bluuebunny/crossref_metadata_2025 huggingface dataset:
Compositional and Structural Disorder in Two-Dimensional AIIIBVI Materials
Authors: Stepanov Roman S. , Marland Pavel I. , Kolobov Alexander V.
Published: August 2023

Two-dimensional (2D) van der Waals (vdW) AIIIBVI semiconductor materials, such as InSe and GaSe, are of considerable interest due to their potential use in various microelectronics applications. The range of properties of materials of this class can be extended further through the use of quasi-binary alloys of the InSe(Te)-GaSe(Te) type. In this work, we study the effect of compositional and structural disorder in 2D In(Ga)Se(Te) on the band structure and electronic properties using first principles modeling. The results for In(Ga)Se demonstrate a noticeable decrease in the band gap for structures with a random distribution of indium and gallium cations, while for In(Ga)Te with a random cation distribution, metallization occurs. Changes in the compositional arrangement of chalcogens (there can be either the same or different atoms on each side of the vdW gap) lead to pronounced changes in the band gap, but no significant changes in topology are observed. In addition, a significant effect of the distance between the layers on the band gap under compression along the c axis was found for both alloys under study. An important point of our study is that van der Waals gap engineering is a very powerful tool to control the properties of 2D materials and its alloys.
Sudden Vision Loss Due to Optic Neuritis—An Uncommon Presentation of Neurosarcoidosis
Authors: Zimna Katarzyna , Szturmowicz Monika , Sobiecka Małgorzata , Błasińska Katarzyna , Bartosiewicz Małgorzata , Tomkowski Witold
Published: August 2023

Sarcoidosis is a systemic, granulomatous disease of unknown etiology, most often manifested by mediastinal and hilar lymph node enlargement and parenchymal nodules in the lungs. However, it may involve any other organ. Neuro-sarcoidosis, a condition that affects up to 20% of sarcoidosis patients, can be found in any part of the central or peripheral nervous system and has important ophthalmic and neuro-ophthalmic manifestations. We present two patients with sudden vision loss due to neurosarcoidosis. In both cases, biopsy of the mediastinal lymph node showed non-caseating granulomas consistent with sarcoidosis. Treatment involved high doses of methylprednisolone intravenously, followed by topical dexamethasone eye drops in the first case and a systemic steroid treatment in the second, resulting in symptom relief. Those two cases demonstrate that sarcoidosis should be considered as a differential diagnosis in cases of optic neuritis.
Establishing a Novel Diagnostic Framework Using Handheld Point-of-Care Focused-Echocardiography (HoPE) for Acute Left-Sided Cardiac Valve Emergencies: A Bayesian Approach for Emergency Physicians in Resource-Limited Settings
Authors: Ekambaram Kamlin , Hassan Karim
Published: August 2023

Acute severe cardiac valve emergencies, such as acute severe mitral regurgitation (AMR) and acute severe aortic regurgitation (AAR), present significant challenges in terms of diagnosis and management. Handheld point-of-care ultrasound devices have emerged as potentially pivotal tools in ensuring the prompt and accurate diagnosis of these left-sided valve emergencies by emergency physicians, particularly in resource-limited settings. Despite the increased utilisation of point-of-care ultrasound by emergency physicians for the management of patients in states of acute cardiorespiratory failure, current diagnostic protocols cannot perform sufficient quantitative assessments of the left-sided cardiac valves. This review elucidates and evaluates the diagnostic utility of handheld point-of-care focused-echocardiography (HoPE) in native AMR and AAR by reviewing the relevant literature and the use of clinical case examples from the Emergency Department at Port Shepstone Regional Hospital (PSRH-ED)—a rural, resource-limited hospital located in KwaZulu-Natal, South Africa. Combining the findings of the review and clinical case illustrations, this review proceeds to synthesise a novel, Bayesian-inspired, iterative diagnostic framework that integrates HoPE into the evaluation of patients with acute cardiorespiratory failure and suspected severe left-sided valve lesions.
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine
Authors: Kufel Jakub , Bargieł-Łączek Katarzyna , Kocot Szymon , Koźlik Maciej , Bartnikowska Wiktoria , Janik Michał , Czogalik Łukasz , Dudek Piotr , Magiera Mikołaj , Lis Anna , Paszkiewicz Iga , Nawrat Zbigniew , Cebula Maciej , Gruszczyńska Katarzyna
Published: August 2023

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.
Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images
Authors: Shaheed Kashif , Abbas Qaisar , Hussain Ayyaz , Qureshi Imran
Published: August 2023

Computed tomography (CT) scans, or radiographic images, were used to aid in the early diagnosis of patients and detect normal and abnormal lung function in the human chest. However, the diagnosis of lungs infected with coronavirus disease 2019 (COVID-19) was made more accurately from CT scan data than from a swab test. This study uses human chest radiography pictures to identify and categorize normal lungs, lung opacities, COVID-19-infected lungs, and viral pneumonia (often called pneumonia). In the past, several CAD systems using image processing, ML/DL, and other forms of machine learning have been developed. However, those CAD systems did not provide a general solution, required huge hyper-parameters, and were computationally inefficient to process huge datasets. Moreover, the DL models required high computational complexity, which requires a huge memory cost, and the complexity of the experimental materials’ backgrounds, which makes it difficult to train an efficient model. To address these issues, we developed the Inception module, which was improved to recognize and detect four classes of Chest X-ray in this research by substituting the original convolutions with an architecture based on modified-Xception (m-Xception). In addition, the model incorporates depth-separable convolution layers within the convolution layer, interlinked by linear residuals. The model’s training utilized a two-stage transfer learning process to produce an effective model. Finally, we used the XgBoost classifier to recognize multiple classes of chest X-rays. To evaluate the m-Xception model, the 1095 dataset was converted using a data augmentation technique into 48,000 X-ray images, including 12,000 normal, 12,000 pneumonia, 12,000 COVID-19 images, and 12,000 lung opacity images. To balance these classes, we used a data augmentation technique. Using public datasets with three distinct train-test divisions (80–20%, 70–30%, and 60–40%) to evaluate our work, we attained an average of 96.5% accuracy, 96% F1 score, 96% recall, and 96% precision. A comparative analysis demonstrates that the m-Xception method outperforms comparable existing methods. The results of the experiments indicate that the proposed approach is intended to assist radiologists in better diagnosing different lung diseases.
Diagnostic of Patients with COVID-19 Pneumonia Using Passive Medical Microwave Radiometry (MWR)
Authors: Emilov Berik , Sorokin Aleksander , Seiitov Meder , Kobayashi Binsei Toshi , Chubakov Tulegen , Vesnin Sergey , Popov Illarion , Krylova Aleksandra , Goryanin Igor
Published: August 2023

Background. Chest CT is widely regarded as a dependable imaging technique for detecting pneumonia in COVID-19 patients, but there is growing interest in microwave radiometry (MWR) of the lungs as a possible substitute for diagnosing lung involvement. Aim. The aim of this study is to examine the utility of the MWR approach as a screening tool for diagnosing pneumonia with complications in patients with COVID-19. Methods. Our study involved two groups of participants. The control group consisted of 50 individuals (24 male and 26 female) between the ages of 20 and 70 years who underwent clinical evaluations and had no known medical conditions. The main group included 142 participants (67 men and 75 women) between the ages of 20 and 87 years who were diagnosed with COVID-19 complicated by pneumonia and were admitted to the emergency department between June 2020 to June 2021. Skin and lung temperatures were measured at 14 points, including 2 additional reference points, using a previously established method. Lung temperature data were obtained with the MWR2020 (MMWR LTD, Edinburgh, UK). All participants underwent clinical evaluations, laboratory tests, chest CT scans, MWR of the lungs, and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2. Results. The MWR exhibits a high predictive capacity as demonstrated by its sensitivity of 97.6% and specificity of 92.7%. Conclusions. MWR of the lungs can be a valuable substitute for chest CT in diagnosing pneumonia in patients with COVID-19, especially in situations where chest CT is unavailable or impractical.
MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook
Authors: Ehmig Jonathan , Engel Günther , Lotz Joachim , Lehmann Wolfgang , Taheri Shahed , Schilling Arndt F. , Seif Amir Hosseini Ali , Panahi Babak
Published: August 2023

Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future.
Autoimmune Encephalitis with Antibodies: Anti-NMDAR, Anti-AMPAR, Anti-GQ1b, Anti-DPPX, Anti-CASPR2, Anti-LGI1, Anti-RI, Anti-Yo, Anti-Hu, Anti-CV2 and Anti-GABAAR, in the Course of Psychoses, Neoplastic Diseases, and Paraneoplastic Syndromes
Authors: Braczkowski Michał , Soszyński Dariusz , Sierakowska Alicja , Braczkowski Ryszard , Kufel Klaudia , Łabuz-Roszak Beata
Published: August 2023

Encephalitis is a condition with a variety of etiologies, clinical presentations, and degrees of severity. The causes of these disorders include both neuroinfections and autoimmune diseases in which host antibodies are pathologically directed against self-antigens. In autoimmune encephalitis, autoantibodies are expressed in the central nervous system. The incidence of this disease is approximately 4% of all reported cases of encephalitis. Autoimmune encephalitis can be induced by antibodies against neuronal surface antigens such as N-methyl-D-aspartate-activated glutamate receptors (NMDAR), α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPAR) or gangliosides GQ1b, DPPX, CASPR2, LGI1, as well as by antibodies against neuronal intracellular antigens. The paper presents a number of both mental and neurological symptoms of autoimmune encephalitis. Moreover, the coexistence of psychoses, neoplastic diseases, and the methods of diagnosing autoimmune encephalitis are discussed. Attention was also drawn to the fact that early diagnosis, as well as early initiation of targeted treatment, increases the chance of a successful course of the therapeutic process. Strategy and Methodology: The articles on which the following paper was based were searched using search engines such as PubMed and Medline. Considering that anti-NMDAR antibodies were first described in 2007, the articles were from 2007 to 2023. The selection of papers was made by entering the phrases “autoimmune encephalitis and psychosis/paraneplastic syndromes or cancer”. The total number of articles that could be searched was 747, of which 100 items were selected, the most recent reports illustrating the presented topic. Thirty-four of them were rejected in connection with case reports or papers that could not be accessed.
Stereomicroscopic Aspects of Non-Carious Cervical Lesions
Authors: Stănuşi Andreea , Stănuşi Adrian Ştefan , Gîngu Oana , Mercuţ Veronica , Osiac Eugen
Published: August 2023

Non-carious cervical lesions (NCCLs) represent a form of tooth wear, characterized by the irreversible loss of dental hard tissues at the enamel–cement junction, without the involvement of caries and dental trauma. The aim of this study was to highlight the morphological elements of NCCLs via their stereomicroscopic examination and to confirm the role of this examination in the diagnosis of early lesions. In addition, the association between the morphological aspects identified during the stereomicroscopic examination of NCCLs and their etiological factors was determined. For this study, extracted teeth with NCCLs were examined with a stereomicroscope. The morphological aspects of NCCLs were evaluated at magnifications up to 75×. In wedge-shaped NCCLs, the stereomicroscopic examination allowed the identification and measurement of scratches, furrows and cracks. In saucer-shaped NCCLs, the stereomicroscopic examination highlighted the smooth appearance of the walls. The presented study highlighted the role of stereomicroscopic examination in the assessment of NCCL morphology and in their early diagnosis. The study confirmed, in particular, the role of occlusal overloads and tooth brushing in determining the morphology of NCCLs.
The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies—A Systematic Review
Authors: Petrila Octavia , Stefan Anca-Elena , Gafitanu Dumitru , Scripcariu Viorel , Nistor Ionut
Published: August 2023

(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: “endometrial cancer artificial intelligence”, “endometrial cancer AI”, “endometrial cancer MRI artificial intelligence”, “endometrial cancer machine learning”, and “endometrial cancer machine learning MRI”. We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.
Precise Traits from Sloppy Components: Perception and the Origin of Phenotypic Response
Authors: Frank Steven A.
Published: August 2023

Organisms perceive their environment and respond. The origin of perception–response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain a memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception–response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection.
What Competencies and Capabilities Identify a Good Teacher? Design of an Instrument to Measure Preservice Teachers’ Perceptions
Authors: Martín-Pastor Elena , Sánchez-Barbero Beatriz , Corrochano Diego , Gómez-Gonçalves Alejandro
Published: August 2023

Teachers should not only be regarded as professionals with a large amount of knowledge that they must transmit but are also expected to fulfil other functions and possess other qualities to overcome the teaching challenge. This paper is aimed at designing and validating an instrument to identify preservice teachers’ perceptions about the competencies and capabilities that a good teacher should have. The study sample is made up of 230 university students who are in their teaching training practises and internships in schools. According to the consulted literature, we elaborated a questionnaire composed of 23 items that are subdivided into four dimensions: personal qualities, training, pedagogical aptitude, and interaction. We analysed reliability (Cronbach’s alpha value was 0.924, and, by dimensions, all values were higher than 0.75; the Orion accuracy coefficients by dimensions were all higher than 0.88) and construct validity, obtaining a good quality of the items (with factor loadings above 0.50), after eliminating one of them. We also obtained a high correlation between dimensions; the lowest value is 0.87. We have therefore achieved a final instrument made up of 22 items that helps identify the set of qualities that a good teacher must have and can serve as a guide for the design and planning of their initial and ongoing training.
Teachers’ Pedagogical Competence in Finnish Early Childhood Education—A Narrative Literature Review
Authors: Ranta Samuli , Kangas Jonna , Harju-Luukkainen Heidi , Ukkonen-Mikkola Tuulikki , Neitola Marita , Kinos Jarmo , Sajaniemi Nina , Kuusisto Arniika
Published: August 2023

Finnish early childhood education (ECE) is defined in education policies and research as an activity with an emphasis on pedagogy as a holistic and Nordic model of education. Pedagogical competence can be viewed as a special expertise of teachers responsible for the implementation of pedagogy in ECE. However, it is unclear how a teacher’s pedagogical competence is defined, understood, and implemented in pedagogical practices and policies. Previous studies define pedagogical competence from different, often narrow, perspectives and various skills have been defined to be a part of teachers’ pedagogical expertise. In this study, we aim to take a closer look at how teachers’ pedagogical competence is defined in research literature in Finland. As our method, we use a narrative literature review of research papers published between 2010 and 2020. We were able to identify fourteen different definitions of pedagogical competencies that could be categorized into three main categories: (a) meta-competencies, (b) professional competencies, and (c) practical competencies. In summary, we argue that pedagogical competence is often narrowly understood. To implement high-quality ECE, teachers’ pedagogical competence should be considered broadly and wholly. Particularly, the shared values and reflection of ethical questions are essential for supporting children’s development and learning through holistic education.
Interactive Alignment in L2 Learning: The Link between Social Interaction and Psycholinguistic Phenomena
Authors: Mifka-Profozic Nadia
Published: August 2023

To engage successfully in conversational activities, participants need to coordinate and synchronise their talk with the talk of their interlocutors. Apart from a set of social strategies and natural routines involved in sequence organisation, a significant contributor to this goal is a psycholinguistic mechanism identified as interactive alignment. The present study set out to examine whether interactive alignment occurs in L2 speech of upper intermediate second language users who have been learning English at school for around 11 years. The participants were a group of twenty Croatian students in their second year of university study, majoring in English. They worked on two collaborative tasks: one carried out in dialogues and the other one in groups of four. Their interactions were analysed both quantitatively and qualitatively, to closely examine how interaction evolves in unscripted task-based L2 production. With a significantly larger number of alignment occurrences recorded in dialogues than in groups of four, both between speaker and within speaker, this study demonstrates that processes in L2 learning and use are interconnected and interdependent at all levels, involving cognitive, psychological, psycholinguistic, and social dimensions.
Board Game Design to Understand the National Power Mix
Authors: Yusa Noritaka , Hamada Ryoju
Published: August 2023

This study reports a board game design that would be an effective tool for teaching and learning the best mix of national power sources in a class concerning energy and sustainability in higher education courses. A board game was developed to understand the characteristics of power sources from a Japanese viewpoint based on an earlier study of the authors. The purpose of the game is to satisfy electricity demands by choosing power sources and procuring the resources necessary for power generation to help develop a country. A total of 50 undergraduate and graduate students were asked to assess the game. The results of the questionnaire-based survey conducted after the game confirmed the students’ evaluation that the game was highly enjoyable and could serve as an effective tool for energy and environmental education in high schools or universities. In addition, the average of “the ratio of the power sources proper to win the game” given by the students was similar to Japanese power mix before the Fukushima disaster, although the game significantly simplified, and even excluded, various factors affecting the national policy of power sources.
Teacher Training and Sustainable Development: Study within the Framework of the Transdisciplinary Project RRREMAKER
Authors: Soto-Solier Pilar Manuela , García-López Ana María , Prados-Peña María Belén
Published: August 2023

The development of the Sustainable Development Goals (SDGs) is a challenge that requires the involvement of the educational system. This study seeks to identify the perception and knowledge of future teachers in relation to sustainable development (SD), the European Green Deal (ECD), and circular economy (CE) at the University of Granada. A qualitative study was carried out, conducted face to face, using an online questionnaire in the classroom. A total of 321 students participated, from bachelor’s degrees in early childhood and primary education to master’s degrees in secondary education. Out of these, 176 validated questionnaires were analyzed. The results showed that future teachers understood the importance of SD education as an important social problem. They believed that education in SD, EGD, or CE could contribute to solving environmental and social problems. Nevertheless, they doubted whether this training should be included by universities in their training actions for teachers; they expressed reservations about the educational effectiveness of the curricula. However, most of them considered the need for more training in this area. Finally, they associated sustainable development mainly with the environmental dimension, followed by the social dimension, and to a lesser extent with the economic dimension.
Educational Computational Chemistry for In-Service Chemistry Teachers: A Data Mining Approach to E-Learning Environment Redesign
Authors: Hernández-Ramos José , Cáceres-Jensen Lizethly , Rodríguez-Becerra Jorge
Published: August 2023

The use of technology in education has experienced significant growth in recent years. In this regard, computational chemistry is considered a dynamic element due to the constant advances in computational methods in chemistry, making it an emerging technology with high potential for application in teaching chemistry. This article investigates the characteristics and perceptions of in-service chemistry teachers who participated in an e-learning educational computational chemistry course. Additionally, it examines how educational data mining techniques can contribute to optimising and developing e-learning environments. The results indicate that teachers view incorporating computational chemistry elements in their classes positively but that this is not profoundly reflected in their teaching activity planning. On the other hand, generated statistical models demonstrate that the most relevant variables to consider in the instructional design of an e-learning educational computational chemistry course are related to participation in various course instances and partial evaluations. In this sense, the need to provide additional support to students during online learning is highlighted, especially during critical moments such as evaluations. In conclusion, this study offers valuable information on the characteristics and perceptions of in-service chemistry teachers and demonstrates that educational data mining techniques can help improve e-learning environments.
Developing Teacher Competencies for Teaching Evolution across the Primary School Curriculum: A Design Study of a Pre-Service Teacher Education Module
Authors: Hanisch Susan , Eirdosh Dustin
Published: August 2023

Numerous studies indicate that evolutionary concepts can and should be taught at the primary school level. However, teaching evolution in primary school is presumably not yet the norm globally. At the same time, the educational potential of evolutionary concepts lies in their applicability to many curriculum topics in the natural and social sciences. Capitalizing on this potential requires broad teacher content knowledge (CK) and pedagogical content knowledge (PCK) about the teaching of evolutionary concepts. However, not much is known regarding ways to develop primary teacher CK and PCK during pre-service training. In this article, we present the iterative design, implementation, and mixed methods formative evaluation (based on a design-based research framework) of a pre-service teacher education module. Its aim was to promote the development of pre-service primary school teacher CK, PCK, as well as motivation and confidence for teaching evolution across the primary school curriculum. Results indicate that pre-service teachers can be supported and motivated to teach evolutionary concepts across various themes in the primary school curriculum through a semester long course. Participants were able to develop core aspects of CK and PCK regarding the teaching of evolutionary concepts in primary school. The resulting module design integrates existing collective PCK on teaching evolution at the primary level as well as novel design considerations and teaching approaches that can be integrated into pre-service teacher education programs. However, challenges remain, particularly regarding the integration of evolutionary concepts in mandated curriculum standards such that the educational potential of evolution can be fully capitalized on by teachers.
Examining the Effects of Supervised Laboratory Instruction on Students’ Motivation and Their Understanding of Chemistry
Authors: Ali Mirtachew T. , Lykknes Annette , Tiruneh Dawit T.
Published: August 2023

This paper examines the impact of supervised laboratory instruction (SLI) on grade 12 students’ understanding of acid–base and solution chemistry topics in the context of Ethiopian secondary schools. A mixed-methods research design was employed, with a purposive sampling of 160 secondary students from six schools in Northwest Ethiopia. The students were divided into two groups: an experimental group (n = 76) and a control group (n = 84). The experimental group attended sessions that were designed based on self-regulated learning (SRL) strategies with SLI, and the control group attended regular instruction designed by the course teacher. Both quantitative and qualitative data were collected to explore the impacts of the experimental and control lessons on improving students’ conceptual understanding and motivation. Descriptive and inferential statistics (for the quantitative data)and reflexive thematic analysis(for the qualitative data)were employed to analyse the data. The findings showed that the SLI-SRL teaching approach for the experimental group resulted in a significantly higher conceptual understanding of the selected chemistry topics than the regular instruction for the control group. In addition, participants from the experimental group indicated that the SLI approach enhanced their motivation towards chemistry. These findings suggest that improving high-school students’ motivation and their conceptual understanding of chemistry requires paying attention to the lesson design.
Performance Enhancement and Stability Improvement in Perovskite Solar Cells via Interface Functionalization
Authors: Falaras Christos , Stathatos Elias
Published: August 2023

Perovskite solar cells (PSCs) have revolutionized the field of photovoltaics, achieving certified power conversion efficiencies reaching 26% at the laboratory scale. High performance, enhanced stability, and long lifetime are prerequisites for the industrialization and commercialization of this class of third-generation photovoltaic technology. Toward the development of well-performing and robust PSCs against environmental stresses, advanced engineering strategies have been employed, targeting the preparation of perovskite absorbing layers with minimal defects and energy-level fine-tuning hydrophobic contacts. Focusing on both the electron transport layer/perovskite and perovskite/hole transport layer interfaces, this review work encompasses some of the most promising engineering methodologies that were recently proposed in order to optimize the device architecture. Machine learning approaches have also been used to validate experimental data and predict with accuracy solar cell parameters, further confirming the significance and justifying the application potential of the proposed innovative interface functionalization approaches.
Energy-Efficient Multiple Autonomous Underwater Vehicle Path Planning Scheme in Underwater Sensor Networks
Authors: Cui Yangfan , Zhu Peibin , Lei Guowei , Chen Peng , Yang Guangsong
Published: August 2023

The issue of limited energy resources is crucial for underwater wireless sensor networks (UWSNs) because these networks operate in remote and harsh environments where access to power sources is limited. Overcoming the energy constraints is necessary to ensure the long-term functionality and sustainability of UWSN, enabling continuous data collection and communication for various applications such as environmental monitoring and surveillance. To solve the problems of limited energy and the difficulty of battery replacement in UWSN, a path planning and energy-saving scheme for charging underwater sensor nodes using AUVs (autonomous underwater vehicles) is proposed. Applying multiple AUVs to charge the sensing network nodes will maximize the size of the underwater sensing network as well as meet the transmission reliability, and the optimal path of AUVs is solved by using a genetic algorithm. Simulation results show that the AUV path planning scheme convergence is faster than that of conventional algorithms, and the lifetime of UWSN is prolonged while energy balancing according to the network size and node density. In high-density networks, the average energy consumption generated by AUVs for exploration is reduced by 15 percent for each additional AUV with our path planning.
Reconsidering Multi-Branch Aggregation for Semantic Segmentation
Authors: Cai Pengjie , Yang Derong , Zou Yonglin , Chen Ruihan , Dai Ming
Published: August 2023

For semantic segmentation tasks, there are problems in using multi-branch structures to enrich feature maps and aggregate different branches of feature maps in a certain network depth, such as the insufficient richness of feature maps and the incomplete aggregation of feature maps. Given multi-branch feature maps and branch aggregation, this study proposes a lightweight method, called multi-branch aggregation atrous spatial pyramidal pooling, by introducing an attention mechanism and CARAFE to enrich the features of the feature maps, giving the feature maps adaptive parameters, periodically adjusting the adaptive parameters, and aggregating the feature maps in both the vertical and horizontal directions. First, the atrous pyramid is retained and the attention mechanism and CARAFE are used to handle the pooling features to obtain 10 different feature maps. Secondly, giving each feature map a cascaded adaptive parameter and periodically adjusting the adaptive parameter to promote or suppress certain feature maps prevents the model from being at a local minimum or saddle point for long periods due to aleatory uncertainty. Finally, the feature maps are vertically aggregated, horizontally aggregated, and weighted for summation. This work demonstrates a competitive performance on the benchmark dataset, with an improvement of 1.88% in MPA, 0.66% in FWIoU, and 1.29% in MIoU compared to atrous spatial pyramid pooling. The benchmark datasets include PASCAL VOC 2012 and CIFAR-100.
SPE-ACGAN: A Resampling Approach for Class Imbalance Problem in Network Intrusion Detection Systems
Authors: Yang Hao , Xu Jinyan , Xiao Yongcai , Hu Lei
Published: August 2023

Network Intrusion Detection Systems (NIDSs) play a vital role in detecting and stopping network attacks. However, the prevalent imbalance of training samples in network traffic interferes with NIDS detection performance. This paper proposes a resampling method based on Self-Paced Ensemble and Auxiliary Classifier Generative Adversarial Networks (SPE-ACGAN) to address the imbalance problem of sample classes. To deal with the class imbalance problem, SPE-ACGAN oversamples the minority class samples by ACGAN and undersamples the majority class samples by SPE. In addition, we merged the CICIDS-2017 dataset and the CICIDS-2018 dataset into a more imbalanced dataset named CICIDS-17-18 and validated the effectiveness of the proposed method using the three datasets mentioned above. SPE-ACGAN is more effective than other resampling methods in improving NIDS detection performance. In particular, SPE-ACGAN improved the F1-score of Random Forest, CNN, GoogLeNet, and CNN + WDLSTM by 5.59%, 3.75%, 3.60%, and 3.56% after resampling.
A Rapidly Adaptive Collision Backoff Algorithm for Improving the Throughput in WLANs
Authors: Lin Cheng-Han , Cheng Ming-Hua , Hwang Wen-Shyang , Shieh Ce-Kuen , Wei Yu-Hang
Published: August 2023

In the 802.11 protocol, the fundamental medium access mechanism is called Distributed Coordination Function (DCF). In DCF, before making any transmission attempt, the nodes count down a timer with a value randomly selected from the Contention Window (CW) size. If the transmitted packet is involved in a collision, the node increases the CW size in an attempt to reduce the collision rate. Conversely, if the packet is transmitted successfully, the node reduces the CW size in order to increase the frequency of the transmission attempts. The growth or reduction in the CW size has a critical effect on the network performance. Several backoff algorithms have been proposed to improve the system throughput. However, none of these methods enable the system to approach the theoretical maximum throughput possible under DCF. Accordingly, this study proposes the Rapidly Adaptive Collision Backoff (RACB) algorithm, in which the CW size is adjusted dynamically based on the collision rate, as analyzed by a mathematical model. Notably, RACB requires no knowledge of the number of nodes in the wireless network and is applicable to both lightly loaded and heavily loaded networks. The numerical results show that, by adjusting the CW size such that the collision rate is maintained at a value close to 0.1, RACB enables the system throughput to approach the maximum DCF throughput in wireless environments containing any number of nodes.
Rigid–Flexible Coupled System Attitude–Orbit Integration Fixed-Time Control
Authors: Zhang Yinghui , Ma Chen , Ma Songjing , Pan Junfeng , Sui Xiaohong , Lin Boxuan , Shi Mengjie
Published: August 2023

A diffractive imaging system consisting of two satellites is analyzed in view of dynamics. The mathematical model of rigid and flexion couples is studied to describe the relative motion of diffractive satellites and imaging satellites. Based on an integrated dynamics model with dual quaternion, a fixed-time non-singular terminal sliding mode controller is designed to meet the requirements of Earth observation. Finally, introducing the non-singular terminal sliding mode as the control group, a comparative simulation of relative motion and control is implemented to verify the controller and dynamics model.
Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols
Authors: Yang Tao , Liu Ziyu , Lu Yu , Zhang Jun
Published: August 2023

Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.
Molecular Characterization and Phylogenetic Analysis of the Pine Tortoise Scale Insect Toumeyella parvicornis (Cockerell) (Hemiptera: Coccidae)
Authors: Di Sora Nicolò , Turco Silvia , Brugneti Federico , Rossini Luca , Mazzaglia Angelo , Contarini Mario , Speranza Stefano
Published: August 2023

Toumeyella parvicornis (Cockerell) (Hemiptera, Coccidae) is becoming a potential main pest of stone pine plants (Pinus pinea L.), both in urban parks and pinewood forests in Europe. Its recent distribution is a source of concern in several regions of Italy and in France. The early detection of this pest plays a fundamental role to contain its geographical expansion, but its taxonomic identification is still based on laborious observations of the morphological traits. The identification is also complicated by the small size of the pest, which makes the observations possible only through a stereomicroscope. Molecular identification is beneficial for detection, but currently, there is only a single gene sequence available for this pest. This study fills this gap in knowledge by providing the sequences of five different genes (COI, 28S, elongation factor (EF-1α), wingless (wg), and histone H3 (HexA)), together with a phylogenetic analysis carried out among species belonging to Coccidae, one of the most important families of scale insects. The results provide new valuable information about T. parvicornis and may represent useful data for its detection and management practices.
Encapsulation of Pineapple Peel Extracts by Ionotropic Gelation Using Corn Starch, Weissella confusa Exopolysaccharide, and Sodium Alginate as Wall Materials
Authors: Polanía Anna María , Ramírez Cristina , Londoño Liliana , Bolívar German , Aguilar Cristobal Noe
Published: August 2023

Phenolic compounds that are present in pineapple by-products offer many health benefits to the consumer; however, they are unstable to many environmental factors. For this reason, encapsulation is ideal for preserving their beneficial effects. In this work, extracts were obtained by the combined method of solid-state fermentation with Rhizopus oryzae and ultrasound. After this process, the encapsulation process was performed by ionotropic gelation using corn starch, sodium alginate, and Weissella confusa exopolysaccharide as wall material. The encapsulates produced presented a moisture content between 7.10 and 10.45% (w.b), a solubility of 53.06 ± 0.54%, and a wettability of 31.46 ± 2.02 s. The total phenolic content (TPC), antioxidant capacity of DPPH, and ABTS of the encapsulates were also determined, finding 232.55 ± 2.07 mg GAE/g d.m for TPC, 45.64 ± 0.9 µm Trolox/mg GAE for DPPH, and 51.69 ± 1.08 µm Trolox/mg GAE for ABTS. Additionally, ultrahigh performance liquid chromatography (UHPLC) analysis allowed us to identify and quantify six bioactive compounds: rosmarinic acid, caffeic acid, p-coumaric acid, ferulic acid, gallic acid, and quercetin. According to the above, using ionotropic gelation, it was possible to obtain microencapsulates containing bioactive compounds from pineapple peel extracts, which may have applications in the development of functional foods.
The Effects of Poly-γ-Glutamic Acid on the Postharvest Physiology and Quality of Strawberry cv. Hongyan during Cold Storage
Authors: Shan Changjuan , Luo Yi , Yang Chen , Gao Xinxia
Published: August 2023

This study investigated the effects of poly-γ-glutamic acid (γ-PGA) on the postharvest physiology and quality of the strawberry cv. Hongyan during cold storage. The results showed that all concentrations of γ-PGA improved decay control and strawberry preservation by enhancing antioxidant capacity, delaying the softening process, and maintaining fruit quality, especially for 100 mg·L−1 γ-PGA. After 14 days of treatment, compared with control, 100 mg·L−1 γ-PGA decreased weight loss, decay rate, and index by 21.9%, 75.0%, and 66.7% and increased the total antioxidant capacity by 43.5% through antioxidant enzymes. In addition, 100 mg·L−1 γ-PGA increased fruit firmness by 53.6% by decreasing the activities of polygalacturonase, pectin lyase, cellulase, and β-galactosidase. In terms of color quality, 100 mg·L−1 γ-PGA improved the values of lightness and yellowness by 30.9% and 52.8%. As regards nutritional quality, 100 mg·L−1 γ-PGA increased the contents of protein, soluble sugars, vitamin C, and total phenols by 106.6%, 80.6%, 51.2%, and 78.4%. In terms of sensory quality, 100 mg·L−1 γ-PGA increased the soluble solids’ content by 19.0% and decreased the titrated acids’ content by 21.1%, which increased the sugar–acid ratio by 50.9%. Our findings suggest that 100 mg·L−1 γ-PGA can be used to improve the decay control and preservation of strawberry cv. Hongyan under cold storage.
Advances in Nutrient-Rich Foods for a Healthy Diet
Authors: González-Palacios Sandra , Fonollá Juristo
Published: August 2023

The nutrient-rich foods are foods with a high content of protein, fiber, vitamins and minerals, but also a low content of saturated fats, added sugar and sodium [...]
The Spousal Unit and Dementia: Investigating the Relational Basis of the Couplehood Concept
Authors: Weicht Bernhard , Tolhurst Edward
Published: August 2023

A strong emphasis is consistently placed upon the relational basis of experience within social scientific dementia research. Within this research corpus, the concept of couplehood is increasingly employed, albeit in rather undefined and loosely theorised ways. Moreover, the evaluation highlights that couplehood is often defined by a normative position that seeks to convey an affirmative perspective on dementia and spousal relationships. The lack of theoretical foundation, however, weakens the explanatory potential of the concept, both for theorising dementia, as well as for empirical research. This article critically evaluates the utility of the couplehood concept by delineating three underlying theoretical conceptions: phenomenological, interactional and relational perspectives. It will be argued that those theoretical threads offer different analytical angles and research opportunities. More thorough ontological development, however, can guide understandings of the complexities that underpin the relational experience of dementia. This will promote a conceptual starting point that offers a more balanced and multifaceted accommodation of two persons and their relationship.