bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Doping Trace Rare Earth Elements on Corrosion Behavior of EH36 Offshore Platform Steel
Authors: Zheng Shujia , Liu Na , Liu Yang , Wang Xiaoning , Qiu Lina , Gong Aijun
Published: March 2024

EH36 offshore platform steel, La (0.0031%) steel, Ce (0.0027%) steel, and Pr (0.0001%) steel were selected as the research objects. The corrosion of four groups of steel was simulated by immersion experiments. In the presence of Cl− the effect of rare earth elements (La, Ce, Pr) on corrosion was investigated by the weight loss method. The morphology of the corrosion products and the apparent morphology after the removal of the corrosion products were observed by scanning electron microscopy (SEM); the main components of four steel corrosion products were analyzed by X-ray diffraction (XRD). The electrochemical behavior and the influence of temperature and Cl− concentration on the corrosion of the four kinds of steel were analyzed by an electrochemical polarization curve and Nyquist diagram. The results showed that the addition of trace rare earth elements, La, Ce, and Pr, to EH36 offshore platform steel can reduce the corrosion rate and the corrosion current density (Icorr), and increase the charge transfer resistance during the corrosion process. The Icorr of La steel, Ce steel, and Pr steel is 6.59 × 10−5 A·cm−2, 7.57 × 10−5 A·cm−2, and 9.53 × 10−5 A·cm−2, respectively, which is lower than that of EH36 steel (Icorr = 1.82 × 10−4 A·cm−2). The influence of Cl− concentration and temperature on the four steels showed the same trend; that is, with the increase in Cl− concentration, the corrosion rate first rises and then slows down, and with the increase in temperature, the corrosion rate gradually accelerates. Rare earth elements promote the production of more α-FeOOH in the rust layer, and the compactness of this product plays a certain role in protecting the steel matrix. The addition of trace rare earth elements, La, Ce, and Pr (less than 0.004%), improves the corrosion resistance of EH36 steel.
Aging Behavior of Polyethylene and Ceramics-Coated Separators under the Simulated Lithium-Ion Battery Service Compression and Temperature Field
Authors: Qian Wang , Wu Shuqiu , Lei Caihong , Xu Ruijie , Wang Yanjie
Published: March 2024

In this paper, a device was set up, which could simulate the separator environment in the battery to track the influence of compression, temperature, and the electrolyte on the structure and electrochemical performance of separators. A commercial polyethylene separator and alumina- or boehmite-coated separators were selected, and the high-temperature cyclic compression was carried out in a mixed solvent environment with a ratio of vinyl carbonate and diethyl carbonate of 1:1. Compared with that compressed for 50 cycles under room temperature, the compression at 60 °C resulted in pore structure deterioration in the polyethylene separator. The oxidative voltage limit was reduced to 3.6 V, and after 200 charge and discharge cycles, the capacity was reduced by more than 50%. For the coated separator, the presence of a coating layer exhibited some protective effects, and the microporous structure in the base membrane was preserved. The oxidative voltage limit was above 4.2 V. However, as a result of the compression, the coating particles were still inserted into the pore structure, leading to a decrease in porosity and a decrease in discharge capacity, especially at a rate of 4 C. Compared with that coated with alumina particles, the interface resistance for the separator coated with boehmite particles was minimally affected, and the electrochemical performance after cyclic compression under 60 °C was better, exhibiting higher application ability.
Energy Efficiency versus Heritage—Proposal for a Replicable Prototype to Maintain the Architectural Values of Buildings in Energy Improvement Interventions on Facades: The Case of the Expansion of San Sebastián
Authors: Sagarna Maialen , Senderos María , Azpiri Ana , Roca Mireia , Mora Fernando , Otaduy Juan Pedro
Published: March 2024

The implementation of energy efficiency improvements in facades, such as ventilated facades or External Thermal Insulation Composite Systems (ETICS), is leading to a widespread modification of these structures. The lack of appropriate regulations to curb the destruction of the built heritage, particularly in historical centers, has been identified. In response to this issue, a project has been developed to create a prototype for determining regulations for facade interventions. The Design Science Research Methodology (DSRM) has been employed in the development of the prototype, which has been tested in a case study of the ensemble formed by the Cortázar and Oriental expansion areas in San Sebastián. The initial prototype has been outlined, corrected, and improved in an iterative process, resulting in a final prototype that enables a faster, more rigorous, and efficient understanding, characterization, classification, study, and definition of intervention criteria, measuring the vulnerability of buildings to these interventions quantitatively. In this study, the vulnerability is defined as the risk that a facade will alter the characteristics of its architectural style and distort the reading of streetscapes that belong to a period with a clearly defined character. The case study results have been compiled into intervention criteria sheets for each of the studied buildings. This material has been presented to the relevant public authorities, with the hope that it may lead to a modification of current legislation, thereby helping to curb the loss of architectural heritage identity.
Nickel Nanoparticles: Insights into Sintering Dynamics
Authors: Bajtošová Lucia , Kihoulou Barbora , Králík Rostislav , Hanuš Jan , Cieslar Miroslav
Published: March 2024

The sintering dynamics of nickel nanoparticles (Ni NPs) were investigated through a comprehensive approach that included in situ transmission electron microscopy annealing and molecular dynamics simulations. This study systematically examines the transformation behaviors of Ni NP agglomerates over a temperature spectrum from room temperature to 850 °C. Experimental observations, supported by molecular dynamics simulations, revealed the essential influence of rotational and translational motions of particles, especially at lower temperatures, on sintering outcomes. The effect of the orientation of particles on the sintering process was confirmed, with initial configurations markedly determining sintering efficiency and dynamics. Calculated activation energies from this investigation follow those reported in the literature, confirming surface diffusion as the predominant mechanism driving the sintering of Ni NPs.
Preparation of Reduced Graphene Oxide Films with High and Uniform Thickness for Electromagnetic Interference Shielding
Authors: Li Meng , Xie Li-Jing , Yi Zong-Lin , Liu Dong , Wang Zheng , Niu Ruo-Han , Jia Hui , Kong Qing-Qiang
Published: March 2024

Reduced graphene films have attracted widespread commercial interest due to high electrical conductivity toward (EMI) shielding. At present, the preparation path of reduced graphene film is to use graphene oxide (GO) as the raw material through self-assembly and high-temperature heat treatment. However, the thickness of reduced graphene films is not high and uniform because of the higher mobility of the graphene oxide slurry, which destroys the reliability of the membrane in the field of electromagnetic interference shielding. Here, we propose the use of sodium carboxymethyl cellulose (CMC) to increase the viscosity of graphene oxide to prepare reduced graphene films with high and uniform thickness. After modification with sodium carboxymethyl cellulose, the EMI shielding effectiveness (EMI SE) of reduced graphene oxide films stabilized at 91–96 dB at 8–12 GHz, which is higher than pure graphene films. Meanwhile, the addition of CMC does not affect the structure of reduced graphene films. This work broadens the application of reduced graphene films in electromagnetic shielding.
High-Performance Tandem White Micro-OLEDs for Virtual Reality and Mixed Reality Displays
Authors: Yang Zhiyong , Hsiang En-Lin , Wu Shin-Tson
Published: March 2024

To achieve wide-gamut and high-efficiency tandem white OLED (WOLED) microdisplays, we propose a new structure leveraging high-order antinodes and patterned microcavities. The color gamut coverages of 95% Rec. 2020 and 92% Rec. 2020 can be achieved in B/G/R tandem WOLED with a moderate microcavity and B/YG tandem WOLED with a strong microcavity, respectively. We have also boosted the optical efficiency by 62% for the tandem B/YG WOLED using the high-order antinodes at optimal conditions. Such a WOLED microdisplay helps reduce the power consumption of virtual reality (VR) and mixed reality (MR) displays while keeping a wide color gamut.
On the Possibility of Using Secondary Alloys in the Production of Aluminum-Based Metal Matrix Composite
Authors: Lattanzi Lucia , Jarfors Anders E. W. , Awe Samuel A.
Published: March 2024

Aluminum-based composites provide tribological performance and thermophysical properties that, combined with being lightweight, are suitable for their application in automotive brake discs. Aluminum alloys allow the use of secondary materials to produce composites, with the drawback of several elements, impurities, and oxides that can harm the mechanical and thermophysical properties. This preliminary study explored the mechanical and thermophysical performance of a composite material produced with a secondary matrix alloy. Overall, the results are promising, with a minimal decrease in mechanical and thermophysical properties despite clustered silicon carbide particles in the composite with the secondary matrix. The challenges in effectively dispersing carbides in the melt seem linked to aluminum oxides, and future microstructural investigations will aim to clarify this aspect.
Effectively Incorporating Small Reserves into National Systems of Protected and Conserved Areas
Authors: Dudley Nigel , Timmins Hannah L. , Stolton Sue , Watson James E. M.
Published: March 2024

Received wisdom has argued that large protected areas are superior to small reserves, based on island biogeography theory, economies of scale, and the need to sustain viable populations of top predators and other large ranging or dispersive species. But this position overlooks evidence that, for many species, strategically placed smaller reserves are extremely important, especially in areas highly altered by humans. Many countries are reluctant or unable to designate additional large protected areas. We provide evidence that carefully designed support networks of smaller protected areas can be an important complement to activities to reach the Global Biodiversity Framework’s target of 30% of the planet in protected and conserved areas by 2030. We identify seven benefits from small reserves, when correctly located and well-managed: (1) conserving critical habitat of range-limited or relic species; (2) conserving remaining areas of sensitive or threatened habitat in altered ecosystems; (3) conserving habitat for sensitive, time-limited lifecycle stages, such as raptor nesting sites and fish spawning grounds; (4) maintaining and enabling connectivity by providing stepping stones of suitable habitat through inhospitable ecosystems; (5) providing increased protection for critical habitat within Category V protected landscapes and seascapes to boost their overall conservation potential; (6) taking advantage of conservation opportunities at cultural sites, sacred natural sites, and other faith-based sites in transformed landscapes; (7) integrating different management approaches and governance types in a range of connected small reserves to multiply conservation impacts. We propose a typology based on these benefits that can guide steps for policy makers to help plan and monitor small reserves in area-based conservation efforts. Using these principles, the role of small reserves in area-based conservation efforts can be further enhanced.
Updated List of Bryophytes from Cape Verde Archipelago
Authors: Sim-Sim Manuela , Martins Anabela , Garcia Cesár Augusto
Published: March 2024

The aim of the present study is to update the list of bryophytes in the Cape Verde archipelago, with a focus on its distinctive terrestrial biota. The research was carried out through a combination of herbarium collections revision and fieldwork conducted from 2016 to 2019. The revised list includes 185 bryophyte taxa (175 species, 5 subspecies, and 5 varieties) from 93 genera and 42 families. Notably, 8 taxa are endemic, and the inclusion of 35 new taxa further enriches the bryophyte diversity of the archipelago. The distribution of bryophyte taxa varies across islands, with Santo Antão, São Nicolau, and Fogo exhibiting particularly high diversity. Acrocarpous mosses are the most common growth form (58.4%), followed by pleurocarpous mosses, thalloid liverworts (15.1% each), and leafy liverworts (11.4%). In conclusion, this study provides valuable insights into the bryophyte diversity of southern Macaronesia enhancing our understanding of its unique flora and emphasizing the imperative need for conservation efforts.
Luxury Car Data Analysis: A Literature Review
Authors: Barakati Pegah , Bertini Flavio , Corsi Emanuele , Gabbrielli Maurizio , Montesi Danilo
Published: March 2024

The concept of luxury, considering it a rare and exclusive attribute, is evolving due to technological advances and the increasing influence of consumers in the market. Luxury cars have always symbolized wealth, social status, and sophistication. Recently, as technology progresses, the ability and interest to gather, store, and analyze data from these elegant vehicles has also increased. In recent years, the analysis of luxury car data has emerged as a significant area of research, highlighting researchers’ exploration of various aspects that may differentiate luxury cars from ordinary ones. For instance, researchers study factors such as economic impact, technological advancements, customer preferences and demographics, environmental implications, brand reputation, security, and performance. Although the percentage of individuals purchasing luxury cars is lower than that of ordinary cars, the significance of analyzing luxury car data lies in its impact on various aspects of the automotive industry and society. This literature review aims to provide an overview of the current state of the art in luxury car data analysis.
The Association between Glymphatic System and Perivascular Macrophages in Brain Waste Clearance
Authors: Kaur Jasleen , Boyd Edward D. , Ding Guangliang , Zhang Li , Luo Hao , Li Qingjiang , Li Lian , Wei Min , Landschoot-Ward Julie , Chopp Michael , Zhang Zhenggang , Jiang Quan
Published: March 2024

The glymphatic system suggests the convective bulk flow of cerebrospinal fluid (CSF) through perivascular spaces and the interstitial spaces of the brain parenchyma for the rapid removal of toxic waste solutes from the brain. However, the presence of convective bulk flow within the brain interstitial spaces is still under debate. We first addressed this argument to determine the involvement of the glymphatic system in brain waste clearance utilizing contrast-enhanced 3D T1-weighted imaging (T1WI), diffusion tensor imaging (DTI), and confocal microscopy imaging. Furthermore, perivascular macrophages (PVMs), which are immune cells located within perivascular spaces, have not been thoroughly explored for their association with the glymphatic system. Therefore, we investigated tracer uptake by PVMs in the perivascular spaces of both the arteries/arterioles and veins/venules and the potential association of PVMs in assisting the glymphatic system for interstitial waste clearance. Our findings demonstrated that both convective bulk flow and diffusion are responsible for the clearance of interstitial waste solutes from the brain parenchyma. Furthermore, our results suggested that PVMs may play an important function in glymphatic system-mediated interstitial waste clearance. The glymphatic system and PVMs could be targeted to enhance interstitial waste clearance in patients with waste-associated neurological conditions and aging.
Homeostasis Model Assessment for Insulin Resistance Mediates the Positive Association of Triglycerides with Diabetes
Authors: Wang Yutang , Fang Yan , Vrablik Michal
Published: March 2024

Elevated circulating triglyceride levels have been linked to an increased risk of diabetes, although the precise mechanisms remain unclear. This study aimed to investigate whether low-density lipoprotein (LDL) cholesterol, homeostatic model assessment (HOMA) for insulin resistance, and C-reactive protein (CRP) served as mediators in this association across a sample of 18,435 US adults. Mediation analysis was conducted using the PROCESS Version 4.3 Macro for SPSS. Simple mediation analysis revealed that all three potential mediators played a role in mediating the association. However, in parallel mediation analysis, where all three mediators were simultaneously included, HOMA for insulin resistance remained a significant mediator (indirect effect coefficient, 0.47; 95% confidence interval [CI], 0.43–0.52; p < 0.05) after adjusting for all tested confounding factors. Conversely, LDL cholesterol (indirect effect coefficient, −0.13; 95% CI, −0.31–0.05; p > 0.05) and C-reactive protein (indirect effect coefficient, 0.01; 95% CI, −0.003–0.02; p > 0.05) ceased to be significant mediators. HOMA for insulin resistance accounted for 49% of the association between triglycerides and diabetes. In conclusion, HOMA for insulin resistance was the dominant mediator underlying the association between triglycerides and diabetes. Therefore, reducing triglyceride levels may hold promise for improving insulin sensitivity in diabetic patients.
Relationship between Prognostic Nutritional Index and Amputation in Patients with Diabetic Foot Ulcer
Authors: Coşkun Belgin , Ayhan Müge , Ulusoy Serap
Published: March 2024

The prognostic nutritional index (PNI) is a new marker used to assess a patient’s nutritional and immune status. It is calculated using serum albumin levels and total lymphocyte count. The aim of this study was to investigate the relationship between PNI and amputation in patients with diabetic foot ulcer (DFU). Patients with DFU were enrolled in this retrospective study. In our study, a total of 386 patient data, of 110 (28.5%) amputated and 276 (71.5%) non-amputated patients, were statistically analyzed. PNI values were significantly lower in the amputated patient group than in the non-amputee patient group (p < 0.001). According to the ROC analysis results, PNI was significant in the prediction of amputation at an excellent level (AUC = 0.937 (0.911–0.963), p < 0.001). The optimal cut-off point for PNI was found to be 39,005. There was classification success for this cut-off point: sensitivity was calculated as 82.7% (74.1–89) and specificity as 93.1% (89.3–95.7). In the multivariate model, the odds ratio (OR) (95% CI) was calculated as 81.8 (38.5–173.7) for PNI. The PNI was associated with an increase in amputation rate in patients with DFU. By using PNI, patients can be directed to advanced centers and have access to early and appropriate interventions.
Improved Applicability and Diagnostic Accuracy of the Novel Spleen-Dedicated Transient Elastography Device for High-Risk Esophageal Varices
Authors: Madir Anita , Barisic Jaman Mislav , Milosevic Marko , Dinjar Kujundžić Petra , Grgurevic Ivica
Published: March 2024

Spleen stiffness measurement (SSM) by transient elastography (TE) has been repeatedly demonstrated as the reliable way to rule out the presence of high-risk esophageal varices (HRV). We aimed to evaluate and compare novel vs. standard TE-SSM module performance in diagnosing HRV in patients with compensated advanced chronic liver disease (cACLD). This retrospective study included patients with cACLD; blood data, upper digestive endoscopy performed within 3 months of TE, SSM@50Hz and SSM@100Hz were collected. Overall, 112 patients with cACLD were analyzed (75.9% males, average age of 66, 43.7% alcohol-related chronic liver disease, 22.3% metabolic-associated steatotic liver disease, 6.2% viral hepatitis). Reliable SSM was possible in 80.3% and 93.8% of patients by using SSM@50Hz and SSM@100Hz probe, respectively. At the cut-off 41.8 kPa and 40.9 kPa (Youden), SSM@50Hz and SSM@100Hz had AUROCs of 0.746 and 0.752, respectively, for diagnosing HRV (p = 0.71). At the respective cut-offs, sensitivities for HRV were 92.9% and 100%, resulting in misclassification rates of 7.1% and 0% by using SSM@50Hz and SSM@100Hz. SSM reliably excludes HRV in cACLD patients, with measurements below 41 kPa potentially avoiding EGD in around 50% of cases, with minimal risk of HRV omission. SSM@100Hz demonstrated less measurement failures and no HRV misclassification.
Incidence of Secondary Sclerosing Cholangitis in Hospitalized Long COVID-19 Patients: A Retrospective Single Center Study
Authors: Werner Christoph R. , Fusco Stefano , Kienzle Katharina , Döbele Stefanie , Artzner Kerstin , Malek Nisar P. , Wichmann Dörte , Göpel Siri
Published: March 2024

Background: SARS-CoV-2 infection and associated COVID-19 disease can lead to critical illness with a risk of developing a multiple organ failure. Subsequently, this may lead to various pathological sequelae, such as secondary sclerosing cholangitis after surviving COVID-19 (SSC-COVID). Objective: The aim is to retrospectively analyze a cohort of hospitalized patients with first-wave (February 2020–June 2020) SARS-CoV-2 infection and persisting unclear cholangiopathy to determine the incidence of SSC-COVID and its risk factors. Results: A total of 249 patients were hospitalized at the university hospital in Tübingen, Germany, with SARS-CoV-2 infection during the first wave of the pandemic. Of these, 35.3% (88/249) required intensive care treatment; 16.5% (41/249) of them died due to the complications of COVID-19; 30.8% (64/208) of surviving patients could be followed up und were retrospectively analyzed at our center. The incidence of confirmed SSC-COVID was 7.8% (5/64). All SSC-COVID patients had an ICU stay >20 days, for invasive ventilation, positioning treatment, vasopressor treatment, but possible risk factors for SSC were not significant due to the small number of patients. Conclusions: SSC-COVID is an emerging disease in post-COVID patients with a high incidence in our single-center cohort. SSC-COVID should be considered as a differential diagnosis, if unclear cholangiopathy or cholestasis persists after SARS-CoV-2 infection.
Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas
Authors: Azamat Sena , Buz-Yalug Buse , Dindar Sukru Samet , Yilmaz Tan Kubra , Ozcan Alpay , Can Ozge , Ersen Danyeli Ayca , Pamir M. Necmettin , Dincer Alp , Ozduman Koray , Ozturk-Isik Esin
Published: March 2024

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann–Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as “en plaque”, and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.
Pilot Study on the Development and Integration of Anthropomorphic Models within the Dental Technician Curriculum
Authors: Bliznakova Kristina , Milev Minko , Dukov Nikolay , Atanasova Virginia , Yordanova Mariana , Bliznakov Zhivko
Published: April 2024

The effectiveness of modern medical education largely depends on the integration and utilization of digital technologies in teaching various disciplines. In this pilot usability study, we introduced 3D printed anthropomorphic dental models, specifically designed for the elective discipline “Digital and Metal-Free Techniques in Dental Technology” from the curriculum of the Dental Technician specialty in the Medical University of Varna. The evaluation focused on dental technician students’ perception of this novel learning environment, its influence on their performance, and the potential for future application of these models and related 3D technologies in their professional practice. A validated satisfaction questionnaire was distributed among 80 students, comprising the total cohort. The results indicated a high acceptance rate, with nearly 95% of participants finding the use of digitally created 3D-printed dental models beneficial. More than 90% believed that exploring digital technologies would enhance their skills. The well-trained instructor’s competence in technology use convinced students of its value, with more than 98% expressing a willingness to incorporate these technologies into their future work for improved precision in dental models. However, due to the current high cost of needed equipment, only 10% of participants may practicably introduce this novel technology into their practical work. The use of anatomically accurate 3D printed models is a valuable addition to the current dental technician curriculum in medical colleges.
A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management
Authors: Kang Tae-Won , Jung Jin-Woo
Published: March 2024

The ongoing expansion of the Fourth Industrial Revolution has led to a diversification of drone applications. Among them, this paper focuses on the critical technology required for load management using drones. Generally, when using autonomous drones, global positioning system (GPS) receivers attached to the drones are used to determine the drone’s position. However, GPS integrated into commercially available drones have an error margin on the order of several meters. This paper, proposes a method that uses fixed-size quick response (QR) codes to maintain the error of drone 3D localization within a specific range and enable accurate mapping. In the drone’s 3D localization experiment, the errors were maintained within a specific range, with average errors ranging from approximately 0 to 3 cm, showing minimal differences. During the mapping experiment, the average error between the actual and estimated positions of the QR codes was consistently around 0 to 3 cm.
Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
Authors: Sun Xinkai , Zhang Sanguo , Ma Shuangge
Published: March 2024

In the classification task, label noise has a significant impact on models’ performance, primarily manifested in the disruption of prediction consistency, thereby reducing the classification accuracy. This work introduces a novel prediction consistency regularization that mitigates the impact of label noise on neural networks by imposing constraints on the prediction consistency of similar samples. However, determining which samples should be similar is a primary challenge. We formalize the similar sample identification as a clustering problem and employ twin contrastive clustering (TCC) to address this issue. To ensure similarity between samples within each cluster, we enhance TCC by adjusting clustering prior to distribution using label information. Based on the adjusted TCC’s clustering results, we first construct the prototype for each cluster and then formulate a prototype-based regularization term to enhance prediction consistency for the prototype within each cluster and counteract the adverse effects of label noise. We conducted comprehensive experiments using benchmark datasets to evaluate the effectiveness of our method under various scenarios with different noise rates. The results explicitly demonstrate the enhancement in classification accuracy. Subsequent analytical experiments confirm that the proposed regularization term effectively mitigates noise and that the adjusted TCC enhances the quality of similar sample recognition.
Evaluating Environmental Sustainability in Africa: The Role of Environmental Taxes, Productive Capacities, and Urbanization Dynamics
Authors: Ben Youssef Adel , Dahmani Mounir
Published: March 2024

This study examines the complex relation among environmental taxes, productive capacities, urbanization, and their collective effects on environmental quality in Africa, drawing on two decades of data from twenty African countries. It situates the study within the broader discourse on sustainable development and economic growth, emphasizing the Environmental Kuznets Curve (EKC) framework to examine the relationship between economic development, characterized by urban expansion and increased productive capacities, and the adoption of environmental taxes amidst the continent’s diverse economic and environmental environments. Using advanced econometric techniques, including the Cross-Section Augmented Autoregressive Distributed Lag (CS-ARDL) model and the Dynamic Common Correlated Effects Mean Group (DCCEMG) estimator, the study addresses data challenges such as cross-sectional dependence and slope heterogeneity. The results provide important insights into the dynamics of environmental quality in relation to economic and urban growth and the role of environmental taxation. The study proposes tailored policy strategies aimed at strengthening sustainable development initiatives in line with international agreements such as the Paris Agreement and the Sustainable Development Goals. These strategies advocate for a nuanced application of environmental taxes and the promotion of productive capacities to enhance environmental sustainability across the African continent.
Loan Portfolio Management and Bank Efficiency: A Comparative Analysis of Public, Old Private, and New Private Sector Banks in India
Authors: Venugopal Santhosh Kumar
Published: March 2024

This comparative study analyzed the impact of loan portfolio composition on the efficiency of different types of banks in India—public sector, old private, and new private banks—in the period between 2013 and 2022. Efficiency was evaluated using data envelopment analysis (DEA). The study considered four loan variables—term lending, working capital, priority sector lending, and secured lending in proportion to the overall loans—as independent factors against the efficiency score as the dependent variable, using a random-effects generalized least squares (GLS) regression framework. The results indicate that there were no significant effects on the efficiency of old private banks, except for working capital, which had a marginally negative impact on bank efficiency. Working capital, priority sector lending, and term lending have been found to significantly impact the efficiency of new private banks. Only term and working capital loans significantly affected the efficiency of public sector banks.
Preparing Teachers to Re-Build Democratic Communities and Disappearing Middle Class to Reverse Global Inequality: A Typology Applying Bourdieusian ‘Taste’ and Dewey-Inspired Tools to Transform World Making
Authors: Taysum Alison , Salha Soheil
Published: March 2024

‘Double structuring’ of objective economic wealth and subjective perception of wealth through language and culture produce a common world of divisions in the classroom from ‘bad taste’ to ‘good taste’. Teachers’ language and culture of instruction and ‘good taste’ perpetuate systemic injustice with unconscious bias. The tool ‘A Blueprint for Character Development for Evolution (ABCDE) offers five steps to prepare teachers to recognize bias and reverse it with a Typology Applying Bourdieusian ‘Taste’ for Making Worlds. A—Ask questions about instructional Language and Culture and Community Languages and Cultures; B—Critique Typology of Taste: C—Develop statistical models with measures to monitor and evaluate incremental progress towards synthesizing/unifying different classroom languages and cultures into one democratic language and culture whilst honouring each; D—Create evidence-informed, logical and ethical qualitative pathways to re-building a democratic community/disappearing middle class; E—Identify a principle of unity that reverses local inequality, and through powerful Higher Education networks, mainstream it to reverse global inequality. A Professional Educators and Administrators Committee for Empowerment (PEACE) offers a systemic multi-level communication infrastructure to enable feedback loops from communities to policy makers to measure inequality and report narrowing income and wealth gaps to reverse the sharpest increase between 2019 and 2020 in global inequality since World War II.
Breaking through Barriers: A Systematic Review of Extended Reality in Education for the Visually Impaired
Authors: Hamash Mahmoud , Ghreir Hanan , Tiernan Peter
Published: March 2024

Education for those who are visually impaired usually relies on modified materials and unique teaching methods. Nonetheless, the advent of Extended Reality marks a considerable change by providing immersive and interactive experiences that can surpass the challenges encountered in conventional learning due to visual impairments. This study aims to systematically review and analyse the existing literature on the use of extended realities in the education of individuals with visual impairment. This systematic review followed the Preferred Reporting Items for Systematic Reviews (PRISMA) statement as a formal systematic review guideline for data collection to ensure the quality and replicability of the revision process. Data were obtained from research studies over the period 2013–2023. The analysis included a total of 71 papers from Science Direct, ERIC, JSTOR, Taylor & Francis Online, and Scopus databases. The results show that Europe had the most publications on these topics during the past decade and that most papers were focused on higher education. Additionally, virtual reality was the most investigated topic. The findings indicate that extended reality has the potential to promote inclusion for the visually impaired in educational settings and provide them with enhanced educational experiences in many educational disciplines.
Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method
Authors: Sun Lina , Wang Hong , Qi Linhai , Yan Jiangyu , Jiang Meijing
Published: March 2024

With the integration of clean energy and new power electronic devices into the power grid, the superposition of harmonic sources has become increasingly apparent and common. There is an urgent need to effectively identify composite harmonic sources in the new energy grid. This article proposes a multi-label composite harmonic source classification method that integrates knowledge representation with the transformer model. First, triplets from harmonic monitoring data are extracted and TransR models are used to train time-frequency feature representation vectors. Then, the transformer model is trained to learn the data features of different harmonic sources. Finally, based on the multi-label classification method, composite harmonic sources are identified. This article integrates the semantic information of time-frequency features into the data samples, increasing the interpretability of the model while expanding the inter-class features, which is conducive to the classification and recognition of the model. Compared with other deep learning recognition methods, verification based on simulation data and measured data shows that this method has low training complexity and higher recognition accuracy.
A Descriptive-Predictive–Prescriptive Framework for the Social-Media–Cryptocurrencies Relationship
Authors: Băroiu Alexandru-Costin , Bâra Adela
Published: March 2024

The research presented in this paper is the first to introduce a thorough Descriptive-Predictive–Prescriptive (DPP) Framework for comprehending the interaction between social media and cryptocurrencies. Recognizing the underexplored domain of the social-media–cryptocurrency interaction, we delve into its many aspects, better understanding present dynamics, forecasting potential future trajectories, and prescribing best solutions for stakeholders. We evaluate social media speech and behavior connected to cryptocurrencies using big data analytics, translating raw data into meaningful insights using Natural Language Processing (NLP) techniques like sentiment analysis. When applied to an experimental dataset, the DPP nets superior results compared to the baseline approach, displaying an improvement of 3.44% of the Root Mean Square Error (RMSE) metric and 4.59% of the Mean Absolute Error (MAE) metric. The unique DPP framework enables a more in-depth assessment of social media’s influence on cryptocurrency trends, and lays the path for strategic decision-making in this nascent but rapidly developing field of study.
Research on a Torque Ripple Suppression Method of Fuzzy Active Disturbance Rejection Control for a Permanent Magnet Synchronous Motor
Authors: Lv Congxin , Wang Bo , Chen Jingbo , Zhang Ruiping , Dong Haiying , Wan Shaoqi
Published: March 2024

In order to meet the necessities of steady and protected operation of a permanent magnet synchronous motor (PMSM) in electromechanical pressure gadget aviation beneath complicated working conditions, a three-phase four-arm inverter fuzzy self-disturbance suppression management (Fuzzy-ADRC) approach for PMSM is proposed to suppress the motor torque pulsation beneath complicated working conditions. Firstly, the defects of the common inverter are analyzed, the three-phase four-bridge inverter is changed via the standard three-phase three-bridge inverter, and the present-day harmonic suppression’s overall performance of the three-phase four-bridge inverter is modeled, analyzed, and verified. Secondly, the ADRC and fuzzy management approach is analyzed, the Kalman filter is delivered into the motor pace loop management to enhance the overall performance of ADRC, and then the fuzzy manipulate and ADRC are blended to similarly enhance the torque ripple suppression’s overall performance of the everlasting magnet synchronous motor. Finally, the proposed three-phase four-arm inverter and fuzzy-ADRC approach are combined, and contrasted with the normal three-phase three-arm inverter and ADRC method. The simulation consequences exhibit that the proposed manipulation technique can efficiently suppress the torque ripple of everlasting magnet synchronous motor and has robust reliability.
How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making
Authors: Wu Guanlin , Zhang Dayu , Miao Zhengyuan , Bao Weidong , Cao Jiang
Published: March 2024

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.
Design of a High-Speed, Low-Power PTL-CMOS Hybrid Multiplier Using Critical-Path Evaluation Model
Authors: Yu Yihe , Pan Wanyuan , Tang Chengcheng , Yin Ningyuan , Yu Zhiyi
Published: March 2024

The multiplier is the fundamental component of many computing modules. As the most important component of a multiplier, the full adder (FA) also has a significant impact on the overall performance. Full adders based on pass transistor logic (PTL) have been a very popular research field in recent years, but the uneven delay makes it difficult to analyze the critical path of multipliers based on PTL full adders. In this paper, we propose a model to evaluate the critical path of the carry save array (CSA) multiplier that could reduce the size of the simulation input set from 4 G to 93 K to finally obtain the maximum delay of the multiplier. We propose a novel low-power, high-speed CSA multiplier based on both PTL full adders and CMOS full adders, using our critical-path evaluation model. The proposed work is implemented in the 28 nm process. We use the model to reduce the worst-case delay by 14.5%. The proposed multiplier improved the power delay product by 9.4% over the conventional full CMOS multiplier.
The Influence of Geometric Parameters for Training an Artificial Neural Network to Predict the Band Structure of 1-D Fishbone Photonic Crystal
Authors: Hsiao Fu-Li , Chen Chien-Chung , Chang Chuan-Yu , Huang Yi-Chia , Tsai Ying-Pin
Published: March 2024

With the rising demand for the transmission of large amounts of information over long distances, the development of integrated light circuits is the key to improving this technology, and silicon photonics have been developed with low absorption in the near-infrared range and with sophisticated fabrication techniques. To build devices that work in different functionalities, photonic crystals are one of the most used structures due to their ability to manipulate light. The investigation of photonic crystals requires the calculation of photonic band structures and is usually time-consuming work. To reduce the time spent on calculations, a trained ANN is introduced in this study to directly predict the band structures using only a minimal amount of pre-calculated band structure data. A well-used 1-D fishbone-like photonic crystal in the form of a nanobeam is used as the training target, and the influence of adjusting the geometric parameters is discussed, especially the lattice constant and the thickness of the nanobeam. To train the ANN with very few band structures, each of the mode points in the band structure is considered as a single datapoint to increase the amount of training data. The datasets are composed of various raw band structure data. The optimized ANN is introduced at the end of this manuscript.
Kalman Filter with Adaptive Covariance Estimation for Carrier Tracking under Weak Signals and Dynamic Conditions
Authors: Cheng Yan , Zhang Shengkang , Wang Xueyun , Wang Haifeng , Yang Huijun
Published: March 2024

Kalman filtering (KF)-based tracking has been commonly employed in global navigation satellite system (GNSS) receivers to achieve robust tracking. However, under more serious conditions, such as severe strength attenuation and abrupt dynamic coexisting environments, it is difficult for KF-based tracking to keep tracking well due to the fixed noise statistics. To further enhance the carrier tracking performance, this paper proposes an adaptive KF carrier tracking method for resisting signal strength fading and high dynamic environments. The proposed method introduces the adaptive factor to adjust the process noise covariance to accommodate the noise statistics in actual variable situations. Moreover, we apply the chi-square hypothesis test to detect system stability. The adaptive factor is only applied when the system is not stable, which can enhance computational efficiency. The proposed method is conducted in the GPS L1 software receivers. According to the results, the proposed algorithm can improve the robustness in tracking performance compared with other tracking methods under signal serious fading and high dynamic conditions. Using the proposed method, GNSS receivers’ navigation performance can be improved under complex conditions.
Architectural Framework and Feasibility of Internet of Things-Driven Mars Exploration via Satellite Constellations
Authors: Ledesma Oscar , Lamo Paula , Fraire Juan A. , Ruiz María , Sánchez Miguel A.
Published: March 2024

This study outlines a technical framework for Internet of Things (IoT) communications on Mars, leveraging Long Range (LoRa) technology to connect Martian surface sensors and orbiting satellites. The designed architecture adapts terrestrial satellite constellation models to Martian environments and the specific needs of interplanetary communication with Earth. It incorporates multiple layers, including Martian IoT nodes, satellite linkage, constellation configuration, and Earth communication, emphasizing potential Martian IoT applications. The analysis covers four critical feasibility aspects: the maximum communication range between surface IoT nodes and orbiting satellites, the satellite constellation’s message processing capacity to determine IoT node volume support, the communication frequency and visibility of IoT nodes based on the satellite constellation arrangement, and the interplanetary data transmission capabilities of LoRa-based IoT devices. The findings affirm LoRa’s suitability for Martian IoT communication, demonstrating extensive coverage, sufficient satellite processing capacity for anticipated IoT node volumes, and effective data transmission in challenging interplanetary conditions. This establishes the framework’s viability for advancing Mars exploration and IoT in space exploration contexts.