bluuebunny/crossref_metadata_2025 huggingface dataset:
Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method
Authors: Chen Zhiwei , Chen Jianneng , Li Yang , Gui Zhiyong , Yu Taojie
Published: July 2023

The precise detection and positioning of tea buds are among the major issues in tea picking automation. In this study, a novel algorithm for detecting tea buds and estimating their poses in a field environment was proposed by using a depth camera. This algorithm introduces some improvements to the YOLOv5l architecture. A Coordinate Attention Mechanism (CAM) was inserted into the neck part to accurately position the elements of interest, a BiFPN was used to enhance the small object detection ability, and a GhostConv module replaced the original Conv module in the backbone to reduce the model size and speed up model inference. After testing, the proposed detection model achieved an mAP of 85.2%, a speed of 87.71 FPS, a parameter number of 29.25 M, and a FLOPs value of 59.8 G, which are all better than those achieved with the original model. Next, an optimal pose-vertices search method (OPVSM) was developed to estimate the pose of tea by constructing a graph model to fit the pointcloud. This method could accurately estimate the poses of tea buds, with an overall accuracy of 90%, and it was more flexible and adaptive to the variations in tea buds in terms of size, color, and shape features. Additionally, the experiments demonstrated that the OPVSM could correctly establish the pose of tea buds through pointcloud downsampling by using voxel filtering with a 2 mm × 2 mm × 1 mm grid, and this process could effectively reduce the size of the pointcloud to smaller than 800 to ensure that the algorithm could be run within 0.2 s. The results demonstrate the effectiveness of the proposed algorithm for tea bud detection and pose estimation in a field setting. Furthermore, the proposed algorithm has the potential to be used in tea picking robots and also can be extended to other crops and objects, making it a valuable tool for precision agriculture and robotic applications.
Relationship between Disaster Shock Experience and Farmers’ Entrepreneurial Inclination: Crisis or Opportunity?
Authors: Zhang Lijun , Gao Wenlin , Ma Xiaoxiao , Gong Rongrong
Published: July 2023

In the context of climate change, it is important to examine the correlation between farmers’ disaster shock experience and their entrepreneurial inclination, as well as its adaptive mechanisms for rural development. We define farmers’ entrepreneurship as farmers engaging in self-employment or business operation, then analyze the positive and negative correlations between disaster shock experience and farmers’ entrepreneurship and propose that there is a U-shaped relationship between them. We develop a mechanism based on the mediating role of farmers’ risk appetite and the moderating role of government support. Based on a survey of 39,113 households from the 2019 China Household Finance Survey, we test our hypotheses using a probit model and structural equation modeling. The findings indicate that disaster shock experience strongly correlates with farmers’ entrepreneurship. Over time, a U-shaped trend emerges between them, in which disaster shock experience can deter farmers’ entrepreneurship through risk appetite, while government support can alleviate this inhibitory effect. Moreover, different types of disasters show the same U-shaped trend, but the relationship differs based on farmers’ gender and income levels. These findings suggest that rural areas should enhance their development capacity and disaster resilience, and the government should establish long-term support mechanisms for entrepreneurship. Additionally, farmers need to accurately understand and cope with disaster shock experience, so they can uncover the potential value and opportunities it presents.
Evaluation of the Genetic Diversity of Greek Garlic (Allium sativum L.) Accessions Using DNA Markers and Association with Phenotypic and Chemical Variation
Authors: Papaioannou Charikleia , Fassou Georgia , Petropoulos Spyridon A. , Lamari Fotini N. , Bebeli Penelope J. , Papasotiropoulos Vasileios
Published: July 2023

The genetic diversity of 27 garlic accessions (local varieties/landraces) was investigated using five simple sequence repeat (SSR) and six inter-simple sequence repeat (ISSR) markers. The SSR genotyping revealed 26 different alleles, while 84 polymorphic bands were produced using the ISSR markers. The average polymorphism information content (PIC) was 0.471 and 0.465 for the SSR and the ISSR markers, respectively. Compared to ISSRs, SSR markers revealed a higher level of redundancy, indicating potential duplicates among the accessions. The analysis of molecular variance (AMOVA) based on both marker systems showed that most of the molecular diversity was attributable to differences within accessions, rather than among them. UPGMA, STRUCTURE, and Principal Coordinate Analysis (PCoA) based on the SSRs produced similar clustering results, although not in agreement with those produced by the ISSR markers. Fisher’s exact tests and multinomial logistic regression analyses identified significant associations between the chemical compounds, the morphological traits of the bulb, and specific SSR and ISSR loci. Our results provide a molecular basis for understanding the genetic diversity of Greek garlic landraces, which could be useful for the conservation and sustainable management of this crop. Potential markers fostering the selection of genotypes in garlic breeding have also been revealed.
Bioremediation of Battery Scrap Waste Contaminated Soils Using Coco Grass (Cyperus rotundus L.): A Prediction Modeling Study for Cadmium and Lead Phytoextraction
Authors: AL-Huqail Arwa A. , Taher Mostafa A. , Širić Ivan , Goala Madhumita , Adelodun Bashir , Choi Kyung Sook , Kumar Piyush , Kumar Vinod , Kumar Pankaj , Eid Ebrahem M.
Published: July 2023

With the increasing demand for electronic devices that use batteries, e-waste is also becoming a major threat to the environment. Battery e-waste contains hazardous heavy metals that affect the health of the soil ecosystem. Thus, the present study evaluates the cadmium (Cd) and lead (Pb) phytoextraction potential of coco grass (Cyperus rotundus L.) grown in soils contaminated with battery scrap waste (BSW). Pot experiments were conducted to grow C. rotundus under different treatments (0%: control, T1: 1%, T2: 2%, T3: 3%, and T4: 4%) of BSW mixed with soil (w/w). The results showed that BSW mixing significantly (p < 0.05) increased the physicochemical properties and heavy metal (Cd and Pb) content in the soil. BSW mixing resulted in a reduction in growth and biochemical traits of C. rotundus and an increase in oxidative stress enzymes with an increase in BSW dose. The Pearson correlation studies also showed that soil HM concentration had a negative influence on the growth and biochemical parameters of C. rotundus. The bioaccumulation and translocation factor analysis showed that C. rotundus was a hyperaccumulator plant with a maximum accumulation of Cd and Pb (38.81 and 109.06 mg·kg−1) in root parts followed by the whole plant (277.43 and 76.10 mg·kg−1) and shoot (21.30 and 22.65 mg·kg−1) parts. Moreover, predictive models based on multiple linear regression (MLR) and artificial neural network (ANN) approaches were developed for Cd and Pb uptake by C. rotundus. Mathematical modeling results showed that soil properties were useful to construct quality MLR and ANN models with good determination coefficient (R2 > 0.98), model efficiency (ME > 0.99), and low root mean square error (RMSE < 5.72). However, the fitness results of the ANN models performed better compared with those of the MLR models. Overall, this study presents an efficient and sustainable strategy to eradicate hazardous HMs by growing C. rotundus on BSW-contaminated soils and reducing its environmental and health consequences.
Comparison of Telomere Length in Age-Matched Primiparous and Multiparous Brahman Cows
Authors: O’Daniel Sydney E. , Kochan Kelli J. , Long Charles R. , Riley David G. , Randel Ronald D. , Welsh Thomas H.
Published: July 2023

Physiological and psychological stressors have been associated with the attrition of telomeres, which are the protective caps of chromosomes. This study compares the telomere length (TL) in 4-year-old Brahman cows grouped by the first parity (n = 8) and the second parity (n = 11). The cows were bled via jugular venipuncture, weighed, and had their body condition scores recorded at Day −28 prior to calving and at Day + 7 and Day + 28 post-calving. The duration of labor (Dlabor) and parturition ease were recorded. The peripheral leukocytes were isolated, the leukocyte blood count with differential was recorded, and the genomic DNA was extracted. The relative quantity of telomere products, which is proportional to the average TL, was determined via multiplex quantitative PCR using the ratio (T/S ratio) of bovine telomere and β-globulin DNA. Standards of the bovine telomere (1012–107 dilution series) and β-globulin (109–104 dilution series) genes were utilized to produce relative copy numbers. The samples were assayed in triplicate and were included if the triplicate Cq difference was less than 0.25 cycles. The parity was the fixed effect, and the random effects included the sire and day repeated with the cow as the subject. Statistical significance was not observed in the leukocyte number or type (p > 0.1). A reduction in the TL of approximately 9225 telomeric copies was found between Parity 1 and Parity 2 (p = 0.02). A trend was found between the TL and Dlabor (p = 0.06). The stress of parturition and raising the first calf of a cow’s life may be responsible for TL attenuation. Parity may be considered a stressor of cow longevity.
Native-Valve Aspergillus Endocarditis: Case Report and Literature Review
Authors: Caroselli Claudio , Suardi Lorenzo Roberto , Besola Laura , Fiocco Alessandro , Colli Andrea , Falcone Marco
Published: July 2023

Aspergillus endocarditis represents the second etiological cause of prosthetic endocarditis following Candida spp. On the other hand, native-valve endocarditis due to Aspergillus are anecdotally reported with increasing numbers in the last decade due to new diagnostic technologies such as polymerase chain reaction (PCR) on samples like valve tissue or entire blood. We performed a review of the literature presenting one case report observed at Pisa University Hospital. Seventy-four case reports have been included in a period between 1950–2022. Immunocompromised status (patients with solid tumor/oncohematological cancer or transplanted patients) was confirmed to be the main risk factor for this rare opportunistic infection with a high rate of metastatic infection (above all, central nervous system) and mortality. Diagnosis relies on serum galactomannan and culture with PCR on valve tissue or whole blood. Cardiac surgery was revealed to be a life-saving priority as well as appropriate antifungal therapy including b-liposomal amphotericin or new triazoles (isavuconazole). The endocarditis team, facing negative blood culture endocarditis affecting an immunocompromised patient, should investigate this difficult-to-treat pathogen.
Electrochemical Assessment of Mitigation of Desulfovibrio ferrophilus IS5 Corrosion against N80 Carbon Steel and 26Cr3Mo Steel Using a Green Biocide Enhanced by a Nature-Mimicking Biofilm-Dispersing Peptide
Authors: Xu Lingjun , Kijkla Pruch , Kumseranee Sith , Punpruk Suchada , Gu Tingyue
Published: July 2023

MIC (microbiologically influenced corrosion) is problematic in many industries, especially in the oil and gas industry. In this work, N80 carbon steel for pipelines was tested with 26Cr3Mo chromium pipeline steel for comparison in SRB (sulfate-reducing bacterium) MIC mitigation using a THPS (tetrakis hydroxymethyl phosphonium sulfate)-based commercial biocide (Biotreat 5475 with 75–80% THPS by mass). Peptide A, a nature-mimicking synthetic cyclic peptide (cys-ser-val-pro-tyr-asp-tyr-asn-trp-tyr-ser-asn-trp-cys) with biofilm dispersal ability was used as a biocide enhancer. Metal coupons covered with 3-d old Desulfovibrio ferrophilus IS5 biofilms were immersed in different biocide solutions. After 1-h treatment, 200 ppm Biotreat 5475, 200 ppm Biotreat 5475 + 200 nM (360 ppb) Peptide A, and 400 ppm Biotreat 5475 achieved 0.5-log, 1.7-log and 1.9-log reductions in sessile cell count on N80, and 0.7-log, 1.7-log, and 1.8-log on 26Cr3Mo, respectively. The addition of 200 nM Peptide A cut the THPS biocide dosage by nearly half. Biocide injection tests in electrochemical glass cells after 1 h exhibited 15%, 70%, and 72% corrosion inhibition efficiency (based on corrosion current density) on N80, and 27%, 79%, 75% on 26Cr3Mo, respectively. Linear polarization resistance and electrochemical impedance spectrometry results also indicated antimicrobial efficacies.
Development of Spray Dried Spirulina Protein-Berry Pomace Polyphenol Particles to Attenuate Pollution-Induced Skin Damage: A Convergent Food-Beauty Approach
Authors: Hoskin Roberta Targino , Grace Mary H. , Guiotto Anna , Pecorelli Alessandra , Valacchi Giuseppe , Lila Mary Ann
Published: July 2023

Spray drying (SD) microencapsulation of phytochemicals from berry pomaces with Spirulina protein (SP) was incorporated into a cosmeceutical topical formulation to mitigate pollution skin damage. Initially, microparticles produced with SP and polyphenols recovered from fruit pomaces (elderberry SP-EB and muscadine grape SP-MG) were characterized regarding physicochemical and phytochemical content (polyphenol load, carotenoid and phycocyanin contents and antioxidant activity). SP had low total phenolic content (7.43 ± 0.23 mg GAE/g DW), but complexation with elderberry or muscadine grape pomaces polyphenols led to a substantial increase (27.63 ± 1.15 SP-EB and 111.0 ± 2.6 mg GAE/g DW SP-MG). SP-MG particles had higher anthocyanin (26.87 ± 1.25 mg/g) and proanthocyanidin (9.02 ± 0.74 mg/g) contents compared to SP-EB particles. SP-MG were prioritized to prepare a topical gel to attenuate skin oxinflammatory markers and prevent skin barrier disruption using ex vivo human biopsies exposed to diesel engine exhaust (DEE). The immunofluorescence results showed increased oxidative protein damage and inflammation associated with impaired skin barrier function after DEE exposure while topical application of gel formulated with SP-MG mitigated these effects. Overall, this study demonstrated that protein-polyphenol complexation is a synergistic strategy to stabilize and deliver residual fruit/algae phytoactives into cosmeceutical products for skin health applications.
Differential Effects of High Fat Diets on Resilience to H2O2-Induced Cell Death in Mouse Cerebral Arteries: Role for Processed Carbohydrates
Authors: Norton Charles E. , Shaw Rebecca L. , Segal Steven S.
Published: July 2023

High fat, western-style diets increase vascular oxidative stress. We hypothesized that smooth muscle cells and endothelial cells adapt during the consumption of high fat diets to become more resilient to acute oxidative stress. Male C57Bl/6J mice were fed a western-style diet high in fat and processed carbohydrates (WD), a high fat diet that induces obesity (DIO), or their respective control (CD) and standard (SD) diets for 16 weeks. Posterior cerebral arteries (PCAs) were isolated and pressurized for study. During acute exposure to H2O2 (200 µM), smooth muscle cell and endothelial cell death were reduced in PCAs from WD, but not DIO mice. WD selectively attenuated mitochondrial membrane potential depolarization and vessel wall Ca2+ influx during H2O2 exposure. Selective inhibition of transient receptor potential (TRP) V4 or TRPC3 channels reduced smooth muscle cell and endothelial cell death in concert with the vessel wall [Ca2+]i response to H2O2 for PCAs from CD mice and eliminated differences between CD and WD. Inhibiting Src kinases reduced smooth muscle cell death along with [Ca2+]i response to H2O2 only in PCAs from CD mice and eliminated differences between diets. However, Src kinase inhibition did not alter endothelial cell death. These findings indicate that consuming a WD, but not high fat alone, leads to adaptations that limit Ca2+ influx and vascular cell death during exposure to acute oxidative stress.
Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
Authors: Zhao Weipeng , Gu Liang
Published: July 2023

In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.
DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes
Authors: William Mathias Vijay Albert , Ramesh Subramanian , Cep Robert , Mahalingam Siva Kumar , Elangovan Muniyandy
Published: July 2023

The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes’ cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng’s method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.
Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
Authors: Baras Nikolaos , Dasygenis Minas
Published: July 2023

In the wake of advancing technology, autonomous vehicles and robotic systems have burgeoned in popularity across a spectrum of applications ranging from mapping and agriculture to reconnaissance missions. These practical implementations have brought to light an array of scientific challenges, a crucial one among them being Coverage Path Planning (CPP). CPP, the strategic planning of a path that ensures comprehensive coverage of a defined area, while being widely examined in the context of a single-robot system, has found its complexity magnified in the multi-robot scenario. A prime hurdle in multi-robot CPP is the division and allocation of the operation area among the robots. Traditional methods, largely reliant on the number of robots and their initial positions to segment the space, often culminate in suboptimal area division. This deficiency can occasionally render the problem unsolvable due to the sensitivity of most area division algorithms to the robots’ starting points. Addressing this predicament, our research introduced an innovative methodology that employs Affinity Propagation (AP) for area allocation in multi-robot CPP. In our approach, the area is partitioned into ‘n’ clusters through AP, with each cluster subsequently assigned to a robot. Although the model operates under the assumption of an unlimited robot count, it offers flexibility during execution, allowing the user to modify the AP algorithm’s similarity function factor to regulate the number of generated clusters. Serving as a significant progression in multi-robot CPP, the proposed model provides an innovative approach to area division and path optimization, thereby setting a strong foundation for future exploration and practical enhancements in this field.
Hyperspectral Point Cloud Projection for the Semantic Segmentation of Multimodal Hyperspectral and Lidar Data with Point Convolution-Based Deep Fusion Neural Networks
Authors: Decker Kevin T. , Borghetti Brett J.
Published: July 2023

The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which enables ingestion and exploitation with point cloud processing neural network methods. Additionally, we present a composite fusion-style, point convolution-based neural network architecture for the semantic segmentation of HSPC and lidar point cloud data. We investigate the effects of the proposed HSPC representation for both unimodal and multimodal networks ingesting a variety of hyperspectral and lidar data representations. Finally, we compare the performance of these networks against each other and previous approaches. This study paves the way for innovative approaches to multimodal remote sensing data fusion, unlocking new possibilities for enhanced data analysis and interpretation.
Influence of Bedding Strength and Angle on Fracture Characteristics of Sandstone under Three-Point Bending Conditions
Authors: Wang Guobo , Meng Tao , Feng Gan , Ma Lifeng , Yang Weimin , He Yi , Zhang Zhijiang , Liang Xufeng
Published: July 2023

The fracture characteristics of bedded sandstone determine the stability and safety of in situ coal gasification technology. Four semicircular stratified sandstone specimens with different strengths (0.3, 0.6, 1.0, and 1.5 times that of rock matrix) and seven different bedding angles (θ = 0°, 15°, 30°, 45°, 60°, 75°, and 90°) were numerically simulated using RFPA2D-Basic V2.0 software. The SCB specimen had no prefabricated crack, and its radius was 25 mm. The loading rate was 0.000001 m/step. The results show that the fracture characteristics of the sandstone are affected by both the strength of the laminae and the angle; the fracture toughness and peak strength of the ultra-weak sandstone, as well as the weak sandstone, are reduced and more easily affected by the bedding angle; the strength rate of the strong sandstone is higher than that of the homogeneous sandstone, and the difference between the fracture characteristics of the two is not significant. This paper suggests that the key mechanism of this phenomenon is the anisotropy between the bedding and the sandstone, along with the competition/synergy between the main crack and the bedding plane bias crack during fracture propagation. These research results can provide a theoretical reference for the safety and stability of underground engineering in China.
Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
Authors: Ma Wenke , Li Bingyang , Cao Yuxue , Wang Pengfei , Liu Mengyue , Chang Chenyang , Peng Shigang
Published: July 2023

As deep space exploration tasks become increasingly complex, the mobility and adaptability of traditional wheeled or tracked probe robots with high functional density are constrained in harsh, dangerous, or unknown environments. A practical solution to these challenges is designing a probe robot for preliminary exploration in unknown areas, which is characterized by robust adaptability, simple structure, light weight, and minimal volume. Compared to the traditional deep space probe robot, the spherical robot with a geometric, symmetrical structure shows better adaptability to the complex ground environment. Considering the uncertain detection environment, the spherical robot should brake rapidly after jumping to avoid reentering obstacles. Moreover, since it is equipped with optical modules for deep space exploration missions, the spherical robot must maintain motion stability during the rolling process to ensure the quality of photos and videos captured. However, due to the nonlinear coupling and parameter uncertainty of the spherical robot, it is tedious to adjust controller parameters. Moreover, the adaptability of controllers with fixed parameters is limited. This paper proposes an adaptive proportion–integration–differentiation (PID) control method based on reinforcement learning for the multi-motion mode spherical probe robot (MMSPR) with rolling and jumping. This method uses the soft actor–critic (SAC) algorithm to adjust the parameters of the PID controller and introduces a switching control strategy to reduce static error. As the simulation results show, this method can facilitate the MMSPR’s convergence within 0.02 s regarding motion stability. In addition, in terms of braking, it enables an MMSPR with random initial speed brake within a convergence time of 0.045 s and a displacement of 0.0013 m. Compared with the PID method with fixed parameters, the braking displacement of the MMSPR is reduced by about 38%, and the convergence time is reduced by about 20%, showing better universality and adaptability.
Special Issue on New Advances and Illustrations in Applied Geochemistry in China
Authors: Gong Qingjie , Shi Zeming
Published: July 2023

The 9th national conference on applied geochemistry in China will be held in Chengdu, Sichuan province, in October 2023, hosted by the committee of applied geochemistry, the Chinese Society for Mineralogy, Petrology and Geochemistry (CSMPG) [...]
Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis–NIR Spectroscopy
Authors: Wu Yiran , Zhu Xinhua , Huang Qiangsheng , Zhang Yuan , Evans Julian , He Sailing
Published: July 2023

Fruit quality assessment plays a crucial role in determining their market value, consumer acceptance, and post-harvest management. In recent years, spectroscopic techniques have gained significant attention as non-destructive methods for evaluating fruit quality. In this study, we propose a novel deep-learning network, called GCNN-LSTM-AT, for the prediction of five important parameters of tangerines using visible and near-infrared spectroscopy (Vis–NIR). The quality attributes include soluble solid content (SSC), total acidity (TA), acid–sugar ratio (A/S), firmness, and Vitamin C (VC). The proposed model combines the strengths of graph convolutional network (GCN), convolutional neural networks (CNNs), and long short-term memory (LSTM) to capture both spatial and sequential dependencies in the spectra data, and incorporates an attention mechanism to enhance the discriminative ability of the model. To investigate the effectiveness and stability of the model, comparisons with three traditional machine-learning algorithms—moving window partial least squares (MWPLS), random forest (RF), and support vector regression (SVR)—and two deep neural networks—DeepSpectra2D and CNN-AT—are provided. The results have shown that the GCNN-LSTM-AT network outperforms other algorithms and models, achieving accurate predictions for SSC (R2: 0.9885, RMSECV: 0.1430 ∘Brix), TA (R2: 0.8075, RMSECV: 0.0868%), A/S (R2: 0.9014, RMSECV: 1.9984), firmness (R2: 0.9472, RMSECV: 0.0294 kg), and VC (R2: 0.7386, RMSECV: 29.4104 mg/100 g) of tangerines.
Light and Affordable Vision System for Mouth Opening–Closing Movement Deviation Assessment in Women with Mild Temporomandibular Joint Disorder
Authors: Goślińska Jagoda , Wareńczak-Pawlicka Agnieszka , Gośliński Jarosław , Owczarek Piotr , Lisiński Przemysław
Published: July 2023

(1) Background: This paper aims to demonstrate a novel and, in terms of application, universal method of the qualitative and quantitative assessment of mandibular movement. (2) Methods: Mandibular movements are recorded by a mono-vision system where a digital camera is fixed on a special extension arm attached to the patient’s head. The described method was used to check lateral deviations in 25 women with mild temporomandibular joint disorder (TMD) and in 25 women from the control group. The maximum deviation (MAX) and mean deviation (MSE) parameters were examined. In addition, the occurrence of parafunctions and joint clicking in the temporomandibular joints was checked in the examined individuals. (3) Results: Among the women with mild TMD, a significantly higher MSE parameter was found compared to the control group. Joint clicking also occurred significantly more frequently. The parameter of MAX did not differ significantly between the examined groups. (4) Conclusions: In women with mild TMD, a lack of movement coordination between the right and left joints can be observed. The method’s high availability combined with simple use makes it a valuable tool for experts in different fields who diagnose and treat stomatognathic system disorders.
A High-Sensitivity Cesium Atomic Magnetometer Based on A Cesium Spectral Lamp
Authors: Lu Yuan-Tian , Liu Lei-Song , Shi Yong-Qi , Zhao Tian , Zhu Wan-Hua , Zhang Tie-Fu , Liu Wu-Ming , Zhang Xiao-Juan
Published: July 2023

Based on a low-noise cesium spectral lamp, a high-sensitivity self-oscillating cesium atomic magnetometer with a wide operating temperature range has been developed, solving problems with existing sensors such as a limited operating temperature range and difficult startup at low temperatures. The temperature feedback mechanism is used to make adjustments to any fluctuations in the cesium lamp’s excitation source in real time, improving the magnetometer’s stability and operating temperature range. Herein, the design and optimization of the cesium atomic magnetometer are presented, and a prototype of the magnetometer is described. The quantum limit sensitivity of the cesium atomic magnetometer is estimated by evaluating the intrinsic relaxation rate in the geomagnetic field. A test demonstrates that the cesium atomic magnetometer’s sensitivity in the geomagnetic background is around 140 fT/Hz at 1 Hz at room temperature, and the operating temperature range is from −50 °C to 70 °C, surpassing most of the commercial products of its kind in terms of sensitivity and operating temperature range.
Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
Authors: Li Mengwei , Zhou Ying , Jia Limin , Qin Yong , Wang Zhipeng
Published: July 2023

A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The results show that a using a fault diagnosis strategy under the condition of unreliable testing is more similar to actual maintenance work, proving the significance of the diagnosis strategy constructed using this method for the practical application of the project.
Life Assessment for Motorized Spindle with Zero Traumatic Failure Data Based on Subdistribution Competing Risk Model
Authors: Zhang Yingzhi , Zhou Yutong , Chen Bingkun , Zhang Han
Published: July 2023

Considering the influence of performance degradation on a product’s traumatic failure, under the condition that only degradation data are observed and no traumatic failure data are observed, this paper proposes a subdistribution competing risk model to achieve a motorized spindle life assessment. This paper assumes that the failure rate ratio of the tested products does not change with time under different stress levels. Basic reliability with zero traumatic failure data is modeled by a unilateral confidence limit method under a two-parameter Weibull distribution. Performance degradation data are taken as covariates. The regression coefficients of the covariates are calculated by SPSS software. Then, a subdistribution competing risk model is constructed, which reflects the dependency relationship between reliability and performance degradation, and the product’s reliability life can be evaluated accordingly. The correctness and advantages of the model built in this paper are verified by a case analysis combined with the performance degradation information of a motorized spindle.
A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism
Authors: Zheng Yanmei , Zhou Guanghui , Lu Bibo
Published: July 2023

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.
Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
Authors: Nair Surya Sasikumar , Trafiałek Joanna , Kolanowski Wojciech
Published: July 2023

This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant in order to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution to extend the shelf life of food products and reduce dependence on petroleum-based resources. This review analyzes the history, production methods, barrier properties, types, and additives of edible packaging systems. The review highlights the advantages and importance of edible packaging materials and describes how they can improve sustainability measures. The market value of edible packaging materials is expanding. Further research on and developments in edible food packaging materials are needed to increase sustainable, eco-friendly packaging practices that are significant for environmental protection and food safety.
HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
Authors: Yin Anchao , Ren Chao , Yan Zhiheng , Xue Xiaoqin , Yue Weiting , Wei Zhenkui , Liang Jieyu , Zhang Xudong , Lin Xiaoqi
Published: July 2023

Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsampling capabilities (UMR module) to capture fine details, enhancing shadowed areas and road boundaries. Next, in the decoding phase, we design a multi-feature fusion module (MPF module) to obtain multi-scale spatial information, enabling better differentiation between roads and objects with similar spectral characteristics. The network simultaneously integrates multi-scale feature information during the downsampling process, producing high-resolution feature maps through progressive cross-layer connections, thereby enabling more effective high-resolution prediction tasks. We conduct comparative experiments and quantitative evaluations of the proposed HRU-Net framework against existing algorithms (U-Net, ResNet, DeepLabV3, ResUnet, HRNet) using the Massachusetts Road Dataset. On this basis, this paper selects three network models (U-Net, HRNet, and HRU-Net) to conduct comparative experiments and quantitative evaluations on the DeepGlobe Road Dataset. The experimental results demonstrate that the HRU-Net framework outperforms its counterparts in terms of accuracy and mean intersection over union. In summary, the HRU-Net model proposed in this paper skillfully exploits information from different resolution feature maps, effectively addressing the challenges of discontinuous road extraction and reduced accuracy caused by shadow occlusion and spectral confusion factors. In complex satellite image scenarios, the model accurately extracts comprehensive road regions.
Research on Construction Sequences and Construction Methods of the Small Clear-Distance, Double-Arch Tunnel under an Asymmetrical Load
Authors: Wu Shan , Wu Jian , Liu Dunwen
Published: July 2023

A small clear-distance, double-arch tunnel under an asymmetrical load combines the characteristics of small clear-distance, tunnels and double-arch tunnels, and the influence of an asymmetrical terrain must be considered. Its construction stability is a problem worth studying. This paper used the Wengcun tunnel as the engineering background. Midas/GTS finite element analysis software was used to study the effects of eight excavation sequences and two excavation methods on tunnel stability. The results showed that the deformation and force of the tunnel were asymmetric under the asymmetrical terrain. Both middle partition walls were deformed towards the shallowly buried side, and the shallowly buried side was deformed to a greater extent. Excavating shallow side tunnels first can effectively mitigate the impact of asymmetric terrain. The arch settlement of the Center Diaphragm Excavation Method is 1.33 cm, which is smaller than the three-step excavation method of 1.48 cm; however, this difference is not significant. The Three-bench Excavation Method was more efficient. Based on the conclusion of a numerical simulation, the construction site adopted the construction sequence of excavating the shallowly buried side tunnel first and adjusted the excavation method to the Three-bench Excavation Method.
The Use of Deep Learning to Improve Player Engagement in a Video Game through a Dynamic Difficulty Adjustment Based on Skills Classification
Authors: Romero-Mendez Edwin A. , Santana-Mancilla Pedro C. , Garcia-Ruiz Miguel , Montesinos-López Osval A. , Anido-Rifón Luis E.
Published: July 2023

The balance between game difficulty and player skill in the evolving landscape of the video game industry is a significant factor in player engagement. This study introduces a deep learning (DL) approach to enhance gameplay by dynamically adjusting game difficulty based on a player’s skill level. Our methodology aims to prevent player disengagement, which can occur if the game difficulty significantly exceeds or falls short of the player’s skill level. Our evaluation indicates that such dynamic adjustment leads to improved gameplay and increased player involvement, with 90% of the players reporting high game enjoyment and immersion levels.
Maximizing Test Coverage for Security Threats Using Optimal Test Data Generation
Authors: Hussain Talha , Faiz Rizwan Bin , Aljaidi Mohammad , Khattak Adnan , Samara Ghassan , Alsarhan Ayoub , Alazaidah Raed
Published: July 2023

As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. It is a constant struggle to maximize test coverage through optimal data generation. We conducted explanatory research to maximize test coverage of security requirements as modeled in the structured misuse case description (SMCD). The acceptance test case is designed through the structured misuse case description for mitigation of security threats. Mal activity is designed from SMCD upon which constraints are specified in object constraint language (OCL) in order to minimize human dependency and improve consistency in the optimal test case design. The study compared two state-of-the-art test coverage maximization approaches through optimal test data generation. It was evident through the results that MC/DC generated optimal test data, i.e., n + 1 test conditions in comparison to the decision coverage approach, i.e., 2n test conditions for security threats. Thus, MC/DC resulted in a significantly lower number of test cases yet maximized test coverage of security threats. We, therefore, conclude that MC/DC maximizes test coverage through optimal test data in comparison to decision coverage at the design level for security threat mitigation.
The Effects of Noise and Reverberation Time on Auditory Sustained Attention
Authors: Li Lei , Liu Yali , Li Ling
Published: July 2023

The indoor acoustic environment is very important for auditory learning, and the completion of various auditory learning tasks cannot be separated from auditory sustained attention. Noise and reverberation time (RT) are the two most common acoustic indicators that affect attention. Based on this, experiments on auditory sustained attention under two factors of noise masking and reverberation effect alone were conducted in an anechoic chamber and a reverberation chamber, respectively. In addition, nine kinds of RT and signal-to-noise ratios (SNR) (3 RT × 3 SNRs) were also designed and implemented in the paper, and the impact of RT and noise on the perception of auditory sustained attention was analyzed through the above three experimental scenarios. The experimental results show the following: (1) noise type has no significant effect on auditory sustained attention performance, and SNR has a significant effect on omission errors (OEs), commission errors (CEs), and mean reaction time (MRT). All three decline with the increase in SNR; when SNR reaches a certain level, it tends to stabilize, and according to the experimental results, good auditory sustained attention SNR needs at least 18 dB; (2) The effect of RT on auditory sustained attention perception has significant differences. As RT increases, the number of inattentive OE increases, the number of impulses CE increases, and the response time becomes longer; (3) SNR has a significant negative impact on college students’ auditory sustained attention performance, while RT has a less negative effect compared to SNR; (4) The functional relationships between auditory sustained attention performance and SNR and RT in three scenarios were obtained, which can be used to predict inattentive OE, impulse, and MRT in noisy and reverberant environments. This study is the first attempt to explore the influence of noise and RT on the auditory sustained attention of Chinese university students, providing theoretical support for the acoustic design of indoor learning environments, and the findings can fill the gap of the lack of basic experimental data in the current standards of the indoor acoustic environment in China.
A Comparative Analysis of the Synoptic Conditions of Two Heavy Rainfall Cases West of the Pearl River Estuary
Authors: Jiao Yang , Xiao Ting , Wu Renguang , Yin Xiaoxue
Published: July 2023

This paper compares the synoptic-scale circulation and thermodynamic conditions of two representative heavy rainfall cases that were concentrated west of the Pearl River Estuary under different seasonal backgrounds. The first case occurred in the early rainy season, with strong precipitation lasting for several hours. The second case occurred in the late rainy season, with extreme flash downpour in just two hours. The analysis reveals that the main cause was different for the two rainfall cases. The main cause of the first rainfall case was converging polar cold air and marine warm air that formed a synoptic scale front that drove the strong ascent. The main cause of the second case was a cold backflow from the Taiwan Strait, resulting in a sharply contrasting equivalent potential temperature front and triggering very strong upward motion. The moist potential vorticity (MPV) showed a negative value before both rainstorms, indicative of the importance of convective instability. A sandwich-like configuration of the MPV led to the development of strong vertical motion for the second case.
Interval-Valued Topology on Soft Sets
Authors: Bayramov Sadi , Aras Çiğdem Gündüz , Kočinac Ljubiša D. R.
Published: July 2023

In this paper, we study the concept of interval-valued fuzzy set on the family SSX,E of all soft sets over X with the set of parameters E and examine its basic properties. Later, we define the concept of interval-valued fuzzy topology (cotopology) τ on SSX,E. We obtain that each interval-valued fuzzy topology is a descending family of soft topologies. In addition, we study some topological structures such as interval-valued fuzzy neighborhood system of a soft point, base and subbase of τ and investigate some relationships among them. Finally, we give some concepts such as direct sum, open mapping and continuous mapping and consider connections between them. A few examples support the presented results.