bluuebunny/crossref_metadata_2025 huggingface dataset:
Targeted Delivery of Chimeric Antigen Receptor into T Cells via CRISPR-Mediated Homology-Directed Repair with a Dual-AAV6 Transduction System
Authors: Moço Pablo D. , Farnós Omar , Sharon David , Kamen Amine A.
Published: September 2023

CAR-T cell therapy involves genetically engineering T cells to recognize and attack tumour cells by adding a chimeric antigen receptor (CAR) to their surface. In this study, we have used dual transduction with AAV serotype 6 (AAV6) to integrate an anti-CD19 CAR into human T cells at a known genomic location. The first viral vector expresses the Cas9 endonuclease and a guide RNA (gRNA) targeting the T cell receptor alpha constant locus, while the second vector carries the DNA template for homology-mediated CAR insertion. We evaluated three gRNA candidates and determined their efficiency in generating indels. The AAV6 successfully delivered the CRISPR/Cas9 machinery in vitro, and molecular analysis of the dual transduction showed the integration of the CAR transgene into the desired location. In contrast to the random integration methods typically used to generate CAR-T cells, targeted integration into a known genomic locus can potentially lower the risk of insertional mutagenesis and provide more stable levels of CAR expression. Critically, this method also results in the knockout of the endogenous T cell receptor, allowing target cells to be derived from allogeneic donors. This raises the exciting possibility of “off-the-shelf” universal immunotherapies that would greatly simplify the production and administration of CAR-T cells.
Performance Analysis of Conventional IPMSM and NCPM Based IPMSM
Authors: Jonnalagadda Vijaya Kumar , Tammminana Narasimhulu , Guntu Raja Rao , Salkuti Surender Reddy
Published: September 2023

This paper proposes a NCPM (Nano-composite coated permanent magnets)-based IPMSM (Interior Permanent Magnet Synchronous Motor) electric drive system, especially applicable for electric vehicles (EV). For an EV, an increase in the “T/A (torque per ampere)” condition is highly recommended, as it directly affects the maximum distance run by EV on a single charge. Due to NCPM, a substantial increase in magnetic flux intensity, resistance to corrosion and Curie temperature are observed. As a result, the proposed drive clearly exhibits a higher power to weight ratio. Also, it is capable of delivering higher T/A to the drive system without any considerable change in two important factors of EV: (1) mass and volume of the drive system (2) battery capacity of the drive system. Moreover, NCPM performance is less susceptible to temperature variation, which makes it an appropriate candidate for vehicular applications, where temperature inconsistency could be a common issue during working conditions. Also, NCPM-based IPMSM offers a quicker speed response than conventional IPMSM, thus providing higher acceleration, which is one of the important performance factors for vehicular applications. A vector controlled mathematical model of IPMSM and NCPM-based IPMSM is tested for various speed commands. Also, the NCPM-based IPMSM, in the proposed configuration, is fed from a three-level DCMLI (diode clamped multi-level inverter), as the drive system is considered for medium to high power applications. A comparative performance analysis is carried out between the proposed drive system and a conventional IPMSM-based drive system using MATLAB/SIMULINK to indicate the efficacy of the proposed configuration.
Credible Pathways to Catching Up with Climate Goals in Nigeria
Authors: Nwokolo Samuel Chukwujindu , Meyer Edson L. , Ahia Chinedu Christian
Published: September 2023

This paper seeks to address Nigeria’s challenges in meeting its climate objectives by investigating feasible pathways that can be implemented to accelerate progress and ensure credibility in meeting these targets. By examining the current policies and practices in place as well as successful strategies employed by other countries, this paper aims to provide strategies and policy implications recommendations for Nigeria to enhance its climate action efforts. The potential scenarios developed in this study ranged from increasing renewable energy capacity to implementing stricter regulations and standards for industries to reduce their carbon footprint, promote sustainable production processes, and strengthen climate governance and policy frameworks. The authors further investigated these measures and discovered that implementing stricter regulations and standards for industries would reduce their carbon footprint, promote sustainable production processes, and strengthen climate governance and policy frameworks. As such, Nigeria will be able to meet its climate goals more quickly as a result of the following factors: preventing environmental degradation, funding environmentally friendly infrastructure, and improving public transportation systems that can reduce vehicle-related greenhouse gas emissions. The authors developed policy measures based on the proposed twelve credible pathways to catching up with climate goals in Nigeria, thereby promoting faster progress by the Nigerian government in achieving climate goals. By adopting these measures, Nigeria’s progress toward the proposed zero net by 2060 will be significantly accelerated. It will position Nigeria as a continental leader in sustainable development and contribute to the overall global efforts to mitigate climate change. This will not only benefit the environment but also lead to financial development and an improved standard of living for its citizens.
Narrative Review of Immunotherapy in Gastroentero-Pancreatic Neuroendocrine Neoplasms
Authors: Kaur Jasmeet , Vijayvergia Namrata
Published: September 2023

Gastroentero-pancreatic Neuroendocrine Neoplasms (GEP-NENs) are a diverse group of rare tumors that arise from neuroendocrine cells in the gastrointestinal tract and pancreas, and they can vary significantly in terms of clinical behavior and prognosis. Immunotherapy, particularly immune checkpoint inhibitors, has shown remarkable success in various malignancies by harnessing the body’s immune system to target and eliminate cancer cells. Immune checkpoint inhibitor clinical studies in GEP-NENs have yielded promising outcomes, particularly in individuals with advanced and refractory disease. Objective responses and disease stabilization have been observed in some cases, even in those previously unresponsive to traditional treatments like chemotherapy or targeted therapies. However, it’s important to note that the efficacy of immunotherapy in GEP-NENs can vary widely depending on tumor characteristics, the immune microenvironment, and patient factors. As such, identifying predictive biomarkers to select the most suitable patients for immunotherapy remains an ongoing challenge. Immunotherapy has considerable potential for treating GEP-NENs, but research is still in its early stages. Several combinations are being explored to enhance the effectiveness of immunotherapy and improve the outcomes of treatment, such as combining immunotherapy with other targeted therapies or chemotherapy.
Two New Edible Lyophyllum Species from Tibetan Areas, China
Authors: Li Shuhong , Tang Songming , He Jun , Zhou Dequn
Published: September 2023

Two new species, Lyophyllum yiqunyang and L. heimogu, that belong to the section Difformia of the genus Lyophyllum, are described based on collections from Tibetan areas, China. The two species are delicious edible low-temperature mushrooms and are widely collected and eaten by local people. Lyophyllum yiqunyang sp. nov. is saprotrophic and has medium-sized basidiomata, olive-grey pileus, cheilocystidia, absent pleurocystidia, globose to subglobose basidiospores (6.12–6.31 × 6.02–6.23 μm) and clamp connections at the pileus context, hymenophoral trama and stipe. Lyophyllum heimogu sp. nov. is saprotrophic and has a dark grey to olive pileus, medium-sized basidiomata and globose to subglobose basidiospores (5.31–5.63 × 5.22–5.41 μm). In the phylogenetic analyses, our two new species formed distinct clades that are well supported by posterior probabilities and bootstrap proportions. Detailed descriptions, colour photos, illustrations and a phylogenetic tree to show the positions of the two new species are presented.
Mesophotic and Bathyal Palaemonid Shrimp Diversity of the Red Sea, with the Establishment of Two New Genera and Two New Species
Authors: Anker Arthur , Vimercati Silvia , Barreca Federica , Marchese Fabio , Chimienti Giovanni , Terraneo Tullia I. , Rodrigue Mattie , Eweida Ameer A. , Qurban Mohammed , Duarte Carlos M. , Pieribone Vincent , Benzoni Francesca
Published: September 2023

The diversity and evolution of the Red Sea invertebrates in mesophotic and deep-water benthic ecosystems remain largely unexplored. The Palaemonidae is a diversified family of caridean shrimps with numerous taxa in need of taxonomic revisions based on recent molecular analyses. The Red Sea mesophotic and bathyal palaemonid shrimps are largely unstudied. During recent expeditions off the Red Sea coast of Saudi Arabia, several palaemonid specimens were collected at a depth range of 88–494 m, spanning the mesophotic and bathyal zones. This material was examined morphologically and genetically to infer phylogenetic relationships among the Red Sea taxa and several other palaemonid genera. The concordant morphological and genetic data led to the description of two new genera and two new species. Moreover, one species was recorded in the Red Sea for the first time, with a new host record, whereas three further deep-water species, which do not occur in the Red Sea, were formally transferred to a different genus. As more exploration efforts are deployed, research on the diversity and evolutionary relationships among marine invertebrates from the Red Sea will further underline the uniqueness of its mesophotic and bathyal fauna.
Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of Intraoperative 68Ga-PSMA-11 PET/CT Images in High-Risk Prostate Cancer: A Pilot Study
Authors: Rovera Guido , Grimaldi Serena , Oderda Marco , Finessi Monica , Giannini Valentina , Passera Roberto , Gontero Paolo , Deandreis Désirée
Published: September 2023

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative 68Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of 68Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97–99%), recall (68–81%), Dice coefficient (80–88%) and Jaccard index (67–79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room.
Volume-Assisted Estimation of Remnant Liver Function Based on Gd-EOB-DTPA Enhanced MR Relaxometry: A Prospective Observational Trial
Authors: Verloh Niklas , Rio Bartulos Carolina , Utpatel Kirsten , Brennfleck Frank , Goetz Andrea , Schicho Andreas , Fellner Claudia , Nickel Dominik , Zeman Florian , Steinmann Johannes F. , Uller Wibke , Stroszczynski Christian , Schlitt Hans-Jürgen , Wiggermann Phillip , Haimerl Michael
Published: September 2023

In the context of liver surgery, predicting postoperative liver dysfunction is essential. This study explored the potential of preoperative liver function assessment by MRI for predicting postoperative liver dysfunction and compared these results with the established indocyanine green (ICG) clearance test. This prospective study included patients undergoing liver resection with preoperative MRI planning. Liver function was quantified using T1 relaxometry and correlated with established liver function scores. The analysis revealed an improved model for predicting postoperative liver dysfunction, exhibiting an accuracy (ACC) of 0.79, surpassing the 0.70 of the preoperative ICG test, alongside a higher area under the curve (0.75). Notably, the proposed model also successfully predicted all cases of liver failure and showed potential in predicting liver synthesis dysfunction (ACC 0.78). This model showed promise in patient survival rates with a Hazard ratio of 0.87, underscoring its potential as a valuable tool for preoperative evaluation. The findings imply that MRI-based assessment of liver function can provide significant benefits in the early identification and management of patients at risk for postoperative liver dysfunction.
Dielectric Characterization of Breast Biopsied Tissues as Pre-Pathological Aid in Early Cancer Detection: A Blinded Feasibility Study
Authors: Canicattì Eliana , Sánchez-Bayuela Daniel Álvarez , Romero Castellano Cristina , Aguilar Angulo Paul Martín , Giovanetti González Rubén , Cruz Hernández Lina Marcela , Ruiz Martín Juan , Tiberi Gianluigi , Monorchio Agostino
Published: September 2023

Dielectric characterization has significant potential in several medical applications, providing valuable insights into the electromagnetic properties of biological tissues for disease diagnosis, treatment planning, and monitoring of therapeutic interventions. This work presents the use of a custom-designed electromagnetic characterization system, based on an open-ended coaxial probe, for discriminating between benign and malignant breast tissues in a clinical setting. The probe’s development involved a well-balanced compromise between physical feasibility and its combined use with a reconstruction algorithm known as the virtual transmission line model (VTLM). Immediately following the biopsy procedure, the dielectric properties of the breast tissues were reconstructed, enabling tissue discrimination based on a rule-of-thumb using the obtained dielectric parameters. A comparative analysis was then performed by analyzing the outcomes of the dielectric investigation with respect to conventional histological results. The experimental procedure took place at Complejo Hospitalario Universitario de Toledo—Hospital Virgen de la Salud, Spain, where excised breast tissues were collected and subsequently analyzed using the dielectric characterization system. A comprehensive statistical evaluation of the probe’s performance was carried out, obtaining a sensitivity, specificity, and accuracy of 81.6%, 61.5%, and 73.4%, respectively, compared to conventional histological assessment, considered as the gold standard in this investigation.
Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury
Authors: Tu Kuan-Chi , Tau Eric nyam tee , Chen Nai-Ching , Chang Ming-Chuan , Yu Tzu-Chieh , Wang Che-Chuan , Liu Chung-Feng , Kuo Ching-Lung
Published: September 2023

Background: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. Method: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. Result: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. Conclusion: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.
Factors Influencing Visual Acuity in Patients with Active Subfoveal Circumscribed Polypoidal Choroidal Vasculopathy and Changes in Imaging Parameters
Authors: Xia Fan , Xing Peiyu , Zhang Hao , Niu Tongtong , Wang Qi , Hua Rui
Published: September 2023

We performed a retrospective, observational study of 51 eyes in 51 treatment-naïve patients with polypoidal choroidal vasculopathy (PCV), whose lesion ranged within the 6 × 6 mm scope of optical coherence tomography angiography (OCTA). The patients were divided into an ill-defined group and a well-defined group based on the pattern of branching vascular network (BVN) on OCTA. BVN morphology was not related to baseline best-corrected visual acuity (BCVA). However, the BCVA in the ill-defined BVN group (−0.18 [interquartile range: −0.40 to 0.00]) was significantly improved after anti-vascular endothelial growth factor (VEGF) injections, compared with that (0.00 [interquartile range: −0.18 to 0.00]) in the well-defined group (z = 2.143, p = 0.032). Multiple logistic regression analysis showed that male sex, fewer injections, and the presence of polypoidal lesions on OCTA images at baseline predicted a poor prognosis in patients with polypoidal lesions on OCTA images after anti-VEGF therapy (all p < 0.05). Finally, BCVA at baseline and the number of injections were protective factors for BCVA after anti-VEGF therapy (all p < 0.05). In contrast, a history of hypertension and macular edema at baseline were risk factors for BCVA after anti-VEGF injections (all p < 0.05). Our results revealed the visual and morphological prognosis of patients with active subfoveal circumscribed PCV after anti-VEGF therapy.
The Utility and Safety of a Continuous Glucose Monitoring System (CGMS) in Asphyxiated Neonates during Therapeutic Hypothermia
Authors: Giordano Lucia , Perri Alessandro , Tiberi Eloisa , Sbordone Annamaria , Patti Maria Letizia , D’Andrea Vito , Vento Giovanni
Published: September 2023

Background: The present study was designed to assess the feasibility and reliability of a Continuous Glucose Monitoring System (CGMS) in a population of asphyxiated neonates during therapeutic hypothermia. Methods: This non-randomized feasibility study was conducted in the Neonatal Intensive Care Unit (NICU) facilities of Fondazione Policlinico A. Gemelli IRCSS. Infants matching the criteria for hypothermic treatment were included in this study and were connected to the CGMS (Medtronic, Northridge, CA, USA) within the first 12 h of life. Hypoglycemia was defined as a glucose value ≤ 47 mg/dL, and hyperglycemia was defined as a glucose value ≥ 180 mg/dL. Data obtained via the CGMS were compared with those obtained via a point-of-care blood glucometer (GTX). Results: The two measuring techniques were compared using the Modified Clarke Error Grid (MCEG). Sixteen infants were enrolled. The sensor had an average (standard deviation) duration of 93 (38) h. We collected 119 pairs of glycemia values (CGMVs) from the CGMS vs. GTX measurements. The CGMS detected twenty-five episodes of hypoglycemia and three episodes of hyperglycemia. All the CGMVs indicating hyperglycemia matched with the blood sample taken via the point-of-care glucometer. Conclusions: The use of a CGMS would be useful as it could detect more episodes of disglycemia than standard care. Our data show poor results in terms of the accuracy of the CGMS in this particular setting.
Biology and Management of High-Grade Myxofibrosarcoma: State of the Art and Future Perspectives
Authors: Nishio Jun , Nakayama Shizuhide
Published: September 2023

Myxofibrosarcoma (MFS) is one of the most common adult soft tissue sarcomas, typically arising in the extremities. Histologically, MFS is classified into three grades: low, intermediate, and high. Histological grades correlate with distant metastases and tumor-associated mortality. The diagnosis of MFS is challenging due to a lack of well-characterized immunohistochemical markers. High-grade MFS displays highly complex karyotypes with multiple copy number alterations. Recent integrated genomic studies have shown the predominance of somatic copy number aberrations. However, the molecular pathogenesis of high-grade MFS remains poorly understood. The standard treatment for localized MFS is surgical resection. The systemic treatment options for advanced disease are limited. This review provides an updated overview of the clinical and imaging features, pathogenesis, histopathology, and treatment of high-grade MFS.
The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging
Authors: Kim Min Ji , Kim Sang Hoon , Kim Suk Min , Nam Ji Hyung , Hwang Young Bae , Lim Yun Jeong
Published: September 2023

Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions.
Relationship between Serum 25-Hydroxyvitamin D Levels and Hormonal Status in Infertile Women: A Retrospective Study
Authors: Kolcsár Melinda , Berecki Bernadett , Gáll Zsolt
Published: September 2023

(1) Background: Infertility is a prevalent issue that affects 10–18% of couples worldwide, and up to 30% in Eastern Europe. Infertility of unknown etiology is one of the most challenging forms of infertility. Vitamin D has recently been extensively studied and researchers are investigating its possible role in ovulation and pregnancy. While the evidence suggests a positive association between vitamin D and ovulation, more research is needed to understand the role that vitamin D plays in ovulation. (2) Methods: In this study, 86 infertile patients were studied retrospectively in a single center by analyzing their anthropometric (body mass index), biochemical (total cholesterol, insulin resistance) and hormonal data in correlation with their vitamin D status. (3) Results: It was found that the mid-luteal progesterone level was significantly related to the 25-hydroxy vitamin D level in a multivariate linear regression model. An analysis of subgroups of ovulatory and anovulatory patients suggests that serum 25-hydroxy vitamin D levels of approximately 20 ng/mL may be necessary to trigger ovulation or to support progesterone secretion. (4) Conclusions: Vitamin D deficiency should be considered when infertility of unknown etiology and progesterone-related disorders occur.
Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate
Authors: Huqh Mohamed Zahoor Ul , Abdullah Johari Yap , AL-Rawas Matheel , Husein Adam , Ahmad Wan Muhamad Amir W , Jamayet Nafij Bin , Genisa Maya , Yahya Mohd Rosli Bin
Published: September 2023

Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. Methods: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. Results: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study’s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. Conclusion: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.
Does University–Industry Engagement Assist Women in Generating Business Income in Emerging Economies? Evidence from Malaysia
Authors: Murdiati Eni , Jawazi Jawazi , Zakaria Nor Balkish , Musa Kazi
Published: September 2023

Women have a significant role in economic development in emerging economies like Malaysia through employment and business ownership. This is crucial for the family’s survival and the prosperity of local economies—especially during an economic crisis or turmoil. Even though SMEs contribute to the local economy, their long-term viability is always uncertain. On the other hand, universities are considered indispensable and requisite contributors to the survival of this SME. This study, hence, evaluates the performance of women-owned small and medium businesses in conjunction with university–industry collaboration. Based on stratified sampling, a questionnaire survey was undertaken among regional SME female owners from various industries closely located to university campuses across different states post-pandemic in June 2021. The 227 female-owned SME responses showed a strong correlation between the university’s contribution to SMEs’ revenue. The findings demonstrate the importance of university support in marketing and training to SMEs’ revenue according to women business owners. These findings accentuate the need for institutional policymakers to generate more profound knowledge and the need to keep ancillary longitudinal initiatives for sustainable business performance, especially among women, via university–industry arrangements.
The Use of Professional Development to Enhance Education of Students with Autism: A Systematic Review
Authors: Petersson-Bloom Linda , Leifler Emma , Holmqvist Mona
Published: September 2023

The study objective in this article was to present the state-of-the-art on teachers’ professional development to enhance educational quality for students with autism spectrum disorders (ASD) in primary and secondary education. A systematic review was conducted, following the PRISMA guidelines. Quantitative, qualitative, and mixed-methods studies were eligible for inclusion. Fifteen studies were identified and summarized. To identify the eligibility criteria, we applied the SPIDER framework. Quality appraisal was applied using the MMAT. A thematic summary and narrative synthesis of the included studies was conducted to establish a comprehensive understanding of what makes a difference in preparation for teachers of autistic students. Two key factors were found regarding the design of professional development for teachers to enhance their skills to teach autistic students. Firstly, teachers’ developed autism-specific knowledge, and secondly, the need for practice-based training with a focus on the challenges teachers meet at their own schools. Multiple ways of addressing teachers’ need for professional development regarding autistic students may further contribute to inclusive values and actions.
An Extracurricular Project-Based Training Course in Innovation and Entrepreneurship Delivered to a Transdisciplinary Group of Students in Engineering, Social Sciences, Arts and Medicine
Authors: Sorici Costin O. , Sălceanu Claudia , Matei Raluca S. , Sburlan Dragoș F. , Țiței Adina , Gîrțu Mihai A.
Published: September 2023

Limited information is available on the design of combined innovation and entrepreneurship training courses, and with even less available on delivering such courses to multidisciplinary teams. We designed an extracurricular project-based training course in innovation and entrepreneurship and delivered it to transdisciplinary teams of students who were then asked to build an autonomous mobile system for healthcare applications and to create a business model to implement it. The course was created through a backward educational design and design thinking to assist teams of students in transforming an idea into a technical solution and developing it into a business model through a project-based learning experience. A transdisciplinary group of 31 students, mostly in bachelor’s degree programs, worked either all together, in plenary sessions, in subject-specific groups, or in teams of 5–6 members, under the guidance of their trainers during a ten-day summer school. We used questionnaires to evaluate the changes in student perceptions and a satisfaction survey to evaluate the students’ experience. The qualitative observations together with the results of the quantitative instruments revealed positive changes in student perceptions towards innovation and entrepreneurship. Additionally, the satisfaction survey offered positive feedback and some recommendations for further improvement. The implementation of the course showed that planning is important, but flexibility is essential, that covering fewer topics might lead to deeper understanding, and that managing expectations and practicing empathy are crucial.
Emotional Competences of Primary Education Teachers: A Need in School Post COVID-19
Authors: Sanz-Ponce Roberto , López-Luján Elena , Serrano-Sarmiento Ángela , Giménez-Beut Juan Antonio
Published: September 2023

The COVID-19 pandemic has increased the number of students with mental health problems: depression, anxiety, stress. Faced with this reality, teachers and schools must be prepared to respond quickly and effectively. Therefore, the objective of this article is to analyze the emotional competences of primary school teachers in the city of Valencia based on the following sociodemographic variables: sex, age, professional experience, type of center and whether they have children. For this purpose, a quantitative methodological approach has been followed, through which the emotional competencies of primary education teachers are analyzed. These results allow us to establish teacher profiles according to sociodemographic variables and help to detect possible training deficiencies. A sample of 371 teachers of primary education in the city of Valencia has been analyzed. The Questionnaire on Teaching Competences of Primary Education Teachers, carried out under the Planned Action Model, has been used, and descriptive, univariate, bivariate and cluster analyses have been carried out. The mean, the standard deviation and the interquartile range (IQR) have been analyzed, as well as non-parametric tests such as the Wilcoxon, Kruskal–Wallis or Z test. The most significant results are that teachers have a greater ability to interpret emotions and to listen to students. On the contrary, it is teachers who most reject prejudice, discrimination and racism. Younger teachers are the ones who implement more inclusive learning environments. Finally, in general, all teachers are very respectful of students and claim to know how to manage classroom conflicts. The results obtained, in general terms, coincide with most of the research on teachers’ emotional competencies. Some aspects simply do not coincide with the literature. The teachers who participated in our research perceive themselves as having a greater capacity to observe and interpret students’ emotions, to generate learning situations that cater to diversity and to listen to their students. Other studies place these competencies at lower levels.
Inorganic Semiconductors in Electronic Applications
Authors: Pisula Wojciech
Published: September 2023

Inorganic semiconductors have a wide range of applications in various fields, including electronics, optoelectronics, photovoltaics, and even catalysis [...]
An Objective Holographic Feedback Linearization Based on a Sliding Mode Control for a Buck Converter with a Constant Power Load
Authors: Li Jiyong , Pi Benquan , Zhou Pengcheng , Li Jingwen , Dong Hao , Chen Peiwen
Published: September 2023

As a typical load, the constant power load (CPL) has negative impedance characteristics. The stability of the buck converter system with a mixed load of CPL and resistive load is affected by the size of the CPL. When the resistive load is larger than the CPL, the buck converter with the output voltage as an output function is a non-minimum phase nonlinear system, because its linear approximation has a right-half-plane pole. The non-minimum phase characteristic limits the application of many control techniques, but the objective holographic feedback linearization control (OHFLC) method is a good control strategy that can bypass the non-minimum phase system and make the system stable. However, the traditional OHFLC method, in designing the controller, generally uses a linear optimal quadratic design method to obtain a linear feedback control law. It requires a state quantity component with a one-order relative degree to the system. But it is not easy to find such a suitable state quantity with a one-order relative degree to the system. In this paper, an improved OHFLC method is proposed for Buck converters with a mixed loads of CPL and resistive loads, using the sliding mode control (SMC) theory to design the controller, so that the output state quantity components with different relative degrees to the system can be used in the holographic feedback linearization method. Finally, the simulation and experimental results also demonstrate that this method has the same, or even better, dynamic response performance and robustness than the traditional OHFLC method.
Target Localization and Grasping of NAO Robot Based on YOLOv8 Network and Monocular Ranging
Authors: Jin Yingrui , Shi Zhaoyuan , Xu Xinlong , Wu Guang , Li Hengyi , Wen Shengjun
Published: September 2023

As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot’s long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method’s advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object.
Electrical Power Edge-End Interaction Modeling with Time Series Label Noise Learning
Authors: Wang Zhenshang , Zhou Mi , Zhao Yuming , Zhang Fan , Wang Jing , Qian Bin , Liu Zhen , Ma Peitian , Ma Qianli
Published: September 2023

In the context of electrical power systems, modeling the edge-end interaction involves understanding the dynamic relationship between different components and endpoints of the system. However, the time series of electrical power obtained by user terminals often suffer from low-quality issues such as missing values, numerical anomalies, and noisy labels. These issues can easily reduce the robustness of data mining results for edge-end interaction models. Therefore, this paper proposes a time–frequency noisy label classification (TF-NLC) model, which improves the robustness of edge-end interaction models in dealing with low-quality issues. Specifically, we employ two deep neural networks that are trained concurrently, utilizing both the time and frequency domains. The two networks mutually guide each other’s classification training by selecting clean labels from batches within small loss data. To further improve the robustness of the classification of time and frequency domain feature representations, we introduce a time–frequency domain consistency contrastive learning module. By classifying the selection of clean labels based on time–frequency representations for mutually guided training, TF-NLC can effectively mitigate the negative impact of noisy labels on model training. Extensive experiments on eight electrical power and ten other different realistic scenario time series datasets show that our proposed TF-NLC achieves advanced classification performance under different noisy label scenarios. Also, the ablation and visualization experiments further demonstrate the robustness of our proposed method.
Research on the Three-Dimensional Electromagnetic Positioning Method Based on Spectrum Line Interpolation
Authors: Jing Zhixin , Gong Yulin , Cai Hua , Tang Haoxiang
Published: September 2023

Medical three-dimensional (3D) positioning technology amalgamates traditional medical instrumentation with computational technology and medical imaging to facilitate the real-time visualization of anatomical lesions, surgical implements, and other relevant objects, thereby augmenting the physician’s diagnostic and therapeutic capabilities. Predominantly, optical positioning technologies are employed; however, they inherently suffer from limitations such as optical occlusion, precluding precise positioning. To surmount these challenges, the present manuscript introduces a phase-based 3D electromagnetic positioning technique predicated on spectral interpolation. Initially, four spectral lines exhibiting maximal signal amplitude are designated for the computation of a correction factor. Subsequently, the functional relationship between this correction factor and frequency offset is modeled via polynomial fitting, from which signal frequency and phase correction equations are derived. Experimental analyses executed within the ANSYS software (2021R2) environment substantiate the utility of the phase correction formula in calculating the phase disparity between the signals of the transmitting and receiving electromagnetic coils. This, in turn, enables the acquisition of the linear distance between these coils, which is instrumental in determining their spatial coordinates. The introduced 3D electromagnetic positioning methodology based on spectral interpolation effectively circumvents the issue of multiple solutions engendered by solving nonlinear equations inherent in traditional electromagnetic positioning systems. Simulation analyses corroborate that the proposed technique enhances phase detection accuracy by 1–2 orders of magnitude over conventional methods, achieving a positional accuracy within the effective measurement space of 3 mm—an improvement of at least 70% in comparison with established positioning paradigms.
Digital Generation Influence on the Post-COVID-19 Use of Digital Technologies in Engineering Education: A Statistical Study
Authors: Antón-Sancho Álvaro , Vergara Diego , Lampropoulos Georgios , Fernández-Arias Pablo
Published: September 2023

The COVID-19 pandemic caused a global health crisis that led to a sudden migration of many educational activities to digital environments. This migration affected the digitization process of higher education. This paper conducts a quantitative statistical analysis of the impact that the COVID-19 pandemic had on the habits of use of information and communication technologies (ICT) among 426 Latin American university professors in engineering areas. In particular, this impact has been analyzed in terms of the digital generation of the participating professors. To achieve this, the responses given by them on a validated questionnaire were examined for the purposes of this research. As a result, the COVID-19 pandemic has led to an increase of up to 47% in the use of ICT among engineering professors, but unevenly according to the different uses of these tools within the teaching activity. In addition, in engineering areas, it seems that digital natives have increased their use of ICT more than digital immigrants after the pandemic (between 8% and 20%, depending on the type of digital tool in question). It is suggested that universities take measures for the digital integration of older professors.
Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning
Authors: Shah Syed Aziz , Abbasi Qammer Hussain , Ahmad Jawad , Imran Muhammad Ali
Published: September 2023

State-of-the-art network architectures ensure fast and dependable real-time communication with abundant data and minimal delays [...]
Activeness: A Novel Neural Coding Scheme Integrating the Spike Rate and Temporal Information in the Spiking Neural Network
Authors: Wang Zongxia , Yu Naigong , Liao Yishen
Published: September 2023

In neuromorphic computing, the coding method of spiking neurons serves as the foundation and is crucial for various aspects of network operation. Existing mainstream coding methods, such as rate coding and temporal coding, have different focuses, and each has its own advantages and limitations. This paper proposes a novel coding scheme called activeness coding that integrates the strengths of both rate and temporal coding methods. It encompasses precise timing information of the most recent neuronal spike as well as the historical firing rate information. The results of basic characteristic tests demonstrate that this encoding method accurately expresses input information and exhibits robustness. Furthermore, an unsupervised learning method based on activeness-coding triplet spike-timing dependent plasticity (STDP) is introduced, with the MNIST classification task used as an example to assess the performance of this encoding method in solving cognitive tasks. Test results show an improvement in accuracy of approximately 4.5%. Additionally, activeness coding also exhibits potential advantages in terms of resource conservation. Overall, activeness offers a promising approach for spiking neural network encoding with implications for various applications in the field of neural computation.
Improved Attention Mechanism for Human-like Intelligent Vehicle Trajectory Prediction
Authors: Shen Chuanliang , Xiao Xiao , Li Shengnan , Tong Yan
Published: September 2023

In order to overcome the low long-term predictive accuracy associated with mainstream prediction models and the limited consideration of driver characteristics, this study presents an enhanced attention mechanism for human-like trajectory prediction, which is based on Long Short-Term Memory (LSTM). A novel database structure is proposed that incorporates data about driving style and driving intent, pertaining to human factors. By utilizing the convolution computation of Convolutional Social-Long Short-Term Memory (CS-LSTM) for surrounding vehicles, spatial feature extraction around the target vehicle is achieved. Simultaneously, we introduce a dynamic driving style recognition model and a human-like driving intent recognition model to fulfill the output of the human-like module. From a temporal perspective, we employ a decoder attention mechanism to reinforce the emphasis on key historical information, while refining the attention mechanism based on driving style for human-like weight assignment. Comparative analysis with other models indicates that the proposed Driving Style-based Attention-enhanced Convolutional Social-Long Short-Term Memory (DACS-LSTM) model exhibits notable advantages in predicting human-like trajectories for long-term tasks. Visualizing the predicted trajectories of both the Attention-enhanced Convolutional Social-Long Short-Term Memory (ACS-LSTM) and our proposed model, and analyzing the impact of the human-like module on the predicted trajectory, shows that our model’s predicted trajectory aligns more closely with the actual one. By comparing the weight distribution of the conventional attention mechanism and the enhanced attention mechanism proposed here, and analyzing the trajectory changes in conjunction with the driving styles, it becomes evident that our proposed model offers a marked improvement.
Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car
Authors: Bulakh Maryna , Klich Leszek , Baranovska Oleksandra , Baida Anastasiia , Myamlin Sergiy
Published: September 2023

The paper presented studies on reducing traction energy consumption with a decrease in the weight of an all-metal gondola car. Based on the proposed mathematical criterion, a new form of a blind floor was obtained, which makes it possible to reduce the weight of an all-metal gondola car. The aim of the paper was to reduce traction energy consumption with a decrease in the weight of an all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, strength studies were performed based on the finite element method. The equivalent stresses of the blind floor of an all-metal gondola car were 140.6 MPa, and the equivalent strains were 7.08 × 10−4. The margin of safety of the blind floor of an all-metal gondola car was 1.57. The weight of an all-metal gondola car with a modified form of a blind floor was reduced by 5.1% compared to a typical all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, a comparison was made of the traction energy consumption with typical all-metal gondola cars. Traction energy consumption with empty all-metal gondola cars were reduced by 2.5–3.1%; with loaded all-metal gondola cars by 2.4–7.3%, depending on the travel time interval.