bluuebunny/crossref_metadata_2025 huggingface dataset:
Resistance to Frankliniella occidentalis during Different Plant Life Stages and under Different Environmental Conditions in the Ornamental Gladiolus
Authors: Wahyuni Dinar S. C. , Klinkhamer Peter G. L. , Choi Young Hae , Leiss Kirsten A.
Published: February 2024

The defense mechanisms of plants evolve as they develop. Previous research has identified chemical defenses against Western flower thrips (WFT) in Gladiolus (Gladiolus hybridus L.). Consequently, our study aimed to explore the consistency of these defense variations against WFT across the various developmental stages of Gladiolus grown under different conditions. Thrips bioassays were conducted on whole plants at three developmental stages, using the Charming Beauty and Robinetta varieties as examples of susceptible and resistant varieties, respectively. Metabolomic profiles of the leaves, buds and flowers before thrips infestation were analyzed. The thrips damage in Charming Beauty was more than 500-fold higher than the damage in Robinetta at all plant development stages. Relative concentrations of triterpenoid saponins and amino acids that were associated with resistance were higher in Robinetta at all plant stages. In Charming Beauty, the leaves exhibited greater damage compared to buds and flowers. The relative concentrations of alanine, valine and threonine were higher in buds and flowers than in leaves. The Metabolomic profiles of the leaves did not change significantly during plant development. In addition, we cultivated plants under different environmental conditions, ensuring consistency in the performance of the two varieties across different growing conditions. In conclusion, the chemical thrips resistance markers, based on the analysis of vegetative plants grown in climate rooms, were consistent over the plant’s lifetime and for plants grown under field conditions.
How Seeds Attract and Protect: Seed Coat Development of Magnolia
Authors: Feng Qiuhong , Cai Ming , Li Honglin , Zhang Xin
Published: February 2024

Seeds are one of the most important characteristics of plant evolution. Within a seed, the embryo, which will grow into a plant, can survive harsh environments. When the seeds are mature, the mother plant will disperse them from its body, allowing them to be taken away to grow in a new place. Otherwise, if the young generation grows alongside the mother plants in the same place, they will compete for sunlight and nutrition. The mother plants use different strategies to send away their seeds. One of these strategies is endozoochory, which means that the seeds disperse via ingestion by animals. There is a conflict between the seeds’ abilities to attract animals and protect the embryo within the digestion systems of animals. Magnolia seeds exhibit typical endozoochory. The seed coats of Magnolia feature sarcotestas and sclerotestas. The sarcotesta, which is fleshy, bright-colored, and edible, attracts animals. The sclerotesta is hard and woody, protecting the embryo from the digestive systems of animals. In this study, we used scanning electron and light microscopes to examine the development of the sarcotesta and sclerotesta of Magnolia stellata seed coats. The results showed that the sarcotesta and sclerotesta come from the outer integument. This result confirms the hypothesis of Asa Gray from 1848. The dependence of the seed dispersal strategy on structural development is discussed.
Antimicrobial Activity on Streptococcus mutans and Enterococcus faecalis of Cyperus articulatus Ethanolic Extracts
Authors: Macambira Daniela Vieira de Castro , Almeida Júnior José Sousa de , Silveira Claudia Fernandes de Magalhães , Sarrazin Sandra Layse Ferreira , Moraes Tânia Mara Pires , da Silva Bruno Alexandre , Minervino Antonio Humberto Hamad , Moraes Waldiney Pires , Barata Lauro Euclides Soares
Published: February 2024

Oral diseases are one of the biggest public health problems worldwide, caused by opportunistic pathogens such as Streptococcus mutans and Enterococcus faecalis. Cyperus articulatus (priprioca) is a plant conventionally used in traditional medicine in the Amazon region. However, little is known about the possible dentistry-related uses of extracts from the rhizomes and solid waste generated by the extraction of essential oils from this vegetable. This study aimed to investigate the chemical composition of volatile compounds and antimicrobial activity through the Minimum Inhibitory Concentration test (MIC and assessment of the toxicity by Hens Egg Test-Chorion Allantoic Membrane (HET-CAM) of the ethanolic extracts from Cyperus articulatus intact rhizomes and solid waste. We identified sesquiterpenes as the main constituents, strong antimicrobial activity of the ethanolic extract of intact rhizomes against S. mutans (MIC = 0.29 mg/mL), moderate antimicrobial activity against E. faecalis of the extract obtained from the solid waste (MIC = 1.17 mg/mL), and absence of toxicity for both tested extracts. The absence of irritation and the antibacterial activity of the ethanolic extract from C. articulatus rhizomes and solid waste reveal its potential for use in the alternative control of bacteria that cause oral infections and may present economic viability as a raw material for dental products.
Ethnomedicinal, Chemical, and Biological Aspects of Lannea Species—A Review
Authors: Malú Quintino , Caldeira Gonçalo I. , Catarino Luís , Indjai Bucar , da Silva Isabel Moreira , Lima Beatriz , Silva Olga
Published: February 2024

Lannea L. genus belongs to the Anacardiaceae botanical family and has long been used in traditional medicinal systems of many countries to manage several health conditions, but no studies have been conducted regarding its usefulness as a source of herbal medicine for human use. A literature review was conducted on scientific papers indexed on B-On, Pubmed, and Web of Science databases. Our results showed that medicinal plants from this botanical genus, mostly constituted by bark and leaf, are often used to approach a wide variety of disease symptoms, like fever, inflammatory states, pain, and gastrointestinal disorders. Phytochemical profiles of Lannea species revealed that phenolic acid derivatives including hydroquinones, phenolic acids, flavonoids, condensed tannins, and triterpenoids are the main classes of secondary metabolites present. Among the total of 165 identified compounds, 57 (34.5%) are flavonoids, mostly quercetin- and myricetin-derived flavonols and catechin and epicatechin flavan-3-ol derivatives also containing a galloyl group. In vitro and in vivo studies allowed the identification of 12 different biological activities, amongst which antimicrobial, antioxidant, anti-inflammatory, and cytotoxic activities were the most frequently cited and observed in in vitro essays. Our review contributes useful information for the scientifical validation of the use of Lannea species in traditional medicinal systems and shows that more research needs to be conducted to better understand the concrete utility of these as herbal medicines.
α-Glucosidase Inhibitory Phytochemical Components of Chinese Endemic Plant Whitfordiodendron filipes var. tomentosum
Authors: Chen Jun-Kun , Ge Zeng-Yue , Liao Xiao-Wen , Xue Jun , Wu Lei , Liang Lin-Fu
Published: February 2024

Whitfordiodendron filipes var. tomentosum is an endemic plant in China. There have been no chemical or pharmacological studies of this plant reported before. In the current research, eight triterpenes and two steroids were obtained. Their structures were established by the analysis of NMR data and comparison with those reported in the literature. These ten structurally diverse compounds comprised five distinct carbon frameworks with different functionalities. The chemotaxonomic significance of these secondary metabolites was discussed, disclosing the common components between the variant W. filipes var. tomentosum and the species W. filipe. Evaluation of α-glucosidase inhibitory activities of these isolates disclosed that compounds 1, 2, 4, and 6 exhibited significant α-glucosidase inhibitory activities (IC50 = 16.6–19.2 μM), which were close in value to the positive control acarbose (IC50 = 11.5 μM). Moreover, the binding modes between the biologically active compounds 1, 2, 4, and 6 and the α-glucosidase protein were preliminarily studied using molecular docking. This study not only showed the chemical and biological profile of the plant W. filipes var. tomentosum but also revealed that these components could be developed as hypoglycemic lead compounds.
Techno-Environmental Evaluation of Alkaline Treatment in Flax Reinforced Thermoplastics
Authors: Vattathurvalappil Suhail Hyder , Shaukat Mian Mobeen , Theravalappil Rajesh , Shuja Shahzada Zaman , Abdelrahman Wael Gamaleldin
Published: February 2024

A combination of thermoplastics and natural fiber reinforcements is considered an ideal choice to mitigate environmental impacts and enhance recyclability or reusability. Chemical treatments are often employed to enhance the thermomechanical properties of natural fiber-reinforced plastics. Nevertheless, it is of paramount importance to assess the techno-economic impact of such chemical treatments and environmentally friendly materials for their implementation in mass productions on an industrial scale. In this work, high-density polyethylene is reinforced with sodium hydroxide (NaOH)-treated and untreated flax fibers to study its impact on mechanical and environmental properties. The composites treated with NaOH exhibited a 37% increase in tensile strength. However, life cycle assessment performed on the NaOH-treated samples showed that they had a global warming potential of 5.8 kg of CO2, a terrestrial acidification potential of 0.0269 kg of SO2, and a human carcinogenic toxicity of 0.031 kg of 1,4-DCB compared to the untreated samples. In summary, the techno-environmental analysis reveals a novel approach to identifying chemical treatments based on their technical and environmental effects.
Investigation on the Extent of Retrograde Condensation of Qianshao Gas Condensate Reservoir Using PVT Experiments and Compositional Reservoir Simulation
Authors: Liu Hailong , Xie Bin , Xin Xiaozhi , Zhao Haining , Liu Yantian
Published: February 2024

In the development of the Qianshao (QS) gas condensate reservoir, it is crucial to consider the phenomenon of retrograde condensation. Understanding the condensate saturation distribution with respect to time and space within the reservoir is essential for planning and implementing effective strategies for the future development of the QS gas condensate reservoir. In this paper, various PVT experiments (including reservoir oil recombination, flash separation, constant composition expansion, and constant volume depletion) were conducted to study the PVT properties and phase behavior of QS gas condensate fluid. Based on experimental data, our in-house PVT computation package was used to determine the appropriate EOS model parameters for the QS gas condensate. A four-step reservoir fluid characterization procedure and workflow for gas condensate reservoirs was developed. Furthermore, by analyzing the pressure-temperature phase envelope, the maximum possible condensate saturation in the QS well area was estimated to be around 3%. Numerical reservoir simulation models were developed using both the EOS model and actual reservoir engineering data. These simulation models were specifically designed to replicate the retrograde condensation process that occurs during production, taking into account both vertical and horizontal wells. By simulating the production process, these single-well reservoir simulation models enable us to quantitatively evaluate the condensate saturation and its distribution over space and time within a specific control area around a single well. Reservoir simulation results show that the condensate build-up around vertical and horizontal wells is quite different. For a vertical well, the maximum condensate oil saturation (30%) around the wellbore is located approximately 5 to 6 m from the well’s center. In contrast, the horizontal well model demonstrates a maximum condensate saturation of no more than 1.5%. This information is crucial for making informed decisions regarding the effective development and management of the QS gas condensate reservoir.
Elimination of Shrinkage in Ductile Iron Castings Using Computer Simulation of Casting and Solidification
Authors: Futas Peter , Pribulova Alena , Sabik Vladimir , Petrik Jozef , Blasko Peter , Brzeziński Marcin
Published: February 2024

The production of high-quality castings without foundry defects at minimal production costs is a constant priority for foundries. Innovation and optimization of production processes are key to achieving this goal. Computer simulation of foundry processes offers a modern alternative to expensive and time-consuming experiments in real foundries and provides a reliable representation and analysis of casting and solidification processes. A detailed analysis of the casting and solidification simulation results allows the prediction of various risks that can cause defects in cast castings, thereby reducing their quality and, last but not least, the cost of their production. This paper deals with the analysis of a computer simulation of the casting of a brake disc in a Slovak foundry. This brake disc has had shrinkages and micro shrinkages that reduce the internal quality of the casting. These defects occurred in the ribs in the upper part of the casting under the feeders. A computer simulation of the casting and solidification of this casting was made according to the real conditions. It turned out that the designed gating system with a system of feeders was not sufficient to eliminate the emerging defects. A new layout for the feeders was proposed, which ultimately eliminated the occurrence of defects based on the results of the computer simulation. The input parameters were set to be as close as possible to the actual needs of the foundry. Moreover, 3D models of the assemblies were designed in SolidWorks Premium 2015 x64 Edition CAD software, and the filling and solidification simulations were performed using the NovaFlow & Solid CV 4.6r42 simulation program.
A Study on the Literacy Rate of Buddhist Sangha in the Tang Dynasty
Authors: Wu Shaowei
Published: February 2024

The Buddhist sangha played a crucial role in ancient China, exerting significant influence on its society through religious identity and cultural knowledge. However, not all members of the monastic community were literate. The Tang Dynasty introduced an examination system that assessed monks’ proficiency in reciting Buddhist scriptures, determining their eligibility for ordination. Simultaneously, efforts to remove unqualified monks and nuns provided an opportunity to estimate the literacy rate within the monastic community. A statistical analysis of the literacy rate offers a novel perspective for understanding the evolution of Buddhism, the intricate relationship between religion and politics, and the role of the monastic community in local society during the Tang Dynasty.
The Mapping of Alpha-Emitting Radionuclides in the Environment Using an Unmanned Aircraft System
Authors: Royo Pablo , Vargas Arturo , Guillot Tania , Saiz David , Pichel Jonathan , Rábago Daniel , Duch María Amor , Grossi Claudia , Luchkov Maksym , Dangendorf Volker , Krasniqi Faton
Published: February 2024

The protection of first responders from radioactive contamination with alpha emitters that may result from a radiological accident is of great complexity due to the short range of alpha particles in the air of a few centimeters. To overcome this issue, for the first time, a system mounted on a UAS for the near-real-time remote measurement of alpha particles has been developed, tested, and calibrated. The new system, based on an optical system adapted to be installed on a UAS in order to measure the UV-C fluorescence emitted by alpha particles in the air, has been tested and calibrated, carried out in the laboratory and in field experiments using UV-C LEDs and 241Am sources. In experimental flights, the probability of detecting a point source was determined to be approximately 60%. In the case of a surface extended source, a detection efficiency per unit surface activity of 10 counts per second per MBq cm−2 was calculated. A background count rate of UV-C of around 26 ± 28 s−1 for an integration time of 0.1 s was measured during flights, which led to a decision threshold surface activity of 5 MBq cm−2.
Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification
Authors: Chu Boce , Zhang Mengxuan , Ma Kun , Liu Long , Wan Junwei , Chen Jinyong , Chen Jie , Zeng Hongcheng
Published: February 2024

Superpixel segmentation has been widely used in the field of computer vision. The generations of PolSAR superpixels have also been widely studied for their feasibility and high efficiency. The initial numbers of PolSAR superpixels are usually designed manually by experience, which has a significant impact on the final performance of superpixel segmentation and the subsequent interpretation tasks. Additionally, the effective information of PolSAR superpixels is not fully analyzed and utilized in the generation process. Regarding these issues, a multiobjective evolutionary superpixel segmentation for PolSAR image classification is proposed in this study. It contains two layers, an automatic optimization layer and a fine segmentation layer. Fully considering the similarity information within the superpixels and the difference information among the superpixels simultaneously, the automatic optimization layer can determine the suitable number of superpixels automatically by the multiobjective optimization for PolSAR superpixel segmentation. Considering the difficulty of the search for accurate boundaries of complex ground objects in PolSAR images, the fine segmentation layer can further improve the qualities of superpixels by fully using the boundary information of good-quality superpixels in the evolution process for generating PolSAR superpixels. The experiments on different PolSAR image datasets validate that the proposed approach can automatically generate high-quality superpixels without any prior information.
A Lightweight Remote Sensing Aircraft Object Detection Network Based on Improved YOLOv5n
Authors: Wang Jiale , Bai Zhe , Zhang Ximing , Qiu Yuehong
Published: February 2024

Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.
Alternative Approach to Tilt-to-Length Coupling Estimation for Laser Ranging Interferometers in Future Gravity Missions
Authors: Wang Zhizhao , Yang Shuju , Jia Fuling , Wu Kaihang , Liao Fangjie , Duan Huizong , Yeh Hsien-Chi
Published: February 2024

Tilt-to-length coupling, a non-constant systematic error source caused by satellite attitude variations, has been observed in the laser ranging signals of the GRACE Follow-On mission. This error can be corrected by certain calibration maneuvers performed regularly in orbit. In this paper, we introduce an alternative approach to tilt-to-length coupling estimation for a laser ranging interferometer in future gravity missions, using the ranging signals without any specific calibration maneuvers, which allows daily estimation. An analytical model of laser ranging signals is derived. The tilt-to-length estimation is performed under different conditions using the least squares method as well as the simulated data. The pointing angle noise is found to be the most significant limiting factor. When the pointing angle noise is below 0.3μrad/Hz1/2, the RMS of the estimation error is below 4 nm, much better than the tilt-to-length error of GRACE Follow-On. In the case of low pointing angle noise, the estimation error of an under 1.5 m offset between the center of mass and the interferometer reference point is not obviously different from the case with only a 0.5 mm offset, which provides installation flexibility for the laser ranging interferometer.
A Low-Profile Circularly Polarized Millimeter-Wave Broadband Antenna Analyzed with a Link Budget for IoT Applications in an Indoor Scenario
Authors: Bhadravathi Ghouse Parveez Shariff , Mane Pallavi R. , Ali Tanweer , Golapuram Dattathreya Goutham Simha , Puthenveettil Gopi Sudheesh , Pathan Sameena , Anguera Jaume
Published: February 2024

Broadband antennas with a low-profile generating circular polarization are always in demand for handheld/ portable devices as CP antennas counter multipath and misalignment issues. Therefore, a compact millimeter-wave antenna is proposed in this article. The proposed antenna structure comprises two circular rings and a circular patch at the center. This structure is further embedded with four equilateral triangles at a 90° orientation. The current entering the radiator is divided into left and right circular directions. The equilateral triangles provide the return path for current at the differential phase of ±90°, generating circular polarization. Structural development and analysis were initially performed through the characteristic mode theory. It showed that Modes 1 to 4 generated good impedance matching from 20 to 30 GHz and Modes 1 to 5, from 30 to 40 GHz. It also demonstrated the summation of orthogonal modes leading to circular polarization. The antenna-measured reflection coefficient |S11| > 10 dB was 19 GHz (23–42 GHz), and the axial ratio at −3 dB was 4.2 GHz (36–40.2 GHz). The antenna gain ranged from 4 to 6.2 dBi. The proposed antenna was tested for link margin estimation for IoT indoor conditions with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The communication reliability with co- and cross-polarization was also studied under these conditions, and the results proved to be satisfactory.
Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases
Authors: Chudzik Artur , Śledzianowski Albert , Przybyszewski Andrzej W.
Published: February 2024

Neurodegenerative diseases (NDs) such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores “digital biomarkers” (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.
Two-Level Spatio-Temporal Feature Fused Two-Stream Network for Micro-Expression Recognition
Authors: Wang Zebiao , Yang Mingyu , Jiao Qingbin , Xu Liang , Han Bing , Li Yuhang , Tan Xin
Published: February 2024

Micro-expressions, which are spontaneous and difficult to suppress, reveal a person’s true emotions. They are characterized by short duration and low intensity, making the task of micro-expression recognition challenging in the field of emotion computing. In recent years, deep learning-based feature extraction and fusion techniques have been widely used for micro-expression recognition, particularly methods based on Vision Transformer that have gained popularity. However, the Vision Transformer-based architecture used in micro-expression recognition involves a significant amount of invalid computation. Additionally, in the traditional two-stream architecture, although separate streams are combined through late fusion, only the output features from the deepest level of the network are utilized for classification, thus limiting the network’s ability to capture subtle details due to the lack of fine-grained information. To address these issues, we propose a new two-level spatio-temporal feature fused with a two-stream architecture. This architecture includes a spatial encoder (modified ResNet) for learning texture features of the face, a temporal encoder (Swin Transformer) for learning facial muscle motor features, a feature fusion algorithm for integrating multi-level spatio-temporal features, a classification head, and a weighted average operator for temporal aggregation. The two-stream architecture has the advantage of extracting richer features compared to the single-stream architecture, leading to improved performance. The shifted window scheme of Swin Transformer restricts self-attention computation to non-overlapping local windows and allows cross-window connections, significantly improving the performance and reducing the computation compared to Vision Transformer. Moreover, the modified ResNet is computationally less intensive. Our proposed feature fusion algorithm leverages the similarity in output feature shapes at each stage of the two streams, enabling the effective fusion of multi-level spatio-temporal features. This algorithm results in an improvement of approximately 4% in both the F1 score and the UAR. Comprehensive evaluations conducted on three widely used spontaneous micro-expression datasets (SMIC-HS, CASME II, and SAMM) consistently demonstrate the superiority of our approach over comparative methods. Notably, our approach achieves a UAR exceeding 0.905 on CASME II, making it one of the few frameworks in the published micro-expression recognition literature to achieve such high performance.
Extrinsic Calibration for a Modular 3D Scanning Quality Validation Platform with a 3D Checkerboard
Authors: Kaiser Mirko , Brusa Tobia , Bertsch Martin , Wyss Marco , Ćuković Saša , Meixner Gerrit , Koch Volker M.
Published: February 2024

Optical 3D scanning applications are increasingly used in various medical fields. Setups involving multiple adjustable systems require repeated extrinsic calibration between patients. Existing calibration solutions are either not applicable to the medical field or require a time-consuming process with multiple captures and target poses. Here, we present an application with a 3D checkerboard (3Dcb) for extrinsic calibration with a single capture. The 3Dcb application can register captures with a reference to validate measurement quality. Furthermore, it can register captures from camera pairs for point-cloud stitching of static and dynamic scenes. Registering static captures from TIDA-00254 to its reference from a Photoneo MotionCam-3D resulted in an error (root mean square error ± standard deviation) of 0.02 mm ± 2.9 mm. Registering a pair of Photoneo MotionCam-3D cameras for dynamic captures resulted in an error of 2.2 mm ± 1.4 mm. These results show that our 3Dcb implementation provides registration for static and dynamic captures that is sufficiently accurate for clinical use. The implementation is also robust and can be used with cameras with comparatively low accuracy. In addition, we provide an extended overview of extrinsic calibration approaches and the application’s code for completeness and service to fellow researchers.
Optoelectronic Torque Measurement System Based on SAPSO-RBF Algorithm
Authors: Xia Kun , Lou Yang , Yuan Qingqing , Zhu Benjing , Li Ruikai , Du Yao
Published: February 2024

The torque is a significant indicator reflecting the comprehensive operational characteristics of a power system. Thus, accurate torque measurement plays a pivotal role in ensuring the safety and stability of the system. However, conventional torque measurement systems predominantly rely on strain gauges adhered to the shaft, often leading to reduced accuracy, poor repeatability, and non-traceability due to the influence of strain gauge adhesion. To tackle the challenge, this paper introduces a photoelectric torque measurement system. Quadrants of photoelectric sensors are employed to capture minute deformations induced by torque on the rotational axis, converting them into measurable voltage. Subsequently, the system employs the radial basis function neural network optimized by simulated annealing combined with particle swarm algorithm (SAPSO-RBF) to establish a correlation between measured torque values and standard references, thereby calibrating the measured values. Experimental results affirm the system’s capability to accurately determine torque measurements and execute calibration, minimizing measurement errors to 0.92%.
Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks
Authors: Hembert Pierre , Ghnatios Chady , Cotton Julien , Chinesta Francisco
Published: February 2024

A deep geological repository for radioactive waste, such as Andra’s Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor’s integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network’s local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra’s Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra’s URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.
Searchable Blockchain-Based Healthcare Information Exchange System to Enhance Privacy Preserving and Data Usability
Authors: Lee Sejong , Kim Yushin , Cho Sunghyun
Published: February 2024

Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.
Pharmaceutical Screening of Bat Feces and Their Applications and Risks in Traditional Chinese Medicine
Authors: Chung Kou-Toung , Lin Ching-Lung , Chuang Wu-Chang , Lee Ming-Chung , Chen Li-Wen , Wu Chung-Hsin
Published: February 2024

Bat feces have been reported in traditional Chinese medicine (TCM) books to have the effect of reducing fever and improving eyesight, but the mechanism of vision improvement still needs further research. To this end, we used 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and liquid chromatography/tandem mass spectrometry (LC/MS/MS) to analyze the antioxidant capacity of and the types of vitamins in bat feces. We hoped to screen the pharmacological components of bat feces and to explain the role that these components may play in treating visual deterioration. Our results found that bat feces had a good antioxidant capacity and mainly contained vitamins B1 (thiamine), B2 (riboflavin), B3 (nicotinamide), B3 (nicotinic acid), and B5 (pantothenic acid). Although these vitamins may help to maintain the health of the optic nerve and cornea, the vitamin content of bat feces is low, but the heavy metal content is high, as shown using inductively coupled plasma–mass spectrometry (ICP-MS) analysis. Therefore, we suggest that the use of bat feces as TCM to improve vision should be strictly restricted.
Adapting for Well-Being: Examining Acculturation Strategies and Mental Health among Latina Immigrants
Authors: Bekteshi Venera , Bellamy Jennifer L.
Published: February 2024

This study investigates how assimilation and integration, two commonly studied acculturation forms, help immigrant Latinas cope with acculturative stress and related psychological distress. It employs the Ecological Framework for Understanding Immigration (EFUI), merging Bronfenbrenner’s Ecological Systems Theory and the Socioecological Model as a comprehensive approach. Through multigroup analysis, various factors significantly affect the psychological distress of immigrant Latinas, including financial constraints, contentment with migration decisions, age, and adherence to familial values. Acculturative stress relates to English proficiency, racial discrimination experiences, U.S. residency duration, and contentment with the migration choice. The moderating effects of assimilation and integration vary, influencing psychological distress and acculturative stress differently in each group. In conclusion, this study uncovers complex coping mechanisms used by immigrant Latina individuals facing acculturative stress and highlights the protective role of assimilation, the importance of familismo, the impact of financial constraints, and the significance of racial discrimination. This finding underscores the need for mental health interventions to respect and incorporate Latinx individuals’ cultural values and beliefs, promoting positive mental health outcomes.
Integrating United Nations Sustainable Development Goals in Soil Science Education
Authors: Mikhailova Elena A. , Post Christopher J. , Nelson Davis G.
Published: February 2024

The United Nations (UN) Sustainable Development Goals (SDGs) offer an opportunity to improve soil science education on sustainability because they provide specific context to educate faculty and students from various disciplines, including Science, Technology, Engineering, and Mathematics (STEM) about SDGs. Soil science is a STEM discipline with a wide range of applications in the SDGs. The objectives of this study were to use a matrix approach (framework for presenting options for discussion and implementation) to integrate SDGs into an existing introductory soil science course taught to undergraduate students from different STEM fields (environmental and natural resources; wildlife biology; and forestry). The course was enriched with a lecture on SDGs and students were asked to link soil properties and class activities to specific SDGs. A post-assessment survey revealed an increase in students’ familiarity with SDGs, and their relevance to soil properties and course activities. Students acknowledged the importance of soils and individual actions for achieving the SDGs. There was an overall increase in student familiarity (+59.4%) with SDGs. Most students agreed (46.7%) and strongly agreed (23.3%) that the course activities were an effective way to learn about SDGs with examples from soil science. Identified learning gaps in subject matter found through the surveys on SDGs were clarified during later classroom discussions. The advantage of this teaching approach is that it seamlessly integrates SDGs with existing course materials while relying on students’ critical thinking skills to effectively analyze soil science information and form a judgement on how it relates to SDGs.
Improving Mental Skills in Precision Sports by Using Neurofeedback Training: A Narrative Review
Authors: Corrado Stefano , Tosti Beatrice , Mancone Stefania , Di Libero Tommaso , Rodio Angelo , Andrade Alexandro , Diotaiuti Pierluigi
Published: February 2024

Primary objectives of neurofeedback training (NFT) are to improve concentration, stress and anxiety management, and performance optimisation. This narrative review examines the role of NFT as a tool to improve mental and cognitive skills of target shooting and archery athletes. Current research discusses how neurofeedback training can act on brain waves by influencing specific EEG frequency bands in order to improve cognitive flexibility. This contribution reports studies that have applied neurofeedback protocols in precision disciplines such as archery and shooting. The results of the studies considered showed that neurofeedback can lead to faster reaction times, more sustained attention, and better emotion management, contributing significantly to athletes’ performance. Furthermore, it is emphasised that neurofeedback could be combined with other techniques such as motor imagination to maximise effectiveness in precision sports training. This review emphasises the importance of future studies that focus on the integration of neurofeedback with biofeedback in neuromodulation protocols. Current perspectives and limitations of research in this area are also indicated. Neuromodulation by means of neurofeedback represents a promising strategy to improve the overall mental and cognitive abilities of target shooting and archery athletes with an interesting potential for high-level performance. Future research should focus on integrated approaches and customised protocols to optimise the use of neurofeedback in a precision sports context.
Understanding Constraints and Enablers of Climate Risk Management Strategies: Evidence from Smallholder Dairy Farmers in Regional South India
Authors: Shantharaju Anupama , Islam Md Aminul , Kath Jarrod M. , Mushtaq Shahbaz , Muniyappa Arun , Singh-Peterson Lila
Published: February 2024

The adoption of effective coping strategies is crucial for successful adaptation to the impacts of climate change in the dairy sector. However, little attention has been paid to understanding the perceived constraints and motivations toward such strategies. A survey was conducted among 104 dairy farmers from three semi-arid regions of South India. The aim of the survey was to explore the dairy farmers’ perception of climate risk, how it impacts their dairy farming system, the coping strategies they employ, and the barriers they face when implementing these strategies. The survey also investigated the factors that facilitate the adoption of adaptation measures. The results indicate dairy farmers in the region perceive drought, pests and diseases, and high temperatures as the major risks associated with climate change, which has resulted in decreased dairy income, animal health problems, reduced fertility, and food intake problems for their cattle. In response to climate variability, dairy farmers have adopted various coping strategies. The most important strategies include buying livestock insurance, keeping low debt obligations, and growing drought-tolerant grass varieties. However, most farmers face significant constraints in adopting these and other strategies including a lack of climate forecast data, the high cost of adaptation activities, and weak institutional support. On the other hand, the key enabling factors that support the adoption of these strategies include milk production security, suitable feed growing conditions, and family interest. Most importantly, the study found that certain factors such as age, education, number of earning family members, annual milk production, monthly cattle expenses, and landholdings significantly influenced dairy farmers’ strategies for adapting to climate change. The study recommends that providing timely climate forecasts, implementing improved policies such as vaccination and cattle health services, and establishing strong institutional support systems can help dairy farmers become more resilient to climate change and protect their livelihoods.
Does Quality Certification or Product Diversification Improve the Performance of Small and Medium Enterprises?
Authors: Le Duc-Niem , Nguyen Van-Hoa
Published: February 2024

This study utilizes a Cobb–Douglas production function and an instrumental variables regression approach to analyze the impact of quality certification and product diversification on asset productivity at the firm level. Analyzing a panel dataset of approximately 2500 firms from 2011, 2013, and 2015 showed that regional norms significantly influence adoption behaviors, which vary across industries and business types. The study reveals that firms employing computers and those with a larger scale are more inclined to adopt these strategies. The results showed that quality certification enhances asset productivity by approximately 26%, highlighting its positive role. In contrast, firms with diversified products exhibit about 18% lower asset productivity than mono-product firms, suggesting a trade-off inherent in diversification. The study concludes with policy recommendations emphasizing the promotion of quality certification while carefully considering the complexities and potential inefficiencies arising from product diversification strategies.
Biological and Chemical Vicissitudes in Soil Rhizosphere Arbitrated under Different Tillage, Residues Recycling and Oilseed Brassica-Based Cropping Systems
Authors: Jat Ram Swaroop , Singh Har Vir , Dotaniya Mohan Lal , Choudhary Ram Lal , Meena Mukesh Kumar , Rai Pramod Kumar
Published: February 2024

In this study, the impacts of long-term soil and crop management practices on crop productivity and soil health in oilseed brassica-based production systems were examined. Different tillage, crop residue recycling and cropping systems (fallow–mustard, cluster bean–mustard, green gram–mustard, maize–mustard, pearl millet–mustard and sesame–mustard) were studied for 5 years at two soil depths (0–15 and 15–30 cm) in a split-plot design with three replications. No-till permanent beds with crop residue (PB + R) noticeably improved soil organic carbon (SOC), microbial biomass carbon (MBC), enzymes (dehydrogenase (DHA) and alkaline phosphatase (AlP)), nitrogen fractions (available and total nitrate) and available phosphorus and potassium content in both soil layers compared to conventional tillage without crop residues. However, the plough soil layer (0–15 cm) showed higher concentrations of soil carbon, enzymes, N fractions and available P than in the subsoil (15–30 cm). The dynamic soil biological and chemical properties also varied with the crop stage, and higher MBC at 30 days, SOC and enzymatic activities at 60 days, and N fractions and available P and K during the harvesting of mustard crop were recorded. Green gram–mustard rotation showed higher values in terms of biological and chemical parameters. Thus, the legume-based mustard crop rotation following no-till permanent beds and residue recycling was found to be holistic in terms of improving soil health and nutrient cycling.