bluuebunny/crossref_metadata_2025 huggingface dataset:
How Do We Learn Skilled Movements?
Authors: Chandy Anisha , Tsay Jonathan , Ivry Rich
Published: May 2022

All athletes were novices at one point in their lives, even Olympic gold medalists and world champions. They committed years of practice to become competitive and even longer to become elite performers. This transformation from novice to expert requires motor learning, which is the process of acquiring and refining motor (movement) skills. Inspired by the observation that motor skills evolve from being effortful to effortless, psychologists have divided motor learning into three separate stages: the cognitive stage, in which we gather information about the actions needed to perform a skill; the associative stage, in which we refine our movements; and the autonomous stage, in which our movements become smooth and automatic. Here, we will explore how a fictional young athlete, Amy, progresses through these three stages and uses different parts of her brain to make the transition from novice to expert.
Influence of Process Operation on the Production of Exopolysaccharides in Arthrospira platensis and Chlamydomonas asymmetrica
Authors: Jung Sun-Hwa , Zell Niklas , Boßle Fabian , Teipel Ulrich , Rauh Cornelia , McHardy Christopher , Lindenberger Christoph
Published: May 2022

Extracellular polysaccharides, or exopolysaccharides are high–molecular weight sugar-based polymers expressed and secreted by many microorganisms. As host organisms, the functions of exopolysaccharides are diverse, ranging from physical protection via biofilm formation, adhesion, and water retention to biological functions that are not entirely understood such as viral attachment inhibition. Industrial applications of exopolysaccharides can be found in food texture modification; for example, utilizing the hydrocolloidal properties of exopolysaccharides for thickening and gelling purposes to improve food quality and texture. Over the last decade, biologically active exopolysaccharides produced by microalgae have received attention for their potential as antiviral, antibacterial and antioxidative compounds and in the applications. However, relatively low yield and productivity are the limiting factors for full-scale industrial application. In this study, the well-known prokaryotic phototrophic microorganismArthrospira platensisand the comparatively unknown eukaryotic unicellular green algaChlamydomonas asymmetricawere used to evaluate the influence of different process parameters on exopolysaccharides formation and productivity. In addition to the essential control variables (light and temperature), the influence of operational techniques (batch and turbidostat) were also investigated. Although the two studied algae are differently affected by above parameters. The light intensity was the most influential parameter observed in the study, leading to differences in exopolysaccharides concentrations by a factor of 10, with the highest measured concentration forA. platensisofcEPS= 0.138 g L−1at 180 μmol m−2s−1and forC. asymmetricaofcEPS= 1.2 g L−1at 1,429 μmol m−2s−1. In continuous systems, the achieved exopolysaccharides concentrations were low compared to batch process, however, slightly higher productivities were reached. Regardless of all screened process parameters,C. asymmetricais the better organism in terms of exopolysaccharides concentrations and productivity.
Risk Stratification of Dysphagia After Surgical Treatment of Hypopharyngeal Cancer
Authors: Joo Hye Ah , Lee Yoon Se , Jung Young Ho , Choi Seung-Ho , Nam Soon Yuhl , Kim Sang Yoon
Published: May 2022

Objective

Hypopharyngeal cancer is managed by either surgical resection or radiation therapy-based treatment. In choosing the treatment modality, the patient’s swallowing function should be considered to achieve optimal treatment outcomes. This study aimed to stratify the risk factors predictive of postoperative dysphagia in hypopharyngeal cancer.

Study Design

Retrospective study.

Setting

Tertiary referral center.

Methods

We enrolled 100 patients who were diagnosed with hypopharyngeal cancer and underwent curative surgery between January 2010 and December 2019, and retrospectively reviewed their medical records.

Results

Postoperative dysphagia occurred in 29 patients (29%) who required a tracheostomy tube or percutaneous gastrostomy tube for feeding or preventing aspiration; additionally, the overall survival rate was lower in those patients than in those without dysphagia. The univariate analysis revealed that postoperative dysphagia was associated with clinical T stage (p = 0.016), N stage (p = 0.002), and surgical resection extent of the larynx and pharynx (p < 0.001). Patients who underwent total laryngectomy with total/partial pharyngectomy were more likely to have dysphagia than those in the larynx-preserving pharyngectomy groups (odds ratio [OR] = 3.208, 95% confidence interval [CI] 1.283–8.024, p = 0.011). Concerning the posterior pharyngeal wall (PPW), which has an important role in swallowing, patients who underwent resection of ≥1/2 of the PPW were more likely to have dysphagia (OR = 7.467, 95% CI 1.799–30.994, p = 0.003).

Conclusions

Surgical resection extent was proportionally associated with dysphagia in hypopharyngeal cancer patients. Patients with smaller lesions but no laryngeal invasion had better postoperative swallowing function than patients with larger lesions or laryngeal involved lesions. Preserving the larynx and hypopharyngeal mucosa (especially the PPW) as much as possible can help preserve postoperative swallowing function.
Involvement of the Actinobacillus pleuropneumoniae ompW Gene in Confrontation of Environmental Pressure
Authors: Chen Xiabing , Shao Zhiyong , Wu Lijun , He Bin , Yang Wenhai , Chen Jie , Jin Erguang , Huang Qi , Lei Liancheng , Xu Jiajia , Li Haotian , Zhang Hui , Wan Yun , Liu Wu , Zhou Rui
Published: May 2022

Actinobacillus pleuropneumoniae causes porcine pleuropneumonia. The function of the outer membrane protein W gene (ompW) of A. pleuropneumoniae has not been evaluated. Thus a deletion mutant of ompW, ΔompW, was constructed to explore the effect of ompW gene deletion on bacterial growth, biofilm formation, bacterial morphology, oxidative tolerance, susceptibility to antibiotics, and the expression of ribosome synthesis and ABC transporter related genes. Results showed that the ompW gene deletion did not affect biofilm formation and the growth of A. pleuropneumoniae but did affect bacterial morphology during steady growth, oxidative tolerance, and bacterial susceptibility to polymyxin B, kanamycin, and penicillin. The ompW gene deletion also affected the expression of ribosome synthesis and ABC transporter related genes. These results suggested that ompW may regulate the biological phenotype of A. pleuropneumoniae.
Endophytic Fungi Accelerate Leaf Physiological Activity and Resveratrol Accumulation in Polygonum cuspidatum by Up-Regulating Expression of Associated Genes
Authors: Sun Rui-Ting , Zhang Ze-Zhi , Feng Xiang-Cao , Zhou Nong , Feng Hai-Dong , Liu Yi-Mei , Harsonowati Wiwiek , Hashem Abeer , Abd_Allah Elsayed Fathi , Wu Qiang-Sheng
Published: May 2022

Polygonum cuspidatum Sieb. et Zucc. is a major raw material for the extraction of drugs such as resveratrol, while the over-exploitation of P. cuspidatum decreases the yield and drug components. The purpose of this study was to analyze the effect of inoculation with root endophytic fungi Funneliformis mosseae and Piriformospora indica singly or in combination in biomass production, physiological activities (e.g., chlorophyll, soluble protein, and gas exchange) and main medicinal ingredients of P. cuspidatum, accompanied by the expression levels of associated genes in resveratrol biosynthesis. Single and co-inoculation with P. indica significantly improved shoot and root biomass production, and single and co-inoculation with F. mosseae and P. indica, especially single P. indica, significantly promoted leaf chlorophyll and soluble-protein concentrations and improved leaf gas exchange, including photosynthetic rate, transpiration rate, stomatal conductance, and intercellular CO2 concentration. The application of endophytic fungi increased resveratrol and polydatin concentrations, while it affected chrysophanol, emodin, and physcion concentrations in a complex manner. In addition, F. mosseae inoculation and co-inoculation induced the expression of PcCRS1, PcRS11, PcRS, and PcSTS, and only single F. mosseae and P. indica inoculation up-regulated the expression of PcCHS1 and PcCHS2. It was concluded that endophytic fungi accelerated biomass production, leaf physiological activity, and resveratrol accumulation in P. cuspidatum, which was associated with the up-regulation of related gene expression in resveratrol biosynthesis.
Phytoremediation of Soil Contaminated with Heavy Metals via Arbuscular Mycorrhiza (Funneliformismosseae) Inoculation Ameliorates the Growth Responses and Essential Oil Content in Lavender (Lavandula angustifolia L.)
Authors: Pirsarandib Yaghoub , Hassanpouraghdam Mohammad Bagher , Rasouli Farzad , Aazami Mohammad Ali , Puglisi Ivana , Baglieri Andrea
Published: May 2022

Phytoremediation of heavy metals (HMs) is an efficient methodology to remove toxic metals from the soil. On the other hand, arbuscular mycorrhizal fungi (AMF) are utilized as biological fertilizers as they improve root expansion, nutrient uptake, shoot growth, and plant biological performance. In this study, the effect of AMF inoculation on the morphological traits, macro- and micronutrient contents, essential oil content (EOC), and essential oil yield (EOY) of lavender (Lavandula angustifolia L.) was investigated, under HM (Pb and Ni) stress in greenhouse conditions. The performed treatments were as follows: AMF (Funneliformis mosseae) inoculation (5 g kg−1 soil), and HM stress, including Pb (150 and 225 mg kg−1 soil from Pb(NO3)2) and Ni (220 and 330 mg kg−1 soil from Ni(NO3)2). The controls were the absence of AMF and HM treatments. The results showed that the contamination with Pb and Ni decreased plant height, branch number, fresh and dry weights of shoots and roots, and P, K, Mg, Fe, Zn, and Mn contents. At the same time, AMF inoculation modulated the adverse effects of Pb and Ni treatments. AMF inoculation and lower concentrations of Pb and Ni increased the EOC and EOY of lavender plants, whereas the higher levels of HMs reduced the morphobiometric traits. AMF inoculation increased the Pb and Ni contents in roots. The treatment with Pb at 220 mg kg−1 led to a higher stress effect than that of Ni treatment. In conclusion, the results recommend AMF inoculation as a helpful procedure to improve the growth responses and EOY of lavender in environments polluted with the tested HMs and suggest that AMF inoculation is potentially efficient in mitigating HM stress effects.
Effect of Harvest Date on Kernel Quality and Antioxidant Activity in su1 Sweet Corn Genotypes
Authors: Ledenčan Tatjana , Horvat Daniela , Špoljarić Marković Sanja , Svečnjak Zlatko , Jambrović Antun , Šimić Domagoj
Published: May 2022

In order to achieve a high-quality product, sweet corn should be harvested at the optimal stage of maturity. The taste of sweet corn depends largely on the kernel moisture (KM) and total sugars (TS) content while its health properties are attributed to the content of total phenols (TPH) and antioxidant activity (AO). This study aimed to estimate quality in sugary (su1) sweet corn hybrids based on the maximum content of kernel chemical compounds and the content change during maturation. During two growing seasons, five harvests by year were made at 2-day intervals from 17 to 25 days after pollination (DAP). KM decreased among hybrids from 6.6% to 14% over an eight-day period, or 0.8% to 1.8% per day. TS content was the highest at 17 DAP (16.5–28.7 mg/g DW) and decreased during maturation from 27% to 67%. Hybrids differed significantly in TPH at 17 DAP (204.4–357.1 mg GAE/100 g DW). TPH change during maturation ranged from the no significant differences up to a 29.3% decrease. DPPH- radical scavenging activity ranged among genotypes from 52.5% to 74.9% inhibition at 17 DAP and significantly increased during maturation. A well-defined hybrid-specific harvest window is necessary to maintain kernel quality in su1 sweet corn.
Endophytic Bacteria Can Replace the Need for Synthetic Auxin during In Vitro Rooting of Pyrus communis
Authors: Nadal Michele Carla , Ferreira Gustavo Magno dos Reis , Andrade Gracielle Vidal Silva , Buttrós Victor Hugo , Rodrigues Filipe Almendagna , da Silva Caroline Marcela , Martins Adalvan Daniel , Rufato Léo , Luz José Magno Queiroz , Dória Joyce , Pasqual Moacir
Published: May 2022

This study aimed to investigate the presence of beneficial microorganisms in the micropropagation of Pyrus communis rootstocks through the isolation, identification, and selection of auxin-producing bacteria. The selected microorganisms were also inoculated in vitro to evaluate their effect on the plant rooting process and their behavior during the acclimatization phase. The results showed the presence of endophytic microorganisms in plant tissue cultures of the ‘OH×F87′ and ‘PDW’ selections. A low diversity was observed in the population of microorganisms isolated from the in vitro culture of the ‘OH×F87’and ‘PDW’ selections, with a predominance of the genera Acinetobacter, Bacillus, and Buttiauxella. The selection of promoting microorganisms was performed based on the auxin production test, in which 30.36% of the microorganisms tested positive. In the in vitro inoculation, it was possible to observe the promotion of growth and emission of roots in the pear rootstocks, from the inoculation with bacteria identified as capable of producing auxin. This process can be used to replace the inclusion of synthetic auxin in the productive chain of woody fruit trees propagated by vegetative means, such as pears. None of the bacterial isolates was notably more promising, but the general similarity of treatments containing the A. septicus and A. ursingii strains, with the synthetic auxin treatment, suggests the possibility of its use on a large scale allowing the adoption of the cheaper method. of rooting. This work opens the door for further research using new, more promising microbial isolates, and also for lower-cost microorganism cultivation techniques, such as low-cost media obtained from agro-industrial residues.
A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading
Authors: Jamshidi Mohammad Behdad , Roshani Sobhan , Talla Jakub , Lalbakhsh Ali , Peroutka Zdeněk , Roshani Saeed , Parandin Fariborz , Malek Zahra , Daneshfar Fatemeh , Niazkar Hamid Reza , Lotfi Saeedeh , Sabet Asal , Dehghani Mojgan , Hadjilooei Farimah , Sharifi-Atashgah Maryam S. , Lalbakhsh Pedram
Published: May 2022

The spread of SARS-CoV-2 can be considered one of the most complicated patterns with a large number of uncertainties and nonlinearities. Therefore, analysis and prediction of the distribution of this virus are one of the most challenging problems, affecting the planning and managing of its impacts. Although different vaccines and drugs have been proved, produced, and distributed one after another, several new fast-spreading SARS-CoV-2 variants have been detected. This is why numerous techniques based on artificial intelligence (AI) have been recently designed or redeveloped to forecast these variants more effectively. The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately. This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19. Sixty-five preprints, peer-reviewed papers, conference proceedings, and book chapters published in 2020 were reviewed. Our criteria to include or exclude references were the performance of these methods reported in the documents. The results revealed that although methods under discussion in this review have suitable potential to predict the spread of COVID-19, there are still weaknesses and drawbacks that fall in the domain of future research and scientific endeavors.
Fatal Infection in an Alpaca (Vicugna pacos) Caused by Pathogenic Rhodococcus equi
Authors: Sting Reinhard , Schwabe Ingo , Kieferle Melissa , Münch Maren , Rau Jörg
Published: May 2022

Rhodococcus (R.) equi is a pathogen primarily known for infections in equine foals, but is also present in numerous livestock species including New World camelids. Moreover, R. equi is considered an emerging zoonotic pathogen. In this report, we describe in detail a fatal rhodococcal infection in an alpaca (Vicugna pacos), to our best knowledge, for the first time. The alpaca died due to a septicemic course of an R. equi infection resulting in emaciation and severe lesions including pyogranulomas in the lungs and pericardial effusion. The onset of the infection was presumably caused by aspiration pneumonia. R. equi could be isolated from the pyogranulomas in the lung and unequivocally identified by MALDI-TOF MS analysis and partial sequencing of the 16S rRNA gene, the 16S-23S internal transcribed spacer (ITS) region and the rpoB gene. The isolate proved to possess the vapA gene in accordance with tested isolates originating from the lungs of infected horses. The R. equi isolates revealed low minimal inhibitory concentrations (MIC values) for doxycycline, erythromycin, gentamycin, neomycin, rifampicin, trimethoprim/sulfamethoxazole, tetracycline and vancomycin in antibiotic susceptibility testing. Investigations on the cause of bacterial, especially fatal, septicemic infections in alpacas are essential for adequately addressing the requirements for health and welfare issues of this New World camelid species. Furthermore, the zoonotic potential of R. equi has to be considered with regard to the One Health approach.
Constraint-Based, Score-Based and Hybrid Algorithms to Construct Bayesian Gene Networks in the Bovine Transcriptome
Authors: Mortazavi Amin , Rashidi Amir , Ghaderi-Zefrehei Mostafa , Moradi Parham , Razmkabir Mohammad , Imumorin Ikhide G. , Peters Sunday O. , Smith Jacqueline
Published: May 2022

Bayesian gene networks are powerful for modelling causal relationships and incorporating prior knowledge for making inferences about relationships. We used three algorithms to construct Bayesian gene networks around genes expressed in the bovine uterus and compared the efficacies of the algorithms. Dataset GSE33030 from the Gene Expression Omnibus (GEO) repository was analyzed using different algorithms for hub gene expression due to the effect of progesterone on bovine endometrial tissue following conception. Six different algorithms (grow-shrink, max-min parent children, tabu search, hill-climbing, max-min hill-climbing and restricted maximum) were compared in three higher categories, including constraint-based, score-based and hybrid algorithms. Gene network parameters were estimated using the bnlearn bundle, which is a Bayesian network structure learning toolbox implemented in R. The results obtained indicated the tabu search algorithm identified the highest degree between genes (390), Markov blankets (25.64), neighborhood sizes (8.76) and branching factors (4.38). The results showed that the highest number of shared hub genes (e.g., proline dehydrogenase 1 (PRODH), Sam-pointed domain containing Ets transcription factor (SPDEF), monocyte-to-macrophage differentiation associated 2 (MMD2), semaphorin 3E (SEMA3E), solute carrier family 27 member 6 (SLC27A6) and actin gamma 2 (ACTG2)) was seen between the hybrid and the constraint-based algorithms, and these genes could be recommended as central to the GSE33030 data series. Functional annotation of the hub genes in uterine tissue during progesterone treatment in the pregnancy period showed that the predicted hub genes were involved in extracellular pathways, lipid and protein metabolism, protein structure and post-translational processes. The identified hub genes obtained by the score-based algorithms had a role in 2-arachidonoylglycerol and enzyme modulation. In conclusion, different algorithms and subsequent topological parameters were used to identify hub genes to better illuminate pathways acting in response to progesterone treatment in the bovine uterus, which should help with our understanding of gene regulatory networks in complex trait expression.
Impact of Three Nonsurgical, Full-Mouth Periodontal Treatments on Total Bacterial Load and Selected Pathobionts
Authors: Abdelbary Mohamed M. H. , Schittenhelm Florian , Yekta-Michael Sareh Said , Reichert Stefan , Schulz Susanne , Kasaj Adrian , Braun Andreas , Conrads Georg , Stein Jamal M.
Published: May 2022

For the treatment of periodontitis stage III/IV, a quadrant/week-wise debridement (Q-SRP) was compared with three full-mouth approaches: full-mouth scaling (FMS, accelerated Q-SRP within 24 h), full-mouth scaling with chlorhexidine-based disinfection (FMD), and FMD with adjuvant erythritol air polishing (FMDAP). The objective of this prospective, randomized study (a substudy of ClinicalTrials.gov, identifier: NCT03509233) was to compare the clinical and microbiological effects of the treatments. In total, 105 patients were randomized to one of the four aforementioned treatment groups, with n = 25, 28, 27, and 25 patients allocated to each group, respectively. At baseline and 3 and 6 months after treatment, the clinical parameters, including the pocket probing depths, clinical attachment level, and bleeding on probing, were recorded, and the prevalence of the total bacteria and four periodontal pathobionts (Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, and Tannerella forsythia) was determined using real-time quantitative PCR. Concerning the clinical outcomes, all the treatment modalities were effective, but the full-mouth approaches, especially FMDAP, were slightly superior to Q-SRP. Using the FMD approach, the reduction in the bacterial load and the number of pathobionts was significantly greater than for FMS, followed by Q-SRP. FMDAP was the least effective protocol for microbial reduction. However, after a temporary increase 3 months after therapy using FMDAP, a significant decrease in the key pathogen, P. gingivalis, was observed. These findings were not consistent with the clinical results from the FMDAP group. In conclusion, the dynamics of bacterial colonization do not necessarily correlate with clinical outcomes after full-mouth treatments for periodontitis stage III/IV.
Comparative Proteomic Analysis Reveals the Effect of Selenoprotein W Deficiency on Oligodendrogenesis in Fear Memory
Authors: Situ Jiaxin , Huang Xuelian , Zuo Mingyang , Huang Yingying , Ren Bingyu , Liu Qiong
Published: May 2022

The essential trace element selenium plays an important role in maintaining brain function. Selenoprotein W (SELENOW), the smallest selenoprotein that has been identified in mammals, is sensitive to selenium levels and abundantly expressed in the brain. However, its biological role in the brain remains to be clarified. Here, we studied the morphological and functional changes in the brain caused by SELENOW deficiency using its gene knockout (KO) mouse models. Histomorphological alterations of the amygdala and hippocampus, specifically in the female SELENOW KO mice, were observed, ultimately resulting in less anxiety-like behavior and impaired contextual fear memory. Fear conditioning (FC) provokes rapidly intricate responses involving neuroplasticity and oligodendrogenesis. During this process, the females generally show stronger contextual FC than males. To characterize the effect of SELENOW deletion on FC, specifically in the female mice, a Tandem mass tag (TMT)-based comparative proteomic approach was applied. Notably, compared to the wildtype (WT) no shock (NS) mice, the female SELENOW KO NS mice shared lots of common differentially expressed proteins (DEPs) with the WT FC mice in the hippocampus, enriched in the biological process of ensheathment and oligodendrocyte differentiation. Immunostaining and Western blotting analyses further confirmed the proteomic results. Our work may provide a holistic perspective of gender-specific SELENOW function in the brain and highlighted its role in oligodendrogenesis during fear memory.
Random Noise vs. State-of-the-Art Probabilistic Forecasting Methods: A Case Study on CRPS-Sum Discrimination Ability
Authors: Koochali Alireza , Schichtel Peter , Dengel Andreas , Ahmed Sheraz
Published: May 2022

The recent developments in the machine-learning domain have enabled the development of complex multivariate probabilistic forecasting models. To evaluate the predictive power of these complex methods, it is pivotal to have a precise evaluation method to gauge the performance and predictability power of these complex methods. To do so, several evaluation metrics have been proposed in the past (such as the energy score, Dawid–Sebastiani score, and variogram score); however, these cannot reliably measure the performance of a probabilistic forecaster. Recently, CRPS-Sum has gained a lot of prominence as a reliable metric for multivariate probabilistic forecasting. This paper presents a systematic evaluation of CRPS-Sum to understand its discrimination ability. We show that the statistical properties of target data affect the discrimination ability of CRPS-Sum. Furthermore, we highlight that CRPS-Sum calculation overlooks the performance of the model on each dimension. These flaws can lead us to an incorrect assessment of model performance. Finally, with experiments on real-world datasets, we demonstrate that the shortcomings of CRPS-Sum provide a misleading indication of the probabilistic forecasting performance method. We illustrate that it is easily possible to have a better CRPS-Sum for a dummy model, which looks like random noise, in comparison to the state-of-the-art method.
EXPLORIA, STEAM Education at University Level as a New Way to Teach Engineering Mechanics in an Integrated Learning Process
Authors: Montés Nicolás , Aloy Paula , Ferrer Teresa , Romero Pantaleon D. , Barquero Sara , Carbonell Alfonso Martinez
Published: May 2022

The objective of our research is the implementation of STEAM (Science Technology Engineering Art Mathematics) learning in the bachelor of Engineering in industrial design and product development at CEU Cardenal Herrera University through the EXPLORIA project. This article implements and develops the proposal for the first year of this bachelor, which includes 24 students aged 18–20. This article focuses on how to integrate STEAM learning within the EXPLORIA project for the improvement in the learning of the physics subject, and in particular, regarding the part of the syllabus related to mechanical engineering through different projects, challenges and milestones that allow the student to see the use in the design and development of products. The EXPLORIA project connects the competencies of the different STEAM subjects included in the bachelor, designing a learning process as a logical, sequential and incremental itinerary. Through concepts on which the fundamentals of design are based: shape, volume, color, space and structure. In particular, this article shows the adaptation made in the physical part to be able to teach the integrated mechanics part in this learning process. The complete learning was carried out through several challenges and two milestones the students had to overcome through the application of the physical knowledge learned in class. To validate the effectiveness of the proposed methodology, at the end of the paper, an ad hoc questionnaire is carried out showing the students’ assessment regarding the new teaching methodology.
Numerical Studies on Rockbursts in Tunnels with High In Situ Stresses and Geothermally Rich Areas
Authors: Luo Zhonglian , Li Jiaming , Tang Shibin , Li Xiaoshuang , Zhang Leitao
Published: May 2022

To further understand the stress evolution and rockburst occurrence mechanism in geothermally rich areas in the Sichuan–Tibet railway project, this work presents a theoretical study of the influence of temperature change on the failure of rock, conducts numerical studies of the temperature and stress evolution in the surrounding rock during high-temperature tunnel excavation, and further studies the possibility of rockbursts under high in situ stress and high-temperature conditions. Rockbursts occur frequently at the junction of a face and tunnel wall, and ventilation and cooling of tunnels reduce the stress and sometimes reduce the possibility of rockbursts. Continuous cooling leads to a larger tensile stress and the possibility of failure of wall rock. In addition, the influence of the convection heat transfer coefficient, in situ stress and fault effect on the stress distribution and possibility of rockbursts are also discussed in detail. The results are beneficial for the prevention and control of rockbursts in high in situ stress and geothermally rich areas.
Computational Modelling for the Effects of Capsular Clustering on Fracture of Encapsulation-Based Self-Healing Concrete Using XFEM and Cohesive Surface Technique
Authors: Hanna John
Published: May 2022

The fracture of microcapsules is an important issue to release the healing agent for healing the cracks in encapsulation-based self-healing concrete. The capsular clustering generated from the concrete mixing process is considered one of the critical factors in the fracture mechanism. Since there is a lack of studies in the literature regarding this issue, the design of self-healing concrete cannot be made without an appropriate modelling strategy. In this paper, the effects of microcapsule size and clustering on the fractured microcapsules are studied computationally. A simple 2D computational modelling approach is developed based on the eXtended Finite Element Method (XFEM) and cohesive surface technique. The proposed model shows that the microcapsule size and clustering have significant roles in governing the load-carrying capacity and the crack propagation pattern and determines whether the microcapsule will be fractured or debonded from the concrete matrix. The higher the microcapsule circumferential contact length, the higher the load-carrying capacity. When it is lower than 25% of the microcapsule circumference, it will result in a greater possibility for the debonding of the microcapsule from the concrete. The greater the core/shell ratio (smaller shell thickness), the greater the likelihood of microcapsules being fractured.
Investigating Industry 5.0 and Its Impact on the Banking Industry: Requirements, Approaches and Communications
Authors: Mehdiabadi Amir , Shahabi Vahid , Shamsinejad Saeed , Amiri Mohammad , Spulbar Cristi , Birau Ramona
Published: May 2022

Technology, along with political and economic factors, is one of the main drivers of the future of banking. Banking managers urgently need to know technological trends to make strategic decisions, know the future accurately, and make the most of existing opportunities. Industry 5.0 is the dream of modern banking, based on strategies for successful entry into the field in a completely different way. Using a complex literature survey, 49 indicators were identified to enter Industry 5.0 and were classified into three categories of insignificant indicators, essential indicators, and very necessary indicators. Then, based on the opinions of 10 experts from ten countries with modern banking in the world, the researchers focused on 14 essential indicators. To analyze the drawn space, structural-interpretive modeling and MICMAC analysis were used and the model was classified into nine levels. The results showed that low-level indices are the most influential (TMBE and HEMS) and higher-level indices are the most influenced (PZM and RNC). Finally, researchers analyzed how to use new technologies in the banking industry with the entry of the Industry 5.0 and revealed what the characteristics of the impact of these indicators on entering Industry 5.0 are.
One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments
Authors: Lv Kexin , Sun Zheng , He Fan , Huang Xiaolin , Yang Jie
Published: May 2022

This paper focuses on the design of a distributed algorithm for generalized eigenvalue problems (GEPs) in one-shot communication. Since existing distributed methods for eigenvalue decomposition cannot be applied to GEP, a general one-shot distributed GEP framework is proposed. The theoretical analysis of the approximation error reveals its relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers. If the symmetric data covariance has repeated eigenvalues in GEP, e.g., in canonical component analysis, we further modify the method for better convergence and prove the necessity experimentally. Numerical experiments validate the effectiveness of the proposed algorithms both on synthetic and real-world datasets.
New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
Authors: Li Der-Chiang , Huang Wen-Kuei , Lin Yao-San
Published: May 2022

The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of α in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of α. In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.
Potential Risks of Microplastic Fomites to Aquatic Organisms with Special Emphasis on Polyethylene-Microplastic-Glyphosate Exposure Case in Aquacultured Shrimp
Authors: Thammatorn Worrayanee , Palić Dušan
Published: May 2022

Plastic litter is increasingly becoming pervasive in aquatic environments, characterized by circulatory patterns between different compartments and continual loading with new debris. Microplastic pollution can cause a variety of effects on aquatic organisms. This review presents the current knowledge of microplastics distribution and sorption capacity, reflecting on possible bioaccumulation and health effects in aquatic organisms. A model case study reveals the fate and toxic effects of glyphosate, focusing on the simultaneous exposure of aquacultured shrimp to polyethylene and glyphosate and their contact route and on the potential effects on their health and the risk for transmission of the contaminants. The toxicity and bioaccumulation of glyphosate-sorbed polyethylene microplastics in shrimp are not well understood, although individual effects have been studied extensively in various organisms. We aim to delineate this knowledge gap by compiling current information regarding the co-exposure to polyethylene microplastic adsorbed with glyphosate to assist in the assessment of the possible health risks to aquacultured shrimp and their consumers.
A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
Authors: Bülbül Mehmet Akif , Harirchian Ehsan , Işık Mehmet Fatih , Aghakouchaki Hosseini Seyed Ehsan , Işık Ercan
Published: May 2022

Determining the risk priorities for the building stock in highly seismic-prone regions and making the final decisions about the buildings is one of the essential precautionary measures that needs to be taken before the earthquake. This study aims to develop an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock. For this purpose, the network parameters in the network structure have been optimized by establishing a hybrid structure with the Genetic Algorithm (GA). As a result, the ANN model can make accurate predictions with maximum efficiency. The suggested ANN model is a feedforward back-propagation network model. It aims to predict the risk priorities for 329 RC buildings in the most successful way, for which the performance score was calculated using the Turkey Rapid Evaluation Method (2013). In this paper, a GA-ANN hybrid model was implemented in which the ANN, using the most successful gene revealed by the model, produced successful results in calculating the performance score. In addition, the required input parameters for obtaining more efficient results in solving such a problem and the parameters that need to be used in establishing such an ANN network structure have been optimized. With the help of such a model, the operation process will be eliminated. The created hybrid model was 98% successful in determining the risk priority in RC buildings.
Analysis and Modeling of Mechanical Ventilation Operation Behaviors of Occupants in Cold Regions of North China
Authors: Zhang Chenchen , Sun Hejiang
Published: May 2022

Mechanical ventilation has a great impact on building simulation performance, such as indoor environment quality and building energy consumption. However, there is still a lack of accurate mechanical ventilation models established from long-term field data that can effectively predict building performance. In this study, one-year measurements on mechanical ventilation operation behavior were collected from 85 apartments, which were conducted with a mechanical ventilation system of the same brand in cold regions of North China. This permitted statistical analysis and clustering of the mechanical ventilation operation behavior by using the K-means method, leading to five behavior patterns. The results showed that 24% households operated mechanical ventilation system nearly all day, and there was a large difference in usage behaviors between the split system and the centralized system. Furthermore, two classes of models based on random forest and logistic regression were developed for predicting mechanical ventilation system operation (on/off) behavior. The models based on random forest showed high accuracy as it resulted in a 0.992 average in predictions. These models using field data can guide the selection of accurate input boundary conditions of mechanical ventilation and improve the accuracy of dwelling numerical simulations.
Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory
Authors: Reza Selim , Ferreira Marta Campos , Machado José J. M. , Tavares João Manuel R. S.
Published: May 2022

Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non-linear behaviour. With the availability of a vast number of data samples, deep neural network-based models are best suited to solve these challenges. However, conventional network-based models lack robustness and accuracy because of their incapability to capture traffic’s spatial and temporal correlations. Besides, they usually require data from adjacent roads to achieve accurate predictions. Hence, this article presents a one-dimensional (1D) convolution neural network (CNN) and long short-term memory (LSTM)-based traffic state prediction model, which was evaluated using the Zenodo and PeMS datasets. The model used three stacked layers of 1D CNN, and LSTM with a logarithmic hyperbolic cosine loss function. The 1D CNN layers extract the features from the data, and the goodness of the LSTM is used to remember the past events to leverage them for the learnt features for traffic state prediction. A comparative performance analysis of the proposed model against support vector regression, standard LSTM, gated recurrent units (GRUs), and CNN and GRU-based models under the same conditions is also presented. The results demonstrate very encouraging performance of the proposed model, improving the mean absolute error, root mean squared error, mean percentage absolute error, and coefficient of determination scores by a mean of 16.97%, 52.1%, 54.15%, and 7.87%, respectively, relative to the baselines under comparison.
Conifers May Ameliorate Urban Heat Waves Better Than Broadleaf Trees: Evidence from Vancouver, Canada
Authors: Eyster Harold N. , Beckage Brian
Published: May 2022

Anthropogenic greenhouse gas emissions are increasing the frequency of deadly heat waves. Heat waves are particularly devastating in cities, where air pollution is high and air temperatures are already inflated by the heat island effect. Determining how cities can ameliorate extreme summer temperature is thus critical to climate adaptation. Tree planting has been proposed to ameliorate urban temperatures, but its effectiveness, particularly of coniferous trees in temperate climates, has not been established. Here, we use remote sensing data (Landsat 8), high-resolution land cover data, and Bayesian models to understand how different tree and land cover classes affect summer surface temperature in Metro Vancouver, Canada. Although areas dominated by coniferous trees exhibited the lowest albedo (95% CrI 0.08–0.08), they were significantly (12.2 °C) cooler than areas dominated by buildings. Indeed, we found that for conifers, lower albedo was associated with lower surface temperatures. Planting and maintaining coniferous trees in cities may not only sequester CO2 to mitigate global climate change, but may also ameliorate higher temperatures and deadly heat waves locally.
A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences
Authors: Pan Jie , Wang Shiwei , Yu Changqing , Li Liping , You Zhuhong , Sun Yanmei
Published: May 2022

Protein–protein interactions (PPIs) are crucial for understanding the cellular processes, including signal cascade, DNA transcription, metabolic cycles, and repair. In the past decade, a multitude of high-throughput methods have been introduced to detect PPIs. However, these techniques are time-consuming, laborious, and always suffer from high false negative rates. Therefore, there is a great need of new computational methods as a supplemental tool for PPIs prediction. In this article, we present a novel sequence-based model to predict PPIs that combines Discrete Hilbert transform (DHT) and Rotation Forest (RoF). This method contains three stages: firstly, the Position-Specific Scoring Matrices (PSSM) was adopted to transform the amino acid sequence into a PSSM matrix, which can contain rich information about protein evolution. Then, the 400-dimensional DHT descriptor was constructed for each protein pair. Finally, these feature descriptors were fed to the RoF classifier for identifying the potential PPI class. When exploring the proposed model on the Yeast, Human, and Oryza sativa PPIs datasets, it yielded excellent prediction accuracies of 91.93, 96.35, and 94.24%, respectively. In addition, we also conducted numerous experiments on cross-species PPIs datasets, and the predictive capacity of our method is also very excellent. To further access the prediction ability of the proposed approach, we present the comparison of RoF with four powerful classifiers, including Support Vector Machine (SVM), Random Forest (RF), K-nearest Neighbor (KNN), and AdaBoost. We also compared it with some existing superiority works. These comprehensive experimental results further confirm the excellent and feasibility of the proposed approach. In future work, we hope it can be a supplemental tool for the proteomics analysis.
Evidence of Failed Resolution Mechanisms in Arrhythmogenic Inflammation, Fibrosis and Right Heart Disease
Authors: Younes Rim , LeBlanc Charles-Alexandre , Hiram Roddy
Published: May 2022

Inflammation is a complex program of active processes characterized by the well-orchestrated succession of an initiation and a resolution phase aiming to promote homeostasis. When the resolution of inflammation fails, the tissue undergoes an unresolved inflammatory status which, if it remains uncontrolled, can lead to chronic inflammatory disorders due to aggravation of structural damages, development of a fibrous area, and loss of function. Various human conditions show a typical unresolved inflammatory profile. Inflammatory diseases include cancer, neurodegenerative disease, asthma, right heart disease, atherosclerosis, myocardial infarction, or atrial fibrillation. New evidence has started to emerge on the role, including pro-resolution involvement of chemical mediators in the acute phase of inflammation. Although flourishing knowledge is available about the role of specialized pro-resolving mediators in neurodegenerative diseases, atherosclerosis, obesity, or hepatic fibrosis, little is known about their efficacy to combat inflammation-associated arrhythmogenic cardiac disorders. It has been shown that resolvins, including RvD1, RvE1, or Mar1, are bioactive mediators of resolution. Resolvins can stop neutrophil activation and infiltration, stimulate monocytes polarization into anti-inflammatory-M2-macrophages, and activate macrophage phagocytosis of inflammation-debris and neutrophils to promote efferocytosis and clearance. This review aims to discuss the paradigm of failed-resolution mechanisms (FRM) potentially promoting arrhythmogenicity in right heart disease-induced inflammatory status.
Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges
Authors: Bi Xinyu , Liu Yanfeng , Li Jianghua , Du Guocheng , Lv Xueqin , Liu Long
Published: May 2022

Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.