bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Six microRNAs as Potential Biomarkers for Pemphigus Vulgaris: From Diagnosis to Pathogenesis
Authors: He Wenxiu , Xing Yixiao , Li Chunlei , Zhou Peiru , Hu Xiaosheng , Hua Hong , Wei Pan
Published: December 2022

Background: Pemphigus vulgaris (PV) is a potentially fatal autoimmune bullous disease. The role of microRNA (miRNA, miR) in the diagnosis and pathogenesis of PV remains unknown. This study aims to provide potential miRNA biomarkers for PV diagnosis and therapy options. Methods: Serum samples were obtained from 22 PV patients, 15 mucous membrane pemphigoid (MMP) patients, and 10 normal controls (NC). Total RNA was extracted from the serum samples, and 12 selected miRNAs were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Bioinformatic analyses including target gene prediction and enrichment analysis were performed. Results: Twelve miRNAs were increased in the serum of the PV group compared with the NC group, in which six miRNAs had good efficacy to diagnose PV from MMP with the area under the receiver operator characteristic curves of 0.970 to 0.988. A series test for the combination of miR-584-5p and miR-155-5p reached the sensitivity and specificity of 95.5% and 100%. Bioinformatic analysis revealed target gene enrichment in the cell adhesion pathways, immune-relating pathways, and P38 mitogen-activated protein kinases signaling pathway. Conclusion: The study provides new insights and targets of miRNAs for the precise diagnosis and the exploration of pathogenesis for PV, which may serve as a reference for further research into autoimmune bullous diseases.
Lichen Sclerosus: A Current Landscape of Autoimmune and Genetic Interplay
Authors: Oyama Noritaka , Hasegawa Minoru
Published: December 2022

Lichen sclerosus (LS) is an acquired chronic inflammatory dermatosis predominantly affecting the anogenital area with recalcitrant itching and soreness. Progressive or persistent LS may cause urinary and sexual disturbances and an increased risk of local skin malignancy with a prevalence of up to 11%. Investigations on lipoid proteinosis, an autosomal recessive genodermatosis caused by loss-of-function mutations in the extracellular matrix protein 1 (ECM1) gene, led to the discovery of a humoral autoimmune response to the identical molecule in LS, providing evidence for an autoimmune and genetic counterpart targeting ECM1. This paper provides an overview of the fundamental importance and current issue of better understanding the immunopathology attributed to ECM1 in LS. Furthermore, we highlight the pleiotropic action of ECM1 in homeostatic and structural maintenance of skin biology as well as in a variety of human disorders possibly associated with impaired or gained ECM1 function, including the inflammatory bowel disease ulcerative colitis, Th2 cell-dependent airway allergies, T-cell and B-cell activation, and the demyelinating central nervous system disease multiple sclerosis, to facilitate sharing the concept as a plausible therapeutic target of this attractive molecule.
DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation
Authors: Chamroukhi Faicel , Brivet Segolene , Savadjiev Peter , Coates Mark , Forghani Reza
Published: December 2022

Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation–maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer.
Evaluation of BD Onclarity™ HPV Assay on Self-Collected Vaginal and First-Void Urine Samples as Compared to Clinician-Collected Cervical Samples: A Pilot Study
Authors: Martinelli Marianna , Giubbi Chiara , Sechi Illari , Bottari Fabio , Iacobone Anna Daniela , Musumeci Rosario , Perdoni Federica , Muresu Narcisa , Piana Andrea , Fruscio Robert , Landoni Fabio , Cocuzza Clementina Elvezia
Published: December 2022

The accuracy of available HPV molecular assays on self-samples needs to be evaluated as compared to clinician-collected samples. This pilot study aimed to investigate the BD Onclarity™ HPV assay on vaginal and first-void urine samples. Sixty-four women referred to colposcopy for cervical dysplasia performed a vaginal self-collection and provided a first-void urine sample, after informed consent. A cervical specimen was collected during the clinician examination. All samples were tested using BD Onclarity™ HPV assay on the BD Viper™ LT System. Overall positive agreement (OPA) between cervical and self-sample results was evaluated using Cohen’s kappa value (κ). Using a clinical cut-off of 38.3 Ct for HPV 16 and 34.2 Ct for other HR genotypes, compared to cervical sample, the self-collected vaginal sample OPA was 85.9%, and κ = 0.699. Without a clinical cut-off, the OPA was 95.3%, and the κ = 0.890. Data obtained comparing cervical and urine samples showed an OPA of 87.5% with a κ = 0.79 using a clinical cut-off, and an OPA of 90.6% with a κ = 0.776 without a clinical cut-off. Data showed a substantial agreement between both self-collected and clinician-collected samples. A specific clinical cut-off analysis should be considered based on type of sample analysed.
Cloning, Expression and Evaluation of Thioredoxin Peroxidase-1 Antigen for the Serological Diagnosis of Schistosoma mekongi Human Infection
Authors: Wanlop Atcharaphan , Angeles Jose Ma. M. , Macalanda Adrian Miki C. , Kirinoki Masashi , Ohari Yuma , Yajima Aya , Yamagishi Junya , Ona Kevin Austin L. , Kawazu Shin-ichiro
Published: December 2022

Schistosoma mekongi, a blood fluke that causes Asian zoonotic schistosomiasis, is distributed in communities along the Mekong River in Cambodia and Lao People’s Democratic Republic. Decades of employing numerous control measures including mass drug administration using praziquantel have resulted in a decline in the prevalence of schistosomiasis mekongi. This, however, led to a decrease in sensitivity of Kato–Katz stool microscopy considered as the gold standard in diagnosis. In order to develop a serological assay with high sensitivity and specificity which can replace Kato–Katz, recombinant S. mekongi thioredoxin peroxidase-1 protein (rSmekTPx-1) was expressed and produced. Diagnostic performance of the rSmekTPx-1 antigen through ELISA for detecting human schistosomiasis was compared with that of recombinant protein of S. japonicum TPx-1 (rSjTPx-1) using serum samples collected from endemic foci in Cambodia. The sensitivity and specificity of rSmekTPx-1 in ELISA were 89.3% and 93.3%, respectively, while those of rSjTPx-1 were 71.4% and 66.7%, respectively. In addition, a higher Kappa value of 0.82 calculated between rSmekTPx-1 antigen ELISA and Kato–Katz confirmed better agreement than between rSjTPx-1 antigen ELISA and Kato–Katz (Kappa value 0.38). These results suggest that ELISA with rSmekTPx-1 antigen can be a potential diagnostic method for detecting active human S. mekongi infection.
Advances in Bone Joint Imaging-Metal Artifact Reduction
Authors: Kohyama Sho , Yoshii Yuichi , Okamoto Yoshikazu , Nakajima Takahito
Published: December 2022

Numerous types of metal implants have been introduced in orthopedic surgery and are used in everyday practice. To precisely evaluate the postoperative condition of arthroplasty or trauma surgery, periprosthetic infection, and the loosening of implants, it is important to reduce artifacts induced by metal implants. In this review, we focused on technical advances in metal artifact reduction using digital tomosynthesis, computed tomography, and magnetic resonance imaging. We discussed new developments in diagnostic imaging methods and the continuous introduction of novel technologies to reduce metal artifacts; however, these innovations have not yet completely removed metal artifacts. Different algorithms need to be selected depending on the size, shape, material and implanted body parts of an implant. Future advances in metal artifact reduction algorithms and techniques and the development of new sequences may enable further reductions in metal artifacts even on original images taken previously. Moreover, the combination of different imaging modalities may contribute to further reductions in metal artifacts. Clinicians must constantly update their knowledge and work closely with radiologists to select the best diagnostic imaging method for each metal implant.
Role of Salivary Biomarkers in Diagnosis and Detection of Dental Caries: A Systematic Review
Authors: Alamoudi Ahmed , Alamoudi Raghad , Gazzaz Yara , Alqahtani Aseel M.
Published: December 2022

Saliva plays a significant role in oral health and tooth integrity. Salivary components reduce tooth surface exposure to demineralization, protect against teeth wear and aid in enamel remineralization. There is a growing attempt to use salivary markers in diagnosing or predicting caries. However, despite the current information, there has yet to be an agreement among scholars. This study seeks to contribute more evidence on the suitability of salivary biomarkers in dental caries diagnosis. Eligible studies were electronically searched on online databases PubMed, Elsevier’s Scopus, EMBASE and Web of Science, and all the studies that met the inclusion criteria were considered. The PECOS criteria guided the study selection process based on the study question. The risk of bias was assessed using the STROBE checklist. Eighteen articles were included in the analysis. All the studies presented relevant data concerning the study objectives. There was evidence of associations between salivary biomarkers and dental caries, and the correlations were either positive or negative. The studies presented significant heterogeneity; thus, a meta-analysis was not possible. Salivary biomarkers appeared to perform crucial and complementary functions toward tooth integrity and thus may be reliable in predicting or diagnosing dental caries in patients.
A Framework for Characterizing Flapping Wing Systems
Authors: Lefik Alex , Marian Romeo , Ogunwa Titilayo , Chahl Javaan
Published: December 2022

Flapping wing systems are being developed by various institutions and research groups around the world with many systems developed that are capable of full flight. However, while instrumentation has been developed that is capable of measuring some of the characteristics of these systems, there is no complete solution. This paper seeks to take the first step toward instrumentation that could be applied to any flapping wing system. This first step is to identify and characterize the forces that are operating on flapping wing systems. This paper presents, in premiere, a systematic analysis of all cases that can create useful or parasitic aerodynamic loads along with the other major loads that would be experienced by these cases and methodology for how these can be measured with the ambition that it can become a framework to be used to characterize any flapping wing system.
Linear System Challenges of Dynamic Factor Models
Authors: Anderson Brian D. O. , Deistler Manfred , Lippi Marco
Published: December 2022

A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed application of the model, be it for example prediction or business cycle determination. Mixed frequency problems are also considered, in which certain entries of an underlying vector process are only available for measurement at a submultiple frequency of the original process. Certain classes of processes are shown to be generically identifiable, and others not to have this property.
Determinants of Banks’ Stability in Jordan
Authors: Kharabsheh Buthiena , Gharaibeh Omar Khlaif
Published: December 2022

This paper aims to examine the determinants of financial stability in Jordanian commercial banks based on annual data for the period from 2011 to 2018. Based on the pooled effect model, this study shows that SME loans and capital adequacy positively and statistically affect the stability of Jordanian commercial banks, while financial inclusion, liquidity risk and credit risk negatively and statistically affect the stability of Jordanian commercial banks. The study recommends increasing the directing of bank loans towards small and medium enterprises, and the necessity for bank managers to commit to capital adequacy requirements because of their positive impact on banks’ stability. This study recommends that banks experiencing financial stability accelerate the increase in the rate of financial inclusion because financial inclusion ratios are very low in most of these stable banks. On the other hand, Jordanian commercial banks that have relatively high rates of financial inclusion should reduce the operating expenses resulting from financial inclusion. Bank managers also have to maintain sufficient liquidity in their banks and enhance credit standards by increasing collateral requirement from customers.
A Social Support and Resource Drain Exploration of the Bright and Dark Sides of Teachers’ Organizational Citizenship Behaviors
Authors: Muntean Arcadius Florin , Curșeu Petru Lucian , Tucaliuc Mihai
Published: December 2022

Our study explores workload and social support as two mechanisms that explain the cost-benefit interplay of organizational citizenship behaviors (OCBs) in relation to teachers’ work performance (WP) and maladaptive work outcomes (MWO, stress and burnout). We use a cross-sectional design to test the bright and dark sides of teachers’ OCBs with a sample of 2224 Romanian teachers (with an average age of 42.72 years old). Workload explains the association between OCBs, WP and MWO for high rather than low levels of OCBs, while social support explains the association irrespective of the OCB level. The association between OCBs targeted towards the organization, WP and MWO is mediated by workload, while the association between OCBs targeted towards individuals, WP and MWO is mediated by social support. Our results join the call for substantial social support offered to teachers and call for more refined and joint evaluations of the type and level of OCB that are conducive for teachers’ performance.
Learning through Collaborative Data Projects: Engaging Students and Building Rapport
Authors: Pietryka Matthew T. , Glazier Rebecca A.
Published: December 2022

Many instructors strive to make students more active participants in their own learning. Yet recommended methods for this purpose, such as project-based learning or providing personalized feedback, are impractical in larger classes or virtual classrooms. To overcome these barriers, we designed a series of collaborative data projects to engage students even in large, online classes. Our goal is to describe and evaluate the efficacy of these projects. The projects offer two major benefits. First, the collective nature of the projects helps students to see how their individual efforts contribute to a larger outcome. Second, the instructor can aggregate the student input and provide personalized feedback using free software, thus, reducing the grading and contact burden while simultaneously building rapport. We examine survey data from about 120 students from four classes during Spring and Fall 2021. The students report that they tend to learn more and find these collaborative assignments more enjoyable compared to a typical college assignment. They also report that receiving individualized feedback increases their interest in the material and makes them feel like the instructor is invested in their learning. We include assignment examples and programming recommendations to help instructors adapt this collaborative engagement method for their own courses.
Deep Feature Extraction for Detection of COVID-19 Using Deep Learning
Authors: Rafiq Arisa , Imran Muhammad , Alhajlah Mousa , Mahmood Awais , Karamat Tehmina , Haneef Muhammad , Alhajlah Ashwaq
Published: December 2022

SARS-CoV-2, a severe acute respiratory syndrome that is related to COVID-19, is a novel type of influenza virus that has infected the entire international community. It has created severe health and safety concerns all over the globe. Identifying the outbreak in the initial phase may aid successful recovery. The rapid and exact identification of COVID-19 limits the risk of spreading this fatal disease. Patients with COVID-19 have distinctive radiographic characteristics on chest X-rays and CT scans. CXR images can be used for people with COVID-19 to diagnose their disease early. This research was focused on the deep feature extraction, accurate detection, and prediction of COVID-19 from X-ray images. The proposed concatenated CNN model is based on deep learning models (Xception and ResNet101) for CXR images. For the extraction of features, CNN models (Xception and ResNet101) were utilized, and then these features were combined using a concatenated model technique. In the proposed scheme, the particle swarm optimization method is applied to the concatenated features that provide optimal features from an overall feature vector. The selection of these optimal features helps to decrease the classification period. To evaluate the performance of the proposed approach, experiments were conducted with CXR images. Datasets of CXR images were collected from three different sources. The results demonstrated the efficiency of the proposed scheme for detecting COVID-19 with average accuracies of 99.77%, 99.72%, and 99.73% for datasets 1, 2 and 3, respectively. Moreover, the proposed model also achieved average COVID-19 sensitivities of 96.6%, 97.18%, and 98.88% for datasets 1, 2, and 3, respectively. The maximum overall accuracy of all classes—normal, pneumonia, and COVID-19—was about 98.02%.
Experimental Validation and Applications of mm-Wave 8 × 8 Antenna-in-Package (AiP) Array Platform
Authors: Li Yifa , Fan Wei , Gao Huaqiang , Zhang Fengchun
Published: December 2022

Phased array antennas play an indispensable role in millimeter-wave (mmWave) communications. The Antenna-in-package (AiP) system combines advanced antenna and packaging technology, making it highly valuable for various cellular, radar and automative applications. The benefits it brings in terms of small size, low development costs, low power consumption and fast beam-steering capability further drive the vast deployment of phased array antennas in mmWave systems. In this paper, an 8 × 8 AiP experimental platform is presented and its operating performance is measured and analyzed. Further, two application examples of the AiP are presented, namely, a platform for investigating the phased array calibration performance of different methods, and an AiP-based channel sounder for channel characterization. The performance of the channel sounder is verified by analysing the angle of arrival (AoA), angle of departure (AoD) and propagation delay of the measured dominant propagation components (DPCs).
High-Frequency Forecasting of Stock Volatility Based on Model Fusion and a Feature Reconstruction Neural Network
Authors: Shi Zhiwei , Wu Zhifeng , Shi Shuaiwei , Mao Chengzhi , Wang Yingqiao , Zhao Laiqi
Published: December 2022

Stock volatility is an important measure of financial risk. Due to the complexity and variability of financial markets, time series forecasting in the financial field is extremely challenging. This paper proposes a “model fusion learning algorithm” and a “feature reconstruction neural network” to forecast the future 10 min volatility of 112 stocks from different industries over the past three years. The results show that the model in this paper has higher fitting accuracy and generalization ability than the traditional model (CART, MLR, LightGBM, etc.). This study found that the “model fusion learning algorithm” can be well applied to financial data modeling; the “feature reconstruction neural network” can well-model data sets with fewer features.
RefinePose: Towards More Refined Human Pose Estimation
Authors: Dong Hao , Wang Guodong , Chen Chenglizhao , Zhang Xinyue
Published: December 2022

Human pose estimation is a very important research topic in computer vision and attracts more and more researchers. Recently, ViTPose based on heatmap representation refreshed the state of the art for pose estimation methods. However, we find that ViTPose still has room for improvement in our experiments. On the one hand, the PatchEmbedding module of ViTPose uses a convolutional layer with a stride of 14 × 14 to downsample the input image, resulting in the loss of a significant amount of feature information. On the other hand, the two decoding methods (Classical Decoder and Simple Decoder) used by ViTPose are not refined enough: transpose convolution in the Classical Decoder produces the inherent chessboard effect; the upsampling factor in the Simple Decoder is too large, resulting in the blurry heatmap. To this end, we propose a novel pose estimation method based on ViTPose, termed RefinePose. In RefinePose, we design the GradualEmbedding module and Fusion Decoder, respectively, to solve the above problems. More specifically, the GradualEmbedding module only downsamples the image to 1/2 of the original size in each downsampling stage, and it reduces the input image to a fixed size (16 × 112 in ViTPose) through multiple downsampling stages. At the same time, we fuse the outputs of max pooling layers and convolutional layers in each downsampling stage, which retains more meaningful feature information. In the decoding stage, the Fusion Decoder designed by us combines bilinear interpolation with max unpooling layers, and gradually upsamples the feature maps to restore the predicted heatmap. In addition, we also design the FeatureAggregation module to aggregate features after sampling (upsampling and downsampling). We validate the RefinePose on the COCO dataset. The experiments show that RefinePose has achieved better performance than ViTPose.
Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
Authors: Bayu Teguh Indra , Huang Yung-Fa , Chen Jeang-Kuo
Published: December 2022

Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability (Prk) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed Prk value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal Prk value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable Prk. The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively.
Study on Price Bubbles of China’s Agricultural Commodity against the Background of Big Data
Authors: Wang Jiayue , Ma Kun , Zhang Ling , Wang Jianzhong
Published: December 2022

Agriculture provides a basis for social and economic development. It is therefore crucial for society and the economy to stabilize agricultural prices. Recent large increases and decreases in Chinese agricultural commodity prices have increased production risks, heightened fluctuations in the domestic agricultural supply, and impacted the stability of the global agricultural market. Meanwhile, big data technology has advanced quickly and now serves as a foundation for the investigation of time series bubbles. Identifying agricultural price bubbles is important for determining agricultural production decisions and policies that control agricultural prices. Using weekly agricultural price data from 2009 to 2021, this paper identifies agricultural price bubbles, pinpoints their time points, and examines their causes. According to our research, prices for corn, hog, green onions, pork, and ginger all have bubbles, but garlic do not. The quantity, length, time distribution, and type of bubbles differ significantly among corn, ginger, green onion, hog, and pork. The main causes for ginger and green onion price bubbles are speculation and natural disasters. Price bubbles for hog and pork are influenced by animal disease and rising costs. Conflicts between supply and demand and changes in price policy cause corn price bubbles to form. This paper advises that the government adopt various regulatory actions to stabilize agricultural prices depending on the characteristics and causes of the various types of agricultural price bubbles, it should also improve the early warning system and response mechanism for agricultural price bubbles and focus on how policies and market processes work together.
Recent Trends in Power Systems Modeling and Analysis
Authors: Araneo Rodolfo , Celozzi Salvatore , Lauria Stefano , Stracqualursi Erika , Di Lorenzo Gianfranco , Graziani Marco
Published: December 2022

In recent years, the explosion of renewable energy sources, the increase in the demand for electrical energy, and several improvements in related technologies have fostered research in many relevant areas of interest [...]
A Multiobjective Artificial-Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources
Authors: Waleed Umar , Haseeb Abdul , Ashraf Muhammad Mansoor , Siddiq Faisal , Rafiq Muhammad , Shafique Muhammad
Published: December 2022

This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.
A Review of Research on Cavity Growth in the Context of Underground Coal Gasification
Authors: Fang Huijun , Liu Yuewu , Ge Tengze , Zheng Taiyi , Yu Yueyu , Liu Danlu , Ding Jiuge , Li Longlong
Published: December 2022

Underground Coal Gasification (UCG) is a leading-edge technology for clean and effective utilization of coal resources, especially for deep coal seams with a depth of more than 1000 m. Since the core operation place of UCG is the cavity, mastering the cavity growth pattern is a prerequisite to ensure the efficient and economic development of UCG. At present, scholars have conducted numerous research works on cavity growth, but the simulation conditions limit the research results. Hence, it is necessary to summarize and sort out the research results of cavity growth patterns, which contribute to deepening the understanding of UCG and pointing out the direction for subsequent research. First of all, this paper summarizes the development history of UCG technology and describes the cavity growth mechanism from chemical reactions and thermo-mechanical failure. Then, the research methods of cavity growth are summarized from three aspects: a field test, laboratory experiment, and numerical simulation. The results show that the appearance of the cavity is teardrop-shaped, and its growth direction is obviously related to the gas injection method, including the injection direction and rate. Subsequently, the factors affecting the cavity growth process are expounded from the geological factors (permeability, moisture content, and coal rank) and operating factors (temperature, pressure, gasification agent’s composition, and gasification agent’s flow pattern). Finally, the existing problems and development trends in the cavity growth are discussed. The follow-up research direction should focus on clarifying the cavity growth mechanism of the controlled-retractable-injection-point (CRIP) method of UCG in the deep coal seam and ascertain the influence of the moisture content in the coal seam on cavity growth.
Research on the Vortex Rope Control Techniques in Draft Tube of Francis Turbines
Authors: Wang Lei , Cui Jiayi , Shu Lingfeng , Jiang Denghui , Xiang Chun , Li Linwei , Zhou Peijian
Published: December 2022

Francis turbines are most widely used in hydropower due to their characteristics which include a fast response and wide time-scale operation. The vortex rope inside Francis turbines is a common flow phenomenon, which always causes strong vibration, pressure pulsations, fatigue load, and even serious failure of the components. Vortex suppression methods can effectively change the velocity and pressure distribution of the flow field in the draft tube, reduce the volume of vortex rope and the amplitude of pressure pulsation, inhibit the development of cavitation erosion, and improve the operation stability of the hydro turbine. However, the vortex suppression method is not suitable for all working conditions, and the vortex suppression effect is also different. There are still many problems with how to analyze the vortex suppression effect and practicability of the turbine from multi-dimensions. It is of great significance to analyze the vortex suppression techniques and their practicability in hydraulic turbines from various aspects. The primary focus of the present study is to analyze the hazards of vortex rope in draft tubes and summarize the methods of suppressing vortex rope and pressure pulsation. This review article provides a basis for controlling the vortex rope in the draft tube, which can help the designers choose the suitable control method to mitigate it. Future research directions are also briefly discussed.
Molecular Phylogenetics of the Orchid Genus Spathoglottis (Orchidaceae: Collabieae) in Peninsular Malaysia and Borneo
Authors: Nordin Farah Alia , Saibeh Kartini , Go Rusea , Mangsor Khairul Nasirudin Abu , Othman Ahmad Sofiman
Published: December 2022

Phylogenetic relationships of the orchid genus Spathoglottis (Orchidaceae: Collabieae) in Peninsular Malaysia and Borneo were inferred using the internal transcribed spacer of a nuclear ribosomal DNA (nrITS), a plastid gene maturaseK (matK) and the plastid region trnL-F. Eleven species and three infraspecific taxa of Spathoglottis were examined, with two outgroup species, were included in the phylogenetic analysis. The combined plastid and nuclear data revealed Spathoglottis as monophyletic. From the maximum likelihood, maximum parsimony and Bayesian analyses, Spathoglottis is divided into four major groups which are, (1) the Dwarf Purple Spathoglottis, (2) the Dwarf Yellow Spathoglottis, (3) the Large Purple Spathoglottis, and (4) the Large Yellow Spathoglottis. The split in the Dwarf and Large Spathoglottis groups might reflect an early differentiation of plant size, flower colours and flower size. Phylogeny reconstruction of the orchid genus Spathoglottis also exhibited strong support towards the taxonomic delimitation of the two mostly debated taxa in the genus, S. aurea and S. microchilina.
In Situ Rainwater Collection and Infiltration System Alleviates the Negative Effects of Drought on Plant-Available Water, Fine Root Distribution and Plant Hydraulic Conductivity
Authors: Ma Changkun , Meng Haobo , Xie Biao , Li Qian , Li Xiangdong , Zhou Beibei , Wang Quanjiu , Luo Yi
Published: December 2022

Soil water status and fine-root characteristics are the foundation for implementing forest water-management strategies in semiarid forest plantations, where rainwater is always the sole source of water for plant growth. Rainwater management and utilization are effective strategies to alleviate water scarcity in semiarid areas as ground water is always inaccessible there. Through the implementation of an in situ rainwater collection and infiltration system (IRCIS), we investigated the effects of IRCIS on soil water and fine-root distributions in the 0–5 m soil profile in a wet (2015, 815 mm) and a dry year (2016, 468 mm) in rainfed Robinia pseudoacacia forests in the Loess Plateau region of China. The results showed drought significantly decreased plant water availability and hydraulic conductivity of roots and branches, but strongly increased soil moisture deficits and fine-root (<2 mm diameter) biomass. With the implementation of IRCIS, soil profile available water and plant hydraulic conductivity can be significantly increased, but soil moisture deficits and fine-root (<2 mm diameter) biomass can be significantly decreased. Drought also significantly influenced the root distribution of Robinia pseudoacacia. The maximum depth of Robinia pseudoacacia roots in the dry year was significantly greater than in the wet year. Therefore, Robinia pseudoacacia can absorb shallow (0–1.5 m) soil water in wet years, while utilizing deep (>1.5 m) soil water in dry years to maintain normal growth and resist drought stress. The results of this study will contribute to the formulation of appropriate strategies for planning and managing rainwater resources in forest plantations.
Study of the Influence of Heat Treatment on OSB Panels Produced with Eucalyptus Wood in Different Layer Compositions
Authors: Sugahara Estefani , Dias Andre , Arroyo Felipe , Christoforo Andre , Costa Michelle L. , Botelho Edson C. , Dias Alfredo M. P. G. , Campos Cristiane
Published: December 2022

In view of the lack of studies aimed at producing and assessing the effects of heat treatment of Oriented Strand Board (OSB) panels, this paper evaluated the thermal degradation kinetics of the raw materials, and the physical and mechanical properties of the panels made with eucalyptus wood and castor oil-based polyurethane adhesive. The OSB panels were subjected to post-production heat treatment (at 175 and 200 °C), replacing the use of wood chemical preservatives. Furthermore, the quantity of materials varied in the face:core:face layers in the proportions of 25:50:25 and 30:40:30, aiming to evaluate the possibility of structural applications for the panels. The results were statistically analyzed and compared with the specifications and classifications for OSB indicated by the European Standard EN 300 and the literature. The application of heat treatment improved the physical properties by decreasing the interaction with water and did not reduce the mechanical properties below the recommended levels. The variation in layer proportions indicated that all of them can be adopted without compromising the panel’s physical-mechanical performance. All treatments are compatible with the EN 300 classification for OSB/4 (heavy duty load-bearing boards for use in humid conditions), presenting technical feasibility and excellent structural profile for civil construction applications.
A Comprehensive Mechanistic Yeast Model Able to Switch Metabolism According to Growth Conditions
Authors: González-Hernández Yusmel , Michiels Emilie , Perré Patrick
Published: December 2022

This paper proposes a general approach for building a mechanistic yeast model able to predict the shift of metabolic pathways. The mechanistic model accounts for the coexistence of several metabolic pathways (aerobic fermentation, glucose respiration, anaerobic fermentation and ethanol respiration) whose activation depends on growth conditions. This general approach is applied to a commercial strain of Saccharomyces cerevisiae. Stoichiometry and yeast kinetics were mostly determined from aerobic and completely anaerobic experiments. Known parameters were taken from the literature, and the remaining parameters were estimated by inverse analysis using the particle swarm optimization method. The optimized set of parameters allows the concentrations to be accurately determined over time, reporting global mean relative errors for all variables of less than 7 and 11% under completely anaerobic and aerobic conditions, respectively. Different affinities of yeast for glucose and ethanol tolerance under aerobic and anaerobic conditions were obtained. Finally, the model was successfully validated by simulating a different experiment, a batch fermentation process without gas injection, with an overall mean relative error of 7%. This model represents a useful tool for the control and optimization of yeast fermentation systems. More generally, the modeling framework proposed here is intended to be used as a building block of a digital twin of any bioproduction process.
Quorum-Sensing Inhibitors from Probiotics as a Strategy to Combat Bacterial Cell-to-Cell Communication Involved in Food Spoilage and Food Safety
Authors: Davares Anyutoulou K. L. , Arsene Mbarga M. J. , Viktorovna Podoprigora I. , Vyacheslavovna Yashina N. , Vladimirovna Zhigunova A. , Aleksandrovna Vasilyeva E. , Nikolayevich Senyagin A. , Nadezhda Sachivkina , Anatolievna Gizinger O. , Nikolaevna Sharova I. , Sergueïevna Das M.
Published: December 2022

Experience-based knowledge has shown that bacteria can communicate with each other through a cell-density-dependent mechanism called quorum sensing (QS). QS controls specific bacterial phenotypes, such as sporulation, virulence and pathogenesis, the production of degrading enzymes, bioluminescence, swarming motility, and biofilm formation. The expression of these phenotypes in food spoiling and pathogenic bacteria, which may occur in food, can have dramatic consequences on food production, the economy, and health. Due to the many reports showing that the use of conventional methods (i.e., antibiotics and sanitizers) to inhibit bacterial growth leads to the emergence of antibiotic resistance, it is necessary to research and exploit new strategies. Several studies have already demonstrated positive results in this direction by inhibiting autoinducers (low-molecular-weight signaling compounds controlling QS) and by other means, leading to QS inhibition via a mechanism called quorum quenching (QQ). Thus far, several QS inhibitors (QSIs) have been isolated from various sources, such as plants, some animals from aqueous ecosystems, fungi, and bacteria. The present study aims to discuss the involvement of QS in food spoilage and to review the potential role of probiotics as QSIs.
Optimization of Medium Components for Fed-Batch Fermentation Using Central Composite Design to Enhance Lichenysin Production by Bacillus licheniformis Ali5
Authors: Pang Zhengjun , Li Yuanzi , Shang Yu , Ali Nawazish , Wang Fenghuan , Zhang Dianwei , Liao Yonghong
Published: December 2022

Lichenysin, an amphiphilic biosurfactant with structural and physicochemical properties similar to surfactin, is produced by Bacillus licheniformis. Its low toxicity, good environmental compatibility, solubilization, foaming, emulsification and detergent activities have led to a wide range of applications in agricultural biocontrol, enhanced oil recovery, foaming agents for cosmetics and detergents for household cleaning products. However, despite the extraordinary surface-active properties and potential applications of lichenysin, the number of wild bacteria found so far is relatively low. Low titers and high costs are the main limiting factors for widespread industrial applications. In this study, a factorial design was used to optimize the composition of the medium for the production of lichenysin by Bacillus licheniformis Ali5. Firstly, the solutions of carbon, nitrogen, amino acids, inorganic salts and trace elements in the medium were evaluated in flasks using a single-factor optimization method. Meanwhile, the operating conditions were optimized in the same way. Afterwards, a partial factorial design was used to investigate the effect of six variables (five medium compositions and inoculum size) on lichenysin production. Based on the results obtained, the concentrations of sucrose and ammonium nitrate and the inoculum size were considered to be important for lichenysin production. Subsequently, a full factorial design was used to optimize these three variables. The optimized medium composition were sucrose 19.8 g/L, NH4NO3 3.9 g/L, K2HPO4·3H2O 4.0 g/L, MgSO4·7H2O 0.6 g/L, FeSO4·7H2O 0.1 g/L, CaCl2 0.01 g/L, NaCl 3.0, trace elements 1.2 mL/L. Finally, the titer of lichenysin after fed-batch fermentation reached 1425.85 mg/L, which was approximately 5.5 times higher than the titer of lichenysin from the original medium. Consequently, the method was further demonstrated to be suitable for lichenysin production.
Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm
Authors: Taha Ashraf A. , Abouroumia Hagar O. , Mohamed Shimaa A. , Amar Lamiaa A.
Published: December 2022

As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption.
A Method for Choosing the Spatial and Temporal Approximations for the LES Approach
Authors: Bakhne Sergei , Sabelnikov Vladimir
Published: December 2022

Analysis and optimization of the hybrid upwind-central numerical methods for modern versions of large eddy simulations (LESs) are presented herein. Optimization was performed based on examination of the characteristics of the spatial and temporal finite-volume approximations of the convective terms of filtered Navier–Stokes equations. A method for selecting level of subgrid viscosity that corresponds to the chosen numerical scheme and makes it possible to obtain an extended inertial interval of the energy spectrum is proposed. A series of LESs of homogeneous isotropic turbulence decay were carried out, and the optimal values of the subgrid model constants included in the hybrid shear stress transport delay detached eddy simulation (SST-DDES) method were determined. A procedure for generating a consistent initial field of subgrid parameters for these simulations is described. The three-stage explicit Runge–Kutta method was demonstrated to be sufficient for stable time integration, while the popular explicit midpoint method was not. The slope of the energy spectrum was shown to be almost independent of the central-difference scheme order and of the mesh spacing when the correct numerical method was applied. Numerical viscosity was found to become much greater than subgrid viscosity when the upwind scheme contributed about 10% or more to the convective flux approximation.
Hypoglycemic Effects and Mechanisms of Buckwheat–Oat–Pea Composite Flour in Diabetic Rats
Authors: Yin Xueqian , Liu Siqi , Zhang Xiaoxuan , Jian Yuanzhi , Wen Jing , Zhou Ruoyu , Yin Ning , Liu Xinran , Hou Chao , Wang Junbo
Published: December 2022

Nutritional intervention is a basic way to prevent and treat diabetes mellitus. Appropriate whole grain intake daily is recommended. The study aimed to explore the feasibility of a kind of buckwheat–oat–pea composite flour (BOP, quality ratio of buckwheat:oats:peas = 6:1:1) as a stable food substitution and its underlying mechanisms. High-fat food (HFD) and streptozotocin injection were used to induce diabetes in rats, and buckwheat, oats, and three different doses of BOP were added to the HFD separately for diet intervention. The whole study lasted for 10 weeks, and the glucose tolerance test, lipids, liver injury, and gut microbiota were evaluated in the last week. The diabetic rat model was successfully induced. The BOP significantly changed the glucose and lipids metabolism, decreased liver injury, and changed the composition of the gut microbiota of diabetic rats. The outcomes of the current study revealed that BOP is a potential stable food substitution.
An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability
Authors: González-Domínguez Raúl , Sayago Ana , Fernández-Recamales Ángeles
Published: December 2022

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.