bluuebunny/crossref_metadata_2025 huggingface dataset:
Acknowledgment to the Reviewers of Physiologia in 2022
Authors:   Physiologia Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Subterranean Clover and Sulla as Valuable and Complementary Sources of Bioactive Compounds for Rainfed Mediterranean Farming Systems
Authors: Molinu Maria Giovanna , Sulas Leonardo , Campesi Giuseppe , Re Giovanni Antonio , Sanna Federico , Piluzza Giovanna
Published: January 2023

Mediterranean pasture and forage legumes are important components of sustainable production systems. Subterranean clover and sulla represent key species having proven high agronomic value and traits for production and multiple services. Our research investigated the potential of the abovementioned species as a source of phenolic compounds and antioxidants for contributing to support their full exploitation in the fodder, animal welfare, and nutraceutical sectors. Antioxidant capacity, as well as the content of total phenolic compounds and individual phenolic compounds, was determined in subterranean clover and sulla shoots at the vegetative, flower bud, flowering, and seed ripening phenological stages. The antioxidant capacity and the phenolic content were affected significantly by harvest time. In subterranean clover, 10 individual phenolic compounds were detected, and isoflavones were the most abundant (3.19–18.27 mg·g−1 DM). Eleven phenolic compounds were identified in sulla shoots, and chlorogenic acid (0.76–3.43 mg·g−1 DM) and diosmin (3.64–4.94 mg·g−1 DM) were the most represented compounds at the vegetative and flower bud stage. On the basis of our findings, a complementary utilization of both legume species is suggested; this might ensure the exploitation of all phenolic compounds in view of the potential benefits for animal production and health.
Genome-Wide Association Study of Rice Grain Shape and Chalkiness in a Worldwide Collection of Xian Accessions
Authors: Wang Nansheng , Chen Huguang , Qian Yingzhi , Liang Zhaojie , Zheng Guiqiang , Xiang Jun , Feng Ting , Li Min , Zeng Wei , Bao Yaling , Liu Erbao , Zhang Chaopu , Xu Jianlong , Shi Yingyao
Published: January 2023

Rice (Oryza sativa L.) appearance quality, which is mainly defined by grain shape and chalkiness, is an important target in rice breeding. In this study, we first re-sequenced 137 indica accessions and then conducted a genome-wide association study (GWAS) for six agronomic traits with the 2,998,034 derived single nucleotide polymorphisms (SNPs) by using the best linear unbiased prediction (BLUP) values for each trait. The results revealed that 195 SNPs had significant associations with the six agronomic traits. Based on the genome-wide linkage disequilibrium (LD) blocks, candidate genes for the target traits were detected within 100 kb upstream and downstream of the relevant SNP loci. Results indicate that six quantitative trait loci (QTLs) significantly associated with six traits (qTGW4.1, qTGW4.2, qGL4.1, qGL12.1, qGL12.2, qGW2.1, qGW4.1, qGW6.1, qGW8.1, qGW8.2, qGW9.1, qGW11.1, qGLWR2.1, qGLWR2.2, qGLWR4.2, qPGWC5.1 and qDEC6.1) were identified for haplotype analysis. Among these QTLs, two (qTGW4.2 and qGW6.1), were overlapped with FLO19 and OsbZIP47, respectively, and the remaining four were novel QTLs. These candidate genes were further validated by haplotype block construction.
Genetic Basis of Resistance to Warrior (-) Yellow Rust Race at the Seedling Stage in Current Central and Northern European Winter Wheat Germplasm
Authors: Shahinnia Fahimeh , Mohler Volker , Hartl Lorenz
Published: January 2023

To evaluate genetic variability and seedling plant response to a dominating Warrior (-) race of yellow rust in Northern and Central European germplasm, we used a population of 229 winter wheat cultivars and breeding lines for a genome-wide association study (GWAS). A wide variation in yellow rust disease severity (based on infection types 1–9) was observed in this panel. Four breeding lines, TS049 (from Austria), TS111, TS185, and TS229 (from Germany), and one cultivar, TS158 (KWS Talent), from Germany were found to be resistant to Warrior (-) FS 53/20 and Warrior (-) G 23/19. The GWAS identified five significant SNPs associated with yellow rust on chromosomes 1B, 2A, 5B, and 7A for Warrior (-) FS 53/20, while one SNP on chromosome 5B was associated with disease for Warrior (-) G 23/19. For Warrior (-) FS 53/20, we discovered a new QTL for yellow rust resistance associated with the marker Kukri_c5357_323 on chromosome 1B. The resistant alleles G and T at the marker loci Kukri_c5357_323 on chromosome 1B and Excalibur_c17489_804 on chromosome 5B showed the largest effects (1.21 and 0.81, respectively) on the severity of Warrior (-) FS 53/20 and Warrior (-) G 23/19. Our results provide the basis for knowledge-based resistance breeding in the face of the enormous impact of the Warrior (-) race on wheat production in Europe.
Improving Dispersion of Carbon Nanotubes in Natural Rubber by Using Waterjet-Produced Rubber Powder as a Carrier
Authors: Guo Xiurui , Guo Shouyun , Liu Gongxu , Bai Lichen , Liu Haichao , Xu Yuan , Zhao Jinyang , Chai Hailin , Jian Xingao , Guo Lei , Liu Fumin
Published: January 2023

Carbon nanotube (CNT), as reinforcing agents in natural rubber (NR), has gained a large amount of consideration due to their excellent properties. Uniform dispersion of CNT is the key to obtaining high-performance NR nanocomposites. In this contribution, a novel ultrasonic grinding dispersion method of CNT with waterjet-produced rubber powder (WPRP) as a carrier is proposed. Microscopic morphologies show that a Xanthium-like structure with WPRP as the core and CNTs as the spikes is formed, which significantly improves the dispersion of CNT in the NR matrix and simultaneously strengthens the bonding of the WPRP and NR matrix. With the increase in the WPRP loading, the Payne effect of CNT/WPRP/NR composites decreases, indicating the effectiveness of the dispersion method. The vulcanization MH and ML value and crosslinking density increase with the increase in the WPRP loading, whereas the scorch time and cure time exhibit a decreasing trend when the WPRP loading is less than 15 phr. It is found that the CNT/WPRP/NR composites filled with 5 phr WPRP have a 4% increase in 300% modulus, a 3% increase in tensile strength, while a 5% decrease in Akron abrasion loss, compared to CNT/NR composites.
Understanding Atherosclerosis Pathophysiology: Can Additive Manufacturing Be Helpful?
Authors: Henriques Joana , Amaro Ana M. , Piedade Ana P.
Published: January 2023

Atherosclerosis is one of the leading causes of death worldwide. Although this subject arouses much interest, there are limitations associated with the biomechanical investigation done in atherosclerotic tissues, namely the unstandardized tests for the mechanical characterization of these tissues and the inherent non-consensual results obtained. The variability of tests and typologies of samples hampers direct comparisons between results and hinders the complete understanding of the pathologic process involved in atherosclerosis development and progression. Therefore, a consensual and definitive evaluation of the mechanical properties of healthy and atherosclerotic blood vessels would allow the production of physical biomodels that could be used for surgeons’ training and personalized surgical planning. Additive manufacturing (AM), commonly known as 3D printing, has attracted significant attention due to the potential to fabricate biomodels rapidly. However, the existing literature regarding 3D-printed atherosclerotic vascular models is still very limited. Consequently, this review intends to present the atherosclerosis disease and the consequences of this pathology, discuss the mechanical characterization of atherosclerotic vessels/plaques, and introduce AM as a potential strategy to increase the understanding of atherosclerosis treatment and pathophysiology.
Acknowledgment to the Reviewers of Polysaccharides in 2022
Authors:   Polysaccharides Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Powder Production via Atomisation and Subsequent Laser Powder Bed Fusion Processing of Fe+316L Steel Hybrid Alloy
Authors: Pramanik Sudipta , Andreiev Anatolii , Hoyer Kay-Peter , Krüger Jan Tobias , Hengsbach Florian , Kircheis Alexander , Zhao Weiyu , Fischer-Bühner Jörg , Schaper Mirko
Published: January 2023

The current investigation shows the feasibility of 316L steel powder production via three different argon gas atomisation routes (closed coupled atomisation, free fall atomisation with and without hot gas), along with subsequent sample production by laser powder bed fusion (PBF-LB). Here, a mixture of pure Fe and atomised 316L steel powder is used for PBF-LB to induce a chemical composition gradient in the microstructure. Optical microscopy and μ-CT investigations proved that the samples processed by PBF-LB exhibit very little porosity. Combined EBSD-EDS measurements show the chemical composition gradient leading to the formation of a local fcc-structure. Upon heat treatment (1100 °C, 14 h), the chemical composition is homogeneous throughout the microstructure. A moderate decrease (1060 to 985 MPa) in the sample’s ultimate tensile strength (UTS) is observed after heat treatment. However, the total elongation of the as-built and heat-treated samples remains the same (≈22%). Similarly, a slight decrease in the hardness from 341 to 307 HV1 is observed upon heat treatment.
A New Model of Temperature Field Accounting for Acid–Rock Reaction in Acid Fracturing in Shunbei Oilfield
Authors: Mou Jianye , He Jiayuan , Zheng Haiqian , Zhang Rusheng , Zhang Lufeng , Gao Budong
Published: January 2023

The Shunbei oil formation is a deep, high-temperature carbonate reservoir. Acid fracturing is an effective technology to stimulate this formation. For acid fracturing, the temperature field is fundamental information for the acid system selection, acid–rock reaction, live acid penetration distance prediction, acid fracturing design, etc. Therefore, in this paper, we conduct a numerical study on the temperature field in acid fracturing to account for the acid–rock reaction in the Shunbei formation. Firstly, a new mathematical model of the fracture temperature field during acid fracturing is established based on the laws of mass and energy conservation and acid–rock reaction kinetics. The fracture model is based on a PKN model, which accounts for a few factors, such as the acid–rock reaction heat, acid–rock reaction rate dependence on the temperature, and the fracture width change with acid erosion. Then, the numerical mode is developed. Next, an extensive numerical study and a parameter analysis are conducted based on the model with the field data from the Shunbei formation. The study shows that the acid–rock reaction in acid fracturing has obvious effects on the temperature field, resulting in a 10~20 °C increase in the Shunbei formation. The acid–rock reaction dependence on temperature is a factor to be accounted for. The rock dissolution increases first and then decreases from the inlet to the tip of the fracture, unlike the monotonous decrease without temperature dependence. The temperature gradient is high near the inlet and then decreases gradually. Beyond half of the fracture, the temperature is close to the formation temperature. The temperature drops fast in the initial injection stage and tends to stabilize at about 50 min.
The Perplexing Mental Health Comorbidity of Alice in Wonderland Syndrome (AIWS): A Case Study
Authors: Hernandez Jennings
Published: January 2023

The Alice in Wonderland Syndrome (AIWS) is an unusual and uncommon condition that falls under the umbrella of neurology and psychiatry. It is characterized by the presence of complex perceptual and visual discord. Additionally, there are visual hallucinations that are multi-dimensional. This syndrome was first described by John Todd in the 1950s, and it was loosely based on the book Alice in Wonderland. A man in his 30s arrived at his doctor’s appointment with a chief complaint of a pounding cluster headache that lasted over a full day. In addition, he mentioned that there was an aura preceding his headaches. The pain was so intense, it was debilitating him from routine activities. Before the headaches, he explained that he would sense bizarre physical and visual behaviors. During these episodes, he explained that things around him appeared distorted and of various sizes. Things in his room appeared to be more distant than they really were and larger in size (macropsia and micropsia). He described the fingers on his right hand to be much smaller compared to his left hand (micropsia). Objects around him were deformed and distorted (metamorphopsia). His symptoms lasted 45 minutes. He did not suffer from any previous headaches or hallucinations. He was a healthy man with a clean bill of health as per his medical records. Upon examination, the attending physician described the patient as alert, oriented to time and place, and under no obvious distress. All labs performed returned normal including a 10-panel drug test. These were tested to see if he was under the influence of any narcotic, stimulant, or other substances. The physician prescribed 500 mg of valproic acid to take daily. Three months later during his follow-up, he mentioned his symptoms had subsided but were still present. His dose was again increased to 1000 mg/day, eventually stopping all further symptoms from surfacing. He has not had another episode in three months. The Alice in Wonderland Syndrome is known to be associated with headaches with preceding auras. It is common in the pediatric and adult populations. In this paper, I introduce a case of a patient who displays migraines with preceding auras, indicative of AIWS.
Jews, Gentiles, and “in Christ” Identity: A Post-Supersessionist Reading of Philippians
Authors: Zoccali Christopher
Published: January 2023

Interpretations of Philippians have commonly suggested that the letter seeks to demonstrate the worthlessness of Paul’s own (former) Jewish identity, and thus that the Philippians should not be led astray by those who would persuade them to adopt the Jewish Law. Accordingly, it is assumed that Paul understands Judaism to have been superseded by Christianity and, moreover, that Christian identity has superseded all other identities that persons may have possessed upon entrance into the Christ community. In contrast to this long-standing interpretive tradition, this article contends that, for Paul, the ethnic distinction between Jew and gentile within the greater Christ community remains intact, along with a continued role for the Torah for both subgroups. Rather than advancing a supersessionist agenda, Paul fundamentally seeks in this letter to strengthen the Philippians’ identity as members of the nations who have, alongside those in Israel, become members of God’s holy, multiethnic people.
Texture-Mapping Error Removal Based on the BRIEF Operator in Image-Based Three-Dimensional Reconstruction
Authors: Yang Junxing , Lu Lu , Peng Ge , Huang He , Wang Jian , Deng Fei
Published: January 2023

In image-based three-dimensional (3D) reconstruction, texture-mapping techniques can give the model realistic textures. When the geometric surface in some regions is not reconstructed, such as for moving cars, powerlines, and telegraph poles, the textures in the corresponding image are textured to other regions, resulting in errors. To solve this problem, this letter proposes an image consistency detection method based on the Binary Robust Independent Elementary Features (BRIEF) descriptor. The method is composed of two parts. First, each triangle in the mesh and its neighboring triangles are sampled uniformly to obtain sampling points. Then, these sampled points are projected into the visible image of the triangle, and the corresponding sampled points and their RGB color values are obtained on the corresponding image. Based on the sampled points on these images, a BRIEF descriptor is calculated for each image corresponding to that triangle. In the second step, the Hamming distance between these BRIEF descriptors is calculated, outliers are removed according to the method, and noisy images are also removed. In addition, we propose adding semantic information to Markov energy optimization to reduce errors further. The two methods effectively reduced errors in texture mapping caused by objects not reconstructed, improving the texture quality of 3D models.
Geometrical Characterization of Hazelnut Trees in an Intensive Orchard by an Unmanned Aerial Vehicle (UAV) for Precision Agriculture Applications
Authors: Vinci Alessandra , Brigante Raffaella , Traini Chiara , Farinelli Daniela
Published: January 2023

Knowledge of tree size is of great importance for the precision management of a hazelnut orchard. In fact, it has been shown that site-specific crop management allows for the best possible management and efficiency of the use of inputs. Generally, measurements of tree parameters are carried out using manual techniques that are time-consuming, labor-intensive and not very precise. The aim of this study was to propose, evaluate and validate a simple and innovative procedure using images acquired by an unmanned aerial vehicle (UAV) for canopy characterization in an intensive hazelnut orchard. The parameters considered were the radius (Rc), the height of the canopy (hc), the height of the tree (htree) and of the trunk (htrunk). Two different methods were used for the assessment of the canopy volume using the UAV images. The performance of the method was evaluated by comparing manual and UAV data using the Pearson correlation coefficient and root mean square error (RMSE). High correlation values were obtained for Rc, hc and htree while a very low correlation was obtained for htrunk. The method proposed for the volume calculation was promising.
Underwater Hyperspectral Imaging System with Liquid Lenses
Authors: Liu Bohan , Men Shaojie , Ding Zhongjun , Li Dewei , Zhao Zhigang , He Jiahao , Ju Haochen , Shen Mengling , Yu Qiuyuan , Liu Zhaojun
Published: January 2023

The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration.
Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China
Authors: Hou Bowen , Wei Caiyong , Liu Xiangnan , Meng Yuanyuan , Li Xiaoyue
Published: January 2023

The evolution of forest landscape patterns can reveal the landscape stability of forest dynamics undergoing complex ecological processes. Analysis of forest landscape dynamics in regions under ecological restoration can evaluate the impact of large-scale afforestation on habitat quality and provide a scientific basis for achieving sustainable eco-environment development. In this study, a method for assessing forest landscape stability by characterizing changes in forest landscape patterns was proposed. Toeplitz inverse covariance-based clustering (TICC) was used to automatically identify landscape pattern evolution by investigating the synergistic changes of two landscape indices—forest cover area (CA) and patch density (PD)—and to extract the short-term processes—degradation, restoration, and stable—that took place between 1987 and 2021. Four long-term evolution modes, no change, increase, decrease, and wave, based on the temporal distribution of short-term change processes, were also defined to assess landscape stability. Our results showed that (i) the forest’s short-term change processes have various forms. The restoration subsequence was the largest and accounted for 46% of the total subsequence and existed in 75% of the landscape units. The time distribution of these three change processes showed that more landscape units have begun to transition into a stable state. (ii) The long-term change modes showed an aggregation distribution law and indicated that 57% of the landscape units were stable and 6.7% were unstable. Therefore, our study can provide a new perspective for the dynamic analysis of landscape patterns and offer insights for formulating better ecological restoration strategies.
Evaluating the Value of CrIS Shortwave-Infrared Channels in Atmospheric-Sounding Retrievals
Authors: Barnet Chris D. , Smith Nadia , Ide Kayo , Garrett Kevin , Jones Erin
Published: January 2023

The Cross-track Infrared Sounder (CrIS), in low Earth orbit since 2011, makes measurements of the top of atmosphere radiance for input into data assimilation (DA) systems as well as the retrieval of geophysical state variables. CrIS measurements have 2211 narrow infrared channels ranging between 650 and 2550 cm−1 (~3.9–15.4 μm) and capture the variation in profiles of atmospheric temperature, water vapor, and numerous trace gas species. DA systems derive atmospheric temperature by assimilating CO2-sensitive channels in the CrIS longwave (LW) band (650–1095 cm−1). Here, we investigate if CO2-sensitive channels in the shortwave (SW) band (2155–2550 cm−1) can similarly be applied. We first evaluated the information content of the CrIS bands followed by an assessment of the performance degradation of retrievals due to the loss of individual CrIS bands. We found that temperature profile retrievals derived from the CrIS SW band were statistically both well-behaved and as accurate as a retrieval utilizing the CrIS LW band. The one caveat, however, is that the higher CrIS instrument noise in the SW band limited its performance under certain conditions. We conclude with a discussion on the implications our results have for channel selection in retrieval and DA systems as well as the design of future space instruments.
A Modified RNN-Based Deep Learning Method for Prediction of Atmospheric Visibility
Authors: Zang Zengliang , Bao Xulun , Li Yi , Qu Youming , Niu Dan , Liu Ning , Chen Xisong
Published: January 2023

Accurate atmospheric visibility prediction is of great significance to public transport safety. However, since it is affected by multiple factors, there still remains difficulties in predicting its heterogenous spatial distribution and rapid temporal variation. In this paper, a recursive neural network (RNN) prediction model modified with the frame-hopping transmission gate (FHTG), feature fusion module (FFM) and reverse scheduled sampling (RSS), named SwiftRNN, is developed. The new FHTG is used to accelerate training, the FFM is used for extraction and fusion of global and local features, and the RSS is employed to learn spatial details and improve prediction accuracy. Based on the ground-based monitoring data of atmospheric visibility from the China Meteorological Information Center during 1 January 2018 to 31 December 2020, the SwiftRNN model and two traditional ConvLSTM and PredRNN models are performed to predict hourly atmospheric visibility in central and eastern China at a lead of 12 h. The results show that the SwiftRNN model has better performance in the skill scores of visibility prediction than those of the ConvLSTM and PredRNN model. The averaged structural similarity (SSIM) of predictions at a lead up to 12 h is 0.444, 0.425 and 0.399 for the SwiftRNN, PredRNN and ConvLSTM model, respectively, and the averaged image perception similarity (LPIPS) is 0.289, 0.315 and 0.328, respectively. The averaged critical success index (CSI) of predictions over 1000 m fog area is 0.221, 0.205 and 0.194, respectively. Moreover, the training speed of the SwiftRNN model is 14.3% faster than the PredRNN model. It is also found that the prediction effect of the SwiftRNN model over 1000 m medium grade fog area is significantly improved along with lead times compared with the ConvLSTM and PredRNN model. All above results demonstrate the SwiftRNN model is a powerful tool in predicting atmospheric visibility.
Improved Bidirectional RRT* Algorithm for Robot Path Planning
Authors: Xin Peng , Wang Xiaomin , Liu Xiaoli , Wang Yanhui , Zhai Zhibo , Ma Xiqing
Published: January 2023

In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.
Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison
Authors: Sintoni Michele , Macrelli Elena , Bellini Alberto , Bianchini Claudio
Published: January 2023

In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time–frequency analysis, frequency analysis and time domain. The results show that this method improves the rotor fault detection in transient conditions. Variable speed drive applications are common in industry. However, traditional condition monitoring methods fail in time-varying conditions or with load oscillations. This method is based on the combined use of the Hilbert and discrete wavelet transforms, which compute the energy in a bandwidth corresponding to the maximum fault signature. Theoretical analysis, numerical simulation and experiments are presented, which confirm the enhanced performance of the proposed method with respect to prior solutions, especially in time-varying conditions. The comparison is based on quantitative analysis that helps in choosing the optimal trade-off between performance and (computational) cost.
Multirotor Motor Failure Detection with Piezo Sensor
Authors: Ambroziak Leszek , Ołdziej Daniel , Koszewnik Andrzej
Published: January 2023

Failure detection of Unmanned Aerial Vehicle (UAV) motors and propulsion systems is the most important step in the implementation of active fault-tolerant control systems. This will increase the reliability of unmanned systems and increase the level of safety, especially in civil and commercial applications. The following paper presents a method of motor failure detection in the multirotor UAV using piezo bars. The results of a real flight, in which the failure of the propulsion system caused the crash of a hybrid VTOL UAV, were presented and analyzed. The conclusions drawn from this flight led to the development of a lightweight, simple and reliable sensor that can detect a failure of the UAV propulsion system. The article presents the outcomes of laboratory tests concerning measurements made with a piezo sensor. An extensive analysis of the obtained results of vibrations recorded on a flying platform arm with a propulsion system is presented, and a methodology for using this type of data to detect failures is proposed. The article presents the possibility of using a piezoelectric sensor to record vibrations on the basis of which it is possible to detect a failure of the UAV propulsion system.
A Delaunay Edges and Simulated Annealing-Based Integrated Approach for Mesh Router Placement Optimization in Wireless Mesh Networks
Authors: Oda Tetsuya
Published: January 2023

Wireless Mesh Networks (WMNs) can build a communications infrastructure using only routers (called mesh routers), making it possible to form networks over a wide area at low cost. The mesh routers cover clients (called mesh clients), allowing mesh clients to communicate with different nodes. Since the communication performance of WMNs is affected by the position of mesh routers, the communication performance can be improved by optimizing the mesh router placement. In this paper, we present a Coverage Construction Method (CCM) that optimizes mesh router placement. In addition, we propose an integrated optimization approach that combine Simulated Annealing (SA) and Delaunay Edges (DE) in CCM to improve the performance of mesh router placement optimization. The proposed approach can build and provide a communication infrastructure by WMNs in disaster environments. We consider a real scenario for the placement of mesh clients in an evacuation area of Kurashiki City, Japan. From the simulation results, we found that the proposed approach can optimize the placement of mesh routers in order to cover all mesh clients in the evacuation area. Additionally, the DECCM-based SA approach covers more mesh clients than the CCM-based SA approach on average and can improve network connectivity of WMNs.
Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning
Authors: Yang Jie , Zheng Jun , Wang Haochen , Li Jiaxing , Sun Haipeng , Han Weifeng , Jiang Nan , Tan Yu-An
Published: January 2023

Federated learning has a distributed collaborative training mode, widely used in IoT scenarios of edge computing intelligent services. However, federated learning is vulnerable to malicious attacks, mainly backdoor attacks. Once an edge node implements a backdoor attack, the embedded backdoor mode will rapidly expand to all relevant edge nodes, which poses a considerable challenge to security-sensitive edge computing intelligent services. In the traditional edge collaborative backdoor defense method, only the cloud server is trusted by default. However, edge computing intelligent services have limited bandwidth and unstable network connections, which make it impossible for edge devices to retrain their models or update the global model. Therefore, it is crucial to detect whether the data of edge nodes are polluted in time. This paper proposes a layered defense framework for edge-computing intelligent services. At the edge, we combine the gradient rising strategy and attention self-distillation mechanism to maximize the correlation between edge device data and edge object categories and train a clean model as much as possible. On the server side, we first implement a two-layer backdoor detection mechanism to eliminate backdoor updates and use the attention self-distillation mechanism to restore the model performance. Our results show that the two-stage defense mode is more suitable for the security protection of edge computing intelligent services. It can not only weaken the effectiveness of the backdoor at the edge end but also conduct this defense at the server end, making the model more secure. The precision of our model on the main task is almost the same as that of the clean model.
Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms
Authors: Herrera-Silva Juan A. , Hernández-Álvarez Myriam
Published: January 2023

Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected dynamic features. Since most of the attributes are shared by diverse ransomware-affected samples, our study can be used for detecting current and even new variants of the threat. This research has the following objectives: (1) Execute experiments with encryptor and locker ransomware combined with goodware to generate JSON files with dynamic parameters using a sandbox. (2) Analyze and select the most relevant and non-redundant dynamic features for identifying encryptor and locker ransomware from goodware. (3) Generate and make public a dynamic features dataset that includes these selected parameters for samples of different artifacts. (4) Apply the dynamic feature dataset to obtain models with machine learning algorithms. Five platforms, 20 ransomware, and 20 goodware artifacts were evaluated. The final feature dataset is composed of 2000 registers of 50 characteristics each. This dataset allows for a machine learning detection with a 10-fold cross-evaluation with an average accuracy superior to 0.99 for gradient boosted regression trees, random forest, and neural networks.
A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices
Authors: Salvador-Ortega Irene , Vivaracho-Pascual Carlos , Simon-Hurtado Arancha
Published: January 2023

In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.
The Design and Development of a Microstrip Antenna for Internet of Things Applications
Authors: Anchidin Liliana , Lavric Alexandru , Mutescu Partemie-Marian , Petrariu Adrian I. , Popa Valentin
Published: January 2023

The Internet of Things (IoT) has become a part of modern life where it is used for data acquisition and long-range wireless communications. Regardless of the IoT application profile, every wireless communication transmission is enabled by highly efficient antennas. The role of the antenna is thus very important and must not be neglected. Considering the high demand of IoT applications, there is a constant need to improve antenna technologies, including new antenna designs, in order to increase the performance level of WSNs (Wireless Sensor Networks) and enhance their efficiency by enabling a long range and a low error-rate communication link. This paper proposes a new antenna design that is able to increase the performance level of IoT applications by means of an original design. The antenna was designed, simulated, tested, and evaluated in a real operating scenario. From the obtained results, it ensured a high level of performance and can be used in IoT applications specific to the 868 MHz frequency band.By inserting two notches along x axis, we find an optimal structure of the microstrip patch antenna with a reflection coefficient of −34.3 dB and a bandwidth of 20 MHz. After testing the designed novel antenna in real IoT operating conditions, we concluded that the proposed antenna can increase the performance level of IoT wireless communications.
Estimation of Sugar Content in Wine Grapes via In Situ VNIR–SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques
Authors: Kalopesa Eleni , Karyotis Konstantinos , Tziolas Nikolaos , Tsakiridis Nikolaos , Samarinas Nikiforos , Zalidis George
Published: January 2023

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR–SWIR spectrum (350–2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.
Control of Frequency Combs with Passive Resonators
Authors: Hendrie James , Hsu Ning , Diels Jean-Claude
Published: January 2023

Tailored optical frequency combs are generated by nesting passive etalons within mode-locked oscillators. In this work, the oscillator generates a comb of 6.8 GHz with 106 MHz side-bands. This tailored comb results from the self-synchronized locking of two cavities with precision optical frequency tuning. In this manuscript, it is demonstrated that these combs can be precisely predicted utilizing a temporal ABCD matrix method and precise comb frequency tuning by scanning over the D1 transition line of 87Rb and observing the fluorescence.
Acknowledgment to the Reviewers of Sexes in 2022
Authors:   Sexes Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Informative g-Priors for Mixed Models
Authors: Chien Yu-Fang , Zhou Haiming , Hanson Timothy , Lystig Theodore
Published: January 2023

Zellner’s objective g-prior has been widely used in linear regression models due to its simple interpretation and computational tractability in evaluating marginal likelihoods. However, the g-prior further allows portioning the prior variability explained by the linear predictor versus that of pure noise. In this paper, we propose a novel yet remarkably simple g-prior specification when a subject matter expert has information on the marginal distribution of the response yi. The approach is extended for use in mixed models with some surprising but intuitive results. Simulation studies are conducted to compare the model fitting under the proposed g-prior with that under other existing priors.
Impact of Graduate Student Expansion and Innovative Human Capital on Green Total Factor Productivity
Authors: Yao Hao , Gu Xiulin , Yu Qing
Published: January 2023

Using data from 30 provinces and cities in China from 2005–2018, panel regression models, mediation models, quantile regressions and threshold regressions were used to examine the relationship between graduate student size expansion, innovative human capital and green total factor productivity (GTFP) and analyze the influence mechanisms and heterogeneity among them. The results of the study are as follows: First, graduate student expansion and innovative human capital are the driving force for GTFP growth in China, and graduate student size expansion indirectly boosts GTFP by promoting the supply of innovative human capital, with a 73% mediating effect. Second, the effects of graduate student expansion and innovative human capital on GTFP show a trend of diminishing marginal benefits, and the estimated coefficient of the effect of graduate student scale share on GTFP has an inverted U-shaped relationship. Third, there is a significant threshold feature of industrial structure upgrading in the influence of graduate student scale expansion on GTFP. The study makes suggestions in terms of expanding of the graduate students, rationalizing the enrollment structure of universities, optimizing the regional industrial structure and creating a social innovation environment.
An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room
Authors: Liu Kuang-Sheng , Muda Iskandar , Lin Ming-Hung , Dwijendra Ngakan Ketut Acwin , Carrillo Caballero Gaylord , Alviz-Meza Aníbal , Cárdenas-Escrocia Yulineth
Published: January 2023

There are no exact criteria for the architecture of openings and windows in office buildings in order to optimize energy consumption. Due to the physical limitations of this renewable energy source and the lack of conscious control over its capabilities, the amount of light entering offices and the role of daylight as a source of energy are determined by how they are constructed. In this study, the standard room dimensions, which are suitable for three to five employees, are compared to computer simulations. DesignBuilder and EnergyPlus are utilized to simulate the office’s lighting and energy consumption. This study presents a new method for estimating conventional energy consumption based on gene expression programming (GEP). A gravitational search algorithm (GSA) is implemented in order to optimize the model results. Using input and output data collected from a simulation of conventional energy use, the physical law underlying the problem and the relationship between inputs and outputs are identified. This method has the advantages of being quick and accurate, with no simulation required. Based on effective input parameters and sensitivity analysis, four models are evaluated. These models are used to evaluate the performance of the trained network based on statistical indicators. Among all the GEP models tested in this study, the one with the lowest MAE (0.1812) and RMSE (0.09146) and the highest correlation coefficient (0.90825) is found to be the most accurate.
Why Do Farmers Support Stable Land Ownership? Marketization with Chinese Characteristics
Authors: Zhang Ximing , Li Xiao , Wang Hui
Published: January 2023

Recent debates regarding marketization have focused on the relationship between the state and the market, while the grassroots and their everyday experiences have arguably been understudied. In this paper, we study marketization with the example of land marketization in China. Out of concern for the grassroots’ perspective, we investigate Chinese farmers’ perceptions with regard to stable land ownership of farmland, which are essential for land marketization in the backdrop of intensive land use conversion in China’s urban periphery. Approximately 1200 farmer households were interviewed around 12 cities in mainland China. An ordered probit regression analysis of the survey results reveals a series of factors that explain the individual farmers’ preference for stable land ownership. Among others, the decreasing size of farmer household and rural women’s insecure property rights in farmland are identified as two grassroots-based characteristics underpinning China’s ongoing transition to a more market-based farmland use institution. An important theoretical implication of our research is that the mainstream literature perhaps over-attributes China’s marketization to the state and the market, while under-evaluating the spontaneous support from bottom-up.
The Association between Street Built Environment and Street Vitality Based on Quantitative Analysis in Historic Areas: A Case Study of Wuhan, China
Authors: Zou Han , Liu Ruichao , Cheng Wen , Lei Jingjing , Ge Jing
Published: January 2023

Over the past decade, enhancing the quality of cities and building vibrant urban streets has become a hot topic in urban planning in China. Although there are many studies on how the built environment affects street vitality, the unique built environment of the street space in historic areas, as the core node of the city, has not been fully explored. This study constructs an association model between the street built environment (SBE) and street vitality in historic areas and evaluates the influence of SBE on street vitality by spatial analysis and statistical analysis methods using POI data, road network data, and Baidu heat map data, taking Wuhan, China, as an example. The results showed that (1) appropriate built environment development intensity, street width-to-height ratio, and facade ratio of historic buildings on the street frontage all can promote street vitality; (2) the spatial distribution of historic buildings converted to commercial functions in historic areas has a high consistency with the spatial distribution of street vitality, and the consistency is significantly higher than that of general urban streets; (3) historic buildings converted to residential functions and those in vacancy or under renovation in historic areas have a significant inhibitory effect on street vitality; and (4) the spatial distribution of transportation facilities and the spatial distribution of street vitality are mutually exclusive in historic areas. This study proposes a method for studying the SBE and street vitality in historic areas and initially explores the relationship between the influences of the SBE on street vitality in historic areas. Since the functional replacement of historic buildings can affect the street vibrancy in historic areas, our findings suggest moderating commercial renovation rather than simply repairing or maintaining the status for enhancing the street vitality. Moreover, the intervention of transportation facilities will reduce the street vitality in historic areas, which provides a basis for the strategy of renewing historic areas into pedestrian street spaces.