bluuebunny/crossref_metadata_2025 huggingface dataset:
Colorectal Cancer Liver Metastases: Is an R1 Hepatic Resection Accepted?
Authors: Symeonidis Dimitrios , Tepetes Konstantinos , Tzovaras George , Kissa Labrini , Samara Athina A. , Bompou Effrosyni , Zacharoulis Dimitrios
Published: December 2022

Metastatic colorectal cancer is associated with a rather dismal 5-year overall survival. The liver is the most commonly affected organ. Improved 5-year survival rates after successful hepatic resections for metastases confined to the liver have been reported. Certainly, a hepatectomy that results in an incomplete tumor resection, in terms of leaving macroscopic residual tumor in the future liver remnant, is not associated with survival benefits. However, the prognostic implications of a microscopically positive surgical margin or a clear margin of less than 1 mm (R1) on pathology are debatable. Although it has been a field of extensive research, the relevant literature often reports contradictory results. The purpose of the present study was to define, assess the risk factors for, and, ultimately, analyze the effect that an R1 hepatic resection for colorectal cancer liver metastases might have on local recurrence rates and long-term prognosis by reviewing the relevant literature. Achieving an R0 hepatic resection, optimally with more than 1 mm of clear margin, should always be the goal. However, in the era of the aggressive multimodality treatment of liver metastatic colorectal cancer, an R1 resection might be the cost of increasing the pool of patients finally eligible for resection. The majority of literature reports have highlighted the detrimental effect of R1 resections on local recurrence and overall survival. However, there are indeed studies that degraded the prognostic handicap as a consequence of an R1 resection in selected patients and highlighted the presence of RAS mutations, the response to chemotherapy, and, in general, factors that reflect the biology of the disease as important, if not the determinant, prognostic factors. In these patients, the aggressive disease biology seems to outperform the resection margin status as a prognostic factor, and the recorded differences between R1 and R0 resections are equalized. Properly and accurately defining this patient group is a future challenge in the field of the surgical treatment of colorectal cancer liver metastases.
A Note on Oil Price Shocks
Authors: Baek Jungho
Published: December 2022

Many empirical studies have examined the role of oil price fluctuations on macroeconomic activities [...]
Study of the Sloshing Dynamics in Partially Filled Rectangular Tanks with Submerged Baffles Using VOF and LES Turbulence Methods for Different Impact Angles
Authors: Vallés Rebollo Xavier , Sadeghi Ehsan , Kusano Ibuki , García-Granada Andrés-Amador
Published: December 2022

This research studies how the angle and dimensions of a single baffle affect the dynamics of a fluid in a closed rectangular tank under an accelerated harmonic vibration in resonance. A half-filled non-deformable rectangular tank with a single centered submerged baffle has been simulated using ANSYS® FLUENT. The study aims to characterize the effect of changing the baffle’s angle; hence, 10 simulations have been performed: without a baffle, 90°, 30°, 60°, 120° and 150°, either maintaining the baffle’s length or the projected height constant. The computational fluid dynamics (CFD) method using volume of fluid (VOF) and large eddy simulation (LES) are used to predict the movement of the fluid in two dimensions, which have been benchmarked against experimental data with excellent agreement. The motion is sinusoidal in the +X direction, with a frequency of oscillation equal to its first vibration mode. The parameters studied have been the free surface elevation, values at three different points and maximum; the center of gravity’s position, velocity, and acceleration; and the forces against the tank’s walls. It has been found that the 90° angle has the most significant damping effect, stabilizing the free-surface elevation, reducing the center of gravity dispersion, and leveling the impacting forces. Smaller angles also tame the sloshing and stabilize it.
Determining the Research Priorities for Adult Primary Brain Tumours in Australia and New Zealand: A Delphi Study with Consumers, Health Professionals, and Researchers
Authors: Halkett Georgia K. B. , Breen Lauren J. , Berg Melissa , Sampson Rebecca , Sim Hao-Wen , Gan Hui K. , Kong Benjamin Y. , Nowak Anna K. , Day Bryan W. , Harrup Rosemary , James Melissa , Saran Frank , Mcfarlane Brett , Tse Chris , Koh Eng-Siew
Published: December 2022

The aim of this project was to determine research priorities, barriers, and enablers for adult primary brain tumour research in Australia and New Zealand. Consumers, health professionals, and researchers were invited to participate in a two-phase modified Delphi study. Phase 1 comprised an initial online survey (n = 91) and then focus groups (n = 29) which identified 60 key research topics, 26 barriers, and 32 enablers. Phase 2 comprised two online surveys to (1) reduce the list to 37 research priorities which achieved consensus (>75% 2-point agreement) and had high mean importance ratings (n = 116 participants) and (2) determine the most important priorities, barriers, and enablers (n = 90 participants). The top ten ranked research priorities for the overall sample and sub-groups (consumers, health professionals, and researchers) were identified. Priorities focused on: tumour biology, pre-clinical research, clinical and translational research, and supportive care. Variations were seen between sub-groups. The top ten barriers to conducting brain tumour research related to funding and resources, accessibility and awareness of research, collaboration, and process. The top ten research enablers were funding and resources, collaboration, and workforce. The broad list of research priorities identified by this Delphi study, together with how consumers, health professionals, and researchers prioritised items differently, and provides an evidence-based research agenda for brain tumour research that is needed across a wide range of areas.
Dietary Association with Midgut Microbiota Components of Eocanthecona furcellata (Wolff)
Authors: Kuang Zhaolang , Wen Jian , Zhu Yongji , He Xiaofang , Chen Kewei
Published: December 2022

Eocanthecona furcellata is an important predatory stinkbug that attacks many lepidopteran pests. For mass-rearing, artificial diets are used to rear this predator in the laboratory; however, the fitness of the predators is reduced, and little is known about the cause. Since gut microbiota plays vital roles in the digestion and development of many hosts and can consequently affect host fitness, an understanding of the microbial community composition of E. furcellata may help to solve this unresolved problem. We compared the development and reproduction of E. furcellata reared on an artificial diet, and a natural (Spodoptera litura) or semi-natural (Tenebrio molitor) diet, and then the midgut microbiota were assessed using high-throughput 16S rRNA. The results of the high-throughput 16S rRNA show that the bacterial richness and diversity in the artificial diet gut samples increased considerably compared with the other samples. Proteobacteria and Firmicutes were the dominant phyla in E. furcellata. At the genus level, Serratia (however, the relative abundance was lower in the artificial diet gut samples), Enterococcus, and an uncultured bacterium genus of family Enterobacteriaceae, were dominant. The midgut microbiota components significantly differed among the diets, indicating that the gut bacteria had a dietary association with E. furcellata. This study provides a better understanding of midgut microbiota and the artificial diets that might affect them in E. furcellata.
Biology, Genetic Diversity, and Ecology of Nitzschia acidoclinata Lange-Bertalot (Bacillariophyta)
Authors: Bagmet Veronika B. , Abdullin Shamil R. , Nikulin Arthur Yu. , Nikulin Vyacheslav Yu. , Gontcharov Andrey A.
Published: December 2022

The diatom Nitzschia acidoclinata is a widespread eurybiontic alga. There is little information on its life cycle properties and cardinal points. To fill this gap, we analyzed six N. acidoclinata clones from a range of habitats in Asiatic Russia regarding their genetic diversity, morphology, morphometry, geography, and ecology. A comparison of 15 N. acidoclinata rbcL sequences sampled across its relatively wide distribution area and contrasting habitats revealed no distinct genotypes in the species. We demonstrated that the valve morphology, their length, and the sexual activity of the investigated clones varied depending on the phase of their life cycle. In this species, abrupt size reduction was observed. It was revealed that N. acidoclinata reproduced by pedogamy, and its auxosporulation was season-dependent and observed in spring and autumn only. The mating activity in our clones was detected only when the cell size was reduced to 9–22 µm in length. The available data on sexual reproduction in the genus Nitzschia suggest that neither clades nor subclades comprise pedogamous or anisogamous taxa at the same time. However, isogamy could occur in the same clade with either pedogamy or anisogamy. These data provide a fundamental basis for the development of N. acidoclinata mass cultivation and long-term maintenance in culture technologies.
Factors Affecting Habitat Selection of Endangered Steppe Eagle (Aquila nipalensis) in Pakistan: Implications for Raptors Conservation
Authors: Ahmad Shakeel , Khattak Romaan Hayat , Teng Liwei , Kaneez Khansa , Liu Zhensheng
Published: December 2022

The steppe eagle (Aquila nipalensis) is an endangered migratory raptor species that migrates in winter to Pakistan and neighbouring countries. In Pakistan, the species migrate at the end of autumn and utilise different habitats across the country. Very little information is available about the species’ population status, distribution, and factors affecting its distribution in Pakistan. In the present study, we predicted the distribution of steppe eagles in Pakistan associated with different environmental variables. We used 149 presence points of the species from an online source (GBIF), published literature, and wildlife photographers. The MaxEnt analysis showed that highly suitable habitats were mostly present in Azad Jammu and Kashmir (AJK), federally administered areas and surrounding areas, southern areas of Sindh Province, and some parts of Khyber Pakhtunkhwa (KP) Province. In addition, some patches were also predicted by MaxEnt in Balochistan Province. Human population density (27.0%), chicken density (16.6%), temperature seasonality (11.1%), and rivers (10.3%) were identified as the main environmental factors that affect the habitat distribution of steppe eagle in Pakistan. Only a small percentage (2.62%) of the total Pakistan area was estimated to be a highly suitable area for steppe eagles, while 20.58% and 7.46% were identified as the least and moderately suitable areas, respectively. Conservation of identified habitats and mitigation of anthropogenic impacts to conserve this endangered eagle species are recommended for immediate and long-term conservation across Pakistan.
Selection of Nesting Habitat and Insular Niche Separation of Two Sympatric Aquila Species
Authors: Solanou Maria , Trypidaki Eirini , Georgopoulou Elisavet , Damianakis Konstantinos , Kardamaki Afroditi , Xirouchakis Stavros M.
Published: December 2022

Aquila chrysaetos and Aquila fasciata are two congeneric eagle species distributed in the Mediterranean region which are supposed to compete for similar breeding and foraging resources. In the present study, bioclimatic, topographic, and human-related habitat parameters were investigated for 64 and 75 nest sites of Golden and Bonelli’s eagles, respectively. The nests were found during fieldwork undertaken from 1995–2020. Overall, the habitat parameters that best discriminated nest site selection were associated with elevation, temperature, and land use with topographic variables being most powerful for niche separation. Univariate analysis, regression, and species distribution modeling identified a strong association of the species with altitude pinpointing the Golden eagle’s mountainous and continental character and the Bonelli’s eagle being a lowland and coastal species. Golden eagle nests were situated away from human settlements on steep cliffs in higher altitude areas with transitional woodland-shrub vegetation. In contrast Bonelli’s eagle nests were located on low-altitude warmer zones, closer to the coast and human settlements with more natural grasslands in their vicinity. The ecological niche separation of the two species was best described by altitude and temperature, though no clear-cut evidence was detected for their competitive exclusion. Inter-specific nearest neighboring distance was found statistically significant only for the Golden eagle which seems to be less tolerant in its co-existence with the Bonelli’s eagle. Conservation measures for both species should target territories under human pressure, though more research should focus on the species range use and habitat heterogeneity within overlapping territories.
Diversity of Volatile Compounds in the Inula candida / I. verbascifolia Group (Asteraceae-Inuleae) and Its Impact on Species Delimitation
Authors: Koutsaviti Aikaterini , Zografaki Maria Eleftheria , Fyllas Nikolaos M. , Tzakou Olga , Constantinidis Theophanis
Published: December 2022

The members of the Inula candida / I. verbascifolia group are perennial and chasmophytic plants attributed to four species and eleven entities at subspecific or varietal level. They are mostly confined to Greece. Volatile compounds of above-ground flowering parts of twenty-three populations covering ten taxonomic entities were obtained after hydrodistillation and analyzed by gas chromatography and mass spectrometry (GC-MS). In most cases, the total percentage of identified constituents reached 89% or above. Seventy-two components were identified. Oxygenated sesquiterpenes prevailed in the I. candida subgroup (47.3–71.5%), with epi-α-cadinol present in all members (13.4–42.7%) but rarely found in the I. verbascifolia subgroup. Considerable amounts of the hydrocarbon aldehydes decanal, undecanal and particularly tridecanal (0.5–35.0%, rarely absent) were predominately found in the I. verbascifolia members but were mostly absent in the I. candida subgroup. Isoalantolactone (12.4–49.5%), identified only in I. subfloccosa, and γ-(Z)-curcumen-12-ol, found only in I. candida subsp. limonella (22.6–42.1%), may serve as chemotaxonomic markers. Two different chemotypes can be distinguished within I. verbascifolia subsp. aschersoniana: a trans-muurola-4(14),5-diene-, γ-cadinene-rich chemotype from north-eastern Greece and a 1-epi-cubenol-, tridecanal-rich chemotype from central Greece. Different statistical algorithms were used to interpret the chemical diversity and identify the most appropriate number of clusters for the taxa. Cluster analyses indicated that the optimum number of clusters that best explain the metabolomic variability of the taxa is two. The degree of membership for each population based on the fuzzy k-means algorithm supported the I. verbascifolia subsp. aschersoniana samples within the I. candida subgroup, whereas I. subfloccosa may belong to any of the two clusters formed, although it also appears to have some unique characteristics.
Factors Affecting Plant Composition in Abandoned Railway Areas with Particular Emphasis on Forest Proximity
Authors: Hutniczak Agnieszka , Urbisz Alina , Urbisz Andrzej , Strzeleczek Łukasz
Published: December 2022

Abandoned railway areas are places for the spontaneous spread of plants and the formation of specific plant communities. However, only limited information on this subject is available in the literature. The study aimed to determine the direction and the rate of forest formation, taking into account selected environmental factors. A floristic, phytosociological, and soil survey was carried out on a set of abandoned railway lines in the Silesia Province (southern Poland). For this purpose, 30 plots of vegetation were selected: 15 located in the vicinity of forests (F) and 15 surrounded by ruderal or segetal communities (NF). As a result, a total of 132 species (121 vascular plants and 11 mosses) were recorded, including 83 species at F plots and 78 species at NF plots. During the research, 13 syntaxa were distinguished. It was found that silt content, nitrate nitrogen (N-NO3), pH, and phosphorous have a significant impact upon which species dominated in the surveyed areas. It was found that the proximity of the forest was a crucial factor in determining the development of forest communities on abandoned railway areas. These results can be helpful for understanding how environmental conditions shape the forest structure on these types of habitats.
Mastitis: Impact of Dry Period, Pathogens, and Immune Responses on Etiopathogenesis of Disease and its Association with Periparturient Diseases
Authors: Egyedy Ashley F. , Ametaj Burim N.
Published: December 2022

Mastitis is an inflammation of the mammary gland initiated by pathogenic bacteria. In fact, mastitis is the second most important reason for the culling of cows from dairy herds, after infertility. In this review we focus on various forms of mastitis, including subclinical and clinical mastitis. We also stress the importance of the dry-off period as an important time when pathogenic bacteria might start their insult to the mammary gland. An important part of the review is the negative effects of mastitis on milk production and composition, as well as economic consequences for dairy farms. The two most important groups of bacteria that are involved in infection of the udder, Gram-negative and Gram-positive bacteria, are also discussed. Although all cows have both innate and adaptive immunity against most pathogens, some are more susceptible to the disease than others. That is why we summarize the most important components of innate and adaptive immunity so that the reader understands the specific immune responses of the udder to pathogenic bacteria. One of the most important sections of this review is interrelationship of mastitis with other diseases, especially retained placenta, metritis and endometritis, ketosis, and laminitis. Is mastitis the cause or the consequence of this disease? Finally, the review concludes with treatment and preventive approaches to mastitis.
Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
Authors: Teramoto Atsushi , Tsukamoto Tetsuya , Michiba Ayano , Kiriyama Yuka , Sakurai Eiko , Imaizumi Kazuyoshi , Saito Kuniaki , Fujita Hiroshi
Published: December 2022

Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.
Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning
Authors: Qasmieh Isam Abu , Alquran Hiam , Zyout Ala’a , Al-Issa Yazan , Mustafa Wan Azani , Alsalatie Mohammed
Published: December 2022

A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.
The Benign Side of the Abdominal Wall: A Pictorial Review of Non-Neoplastic Diseases
Authors: Porrello Giorgia , Vernuccio Federica , Alvarez-Hornia Pérez Eduardo , Brancatelli Giuseppe , Cannella Roberto
Published: December 2022

The abdominal wall is the location of a wide spectrum of pathological conditions, from benign to malignant ones. Imaging is often recommended for the evaluation of known palpable abdominal masses. However, abdominal wall pathologies are often incidentally discovered and represent a clinical and diagnostic challenge. Knowledge of the possible etiologies and complications, combined with clinical history and laboratory findings, is crucial for the correct management of these conditions. Specific imaging clues can help the radiologist narrow the differential diagnosis and distinguish between malignant and benign processes. In this pictorial review, we will focus on the non-neoplastic benign masses and processes that can be encountered on the abdominal wall on cross-sectional imaging, with a particular focus on their management. Distinctive sonographic imaging clues, compared with computed tomography (CT) and magnetic resonance (MR) findings will be highlighted, together with clinical and practical tips for reaching the diagnosis and guiding patient management, to provide a complete diagnostic guide for the radiologist.
Noise Enhancement of Neural Information Processing
Authors: Destexhe Alain
Published: December 2022

Cortical neurons in vivo function in highly fluctuating and seemingly noisy conditions, and the understanding of how information is processed in such complex states is still incomplete. In this perspective article, we first overview that an intense “synaptic noise” was measured first in single neurons, and computational models were built based on such measurements. Recent progress in recording techniques has enabled the measurement of highly complex activity in large numbers of neurons in animals and human subjects, and models were also built to account for these complex dynamics. Here, we attempt to link these two cellular and population aspects, where the complexity of network dynamics in awake cortex seems to link to the synaptic noise seen in single cells. We show that noise in single cells, in networks, or structural noise, all participate to enhance responsiveness and boost the propagation of information. We propose that such noisy states are fundamental to providing favorable conditions for information processing at large-scale levels in the brain, and may be involved in sensory perception.
ECSS: High-Embedding-Capacity Audio Watermarking with Diversity Reception
Authors: Wu Shiqiang , Huang Ying , Guan Hu , Zhang Shuwu , Liu Jie
Published: December 2022

Digital audio watermarking is a promising technology for copyright protection, yet its low embedding capacity remains a challenge for widespread applications. In this paper, the spread-spectrum watermarking algorithm is viewed as a communication channel, and the embedding capacity is analyzed and modeled with information theory. Following this embedding capacity model, we propose the extended-codebook spread-spectrum (ECSS) watermarking algorithm to heighten the embedding capacity. In addition, the diversity reception (DR) mechanism is adopted to optimize the proposed algorithm to obtain both high embedding capacity and strong robustness while the imperceptibility is guaranteed. We experimentally verify the effectiveness of the ECSS algorithm and the DR mechanism, evaluate the performance of the proposed algorithm against common signal processing attacks, and compare the performance with existing high-capacity algorithms. The experiments demonstrate that the proposed algorithm achieves a high embedding capacity with applicable imperceptibility and robustness.
Dynamic Banking Systemic Risk Accumulation under Multiple-Risk Exposures
Authors: Fan Hong , Tang Miao
Published: December 2022

Much of the existing research on banking systemic risk focuses on static single-risk exposures, and there is a lack of research on multiple-risk exposures. The reality is that the banking system is facing an increasingly complex environment, and dynamic measures of multiple-risk integration are essential. To reveal the risk accumulation process under the multi-risk exposures of the banking system, this article constructs a dynamic banking system as the research object and combines geometric Brownian motion, the BSM model, and the maximum likelihood estimate method. This article also aims to incorporate three types of exposures (interbank lending market risk exposures, entity industry credit risk exposures, and market risk exposures) within the same framework for the first time and builds a model of the dynamic evolution of banking systemic risk under multiple exposures. This study included the collection of a large amount of real data on banks, entity industries, and market risk factors, and used the ΔCoVaR model to evaluate the systemic risk of the China banking system from the point of view of the accumulation of risk from different exposures, revealing the dynamic process of risk accumulation under the integration of multiple risks within the banking system, as well as the contribution of different exposures to banking systemic risk. The results showed that the banking systemic risk of China first increased and then decreased with time, and the rate of risk accumulation is gradually slowing down. In terms of the impact of different kinds of exposures on system losses, the credit risk exposure of the entity industry had the greatest impact on the banking systemic risk among the three kinds of exposures. In terms of the contribution of the interbank lending market risk to the systemic risk, the Bank of Communications, China Everbright Bank, and Bank of Beijing contributed the most. In terms of the contribution of the bank–entity industry credit risk to the systemic risk, the financial industry, accommodation and catering industry, and manufacturing industry contributed the most. Considering the contribution of market risk to the systemic risk, the Shanghai Composite Index, the Hang Seng Composite Index, and the Dow Jones Index contributed the most. The research in this paper enriches the existing banking systemic risk research perspective and provides a reference for the regulatory decisions of central banks.
Exact Solution of a Time-Dependent Quantum Harmonic Oscillator with Two Frequency Jumps via the Lewis–Riesenfeld Dynamical Invariant Method
Authors: Coelho Stanley S. , Queiroz Lucas , Alves Danilo T.
Published: December 2022

Harmonic oscillators with multiple abrupt jumps in their frequencies have been investigated by several authors during the last decades. We investigate the dynamics of a quantum harmonic oscillator with initial frequency ω0, which undergoes a sudden jump to a frequency ω1 and, after a certain time interval, suddenly returns to its initial frequency. Using the Lewis–Riesenfeld method of dynamical invariants, we present expressions for the mean energy value, the mean number of excitations, and the transition probabilities, considering the initial state different from the fundamental. We show that the mean energy of the oscillator, after the jumps, is equal or greater than the one before the jumps, even when ω1<ω0. We also show that, for particular values of the time interval between the jumps, the oscillator returns to the same initial state.
Analysis and Comparison of International Digital Competence Frameworks for Education
Authors: Mattar Joao , Santos Cassio Cabral , Cuque Lucia Maria
Published: December 2022

Different frameworks are available for assessing and developing digital competences, which poses a choice challenge for potential users. This article aims to analyze and compare international digital competence frameworks for education. The study compares characteristics such as the frameworks’ purpose, structure, competences, and levels, as well as indicators for instrument development. The results indicate that the objective, the theoretical background, and the target group define the framework characteristics. Most analyzed frameworks focus on teacher training. The comparison identified common competences: communication, collaboration, sharing, information and data, content, technical, teaching, learning, and ethics. All frameworks include profiles, objectives, descriptors, activities, examples, and cases of knowledge, skills, and attitudes. The article concludes that digital competence frameworks should be segmented by educational actors (students, teachers, and administrators) and levels (early childhood, primary, higher, and corporate) with corresponding assessment instruments.
The Development of an ‘Attitudes to Science and Religion’ Instrument for Secondary School Students: How Are the Attitudes of Students to Science and Religion Associated with Student Religion and Other Characteristics?
Authors: Mujtaba Tamjid , Reiss Michael
Published: December 2022

This article uses data from students in England to pilot and validate an ‘Attitudes to Science and Religion’ instrument which explores secondary school students’ perceptions of the relationship between science and religion, as well as their attitudes towards science and towards religion. This instrument was developed in part from previous studies and is intended for educators and researchers for both pedagogical and research purposes. The post-pilot questionnaire was then used with 1102 Year 9/10 students from 18 schools in England to answer two research questions: (1) Is there a relationship between students’ attitudes towards science and their perceptions as to whether science and religion are compatible? (2) What are the characteristics of students who report that science and religion are compatible? Students who reported that science and religion were compatible had more positive perceptions of science and of their ability in science, were more likely to have future aspirations in science and showed more positive attitudes towards science education. There was no statistically significant difference between Christian and Muslim students in their responses about the compatibility of science and religion, nor about whether evolution and creationism should be taught in the science classroom. Muslim students were as positive as were Christian students about their science education and the benefits of science; however, they were less positive about the role of science in explaining the world. Students who reported science and religion as being compatible were more likely to hold religious beliefs than a belief in scientism. Implications of this research are that there ought to be a place for discussion about the relationship between science and religion within school lessons and that high quality discussion is likely to be fostered by developing students’ critical thinking skills.
Governance Boards and Student Activism: Responding to Racism
Authors: Cho Katherine S. , Cesar-Davis Natacha
Published: December 2022

Colleges and universities continue to contend with issues of campus racism, often illuminated by student concerns. Within these ongoing conversations, governance boards play a critical part in engaging with campus issues. Utilizing critical discourse analysis, this study examines two universities through 2000 documents of board meeting minutes, agendas, student newspapers, and campus archives to scrutinize the language, framing, and decision-making of board efforts with diversity, equity, inclusion, and addressing student concerns. Findings illuminate aspects of the Institutional Response Framework and interest-convergence in the ways boards rationalize decisions through concerns about reputation and protecting the university’s best interests.
Study of Beam–Wave Interaction in a Sub-THz Traveling Wave Tube with a Converging Sheet Electron Beam Focused by a Uniform Magnetic Field
Authors: Titov Vladimir , Ploskih Andrey , Ryskin Nikita
Published: December 2022

A traveling-wave tube (TWT) with a sheet electron beam and staggered double-grating slow-wave structure (SWS) is a promising high-power, wideband terahertz amplifier. In such tubes, electron-optical systems (EOSs) with a converging sheet beam are mostly used, which allow a reduction of the cathode load, increase the lifetime, and enable operation in a continuous-wave (CW) mode. This paper presents the results of a 3D particle-in-cell (PIC) simulation of the 0.22 THz TWT driven with a converged sheet beam, which is compressed to less than 100 μm thickness in the EOS with a magnetically shielded cathode. The beam with high compression has a significant transversal velocity spread and essentially non-uniform current density distribution over the cross-section. These factors significantly affect the beam–wave interaction. We compare the performance of the TWT driven by the compressed sheet beam and by an idealized initially rectilinear beam without any velocity spread.
Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video
Authors: Selvi Esakky , Adimoolam Malaiyalathan , Karthi Govindharaju , Thinakaran Kandasamy , Balamurugan Nagaiah Mohanan , Kannadasan Raju , Wechtaisong Chitapong , Khan Arfat Ahmad
Published: December 2022

Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.
A Cooperative Control Strategy for a Hydraulic Regenerative Braking System Based on Chassis Domain Control
Authors: Li Ning , Jiang Junping , Sun Fulu , Ye Mingrui , Ning Xiaobin , Chen Pengzhan
Published: December 2022

In order to solve the problems of wheel locking and loss of vehicle control due to understeering or oversteering during the braking energy-recovery process of the hydraulic regenerative braking system (HRBS), aiming at the characteristics of chassis domain control that can realize coordinated work among various chassis systems, a cooperative control strategy of HRBS based on chassis domain control was proposed. Firstly, a HRBS test bench was built, and the accuracy of the simulation model was verified by comparing it with the test. Next, the proposed cooperative control strategy was designed, which coordinates the wheel anti-lock actuation system (WAAS) to adjust the wheel cylinder pressure to solve the wheel locking problem of HRBS in the process of braking energy recovery and coordinate the vehicle anti-loss control actuation system (VACAS) to generate a yaw compensation moment to solve the vehicle loss of the control problem of HRBS in the process of braking energy recovery by detecting the wheel slip ratio, yaw rate and sideslip angle. Finally, the established control strategy was verified through the co-simulation of Carsim and Matlab software, and the results showed that the control strategy proposed in this paper could not only avoid wheel locking and loss of vehicle control during turning braking on low-adhesion roads, but also improve the energy-recovery efficiency by 29.64% compared with a vehicle that only controls the slip ratio.
A Deep Learning Approach for Efficient Electromagnetic Analysis of On-Chip Inductor with Dummy Metal Fillings
Authors: Li Xiangliang , Tang Yijie , Zhao Peng , Chen Shichang , Xu Kuiwen , Wang Gaofeng
Published: December 2022

A deep learning approach for the efficient electromagnetic analysis of an on-chip inductor with dummy metal fillings (DMFs) is proposed. By comparing different activation functions and loss functions, a deep neural network for DMF modeling is built using a smooth maximum unit activation function and log-cosh loss function. The parasitic capacitive effect of DMFs is quickly and accurately extracted though this model, and the effective permittivity can be obtained subsequently. An on-chip inductor containing DMFs with different filling densities is analyzed using this proposed method and compared with the electromagnetic simulation of entire structures. The results validate the accuracy and efficiency of this proposed method.
A Gate-Level Information Leakage Detection Framework of Sequential Circuit Using Z3
Authors: Zhang Qizhi , Liu Liang , Yuan Yidong , Zhang Zhe , He Jiaji , Gao Ya , Li Yao , Guo Xiaolong , Zhao Yiqiang
Published: December 2022

Hardware intellectual property (IP) cores from untrusted vendors are widely used, raising security concerns for system designers. Although formal methods provide powerful solutions for detecting malicious behaviors in hardware, the participation of manual work prevents the methods from reaching practical applications. For example, Information Flow Tracking (IFT) represents a powerful approach to preventing leakage of sensitive information. However, existing IFT solutions either introduce hardware overheads or lack practical automatic working procedures, especially for hardware sequential logic. To alleviate these challenges, we propose a framework that fully automates information leakage detection at the gate level of hardware. This framework introduces Z3, an SMT solver, to automatically check the violation of confidentiality. On the other hand, an automatic tool is developed to remove the manual workload further. In this tool, the gate level hardware is converted to the formal model firstly, and the integrity of the model is assessed. Along with the model converting step, the property for leakage detection is generated as well. The proposed solution is tested on 25 gate-level netlist benchmarks, where sequential designs are included to validate the effectiveness. As a result, Trojans leaking information from circuit outputs can be automatically detected. The measured time consumption of the entire working procedure validates the efficiency of the proposed approach.
Integral Windup Resetting Enhancement for Sliding Mode Control of Chemical Processes with Longtime Delay
Authors: Prado Alvaro Javier , Herrera Marco , Dominguez Xavier , Torres Jose , Camacho Oscar
Published: December 2022

The effects of the windup phenomenon impact the performance of integral controllers commonly found in industrial processes. In particular, windup issues are critical for controlling variable and longtime delayed systems, as they may not be timely corrected by the tracking error accumulation and saturation of the actuators. This work introduces two anti-windup control algorithms for a sliding mode control (SMC) framework to promptly reset the integral control action in the discontinuous mode without inhibiting the robustness of the overall control system against disturbances. The proposed algorithms are intended to anticipate and steer the tracking error toward the origin region of the sliding surface based on an anti-saturation logistic function and a robust compensation action fed by system output variations. Experimental results show the effectiveness of the proposed algorithms when they are applied to two chemical processes, i.e., (i) a Variable Height Mixing Tank (VHMT) and (ii) Continuous Stirred Tank Reactor (CSTR) with a variable longtime delay. The control performance of the proposed anti-windup approaches has been assessed under different reference and disturbance changes, exhibiting that the tracking control performance in the presence of disturbances is enhanced up to 24.35% in terms of the Integral Square Error (ISE) and up to 88.7% regarding the Integral Time Square Error (ITSE). Finally, the results of the proposed methodology demonstrated that the excess of cumulative energy by the actuator saturation could reduce the process resources and also extend the actuator’s lifetime span.
A Substation Fire Early Warning Scheme Based on Multi-Information Fusion
Authors: Miao Junjie , Li Bingyu , Du Xuhao , Wang Haobin
Published: December 2022

In view of the substation fire early warning using a single information sensor monitoring, it is easy to make mistakes and omissions. Taking the cable in substation as the research object, a multi-information fusion fire prediction model based on back propagation neural network (BPNN) and fuzzy set theory is proposed. Firstly, the BPNN model is trained by using the existing data. Secondly, the artificial fish swarm algorithm (AFSA) is used to optimize the BPNN, which speeds up convergence speed of the model and improves the accuracy of prediction. The fuzzy set theory is applied to fuse the predicted fire probability to obtain the optimal fire prevention and control decision. Finally, the fire protection measures are taken according to the fire decision. The experimental show that the average absolute errors of no fire, smoldering and open fire decreased by 26.06%, 38.5% and 43.13% respectively. The model has higher prediction accuracy, can reasonably output different levels of fire alarm signals, establish substation fire warning and prevention and control system, and provide reference for future substation fire and other disasters warning and prevention and control.
Design of an Adaptive Distributed Drive Control Strategy for a Wheel-Side Rear-Drive Electric Bus
Authors: Chen Huipeng , Wang Weiyang , Zhu Shaopeng , Chen Sen , Gao Jian , Zhou Rougang , Wei Wei
Published: December 2022

A wheel motor simplifies the chassis structure of an electric bus, greatly improving its response speed and controllability. How to improve the lateral stability of the vehicle under complex and changeable driving conditions is a major problem in the motion control of electric buses. This study proposed an adaptive distributed drive control strategy for a rear-wheel drive electric bus. An adaptive fuzzy controller was designed to obtain the additional yaw moment of the vehicle and then combined with a rule distribution method to modify the steering characteristics of the vehicle to obtain the optimal driving torque distribution. Hardware-in-the-loop test results showed that under adaptive fuzzy control, the yaw rate deviations under low- and high-speed conditions were reduced from 18% and 42% without control to 10% and 23% with control, respectively. Under sine wave conditions, the deviation of the yaw rate and the vehicle’s sideslip angle were reduced from 83% and 852% without control to 12% and 15% with control, respectively. It was verified that the electric bus with adaptive fuzzy control could maintain good vehicle stability at full speed.
Ability-Restricted Indoor Reconnaissance Task Planning for Multiple UAVs
Authors: Zhang Ruowei , Dou Lihua , Wang Qing , Xin Bin , Ding Yulong
Published: December 2022

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal environment of buildings has a great impact on the path planning of UAVs. In this paper, the ability-restricted indoor reconnaissance task-planning (ARIRTP) problem is solved by a bi-level problem-solving framework. In the upper level, an iterative search algorithm is used to solve the task assignment problem. According to the characteristics of the problem, a solution-space compression mechanism (SSCM) is proposed to exclude solutions that do not satisfy the task requirements. In the lower level, based on a topological map, the nearest neighbor (NN) algorithm is used to quickly construct the path sequence of a UAV. Finally, the genetic algorithm (GA) and simulated annealing (SA) algorithm are applied to the upper level of the framework as iterative search algorithms, which produces two hybrid algorithms named the GA-NN and SA-NN, respectively. ARIRTP instances of different scales are designed to verify the effectiveness of the SSCM and the performance of the GA-NN and SA-NN methods. It is demonstrated that the SSCM can significantly compress the solution space and effectively improve the performance of the algorithms. The proposed bi-level problem-solving framework provides a methodology for the cooperation of multi-UAV to perform reconnaissance tasks in indoor environments. The experimental results show that the GA-NN and SA-NN methods can quickly and efficiently solve the ARIRTP problem. The performance of the GA-NN method is similar to that of the SA-NN method. The GA-NN method runs slightly faster. In large-scale instances, the performance of the SA-NN method is slightly better than that of the GA-NN method.