bluuebunny/crossref_metadata_2025 huggingface dataset:
Overexpression of SCYL1 Is Associated with Progression of Breast Cancer
Authors: Sun Aiqin , Tian Xianyan , Yang Wannian , Lin Qiong
Published: September 2022

SCYL1 is a pseudokinase and plays roles in cell division and gene transcription, nuclear/cytoplasmic shuttling of tRNA, protein glycosylation, and Golgi morphology. However, the role of SCYL1 in human breast cancer progression remains largely unknown. In this study, we determined expression of SCYL1 in breast cancer by searching the Cancer Genome Atlas (TCGA) and Tumor Immunoassay Resource (TIMER) databases. Meanwhile, we collected breast tumor tissue samples from 247 cases and detected expression of SCYL1 in the tumors using the tissue microarray assay (TMA). Association of SCYL1 with prognosis of breast cancer was determined based on the PrognoScan database. The results have shown that SCYL1 is overexpressed in breast cancer, and the expression of SCYL1 is associated with poor clinical outcomes of breast cancer patients. Furthermore, knockdown of SCYL1 by shRNAs significantly inhibited the proliferation and migration of breast cancer cells. Taken together, our data suggest that SCYL1 is a biomarker for poor prognosis of breast cancer, has a promoting role in breast cancer progression, and is a potential target for breast cancer therapy.
Impact of Nutrition Counseling in Head and Neck Cancer Sufferers Undergoing Antineoplastic Therapy: A Randomized Controlled Pilot Study
Authors: Dai Wangshu , Wang Shu-An , Wang Kongcheng , Chen Chen , Wang Juan , Chen Xiaotian , Yan Jing
Published: September 2022

Head and neck cancer (HNC) sufferers usually encounter arduous nutritional problems when they are receiving antineoplastic therapy. Consequently, the presence of anxiety and depression is commonly observed in this population. This study aimed to explore the physical and psychological influence of nutritional counseling in patients with HNC. Patients receiving concurrent chemo-radiotherapy were randomly assigned to the nutritional counseling group (n = 32, 52.45%) and the control group (n = 29, 47.54%) according to their treatment patterns. In the nutritional counseling group, registered dietitians provided face-to-face counseling during the antineoplastic treatment course at least every two weeks. Nutrient intake amount, relevant nutritional indexes, quality of life, and the degree of anxiety and depression were compared between the two groups. We observed a decrease in the calorie and protein intake amount in both groups, while the decrease in the control group is even worse. The weight loss is more obvious in the control group. The HADS scores in the intervention group were significantly lower than that in the control group (p < 0.05). The Karnofsky Performance Status (KPS) scores in the intervention group were significantly higher than that in the control group (p < 0.05). The level of serum total protein, serum albumin, transferrin, and the thickness of the triceps skin fold decreased less in the intervention group (p < 0.05). Our findings suggest that nutrition counseling is essential for the maintenance of calorie and protein intake in HNC suffers, which contributes to an improvement in the physical and psychological states. The impacts observed in this pilot study warrant further exploration in a larger prospective trial.
Circular Bedforms Due to Pit Foraging of Greater Flamingo Phoenicopterus roseus in a Back-Barrier Intertidal Habitat
Authors: Salvador Paolo , Bezzi Annelore , Martinucci Davide , Sponza Stefano , Fontolan Giorgio
Published: September 2022

The Greater Flamingo Phoenicopterus roseus is known as an ecosystem engineer, rearranging sediment in peculiar bedforms as a consequence of its filter-feeding behaviour. In recent decades, the populations of the Greater Flamingo have notably increased, and now the species is one of the most abundant waterbirds in Mediterranean wetlands. Owing to its range expansion, it inhabits and exploits new and suitable foraging sites detectable by foraging structures left on the sediment. There are few images of the foraging morphologies in the literature, possibly due to their ephemeral nature and difficulty in detecting them. In this manuscript, we present a very detailed UAV (Unmanned Aerial Vehicle) image of an aggregate of pit foraging structures of Greater Flamingo discovered on a back-barrier washover fan in the Marano and Grado Lagoon (Northern Adriatic, Italy).
New Records of the Psychrophilic Tetracladium Species Isolated from Freshwater Environments in Korea
Authors: Goh Jaeduk , Jeon Yu Jeong , Mun Hye Yeon
Published: September 2022

We investigated the diversity of aquatic fungi from freshwater environments in Korea. From the survey of aquatic fungi, three Tetracladium (Leotiomycetes, Helotiales) strains, NNIBRFG814, NNIBRFG1997, and NNIBRFG3891, were isolated from decaying leaves of Fraxinus rhynchophylla located in Taebaek-si, Gangwon-do; freshwater foam in the freshwater of the Gam stream in Gimcheon-si; and from filtered freshwater of the Yangsan stream in Mungyeong-si, Gyeongsangbuk-do, respectively. The isolated fungal strains were identified using ITS sequence analysis and morphological characters. A sequence analysis indicated that the fungal isolates were closely related to Tetracladium breve MK371730 (100%, NNIBRFG814), Tetracladium setigerum KU519120 (98.96%, NNIBRFG1997), and T. marchalianum MK353126 (98.14%, NNIBRFG3891). In terms of growth conditions, it was confirmed that NNIBRFG814 grew best at 20 °C, did not grow above 30 °C, and grew at 5 °C as a psychrophilic fungus. NNIBRFG814 was included in the T. breve clade but its morphology was not similar to that of T. breve. Thus, NNIBRFG814 is a new species, and T. fraxineum sp. nov. NNIBRFG1997 and NNIBRFG3891 are the first records in Korea of T. setigerum and T. marchalianum, respectively.
Freshwater Fishes of Central America: Distribution, Assessment, and Major Threats
Authors: Contreras-MacBeath Topiltzin , Ardón Diego A. , Quintana Yasmin , Angulo Arturo , Lyons Tim , Lardizabal Claudia , McMahan Caleb D. , Elías Diego J. , Matamoros Wilfredo A. , Barraza José Enrique , González Rigoberto , Fuentes-Montejo César E. , Ambruster Jonathan W. , Carrasco Juan Carlos , Brito Rodriguez Marlem
Published: September 2022

Central America contains a rich diversity of freshwater habitats that support more than 600 species of freshwater fishes. However, despite several perceived threats to the integrity of the freshwater habitats throughout the region, a formal analysis of extinction risk for the region’s ichthyofauna is lacking. In this manuscript, we report an updated checklist of species and a novel comprehensive assessment of the conservation status of Central American freshwater fishes by applying the IUCN Red List Categories and Criteria to species at the global level. We also analyze the distribution of freshwater fishes across Central America and generate baseline geospatial data that can be used in multi-species conservation planning processes, which is available through the Red List Website. Our results indicate that between 15 and 28% of freshwater fishes in the region are threatened with extinction, with considerable uncertainty resulting from elevated data deficiency. We identify major and widespread threats in the region, including pollution, agriculture, aquaculture, biological resource use, natural system modifications, invasive species, and land development. This analysis represents an important first step in formulating effective conservation planning and action initiatives for a taxonomic group that historically has received few protections and can be used to inform conservation priorities of freshwater ecosystems at both national and regional scales.
Hotspots of Subterranean Biodiversity Redux
Authors: Deharveng Louis , Pipan Tanja , Bedos Anne , Culver David C.
Published: September 2022

For most plants and animals the broad outlines of global species richness are well known, and often in some detail [...]
A Comparative Analyzing of Zooplankton Community Diversity in Surface Layer Water of Reservoir Via eDNA Metabarcoding and Microscopy
Authors: Ji Chang Woo , Oh Hye-Ji , Chang Kwang-Hyeon , Park Young-Seuk , Kwak Ihn-Sil
Published: September 2022

We compared two sampling methods, eDNA metabarcoding and microscope identification (MSI), for the analysis of zooplankton diversity in reservoirs with its inflow and outflow streams. The dynamic patterns of Cladocera and Rotifera at different time points were similar between the two sampling methods, but there was a slight difference in the Copepoda. Specifically, the members of the Copepoda subclass could not be easily classified using the MSI method, whereas eDNA metabarcoding could detect minor taxa of Cladocera and Rotifera. Upon comparing the list of zooplankton communities in Korea with the gene database of NCBI, only ~56% of the zooplankton genera reported in Korea could be detected based on the 18S rRNA gene. However, eDNA metabarcoding detected a more diverse range of zooplankton despite the lack of genetic information. As water temperature increased after May, the zooplankton diversity decreased according to the MSI method but increased according to the eDNA metabarcoding method. Although eDNA metabarcoding has some limitations, it was able to detect a wider diversity of zooplankton compared to the MSI. eDNA metabarcoding provides a more reliable means to identify zooplankton.
Diagnostic Value of Inflammatory Markers in Elderly Arab Women with Sarcopenia
Authors: Aldisi Dara , Abulmeaty Mahmoud M. A. , Alsaawi Tafany A. , Alorf Abeer S. , Mujlli Gadah , Alshahrani Atheer M. , Alahmari Rajwa M. , Alquraishi Mohammed , Al-Daghri Nasser M. , Alruwaili Nawaf W. , Sabico Shaun
Published: September 2022

Several studies have found a correlation between inflammatory markers and sarcopenia; however, limited research has been conducted on the Arabic population. Therefore, this study aimed to investigate the value of inflammatory parameters in Saudi elderly women with sarcopenia. In this cross-sectional study, 76 elderly Saudi women (>65 years) were stratified according to the presence (n = 26) or absence (n = 50) of sarcopenia, using the operational definition of the Asian Working Group for Sarcopenia (AWGS). Demographics and clinical data were collected. Muscle mass, muscle strength, and physical performance were assessed using bioelectrical impedance, hand grip and timed-up-and-go (TUG) tests, respectively. Inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α) and C-reactive protein (CRP) were assessed using commercially available assays. Muscle mass and strength indicators were lower in the sarcopenia group (p-value < 0.05). Moreover, interleukin 6 (IL-6) was positively correlated with TUG (r = 0.48, p-value < 0.05), while CRP showed an inverse correlation with the right leg muscle (R-Leg-M) and a positive correlation with triceps skinfold (TSF) (r = −0.41, r = 0.42, respectively, p-values < 0.05). Additionally, TSF and R-Leg-M were independent predictors of CRP variation (R2 = 0.35; p < 0.01). Lastly, participants with a TNF-α > 71.2 were five times more likely to have sarcopenia [(OR = 5.85), 95% CI: 1.07–32.08; p = 0.04]. In conclusion, elevated levels of TNF-α are significantly associated with the risk of sarcopenia, while variations perceived in circulating CRP can be explained by changes in the muscle mass indices only among individuals with sarcopenia. The present findings, while promising, need further investigations on a larger scale to determine whether inflammatory markers hold any diagnostic value in assessing sarcopenia among elderly Arab women.
Perspectives in Therapy of Chronic Rhinosinusitis
Authors: Brzost Jacek , Czerwaty Katarzyna , Dżaman Karolina , Ludwig Nils , Piszczatowska Katarzyna , Szczepański Mirosław
Published: September 2022

The recent classification of chronic rhinosinusitis (CRS) focusses on investigating underlying immunopathophysiological mechanisms. Primary CRS is subdivided based on endotype dominance into type 2 (that relates mostly to the Th2 immune response with high levels of IL-5, IL-13, and IgE), or non-type 2 (that corresponds to the mix of type 1 and type 3). The treatment selection of CRS is dependent on endotype dominance. Currently, the majority of patients receive standardized care—traditional pharmacological methods including local or systemic corticosteroids, nasal irrigations or antibiotics (for a selected group of patients). If well-conducted drug therapy fails, endoscopic sinus surgery is conducted. Aspirin treatment after aspirin desensitization (ATAD) with oral aspirin is an option for the treatment in nonsteroidal anti-inflammatory drug (NSAID)-exacerbated respiratory disease (N-ERD) patients. However, in this review the focus is on the role of biological treatment—monoclonal antibodies directed through the specific type 2 immune response targets. In addition, potential targets to immunotherapy in CRS are presented. Hopefully, effective diagnostic and therapeutic solutions, tailored to the individual patient, will be widely available very soon.
Pericarditis and Takotsubo Syndrome—Diagnosis of Cardiac Complications of Post-Acute COVID-19 Syndrome in a 77-Year-Old Woman
Authors: Zalewska-Adamiec Malgorzata , Bachorzewska-Gajewska Hanna , Dobrzycki Slawomir
Published: September 2022

The SARS-CoV-2 virus infection most often takes the form of acute COVID-19 respiratory disease, but in some patients, it turns into acute COVID-19 syndrome after a few weeks. Cardiac complications occur in the form of acute and post-acute diseases and the most common are myocarditis, pericarditis, arrhythmias, and acute coronary syndromes or Takotsubo syndrome. Cardiovascular complications are often the cause of hospitalization and death in COVID-19 patients. We present the case of a 77-year-old woman who was admitted to the clinic with suspected myocardial infarction. Coronary arteriography revealed atherosclerotic wall lesions, and echocardiography showed a good contractility of the left ventricle and fluid in the pericardial sac. Pericarditis was diagnosed. In the following days, acute kidney damage was observed, and one hemodialysis session was performed. On the sixth day of hospitalization, a sudden cardiac arrest occurred, and the patient was resuscitated. The echocardiogaphy showed abnormal contractility of the left ventricular with the ejection fraction of 15%—Takotsubo image. After a few hours, a cardiac arrest occurred again, and the patient died.
Focal Uptake in the Sternum on 18F-FDG-PET/CT Caused by G-CSF Therapy after Chemotherapy Mimicking Bone Metastasis of Breast Cancer
Authors: Hayashi Kumiko , Fujioka Tomoyuki , Hara Masatake , Kumaki Yuichi , Oda Goshi , Yamaga Emi , Mori Mio , Ohnishi Iichiroh , Kubota Kazunori , Nakagawa Tsuyoshi
Published: September 2022

A woman in her 70s was diagnosed with left breast cancer and left axillary lymph node metastasis by an ultrasound-guided biopsy. 18F-FDG-PET/CT showed strong FDG accumulation in the tumor in the left breast and a left axillary lymph node. Neoadjuvant chemotherapy (NAC) was administered in combination with a G-CSF injection to prevent febrile neutropenia. The post-treatment 18F-FDG-PET/CT showed the disappearance of the left breast tumor and left axillary lymph node and revealed a solitary new area of strong FDG accumulation in the sternum. To rule out the possibility of sternal metastasis, a sternal biopsy was performed at the same time as surgery, which revealed no malignant findings. Although very rare, focal uptake on 18F-FDG-PET/CT performed after anticancer drug therapy with G-CSF may mimic a solitary bone metastasis. A bone biopsy may be a useful technique to avoid an immediate misdiagnosis of bone metastasis.
The Onset of Subtalar Joint Monoarthritis in a Patient with Rheumatoid Arthritis
Authors: Wakabayashi Hiroki , Nakata Kenta , Nishimura Akinobu , Hasegawa Masahiro , Sudo Akihiro
Published: September 2022

The involvement of the subtalar joint is uncommon in the early stages of rheumatoid arthritis (RA). We report a case of a 47-year-old female who had RA with isolated subtalar joint arthritis. The clinical history, magnetic resonance imaging, and pathological findings of the patient are presented. A careful evaluation of the patients for chronic ankle-to-heel pain should be conducted, and concomitant evaluation for inflammatory arthritis, including RA, should be considered.
Anterior Cruciate Ligament Tear Detection Based on Deep Convolutional Neural Network
Authors: Joshi Kavita , Suganthi K.
Published: September 2022

Anterior cruciate ligament (ACL) tear is very common in football players, volleyball players, sprinters, runners, etc. It occurs frequently due to extra stretching and sudden movement and causes extreme pain to the patient. Various computer vision-based techniques have been employed for ACL tear detection, but the performance of most of these systems is challenging because of the complex structure of knee ligaments. This paper presents a three-layered compact parallel deep convolutional neural network (CPDCNN) to enhance the feature distinctiveness of the knee MRI images for anterior cruciate ligament (ACL) tear detection in knee MRI images. The performance of the proposed approach is evaluated for the MRNet knee images dataset using accuracy, recall, precision, and the F1 score. The proposed CPDCNN offers an overall accuracy of 96.60%, a recall rate of 0.9668, a precision of 0.9654, and an F1 score of 0.9582, which shows superiority over the existing state-of-the-art methods for knee tear detection.
Microstructure and Mechanical Properties of Novel High-Strength, Low-Activation Wx(TaVZr)100−x (x = 5, 10, 15, 20, 25) Refractory High Entropy Alloys
Authors: Zhang Jingsai , Chen Shunhua , Liu Jiaqin , Qing Zhenhua , Wu Yucheng
Published: September 2022

In this work, novel high-strength, low-activation Wx(TaVZr)100−x (x = 5, 10, 15, 20, 25) refractory high entropy alloys (RHEAs) were prepared by vacuum arc melting. Their microstructure, compressive mechanical properties, hardness, and fracture morphology were investigated and analyzed. The results show that the RHEAs possess a disordered BCC phase, ordered Laves phase, and Zr-rich HCP phase. Their dendrite structures were observed, and the distribution of dendrites became gradually more dense with an increase in W content. The RHEAs demonstrate high strength and hardness, with these properties being higher than in most reported tungsten-containing RHEAs. For example, the typical W20(TaVZr)80 RHEA has a yield strength of 1985 MPa and a hardness of 636 HV, respectively. The improvement in terms of strength and hardness are mainly due to solid solution strengthening and the increase in dendritic regions. During compression, with the increase in the applied load, the fracture behavior of RHEAs changed from initial intergranular fractures to a mixed mode combining both intergranular and transgranular fractures.
An OpenCL-Based FPGA Accelerator for Faster R-CNN
Authors: An Jianjing , Zhang Dezheng , Xu Ke , Wang Dong
Published: September 2022

In recent years, convolutional neural network (CNN)-based object detection algorithms have made breakthroughs, and much of the research corresponds to hardware accelerator designs. Although many previous works have proposed efficient FPGA designs for one-stage detectors such as Yolo, there are still few accelerator designs for faster regions with CNN features (Faster R-CNN) algorithms. Moreover, CNN’s inherently high computational complexity and high memory complexity bring challenges to the design of efficient accelerators. This paper proposes a software-hardware co-design scheme based on OpenCL to implement a Faster R-CNN object detection algorithm on FPGA. First, we design an efficient, deep pipelined FPGA hardware accelerator that can implement Faster R-CNN algorithms for different backbone networks. Then, an optimized hardware-aware software algorithm was proposed, including fixed-point quantization, layer fusion, and a multi-batch Regions of interest (RoIs) detector. Finally, we present an end-to-end design space exploration scheme to comprehensively evaluate the performance and resource utilization of the proposed accelerator. Experimental results show that the proposed design achieves a peak throughput of 846.9 GOP/s at the working frequency of 172 MHz. Compared with the state-of-the-art Faster R-CNN accelerator and the one-stage YOLO accelerator, our method achieves 10× and 2.1× inference throughput improvements, respectively.
Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures
Authors: Hinchliffe Chloe , Yogarajah Mahinda , Elkommos Samia , Tang Hongying , Abasolo Daniel
Published: September 2022

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.
Bibliometric Analysis of Information Theoretic Studies
Authors: Lam Weng Hoe , Lam Weng Siew , Jaaman Saiful Hafizah , Lee Pei Fun
Published: September 2022

Statistical information theory is a method for quantifying the amount of stochastic uncertainty in a system. This theory originated in communication theory. The application of information theoretic approaches has been extended to different fields. This paper aims to perform a bibliometric analysis of information theoretic publications listed on the Scopus database. The data of 3701 documents were extracted from the Scopus database. The software used for analysis includes Harzing’s Publish or Perish and VOSviewer. Results including publication growth, subject areas, geographical contributions, country co-authorship, most cited publications, keyword co-occurrence analysis, and citation metrics are presented in this paper. Publication growth has been steady since 2003. The United States has the highest number of publications and received more than half of the total citations from all 3701 publications. Most of the publications are in computer science, engineering, and mathematics. The United States, the United Kingdom, and China have the highest collaboration across countries. The focus on information theoretic is slowly shifting from mathematical models to technology-driven applications such as machine learning and robotics. This study highlights the trends and developments of information theoretic publications, which helps researchers to understand the state of the art of information theoretic approaches for future contributions in this research domain.
Switching Adaptive Control with Applications on Robot Manipulators
Authors: Wang Shihao , Zheng Shiqi , Deng Yushu , Jiang Zhouxiang , Song Bao , Tang Xiaoqi
Published: September 2022

This paper concentrates on the study of logic-based switching adaptive control. Two different cases will be considered. In the first case, the finite time stabilization problem for a class of nonlinear system is studied. Based on the recently developed adding a barrier power integrator technique, a new logic-based switching adaptive control method is proposed. In contrast with the existing results, finite time stability can be achieved when the considered systems contain both fully unknown nonlinearties and unknown control direction. Moreover, the proposed controller has a very simple structure and no approximation methods, e.g., neural networks/fuzzy logic, are needed. In the second case, the sampled-data control for a class of nonlinear system is investigated. New sampled-data logic-based switching mechanism is proposed. Compared with previous works, the considered nonlinear system has an uncertain linear growth rate. The control parameters and the sampling time can be adjusted adaptively to render the exponential stability of the closed loop system. Applications in robot manipulators are conducted to verify the proposed results.
Using Critical Integrative Argumentation to Assess Socioscientific Argumentation across Decision-Making Contexts
Authors: Sparks Rachel A. , Jimenez P. Citlally , Kirby Caitlin K. , Dauer Jenny M.
Published: September 2022

Socioscientific issues (SSI) are often used to facilitate students’ engagement in multiple scientific practices such as decision-making and argumentation, both of which are goals of STEM literacy, science literacy, and integrated STEM education. Literature often emphasizes scientific argumentation over socioscientific argumentation, which involves considering social factors in addition to scientific frameworks. Analyzing students’ socioscientific arguments may reveal how students construct such arguments and evaluate pedagogical tools supporting these skills. In this study, we examined students’ socioscientific arguments regarding three SSI on pre- and post-assessments in the context of a course emphasizing SSI-based structured decision-making. We employed critical integrative argumentation (CIA) as a theoretical and analytical framework, which integrates arguments and counterarguments with stronger arguments characterized by identifying and refuting counterarguments. We hypothesized that engaging in structured decision-making, in which students integrate multidisciplinary perspectives and consider tradeoffs of various solutions based upon valued criteria, may facilitate students’ development of integrated socioscientific arguments. Findings suggest that students’ arguments vary among SSI contexts and may relate to students’ identities and perspectives regarding the SSI. We conclude that engaging in structured decision-making regarding personally relevant SSI may foster more integrated argumentation skills, which are critical to engaging in information-laden democratic societies.
Case Technology in Teaching Professional Foreign Communication to Law Students: Comparative Analysis of Distance and Face-to-Face Learning
Authors: Sheredekina Oksana , Karpovich Irina , Voronova Larisa , Krepkaia Tatyana
Published: September 2022

Foreign language speaking skills need much practice in order to be improved, which is why it is essential to use different teaching techniques to instruct students effectively. The multiplicity and multitasking of the foreign language teaching process requires the introduction of technology that ensures consistently effective results of professional foreign language learning, i.e., case technology in the algorithmized educational process. The conducted research contributes to the issue of case technology implementation—the algorithm of professional task solutions, aimed at improving students’ English-speaking skills—in teaching professional legal English. The aim of the study is to investigate the effectiveness of case technology in the improvement of ESP speaking skills in the course of distance and face-to-face learning. Comparative analysis of the distance and face-to-face format of case technology implementation in teaching professional foreign communication to law students was carried out in the course of the two-semester study conducted at SPbPU (N 120) in the period from January 2020 to July 2021. Mixed qualitative and quantitative methods were applied to collect and analyse data for the study. The results of the study showed that, for such assessment criteria as task completion, discourse competence and meaningfulness, the use of case technology in a face-to-face format was more effective than the one carried out on-line. As it can have some pedagogical implications on the improvement of English-speaking skills while teaching professional legal English at university, additional effort should be taken to ensure the effectiveness of case technology in the course of distance learning.
High-Resolution Range Profile Feature Transformation Method Based on Amplitude-Phase Modulation Metasurface
Authors: Sui Ran , Wang Junjie , Xu Yong , Feng Dejun
Published: September 2022

Time-modulated metasurfaces have attracted much attention due to their flexible manipulation of electromagnetic waves. Time-varying response leads to spread-spectrum and reallocation of energy. Thus, an amplitude-phase joint modulation (TAJM) method is proposed based on a time-modulated active polarization conversion metasurface (APCM). Taking radar high resolution range profile (HRRP) as the object, the distance transformation and multi-target phenomena are further discovered. The proposed method achieves a flexible manipulation of target amplitude and phase. The relationship between the modulation parameters and HRRP feature is studied. It is revealed that the absorption depth is the dominant factor for the energy distribution of TAJM. The method can be applied to multi-target generation and target feature manipulation.
Multi-Underwater Gliders Coverage Path Planning Based on Ant Colony Optimization
Authors: Ji Haijun , Hu Hao , Peng Xingguang
Published: September 2022

Underwater gliders (UGs) are widely applied to regional exploration to find potential targets. However, the complex marine environment and special movement patterns make it difficult to plan their coverage path. In this paper, a novel multi-underwater gliders coverage path planning algorithm based on ant colony optimization (MGCPP-ACO) is proposed. First, according to the detection radius of the sonar and the motion process of the UGs, we establish a detection coverage model. Then, considering the motion constraints of the UGs and optimization objectives, we redesign the feasible region, transition probability, pheromone update rule and heuristic function of the ACO algorithm. Finally, we carry out three groups of experiments. The simulation results show that the MGCPP-ACO can cover almost the entire sea area and adapt to different initial positions and heading angles. In addition, compared with the traditional scan-line (SCAN) algorithm, the MGCPP-ACO has a higher coverage efficiency and lower coverage cost.
Efficient Resource Allocation for Security-Aware Task Offloading in MEC System Using DVS
Authors: Wang Yanli , Zhang Wanli , Deng Haiquan , Li Xianwei
Published: September 2022

With the Internet of Things (IoT) and communication technologies are snowballing, various applications (e.g., e-health and face recognition) are generated by IoT devices (IoTDs). Nevertheless, these IoTDs generally have constrained computation resources. By offloading the IoT applications to be processed by the MEC servers, mobile edge computing (MEC) is envisioned as a promising and effective solution to address this problem. Meanwhile, security is a critical issue for task offloading in MEC. While plenty of studies have focused on IoT tasks offloading, many of them ignored the security issue. Moreover, many previous works ignored the resource allocation of MEC servers. In addition, as dynamic voltage scaling (DVS) technology is flexible in the design of MEC systems, we integrate this technology with task offloading. In this paper, the problem of IoT applications offloading in an MEC system is studied, whose goal is to minimize computation overheads measured by the task processing delay and energy consumption of IoTDs. The AES cryptographic technique is adopted to make sure that the security of the data of the offloaded tasks is guaranteed. An optimization problem of security-aware task offloading is formulated and solved by proposing an efficient resource-allocation scheme. Experimental results are performed to evaluate and confirm the performance of the proposed security model.
Bi-Level Fuzzy Expectation-Based Dynamic Anti-Missile Weapon Target Allocation in Rolling Horizons
Authors: Zhu Xiaowen , Fan Chengli , Liu Shengli , Xing Huaixi , Qi Cheng
Published: September 2022

The weapon target allocation (WTA) problem is a crucial issue in anti-missile command decisions. However, the current anti-missile weapon target allocation models ignore the dynamic complexity, cooperation, and uncertainty in the actual combat process, which results in the misclassification and omission of targets. Therefore, we propose a bi-level dynamic anti-missile weapon target allocation model based on rolling horizon optimization and marginal benefit reprogramming to achieve rapid impact on static and dynamic uncertainties in the battlefield environment. Further, we also propose an improved bi-level recursive BBO algorithm based on hybrid migration and variation to perform fast and efficient optimization of the model objective function. A simulation analysis demonstrate that the model is suitable for larger-scale, complex, dynamic anti-missile operations in uncertain environments, while the algorithm achieves better solution efficiency and solution time compared with the same type of heuristic algorithm, which meet the requirements of solution accuracy and timeliness. In addition, we obtain better rolling horizon parameters to further optimize its performance.
Context-Based, Predictive Access Control to Electronic Health Records
Authors: Psarra Evgenia , Apostolou Dimitris , Verginadis Yiannis , Patiniotakis Ioannis , Mentzas Gregoris
Published: September 2022

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.
A Survey on Particle Swarm Optimization for Association Rule Mining
Authors: Li Guangquan , Wang Ting , Chen Qi , Shao Peng , Xiong Naixue , Vasilakos Athanasios
Published: September 2022

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.
Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification
Authors: Almuayqil Saleh Naif , Humayun Mamoona , Jhanjhi N. Z. , Almufareh Maram Fahaad , Javed Danish
Published: September 2022

Social networks such as twitter have emerged as social platforms that can impart a massive knowledge base for people to share their unique ideas and perspectives on various topics and issues with friends and families. Sentiment analysis based on machine learning has been successful in discovering the opinion of the people using redundantly available data. However, recent studies have pointed out that imbalanced data can have a negative impact on the results. In this paper, we propose a framework for improved sentiment analysis through various ordered preprocessing steps with the combination of resampling of minority classes to produce greater performance. The performance of the technique can vary depending on the dataset as its initial focus is on feature selection and feature combination. Multiple machine learning algorithms are utilized for the classification of tweets into positive, negative, or neutral. Results have revealed that random minority oversampling can provide improved performance and it can tackle the issue of class imbalance.
Simulation Tool for the Development of a Staged Combustion Pellet Stove Controller
Authors: Lustenberger Daniel , Strassburg Joris , Strebel Tom , Mangold Fabienne , Griffin Timothy
Published: September 2022

Optimizing the combustion control concepts on a pellet stove with very low heat output is time-consuming and costly. In order to shorten the required laboratory test time, a 0-D transient tool was developed within the ERA-NET project “LowEmi-MicroStove”, which simulates a 4 kW pellet stove with staged combustion and heat transfer. This approach was chosen in order to greatly simplify the description of the combustion processes and so reduce the computational complexity and simulation time. The combustion of a bed of pellets is modeled as a superposition of the combustion cycles of individual pellets, assuming no interactions between pellets. A test setup was developed and used to determine the ignition and burning cycle of individual pellets. The description of the CO emissions behavior is based upon an empirically grounded relation which is in turn based on the air/fuel ratio and the combustion chamber temperature. For the validation of the 0-D simulation results, a test rig for a 4 kW pellet stove was built. Despite its simplistic approach, good agreement was found between the simulation and 4 kW pellet stove test results for the mean values and temporal fluctuations of flue gas temperature and oxygen and carbon monoxide content during start up, stable operation and load changes. The simulation could thus be used to quantify the effect of air flow rates and distribution as well as load changes on performance and draw conclusions regarding different process control strategies. A control strategy which can operate the stove at high temperatures near the air stoichiometric limit with acceptable CO emissions has been proven to be the most promising. Additionally, the model can be used to quantify the effects of variations in other process parameters, for example the impact of fluctuations in the pellet feed. Due to its effectiveness and simplicity, this model approach can be applied for the development of control strategies for other staged, pellet combustion systems.
Fault-Tolerant Operation of Bidirectional ZSI-Fed Induction Motor Drive for Vehicular Applications
Authors: Sharma Vivek , Hossain Md. Jahangir , Mukhopadhyay Subhas
Published: September 2022

This paper presents an efficient and fast fault-tolerant control scheme for a bidirectional Z-source inverter (BiZSI)-fed induction-motor drive system for vehicular applications. The proposed strategy aims for the fault detection, localization and diagnosis of the proposed system during switch failures in the inverter module. Generally, power–semiconductor switch failures in inverter modules occur due to open- and short-circuit faults. An efficient modulation scheme is proposed and design specifications are thoroughly derived to obtain high voltage gains across the BiZSI network. A suitably fast detection and diagnosis scheme to isolate the faulty leg and resume the normal operation is discussed in this paper. The control scheme is provided such that the faulty leg is isolated and the motor phase is fed from a redundant leg to resume the operation. A feasible localization algorithm based on experimentally derived values and switching vectors is implemented. In addition, a fast fault diagnosis method based on current estimation and motor speed variation is designed and implemented. Moreover, the most important advantages of the proposed strategy include lower hardware requirements and less harmonic distortion in the output currents. Finally, the simulation and experimental results are presented to validate the feasibility of the theoretical analysis. An extensive performance evaluation of the proposed system with fault ride-through capabilities is performed to prove its suitability for vehicular applications. To validate its merits, the proposed strategy is compared with similar fault-tolerant schemes currently used in the industry.
An Optimized Fuzzy Based Control Solution for Frequency Oscillation Reduction in Electric Grids
Authors: Maqbool Hina , Yousaf Adnan , Asif Rao Muhammad , Rehman Ateeq Ur , Eldin Elsayed Tag , Shafiq Muhammad , Hamam Habib
Published: September 2022

The demand for uninterruptible electricity supply is increasing, and the power engineering sector has started researching alternative solutions. Distributed generation (DG) dissemination into the electric grid to cope with the accelerating demand for electricity is taken into consideration. However, its integration with the traditional grid is a key task as sudden changes in load and their fickle nature cause the frequency to deviate from its adjusted range and affect the grid’s reliability. Moreover, the increased use of DG will significantly impact power system frequency response, posing a new challenge to the traditional power system frequency framework. Therefore, maintaining the frequency within the nominal range can improve its reliability. This deviation should be removed within a few seconds to keep the system’s frequency stable so that supply and demand are balanced. In a traditional grid system, the controllers installed at the generation side help to control the system’s frequency. These generators have capital installation costs that are not desirable for system operators. Therefore, this article proposed a comprehensive control framework to enable high penetration of DG while still providing adequate frequency response. This is accomplished by investigating a grasshopper optimization algorithm-based (GOA) fuzzy PD-PI controller (FPD-PI) for analyzing frequency control and optimizing the FPD-PI controller gains to minimize the frequency fluctuations. In this paper, interconnected hybrid power systems (HPS) are considered. In this study, the response of a system is analyzed, and the results validate that the oscillations in frequency are substantially reduced by the proposed controller. Moreover, our model is the best option for controlling frequency instead of conventional controllers, as it is efficient and fast to regulate frequency by switching the preferred loads on or off.
Measurement of the Kinetics and Thermodynamics of the Thermal Degradation for a Flame Retardant Polyurethane-Based Aerogel
Authors: Wang Xinyang , Ding Yan , Chen Zhanwen , Tang Chuyan , Ren Xingyu , Hu Hongyun , Fang Qingyan
Published: September 2022

The current work aims to study the thermal degradation of the flame retardant polyurethane aerogel (FR_PU_aerogel) through multiple milligram-scale experimental methods. A systemic methodology for measuring the reaction kinetics and thermodynamics of the thermal degradation of FR_PU_aerogel is detailed. Specifically, the thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were performed simultaneously in inert atmosphere to measure the mass loss and heat flow data, and a numerical framework called ThermaKin2Ds was used to inversely model these experimental data. First, a reaction mechanism with six first-order consecutive reactions was developed based on the inverse analysis of the TGA data. The corresponding reaction kinetics were optimized using the hill climbing optimization algorithm. Subsequently, the heat capacities of each condensed phase component and the heat of the reactions were obtained through inversely modeling the heat flow data. Furthermore, the heat of the complete combustion of each gaseous component were derived based on the heat release rates measured in the milligram-scale combustion calorimeter (MCC) experiments. It is noted that the developed reaction mechanism was further validated against the mass loss data obtained at different heating rates. The parameters determined in this work serve as a core subset of inputs for the pyrolysis model development, which is essential for the quantitative understanding of the ignition and the combustion behavior of solid materials.
Livestock’s Urine-Based Plant Microbial Fuel Cells Improve Plant Growth and Power Generation
Authors: Apollon Wilgince , Vidales-Contreras Juan , Rodríguez-Fuentes Humberto , Gómez-Leyva Juan , Olivares-Sáenz Emilio , Maldonado-Ruelas Víctor , Ortiz-Medina Raúl , Kamaraj Sathish-Kumar , Luna-Maldonado Alejandro
Published: September 2022

Plant microbial fuel cells (P-MFCs) are sustainable and eco-friendly technologies, which use plant root exudates to directly nourish the electrochemically active bacteria (EABs) to generate sustainable electricity. However, their use in evaluating plant growth has been insufficiently studied. In this study, interconnection between plant growth and the production of bioelectricity was evaluated by using P-MFCs inoculated with 642.865 mL ≅ 643 mL of livestock’s urine such as cow urine, goat urine, and sheep urine. The greatest mean stem diameter of 0.52 ± 0.01 cm was found in P-MFC-3 inoculated with goat urine, while the P-MFC-2 treated with cow urine reached a higher average number of roots with a value of 86 ± 2.50 (95% improvement) (p < 0.05). Besides, P-MFC-4 presented greater height of 50.08 ± 0.67 cm. For polarization curve experiment a higher maximum power density of 132 ± 11.6 mW m−2 (931 mA m−2) was reached with cow urine; in turn, with regard to the long-term operation, the same reactor indicated a higher maximum average power density of 43.68 ± 3.05 mW m−2. The study’s findings indicated that Stevia P-MFC inoculated with urine was a good option to increase the biomass amount for the agricultural plants along with power generation. Further, this study opens the way for more investigation of evaluating the impact of P-MFC on plant growth.