bluuebunny/crossref_metadata_2025 huggingface dataset:
Spectral Relaxation Methodology for Chemical and Bioconvection Processes for Cross Nanofluid Flowing around an Oblique Cylinder with a Slanted Magnetic Field Effect
Authors: Rasool Ghulam , Shah Syed Zahir Hussain , Sajid Tanveer , Jamshed Wasim , Cieza Altamirano Gilder , Keswani Bright , Artidoro Sandoval Núñez Rafaél , Sánchez-Chero Manuel
Published: October 2022

The current investigation explains the chemical reaction and bioconvection process for an inclined magnetized Cross nanofluid over an inclined cylinder using a spectral relaxation approach. Additionally, the facts concerning swimming gyrotactic microorganisms, non-uniform thermal conductivity, and variable decrease or increase in heat sources are taken together. Each profile is checked for inclined and orthogonal magnetic impact. Appropriate transformations made for conversion of nonlinear PDEs into systems of ODEs. For obtaining numerical results, a spectral relaxation approach is utilized, and graphs are plotted with each physical parameter attached. It is well established that the temperature field intensifies owing to an amplification of thermal conduction and Brownian diffusivity phenomena. The heat transfer rate amplifies owing to a magnification in magnetic parameter and thermal conductivity, but the velocity field diminishes as a result of magnification in the Weissenberg number and power law index. Amplification in the reaction rate constant parameter diminishes the concentration field. Activation energy is the key factor responsible for magnification in the concentration field. Furthermore, smooth agreement is found during comparison with the existing literature. Statistical analysis is also conducted for physical quantities.
Electronic Structure and Optical Properties of Cu2ZnSnS4 under Stress Effect
Authors: Yang Xiufan , Qin Xinmao , Yan Wanjun , Zhang Chunhong , Zhang Dianxi , Guo Benhua
Published: October 2022

By using the pseudopotential plane-wave method of first principles based on density functional theory, the band structure, density of states and optical properties of Cu2ZnSnS4 under isotropic stress are calculated and analyzed. The results show that Cu2ZnSnS4 is a direct band gap semiconductor under isotropic stress, the lattice is tetragonal, and the band gap of Cu2ZnSnS4 is 0.16 eV at 0 GPa. Stretching the lattice causes the bottom of the conduction band of Cu2ZnSnS4 to move toward lower energies, while the top of the valence band remains unchanged and the band gap gradually narrows. Squeezing the lattice causes the bottom of the conduction band to move toward the high-energy direction, while the top of the valence band moves downward toward the low-energy direction, and the Cu2ZnSnS4 band gap becomes larger. The static permittivity, absorption coefficient, reflectivity, refractive index, electrical conductivity, and energy loss function all decrease when the lattice is stretched, and the above optical parameters increase when the lattice is compressed. When the lattice is stretched, the optical characteristic peaks such as the dielectric function shift to the lower-energy direction, while the optical characteristic peak position shifts to the higher-energy direction when the lattice is compressed.
Eshelby Tensors for Two-Dimensional Decagonal Piezoelectric Quasicrystal Composites
Authors: Li Guangfang , Li Lianhe
Published: October 2022

The Eshelby tensor for two-dimensional (2D) piezoelectric quasicrystal composites (QCs) is considered. The explicit expressions of Eshelby tensors for 2D piezoelectric QCs are given using the Green’s function method and the interior polarization tensor method, respectively. On this basis, numerical examples of the Eshelby tensor for 2D piezoelectric QCs with ellipsoidal inclusions are discussed in detail.
2D Monte Carlo Simulation of Cocrystal Formation Using Patchy Particles
Authors: Ranguelov Bogdan , Nanev Christo
Published: October 2022

Cocrystals of Active Pharmaceutical Ingredients (APIs) are an attractive therapeutic alternative to salt formations. However, due to the molecular scale processes involved, the earliest stages of cocrystal formation remain poorly understood. In this paper, some light is shed on the thermodynamics and kinetics of co-crystallization. Importantly, to mimic the molecular scale processes of cocrystal formation, we use 2D Monte Carlo simulations and a computational model with short-range attraction and a mixture of two types of patchy particles (PPs) monomers. Each type possesses four patches, grouped in two by two, and each couple of patches is characterized by its specific placement on the circumference of the monomer and corresponding patch strength (a strong and narrow or weak and wide interaction). The spatial placement of the patches on both PPs monomers (alternating periodically through 60 and 120 degrees and vice versa) selected by us shows the emergence of both rhombohedral (metastable) and trihexagonal (stable) Kagome-like structures. The Kagome-like structures are preceded by formation of two types of trimers involving strong bonds only, or mixed trimers of strong and weak bonds, the later serving as building blocks for the finally generated Kagome patchy cocrystal, after prolonged simulation times. The step-by step process governing the cocrystal formation is discussed in detail, concerning the temperature interval, concentrations of PPs, the specific patch geometry and patch anisotropy as well. It is to be hoped that an understanding of the mechanisms of co-crystallization can help to control practical cocrystal synthesis and the possible phase transformations.
Clinical Factors Associated with Long-Term Survival in Metastatic Melanoma Treated with Anti-PD1 Alone or in Combination with Ipilimumab
Authors: Goutam Siddhartha , Stukalin Igor , Ewanchuk Benjamin , Sander Michael , Ding Philip Q. , Meyers Daniel E. , Heng Daniel , Cheung Winson Y. , Cheng Tina
Published: October 2022

Immune checkpoint inhibitors (ICIs) for treatment of metastatic melanoma (MM) offer lasting overall survival (OS) benefit in a subset of patients. However, outcomes remain poor for non-responders. Clinical predictors of long-term survival remain elusive. We utilized the Alberta Immunotherapy Database to investigate the association of host and disease characteristics, and treatment factors with overall survival (OS) greater than 3 years. We identified patients treated between August 2013 and May 2020 with single-agent anti-PD1 or combination (anti-PD1 and anti-CTLA4) ICI regimens. A logistic regression model was used to assess for independent association between clinical factors captured and survival greater than 3 years. Statistically significant factors on univariable analysis were assessed using multivariable analysis. In total, 284 of 460 patients were identified to have short-term (<1 year) or long-term (>3 years) survival with 186 surviving <1 year and 98 surviving >3 years. The median age was 64 and 18.4% of patients were ECOG ≥ 2. On logistic regression, Breslow’s Depth ≤ 4 mm, normal serum LDH, normal serum albumin and M-stage 1a/b were associated with OS > 3 years on univariable and multivariable analysis. ECOG < 2, dNLR ≤ 3, normal hemoglobin were only associated with survival on the univariable analysis but not in the multivariable analysis. The objective response rate in long-term survivors was 83.7% compared to 7.5% in the short-term survivors. Our study identifies four easily accessible predictors of long-term survival in a large real-world MM cohort treated with ICI.
Rationale Efficacy and Safety Evidence of Lenvatinib and Pembrolizumab Association in Anaplastic Thyroid Carcinoma
Authors: Boudin Laurys , Morvan Jean-Baptiste , Thariat Juliette , Métivier Denis , Marcy Pierre-Yves , Delarbre David
Published: October 2022

Anaplastic thyroid carcinoma (ATC) are highly aggressive malignant tumors with poor overall prognosis despite multimodal therapy. As ATC are extremely rare, no randomized controlled study has been published for metastatic disease. Thyrosine kinase inhibitors, especially lenvatinib and immune checkpoint inhibitors such as pembrolizumab, are emerging drugs for ATC. Few studies have reported the efficacity of pembrolizumab and lenvatinib association, resulting in its frequent off-label use. In this review, we discuss rationale efficacy and safety evidence for the association of lenvatinib and pembrolizumab in ATC. First, we discuss preclinical rationale for pembrolizumab monotherapy, lenvatinib monotherapy and synergistic action of pembrolizumab and lenvatinib in the metastatic setting. We also discuss clinical evidence for immunotherapy and pembrolizumab in ATC through the analysis of studies evaluating immunotherapy, lenvatinib and pembrolizumab lenvatinib association in ATC. In addition, we discuss the safety of this association and potential predictive biomarkers of efficiency.
Waterbodies in the Floodplain of the Drava River Host Species-Rich Macrophyte Communities despite Elodea Invasions
Authors: Zelnik Igor , Germ Mateja , Kuhar Urška , Gaberščik Alenka
Published: October 2022

The contribution discusses macrophyte communities in natural and man-made waterbodies located on the active floodplain along the Drava river (Slovenia). We presumed that these different types of wetlands host a great number of macrophyte species, but this diversity may be affected by the presence of alien invasive species Elodea canadensis and E. nuttallii. Presence, relative abundance, and growth forms of plant species along with selected environmental parameters were monitored. Correlation analyses and direct gradient analyses were performed to reveal the possible relations between the structure of macrophyte community and environmental parameters. Number of macrophytes in surveyed water bodies varied from 1 to 23. Besides numerous native species we also recorded Elodea canadensis and E. nuttallii, which were present in 19 out of 32 sample sites, with E. nuttallii prevailing. The less invasive E. canadensis was absent from ponds and oxbow lakes but relatively abundant in side-channels, while E. nuttallii was present in all types but dominant in ponds. The most abundant native species were Myriophyllum spicatum and M. verticillatum, Ceratophyllum demersum and Potamogeton natans. Correlation analyses showed no negative effect of the invasive alien Elodea species to the species richness and diversity of native flora. Positive correlation between the abundance of E. nuttallii and temperature of the water was obtained.
Bio-Based Adhesives for Orthopedic Applications: Sources, Preparation, Characterization, Challenges, and Future Perspectives
Authors: Nuswantoro Nuzul , Lubis Muhammad , Juliadmi Dian , Mardawati Efri , Antov Petar , Kristak Lubos , Hua Lee
Published: October 2022

Bone fracture healing involves complex physiological processes that require biological events that are well coordinated. In recent decades, the process of fracture healing has been upheld through various treatments, including bone implants and bio-adhesive utilization. Bio-adhesion can be interpreted as the process in which synthetic or natural materials adhere to body surfaces. Bio-based adhesives have superiority in many value-added applications because of their biocompatibility, biodegradability, and large molecular weight. The increased variety and utilization of bio-based materials with strong adhesion characteristics provide new possibilities in the field of orthopedics in terms of using bio-based adhesives with excellent resorbability, biocompatibility, ease of use, and low immunoreactivity. The aim of this review is to provide comprehensive information and evaluation of the various types of bio-based adhesives used clinically with a specific focus on their application in orthopedics. The main properties of bio-based adhesives, their benefits, and challenges compared with the traditional bio-based materials in orthopedics, as well as the future perspectives in the field, have also been outlined and discussed.
Deep Learning Multi-Domain Model Provides Accurate Detection and Grading of Mucosal Ulcers in Different Capsule Endoscopy Types
Authors: Kratter Tom , Shapira Noam , Lev Yarden , Mauda Or , Moshkovitz Yehonatan , Shitrit Roni , Konyo Shani , Ukashi Offir , Dar Lior , Shlomi Oranit , Albshesh Ahmad , Soffer Shelly , Klang Eyal , Ben Horin Shomron , Eliakim Rami , Kopylov Uri , Margalit Yehuda Reuma
Published: October 2022

Background and Aims: The aim of our study was to create an accurate patient-level combined algorithm for the identification of ulcers on CE images from two different capsules. Methods: We retrospectively collected CE images from PillCam-SB3′s capsule and PillCam-Crohn’s capsule. ML algorithms were trained to classify small bowel CE images into either normal or ulcerated mucosa: a separate model for each capsule type, a cross-domain model (training the model on one capsule type and testing on the other), and a combined model. Results: The dataset included 33,100 CE images: 20,621 PillCam-SB3 images and 12,479 PillCam-Crohn’s images, of which 3582 were colonic images. There were 15,684 normal mucosa images and 17,416 ulcerated mucosa images. While the separate model for each capsule type achieved excellent accuracy (average AUC 0.95 and 0.98, respectively), the cross-domain model achieved a wide range of accuracies (0.569–0.88) with an AUC of 0.93. The combined model achieved the best results with an average AUC of 0.99 and average mean patient accuracy of 0.974. Conclusions: A combined model for two different capsules provided high and consistent diagnostic accuracy. Creating a holistic AI model for automated capsule reading is an essential part of the refinement required in ML models on the way to adapting them to clinical practice.
Demarcation Line Determination for Diagnosis of Gastric Cancer Disease Range Using Unsupervised Machine Learning in Magnifying Narrow-Band Imaging
Authors: Okumura Shunsuke , Goudo Misa , Hiwa Satoru , Yasuda Takeshi , Kitae Hiroaki , Yasuda Yuriko , Tomie Akira , Omatsu Tatsushi , Ichikawa Hiroshi , Yagi Nobuaki , Hiroyasu Tomoyuki
Published: October 2022

Background and Aims: It is important to determine an accurate demarcation line (DL) between the cancerous lesions and background mucosa in magnifying narrow-band imaging (M-NBI)-based diagnosis. However, it is difficult for novice endoscopists. We aimed to automatically determine the accurate DL using a machine learning method. Methods: We used an unsupervised machine learning approach to determine the DLs. Our method consists of the following four steps: (1) an M-NBI image is segmented into superpixels using simple linear iterative clustering; (2) the image features are extracted for each superpixel; (3) the superpixels are grouped into several clusters using the k-means method; and (4) the boundaries of the clusters are extracted as DL candidates. The 23 M-NBI images of 11 cases were used for performance evaluation. The evaluation investigated the similarity of the DLs identified by endoscopists and our method, and the Euclidean distance between the two DLs was calculated. For the single case of 11 cases, the histopathological examination was also conducted to evaluate the proposed system. Results: The average Euclidean distances for the 11 cases were 10.65, 11.97, 7.82, 8.46, 8.59, 9.72, 12.20, 9.06, 22.86, 8.45, and 25.36. The results indicated that the proposed method could identify similar DLs to those identified by experienced doctors. Additionally, it was confirmed that the proposed system could generate pathologically valid DLs by increasing the number of clusters. Conclusions: Our proposed system can support the training of inexperienced doctors as well as enrich the knowledge of experienced doctors in endoscopy.
Genome-Wide Association Study Identifies Genetic Variants Associated with Rotator Cuff Tear—A Pilot Study
Authors: An Hyun-Ju , Kim Jae-Hwa , Yoon Siyeong , Choi Junwon , Koo Jeongmo , Lee Soonchul
Published: October 2022

A rotator cuff is a muscle and tendon surrounding the shoulder joint, and a rotator cuff tear can be caused by overuse or injury, which leads to great pain in affected individuals. However, rotator cuff tear is a multifactorial process whose underlying mechanism is still unclear. Many previous studies have suggested an important role of genetic predisposition, such as single-nucleotide polymorphisms (SNPs), in explaining the genesis of tendinopathy. This study aimed to identify specific genes or genetic variants associated with rotator cuff tears by performing a genome-wide association study (GWAS) using an independent case of rotator cuff tears. GWAS was performed using data from CHA Bundang Medical Center with 20 cases of rotator cuff tears, and 20 cases of healthy controls genotyped on the Illumina HiSeq 2500. Tests of association were performed using the Burrows–Wheeler Aligner (BWA) software at 284,246 SNPs. Data were filtered based on sequence ontology, minor allele frequency, and Hardy–Weinberg equilibrium values, and SNPs were considered significant if the p-value was <0.05. The tests of association revealed more than 20 significantly associated SNPs. SNPs showing the highest significance occurred in candidate genes, including LAIR2 (rs2287828, OR 9.116, p-value 5.49 × 10−4) on chromosome 19 and CRIPAK (rs9328733, OR 6, p-value 1.11 × 10−3) and REST (rs2228991, OR 8.222, p-value 1.20 × 10−3) on chromosome 4. This study attempted to identify genetic variants influencing rotator cuff tears through a genome-wide association study using a dense set of SNPs. More than 20 SNPs were significantly associated with rotator cuff tears. The major limitation of this study is that it was conducted on a small study group and requires further validation. Nevertheless, the identification of potential genetic variants related to rotator cuff injury would aid in the early detection of individuals at risk for the development of tendinopathy and will provide insight into future gene therapies.
Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification
Authors: Khristoforova Yulia , Bratchenko Ivan , Bratchenko Lyudmila , Moryatov Alexander , Kozlov Sergey , Kaganov Oleg , Zakharov Valery
Published: October 2022

In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences.
Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method
Authors: Aldhyani Theyazn H. H. , Nair Rajit , Alzain Elham , Alkahtani Hasan , Koundal Deepika
Published: October 2022

Breast cancer can develop when breast cells replicate abnormally. It is now a worldwide issue that concerns people’s safety all around the world. Every day, women die from breast cancer, which is especially common in the United States. Mammography, CT, MRI, ultrasound, and biopsies may all be used to detect breast cancer. Histopathology (biopsy) is often carried out to examine the image and discover breast cancer. Breast cancer detection at an early stage saves lives. Deep and machine learning models aid in the detection of breast cancer. The aim of the research work is to encourage medical research and the development of technology by employing deep learning models to recognize cancer cells that are small in size. For histological annotation and diagnosis, the proposed technique makes use of the BreCaHAD dataset. Color divergence is caused by differences in slide scanners, staining procedures, and biopsy materials. To avoid overfitting, we used data augmentation with 19 factors, such as scale, rotation, and gamma. The proposed hybrid dilation deep learning model is of two sorts. It illustrates edges, curves, and colors, and it improves the key traits. It utilizes dilation convolution and max pooling for multi-scale information. The proposed dilated unit processes the image and sends the processed features to the Alexnet, and it can recognize minute objects and thin borders by using the dilated residual expanding kernel model. An AUC of 96.15 shows that the new strategy is better than the old one.
Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals
Authors: Uyanık Hakan , Ozcelik Salih Taha A. , Duranay Zeynep Bala , Sengur Abdulkadir , Acharya U. Rajendra
Published: October 2022

Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelectrical signals that are frequently used because of the many advantages they offer in the field of emotion recognition. This study proposes an improved approach for EEG-based emotion recognition on a publicly available newly published dataset, VREED. Differential entropy (DE) features were extracted from four wavebands (theta 4–8 Hz, alpha 8–13 Hz, beta 13–30 Hz, and gamma 30–49 Hz) to classify two emotional states (positive/negative). Five classifiers, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naïve Bayesian (NB), Decision Tree (DT), and Logistic Regression (LR) were employed with DE features for the automated classification of two emotional states. In this work, we obtained the best average accuracy of 76.22% ± 2.06 with the SVM classifier in the classification of two states. Moreover, we observed from the results that the highest average accuracy score was produced with the gamma band, as previously reported in studies in EEG-based emotion recognition.
Hypertension, Anxiety and Obstructive Sleep Apnea in Cardiovascular Disease and COVID-19: Mediation by Dietary Salt
Authors: Brown Ronald B.
Published: October 2022

This perspective paper used a grounded theory method to synthesize evidence proposing that sodium toxicity from excessive dietary salt intake is a potential common pathophysiological mechanism that mediates the association of hypertension, obstructive sleep apnea, and anxiety with cardiovascular disease and COVID-19. Increased anxiety in these conditions may be linked to a high-salt diet through stimulation of the sympathetic nervous system, which increases blood pressure while releasing catecholamines, causing a “fight or flight” response. A rostral shift of fluid overload from the lower to the upper body occurs in obstructive sleep apnea associated with COVID-19 and cardiovascular disease, and may be related to sodium and fluid retention triggered by hypertonic dehydration. Chronic activation of the renin-angiotensin-aldosterone system responds to salt-induced dehydration by increasing reabsorption of sodium and fluid, potentially exacerbating fluid overload. Anxiety may also be related to angiotensin II that stimulates the sympathetic nervous system to release catecholamines. More research is needed to investigate these proposed interrelated mechanisms mediated by dietary salt. Furthermore, dietary interventions should use a whole-food plant-based diet that eliminates foods processed with salt to test the effect of very low sodium intake levels on hypertension, anxiety, and obstructive sleep apnea in cardiovascular disease and COVID-19.
SoK: Benchmarking the Performance of a Quantum Computer
Authors: Wang Junchao , Guo Guoping , Shan Zheng
Published: October 2022

The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer’s benchmarking and propose setting up the QTOP100.
GDPR Compliant Data Storage and Sharing in Smart Healthcare System: A Blockchain-Based Solution
Authors: Bai Pinky , Kumar Sushil , Kumar Kirshna , Kaiwartya Omprakash , Mahmud Mufti , Lloret Jaime
Published: October 2022

Smart healthcare systems provide user-centric medical services to patients based on collected information of patients inducing personal health information (PHI) and personal identifiable information (PII). The information (PII and PHI) flows into the smart healthcare system with or without any regulation and patient concern with the help of new information and communication technologies (ICT). The use of ICT comes with the security and privacy issues of collected PII and PHI data. The Europe Union has published the General Data Protection Regulation (GDPR) to regulate the flow of personal information. Towards this end, this paper proposes a blockchain-based data storage and sharing framework for a smart healthcare system that complies with the “Privacy by Design” rule of the GDPR. The personal information collected from patients is stored on off-chain storage (IPFS), and other information is stored on the blockchain ledger, which is visible to all participants. The smart contracts are designed to share the PII data with another participant based on prior permission of the data owner. The proposed framework also includes the deletion of PII and PHI in the system as per the “Right to be Forgotten” GDPR rule. Security and privacy analyses are performed for the framework to demonstrate the security and privacy of data while sharing and at rest. The comparative performance analysis demonstrates the benefit of the proposed GDPR-compliant data storage and sharing framework using blockchain. It is evident from the reported results that the proposed framework outperforms the state-of-the-art techniques in terms of performance metrics in a smart healthcare system.
A Large Scale Evolutionary Algorithm Based on Determinantal Point Processes for Large Scale Multi-Objective Optimization Problems
Authors: Okoth Michael , Shang Ronghua , Jiao Licheng , Arshad Jehangir , Rehman Ateeq , Hamam Habib
Published: October 2022

Global optimization challenges are frequent in scientific and engineering areas where loads of evolutionary computation methods i.e., differential evolution (DE) and particle-swarm optimization (PSO) are employed to handle these problems. However, the performance of these algorithms declines due to expansion in the problem dimension. The evolutionary algorithms are obstructed to congregate with the Pareto front rapidly while using the large-scale optimization algorithm. This work intends a large-scale multi-objective evolutionary optimization scheme aided by the determinantal point process (LSMOEA-DPPs) to handle this problem. The proposed DPP model introduces a mechanism consisting of a kernel matrix and a probability model to achieve convergence and population variety in high dimensional relationship balance to keep the population diverse. We have also employed elitist non-dominated sorting for environmental selection. Moreover, the projected algorithm also demonstrates and distinguishes four cutting-edge algorithms, each with two and three objectives, respectively, and up to 2500 decision variables. The experimental results show that LSMOEA-DPPs outperform four cutting-edge multi-objective evolutionary algorithms by a large margin.
A Novel Ring-Gate AlGaN/GaN HEMT Device and Electrode Structure Optimization
Authors: Zhu Yanxu , Song Xiaomeng , Li Jianwei , Li Jinheng , Fei Baoliang , Li Peiyang , Li Fajun
Published: October 2022

In this paper, a novel ring-gate structure AlGaN/GaN HEMT device is proposed and fabricated successfully. When the gate-source spacing Lgs = 5 μm, gate-drain spacing Lgd = 7 μm, gate length Lg = 3 μm, the maximum drain current Idmax of this ring-gate AlGaN/GaN HEMT device improved by 161.8% comparing with the conventional structure device, the threshold voltage Vth increased by 66.7% from 1.65 V to 2.5 V. In order to further improve the performance of the device, a series of electrode structure optimization designs have been carried out. Firstly, the effect of source-drain electrode alloy type and etching depth under source-drain region on the transfer and output characteristics was investigated, we fabricated devices with two alloy electrodes of multi-layer Ti/Al/Ti/Al/Ti/Al/Ni/Au and single layer Ti/Al/Ni/Au, then perform groove etching under the source and drain electrodes, the etching depth is set to 10/20 nm, after analysis and calculation, it is found that among ring-gate and conventional-gate devices, the device with multi-layer electrodes and an etched depth of 10 nm performs best. Then, the influence of device size parameters on transfer and output characteristics was explored, devices with different Lg and Lgd were prepared, after testing it is found that with the increase of Lg, the Vth of the conventional-gate and ring-gate HEMT devices both showed a positive-shift trend, in conventional device Vth increased from 1.53 V to 1.7 V, and this value increased from 1.5 V to 2.5 V in ring-gate device; the saturation drain current decreases when Lg increasing, and the decrease of the ring-gate device is more obvious, from 51.28 mA at Lg = 3 μm to 24.48 mA at Lg = 6 μm; when Lds decreases, the Vth of the two structures doesn’t change significantly, but the output current increases with the reduction of Lds, among them, the Idmax of the conventional structure device at Lgd = 19 um is 79.07% lower than that at Lgd = 7 μm; the value of the ring-gate device is reduced by 113.7%. In addition, among all the above devices, the ring-gate devices all show better output characteristics and higher Vth than conventional devices.
Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles
Authors: Ayaz Ferheen , Sheng Zhengguo , Tian Daxin , Nekovee Maziar , Saeed Nagham
Published: October 2022

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.
Performance of Parabolic Trough Collector with Different Heat Transfer Fluids and Control Operation
Authors: Kannaiyan Surender , Bokde Neeraj Dhanraj
Published: October 2022

Electricity generation from solar energy has become very desirable because it is abundantly available and eco-friendly. Mathematical modeling of various components of a Solar Thermal Power plant (STP) is warranted to predict the optimal and efficient operation of the plant. The efficiency and reliability of STPs are maximized based on different operating strategies. Opting for proper Heat Transfer Fluid (HTF), which is proposed in this paper, helps in reducing operating complexity and lowering procurement cost. The Parabolic Trough Collector (PTC) is the heart of STP, where proper focusing of PTC towards solar radiation is the primary task to maximize the outlet temperature of HTF. This maximum temperature plays a major factor due to diurnal solar radiation variation, and its disturbance nature, with the frequent startup and shutdown of STP, is avoided. In this paper, the PTC component is modeled from the first principle, and, with different HTF, the performance of PTC with constant and quadratic solar disturbances is analyzed along with classical control system designs. Through this, the operator will be able to choose proper HTF and resize the plant components depending on plant location and weather conditions. Furthermore, the thermal energy is collected for therminol oil, molten salt, and water; and its performance with different inputs of solar radiation is analyzed along with closed-loop controllers. Thermal energy extracted by therminol oil, molten salt, and water with constant solar radiation results in 81.7%,73.7% and 18.7%, respectively.
A Thermal Model to Estimate PV Electrical Power and Temperature Profile along Panel Thickness
Authors: Nicoletti Francesco , Cucumo Mario Antonio , Ferraro Vittorio , Kaliakatsos Dimitrios , Gigliotti Albino
Published: October 2022

The production of electricity from photovoltaic panels has experienced significant developments. To manage the energy flows introduced into the electricity grid, it is necessary to estimate the productivity of PV panels under the climatic conditions. In this study, a photovoltaic panel is modelled from thermal and electrical points of view to evaluate electrical performance and identify the temperature distribution in the layers. The analysis performed is time dependent and the problem is solved using the finite difference technique. A methodology is introduced to estimate the cloudiness of the sky, which affects radiative heat exchange. The calculation method is validated using experimental data recorded in a laboratory of the University of Calabria. Temperature and electrical power are predicted with RMSE of 1.5–2.0 °C and NRMSE of 1.2–2.1%, respectively. The evaluation of the temperature profile inside the panel is essential to understand how heat is dissipated. The results show that the top surface (glass) is almost always colder than the back of the panel, despite being exposed to radiation. In addition, the upper surface dissipates more heat power than the lower one. Cooling systems, such as spray cooling, work better if they are installed on the back of the panel.
Energy Performance of Water Generators from Gaseous Mixtures by Condensation: Climatic Datasets Choice
Authors: Cattani Lucia , Magrini Anna , Leoni Valentina
Published: October 2022

Due to the growing issues related to water scarcity and pollution, water extraction from gaseous mixtures, such as atmospheric air, or fumes from combustion, is acquiring increasing importance. Nevertheless, one of the main concerns is the energy consumption that affects the use of any kind of Air(/Gas) to Water Generator (AWG). Referring specifically to water extraction from humid environmental air, AWG behaviour depends upon the air thermo-dynamic conditions and thus upon weather data. To evaluate the water extraction energy efficiency, two interesting tools can be applied: the WET (Water Energy Transformation) indicator, concerning the specific AWG machine behaviour, and the MHI (Moisture Harvesting Index), focused on climate suitability evaluation. Those tools require the knowledge of weather data to be applied. When hourly data for the entire year are available, the application of these tools leads to reliable results. However, in many cases, only average climatic data are available. Today, there are no indications about the reliability of results coming from the use of those less accurate data sets: the research aims to provide a preliminary assessment of the conditions under which average climatic data can be employed without losing meaning. This target was pursued by calculating WET and MHI with three different data sets and five meaningful climate examples. By comparing results, it was possible to provide indications about the most suitable use of average data.
Thermal Effect of Cylindrical Heat Sink on Heat Management in LED Applications
Authors: Ekpu Mathias , Ogbodo Eugene A. , Ngobigha Felix , Njoku Jude E.
Published: October 2022

Light Emitting Diode (LED) applications are increasingly used in various microelectronic devices due to their efficient light generation. The miniaturisation of the LED and its integration into compact devices within the weight limit have resulted in excessive heat generation, and inefficient management of this heat could lead to the failure of the entire system. Passive and/or active heat sinks are used for dissipating heat from the system to the environment to improve performance. An ANSYS design modeller and transient thermal conditions were utilised in this study to design and simulate the LED system. The modeller performs its function by utilising the Finite Element Method (FEM) technique. The LED system considered in this work consists of a chip, thermal interface material, and a cylindrical heat sink. The thickness of the Cylindrical Heat Sink (CHS) fins used in the investigation is between 2 mm and 6 mm, whilst ensuring the mass of heat sinks is not more than 100 g. The input power of the LED chip is between 4.55 W and 25.75 W, as required by some original equipment manufacturers (OEMs). A mesh dependency study was carried out to ensure the results were synonymous with what can be obtained practically. The simulation results suggest that the power ratings did not affect the thermal resistance of the CHS. In addition, the thermal resistance increased with the increased thickness of the CHS fin. The efficiencies of the heat sink were found to increase with an increased thickness of the cylindrical fin and the accuracy between the calculated and simulated thermal efficiency ranges from 84.33% to 98.80%. Evidently, the CHS fin of 6 mm thickness is more efficient than the other CHS fins, as depicted in this study.
Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load
Authors: Jin Bingjie , Zeng Guihua , Lu Zhilin , Peng Hongqiao , Luo Shuxin , Yang Xinhe , Zhu Haojun , Liu Mingbo
Published: October 2022

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.
Types of Policies for the Joint Diffusion of Electric Vehicles with Renewable Energies and Their Use Worldwide
Authors: Sousa Cristina , Costa Evaldo
Published: October 2022

A large reduction in greenhouse gas (GHG) emissions will be needed in the coming decades to keep global warming well below 2 °C. Together, energy and transport sectors are responsible for around ¾ of carbon dioxide (CO2) emissions worldwide. The transition to a low-carbon economy involves an increase in the share of renewable energies in power grid and the electrification of the transports thus demanding policies focused on the joint diffusion of the two technologies This study adopted a hybrid methodology to examine several types of policies and their implications in the use and diffusion of the electric vehicles charged with renewables. We argue that it is not enough to have policies to expand electric mobility with renewable sources. The results reveal that production and technology represent ⅓ of electric vehicles and renewables policies. Broad coverage of policies does not translate into market gains since 20% of the countries that encourage electric vehicles have a low market share. Policies need to be broad, consistent, reach more countries and promote synergies between renewables and electric mobility to provide the conjoint diffusion of both technologies and allow the CO2 emission mitigation targets for 2030 to be achieved. This study contributes to research on sustainable policies and innovations to decarbonizing the energy and transport sectors.
Numerical Modelling of Forced Convection of Nanofluids in Smooth, Round Tubes: A Review
Authors: Cieśliński Janusz T.
Published: October 2022

A comprehensive review of published works dealing with numerical modelling of forced convection heat transfer and hydrodynamics of nanofluids is presented. Due to the extensive literature, the review is limited to straight, smooth, circular tubes, as this is the basic geometry in shell-and-tube exchangers. Works on numerical modelling of forced convection in tubes are presented chronologically in the first part of the article. Particular attention was paid to the method of the solution of governing equations, geometry of the heating section, and boundary conditions assumed. Influence of nanoparticles on heat transfer and flow resistance are discussed. Basic information is summarized in tabular form, separately for single-phase approach and two-phase models. The second part of the article contains the correlation equations proposed in the presented papers for the calculation of the Nusselt (Nu) number or heat transfer coefficient, separately for laminar and turbulent flow. Details of the type of nanofluids, the concentration of nanoparticles, and the Reynolds (Re) number range are also presented. Finally, advantages and disadvantages of individual numerical approaches are discussed.
E-Scooter Micro-Mobility Application for Postal Service: The Case of Turkey for Energy, Environment, and Economy Perspectives
Authors: Ayözen Yunus Emre , İnaç Hakan , Atalan Abdulkadir , Dönmez Cem Çağrı
Published: October 2022

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver’s gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas.
Solar Pyrolysis of Spirulina platensis Assisted by Fresnel Lens Using Hydrocalumite-Type Precursors
Authors: Martins Marcus P. B. , Hori Carla E. , Barrozo Marcos A. S. , Vieira Luiz G. M.
Published: October 2022

Solar pyrolysis is a promising technology as it combines use of biomass and solar energy to generate transportable and storable fuels, as well as chemicals of interest. The most desired product of rapid pyrolysis of microalgae is bio-oil, a liquid and viscous mixture composed of hundreds of chemicals. Among these compounds are many oxygenates that usually bring some undesirable properties to bio-oil, e.g., instability. This study aimed to investigate the potential of Spirulina platensis to produce bio-oil from catalytic solar pyrolysis assisted by Fresnel lens. The performance of the mixed oxides derived from hydrocalumite was evaluated, aiming to improve the yield and quality of the liquid product. The effects of reaction time and percentage of catalyst on the product distribution and bio-oil composition were quantified. An optimization study was performed using the differential evolution (DE) algorithm in order to maximize the bio-oil yield. The results showed that the highest liquid yield (43.4%) was obtained in 23.4 min using a catalyst percentage of 58.6%. The mixed oxides derived from hydrocalumite contributed to the improvement in the bio-oil quality, which presented in its composition a low quantity of oxygenated compounds and a higher percentage of hydrocarbons.