bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell and Module Models
Authors: Memon Zulfiqar Ali , Akbari Mohammad Amin , Zare Mohsen
Published: September 2023

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.
Some Model Theoretic Properties for Pavelka-Style Gödel Logic, RGL* and Gödel Logic with Δ
Authors: Roshandel Tavana Nazanin
Published: September 2023

Pavelka-style (rational) Gödel logic is an extension of Gödel logic which is denoted by RGL*. In this article, due to the approximate Craig interpolation property for RGL*, the Robinson theorem and approximate Beth theorem are presented and proved. Then, the omitting types theorem for this logic is expressed and proved. At the end, as a reduction, the omitting types theorem for standard Gödel logic with Δ is studied.
Repeated Transcranial Photobiomodulation with Light-Emitting Diodes Improves Psychomotor Vigilance and EEG Networks of the Human Brain
Authors: Chaudhari Akhil , Wang Xinlong , Wu Anqi , Liu Hanli
Published: September 2023

Transcranial photobiomodulation (tPBM) has been suggested as a non-invasive neuromodulation tool. The repetitive administration of light-emitting diode (LED)-based tPBM for several weeks significantly improves human cognition. To understand the electrophysiological effects of LED-tPBM on the human brain, we investigated alterations by repeated tPBM in vigilance performance and brain networks using electroencephalography (EEG) in healthy participants. Active and sham LED-based tPBM were administered to the right forehead of young participants twice a week for four weeks. The participants performed a psychomotor vigilance task (PVT) during each tPBM/sham experiment. A 64-electrode EEG system recorded electrophysiological signals from each participant during the first and last visits in a 4-week study. Topographical maps of the EEG power enhanced by tPBM were statistically compared for the repeated tPBM effect. A new data processing framework combining the group’s singular value decomposition (gSVD) with eLORETA was implemented to identify EEG brain networks. The reaction time of the PVT in the tPBM-treated group was significantly improved over four weeks compared to that in the sham group. We observed acute increases in EEG delta and alpha powers during a 10 min LED-tPBM while the participants performed the PVT task. We also found that the theta, beta, and gamma EEG powers significantly increased overall after four weeks of LED-tPBM. Combining gSVD with eLORETA enabled us to identify EEG brain networks and the corresponding network power changes by repeated 4-week tPBM. This study clearly demonstrated that a 4-week prefrontal LED-tPBM can neuromodulate several key EEG networks, implying a possible causal effect between modulated brain networks and improved psychomotor vigilance outcomes.
MRI-Based Deep Learning Method for Classification of IDH Mutation Status
Authors: Bangalore Yogananda Chandan Ganesh , Wagner Benjamin C. , Truong Nghi C. D. , Holcomb James M. , Reddy Divya D. , Saadat Niloufar , Hatanpaa Kimmo J. , Patel Toral R. , Fei Baowei , Lee Matthew D. , Jain Rajan , Bruce Richard J. , Pinho Marco C. , Madhuranthakam Ananth J. , Maldjian Joseph A.
Published: September 2023

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin–Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.
Association between Combination Antiretroviral Therapy and Telomere Length in People Living with Human Immunodeficiency Virus
Authors: Bukic Ena , Milasin Jelena , Toljic Bosko , Jadzic Jelena , Jevtovic Djordje , Obradovic Bozana , Dragovic Gordana
Published: September 2023

Long-term exposure to combination antiretroviral therapy (cART) may be associated with accelerated ageing. Telomere length is considered to be reliable aging biomarker. The aim of this study was to compare patients’ relative telomere length (RTL) between and within different cART classes and to estimate the impact of certain HIV-related variables on RTL. The study was conducted in 176 HIV-infected male patients receiving cART, with ≤50 copies HIV RNA/mL plasma. RTL was determined from mononuclear cells by quantitative polymerase chain reaction. Standard statistical tests and unsupervised machine learning were performed. The mean RTL was 2.50 ± 1.87. There was no difference (p = 0.761) in RTL between therapeutic groups: two nucleoside reverse transcriptase inhibitors as the backbone treatment, combined with either integrase inhibitor, protease inhibitor, or non-nucleoside reverse transcriptase inhibitor (NNRTI). Machine learning results suggested duration of HIV infection, CD4+ T-cell count, and cART, including NNRTI, as potentially significant variables impacting RTL. Kendall’s correlation test excluded duration of HIV infection (p = 0.220) and CD4+ T-cell count (p = 0.536) as significant. The Mann–Whitney test confirmed that cART containing NNRTI impacted RTL (p = 0.018). This was the first study to show that patients using efavirenz within cART had significantly shorter telomeres than patients using nevirapine.
Daily Rhythmicity of Muscle-Related and Rhythm Genes Expression in Mackerel Tuna (Euthynnus affinis)
Authors: Wang Wenwen , Dai Shiming , Liu Longlong , Fu Zhengyi , Yang Rui , Yu Gang , Ma Zhenhua , Zong Humin
Published: September 2023

The aim of this study was to investigate the circadian rhythm of muscle-related gene expression in mackerel tuna under different weather conditions. The experiment was carried out under two weather conditions at four sampling times (6:00, 12:00, 18:00, and 24:00) to determine the expression of growth, function, and rhythm genes: white muscle rhythm genes were rhythmic on sunny and cloudy days, except for PER3 and RORA; all functional genes had daily rhythmicity. Red muscle had daily rhythmicity on both sunny and cloudy days; functional genes had daily rhythmicity except for MBNL. The expression levels of the rhythm gene PER1 were determined to be significantly different by independent t-test samples in white muscle at 6:00, 12:00, 18:00, and 24:00 under different weather conditions; the expression levels of the functional genes MBNL and MSTN were both significantly different. In the red muscle, the expression of the rhythm genes PER3, REVERBA, and BMAL1 was determined by independent t-test samples at 6:00, 12:00, 18:00, and 24:00 on cloudy and sunny days; the functional gene MBNL was significantly different. The present study showed that mackerel tuna muscle rhythm genes and functional genes varied significantly in expression levels depending on weather, time of day, and light intensity and that the expression levels of myogenic genes were closely related to clock gene expression. The fish were also able to adapt to changes in light intensity in different weather conditions through positive physiological regulation.
Exploring the Potential of Sulfur Moieties in Compounds Inhibiting Steroidogenesis
Authors: Wróbel Tomasz M. , Sharma Katyayani , Mannella Iole , Oliaro-Bosso Simonetta , Nieckarz Patrycja , Du Toit Therina , Voegel Clarissa Daniela , Rojas Velazquez Maria Natalia , Yakubu Jibira , Matveeva Anna , Therkelsen Søren , Jørgensen Flemming Steen , Pandey Amit V. , Pippione Agnese C. , Lolli Marco L. , Boschi Donatella , Björkling Fredrik
Published: September 2023

This study reports on the synthesis and evaluation of novel compounds replacing the nitrogen-containing heterocyclic ring on the chemical backbone structure of cytochrome P450 17α-hydroxylase/12,20-lyase (CYP17A1) inhibitors with a phenyl bearing a sulfur-based substituent. Initial screening revealed compounds with marked inhibition of CYP17A1 activity. The selectivity of compounds was thereafter determined against cytochrome P450 21-hydroxylase, cytochrome P450 3A4, and cytochrome P450 oxidoreductase. Additionally, the compounds showed weak inhibitory activity against aldo-keto reductase 1C3 (AKR1C3). The compounds’ impact on steroid hormone levels was also assessed, with some notable modulatory effects observed. This work paves the way for developing more potent dual inhibitors specifically targeting CYP17A1 and AKR1C3.
The Role of Gli1+ Mesenchymal Stem Cells in Osteogenesis of Craniofacial Bone
Authors: Wu Laidi , Liu Zhixin , Xiao Li , Ai Mi , Cao Yingguang , Mao Jing , Song Ke
Published: September 2023

Glioma-associated oncogene homolog 1 (Gli1) is a transcriptional activator of hedgehog (Hh) signaling that regulates target gene expression and several cellular biological processes. Cell lineage tracing techniques have highlighted Gli1 as an ideal marker for mesenchymal stem cells (MSCs) in vivo. Gli1+ MSCs are critical for the osteogenesis of the craniofacial bone; however, the regulatory mechanism by which Gli1+ MSCs mediate the bone development and tissue regeneration of craniofacial bone has not been systematically outlined. This review comprehensively elucidates the specific roles of Gli1+ MSCs in craniofacial bone osteogenesis. In addition to governing craniofacial bone development, Gli1+ MSCs are associated with the tissue repair of craniofacial bone under pathological conditions. Gli1+ MSCs promote intramembranous and endochondral ossification of the craniofacial bones, and assist the osteogenesis of the craniofacial bone by improving angiopoiesis. This review summarizes the novel role of Gli1+ MSCs in bone development and tissue repair in craniofacial bones, which offers new insights into bone regeneration therapy.
Integrated Metabolomic and Transcriptomic Analysis of the Quinoa Seedling Response to High Relative Humidity Stress
Authors: Li Xinyi , Zhang Ping , Liu Jia , Wang Hongxin , Liu Junna , Li Hanxue , Xie Heng , Wang Qianchao , Li Li , Zhang Shan , Huang Liubin , Liu Chenghong , Qin Peng
Published: September 2023

Quinoa is of great interest because it is cold- and drought-resistant; however, little research has been performed on quinoa under high relative humidity (RH) stress. In this study, quinoa seedlings of a highly HR-resistant variety (“Dianli-439”) and a sensitive variety (“Dianli-969”) were subjected to morphological and physiological measurements and metabolome and transcriptome analyses to investigate their response to high RH stress. In total, 1060 metabolites were detected, and lipids and flavonoids were the most abundant, with 173 and 167 metabolites, respectively. In total, 13,095 differentially expressed genes were identified, and the results showed that abscisic acid, auxin, and jasmonic-acid-related genes involved in plant hormone signaling may be involved in the response of quinoa seedlings to high RH stress. The analysis of the transcription factors revealed that the AP2/ERF family may also play an important role in the response to high RH stress. We identified the possible regulatory mechanisms of the hormone signaling pathways under high RH stress. Our findings can provide a basis for the selection and identification of highly resistant quinoa varieties and the screening of the metabolite-synthesis- and gene-regulation-related mechanisms in quinoa in response to RH stress.
Thromboelastometry-Guided Individualized Fibrinolytic Treatment for COVID-19-Associated Severe Coagulopathy Complicated by Portal Vein Thrombosis: A Case Report
Authors: Forgács Robin , Bokrétás Gergely Péter , Monori Zoltán , Molnár Zsolt , Ruszkai Zoltán
Published: September 2023

COVID-19-associated coagulopathy (CAC), mainly characterized by hypercoagulability leading to micro- and macrovascular thrombotic events due to the fibrinolysis shutdown phenomenon, is a life-threatening complication of severe SARS-CoV-2 infection. However, optimal criteria to assess patients with the highest risk for progression of severe CAC are still unclear. Bedside point-of-care viscoelastic testing (VET) appears to be a promising tool to recognize CAC, to support the appropriate therapeutic decisions, and to monitor the efficacy of the treatment. The ClotPro VET has the potential to reveal fibrinolysis resistance indicated by a clot lysis time (LT) > 300 s on the TPA-test. We present a case of severe SARS-CoV-2 infection complicated by CAC-resulting portal vein thrombosis (PVT) and subsequent liver failure despite therapeutic anticoagulation. Since fibrinolysis shutdown (LT > 755 s) caused PVT, we performed a targeted systemic fibrinolytic therapy. We monitored the efficacy of the treatment with repeated TPA assays every three hours, while the dose of recombinant plasminogen activator (rtPA) was adjusted until fibrinolysis shutdown completely resolved and portal vein patency was confirmed by an ultrasound examination. Our case report highlights the importance of VET-guided personalized therapeutic approach during the care of severely ill COVID-19 patients, in order to appropriately treat CAC.
The Effect of Three Desensitizing Toothpastes on Dentinal Tubules Occlusion and on Dentin Hardness
Authors: Bologa Emilia , Stoleriu Simona , Nica Irina , Tărăboanță Ionuț , Georgescu Andrei , Matei Ruxandra Ilinca , Andrian Sorin
Published: September 2023

There are two main methods used for dentin hypersensitivity (DH) treatment: dentinal tubule occlusion and blockage of nerve activity. Dentifrices are the most common vehicles for active ingredients used for DH treatment. The aim of this study was to evaluate the efficacy of three toothpastes on dentinal tubule occlusion, mineral acquisition, and dentin hardness. Forty human dentin disks were submerged in 40% citric acid for 30 s and then exposed to tooth brushing for 2 min twice a day for 14 days using three toothpastes: Dontodent Sensitive (group 1), Dr. Wolff’s Biorepair (group 2), and Sensodyne Repair and Protect (group 3). In the control group (group 4), the samples were brushed with water. All of the samples were evaluated using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), and Vickers dentin hardness determination. On SEM images, the degree of dentinal tubule occlusion was assessed using a five-grade scale. The mean score values in groups 1–4 were 3.60 ± 0.69, 2.20 ± 0.91, 2.30 ± 1.16, and 5.00 ± 0.00, significantly higher in study groups when compared to the control group (Kruskal Wallis test p < 0.05). EDX evaluation showed significantly higher calcium and phosphorus concentrations in groups 1 and 3 when compared to control group d. The mean values of Vickers dentin hardness numbers in groups 1–4 were 243.03 ± 10.014, 327.38 ± 56.65, 260.29 ± 37.69, and 225.83 ± 29.93, respectively. No statistically significant results were obtained when comparing the hardness mean values in groups (Kruskal-Wallis statistical test, p = 0.372 > 0.05). All three toothpastes tested demonstrated significant occlusion of dentinal tubules. Dontodent Sensitive and Sensodyne Repair and Protect toothpastes enhanced the calcium and phosphorus content of the dentin surface. None of the toothpastes increased dentin hardness as a result of mineral acquisition.
Post-Earthquake Housing Reconstruction Management and Implementation in Rural Areas: Review and Lessons from Dujiangyan, Wenchuan Earthquake
Authors: Li Qiushan , Wumaier Kabilijiang , Osamu Koide
Published: September 2023

Housing reconstruction plays a crucial role in renovating disaster-hit areas. Rural areas are considerably different from urban areas in terms of geographic environment, building size, residential culture, and social organization. Therefore, post-disaster recovery and reconstruction models for urban areas cannot be applied directly to disaster-hit rural areas. This study, based on the experience of rural housing reconstruction after the Wenchuan earthquake, identified key strategic issues in housing reconstruction that must be addressed to achieve the goal of “building back better” in the future. By taking the experience of Dujiangyan as our reference, the study found that the following strategies are important for successful housing reconstruction in rural areas: (1) actively involve disaster victims through a participatory institutional design; (2) coordinate the interests of governments, markets, and disaster victims and the functions of living, production, and ecology through a classified housing reconstruction system; and (3) activate the quota for rural collective construction land and create a new source of funding for housing reconstruction through the market circulation of urban-rural land. Additionally, in the context of urban-rural integration, changes in land use can lead to rural spatial reconstruction and sustainable regional development, providing a reference for formulating optimal post-disaster reconstruction strategies.
Transformation of Furfural-Acetone Condensation Adduct over Mo/SBA-15 Catalysts under Atmospheric Pressure
Authors: Teles Camila A. , Ciotonea Carmen , Royer Sébastien , Richard Frédéric
Published: September 2023

The transformation of the furfural-acetone condensation adduct (FAc) was investigated under atmospheric pressure at 300 °C over a series of molybdenum supported on SBA-15 doped with different acid/oxophilic species (Zr, Fe and Al). The FAc underwent several reactions including mainly hydrogenation, deoxygenation and cyclization. The order of activity was Mo/Zr-SBA > Mo/Al-SBA > Mo/Fe-SBA ≅ Mo/SBA, demonstrating the positive effect of dopants. Likewise, the synergy between molybdenum with the oxophilic Zr4+ species significantly increased the selectivity toward the partially deoxygenated products.
Efficient Synthesis of Furfural from Corncob by a Novel Biochar-Based Heterogeneous Chemocatalyst in Choline Chloride: Maleic Acid–Water
Authors: Yang Linsong , Li Yucheng , Wu Yuqi , He Yucai , Ma Cuiluan
Published: September 2023

The use of plentiful and renewable feedstock for producing chemicals is fundamental for the development of sustainable chemical processes. Using fish scale as a biobased carrier, a novel biochar SO42−/SnO2-FFS heterogeneous chemocatalyst was prepared to catalyze furfural production from xylose-rich corncob-hydrolysates obtained from acid hydrolysis of corncob in a deep eutectic solvent (DES)–water system. By characterizing the physical as well as chemical properties of SO42−/SnO2-FFS by NH3-TPD, FT-IR, XPS, XRD, and SEM, it was shown that the chemocatalyst had Lewis/Brönsted acid centers, and its surface roughness could be well expanded to contact substrates. The corncob was initially hydrolyzed at 140 °C to obtain xylose-rich hydrolysate. Subsequently, SO42−/SnO2-FFS (3.6 wt.%) was used to catalyze the corn cob hydrolysate containing D-xylose (20.0 g/L) at a reaction temperature of 170 °C for 15 min. Additionally, ZnCl2 (20.0 g/L) was added. Ultimately, furfural (93.8 mM, 70.5% yield) was produced in the deep eutectic solvent ChCl:maleic acid–water (DESMLA–water = 10:90, v/v). A synergistic catalytic mechanism for transforming xylose-rich corncob-hydrolysate into furfural and byproducts were proposed using SO42−/SnO2-FFS as a chemocatalyst in DESMLA–water containing ZnCl2. Consequently, the efficient use of biochar SO42−/SnO2-FFS chemocatalysts for the sustainable synthesis of biobased furan compounds from biomass holds great promise in the future.
Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients
Authors: Corzo-Gómez Josselin , Guzmán-Aquino Susana , Vargas-De-León Cruz , Megchún-Hernández Mauricio , Briones-Aranda Alfredo
Published: September 2023

The current contribution aimed to evaluate the capacity of the naive Bayes classifier to predict the progression of dengue fever to severe infection in children based on a defined set of clinical conditions and laboratory parameters. This case-control study was conducted by reviewing patient files in two public hospitals in an endemic area in Mexico. All 99 qualifying files showed a confirmed diagnosis of dengue. The 32 cases consisted of patients who entered the intensive care unit, while the 67 control patients did not require intensive care. The naive Bayes classifier could identify factors predictive of severe dengue, evidenced by 78% sensitivity, 91% specificity, a positive predictive value of 8.7, a negative predictive value of 0.24, and a global yield of 0.69. The factors that exhibited the greatest predictive capacity in the model were seven clinical conditions (tachycardia, respiratory failure, cold hands and feet, capillary leak leading to the escape of blood plasma, dyspnea, and alterations in consciousness) and three laboratory parameters (hypoalbuminemia, hypoproteinemia, and leukocytosis). Thus, the present model showed a predictive and adaptive capacity in a small pediatric population. It also identified attributes (i.e., hypoalbuminemia and hypoproteinemia) that may strengthen the WHO criteria for predicting progression to severe dengue.
Aluminum Chromium Nitride Coating on a Laser Shock Micro-Molded Surface of E690 High-Strength Steel and Its Antifriction Mechanism
Authors: Cao Yupeng , Bao Haidong , Shi Weidong , Wang Zhengang , Zhang Jinchao
Published: September 2023

Aiming its analysis at the poor hardness and wear-resistance of E690 high-strength steel, and the high hardness and good wear-resistance of AlCrN-coated, combined with the laser impact micro-modeling which can store oil lubrication, this paper carries out research on the synergistic wear reduction mechanism of laser impact micro-modeling AlCrN coated on the surface of E690 high-strength steel. Multi-arc ion plating technology is used to prepare the AlCrN coating on the laser-impact micro-modeling specimen; the micro-modeling AlCrN-coated specimen is subjected to a reciprocating friction test, and the hardness and residual stress of the coated surface are measured by equipment such as a residual stress meter and a microhardness tester. The microstructure and physical elements of the surface wear before and after the preparation of the coating are analyzed by scanning electron microscope (SEM), confocal three-dimensional morphometer and XRD diffractometer, respectively. The results show that the prepared AlCrN-coated materials were well-bonded to the substrate. Compared with the micro-molding-only specimens, the average friction coefficient and wear amount of the micro-molded AlCrN-coated specimens with different micro-molding densities and depths decreased compared with the micro-molded specimens; among them, the average friction coefficient of the specimens with a micro-molding density of 19.6% and a depth of 7.82 µm was 0.0936, which was the lowest. Additionally, the AlCrN coating enhances the stability of the friction process of the specimen and reduces the amount of wear of the specimen. Under the premise of ensuring the anti-wear and stability properties of the material, the best integrated friction performance was achieved at a micro-molding density of 19.6% and a depth of 24.72 µm. A synergistic wear reduction and lubrication model of micro-molding and AlCrN-coating was established.
Novel Deep Feature Fusion Framework for Multi-Scenario Violence Detection
Authors: Jebur Sabah Abdulazeez , Hussein Khalid A. , Hoomod Haider Kadhim , Alzubaidi Laith
Published: September 2023

Detecting violence in various scenarios is a difficult task that requires a high degree of generalisation. This includes fights in different environments such as schools, streets, and football stadiums. However, most current research on violence detection focuses on a single scenario, limiting its ability to generalise across multiple scenarios. To tackle this issue, this paper offers a new multi-scenario violence detection framework that operates in two environments: fighting in various locations and rugby stadiums. This framework has three main steps. Firstly, it uses transfer learning by employing three pre-trained models from the ImageNet dataset: Xception, Inception, and InceptionResNet. This approach enhances generalisation and prevents overfitting, as these models have already learned valuable features from a large and diverse dataset. Secondly, the framework combines features extracted from the three models through feature fusion, which improves feature representation and enhances performance. Lastly, the concatenation step combines the features of the first violence scenario with the second scenario to train a machine learning classifier, enabling the classifier to generalise across both scenarios. This concatenation framework is highly flexible, as it can incorporate multiple violence scenarios without requiring training from scratch with additional scenarios. The Fusion model, which incorporates feature fusion from multiple models, obtained an accuracy of 97.66% on the RLVS dataset and 92.89% on the Hockey dataset. The Concatenation model accomplished an accuracy of 97.64% on the RLVS and 92.41% on the Hockey datasets with just a single classifier. This is the first framework that allows for the classification of multiple violent scenarios within a single classifier. Furthermore, this framework is not limited to violence detection and can be adapted to different tasks.
Robust γ-TiAl Dual Microstructure Concept by Advanced Electron Beam Powder Bed Fusion Technology
Authors: Reith Marcel , Franke Martin , Körner Carolin
Published: September 2023

The dual microstructure concept for gamma titanium aluminides (γ-TiAl) processed via electron beam–powder bed fusion (PBF-EB) provides a huge potential for more efficient jet turbine engines. While the concept is feasible and the mechanical properties are promising, there are still some challenges. For an industrial application, the heat treatment window has to match the conditions in industrial furnaces. This study shows how the required heat treatment window can be achieved via advanced PBF-EB technology. Through using an electron beam with 150 kV acceleration voltage, the difference in aluminum between the designed aluminum-rich and aluminum-lean regions of the part is increased. Moreover, the aluminum content within each of these regions, respectively, is more homogenous compared to the 60 kV acceleration voltage. This combination provides a heat treatment window of 25 °C, enabling the industrial application of the dual microstructure concept for γ-TiAl.
Growth and Characterization of Organic 2-Chloro 5-Nitroaniline Crystal Using the Vertical Bridgman Technique
Authors: Karuppusamy Shanmugam , Sathishkumar Veerappampalayam Easwaramoorthy , Dinesh Babu Kanagaraj , Sakthivel Pandurengan
Published: September 2023

In this article, we discuss the preparation of organic 2-chloro-5 nitroaniline (2C5NA) crystals and their different kinds of physical, chemical, and mechanical properties. The vertical Bridgman approach was used to effectively produce the bulk organic 2C5NA crystal. To produce a good-quality bulk crystal, the shape, dimensions, and cone angle of the ampoule were optimized. Also, the temperature profile was set for the 2C5NA crystal. The growth atmosphere and the lowering rate were identified to obtain a homogeneous mixture of the compounds and initiate the nucleation process. Single-crystal X-ray diffraction (XRD), powder XRD, proton Fourier transform nuclear magnetic resonance (FT-NMR), and Fourier transform infrared investigations were used to confirm the crystal structure, molecular structure, and presence of functional groups in the formed crystal. The formed crystal has a monoclinic crystal structure with the space group P21/c, according to single-crystal XRD analysis. The thermal stability and kinetic parameters were examined using thermogravimetric analysis and differential thermal curves. From dielectric analysis, the electrical conductivity and dielectric behavior of 2C5NA were investigated with variations in frequency and temperature. The organic 2-chloro-5-nitroaniline crystal demonstrates that the indentation size effect is observed in the Vickers micro-hardness test, which was also carried out.
The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy
Authors: Popovic Dusan , Glisic Tijana , Milosavljevic Tomica , Panic Natasa , Marjanovic-Haljilji Marija , Mijac Dragana , Stojkovic Lalosevic Milica , Nestorov Jelena , Dragasevic Sanja , Savic Predrag , Filipovic Branka
Published: September 2023

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer’s ability to achieve human cognitive performance, which is accomplished through enabling computer “learning”. This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett’s esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.
Personalized Antiarrhythmic Therapy Using a Self-Managed Daily-ECG Device
Authors: Mattei Eugenio , Lino Stefano , Censi Federica , Calcagnini Giovanni , Calò Leonardo
Published: September 2023

A 50-year-old Caucasian man arrived at the emergency department presenting paucisymptomatic atrial fibrillation. Once discharged after the appropriate treatments, the patient continued to have paucisymptomatic episodes. For this reason, he was provided with the Cardionica device which made it possible to better investigate the type of arrhythmic episodes, in order to tailor his therapy and to finally restore a normal sinus rhythm in the patient.
Contributions to Image Transmission in Icing Conditions on Unmanned Aerial Vehicles
Authors: Rodríguez Marco José Enrique , Sánchez Rubio Manuel , Martínez Herráiz José Javier , González Armengod Rafael , Del Pino Juan Carlos Plaza
Published: September 2023

In terms of manned aircraft, pilots usually detect icing conditions by visual cues or by means of ice detector systems. If one of these cues is seen by the crew or systems detect icing conditions, they have to apply the evasive procedure as defined within the aircraft flight manual (AFM). However, as regards unmanned aircraft, there are not pilots on board and, consequently, nobody can act immediately when icing conditions occur. This article aims to propose new techniques of sending information to ground which make possible to know the aircraft performance correctly in icing conditions. For this goal, three contributions have been developed for the unmanned aircraft Milano. Since icing conditions are characterized quantitatively by the droplet size, the liquid water content, and the total air temperature, when these parameters are between certain limits ice formation on aircraft may occur. As a result of these contributions, in that moment, high-quality images of the wing leading edge, tail leading edge and meteorological probes will be captured and sent to ground making possible that remote pilots or artificial intelligent (AI) systems can follow the appropriate procedures, avoid encounters with severe icing conditions and perform real-time decision making. What is more, as information security is becoming an inseparable part of data communication, it is proposed how to embed relevant information within an image. Among the improvements included are image compression techniques and steganography methods.
20th Congress of the European Burns Association (EBA)
Authors: Depetris Nadia , De Jong Alette , Schiestl Clemens , Duteille Franck , Meirte Jill , Barret-Nerin Joan P. , Van Zuijlen Paul , Vuola Jyrki , Almeland Stian
Published: September 2023

Abstracts of the plenary and special interest sessions, workshops, and oral and poster presentations of the 20th EBA Congress in Nantes, France from 6–9 September 2023.
Collaboration and Shared Responsibility in Team Teaching: A Large-Scale Survey Study
Authors: Decuyper Aron , Tack Hanne , Vanblaere Bénédicte , Simons Mathea , Vanderlinde Ruben
Published: September 2023

The practice of team teaching—how teachers deliver team teaching in the classroom—substantially determines its effect. The collaboration between the teachers and the level of shared responsibility between them are two important dimensions of the team teaching practice. To date, no instrument exists to measure these dimensions. However, in view of empirical research within the context of team teaching, such an instrument is important. Therefore, the Collaboration and Shared Responsibility in Team Teaching (CSTT) scale is developed, making it possible to assess these two important dimensions. The CSTT scale was used in a large-scale cross-sectional survey study (n = 555). Next to a validation of the scale, this study provides empirical evidence on the differences between groups of teachers regarding (a) teaching experience, (b) education type, and (c) frequency of team teaching. Results show that teachers overall report high scores on both dimensions. Further, this study indicates that there are no significant differences between the groups based on (a) teaching experience and (b) education type for both collaboration and shared responsibility. There are, however, significant differences between groups in terms of the (c) frequency of team teaching.
Theory for Electrochemical Heat Sources and Exothermic Explosions: The Akbari–Ganji Method
Authors: Vanaja Ramalingam , Jeyabarathi Ponraj , Rajendran Lakshmanan , Lyons Michael Edward Gerard
Published: September 2023

A device that transforms chemical energy into electrical energy is an electrochemical cell. The reaction type inside the cell determines whether it is exothermic or endothermic. This paper discusses the mathematical modelling of exothermic explosions in a slab. This model is based on a nonlinear equation containing a nonlinear term related to Arrhenius, bimolecular, and sensitised laws of reaction kinetics. The absolute temperature can be derived by solving the nonlinear equation using the Akbari–Ganji technique. The mathematical model also numerically solved and simulated in the MATLAB® v2016b software. The new simple theoretical result is validated with previously identified analytical and numerical findings. The influence of the parameters of Frank-Kamenetskii number, activation energy and the numerical exponent on temperature is discussed. The Frank-Kamenetskii number is observed to drop as the temperature is found to decrease, while the activation energy parameter is shown to increase. The numerical exponent has little or no effect on the temperature. An extension of this model to cylinder and sphere geometry is also provided.
Identification and Analysis of Low-Frequency Oscillation in a Multi-Grid-Forming-VSC Grid-Connected System
Authors: Zhang Min , Fan Rui , Li Huipeng , Zhao Jun , Wang Tengxin , Chen Lin
Published: September 2023

The existing low-frequency oscillation analysis method of a multi-grid-forming-VSC (voltage source converter) is greatly affected by modeling accuracy, and its oscillation mode can only be determined by acquiring the control parameters of the system. Therefore, a method of identifying low-frequency oscillation characteristics of multi-VSC based on VMD (variational mode decomposition) and a Prony algorithm was proposed in this paper. The Prony algorithm is sensitive to noise, and its identification accuracy is greatly affected by noise. Thus, the VMD algorithm was utilized to denoise the measured data. Then, the Prony algorithm was applied to analyze the low-frequency oscillation of the measured data of single VSC and multi-VSC grid-connected systems, and its applicability to different grid-forming VSCs was verified. The error comparison results showed that the proposed low-frequency oscillation identification method had high accuracy. Furthermore, the influence of the number of parallel VSCs, grid strength and active output on the low-frequency oscillation of the system was investigated. Finally, the effectiveness of the proposed low-frequency oscillation method was verified by building a physical experimental platform.
Efficient Distributed Mapping-Based Computation for Convolutional Neural Networks in Multi-Core Embedded Parallel Environment
Authors: Jia Long , Li Gang , Lu Meili , Wei Xile , Yi Guosheng
Published: September 2023

Embedded systems are the best solution to achieve high-performance edge terminal computing tasks. With the rapid increase in the amount of data generated by edge devices, it is imperative to implement intelligent algorithms with large amounts of data and computation on embedded terminal systems. In this paper, a novel multi-core ARM-based embedded hardware platform with a three-dimensional mesh structure was first established to support the decentralized algorithms. To deploy deep convolutional neural networks (CNNs) in this embedded parallel environment, a distributed mapping mechanism was proposed to efficiently decentralize computation tasks in the form of a multi-branch assembly line. In addition, a dimensionality reduction initialization method was also utilized to successfully resolve the conflict between the storage requirement of computation tasks and the limited physical memories. LeNet-5 networks with different sizes were optimized and implemented in the embedded platform to verify the performance of our proposed strategies. The results showed that memory usage can be controlled within the usable range through dimensionality reduction. The down-sampling layer as the base point of the mapping for the inter-layer segmentation achieved the optimal operation in lateral dispersion with a reduction of around 10% in the running time compared with the other layers. Further, the computing speed for a network with an input size of 105 × 105 in the multi-core parallel environment is nearly 20 times faster than that in a single-core system. This paper provided a feasible strategy for edge deployments of artificial intelligent algorithms on multi-core embedded devices.
Phased Array Radar Resource Consumption Method Based on Phase-Switched Screen
Authors: Hao Guoqing , Feng Dejun , Wang Junjie , Zhou Zimeng , Wang Ling
Published: September 2023

The consumption of phased array radar (PAR) resources affects the accuracy of its multi-target searching and tracking abilities. The jamming methods aimed at PAR involve deceptive jamming and comprehensive jamming strategies, but have the disadvantages of high cost and complexity. Given this context, this paper proposes a PAR resource consumption method based on phase-switched screen (PSS), and derives a relationship between the targets and the evaluation indicators. By implementing periodic modulation of PSS, a controlled number of deceptive multiple false targets can be generated, thereby enticing the radar system to engage in activities such as searching and tracking targets. When the quantity of false targets is increased, the number of beam requests becomes five times higher than compared to the scenario without false targets. This has also led the margin of time resources to exceed 50%, successfully achieving the objective of consuming radar resources. Furthermore, this method offers straightforward manipulation, flexibility, and the potential to significantly consume radar resources. The efficacy of the proposed method is confirmed with the simulation outcomes.
GPU-Accelerated Interaction-Aware Motion Prediction
Authors: Hortelano Juan Luis , Trentin Vinicius , Artuñedo Antonio , Villagra Jorge
Published: September 2023

Before their massive deployment, autonomous vehicles need to prove in complex scenarios such that they can reach human driving proficiency while guaranteeing higher safety levels. One of the most important human traits to negotiating traffic is the ability to predict the future behavior of surrounding vehicles as a basis for agile and safe navigation. This capability is particularly challenging for an autonomous system in highly interactive driving situations, such as intersections or roundabouts. In this paper, a set of techniques to bring a computationally expensive state-of-the-art motion prediction algorithm to real-time execution are presented with the goal of meeting a standard motion-planning algorithm execution frequency of 5 Hz, which is the primary consumer of motion predictions. This is achieved by applying novel and existing parallelization algorithms that take advantage of graphic processing units (GPUs) through the compute unified device architecture (CUDA) programming language and managing to produce an average 5× speedup over raw C++ in the cases studied. The optimizations are then evaluated in public datasets and a real vehicle on a test track.
Effect of Zinc Excess on Some Physiological Parameters and on the Fatty Acids Profile of Sinapis alba L. and Brassica juncea L. (Czern)
Authors: Repkina Natalia , Murzina Svetlana A. , Voronin Viktor P. , Kaznina Natalia
Published: September 2023

Zinc (Zn) is a vital micronutrient for all living organisms, but in high concentrations, it is a major anthropogenic pollutant. In this study, it was investigated the effect of zinc excess concentrations (50, 100, and 150 mg kg−1) in the substrate on some physiological parameters and the dynamics of fatty acid (FA) content in yellow mustard (Sinapis alba L. cv. Belgia) and oriental mustard (Brassica juncea L. (Czern) cv. Slavanka). The Zn concentration of 50 mg kg−1 did not affect the physiological parameters of yellow mustard, whereas some physiological parameters slightly decreased in oriental mustard. Moreover, this concentration stimulated an increase in total and unsaturated FAs content in leaves of both species, along with high Zn accumulation by both species. The Zn concentrations of 100 and 150 mg kg−1 in substrate had a negative effect on the state of plants as measured by the studied physiological parameters and caused different changes in FAs content in yellow and oriental mustard. Particularly, the total amount of FA increased in comparison to the control, but this increase was due to an elevation in saturated FA (SFA) content. At the same time, the total FA content in oriental mustard slightly decreased while the proportion of unsaturated FAs to SFA kept rising. According to the present data and analysis of the available literature, two strategies of adaptation to heavy metals were suggested through the changes in FAs: height-resistant horticultural plant species on the one side and sensitive species on the other.
Epidemiology and Transmission Dynamics of Viral Encephalitides in West Africa
Authors: Akinsulie Olalekan Chris , Adesola Ridwan Olamilekan , Aliyu Victor Ayodele , Oladapo Ifeoluwa Peace , Hamzat Abdulafees
Published: September 2023

Encephalitis is an inflammation of the brain, often caused by an autoimmune reaction, or in most cases because of a direct viral, bacterial, or parasitic infection. Viral encephalitides (VE) presents a significant public health concern globally, especially in West Africa. There are more than five hundred known arthropod-borne viruses (arboviruses), with over a hundred of them identified to cause encephalitic diseases in humans and animals, giving rise to a tremendous burden of the diseases and socioeconomic strains in tropical and subtropical regions worldwide. Despite their importance, few effective preventive and control measures in the form of vaccines and therapies are available, and when they are, their use is limited. These limitations are largely hinged on the paucity of information about the molecular epidemiology and transmission patterns of VE in West Africa. Here, we reviewed the transmission dynamics, molecular epidemiology, and the ecological drivers of VE in West Africa. Collectively, timely and accurate interventions are essential for encephalitic viral disease control. Moreover, the integrated health system approach, combining surveillance, vaccination, vector control, and community engagement, could be effective in preventing viral encephalitis globally.