bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
Authors: Wang Lei , Zhu Benchao , Chen Jingcao , Zhou Mingchao , Liu Zhengyan , Li Jinyan , Xu Chunmei
Published: January 2023

In a 25-Hz phase-sensitive track circuit, traction backflow is unevenly distributed in the two rails, resulting in interference caused by the 50 Hz unbalanced current, which leads to misoperation of relays and other equipment in the circuit. Focusing on the mechanism of unbalanced current generation, this paper probes the causes of track circuit equipment interference and innovatively analyzes the mechanism of the choke transformer and relay affected, in order to find a method to suppress the interference of the 25 Hz phase-sensitive track equipment. Firstly, the mechanism of unbalanced current generation is explained, and the influence of the unbalanced impulse current on the choke transformer and binary two-bit relay is analyzed. Secondly, the DC magnetic bias, the second side voltage of the choke transformer and the excitation current, flux density, core loss of choke transformer and relay under a different unbalance impulse current are simulated. Then, the unbalanced current simulation test, unbalanced current test during driving and grounding wire test are carried out. Finally, it is concluded that the unbalanced impulse current causes magnetic saturation of the choke transformer, then affects voltage sag of the relay coil, resulting in misoperation of equipment. The conclusions of this paper can play an important guiding role in studying the influence of unbalanced current and restraining the interference of the 25 Hz phase-sensitive track circuit.
Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
Authors: Alabdulatif Abdulatif , Al Asqah Muneerah , Moulahi Tarek , Zidi Salah
Published: January 2023

Machine learning-based (ML) systems are becoming the primary means of achieving the highest levels of productivity and effectiveness. Incorporating other advanced technologies, such as the Internet of Things (IoT), or e-Health systems, has made ML the first choice to help automate systems and predict future events. The execution environment of ML is always presenting contrasting types of threats, such as adversarial poisoning of training datasets or model parameters manipulation. Blockchain technology is known as a decentralized network of blocks that symbolizes means of protecting block content integrity and ensuring secure execution of operations.Existing studies partially incorporated Blockchain into the learning process. This paper proposes a more extensive secure way to protect the decision process of the learning model. Using smart contracts, this study executed the model’s decision by the reversal engineering of the learning model’s decision function from the extracted learning parameters. We deploy Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers decision functions on-chain for more comprehensive integration of Blockchain. The effectiveness of this proposed approach is measured by applying a case study of medical records. In a safe environment, SVM prediction scores were found to be higher than MLP. However, MLP had higher time efficiency.
Multi-Task Learning for Building Extraction and Change Detection from Remote Sensing Images
Authors: Hong Danyang , Qiu Chunping , Yu Anzhu , Quan Yujun , Liu Bing , Chen Xin
Published: January 2023

Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images is a crucial step toward practical BE and CD solutions, as in other DL-based applications. To better exploit the available labeled training data for representation learning, we propose a multi-task learning (MTL) network for simultaneous BE and CD, comprising the state-of-the-art (SOTA) powerful Swin transformer as a shared backbone network and multiple heads for predicting building labels and changes. Using the popular CD dataset the Wuhan University building change detection dataset (WHU-CD), we benchmarked detailed designs of the MTL network, including backbone and pre-training choices. With a selected optimal setting, the intersection over union (IoU) score was improved from 70 to 81 for the WHU-CD. The experimental results of different settings demonstrated the effectiveness of the proposed MTL method. In particular, we achieved top scores in BE and CD from optical images in the 2021 Gaofen Challenge. Our method also shows transferable performance on an unseen CD dataset, indicating high label efficiency.
Optimizing the Seeding Density of Human Mononuclear Cells to Improve the Purity of Highly Proliferative Mesenchymal Stem Cells
Authors: Nagai Hiroyuki , Miwa Akihiro , Yoneda Kenji , Fujisawa Koichi , Takami Taro
Published: January 2023

Mesenchymal stem cells (MSCs) hold considerable promise for regenerative medicine. Optimization of the seeding density of mononuclear cells (MNCs) improves the proliferative and differentiation potential of isolated MSCs. However, the underlying mechanism is unclear. We cultured human bone marrow MNCs at various seeding densities (4.0 × 104, 1.25 × 105, 2.5 × 105, 6.0 × 105, 1.25 × 106 cells/cm2) and examined MSC colony formation. At lower seeding densities (4.0 × 104, 1.25 × 105 cells/cm2), colonies varied in diameter and density, from dense to sparse. In these colonies, the proportion of highly proliferative MSCs increased over time. In contrast, lower proliferative MSCs enlarged more rapidly. Senescent cells were removed using a short detachment treatment. We found that these mechanisms increase the purity of highly proliferative MSCs. Thereafter, we compared MSCs isolated under optimized conditions with a higher density (1.25 × 106 cells/cm2). MSCs under optimized conditions exhibited significantly higher proliferative and differentiation potential into adipocytes and chondrocytes, except for osteocytes. We propose the following conditions to improve MSC quality: (1) optimizing MNC seeding density to form single-cell colonies; (2) adjusting incubation times to increase highly proliferative MSCs; and (3) establishing a detachment processing time that excludes senescent cells.
Identification and Verification of Biomarkers and Immune Infiltration in Obesity-Related Atrial Fibrillation
Authors: Xie Zhonghui , Liu Chuanbin , Lu Xu , Chen Zhijie , Zhang Nan , Wang Xinyan , Li Xiaoqian , Li Yang
Published: January 2023

Obesity is an independent risk factor for atrial fibrillation (AF). However, the mechanisms underlying this crosstalk are still being uncovered. Co-differentially expressed genes (co-DEGs) of AF and obesity microarrays were identified by bioinformatics analysis. Subsequently, functional enrichment, cell-type enrichment, and protein–protein interaction network analyses of co-DEGs were carried out. Then, we validated the hub genes by qRT-PCR of patients’ blood samples. Finally, CIBERSORT was utilized to evaluate the AF microarray to determine immune infiltration and the correlation between validated hub genes and immune cells. A total of 23 co-up-regulated DEGs in AF and obesity microarrays were identified, and these genes were enriched in inflammation- and immune-related function. The enriched cells were whole blood, CD33+ myeloid, and CD14+ monocytes. The hub genes were identified as MNDA, CYBB, CD86, FCGR2C, NCF2, LCP2, TLR8, HLA-DRA, LCP1, and PTPN22. All hub genes were only elevated in blood samples of obese-AF patients. The CIBERSORT analysis revealed that the AF patients’ left atrial appendage had increased infiltration of naïve B cells and decreased infiltration of memory B cells. The hub genes were related positively to naïve B cells and negatively to memory B cells. Ten hub genes may serve as biomarkers for obesity-related AF. These findings may also aid in comprehending pathophysiological mechanisms for obesity-related AF.
The Interaction between Mushroom Polysaccharides and Gut Microbiota and Their Effect on Human Health: A Review
Authors: Zhao Jiahui , Hu Yixin , Qian Chao , Hussain Muhammad , Liu Shizhu , Zhang Anqiang , He Rongjun , Sun Peilong
Published: January 2023

Mushroom polysaccharides are a kind of biological macromolecule extracted from the fruiting body, mycelium or fermentation liquid of edible fungi. In recent years, the research on mushroom polysaccharides for alleviating metabolic diseases, inflammatory bowel diseases, cancers and other symptoms by changing the intestinal microenvironment has been increasing. Mushroom polysaccharides could promote human health by regulating gut microbiota, increasing the production of short-chain fatty acids, improving intestinal mucosal barrier, regulating lipid metabolism and activating specific signaling pathways. Notably, these biological activities are closely related to the molecular weight, monosaccharide composition and type of the glycosidic bond of mushroom polysaccharide. This review aims to summarize the latest studies: (1) Regulatory effects of mushroom polysaccharides on gut microbiota; (2) The effect of mushroom polysaccharide structure on gut microbiota; (3) Metabolism of mushroom polysaccharides by gut microbiota; and (4) Effects of mushroom polysaccharides on gut microbe-mediated diseases. It provides a theoretical basis for further exploring the mechanism of mushroom polysaccharides for regulating gut microbiota and gives a reference for developing and utilizing mushroom polysaccharides as promising prebiotics in the future.
Plier Ligands for Trapping Neurotransmitters into Complexes for Sensitive Analysis by SERS Spectroscopy
Authors: Eremina Olga E. , Kapitanova Olesya O. , Medved'ko Alexei V. , Zelenetskaya Alexandra S. , Egorova Bayirta V. , Shekhovtsova Tatyana N. , Vatsadze Sergey Z. , Veselova Irina A.
Published: January 2023

Catecholamines–dopamine, noradrenaline and adrenaline are important biomarkers of neurotransmitter metabolism, indicating neuroendocrine tumors and neurodegenerative diseases. Surface-enhanced Raman spectroscopy (SERS) is a promising analytical technique with unprecedented multiplexing capabilities. However, not all important analytes exhibit strong SERS signals on stable and robust nanostructured substrates. In this work, we propose a novel indicator system based on the formation of mixed ligand complexes with bispidine-based bis-azole ligands which can serve as pliers to trap Cu(II) ions and stabilize its complexes with catecholamines. Four synthesized ligands with different functional groups: carboxyl, amino, benzyl, and methoxybenzyl, were applied for forming stable complexes to shift maximum absorbance of catecholamines from the ultraviolet region to 570–600 nm. A new absorbance band in the visible range resonates with the local surface plasmon resonance (LSPR) band of metal nanoparticles and most used laser wavelengths. This match allowed use of Molecular Immobilization and Resonant Raman Amplification by Complex-Loaded Enhancers (MIRRACLE) methodology to measure intense Raman signals on a nanostructured silver-based SERS-active substrate. The synthesized plier-like ligands fixed and stabilized catecholamine complexes with Cu(II) on the SERS sensor surface, which facilitated the determination of dopamine in a 3.2 × 10−12–1 × 10−8 M concentration range.
The Experience of Self-Transcendence in Social Activists
Authors: Barton Carol , Hart Rona
Published: January 2023

Every day the wellbeing of disadvantaged individuals and communities is being transformed through the activities of self-transcendent social activists. The positive contagion generated by their actions is felt globally through influence, replication, leadership training and education. These people are visionary, brave, and describe their lives as joyful, deeply fulfilled, and impactful. Seeking no personal recognition or accolade, born from a deep feeling of connectedness and a vision of how life could be better, participants describe the factors that influenced their decision to dedicate their lives to serving the greater good. Using Constructivist Grounded Theory, in-depth semi structured interviews were carried out with eight participants who self-identified as self-transcendent social activists, who have initiated non-mandated and not-for-profit community action. Data was analyzed to explore each participant’s personal experiences of self-transcendence and how being self-transcendent has manifested their life choices. The findings present a definition of ‘self-transcendent social activism’ and a theoretical model that explains the development of participants’ activism: trigger, activate, maintain and sustain, resulting in an impact experienced at three levels - individual, community and global. Theoretical and practical implications are discussed.
A Bibliometric and Content Review of Carbon Emission Analysis for Building Construction
Authors: Wang Yuanhang , Jiang Zhiwen , Li Lingzhi , Qi Yujun , Sun Jianwei , Jiang Zhangzhang
Published: January 2023

To combat climate change and meet energy conservation and emission reduction goals, the building sector must adopt low-carbon technologies and low-carbon management methods. To systematically explore existing research areas and track future research trends of carbon emission in the construction stage (CECS), this study conducts a bibliometric and content analysis of CECS studies. 563 relevant publications published between 2000 and 2022 are examined and analyzed using data from the Web of Science (WoS) core collection database. The findings reveal that studies of CECS have evolved through three stages: preliminary exploratory period, stable development period, and rapid development period. In addition, the literature co-citation network and content analysis classify the 13 found co-citation clusters into four knowledge domains: sources definition, data statistics, assessment methods, and carbon reduction strategies. Finally, a knowledge map of CECS studies is presented, outlining significant aspects of research, existing gaps in knowledge, and directions for future study. This work will make it easier for academics and professionals to pinpoint promising areas of study, fill in knowledge gaps, and broaden the scope of existing research on CECS.
Embodied Carbon Emissions in China’s Building Sector: Historical Track from 2005 to 2020
Authors: Zhu Chen , Yang Zhihan , Huang Boyu , Li Xiaodong
Published: January 2023

China’s large-scale construction has led to massive energy consumption and carbon emissions. The embodied carbon emissions (ECs) of China’s building sector play a key role in realizing national emission reduction targets. Currently, the understanding of the status quo of ECs is vague and inconsistent, and the existing accounting models still have several limitations. Therefore, this study develops two improved models (i.e., the process-based and the input-output-based life cycle assessment models) and dynamic accounting datasets to reveal historical trends and emission characteristics of ECs from 2005 to 2020. The results show that the total ECs in 2020 were as high as 2.28 billion tCO2, accounting for 25.2% of China’s total energy-related carbon emissions. The indirect ECs are the largest contributor, representing 95.9% of the total building ECs. The ECs increased quickly at first and entered a plateau, stable at about 2.2 billion tCO2 after 2015. From 2005 to 2020, the total building ECs contributed 38.7% to the national carbon emission growth, while the intensity of ECs showed a downward trend, indicating that the increase in China’s building ECs is scale-driven. This study provides sound methodological, and data support for emission tracing and the low-carbon development of China’s building sector.
Construction of Multi-Defective ZnMn2O4/Carbon Nitride Three-Dimensional System for Highly Efficient Photocatalytic Sulfamethoxazole Degradation
Authors: Xu Yandong , Liao Jianjun , Zhang Linlin , Li Yakun , Ge Chengjun
Published: January 2023

Rational design of composite nanostructured photocatalytic systems with good sunlight absorption capacity and efficient charge separation and transfer ability is an urgent problem to be solved in photocatalysis research. Here, a ZnMn2O4 decorated three-dimensional carbon nitride with O, C co-doping, and nitrogen defect composite photocatalytic system was prepared using a simple hydrothermal method and subsequent calcination method. For the photocatalytic reactions, the presence of heterostructures, C, O co-doping, and nitrogen defects greatly promotes the separation and transfer of charges at the semiconductor/semiconductor interface under the local electric field, thereby extending its service life. The photocatalytic degradation efficiency of sulfamethoxazole in water is as high as 94.3% under the synergistic effects, which is also suitable for the complex water environment. In addition, the synthesized photocatalyst has good chemical stability and recyclability. This study provides a new opportunity to solve the problem of environmental pollution.
Large-Scale Preparation of Ultrathin Bimetallic Nickel Iron Sulfides Branch Nanoflake Arrays for Enhanced Hydrogen Evolution Reaction
Authors: Deng Shengjue , Liu Changsheng , Zhang Yan , Ji Yingxi , Mei Bingbao , Yao Zhendong , Lin Shiwei
Published: January 2023

Developing the large-scale preparation of non-noble metal catalysts with high performance is crucial for promoting the electrochemical production of hydrogen from water. In this work, a novel TiO2@FeNi2S4 (TiO2@FNS) branch nanoflake array on Ni foam can be prepared at a large scale (50 cm2) by combining an atomic-layer-deposited (ALD) TiO2 skeleton with one-step facile low-temperature (<100 °C) sulfurization method. As-prepared TiO2@FNS arrays exhibit excellent hydrogen evolution reaction (HER) performance with an overpotential of 97 mV at 10 mA cm−2, superior to the FNS counterpart (without TiO2 coating) and other reported catalysts. The enhanced HER catalytic performance of TiO2@FNS is attributed to the increased specific surface area and improved structural stability due to the introduction of TiO2 coating. Moreover, theoretical calculations also show that the bimetallic NFS structure is more favorable to the dissociation of water molecule and the desorption of H than the monometallic Ni3S2 counterpart. With the combination of experimental results and theoretical calculations, this work has enlightened a new way of exploring high-efficient catalysts for HER.
Applications of Fluorescent Carbon Dots as Photocatalysts: A Review
Authors: Sendão Ricardo M. S. , Esteves da Silva Joaquim C. G. , Pinto da Silva Luís
Published: January 2023

Carbon dots (CDs) have attracted considerable interest from the scientific community due to their exceptional properties, such as high photoluminescence, broadband absorption, low toxicity, water solubility and (photo)chemical stability. As a result, they have been applied in several fields, such as sensing, bioimaging, artificial lighting and catalysis. In particular, CDs may act as sole photocatalysts or as part of photocatalytic nanocomposites. This study aims to provide a comprehensive review on the use of CDs as sole photocatalysts in the areas of hydrogen production via water splitting, photodegradation of organic pollutants and photoreduction and metal removal from wastewaters. Furthermore, key limitations preventing a wider use of CDs as photocatalysts are pointed out. It is our hope that this review will serve as a basis on which researchers may find useful information to develop sustainable methodologies for the synthesis and use of photocatalytic CDs.
Retinoic Acid Signalling in the Pineal Gland Is Conserved across Mammalian Species and Its Transcriptional Activity Is Inhibited by Melatonin
Authors: Ashton Anna , Clark Jason , Fedo Julia , Sementilli Angelo , Fragoso Yara D. , McCaffery Peter
Published: January 2023

The pineal gland is integral to the circadian timing system due to its role in nightly melatonin production. Retinoic acid (RA) is a potent regulator of gene transcription and has previously been found to exhibit diurnal changes in synthesis and signalling in the rat pineal gland. This study investigated the potential for the interaction of these two systems. PCR was used to study gene expression in mouse and human pineal glands, ex-vivo organotypic cultured rat pineal gland and cell lines. The mouse and human pineal glands were both found to express the necessary components required for RA signalling. RA influences the circadian clock in the brain, therefore the short-term effect of RA on clock gene expression was determined in ex vivo rat pineal glands but was not found to rapidly regulate Per1, Per2, Bmal1, or Cry1. The interaction between RA and melatonin was also investigated and, unexpectedly, melatonin was found to suppress the induction of gene transcription by RA. This study demonstrates that pineal expression of the RA signalling system is conserved across mammalian species. There is no short-term regulation of the circadian clock but an inhibitory effect of melatonin on RA transcriptional activity was demonstrated, suggesting that there may be functional cross-talk between these systems.
Etiological Roles of p75NTR in a Mouse Model of Wet Age-Related Macular Degeneration
Authors: Subirada Paula Virginia , Tovo Albana , Vaglienti María Victoria , Luna Pinto José Domingo , Saragovi Horacio Uri , Sánchez Maria Cecilia , Anastasía Agustín , Barcelona Pablo Federico
Published: January 2023

Choroidal neovascularization (CNV) is a pathological angiogenesis of the choroidal plexus of the retina and is a key feature in the wet form of age-related macular degeneration. Mononuclear phagocytic cells (MPCs) are known to accumulate in the subretinal space, generating a chronic inflammatory state that promotes the growth of the choroidal neovasculature. However, how the MPCs are recruited and activated to promote CNV pathology is not fully understood. Using genetic and pharmacological tools in a mouse model of laser-induced CNV, we demonstrate a role for the p75 neurotrophin receptor (p75NTR) in the recruitment of MPCs, in glial activation, and in vascular alterations. After laser injury, expression of p75NTR is increased in activated Muller glial cells near the CNV area in the retina and the retinal pigmented epithelium (RPE)-choroid. In p75NTR knockout mice (p75NTR KO) with CNV, there is significantly reduced recruitment of MPCs, reduced glial activation, reduced CNV area, and the retinal function is preserved, as compared to wild type mice with CNV. Notably, a single intravitreal injection of a pharmacological p75NTR antagonist in wild type mice with CNV phenocopied the results of the p75NTR KO mice. Our results demonstrate that p75NTR is etiological in the development of CNV.
The Correlation between Air and Water Vapour Permeability of Textiles
Authors: Adámek Karel , Havelka Antonin , Kůs Zdenek , Mazari Adnan
Published: January 2023

The permeability of fiber-based materials and general medical textiles is very important, and it is commonly believed that air permeability and water permeability are correlated. This property is expressed as the breathability of the material. Knowing the air permeability is helpful in predicting the water vapour permeability as well, but whether this really works for textiles at different pressures and humidities still needs to be explored. In this study, this hypothesis is examined, and it is discovered that this phenomenon does not show significant corelation. Moreover, the non-standardised unit of measurement of breathability makes the comparison much more complex in terms of textiles. The articles focuses on any possible relationship between the air and water vapour permeability of textiles.
An Experimental Study on Adhesion Strength of Offshore Atmospheric Icing on a Wind Turbine Blade Airfoil
Authors: Mu Zhongqiu , Li Yan , Guo Wenfeng , Shen He , Tagawa Kotaro
Published: January 2023

When wind turbines work in a cold and humid environment, especially offshore condition, ice accretion on the blade surfaces has a negative effect on the aerodynamic performance. In order to remove the ice from the wind turbine blade, the adhesive characteristics of atmospheric icing on the blade surface should be mastered under various conditions. The objective of this study is to evaluate the effects of offshore atmospheric conditions, including wind speeds, ambient temperatures and, especially, the salt contents on ice adhesion strength for wind turbine blades. The experiments were conducted on a NACA0018 blade airfoil under conditions including an ambient temperature of −3 °C~−15 °C, wind speed of 6 m/s~15 m/s and salt content of 1~20 mg/m3. The results showed that salt content was the most important factor affecting the ice adhesion strength, followed by ambient temperature and wind speed. The interactive effect of wind speed and salt content, ambient temperature and salt content were extremely significant. The research can provide a reference for the anti-icing for offshore wind turbines.
An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments
Authors: Oliveira Rita , Ferreira Joana , Azevedo Luís Filipe , Almeida Isabel F.
Published: January 2023

Skin type classification is important because it provides guidance for professionals and consumers to recommend and select the most appropriate cosmetic products and skin care protocols and it is also important in clinical research. Several methods have been proposed for classifying skin typologies such as non-invasive bioengineering tools (examples: Corneometer® and Sebumeter®), visual and tactile methods (subjective methods that evaluate skin appearance, texture, temperature, and abnormalities), artificial intelligence-based tools and instruments (examples: visual rating scales, and self-report instruments). Examples of known visual rating scales used to classify skin aging are the Griffiths Photonumeric Scale, the Glogau Scale, and the SCINEXA Scale. The Fitzpatrick Skin Phototype Classification and the Baumann Skin Type System are some of the self-report instruments used for skin type classification. Despite the diversity of methods to classify skin type and degree of skin aging, data on instruments are scarce and not adequately compiled. Validation in larger samples and with individuals of different ethnicities and geographic locations is needed to promote a more universal use. Visual rating scales and instruments are interesting tools that allow the skin to be promptly and efficiently examined, without using costly or complex equipment, and are very useful in a clinical or self-assessment context.
Introducing UWF-ZeekData22: A Comprehensive Network Traffic Dataset Based on the MITRE ATT&CK Framework
Authors: Bagui Sikha S. , Mink Dustin , Bagui Subhash C. , Ghosh Tirthankar , Plenkers Russel , McElroy Tom , Dulaney Stephan , Shabanali Sajida
Published: January 2023

With the rapid rate at which networking technologies are changing, there is a need to regularly update network activity datasets to accurately reflect the current state of network infrastructure/traffic. The uniqueness of this work was that this was the first network dataset collected using Zeek and labelled using the MITRE ATT&CK framework. In addition to identifying attack traffic, the MITRE ATT&CK framework allows for the detection of adversary behavior leading to an attack. It can also be used to develop user profiles of groups intending to perform attacks. This paper also outlined how both the cyber range and hadoop’s big data platform were used for creating this network traffic data repository. The data was collected using Security Onion in two formats: Zeek and PCAPs. Mission logs, which contained the MITRE ATT&CK data, were used to label the network attack data. The data was transferred daily from the Security Onion virtual machine running on a cyber range to the big-data platform, Hadoop’s distributed file system. This dataset, UWF-ZeekData22, is publicly available at datasets.uwf.edu.
MoS2 Quantum Dot Modified Electrode: An Efficient Probe for Electrochemical Detection of Hydrazine
Authors: Roy Susmita , Sharma Sarda , Chappanda Karumbaiah N. , Chakraborty Chanchal
Published: January 2023

The development of an effective sensor system that can detect carcinogenic hydrazine is of prime scientific interest for the protection of human health and the environment. In the present study, MoS2 quantum dots (QDs) with an average diameter of ~5 nm were synthesized using a facile one-step, bottom-up hydrothermal method using cysteine as reducing as well as capping agents. The presence of cysteine was evaluated by FTIR spectroscopy. The synthesized MoS2 QDs were applied to modify the conventional glassy carbon electrode (GCE) in order to detect hydrazine electrochemically in neutral pH conditions. In the cyclic voltammetry (CV) study, the MoS2 QDs-modified electrode revealed much better catalytic activities for hydrazine electro-oxidation compared to the bare GCE surface. The smaller size of the QDs with high surface area and the presence of carboxylic acid containing cysteine on the surface of the QDs enhanced the adsorption as well as the electrocatalytic activity. The amperometric response of MoS2-QD-modified GCE unveiled excellent electrocatalytic sensing properties towards neurotoxic hydrazine with a very high sensitivity of 990 μAmM−1cm−2 (R2 = 0.998), low LOD of 34.8 μM, and a broad linear range. Moreover, this high-sensitive, binder and conducting filler-free MoS2-QD-based sensing system is very promising in agile amperometric detection of neurotoxic hydrazine for environmental monitoring in industrial sectors.
Malignant Prostate Tissue Is Associated with Different Microbiome Gene Functions
Authors: Kim Jae Heon , Seo Hoonhee , Kim Sukyung , Ul-Haq Asad , Song Ho-Yeon , Song Yun Seob
Published: January 2023

Specific microorganisms and changes in the constituents of the microbiome are linked with pathologies in humans, such as malignancy. Within the prostate, certain bacterial communities may locate advantageous conditions and establish themselves, thus outperforming alternative species. In this study, a comparison of malignant (MT) and benign prostate tissues (BT) or benign prostate hyperplasia (BPH) was performed in order to delineate the respective microbiomes in each sample type and to determine their pertinence to prostatic tumourigenesis. Specimens of MT (n = 26) and PT (n = 13)/BPH (n = 10) were acquired from patients. No variations in the make-up of the microbiome were seen when MT and PT specimens were compared. Changes in the bacterial constituents and functional genes were seen in the specimens obtained from patients with MT when contrasted against samples from those with BPH. Pelomonas was the genus with the highest abundance in MT specimens. It is proposed that dissimilar microbiome gene functions are present in the contexts of MT and PT samples.
Potentially Virulent Multi-Drug Resistant Escherichia fergusonii Isolated from Inanimate Surface in a Medical University: Omphisa fuscidentalis as an Alternative for Bacterial Virulence Determination
Authors: Ilsan Noor Andryan , Yunita Melda , Dewi Nurul Kusuma , Irham Lalu Muhammad , Sipriyadi  , Nurfajriah Siti , Inggraini Maulin
Published: January 2023

Multi-drug resistant (MDR) bacteria are becoming a worldwide problem due to limited options for treatment. Moreover, patients infected by MDR with highly virulent accessories are worsening the symptoms, even to the point of causing death. In this study, we isolated bacteria from 14 inanimate surfaces that could potentially be reservoirs for the spread of bacterial infections in the medical university. Blood agar media was used for bacterial isolation. The bacterial colony that showed hemolytic activities on each surface was tested for antimicrobial susceptibility against eight different antibiotics. We found that MDR bacterium, namely TB1, which was isolated from a toilet bowl, was non-susceptible to ampicillin, imipenem, chloramphenicol, amoxicillin-clavulanic acid, gentamicin, and tetracycline. Another MDR bacterium isolated from the mobile phone screen of security officers, namely HSO, was resistant to chloramphenicol, gentamicin, tetracycline, and cefixime. An in vivo virulence test of bacterial isolates used Omphisa fuscidentalis larvae as an alternative to Galleria mellonella larvae for the infection model. A virulence test of TB1 in O. fuscidentalis larvae revealed 20% survival in the bacterial density of 104 and 105 CFU/larvae; and 0% survival in the bacterial density of 106 CFU/larvae at 24 h after injection. Bacterial identification was performed for TB1 as a potential virulent isolate. Bacterial identification using partial 16s rRNA gene showed that TB1 exhibited 99.84% identity to Escherichia fergusonii 2611. This study concludes that TB1 is a potentially virulent MDR E. fergusonii isolated from toilet bowls at a medical university.
Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions
Authors: Bhachawat Saransh , Shriram Eashwar , Srinivasan Kathiravan , Hu Yuh-Chung
Published: January 2023

Degenerative nerve diseases such as Alzheimer’s and Parkinson’s diseases have always been a global issue of concern. Approximately 1/6th of the world’s population suffers from these disorders, yet there are no definitive solutions to cure these diseases after the symptoms set in. The best way to treat these disorders is to detect them at an earlier stage. Many of these diseases are genetic; this enables machine learning algorithms to give inferences based on the patient’s medical records and history. Machine learning algorithms such as deep neural networks are also critical for the early identification of degenerative nerve diseases. The significant applications of machine learning and deep learning in early diagnosis and establishing potential therapies for degenerative nerve diseases have motivated us to work on this review paper. Through this review, we covered various machine learning and deep learning algorithms and their application in the diagnosis of degenerative nerve diseases, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, we also included the recent advancements in each of these models, which improved their capabilities for classifying degenerative nerve diseases. The limitations of each of these methods are also discussed. In the conclusion, we mention open research challenges and various alternative technologies, such as virtual reality and Big data analytics, which can be useful for the diagnosis of degenerative nerve diseases.
Aero-Propulsive Interactions between UAV Wing and Distributed Propellers Due to Their Relative Position
Authors: Ciliberti Danilo , Della Vecchia Pierluigi , Orticalco Vincenzo , Nicolosi Fabrizio
Published: January 2023

The purpose of this paper is the evaluation of the aero-propulsive effects on a UAV wing model with distributed propulsion. An array of three propellers is placed ahead of the leading edge of a rectangular wing with flap. The investigation was performed with high-fidelity numerical analyses to provide insights into the phenomenology and to screen the interesting positions to be validated in the wind tunnel. The propellers’ array is moved into twelve different positions, allowing longitudinal and vertical translations. The wing has an untwisted and constant section profile, with a single slot trailing-edge flap that is deflected into three positions. The flap span is entirely covered by the propellers’ blowing. Results show an increment of lift, drag, and pitching moment coefficients with distributed propellers enabled. For a given thrust level, the magnitude of such increments depends on the propellers’ positions, the flap configuration, and the angle of attack. The lift enhancement sought in distributed propulsion applications comes at the expense of a significant increase in drag and pitching moment magnitude. In some combinations, the wing’s contribution to the aircraft longitudinal stability is severely affected. Conversely, the propellers’ inflow is altered such that thrust is increased in all the investigated configurations, with a small reduction of propulsive efficiency.
Research on Adaptive Prescribed Performance Control Method Based on Online Aerodynamics Identification
Authors: An Shuaibin , Zang Jianwen , Yan Ming , Zhu Baiyang , Liu Jun
Published: January 2023

Wide-speed-range vehicles are characterized by high flight altitude and high speed, with significant changes in the flight environment. Due to the strong uncertainty of its aerodynamic characteristics, higher requirements are imposed on attitude control. In this paper, an adaptive prescribed performance control method based on online aerodynamic identification is proposed, which consists of two parts: an online aerodynamic parameter identification method and an adaptive attitude control method based on the pre-defined parameters of the control system. The aerodynamic parameter identification is divided into offline design and online design. In the offline design, neural networks are used to fit nonlinear aerodynamic characteristics. In the online design, a nonlinear recursive identification method is used to correct the errors of the offline fitted model. The adaptive attitude control is based on the conventional control method and updates the control gain in real time according to the desired system parameters to enhance the robustness of the controller. Finally, the effectiveness of the offline neural network and online discrimination correction is verified by mathematical simulations, and the effectiveness and robustness of the adaptive control proposed in this paper are verified by comparative simulation.
Narrating Ancient Roman Heritage through Drawings and Digital Architectural Representation: From Historical Archives, UAV and LIDAR to Virtual-Visual Storytelling and HBIM Projects
Authors: Banfi Fabrizio , Roascio Stefano , Mandelli Alessandro , Stanga Chiara
Published: January 2023

One of the main objectives of today’s archaeological sites and museums is the development of research, understood as the interpretation and contextualisation of tangible and intangible cultural heritage to broaden the knowledge and accessibility of archaeological parks often unknown to visitors and the public on a large scale. In this perspective, the Appia Antica Archaeological Park aims to support research in digitising infrastructures and archaeological contexts of high historical and cultural value to plan short- and medium-term preservation and maintenance projects. In this context, unmanned aerial vehicles (UAVs) are tools with enormous potential in survey, inspection and digitisation, providing the basis for the subsequent phases of data interpretation, representation and material analysis. Thanks to the photorealistic reconstruction of dense structure from motion (DSfM) in the application of structural inspections, today it is possible to intercept the geometry and material conditions of small, medium and large structures, reducing the costs of inspections, limiting the interruption of the public and providing professionals and visitors with a better volumetric understanding of the system. However, inserting information that gradually accumulates throughout the process requires advanced 3D digital representation techniques, such as HBIM (historic building information modelling), scan-to-BIM approach and interactive forms, such as virtual and augmented reality (VR-AR). For these reasons, this study summarises the experience and lessons learned from the UAV inspection of three research case studies at archaeological, architectural, and infrastructure scales to increase awareness of the Roman-built heritage.
Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction
Authors: Zhang Mingyu , Gao Fei , Yang Wuping , Zhang Haoran
Published: January 2023

This work suggests an enhanced natural environment animal detection algorithm based on YOLOv5s to address the issues of low detection accuracy and sluggish detection speed when automatically detecting and classifying large animals in natural environments. To increase the detection speed of the model, the algorithm first enhances the SPP by switching the parallel connection of the original maximum pooling layer for a series connection. It then expands the model’s receptive field using the dataset from this paper to enhance the feature fusion network by stacking the feature pyramid network structure as a whole; secondly, it introduces the GSConv module, which combines standard convolution, depth-separable convolution, and hybrid channels to reduce network parameters and computation, making the model lightweight and easier to deploy to endpoints. At the same time, GS bottleneck is used to replace the Bottleneck module in C3, which divides the input feature map into two channels and assigns different weights to them. The two channels are combined and connected in accordance with the number of channels, which enhances the model’s ability to express non-linear functions and resolves the gradient disappearance issue. Wildlife images are obtained from the OpenImages public dataset and real-life shots. The experimental results show that the improved YOLOv5s algorithm proposed in this paper reduces the computational effort of the model compared to the original algorithm, while also providing an improvement in both detection accuracy and speed, and it can be well applied to the real-time detection of animals in natural environments.
An Artificial Intelligence-Assisted Expectation Propagation Detection for MIMO Systems
Authors: Xin Pengzhe , Wang Hailong , Liu Yu , Chen Jianping , Song Tiecheng , Wang Dongming
Published: January 2023

Multiple-input multiple-output (MIMO) technology is one of the key technical approaches to improve the spectrum efficiency of wireless communication. Modern communication systems employ MIMO and high-order quadrature amplitude modulation (QAM) to maximize spectral efficiency. However, with the increase in the number of antennas and modulation orders, it is very challenging to design a low-complexity and high-efficiency MIMO receiver. In recent years, with the rapid development of new technologies such as artificial intelligence, more and more researchers have tried to apply machine learning techniques in the field of communication to break through the performance of traditional communication algorithms. In this paper, we propose a new low-complexity MIMO detection algorithm: an artificial intelligence-assisted expectation propagation (EP) detection algorithm. Neural network models are used to learn and map some of the time-consuming steps in the EP detection algorithm, converting the complex operation process into a few matrix multiplication operations in order to reduce the complexity of the detection algorithm. It is verified that the method proposed in this paper can approximate the performance of the original EP detection algorithm with reduced complexity and is applicable in different scenarios.
Numerical Robustness Evaluation of Floating-Point Closed-Loop Control Based on Interval Analysis
Authors: Savi Filippo , Farjudian Amin , Buticchi Giampaolo , Barater Davide , Franceschini Giovanni
Published: January 2023

Power-electronics-based systems have penetrated into several critical sectors, such as the industry, power generation, energy transmission and distribution, and transportation. In this context, the system’s control, often implemented in real-time processing units, has to meet stringent requirements in terms of safety and repeatability. Given the growing complexity of the implemented algorithms, floating-point arithmetic is being increasingly adopted for high-performance systems. This paper proposes to assess the numerical stability of the control algorithms by means of an interval analysis. The case study of an electric drive is considered, given the wide adoption of such systems and the importance they hold for the safety of the applications. In particular, two different control strategies—the resonant control and the vector space decomposition—are examined, and a sensitivity analysis based on the proposed technique highlights the different characteristics of the two with respect to numerical stability. The proposed method shows how the resonant control is more robust to variations of the controller gain coefficients with respect to the numerical stability, which could make it the preferred choice for mission-critical electric drive control.
A High-Capacity Coverless Information Hiding Based on the Lowest and Highest Image Fragments
Authors: Anggriani Kurnia , Chiou Shu-Fen , Wu Nan-I , Hwang Min-Shiang
Published: January 2023

Coverless data hiding is resistant to steganalytical tool attacks because a stego image is not altered. On the other hand, one of its flaws is its limited hiding capacity. Recently, a coverless data-hiding method, known as the coverless information-hiding method based on the most significant bit of the cover image (CIHMSB), has been developed. This uses the most significant bit value in the cover image by calculating the average intensity value on the fragment and mapping it with a predefined sequence. As a result, CIHMBS is resistant to attack threats such as additive Gaussian white noise (AGWN), salt-and-pepper noise attacks, low-pass filtering attacks, and Joint Photographic Experts Group (JPEG) compression attacks. However, it only has a limited hiding capacity. This paper proposes a coverless information-hiding method based on the lowest and highest values of the fragment (CIHLHF) of the cover image. According to the experimental results, the hiding capacity of CIHLHF is twice that of CIHMSB.
An Assessment of the Risk Factors Associated with Disease Outbreaks across Tilapia Farms in Central and Southern Zambia
Authors: Ndashe Kunda , Hang’ombe Bernard Mudenda , Changula Katendi , Yabe John , Samutela Mulemba Tillika , Songe Mwansa M. , Kefi Alexander Shula , Njobvu Chilufya Loziwe , Sukkel Martin
Published: January 2023

The study investigated the management practices that contribute to disease outbreaks in farmed tilapia in Lusaka and central and southern provinces in Zambia. It was a cross-sectional qualitative study undertaken from January to March 2021 in which questionnaires were administered to 49 farmers to assess their fish health management and biosecurity competence. Data were analysed using means, percentages, ratios, and logistical regression. The results showed that the majority of the farms had high stocking densities (>8 fish/m2, 44.4%), reared Nile tilapia (67.7%), and sourced water for farming from rivers and streams (45.7%). A few farmers measured water quality parameters daily (16.7%) and removed dead fish from ponds daily (20.8%). The stocking density (p = 0.013), fish species (p = 0.031), dead fish disposal methods (p = 0.023), and control of predator birds (p = 0.016) influenced the total mortality recorded on farms, while pond type (p = 0.031 and p = 0.045), water source (p = 0.023), and stocking density (p = 0.027) influenced the duration of a mortality episode. It is evident that some fish health management practices and biosecurity concepts among tilapia farmers in the study area are inadequate and may not contain disease outbreaks or the spread of pathogens.