bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultrasonographic Evaluation of Entheseal Fibrocartilage in Patients with Psoriatic Arthritis, Athletes and Healthy Controls: A Comparison Study
Authors: Perrotta Fabio Massimo , Ronga Mario , Scriffignano Silvia , Lubrano Ennio
Published: April 2023

The aims of this study were as follows: (1) To evaluate the entheseal fibrocartilage (EF) during Achilles tendon insertion in patients with Psoriatic Arthritis (PsA) by using power Doppler ultrasound (PDUS), (2) to assess the intra and inter-reader reliability of the evaluation of EF thickness, (3) to compare the EF thickness of PsA patients, athletes and healthy controls (HCs), and (4) to evaluate the correlations between EF abnormalities, disease activity and functional indices in PsA. Methods: Consecutive PsA patients attending our unit were asked to participate. HCs and agonist athletes were enrolled as a control group. A bilateral PDUS evaluation of Achilles tendons was performed in order to evaluate the EF in all patients and controls. Results: In total, 30 PsA patients, 40 athletes and 20 HCs were enrolled. The median (IQR) EF thickness among the PsA patients, athletes and HCs was 0.035 cm (0.028–0.04) cm, 0.036 (0.025–0.043) cm and 0.030 (0.020–0.038) cm, respectively (p = 0.05 between PsA patients and HCs). The intra-reader reliability was excellent [ICC (95% CI) of 0.91 (0.88–0.95)] and the inter-reader reliability was good (0.80 (0.71–0.86). The assessment of EF was feasible, with a mean time of 2 min. No correlations were found with disease activity indices in PsA patients. Conclusion: The assessment of EF is a feasible and reproducible test and may be explored as a potential imaging biomarker.
Long-Term Adherence in Overweight Patients with Obstructive Sleep Apnea and Hypertension—A Pilot Prospective Cohort Study
Authors: Zota Ioana , Roca Mihai , Leon Maria , Cozma Corina , Anghel Larisa , Statescu Cristian , Sascau Radu , Hancianu Monica , Mircea Cornelia , Ciocoiu Manuela , Cumpat Carmen , Mitu Florin
Published: April 2023

Obstructive sleep apnea (OSA) is associated with increased cardiovascular risk, sedentarism, depression, anxiety and impaired quality of life. The long-term effectiveness of positive airway pressure (PAP) is insufficiently studied and limited by poor patient compliance. The aim of this pilot prospective cohort study was to evaluate long-term adherence in overweight patients with moderate–severe OSA and hypertension and to analyze changes in weight, sleepiness and quality of life. We performed a prospective study that included overweight patients with moderate–severe OSA and hypertension who had not undergone previous PAP therapy. All subjects received a standard physical examination, education regarding lifestyle changes and free PAP therapy for 2 months. After five years, the patients were invited to participate in a telephone-based interview regarding PAP compliance and completed standard questionnaires assessing adherence to medication, physical activity, diet, anxiety and quality of life (QoL). Only 39.58% of the patients were adherent to PAP 5 years (58.42 ± 3.70 months) after being diagnosed with moderate–severe OSA. Long-term PAP use results in sustained weight loss; improved blood pressure control, sleepiness and QOL; and lower anxiety and depression scores. PAP compliance was not associated with a higher level of daily physical activity or a healthier diet.
Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features
Authors: Rasheed Mehwish , Iqbal Muhammad Waseem , Jaffar Arfan , Ashraf Muhammad Usman , Almarhabi Khalid Ali , Alghamdi Ahmed Mohammed , Bahaddad Adel A.
Published: April 2023

The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.
Higher Number of Tumor-Infiltrating PD-L1+ Cells Is Related to Better Response to Multikinase Inhibitors in Hepatocellular Carcinoma
Authors: Han Ji Won , Kim Ji Hoon , Kim Dong Hyun , Jang Jeong Won , Bae Si Hyun , Choi Jong Young , Yoon Seung Kew , Ahn Jaegyoon , Yang Hyun , Sung Pil Soo
Published: April 2023

Multikinase inhibitors (MKIs) such as sorafenib and lenvatinib are first-line treatments for unresectable hepatocellular carcinoma (HCC) and are known to have immunomodulatory effects. However, predictive biomarkers of MKI treatment in HCC patients need to be elucidated. In the present study, thirty consecutive HCC patients receiving lenvatinib (n = 22) and sorafenib (n = 8) who underwent core-needle biopsy before treatment were enrolled. The associations of CD3, CD68, and programmed cell death-ligand-1 (PD-L1) immunohistochemistry with patient outcomes, including overall survival (OS), progression-free survival (PFS), and objective response rate (ORR), were evaluated. High and low subgroups were determined according to median CD3, CD68, and PD-L1 values. Median CD3 and CD68 counts were 51.0 and 46.0 per 20,000 µm2, respectively. The median combined positivity score (CPS) of PD-L1 was 2.0. Median OS and PFS were 17.6 and 4.4 months, respectively. ORRs of the total, lenvatinib, and sorafenib groups were 33.3% (10/30), 12.5% (1/8), and 40.9% (9/22), respectively. The high CD68+ group had significantly better PFS than the low CD68+ group. The high PD-L1 group had better PFS than the low subgroup. When we analyzed the lenvatinib subgroup, PFS was also significantly better in the high CD68+ and PD-L1 groups. These findings suggest that high numbers of PD-L1-expressing cells within tumor tissue prior to MKI treatment can serve as a biomarker to predict favorable PFS in HCC patients.
ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction
Authors: Hussain Shah , Haider Shahab , Maqsood Sarmad , Damaševičius Robertas , Maskeliūnas Rytis , Khan Muzammil
Published: April 2023

Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of tumor volume instead of size may improve the accuracy of survival prediction. In response to this need, we propose a novel model, the enhanced brain tumor identification and survival time prediction (ETISTP), which computes tumor volume, classifies it into low- or high-grade glioma, and predicts survival time with greater accuracy. The ETISTP model integrates four parameters: patient age, survival days, gross total resection (GTR) status, and tumor volume. Notably, ETISTP is the first model to employ tumor volume for prediction. Furthermore, our model minimizes the computation time by allowing for parallel execution of tumor volume computation and classification. The simulation results demonstrate that ETISTP outperforms prominent survival prediction models.
Interventional Treatment of Budd–Chiari Syndrome
Authors: Rössle Martin
Published: April 2023

Medical treatment is regarded as the primary course of action in patients with Budd–Chiari syndrome (BCS). Its efficacy, however, is limited, and most patients require interventional treatment during follow-up. Short-segment stenosis or the occlusion (the so-called web) of hepatic veins or the inferior vena cava are frequent in Asian countries. An angioplasty with or without stent implantation is the treatment of choice to restore hepatic and splanchnic blood flow. The long-segment thrombotic occlusion of hepatic veins, common in Western countries, is more severe and may require a portocaval shunting procedure to relieve hepatic and splanchnic congestion. Since it was first proposed in a publication in 1993, the transjugular intrahepatic portosystemic shunt (TIPS) has gained more and more attention, and in fact it has been so successful that previously utilized surgical shunts are only used for few patients for whom it does not work. Both interventional treatment options can be performed successfully in about 95% of patients even after the complete obliteration of the hepatic veins. The long-term patency of the TIPS, a considerable problem in its early years, has been improved with PTFE-covered stents. The complication rates of these interventions are low and the survival rate is excellent with five- and ten-year survival rates of 90% and 80%, respectively. Present treatment guidelines recommend a step-up approach indicating interventional treatment after the failure of medical treatment. However, this widely accepted algorithm has several points of contention, and early interventional treatment is proposed instead.
Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases
Authors: Wang Chung-Ting , Chang Yin-Hsi , Tan Gavin S. W. , Lee Shu Yen , Chan R. V. Paul , Wu Wei-Chi , Tsai Andrew S. H.
Published: April 2023

Indirect ophthalmoscopy and handheld retinal imaging are the most common and traditional modalities for the evaluation and documentation of the pediatric fundus, especially for pre-verbal children. Optical coherence tomography (OCT) allows for in vivo visualization that resembles histology, and optical coherence tomography angiography (OCTA) allows for non-invasive depth-resolved imaging of the retinal vasculature. Both OCT and OCTA were extensively used and studied in adults, but not in children. The advent of prototype handheld OCT and OCTA have allowed for detailed imaging in younger infants and even neonates in the neonatal care intensive unit with retinopathy of prematurity (ROP). In this review, we discuss the use of OCTA and OCTA in various pediatric retinal diseases, including ROP, familial exudative vitreoretinopathy (FEVR), Coats disease and other less common diseases. For example, handheld portable OCT was shown to detect subclinical macular edema and incomplete foveal development in ROP, as well as subretinal exudation and fibrosis in Coats disease. Some challenges in the pediatric age group include the lack of a normative database and the difficulty in image registration for longitudinal comparison. We believe that technological improvements in the use of OCT and OCTA will improve our understanding and care of pediatric retina patients in the future.
Neural Network and Dynamic Inversion Based Adaptive Control for an HALE-UAV against Icing Effects
Authors: Li Yiyang , Cheng Lingquan , Yuan Jiayi , Ai Jianliang , Dong Yiqun
Published: April 2023

In the past few decades, in-flight icing has become a common problem for many missions, potentially leading to a reduction in control effectiveness and flight stability, which would threaten flight safety. One of the most popular methods to address this problem is adaptive control. This paper establishes a dynamic model of an iced high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) with disturbance and measurement noise. Then, by combining multilayer perceptrons (MLP) with a nonlinear dynamic inversion (NDI) controller, we propose an MLP-NDI controller to compensate for online inversion errors and provide a brief proof of control stability. Two experiments were conducted: on one hand, we compared the MLP-NDI controller with other typical controllers; on the other hand, we evaluated its robustness and adaptiveness under different icing conditions. Results indicate that the MLP-NDI controller outperforms other typical controllers with higher tracking accuracy and exhibits strong robustness in the presence of icing errors and measurement noise, which has huge potential to ensure flight safety.
Transmission Line Segmentation Solutions for UAV Aerial Photography Based on Improved UNet
Authors: He Min , Qin Liang , Deng Xinlan , Zhou Sihan , Liu Haofeng , Liu Kaipei
Published: April 2023

The accurate and efficient detection of power lines and towers in aerial drone images with complex backgrounds is crucial for the safety of power grid operations and low-altitude drone flights. In this paper, we propose a new method that enhances the deep learning segmentation model UNet algorithm called TLSUNet. We enhance the UNet algorithm by using a lightweight backbone structure to extract the features and then reconstructing them with contextual information features. In this network model, to reduce its parameters and computational complexity, we adopt DFC-GhostNet (Dubbed Full Connected) as the backbone feature extraction network, which is composed of the DFC-GhostBottleneck structure and uses asymmetric convolution to capture long-distance targets in transmission lines, thus enhancing the model’s extraction capability. Additionally, we design a hybrid feature extraction module based on convolution and a transformer to refine deep semantic features and improve the model’s ability to locate towers and transmission lines in complex environments. Finally, we adopt the up-sampling operator CARAFE (Content-Aware Re-Assembly of FEature) to improve segmentation accuracy by enhancing target restoration using contextual neighborhood pixel information correlation under feature decoding. Our experiments on public aerial photography datasets demonstrate that the improved model requires only 8.3% of the original model’s computational effort and has only 21.4% of the original model’s parameters, while achieving a reduction in inference speed delay by 0.012 s. The segmentation metrics also showed significant improvements, with the mIOU improving from 79.75% to 86.46% and the mDice improving from 87.83% to 92.40%. These results confirm the effectiveness of our proposed method.
Nonlinear Fault-Tolerant Vibration Control for Partial Actuator Fault of a Flexible Arm
Authors: Li Ximei , Jin Guang , Deng Mingcong
Published: April 2023

This paper presents a nonlinear fault-tolerant vibration control system for a flexible arm, considering partial actuator fault. A lightweight flexible arm with lower stiffness will inevitably cause vibration which will impair the performance of the high-precision control system. Therefore, an operator-based robust nonlinear vibration control system is integrated by a double-sided interactive controller actuated by the Shape Memory Alloy (SMA) actuators for the flexible arm. Furthermore, to improve the safety and reliability of the safety-critical application, fault-tolerant dynamics for partial actuator fault are considered as an essential part of the proposed control system. The experimental cases are set to the partial actuator as faulty conditions, and the proposed vibration control scheme has fault-tolerant dynamics which can still effectively stabilize the vibration displacement. The reconfigurable controller improves the fault-tolerant performance by shortening the vibration time and reducing the vibration displacement of the flexible arm. In addition, compared with a PD controller, the proposed nonlinear vibration control has better performance than the traditional controller. The experimental results show that the effectiveness of the proposed method is confirmed. That is, the safety and reliability of the proposed fault-tolerant vibration control are verified even if in the presence of an actuator fault.
The Compression Optimality of Asymmetric Numeral Systems
Authors: Pieprzyk Josef , Duda Jarek , Pawłowski Marcin , Camtepe Seyit , Mahboubi Arash , Morawiecki Paweł
Published: April 2023

Source coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming, for instance) renews interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from having a beautiful mathematical structure, it is very efficient and offers compression with a very low coding redundancy. ANS works well for any symbol source statistics, and it has become a preferred compression algorithm in the IT industry. However, designing an ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression ratio. The paper investigates the compression optimality of ANS. It shows that ANS is optimal for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to precisely determine the ANS compression rate. We present two algorithms for finding ANS instances with a high compression ratio. The first explores state probability approximations in order to choose ANS instances with better compression ratios. The second algorithm is a probabilistic one. It finds ANS instances whose compression ratios can be made as close to the best ratio as required. This is done at the expense of the number θ of internal random “coin” tosses. The algorithm complexity is O(θL3), where L is the number of ANS states. The complexity can be reduced to O(θLlog2L) if we use a fast matrix inversion. If the algorithm is implemented on a quantum computer, its complexity becomes O(θ(log2L)3).
A Second-Order Network Structure Based on Gradient-Enhanced Physics-Informed Neural Networks for Solving Parabolic Partial Differential Equations
Authors: Sun Kuo , Feng Xinlong
Published: April 2023

Physics-informed neural networks (PINNs) are effective for solving partial differential equations (PDEs). This method of embedding partial differential equations and their initial boundary conditions into the loss functions of neural networks has successfully solved forward and inverse PDE problems. In this study, we considered a parametric light wave equation, discretized it using the central difference, and, through this difference scheme, constructed a new neural network structure named the second-order neural network structure. Additionally, we used the adaptive activation function strategy and gradient-enhanced strategy to improve the performance of the neural network and used the deep mixed residual method (MIM) to reduce the high computational cost caused by the enhanced gradient. At the end of this paper, we give some numerical examples of nonlinear parabolic partial differential equations to verify the effectiveness of the method.
Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing
Authors: Achar Sandesh , Faruqui Nuruzzaman , Whaiduzzaman Md , Awajan Albara , Alazab Moutaz
Published: April 2023

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month.
Multiagent Manuvering with the Use of Reinforcement Learning
Authors: Orłowski Mateusz , Skruch Paweł
Published: April 2023

This paper presents an approach for defining, solving, and implementing dynamic cooperative maneuver problems in autonomous driving applications. The formulation of these problems considers a set of cooperating cars as part of a multiagent system. A reinforcement learning technique is applied to find a suboptimal policy. The key role in the presented approach is a multiagent maneuvering environment that allows for the simulation of car-like agents within an obstacle-constrained space. Each of the agents is tasked with reaching an individual goal, defined as a specific location in space. The policy is determined during the reinforcement learning process to reach a predetermined goal position for each of the simulated cars. In the experiments, three road scenarios—zipper, bottleneck, and crossroads—were used. The trained policy has been successful in solving the cooperation problem in all scenarios and the positive effects of applying shared rewards between agents have been presented and studied. The results obtained in this work provide a window of opportunity for various automotive applications.
Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization
Authors: Jalil Zakia , Nasir Muhammad , Alazab Moutaz , Nasir Jamal , Amjad Tehmina , Alqammaz Abdullah
Published: April 2023

In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%.
Anti-Offset High-Voltage Wireless Power Transfer System Based on Many-to-One Topology
Authors: Chen Jing , Li Rui , Yu Shuai , Zhang Zhengqing , Cai Jiuqing , Zhang Weilong , Sun Haijing
Published: April 2023

Aiming at the problems of limited semiconductor performance and positioning difficulties in the wireless power transfer (WPT) system of electric vehicles, the paper proposes a many-to-one high-voltage wireless charging anti-offset system. In order to be adapted to high-voltage applications, the system is designed with a series input inverter. At the same time, a multi-winding transformer is used to realize the equivalent parallel output of inverters and the function of transmitting power to primary loops. The many-to-one topology aims to expand the positioning range of electric vehicles to realize the anti-offset of wireless charging. In order to analyze the working mechanism of the multi-winding transformer and study the power transmission characteristics of the many-to-one topology, the equivalent circuit analysis and Matlab simulation are carried out, and a laboratory prototype for experimental verification is made. Based on the experimental and simulation results, the paper proposes a hybrid operating mode based on many-to-one WPT topology, which can effectively expand the positioning range of electric vehicles during wireless charging. Both the analytical and experimental results show that the topology proposed in the paper can effectively increase the input voltage of the system for high-voltage scenarios, and effectively expand the positioning range of the WPT system.
Characterization of Sn-xIn Solders and Thermomigration-Induced Interfacial IMC Growth of Cu/Sn-xIn/Cu Micro Solder Joints
Authors: Du Yanfeng , Qiao Yuanyuan , Ren Xiaolei , Lai Yanqing , Zhao Ning
Published: April 2023

The melting behavior and microstructure of bulk Sn-xIn (x = 6, 12 and 24, wt.%) solders and the thermomigration, elemental distribution and intermetallic compound (IMC) growth in Cu/Sn-xIn/Cu micro solder joints during soldering and aging under temperature gradient (TG) were investigated. The results indicate that In addition effectively decreased the melting temperature of the bulk solders. Only the InSn4 phase was detected when In addition was increased to 24 wt.%. During soldering under TG, the growth rate of the interfacial IMC layer at the cold end interfaces gradually decreased as the In content increased. The mechanism of microstructure evolution and elemental distribution in the micro solder joints was revealed based on the TG-induced atomic thermomigration (TM). The chemical potential gradient of atoms was enhanced by TM, and the rapid diffusion of atoms in the liquids resulted in a uniform distribution of In element in both solders and the IMC phase during soldering. While during aging under TG, there was a smaller chemical potential gradient due to the slow atomic diffusion rate. At this time, TG dominated the atomic migration, which resulted in a nonuniform distribution of the In element in the whole joints. This study provides further insight into the application of In-containing solders in electronic packaging.
A Novel End-to-End Turkish Text-to-Speech (TTS) System via Deep Learning
Authors: Oyucu Saadin
Published: April 2023

Text-to-Speech (TTS) systems have made strides but creating natural-sounding human voices remains challenging. Existing methods rely on noncomprehensive models with only one-layer nonlinear transformations, which are less effective for processing complex data such as speech, images, and video. To overcome this, deep learning (DL)-based solutions have been proposed for TTS but require a large amount of training data. Unfortunately, there is no available corpus for Turkish TTS, unlike English, which has ample resources. To address this, our study focused on developing a Turkish speech synthesis system using a DL approach. We obtained a large corpus from a male speaker and proposed a Tacotron 2 + HiFi-GAN structure for the TTS system. Real users rated the quality of synthesized speech as 4.49 using Mean Opinion Score (MOS). Additionally, MOS-Listening Quality Objective evaluated the speech quality objectively, obtaining a score of 4.32. The speech waveform inference time was determined by a real-time factor, with 1 s of speech data synthesized in 0.92 s. To the best of our knowledge, these findings represent the first documented deep learning and HiFi-GAN-based TTS system for Turkish TTS.
Security and Internet of Things: Benefits, Challenges, and Future Perspectives
Authors: Taherdoost Hamed
Published: April 2023

Due to the widespread use of the Internet of Things (IoT), organizations should concentrate their efforts on system security. Any vulnerability could lead to a system failure or cyberattack, which would have a large-scale impact. IoT security is a protection strategy and defense mechanism that protects against the possibility of cyberattacks that specifically target physically linked IoT devices. IoT security teams are currently dealing with growing difficulties, such as inventories, operations, diversity, ownership, data volume, threats, etc. This review examines research on security and IoT with a focus on the situation, applications, and issues of the present as well as the potential for the future. IoT network security has received greater attention from interdisciplinary and geographically scattered researchers in recent years. Data integrity, secrecy, authentication, and authorization should be guaranteed due to the large amount of data that flows across network devices. However, the area of IoT security still has a lot of room for growth.
A Systematic Approach to Stable Grasping of a Two-Finger Robotic Hand Activated by Jamming of Granular Media
Authors: Fakhri Osamah , Youssef George , Nacy Somer , Jameel Al-Tamimi Adnan Naji , Hussein O. , Abbood Wisam T. , Abdullah Oday Ibraheem , AL-Karkhi Nazar Kais
Published: April 2023

A systematic approach is presented to achieve the stable grasping of objects through a two-finger robotic hand, in which each finger cavity was filled with granular media. The compaction of the latter, controlled by vacuum pressure, was used to adjust the structural and contact stiffness of the finger. The grasping stability was studied under the concurrent effect of an external torque and applied vacuum pressure. Stable grasping was defined as the no slippage condition between the grasped object and the two fingers. Three control schemes were adopted and applied experimentally to ensure the effectiveness of the grasping process. The results showed that stable and unstable grasping regions exist for each combination of applied torque and vacuum pressure. The two-finger robotic hands can be further improved for applications that require high load-carrying capabilities.
Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter
Authors: Sun Jing , Ji Yi-Mu , Liu Shang-Dong
Published: April 2023

Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.
Functional Damage Assessment Method for Preformed Fragment Warheads to Evaluate the Effect on the Phased-Array Antenna
Authors: Zou Shaoxin , Gu Wenbin , Ren Wen , Shen Chaohu , Chen Zhaoyi , Hao Likai
Published: April 2023

Damage assessment of a small-weight precision-guided warhead to evaluate the effect on the performance of high-value vulnerable targets should focus on functional damage instead of physical damage. Developing a damage assessment model that considers target function attenuation as a damage criterion for warhead design and tactical application is of great significance. In this paper, an accurate mathematical description of a preformed fragment warhead is provided. The failure of phased-array antenna elements in different initiation conditions is predicted by a shot-line model and spatial coordinate mapping. A real-time co-simulation model of the explosion damage field and the phased-array antenna electrical performance is developed and the effective damage modes are obtained through multi-condition simulation and electrical performance parameters analysis, which has more tactical application value than the structural damage assessment method with equivalent targets and the electrical simulation in artificial or random phrased-array-element failure conditions.
Assessing the Performance of Small Wind Energy Systems Using Regional Weather Data
Authors: Früh Wolf-Gerrit
Published: April 2023

While large renewable power generation schemes, such as wind farms, are well monitored with a wealth of data provided through a SCADA system, the only information about the behaviour of small wind turbines is often only through the metered electricity production. Given the variability of electricity output in response to the local wind or radiation condition, it is difficult to ascertain whether particular electricity production in a metering period is the result of the system operating normally or if a fault is resulting in a sub-optimal production. This paper develops two alternative methods to determine a performance score based only on electricity production and proxy wind data obtained from the nearest available weather measurement. One method based on partitioning the data, consistent with a priori expectations of turbine performance, performs well in common wind conditions but struggles to reflect the effects of different wind directions. An alternative method based on Principal Component Analysis is less intuitive but shown to be able to incorporate wind direction.
An Online Control Method of Reactive Power and Voltage Based on Mechanism–Data Hybrid Drive Model Considering Source–Load Uncertainty
Authors: Huang Xu , Zu Guoqiang , Ding Qi , Wei Ran , Wang Yudong , Wei Wei
Published: April 2023

The uncertainty brought about by the high proportion of distributed generations poses great challenges to the operational safety of novel distribution systems. Therefore, this paper proposes an online reactive power and voltage control method that integrates source–load uncertainty and a mechanism–data hybrid drive (MDHD) model. Based on the concept of a mechanism and data hybrid drive, the mechanism-driven deterministic reactive power optimization strategy and the stochastic reactive power optimization strategy are used as training data. By training the data-driven CNN–GRU network model offline, the influence of source–load uncertainty on reactive power optimization can be effectively assessed. On this basis, according to the online source and load predicted data, the proposed hybrid-driven model can be applied to quickly obtain the reactive power optimization strategy to enable fast control of voltage. As observed in the case studies, compared with the traditional deterministic and stochastic reactive power optimization models, the hybrid-driven model not only satisfies the real-time requirement of online voltage control, but also has stronger adaptability to source–load uncertainty.
Exergy Load Distribution Analysis Applied to the Dehydration of Ethanol by Extractive Distillation
Authors: Guerrero-Martin Camilo Andrés , Fernández-Ramírez Juan Sebastián , Arturo-Calvache Jaime Eduardo , Milquez-Sanabria Harvey Andrés , da Silva Fernandes Fernando Antonio , Costa Gomes Vando José , Lima e Silva Wanessa , Dutra Valente Duarte Emanuele , Guerrero-Martin Laura Estefanía , Lucas Elizabete Fernandes
Published: April 2023

This study presents the analysis of the exergy load distribution in a separation process by extractive distillation for ethanol dehydration. The methodology carried out is divided into three parts: the calculation of the flow exergy considering the physical and chemical exergies of the distillation process; the calculation of the primary and transformed exergy contributions considering the consumed exergy; and finally, the overall process efficiency, which shows the real percentage of energy being used in the process. The simulation of an extractive distillation separation system is carried out using Aspen Plus®, from Aspen Tech Version 9. In general, heat transfer processes (heating or cooling) are the ones that generate the greatest exegetic destruction, which is why they must be the operations that must be optimized. As a result of our case study, the local exergy efficiency of the extractive distillation column is 13.80%, which is the operation with the greatest energy loss, and the overall exergy efficiency of the separation system is 30.67%. Then, in order to increase exergy efficiency, a sensitivity analysis is performed with the variation of the azeotrope feed, number of stages, reflux ratio, and solvent feed variation on ethanol purity to reach an overall efficiency of 33.53%. The purity of ethanol is classified as higher than that of the specified, 99.65%.
Waste Energy Recovery and Valorization in Internal Combustion Engines for Transportation
Authors: Di Battista Davide , Cipollone Roberto
Published: April 2023

Internal Combustion Engines (ICE) are experiencing a transition era in which research and innovation are mainly pushed by environmental issues: emission reduction and fuel saving are indispensable requirements of the new technologies, otherwise the end of ICE is proposed in Europe. Modifications, in reality, are under discussion by 2026 but the environmental issues are anyway welcomed. In the transportation sector, today dominated by ICEs, it appears that the reduction in the propulsion power, hybridization at various degrees, and exhaust post-treatment improvements will guarantee technological solutions able to support the transition in the next couple of decades toward full electric propulsion. Waste Heat Recovery (WHR) is a very interesting opportunity since almost two-thirds of fuel energy is not converted into mechanically useful energy. Moreover, the integration with other thermal streams on board (cooling and lubricating mediums, EGR cooling) can add further value to the recovery opportunity as well as the concept of managing the engine thermal management which can produce a sensible contribution that is appreciated mainly during urban driving. A huge scientific effort is underway, and a great expectation is perceptible. More generally, the technological options that can achieve a reduction in overall fuel consumption and, thus, the improvement of global engine efficiency, are the most valuable when they can be introduced without massive changes to the engine layout. This happens in all the energy applications in which ICEs are involved since the recovery unit can be introduced in the exhaust line. The mechanical energy recovered can be easily transformed into electrical energy, so represents an interesting integration with the hybrid propulsion powertrains. In this paper, a review of the most important technologies referred to the WHR is presented, outlining advantages and drawbacks, and setting up the presently available technologies referred to the transportation sector.
Post-Consumer Plastic Waste Management: From Collection and Sortation to Mechanical Recycling
Authors: Bernat Katarzyna
Published: April 2023

Challenges associated with plastic waste management range from littering to high collection costs to low recycling rates. Effective collection of plastics is obviously an important step in the management of plastic waste and has an impact on recycling rates. For this reason, several countries have transformed their collection systems in recent decades. Collecting more plastic packaging comes at a cost, as the feedstock for the sorting process becomes more complex and leads to cross-contamination within the sorted fractions. Therefore, a balance must be obtained between some elements, such as the design of packaging, collection and recycling rates, and finally, the quality of fractions that have been sorted. Further investment to improve pretreatment, sorting, and recycling technologies and simpler recyclable packaging designs are, therefore, key to further increasing plastic recycling rates. It is essential to possess more data, especially on the type of containers and plastics, and examine how often unsorted waste is collected. The automated waste collection monitoring system is a step forward in automating manual waste collection and sorting. Multi-sensory artificial intelligence (AI) for sorting plastic waste and the blockchain sorting platform for the circular economy of plastic waste are forward-looking activities that will increase the efficiency of recycling plastic waste. This review focuses on the development of collection systems and sorting processes for post-consumer plastic recycling. The focus is on best practices and the best available technology. Separate collection systems for recyclable plastics are presented and discussed along with their respective technical collection and sorting solutions, taking into consideration that progress in separation and sorting systems are implicitly linked to approaches to waste collection.
Molecular Filters in Medicinal Chemistry
Authors: Kralj Sebastjan , Jukič Marko , Bren Urban
Published: April 2023

Efficient chemical library design for high-throughput virtual screening and drug design requires a pre-screening filter pipeline capable of labeling aggregators, pan-assay interference compounds (PAINS), and rapid elimination of swill (REOS); identifying or excluding covalent binders; flagging moieties with specific bio-evaluation data; and incorporating physicochemical and pharmacokinetic properties early in the design without compromising the diversity of chemical moieties present in the library. This adaptation of the chemical space results in greater enrichment of hit lists, identified compounds with greater potential for further optimization, and efficient use of computational time. A number of medicinal chemistry filters have been implemented in the Konstanz Information Miner (KNIME) software and analyzed their impact on testing representative libraries with chemoinformatic analysis. It was found that the analyzed filters can effectively tailor chemical libraries to a lead-like chemical space, identify protein–protein inhibitor-like compounds, prioritize oral bioavailability, identify drug-like compounds, and effectively label unwanted scaffolds or functional groups. However, one should be cautious in their application and carefully study the chemical space suitable for the target and general medicinal chemistry campaign, and review passed and labeled compounds before taking further in silico steps.
Investigation of a Broad-Bean Based Low-Cost Medium Formulation for Bacillus subtilis MSCL 897 Spore Production
Authors: Grigs Oskars , Didrihsone Elina , Bolmanis Emils
Published: April 2023

Bacillus subtilis (Bs) is a bacterium that benefits plants and is used in the production of bio-fungicides. The cultivation of Bs is a crucial step in bio-control preparation production, as it greatly impacts the quality and price of the final product. In a series of shake flask experiments, we investigated the economically feasible broth composition for spore production of Bacillus subtilis MSCL 897, a Latvian soil isolate. Our study investigated the impact of utilizing legume-based flours (such as broad bean, grey pea, and soybean) as the primary nitrogen source, along with sugar-beet molasses, sucrose, or glucose as the carbon source, and yeast extract, peptone, and corn-steep liquor as growth factor additives. Additionally, we examined the effect of using (NH3)2HPO4 or urea as supplementary nitrogen sources, as well as previously established media formulations, on spore yield. Our results showed that a culture medium composed of broad bean flour (10 g/L) and molasses (10 g/L) led to spore productivity of 1.35 ± 0.47 × 108 CFU/mL at 48 h. By enriching the culture medium base constituents with a minor (0.5–1.0 g/L) yeast extract or corn-steep liquor additive, a notable increase in spore productivity was observed, with values of 2.00 ± 0.28 × 108 and 2.34 ± 0.18 × 108 CFU/mL at 48 h, respectively, and sporulation efficiency > 80–90%. As a result, we achieved a high spore yield of the Bacillus subtilis MSCL 897 strain, demonstrating the competitiveness of our approach, which relied on a low-cost medium made mainly from locally available and renewable raw materials.
Supplementing Yogurt with Probiotic Bifidobacteria to Counter Chronic Kidney Disease
Authors: Stuivenberg Gerrit A. , Chmiel John A. , Akouris Polycronis P. , White Jessica , Wilcox Hannah , Seney Shannon , Burton Jeremy P. , Reid Gregor
Published: April 2023

Chronic kidney disease (CKD) disproportionately affects populations in developing countries. In sub-Saharan Africa, CKD prevalence is high (12–23%) and is associated with cardiovascular manifestations. Uremic toxins, especially p-cresol and p-cresyl sulfate, are associated with the disease. Reducing uremic toxins in the body slows disease progression and improves patient outcomes. Probiotic Bifidobacterium breve HRVD521-US, B. animalis HRVD524-US, B. longum SD-BB536-JP, and B. longum SD-CECT7347-SP internalize p-cresol and improve longevity in vivo. In 2002, Tanzanian communities were taught to produce probiotic yogurt (Fiti®) supplemented with Lacticaseibacillus rhamnosus GR-1. This has expanded to over 100 community producers across the country. To produce yogurt that could reduce the burden of CKD by sequestering uremic toxins, we decided to test the addition of p-cresol-clearing bifidobacterial strains. By repeating the Fiti® production process performed in Tanzanian communities and adding a bifidobacterial strain, we found that they were successfully incorporated into the yogurt without any detrimental effect on sensory properties or viable counts. Three of the four strains significantly reduced p-cresol when added to a simulated colonic environment. In conclusion, this study has shown that Fiti® sachets provided to Tanzanian communities to produce yogurt can be supplemented with strains that can potentially confer additional health benefits.