bluuebunny/crossref_metadata_2025 huggingface dataset:
Arbutus unedo: Innovative Source of Antioxidant, Anti-Inflammatory and Anti-Tyrosinase Phenolics for Novel Cosmeceuticals
Authors: Habachi Emna , Rebey Iness Bettaieb , Dakhlaoui Sarra , Hammami Majdi , Sawsen Selmi , Msaada Kamel , Merah Othmane , Bourgou Soumaya
Published: December 2022

Phenolic compounds are valuable cosmetic ingredients. They display skin protective potential and play an important role in preserving cosmetic formulations due to their ability to neutralize free radicals. Considering this fact, the current study aims to obtain a phenolic-enriched fraction from Arbutus unedo for topical application in cosmeceutical products. The chemical composition and the antioxidant, anti-inflammatory, and anti-tyrosinase activities of different extracts from the plant were investigated and compared. Samples were obtained by maceration, reflux, and ultrasound using water and ethanol. The findings indicated that the extraction methods impacted the phytochemical composition of the extracts. The high-performance liquid chromatography with diode-array detection (HPLC–DAD) analysis showed a wide range of phenolic compounds, comprising phenolic acids and flavonoids. Among the extracts, the water reflux had significant levels of both total polyphenols, flavonoids, and tannins and possessed the most important content on hyperoside. It displayed the most significant antioxidant activities with high antiradical and reducing power, as well as strong total antioxidant activity. It possesses a promising whitening effect with high anti-tyrosinase activities. Furthermore, it shows no cytotoxicity and moderate anti-inflammatory activity. Finally, due to its high yield efficiency and activities, water reflux was selected to formulate a cosmeceutical oil-in-water nanoemulsion that displayed optimal pH and stability.
Morphology Transition of Te-Doped InAs Nanowire on InP(111)B Grown Using MOCVD Method
Authors: Song Chang-Hun , Kong Minwoo , Jang Hyunchul , Lee Sang Tae , Park Hyeong-Ho , Kim Donghyun , Song Keunman , Ko Dae-Hong , Shin Chan-Soo
Published: December 2022

In this paper, we reported changes in the growth morphology of n+InAs nanowires (NWs) doped with Te which were selectively grown on nano-hole patterned InP(111)B substrates using an MOCVD method. While the vertical growth of InAs NWs in the <111> direction was extremely suppressed, their lateral growth was enhanced when the diethyl-tellurium (DETe) flow rate was increased as they grew. Moreover, the sidewall planes evolved from (11¯0) (90° against the (111) plane) to a reverse-tapered morphology, which had a 62° slope against the InP (111)B plane, when the Te flow rate and growth time were increased. This indicates that the surfactant effect of adsorbed Te atoms on InAs changes the relative growth rate between (111) and (11¯0) due to the increase in surface free energy in the growth plane.
Correction: Li et al. Characterizations of Single-Crystal Lithium Niobate Thin Films. Crystals 2022, 12, 667
Authors: Li Qingyun , Zhang Honghu , Zhu Houbin , Hu Hui
Published: December 2022

The authors wish to make the following correction to the Conflict of Interests section in their published paper [...]
Combustion Synthesis of Metal-Intermetallic-Ceramic Laminate AlMg6-NiAl-TiC Composite
Authors: Denisov Igor , Shakhray Denis , Malakhov Andrey , Seropyan Stepan
Published: December 2022

In this study, SHS was used to produce metal-intermetallic-ceramic laminate AlMg6-NiAl-TiC composite. The experiment conducted without a cylindrical powder pellet holder produced no joint between the NiAl and AlMg6 sheet. On the other hand, the experiment conducted inside a cylindrical powder pellet holder (CPPH) with a blind hole produced a joint. It was found that the AlMg6 sheet had a temperature of 400–550 °C across its entire thickness during SHS. The study of the microstructure and energy-dispersive analysis (EDS) of AlMg6-NiAl-TiC composite showed that it had five layers: (1) ceramic layer of 7-mm-thick TiC; (2) the upper diffusion layer that formed at the interface between NiAl and TiC consisted of TiC + NiAl; (3) an intermetallic layer, which consisted of 13-mm-thick NiAl; (4) the lower diffusion layer, which formed at the interface between NiAl and AlMg6; and (5) a layer of 4-mm-thick aluminum alloy AlMg6. The EDS showed that during the synthesis of NiAl and its interaction with the surface of the AlMg6 sheet, mixing of the components of the initial materials (NiAl, AlMg6) in the joint interface occurs. At the interface of NiAl and AlMg6, the microhardness was 790–870 HV, which indicates the presence of quenching structures in the melted zones.
Abstracts of the Cell Therapy Transplant Canada 2022 Annual Conference
Authors: Maier Stephanie A. , Berg Tobias , Berrigan Susan , Bramson Jonathan , Bredeson Chris , Cantin Guy , Daly Andrew , Davies Gwynivere A. , Elsawy Mahmoud , Garcia-Horton Alejandro , Lam Wilson , Lapworth Alix , Lepic Kylie , Melo Garcia Luciana , Schultz Kirk R. , Vasudevan Nampoothiri Ram , White Darrell , Delisle Jean-Sébastien
Published: December 2022

On behalf of Cell Therapy Transplant Canada (CTTC), we are pleased to present the Abstracts of the CTTC 2022 Annual Conference. The conference was held in-person 15–18 June 2022, in Niagara Falls, Ontario. Poster authors presented their work during a lively and engaging welcome reception on Thursday, 16 June, and oral abstract authors were featured during the oral abstract session in the afternoon on Friday, 17 June 2022. Thirty-three (33) abstracts were selected for presentation as posters and six (6) as oral presentations. The top abstracts in each of four (4) categories, (1) Basic/Translational sciences, (2) Clinical Trials/Observations, (3) Laboratory/Quality, and (4) Pharmacy/Nursing/Other Transplant Support, received awards for both the oral and poster presentations. All of these were marked as “Award Recipient” with the relevant category. We congratulate all the presenters on their research and contribution to the field.
Discovery of the First Blattinopsids of the Genus Glaphyrophlebia Handlirsch, 1906 (Paoliida: Blattinopsidae) in the Upper Carboniferous of Southern France and Spain and Hypothesis on the Diversification of the Family
Authors: Nel André , Garrouste Romain , Peñalver Enrique , Hernández-Orúe Antonio , Jouault Corentin
Published: December 2022

Glaphyrophlebia victoiriensis sp. nov. (Paoliida: Blattinopsidae) is the third Gzhelian representative of the genus and is described based on a beautiful forewing from the Var department in Southern France. Together with the description of another forewing fragment of a Glaphyrophlebia sp. from the Province of León in NW Spain, they improve our knowledge of fossil insects from French and Spanish upper Carboniferous deposits. The specimen of Glaphyrophlebia sp. is the first mention of the family in the Carboniferous of Spain and extends the geographical distribution of the genus. These descriptions suggest that the genus Glaphyrophlebia was speciose during the Upper Pennsylvanian, while otherwise very diverse in the lower and middle Permian strata of the Russian Federation. We proposed the first hypothesis to explain the diversification of the family and of its most speciose genera and to argue that their diversity dynamics were likely linked with the major environmental changes that followed the collapse of the Carboniferous rainforest, notably the extension of arid biomes during the Permian period. The exquisite preservation and the fineness of the sediment from Tante Victoire, in which the new species was found, suggests that the locality is suitable for preserving other fossil insects and will require additional investigations.
Large-Scale In Vitro Multiplication and Phytochemical Analysis of Himantoglossum affine (Boiss.) Schltr.: An Endangered Euro-Mediterranean Terrestrial Orchid
Authors: Fatahi Mozhgan , Vafaee Yavar , Tahir Nawroz Abdul-razzak , Khorshidi Jalal
Published: December 2022

Himantoglossum affine is a threatened terrestrial orchid. We aimed to optimize asymbiotic seed germination and direct embryogenesis and to analyze the phytochemical profile and physico-biochemical analysis of leaf and tuber. The individual use of organic nitrogen compounds resulted in higher germination efficiencies, while the shortest times to germination were observed using coconut water plus casein hydrolysate. Plantlets grown on media supplemented with pineapple juice and peptone had the highest plantlet length and weight. For embryogenesis, the highest regeneration rate (44%) and embryo number/explant (10.12 ± 2.08) were observed in young protocorm-like body (PLB) explants with 0.5 mg/L naphthalene acetic acid (NAA) and 1 mg/L thidiazuron (TDZ). During the acclimatization process, the scattered vascular tubes converted to fully developed vascular tissues, ensuring maximum sap flux. Gas chromatography–mass spectrometry analysis identified 1,2,3-propanetriol, monoacetate, 4H-pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl, and 2-butenedioic acid, 2-methyl-, (E)- as the most prevalent compounds. We reported higher contents of total phenolics and flavonoids and antioxidant activity compared to other terrestrial orchids. The glucomannan content (36.96%) was also higher than starch content (31.31%), comparable to those reported in other tuberous orchids. Based on the fragmentation of H. affine populations in the Middle East and Euro-Mediterranean countries due to over-harvesting, climate change, and/or human impact, our procedure offers a tool for the re-introduction of in vitro-raised plants to threatened areas.
Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques
Authors: ArulDass Stephen Dass , Jayagopal Prabhu
Published: December 2022

Many scientific researchers’ study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling.
Diagnostic Accuracy of AI for Opportunistic Screening of Abdominal Aortic Aneurysm in CT: A Systematic Review and Narrative Synthesis
Authors: Kodenko Maria R. , Vasilev Yuriy A. , Vladzymyrskyy Anton V. , Omelyanskaya Olga V. , Leonov Denis V. , Blokhin Ivan A. , Novik Vladimir P. , Kulberg Nicholas S. , Samorodov Andrey V. , Mokienko Olesya A. , Reshetnikov Roman V.
Published: December 2022

In this review, we focused on the applicability of artificial intelligence (AI) for opportunistic abdominal aortic aneurysm (AAA) detection in computed tomography (CT). We used the academic search system PubMed as the primary source for the literature search and Google Scholar as a supplementary source of evidence. We searched through 2 February 2022. All studies on automated AAA detection or segmentation in noncontrast abdominal CT were included. For bias assessment, we developed and used an adapted version of the QUADAS-2 checklist. We included eight studies with 355 cases, of which 273 (77%) contained AAA. The highest risk of bias and level of applicability concerns were observed for the “patient selection” domain, due to the 100% pathology rate in the majority (75%) of the studies. The mean sensitivity value was 95% (95% CI 100–87%), the mean specificity value was 96.6% (95% CI 100–75.7%), and the mean accuracy value was 95.2% (95% CI 100–54.5%). Half of the included studies performed diagnostic accuracy estimation, with only one study having data on all diagnostic accuracy metrics. Therefore, we conducted a narrative synthesis. Our findings indicate high study heterogeneity, requiring further research with balanced noncontrast CT datasets and adherence to reporting standards in order to validate the high sensitivity value obtained.
Additional Diffusion-Weighted Imaging with Background Body Signal Suppression (DWIBS) Improves Pre-Therapeutical Detection of Early-Stage (pT1a) Glottic Cancer: A Feasibility and Interobserver Reliability Study
Authors: Schleder Stephan , May Matthias , Habicher Werner , Dinkel Johannes , Schreyer Andreas G. , Gostian Antoniu-Oreste , Schicho Andreas
Published: December 2022

(1) Background: Early-stage glottic cancer is easily missed on magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) may improve diagnostic accuracy. Therefore, our aim was to assess the value of adding diffusion-weighted imaging with background body signal suppression (DWIBS) to pre-therapeutic MRI staging. (2) Methods: Two radiologists with 8 and 13 years of experience, blinded to each other’s findings, initially interpreted only standard MRI, later DWIBS alone, and afterward, standard MRI + DWIBS in 41 patients with histopathologically proven pT1a laryngeal cancer of the glottis. (3) Results: Detectability rates with standard MRI, DWIBS only, and standard MRI + DWIBS were 68–71%, 63–66%, and 73–76%, respectively. Moreover, interobserver reliability was calculated as good (κ = 0.712), very good (κ = 0.84), and good (κ = 0.69) for standard MRI, DWIBS only, and standard MRI + DWIBS, respectively. (4) Conclusions: Standard MRI, DWIBS alone, and standard MRI + DWIBS showed an encouraging detection rate, as well as distinct interobserver reliability in the diagnosis of early-stage laryngeal cancer when compared to the definitive histopathologic report.
Clinical Performance Evaluation of the NeuMoDx Flu A-B/RSV/SARS-CoV-2 Vantage Assay
Authors: Meletis Georgios , Tychala Areti , Gkeka Ioanna , Gkotzia Athanasia , Triantafyllou Aikaterini , Pappa Styliani , Exindari Maria , Gioula Georgia , Papa Anna , Skoura Lemonia
Published: December 2022

SARS-CoV-2 infections may present with various symptoms that are similar to those of other respiratory diseases. For this reason, the need for simultaneous detection of at least RSV and influenza viruses together with SARS-CoV-2 was evident from the early stages of the pandemic. In the present study, we evaluated the clinical performance of the NeuMoDx™ Flu A-B/RSV/SARS-CoV-2 Vantage Assay against the conventional low-plex PCR utilized to detect influenza A-B, RSV, and SARS-CoV-2. There were 115 known positive clinical samples and 35 negative controls obtained from asymptomatic health-care workers included in the study; 25 samples were positive for influenza viruses, 46 for RSV, and 44 for SARS-CoV-2. The sensitivity, specificity, positive predictive value, and negative predictive value of the evaluated method for influenza and SARS-CoV-2 were 100%. The Spearman correlation coefficient was 0.586 (p < 0.05) for influenza and 0.893 (p < 0.05) for SARS-CoV-2. The sensitivity of the aforementioned assay for RSV was 93.47%; the specificity and the positive predictive value were 100%, and the negative predictive value was 92.10%, while the Spearman correlation coefficient was not applicable for the RSV. Overall, the assay under evaluation was shown to be a reliable alternative for the simultaneous detection of influenza viruses, RSV and SARS-CoV-2.
Antibiotic Misuse in Wound Care: Can Bacterial Localization through Fluorescence Imaging Help?
Authors: Caputo Wayne J. , Monterosa Patricia , Beggs Donald
Published: December 2022

(1) Background: Systemic antibiotic use in chronic wounds is alarmingly high worldwide. Between 53% to 71% of patients are prescribed at least one course per chronic wound. Systemic antibiotic use should follow antibiotic stewardship guidelines and ought to be reserved for situations where their use is deemed supported by clinical indications. Unfortunately, in the field of wound care, indiscriminate and often inadequate use of systemic antibiotics is leading to both patient complications and worsening antibiotic resistance rates. Implementing novel tools that help clinicians prevent misuse or objectively determine the true need for systemic antibiotics is essential to reduce prescribing rates. (2) Methods: We present a compendium of available systemic antibiotic prescription rates in chronic wounds. The impact of various strategies used to improve these rates, as well as preliminary data on the impact of implementing fluorescence imaging technology to finesse wound status diagnosis, are presented. (3) Results: Interventions including feedback from wound care surveillance and treatment data registries as well as better diagnostic strategies can ameliorate antibiotic misuse. (4) Conclusions: Interventions that mitigate unnecessary antibiotic use are needed. Effective strategies include those that raise awareness of antibiotic overprescribing and those that enhance diagnosis of infection, such as fluorescence imaging.
Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction
Authors: Almulihi Ahmed , Saleh Hager , Hussien Ali Mohamed , Mostafa Sherif , El-Sappagh Shaker , Alnowaiser Khaled , Ali Abdelmgeid A. , Refaat Hassan Moatamad
Published: December 2022

Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the “silent killer,” is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper’s aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
Chronic Active T-Cell Mediated Kidney Rejection as a Clinically Significant Type of Allograft Loss?
Authors: Mizera Jakub , Pilch Justyna , Kamińska Dorota , Krajewska Magdalena , Donizy Piotr , Banasik Mirosław
Published: December 2022

The purpose of this article is to assess the present knowledge about chronic active (CA) T-cell mediated rejection (TCMR) of a kidney. In the research authors review current Banff diagnostic criteria used in kidney rejection, focus on their possible future evolution, and investigate the role of currently available molecular methods that could be implemented into the diagnostic scheme. Research also points out previously and currently available treatment methods applied to CA TCMR and takes into account possible side effects consequent upon the therapy. Moreover, attention is being paid to the CA TCMR coincidence with other kidney rejection types such as antibody-mediated rejection (ABMR) and its influence on the treatment approach. Authors also mark the possibility of non-HLA antibodies coexistence in patients with CA TCMR and describe its possible resonance on kidney allograft function. Nonetheless, it seems that current knowledge about CA TCMR is not sufficient and requires further investigation.
An In Vivo Study on the Development of Bacterial Microbiome on Clear Orthodontic Retainer
Authors: Velliyagounder Kabilan , Ardeshna Anil , Shah Serena
Published: December 2022

Objectives: The objective of this study was to see how the bacterial composition changes on clear orthodontic retainer over a 14-day period. Methods: Saliva and plaque samples collected from a clear retainer surface were obtained from five healthy volunteers receiving retainer treatment. Prior to clear retainer delivery, patients had not been wearing any other appliances. Patients were instructed to wear their clear retainer for the 14-day period, taking them off to eat and to clean them with a soft-bristle toothbrush. The bacterial composition was determined via Illumina MiSeq sequencing of the bacterial 16S rRNA. After bioinformatics processing using the QIIME pipeline, the intra- and intergroup biodiversity of the sample was analyzed. Results: The bacterial composition changed over a 14-day period in the saliva and on the clear retainer. When comparing the different phylum levels between saliva and clear retainer’ microbiota, the Firmicutes were significantly increased 1.26-fold (p = 0.0194) and 1.34-fold (p = 0.0123) after 7 and 14 days of retainer treatment when compared to saliva, respectively. The Campylobacteriota were significantly decreased 1.80-fold (p = 0.05) in the clear retainer when compared to saliva at 7 days. At the genus level, several microbiota were significantly increased in relative abundance in the clear retainer after the 14-day period. Conclusion: These findings reveal that the presence of a clear retainer in the mouth might lead to enamel changes or periodontal tissue destruction, especially after 14 days of use.
Multi-Task Learning for Compositional Data via Sparse Network Lasso
Authors: Okazaki Akira , Kawano Shuichi
Published: December 2022

Multi-task learning is a statistical methodology that aims to improve the generalization performances of estimation and prediction tasks by sharing common information among multiple tasks. On the other hand, compositional data consist of proportions as components summing to one. Because components of compositional data depend on each other, existing methods for multi-task learning cannot be directly applied to them. In the framework of multi-task learning, a network lasso regularization enables us to consider each sample as a single task and construct different models for each one. In this paper, we propose a multi-task learning method for compositional data using a sparse network lasso. We focus on a symmetric form of the log-contrast model, which is a regression model with compositional covariates. Our proposed method enables us to extract latent clusters and relevant variables for compositional data by considering relationships among samples. The effectiveness of the proposed method is evaluated through simulation studies and application to gut microbiome data. Both results show that the prediction accuracy of our proposed method is better than existing methods when information about relationships among samples is appropriately obtained.
SINR- and MI-Based Double-Robust Waveform Design
Authors: Xin Fengming , Li Jing , Wang Yan , Zhang Mingfeng
Published: December 2022

Owing to cognitive radar breaking the open-loop receiving–transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.
EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision
Authors: Xin Gangtao , Fan Pingyi
Published: December 2022

Semantic communication is not focused on improving the accuracy of transmitted symbols, but is concerned with expressing the expected meaning that the symbol sequence exactly carries. However, the measurement of semantic messages and their corresponding codebook generation are still open issues. Expansion, which integrates simple things into a complex system and even generates intelligence, is truly consistent with the evolution of the human language system. We apply this idea to the semantic communication system, quantifying semantic transmission by symbol sequences and investigating the semantic information system in a similar way as Shannon’s method for digital communication systems. This work is the first to discuss semantic expansion and knowledge collision in the semantic information framework. Some important theoretical results are presented, including the relationship between semantic expansion and the transmission information rate. We believe such a semantic information framework may provide a new paradigm for semantic communications, and semantic expansion and knowledge collision will be the cornerstone of semantic information theory.
Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm
Authors: Zhao Ji , Mu Yuzong , Qiao Yanping , Li Qiang
Published: December 2022

For system identification under impulsive-noise environments, the gradient-based generalized maximum correntropy criterion (GB-GMCC) algorithm can achieve a desirable filtering performance. However, the gradient method only uses the information of the first-order derivative, and the corresponding stagnation point of the method can be a maximum point, a minimum point or a saddle point, and thus the gradient method may not always be a good selection. Furthermore, GB-GMCC merely uses the current input signal to update the weight vector; facing the highly correlated input signal, the convergence rate of GB-GMCC will be dramatically damaged. To overcome these problems, based on the Newton recursion method and the data-reusing method, this paper proposes a robust adaptive filtering algorithm, which is called the Newton recursion-based data-reusing GMCC (NR-DR-GMCC). On the one hand, based on the Newton recursion method, NR-DR-GMCC can use the information of the second-order derivative to update the weight vector. On the other hand, by using the data-reusing method, our proposal uses the information of the latest M input vectors to improve the convergence performance of GB-GMCC. In addition, to further enhance the filtering performance of NR-DR-GMCC, a random strategy can be used to extract more information from the past M input vectors, and thus we obtain an enhanced NR-DR-GMCC algorithm, which is called the Newton recursion-based random data-reusing GMCC (NR-RDR-GMCC) algorithm. Compared with existing algorithms, simulation results under system identification and acoustic echo cancellation are conducted and validate that NR-RDR-GMCC can provide a better filtering performance in terms of filtering accuracy and convergence rate.
Initial Solution Generation and Diversified Variable Picking in Local Search for (Weighted) Partial MaxSAT
Authors: Zhang Zaijun , Zhou Jincheng , Wang Xiaoxia , Yang Heng , Fan Yi
Published: December 2022

The (weighted) partial maximum satisfiability ((W)PMS) problem is an important generalization of the classic problem of propositional (Boolean) satisfiability with a wide range of real-world applications. In this paper, we propose an initialization and a diversification strategy to improve local search for the (W)PMS problem. Our initialization strategy is based on a novel definition of variables’ structural entropy, and it aims to generate a solution that is close to a high-quality feasible one. Then, our diversification strategy picks a variable in two possible ways, depending on a parameter: continuing to pick variables with the best benefits or focusing on a clause with the greatest penalty and then selecting variables probabilistically. Based on these strategies, we developed a local search solver dubbed ImSATLike, as well as a hybrid solver ImSATLike-TT, and experimental results on (weighted) partial MaxSAT instances in recent MaxSAT Evaluations show that they outperform or have nearly the same performances as state-of-the-art local search and hybrid competitors, respectively, in general. Furthermore, we carried out experiments to confirm the individual impacts of each proposed strategy.
Effect of Back Pressure on Performances and Key Geometries of the Second Stage in a Highly Coupled Two-Stage Ejector
Authors: Yan Jia , Shu Yuetong , Wang Chen
Published: December 2022

In this paper, for a highly coupled two-stage ejector-based cooling cycle, the optimization of primary nozzle length and angle of the second-stage ejector under varied primary nozzle diameters of the second stage was conducted first. Next, the evaluation for the influence of variable back pressure on ER of the two-stage ejector was performed. Last, the identification of the effect of the variable back pressure on the key geometries of the two-stage ejector was carried out. The results revealed that: (1) with the increase of the nozzle diameter at the second stage, the ER of both stages decreased with the increases of the length and angle of the converging section of the second-stage primary nozzle; (2) the pressure lift ratio range of the second-stage ejector in the critical mode gradually increased with the increase of the nozzle diameter of the second-stage; (3) when the pressure lift ratio increased from 102% to 106%, the peak ER of the second-stage decreased, and the influence of the area ratio and nozzle exit position of the second-stage ejector on its ER was reduced; (4) with the increase of nozzle diameter of the second-stage, the influence of area ratio and nozzle exit position of the second-stage on the second-stage performance decreased; and (5) the optimal AR of the second stage decreased but the optimal nozzle exit position of the second stage kept constant with the pressure lift ratio of the two-stage ejector.
Methodology for Constructing an Experimental Investment Strategy Formed in Crisis Conditions
Authors: Ivanyuk Vera
Published: December 2022

This article proposes a neoclassical stock market portfolio based on the principles of dynamic response and constant adaptation to the market. The construction of a neoclassical investment portfolio begins with the conceptual development of an adaptive investment strategy. We suggest an algorithm for creating an adaptive investment portfolio. The conceptual model of the investment strategy is presented including the following mandatory components: evaluation, forecasting, investment, and adaptation. This model has the ability to adapt both in normal and in crisis periods of the market. As a description of the forecasting component, an additive mathematical model of the predictive ensemble is used, including seasonal, regression, and shock elements as well as a neural network.
How Corruption Is and Should Be Investigated by Economic Theory
Authors: Wawrosz Petr
Published: December 2022

The article analyzes how economic theory usually investigates corruption. It describes the main traditional economic theories dealing with this issue—principal-agent theory (agency theory) and rent-seeking theory—and it emphasizes that both face some problems, especially their neglect of some important factors as to why corruption occurs which prevents them from accurately analyzing this phenomenon and proposing solutions on how to fight against it. The article further discusses whether institutional economics can overcome these problems. We show that it does, but that it needs to more seriously consider the environment in which corruption occurs. Redistribution system theory can serve as a useful aid here because it reveals that the source of corruption is an environment of undesirable redistribution. The article provides the characteristics of this type of redistribution and shows how its reduction also leads to the reduction in corruption. It can be concluded that economic theory should not rely only on traditional approaches to corruption but should at least add institutional economics and redistribution system theory to its methods of corruption analysis.
School Adjustment and Academic Success in Qatari Secondary School Students: Associations with Well-Being and Emotional and Behavioral Problems
Authors: Al-Hendawi Maha , Kliewer Wendy , Hussein Esraa
Published: December 2022

Although the Qatari government has invested significantly in education over the past two decades, little research has comprehensively assessed factors associated with high school adjustment and academic success in the country. The present study addressed this gap with a sample of 576 students attending high school in Qatar (M age = 16.32, SD = 1.09; 63.7% male). Students self-reported their school adjustment and perceived academic performance as well as positive and negative indicators of health, including well-being and emotional and behavioral problems. Path models revealed that both well-being and problems contributed to school adjustment. Well-being and problems also contributed to academic performance, but these paths differed for males and females. For females, but not males, well-being was directly related to better performance; for males, but not females, problems contributed directly to worse performance and also operated through school adjustment to affect performance. Implications for promoting optimal school adjustment are discussed.
YOLO-RFF: An Industrial Defect Detection Method Based on Expanded Field of Feeling and Feature Fusion
Authors: Li Gang , Zhao Shilong , Zhou Mingle , Li Min , Shao Rui , Zhang Zekai , Han Delong
Published: December 2022

Aiming at the problems of low efficiency, high false detection rate, and poor real-time performance of current industrial defect detection methods, this paper proposes an industrial defect detection method based on an expanded perceptual field and feature fusion for practical industrial applications. First, to improve the real-time performance of the network, the original network structure is enhanced by using depth-separable convolution to reduce the computation while ensuring the detection accuracy, and the critical information extraction from the feature map is enhanced by using MECA (More Efficient Channel Attention) attention to the detection network. To reduce the loss of small target detail information caused by the pooling operation, the ASPF (Atrous Spatial Pyramid Fast) module is constructed using dilate convolution with different void rates to extract more contextual information. Secondly, a new feature fusion method is proposed to fuse more detailed information by introducing a shallower feature map and using a dense multiscale weighting method to improve detection accuracy. Finally, in the model optimization process, the K-means++ algorithm is used to reconstruct the prediction frame to speed up the model’s convergence and verify the effectiveness of the combination of the Mish activation function and the SIoU loss function. The NEU-DET steel dataset and PCB dataset is used to test the effectiveness of the proposed model, and compared to the original YOLOv5s, our method in terms of mAP metrics by 6.5% and 1.4%, and in F1 by 5.74% and 1.33%, enabling fast detection of industrial surface defects to meet the needs of real industry.
Simulation Study on Variable Pressure Margin Energy Recovery of Electric Loader Actuator
Authors: Mu Hongyun , Luo Yanlei , Luo Yu , Chen Lunjun
Published: December 2022

The conventional electric loader uses a motor instead of an engine, which aligns with the current energy-saving and emission-reduction trend. However, the motor’s speed control performance and overload capacity are under-utilized, and the actuator suffers from the potential energy waste problem of the boom arm. This study proposes a variable pressure margin energy recovery system for the electric loader actuator. It uses a combination of a permanent magnet synchronous motor (PMSM) and a quantitative pump. It can achieve variable pressure margin control and energy recovery through the pressure feedback closed-loop control. AMESim is used to build the simulation model based on the system control strategy, actuator, supercapacitor, and PMSM mathematical mode. Under typical working conditions, the simulation study is conducted on a 50-type wheel loader to obtain cylinder displacement, system energy recovery, and energy-saving performance. The simulation results show that the system is feasible and can effectively reduce energy consumption. Its energy recovery efficiency is 65.4%. The PMSM energy supply is reduced by 28.6% with the variable pressure margin control. It has high energy-saving performance, and the energy-saving efficiency is 38.5%. It provides a reference for research on energy-saving systems for electric construction machinery.
Analysis of the Influencing Factors on S-Band Sea Spikes
Authors: Zhao Peng , Wu Zhensen , Zhang Yushi , Zhu Dong
Published: December 2022

While detecting targets on the sea by radar that looks downward, sea spikes similar to the target echoes cause false alarms, especially at low grazing angles. To suppress the interference of sea spikes on target detection, it is important to study the factors influencing the occurrence probability of sea spikes during radar inspections. Based on measured sea clutter data, this study uses the amplitude threshold, duration time, and interval time to identify sea spikes from sea clutter and uses the pulse number per unit time to characterize their occurrence probability. The influences of the grazing angle, wave height, wave direction, and wind speed on the occurrence probability of sea spikes were analyzed. The results indicate that the occurrence probability of sea spikes increases exponentially with decreasing grazing angle in the range from 0.7° to 7.1°, and linearly with increasing wind speed. Wave direction has little or no influence on the probability of sea spikes. At the smaller grazing angles, from 0.7° to 1.7°, the influence of wave height on the probability of sea spikes’ occurrence is obvious, showing a linear trend, but in the range of 2.6° to 7.1°, the influence is not obvious. In addition, the occurrence probability of sea spikes is found greater at wave heights from 0.9 m to 1.1 m relative to other wave heights, which is worthy of further study.
Computer Vision-Based Kidney’s (HK-2) Damaged Cells Classification with Reconfigurable Hardware Accelerator (FPGA)
Authors: Ghani Arfan , Hodeify Rawad , See Chan , Keates Simeon , Lee Dah-Jye , Bouridane Ahmed
Published: December 2022

In medical and health sciences, the detection of cell injury plays an important role in diagnosis, personal treatment and disease prevention. Despite recent advancements in tools and methods for image classification, it is challenging to classify cell images with higher precision and accuracy. Cell classification based on computer vision offers significant benefits in biomedicine and healthcare. There have been studies reported where cell classification techniques have been complemented by Artificial Intelligence-based classifiers such as Convolutional Neural Networks. These classifiers suffer from the drawback of the scale of computational resources required for training and hence do not offer real-time classification capabilities for an embedded system platform. Field Programmable Gate Arrays (FPGAs) offer the flexibility of hardware reconfiguration and have emerged as a viable platform for algorithm acceleration. Given that the logic resources and on-chip memory available on a single device are still limited, hardware/software co-design is proposed where image pre-processing and network training were performed in software, and trained architectures were mapped onto an FPGA device (Nexys4DDR) for real-time cell classification. This paper demonstrates that the embedded hardware-based cell classifier performs with almost 100% accuracy in detecting different types of damaged kidney cells.
A Systematic Review on Machine Learning Techniques for Early Detection of Mental, Neurological and Laryngeal Disorders Using Patient’s Speech
Authors: Sayadi Mohammadjavad , Varadarajan Vijayakumar , Langarizadeh Mostafa , Bayazian Gholamreza , Torabinezhad Farhad
Published: December 2022

There is a substantial unmet need to diagnose speech-related disorders effectively. Machine learning (ML), as an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve these issues. The purpose of this study was to categorize and compare machine learning methods in the diagnosis of speech-based diseases. In this systematic review, a comprehensive search for publications was conducted on the Scopus, Web of Science, PubMed, IEEE and Cochrane databases from 2002–2022. From 533 search results, 48 articles were selected based on the eligibility criteria. Our findings suggest that the diagnosing of speech-based diseases using speech signals depends on culture, language and content of speech, gender, age, accent and many other factors. The use of machine-learning models on speech sounds is a promising pathway towards improving speech-based disease diagnosis and treatments in line with preventive and personalized medicine.
Characteristics of Dye-Sensitized Solar Cell under PWM Illumination: Toward Indoor Light-Energy Harvesting in the Solid-State Lighting Era
Authors: Tada Kazuya
Published: December 2022

The dye-sensitized solar cell (DSSC) has been on the market as a permanent power source for indoor IoT edge devices. In recent years, indoor illumination technology has been experiencing a drastic transition from incandescent and fluorescent lamps toward solid-state lighting devices with light-emitting diodes (LEDs). In addition to the high power efficiency, a virtue of LEDs is their prompt response, which enables precise change of the illumination level using pulse-width modulation (PWM) of the current source, and thus PWM illumination is commonly installed in society. The light intensity change from off to on states of an LED under PWM driving is literally infinity, which causes the lighting to flicker. The lighting flicker induces not only an optical illusion but also biological effects, including serious health problems, which can be mitigated by raising the modulation frequency. Because the peak intensity of a PWM illumination can be 100 times that of the average intensity, the indoor solar cell, which has a relatively high series resistance, is expected to underperform. In this paper, the characteristics of a commercial indoor DSSC under PWM illumination are studied. It is found that while PWM illumination at low frequency seriously deteriorates the performance of the DSSC, it recovers at high frequency. The latter feature is not found in indoor amorphous-Si solar cells, and the electrochemical impedance spectroscopy revealed that it stems from the electrochemical nature of some components of the series impedance in the DSSC, offering a key piece of evidence of the superiority for use in the modern indoor application of the DSSC over traditional amorphous-Si solar cells.
An Overview of Energy Access Solutions for Rural Healthcare Facilities
Authors: Olatomiwa Lanre , Sadiq Ahmad A. , Longe Omowunmi Mary , Ambafi James G. , Jack Kufre Esenowo , Abd'azeez Toyeeb Adekunle , Adeniyi Samuel
Published: December 2022

Quality in healthcare service is essential in giving rural dwellers a good standard of living. It has been established that many rural locations in Sub-Saharan Africa away from the grid connection have difficulty accessing electricity. The inaccessibility of reliable energy and essential medical equipment was the leading barrier to improved healthcare delivery in these rural locations. The deficiency of basic medical equipment to power essential services due to limited or unreliable electricity access has reduced rural healthcare workers’ care capabilities, resulting in higher mortality rates. This paper, therefore, reviews the existing energy solutions for rural healthcare facilities, thereby analysing different approaches and the geographical energy mix and ascertaining the effectiveness of various techniques and energy mix as solutions to effective healthcare delivery in healthcare centres. Hybrid Renewable Energy Sources (HRES) microsystems, like microgrids incorporated with solar panels and battery, is identified to ensure higher and more reliable energy access in rural healthcare centres. At the same time, the adoption of Demand Side Management (DSM) in the HRES deployment in countryside healthcare facilities is reported to decrease the initial cost of installation and improve efficiency. Lastly, in improving energy access, rural electrification planning is achieved through modelling tools related to energy access modelling.
Design and Analysis of an Offshore Wind Power to Ammonia Production System in Nova Scotia
Authors: Cunanan Carlo James , Elorza Casas Carlos Andrés , Yorke Mitchell , Fowler Michael , Wu Xiao-Yu
Published: December 2022

Green ammonia has potential as a zero-emissions energy vector in applications such as energy storage, transmission and distribution, and zero-emissions transportation. Renewable energy such as offshore wind energy has been proposed to power its production. This paper designed and analyzed an on-land small-scale power-to-ammonia (P2A) production system with a target nominal output of 15 tonnes of ammonia per day, which will use an 8 MW offshore turbine system off the coast of Nova Scotia, Canada as the main power source. The P2A system consists of a reverse osmosis system, a proton exchange membrane (PEM) electrolyser, a hydrogen storage tank, a nitrogen generator, a set of compressors and heat exchangers, an autothermal Haber-Bosch reactor, and an ammonia storage tank. The system uses an electrical grid as a back-up for when the wind energy is insufficient as the process assumes a steady state. Two scenarios were analyzed with Scenario 1 producing a steady state of 15 tonnes of ammonia per day, and Scenario 2 being one that switched production rates whenever wind speeds were low to 55% the nominal capacity. The results show that the grid connected P2A system has significant emissions for both scenarios, which is larger than the traditional fossil-fuel based ammonia production, when using the grid in provinces like Nova Scotia, even if it is just a back-up during low wind power generation. The levelized cost of ammonia (LCOA) was calculated to be at least 2323 CAD tonne−1 for both scenarios which is not cost competitive in this small production scale. Scaling up the whole system, reducing the reliance on the electricity grid, increasing service life, and decreasing windfarm costs could reduce the LCOA and make this P2A process more cost competitive.