bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Application of a Remote Monitoring System for Agricultural Machinery Operation in Conservation Tillage
Authors: Luo Changhai , Chen Jingping , Guo Shuxia , An Xiaofei , Yin Yanxin , Wen Changkai , Liu Huaiyu , Meng Zhijun , Zhao Chunjiang
Published: September 2022

There is an increasing demand for remote monitoring and management of agricultural machinery operation in conservation tillage. Considering the problems of large errors in detecting operation quality parameters, such as tillage depth and corn straw cover rate, in complex farmland environments, this paper proposes a tillage depth measurement method based on the dual attitude compound of a tractor body and three-point hitch mechanism with lower pull rod and an online measurement method based on K-means clustering of the corn straw cover rate on farmland surface. An operation monitoring terminal was developed for the remote collection of quality parameters of conservation tillage field operation. A remote monitoring system of agricultural machinery operation was constructed and applied over a large area. The field tests showed that the static mean error and root-mean-square error of this method were 0.16 and 0.67 cm for uphill and 0.36 and 0.57 cm for downhill, respectively. For the 28 and 33 cm tillage depth tests, the mean dynamic measurement errors of this method were 0.55 and 0.61 cm, and the root means square errors were 0.64 and 0.73 cm, respectively, and the coefficient of variation of tillage depth did not exceed 3%. The correlation coefficient between the corn straw cover rate detection algorithm based on K-means clustering and the manual image marking method reached 0.92, with an average error of 9.69%, and the accuracy filled the demand for straw cover rate detection. The detection accuracy of tillage depth and straw cover rate was high and thus provides an effective means of information technology support for the quality monitoring and production management of conservation tillage farming operations.
Effect of Extraction Type on Bioactive Compounds and Antioxidant Activity of Moringa oleifera Lam. Leaves
Authors: Olvera-Aguirre Gabriel , Mendoza-Taco Miriam Marleny , Moo-Huchin Victor Manuel , Lee-Rangel Héctor Aarón , Roque-Jiménez José Alejandro , Gómez-Vázquez Armando , Dzib-Cauich Dany A. , Vargas-Bello-Pérez Einar , Chay-Canul Alfonso Juventino
Published: September 2022

The objective of this study was to determine the extraction yield, the content of bioactive compounds and in vitro antioxidant activity of different extracts from Moringa oleifera (MO) leaves, and their potential use as a natural feed additive for ruminant diets. For the preparation of MO extracts, three treatments were used: (1) MO extract in distilled water (MOEW, 0%), (2) ethanol-water (MOEEW, 50%) and (3) absolute ethanol (MOEE, 100%). The extraction yield and the antioxidant activity measured with the DPPH assay in the MO extracts were higher for MOEW and MOEEW. From all treatments, MOEEW had the highest antioxidant activity evaluated with the ABTS assay and showed a higher content of bioactive compounds. On the other hand, the principal component analysis showed that the first two principal components explained 96.5% of the variability of the data. The variables that contributed to the greatest variation were condensed tannins (CT), total phenolic compounds (TPC), total flavonoids (TF), and extraction yield. A high correlation (p ≤ 0.001) was observed between TPC and extraction yield with r2 = 0.989. The content of bioactive compounds and antioxidant activity was higher in the MOEEW extract; therefore, its inclusion in ruminant diets can be suggested to potentially improve their productivity and product quality.
Does the Adoption of Digital Payment Improve the Financial Availability of Farmer Households? Evidence from China
Authors: Chen Baozhen , Ren Jinzheng
Published: September 2022

Digital finance carries the expectation of achieving inclusiveness. The purpose of this paper is to explore how digital finance can improve the financial availability and the extent to which digital finance can improve the financial availability of farmer households. Based on micro-rural survey data in China from 2017 to 2019, employing the Cov-AHP weighting method, this study measured the index of financial availability (IFA) of farmer households in terms of three dimensions: investment, bank loans, and private finance. We analyzed the mechanism of how digital payment adoption affects the IFA of farmer households based on the Long Tail Effect theory of Anderson. Ordinary least squares method and ordered probit model was constructed to empirically test the impact of payment adoption on the IFA of farmer households. The research results show that (1) the IFA of Chinese rural households is still at a low level; (2) while the availability of investment is very low, the availability of bank loans is relatively high; and (3) the adoption of digital payment has a positive impact on improving the IFA of farmer households, including the availability of investments, bank loans, and private finance. The results are robust to model misspecification and reverse causality. The evidence also suggests that the adoption of digital payment mainly affects the financial availability of farmer households through information effects. Therefore, attention should be paid to broadening information channels and promoting the adoption of digital payments to improve financial access for farmer households. This study contributes to the comprehensive understanding of the financial situation of households by constructing a financial availability indicator system from three dimensions. By analyzing the impact of digital payment adoption on farmers’ financial availability, this study helps to understand how digital finance can play a positive role in farmer households’ financial conditions.
Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal
Authors: Nyasulu Chimango , Diattara Awa , Traore Assitan , Deme Abdoulaye , Ba Cheikh
Published: September 2022

To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather information and services are essential for farmers to more effectively survive the increasing occurrence of extreme weather events due to climate change. Weather information is important for resource management in agricultural production and helps farmers plan their farming activities in advance. Machine Learning is one of the technologies used in agriculture for weather forecasting and crop disease detection among others. The objective of this study is to develop Machine Learning-based models adapted to the context of daily weather forecasting for Rainfall, Relative Humidity, and Maximum and Minimum Temperature in Senegal. In this study, we made a comparison of ten Machine Learning Regressors with our Ensemble Model. These models were evaluated based on Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Coefficient of Determination. The results show that the Ensemble Model performs better than the ten base models. The Ensemble Model results for each parameter are as follows; Relative Humidity: Mean Absolute Error was 4.0126, Mean Squared Error was 29.9885, Root Mean Squared Error was 5.4428 and Coefficient of Determination was 0.9335. For Minimum Temperature: Mean Absolute Error was 0.7908, Mean Squared Error was 1.1329, Root Mean Squared Error was 1.0515 and Coefficient of Determination was 0.9018. For Maximum Temperature: Mean Absolute Error was 1.2515, Mean Squared Error was 2.8038, Root Mean Squared Error was 1.6591 and Coefficient of Determination was 0.8205. For Rainfall: Mean Absolute Error was 0.2142, Mean Squared Error was 0.1681, Root Mean Squared Error was 0.4100 and Coefficient of Determination was 0.7733. From the present study, it has been observed that the Ensemble Model is a feasible model to be used for Rainfall, Relative Humidity, and Maximum and Minimum Temperature forecasting.
Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop
Authors: Colovic Milica , Yu Kang , Todorovic Mladen , Cantore Vito , Hamze Mohamad , Albrizio Rossella , Stellacci Anna Maria
Published: September 2022

The deployment of novel technologies in the field of precision farming has risen to the top of global agendas in response to the impact of climate change and the possible shortage of resources such as water and fertilizers. The present research addresses the performance of water and nitrogen-sensitive narrow-band vegetation indices to evaluate the response of sweet maize (Zea mays var. saccharata L.) to different irrigation and nitrogen regimes. The experiment was carried out in Valenzano, Bari (Southern Italy), during the 2020 growing season. Three irrigation regimes (full irrigation, deficit irrigation, and rainfed) and two nitrogen levels (300 and 50 kg ha−1) were tested. During the growing season, a Field Spec Handheld 2 spectroradiometer operating in the range of 325–1075 nm was utilized to capture spectral data regularly. In addition, soil water content, biometric parameters, and physiological parameters were measured. The DATT index, based on near-infrared and red-edge wavelengths, performed better than other indices in explaining the variation in chlorophyll content, whereas the double difference index (DD) showed the greatest correlation with the leaf–gas exchange. The modified normalized difference vegetation index (NNDVI) and the ratio of water band index to normalized difference vegetation index (WBI/NDVI) showed the highest capacity to distinguish the interaction of irrigation x nitrogen, while the best discriminating capability of these indices was under a low nitrogen level. Moreover, red-edge-based indices had higher sensitivity to nitrogen levels compared to the structural and water band indices. Our study highlighted that it is critical to choose proper narrow-band vegetation indices to monitor the plant eco-physiological response to water and nitrogen stresses.
Transcriptome Analyses Provide Insights into the Auditory Function in Trachemys scripta elegans
Authors: Lu Ningning , Chen Bo , Qing Jiao , Lei Jinhong , Wang Tongliang , Shi Haitao , Wang Jichao
Published: September 2022

An auditory ability is essential for communication in vertebrates, and considerable attention has been paid to auditory sensitivity in mammals, birds, and frogs. Turtles were thought to be deaf for a long time; however, recent studies have confirmed the presence of an auditory ability in Trachemys scripta elegans as well as sex-related differences in hearing sensitivity. Earlier studies mainly focused on the morphological and physiological functions of the hearing organ in turtles; thus, the gene expression patterns remain unclear. In this study, 36 transcriptomes from six tissues (inner ear, tympanic membrane, brain, eye, lung, and muscle) were sequenced to explore the gene expression patterns of the hearing system in T. scripta elegans. A weighted gene co-expression network analysis revealed that hub genes related to the inner ear and tympanic membrane are involved in development and signal transduction. Moreover, we identified six differently expressed genes (GABRA1, GABRG2, GABBR2, GNAO1, SLC38A1, and SLC12A5) related to the GABAergic synapse pathway as candidate genes to explain the differences in sexually dimorphic hearing sensitivity. Collectively, this study provides a critical foundation for genetic research on auditory functions in turtles.
CoFee-L: A Model of Animal Displacement in Large Groups Combining Cohesion Maintenance, Feeding Area Search and Transient Leadership
Authors: Gavrilitchenko Nikita , Gazagne Eva , Vandewalle Nicolas , Delcourt Johann , Hambuckers Alain
Published: September 2022

In the tropics, the conservation of tree species is closely linked to that of animals, as a large proportion of trees are zoochoric and therefore rely on the movement of animals to disperse their seeds in order to increase the chances of the survival of progeny and to allow migration in the face of climate change. Research into the prediction of animal movements is therefore critical but has so far focused only on particular features of collective behavior. In contrast, we included the concepts of cohesion maintenance, feeding area search and transient leadership in a single model, CoFee-L, and tested it to simulate the movement of a wild-ranging troop of primates (Macaca leonina). We analyzed and compared observations and simulations with a statistical physics tool (mean squared displacement) and with histograms and χ2 (for the step length and turning angle distributions). CoFee-L allowed us to simulate the physical properties of the troop’s center of mass trajectory as well as the step length and angle distributions of the field data. The parametrization of CoFee-L was rather straightforward, as it was sufficient to fix a set of parameters easily observable in the field and then to adjust the values of four parameters that have biological meaning.
The Effects of Oncolytic Pseudorabies Virus Vaccine Strain Inhibited the Growth of Colorectal Cancer HCT-8 Cells In Vitro and In Vivo
Authors: Chai Chunxia , Zhang Jinlong , Zhou Yanyan , Yin Hua , Zhang Fan , Diao Yun , Zan Xiaohui , Ma Yanhua , Wang Yan , Wu Youzhi , Wang Wei
Published: September 2022

Oncolytic viral therapy is a promising treatment approach for a variety of tumor forms. Although a number of studies have demonstrated that the pseudorabies virus (PRV) may be applied as an oncolytic carrier, the anti-colorectal cancer impact of the virus and the mechanism of its cytotoxic effect remain elusive. In this study, the replication capacity and cell activity of PRV attenuated live vaccines Bartha K61 and HB98 in HCT-8 cells in vitro were investigated. Next, the antitumor ability and safety were evaluated in a mouse model of HCT-8 tumor transplantation. Both PRV strains were able to suppress tumor growth and HB98 showed higher safety and efficiency than the Bartha K61 strain. Finally, flow cytometry and immunohistochemistry examination were performed to investigate its possible cytotoxic mechanism. The results showed that PRV inhibited tumor proliferation both in vitro and in vivo by inducing apoptosis. In summary, our study discovered for the first time that the live attenuated PRV has an oncolytic effect on HCT-8 cells with high efficacy and safety.
Suppression of NRF2 Activity by HIF-1α Promotes Fibrosis after Ischemic Acute Kidney Injury
Authors: Bondi Corry D. , Rush Brittney M. , Hartman Hannah L. , Wang Jiaxuan , Al-Bataineh Mohammad M. , Hughey Rebecca P. , Tan Roderick J.
Published: September 2022

Acute kidney injury (AKI) is a rapid decline in renal function and can occur after ischemia/reperfusion injury (IRI) to the tubular epithelia. The nuclear factor erythroid-2-related factor 2 (NRF2) pathway protects against AKI and AKI-to-chronic kidney disease (CKD) progression, but we previously demonstrated that severe IRI maladaptively reduced NRF2 activity in mice. To understand the mechanism of this response, we subjected C57BL/6J mice to unilateral kidney IRI with ischemia times that were titrated to induce mild to severe injury. Mild IRI increased NRF2 activity and was associated with renal recovery, whereas severe IRI decreased NRF2 activity and led to progressive CKD. Due to these effects of ischemia, we tested the hypothesis that hypoxia-inducible factor-1α (HIF-1α) mediates NRF2 activity. To mimic mild and severe ischemia, we activated HIF-1α in HK-2 cells in nutrient-replete or nutrient-deficient conditions. HIF-1α activation in nutrient-replete conditions enhanced NRF2 nuclear localization and activity. However, in nutrient-deficient conditions, HIF-1α activation suppressed NRF2 nuclear localization and activity. Nuclear localization was rescued with HIF-1α siRNA knockdown. Our results suggest that severe ischemic AKI leads to HIF-1α-mediated suppression of NRF2, leading to AKI-to-CKD progression.
Methanol Extract of Commelina Plant Inhibits Oral Cancer Cell Proliferation
Authors: Liu Wangta , Hsu Yin-Yin , Tang Jen-Yang , Cheng Yuan-Bin , Chuang Ya-Ting , Jeng Jiiang-Huei , Yen Chia-Hung , Chang Hsueh-Wei
Published: September 2022

Data regarding the effects of crude extract of Commelina plants in oral cancer treatment are scarce. This present study aimed to assess the proliferation-modulating effects of the Commelina sp. (MECO) methanol extract on oral cancer cells in culture, Ca9-22, and CAL 27. MECO suppressed viability to a greater extent in oral cancer cells than in normal cells. MECO also induced more annexin V, apoptosis, and caspase signaling for caspases 3/8/9 in oral cancer cells. The preferential antiproliferation and apoptosis were associated with cellular and mitochondrial oxidative stress in oral cancer cells. Moreover, MECO also preferentially induced DNA damage in oral cancer cells by elevating γH2AX and 8-hydroxyl-2′-deoxyguanosine. The oxidative stress scavengers N-acetylcysteine or MitoTEMPO reverted these preferential antiproliferation mechanisms. It can be concluded that MECO is a natural product with preferential antiproliferation effects and exhibits an oxidative stress-associated mechanism in oral cancer cells.
Transformer-Based Multilingual Speech Emotion Recognition Using Data Augmentation and Feature Fusion
Authors: Al-onazi Badriyya B. , Nauman Muhammad Asif , Jahangir Rashid , Malik Muhmmad Mohsin , Alkhammash Eman H. , Elshewey Ahmed M.
Published: September 2022

In recent years data science has been applied in a variety of real-life applications such as human-computer interaction applications, computer gaming, mobile services, and emotion evaluation. Among the wide range of applications, speech emotion recognition (SER) is also an emerging and challenging research topic. For SER, recent studies used handcrafted features that provide the best results but failed to provide accuracy while applied in complex scenarios. Later, deep learning techniques were used for SER that automatically detect features from speech signals. Deep learning-based SER techniques overcome the issues of accuracy, yet there are still significant gaps in the reported methods. Studies using lightweight CNN failed to learn optimal features from composite acoustic signals. This study proposed a novel SER model to overcome the limitations mentioned earlier in this study. We focused on Arabic vocal emotions in particular because they received relatively little attention in research. The proposed model performs data augmentation before feature extraction. The 273 derived features were fed as input to the transformer model for emotion recognition. This model is applied to four datasets named BAVED, EMO-DB, SAVEE, and EMOVO. The experimental findings demonstrated the robust performance of the proposed model compared to existing techniques. The proposed SER model achieved 95.2%, 93.4%, 85.1%, and 91.7% accuracy on BAVED, EMO-DB, SAVEE, and EMOVO datasets respectively. The highest accuracy was obtained using BAVED dataset, indicating that the proposed model is well suited to Arabic vocal emotions.
Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
Authors: Wang Xuan , Lyu Haiyang , He Weiji , Chen Qian
Published: September 2022

The LiDAR point cloud has been widely used in scenarios of automatic driving, object recognition, structure reconstruction, etc., while it remains a challenging problem in line structure extraction, due to the noise and accuracy, especially in data acquired by consumer electronic devices. To address the issue, a line structure extraction method based on the persistence of tensor feature is proposed, and subsequently applied to the data acquired by an iPhone-based LiDAR sensor. The tensor of each point is encoded, voted, and aggregated by its neighborhood, and further decomposed into different geometric features in each dimension. Then, the line feature in the point cloud is represented and computed using the persistence of the tensor feature. Finally, the line structure is extracted based on the persistent homology according to the discrete Morse theory. With the LiDAR point cloud collected by the iPhone 12 Pro MAX, experiments are conducted, line structures are extracted from two different datasets, and results perform well in comparison with other related results.
Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
Authors: Zhu Xin-Cheng , Zhao Deng-Huang , Zhang Yi-Hua , Zhang Xiao-Jun , Tao Zhi
Published: September 2022

Due to the complexity and non-stationarity of the voice generation system, the nonlinearity of speech signals cannot be accurately quantified. Recently, the recurrence quantification analysis method has been used for voice disorder detection. In this paper, multiscale recurrence quantification measures (MRQMs) are proposed. The signals are reconstructed in the high-dimensional phase space at the equivalent rectangular bandwidth scale. Recurrence plots (RPs) combining the characteristics of human auditory perception are drawn with an appropriate recurrence threshold. Based on the above, the nonlinear dynamic recurrence features of the speech signal are quantized from the recurrence plot of each frequency channel. Furthermore, this paper explores the recurrence quantification thresholds that are most suitable for pathological voices. Our results show that the proposed MRQMs with support vector machine (SVM), random forest (RF), Bayesian network (BN) and Local Weighted Learning (LWL) achieve an average accuracy of 99.45%, outperforming traditional features and other complex measurements. In addition, MRQMs also have the potential for multi-classification of voice disorder, achieving an accuracy of 89.05%. This study demonstrates that MRQMs can characterize the recurrence characteristic of pathological voices and effectively detect voice disorders.
Optimized-Based Fault-Tolerant Control of an Electro-Hydraulic System with Disturbance Rejection
Authors: Phan Van Du , Ahn Kyoung Kwan
Published: September 2022

In this article, the design and implementation of a fault-tolerant controller are proposed for an electro-hydraulic actuator (EHA) in the presence of disturbances and actuator faults. The existence of nonlinearities, uncertainties, and a bias fault (i.e., internal leakage fault) in the system dynamics significantly decreases the desired performance. The nonlinear disturbance observers (NDO) are constructed to handle the adverse influences caused by the above disadvantages. The whole fault-tolerant control (FTC) scheme consists of two design loops: an inner force control loop and an outer position control loop. The inner loop is based on an optimized backstepping framework to achieve the optimal performance, whilst the problem of uncertainties and disturbances is dealt with using a terminal sliding mode directly designed from the position tracking error. It is shown by theoretical analysis that system stability is ensured under faulty conditions. Finally, simulation results and comparison studies are conducted to further verify the effectiveness of the proposed approach.
PickingDK: A Framework for Industrial Bin-Picking Applications
Authors: Ojer Marco , Lin Xiao , Tammaro Antonio , Sanchez Jairo R.
Published: September 2022

This work presents an industrial bin-picking framework for robotics called PickingDK. The proposed framework employs a plugin based architecture, which allows it to integrate different types of sensors, robots, tools, and available open-source software and state-of-the-art methods. It standardizes the bin-picking process with a unified workflow based on generally defined plugin interfaces, which promises the hybridization of functional/virtual plugins for fast prototyping and proof-of-concept. It also offers different levels of controls according to the user’s expertise. The presented use cases demonstrate flexibility when building bin-picking applications under PickingDK framework and the convenience of exploiting hybrid style prototypes for evaluating specific steps in a bin-picking system, such as parameter fine-tuning and picking cell design.
Frequency Response Analysis: An Enabling Technology to Detect Internal Faults within Critical Electric Assets
Authors: Al-Ameri Salem Mgammal , Alawady Ahmed Allawy , Abdul-Malek Zulkurnain , Noorden Zulkarnain Ahmad , Yousof Mohd Fairouz Mohd , Salem Ali Ahmed , Mosaad Mohamed Ibrahim , Abu-Siada Ahmed
Published: September 2022

Frequency Response Analysis (FRA) technique has been recognized by worldwide utilities as a matured technology to assess the mechanical integrity of power transformers. While some industrial critical assets such as induction motors have the same construction principle as power transformers, the application of FRA technique to induction motors has not yet been fully explored. This paper presents analogical experimental studies for the application of FRA on power transformers and induction motors. For a consistent analogy, the FRA technique has been employed to detect short and open circuit turns in both appliances, which helps explore a wider scope of the FRA applications on rotating machines. In this regard, experimental FRA measurements are performed on an 11/0.415 kV, 500 kVA, three-phase distribution transformer and a 5.5 HP three-phase induction motor. Several short and open circuit faults are staged on the windings of both tested equipment and the FRA signature is recorded and compared with the reference signature at no fault. To quantify the impact of faults on the FRA signature, several statistical indicators are used and threshold limits for these indicators are proposed to automate the interpretation process. Results reveal a good correlation between the FRA signatures of induction motors and power transformers that attests to the feasibility of using FRA technique to detect various faults within large rotating machines.
Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning
Authors: Oh Dongsuk , Lim Jungwoo , Park Kinam , Lim Heuiseok
Published: September 2022

The commonsense question and answering (CSQA) system predicts the right answer based on a comprehensive understanding of the question. Previous research has developed models that use QA pairs, the corresponding evidence, or the knowledge graph as an input. Each method executes QA tasks with representations of pre-trained language models. However, the ability of the pre-trained language model to comprehend completely remains debatable. In this study, adversarial attack experiments were conducted on question-understanding. We examined the restrictions on the question-reasoning process of the pre-trained language model, and then demonstrated the need for models to use the logical structure of abstract meaning representations (AMRs). Additionally, the experimental results demonstrated that the method performed best when the AMR graph was extended with ConceptNet. With this extension, our proposed method outperformed the baseline in diverse commonsense-reasoning QA tasks.
CSECMAS: An Efficient and Secure Certificate Signing Based Elliptic Curve Multiple Authentication Scheme for Drone Communication Networks
Authors: Li Lin , Lian Xiaofeng , Wang Yulong , Tan Li
Published: September 2022

With the rapid development of communication technology, unmanned aerial vehicles (UAVs) have been widely used in the military, rescue, agricultural and other fields. The security and privacy issues are one of the biggest challenges for UAV communications. In this paper, we propose a certificate signing based on an Elliptic Curve multiple authentication scheme (CSECMAS). The scheme employs multiple authentication tools to ensure the legitimacy of the identity and the integrity of the information. We treat multiple UAVs as a cluster, and after establishing a secure session between the ground control station (GCS) and the UAV, we use the fitness function to select one node from them as the key node. Mutual authentication between the UAVs through the key node establishes secure conversations while increasing the security and resistance to destruction of the entire communication network. The proposed protocol is formally analyzed and verified by using the model-checking tool SPIN. The informal analysis shows that the protocol can resist various attacks, such as man-in-the-middle attacks, replay attacks, and DoS attacks. The performance analysis shows that the proposed protocol is superior compared to other related protocols.
Hybridization of DRASTIC Method to Assess Future GroundWater Vulnerability Scenarios: Case of the Tebessa-Morsott Alluvial Aquifer (Northeastern Algeria)
Authors: Boufekane Abdelmadjid , Belloula Moufida , Busico Gianluigi , Drias Tarek , Reghais Azzeddine , Maizi Djamel
Published: September 2022

In this study, a new approach integrating a groundwater vulnerability method and a numerical model for predicting groundwater resource sustainability under actual and future conditions of exploitation (2010–2030) is proposed in the semi-arid region of the Tebessa-Morsott alluvial aquifer (northeastern Algeria). The groundwater vulnerability method-based DRASTIC model was used to evaluate and delineate the vulnerable areas using a GIS technique. The MODFLOW code, on the other hand, was used to calculate the dynamics of groundwater level under actual and future conditions of exploitation considering two scenarios. The results of the application of the DRASTIC method to the reference year conditions (year 2010) showed that the high and average vulnerability classes covered a wide zone of the study area, about 97%. These results were validated based on the nitrate concentration values (R2 = 0.955). However, the results for predicting future groundwater vulnerability showed that groundwater vulnerability variation over time (period 2010–2030) was closely related to groundwater depth variation caused by the pumping rate, since the decreases in the piezometric level produce a worsening of groundwater vulnerability. To achieve better groundwater management, an experimental site for artificial recharge supplemented by hydro-chemical monitoring of the groundwater could be an effective remediation strategy.
Combining UNet 3+ and Transformer for Left Ventricle Segmentation via Signed Distance and Focal Loss
Authors: Liu Zhi , He Xuelin , Lu Yunhua
Published: September 2022

Left ventricle (LV) segmentation of cardiac magnetic resonance (MR) images is essential for evaluating cardiac function parameters and diagnosing cardiovascular diseases (CVDs). Accurate LV segmentation remains a challenge because of the large differences in cardiac structures in different research subjects. In this work, a network based on an encoder–decoder architecture for automatic LV segmentation of short-axis cardiac MR images is proposed. It combines UNet 3+ and Transformer to jointly predict the segmentation masks and signed distance maps (SDM). UNet 3+ can extract coarse-grained semantics and fine-grained details from full scales, while a Transformer is used to extract global features from cardiac MR images. It solves the problem of low segmentation accuracy caused by blurred LV edge information. Meanwhile, the SDM provides a shape-aware representation for segmentation. The performance of the proposed network is validated on the 2018 MICCAI Left Ventricle Segmentation Challenge dataset. The five-fold cross-validation evaluation was performed on 145 clinical subjects, and the average dice metric, Jaccard coefficient, accuracy, and positive predictive value reached 0.908, 0.834, 0.979, and 0.903, respectively, showing a better performance than that of other mainstream ones.
Adsorption of Emerging Pollutant by Pecan Shell-Based Biosorbent
Authors: Cordeiro Sabrina Grando , Weber Ani Caroline , Costa Bruna , Dahmer Bruno Rampanelli , Kuhn Daniel , Ethur Eduardo Miranda , Corbellini Valeriano Antonio , Hoehne Lucélia
Published: September 2022

The insertion of antibiotics in water resources results from anthropogenic sources; however, at residual concentrations, they characterize potential risks to the ecosystem, such as the emergence of multi-resistant bacteria. It is necessary to develop technologies to provide sustainable solutions for low- and middle-income countries. Thus, the present study aims to evaluate the ability to remove the antibiotic ciprofloxacin (CPX) with a biosorbent produced with pecan shells (PSB). The PSB structure was determined by scanning electron microscopy and spectroscopy in the infrared region by Fourier Transform. For adsorption assays, solutions of 10 mg L−1 of CPX were used. The results show that the process reaches equilibrium at 240 min, and follows the pseudo-second order model kinetic and the Freundlich equilibrium model. The increase in temperature and the pH variation of the solution strongly influence the process. In general, the adsorption of CPX using PS is a potential method for treating water and contaminated effluents, as well as being a low-cost method; this is because it uses a byproduct from the agricultural industry that results in a reduction of approximately 60% of the antibiotic load contained in the liquid effluent.
Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
Authors: Calabrese Francesca , Regattieri Alberto , Bortolini Marco , Galizia Francesco Gabriele
Published: September 2022

The pervasive digital innovation of the last decades has led to a remarkable transformation of maintenance strategies. The data collected from machinery and the extraction of valuable information through machine learning (ML) have assumed a crucial role. As a result, data-driven predictive maintenance (PdM) has received significant attention from academics and industries. However, practical issues are limiting the implementation of PdM in manufacturing plants. These issues are related to the availability, quantity, and completeness of the collected data, which do not contain all machinery health conditions, are often unprovided with the contextual information needed by ML models, and are huge in terms of gigabytes per minute. As an extension of previous work by the authors, this paper aims to validate the methodology for streaming fault and novelty detection that reduces the quantity of data to transfer and store, allows the automatic collection of contextual information, and recognizes novel system behaviors. Five distinct datasets are collected from the field, and results show that streaming and incremental clustering-based approaches are effective tools for obtaining labeled datasets and real-time feedback on the machinery’s health condition.
A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell
Authors: Dang Wei , Guo Jialiang , Liu Mingzhe , Liu Shan , Yang Bo , Yin Lirong , Zheng Wenfeng
Published: September 2022

At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfactory data collection is relatively cumbersome, and it is more difficult to collect labeled data. However, in many scenarios, to use a small amount of labeled data to train a good-performing classifier, it is not feasible to rely only on supervised learning algorithms, but semi-supervised learning algorithms can better cope with only a small amount of labeled data and a large amount of unlabeled data. This study combines the new weighted kernel with SKELM and proposes a semi-supervised extreme learning machine algorithm based on the weighted kernel, SELMWK. The experimental results show that the proposed SELMWK algorithm has good classification performance and can solve the semi-supervised gas classification task of the same domain data well on the used dataset.
Adaptive Control Strategy of a 2-Axes Gimbal Structure Loading Device for a Stair-Climbing Delivery Robot
Authors: Sim Yeri , Jin Sangrok
Published: September 2022

This paper studies an adaptive control strategy for a 2-axes gimbal structure loading device for a stair-climbing delivery robot. The properties of the loading device change depending on the luggage, and the loaded luggage cannot be specified. It is difficult to design an appropriate controller with fixed PID gains. To optimize the controller regardless of luggage condition, the adaptive control strategy is conducted by system identification and gain scheduling. The system identification estimates the parameters of the dynamic equations. The gain scheduler optimizes the PID controller using the estimated parameters. The system identification technique is based on the least-squares method. The accuracy of system identification is improved by a null-space solution. The gain scheduler consists of surface functions defined by interpolation of the optimized PID gains. The system identification techniques are verified by simulation, comparing a specific system assumed as a real system and the estimated system. The experiments using the motion platform verify the adaptive control strategy. This proposed control strategy adapts the controller to the system of the loading device.
Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
Authors: Ahmad Ashfaq , Faisal Farooq , Ullah Sadiq , Choi Dong-You
Published: September 2022

This paper presents the design of three types of dual band (2.5 & 5.2 GHz) wearable microstrip patch antennas. The first one is based on a conventional ground plane, whereas the other two antennas are based on two different types of two-dimensional electromagnetic band gap (EBG) structures. The design of these two different dual-band EBG structures using wearable substrates incorporates several factors in order to improve the performance of the proposed conventional ground plane (dual band) wearable antenna. The second EBG with plus-shaped slots is about 22.7% more compact in size relative to the designed mushroom-like EBG. Subsequently, we have demonstrated that the mushroom-like EBG and the EBG with plus-shaped slots improve the bandwidth by 5.2 MHz and 7.9 MHz at lower resonance frequencies and by 33.6 MHz and 16.7 MHz at higher resonance frequencies, respectively. Furthermore, improvements in gain of 4.33% and 16.5% at a frequency of 2.5 GHz and improvements in gain of 30.43% and 4.57% at 5.2 GHz have been achieved by using the mushroom-like EBG and EBG with plus-shaped slots, respectively. The operation of the conventional ground plane antenna is investigated under different bending conditions, such as wrapped around different rounded body parts. The proposed conventional ground plane antenna is placed over a three-layered (flat body phantom (chest)) and four-layered (rounded body parts) tissue models, and a thorough SAR analysis has been performed. It is concluded that the proposed antenna reduces SAR effects (<2 W/kg) on the human body, thereby making it useful for numerous critical wearable applications.
Optical Bullets and Their Modulational Instability Analysis
Authors: Al-Ghafri Khalil S. , Krishnan Edamana V. , Khan Salam , Biswas Anjan
Published: September 2022

The current work is devoted to investigating the multidimensional solitons known as optical bullets in optical fiber media. The governing model is a (3+1)-dimensional nonlinear Schrödinger system (3D-NLSS). The study is based on deriving the traveling wave reduction from the 3D-NLSS that constructs an elliptic-like equation. The exact solutions of the latter equation are extracted with the aid of two analytic approaches, the projective Riccati equations and the Bernoulli differential equation. Upon applying both methods, a plethora of assorted solutions for the 3D-NLSS are created, which describe mixed optical solitons having the profiles of bright, dark, and singular solitons. Additionally, the employed techniques provide several kinds of periodic wave solutions. The physical structures of some of the derived solutions are depicted to interpret the nature of the medium characterized by the 3D-NLSS. In addition, the modulation instability of the discussed model is examined by making use of the linear stability analysis.
What Do Prescribers of Bone Modifying Agents Know about Medication-Related Osteonecrosis of the Jaw? Is Current Prevention Enough?
Authors: Golu Mihai Vlad , Pașcanu Ionela , Togănel Cornelia , Petrovan Cecilia , Cosarcă Adina , Bereczki Temistocle Despina Luciana , Ormenișan Alina
Published: September 2022

Osteonecrosis of the jaw represents interference by external and internal factors in the natural bone remodeling system. Numerous bone remodeling agents (BMAs), such as bisphosphonates, denosumab, and tyrosine kinase inhibitors, can lead to medication-related osteonecrosis of the jaw (MRONJ). This is a serious condition that ocurs as a side-effect of treatment in patients with osteoporosis or malignancies. Antiresorptive drugs are medications that target osteoclasts with the aim of preventing bone resorption and are used to treat osteoporosis, osteopenia, and a variety of other conditions, such as Paget’s disease. They are also used in cancer patients with active bone metastases where antiresorptive treatment is used for the prevention of skeletal complications. Poor dental health, infections, and especially dental surgery are the primary causes of MRONJ, while other risk factors, such as smoking, alcohol abuse, and diabetes mellitus, can also influence its development. Prevention is the key component of management, and thus awareness of the risk factors among prescribers is very important. The aim of our study was to evaluate current knowledge about MRONJ among BMA prescribers in an academic hospital and their awareness about oral health and dental check-ups. By using a custom-designed questionnaire addressed to general and internal medical practitioners, endocrinologists, rheumatologists, and oncologists as an instrument for collecting data, we tried to identify trends in BMA prescription among different specialists and their recommended preventative measures, with the aim of creating new strategies to prevent the occurrence of MRONJ. The survey revealed a low awareness among physicians of the potential risk factors and underlined the need for a concerted effort to improve patient management. In this sense, a multidisciplinary team approach that includes the patient, the drug prescriber, the dentist, and the oral surgeon could significantly improve the quality of life of patients with MRONJ.
Design and Analysis of Dual-Band High-Gain THz Antenna Array for THz Space Applications
Authors: Shihzad Waleed , Ullah Sadiq , Ahmad Ashfaq , Abbasi Nisar Ahmad , Choi Dong-you
Published: September 2022

In this paper, a high-gain THz antenna array is presented. The array uses a polyimide substrate with a thickness of 10 μm, a relative permittivity of 3.5, and an overall volume of 2920 μm × 1055 μm × 10 μm, which can be employed for THz band space communication and other interesting applications. The dual-band single-element antenna is designed in four steps, while operating at 0.714 and 0.7412 THz with −10 dB bandwidths of 4.71 and 3.13 GHz, providing gain of 5.14 and 5 dB, respectively. In order to achieve a high gain, multiple order antenna arrays are designed such as the 2 × 1 antenna array and the 4 × 1 antenna array, named type B and C, respectively. The gain and directivity of the proposed type C THz antenna array are 12.5 and 11.23 dB, and 12.532 and 11.625 dBi at 0.714 and 0.7412 THz, with 99.76 and 96.6% radiation efficiency, respectively. For justification purposes, the simulations of the type B antenna are carried out in two simulators such as the CST microwave studio (CSTMWS) and the advance design system (ADS), and the performance of the type B antenna is compared with an equivalent circuit model on the bases of return loss, resulting in strong agreement. Furthermore, the parametric analysis for the type C antenna is done on the basis of separation among the radiating elements in the range 513 to 553 μm. A 64 × 1 antenna array is used to achieve possible gains of 23.8 and 24.1 dB, and directivity of 24.2 and 24.5 dBi with good efficiencies of about 91.66 and 90.35% at 0.7085 and 0.75225 THz, respectively, while the 128 × 1 antenna array provides a gain of 26.8 and 27.2 dB, and directivity of 27.2 and 27.7 dBi with good efficiency of 91.66 and 90.35% at 0.7085 and 0.75225 THz, respectively. All the results achieved in this manuscript ensure the proposed design is a feasible candidate for high-speed and free space wireless communication systems.
Research on Morphology Detection of Metal Additive Manufacturing Process Based on Fringe Projection and Binocular Vision
Authors: Wang Min , Zhang Qican , Li Qian , Wu Zhoujie , Chen Chaowen , Xu Jin , Xue Junpeng
Published: September 2022

This paper considers the three-dimensional (3D) shape measurement of metal parts during an additive manufacturing process in a direct energy deposition (DED) printing system with high temperature and strong light; a binocular measurement system based on ultraviolet light source projection is built using fringe projection and Fourier analysis. Firstly, ultraviolet light projection and an optical filter are used to obtain high-quality fringe patterns in an environment with thermal radiation. Then, Fourier analysis is carried out by using a single deformed fringe, and a spatial phase unwrapping algorithm is applied to obtain an unambiguous unwrapping phase, which is used as the guiding basis for the binocular matching process and 3D shape reconstruction. Finally, the accuracy of the measuring system is evaluated using a standard ball-bar gauge and the measurement error of this system is within 0.05 mm @ 100 × 100 mm. The results show that the system can measure 3D shape changes of metal parts in the additive manufacturing process. The proposed method and system have the potential to be used for online inspection and quality control of additive manufacturing.
Evaluation of GNSS Radio Occultation Profiles in the Vicinity of Atmospheric Rivers
Authors: Murphy Michael J. , Haase Jennifer S.
Published: September 2022

Increasing the density of Global Navigation Satellite System radio occultation (RO) with commercial Smallsats and the next generation COSMIC-2 constellation is expected to improve analyses of the state of atmosphere, which is essential for numerical weather prediction. High vertical resolution RO profiles could be useful to observe atmospheric rivers (ARs) over the ocean, which transport water vapor in shallow, elongated corridors that frequently impact the west coasts of continents. The multi-year AR Reconnaissance campaign has extensively sampled ARs over the northeastern Pacific with dropsondes, providing an invaluable dataset to evaluate the reliability of RO retrievals. These dropsondes, and a reanalysis product that assimilates them, are compared to three RO datasets: (1) established operational missions, (2) COSMIC-2, and (3) the commercial Spire constellation. Each RO dataset has biases relative to reanalyses of less than 0.5% N in the upper troposphere and negative biases in the lower troposphere. Direct colocations with dropsondes indicate that vertical refractivity gradients present within ARs may be contributing to negative biases at higher altitudes inside than outside ARs, where the greatest variability and vertical gradients are at the well-defined boundary layer top. Observations from Spire are overly smooth, affecting the ability to resolve the low-level structure of an AR. Surprisingly, the depth of penetration into the lower troposphere is greater inside an AR than outside for all datasets. The results indicate that the observation errors used for assimilation of RO within ARs should consider the height dependent biases that are associated with the structure of the atmosphere.