bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Implementation of a Personalizable Alternative Mouse and Keyboard Interface for Individuals with Limited Upper Limb Mobility
Authors: Andreas Daniel , Six Hannah , Bliek Adna , Beckerle Philipp
Published: November 2022

People with neuromuscular diseases often experience limited upper limb mobility, which makes the handling of standard computer mice and keyboards difficult. Due to the importance of computers in private and professional life, this work aims at implementing an alternative mouse and keyboard interface that will allow for their efficient use by people with a neuromuscular disease. Due to the strongly differing symptoms of these diseases, personalization on the hardware and software levels is the focus of our work. The presented mouse alternative is based on a spectacle frame with an integrated motion sensor for head tracking, which enables the control of the mouse cursor position; the keyboard alternative consists of ten keys, which are used to generate word suggestions for the user input. The interface was tested in a user study involving three participants without disabilities, which showed the general functionality of the system and potential room for improvement. With an average throughput of 1.56 bits per second achieved by the alternative mouse and typing speeds of 8.44 words per minute obtained using the alternative keyboard, the proposed interface could be a promising input device for people with limited upper limb mobility.
Anaemia and Nutritional Status during HIV and Helminth Coinfection among Adults in South Africa
Authors: Mpaka-Mbatha Miranda N. , Naidoo Pragalathan , Islam Md Mazharul , Singh Ravesh , Mkhize-Kwitshana Zilungile L.
Published: November 2022

Sub-Saharan Africa is burdened with helminthiasis and HIV/AIDS, and there is a significant overlap between these infections. However, little is known about the extent of anaemia and malnutrition in HIV/AIDS and helminth coinfected adults. The study investigated the anaemia profiles and nutritional status of HIV and helminth coinfected adult South Africans. Stool samples were collected from participants (N = 414) for parasite detection using the Kato–Katz and Mini Parasep® SF techniques. Blood was collected to determine participants’ HIV status, micro- and macronutrients, haematological parameters, and Ascaris lumbricoides-specific IgE and IgG4 levels. Thereafter, participants were stratified into single infection (HIV or helminths), coinfection, and uninfected controls (no HIV and helminth) groups. The majority (74.9%) of participants had CD4 counts of >500 cells/μL, indicating no significant immunosupression. The coinfected group had an overall anaemia prevalence of 16.9%, which was lower than that of the HIV-infected group (44.6%) and higher than helminth infected group (15.4%). Overall helminth prevalence was 33%, with Ascaris lumbricoides being the most prevalent. The coinfected group also had lower vitamin A (p = 0.0107), calcium (p = 0.0002), and albumin (p < 0.0001) levels compared to HIV/helminth uninfected controls. Unexpectedly, the coinfected group had the highest serum iron levels, followed by the helminth-infected and control groups, both of which had similar iron levels, and finally, the HIV-infected group, which had the lowest iron levels (p = 0.04). Coinfected adults may be prone to micronutrient deficiency and anaemia. Further research and intervention programmes are required in this neglected field.
The Impact of the COVID-19 Lockdown on Sugar-Sweetened Beverage Consumption in Children in Saudi Arabia: A Mixed-Methods Study
Authors: Baghlaf Khlood , Bormah Dania , Hakami Anwar , Bagher Sara M.
Published: November 2022

(1) Background: In 2020, there seems to have been a global shift in lifestyle and eating habits with the emergence of the coronavirus disease 2019 (COVID-19) and the ensuing lockdowns implemented by national governments. This study assessed the impact of the COVID-19 lockdown on SSBs consumption among healthy 6–11 years old children in Saudi Arabia; (2) Methods: This is a mixed-methods study, incorporating a quantitative component, which was a validated Arabic online questionnaire completed by parents, and a qualitative component, involving structured interviews with 10 selected parents using a criterion sampling method; (3) Results: There was a small decrease in consumption reported during lockdown across all SSBs types (soft drinks, n = 58 (13.9%); juices: n = 115 (27.6%); flavored milk: n = 93 (22.3%)). The results showed that with every increase in the dental pain scale there was a positive odd (AOR:0.64; p = 0.001) of decreased consumption of SSBs. Several themes related to increase and decrease SSBs consumption emerged; (4) Conclusions: There was a small decrease in SSBs consumption during lockdown reported by parents. Several themes emerged that can be used to strategize against problematic eating behavior, enabling such provisions as family dietary interventions, which target both parents and children.
Ketogenic Diets in the Management of Lennox-Gastaut Syndrome—Review of Literature
Authors: Skrobas Urszula , Duda Piotr , Bryliński Łukasz , Drożak Paulina , Pelczar Magdalena , Rejdak Konrad
Published: November 2022

Epilepsy is an important medical problem with approximately 50 million patients globally. No more than 70% of epileptic patients will achieve seizure control after antiepileptic drugs, and several epileptic syndromes, including Lennox-Gastaut syndrome (LGS), are predisposed to more frequent pharmacoresistance. Ketogenic dietary therapies (KDTs) are a form of non-pharmacological treatments used in attempts to provide seizure control for LGS patients who experience pharmacoresistance. Our review aimed to evaluate the efficacy and practicalities concerning the use of KDTs in LGS. In general, KDTs are diets rich in fat and low in carbohydrates that put the organism into the state of ketosis. A classic ketogenic diet (cKD) is the best-evaluated KDT, while alternative KDTs, such as the medium-chain triglyceride diet (MCT), modified Atkins diet (MAD), and low glycemic index treatment (LGIT) present several advantages due to their better tolerability and easier administration. The literature reports regarding LGS suggest that KDTs can provide ≥50% seizure reduction and seizure-free status in a considerable percentage of the patients. The most commonly reported adverse effects are constipation, diarrhea, and vomiting, while severe adverse effects such as nephrolithiasis or osteopenia are rarely reported. The literature review suggests that KDTs can be applied safely and are effective in LGS treatment.
Altered Gastric Microbiota and Inflammatory Cytokine Responses in Patients with Helicobacter pylori-Negative Gastric Cancer
Authors: Kim Han-Na , Kim Min-Jeong , Jacobs Jonathan P. , Yang Hyo-Joon
Published: November 2022

The role of the gastric mucosal microbiome in Helicobacter pylori-negative gastric cancer (GC) remains unclear. Therefore, we aimed to characterize the microbial alterations and host inflammatory cytokine responses in H. pylori-negative GC. Gastric mucosal samples were obtained from 137 H. pylori-negative patients with GC (n = 45) and controls (chronic gastritis or intestinal metaplasia, n = 92). We performed 16S rRNA gene sequencing (n = 67), a quantitative reverse transcription-polymerase chain reaction to determine the relative mRNA expression levels of TNF (tumor necrosis factor), IL1B (interleukin 1 beta), IL6 (interleukin 6), CXCL8 (C-X-C motif chemokine ligand 8), IL10 (interleukin 10), IL17A (interleukin 17A), TGFB1 (transforming growth factor beta 1) (n = 113), and the correlation analysis between sequencing and expression data (n = 47). Gastric mucosal microbiota in patients with GC showed reduced diversity and a significantly different composition compared to that of the controls. Lacticaseibacillus was significantly enriched, while Haemophilus and Campylobacter were depleted in the cancer group compared to the control group. These taxa could distinguish the two groups in a random forest algorithm. Moreover, the combined relative abundance of these taxa, a GC microbiome index, significantly correlated with gastric mucosal IL1B expression, which was elevated in the cancer group. Overall, altered gastric mucosal microbiota was found to be associated with increased mucosal IL1B expression in H. pylori-negative GC.
Fat-Soluble Vitamers: Parent-Child Concordance and Population Epidemiology in the Longitudinal Study of Australian Children
Authors: Arachchige Githal Randunu Porawakara , Pook Chris James , Jones Beatrix , Coe Margaret , Saffery Richard , Wake Melissa , Thorstensen Eric Bruce , O’Sullivan Justin Martin ,   on behalf of the Child CheckPoint Team
Published: November 2022

Fat-soluble vitamers (FSV) are a class of diverse organic substances important in a wide range of biological processes, including immune function, vision, bone health, and coagulation. Profiling FSV in parents and children enables insights into gene-environment contributions to their circulating levels, but no studies have reported on the population epidemiology of FSV in these groups as of yet. In this study, we report distributions of FSV, their parent-child concordance and variation by key characteristics for 2490 children (aged 11–12 years) and adults (aged 28–71 years) in the Child Health CheckPoint of the Longitudinal Study of Australian Children. Ten A, D, E and K vitamers were quantified using a novel automated LC-MS/MS method. All three K vitamers (i.e., K1, MK-4, MK-7) and 1-α-25(OH)2D3 were below the instrument detection limit and were removed from the present analysis. We observed a strong vitamer-specific parent-child concordance for the six quantifiable A, D and E FSVs. FSV concentrations all varied by age, BMI, and sex. We provide the first cross-sectional population values for multiple FSV. Future studies could examine relative genetic vs. environmental determinants of FSV, how FSV values change longitudinally, and how they contribute to future health and disease.
Association between Dietary Habit and Clinical Parameters in Patients with Chronic Periodontitis Undergoing Supportive Periodontal Therapy
Authors: Tabe Shinichi , Nakayama Yohei , Kobayashi Ryoki , Oyama Kstsunori , Kitano Daisuke , Ogihara Jun , Senpuku Hidenobu , Ogata Yorimasa
Published: November 2022

The recurrence risk evaluation has been emphasized in periodontal stabilization during supportive periodontal therapy (SPT). However, nutritional factors, e.g., dietary habits such as the frequency of eating vegetables, are rarely included in the evaluation. In this study, the effect of nutritional factors on clinical periodontal parameters was examined in a lifestyle-related investigation and a periodontal examination in patients with periodontitis undergoing SPT. A total of 106 patients were recruited. Tendencies toward a negative correlation were found between rate of a probing depth (PD) of 4–5 mm, rate of PD ≥ 6 mm, the bleeding on probing (BOP) rate, periodontal inflamed surface area (PISA), and various nutritional factors. The number of teeth was a clinical parameter with a significantly high R2 (≥0.10) influenced by environmental factors, whereas PD, PD of 4–5 mm, the BOP rate, and PISA were influenced by nutritional factors. These results suggested that environmental factors reflected clinical parameters showing long-term pathophysiology, such as the PD rate. Nutritional factors tended to affect the current inflammatory pathophysiology, such as the BOP rate, PISA, and PISA/periodontal epithelial surface area. Therefore, environmental and nutritional factors appear to be useful for evaluating the risk of periodontitis during SPT.
Diosgenin Ameliorated Type II Diabetes-Associated Nonalcoholic Fatty Liver Disease through Inhibiting De Novo Lipogenesis and Improving Fatty Acid Oxidation and Mitochondrial Function in Rats
Authors: Zhong Yujie , Li Zhiman , Jin Ruyi , Yao Yanpeng , He Silan , Lei Min , Wang Xin , Shi Chao , Gao Li , Peng Xiaoli
Published: November 2022

Diosgenin (DIO) is a dietary and phytochemical steroidal saponin representing multiple activities. The present study investigated the protective effect of DIO on type II diabetes-associated nonalcoholic fatty liver disease (D-NAFLD). The rat model was established by high-fat diet and streptozotocin injection and then administered DIO for 8 weeks. The results showed that DIO reduced insulin resistance index, improved dyslipidemia, and relieved pancreatic damage. DIO decreased hepatic injury markers, including aspartate aminotransferase (AST) and alanine aminotransferase (ALT). H&E staining showed that DIO relieved hepatic lipid deposition. Mechanistically, DIO inhibited hepatic de novo lipogenesis (DNL) and increased fatty acid β-oxidation (FAO) through regulation of the AMPK-ACC/SREBP1 pathway. Endoplasmic reticulum (ER) stress was inhibited by DIO through regulation of PERK and IRE1 arms, which may then inhibit DNL. DIO also decreased reactive oxygen species (ROS) and enhanced the antioxidant capacity via an increase in Superoxide dismutase (SOD), Catalase (CAT), and Glutathione peroxidase (GPx) activities. The mitochondria are the site for FAO, and ROS can damage mitochondrial function. DIO relieved mitochondrial fission and fusion disorder by inhibiting DRP1 and increasing MFN1/MFN2 expressions. Mitochondrial apoptosis was then inhibited by DIO. In conclusion, the present study suggests that DIO protects against D-NAFLD by inhibiting DNL and improving FAO and mitochondrial function.
Evaluation of the Use of UAV-Derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring Based on Machine Learning Algorithms and SHAP Analysis
Authors: Wei Hsiang-En , Grafton Miles , Bretherton Mike , Irwin Matthew , Sandoval Eduardo
Published: November 2022

Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. Although unmanned aerial vehicles (UAVs) can provide efficient mapping across the entire vineyard, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band, which makes the monitoring of GWS problematic. The goal of this study is to explore whether and which of the ancillary variables (vegetation characteristics, temporal trends, weather conditions, and soil/terrain data) may improve the accuracy of GWS estimation using multispectral UAV and provide insights into the contribution, in terms of direction and intensity, for each variable contributing to GWS variation. UAV-derived vegetation indices, slope, elevation, apparent electrical conductivity (ECa), weekly or daily weather parameters, and day of the year (DOY) were tested and regressed against stem water potential (Ψstem), measured by a pressure bomb, and used as a proxy for GWS using three machine learning algorithms (elastic net, random forest regression, and support vector regression). Shapley Additive exPlanations (SHAP) analysis was used to assess the relationship between selected variables and Ψstem. The results indicate that the root mean square error (RMSE) of the transformed chlorophyll absorption reflectance index-based model improved from 213 to 146 kPa when DOY and elevation were included as ancillary inputs. RMSE of the excess green index-based model improved from 221 to 138 kPa when DOY, elevation, slope, ECa, and daily average windspeed were included as ancillary inputs. The support vector regression best described the relationship between Ψstem and selected predictors. This study has provided proof of the concept for developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as providing individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.
Coastline Recognition Algorithm Based on Multi-Feature Network Fusion of Multi-Spectral Remote Sensing Images
Authors: Qiu Shi , Ye Huping , Liao Xiaohan
Published: November 2022

Remote sensing images can obtain broad geomorphic features and provide a strong basis for analysis and decision making. As 71% of the earth is covered by water, shipping has become an efficient means of international trade and transportation, and the development level of coastal cities will directly reflect the development level of a country. The coastline is the boundary line between seawater and land, so it is of great significance to accurately identify it to assist shipping traffic and docking, and this identification will also play a certain auxiliary role in environmental analysis. Currently, the main problems of coastline recognition conducted by remote sensing images include: (1) in the process of remote sensing, image transmission inevitably brings noise causing poor image quality and difficult image quality enhancement; (2) s single scale does not allow for the identification of coastlines at different scales; and (3) features are under-utilized, false detection is high and intuitive measurement is difficult. To address these issues, we used the following multispectral methods: (1) a PCA-based image enhancement algorithm was proposed to improve image quality; (2) a dual attention network and HRnet network were proposed to extract suspected coastlines from different levels; and (3) a decision set fusion approach was proposed to transform the coastline identification problem into a probabilistic problem for coastline extraction. Finally, we constructed a coastline straightening model to visualize and analyze the recognition effect. Experiments showed that the algorithm has an AOM greater than 0.88 and can achieve coastline extraction.
A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages
Authors: Sun Jin , Chen Zhengyu , Wang Fu
Published: November 2022

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.
Satellite Remote Sensing Identification of Discolored Standing Trees for Pine Wilt Disease Based on Semi-Supervised Deep Learning
Authors: Wang Jiahao , Zhao Junhao , Sun Hong , Lu Xiao , Huang Jixia , Wang Shaohua , Fang Guofei
Published: November 2022

Pine wilt disease (PWD) is the most dangerous biohazard of pine species and poses a serious threat to forest resources. Coupling satellite remote sensing technology and deep learning technology for the accurate monitoring of PWD is an important tool for the efficient prevention and control of PWD. We used Gaofen-2 remote sensing images to construct a dataset of discolored standing tree samples of PWD and selected three semantic segmentation models—DeepLabv3+, HRNet, and DANet—for training and to compare their performance. To build a GAN-based semi-supervised semantic segmentation model for semi-supervised learning training, the best model was chosen as the generator of generative adversarial networks (GANs). The model was then optimized for structural adjustment and hyperparameter adjustment. Aimed at the characteristics of Gaofen-2 images and discolored standing trees with PWD, this paper adopts three strategies—swelling prediction, raster vectorization, and forest floor mask extraction—to optimize the image identification process and results and conducts an application demonstration study in Nanping city, Fujian Province. The results show that among the three semantic segmentation models, HRNet was the optimal conventional semantic segmentation model for identifying discolored standing trees of PWD based on Gaofen-2 images and that its MIoU value was 68.36%. Additionally, the GAN-based semi-supervised semantic segmentation model GAN_HRNet_Semi improved the MIoU value by 3.10%, and its recognition segmentation accuracy was better than the traditional semantic segmentation model. The recall rate of PWD discolored standing tree monitoring in the demonstration area reached 80.09%. The combination of semi-supervised semantic segmentation technology and high-resolution satellite remote sensing technology provides new technical methods for the accurate wide-scale monitoring, prevention, and control of PWD.
The Grain for Green Program Enhanced Synergies between Ecosystem Regulating Services in Loess Plateau, China
Authors: Yu Ziyue , Deng Xiangzheng , Cheshmehzangi Ali
Published: November 2022

Decades of reckless deforestation have caused serious soil erosion and land desertification issues in the Loess Plateau (LP). “Grain for Green” Program (GFGP), one of the world’s largest ecological restoration projects, is crucial to improve the ecological environment. Previous studies have demonstrated that GFGP lowers soil erosion in the LP. However, there are trade-offs and synergies between ecological services. Does strengthening soil conservation prevent enhancing other ecosystem services? Consequently, can the GFGP improve many ecological services simultaneously? This study compares changes in NDVI prior to and following the implementation of the GFGP in LP to the enhancement of ecosystem services. During the research period, the LP’s overall vegetation cover rose significantly, particularly in the GFGP’s major counties. Significant improvements were made to ecosystem services such as carbon sequestration, soil conservation, and habitat quality. The GFGP enhanced the synergistic linkages between ecological services. The implementation of the GFGP decreased water yield, suggesting trade-offs with other ecosystem services. Additionally, we investigate regional trade-offs/synergies between ecosystem services and their influencing factors, which were influenced by topographic and climatic variables. To maximize the benefits of ecological restoration efforts, we need a deeper understanding of the relationships between ecosystem services and the mechanisms that drive them. Thus, policymakers can scientifically exert control over local influences on ecosystem services, either by boosting the provision of specific services or by limiting specific influences in order to maintain ecosystem stability.
Deep Learning Anomaly Classification Using Multi-Attention Residual Blocks for Industrial Control Systems
Authors: Jiang Jehn-Ruey , Lin Yan-Ting
Published: November 2022

This paper proposes a novel method monitoring network packets to classify anomalies in industrial control systems (ICSs). The proposed method combines different mechanisms. It is flow-based as it obtains new features through aggregating packets of the same flow. It then builds a deep neural network (DNN) with multi-attention blocks for spotting core features, and with residual blocks for avoiding the gradient vanishing problem. The DNN is trained with the Ranger (RAdam + Lookahead) optimizer to prevent the training from being stuck in local minima, and with the focal loss to address the data imbalance problem. The Electra Modbus dataset is used to evaluate the performance impacts of different mechanisms on the proposed method. The proposed method is compared with related methods in terms of the precision, recall, and F1-score to show its superiority.
Advanced Sensing, Fault Diagnostics, and Structural Health Management
Authors: Li Yongbo , Li Bing , Ji Jinchen , Kalhori Hamed
Published: November 2022

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries [...]
Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network
Authors: Wang Haitao , Yang Jie , Shi Lichen , Wang Ruihua
Published: November 2022

The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantial amount of prior knowledge to extract degraded features. A large number of recurrent neural networks (RNNs) have been applied to RUL, but their shortcomings of long-term dependence and inability to remember long-term historical information can result in low RUL prediction accuracy. To address this limitation, this paper proposes an RUL prediction method based on adaptive shrinkage processing and a temporal convolutional network (TCN). In the proposed method, instead of performing the feature extraction to preprocess the original data, the multi-channel data are directly used as an input of a prediction network. In addition, an adaptive shrinkage processing sub-network is designed to allocate the parameters of the soft-thresholding function adaptively to reduce noise-related information amount while retaining useful features. Therefore, compared with the existing RUL prediction methods, the proposed method can more accurately describe RUL based on the original historical data. Through experiments on a PHM2012 rolling bearing data set, a XJTU-SY data set and comparison with different methods, the predicted mean absolute error (MAE) is reduced by 52% at most, and the root mean square error (RMSE) is reduced by 64% at most. The experimental results show that the proposed adaptive shrinkage processing method, combined with the TCN model, can predict the RUL accurately and has a high application value.
Smart Shirt for Measuring Trunk Orientation
Authors: Simegnaw Abdella Ahmmed , Teyeme Yetanawork , Malengier Benny , Tesfaye Tamrat , Daba Hundessa , Esmelealem Kaledawit , Langenhove Lieva Van
Published: November 2022

Improper cycling posture is linked to a variety of spinal musculoskeletal diseases, including structural malformation of the spine and back discomfort. This paper presents a novel smart shirt integrated tri-axial gyroscope and accelerometer that can detect postural variation in terms of spinal curvature changes. To provide accurate feedback to the wearer and improve the wearer’s correct movement, the garment is able to recognize trunk body posture. The gyroscope/accelerometer was placed around the upper and mid trunk of the user to record tri-axial angular velocity data. The device can also be used to help determine the trunk bending angle and monitor body postures in order to improve optimal orientation and position. The garment enables continuous measurement in the field at high sample rates (50 Hz), and the sensor has a large measurement range (16 g, 2000°/s). As electronic components are non-washable, instead of encapsulating them, a detachable module was created. In this, magnets are embedded in the jersey, and allow the positioning and removal of the sensor. The test results show that the average trunk-bending angle was 21.5°, and 99 percent of the observed angle fell within the standard (ranging from 8° to 35°). The findings demonstrate the feasibility of employing the smart shirt sensor to estimate trunk motions in the field on a regular basis.
Lower Limb Exoskeleton Sensors: State-of-the-Art
Authors: Neťuková Slávka , Bejtic Martin , Malá Christiane , Horáková Lucie , Kutílek Patrik , Kauler Jan , Krupička Radim
Published: November 2022

Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human–machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.
Can Shared Control Improve Overtaking Performance? Combining Human and Automation Strengths for a Safer Maneuver
Authors: Marcano Mauricio , Tango Fabio , Sarabia Joseba , Chiesa Silvia , Pérez Joshué , Díaz Sergio
Published: November 2022

The Shared Control (SC) cooperation scheme, where the driver and automated driving system control the vehicle together, has been gaining attention through the years as a promising option to improve road safety. As a result, advanced interaction methods can be investigated to enhance user experience, acceptance, and trust. Under this perspective, not only the development of algorithms and system applications are needed, but it is also essential to evaluate the system with real drivers, assess its impact on road safety, and understand how drivers accept and are willing to use this technology. In this sense, the contribution of this work is to conduct an experimental study to evaluate if a previously developed shared control system can improve overtaking performance on roads with oncoming traffic. The evaluation is performed in a Driver-in-the-Loop (DiL) simulator with 13 real drivers. The system based on SC is compared against a vehicle with conventional SAE-L2 functionalities. The evaluation includes both objective and subjective assessments. Results show that SC proved to be the best solution for assisting the driver during overtaking in terms of safety and acceptance. The SC’s longer and smoother control transitions provide benefits to cooperative driving. The System Usability Scale (SUS) and the System Acceptance Scale (SAS) questionnaire show that the SC system was perceived as better in terms of usability, usefulness, and satisfaction.
Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study
Authors: Lytridis Chris , Bazinas Christos , Pachidis Theodore , Chatzis Vassilios , Kaburlasos Vassilis G.
Published: November 2022

The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, and efficient operation. This paper presents a simulation study that demonstrates a robust coordination strategy for the navigation of two heterogeneous robots, where one robot is the expert and the second robot is the helper in a vineyard. The robots are equipped with localization and navigation capabilities so that they can navigate the environment and appropriately position themselves in the work area. A modular collaborative algorithm is proposed for the coordinated navigation of the two robots in the field via a communications module. Furthermore, the robots are also able to position themselves accurately relative to each other using a vision module in order to effectively perform their cooperative tasks. For the experiments, a realistic simulation environment is considered, and the various control mechanisms are described. Experiments were carried out to investigate the robustness of the various algorithms and provide preliminary results before real-life implementation.
Study of a Fiber Optic Fabry-Perot Strain Sensor for Fuel Assembly Strain Detection
Authors: Jiao Jianan , Chen Jianjun , Wang Ning , Zhang Jie , Zhu Yong
Published: November 2022

This paper proposes a fiber optic Fabry-Perot (F-P) strain sensing system using non-scan correlation demodulation applied to the health monitoring of the pressurized water reactor’s fuel assembly structures. The structural design and sensing mechanism analysis of the sensor were carried out, and the strain transfer model from the fuel sheet to the strain gauge was established. After the sensor fabrication and installation, the static tests have been conducted, and the results indicate that the sensing system can accurately measure the microstrain with a sensitivity of up to 12.6 nm/με at a high temperature (300 °C). The dynamic testing shows that the sensing system has a good frequency adaptation at 10–500 Hz. Thermal-hydraulic experiments show that the sensing system can run stably in a nuclear reactor, with high temperature, high pressure, and high-velocity flow flushing; additionally, the consistency deviation of the measured data is less than 1.5%.
Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture
Authors: Bristow Noel , Rengaraj Saravanan , Chadwick David R. , Kettle Jeff , Jones Davey L.
Published: November 2022

Crop productivity is highly dependent on the availability of soluble nitrogen (N), e.g. nitrate, in soil. When N levels are low, fertilisers are applied to replenish the soil’s reserves. Typically the timing of these applications is based on paper-based guidance and sensor-based measurements of canopy greenness, which provides an indirect measure of soil N status. However this approach often means that N fertiliser is applied inappropriately or too late, resulting in excess N being lost to the environment, or too little N to meet crop demand. To promote greater N use efficiency and improve agricultural sustainability, we developed an Internet of Things (IoT) approach for the real-time measurement of soil nitrate levels using ion-selective membrane sensors in combination with digital soil moisture probes. The node incorporates state-of-the-art IoT connectivity using a LoRaWAN transceiver. The sensing platform can transfer real-time data via a cloud-connected gateway for processing and storage. In summary, we present a validated soil sensor system for real-time monitoring of soil nitrate concentrations, which can support fertiliser management decisions, improve N use efficiency and reduce N losses to the environment.
Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods
Authors: Gojić Gorana , Petrović Veljko B. , Dragan Dinu , Gajić Dušan B. , Mišković Dragiša , Džinić Vladislav , Grgić Zorka , Pantelić Jelica , Oros Ana
Published: November 2022

Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation masks remains unexplored. In this paper, we perform a pilot study to assess the clinical viability of automatically generated segmentation masks in the diagnosis of diseases affecting retinal vascularization. Five ophthalmologists with clinical experience were asked to participate in the study. The results demonstrate low classification accuracy, inferring that generated segmentation masks cannot be used as a standalone resource in general clinical practice. The results also hint at possible clinical infeasibility in experimental design. In the follow-up experiment, we evaluate the clinical quality of masks by having ophthalmologists rank generation methods. The ranking is established with high intra-observer consistency, indicating better subjective performance for a subset of tested networks. The study also demonstrates that objective metrics are not correlated with subjective metrics in retinal segmentation tasks for the methods involved, suggesting that objective metrics commonly used in scientific papers to measure the method’s performance are not plausible criteria for choosing clinically robust solutions.
Securing Session Initiation Protocol
Authors: Younes Osama , Albalawi Umar
Published: November 2022

The session initiation protocol (SIP) is widely used for multimedia communication as a signaling protocol for managing, establishing, maintaining, and terminating multimedia sessions among participants. However, SIP is exposed to a variety of security threats. To overcome the security flaws of SIP, it needs to support a number of security services: authentication, confidentiality, and integrity. Few solutions have been introduced in the literature to secure SIP, which can support these security services. Most of them are based on internet security standards and have many drawbacks. This work introduces a new protocol for securing SIP called secure-SIP (S-SIP). S-SIP consists of two protocols: the SIP authentication (A-SIP) protocol and the key management and protection (KP-SIP) protocol. A-SIP is a novel mutual authentication protocol. KP-SIP is used to secure SIP signaling messages and exchange session keys among entities. It provides different security services for SIP: integrity, confidentiality, and key management. A-SIP is based on the secure remote password (SRP) protocol, which is one of standard password-based authentication protocols supported by the transport layer security (TLS) standard. However, A-SIP is more secure and efficient than SRP because it covers its security flaws and weaknesses, which are illustrated and proven in this work. Through comprehensive informal and formal security analyses, we demonstrate that S-SIP is secure and can address SIP vulnerabilities. In addition, the proposed protocols were compared with many related protocols in terms of security and performance. It was found that the proposed protocols are more secure and have better performance.
Application of Transformer Models to Landslide Susceptibility Mapping
Authors: Bao Shuai , Liu Jiping , Wang Liang , Zhao Xizhi
Published: November 2022

Landslide susceptibility mapping (LSM) is of great significance for the identification and prevention of geological hazards. LSM is based on convolutional neural networks (CNNs); CNNs use fixed convolutional kernels, focus more on local information and do not retain spatial information. This is a property of the CNN itself, resulting in low accuracy of LSM. Based on the above problems, we use Vision Transformer (ViT) and its derivative model Swin Transformer (Swin) to conduct LSM for the selected study area. Machine learning and a CNN model are used for comparison. Fourier transform amplitude, feature similarity and other indicators were used to compare and analyze the difference in the results. The results show that the Swin model has the best accuracy, F1-score and AUC. The results of LSM are combined with landslide points, faults and other data analysis; the ViT model results are the most consistent with the actual situation, showing the strongest generalization ability. In this paper, we believe that the advantages of ViT and its derived models in global feature extraction ensure that ViT is more accurate than CNN and machine learning in predicting landslide probability in the study area.
Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter
Authors: Zhang Guowei , Yin Jiyao , Deng Peng , Sun Yanlong , Zhou Lin , Zhang Kuiyuan
Published: November 2022

As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at the issues of the occlusion, background noise, and motion state violent change for multi-object in a complex scene, an improved DeepSORT algorithm based on YOLOv5 is proposed for multi-object tracking to enhance the speed and accuracy of tracking. Firstly, a general object motion model is devised, which is similar to the variable acceleration motion model, and a multi-object tracking framework with the general motion model is established. Then, the latest YOLOv5 algorithm, which has satisfactory detection accuracy, is utilized to obtain the object information as the input of multi-object tracking. An unscented Kalman filter (UKF) is proposed to estimate the motion state of multi-object to solve nonlinear errors. In addition, the adaptive factor is introduced to evaluate observation noise and detect abnormal observations so as to adaptively adjust the innovation covariance matrix. Finally, an improved DeepSORT algorithm for multi-object tracking is formed to promote robustness and accuracy. Extensive experiments are carried out on the MOT16 data set, and we compare the proposed algorithm with the DeepSORT algorithm. The results indicate that the speed and precision of the improved DeepSORT are increased by 4.75% and 2.30%, respectively. Especially in the MOT16 of the dynamic camera, the improved DeepSORT shows better performance.
SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System
Authors: Abdou Mohammed , Kamal Hanan Ahmed
Published: November 2022

Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.
MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution
Authors: Ma Jian , Han Xiyu , Zhang Xiaoyin , Li Zhipeng
Published: November 2022

Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR; however, the existing CNN-based SR methods cannot fully search for background information in the measurement of feature extraction. In addition, in most cases, different scale factors of image SR are assumed to be different assignments and completed by training different models, which does not meet the actual application requirements. To solve these problems, we propose a multi-scale learning wavelet attention network (MLWAN) model for image SR. Specifically, the proposed model consists of three parts. In the first part, low-level features are extracted from the input image through two convolutional layers, and then a new channel-spatial attention mechanism (CSAM) block is concatenated. In the second part, CNN is used to predict the highest-level low-frequency wavelet coefficients, and the third part uses recursive neural networks (RNN) with different scales to predict the wavelet coefficients of the remaining subbands. In order to further achieve lightweight, an effective channel attention recurrent module (ECARM) is proposed to reduce network parameters. Finally, the inverse discrete wavelet transform (IDWT) is used to reconstruct HR image. Experimental results on public large-scale datasets demonstrate the superiority of the proposed model in terms of quantitative indicators and visual effects.
CFX: Contention-Free Channel Access for IEEE 802.11ax
Authors: Lee Kyu-haeng , Kim Daehee
Published: November 2022

Orthogonal frequency-division multiple access (OFDMA) has attracted great attention as a key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network congestion and channel access delay. Unfortunately, the traditional random access protocol of Wi-Fi seldom allows these benefits to be achieved, especially in dense network environments, as the access point (AP) rarely gains the channel access needed to trigger OFDMA uplink transmissions due to severe frame collisions. To address this problem, we propose a new channel access scheme called Contention-Free Channel Access for 802.11ax (CFX). In the proposed scheme, users can access the channel without contention, since they are guaranteed a transmission opportunity immediately after another user’s transmission. To realize CFX on top of the existing Buffer Status Report/BSR Poll (BSR/BSRP) exchange protocol of 802.11ax, we develop an additional scheme based on shared channel access that helps the AP to obtain the buffer status of users and manage a contention-free channel access schedule. In addition, in order to appropriately utilize the savings from the reduced frame collisions, we conduct sum throughput maximization using an actor-critic proximal policy optimization (PPO)-based deep reinforcement learning approach. The results of an extensive evaluation show that CFX not only significantly improves the uplink performance of Wi-Fi in terms of throughput and channel access delay but can also dynamically adjust the parameters in response to changes in the network status.