bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
Authors: Jawad Khurram , Mahto Rajul , Das Aryan , Ahmed Saboor Uddin , Aziz Rabia Musheer , Kumar Pavan
Published: April 2023

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.
A Review of Blast Loading in the Urban Environment
Authors: Ratcliff Adam , Rigby Sam , Clarke Sam , Fay Stephen
Published: April 2023

Urban blasts have become a significant concern in recent years. Whilst free-field blasts are well understood, the introduction of an urban setting (or any complex geometry) gives rise to multiple blast wave interactions and unique flow complexities, significantly increasing the difficulty of loading predictions. This review identifies commonly agreed-upon concepts or behaviours that are utilised to describe urban shock wave propagation, such as channelling and shielding, in conjunction with exploring urban characterisation metrics that aim to predict the effects on global blast loading for an urban blast. Likewise, discrepancies and contradictions are highlighted to promote key areas that require further work and clarification. Multiple numerical modelling programmes are acknowledged to showcase their ability to act as a means of validation and a preliminary testing tool. The findings contained within this review aim to inform future research decisions and topics better.
Experimental Analysis and Optimisation of a Novel Laser-Sintering Process for Additive Manufacturing of Continuous Carbon Fibre-Reinforced Polymer Parts
Authors: Baranowski Michael , Völger Lukas , Friedmann Marco , Fleischer Jürgen
Published: April 2023

Additive manufacturing of continuous carbon fibre-reinforced polymer (CCFRP) parts enables the production of high-strength parts for aerospace, engineering and other industries. Continuous fibres allow for parts to be reinforced along the load path, multiplying their mechanical properties. However, current additive manufacturing processes for producing CCFRP parts do not optimally meet the requirements of the matrix. With resin- and extrusion-based processes, the time-consuming and costly post-processing required to remove support structures severely limits design freedom, and producing small batches requires increased effort. In contrast, laser sintering has proven to be a promising alternative in an industrial environment, allowing the production of robust parts without support structures in a time-efficient and economical manner for single and small-batch production. Based on a novel laser-sintering machine with the automated integration of continuous fibres, a combination of the advantages of the laser-sintering process and the advantages of continuous fibres is to be achieved. This paper describes an experimental analysis and optimisation of this laser-sintering machine using design of experiments. The processing time for fibre integration could be reduced by a factor of three compared to the initial state.
A Superior Lithium-Ion Capacitor Based on Ultrafine MnO/Dual N-Doped Carbon Anode and Porous Carbon Cathode
Authors: Lei Da , Gao Yuyang , Hou Zhidong , Ren Lingbo , Jiang Mingwei , Cao Yunjing , Zhang Yu , Wang Jian-Gan
Published: April 2023

Owing to the unique virtues of specific energy/power densities, lithium-ion capacitors (LICs) have been increasingly attracting research attention. However, the LICs are greatly restrained by the slow Li+-reaction kinetics of battery-type anodes, which is still a challenging task. In this work, we construct a superior LIC using ultrafine MnO/dual N-doped carbon (MnO/DNC) anode and activated N-doped porous carbon (ANC) derived from a homologous polypyrrole precursor. The uniform MnO ultrafine particles (~10 nm size) are well encapsulated into a dual-carbon framework, which provides fast ion/electron transportation and structural cushion for high-rate and long-durable energy storage. Accordingly, the anodic MnO/DNC achieves an impressive rate performance (179 mAh g−1 @10 A g−1) and a stable 500-cycling lifespan. The as-constructed LICs could deliver a large specific energy of 172 Wh kg−1 at 200 W kg−1 and retain at 37 Wh kg−1 even at a high specific power of 15 kW kg−1. It is believed that the design strategy of confining ultrafine conversion-type anode materials into a dual-carbon structure will expedite the development of advanced LICs.
A High-Performance Li-O2/Air Battery System with Dual Redox Mediators in the Hydrophobic Ionic Liquid-Based Gel Polymer Electrolyte
Authors: Feng Ningning , Wang Chaoqiang , Wang Jing , Lin Yang , Yang Gang
Published: April 2023

Lithium–oxygen (Li-O2) batteries have captured worldwide attention owing to their highest theoretical specific energy density. However, this promising system still suffers from huge discharge/charge overpotentials and poor cycling stability, which are related to the leakage/volatilization of organic liquid electrolytes and the inefficiency of solid catalysts. A mixing ionic liquid-based gel polymer electrolyte (IL-GPE)-based Li-O2 battery, consisting of a 20 mM 2,5-di-tert-butyl-1,4-benzoquinone (DBBQ) 40 mM N-methylphenothiazine (MPT)-containing IL-GPE and a single-walled carbon nanotube cathode, is designed for the first time here. This unique dual redox mediators-based GPE, which contains a polymer matrix immersed with mixed ionic liquid electrolyte, provides a proper ionic conductivity (0.48 mS cm−1) and effective protection for lithium anode. In addition, DBBQ, as the catalyst for an oxygen reduction reaction, can support the growth of discharge products through the solution–phase pathway. Simultaneously, MPT, as the catalyst for an oxygen evolution reaction, can decompose Li2O2 at low charge overpotentials. Hence, the DBBQ-MPT-IL-GPE-based Li-O2 battery can operate for 100 cycles with lower charge/discharge overpotentials. This investigation may offer a promising method to realize high-efficiency Li-O2/air batteries.
Machine Learning and Explainable Artificial Intelligence Using Counterfactual Explanations for Evaluating Posture Parameters
Authors: Dindorf Carlo , Ludwig Oliver , Simon Steven , Becker Stephan , Fröhlich Michael
Published: April 2023

Postural deficits such as hyperlordosis (hollow back) or hyperkyphosis (hunchback) are relevant health issues. Diagnoses depend on the experience of the examiner and are, therefore, often subjective and prone to errors. Machine learning (ML) methods in combination with explainable artificial intelligence (XAI) tools have proven useful for providing an objective, data-based orientation. However, only a few works have considered posture parameters, leaving the potential for more human-friendly XAI interpretations still untouched. Therefore, the present work proposes an objective, data-driven ML system for medical decision support that enables especially human-friendly interpretations using counterfactual explanations (CFs). The posture data for 1151 subjects were recorded by means of stereophotogrammetry. An expert-based classification of the subjects regarding the presence of hyperlordosis or hyperkyphosis was initially performed. Using a Gaussian progress classifier, the models were trained and interpreted using CFs. The label errors were flagged and re-evaluated using confident learning. Very good classification performances for both hyperlordosis and hyperkyphosis were found, whereby the re-evaluation and correction of the test labels led to a significant improvement (MPRAUC = 0.97). A statistical evaluation showed that the CFs seemed to be plausible, in general. In the context of personalized medicine, the present study’s approach could be of importance for reducing diagnostic errors and thereby improving the individual adaptation of therapeutic measures. Likewise, it could be a basis for the development of apps for preventive posture assessment.
Imaging Markers for Normal Pressure Hydrocephalus: An Overview
Authors: Pyrgelis Efstratios-Stylianos , Velonakis Georgios , Papageorgiou Sokratis G. , Stefanis Leonidas , Kapaki Elisabeth , Constantinides Vasilios C.
Published: April 2023

Idiopathic bormal pressure hydrocephalus (iNPH) is a neurological syndrome that clinically presents with Hakim’s triad, namely cognitive impairment, gait disturbances, and urinary incontinence. The fact that iNPH is potentially reversible makes its accurate and early diagnosis of paramount importance. Its main imaging characteristic is the dilation of the brain’s ventricular system and the imaging parameters are also included in its diagnostic criteria along with clinical data. There is a variety of different modalities used and a great number of imaging markers that have been described while assessing iNPH patients. The present literature review attempts to describe the most important of these imaging markers and to shed some light on their role in diagnosis, differential diagnosis, and possibly prognosis of this potentially reversible neurological syndrome.
miRNAs’ Cross-Involvement in Skin Allergies: A New Horizon for the Pathogenesis, Diagnosis and Therapy of Atopic Dermatitis, Allergic Contact Dermatitis and Chronic Spontaneous Urticaria
Authors: Brancaccio Raffaele , Murdaca Giuseppe , Casella Rossella , Loverre Teresa , Bonzano Laura , Nettis Eustachio , Gangemi Sebastiano
Published: April 2023

Skin inflammation is a common underlying feature of atopic dermatitis, allergic contact dermatitis and chronic spontaneous urticaria. The pathogenetic mechanisms have not been fully elucidated. The purpose of this study was to examine whether miRNA, by regulating inflammatory mechanisms through the modulation of innate and adaptive immune responses, could play a major role in the pathogenesis of these skin conditions. We conducted a narrative review using the Pubmed and Embase scientific databases and search engines to find the most relevant miRNAs related to the pathophysiology, severity and prognosis of skin conditions. The studies show that miRNAs are involved in the pathogenesis and regulation of atopic dermatitis and can reveal an atopic predisposition or indicate disease severity. In chronic spontaneous urticaria, different miRNAs which are over-expressed during urticaria exacerbations not only play a role in the possible response to therapy or remission, but also serve as a marker of chronic autoimmune urticaria and indicate associations with other autoimmune diseases. In allergic contact dermatitis, miRNAs are upregulated in inflammatory lesions and expressed during the sensitization phase of allergic response. Several miRNAs have been identified as potential biomarkers of these chronic skin conditions, but they are also possible therapeutic targets.
Presence of Choroidal Caverns in Patients with Posterior and Panuveitis
Authors: Begaj Tedi , Yuan Amy , Lains Ines , Li Ashley , Han Samuel , Susarla Gayatri , Parikh Ravi , Sobrin Lucia
Published: April 2023

Choroidal caverns (CCs) have been described in association with age-related macular degeneration and pachychoroid disease. However, it is unknown if caverns are found in patients with chronic non-infectious uveitis (NIU). Herein, we evaluated patients with NIU who had optical coherence tomography and indocyanine green angiography for CCs. Clinical and demographic characteristics were extracted from the chart review. Univariate and multivariate mixed-effects logistical models were used to assess the association between clinical and demographic factors and the presence of CCs. One hundred thirty-five patients (251 eyes) met the inclusion criteria: 1 eye had anterior uveitis, 5 had intermediate uveitis, 194 had posterior uveitis, and 51 had panuveitis. The prevalence of CCs was 10%. CCs were only observed in patients with posterior and panuveitis, with a prevalence of 10.8% and 7.8%, respectively. Multifocal choroiditis (MFC) was the type of uveitis where CCs were most frequently observed, with 40% of eyes with MFC having CCs. In addition, male sex (p = 0.024) was associated with CCs. There was no significant difference in the degree of intraocular inflammation or mean subfoveal choroidal thickness between CC+ and CC− eyes. This is the first study to describe CCs in uveitis. Overall, these findings suggest that caverns may be a sequela of structural and/or vascular perturbations in the choroid from uveitis.
The Clinical and Biological Effects of Receptor Expression-Enhancing Protein 6 in Tongue Squamous Cell Carcinoma
Authors: Tseng Chung-Chih , Hung Chung-Ching , Shu Chih-Wen , Lee Cheng-Hsin , Chen Chun-Feng , Kuo Mei-Shu , Kao Yu-Ying , Chen Chun-Lin , Ger Luo-Ping , Liu Pei-Feng
Published: April 2023

There are currently no effective biomarkers for the diagnosis and treatment of tongue squamous cell carcinoma (TSCC), which causes a poor 5-year overall survival rate. Thus, it is crucial to identify more effective diagnostic/prognostic biomarkers and therapeutic targets for TSCC patients. The receptor expression-enhancing protein 6 (REEP6), a transmembrane endoplasmic reticulum resident protein, controls the expression or transport of a subset of proteins or receptors. Although it was reported that REEP6 plays a role in lung and colon cancers, its clinical impact and biological role in TSCC are still unknown. The present study aimed to identify a novel effective biomarker and therapeutic target for TSCC patients. Expression levels of REEP6 in specimens from TSCC patients were determined with immunohistochemistry. Gene knockdown was used to evaluate the effects of REEP6 in cancer malignancy (colony/tumorsphere formation, cell cycle regulation, migration, drug resistance and cancer stemness) of TSCC cells. The clinical impact of REEP6 expression and gene co-expression on prognosis were analyzed in oral cancer patients including TSCC patients from The Cancer Genome Atlas database. Tumor tissues had higher levels of REEP6 compared to normal tissues in TSCC patients. Higher REEP6 expression was related to shorter disease-free survival (DFS) in oral cancer patients with poorly differentiated tumor cells. REEP6-knocked-down TSCC cells showed diminished colony/tumorsphere formation, and they also caused G1 arrest and decreased migration, drug resistance and cancer stemness. A high co-expression of REEP6/epithelial–mesenchymal transition or cancer stemness markers also resulted in poor DFS in oral cancer patients. Thus, REEP6 is involved in the malignancy of TSCC and might serve as a potential diagnostic/prognostic biomarker and therapeutic target for TSCC patients.
Augmented Harris Hawks Optimizer with Gradient-Based-like Optimization: Inverse Design of All-Dielectric Meta-Gratings
Authors: Edee Kofi
Published: April 2023

In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking prey. The hunting strategy is divided into two phases: exploration and exploitation. However, the original HHO algorithm performs poorly in the exploitation phase and may get trapped and stagnate in a basin of local optima. To improve the algorithm, we propose pre-selecting better initial candidates obtained from a gradient-based-like (GBL) optimization method. The main drawback of the GBL optimization method is its strong dependence on initial conditions. However, like any gradient-based method, GBL has the advantage of broadly and efficiently spanning the design space at the cost of computation time. By leveraging the strengths of both methods, namely GBL optimization and HHO, we show that the proposed hybrid approach, denoted as GBL–HHO, is an optimal scenario for efficiently targeting a class of unseen good global optimal solutions. We apply the proposed method to design all-dielectric meta-gratings that deflect incident waves into a given transmission angle. The numerical results demonstrate that our scenario outperforms the original HHO.
Mechanosynthesis of Diaminobiphenyls-Based Schiff’s Bases as Simple Probes for the Naked-Eye Detection of Cyanide Ion
Authors: Al-Ithawi Wahab K. A. , Khasanov Albert F. , Kovalev Igor S. , Nikonov Igor L. , Kopchuk Dmitry S. , Platonov Vadim A. , Santra Sougata , Zyryanov Grigory V. , Ranu Brindaban C.
Published: April 2023

Cyanide ions are known to be lethal for insects and mammals and harmful for the environment, and new methods for their selective detection are in high demand. Herein, the mechanosynthesis of simple Schiff’s bases-based probes S1–S3 for visual detection of CN− anion is reported. These probes were obtained by means of a reaction between isomeric 4,4-, 3,3- and 2,2-diaminobiphenyls and 4-nitrobenzaldehyde under ball milling conditions. The probes showed high selectivity and sensitivity toward CN− anion via a dramatic “yellow-to-dark purple” color change with a detection limit of 26 × 103, 8.7 × 103 and 14 × 103 ppm for S1–S3, respectively. The proposed mechanism of the detection suggests the deprotonation of a proton from an imine moiety, followed by the formation of charge transfer complexes (CTC).
Porous Copper Oxide Thin Film Electrodes for Non-Enzymatic Glucose Detection
Authors: Carinelli Soledad , Salazar-Carballo Pedro A. , De la Rosa Melián Julio Ernesto , García-García Francisco
Published: April 2023

The present work describes novel copper oxide thin film-modified indium tin oxide electrodes prepared by magnetron sputtering and their application for glucose sensing. Copper oxide-modified sensors were characterized by electrochemical techniques, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The deposited thin film (of about 400 nm of thickness) consisted of Cu2O/CuO nanocolumns of ca. 80 nm in diameter. After optimizing the main experimental parameters, the electrodes showed noteworthy electrocatalytic properties for glucose detection (sensitivity ca. 2.89 A M−1 cm−2 and limit of detection ca. 0.29 μM (S/N = 3)). The sensor showed negligible response against common electroactive species and other sugars. Finally, recovery experiments in commercial soda drinks and the determination of glucose content in different commercial drinks, such as soda, tea, fruit juices, and sports drinks, are described.
Beyond Screen Time: The Different Longitudinal Relations between Adolescents’ Smartphone Use Content and Their Mental Health
Authors: Huang Shunsen , Lai Xiaoxiong , Li Yajun , Cui Yang , Wang Yun
Published: April 2023

Purpose: Previous studies focusing on the relationship between adolescents’ screen time and mental health have uncovered contradictory results. By focusing on smartphone use content (SUC), this study uses specification curve analysis to explore the different effects of SUCs on mental health-based on longitudinal data. Methods: A total of 2552 adolescents were surveyed in the first (July 2020) and second year (April 2021). A total of 2049 eligible participants (average age = 14.39 ± 2.27, female = 1062) are included in the analysis. Participants reported 20 types of content used by them during smartphone use and their mental health (depression, anxiety, and somatization). Specification curve analysis was used to examine the longitudinal relationship between SUCs and their mental health. Results: Smartphone use for listening to music (median β = 0.18, p < 0.001, NSRPD = 25/27, p < 0.05), chatting online (median β = 0.15, p < 0.001, NSRPD = 24/27, p < 0.05), watching TV (median β = 0.14, p < 0.001, NSRPD = 24/27, p < 0.05), and playing games (median β = 0.09, p < 0.001, NSRPD = 19/27, p < 0.05) produce high to medium negative effects on subsequent mental health. Only using smartphones for online courses exerts no effect on their subsequent mental health (median β = 0.01, p > 0.05, NSRPD = 0/27, p > 0.05). The left 15 types of smartphone content showed unstable effects on future mental health. Depending on the types of content used, these effects ranged from high, medium, and small to none. The relatively descending order of effect on mental health is listening to music, chatting online, watching TV, playing games, and types of content (e.g., browsing social media, making payments, reading online novels) with high but unstable effects, types of content with medium (e.g., browsing news and posting/sharing) but unstable effects, types of content (e.g., using the camera, obtaining life information, and making calls) with small but unstable effects, such as finishing homework and taking online courses. Conclusions: This study enlightens researchers and policymakers to update their understanding of adolescents’ technology use, especially to adopt a differentiated attitude towards different media use content. As nutritionists often do, a “nutritionally balanced” digital diet for young people should be recommended to the public, rather than just suggesting limits on the amount of time they can spend using digital media.
Evaluation of a Virtual Dance Class for Cancer Patients and Their Partners during the Corona Pandemic—A Real-World Observational Study
Authors: Hübner Jutta , Rudolph Ivonne , Wozniak Tobias , Pietsch Ronny , Margolina Mascha , Garcia Isabel , Mayr-Welschlau Katharina , Schmidt Thorsten , Keinki Christian
Published: April 2023

Background: During the corona pandemic, all courses on physical activity for cancer patients were canceled. The aim of our study was to evaluate the feasibility of switching dancing classes for patients and their partners to online classes. Methods: Patients and partners from courses at four different locations who consented to the online course offer were asked to fill in a pseudonymous questionnaire on access to the training, technical challenges, acceptance and well-being (1-item visual analog scale from 1 to 10) before and after the training. Results: Sixty-five participants returned the questionnaire (39 patients and 23 partners). Fifty-eight (89.2%) had danced before, and forty-eight (73.8%) had visited at least one course of ballroom dancing for cancer patients before. The first access to the online platform was difficult for 39 participants (60%). Most participants (57; 87.7%) enjoyed the online classes, but 53 (81.5%) rated them as less fun than the real classes as direct contact was missing. Well-being increased significantly after the lesson and remained improved for several days. Conclusion: Transforming a dancing class is feasible for participants with digital experience and goes along with technical difficulties. It is a substitute for real classes if mandatory and improves well-being.
Performance Assessment in a “Lane Departure” Scenario of Impending Collision for an ADAS Logic Based on Injury Risk Minimisation
Authors: Gulino Michelangelo-Santo , Vangi Dario , Damaziak Krzysztof
Published: April 2023

The current prioritisation of road safety enhancement in the automotive sector is leading toward the near future implementation of Advanced Driver Assistance Systems (ADASs), aiming at the simultaneous intervention of braking and steering for impact avoidance in case of an impending collision. However, it is partially unclear how new technologies for controlling the steering will actually behave in the case of inevitable collision states; the need consequently emerges to propose and tune efficient ADAS strategies to handle the complexity of critical road scenarios. An adaptive intervention logic on braking and steering for highly automated vehicles is applied in the context of a “lane departure”, two-vehicle critical road scenario; the ADAS implementing the logic activates to minimise the injury risk for the ego vehicle’s occupants at each time step, adapting to the eventual scenario evolution consequent to actions by other road users. The performance of the adaptive logic is investigated by a software-in-the-loop approach, varying the mutual position of the involved vehicles at the beginning of the criticality and comparing the injury risk outcomes of the eventual impacts with those connected to the Autonomous Emergency Braking (AEB). The results highlight a twofold benefit from the adaptive logic application in terms of road safety: (1) it decreases the frequency of impacts compared to the AEB function; (2) in inevitable collision states, it decreases injury risk for the vehicles’ occupants down to 40% compared to the AEB. This latter condition is achieved thanks to the possibility of reaching highly eccentric impact conditions (low impact forces and occupants’ injury risk as a consequence). The obtained highlights expand the literature regarding the adaptive logic by considering a diverse critical road scenario and investigating how fine variations on the vehicles’ mutual position at the beginning of the criticality reflect on the injury outcomes for different types of intervention logic.
Psychological Implicit Motives Construct as an Emergent Fractal Attractor from Intermittent Neurophysiological Responses: Simulation and Entropy-like Characterization
Authors: Martín Miguel Ángel , Vara Celia , García-Gutiérrez Carlos
Published: April 2023

Implicit Motives are non-conscious needs that drive human behavior towards the achievement of incentives that are affectively incited. Repeated affective experiences providing satisfying rewards have been held responsible for the building of Implicit Motives. Responses to rewarding experiences have a biological basis via close connections with neurophysiological systems controlling neurohormone release. We propose an iteration random function system acting in a metric space to model experience–reward interactions. This model is based on key facts of Implicit Motive theory reported in a broad number of studies. The model shows how (random) responses produced by intermittent random experiences create a well-defined probability distribution on an attractor, thus providing an insight into the underlying mechanism leading to the emergence of Implicit Motives as psychological structures. Implicit Motives’ robustness and resilience properties appear theoretically explained by the model. The model also provides uncertainty entropy-like parameters to characterize Implicit Motives which hopefully might be useful, beyond the mere theoretical frame, when used in combination with neurophysiological methods.
The Collective Dimension in the Activity of Physical Education Student-Teachers to Cope with Emotionally Significant Situations
Authors: Descoeudres Magali
Published: April 2023

The entry into the teaching profession is identified in the literature as a special, complex, and emotionally intense stage. Some teachers adopt turnover or attrition as coping tactics. The aim of this study is to understand the effect of the collective dimension on the professional development of physical education student-teachers in how they deal with emotionally significant situations. To avoid dropping out and to foster their well-being, beginners should develop their abilities by sharing their experiences. A mixed research design using a questionnaire and implementing a clinical activity procedure was adopted. Student-teachers (n = 139) had to write about 2 emotionally significant situations that they experienced during teaching, and they then shared or not with others. The second part of the study involved following up five student-teachers over the course of one year. A total of 32 filmed lessons with an emotionally significant situation served as support to self-confrontation and crossed interviews. The data were processed using the method of Bruno and Méard. The results show that student-teachers experience many emotionally significant situations, and this highlights the importance of using a collective dimension to help them cope with this emotional overload. Finally, these results open the potential value of a hybridised teacher education model of student-teachers, particularly when considering the emotional nature of the teaching profession and the necessity of sharing experiences in order to better deal with them.
Using Big Data for Educational Decisions: Lessons from the Literature for Developing Nations
Authors: Taylor Zach W. , Charran Chelseaia , Childs Joshua
Published: April 2023

Educational leaders from developing countries may be tasked with using big data to help inform educational decisions. Although many researchers have explored how to use big data or datasets to help solve educational problems, few studies have articulated how educational researchers and leaders from developing nations can use big data to make educational decisions. This study provides a literature review and takes a position to help educational leaders from developing nations use big data to make educational decisions and understand the strengths and weaknesses of using data to drive decision making. Moreover, this study addresses how datasets may be limited and how educational leaders can understand these limitations when using big data.
Data-Driven, Short-Term Prediction of Charging Station Occupation
Authors: Aghsaee Roya , Hecht Christopher , Schwinger Felix , Figgener Jan , Jarke Matthias , Sauer Dirk Uwe
Published: April 2023

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events.
Modelling of Electric Power Generation Plant Based on Gas Turbines with Agricultural Biomass Fuel
Authors: Rico-Riveros Luis Fernando , Trujillo-Rodríguez César Leonardo , Díaz-Aldana Nelson Leonardo , Rus-Casas Catalina
Published: April 2023

To ensure the survival of society, an enormous amount of energy is required to sustain the economic and social development of communities. In addition, there is a pressing need to achieve significant reductions in climate change and the associated costs of implementing systems based on traditional energy sources, as well as addressing the issue of providing electricity to isolated areas. In rural environments, there is an alternative energy source with enormous potential, agricultural biomass, which can produce electrical and thermal energy and can progressively help to reduce dependence on fossil fuels. The purpose of this work is to present a dynamic simulation model of a power generation plant that uses the Joule Brayton thermodynamic cycle, based on a gas turbine which is fueled by residual agricultural biomass; the cycle converts mechanical energy to electrical energy. The problem is approached through the characterization of the biomass, mathematical models of the plant components, and simulation of the system behavior in different scenarios. The simulations are processed in Matlab/Simulink, which allows the model to be verified, validating the equilibrium relationship between generation and load demand.
The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches
Authors: Muhammad Yar , Hassan Muhammad Abul , Almotairi Sultan , Farooq Kawsar , Granelli Fabrizio , Strážovská Ľubomíra
Published: April 2023

Nowadays, we are living in the modern era of technological revolution and globalization, where people are giving more priority to proper education to compete among the top countries and to achieve something in their lives. Education improves a person’s abilities and creativity, which in turn have a positive effect on the development of a nation’s or an individual’s economy and also play a productive role in it. The traditional approaches are based only on statistical measures and are not capable of figuring out the most significant socioeconomic factors affecting the performance of a student. Keeping in mind the significance of socioeconomic status (SES) in improving the performance of a student, this study analyzes the important socioeconomic factors that affect the performance of a student in Khyber Pakhtunkhwa, Pakistan. We developed our own dataset by collecting data from 100 different schools (both government and private) in Khyber Pakhtunkhwa, Pakistan, consisting of more than 5550 students who were given a proper questionnaire survey. The created dataset consists of a total of 18 features and a target class. In this research, we used different statistical and machine learning (ML) methodologies to identify the most crucial elements that significantly affect the academic achievements of a student and have a strong correlation with the target class. To select the most prominent features from the dataset, we used two different feature selectors (FCBF and relief) and measured their performances along with ML models. To measure the significance rate of each ML algorithm using the full and selected feature space, we used different performance measures such as accuracy, precision, recall, sensitivity, specificity, etc. The experimental outcomes show that the feature selection algorithms significantly improve the performance of the classification models by providing more relevant features that have a strong association with the target class. This study also offered some advice for decision-makers, particularly in the respective education sector and other authorities, to develop specific solution strategies, plans, and initiatives to address the issue. It is envisioned that the suggested scheme will help the residents of Khyber Pakhtunkhwa province, in particular, obtain a high-quality education that can help pave the way for an educated and developed Pakistan.
Optimized Resonant Network Design for High Energy Transfer Efficiency of the WPT System
Authors: Li Weijie , Diao Lijun , Mei Weiyao , Dongye Zhonghao , Qin Xuqing , Jin Zheming
Published: April 2023

This paper proposes an active resonant network based on variable resonant capacitances to improve the operating performance of the LCC-S compensated topology in the wireless power transfer (WPT) system for electric vehicles under coil-misaligned conditions. By adjusting the series and parallel compensation capacitances on the transmitting side, the output voltage can be kept constant and the energy transfer efficiency can be improved under different coil offsets. The switch-controlled capacitors (SCCs) are used to change the compensation capacitances continuously. To find the proper compensation capacitances to achieve the excellent performance of the system, the optimization algorithm is applied, and the corresponding digital control strategy is described to regulate the equivalent capacitances of SCCs. Experimental results with a 2.7 kW power scale show that the output voltage is constant, and the operating efficiency is always over 90% in the WPT system with an active resonant network under different misalignment conditions. In addition, the system is delivering an equal amount of energy for all misalignments within the range of 80 mm, which improves the expected value of transferred energy by about 29%.
Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis
Authors: Haleem Muhammad Salman , Ekuban Audrey , Antonini Alessio , Pagliara Silvio , Pecchia Leandro , Allocca Carlo
Published: April 2023

With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle.
Analytical Investigation of Magnetic Scalar Potentials Oscillation in Spoke PM Flux Modulation Machines
Authors: Ur Rahman Lutf , Rehman Abdur , Kim Byungtaek
Published: April 2023

In this paper, the oscillation phenomena of the scalar magnetic potential of iron pieces of spoke permanent magnet (PM) machines are analyzed and the effects of the oscillation on the air gap flux and back electro-motive force (EMF) are deeply investigated, especially for flux modulation machines such as vernier and flux switching PM (FSPM) machines. To these ends, the formula of the scalar magnetic potential is derived for a generalized spoke PM structure. It reveals that the oscillation phenomena depend on the slot/pole combination, consequently resulting in different behavior according to the machine types such as vernier and FSPM machines. Next, each core’s potential given as a discrete function is developed into a continuous function of an air gap magneto-motive force (MMF) rotating and oscillating through Fourier series expansion. Making use of the developed MMF and the specific permeance of air gap, the equations of air gap flux density and back EMF are derived, which enable accurately estimating the suppression of the modulation flux and the back EMF due to the potential oscillation for different types of spoke PM machines. For validation, various magnetic characteristics are quantitatively examined for different type of spoke PM structures, including PM vernier and FSPM machines, and verified by comparing with FEM simulation results.
Numerical Analysis of the Influence of Inner Tubes Arrangement on the Thermal Performance of Thermal Energy Storage Unit
Authors: Younis Obai , Samir Laouedj , Belazreg Abdeldjalil , Qasem Naef
Published: April 2023

The container shape and arrangement of the thermal storage systems (TES) play a vital role in enhancing thermal performance. In the current investigation, the impact of inner tube dimensions and arrangements of TES on the thermal performance of a PCM-based triplex-tube latent heat storage exchanger (TTHX) is numerically analyzed. COMSOL Multiphysics commercial software was employed to obtain the numerical solution of the governing equations. Eight different cases with the same volume of PCM and various configurations of the inner tubes were investigated. The results of the current study were presented in terms of temperature contours, liquid fraction, Bejan number, average temperature, and average Nusslet number. The shortest melting time was 48 min, which was achieved by a single inner tube configuration with a quicker melting time of >62% compared to other cases. While for multi-tubes, the shortest time was 78 min, which was achieved by the configuration of three tubes (two horizontal and the third placed at the lower section) with an enhancement of melting time reduction of >12% compared to other cases, except for a single inner tube configuration. Regarding the entropy generation, the single tube configuration achieved the lower Bejan number. Therefore, single tube configuration was found to be the best option for maximizing the thermal performance of the studied TTHX.
Carbon Nanotubes as an Alternative to Copper Wires in Electrical Machines: A Review
Authors: Sivasubramaniyam Vigneselvan , Ramasamy Suganthi , Venkatraman Manikandan , Gatto Gianluca , Kumar Amit
Published: April 2023

The surge in electric vehicles (EVs) and their electrical appliances requires highly efficient, lightweight electrical machines with better performance. However, conventional wire used for electrical machine windings have certain limits to the current requirements. Copper is a commonly used material in electrical windings, and due to its ohmic resistance, it causes 75% of total losses in electrical machines (copper losses). The high mass of the copper results in a bulky system size, and the winding temperature of copper is always maintained at less than 150 °C to preserve the thermal insulation of the electric machine of the windings. On the other hand, carbon nanotubes and carbon nanotube materials have superior electrical conductivity properties and mechanical properties. Carbon nanotubes ensure 100 MS/m of electrical conductivity, which is higher than the copper electrical conductivity of 59.6 MS/m. In the literature, various carbon nanotubes have been studied based on electrical conductivity, temperature co-efficient with resistivity, material thickness and strength, insulation, and efficiency of the materials. Here, we review the electrical and mechanical properties of carbon nanotubes, and carbon nanotube composite materials are reviewed with copper windings for electrical wires.
Analyzing the Performance of Thermoelectric Generators with Inhomogeneous Legs: Coupled Material–Device Modelling for Mg2X-Based TEG Prototypes
Authors: Camut Julia , Müller Eckhard , de Boor Johannes
Published: April 2023

Thermoelectric generators (TEGs) possess the ability to generate electrical power from heat. As TEGs are operated under a thermal gradient, inhomogeneous material properties—either by design or due to inhomogeneous material degradation under thermal load—are commonly found. However, this cannot be addressed using standard approaches for performance analysis of TEGs in which spatially homogeneous materials are assumed. Therefore, an innovative method of analysis, which can incorporate inhomogeneous material properties, is presented in this study. This is crucial to understand the measured performance parameters of TEGs and, from this, develop means to improve their longevity. The analysis combines experimental profiling of inhomogeneous material properties, modelling of the material properties using a single parabolic band model, and calculation of device properties using the established Constant Property Model. We compare modeling results assuming homogeneous and inhomogeneous properties to the measurement results of an Mg2(Si,Sn)-based TEG prototype. We find that relevant discrepancies lie in the effective temperature difference across the TE leg, which decreases by ~10%, and in the difference between measured and calculated heat flow, which increases from 2–15% to 9–16% when considering the inhomogeneous material. The approach confirms additional resistances in the TEG as the main performance loss mechanism and allows the accurate calculation of the impact of different improvements on the TEG’s performance.
Wind Energy Supply Profiling and Offshore Potential in South Africa
Authors: Karamanski Stefan , Erfort Gareth
Published: April 2023

South Africa’s energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa’s coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied.
Supercritical Heat Transfer and Pyrolysis Characteristics of n-Decane in Circular and Rectangular Channels
Authors: Lei Zhiliang , Bao Zewei
Published: April 2023

In this research, the effects of different channel cross-section shapes on the flow, heat transfer and pyrolysis characteristics of n-decane were analyzed and compared based on CFD simulations. The interactions between cracking, heat transfer and flow field in a circular tube and a rectangular tube were studied. The results showed that the mean pressure drop in the rectangular channel is 1.18 times as high as that in the circular channel with pyrolysis due to its smaller equivalent diameter. The maximum value of the chemical heat sink in the rectangular channel is 1.6 times as high as that in the circular channel. The high temperature zone of any cross section in the rectangular channel is much larger than that in the circular channel due to the superposition of the boundary layer and lower turbulent kinetic energy in the corners of the rectangular channel. The maximum value of the Nu in the circular channel is 1.3 times as high as that in the rectangular channel with pyrolysis due to larger heat capacity, lower viscosity and higher wall shear stress.