bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome-Wide Association Study (GWAS) Reveals an SNP Associated with Waxy Trait and Development of a Functional Marker for Predicting Waxy Maize (Zea mays L. var. ceratina)
Authors: Ruanjaichon Vinitchan , Yin Khin Kyawt , Thunnom Burin , Khammona Kanogporn , Suriharn Khundej , Simla Sakunkan , Kerdsri Chalong , Aesomnuk Wanchana , Yongsuwan Arweewut , Chaomueang Naraporn , Oo Nay Nay , Unartngam Jintana , Arikit Siwaret , Wanchana Samart , Toojinda Theerayut
Published: September 2022

Waxy maize (Zea mays L. var. ceratina) is a special type of maize characterized by a sticky texture when cooked, due to high amylopectin content in the endosperm. Waxy maize is popular in China and Southeast Asia for fresh consumption. Breeding strategies have been used to improve the quality of waxy maize, including hybrid breeding by crossing super sweet maize and waxy maize. However, the lack of a marker has limited the efficiency of breeding for the waxy trait, especially because the waxy allele is recessive. In this study, we conducted a genome-wide association study (GWAS) in an association panel consisting of 213 inbred lines and recombinant inbred lines (RILs) of field maize and waxy maize to identify loci associated with the waxy kernel phenotype. The genotypic data were 155,768 SNPs derived from the high-density 600 K maize genotyping array for single-nucleotide polymorphisms (SNPs). The GWAS results identified the qWx9 locus on chromosome 9 (25.06–25.18 Mb) associated with the trait. Based on the most significantly associated SNP (AX-90613979, −log10(P) = 6.8)), which was located on Wx1, a MassArray marker was developed and validated in a panel of 139 maize lines containing waxy maize and sweet maize with different amylose content. The newly developed marker had a significant association with amylose content (R2 value of 0.81, p < 0.001) and clearly distinguished between waxy maize and sweet maize lines that had different amylose content. This marker will be useful for maize breeding programs for the waxy trait, as well as for breeding programs for hybrid maize combining the sweetness and waxy traits. The gene-based SNP markers could aid breeders by eliminating the costs and time required to perform lengthy field trials and help to accelerate sweet maize and waxy maize breeding programs.
Investigating the Potential of Streptomyces spp. in Suppression of Rhizoctonia solani (AG1-IA) Causing Rice Sheath Blight Disease in Northern Iran
Authors: Soltani Nejad Meysam , Samandari Najafabadi Neda , Aghighi Sonia , Shahidi Bonjar Amir Hashem , Murtazova Kheda Magomed-Salihovna , Nakhaev Magomed Ramzanovich , Zargar Meisam
Published: September 2022

A study was conducted in the Guilan Province of Iran, using a variety of Actinomycetes species isolated from the rice fields, with the intention of identifying useful biocontrol agents to lessen rice sheath blight disease. The antagonistic effects against the rice pathogen agent were also assessed both in vitro and in vivo. The antifungal abilities of more than 30 Actinomycetes isolates against the Rhizoctonia solani Kühn (AG1-IA) were used. The biocontrol abilities of the most active isolates were studied in a greenhouse. The size of the inhibition zone against pathogen development and the most potent antagonist Actinomycetes isolates were determined based on the dual culture screening test findings. The ability to create hydrolytic enzymes including amylase, chitinase, protease, and lipase were shown by hydrolytic enzyme assays on the putative antagonists. Antifungal activities of Streptomyces isolates against fungus mycelia were also studied using SEM since, compared to the control grown mycelia and mycelia adjacent to the inhibition zone in the plate, tests revealed an unusual and deformed structure; in our opinion, the chitinase secreted can destroy fungal mycelium. Chloroform test showed that its antifungal effect persists upon exposure to chloroform. All possible isolates belonged to the Streptomyces species, according to the 16S rDNA molecular analysis of the majority of active isolates. Comparing isolates, G had the highest impact in reducing sheath blight disease. The Iranian strain of the Streptomyces has antifungal capabilities, highlighting its potential as a viable biocontrol agent to be used in an Integrated Disease Management (IDM) program to control the rice sheath blight disease.
A Bibliometric Review of Plant Growth-Promoting Rhizobacteria in Salt-Affected Soils
Authors: Ma Xixi , Pan Jing , Xue Xian , Zhang Jun , Guo Qi
Published: September 2022

As a primary form of land degradation in arid and semi-arid areas, soil salinity can adversely affect plant nutrient balance, photosynthesis, protein synthesis, energy metabolism, and other functions. Plant growth-promoting rhizobacteria (PGPR) inoculation of plants is an environmentally friendly strategy to alleviate salt stress and improve salt tolerance. Based on the Web of Science Core Collection (WoSCC) database, in terms of the number of publications and citations, collaboration networks, and keywords, this bibliometric analysis employed VOSviewer 1.6.17 and HistCite Pro 2.1 software to map the scientific knowledge of related research, comprehensively review knowledge structure and provide an outlook on future research topics. The results showed that publications and citations increased exponentially between 1978 and 2021. Regarding knowledge structure, Asian nations conducted research in a more concentrated manner, developed close collaborative relationships, and produced rich research results. Halotolerant PGPR, sustainable agriculture, microbial community, soil salinization, microbiome, oxidative stress, and biofertilizer, are currently hot topics. This bibliometric study will provide a meaningful reference for investigating the field’s evolution and pinpointing the research frontiers.
Nitrous Oxide Emission and Grain Yield in Chinese Winter Wheat–Summer Maize Rotation: A Meta-Analysis
Authors: Yao Chengcheng , Wu Xiongwei , Bai He , Gu Jiangxin
Published: September 2022

A systematic understanding of nitrous oxide (N2O) emission and grain yield in winter wheat–summer maize rotation, one of the most important cereal cropping systems in China, is still lacking. The primary aim of this study was to quantify the N2O emissions and grain yield, as well as responses to mitigation strategies, in this intensively managed agroecosystem. We conducted a pairwise meta-analysis by compiling a comprehensive dataset of annual N2O emissions (n = 530) and grain yields (n = 352) from peer−reviewed publications. The N2O emissions increased with nitrogen (N) fertilizer input rates following a linear model (r2 = 0.295, p < 0.001), giving a specific emission coefficient and background emission of 0.71% and 0.5 kg N ha−1 yr−1, respectively. The grain yields responded to the N input rates following a linear-plateau model (r2 = 0.478, p < 0.001), giving an optimal N input rate and maximum grain yield of 405 kg N ha−1 yr−1 and 15.5 t ha−1 yr−1, respectively. The meta-analyses revealed that reducing N fertilizers (approximately 50% of the full N input), water-saving irrigation, reduced or no tillage, and applying enhanced efficiency fertilizers significantly decreased N2O emissions (range: −45% to −9%) and increased or did not impact grain yields (range: −1% to 3%). We recommend that reducing agricultural inputs (i.e., N fertilizers, irrigation, and tillage) is a feasible N2O mitigation strategy in the intensively managed winter wheat–summer maize rotation that can be employed without additional environmental risks.
Sirtuin 3 Plays a Critical Role in the Antidepressant- and Anxiolytic-like Effects of Kaempferol
Authors: Li Hao-Yuan , Wang Jing , Liang Ling-Feng , Shen Shi-Yu , Li Wei , Chen Xiao-Rong , Li Bing , Zhang Yu-Qiu , Yu Jin
Published: September 2022

An estimated 20% of women experience depression at some point during menopause. Hormone replacement therapy (HRT), as the main therapy for depression and other menopausal syndromes, comes with a few undesirable side effects and a potential increase in cancer and cardiovascular risk. Consequently, there is a dire need for the development of new therapies to treat menopausal depression. Oxidative stress combined with the decline in sex hormones might explain the occurrence of psychological symptoms characteristic of menopause. Therefore, antioxidants have been suggested as a promising therapy for aging-associated diseases, such as menopausal depression. As a flavonoid antioxidant, kaempferol might have a potential neuroprotective action. Hence, the study was conducted to assess the potential antidepressant action of kaempferol and clarify the underlying mechanism. The results show that kaempferol has potential beneficial effects on VCD-induced rodent model of menopausal depression and produces antioxidant effects as well as increases the deacetylation of superoxide dismutase 2 (SOD2) and the protein level of Sirtuin3 (Sirt3) in the hippocampus. On the contrary, Sirt3 depletion abrogated the antidepressant- and anxiolytic-like effects as well as antioxidant effects of kaempferol. In conclusion, kaempferol might produce antidepressant effects via upregulating the expression of Sirt3, the major deacetylase in mitochondria, and subsequently activate the mitochondrial antioxidases. These findings shed some light on the use of kaempferol or vegetables and herbs that contain kaempferol as a complementary therapy for menopausal depression.
Oddioside A, a New Phenolic Glycoside Isolated from the Fruits of Morus alba (Mulberry), Protects TNF-α-Induced Human Dermal Fibroblast Damage
Authors: Kim Kang Sub , Kim Ranhee , Son So-Ri , Kang Ki Sung , Jang Dae Sik , Lee Sullim
Published: September 2022

In our preliminary study, a hot water extract from the fruits of Morus alba (mulberry) inhibited the secretion of metalloproteinase-1 (MMP-1) against tumor necrosis factor-α (TNF-α)-stimulated human dermal fibroblasts (HDFs), and therefore we researched its active compounds. In the present study, a new phenolic glycoside (oddioside A, 1) and 21 known compounds (2−22) were isolated from the hot water extract from the fruits of M. alba by repeated chromatography. The chemical structure of the new compound 1 was elucidated by its spectroscopic data (1D− and 2D−NMR and HRMS) measurement and by acidic hydrolysis. The presence of sargentodoside E (2), eugenyl glucoside (6), 2-O-β-d-glucopyranosyl-4,6-dihydroxybenzaldehyde (7), 7S,8R-erythro-7,9,9’-trihydroxy-3,3’-dimethoxy-8-O-4’-neolignan-4-O-β-d-glucopyranoside (11), pinoresinol-4-O-β-d-glucopyranoside (12), taxifolin-7-O-β-d-glucopyranoside (20), and pinellic acid (21) were reported from M. alba for the first time in this study. The new compound oddioside A (1) suppressed the secretion of MMP-1 and increased collagen in TNF-α-stimulated HDFs. In addition, the phosphorylation of mitogen-activated protein kinases (MAPKs) was inhibited by oddioside A. In conclusion, the extract from fruits of M. alba and its constituent oddioside A may be a potential agent to prevent inflammation-related skin aging and other skin disorders.
GPX4 Alleviates Diabetes Mellitus-Induced Erectile Dysfunction by Inhibiting Ferroptosis
Authors: Xu Wenchao , Sun Taotao , Wang Jiaxin , Wang Tao , Wang Shaogang , Liu Jihong , Li Hao
Published: September 2022

Pharmacological therapy of diabetes mellitus-induced erectile dysfunction (DMED) is intractable owig to the poor response to phosphodiesterase type 5 inhibitors (PDE5i). The surge in the number of diabetic patients makes it extremely urgent to find a novel therapy for DMED. Ferroptosis is a recently discovered form of cell death evoked by lipid peroxidation and is related to several diabetic complications. GPX4, an important phospholipid hydroperoxidase, can alleviate ferroptosis and maintain redox balance via reducing lipid peroxides. However, whether GPX4 can be a prospective target of DMED needs to be determined. Fifty rats were randomly divided into control group, DMED group, DMED + negative control group (DMED + NC group), DMED + low-dose group (1 × 106 infectious units), and DMED + high-dose group (2 × 106 infectious units). Erectile function was assessed 4 weeks after intracavernous injection of GPX4 or negative control lentivirus. The penile shafts were collected for subsequent molecular biological and histological analysis. The results demonstrated that erectile function of the rats in DMED and DMED + NC groups was extremely impaired and was improved in a dose-dependent manner with GPX4 lentivirus (GPX4-LV) injection. Additionally, upregulation of the ACSL4-LPCAT3-LOX pathway, iron overload, oxidative stress, fibrosis, and decreased endothelial and smooth muscle cell numbers were observed in the corpus cavernosum of DMED group. Meanwhile, the nitric oxide (NO)/cyclic guanosine monophosphate (cGMP) pathway was inhibited, and the Ras homolog gene family member A (RhoA)/Rho-associated protein kinase (ROCK) pathway was promoted in DMED rats. The above histologic alterations and related molecular changes were alleviated after GPX4-LV injection. The results revealed that GPX4 improved erectile function by modulating ferroptosis during DMED progression. This finding is of paramount significance in deciphering the molecular mechanism of hyperglycemia-induced ferroptosis, thereby providing a prospective target for preventing the development of DMED.
Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
Authors: Saarela Mirka , Geogieva Lilia
Published: September 2022

Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will allow self-screening and automatic analysis in the future. Especially, the recent success of various deep machine learning models shows promise that, in the future, patients could self-analyse their external signs of skin cancer by uploading pictures of these signs to an artificial intelligence system, which runs such a deep learning model and returns the classification results. However, both patients and dermatologists, who might use such a system to aid their work, need to know why the system has made a particular decision. Recently, several explanation techniques for the deep learning algorithm’s decision-making process have been introduced. This study compares two popular local explanation techniques (integrated gradients and local model-agnostic explanations) for image data on top of a well-performing (80% accuracy) deep learning algorithm trained on the HAM10000 dataset, a large public collection of dermatoscopic images. Our results show that both methods have full local fidelity. However, the integrated gradients explanations perform better with regard to quantitative evaluation metrics (stability and robustness), while the model-agnostic method seem to provide more intuitive explanations. We conclude that there is still a long way before such automatic systems can be used reliably in practice.
Transient Linear Circuit Analysis Using Finite Element Technique
Authors: Jurić-Grgić Ivica , Modrić Tonći , Mandić Marin
Published: September 2022

In this paper, a novel algorithm for a transient linear circuit analysis based on the Finite Element Technique (FET) was established. The FET procedure allows a straightforward solution for complex electric circuits since it is based on the Finite Element Method (FEM) approach. The developed algorithm allows us to select various types of time integration schemes when forming a local system of equations for a coupled circuit finite element. To illustrate the basic principle of the developed FET-based algorithm and to perform transient analysis, a random coupled linear circuit was analyzed. Numerical solutions obtained using Heun’s and the generalized trapezoidal rule (ϑ-method) integration schemes were compared to the solution obtained by Matlab Simulink software. It was shown that the accuracy of results depends on the employed time integration scheme when performing the FET procedure. It was shown that using Heun’s method as the time integration scheme yields more accurate results than using the ϑ-method time integration scheme.
Special Issue on Applied Microbiology in Food Technology
Authors: Betoret Ester , Betoret Noelia
Published: September 2022

Food science and technology plays a very important role in designing and developing new foods, and in improving the safety, nutritional and organoleptic quality of processed foods [...]
Fermentative Hydrogen Production from Lignocellulose by Mesophilic Clostridium populeti FZ10 Newly Isolated from Microcrystalline Cellulose-Acclimated Compost
Authors: Zhang Jingnan , Jiang Baoxuan , Zhang Haokun , Qian Sitong , Wei Tao , Zhang Zhiping , Song Lili , Yang Xu
Published: September 2022

Screening new Clostridium strains that can efficiently utilize lignocellulose to produce hydrogen is extremely important for dark fermentative hydrogen production. In this study, a mesophilic hydrogen-producing bacterium, identified as Clostridium populeti FZ10, was newly isolated from compost acclimated by microcrystalline cellulose. The strain could produce hydrogen from various cellulosic substrates. The performances of hydrogen production from microcrystalline cellulose (MCC) and corn stalk (CS) were especially investigated. The maximum hydrogen yield and hydrogen production rate from MCC were 177.5 ± 4.8 mL/g and 7.7 ± 0.2 mL·g−1·h−1, respectively. Furthermore, scanning electron microscopy (SEM) images showed that the structure of CS was destroyed after fermentation, which could be attributed to the presence of exoglucanase, endoglucanase, β-glucosidase and xylanase produced by Clostridium populeti FZ10. The maximum hydrogen yield and hydrogen production rate from CS were 92.5 ± 3.7 mL/g and 5.9 ± 0.2 mL·g−1·h−1,respectively, with a cellulose degradation of 47.2 ± 2.3% and a hemicellulose degradation of 58.1 ± 2.0%. This study demonstrates that Clostridium populeti FZ10 is an ideal candidate for directly converting lignocellulose into biohydrogen under mesophilic conditions. The discovery of strain C. populeti FZ10 has special significance in the field of bioenergy.
Numerical Simulation and Field Test of the Interaction between Existing Station and Enclosure in Open Excavation and Adding Stories Construction
Authors: Wang Jun , Li Haifeng , Wu Shengzhi , Liang Erbin , Wang Zhikang
Published: September 2022

When constructing open excavation and adding stories on the upper part of an existing station structure, the new foundation pit contacts with the existing station at zero distance; subsequently, the construction has a greater impact than that of other close constructions. To explore the cross-impact of new and existing structures on open excavation and story-adding above the existing station, in this study, through numerical simulation and field measurement, we analyze the deformation law of the existing station and foundation pit retaining structure in the excavation and story-adding construction above the existing station. The following conclusions are obtained. The excavation of the upper foundation pit leads to the overall uplift of the existing station, and the sidewall exhibits an internal extrusion trend, which is caused by an increase in the horizontal load on the station caused by the unloading of the vault and expansion of the formation plastic zone. Affected by the existing station structure, some foundation pit retaining structures are end-suspended piles; the bottoms of the end-suspended pile slips are evident. This study offers control measures for the primary support connection between the pile bottom and initial retention and protection of the existing station, deep hole grouting reinforcement of the stratum behind the pile, and reserving earth berm at the pit bottom to reduce the deformation of the end-suspended pile. The research results can provide a reference for constructing the added story of a subway transfer station.
Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets
Authors: Pandeya Yagya Raj , Bhattarai Bhuwan , Lee Joonwhoan
Published: September 2022

This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories of Pansori. We propose two datasets; one is for rhythm classification and one is for segmentation. Two classification and two segmentation neural networks are trained and tested in an end-to-end manner. The standard HR network and DeepLabV3+ network are used for rhythm segmentation. A modified HR network and a novel GlocalMuseNet are used for the classification of music rhythm. The GlocalMuseNet outperforms the HR network for Pansori rhythm classification. A novel segmentation model (a modified HR network) is proposed for Pansori rhythm segmentation. The results show that the DeepLabV3+ network is superior to the HR network. The classifier networks are used for time-varying rhythm classification that behaves as the segmentation using overlapping window frames in a spectral representation of audio. Semantic segmentation using the DeepLabV3+ and the HR network shows better results than the classification-based structural analysis methods used in this work; however, the annotation process is relatively time-consuming and costly.
Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT
Authors: Tareq Imad , Elbagoury Bassant M. , El-Regaily Salsabil , El-Horbaty El-Sayed M.
Published: September 2022

The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification.
Malware Variants Detection Model Based on MFF–HDBA
Authors: Wang Shuo , Wang Jian , Song Yafei , Li Sicong , Huang Wei
Published: September 2022

A massive proliferation of malware variants has posed serious and evolving threats to cybersecurity. Developing intelligent methods to cope with the situation is highly necessary due to the inefficiency of traditional methods. In this paper, a highly efficient, intelligent vision-based malware variants detection method was proposed. Firstly, a bilinear interpolation algorithm was utilized for malware image normalization, and data augmentation was used to resolve the issue of imbalanced malware data sets. Moreover, the paper improved the convolutional neural network (CNN) model by combining multi-scale feature fusion (MFF) and channel attention mechanism for more discriminative and robust feature extraction. Finally, we proposed a hyperparameter optimization algorithm based on the bat algorithm, referred to as HDBA, in order to overcome the disadvantage of the traditional hyperparameter optimization method based on manual adjustment. Experimental results indicated that our model can effectively and efficiently identify malware variants from real and daily networks, with better performance than state-of-the-art solutions.
Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning
Authors: Yue Yiqun , Zhou Yang , Xu Lijuan , Zhao Dawei
Published: September 2022

Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of evolutionary algorithms are very suitable for finding optimal strategies. However, the more common evolutionary algorithms currently used have relatively large limitations in convergence accuracy and convergence speed, such as PSO, DE, GA, etc. Therefore, this paper proposes a hybrid strategy differential evolution algorithm based on reinforcement learning and opposition-based learning to construct the optimal security strategy. It greatly improved the common problems of evolutionary algorithms. This paper first scans the vulnerabilities of the water distribution system and generates an attack graph. Then, in order to solve the balance problem of cost and benefit, a cost–benefit-based objective function is constructed. Finally, the optimal security strategy set is constructed using the algorithm proposed in this paper. Through experiments, it is found that in the problem of security strategy construction, the algorithm in this paper has obvious advantages in convergence speed and convergence accuracy compared with some other intelligent strategy selection algorithms.
Behavior of Sedimentary Rock Tunnel against Rigid Projectile Impact
Authors: Alsabhan Abdullah H. , Sadique Md. Rehan , Alqarni Ali S. , Alam Shamshad , Suh Wonho
Published: September 2022

The tunnels in present-day cities are experiencing varying degrees of loading conditions ranging from static to extreme loading. Therefore, the stability of underground tunnels needs to be analyzed and understood for safer and strengthened design. The present study was conducted to simulate the impact loading conditions due to a missile traveling at a velocity of 5 Mach for different rock tunnels. The nonlinear continuum finite element analysis has been carried out through Abaqus and Explicit. The four different types of sandstones considered in the present study include Kota, Jamrani, Singrauli, and Jhingurda sandstones. An elastoplastic Mohr–Coulomb constitutive material model has been considered to model the behavior of rock surrounding the tunnel opening. The tunnel has an opening of 7 m in diameter (d), and 50 m in height and breadth, with 50 m of longitudinal length. The deformation and stress in the rock and the damage to the concrete lining have been compared in different cases. The Concrete–Damage–Plasticity (CDP) model and the Johnson–Cook model were considered for modelling of the RC lining and steel reinforcement. It was concluded that Jhingurda sandstone has maximum deformations due to impacts caused by missiles.
AI-Based Prediction of Myocardial Infarction Risk as an Element of Preventive Medicine
Authors: Rojek Izabela , Kozielski Mirosław , Dorożyński Janusz , Mikołajewski Dariusz
Published: September 2022

The incidence of myocardial infarction (MI) is growing year on year around the world. It is considered increasingly necessary to detect the risks early, respond through preventive medicines and, only in the most severe cases, control the disease with more effective therapies. The aim of the project was to develop a relatively simple artificial-intelligence tool to assess the likelihood of a heart infarction for preventive medicine purposes. We used binary classification to determine from a wide variety of patient characteristics the likelihood of heart disease and, from a computational point of view, determine what the minimum set of characteristics permits. Factors with the highest positive influence were: cp, restecg and slope, whilst factors with the highest negative influence were sex, exang, oldpeak, ca, and thal. The novelty of the described system lies in the development of the AI for predictive analysis of cardiovascular function, and its future use in a specific patient is the beginning of a new phase in this field of research with a great opportunity to improve pre-clinical care and diagnosis, and accuracy of prediction in preventive medicine.
Numerical Simulation of Adaptive Radial Basis NN-Based Non-Singular Fast Terminal Sliding Mode Control with Time Delay Estimator for Precise Control of Dual-Axis Manipulator
Authors: Wu Jim-Wei , Cen Wen-Shan , Ho Cheng-Chang
Published: September 2022

Robotic manipulators can reduce the cost of production and improve productivity; however, controlling a manipulator to follow a desired trajectory is a thorny problem. In this study, we introduced various forms of interference to facilitate the modeling of a dual-axis manipulator. The interference associated with the payload is handled by an adaptive radial basis neural network (ARBNN) controller, while other interference is estimated by a time delay estimator (TDE). The control signal is output by a non-singular fast terminal sliding mode controller (NFTSMC) to minimize further interference. Since the proposed controller can deal with the payload, system uncertainties, external disturbances, friction, and backlash, compared with conventional control methods, it has better tracking accuracy and stability.
Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
Authors: Peña Carlos , Romero David , Noguez Julieta
Published: September 2022

In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks.
Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
Authors: Harishidayat Dicky , Al-Shuhail Abdullatif , Randazzo Giovanni , Lanza Stefania , Muzirafuti Anselme
Published: September 2022

Seismic reflection utilizes sound waves transmitted into the subsurface, reflected at rock boundaries, and recorded at the surface. Interpretation of their travel times and amplitudes are the key for reconstructing various geomorphological features across geological time (e.g., reefs, dunes, and channels). Furthermore, the integration of surface geomorphology technique mapping, such as digital elevation models, with seismic geomorphology can increase land and marine feature modelling and reduce data uncertainty, as well. This paper presents an overview of seismic and surface geomorphology techniques and proposes an integrated workflow for better geological mapping, 3D surface imaging, and reconstruction. We intend to identify which techniques are more often used and which approaches are more appropriate for better output results. We noticed that an integration of surface and subsurface geomorphology techniques could be beneficial for society in landscape mapping, reservoir characterization, and city/regional planning.
Evaluation of Viscoelastic Adhesion Strength and Stability of Composite Waterproofing Sheet Using Non-Hardening Viscoelastic Synthetic Polymer-Based Rubber Gel
Authors: Jeon Junhong , Oh Kyuhwan , Kim Sooyeon , Jiang Bo , He Xingyang , Oh Sangkeun
Published: September 2022

Recently, a non-hardening type synthetic polymerized rubber gel (hereinafter referred to as NHV-SPRG) composite waterproofing sheet has been used on construction site as a new waterproofing technology. In this study, a test method is proposed in which a composite waterproof sheet is attached to an area of 150 mm in length and 100 mm in width on the mortar-based substrate specimen, and subsequently peeled off at 180 ° vertically to measure the “peel load (N)” at 10 points of 10 mm intervals (P1~P10, from 30 mm point to 120 mm point). Value obtained by dividing the average value of the measured peeling load by a width of 100 mm was defined as “viscoelastic adhesion strength (N/mm)”. The viscoelastic adhesive strength evaluated for the NHV-SPRG composite waterproof sheet of 4 types (VG-1, VG-2, VG-3, VG-4) was an average of 1.99 N/mm. To examine the effectiveness of the viscoelastic adhesive strength, the adhesion stability was evaluated based on the peel load’s coefficient of variation, grade of the linear regression line of the peel load, and peel load percent relative range in peel load for each section of the composite waterproofing layer. As a result, the VG-2 type was measured to have the highest viscoelastic adhesive strength, but the VG-1 type was confirmed to have the highest adhesive stability. Considering these results, it is judged that even a product with high adhesion can have varying stability in terms of performance that can affect construction precision. The test method and performance standard value for the viscoelastic adhesion strength proposed as an evaluation index through this study are expected to be used as a quality control standard to secure the on-site adhesion stability of the NHV-SPRG composite waterproof sheet.
One Novel Dynamic-Load Time-Domain-Identification Method Based on Function Principle
Authors: Li Hongqiu , Jiang Jinhui , Cui Wenxu , Zhao Jiamin , Mohamed M. Shadi
Published: September 2022

In order to ensure the reliability of the structural design, it is necessary to know the external loads acting on the structure. In this paper, we propose a novel method to identify the dynamic loads based on function principles in the time domain. Assuming the external load remains constant within one micro segment, we establish a linear relationship between external load and structural response in the micro segments based on the mechanical energy conservation law. Next, the external load is obtained by solving the inverse problem in each micro-segment. Finally, the external load in the whole time domain is achieved by fitting the load-identification results in each micro segment. In order to verify the effectiveness and accuracy, single-force and two-force identification, and load identification with noise simulations, are performed on the structures, and the identification results are compared to the ones of the traditional time-domain method with a deviation of less than 5%. The proposed method can effectively solve the problem of cumulative errors in the time-domain method, while its resistance to noise interference is also strong. At last, we verify the experimental performance of the proposed method. The experimental results show the effectiveness and high accuracy of the proposed method. This work presents a first attempt to solve the structural dynamic load with an approach based on a function principle.
FPSO Hull Structures with Sandwich Plate System in Cargo Tanks
Authors: Silva João Pedro , Chen Bai-Qiao , Videiro Paulo Mauricio
Published: September 2022

Nowadays, the floating production storage and offload system (FPSO) is one of the most common platform types for offshore oil production. The traditional arrangement of the stiffened panels creates obstacles for automated cleaning and inspections by remote devices. This paper summarizes the results of an initial study for the design and construction of FPSO hulls with SPS in order to overcome this problem. The main goal is to have the walls and bottom of the cargo tanks free of stiffeners. This research is conducted by first designing the hull with a conventional structural arrangement using steel according to the ABS rules as a benchmark. Following that, the equivalent hull structure with sandwich plates is designed in accordance with the guidelines for SPS construction from DNV rules. Finally, this paper provides the results of a finite element analysis to compare the stresses and ultimate strengths of both types of structures. Briefly, the main results are that the SPS design provides a reduction of 2.8% of the total weight and a better overall structural performance by an increase of 26% for the ultimate strength of the hull.
Partial Ionization Cross Sections of Tungsten Hexafluoride Due to Electron Impact
Authors: Goswami Kanupriya , Luthra Meetu , Bharadvaja Anand , Baluja Kasturi Lal
Published: September 2022

The ionization data of a neutral molecule are crucial to model the energy deposition and dissociative ionization process. We study theoretically the electron impact ionization process and report on the dissociative ionization cross sections of the tungsten hexafluoride cations invoking the modified-binary-encounter-Bethe model. In this model, the binary-encounter-Bethe model is modified by applying the transformation to the binding energies of the molecular orbitals and then normalizing the partial ionization cross sections of the cations using the branching ratios. The normalization is performed at a particular energy and ensures that the branching ratios of different fragments are summed to unity. The model yielded satisfactory results for both the singly and doubly ionized ions. The approach validates the results of Basner et al. The advantages and limitations of this model are also discussed. This work corroborates the importance of mass spectrometry data in the proper understanding of the ionization process.
Bi2Se3 Nanostructured Thin Films as Perspective Anodes for Aqueous Rechargeable Lithium-Ion Batteries
Authors: Lazarenko Vitalijs , Rublova Yelyzaveta , Meija Raimonds , Andzane Jana , Voikiva Vanda , Kons Artis , Sarakovskis Anatolijs , Viksna Arturs , Erts Donats
Published: September 2022

In recent years, aqueous rechargeable lithium-ion batteries (ARLIBs) have attracted attention as an alternative technology for electrical storage. One of the perspective battery anode materials for application in ARLIBs is Bi2Se3, which has already shown good perspectives in the application of conventional lithium-ion batteries (LIBs) that use organic electrolytes. In this study, the electrochemical properties of Bi2Se3 thin films with two different layers on the electrode surface—the solid electrolyte interphase (SEI) and the Bi2O3 layer—were investigated. The results of this work show that the formation of the SEI layer on the surface of Bi2Se3 thin films ensures high diffusivity of Li+, high electrochemical stability, and high capacity up to 100 cycles, demonstrating the perspectives of Bi2Se3 as anode material for ARLIBs.
Biochemical Analysis of Urine Samples from Diabetic and Hypertensive Patients without Renal Dysfunction Using Spectrophotometry and Raman Spectroscopy Techniques Aiming Classification and Diagnosis
Authors: de Sousa Vieira Elzo Everton , Silveira Landulfo , Carvalho Henrique Cunha , Bispo Jeyse Aliana Martins , Fernandes Fernanda Barrinha , Fernandes Adriana Barrinha
Published: September 2022

The purpose of this study was to perform a comparative biochemical analysis between conventional spectrophotometry and Raman spectroscopy, techniques used for diagnoses, on the urine of healthy (CT) and diabetic and hypertensive patients (DM&HBP). Urine from 40 subjects (20 in the CT group and 20 in the DM&HBP group) was examined in a dispersive Raman spectrometer (an 830 nm excitation and a 350 mW power). The mean Raman spectra between both groups showed a significant difference in peaks of glucose; exploratory analysis by principal component analysis (PCA) identified spectral differences between the groups, with higher peaks of glucose and proteins in the DM&HBP group. A partial least squares (PLS) regression model estimated by the Raman data indicated the concentrations of urea, creatinine, glucose, phosphate, and total protein; creatinine and glucose were the biomarkers that presented the best correlation coefficient (r) between the two techniques analyzed (r = 0.68 and r = 0.98, respectively), both with eight latent variables (LVs) and a root mean square error of cross-validation (RMSecv) of 3.6 and 5.1 mmol/L (41 and 92 mg/dL), respectively. Discriminant analysis (PLS-DA) using the entire Raman spectra was able to differentiate the samples of the groups in the study, with a higher accuracy (81.5%) compared to the linear discriminant analysis (LDA) models using the concentration values of the spectrometric analysis (60.0%) and the concentrations predicted by the PLS regression (69.8%). Results indicated that spectral models based on PLS applied to Raman spectra may be used to distinguish subjects with diabetes and blood hypertension from healthy ones in urinalysis aimed at population screening.
Encoder-Controlled Functional Electrical Stimulator for Bilateral Wrist Activities—Design and Evaluation
Authors: Solomons Cassandra D. , Shanmugasundaram Vivekanandan , Balasubramanian Sivakumar
Published: September 2022

Upper limb impairment following stroke is often characterized by limited voluntary control in the affected arm. In addition, significant motor coordination problems occur on the unaffected arm due to avoidance of performing bilateral symmetrical activities. Rehabilitation strategies should, therefore, not only aim at improving voluntary control on the affected arm, but also contribute to synchronizing activity from both upper limbs. The encoder-controlled functional electrical stimulator, described in this paper, implements precise contralateral control of wrist flexion and extension with electrical stimulation. The stimulator is calibrated for each individual to obtain a table of stimulation parameters versus wrist angle. This table is used to set stimulation parameters dynamically, based on the difference in wrist angle between the set and stimulated side, which is continuously monitored. This allows the wrist on the stimulated side to follow flexion and extension patterns on the set side, thereby mirroring wrist movements of the normal side. This device also gives real-time graphical feedback on how the stimulated wrist is performing in comparison to the normal side. A study was performed on 25 normal volunteers to determine how closely wrist movements on the set side were being followed on the stimulated side. Graphical results show that there were minor differences, which were quantified by considering the peak angles of flexion and extension on the set and stimulated side for each participant. The mean difference in peak flexion and extension range of movement was 2.3 degrees and 1.9 degrees, respectively, with a mean time lag of 1 s between the set and the stimulated angle graphs.
Genomic Effect of DNA Methylation on Gene Expression in Colorectal Cancer
Authors: Hong Juyeon , Rhee Je-Keun
Published: September 2022

The aberrant expression of cancer-related genes can lead to colorectal cancer (CRC) carcinogenesis, and DNA methylation is one of the causes of abnormal expression. Although many studies have been conducted to reveal how DNA methylation affects transcription regulation, the ways in which it modulates gene expression and the regions that significantly affect DNA methylation-mediated gene regulation remain unclear. In this study, we investigated how DNA methylation in specific genomic areas can influence gene expression. Several regression models were constructed for gene expression prediction based on DNA methylation. Among these models, ElasticNet, which had the best performance, was chosen for further analysis. DNA methylation near transcription start sites (TSS), especially from 2 kb upstream to 7 kb downstream of TSS, had an essential regulatory role in gene expression. Moreover, methylation-affected and survival-associated genes were compiled and found to be mainly enriched in immune-related pathways. This study investigated genomic regions in which methylation changes can affect gene expression. In addition, this study proposed that aberrantly expressed genes due to DNA methylation can lead to CRC pathogenesis by the immune system.
Effects of Physical Exercises Alone on the Functional Capacity of Individuals with Obesity and Knee Osteoarthritis: A Systematic Review
Authors: Caiado Vanessa Silva , Santos Aline Cristina Gomes , Moreira-Marconi Eloá , Moura-Fernandes Marcia Cristina , Seixas Adérito , Taiar Redha , Lacerda Ana Cristina Rodrigues , Sonza Anelise , Mendonça Vanessa Amaral , Sá-Caputo Danúbia Cunha , Bernardo-Filho Mario
Published: September 2022

The association between obesity and knee osteoarthritis (KOA) is reported in the literature. The inflammatory factors described in obesity associated with mechanical overload on the knee joint lead to KOA development and reduced functional capacity in these individuals. Most physical exercise (PE) protocols associate a diet program to improve the functional capacity of individuals with concomitant KOA and obesity. There is a lack of published protocols performing PE alone, which would be without an associated diet program in individuals with both clinical conditions. In this systematic review, the authors summarize the effects of the application of PE alone, describing each protocol and reporting the improvement in the function of people with these clinical conditions. This investigation was conducted according to the PRISMA guidelines and registered in PROSPERO. Five databases (MEDLINE/PubMed, PEDro, Scopus, CINAHL and Web of Science) were used up to July 2022 and ten studies, including 534 participants, met the inclusion criteria. The PEDro scale, Cochrane collaborations and ROBINS-I tools were used to evaluate the methodological quality and risk of bias. It was concluded that PE performed alone seems to provide an improvement in the functional capacity of these individuals even without an associated diet plan in the condition of obesity.