathematical models are developed for nonlinear factors such as wheel–rail contact, car–end contact, and coupler overload representation in the model. Then, the Train Collision State Score (TCSS) is proposed, enabling quantitative safety evaluation under different collision conditions. Finally, the influence of the energy-absorbing device’s initial attitude and leading car parameters on collisions is studied. The results indicate that initial vertical height difference and pitching angle significantly increase TCSS. As the force level of the energy-absorbing device of the leading car increases, the effects of two intermediate coupler overload states on TCSS show opposite trends.
Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
Authors: Shiao Yaojung , Bhagat Ritik
Published: September 2023

Knee orthosis plays a vital role in enhancing the wellbeing and quality of life of individuals suffering from knee arthritis. This study explores a machine-learning-based methodology for predicting a user’s gait subphase using inertial measurement units (IMUs) for a semiactive orthosis. A musculoskeletal simulation is employed with the help of existing experimental motion-capture data to obtain essential metrics related to the gait cycle, which are then normalized and scaled. A meticulous data capture methodology using foot switches is used for precise synchronization with IMU data, resulting in comprehensive labeled subphase datasets. The integration of simulation results and labeled datasets provides activation data for effective knee flexion damping following which multiple supervised machine learning algorithms are trained and evaluated for performances. The time series forest classifier emerged as the most suitable algorithm, with an accuracy of 86 percent, against randomized convolutional kernel transform, K-neighbor time series classifier, and long short-term memory–fully convolutional network, with accuracies of 68, 76, and 78, respectively, showcasing exceptional performance scores, thereby rendering it an optimal choice for identifying gait subphases and achieving the desired level of damping for magnetorheological brake-mounted knee orthosis based on simulated results.
Review of Mechanisms and Suppression Methods for Low-Frequency Pressure Fluctuations in Open-Jet Wind Tunnels
Authors: Jin Ling , Deng Xiao Bing , Wang Xunnian , Zhang Junlong , Zeng Weiping
Published: September 2023

Low-frequency pressure fluctuations are common in open-jet wind tunnels, affecting test accuracy and posing safety risks to the wind tunnels. These oscillations can be caused by different mechanisms in different wind tunnels, and it is often necessary to identify the specific mechanism responsible for the oscillation and develop appropriate control methods. This paper presents a comprehensive review of the current state of research on low-frequency pressure fluctuations in subsonic open-jet wind tunnels, with a particular emphasis on their generation mechanisms and control strategies. The primary source of excitation is attributed to the edgetone feedback formed by the impingement of the jet on the collector. The sound wavelength corresponding to the edgetone frequency is close to that of the plenum scale, facilitating resonance with both plenum-associated vibration modes and specific-order standing wave modes within the circuit loop, resulting in significant low-frequency pulsations. Passive control methods such as nozzle vortex generators and collector breathing gaps have been extensively employed due to their cost-effectiveness and efficiency. The concluding section highlights some unresolved issues that require further investigation in this field.
Analysis of Low-Frequency Communication of Hypersonic Vehicles in Thermodynamic and Chemical Non-Equilibrium State
Authors: Wang Kaijie , Chen Xuequan , Wen Zhongkai
Published: September 2023

A plasma sheath will be developed surrounding a hypersonic vehicle in flight, which can reflect, absorb, and scatter electromagnetic (EM) waves of lower frequencies than its own, resulting in a communication blackout. This paper focuses on knowing how to limit the absorption and reflection of low-frequency EM waves by plasma sheath in a thermodynamic and chemical non-equilibrium state. According to the temperature increment model, the energy of high-power microwave (HPM) irradiation is translated into the temperature increment of heavy particles in plasma. As a result of this modification process, the transmittance of low-frequency EM waves going through the plasma sheath in a certain time frame rises, potentially easing the communication blackout problem.
Study on Ground Settlement Patterns and Prediction Methods in Super-Large-Diameter Shield Tunnels Constructed in Composite Strata
Authors: Li Jinlan , Liu Anyu , Xing Huang
Published: September 2023

This study focuses on investigating the surface settlement characteristics induced by the construction of a super-large-diameter shield tunnel in composite strata. By utilizing a combination of field monitoring and numerical simulation analysis, the surface settlement patterns encountered during the construction process in horizontally distributed typical soil–rock composite strata were summarized based on the 16.03 m super-large-diameter shield tunnel project in the southerly extension of He’ping Avenue in Wuhan. In addition, the collected data were used to enhance the Peck empirical formula. The results of the study show the following: (1) Significant non-uniform settlement occurs along the tunneling direction when the shield machine passes through soil–rock composite strata. The range of non-uniform settlement is approximately 3.1 times the tunnel diameter (D) in soil sections and 1.9 times the tunnel diameter (D) in rock sections. (2) The impact of composite strata on the maximum settlement is greater than its effect on the settlement trough width, with a larger impact within the soil sections compared to the rock sections. (3) The Peck correction formula, which takes into account the distance between the monitoring cross-section and the composite interface, provides more accurate predictions than the original Peck empirical formula.
A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
Authors: Xu Menglong , Yang Yabin , Wu Yangang
Published: September 2023

We developed an improved depth of extreme point (DEXP) method, characterized as an effective and rapid imaging method that can estimate the depth and distribution of a source quickly. Its main purpose is to solve various challenges. The automatic calculation aspect of the traditional method is often limited; namely, there is a problem with achieving automatic and reliable processing when the observed surface presents undulating topography, and this problem cannot be ignored. Therefore, we propose the addition of the constant method and the hypothetical observed surface method to achieve improvements in the traditional method. Firstly, we test the improved method on the synthetic models to demonstrate its notable advantage: the achievement of a fully automatic calculation without requiring any other additional information such as structural index (SI) values and threshold values. Meanwhile, we also demonstrate its ability and reliability to handle undulating topography with acceptable accuracy for imaging results. Furthermore, we verify the robustness of the improved method by applying it to real gravity data from the potash salt deposit in the Sakhon Nakhon basin, Laos. In this case, the improved DEXP method effectively identified the location of the potash deposit. Moreover, combined with the optimal edge detection method, gravity prospecting for potash salt deposits exhibited significant advantages.
Wine Production through Virtual Environments with a Focus on the Teaching–Learning Process
Authors: Tapia Danis , Illescas Diego , Santamaría Walter , Ortiz Jessica S.
Published: September 2023

This paper focuses on the application of the hardware-in-the-loop (HIL) technique in the winemaking process. The HIL technique provides an effective methodology to test and verify the automatic control of industrial processes in 3D laboratory environments. Two parts are considered: (i) software, which consists of the virtualization of the wine process in order to generate a realistic work environment that allows the student to manipulate the system while visualizing the changes in the process; and (ii) hardware, through which the process control is implemented in ladder language in a PLC S7 1200 AC/DC/RLY (programmable logic controller). Bidirectional Ethernet TCP/IP communication is established, achieving a client–server architecture. This article highlights the main advantages of the HIL technique, such as its ability to simulate complex and extreme scenarios that would be difficult or expensive to recreate in a real environment. In addition, real-time testing of the hardware and software to implement the control system is performed, allowing for fast and accurate responses. Finally, a usability table is obtained that demonstrates the benefits of performing industrial process control work in virtual work environments, focusing the development on meaningful learning processes for engineering students.
Laser Fabrication: A Solid Present for the Brightest Future
Authors: Gaudiuso Caterina
Published: September 2023

“A solution seeking a problem”: this is how the laser was famously defined by its own developer upon its first appearance on the scientific and technological stage [...]
Sound-to-Imagination: An Exploratory Study on Cross-Modal Translation Using Diverse Audiovisual Data
Authors: Fanzeres Leonardo A. , Nadeu Climent
Published: September 2023

The motivation of our research is to explore the possibilities of automatic sound-to-image (S2I) translation for enabling a human receiver to visually infer occurrences of sound-related events. We expect the computer to ‘imagine’ scenes from captured sounds, generating original images that depict the sound-emitting sources. Previous studies on similar topics opted for simplified approaches using data with low content diversity and/or supervision/self-supervision for training. In contrast, our approach involves performing S2I translation using thousands of distinct and unknown scenes, using sound class annotations solely for data preparation, just enough to ensure aural–visual semantic coherence. To model the translator, we employ an audio encoder and a conditional generative adversarial network (GAN) with a deep densely connected generator. Furthermore, we present a solution using informativity classifiers for quantitatively evaluating the generated images. This allows us to analyze the influence of network-bottleneck variation on the translation process, highlighting a potential trade-off between informativity and pixel space convergence. Despite the complexity of the specified S2I translation task, we were able to generalize the model enough to obtain more than 14%, on average, of interpretable and semantically coherent images translated from unknown sounds.
Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM
Authors: Wu Yuezhou , Li Guimin , Fu Qiang
Published: September 2023

In the current field of air traffic control speech, there is a lack of effective objective speech quality evaluation methods. This paper proposes a new network framework based on ResNet–BiLSTM to address this issue. Firstly, the mel-spectrogram of the speech signal is segmented using the sliding window technique. Next, a preceding feature extractor composed of convolutional and pooling layers is employed to extract shallow features from the mel-spectrogram segment. Then, ResNet is utilized to extract spatial features from the shallow features, while BiLSTM is used to extract temporal features, and these features are horizontally concatenated. Finally, based on the concatenated spatiotemporal features, the final speech quality score is computed using fully connected layers. We conduct experiments on the air traffic control speech database and compare the objective scoring results with the subjective scoring results. The experimental results demonstrate that the proposed method has a high correlation with the mean opinion score (MOS) of air traffic control speech.
The Impact of the Growing Substrate on Morphological and Biochemical Features of Salicornia europaea L.
Authors: Constantin Carmen Gabriela , Zugravu Mihaela Maria , Georgescu Mihaela , Constantin Mugurași Florin , Moț Andrei , Paraschiv Maria , Dobrin Aurora
Published: September 2023

Nowadays, intensive agriculture correlated with the impact of climate change has led to nutrient soil depletion and the salinization of agricultural lands, making them unsuitable for conventional agricultural crops, with a direct impact on the food industry. Therefore, it is necessary to find sustainable alternative solutions that satisfy the needs of both consumers and food production. One such solution may be represented by salt-tolerant species that can fulfill food requirements. One of the most promising salt-tolerant plant species that can be used is Salicornia europaea L. The present work was conducted in greenhouse conditions, and the adaptability of the species on different cultivation substrates was investigated by means of monitoring the plant indicators such as cuticle, epidermis, parenchyma, polyphenols content, and minerals. Moreover, the correlation between the polyphenol and mineral contents was highlighted. Therefore, three cultivation substrates with different levels of salinity/electrical conductivity were used. The reference (I) for biochemical indicators was represented by the plant grown in natural salinity conditions in the Southeast region of Romania. The results indicate that Salicornia europaea L. can be grown on different cultivation substrates other than salted soils, the plant showing the capacity to accumulate bioactive compounds similar to natively grown ones.
Deep-Learning Software Reliability Model Using SRGM as Activation Function
Authors: Kim Youn Su , Pham Hoang , Chang In Hong
Published: September 2023

Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.
Applied Machine Learning: New Methods, Applications, and Achievements
Authors: Dudek Grzegorz
Published: September 2023

The realm of machine learning (ML) is one of the most dynamic and compelling domains within the computing landscape today [...]
Generation of Realistic Cut-In Maneuvers to Support Safety Assessment of Advanced Driver Assistance Systems
Authors: Kayatas Zafer , Bestle Dieter , Bestle Pascal , Reick Robin
Published: September 2023

Advanced Driver Assistance Systems (ADASs) attract constantly growing attention from academics and industry as more and more vehicles are equipped with such technology. Level-3 ADASs, like the DRIVE PILOT from Mercedes-Benz AG, are expected to appear more and more on the market in the next few years. However, automated driving raises new challenges for the system validation required for series approval. The replacement of a human driver as control instance expands the range of variants to be validated and verified. The scenario-based validation approach meets these challenges by simulating only specific safety-critical driving scenarios using software-in-the-loop simulation. According to the current state of the art, various safety-relevant driving scenarios are parameterized as idealized maneuvers which, however, requires a great modeling effort, and at the same time, such simplifications may bias the safety assessment. Therefore, a novel approach using artificial intelligence methods is taken here to generate more realistic driving scenarios. Namely, a generative model based on a variational autoencoder is trained with real-world data and then used to generate trajectories for a specific driving maneuver. Through a comprehensive analysis of the synthetic trajectories, it becomes clear that the generative model can learn and replicate relevant properties of real driving data as well as their probabilistics much better than the mathematical models used so far. Furthermore, it is proven that both the statistical properties and the time characteristics are almost equal to those of the input data.
Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms
Authors: Jumiawi Walaa Ali H. , El-Zaart Ali
Published: September 2023

In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.