bluuebunny/crossref_metadata_2025 huggingface dataset:
Observing Spatiotemporal Inconsistency of Erosion and Accretion in the Barak River Using Remote Sensing and GIS Techniques
Authors: Sil Briti Sundar , Ashwini Kumar , Annayat Wajahat , Debnath Jatan , Farooq Majid , Meraj Gowhar
Published: December 2022

Alluvial rivers all over the world have one common problem, which is their meandering pattern. This meander formation is because of natural and anthropogenic processes. Barak River is dynamic, and due to this, it is exposed to regular shifting and creates many problems for the people who reside near the river. The livelihood of many people depends on agriculture, which they conduct on the nearby sides of the river. However, the regular shifting of riverbanks makes their life miserable and leads to severe economic losses. Further, roadways and railways run along the banks of the Barak River, and during monsoon, Assam (Silchar), along with three states, Mizoram, Manipur, and Tripura, become disconnected from the rest of India because the road and rail connections fail due to riverbank erosion. Therefore, considering the catchment area and the importance of this river, we have tried to understand the spatiotemporal changes (erosion, deposition, and unchanged area) in the Barak River. From our analysis, we found that the maximum and minimum amount of erosion occurred from 2012–2017 and 2002–2012 and were 727.56 ha and 332.69 ha, respectively. While the highest amount of deposition that occurred during 1984–2017 was 1054.21 ha, the minimum amount of deposition that occurred during 2012–2017 was 351.32. Overall, it was identified that the area under the deposition was more dynamic than the erosion from 1984–2017. Moreover, from the temporal analysis of land use/land cover from 1984–2017, it was found that the area that comes under the settlement and arable land has increased by 10.47% and 5.05%, respectively. The dynamic factors, such as the nature of channel gradient, land use/land cover, and riparian vegetative cover, could be the probable driving forces that cause changes in the erosional and depositional areas. This study will help us understand the dynamics of the Barak River and other rivers of this type worldwide. This study shall help implement strategies that will help manage bank erosion by adapting scientific bank protection measures.
A Cistus incanus Extract Blocks Psychological Stress Signaling and Reduces Neurogenic Inflammation and Signs of Aging in Skin, as Shown in In-Vitro Models and a Randomized Clinical Trial
Authors: Havas Fabien , Cohen Moshe , Krispin Shlomo , Loing Estelle , Attia-Vigneau Joan
Published: December 2022

Psychological stress exerts its effects mainly through the release of corticotropin releasing hormone (CRH), which activates inflammatory pathways in skin (inter alia), resulting in redness, extracellular matrix degradation, loss of skin elasticity and firmness, and the appearance of wrinkles—namely, accelerated skin aging. In order to propose a solution to this neurogenic aging phenomenon, we report here on studies using a myricitrin-rich extract of Cistus incanus, a Mediterranean shrub used in traditional medicine for the treatment of inflammatory and other diseases. These studies include a CRH receptor (CRH-R1) blocking assay; in vitro inflammatory cytokine reduction under CRH stimulation, and ex vivo NF-kB inhibition; and a double-blind clinical trial performed on highly stressed panelists, evaluating skin inflammation and wrinkling (active formulation vs. placebo control, applied split-face following a computer-generated randomization scheme; 36 subjects recruited and randomized, 30 analyzed; no adverse effects recorded; EMA/INFARMED registration #118505, internally funded). The results show that this extract can effectively block the CRH-R1 receptor, preventing NF-κB activation and the production of related pro-inflammatory cytokines. In a clinical setting, this same extract delivered significant anti-inflammatory and anti-aging effects. Taken together, these results demonstrate the value of this extract as a cosmetic active to counter neurogenic inflammation and skin aging.
Cassia alata (Linnaeus) Roxburgh for Skin: Natural Remedies for Atopic Dermatitis in Asia and Their Pharmacological Activities
Authors: Yon Jessica-Ai-Lyn , Lee Sue-Kei , Keng Jing-Wen , Chow Sek-Chuen , Liew Kai-Bin , Teo Swee-Sen , Shaik Mossadeq Wan Mastura , Marriott Philip J. , Akowuah Gabriel Akyirem , Ming Long Chiau , Goh Bey Hing , Chew Yik-Ling
Published: December 2022

Atopic dermatitis (AD) is an inflamed skin condition with relapsing pruritus and cutaneous physiological dysfunction. This skin disorder is widespread around the world and frequently affects infants, children and adults. Natural products with bioactive lead compounds are the source of natural medicines for complementary and alternative therapy in managing AD. Cassia alata has been used traditionally as a remedy for a variety of health issues. In Asian countries, it is used as an ethnomedicine to treat skin conditions such pityriasis versicolor, ringworm, scabies, shingles, urticaria and itching. According to previously published studies, the phytochemicals in C. alata may have a wide range of significant pharmacological effects. AD management is highlighted here, as this review explores the literature on the pharmacological effects of C. alata and its phytochemical content. Specifically, antibacterial, wound healing, anti-inflammatory and antioxidant effects are reviewed and discussed in relation to AD management.
Workforce Management during the Time of COVID-19—Lessons Learned and Future Measures
Authors: Bardhan Rupkatha , Byrd Traci , Boyd Julie
Published: December 2022

Industries worldwide have faced continuous burdens since the beginning of the COVID-19 pandemic, while adjusting to rapidly changing rules and regulations. Industries need to be prepared to remain operational and productive in the face of current and emergent pathogens. While several businesses could remain functional through remote work, critical industries faced closings, worker shortages, and loss of productivity. Pharmaceutical industries were blessed with an increase in the stock market and creation of new jobs, but faced serious severe challenges due to shortage of medicines and drugs. Critical infrastructures such as healthcare, food and agriculture, manufacturing, construction, transportation, retail, waterworks, and waste management took a significant hit during the pandemic, and are still suffering from worker shortages to function optimally. Above all odds, companies were able to maintain the necessities by implementing strict safety protocols such as thorough and repeated cleaning, use of hand sanitizer/disinfectants, wearing face masks and personal protective equipment, and maintaining social distancing. This article addresses how COVID-19 disrupted normal operations on a large scale, and how essential businesses have learned to assess the impact, handle situations effectively, and become resilient for future crises. Best practices were tailored to each industry sector to prepare for and address the pandemic.
Characterization and Analysis of Micromechanical Properties on DNTF and CL-20 Explosive Crystals
Authors: Nan Hai , Zhu Yiju , Niu Guotao , Wang Xuanjun , Sun Peipei , Jiang Fan , Bu Yufan
Published: December 2022

To study the crystal mechanical properties of 3,4-dinitrofurazanofuroxan (DNTF) and hexanitrohexaazaisowurtzitane (CL-20) deeply, the crystals of DNTF and CL-20 were prepared by the solvent evaporation method. The crystal micromechanical loading procedure was characterized by the nanoindentation method, and then obtained the mechanical parameters. In addition, the crystal fracture behaviors were investigated with scanning probe microscopy (SPM). The results show that the hardness for DNTF and CL-20 was 0.57 GPa and 0.84 GPa, and the elastic modulus was 10.34 GPa and 20.30 GPa, respectively. CL-20 obviously exhibits a higher hardness, elastic modulus and local energy-dissipation and a smaller elastic recovery ability of crystals than those of DNTF. CL-20 crystals are more prone to cracking and have a lower fracture toughness value than DNTF. Compared to DNTF crystals, CL-20 is a kind of brittle material with higher modulus, hardness and sensitivity than that of DNTF, making the ignition response more likely to happen.
Variation in Body Mass and Skeletal Muscle Indices in Head and Neck Cancer Patients Undergoing (Chemo)Radiotherapy and Nutritional Intervention
Authors: Pisani Carla , Mastroleo Federico , Collo Alessandro , Ferrante Daniela , Carabelli Greta , Franco Pierfrancesco , Riso Sergio , Dell’Era Valeria , Garzaro Massimiliano , Aluffi Valletti Paolo , Krengli Marco
Published: December 2022

The aim of this study was to analyze variation in body mass index (BMI) and skeletal muscle index (SMI) in head and neck squamous cell carcinoma (HNSCC) patients who underwent exclusive radiotherapy (RT) or concurrent chemo-radiotherapy (RT-CHT). We enrolled 73 HNSCC pts treated with definitive or post-operative RT (14 pts) or RT-CHT (59 pts). At the time of diagnosis (t0) and 3 months after treatment completion (t3), CT scans were retrieved to measure skeletal muscle at the level of the C3 vertebra. Median follow-up was 16 months. Nine disease progressions with distant metastases and eleven local relapses were observed. Fifty-three pts were free from progression at 1 year. At t0, average BMI was 25.8 (SD 4.1), while at t3 it was 24.5, with no reduction in 54 pts. A BMI decrease of −1.3 (p-value < 0.0001) between t0 and t3 was found with the Wilcoxon signed-rank test. SMI was 57.1 and 59.2 at t0 and t3, respectively (p-value = 0.005). According to our analysis, SMI variation seems to reflect the effect of an appropriate nutritional intervention and may represent a reliable, simple tool for muscle mass analysis.
Impact of COVID-19 Epidemic on Psycho-Oncological Distress in Neuro-Oncological Patients
Authors: Staub-Bartelt Franziska , Steinmann Julia , Radtke Oliver , Hänggi Daniel , Sabel Michael , Rapp Marion
Published: December 2022

Up to 40% of neuro-oncological patients already deal with high levels of distress under conventional circumstances. Due to COVID-19, pandemic hospital visitor rules have been restricted and patients did not receive the same level of supporting caregiver network as before COVID. The aim of the present study was to analyse the impact of the COVID pandemic on the prevalence of distress, anxiety and depression in neuro-oncological patients. Patients admitted for brain tumour surgery were screened regarding distress, anxiety and depression. Furthermore, aspects of patients’ quality of life and clinical data were covered. Retrospectively available data of patients treated pre-pandemic (group A) and throughout the COVID-19 pandemic (group B) were statistically analysed using Chi-square tests and independent-sample t-tests, and regression analysis was performed to support statistical findings. Data from 110 patients were available. In all, 48 patients were included pre-COVID-19 and 62 during the COVID-19 pandemic. The authors found no significant difference between pre-COVID-19 prevalence of distress (p = 0.112), anxiety (p = 0.385) or depression (p = 0.084). Regression analyses additionally did not show any significant influence of COVID-19 on the above analysed parameter. Analyses of our cohort’s data could not underline the negative impact of COVID-19 restrictions, shortcuts of professional and remodelled caregiver support on psycho-oncological outcomes.
Two- and Three-Dimensional Computer Vision Techniques for More Reliable Body Condition Scoring
Authors: O’Mahony Niall , Krpalkova Lenka , Sayers Gearoid , Krump Lea , Walsh Joseph , Riordan Daniel
Published: December 2022

This article identifies the essential technologies and considerations for the development of an Automated Cow Monitoring System (ACMS) which uses 3D camera technology for the assessment of Body Condition Score (BCS). We present a comparison of a range of common techniques at the different developmental stages of Computer Vision including data pre-processing and the implementation of Deep Learning for both 2D and 3D data formats commonly captured by 3D cameras. This research focuses on attaining better reliability from one deployment of an ACMS to the next and proposes a Geometric Deep Learning (GDL) approach and evaluating model performance for robustness from one farm to another in the presence of background, farm, herd, camera pose and cow pose variabilities.
Update on Corneal Confocal Microscopy Imaging
Authors: Cañadas Pilar , Alberquilla García-Velasco Marta , Hernández Verdejo José Luis , Teus Miguel A.
Published: December 2022

In vivo corneal confocal microscopy (IVCM) is a non-invasive ophthalmic imaging technique that provides images of the cornea at the cellular level. Despite the uses in ocular surface pathologies, in the last decades IVCM has been used to provide more knowledge in refractive surgery wound healing, in neuropathies diagnosis, etc. The observation of the corneal cells, both normal and inflammatory, and the possibility of quantification of the corneal nerve density with manual or automated tools, makes IVCM have a significant potential to improve the diagnosis and prognosis in several systemic and corneal conditions.
Effect of Targeted Embolization on Seizure Outcomes in Patients with Brain Arteriovenous Malformations
Authors: Nurimanov Chingiz , Babi Aisha , Menlibayeva Karashash , Makhambetov Yerbol , Kaliyev Assylbek , Zholdybayeva Elena , Akshulakov Serik
Published: December 2022

Background: Seizures are one of the most debilitating manifestations of brain arteriovenous malformations (AVMs). This study aimed to evaluate the effect of curative embolization on brain AVM patients presenting with seizures. Methods: The records of patients who underwent embolization for brain AVM from January 2012 to December 2020 were evaluated and patients presenting with seizures were interviewed. Patient responses were evaluated according to the International League Against Epilepsy (ILAE) and Engel classifications. Statistical analyses of factors associated with seizure outcomes and complications were performed using ANOVA and Fischer’s exact tests. Results: The mean age of the participants was 35.2 ± 10.7 years. More than 80% of the patients received no or suboptimal dosages of antiepileptic drugs (AEDs) prior to embolization. Positive seizure dynamics were observed in 50% of the patients post-procedure. A correlation was found between length of seizures in anamnesis and outcomes of both Engel and ILAE score, where shorter length was associated with better outcomes. Post-embolization hemorrhage was associated with initial presentation with hemorrhage. Conclusions: The embolization of brain AVMs had a positive effect on seizure presentation and a relatively low prevalence of complications. However, the results of the study are obscured by inadequate AED treatment received by the patients, which prompts prospective studies on the topic with careful patient selection.
Prevalence of Myocardial Injury and Myocardial Infarction in Patients with a Hypertensive Emergency: A Systematic Review
Authors: Talle Mohammed A. , Ngarande Ellen , Doubell Anton F. , Herbst Philip G.
Published: December 2022

Myocardial injury and myocardial infarction can complicate a hypertensive emergency, and both are associated with poor prognosis. However, little is known about the prevalence of myocardial injury and the different subtypes of myocardial infarction in patients with hypertensive emergencies. This systematic review aims to determine the prevalence of myocardial infarction and its subtypes, and the prevalence of myocardial injury in patients with hypertensive emergencies following the PRISMA guideline. A systematic search of PubMed, Web of Science, and EBSCOHost (MEDLINE) databases was carried out from inception to identify relevant articles. A total of 18 studies involving 7545 patients with a hypertensive emergency were included. Fifteen (83.3%) studies reported on the prevalence of myocardial infarction ranging from 3.6% to 59.6%, but only two studies specifically indicated the prevalence of ST-elevation and non-ST-elevation myocardial infarction. The prevalence of myocardial injury was obtained in three studies (16.7%) and ranged from 15% to 63%. Despite being common, very few studies reported myocardial injury and the subtypes of myocardial infarction among patients presenting with a hypertensive emergency, highlighting the need for more research in this area which will provide pertinent data to guide patient management and identify those at increased risk of major adverse cardiovascular events.
A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer
Authors: Walsh Ricky , Tardy Mickael
Published: December 2022

Tools based on deep learning models have been created in recent years to aid radiologists in the diagnosis of breast cancer from mammograms. However, the datasets used to train these models may suffer from class imbalance, i.e., there are often fewer malignant samples than benign or healthy cases, which can bias the model towards the healthy class. In this study, we systematically evaluate several popular techniques to deal with this class imbalance, namely, class weighting, over-sampling, and under-sampling, as well as a synthetic lesion generation approach to increase the number of malignant samples. These techniques are applied when training on three diverse Full-Field Digital Mammography datasets, and tested on in-distribution and out-of-distribution samples. The experiments show that a greater imbalance is associated with a greater bias towards the majority class, which can be counteracted by any of the standard class imbalance techniques. On the other hand, these methods provide no benefit to model performance with respect to Area Under the Curve of the Recall Operating Characteristic (AUC-ROC), and indeed under-sampling leads to a reduction of 0.066 in AUC in the case of a 19:1 benign to malignant imbalance. Our synthetic lesion methodology leads to better performance in most cases, with increases of up to 0.07 in AUC on out-of-distribution test sets over the next best experiment.
An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images
Authors: Barriada Rubén G. , Masip David
Published: December 2022

Cardiovascular diseases (CVDs) are one of the most prevalent causes of premature death. Early detection is crucial to prevent and address CVDs in a timely manner. Recent advances in oculomics show that retina fundus imaging (RFI) can carry relevant information for the early diagnosis of several systemic diseases. There is a large corpus of RFI systematically acquired for diagnosing eye-related diseases that could be used for CVDs prevention. Nevertheless, public health systems cannot afford to dedicate expert physicians to only deal with this data, posing the need for automated diagnosis tools that can raise alarms for patients at risk. Artificial Intelligence (AI) and, particularly, deep learning models, became a strong alternative to provide computerized pre-diagnosis for patient risk retrieval. This paper provides a novel review of the major achievements of the recent state-of-the-art DL approaches to automated CVDs diagnosis. This overview gathers commonly used datasets, pre-processing techniques, evaluation metrics and deep learning approaches used in 30 different studies. Based on the reviewed articles, this work proposes a classification taxonomy depending on the prediction target and summarizes future research challenges that have to be tackled to progress in this line.
Left Ventricular Fibrosis Assessment by Native T1, ECV, and LGE in Pulmonary Hypertension Patients
Authors: Cerne John W. , Pathrose Ashitha , Sarnari Roberto , Veer Manik , Chow Kelvin , Subedi Kamal , Allen Bradley D. , Avery Ryan J. , Markl Michael , Carr James C.
Published: December 2022

Cardiac magnetic resonance imaging (MRI) is emerging as an alternative to right heart catheterization for the evaluation of pulmonary hypertension (PH) patients. The aim of this study was to compare cardiac MRI-derived left ventricle fibrosis indices between pre-capillary PH (PrePH) and isolated post-capillary PH (IpcPH) patients and assess their associations with measures of ventricle function. Global and segmental late gadolinium enhancement (LGE), longitudinal relaxation time (native T1) maps, and extracellular volume fraction (ECV) were compared among healthy controls (N = 25; 37% female; 52 ± 13 years), PH patients (N = 48; 60% female; 60 ± 14 years), and PH subgroups (PrePH: N = 29; 65% female; 55 ± 12 years, IpcPH: N = 19; 53% female; 66 ± 13 years). Cardiac cine measured ejection fraction, end diastolic, and end systolic volumes and were assessed for correlations with fibrosis. LGE mural location was qualitatively assessed on a segmental basis for all subjects. PrePH patients had elevated (apical-, mid-antero-, and mid-infero) septal left ventricle native T1 values (1080 ± 74 ms, 1077 ± 39 ms, and 1082 ± 47 ms) compared to IpcPH patients (1028 ± 53 ms, 1046 ± 36 ms, 1051 ± 44 ms) (p < 0.05). PrePH had a higher amount of insertional point LGE (69%) and LGE patterns characteristic of non-vascular fibrosis (77%) compared to IpcPH (37% and 46%, respectively) (p < 0.05; p < 0.05). Assessment of global LGE, native T1, and ECV burdens did not show a statistically significant difference between PrePH (1.9 ± 2.7%, 1056.2 ± 36.3 ms, 31.2 ± 3.7%) and IpcPH (2.7 ± 2.7%, 1042.4 ± 28.1 ms, 30.7 ± 4.7%) (p = 0.102; p = 0.229 p = 0.756). Global native T1 and ECV were higher in patients (1050.9 ± 33.8 and 31.0 ± 4.1%) than controls (28.2 ± 3.7% and 1012.9 ± 29.4 ms) (p < 0.05). Cardiac MRI-based tissue characterization may augment understanding of cardiac involvement and become a tool to facilitate PH patient classification.
Implant-Supported Prostheses in the Edentulous Mandible: Biomechanical Analysis of Different Implant Configurations via Finite Element Analysis
Authors: Anitua Eduardo , Larrazabal Saez de Ibarra Naiara , Saracho Rotaeche Luis
Published: December 2022

This study explores the implant-supported prosthetic treatment alternatives of the edentulous mandible from a biomechanical point of view by means of a Finite Element Analysis (FEA). Finite element (FE) models were used to simulate cases treated with six, five, and four, implants and a fixed prosthesis with a cantilever. In the four implant treatments, three cases were analyzed; the posterior implants were placed in axial positions, angled at 30° and 45°. Cases with six and four axially placed implants were also analyzed by placing the posterior implants distally to the foramen, thus eliminating the cantilever in the prostheses. In the cases with implants between foramina, the highest values for the principal strains and von Mises stresses were observed in the case with four implants where the posterior implants were angled at 45°. Cases with implants placed distally to the foramen and without a cantilever showed much lower bone stress and strain levels compared to cases with implants between foramina. From a biomechanical point of view, it seems to be a better option to use implants positioned distally to the foramen, eliminating cantilevers.
Onboard Distributed Trajectory Planning through Intelligent Search for Multi-UAV Cooperative Flight
Authors: Lu Kunfeng , Hu Ruiguang , Yao Zheng , Wang Huixia
Published: December 2022

Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results.
Liutex-Represented Vortex Spectrum in Turbulence
Authors: Yan Bowen , Wang Yiqian , Liu Chaoqun
Published: December 2022

The Liutex vector is new quantity introduced to represent the rigid-body rotation part of fluid motion and thus to define and identify vortices in various flows. In this work, the intermittency and power-law similarity of the Liutex vector in homogeneous, isotropic turbulence and a turbulent channel are explored. First, we found that the Liutex vector is more intermittent than the vorticity vector in the considered turbulent flows, which indicates that an iso-surface of a Liutex magnitude with an appropriate threshold could capture the major rotating motions or vortical motions of the flow. Second, the three-dimensional energy spectrums of velocity, vorticity (enstrophy spectrum) and the Liutex vector in homogeneous isotropic turbulence are shown to exhibit power laws of −5/3, 1/3 and 1/3 in the inertial subrange, respectively, whilst the Liutex energy spectrum particularly satisfies an additional −10/3 power law in the viscous subrange. This viscous similarity of the Liutex vector is the only power law that survived from the wall presence and is argued to originate from the fact that the Liutex vector represents the rigid part of fluid motion and is free from any shear contamination. The existence of such a viscous similarity law indicates a certain coherence of the small scales of turbulence and could possibly help understand and model turbulence.
Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports
Authors: Birant Kokten Ulas , Birant Derya
Published: December 2022

The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (single-instance) classification techniques. However, a team-centric classification is required since team cooperation is essential in completing game missions and achieving final success. To bridge this gap, in this study, we propose a new approach, called Multi-Objective Multi-Instance Learning (MOMIL). It is the first study that applies the multi-instance learning technique to make win predictions in eSports. The proposed approach jointly considers the objectives of the players in a team to capture relationships between players during the classification. In this study, entropy was used as a measure to determine the impurity (uncertainty) of the training dataset when building decision trees for classification. The experiments that were carried out on a publicly available eSports dataset show that the proposed multi-objective multi-instance classification approach outperforms the standard classification approach in terms of accuracy. Unlike the previous studies, we built the models on season-based data. Our approach is up to 95% accurate for win prediction in eSports. Our method achieved higher performance than the state-of-the-art methods tested on the same dataset.
Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data
Authors: Mirkes Evgeny M. , Bac Jonathan , Fouché Aziz , Stasenko Sergey V. , Zinovyev Andrei , Gorban Alexander N.
Published: December 2022

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets into a common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.
Combining Fractional Derivatives and Machine Learning: A Review
Authors: Raubitzek Sebastian , Mallinger Kevin , Neubauer Thomas
Published: December 2022

Fractional calculus has gained a lot of attention in the last couple of years. Researchers have discovered that processes in various fields follow fractional dynamics rather than ordinary integer-ordered dynamics, meaning that the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of which is that fractional derivatives inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It has provided many further insights and discoveries in various scientific disciplines. As these two modern-day topics hold a lot of potential for combined approaches in terms of describing complex dynamics, this article review combines approaches from fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there is already extensive literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning and fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools, and enhance their existing approaches.
A Lightweight Method for Defense Graph Neural Networks Adversarial Attacks
Authors: Qiao Zhi , Wu Zhenqiang , Chen Jiawang , Ren Ping’an , Yu Zhiliang
Published: December 2022

Graph neural network has been widely used in various fields in recent years. However, the appearance of an adversarial attack makes the reliability of the existing neural networks challenging in application. Premeditated attackers, can make very small perturbations to the data to fool the neural network to produce wrong results. These incorrect results can lead to disastrous consequences. So, how to defend against adversarial attacks has become an urgent research topic. Many researchers have tried to improve the model robustness directly or by using adversarial training to reduce the negative impact of an adversarial attack. However, the majority of the defense strategies currently in use are inextricably linked to the model-training process, which incurs significant running and memory space costs. We offer a lightweight and easy-to-implement approach that is based on graph transformation. Extensive experiments demonstrate that our approach has a similar defense effect (with accuracy rate returns of nearly 80%) as existing methods and only uses 10% of their run time when defending against adversarial attacks on GCN (graph convolutional neural networks).
On the Consensus Performance of Multi-Layered MASs with Various Graph Parameters—From the Perspective of Cardinalities of Vertex Sets
Authors: Huang Da , Yu Zhiyong
Published: December 2022

This work studies the first-order coherence of noisy multi-agent networks with multi-layered structures. The coherence, which is a sort of performance index of networks, can be seen as a sort of measurement for a system’s robustness. Graph operations are applied to design the novel multi-layered networks, and a graph spectrum approach, along with analysis methods, is applied to derive the mathematical expression of the coherence, and the corresponding asymptotic results on the performance index have been obtained. In addition, the coherence of these non-isomorphic multi-layered networks with three different graph parameters are compared and analyzed. We find that, when the cardinalities of the vertex sets of corresponding counterpart layers are the same, the multi-layered topology class with a balanced, complete, multi-partite structure has the best robustness of all the considered networks, if the sufficient conditions for the node-related parameters hold. Finally, simulations are given to verify the asymptotic results.
Online Testing as a Means of Enhancing Students’ Academic Motivation during the Coronavirus Pandemic
Authors: Stoyanova Stanislava , Giannouli Vaitsa
Published: December 2022

Although it is widely believed that online testing may be applied as a way of enhancing academic motivation, thus far we know little about this topic for Bulgarian students. For this purpose, we conducted research during the COVID-19 pandemic focusing only on university students (n = 80; 74 women, 63 full-time, 17 part-time, 41 bachelor students, 39 master’s students). Participants studied online and filled in several tests online as a part of their interim control. Nine tests were created measuring knowledge and skills related to psychological measurements. Each student was provided the opportunity to respond an unlimited number of times to each test and, therefore, data were collected from 1226 testing procedures that permitted the comparison of 911 responses from full-time students with 315 responses from part-time students. Findings support the conclusion that the highest academic motivation was manifested in the best students’ performance, because the students with high academic motivation had the highest test scores/attempts in online testing. The lowest academic motivation was expressed in the least efforts put into the learning process, because the students with the lowest motivation made the least number of attempts to respond to a test, compared with the students with medium and high academic motivation.
Analysis of the Control System for a Soft Starter of an Induction Motor Based on a Multi-Zone AC Voltage Converter
Authors: Kosykh Evgeniy , Udovichenko Aleksey , Lopatkin Nikolay , Zinoviev Gennadiy , Grishanov Evgeniy , Sarakhanova Regina
Published: December 2022

The development trends of the modern world of power electronics dictate the requirements for the use of AC voltage converters as soft starters for induction motors. A direct connection of the motors to the mains voltage negatively affects both the motor itself and the mains system as a whole due to high starting currents values, which entail, as a rule, more frequent accidents and shorten the drive system service life. Modern methods of motor acceleration are implemented in practice by means of frequency converters, which require the presence of both a rectifier and an inverter in the structure of the device. The paper presents a study of the control system of a multi-zone AC voltage single-stage regulator. The use of capacitive voltage dividers will also compensate for the consumed reactive power. The article analyzes the features of modern soft starters, describes the circuit design, presents a mathematical calculation by the differential equations algebraization method, a performed simulation modeling in Matlab/Simulink, and also an assembled experimental stand. Particular attention is paid to the definition of the multizonality concept of the proposed converter and the analysis of the control method. The developed algorithm of the double-loop automatic control system will minimize the influence of induction motors on the mains voltage and thus improve electromagnetic compatibility.
Emulation of the Rician K-Factor of 5G Propagation in a Source Stirred Reverberation Chamber
Authors: De Leo Alfredo , Russo Paola , Mariani Primiani Valter
Published: December 2022

This paper presents a novel algorithm to emulate the Rician K-factor of a wireless communication environment using a reverberation chamber. In particular, it was focused on the frequency bands used by 5G technology for data transmission. Unlike the current state of the art, the increase of the Rician K-factor is not achieved by inserting lossy material in the chamber, but through the proposed algorithm, which selects a subset of electromagnetic field realizations inside the chamber. The algorithm was first applied to the results of a simulation obtained by using an analytical model that predicts the main physical quantities inside a rectangular cavity where a multiple monopole source stirring action is implemented. Subsequently, the robustness of the algorithm and its applicability was tested on a set of data retrieved from experimental measurements. In all of the considered scenarios, the validity of the proposed method was demonstrated.
Open-Source Culture Platform for Multi-Cell Type Study with Integrated Pneumatic Stimulation
Authors: Cacocciola Nicolò , Marasso Simone Luigi , Canavese Giancarlo , Cocuzza Matteo , Pirri Candido Fabrizio , Frascella Francesca
Published: December 2022

Mechanical forces can influence the structure and development of healthy and cancerous cells and tissue microenvironments, acting on their physical shape and promoting non-genetic alterations during growth. For this reason, it is interesting to investigate the role of dynamic hydrostatic compression on such cultures, to assess the role of such stimuli on key parameters, such as cell differentiation, cell stiffness and cytoskeleton rearrangements. In this work, we present a versatile Arduino-based pneumatic system for the stimulation of a cell culture performed in a standard multi-well plate, designed to work inside a CO2 incubator. The system is capable of modifying the hydrostatic pressure inside a dedicated culture chamber following the desired pattern, and, thus, providing a mechanical hydrostatic stimulus to a cell culture growing inside it. In the present work, a human respiration-like compression pattern was used, to mimic the mechanical stress conditions inside the human lung alveoli, and make the platform compatible with the development of lung tissues and organoids.
Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
Authors: Boujerfaoui Said , Riad Rabia , Douzi Hassan , Ros Frédéric , Harba Rachid
Published: December 2022

Currently, most transactions and exchanges are conducted through the Internet thanks to technological tools, running the risk of the falsification and distortion of information. This is due to the massive demand for the virtual world and its easy access to anyone. Image watermarking has recently emerged as one of the most important areas for protecting content and enhancing durability and resistance to these kinds of attacks. However, there is currently no integrated technology able to repel all possible kinds of attacks; the main objective of each technology remains limited to specific types of applications, meaning there are multiple opportunities to contribute to the development of this field. Recently, the image watermarking field has gained significant benefits from the sudden popularity of deep learning and its outstanding success in the field of information security. Thus, in this article, we will describe the bridge by which the watermarking field has evolved from traditional technology to intelligent technologies based on deep learning.
Wafer Surface Defect Detection Based on Feature Enhancement and Predicted Box Aggregation
Authors: Zheng Jiebing , Dang Jiangtao , Zhang Tao
Published: December 2022

For wafer surface defect detection, a new method based on improved Faster RCNN is proposed here to solve the problems of missing detection due to small objects and multiple boxes detection due to discontinuous objects. First, focusing on the problem of small objects missing detection, a feature enhancement module (FEM) based on dynamic convolution is proposed to extract high-frequency image features, enrich the semantic information of shallow feature maps, and improve detection performance for small-scale defects. Second, for the multiple boxes detection caused by discontinuous objects, a predicted box aggregation method is proposed to aggregate redundant predicted boxes and fine-tune real predicted boxes to further improve positioning accuracy. Experimental results show that the mean average precision of the proposed method, when validated on a self-developed dataset, reached 87.5%, and the detection speed was 0.26 s per image. The proposed method has a certain engineering application value.
A Novel COVID-19 Image Classification Method Based on the Improved Residual Network
Authors: Chen Hui , Zhang Tian , Chen Runbin , Zhu Zihang , Wang Xu
Published: December 2022

In recent years, chest X-ray (CXR) imaging has become one of the significant tools to assist in the diagnosis and treatment of novel coronavirus pneumonia. However, CXR images have complex-shaped and changing lesion areas, which makes it difficult to identify novel coronavirus pneumonia from the images. To address this problem, a new deep learning network model (BoT-ViTNet) for automatic classification is designed in this study, which is constructed on the basis of ResNet50. First, we introduce multi-headed self-attention (MSA) to the last Bottleneck block of the first three stages in the ResNet50 to enhance the ability to model global information. Then, to further enhance the feature expression performance and the correlation between features, the TRT-ViT blocks, consisting of Transformer and Bottleneck, are used in the final stage of ResNet50, which improves the recognition of complex lesion regions in CXR images. Finally, the extracted features are delivered to the global average pooling layer for global spatial information integration in a concatenated way and used for classification. Experiments conducted on the COVID-19 Radiography database show that the classification accuracy, precision, sensitivity, specificity, and F1-score of the BoT-ViTNet model is 98.91%, 97.80%, 98.76%, 99.13%, and 98.27%, respectively, which outperforms other classification models. The experimental results show that our model can classify CXR images better.
Controllable Wireless Spoofing Attack Based on Conditional BEGAN and Auxiliary Channel Sensing
Authors: Ma Mingjun , Zhang Yan , Zhao Tianyu , Zhang Wancheng , He Zunwen
Published: December 2022

This paper investigates how to build a controllable wireless spoofing attack launch framework that is driven by fundamental channel modeling and practical wireless datasets. First, we propose a wireless spoofing attack scheme against the defense mechanism with adversarial deep learning. To obtain channel characteristics and facilitate offline training of the attack model, auxiliary channel sensing is proposed with fundamental channel modeling. Based on these, a conditional boundary equilibrium generative adversarial network (CBEGAN) is designed with adversarial autoencoder (AAE), which takes true labels of signals and channel characteristics as conditions and enables the generation of controllable spoofing signals to fool the protected legitimate classifier. We verify the performance of the proposed spoofing attack scheme with CBEGAN and channel sensing by using wireless datasets, which contain signal data of multiple emitters and modulation types. Results show that the proposed scheme outperforms random attack, replay attack, and the recent attack scheme based on generative adversarial network (GAN) when a single legitimate emitter sends a fixed modulation type. It is also shown that the average attack success probability of the proposed CBEGAN attack model can reach more than 80% while mimicking multiple emitters and modulation types. The performance of the proposed scheme on different channel conditions including signal-to-noise ratio (SNR) and K-factor of the Rician fading channel is evaluated.
An Algorithm Based on Improved K_shell Key Node Identification of the Power Grid Integrated with Renewable Energy
Authors: Zhang Di , Kang Yaxiong , Ji Li , Shi Ruifeng , Jia Limin
Published: December 2022

In this paper, we propose an improved K_shell algorithm for identifying the key nodes of a power grid. This method is improved on the basis of the original Ks value calculation with the degree as the index. The electrical characteristics in the power grid are weighted to the network measure and then added as the new Ks value. The new key nodes are selected by iteratively refreshing the network. Additionally, combined with an entropy weight method, the comprehensive weights of the above indicators are reported from objective viewpoints to obtain key nodes of the power grid. Then, an IEEE 39-bus system is used for simulation. The results show that the key nodes can be identified more accurately by comprehensively considering the structural and electrical characteristics of the power grid by establishing multidimensional indicators and comparing the results with those of other studies. Finally, taking full account of the electrical information of the grid node and its neighboring nodes, a reasonable load redistribution strategy for faulty nodes is formulated, which more effectively reflects the grid performance by comparing it with the Thiel entropy method and the maximum flow method in the literature. The results show that the proposed method improves the influences of key nodes on the grid load by 5.6%, and improves the network efficiency by 15.7%.