bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxidation Resistance of Ir/HfO2 Composite Coating Prepared by Chemical Vapor Deposition: Microstructure and Elemental Migration
Authors: Zhu Junyu , Li Wenting , Cai Hongzhong , Wang Xian , Wang Xingqiang , Wei Yan , Hu Changyi , Zhao Xingdong , Zhang Xuxiang
Published: June 2024

In this study, a HfO2 coating was developed on an Ir matrix using a customized open-tube airflow, cold-wall chemical vapor deposition instrument. The preparation process and structure of the as-prepared coating were investigated to gain insights into its characteristics. The HfO2 coating effectively prevents direct contact between Ir and O, leading to a reduction in the oxidation rate of Ir. Furthermore, defects such as micropores and cracks generated during sealed oxidation erosion contribute to Ir’s decelerated oxidation failure. The as-prepared HfO2 coating exhibits low thermal conductivity and a high heat radiation rate, reducing the coating’s surface temperature. These characteristics significantly enhance adversity tolerance and increase the working temperature of the coating. Moreover, the as-prepared HfO2 coating can serve as a diffusion barrier, blocking both the direct contact of O with the Ir coating and the diffusion of other elements to the Ir coating. As a result, the rates of diffusion of other elements to the Ir coating are reduced.
Calcium Phosphate Coatings Deposited on 3D-Printed Ti–6Al–4V Alloy by Plasma Electrolytic Oxidation
Authors: Sagidugumar Amangeldi , Dogadkin Dmitriy , Turlybekuly Amanzhol , Kaliyev Daniyar
Published: June 2024

In this article, the process of creating calcium phosphate coatings through plasma electrolytic oxidation was investigated. Calcium phosphate coatings were deposited onto titanium substrates fabricated via the selective laser melting (SLM) method. The correlation between the characteristics of the coating and the applied voltage (200, 250, and 300 V) of PEO was studied. The surface morphology analysis indicates that an increase in applied voltage results in a larger pore size. It was discovered that, when a voltage of 300 V was applied, a layer of hydroxyapatite formed. However, at 300 V, the coating cracked, producing a significantly rough surface. Our analysis of the elemental composition of sample cross sections indicates the presence of TiO2 layers that are enriched with calcium (Ca) and phosphorus (P). The coefficient of friction and wear rate are primarily influenced by the morphology, pore size, and density of the titanium dioxide layer. Furthermore, a rise in the quantity of the beta phase of the titanium on the surface can be noticed as the applied voltage increases. As a result, it also affects the mechanical and tribological characteristics of the coating. The sample treated to a voltage of 250 V demonstrates a higher resistance to wear and a lower elastic modulus in comparison to the other two coatings.
Optimization of Epitaxial Structures on GaN-on-Si(111) HEMTs with Step-Graded AlGaN Buffer Layer and AlGaN Back Barrier
Authors: Kim Jeong-Gil
Published: June 2024

Recently, crack-free GaN-on-Si growth technology has become increasingly important due to the high demand for power semiconductor devices with high performances. In this paper, we have experimentally optimized the buffer structures such as the AlN nucleation layer and step-graded AlGaN layer for AlGaN/GaN HEMTs on Si (111) substrate by varying growth conditions and thickness, which is very crucial for achieving crack-free GaN-on-Si epitaxial growth. Moreover, an AlGaN back barrier was inserted to reduce the buffer trapping effects, resulting in the enhancement of carrier confinement and suppression of current dispersion. Firstly, the AlN nucleation layer was optimized with a thickness of 285 nm, providing the smoothest surface confirmed by SEM image. On the AlN nucleation layer, four step-graded AlGaN layers were sequentially grown by increasing the Al composition from undermost layer to uppermost layer, meaning that the undermost one was close to AlN, and the uppermost was close to GaN, to reduce the stress and strain in the epitaxial layer gradually. It was also verified that the thicker step-graded AlGaN buffer layer is suitable for better crystalline quality and surface morphology and lower buffer leakage current, as expected. On these optimized buffer structures, the AlGaN back barrier was introduced, and the effects of the back barrier were clearly observed in the device characteristics of the AlGaN/GaN HEMTs on Si (111) substrate such as the transfer characteristics, output characteristics and pulsed I-V characteristics.
Use of Organic Acids as Additives for Plasma Electrolytic Oxidation (PEO) of Titanium
Authors: Ceriani Federica , Casanova Luca , Ormellese Marco
Published: June 2024

The present study investigates the influence of organic acids, added to the electrolytic solution, on the structure, morphology, and corrosion behaviour of plasma electrolytic oxidation (PEO) coatings produced on titanium grade 2. Particular attention is paid to the role of functional groups in the modification of the oxide’s properties. For this reason, all three selected acids, namely glutaric, glutamic, and tartaric acid, display two carboxylic groups, thus they interact with the substrate material mainly through –COO− adsorption. However, glutamic acid also has an amine group, while tartaric acid has two hydroxyl groups. The presence of such additional functional groups is found to impact the formation of the PEO coatings. According to scanning electron microscopy (SEM) analyses, the number of defects and their dimension increase with an increasing number of active groups present in the organic molecules. Then, when glutaric acid with only two carboxyl groups, is employed as an additive, smaller pores are produced. The dimension of defects increases when glutamic and tartaric acid are used. X-ray diffraction (XRD) testing demonstrates that rutile and anatase are present in all the coatings and that when using tartaric acid, a relatively high level of amorphism is reached. The electrochemical and corrosion behaviours are evaluated by potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) performed in a heated sulphuric acid solution. It is found that all types of coatings provide protection against corrosion, with oxides produced using glutamic acid showing the lowest corrosion current density (0.58 mA·m−2) and low corrosion rate (1.02 μm·y−1).
Flexural and Shear Strengthening of Reinforced-Concrete Beams with Ultra-High-Performance Concrete (UHPC)
Authors: Ahmed Farabi Bin , Biswas Rajib Kumar , Sen Debasish , Tasnim Sumaiya
Published: June 2024

Ultra-high-performance concrete (UHPC) is considered to be a promising material for the strengthening of damaged reinforced concrete (RC) members due to its high mechanical strength and low permeability. However, its high material cost, limited code provisions, and scattered material properties limit its wide application. There is a great need to review existing articles and create a database to assist different technical committees for future code provisions on UHPC. This study presents a comprehensive overview focusing on the effect of the UHPC layer on the flexural and shear strengthening of RC beams. From this review, it was evident that (1) different retrofitting configurations have a remarkable effect on the cracking moment compared to the maximum moment in the case of flexural strengthening; (2) the ratios of the shear span and UHPC layer thickness have a notable effect on shear strengthening and the failure mode; and (3) different bonding techniques have insignificant effects on shear strengthening but a positive impact on flexural strengthening. Overall, it can be concluded that three-side strengthening has a higher increment range for flexural (maximum, 81%–120%; cracking, 300%–500%) and shear (maximum, 51%–80%; cracking, 121%–180%) strengthening. From this literature review, an experimental database was established, and different failure modes were identified. Finally, this research highlights current issues with UHPC and recommends some future works.
Deodorant Efficacy of Xylityl Sesquicaprylate Vehiculated into Roll-on and Stick Prototype Formulations
Authors: Mussi Lilian , Baby André Rolim , Nogueira Cecilia , de Camargo Junior Flávio Bueno , Magalhães Wagner Vidal
Published: June 2024

Given the burgeoning global market for deodorant products, it is paramount to develop novel, safe, and efficacious molecules that align with the cosmetic industry’s trend toward active compounds sourced from natural, sustainable, and renewable sources. In this context, we in vitro and in vivo investigated the deodorant potential of xylityl sesquicaprylate, a compound that, besides other functions, has antimicrobial activity. We performed the time–kill test to challenge the xylityl sesquicaprylate against Staphylococcus epidermidis, Staphylococcus aureus, and Corynebacterium xerosis and in vivo trial efficacy was established through a sniff test using two deodorant prototype formulations containing xylityl sesquicaprylate at 0.35% (w/w). The xylityl sesquicaprylate at 0.35% (w/w) in glycerin presented in vitro deodorant activity through a bactericide/bacteriostatic profile against S. epidermidis, S. aureus, and C. xerosis. The in vivo efficacy trial performed by the sniffers through a single application of the roll-on and the stick prototype formulations added to the developed active compound deodorant’s effectiveness with a reduction in axillary bad odor, in comparison to the respective blank sample, for 2, 4, 8, and 12 h. When the deodorant efficacy was evaluated subjectively by the participants, there was always no difference between the stick sample and the blank; however, the roll-on deodorant was perceived as effective after 4 and 8 h of a single application of the sample, as established by the volunteers.
An Energy-Optimized Artificial Intelligence of Things (AIoT)-Based Biosensor Networking for Predicting COVID-19 Outbreaks in Healthcare Systems
Authors: Pahuja Monika , Kumar Dinesh
Published: June 2024

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today’s environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices’ limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model’s efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems.
Securing Data Exchange with Elliptic Curve Cryptography: A Novel Hash-Based Method for Message Mapping and Integrity Assurance
Authors: Lahraoui Younes , Lazaar Saiida , Amal Youssef , Nitaj Abderrahmane
Published: June 2024

To ensure the security of sensitive data, elliptic curve cryptography (ECC) is adopted as an asymmetric method that balances security and efficiency. Nevertheless, embedding messages into elliptic curve (EC) points poses a significant challenge. The intricacies of this process can greatly affect the overall security and efficiency of the cryptosystem, reflecting security vulnerabilities observed in many existing schemes that utilize ElGamal ECC-based encryption. In this paper, we introduce an innovative hash-based technique for securely embedding messages into EC points before encryption. A random parameter and a shared secret point generated through the EC Diffie–Hellman protocol are used to bolster the scheme’s security. The security of the proposed method is evaluated against various attack models; moreover, the complexity, and sensitivity of the encryption scheme, as well as its inputs, are analyzed. The randomness assessment of the ciphertext was performed using the NIST statistical test suite. Additionally, we propose a mechanism to ensure the integrity of the message by securely appending a tag to the ciphertext. As a consequence, a comprehensive analysis of our scheme demonstrates its effectiveness in maintaining data security and integrity against various attack models. The algorithm also meets more criteria such as the strict avalanche criterion, linear complexity, and operability.
Opioids and Cancer: Current Understanding and Clinical Considerations
Authors: Sah Dhananjay , Shoffel-Havakuk Hagit , Tsur Nir , Uhelski Megan L. , Gottumukkala Vijaya , Cata Juan P.
Published: June 2024

Pain is one of the most common symptoms in patients with cancer. Pain not only negatively affects the quality of life of patients with cancer, but it has also been associated with reduced survival. Pain management is therefore a critical component of cancer care. Prescription opioids remain the first-line approach for the management of moderate-to-severe pain associated with cancer. However, there has been increasing interest in understanding whether these analgesics could impact cancer progression. Furthermore, epidemiological data link a possible association between prescription opioid usage and cancer development. Until more robust evidence is available, patients with cancer with moderate-to-severe pain may receive opioids to decrease suffering. However, future studies should be conducted to evaluate the role of opioids and opioid receptors in specific cancers.
Evaluation of Tumor Control and Normal Tissue Complication Probabilities in Patients Receiving Comprehensive Nodal Irradiation for Left-Sided Breast Cancer
Authors: Flores-Balcázar Christian H. , Urías-Arce Dulce M.
Published: June 2024

Women with left-sided breast cancer receiving adjuvant radiotherapy have increased incidence of cardiac mortality due to ischemic heart disease; to date, no threshold dose for late cardiac/pulmonary morbidity or mortality has been established. We investigated the likelihood of cardiac death and radiation pneumonitis in women with left-sided breast cancer who received comprehensive lymph node irradiation. The differences in dosimetric parameters between free-breathing (FB) and deep inspiration breath hold (DIBH) techniques were also addressed. Based on NTCP calculations, the probability of cardiac death was significantly reduced with the DIBH compared to the FB technique (p < 0.001). The risk of radiation pneumonitis was not clinically significant. There was no difference in coverage between FB and DIBH plans. Doses to healthy structures were significantly lower in DIBH plan than in FB plan for V20, V30, and ipsilateral total lung volume. Inspiratory gating reduces the dose absorbed by the heart without compromising the target range, thus reducing the likelihood of cardiac death.
Radiation Oncologists’ Perspectives on Oligometastatic Prostate Cancer: A Survey from Korean Oligometastasis Working Group
Authors: Yoo Gyu Sang , Park Sunmin , Rim Chai Hong , Cho Won Kyung , Chang Ah Ram , Kim Young Seok , Ahn Yong Chan , Chie Eui Kyu
Published: June 2024

Background: Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy (MDRT) in OMPC. However, the recognition regarding the adopted definitions, methodologies of assessment, and therapeutic approaches is diverse among radiation oncologists. This study aims to evaluate the level of agreement for issues in OMPC among radiation oncologists. Methods: We generated 15 key questions (KQs) for OMPC relevant to definition, diagnosis, local therapies, and endpoints. Additionally, three clinical scenarios representing synchronous metastatic prostate cancer (mPC) (case 1), metachronous mPC with visceral metastasis (case 2), and metachronous mPC with castration-resistance and history of polymetastasis (case 3) were developed. The 15 KQs were adapted according to each scenario and transformed into 23 questions with 6–9 per scenario. The survey was distributed to 80 radiation oncologists throughout the Republic of Korea. Answer options with 0.0–29.9%, 30–49.9%, 50–69.9%, 70–79.9%, 80–89.9%, and 90–100% agreements were considered as no, minimal, weak, moderate, strong, and near perfect agreement, respectively. Results: Forty-five candidates voluntarily participated in this study. Among 23 questions, near perfect (n = 4), strong (n = 3), or moderate (n = 2) agreements were shown in nine. For the case recognized as OMPC with agreements of 93% (case 1), near perfect agreements on the application of definitive radiation therapy (RT) for whole metastatic lesions were achieved. While ≥70% agreements regarding optimal dose-fractionation for metastasis-directed RT (MDRT) has not been achieved, stereotactic body RT (SBRT) is favored by clinicians with higher clinical volume. Conclusion: For the case recognized as OMPC, near perfect agreement for the application of definitive RT for whole metastatic lesions was reached. SBRT was more favored as a MDRT by clinicians with a higher clinical volume.
Data on Stark Broadening of N VI Spectral Lines
Authors: Dimitrijević Milan S. , Christova Magdalena D. , Sahal-Bréchot Sylvie
Published: June 2024

Data on Stark broadening parameters, spectral line widths, and shifts for 15 multiplets of N VI, whose spectral lines are broadened by collisions with electrons, protons, alpha particles (He III) and B III, B IV, B V and B VI ions, are presented. They have been calculated using the semiclassical perturbation theory, for temperatures from 50,000 K to 2,000,000 K, and perturber densities from 1016 cm−3 up to 1024 cm−3. The data for e, p and He III are of particular interest for the analysis and modelling of atmospheres of hot and dense stars, as, e.g., white dwarfs, and for investigation of their spectra, and data for boron ions are used for analysis and modelling of laser-driven plasma in proton–boron fusion research.
Patient-Reported Outcome Measures in Patients with and without Non-Expandable Lung Secondary to Malignant Pleural Effusion—A Single-Centre Observational Study
Authors: Petersen Jesper Koefod , Fjaellegaard Katrine , Rasmussen Daniel Bech , Alstrup Gitte , Høegholm Asbjørn , Sidhu Jatinder Sing , Bhatnagar Rahul , Clementsen Paul Frost , Laursen Christian B. , Bodtger Uffe
Published: June 2024

Background: Malignant pleural effusion (MPE) affects up to 15% of patients with malignancy, and the prevalence is increasing. Non-expandable lung (NEL) complicates MPE in up to 30% of cases. However, it is not known if patients with malignant pleural effusion and NEL are more symptomatic in activities of daily living compared to patients with MPE with expandable lung. Methods: This was an observational study on consecutively recruited patients with MPE from our pleural clinic. Before thoracentesis, patients completed patient-reported outcomes on cancer symptoms (ESAS), health-related quality of life (5Q-5D-5L), and dyspnoea scores. Following thoracentesis, patients scored dyspnoea relief and symptoms during thoracentesis. Data on focused lung ultrasound and pleural effusion biochemistry were collected. The non-expandable lung diagnosis was made by pleural experts based on radiological and clinical information. Results: We recruited 43 patients, including 12 with NEL (28%). The NEL cohort resembled those from previous studies concerning ultrasonography, pleural fluid biochemistry, and fewer cases with high volume thoracentesis. Patients with and without NEL were comparable concerning baseline demography. The 5Q-5D-5L utility scores were 0.836 (0.691–0.906) and 0.806 (0.409–0.866), respectively, for patients with and without NEL. We observed no between-group differences in symptom burden or health-related quality of life. Conclusion: While the presence of NEL affects the clinical management of recurrent MPE, the presence of NEL seems not to affect patients’ overall symptom burden in patients with MPE.
Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening
Authors: Apostolopoulos Ioannis D. , Papathanasiou Nikolaos D. , Apostolopoulos Dimitris J. , Papandrianos Nikolaos , Papageorgiou Elpiniki I.
Published: June 2024

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules’ (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier. Two medical experts provided their malignancy likelihood scores, taking into account the patient’s clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29–95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings.
The COVID-19 Experience in Adolescents: Emotional and Behavioral Recall at the End of the Pandemic
Authors: Zaccagni Luciana , De Luca Federica , Rinaldo Natascia , Mazzoni Gianni , Mandini Simona , Gualdi-Russo Emanuela
Published: June 2024

The COVID-19 pandemic and the resulting lockdown countermeasure may have significantly affected adolescents’ physical and mental health. This study aims to assess adolescents’ recollections of this period, also analyzing their current weight status along with factors they traced back to the epidemic phase and their current sports practice. A survey among 233 Italian adolescents aged 12.4 ± 0.9 years was conducted in October 2023. To achieve the research objectives, a new questionnaire was developed: the COVID-19 AdolesceNt/chilDren Lockdown Experience questionnaire (CANDLE). The new questionnaire was employed to gather data on the adolescents’ recollections of the lockdown situation they experienced. The stature and weight of participants were measured directly. The results indicated that middle schoolers remember both positive and negative experiences of the lockdown: the change perceived as the most positive was spending more time with family, while social detachment from peers represents the most negative aspect. According to multivariate regression analysis, certain behaviors they assumed during the lockdown, such as comfort food consumption in boys and sleeping disturbances in girls, in addition to their current sports practice, affected their actual Body Mass Index. This study supports the evidence that changes caused by the COVID-19 lockdown affected adolescents’ physical and mental health, albeit with sex differences.
Management of Calcified Canals with a New Type of Endodontic Static Guide: A Case Report
Authors: Fornara Roberto , Pisano Massimo , Salvati Giovanni , Malvicini Giulia , Iandolo Alfredo , Gaeta Carlo
Published: June 2024

(1) Background: Pulp canal obliteration (PCO) is a common condition characterized by an abnormal dentinal apposition within the canal wall, which often rises as a consequence of dental trauma. In recent years, “guided endodontics” has offered a reliable and safer procedure for endodontic access in case of PCO. The present case report aims to introduce a new endodontic guided system with a sleeveless, open-frame titanium guide designed to reduce costs compared to traditional templates. (2) Methods: The patient is a 19-year-old female who was referred to a private clinic to treat a calcified and symptomatic tooth (3.3) with apical periodontitis. Following the first unsuccessful treatment attempt using the operating microscope, a three-dimensional cone beam computed tomography (CBCT) examination and an optical impression were performed in agreement with the patient. The Digital Imaging and Communications in Medicine (DICOM) and Standard Tessellation Language (STL) files were sent to the lab for the template packaging, and the patient was scheduled for a second appointment. The endodontic guide was accurately positioned, and shaping and filling were concluded with success. The canal was sealed with the single-cone technique and bioceramic sealer. (3) Results: The patient reported no significant post-operative symptoms. Notably, the treatment achieved total symptom resolution, as evidenced by radiographic assessments conducted 3 and 24 months post-treatment, confirming the procedure’s success. (4) Conclusions: This innovative sleeveless, open-frame static titanium guide presents a promising advancement in guided endodontics for PCO treatment. The advantages include preserving healthy dental tissue, reduced chairside operating time, and cost savings compared to conventional templates. This approach holds promise for enhancing the quality and efficiency of endodontic procedures in cases of PCO. While the static guide holds promise, larger prospective studies are necessary to validate its efficacy, safety, and broader applicability in routine endodontic procedures.
Concordance Analysis of Lower Third Molar Surgery Classifications: A Comparative Study
Authors: Barone Selene , Bennardo Francesco , Salviati Marianna , Cosentino Vincenzo , Finamore Riccardo , Greco Vincenzo , Madonna Antonio , Procopio Anna , Antonelli Alessandro , Giudice Amerigo
Published: June 2024

The high frequency and complexity of mandibular third molar (M3M) surgery have led several authors to the development of classification systems for better evaluation and management in oral surgery. This study compared the classifications of Juodzabalys and Daugela et al. (JD), Sammartino et al., Chang et al., Jhamb et al., Maglione et al., and Nemsi et al. to understand the concordance between the scores of M3M surgery. Two types of analysis were conducted: the relationship between the M3M and the inferior alveolar nerve (IAN), and the overall difficulty score based on the tooth’s angulation and its spatial position with the adjacent structure. The analysis of the classifications on the relationship between M3M and IAN resulted in a concordance of 26.1%. In the pairwise comparisons, the classifications of Nemsi et al. and Jhamb et al. showed the highest concordance of 59.5%. Analyzing the total scores difficulty, the JD et al., Chang et al., and Sammartino et al. classifications demonstrated a concordance level of 25.5%. A pairwise assessment revealed a higher concordance degree between the classifications of Sammartino et al. and Chang et al. (57.4%). The results highlight the limits in establishing a comprehensive and objective classification for the surgical difficulty of M3M, possibly attributed to variations in the methodology for computing total scores. An objective, automated, and non-operator-dependent classification method for assessing the surgical difficulty of M3M is still needed.
Correlations of Cross-Entropy Loss in Machine Learning
Authors: Connor Richard , Dearle Alan , Claydon Ben , Vadicamo Lucia
Published: June 2024

Cross-entropy loss is crucial in training many deep neural networks. In this context, we show a number of novel and strong correlations among various related divergence functions. In particular, we demonstrate that, in some circumstances, (a) cross-entropy is almost perfectly correlated with the little-known triangular divergence, and (b) cross-entropy is strongly correlated with the Euclidean distance over the logits from which the softmax is derived. The consequences of these observations are as follows. First, triangular divergence may be used as a cheaper alternative to cross-entropy. Second, logits can be used as features in a Euclidean space which is strongly synergistic with the classification process. This justifies the use of Euclidean distance over logits as a measure of similarity, in cases where the network is trained using softmax and cross-entropy. We establish these correlations via empirical observation, supported by a mathematical explanation encompassing a number of strongly related divergence functions.
Under the Radar: A Survey of Students’ Experiences of Discrimination in the German University Context
Authors: Führer Amand , Wagner Karoline , Reinhardt Zoë , Wienke Andreas
Published: June 2024

Universities are commonly imagined as “enlightened institutions” that leave no room for discrimination. While studies from other countries and some studies from German universities cast doubt on this notion, the extent and characteristics of university students’ experiences of discrimination in Germany are not yet sufficiently researched. Therefore, the aim of this study was to assess university students’ experiences of discrimination in a cross-sectional online survey. A total of 890 students completed the questionnaire. Of these, 45% reported that they had witnessed discrimination in the university, while 28% reported first-hand experiences. For those who had experienced discrimination themselves, it occurred repeatedly (68%) or regularly (15.5%). The most common grounds for discrimination were sex or gender (33% of all reported grounds for discrimination), non-German origin (12.5%) and having a chronic illness (7%). University lecturers were mentioned most often (by 80% of those who reported discrimination) as the perpetrators of discrimination. Taking these findings into consideration, universities should revisit their policies and structures that protect students against discrimination and support them in the case of harassment. This could entail obligatory training for university employees but should also consider that the university’s hierarchical structure facilitates abuses of power and can therefore be considered a variable in its own right.
On the Construct of Subjective Risk Intelligence and Its Relationships with Personality, Emotional Intelligence and Coping Strategies: A Comparison between Adolescents and Adults
Authors: Guarnera Maria , Zarbo Rita , Buccheri Stefania Lucia , Magnano Paola
Published: June 2024

The complexity of today’s scenario has made it necessary to investigate the need for individuals to make choices that entail increasing exposure to risk and uncertainty. Among the individual resources that could help people to cope with situations of uncertainty, the new construct of subjective risk intelligence (SRI), known as a person’s ability to effectively weigh the pros and cons of a decision in situations where not all the outcomes are foreseen, would seem to play a prominent role. Considering that personality and coping strategies have been shown to be significantly related in previous research, the present study investigates the relationships between subjective risk intelligence, emotional intelligence, personality traits and coping strategies in both adults and adolescents. This cross-sectional study was conducted on 1390 Italian people, divided into two subsamples of 641 adolescents and 749 adults. The results showed that SRI mediated the roles that personality traits and emotional intelligence have in coping strategies differently in the adult sample, in which the mediating role of SRI was found for avoidance coping, and in the adolescent sample, in which SRI influenced all of the antecedents analysed in the study for almost all of the identified coping strategies. In light of these findings, subjective risk intelligence could be activated to deal with uncertain and risky situations, influencing the choice of effective or ineffective strategies in both adults and adolescents.
Academic Burnout, Personality, and Academic Variables in University Students
Authors: Cuevas-Caravaca Elena , Sánchez-Romero Elisa Isabel , Antón-Ruiz Josefa A.
Published: June 2024

This study examines academic burnout syndrome and its relation to personal and academic variables among university students in nursing and early childhood education programs in Spain. A total of 606 university students (primary education: 49.7%; nursing: 49.7%) of both sexes (71.5% female) with an average age of 20.68 years (SD = 1.65) participated. An ex post facto retrospective single-group design was planned. The instruments used were the Maslach Burnout Inventory-Student Survey (MBI-SS) and the NEO Five-Factor Inventory (NEO-FFI). Nursing students, who reported more study hours, less sleep, and lower grades, had higher academic burnout scores. Linear regression models were proposed to analyze the relationship between academic burnout, personality, and sociodemographic variables. Nursing students scored higher in emotional exhaustion and lower in cynicism, and they scored higher in neuroticism and openness. Furthermore, 16.1% of the variance in academic burnout was explained by personality variables as well as the degree studied, course year, and study hours. These findings suggest the importance of considering both academic and personality variables in understanding academic burnout in university students.
Graph-Based Modeling and Optimization of WPT Systems for EVs
Authors: Hansen Matthew J. , Droge Greg , Kamineni Abhilash
Published: June 2024

A model of a system of wireless power transfer (WPT) pads is developed, where each WPT pad is modeled as a node and the coupling between pads is modeled as graph edges. This modeling approach is generalized to admit primary, secondary, and booster coils, where power can flow among the pads and a pad can fill multiple roles. An excitation in one pad induces voltage and current in all neighboring pads, causing each pad to act as both a booster coil and either a transmitter or a receiver. Power flow through the entire system can be modeled with the graph structure; the power flow can then be optimized by alternating the phases of the WPT excitations to maximize power transfer. An example is shown where exploiting the graph-based WPT system modeling increases total energy transfer by 25% compared to another method. This increase occurs without altering the geometry of the pads or the magnitude of the pad excitations.
A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration
Authors: Aly Hamed H.
Published: June 2024

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models.
Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks
Authors: Brayshaw George , Ward-Cherrier Benjamin , Pearson Martin J.
Published: June 2024

The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%).
Wireless Power Transfer System Model Reduction with Split Frequency Matching
Authors: Wang Ke , Wu Qingyu , Peng Jing , Li Hongchang
Published: June 2024

Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise mathematical formula. However, the model relies on the assumption of loose coupling and loses accuracy when the coupling becomes stronger. In this paper, a model reduction method with split frequency matching is proposed to improve model accuracy under relatively strong coupling conditions, which is suitable for most short-distance WPT applications, such as wireless electrical vehicle charging. Split frequency matching is achieved through a pair of conjugate equivalent mutual inductances, which are derived from the asymmetry characteristics of the full-order dynamic phasor model in the positive and negative frequency domains. The proposed model retains the advantages of the existing model while significantly improving the accuracy under strong coupling conditions. Its characteristics are verified by comparing the experimental results and model predictions under both large step changes and small-signal perturbations.
Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images
Authors: Altamimi Amal , Ben Youssef Belgacem
Published: June 2024

In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized.
The Electromagnetic Shielding Properties of Biodegradable Carbon Nanotube–Polymer Composites
Authors: Pietrzak Łukasz , Stano Ernest , Szymański Łukasz
Published: June 2024

In this article, the electromagnetic shielding properties of carbon nanotube–polymer nanocomposites are presented. The composite fabrication technique is spray-drying, the usage of which leads to a uniform dispersion of carbon nanotubes (CNTs) in a polymer matrix. Obtaining good filler dispersion is necessary to form a continuous, electrically conductive network of CNTs inside the polymer matrix. In the described nanocomposites, the network of conductive filler particles acts as an electromagnetic radiation barrier. For this reason, developing a highly effective fabrication method is very important. Also, the method should be simple enough to be easily adopted in an industrial environment. The authors shows in this text that both goals mentioned are achieved. The obtained nanocomposite material not only has electrostatic shielding capabilities but comprises electromagnetic shielding properties, which fulfills the main goal of the presented work. It is also worth mentioning that the developed manufacturing method allows for the usage of different fillers and polymers and thus the fabrication of materials capable of meeting a wide range of requirements.
Fast and Lightweight Vision-Language Model for Adversarial Traffic Sign Detection
Authors: Mumcu Furkan , Yilmaz Yasin
Published: June 2024

Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have proven to be vulnerable. Despite the increasing research on adversarial ML attacks against road sign recognition models, there is little to no focus on defending against these attacks. In this paper, we propose the first defense method specifically designed for autonomous vehicles to detect adversarial ML attacks targeting road sign recognition models, which is called ViLAS (Vision-Language Model for Adversarial Traffic Sign Detection). The proposed defense method is based on a custom, fast, lightweight, and salable vision-language model (VLM) and is compatible with any existing traffic sign recognition system. Thanks to the orthogonal information coming from the class label text data through the language model, ViLAS leverages image context in addition to visual data for highly effective attack detection performance. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. When tested against four state-of-the-art attacks targeting four popular action recognition models, our proposed detector achieves an average AUC of 0.94. This result achieves a 25.3% improvement over a state-of-the-art defense method proposed for generic image attack detection, which attains an average AUC of 0.75. We also show that our custom VLM is more suitable for an autonomous vehicle compared to the popular off-the-shelf VLM and CLIP in terms of speed (4.4 vs. 9.3 milliseconds), space complexity (0.36 vs. 1.6 GB), and performance (0.94 vs. 0.43 average AUC).
MST-DGCN: A Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network for Electroencephalogram Recognition of Motor Imagery
Authors: Chen Yuanling , Liu Peisen , Li Duan
Published: June 2024

The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems.
A Privacy-Preserving Friend Matching Scheme Based on Attribute Encryption in Mobile Social Networks
Authors: Yu Li , Nan Xingxing , Niu Shufen
Published: June 2024

In mobile social networks, users can easily communicate with others through smart devices. Therefore, the protection of user privacy in social networks is becoming a significant subject. To solve this problem, this paper proposes a fine-grained data access control scheme that uses attributes to match friends. In our scheme, the friend-making parties generate friend preference and self-description lists, respectively, realizing attribute hiding by converting friendship preference into ciphertext access control policies and self-description into attribute keys. The social platform matches user profiles to quickly eliminate unmatched users and avoids invalid decryption. In order to reduce the computational burden and communication cost of mobile devices, we adopt an algorithm mechanism for outsourcing decryption. When the user meets the matching conditions, the algorithm outsources the bilinear pair operation with large computation to the friend server. After that, the user finally decrypts the ciphertext. Security analysis shows that our scheme is safe and effective. In addition, performance evaluation shows that the proposed scheme is efficient and practical.