bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Class of Potent Anti-Tyrosinase Compounds with Antioxidant Activity, 2-(Substituted phenyl)-5-(trifluoromethyl)benzo[d]thiazoles: In Vitro and In Silico Insights
Authors: Hwang YeJi , Lee Jieun , Jung Hee Jin , Ullah Sultan , Ko Jeongin , Jeong Yeongmu , Park Yu Jung , Kang Min Kyung , Yun Hwayoung , Kim Min-Soo , Chun Pusoon , Chung Hae Young , Moon Hyung Ryong
Published: July 2022

Sixteen compounds bearing a benzothiazole moiety were synthesized as potential tyrosinase inhibitors and evaluated for mushroom tyrosinase inhibitory activity. The compound 4-(5-(trifluoromethyl)benzo[d]thiazol-2-yl)benzene-1,3-diol (compound 1b) exhibited the highest tyrosinase activity inhibition, with an IC50 value of 0.2 ± 0.01 μM (a potency 55-fold greater than kojic acid). In silico results using mushroom tyrosinase and human tyrosinase showed that the 2,4-hydroxyl substituents on the phenyl ring of 1b played an important role in the inhibition of both tyrosinases. Kinetic studies on mushroom tyrosinase indicated that 1b is a competitive inhibitor of monophenolase and diphenolase, and this was supported by docking results. In B16F10 murine melanoma cells, 1a and 1b dose-dependently and significantly inhibited melanin production intracellularly, and melanin release into medium more strongly than kojic acid, and these effects were attributed to the inhibition of cellular tyrosinase. Furthermore, the inhibition of melanin production by 1b was found to be partially due to the inhibition of tyrosinase glycosylation and the suppression of melanogenesis-associated genes. Compound 1c, which has a catechol group, exhibited potent antioxidant activities against ROS, DPPH, and ABTS, and 1b also had strong ROS and ABTS radical scavenging activities. These results suggest that 5-(trifluoromethyl)benzothiazole derivatives are promising anti-tyrosinase lead compounds with potent antioxidant effects.
Inhibition of GCN2 Alleviates Cardiomyopathy in Type 2 Diabetic Mice via Attenuating Lipotoxicity and Oxidative Stress
Authors: Yuan Juntao , Li Fang , Cui Bingqing , Gao Junling , Yu Zhuoran , Lu Zhongbing
Published: July 2022

Diabetic cardiomyopathy (DCM) is a kind of heart disease that affects diabetic patients and is one of the primary causes of death. We previously demonstrated that deletion of the general control nonderepressible 2 (GCN2) kinase ameliorates cardiac dysfunction in diabetic mice. The aim of this study was to investigate the protective effect of GCN2iB, a GCN2 inhibitor, in type 2 diabetic (T2D) mice induced by a high-fat diet (HFD) plus low-dose streptozotocin (STZ) treatments or deletion of the leptin receptor (db/db). GCN2iB (3 mg/kg/every other day) treatment for 6 weeks resulted in significant decreases in fasting blood glucose levels and body weight and increases in the left ventricular ejection fraction. GCN2iB treatment also attenuated myocardial fibrosis, lipid accumulation and oxidative stress in the hearts of T2D mice, which was associated with decreases in lipid metabolism-related genes and increases in antioxidative genes. Untargeted metabolomics and RNA sequencing analysis revealed that GCN2iB profoundly affected myocardial metabolomic profiles and gene expression profiles. In particular, GCN2iB increased myocardial phosphocreatine and taurine levels and upregulated genes involved in oxidative phosphorylation. In conclusion, the data provide evidence that GCN2iB effectively protects against cardiac dysfunction in T2D mice. Our findings suggest that GCN2iB might be a novel drug candidate for DCM therapy.
Uwhangchungsimwon Inhibits Oxygen Glucose Deprivation/Re-Oxygenation-Induced Cell Death through Neuronal VEGF and IGF-1 Receptor Signaling and Synaptic Remodeling in Cortical Neurons
Authors: Hong Jin Young , Kim Hyunseong , Yeo Changhwan , Jeon Wan-Jin , Lee Junseon , Baek Seung Ho , Lee Yoon Jae , Ha In-Hyuk
Published: July 2022

Uwhangchungsimwon (UCW), a multi-component herbal product, has long been used to treat vascular diseases such as headache, dizziness, high blood pressure, and stroke. Though the prophylactic actions of UCW are well known, insufficient experimental evidence exists on its effectiveness against stroke. Here, we investigated the mechanism underlying the efficacy of UCW in oxygen glucose deprivation/re-oxygenation (OGD/R)-injury to the primary cortical neurons using an in vitro ischemia model. Neurons secrete vascular endothelial growth factor (VEGF), which acts as a neurotrophic factor in response to an ischemic injury. VEGF modulates neuroprotection and axonal outgrowth by activating the VEGF receptors and plays a critical role in vascular diseases. In this study, cortical neurons were pretreated with UCW (2, 10, and 50 µg/mL) for 48 h, incubated in oxygen-glucose-deprived conditions for 2 h, and further reoxygenated for 24 h. UCW effectively protected neurons from OGD/R-induced degeneration and cell death. Moreover, the role of UCW in sustaining protection against OGD/R injury is associated with activation of VEGF-VEGFR and insulin-like growth factor 1 receptor expression. Therefore, UCW is a potential herbal supplement for the prevention of hypoxic-ischemic neuronal injury as it may occur after stroke.
Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
Authors: Yu Min , Yao Shuyin , Wu Xuan , Chen Liang
Published: July 2022

Owing to the heterogeneity of software and hardware in different types of mobile terminals, the received signal strength indication (RSSI) from the same Wi-Fi access point (AP) varies in indoor environments, which can affect the positioning accuracy of fingerprint methods. To solve this problem and consider the nonlinear characteristics of Wi-Fi signal strength propagation and attenuation, we propose a whale optimisation algorithm-back-propagation neural network (WOA-BPNN) model for indoor Wi-Fi RSSI calibration. Firstly, as the selection of the initial parameters of the BPNN model has a considerable impact on the positioning accuracy of the calibration algorithm, we use the WOA to avoid blindly selecting the parameters of the BPNN model. Then, we propose an improved nonlinear convergence factor to balance the searchability of the WOA, which can also help to optimise the calibration algorithm. Moreover, we change the structure of the BPNN model to compare its influence on the calibration effect of the WOA-BPNN calibration algorithm. Secondly, in view of the low positioning accuracy of indoor fingerprint positioning algorithms, we propose a region-adaptive weighted K-nearest neighbour positioning algorithm based on hierarchical clustering. Finally, we effectively combine the two proposed algorithms and compare the results with those of other calibration algorithms such as the linear regression (LR), support vector regression (SVR), BPNN, and genetic algorithm-BPNN (GA-BPNN) calibration algorithms. The test results show that among different mobile terminals, the proposed WOA-BPNN calibration algorithm can increase positioning accuracy (one sigma error) by 41%, 42%, 44% and 36%, on average. The indoor field tests suggest that the proposed methods can effectively reduce the indoor positioning error caused by the heterogeneous differences of software and hardware in different mobile terminals.
In Search of a Dynamical Vocabulary: A Pipeline to Construct a Basis of Shared Traits in Large-Scale Motions of Proteins
Authors: Tarenzi Thomas , Mattiotti Giovanni , Rigoli Marta , Potestio Raffaello
Published: July 2022

The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.
Intelligent Detection of Parcels Based on Improved Faster R-CNN
Authors: Zhao Ke , Wang Yaonan , Zhu Qing , Zuo Yi
Published: July 2022

Parcel detection is crucial to achieving automatic sorting in intelligent logistics systems. Most parcels in logistics centers are currently detected manually, imposing low efficiency and high error rate, severely limiting logistics transportation efficiency. Therefore, there is an urgent need for automated parcel detection. However, parcels in logistics centers have challenges such as dense stacking, occlusion and background interference, making it difficult for existing methods to detect parcels accurately. To address the above problem, we developed an improved Faster R-CNN-based parcel detection model spurred by current deep-learning-based object detection trends. The proposed method first solves the false detection problem due to parcel mutual occlusion by augmenting Faster R-CNN with an edge detection branch and adding object edge loss to the loss function. Furthermore, the self-attention ROI Align module is proposed to address the problem of feature misalignment caused by the quantization rounding operation in the ROI Pooling module. The module uses an attention mechanism to filter and enhance the features and uses bilinear interpolation to calculate the feature pixel values, improving detection accuracy. The implementation of the proposed method was validated using parcel images collected in the field and the public dataset SKU110K and compared with four existing parcel detection methods. The results show that our method’s Recall, Precision, map@0.5 and Fps are 96.89%, 98.76%, 98.42% and 22.83%, respectively, which significantly improves the parcel detection accuracy.
A New Grey Target Evaluation Method of Science and Technology Talents Considering ‘Dominant-Implicit’ Reference Points
Authors: Jiang Lin , Zhu Jianjun
Published: July 2022

This paper studies the grey target evaluation model of science and technology talents by drawing on the ‘dominant-implicit’ double reference point. Input and output perspectives are adopted and developed to establish a dominant reference point for the evaluation of science and technology talents, which takes into account the ‘Peer-Status quo’ and ‘Baseline-History’, as well as an implicit reference point, such as the ‘Expectation-Opportunity’ and ‘Goal-Preference’. The framework of the grey target evaluation model for science and technology talents is then constructed and given a dual reference (dominant–implicit). The spherical grey target on the maximum, minimum and arithmetic mean values of the target effect measure values of each reference point is established to determine the reference point weight under the input and output indicators. This research will contribute to the comprehensive effect measure value and the ranking of results that relate to science and technology talents and will demonstrate that its applied method is feasible.
The Importance of Multidisciplinary Analytical Strategies to Solve Identification and Characterization Challenges in Gemology: The Example of the “Green Stones”
Authors: Musa Maya
Published: July 2022

The present review aims to discuss the importance of a multidisciplinary approach in cultural heritage and archaeometry investigations. The analytical methods used to identify and characterize “Green Stones” are discussed as an example. In the present paper, the term Green Stones is applied but not limited to jade materials, which have considerable importance in cultural heritage studies. In fact, archaeological samples made in Green Stones have been discovered worldwide, with many dating back to the Neolithic Age. Moreover, these materials represent an interesting analytical challenge, starting with their nomenclature and, in most cases, the nature of their polycrystalline samples and their heterogeneity. Indeed, after a brief introduction about the advantages of the non-destructive analytical techniques commonly used for gemstones and cultural heritage samples analyses, the limits of the same have been discussed on the basis of Green Stones applicability. Finally, a multidisciplinary methodology for Green Stones identification and full characterization, which considers materials’ heterogeneity and information, has been proposed and based on different references.
The Effect of Pectin Branching on the Textural and Swelling Properties of Gel Beads Obtained during Continuous External Gelation Process
Authors: Pancerz Michał , Kruk Joanna , Ptaszek Anna
Published: July 2022

The aim of the study was to produce gel beads under continuous conditions. Pectins obtained from black and red currants and commercial apple pectin were used as the material. For the production of gel beads, a self-designed device was used. The designed device allows for the production of gel beads in a continuous process, the properties of which are similar to those obtained in the classic, batch process. Thanks to the device, it is possible to obtain a repeatable product while reducing the workload. The produced gel beads were tested for water absorption and textural properties. The water absorption of the obtained gel capsules is strongly influenced by the pectin chain structure. Pectin beads obtained from currant pectins have a less hard structure and are more sensitive to deformation than those from apple pectin. Shorter and more branched chains of currant pectin than apple pectin form gels with a delicate structure, which strongly absorbs water, and unlike apple pectin gel, it disintegrates. The results show that the use of raw material obtained from different sources allows for obtaining products with various properties, using the same method; moreover, the used device is fully scalable and can be used in large scale.
An Attempt to Study Foundation Anchoring Conditions in Sedimentary Estuaries Using Integrated Methods
Authors: Gounou Pokam Blaise Pascal , Domra Kana Janvier , Meli’i Jorelle Larissa , Ariane Gweth Marthe Mbond , Pokam Kegni Serges Hugues , Njock Michel Constant , Mbouombouo Ngapouth Ibrahim , Pouth Nkoma Michel André , Mbono Samba Yves Christian , Njandjock Nouck Philippe
Published: July 2022

The search for and knowledge of the best conditions for anchoring the foundations of certain structures such as bridges, tunnels and quays in sedimentary estuaries is a challenge, for both scientists in general and engineers in particular. Indeed, wharves are structures that receive a lot of stresses and therefore require anchoring to avoid tilting and to guarantee their stability during service. This work, based on the analysis of data from seismic refraction methods, mechanical soundings and laboratory tests, characterises the terrain of the Wouri estuary in Central Africa. The objective is to determine and present the subsurface layers encountered as well as their characteristics, in order to define the best conditions for anchoring the foundations to ensure the stability of the quays to be built there. The seismic refraction campaign shows that the study area is relatively heterogeneous over the first 25 m, with velocities measured in the range 1520–1750 m/s; modulated in two distinct ranges, between 1520–1580 m/s characteristic of mud and loose sediments (alternating layers of clay, sand, loose silt) and the range 1580–1750 m/s corresponding to the signature of sandy-silty or compact clays. The mechanical tests show sedimentary soils, with alternating layers of sandy clay and clayey sand over the 42 m drilled, loose over the first 30 m in the bank area and over the first 15 m in the canal or dredge area, with a limit pressure of less than 1 MPa. Similarly, the soil samples taken and tested in the laboratory show that the soils are clayey over the first 30 metres, plastic and liquid with respect to their water content, respectively, below and above the liquidity limits, confirming their loose character. The results of seismic refraction, mechanical soundings and laboratory tests show that, in estuarine areas characterised by alternating sandy clay and clayey sand, there are not always hard formations in the first 25 metres of depth but, from a depth of 30 m, the soils become moderately compact and begin to form an anchoring layer sufficient to guarantee the stability of the quays against earth pressure forces.
Explaining the Mean and Variability of Extra Travel Time in Traffic with Interacting Fast and Slow Vehicles
Authors: Carrillo-González José Gerardo , Perez-Martinez Francisco
Published: July 2022

In this work, the authors developed procedures to explain mean extra travel time (T) and extra travel time variability (V). This was carried out for situations (through simulations) where the fast vehicles’ travel time, whose speed tendency (sp1) is the permitted speed limit, is negatively affected (i.e., increasing travel time) by the presence of slow vehicles, whose speed tendency (sp2) is half the speed limit. The speed limit was set in the range of 60 km/h to 90 km/h, with seven cases, and every case had eight simulations, each with different p1 (fast vehicles’ percentage) and p2= 100% − p1 (slow vehicles’ percentage) values. p2 ranged from 10% to 80% at intervals of 10%, for a total of 56 simulations. From the simulations’ data, we calculated the fast vehicles’ extra travel time, which is the additional time to traverse an avenue segment owing to the presence of slow vehicles. The fast and slow vehicles recreate heterogenous traffic in terms of speed. We developed procedures for modeling T and V with p2, and V with T. For modeling, ~71.42% of the data from simulations was used. We find that the models’ parameters values can be used for explaining the remaining data. In addition, we discovered that the pattern of p2 vs. V, for p2 ranging from 50% to 80%, is different among sp1 cases and not linear.
Deep Knowledge Tracing Based on Spatial and Temporal Representation Learning for Learning Performance Prediction
Authors: Lyu Liting , Wang Zhifeng , Yun Haihong , Yang Zexue , Li Ya
Published: July 2022

Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students’ learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students’ learning history sequence, and then further extracts temporal features to extract deeper hidden information. Specifically, firstly, the DKT-STDRL model uses CNN to extract the spatial feature information of students’ exercise sequences. Then, the spatial features are connected with the original students’ exercise features as joint learning features. Then, the joint features are input into the BiLSTM part. Finally, the BiLSTM part extracts the temporal features from the joint learning features to obtain the prediction information of whether the students answer correctly at the next time step. Experiments on the public education datasets ASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTchall, and Statics2011 prove that DKT-STDRL can achieve better prediction effects than DKT and CKT.
Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation
Authors: Yan Rong , Li Jiang , Su Xiangdong , Wang Xiaoming , Gao Guanglai
Published: July 2022

Previous works trained the Transformer and its variants end-to-end and achieved remarkable translation performance when there are huge parallel sentences available. However, these models suffer from the data scarcity problem in low-resource machine translation tasks. To deal with the mismatch problem between the big model capacity of the Transformer and the small parallel training data set, this paper adds the BERT supervision on the latent representation between the encoder and the decoder of the Transformer and designs a multi-step training algorithm to boost the Transformer on such a basis. The algorithm includes three stages: (1) encoder training, (2) decoder training, and (3) joint optimization. We introduce the BERT of the target language in the encoder and the decoder training and alleviate the data starvation problem of the Transformer. After the training stage, the BERT will not further attend the inference section explicitly. Another merit of our training algorithm is that it can further enhance the Transformer in the task where there are limited parallel sentence pairs but large amounts of monolingual corpus of the target language. The evaluation results on six low-resource translation tasks suggest that the Transformer trained by our algorithm significantly outperforms the baselines which were trained end-to-end in previous works.
An Innovative Process Design Model for Machined Surface Error Distribution Consistency in High-Efficiency Milling
Authors: Zhao Peiyi , Jiang Yupeng , Jiang Bin , Ma Chaoyang
Published: July 2022

In the process of high-efficiency milling, due to the impact of plunge caused by the intermittent cutting of the milling cutter, there is a high frequency and small vibration between the milling cutter and the workpiece. In this paper, we construct a transient cutting model for high-efficiency milling tools under the action of vibration, and analyze the factors influencing the consistency of the machined surface error and its distribution. The design variables for the consistency of error distribution of high-efficiency milling are obtained, the significance analysis of the influencing factors is carried out using the central composite experimental design and the response surface method, the interaction between the influencing factors is studied, the evaluation index of the consistency of error distribution of milling is proposed, and the process design method for the consistency of error distribution of the machined surface is proposed and validated. The results of the verification experiments show that, compared with the original process scheme, the two maximum errors corresponding to the new process scheme are on average 61.3% smaller than those of the old process scheme, and the consistency of the machined surface error distribution of the new process scheme is significantly improved compared with the old process scheme.
Fatigue Life Analysis of Main Reducer Gears for Battery Electric Bus Considering Regenerative Braking
Authors: Du Jinfu , Wu Xingrong , Mao Jin
Published: July 2022

The braking mode of the battery electric urban bus (BEUB) is different from the friction braking of the traditional fuel bus due to the introduction of a regenerative braking system. The intervention of electromagnetic braking changes the working condition of the main reducer gears, thus affecting their service lives. Based on the Urban Dynamometer Driving Schedule (UDDS) driving cycle condition, the stress–time history of the main reducer gears is calculated. Combined with the static analysis results and the S-N curve of the material, the fatigue lives of the main reducer gears considering electromagnetic braking and traditional friction braking are analyzed. The reverse torque on the driving axle during electromagnetic braking is taken into account to be closer to the real situation. Results show that, under the electromagnetic braking mode, the bending fatigue lives of the tooth root on the convex and concave surfaces of the pinion are 78.5% and 78.9% of that under the traditional friction braking mode, respectively, while the contact fatigue life of the pinion working surface is 78.2% of that under the friction braking mode, indicating that the introduction of the regenerative braking system into the BEUB will cause a significant reduction in the service life of the main reducer gears. This study provides a high-precision fatigue life calculation method for the BEUB main reducer gears and the accurate prediction of their remaining life.
Influence of 17% EDTA and Sapindus mukorossi on the Surface Property of Protaper Gold Rotary Endodontic Instruments
Authors: Ahmed Riaz , Choudhry Ziaullah , Qazi Fazal Ur-Rehman , Malik Sofia , Ahmed Shahbaz , Saadat Sohail , AlMokhatieb Ahmed A. , Abdulwahed Abdulaziz , Alkahtany Mazen F. , Ahmed Muhammad Adeel , Al-Aali Khulud A. , Vohra Fahim , Abduljabbar Tariq
Published: July 2022

The aim of the study was to evaluate the influence of 17% ethylenediaminetetraacetic acid (EDTA) and ethanolic extract of Sapindus mukorossi (S. mukorossi) in combination with canal shaping on surface properties of ProTaper Gold rotary endodontic file. Sixteen F1 ProTaper Gold rotary files underwent the standard protocol for root canal treatment using two irrigants (S. mukorossi and 17% EDTA) in single-rooted decoronated teeth. Eight unused files were used as a control. All files were examined under an atomic force microscope (AFM) to evaluate surface roughness (Ra) and root mean square (RMS). Data were analyzed using the t-test. RMS and Ra values of the control file were 1.37 and 0.607, respectively. The mean RMS (3.70 ± 1.41) and mean Ra (2.89 ± 1.41) in EDTA group were significantly higher than the control file (p < 0.05). The mean RMS in the S. mukorossi group (1.77 ± 0.66) did not show any significant difference with the control (p > 0.05). The Ra of S. mukorossi group (1.09 ± 0.05) was significantly higher than the control file (p < 0.05). Moreover, the mean RMS and Ra were found to be significantly higher in the EDTA group compared to the S. mukorossi group (p < 0.05). The mean Ra and RMS values of the ProTaper Gold files used to prepare canals using 17% EDTA and S. mukorossi irrigation were significantly higher than the control files. The EDTA-treated ProTaper Gold rotary files demonstrated higher surface Ra and RMS than the S. mukorossi files.
Research on the Stress Characteristics of Initial Tunnel Supports Based on Active Load Adjustment
Authors: Ma Ke , Chen Li-Ping , Fang Qian
Published: July 2022

Initial tunnel support takes on a critical significance in the stability control of surrounding rocks and the core content of tunnel support design. Its stability and safety are essential to the tunnel. The support load was optimized using the active surrounding rock load intervention scheme in accordance with the section form and bearing characteristics of the support structure. The optimization scheme of active surrounding rock load was obtained by applying active intervention load to the initial support of the tunnel to minimize the peak moment of the support structure. An active adjustment system for tunnel-surrounding rock loads was developed using the function of hydraulic load transfer and transmission combined with load proportion control. Based on the actual project, the implementation effect of the surrounding rock load active intervention scheme was verified by analyzing the measurement results of the supporting structure in the test section and the comparison section. The results suggest that the application of active intervention load can effectively improve the stress state of the tunnel initial support structure, significantly reduce the tunnel surrounding rock bias pressure and the structural peak bending moment, and increase the stability of the support structure. To control the peak moment of the supporting structure, an active intervention method and its implementation scheme for the stress of the tunnel supporting structure were proposed, which reduces the deficiency in which the conventional supporting structure can only passively bear the deformation pressure of the surrounding rock, effectively improves the stress state of the supporting structure, and can provide a reference for the development of novel tunnel supporting forms.
Moment Magnitude Homogenization Relations in the South Korean Region from 1900 to 2020
Authors: Yee Eric , Park Wonsick
Published: July 2022

Utilizing several international and local earthquake catalogs, a set of regression models converting magnitude types typically found in the South Korean region to moment magnitude MW is presented. These linear regressions were performed using ordinary and total least squares techniques. A standard deviation applied to the residuals was used as a metric for ranking the MW regressions. This resulted in MW regressions from MV,JMA, MD,JMA, MS,MOS, and MS,BJI having the lowest standard deviations, while ML,IDC showed the highest standard deviation. Other magnitude types ranked differently depending on regression type. An examination of the residuals showed a linear model was appropriate for most magnitude types, with the exception of ML,KMA and ML,IDC. Residuals suggested a bilinear model worked well for ML,KMA, while ML,IDC showed a monotonic trend.
Dynamic Stability Analysis of Isolated Power System
Authors: Deltuva Ramūnas , Lukočius Robertas , Otas Konstantinas
Published: July 2022

The islanded mode of operation of an electric power system (EPS) that has generation capabilities provided by conventional thermal power plants, by a pumped-storage power station, or from an interlink with a neighboring electric power system through an HVDC BtB converter is addressed in this paper. The risk for electrical power systems to fall into an islanded mode has recently grown, as it is caused not just by technical reasons but by a geopolitical situation as well. The current strains demand the close consideration of problems related to EPS operation in an islanded mode. This paper considers several. The research covers the following issues. The response of the islanded system to a sudden and spasmodic load change is analyzed in cases when the system deals with the disturbance with internal resources alone and with the help of an HVDC BtB converter’s frequency control functionality. Analysis of the impact of the settings of the HVDC BtB converter on the system’s response to disturbances is presented and the optimal set of parameters found. The impact of the system’s extended inertia on the system’s response is evaluated by using an additional unit of the pumped-storage power station in synchronous condenser mode. Transients in the system when switching a unit operating in synchronous condenser mode on and off are analyzed. The capability of the system to withstand major disturbances, such as disconnection of the pumped-storage power station’s unit operating in a pump mode and disconnection of the HVDC BtB converter in emergency modes, if a situation demands, is researched. The research is carried out by numerical simulations using PSS Sincal Electricity Basic software. Updated operating parameters of the isolated power system and the LCC HVDC BtB converter, as well as frequency control automation provided by ABB, were used in the simulations.
Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework
Authors: Li Ziqian , Jia Jiwei , Lu Zheng , Jiao Jian , Yu Ping
Published: July 2022

Accurate and efficient reconstruction of hidden geological structures under the surface is the main task of high-resolution Velocity Model Building (VMB). The most commonly used methods in practice are Tomography and Full Waveform Inversion (FWI), which rely heavily on the initial model. Recently, deep learning types of methods have received widespread attention and have performed well in many tasks such as image segmentation and classification. Therefore, it is of great significance to introduce deep learning algorithms into the VMB procedure to accelerate the production cycle, especially for the velocity anomalies detection, which is crucial for a high-resolution initial model. In this paper, a modified U-Net framework is proposed and applied directly on the seismic shot gathers to identify anomalies in the early stage of VMB, which can provide a suitable initial guess for the following large-scale VMB procedures such as FWI. The numerical examples show the power of the proposed method on synthetic data.
A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules
Authors: Yeom Chan-Uk , Kwak Keun-Chang
Published: July 2022

This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and output spaces. However, the prediction performance decreases when the specific data points with geometric features are used. The CGK clustering solves the above situation by generating valid clusters considering the geometric attributes of data in input and output spaces with the aid of the Mahalanobis distance. However, it is necessary to generate rational information granules (IGs) because there is a significant change in performance according to the context generated in the output space and the shape, size, and several clusters generated in the input space. As a result, the rational IGs are obtained by considering the relationship between the coverage and specificity of IG using the genetic algorithm (GA). Thus, the optimized CGK-based fuzzy granular models with the aggregated structure are designed based on rational IGs. The prediction performance was compared using the two databases to verify the validity of the proposed method. Finally, the experiments revealed that the performance of the proposed method is higher than that of the previous model.
Using Deep 1D Convolutional Grated Recurrent Unit Neural Network to Optimize Quantum Molecular Properties and Predict Intramolecular Coupling Constants of Molecules of Potential Health Medications and Other Generic Molecules
Authors: Oyewola David Opeoluwa , Dada Emmanuel Gbenga , Emebo Onyeka , Oluwagbemi Olugbenga Oluseun
Published: July 2022

A molecule is the smallest particle in a chemical element or compound that possesses the element or compound’s chemical characteristics. There are numerous challenges associated with the development of molecular simulations of fluid characteristics for industrial purposes. Fluid characteristics for industrial purposes find applications in the development of various liquid household products, such as liquid detergents, drinks, beverages, and liquid health medications, amongst others. Predicting the molecular properties of liquid pharmaceuticals or therapies to address health concerns is one of the greatest difficulties in drug development. Computational tools for precise prediction can help speed up and lower the cost of identifying new medications. A one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) was used in this study to offer a novel forecasting model for molecular property prediction of liquids or fluids. The signal data from molecular properties were pre-processed and normalized. A 1D convolutional neural network (1D-CNN) was then built to extract the characteristics of the normalized molecular property of the sequence data. Furthermore, gated recurrent unit (GRU) layers processed the extracted features to extract temporal features. The output features were then passed through several fully-connected layers for final prediction. For both training and validation, we used molecular properties obtained from the Kaggle database. The proposed method achieved a better prediction accuracy, with values of 0.0230, 0.1517, and 0.0693, respectively, in terms of the mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE).
Differences in External and Internal Load in Elite Youth Soccer Players within Different Match Timing Zones
Authors: Kunzmann Egon , Ford Kevin R. , Sugimoto Dai , Baca Arnold , Hank Mikulas , Bujnovsky David , Mala Lucia , Zahalka Frantisek , Maly Tomas
Published: July 2022

The aim of this study was to determine and analyze the differences between the players’ internal (IL) and external load (EL) in different time zones (T1: 0–45 min, T2: 45–70 min, T3: 0–70 min, T4: 70–90 min, T5: 45–90 min, and T6: 0–90 min) in elite youth soccer matches (U17–19 age category). The monitored group comprised elite youth soccer players (n = 66; age = 17.5 ± 1.2 years; body height = 178.5 ± 8.7 cm; body mass = 70.4 ± 6.3 kg). Multivariate analysis of variances was used to compare the following variables: relative total distance covered (TDCrel), distance covered in different speed zones (Z1–Z6), high-metabolic load distance (HMLD), maximum running speed (Smax), number of acceleration (ACC) and deceleration (DCC) entries in different speed zones (Z1, Z2, Z3), and maximum heart rate (HRmax). Results revealed significant differences (p < 0.05) in the first 70 min (T3) compared to the last 20 min of the match (T4) in the following: TDCrel was higher, up to 6.6% (123.09 ± 9.48 vs. 115.03 ± 9.42 m.min−1); distance in Z5 (22.4%, 6.08 ± 1.82 vs. 4.72 ± 1.72 m.min−1); Z4 (18.6%, 20.15 ± 4.82 vs. 16.40 ± 3.48 m.min−1); Z3 (10.4%, 53.06 ± 47.52 m.min−1); and HMLD (16.1%, 34.86 ± 5.67 vs. 29.26 ± 5.11 m.min−1). We also found higher running performance in the first half (T1) than in the second half (T5) in the following parameters: TDCrel, Z5, Z4, ACCZ1, ACCZ2, and DCCZ1. With progressive time (in T4) we found a significant decrease in physical running performance, probably due to fatigue, which can lead to potential injury or losing in a match. The results of this study may provide helpful information in developing training strategies for coaches and in the set-up of plan for potential substitution of exhausted players mainly for the last 20 min of a match (T4). The present results are expressed as a relative value and should be compared with other study results with irregular timing zones.
The Effect of Cation Exchange on the Pore Geometry of Zeolite L
Authors: Price Lisa A. , Jones Zöe , Nearchou Antony , Stenning Gavin , Nye Daniel , Sartbaeva Asel
Published: July 2022

Zeolites with the LTL framework topology are attractive materials for use in optoelectronics, gas adsorption and as chemical reactors. This is due to their unique, one-dimensional (1D) channel systems which are large enough to act as hosts for organic dye molecules and other guest materials. Here, we use high-resolution X-ray diffraction to show the effect of cation exchange on the pore geometry of LTL-type zeolites. The nature of the exchanging cation is shown to influence the free access diameter, volume and water content of the 12-ring (12R) channel systems. As such, cation exchange can be used to tune the molecular sieving and adsorption properties of LTL-type zeolites. This offers new possibilities for these materials in technologically relevant applications.
Impacts of Heavy and Persistent Precipitation on Railroad Infrastructure in July 2021: A Case Study from the Ahr Valley, Rhineland-Palatinate, Germany
Authors: Szymczak Sonja , Backendorf Fabia , Bott Frederick , Fricke Katharina , Junghänel Thomas , Walawender Ewelina
Published: July 2022

In contrast to river floods, the enormous erosion potential in catchments contributes significantly to the extent of damage to infrastructure in valleys. This paper investigates the impact of the heavy precipitation event of 14–15 July 2021 on the railroad in the Ahr valley in Rhineland-Palatinate, Germany. In a first step, a detailed overview of the climatological and hydrological drivers using spatially high-resolved precipitation distribution and peak discharge modeling is provided, and the event is placed in a broader context by comparing it to past flash flood events from 1910 and 2016. In a second step, a detailed mapping of damages along the railroad line is performed using aerial photographs. The mapping revealed that bridges are the weakest point during a flood event and that they contribute to an increase and modification of the flood wave through backwater effects. Since flood events are expected to increase in the future, there is an urgent need to increase the resilience of transportation to this hazard and to answer the question of what magnitudes and return periods of events should be used in future sizing of rail infrastructure.
Development of an Indexed Score to Identify the Most Suitable Sampling Method to Assess Occupational Exposure to Fungi
Authors: Cervantes Renata , Dias Marta , Gomes Bianca , Carolino Elisabete , Viegas Carla
Published: July 2022

The sampling approach is of utmost importance to obtain data regarding microbial viability and truly assess workers’ potential health effects. The purpose of this assessment is to create a score which will provide up-to-date information to identify the most suitable sampling method to assess occupational exposure to fungi. Data from a sampling campaign performed at Firefighters Headquarters (FFH) was analysed and a score was calculated from one (1) to three (3) for five (5) distinct sample parameters: (a) accuracy; (b) complexity of the field work; (c) cost; (d) complexity in laboratory work; and (e) time taken since the fieldwork until obtaining the fungal contamination characterization. The statistical analysis allowed us to conclude that settled dust and Andersen six-stage were the best sampling methods to perform the assessment of the occupational exposure to fungi at FFH, when considering the number of species. As for the final score, the results showed that surface swabs were the best sampling method. The results obtained for surface swabs highlights the low complexity of this processing combined with the fact that it is a low-cost sampling method. This study reinforces the need to use a wide array of sampling methods when assessing occupational exposure to fungal contamination to ensure an accurate risk characterization.
Prediction Model of Carbon Dioxide Concentration in Pig House Based on Deep Learning
Authors: Zang Jianjun , Ye Shuqin , Xu Zeying , Wang Junjun , Liu Wenchao , Bai Yungang , Yong Cheng , Zou Xiuguo , Zhang Wentian
Published: July 2022

The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment.
Families of Ramanujan-Type Congruences Modulo 4 for the Number of Divisors
Authors: Merca Mircea
Published: July 2022

In this paper, we explore Ramanujan-type congruences modulo 4 for the function σ0(n), counting the positive divisors of n. We consider relations of the form σ08(αn+β)+r≡0(mod4), with (α,β)∈N2 and r∈{1,3,5,7}. In this context, some conjectures are made and some Ramanujan-type congruences involving overpartitions are obtained.
Recent Health Diagnosis Methods for Lithium-Ion Batteries
Authors: Li Yaqi , Guo Jia , Pedersen Kjeld , Gurevich Leonid , Stroe Daniel-Ioan
Published: July 2022

Lithium-ion batteries have good performance and environmentally friendly characteristics, so they have great potential. However, lithium-ion batteries will age to varying degrees during use, and the process is irreversible. There are many aging mechanisms of lithium batteries. In order to better verify the internal changes of lithium batteries when they are aging, post-mortem analysis has been greatly developed. In this article, we summarized the electrical properties analysis and post-mortem analysis of lithium batteries developed in recent years and compared the advantages of varieties of both destructive and non-destructive methods, for example, open-circuit-voltage curve-based analysis, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, X-ray photoelectron spectroscopy and X-ray diffraction. On this basis, new ideas could be proposed for predicting and diagnosing the aging degree of lithium batteries, at the same time, further implementation of these technologies will support battery life control strategies and battery design.
Real-Time End-to-End Speech Emotion Recognition with Cross-Domain Adaptation
Authors: Wongpatikaseree Konlakorn , Singkul Sattaya , Hnoohom Narit , Yuenyong Sumeth
Published: July 2022

Language resources are the main factor in speech-emotion-recognition (SER)-based deep learning models. Thai is a low-resource language that has a smaller data size than high-resource languages such as German. This paper describes the framework of using a pretrained-model-based front-end and back-end network to adapt feature spaces from the speech recognition domain to the speech emotion classification domain. It consists of two parts: a speech recognition front-end network and a speech emotion recognition back-end network. For speech recognition, Wav2Vec2 is the state-of-the-art for high-resource languages, while XLSR is used for low-resource languages. Wav2Vec2 and XLSR have proposed generalized end-to-end learning for speech understanding based on the speech recognition domain as feature space representations from feature encoding. This is one reason why our front-end network was selected as Wav2Vec2 and XLSR for the pretrained model. The pre-trained Wav2Vec2 and XLSR are used for front-end networks and fine-tuned for specific languages using the Common Voice 7.0 dataset. Then, feature vectors of the front-end network are input for back-end networks; this includes convolution time reduction (CTR) and linear mean encoding transformation (LMET). Experiments using two different datasets show that our proposed framework can outperform the baselines in terms of unweighted and weighted accuracies.