bluuebunny/crossref_metadata_2025 huggingface dataset:
Modified Maximum Likelihood Estimation Metal Magnetic Memory Quantitative Identifying Model of Weld Defect Levels Based on Dempster–Shafer Theory
Authors: Xing Haiyan , Xu Cheng , Yi Ming , Gao Shenrou , Liu Weinan
Published: July 2023

Metal magnetic memory (MMM) is a nondestructive testing technology based on the magnetomechanical effect, which is widely used in the qualitative detection of stress concentration zones for welded joints. However, there is inevitable residual stress after welding, which brings the bottleneck of quantitative identification between the weld residual stress concentration and the early hidden damage. In order to overcome the bottleneck of quantitative identification of weld defect levels with MMM technology, a modified maximum likelihood estimation (MLE) MMM quantitative identifying model is first proposed. The experimental materials are Q235B welded plate specimens. Fatigue tension experiments were operated to find the MMM feature laws of critical hidden crack by comparing with synchronous X-ray detection results. Six MMM characteristic parameters, which are, ΔHp(x), Gxmax, Zxmax, ΔHp(y), Gymax and Zymax, are extracted corresponding to the normal state, the hidden crack state and the macroscopic crack, respectively. The MLE values of the six parameters are obtained by the kernel density functions with optimized bandwidth from the view of mathematical statistics. Furthermore, the modified MLE MMM quantitative identifying model is established based on D–S theory to overcome the partial overlap of MLE values among different defect levels, of which the uncertainty is as low as 0.3%. The verification result from scanning electron microscopy (SEM) is consistent with the prediction of the modified MLE MMM model, which provides a new method for quantitative identification of weld defect levels.
The Spatial and Temporal Distribution of Mangrove Forest Cover from 1973 to 2020 in Chwaka Bay and Menai Bay, Zanzibar
Authors: Mohamed Mohamed Khalfan , Adam Elhadi , Jackson Colbert M.
Published: July 2023

Mangroves in Zanzibar have a high value for biodiversity and scenic beauty. However, mangroves are susceptible to anthropogenic and natural environmental disturbances. Although global mangrove monitoring systems exist, it is the practitioners focused on small mangrove areas who are knowledgeable about the area in which they work. This study examined the change in mangrove cover in Chwaka Bay and Menai Bay, between 1973 and 2020, using Landsat satellite data. The transformed divergence (TD) index and Jeffries–Matusita (J-M) distance were used to calculate separability of land cover classes before classification. The overall accuracies ranged between 82.5 and 92.7% for Chwaka Bay and 85.5 and 94.5% for Menai Bay. The kappa coefficients (ĸ) were in the range of 0.72–0.90, while the producer’s and user’s accuracies were between 72 and 100%. Chwaka Bay’s annual decrease in mangrove cover between 1973 and 2020 was 48.5 ha, compared to Menai’s 6.8 ha. The loss of mangroves in Zanzibar is linked to illegal timber/pole cutting, firewood collection, charcoal burning, unsustainable aquaculture, and agricultural and urban development. Others are changes in environmental conditions that are suitable for mangroves and climate change. This study is crucial in providing a basis for the ecological restoration and management of Zanzibar’s mangrove ecosystem.
Classification of Red Blood Cells Using Time-Distributed Convolutional Neural Networks from Simulated Videos
Authors: Molčan Samuel , Smiešková Monika , Bachratý Hynek , Bachratá Katarína , Novotný Peter
Published: July 2023

The elasticity of red blood cells (RBCs) plays a vital role in their efficient movement through blood vessels, facilitating the transportation of oxygen within the bloodstream. However, various diseases significantly impact RBC elasticity, making it an important parameter for diagnosing and monitoring health conditions. In this study, we propose a novel approach to determine RBC elasticity by analyzing video recordings and using a convolutional neural network (CNN) for classification. Due to the scarcity of available blood flow recordings, computer simulations based on a numerical model are employed to generate a substantial amount of training data. The simulation model incorporates the representation of RBCs as elastic objects within a fluid flow, allowing for a detailed understanding of their behavior. We compare the performance of different CNN architectures, including ResNet and EfficientNet, for video classification of RBC elasticity. Our results demonstrate the potential of using CNNs and simulation-based data for the accurate classification of RBC elasticity.
Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
Authors: Hubert Delisle Maxime , Christidi-Loumpasefski Olga-Orsalia , Yalçın Barış C. , Li Xiao , Olivares-Mendez Miguel , Martinez Carol
Published: July 2023

Active debris removal (ADR) is positioned by space agencies as an in-orbit task of great importance for stabilizing the exponential growth of space debris. Most of the already developed capturing systems are designed for large specific cooperative satellites, which leads to expensive one-to-one solutions. This paper proposed a versatile hybrid-compliant mechanism to target a vast range of small uncooperative space debris in low Earth orbit (LEO), enabling a profitable one-to-many solution. The system is custom-built to fit into a CubeSat. It incorporates active (with linear actuators and impedance controller) and passive (with revolute joints) compliance to dissipate the impact energy, ensure sufficient contact time, and successfully help capture a broader range of space debris. A simulation study was conducted to evaluate and validate the necessity of integrating hybrid compliance into the ADR system. This study found the relationships among the debris mass, the system’s stiffness, and the contact time and provided the required data for tuning the impedance controller (IC) gains. This study also demonstrated the importance of hybrid compliance to guarantee the safe and reliable capture of a broader range of space debris.
Eccentric Resistance Training: A Methodological Proposal of Eccentric Muscle Exercise Classification Based on Exercise Complexity, Training Objectives, Methods, and Intensity
Authors: Burgos-Jara Carlos , Cerda-Kohler Hugo , Aedo-Muñoz Esteban , Miarka Bianca
Published: July 2023

Eccentric resistance training that focuses on the lengthening phase of muscle actions has gained attention for its potential to enhance muscle strength, power, and performance (among others). This review presents a methodological proposal for classifying eccentric exercises based on complexity, objectives, methods, and intensity. We discuss the rationale and physiological implications of eccentric training, considering its benefits and risks. The proposed classification system considers exercise complexity and categorizing exercises by technical requirements and joint involvement, accommodating various skill levels. Additionally, training objectives are addressed, including (i) Sports Rehabilitation and Return To Sport, (ii) Muscle Development, (iii) Injury Prevention, (iv) Special Populations, and (v) Sporting Performance, proposing exercise selection with desired outcomes. The review also highlights various eccentric training methods, such as tempo, isoinertial, plyometrics, and moderate eccentric load, each with different benefits. The classification system also integrates intensity levels, allowing for progressive overload and individualized adjustments. This methodological proposal provides a framework for organizing eccentric resistance training programs, facilitating exercise selection, program design, and progression. Furthermore, it assists trainers, coaches, and professionals in optimizing eccentric training’s benefits, promoting advancements in research and practical application. In conclusion, this methodological proposal offers a systematic approach for classifying eccentric exercises based on complexity, objectives, methods, and intensity. It enhances exercise selection, program design, and progression in eccentric resistance training according to training objectives and desired outcomes.
Solution for the Mathematical Modeling and Future Prediction of the COVID-19 Pandemic Dynamics
Authors: Stăncioi Cristina-Maria , Ștefan Iulia Adina , Briciu Violeta , Mureșan Vlad , Clitan Iulia , Abrudean Mihail , Ungureșan Mihaela-Ligia , Miron Radu , Stativă Ecaterina , Nanu Michaela , Topan Adriana , Toader Daniela Oana , Nanu Ioana
Published: July 2023

The COVID-19 infectious disease spread in the world represents, by far, one of the most significant moments in humankind’s recent history, affecting daily activities for a long period of time. The data available now allow important modelling developments for the simulation and prediction of the process of an infectious disease spread. The current work provides strong insight for estimation and prediction mathematical model development with emphasis on differentiation between three distinct methods, based on data gathering for Romanian territory. An essential aspect of the research is the quantification and filtering of the collected data. The current work identified five main categories considered as the model’s inputs: inside temperatures (°C), outside temperatures (°C), humidity (%), the number of tests and the quantified value of COVID-19 measures (%) and, as the model’s outputs: the number of new cases, the number of new deaths, the total number of cases or the total number of deaths. Three mathematical models were tested to find the optimal solution: transfer vector models using transfer functions as elements, autoregressive-exogenous (ARX) models, and autoregressive-moving-average (ARMAX) models. The optimal solution was selected by comparing the fit values obtained after the simulation of all proposed models. Moreover, the manuscript includes a study of the complexity of the proposed models. Based on the gathered information, the structure parameters of the proposed models are determined and the validity and the efficiency of the obtained models are proven through simulation.
Field Measurement Study on Dynamic Characteristics of the Shanghai World Financial Center
Authors: Wang Xu , Kong Hu , Zhang Guoliang , Zhao Peng
Published: July 2023

It is of great practical importance to study the vibration response characteristics of super high-rise buildings under an earthquake action to provide a basis for seismic design and later maintenance of structures in coastal areas. During this study, the Shanghai World Financial Center (SWFC)’s health monitoring system was utilized to monitor earthquakes of magnitude 6.4 in Taiwan, 6.0 in Japan, 7.2 in the East China Sea, and 4.4 in Jiangsu, in real-time. Through the improved Envelope Random Decrement Technique (E-RDT), the dynamic properties of super high-rise buildings were examined under different earthquake effects in terms of the acceleration power spectrum, natural frequency, damping ratio, and mode shape. The results demonstrated that (1) the vibration responses of the structure in X (East–West) and Y (North–South) directions under four earthquakes were consistent, and with increasing floor height, the discreteness of the amplitude and acceleration signals of vibration responses increased. (2) The first two natural frequencies of the structure in X and Y directions decreased with the increase in amplitude, but the damping ratio increased with the increase in amplitude. The minimum values of the first two natural frequencies are 0.1498 Hz and 0.4312 Hz, respectively, and the maximum values of the first two damping ratios are 0.0086 and 0.0068, respectively. (3) Under different earthquake excitations, the SWFC’s mode shape’s estimates were similar, and their change trends in the X and Y directions were nonlinear as the number of floors increased. The structure was not seriously damaged by the four earthquakes. This study can provide helpful information for the seismic design of super high-rise buildings based on its findings.
Research on a Ship Mooring Motion Suppression Method Based on an Intelligent Active Anti-Roll Platform
Authors: Gao Feng , Tang Yougang , Hu Chuanqi , Xie Xiaolei
Published: July 2023

Conventional ship mooring in ports has many shortcomings such as a high safety risk, low efficiency and high labor intensity. In order to explore and develop the theory and key technologies of intelligent automatic mooring systems, this paper takes an intelligent mooring system based on a parallel anti-rolling mechanism as the research and development object. A new mooring method integrating ship hydrodynamics, mechanism kinematics and intelligent algorithms is proposed. Through numerical simulation and comparative analysis of the model, the motion inhibition effect of mooring ships under different working conditions is obtained. The results show that the control strategy and intelligent algorithm of the system can realize the active control of the wharf mooring ships and achieve the goal of improving wharf stability conditions through an intelligent mooring system.
Identification of Product Innovation Path Incorporating the FOS and BERTopic Model from the Perspective of Invalid Patents
Authors: Zhang Dingtang , Wu Xuan , Liu Peng , Qin Hao , Zhou Wei
Published: July 2023

Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises’ own technology. Due to the cost of patent research and development and the risk of infringement, this paper constructs a product innovation identification path that integrates the FOS and BERTopic model from the perspective of invalid patents. The path consists of three stages, including the identification of the problem to be solved by the product based on functional analysis, the extraction of the subject matter elements based on the core failed patent technology, and the generation and evaluation of innovative solutions based on TRIZ theory and the best- worst method (BWM). Finally, the feasibility of the path constructed in this paper is verified by taking a slurry pump as an example. The application results show that the product innovation identification path constructed in this paper can provide theoretical support for enterprises to carry out technological innovation activities efficiently.
Studies on the Manufacturing of Food Products Using Unconventional Raw Materials
Authors: Dabija Adriana , Rusu Lăcrămioara , Codină Georgiana Gabriela
Published: July 2023

Today, companies blend innovation with tradition to create new products, as the food business is continually looking for fresh product ideas that reflect worldwide trends. [...]
Uniform Circle Formation for Fully, Semi-, and Asynchronous Opaque Robots with Lights
Authors: Feletti Caterina , Mereghetti Carlo , Palano Beatrice
Published: July 2023

In the field of robotics, a lot of theoretical models have been settled to formalize multi-agent systems and design distributed algorithms for autonomous robots. Among the most investigated problems for such systems, the study of the Uniform Circle Formation (UCF) problem earned a lot of attention for the properties of such a convenient disposition. Such a problem asks robots to move on the plane to form a regular polygon, running a deterministic and distributed algorithm by executing a sequence of look–compute–move cycles. This work aims to solve the UCF problem for a very restrictive model of robots: they are punctiform, anonymous, and indistinguishable. They are completely disoriented, i.e., they share neither the coordinate system nor chirality. Additionally, they are opaque, so collinearities can hide important data for a proper computation. To tackle these system limitations, robots are equipped with a persistent light used to communicate and store a constant amount of information. For such a robot model, this paper presents a solution for UCF for each of the three scheduling modes usually studied in the literature: fully synchronous, semi-synchronous, and asynchronous. Regarding the time complexity, the proposed algorithms use a constant number of cycles (epochs) for fully synchronous (semi-synchronous) robots, and linearly, many epochs in the worst case for asynchronous robots.
A Novel Approach Proposal for Estimation of Ultimate Pile Bearing Capacity Based on Pile Loading Test Data
Authors: Vural İsa , Kabaca Halil , Poyraz Semiha
Published: July 2023

Determining the ultimate bearing capacity of piled foundations has been one of the most important problems in geotechnical engineering. It has been observed that the pile loading test evaluation methods based on the mathematical model give values far away from the failure load, even in piles that have reached the failure state. For this reason, it is aimed at developing a new mathematical pile loading test evaluation method based on the load-settlement curve. It has been thought that the main problem with the pile loading test evaluation methods based on other mathematical models giving values far from the failure load is that the methods iterate excessively. The new method proposed in the study was developed by taking this situation into account. The performance of the proposed new method was investigated using eight pile loading tests conducted in various provinces of Turkey. In order to verify the reliability of the newly developed method, the study was completed by applying multiple comparison tests with other methods in the literature (theoretical methods, finite element analysis methods, and pile loading test evaluation methods). According to the applied analysis of variance, it was concluded that the proposed new method remained within the 95% confidence interval and was usable.
A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
Authors: Teca Geovani , Natkaniec Marek
Published: July 2023

Vendors implement the MAC address randomization technique to prevent IEEE 802.11 client station devices from being tracked. Although it conceals device identity, it cannot hide its occurring data transmission. This paper presents a novel covert channel that leverages the MAC address randomization technique to create a covert channel to hide data transmission inside IEEE 802.11 networks. The secret data are a disposable random MAC address generated by the IEEE 802.11 station as part of the probe request frame while scanning the network. The paper presents the concept of the covert channel, its implementation, and performance metrics. The study covers diverse scenarios, including the adaptation of the modified Selective Repeat ARQ protocol to alleviate the impact of the number of client stations and their offered loads on the covert channel. The results show that with the appropriate parameter selections, we can adapt the covert channel to produce excellent throughput, efficiency, delay, and jitter according to the environment in which it is installed.
RETRACTED: Continual Learning Approach for Continuous Data Stream Analysis in Dynamic Environments
Authors: Prasanna K. , Khan Mudassir , Alshahrani Saeed , Kiran Ajmeera , Phanindra Kumar Reddy P. , Alymani Mofadal , Chinna Babu J.
Published: July 2023

Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning methodologies have been significantly systematic in recent years. The research introduces a novel approach using the fully connected committee machine (FCM) and different activation functions to address conceptual changes in continuous data streams. It explores scenarios of continual learning and investigates the effects of over-learning and weight decay on concept drift. The findings demonstrate the effectiveness of the FCM framework and provide insights into improving machine learning approaches for continuous data stream analysis. We used a layered neural network framework to experiment with different scenarios of continual learning on continuous data streams in the presence of change in the data distribution using a fully connected committee machine (FCM). In this research, we conduct experiments in various scenarios using a layered neural network framework, specifically the fully connected committee machine (FCM), to address conceptual changes in continuous data streams for continual learning under a conceptual change in the data distribution. Sigmoidal and ReLU (Rectified Linear Unit) activation functions are considered for learning regression in layered neural networks. When the layered framework is trained from the input data stream, the regression scheme changes consciously in all scenarios. A fully connected committee machine (FCM) is trained to perform the tasks described in continual learning with M hidden units on dynamically generated inputs. In this method, we run Monte Carlo simulations with the same number of units on both sides, K and M, to define the advancement of intersections between several hidden units and the calculation of generalization error. This is applied to over-learnability as a method of over-forgetting, integrating weight decay, and examining its effects when a concept drift is presented.
Adipolin, Chemerin, Neprilysin and Metabolic Disorders Associated with Obesity
Authors: Sperling Marcelina , Grzelak Teresa , Pelczyńska Marta , Bogdański Paweł , Czyżewska Krystyna , Formanowicz Dorota
Published: July 2023

Adipolin, neprilysin, and chemerin have pleiotropic properties; therefore, their concentrations may influence health complications associated with obesity. The study aimed to search the relationship between adipokine levels and anthropometric and metabolic parameters as well as blood pressure values, taking into account the influence of gender and age. The study group consisted of 88 people aged 30–60 years. It was found that chemerin concentration is positively correlated with glycaemia in the 120′ OGTT (rs = 0.412; p = 0.030) in obese women. There was a negative correlation between adipolin and LDL-C serum concentration (rs = −0.414; p = 0.044) in obese individuals with normal glucose tolerance and a positive correlation between chemerin concentration and triglyceridemia (rs = 0.333; p = 0.033) in the men. Moreover, high chemerin levels (above median values) were observed 3.79 times more frequently (OR = 3.79; 95% CI: 1.03–13.91; p = 0.040) in the male population characterized by elevated triglyceride levels (above 1.7 mmol/L). In the logistic regression analyses, we found that the frequency of high plasma adipolin concentrations increases with age (p = 0.005) only in people with a BMI < 30 kg/m2. It was also shown that the concentrations of the studied adipokines are interrelated. Adipolin, neprilysin and chemerin concentrations are associated with selected anthropometric and metabolism exponents.
Development of a Method for Assessing the Resistance of Building Coatings to Phoatoautotrophic Biofouling
Authors: Komar Michał , Szulc Justyna , Kata Iwona , Szafran Krzysztof , Gutarowska Beata
Published: July 2023

The aim of this study was to develop a method for assessing the growth of photoautotrophs on plaster coatings, which will be used to reliably assess the resistance of these materials to photoautotrophic growth in the simulation of long-term exposure. In the course of the study, mineral and silicone plaster substrates were inoculated with a mixture of Stichococcus bacillaris, Nostoc commune, Pseudochlorella signiensis, and Coenochloris signiensis, and incubated for 28 days in model conditions. At 14 and 28 days after inoculation, the degree of photoautotrophic growth was determined using hemocytometer cell counting, a HY-LiTE 2 ATP measuring system, chlorophyll a concentration quantification, CIE L*a*b spectrophotometric color change evaluation, and visual assessment. The acquired results allowed us to select visual assessment and spectrophotometric color change evaluation as quick-to-perform and reliable techniques for further laboratory studies. The impact of minor changes introduced in the inoculation and incubation procedures on the rate of biofilm formation and severity of microbial fouling was studied. Differences in inoculation and incubation procedures strongly affected the results of the performed tests. Both methods have shown high potential and should be further expanded upon in environmental studies.
Band Gap Properties in Metamaterial Beam with Spatially Varying Interval Uncertainties
Authors: He Feiyang , Shi Zhiyu , Zhang Zexin , Qian Denghui , Feng Xuelei
Published: July 2023

First, this study proposed a metamaterial beam model with spatially varying interval density. The interval dynamic equation of this model could be established by incorporating the decomposition results of the interval field based on Karhunen–Loeve expansion into the finite element method. An interval perturbation finite element method was developed to evaluate the bounds of the dynamic response interval vector. Then, an interval vibration transmission analysis could be performed, and the frequency range of the safe band gap could be determined. Meanwhile, Monte Carlo simulations and the vertex method are also presented to provide reference solutions. By comparison, it was found that the calculation accuracy of the interval perturbation finite element method was acceptable. The numerical results also showed that the safe band gap range was significantly smaller than that of the deterministic band gap.
Machine Learning Approaches to Intracranial Pressure Prediction in Patients with Traumatic Brain Injury: A Systematic Review
Authors: Bradley George R. E. , Roldán María , Kyriacou Panayiotis A.
Published: July 2023

Purpose: Intracranial pressure (ICP) monitoring is a “gold standard” monitoring modality for severe traumatic brain injury (TBI) patients. The capacity to predict ICP crises could further minimise the rate of secondary brain injury and improve the outcomes of TBI patients by facilitating timely intervention prior to a potential crisis. This systematic review sought (i) to identify the most efficacious approaches to the prediction of ICP crises within TBI patients, (ii) to access the clinical suitability of existing predictive models and (iii) to suggest potential areas for future research. Methods: Peer-reviewed primary diagnostic accuracy studies, assessing the performance of ICP crisis prediction methods within TBI patients, were included. The QUADAS-2 tool was used to evaluate the quality of the studies. Results: Three optimal solutions to predicting the ICP crisis were identified: a long short-term memory (LSTM) model, a Gaussian processes (GP) approach and a logistic regression model. These approaches performed with an area under the receiver operating characteristics curve (AUC-ROC) ranging from 0.86 to 0.95. Conclusions: The review highlights the existing disparity of the definition of an ICP crisis and what prediction horizon is the most clinically relevant. Moreover, this review draws attention to the existing lack of focus on the clinical intelligibility of algorithms, the measure of how algorithms improve patient care and how algorithms may raise ethical, legal or social concerns. The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022314278).
A Study on OLED Cell Simulation and Detection Phases Based on the A2G Algorithm for Artificial Intelligence Application
Authors: Han Dong-Hun , Jeong Yeong-Hoon , Kang Min-Soo
Published: July 2023

In this study, we demonstrate the viability of applying artificial intelligence (AI) techniques to conduct inspections at the OLED cell level using simulated data. The implementation of AI technologies necessitates training data, which we addressed by generating an OLED dataset via our proprietary A2G algorithm, integrating the finite element method among concerns over data security. Our A2G algorithm is designed to produce time-dependent datasets and establish threshold conditions for the expansion of dark spots based on OLED parameters and predicted lifespan. We explored the potential integration of AI in the inspection phase, performing cell-based evaluations using three distinct convolutional neural network models. The test results yielded a promising 95% recognition rate when classifying OLED data into pass and fail categories, demonstrating the practical effectiveness of this approach. Through this research, we not only confirmed the feasibility of using simulated OLED data in place of actual data but also highlighted the potential for the automation of manual inspection processes. Furthermore, by introducing OLED defect detection models at the cell level, as opposed to the traditional panel level during inspections, we anticipate higher classification rates and improved yield. This forward-thinking approach underscores significant advancement in OLED inspection processes, indicating a potential shift in industry standards.
AutoFL: Towards AutoML in a Federated Learning Context
Authors: Preuveneers Davy
Published: July 2023

Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with classical machine learning. The recent literature on federated learning often focuses on domain-specific use cases (e.g., IoT), investigates various privacy concerns (e.g., membership inference), or analyzes the impact of adversarial attacks (e.g., poisoning) and possible countermeasures. In these works, it is common for the server to have already chosen a specific machine-learning model and predefined hyperparameters prior to initiating the distributed training process. This decision is based on the server’s ability to accomplish the task by either reusing well-established neural network architectures suitable for the specific task (e.g., ResNet-50 for image classification) or evaluating the adequacy of a model using the limited data it has access to. Additionally, the server may also assess publicly available datasets, which may or may not accurately represent real-world data distributions. In this paper, we address the challenge where this step—i.e., the ML model selection and hyperparameter optimization—is not possible in a centralized manner. In such a context, the data of a single client may not be sufficient or not representative enough to construct an ML model configuration that is effective for all clients. In real-world deployments, the data on the different clients may be imbalanced and heterogeneously distributed, and the performance impact of countermeasures is often unclear upfront. While various automated machine learning (AutoML) frameworks have been proposed for classical machine learning and deep learning in a centralized setting, we investigated the practical feasibility of AutoML in a federated learning context while taking into account the presence of security and privacy countermeasures. We implemented and validated our proof-of-concept framework, called AutoFL, on top of open-source libraries for machine learning, federated learning, and hyperparameter optimization, and have demonstrated the added value of our framework with public datasets in different scenarios.
Incidence of Carpal Tunnel Syndrome and Other Coexisting Brachial Plexus Neuropathies in Bullseye Shooters—A Pilot Retrospective Clinical and Neurophysiological Assessment
Authors: Rajczewski Aleksander , Daroszewski Przemysław , Fabijański Artur , Bogusławski Ksawery , Kaźmierczak Michał , Huber Juliusz
Published: July 2023

Shooting may impact the frequency of neuropathies in the upper extremity nerves or of cervical disc–root conflicts. This study was undertaken to assess whether shooting sports trained with a handgun by civilians may influence the risk factor for carpal tunnel syndrome (CTS) and other neuropathies of the brachial plexus nerve fibers. Neurophysiological studies using surface electromyography (rEMG at rest and mcEMG during maximal contraction), electroneurography (ENG), and motor-evoked potential recordings (MEPs) were performed in a select population of nine shooters, which were rigorously screened as positive through a clinical examination for carpal tunnel syndrome and other brachial plexus neuropathies among a population of forty-two subjects, to confirm the existence of pathologies in the upper extremities. Increased muscle tension in rEMG and a simultaneous decrease in motor unit activity in mcEMG were recorded both in the proximal and distal muscles of the upper extremities more frequently in the shooters than in the healthy controls—volunteers. An ENG examination confirmed CTS in the shooting hand of four subjects (4/42; 9.5%), additionally revealing a significantly decreased F-wave at the C6–C7 levels in the dominant extremities of the shooting group in comparison to the control population (p = 0.05). All the examined subjects had revealed brachial plexus pathologies on both sides according to the results of the MEP recordings upon stimulation at the C4–C8 levels (various significant differences between the shooters and control group were found), and two had ulnar neuropathy in the wrist on the shooting side. It was concluded that shooting sports are a moderate risk factor for carpal tunnel syndrome and that they significantly influence the development of other brachial plexus neuropathies.
Q-MeaMetaVC: An MVC Solver of a Large-Scale Graph Based on Membrane Evolutionary Algorithms
Authors: Liao Chunmei , Guo Ping , Gu Jiaqi , Deng Qiuju
Published: July 2023

In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor networks. Minimum vertex cover (MVC) is an important problem in large-scale graph research. This paper proposes a solver Q-MeaMetaVC based on the MVC framework PEAF and the membrane evolution algorithm framework MEAF. First, the graph is reduced and divided into two types of connected components (bipartite graph and non-bipartite graph) to reduce the scale of the problem. Second, different membrane structures are designed for different types of connected components to better represent the connected component features and facilitate solutions. Third, a membrane evolution algorithm (MEA), which includes fusion, division, cytolysis, and selection operators, is designed to solve the connected components. Then, Q-MeaMetaVC is compared with the best MVC solver in recent years on the test set, and good experimental results that are obtained verify the feasibility and effectiveness of Q-MeaMetaVC in solving the MVC of large-scale graphs.
FC-ResNet: A Multilingual Handwritten Signature Verification Model Using an Improved ResNet with CBAM
Authors: Muhtar Yusnur , Muhammat Mahpirat , Yadikar Nurbiya , Aysa Alimjan , Ubul Kurban
Published: July 2023

Offline signature verification is a widely used biometric method in finance, law, and administrative procedures. However, existing deep convolutional neural network models perform poorly on signature datasets that span different regions and ethnic people, while also suffering from problems such as large parameter counts and slow inference speeds. To address these issues, we propose an improved residual network model (FC-ResNet). This model introduces a convolutional block attention module into the classical residual network to adapt to the diversity and variability of signatures, while also compressing the model for lightweight deployment. Due to the lack of public, offline handwritten signature datasets for ethnic people, we collected a large-scale offline handwritten signature dataset, including genuine signatures and forged signatures in Chinese, Uyghur, Kazakh, and Kirgiz, totaling 38,400 images. Our FC-ResNet model achieved an accuracy of over 96% for each language in our self-built dataset, as well as accuracy rates of 96.21%, 98.42%, and 97.28% on the public datasets CEDAR, BHSig-B, and BHSig-H, respectively. Based on the above experimental results, our proposed model demonstrates great potential for both public and self-built signature datasets, while also exhibiting significant advantages in lightweight model deployment. We believe that this work can provide a feasible solution for ethnic people signature verification.
Amplitude and Phase Information Interaction for Speech Enhancement Method
Authors: Yu Qiuyu , Zhou Ruohua
Published: July 2023

In order to improve the speech enhancement ability of FullSubNet model, an improved method FullSubNet-pMix is proposed. Specifically, pMix module is added to the structure of full-band frequency domain information processing, which realizes the information interaction between amplitude spectrum and phase spectrum. At the same time, the hyperparameters used in training are optimized so that the full-band and sub-band structure of the system can play a better role. Experiments are carried out on selected test sets. The experimental results show that the proposed method can independently improve the speech enhancement effect of the model, and the effect on the four evaluation indicators of WB-PESQ, NB-PESQ, STOI, and SI-SDR is better than the original model. Therefore, the FullSubNet-pMix method proposed in this paper can effectively enhance the ability of the model to extract and use voice information. The impact of different loss functions on the training performance was also verified.
Adaptive Finite-Time Trajectory Tracking Control for Coaxial HAUVs Facing Uncertainties and Unknown Environmental Disturbances
Authors: Lu Mingqing , Liao Fei , Xing Beibei , Fan Zhaolin , Su Yumin , Wu Wenhua
Published: July 2023

In this paper, the problems of system design, dynamic modeling, and trajectory tracking control of coaxial hybrid aerial underwater vehicles (HAUVs) with time-varying model parameters and composite marine environment disturbances are investigated. It is clear that a stable transition between different media remains a challenge in the practical implementation of amphibious tasks. For HAUVs, accurate dynamic modeling to describe complex dynamic variations during vehicle takeoff from underwater to air is a huge challenge. Meanwhile, due to the rapid changes in model parameters and the external environment, vehicles are likely to fall into the sea during the cross-domain process. An integrated continuous dynamic model considering hydrodynamic changes is established by introducing a linear switching coefficient during the process of trans-medium motion. A nonsingular fast terminal sliding-mode control (NFTSMC) algorithm combined with adaptive technology is used to design the position and attitude of the controller. With no previous knowledge of external interferences and lumped uncertainties of the HAUV, the adaptive NFTSMC (ANFTSMC) algorithm achieves the control objectives; at the same time, the inherent chattering problems of sliding mode control (SMC) are weakened. The finite-time stability of the global system is proven strictly using a series of mathematical derivations based on Lyapunov theory. The effect of the controller applied is analyzed through a series of simulations with representative working conditions. The results show that the proposed ANFTSMC can realize a “seamless” air–water trans-medium process, which proves the superiority and robustness of the proposed control algorithm.
Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
Authors: Abbasi Saad Jamshed , Lee Sangchul
Published: July 2023

Trajectory tracking is a crucial aspect of controlling nonlinear systems and is an important area of research. Researchers have proposed several strategies to perform this task in the presence of perturbations, which are the sum of a system’s uncertainty, modeling errors, and external disturbances. Nonlinear systems, such as robot manipulators, have complex dynamics, and deriving their exact mathematical models is a tedious task. Therefore, the objective of this research is to design a model-free form of control for such systems. To achieve this goal, a sliding mode control (SMC) with a proportional-integral-derivative (PID) sliding surface was designed and integrated with a saturation-function-based extended-state observer (ESO). In an extended-state observer (ESO), the primary concept is to define the system’s perturbation. The ESO estimates the system’s states and perturbation, including the known and unknown dynamics, uncertainties, and external disturbances, which are considered as perturbations. The estimated perturbation is used in a closed loop to cancel the actual perturbation. This perturbation-rejection technique improved the controller’s performance, resulting in reduced position error, reduced sensitivity to low-frequency elements of perturbation, and a small magnitude of switching gain. The designed control algorithm requires minimal information about the system, specifically position feedback, and, therefore, there is no need to identify the system parameters. A mathematical analysis of the designed algorithm was performed in detail, and the algorithm was compared with the existing ESO-based SMC algorithm. Simulations were conducted using MATLAB/SimMechanics on two different systems, and the comparison results validated the performance of the designed algorithm in comparison to previous research.
Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network
Authors: Yoshimura Takaaki , Manabe Keisuke , Sugimori Hiroyuki
Published: July 2023

The Gleason score (GS) is essential in categorizing prostate cancer risk using biopsy. The aim of this study was to propose a two-class GS classification (< and ≥GS 7) methodology using a three-dimensional convolutional neural network with semantic segmentation to predict GS non-invasively using multiparametric magnetic resonance images (MRIs). Four training datasets of T2-weighted images and apparent diffusion coefficient maps with and without semantic segmentation were used as test images. All images and lesion information were selected from a training cohort of the Society of Photographic Instrumentation Engineers, the American Association of Physicists in Medicine, and the National Cancer Institute (SPIE–AAPM–NCI) PROSTATEx Challenge dataset. Precision, recall, overall accuracy and area under the receiver operating characteristics curve (AUROC) were calculated from this dataset, which comprises publicly available prostate MRIs. Our data revealed that the GS ≥ 7 precision (0.73 ± 0.13) and GS < 7 recall (0.82 ± 0.06) were significantly higher using semantic segmentation (p < 0.05). Moreover, the AUROC in segmentation volume was higher than that in normal volume (ADCmap: 0.70 ± 0.05 and 0.69 ± 0.08, and T2WI: 0.71 ± 0.07 and 0.63 ± 0.08, respectively). However, there were no significant differences in overall accuracy between the segmentation and normal volume. This study generated a diagnostic method for non-invasive GS estimation from MRIs.
AIQS-DB: Revolutionizing the Simultaneous Analysis of Organic Compounds
Authors: Bui Quang Minh , Nguyen Huynh Nhat Minh , Le Van Nhan , Nguyen Thanh Thao , Truong Ngoc Minh , Nguyen Ngoc Tung , Le Quang Huong , Nguyen Quang Trung
Published: July 2023

This paper reports a database, namely, the Automated Identification and Quantification Database System (AIQS-DB), which consists of three components, including retention times, mass data, and calibration curves, without the requirement to analyze standard substances. The AIQS-DB that are pre-registered in the database are used as the replacement for the process of measuring chemical standards. Both the target and unknown substances in the real samples were determined by the same conditions of GC-MS as those used for the initial database register in the AIQS-DB system. The article provides a comprehensive overview of the wide-ranging applications of AIQS-DB in various fields and highlights its usefulness as a tool for the simultaneous analysis of organic compounds in different matrixes such as water, soil, sediment and air, etc. It could be considered as the basis in further applications of the AIQS-DB method in determining organic compounds in other fields, specifically biology, food, agriculture, medicine, etc., allowing assessment and reflection on the quality and status of the studied products quickly and cost-effectively.
Design and Assessment of Digital Musical Devices Yielding Vibrotactile Feedback
Authors: Papetti Stefano , Järveläinen Hanna , Fontana Federico
Published: July 2023

Touch has a pivotal importance in determining the expressivity of musical performance for a number of musical instruments. However, most digital musical devices provide no interactive force and/or vibratory feedback to the performer, thus depriving the somatosensory channel of a number of cues. Is the lack of haptic feedback only an aesthetic issue, or does it remove cues essential for digital instrument playing? If so, at which level is the interaction objectively impoverished? What are the effects on musical performance? In this survey article we illustrate our recent research about the use of vibrotactile feedback in three digital instrument interfaces, using tools that we developed over several years and made available to the community in open-source form. These interfaces span a wide range of familiarity and gestural opportunities, enabling us to explore the impact of haptic feedback on different types of digital instruments. We conducted experiments with professional musicians to assess the impact of vibratory cues on both the perceived quality of the instrument and the playing experience, as well as on musical performance. Particular attention was paid to scientific rigor and repeatability of the results, so as to serve as a reference for researchers and practitioners of the musical haptics community. Our results suggest a significant role of vibrotactile feedback in shaping the perception of digital musical instruments, although the effects on musical performance varied depending on the interfaces tested.
Perceptual Relevance of Haptic Feedback during Virtual Plucking, Bowing and Rubbing of Physically-Based Musical Resonators
Authors: Onofrei Marius George , Fontana Federico , Serafin Stefania
Published: July 2023

The physics-based design and realization of a digital musical interface asks for the modeling and implementation of the contact-point interaction with the performer. Musical instruments always include a resonator that converts the input energy into sound, meanwhile feeding part of it back to the performer through the same point. Specifically during plucking or bowing interactions, musicians receive a handful of information from the force feedback and vibrations coming from the contact points. This paper focuses on the design and realization of digital music interfaces realizing two physical interactions along with a musically unconventional one, rubbing, rarely encountered in assimilable forms across the centuries on a few instruments. Therefore, it aims to highlight the significance of haptic rendering in improving quality during a musical experience as opposed to interfaces provided with a passive contact point. Current challenges are posed by the specific requirements of the haptic device, as well as the computational effort needed for realizing such interactions without occurrence during the performance of typical digital artifacts such as latency and model instability. Both are however seemingly transitory due to the constant evolution of computer systems for virtual reality and the progressive popularization of haptic interfaces in the sonic interaction design community. In summary, our results speak in favor of adopting nowadays haptic technologies as an essential component for digital musical interfaces affording point-wise contact interactions in the personal performance space.