bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Efficacy and Safety of Platinum-Based Chemotherapy as First-Line Therapy between B3 Thymoma and Thymic Carcinoma
Authors: Hao Yue , Si Jinfei , Jin Jianan , Wei Jingwen , Xiang Jing , Xu Chunwei , Song Zhengbo
Published: December 2022

Background: B3 type thymoma is defined as a well-differentiated thymic carcinoma and is similar to a thymic carcinoma. However, the differences between them are not well defined. In addition, the data to compare the efficacy and safety of platinum-based chemotherapy as first-line therapy between B3 thymoma and thymic carcinoma are lacking. Methods: The efficacy and safety of platinum-based chemotherapy as first-line therapy was retrospectively compared between a group of 36 patients with type B3 thymoma and a group of 127 patients with thymic carcinoma treated between January 2009 and March 2022 at the Zhejiang Cancer Hospital. Objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse events were analyzed. Results: The ORRs for B3 thymoma and thymic carcinoma were 36.1% and 28.3%, respectively (p = 0.370). Among all patients, the difference in PFS between B3 thymoma and thymic carcinoma was not significant (11.3 vs. 10.1 months, p = 0.118). The squamous carcinoma subgroup did not exhibit any differences in PFS compared to B3 thymoma (11.7 vs. 11.3 months, p = 0.161). The result for the non-squamous carcinoma subgroup was similar (6.5 vs. 11.3 months, p = 0.128). Furthermore, the OS values for B3 thymoma and thymic carcinoma were not significantly different (58.3 vs. 35.1 months, p = 0.067). However, there were differences in OS between B3 thymoma and non-squamous carcinoma (58.3 vs. 30.6 months, p = 0.031). Conclusions: B3 thymoma and especially squamous carcinoma patients may be treated using a similar therapy scheme as that utilized for thymic carcinoma.
Integrating Systematic Reviews into Supportive Care Trial Design: The Rethinking Clinical Trials (REaCT) Program
Authors: Alshamsan Bader , Hutton Brian , Liu Michelle , Vandermeer Lisa , Clemons Mark
Published: December 2022

Purpose: To review the successes and challenges of integrating systematic reviews (SRs) into the Rethinking Clinical Trials (REaCT) Program. Methods: All REaCT program SRs were evaluated and descriptive summaries presented. Results: Twenty-two SRs have been performed evaluating standard of care interventions for the management of: breast cancer (n = 15), all tumour sites (n = 4), breast and prostate cancers (n = 2), and prostate cancer (n = 1). The majority of SRs were related to supportive care (n = 14) and survivorship (n = 5) interventions and most (19/22, 86%) confirmed the existence of uncertainty relating to the clinical question addressed in the SR. Most SRs (15/22, 68%) provided specific recommendations for future studies and results were incorporated into peer-reviewed grant applications (n = 6) and clinical trial design (n = 12). In 12/22 of the SRs, the first author was a trainee. All SRs followed PRISMA guidelines. Conclusion: SRs are important for identifying and confirming clinical equipoise and designing trials. SRs provide an excellent opportunity for trainees to participate in research.
Climate Change Is Driving Shifts in Dragonfly Species Richness across Europe via Differential Dynamics of Taxonomic and Biogeographic Groups
Authors: Olsen Kent , Svenning Jens-Christian , Balslev Henrik
Published: December 2022

Understanding how changes in species richness pattern correlate with range changes in different taxonomic and biogeographic groups is important for conservation because it allows for generalizations about which species are at greatest risk. Here, we assessed whether changes in species richness patterns result from generalized range shifts across taxonomic and biogeographic groups or from changes in specific subsets of species. Using data from 1988 and from 2010, we studied changes in distributional range of European dragonfly species, using outline distribution maps for all dragonflies combined and separately for taxonomic suborders (Zygoptera and Anisoptera) and biogeographic groups (Boreo-alpine, Eurasian, Mediterranean, and Tropical). The results demonstrated differing range dynamics for Zygoptera and Anisoptera, with Anisoptera driving local turnover in species richness to a greater extent than Zygoptera. The distributional range of Tropical and Mediterranean species had expanded to a much greater extent than that of Eurasian and Boreo-alpine species. Large-scale changes in species richness arose from several divergent, group-specific processes. Overall, local diversity especially declined in parts of southern and south-eastern Europe, reflecting local losses in multiple species rather than major range contractions among Mediterranean or Eurasian species. In fact, among the biogeographic groups, overall range declines were most prominent among Boreo-alpine species, highlighting the particular threat from climate change to this group.
Effects of Macrobenthos Relative to Floating-Leaved Plants on the Wintering Shorebird Assemblages at Shengjin Lake, China
Authors: Zhang Zhongfang , Xia Shanshan , Sun Xuetao , Zhou Lizhi
Published: December 2022

In shallow lakes, floating-leaved plants can produce dense lakebeds that dramatically alter freshwater ecosystems and impact macrobenthic communities. Shorebirds are morphologically diverse and utilize different foraging strategies; they can partition food resources to achieve coexistence due to differences in food availability. In this study, we defined shorebird foraging guilds using a principal component analysis and explained differences in shorebird composition in terms of food availability by comparing macrobenthic and shorebird communities in Euryale ferox artificial planting areas, Trapa spp. natural growth areas, and control areas. The Mantel test and a Spearman analysis were used to correlate macrobenthic taxa with shorebird foraging guilds. We recorded four different macrobenthic taxa in the three study areas, including insects, gastropods, oligochaetes, and bivalves. Fifteen species belonging to three shorebird families were recorded across the three study areas. Our results suggest that floating-leaved plants are an important cause of differences in macrobenthic communities, and epifaunal macrobenthos (insects and gastropods) and infaunal macrobenthos (oligochaetes and bivalves) take on different patterns of diversity composition in different habitats. The macrobenthic and shorebird communities were potentially coherent. Different shorebird foraging guilds were limited by food availability and thus correlated differently with different macrobenthos. Therefore, differences in macrobenthic communities relative to floating-leaved plants can affect shorebird assemblages by affecting the availability of food resources.
A Bibliometric Analysis of the Global Research in Odonata: Trends and Gaps
Authors: Oliveira-Junior José Max Barbosa , Rocha Tainã Silva , Vinagre Suellen Furtado , Miranda-Filho Jair Costa , Mendoza-Penagos Cristian Camilo , Dias-Silva Karina , Juen Leandro , Calvão Lenize Batista
Published: December 2022

Insects of the order Odonata have been used as indicators of environmental quality in different aquatic systems around the world. In this context, we conducted a bibliometric analysis to understand the general patterns of research on Odonata published in the past decade (2012–2021). We extracted literature from the Web of Science (WoS) in the advanced search option and used search terms related to Odonata plus search strings for each term. A total of 2764 Odonata publications were identified. The journals with the most published articles on Odonata were Zootaxa, International Journal of Odonatology and Odonatologica. The countries with the most Odonata publications were the USA, Brazil and China. Most studies were conducted on streams, ponds and rivers. Ecology, taxonomy and behavior were the main study topics. Of the total articles on Odonata, 982 involved Zygoptera and 946 Anisoptera. Another 756 studies were focused on both suborders. The increase in ecological and taxonomic studies of Odonata reflects the dynamic characteristics of this order, and its relatively well-defined systematics, especially in the case of adults. Despite the recent increase in the number of publications, there are still many gaps related to topics such as biogeography, parasitism, competition within and between species, evolutionary and phylogenetic relationships, as well as studies of the eggs (e.g., their development) and larval exuviae (e.g., their morphological features).
Enhanced Diagnostics for Corneal Ectatic Diseases: The Whats, the Whys, and the Hows
Authors: Esporcatte Louise Pellegrino Gomes , Salomão Marcella Q. , Neto Alexandre Batista da Costa , Machado Aydano P. , Lopes Bernardo T. , Ambrósio Renato
Published: December 2022

There are different fundamental diagnostic strategies for patients with ectatic corneal diseases (ECDs): screening, confirmation of the diagnosis, classification of the type of ECD, severity staging, prognostic assessment, and clinical follow-up. The conscious application of such strategies enables individualized treatments. The need for improved diagnostics of ECD is related to the advent of therapeutic refractive procedures that are considered prior to keratoplasty. Among such less invasive procedures, we include corneal crosslinking, customized ablations, and intracorneal ring segment implantation. Besides the paradigm shift in managing patients with ECD, enhancing the sensitivity to detect very mild forms of disease, and characterizing the inherent susceptibility for ectasia progression, became relevant for identifying patients at higher risk for progressive iatrogenic ectasia after laser vision correction (LVC). Moreover, the hypothesis that mild keratoconus is a risk factor for delivering a baby with Down’s syndrome potentially augments the relevance of the diagnostics of ECD. Multimodal refractive imaging involves different technologies, including Placido-disk corneal topography, Scheimpflug 3-D tomography, segmental or layered tomography with layered epithelial thickness using OCT (optical coherence tomography), and digital very high-frequency ultrasound (VHF-US), and ocular wavefront. Corneal biomechanical assessments and genetic and molecular biology tests have translated to clinical measurements. Artificial intelligence allows for the integration of a plethora of clinical data and has proven its relevance in facilitating clinical decisions, allowing personalized or individualized treatments.
Pulmonary Rehabilitation in SARS-CoV-2: A Systematic Review and Meta-Analysis of Post-Acute Patients
Authors: Reinert Glenn , Müller Daniel , Wagner Pit , Martínez-Pozas Oliver , Cuenca-Záldivar Juan Nicolás , Fernández-Carnero Josué , Sánchez Romero Eleuterio A. , Corbellini Camilo
Published: December 2022

Background: Pulmonary Rehabilitation (PR) was initially developed for the management of Chronic Obstructive Pulmonary Disease (COPD) and is now recognized as a core management of COVID-19 patients. This systematic review and meta-analysis examined the efficacy of PR in patients with post-acute COVID-19 infection. Methods: A literature search was conducted in PubMed, the Web of Science (WoS), and the Cochrane Library from their inceptions until October 2022, and randomized controlled trials and observational studies were considered. The outcomes measured included dyspnea, physical function, and quality of life. Results: Eleven studies including 677 participants with post-acute COVID-19 were included in this analysis. From a qualitative point of view and analyzing the studies separately, PR improves dyspnea, physical function, and quality of life in patients with post-acute COVID-19. However, in pooling the data of all the studies, no significant changes pre-postintervention, compared to the control, were found among the experimental studies included in the analysis in any outcome measures, due to the high heterogeneity between the studies, as well as no significant improvements being found in the observational studies. A subgroup analysis revealed significant differences in all the included outcomes. Future studies should include the same scale to assess the actual efficacy of PR. Conclusion: From a qualitative analysis point of view, PR is effective in improving physical function, reducing dyspnea, and improving quality of life in patients with post-acute COVID-19. However, an exploratory meta-analysis was performed to evaluate, by subgroups, the efficacy of PR, and positive results were found in favor of PR.
RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features
Authors: Hassan Arfa , Alkhalifah Tamim , Alturise Fahad , Khan Yaser
Published: December 2022

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes’ standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.
Cascaded Hough Transform-Based Hair Mask Generation and Harmonic Inpainting for Automated Hair Removal from Dermoscopy Images
Authors: Ashour Amira S. , El-Wahab Basant S. Abd , Wahba Maram A. , Mansour Diaa-Eldin A. , Hodeib Abeer Abd Elhakam , Khedr Rasha Abd El-Ghany , Hassan Ghada F. R.
Published: December 2022

Restoring information obstructed by hair is one of the main issues for the accurate analysis and segmentation of skin images. For retrieving pixels obstructed by hair, the proposed system converts dermoscopy images into the L*a*b* color space, then principal component analysis (PCA) is applied to produce grayscale images. Afterward, the contrast-limited adaptive histogram equalization (CLAHE) and the average filter are implemented to enhance the grayscale image. Subsequently, the binary image is generated using the iterative thresholding method. After that, the Hough transform (HT) is applied to each image block to generate the hair mask. Finally, the hair pixels are removed by harmonic inpainting. The performance of the proposed automated hair removal was evaluated by applying the proposed system to the International Skin Imaging Collaboration (ISIC) dermoscopy dataset as well as to clinical images. Six performance evaluation metrics were measured, namely the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), the signal-to-noise ratio (SNR), the structural similarity index (SSIM), the universal quality image index (UQI), and the correlation (C). Using the clinical dataset, the system achieved MSE, PSNR, SNR, SSIM, UQI, and C values of 34.7957, 66.98, 42.39, 0.9813, 0.9801, and 0.9985, respectively. The results demonstrated that the proposed system could satisfy the medical diagnostic requirements and achieve the best performance compared to the state-of-art.
The Clinical Utility of Droplet Digital PCR for Profiling Circulating Tumor DNA in Breast Cancer Patients
Authors: Gezer Ugur , Bronkhorst Abel J. , Holdenrieder Stefan
Published: December 2022

Breast cancer is the most common cancer affecting women worldwide. It is a malignant and heterogeneous disease with distinct molecular subtypes, which has prognostic and predictive implications. Circulating tumor DNA (ctDNA), cell-free fragmented tumor-derived DNA in blood plasma, is an invaluable source of specific cancer-associated mutations and holds great promise for the development of minimally invasive diagnostic tests. Furthermore, serial monitoring of ctDNA over the course of systemic and targeted therapies not only allows unparalleled efficacy assessments but also enables the identification of patients who are at risk of progression or recurrence. Droplet digital PCR (ddPCR) is a powerful technique for the detection and monitoring of ctDNA. Due to its relatively high accuracy, sensitivity, reproducibility, and capacity for absolute quantification, it is increasingly used as a tool for managing cancer patients through liquid biopsies. In this review paper, we gauge the clinical utility of ddPCR as a technique for mutational profiling in breast cancer patients and focus on HER2, PIK3CA, ESR1, and TP53, which represent the most frequently mutated genes in breast cancers.
Radiation Exposure and Lifetime Attributable Risk of Cancer Incidence and Mortality from Low- and Standard-Dose CT Chest: Implications for COVID-19 Pneumonia Subjects
Authors: Garg Mandeep , Karami Vahid , Moazen Javad , Kwee Thomas , Bhalla Ashu Seith , Shahbazi-Gahrouei Daryoush , Shao Yu-Hsuan Joni
Published: December 2022

Since the novel coronavirus disease 2019 (COVID-19) outbreak, there has been an unprecedented increase in the acquisition of chest computed tomography (CT) scans. Nearly 616 million people have been infected by COVID-19 worldwide to date, of whom many were subjected to CT scanning. CT exposes the patients to hazardous ionizing radiation, which can damage the genetic material in the cells, leading to stochastic health effects in the form of heritable genetic mutations and increased cancer risk. These probabilistic, long-term carcinogenic effects of radiation can be seen over a lifetime and may sometimes take several decades to manifest. This review briefly describes what is known about the health effects of radiation, the lowest dose for which there exists compelling evidence about increased radiation-induced cancer risk and the evidence regarding this risk at typical CT doses. The lifetime attributable risk (LAR) of cancer from low- and standard-dose chest CT scans performed in COVID-19 subjects is also discussed along with the projected number of future cancers that could be related to chest CT scans performed during the COVID-19 pandemic. The LAR of cancer Incidence from chest CT has also been compared with those from other radiation sources, daily life risks and lifetime baseline risk.
Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
Authors: Yeh Chih-Yang , Lee Hsun-Hua , Islam Md. Mohaimenul , Chien Chiu-Hui , Atique Suleman , Chan Lung , Lin Ming-Chin
Published: December 2022

Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients’ medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis.
Correlation between Hand Grip Strength and Peak Inspiratory Flow Rate in Patients with Stable Chronic Obstructive Pulmonary Disease
Authors: Suriyakul Apisara , Saiphoklang Narongkorn , Barjaktarevic Igor , Cooper Christopher B.
Published: December 2022

Optimal peak inspiratory flow rate (PIFR) is required for effective drug delivery to distal airways when using dry powder inhalers (DPIs). This study aimed to examine the association between PIFR and hand grip strength (HGS) in stable COPD patients. A cross-sectional study was conducted. PIFR was measured using the In-check DIAL to assess for Accuhaler and Turbuhaler DPIs. HGS was measured using a handheld dynamometer. A PIFR of <60 L/min was considered suboptimal PIFR. Demographics, clinical data, and spirometric data were collected and compared. Eighty-one patients (86% men) were included. Mean age was 73.3 ± 8.9 years. FEV1 was 65.3 ± 23.7%. The prevalence of suboptimal PIFR was 38% and 59% for Accuhaler and Turbuhaler, respectively. HGS in the suboptimal PIFR group was lower than in the optimal PIFR group for Accuhaler (22.8 ± 4.7 vs. 33.2 ± 6.9 kg, p < 0.001) and for Turbuhaler (25.3 ± 6.4 vs. 35.1 ± 6.3 kg, p < 0.001). The equation for predicted Accuhaler PIFR (L/min) was −30.340 + (0.274 × hand grip strength) − (0.206 × age) + (0.219 × height) + (1.019 × FVC). The equation for predicted Turbuhaler PIFR (L/min) was 56.196 + (0.321 × hand grip strength) − (0.196 × female) − (0.224 × age) + (0.304 × FVC). The best cutoff values of HGS for predicting optimal PIFR in Accuhaler and Turbuhaler were 26.8 kg (with 82% sensitivity and 84% specificity) and 31.9 kg (with 79% sensitivity and 90% specificity), respectively. In conclusion, HGS correlated with PIFR in patients with clinically stable COPD, especially in the group with pronounced symptoms without frequent exacerbations. HGS threshold values associated with suboptimal PIFR were identified. HGS may be used as an alternative tool to assess an optimal inspiratory force for DPIs.
Experiences of Individuals Living with Spinal Cord Injuries (SCI) and Acquired Brain Injuries (ABI) during the COVID-19 Pandemic
Authors: Wong Michelle M. Y. , Seliman Merna , Loh Eldon , Mehta Swati , Wolfe Dalton L.
Published: December 2022

The COVID-19 pandemic presents unique challenges for people living with acquired neurological conditions. Due to pandemic-related societal restrictions, changes in accessibility to medical care, equipment, and activities of daily living may affect the mental health of individuals with a SCI or ABI. This study aimed to understand the impact of the pandemic on psychological wellbeing, physical health, quality of life, and delivery of care in persons living with SCI and ABI. A secondary objective included exploring the use of virtual services designed to meet these challenges. In a companion study, participants were surveyed using validated scales of psychosocial health, physical health and healthcare access. In this study, 11 individuals gathered from the survey participated in virtual individual semi-structured interviews to provide accounts of lived experiences regarding critical health challenges and eHealth. Two researchers independently coded interviews for themes using a hermeneutic phenomenological approach. Through analysis of interviews, 5 themes were identified regarding COVID-19 and recovery, access to care, virtual healthcare, systemic barriers, and coping. Overall, limited opportunities due to the pandemic led to a need for adaptation and multifaceted outcomes on one’s wellbeing, which provides guidance for future clinical practice.
Digital Quantum Simulation of the Spin-Boson Model under Markovian Open-System Dynamics
Authors: Burger Andreas , Kwek Leong Chuan , Poletti Dario
Published: December 2022

Digital quantum computers have the potential to simulate complex quantum systems. The spin-boson model is one of such systems, used in disparate physical domains. Importantly, in a number of setups, the spin-boson model is open, i.e., the system is in contact with an external environment which can, for instance, cause the decay of the spin state. Here, we study how to simulate such open quantum dynamics in a digital quantum computer, for which we use an IBM hardware. We consider in particular how accurate different implementations of the evolution result as a function of the level of noise in the hardware and of the parameters of the open dynamics. For the regimes studied, we show that the key aspect is to simulate the unitary portion of the dynamics, while the dissipative part can lead to a more noise-resistant simulation. We consider both a single spin coupled to a harmonic oscillator, and also two spins coupled to the oscillator. In the latter case, we show that it is possible to simulate the emergence of correlations between the spins via the oscillator.
Non-Additive Entropy Composition Rules Connected with Finite Heat-Bath Effects
Authors: Biró Tamás Sándor
Published: December 2022

Mathematical generalizations of the additive Boltzmann–Gibbs–Shannon entropy formula have been numerous since the 1960s. In this paper we seek an interpretation of the Rényi and Tsallis q-entropy formulas single parameter in terms of physical properties of a finite capacity heat-bath and fluctuations of temperature. Ideal gases of non-interacting particles are used as a demonstrating example.
Multi-Qubit Bose–Einstein Condensate Trap for Atomic Boson Sampling
Authors: Tarasov Sergey , Shannon William , Kocharovsky Vladimir , Kocharovsky Vitaly
Published: December 2022

We propose a multi-qubit Bose–Einstein-condensate (BEC) trap as a platform for studies of quantum statistical phenomena in many-body interacting systems. In particular, it could facilitate testing atomic boson sampling of the excited-state occupations and its quantum advantage over classical computing in a full, controllable and clear way. Contrary to a linear interferometer enabling Gaussian boson sampling of non-interacting non-equilibrium photons, the BEC trap platform pertains to an interacting equilibrium many-body system of atoms. We discuss a basic model and the main features of such a multi-qubit BEC trap.
Quantum Spatial Search with Electric Potential: Long-Time Dynamics and Robustness to Noise
Authors: Fredon Thibault , Zylberman Julien , Arnault Pablo , Debbasch Fabrice
Published: December 2022

We present various results on the scheme introduced in a previous work, which is a quantum spatial-search algorithm on a two-dimensional (2D) square spatial grid, realized with a 2D Dirac discrete-time quantum walk (DQW) coupled to a Coulomb electric field centered on the the node to be found. In such a walk, the electric term acts as the oracle of the algorithm, and the free walk (i.e., without electric term) acts as the “diffusion” part, as it is called in Grover’s algorithm. The results are the following. First, we run long time simulations of this electric Dirac DQW, and observe that there is a second localization peak around the node marked by the oracle, reached in a time O(N), where N is the number of nodes of the 2D grid, with a localization probability scaling as O(1/lnN). This matches the state-of-the-art 2D-DQW search algorithms before amplitude amplification We then study the effect of adding noise on the Coulomb potential, and observe that the walk, especially the second localization peak, is highly robust to spatial noise, more modestly robust to spatiotemporal noise, and that the first localization peak is even highly robust to spatiotemporal noise.
Information and Communication Technologies in Peruvian University Students: A Confirmatory Analysis of Their Frequency and Extent of Use
Authors: Vargas-Merino Jorge Alberto , Olórtegui-Alcalde Luis Miguel , Córdova-Berona Heli Alejandro , Mauricci-Zuloeta José Jorge , Panez-Bendezú Miguel Humberto
Published: December 2022

Pedagogical strategies with technology have an impact on university academic teaching. Hence, there is a need to develop competencies that allow the efficient use of various digital resources. The present study validated through a confirmatory factor analysis the constructs proposed in the CUTIC-28 in a sample of 318 Peruvian university students. It was a quantitative approach research at a descriptive level and based on a non-experimental design. The results demonstrated, as reflected in each metric of the confirmatory factor analysis (CFA), the theoretical and empirical sustainability of the original questionnaire to assess the frequency and extent of ICT use in Peruvian university students; the set of data reported offers the certainty that it is a defensible and sustainable factorial model. The covariances and correlations between the dimensions and subdimensions are highly significant and positive, and, therefore, the factorial structure is confirmed by the sample data. The confirmed scale has adequate properties that allow it to be considered a valid and reliable measure in future research, even after adding other variables, such as gender, age, and type of university, among the variables of interest that show significant differences. The results also show that there is still a knowledge gap to be covered.
The Future of Higher Education: Identifying Current Educational Problems and Proposed Solutions
Authors: Halabieh Haya , Hawkins Sasha , Bernstein Alexandra E. , Lewkowict Sarah , Unaldi Kamel Bukle , Fleming Lindsay , Levitin Daniel
Published: December 2022

It is widely acknowledged that higher education is failing to meet the needs of students and employers, while educational costs and student debt are rapidly increasing. Our aim was to address these issues in an innovative fashion through a structured review combined with recommendations for best practices. Specifically, we aimed to identify and systemize failings of higher ed based on current scholarship, propose solutions, and identify institutions of higher education (IHEs) that have begun to successfully put these solutions in practice. Based on our literature review, this is the first time such a study has been conducted. We performed a structured literature review and identified four key failings in higher education: quality, relevance, access, and cost. From the reviewed literature we extracted a rubric to identify and evaluate twelve IHEs that are effectively applying new and innovative models that address these four problems. We conclude by recommending best practices for the successful redesign of IHEs. The overarching problem we identified was lack of student preparedness to succeed in a highly complex, competitive, and increasingly global, digital world—curricula lack relevance. IHEs are failing to teach the skills and tools needed for sustained success in the workplace: critical and creative thinking, problem-solving, co-operation, tolerance, and collaboration (which incidentally align with the skills and tools needed for effective citizenship) and when they do, they are not using evidence-based pedagogical strategies drawn from research on the science of learning. Additionally, IHEs are failing to provide accessible, high-quality, affordable postsecondary education. Financial and geographic inaccessibility, opaque admissions processes, attrition, poor attention to student health and well-being, lack of Indigenous inclusion, weak utilization of technology, and outmoded teaching methods and content contribute to the barriers to student success. The twelve IHEs we identified are geographically, economically, and pedagogically diverse, each serving as a model for the future of higher education. The novel contributions offered here are (i) a systematic review of higher education’s failings as they impact students and employers, (ii) identification of specific programs and initiatives that can ameliorate these failings, and (iii) identification of IHEs that are engaging in best practices with respect to (i) and (ii).
Impacts of Technology in Learning: Mobile Typing Applications for Writing and Accomplishing Academic Tasks among Arabic-Speaking Undergraduate Students
Authors: Alsubaie Merfat Ayesh
Published: December 2022

In recent years, especially during and after the COVID-19 pandemic, the use of mobile applications (apps) has become common in all areas of life for carrying out tasks, facilitating communication, and completing work. Education has been one of the most prominent areas that has presented a strong trend for using mobile apps. For instance, a huge number of Arabic-speaking undergraduate students in Saudi Arabia have been using mobile typing apps to accomplish their academic tasks and for other educational purposes. This phenomenon has influenced the academic writing skills of undergraduate students. In this study, we identify the relationship between academic tasks, academic writing skills, and the use of mobile typing apps among Arabic-speaking undergraduate students by analyzing which skills were affected by typing via mobile apps. A total of 276 Arabic-speaking undergraduate students in Saudi Arabia participated in this study. By applying structural equation modeling to analyze the quantitative data, results revealed that use of mobile typing apps had the strongest effect on the academic writing skills of clarity and cohesiveness. Meanwhile, using mobile typing apps to accomplish academic tasks had less impact on accuracy and vocabulary. Several pedagogical and technical implications are presented at the end of the study.
Analysis and Optimization Based on Factors Affecting the Spiral Climbing Locomotion of Snake-like Robot
Authors: Zhang Peng , Zang Yong , Guan Ben , Wu Zhaolin , Gao Zhiying
Published: December 2022

The snake-like robot is a limbless bionic robot widely used in unstructured environments to perform tasks with substantial functional flexibility and environmental adaptability in complex environments. In this paper, the spiral climbing motion of a snake-like robot on the outer surface of a cylindrical object was studied based on the three-dimensional motion of a biological snake, and we carried out the analysis and optimization of the motion-influencing factors. First, the spiral climbing motion of the snake-like robot was implemented by the angle control method, and the target motion was studied and analyzed by combining numerical and environmental simulations. We integrated the influence of kinematics and dynamics factors on the spiral climbing motion. Based on this, we established a multi-objective optimization function that utilized the influence factors to optimize the joint module. In addition, through dynamics simulation analysis, the change of the general clamping force of the snake-like robot’s spiral climbing motion was transformed into the analysis of the contact force between the joint module and the cylinder. On the basis of the results, the effect of the control strategy adopted in this paper on the motion and change rule of the spiral climbing motion was analyzed. This paper presents the analysis of the spiral climbing motion, which is of great theoretical significance and engineering value for the realization of the three-dimensional motion of the snake-like robot.
Survey of Credit Card Anomaly and Fraud Detection Using Sampling Techniques
Authors: Alamri Maram , Ykhlef Mourad
Published: December 2022

The rapid growth in e-commerce has resulted in an increasing number of people shopping online. These shoppers depend on credit cards as a payment method or use mobile wallets to pay for their purchases. Thus, credit cards have become the main payment method in the e-world. Given the billions of transactions that occur daily, criminals see tremendous opportunities to be gained from finding different ways of attacking and stealing credit card information. Fraudulent credit card transactions are a serious business issue, and such ‘scams’ can result in significant financial and personal losses. As a result, businesses are increasingly investing in the development of new ideas and methods for detecting and preventing fraud to secure their customers’ trust to protect their privacy. In recent years, learning algorithms have emerged as important in research areas aimed at developing optimal solutions to this issue. The core challenge currently facing researchers is that of the imbalanced credit card dataset, in which the data are highly skewed and the number of normal transactions is much higher than fraudulent transactions, which thus negatively affects the performance of credit card fraud detection. This paper reviews the sampling techniques and their importance in solving the imbalanced data problem. Past research is found to show that hybrid sampling techniques will produce excellent results that can improve the fraud detection system.
Application of Skeletonization-Based Method in Solving Inverse Scattering Problems
Authors: Zhang Xinhui , Liang Bingyuan , Ye Xiuzhu
Published: December 2022

In electromagnetic inverse scattering problems, Scattered field commonly needs to be measured by a large number of receiving antennas to provide enough scattered information for image reconstruction, which may increase the cost of the experimental system and require a long testing time. In this paper, a skeletonization-based method was proposed to reduce the number of actual receiving antennas involved in an inverse scattering system. The skeleton points were obtained by performing a strong-rank-revealing QR factorization of Green’s function matrix. By measuring the scattered field only at the skeleton points, the number of receiving antennas could be effectively reduced, while the scattered field data at other receiving points could be accurately restored from the skeleton points. The numerical results show that, compared with the frequency domain zero-padding (FDZP) method, the skeletonization-based method was more accurate for antennas distributed in an elliptical shape (such as thorax imaging). In addition, the inverse scattering method using the skeletonization-based method was able to reduce the number of measurements while maintaining an image quality comparable to that of the actual full measurement system. The proposed method can serve as a guidance for building an experimental system for inverse scattering problems, especially for cases when the antennas are elliptically distributed.
An Approach for Analyzing Cyber Security Threats and Attacks: A Case Study of Digital Substations in Norway
Authors: Yildirim Yayilgan Sule , Holik Filip , Abomhara Mohamed , Abraham Doney , Gebremedhin Alemayehu
Published: December 2022

In this paper, we provide an approach for analyzing cyber security threats and attacks in digital substations, which is based on several steps we performed within our work on two Research Council of Norway (RCN) projects. In the literature, there are various separate or theoretical concepts to understand and follow a security analysis of smart grids in general, but none is focused specifically on digital substations. Moreover, none is showing real applicability on an existing use case, making the implementation difficult. The approach we propose here is a result of our attempts to create a comprehensive overview of the individual steps we have been taking to do the analysis. For that reason, firstly, we start with defining and explaining a digital substation and its concepts, and the security challenges related to digital substations. Afterwards, we present the main steps of the security analysis for digital substation. The first step is the security pyramid. The following steps are threat analysis, threat modeling, risk assessment and the simulation impact analysis, which are another contribution from our group presented in this paper. Considering that the main goal of a security analysis is to create awareness for the stakeholders of digital substations, such an impact simulation provides a flexible way for stakeholders to see and to understand the consequences of security threats and attacks. We summarize the paper with an illustration of the steps we are taking in the form of the approach for digital substation.
Eigenstructure through Matrix Adjugates and Admissible Pairs
Authors: El-Ghezawi Omar M. E.
Published: December 2022

The traditional eigenstructure assignment method is reconsidered in this paper, shedding light on its nature and some of its properties. The approach is through matrix adjugates which enable a more eloquent decomposition compared to the lumped solution usually obtained through matrix null spaces. Based on the new approach, the admissible pair (w,z) is formalized. The new approach enables an alternative methodology to the determination of the permissible closed loop eigenvector subspaces and the might be termed the companion input-subspace. Such approach renders the method a formulae-based method as w and z are now obtained formula-wise as opposed to being extracted out of a lumped solution. Compared to the traditional method, the study reveals information such as w and z are independently and explicitly determined. Moreover, newly assigned eigenvalues always result in z≠0, the input matrix B does not influence z and a fundamental feature of the open loop characteristic polynomial is exposed. Furthermore, closed loop eigenvectors associated with repeated eigenvalues can be explicitly computed by means of differentiation as opposed to null space determination. A special form of system representation is highlighted, which considerably eases the calculations. The myriad concepts pointed out have been demonstrated and authenticated through carefully selected examples involving real, complex, repeated eigenvalues, and two practical systems of an electrical nature.
Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners
Authors: Ye Zi , Jiang Lei , Li Yang , Wang Zhaoting , Zhang Guodao , Chen Huiling
Published: December 2022

Self-regulated learning is one of the important skills to achieve learning goals and is also the key factor to ensure the quality of online learning. With the rapid development of intelligent education and information technology, online learning behavior has become a new trend in the development of education modernization. Behavior data of online learning platforms are an important carrier to reflect the learners’ initiative to plan, monitor, and regulate their learning process. Self-regulated learning (SRL) is one of the important skills to achieve learning goals and is an essential means to ensure the quality of online learning. However, there are still great challenges in studying the types and sequential patterns of learners’ self-regulated learning behaviors in online environments. In addition, for higher education, the defects of the traditional education mode are increasingly prominent, and self-regulated learning (SRL) has become an inevitable trend. Based on Zimmerman’s self-regulation theory model, this paper first classifies learning groups using the hierarchical clustering method. Then, lag sequence analysis is used to explore the most significant differences in SRL behavior and its sequence patterns among different learning groups. Finally, the differences in academic achievement among different groups are discussed. The results are as follows: (1) The group with more average behavior frequency tends to solve online tasks actively, presenting a “cognitive oriented” sequential pattern, and this group has the best performance; (2) the group with more active behavior frequency tends to improve in the process of trial and error, showing a “reflective oriented” sequence pattern, and this group has better performance; (3) the group with the lowest behavior frequency tends to passively complete the learning task, showing a “negative regulated” sequence pattern, and this group has poor performance. From the aspects of stage and outcome of self-regulated learning, the behavior sequence and learning performance of online learning behavior mode are compared, and the learning path and learning performance of different learning modes are fully analyzed, which can provide reference for the improvement of online learning platform and teachers’ teaching intervention.
From Ranking Search Results to Managing Investment Portfolios: Exploring Rank-Based Approaches for Portfolio Stock Selection
Authors: Alsulmi Mohammad
Published: December 2022

The task of investing in financial markets to make profits and grow one’s wealth is not a straightforward task. Typically, financial domain experts, such as investment advisers and financial analysts, conduct extensive research on a target financial market to decide which stock symbols are worthy of investment. The research process used by those experts generally involves collecting a large volume of data (e.g., financial reports, announcements, news, etc.), performing several analytics tasks, and making inferences to reach investment decisions. The rapid increase in the volume of data generated for stock market companies makes performing thorough analytics tasks impractical given the limited time available. Fortunately, recent advancements in computational intelligence methods have been adopted in various sectors, providing opportunities to exploit such methods to address investment tasks efficiently and effectively. This paper aims to explore rank-based approaches, mainly machine-learning based, to address the task of selecting stock symbols to construct long-term investment portfolios. Relying on these approaches, we propose a feature set that contains various statistics indicating the performance of stock market companies that can be used to train several ranking models. For evaluation purposes, we selected four years of Saudi Stock Exchange data and applied our proposed framework to them in a simulated investment setting. Our results show that rank-based approaches have the potential to be adopted to construct investment portfolios, generating substantial returns and outperforming the gains produced by the Saudi Stock Market index for the tested period.