bluuebunny/crossref_metadata_2025 huggingface dataset:
Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges
Authors: Rogulj Katarina , Kilić Pamuković Jelena , Jajac Nikša
Published: January 2021

A systematic methodology for condition assessment of the historic road bridges was needed because of the poor and inadequate condition of bridges which cannot satisfy everyday-day dynamic loads and deteriorations due to the aging process. Thus in this study, a new expert system based on the knowledge approach has been proposed to develop a systematic procedure for condition assessment of these bridges using fuzzy logic and sets of α-cuts. Each bridge is divided into three components: superstructure, substructure, and equipment, and each component is divided into relevant elements. These elements are evaluated by an expert and their ratings are fuzzified according to defined fuzzy sets, their membership functions, and linguistic values. Furthermore, fuzzy structural importance is given to ratings of each element. Combinations of these two values are calculated to obtain a fuzzy rating of the component using the Fuzzy Weighted Geometric Mean (FWGM). Finally, for the defuzzification of the component rating, the centroid method is proposed. The Analytic Hierarchy Process (AHP) is used for comparison of the components. The bridge condition rating is achieved by summering all the components ratings multiplied by their relative importance, and it is presented as a value of the Historic Road Bridge Condition Assessment Index (HRBCAI). The validation is conducted on the bridges built until the end of the Austro-Hungarian Monarchy in Split-Dalmatia County, Croatia.
Enhanced Robust Design Optimization in Seat Belt Anchorage Strength for Front Crash Safety of Multi-Purpose Vehicle
Authors: Song Chang Yong
Published: January 2021

This paper deals with an enhanced robust design optimization (RDO) method and its application to the strength design problem of seat belt anchorage, related to the front crash safety of multi-purpose vehicles. In order to determine the rational design safety of the newly developed automotive part, such as the seat, in which the reliability of the evaluation data is not sufficient at the design stage, it is necessary to implement a probabilistic design considering uncertainties. Thickness size variables of the seat frame structure’s members were considered random design variables, including uncertainties such as manufacturing tolerance, which are an inevitable hazard in the design of automotive parts. Probabilistic constraints were selected from the strength performances of the seat belt anchorage test, which are regulated in Economic Commission for Europe (ECE) and Federal Motor Vehicle Safety Standard (FMVSS), and the strength performances were evaluated by finite element analyses. The RDO problem was formulated such that the random design variables were determined by minimizing the seat frame weight subject to the probabilistic strength performance constraints evaluated from the reliability analyses. Three sigma level quality was considered for robustness in side constraints. The mean value reliability method (MVRM) and adaptive importance sampling method (AISM) were used for the reliability analyses in the RDO, and reliability probabilities from the MVRM and the AISM on the probabilistic optimum design were assessed by Monte Carlo simulation (MCS). The RDO results according to the reliability analysis methods were compared to determine the optimum design results. In the case of the RDO with the AISM, the structure reliability was fully satisfied for all the constraint functions, so the most reliable structural safety was guaranteed for the seat frame design.
Reasons for the High Electrical Conductivity of Bismuth Ferrite and Ways to Minimize It
Authors: Andryushin Konstantin , Sakhnenko Vladimir , Turik Anatoliy , Shilkina Lidiya , Pavelko Alexey , Dudkina Svetlana , Rudskaya Angela , Rudskiy Daniil , Verbenko Iliya , Hasbulatov Sidek , Reznichenko Larisa , Parinov Ivan , Chang Shun-Hsyung , Wang Hung-Yu
Published: January 2021

The reasons for the high electrical conductivity of bismuth ferrite due to its natural composite structure, structural non-stoichiometry, redox processes, and the boundary position in the perovskite family have been considered. It has been shown that it is possible to significantly (2–3 orders of magnitude) reduce the conductivity of BiFeO3 by introducing large-sized ions of rare-earth elements (REE: La, Pr, Nd, Sm, Eu, Gd with 0.94 ≤ R¯ ≤ 1.04 Å) in amounts of up to 10 mol %. An interpretation of the observed effects has been given. A consideration about the appropriateness of taking into account the presented results when developing devices using materials of the BiFeO3/REE type has been expressed.
Prediction of Rolling Contact Fatigue Behavior in Rails Using Crack Initiation and Growth Models along with Multibody Simulations
Authors: Rodríguez-Arana Borja , San Emeterio Albi , Alvarado Unai , Martínez-Esnaola José M. , Nieto Javier
Published: January 2021

Rolling contact fatigue (RCF) is a common cause of rail failure due to repeated stresses at the wheel-rail contact. This phenomenon is a real problem that greatly affects the safety of train operation. Preventive and corrective maintenance tasks have a big impact on the Life Cycle Cost (LCC) of railway assets, and therefore cutting-edge strategies based on predictive functionalities are needed to reduce it. A methodology based on physical models is proposed to predict the degradation of railway tracks due to RCF. This work merges a crack initiation and a crack growth model along with a fully nonlinear multibody model. From a multibody assessment of the vehicle-track interaction, an energy dissipation method is used to identify points where cracks are expected to appear. At these points, crack propagation is calculated considering the contact conditions as a function of crack depth. The proposed methodology has been validated with field measurements, conducted using Eddy Currents provided by the infrastructure manager Network Rail. Validation results show that RCF behavior can be predicted for track sections with different characteristics without the necessity of previous on-track measurements.
Split-Based Algorithm for Weighted Context-Free Grammar Induction
Authors: Gabor Mateusz , Wieczorek Wojciech , Unold Olgierd
Published: January 2021

The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the weights of rules are estimated using a novel Inside–Outside Contrastive Estimation algorithm. The results showed that our approach outperforms in terms of F1 scores of other state-of-the-art methods.
Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data
Authors: Zhao Tianyi , Zhang Chengyu , Ujeed Terigele , Ma Liangdong
Published: January 2021

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.
Application of DMLF in Pulse Ranging LiDAR System
Authors: Guo Dongbing , Wang Chunhui , Qi Baoling
Published: January 2021

A novel differential method based on linear fitting is proposed to improve the accuracy of the pulse-ranging system. In this method, the Peak Points (PPs) of the traditional echo signal transformed into the Positive-Going Zero-Crossing Points (PGZCPs) of the differential echo signal. Then, we obtained the true zero-crossing point by the linear fitting of points near PGZCPs. We compared the differential method based on linear fitting (DMLF) and peak method. The Root Mean Square Error (RMSE) found with our method (0.1661 ns) is nearly a 50% reduction compared to that of the peak method (0.3318 ns), and a large number of experiments have demonstrated that the Relative Error (RE) of our proposed method is less than 30 ppm.
Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training
Authors: Condino Sara , Turini Giuseppe , Mamone Virginia , Parchi Paolo Domenico , Ferrari Vincenzo
Published: January 2021

Simulation for surgical training is increasingly being considered a valuable addition to traditional teaching methods. 3D-printed physical simulators can be used for preoperative planning and rehearsal in spine surgery to improve surgical workflows and postoperative patient outcomes. This paper proposes an innovative strategy to build a hybrid simulation platform for training of pedicle screws fixation: the proposed method combines 3D-printed patient-specific spine models with augmented reality functionalities and virtual X-ray visualization, thus avoiding any exposure to harmful radiation during the simulation. Software functionalities are implemented by using a low-cost tracking strategy based on fiducial marker detection. Quantitative tests demonstrate the accuracy of the method to track the vertebral model and surgical tools, and to coherently visualize them in either the augmented reality or virtual fluoroscopic modalities. The obtained results encourage further research and clinical validation towards the use of the simulator as an effective tool for training in pedicle screws insertion in lumbar vertebrae.
Optimal Segment Control of Active Twist Rotor for Power Reduction in Forward Flight
Authors: Zhang Xiaochi , Wan Zhiqiang , Yan De
Published: January 2021

The segment control of active twist rotor is investigated to evaluate the effectiveness in rotor power reduction. A numerical model for predicting the isolated rotor power and loads in steady level flights is deployed and validated. A parametric sweep of the amplitude and phase angle for uniform single-harmonic active twist control is conducted to demonstrate the mechanism of active twist control in rotor power reduction. The optimal control schedules and segment layouts of the segment twist control for power reduction while considering saturation limits are obtained using an optimization framework based on genetic algorithm. Up to 5-seg configuration is considered. The results indicate that the segment twist control reduces the rotor power more than the uniform twist control by applying divergent control schedules to each segment. The load distribution of the rotor disk is harmonized in both circumferential and spanwise directions. The 2-seg and 3-seg control configurations are appropriate, while the configurations with more segments yield limited benefits and they may be penalized with an increase in system complexity.
Investigation of Visual Stimulus Signals Using Hue Change for SSVEP
Authors: Sato Yoshihiro , Kitamura Yuichiro , Hirata Takamichi , Bao Yue
Published: January 2021

This study focuses on the problem of eye irritation when measuring steady-state visual evoked potentials (SSVEPs) using a brain–computer interface and aims to clarify experimentally visual stimulus signals that do not cause discomfort to users. To this end, a method is proposed that introduces a flash stimulus in which the color is changed by changing its hue. This reduces the change in brightness while providing a color change, thereby facilitating visual stimulation with less discomfort. In experiments conducted, flash stimuli of the primary colors red, green, and blue and colors with different hues of 5–45° from these primary colors were generated to investigate the algorithm accuracy of SSVEP and discomfort. Subjective questionnaire and CFF values, which are ophthalmic parameters, were obtained for the subjects and compared to the discrimination rate. As a result of the comparison, it was confirmed that the fatigue level of the visual stimulus generated by the proposed hue change was lower than that of the conventional black-and-white stimulus. It was also confirmed that the combination of the hue difference and frequency could obtain the same discrimination rate as the conventional method.
Evaluating the Sustainability and Inherent Safety of a Crude Palm Oil Production Process in North-Colombia
Authors: González-Delgado Angel Darío , Barajas-Solano Andrés F. , Leon-Pulido Jeffrey
Published: January 2021

The African palm is the main source of vegetable oil worldwide, representing about 29.60% of the total oil and fat production around the world. The rapid expansion of this sector has faced several concerns related to environmental and social aspects that have driven the search for sustainable alternatives. In this work, the inherent safety analysis and sustainability evaluation for the crude palm oil production process was performed using the inherent safety index (ISI) method and the sustainable weighted return on investment metric (SWROIM), respectively. The process was designed for a processing capacity of 30 t/h of palm bunches and under North-Colombian conditions. Three technical indicators were considered to evaluate the process sustainability including exergy efficiency, potential environmental impacts output (PEI output), and the total inherent safety index (ITI). The economic factor is directly considered since the SWROIM is an extension of the conventional return on investment (ROI). The resulting ITI at 11 indicated an inherently safe process, and the highest risk was observed for the process equipment safety subindex. The SWROIM reached a higher value (53%) compared to the conventional ROI (49.39%), which suggests positive impacts on sustainability. The novelty of this work lies in detecting the inherent risks and providing a decision making criteria for this project through a complete evaluation that relates economic, energy, environmental, and safety criteria.
Assessment of an Improved Three-Diode against Modified Two-Diode Patterns of MCS Solar Cells Associated with Soft Parameter Estimation Paradigms
Authors: Bayoumi Ahmed S. , El-Sehiemy Ragab A. , Mahmoud Karar , Lehtonen Matti , Darwish Mohamed M. F.
Published: January 2021

Recently, the use of multi-crystalline silicon solar cells (MCSSCs) has been increasing worldwide. This work proposes a novel MCSSC pattern for achieving a more accurate emulation of the electrical behavior of solar cells. Specifically, this pattern is dependent on the modification of the double diode model of MCSSCs. Importantly, the proposed pattern has an extra diode compared to the previously modified double-diode model (MDDM) described in the literature for considering the defect region of MCSSC to form a modified three diode model (MTDM). For estimating the parameters of the proposed MTDM, two metaheuristic algorithms called closed-loop particle swarm optimization (CLPSO) and elephant herd optimization (EHO) are developed, which have superior convergence rates. The competitive algorithms are executed on experimental data based on a MCSSC of area 7.7 cm2 from Q6-1380 and CS6P-240P solar modules under different irradiance and temperature levels for both MDDM and MTDM. Also, the proposed elephant herd optimization soft paradigm is extended for a high irradiance level at 1000 W/m2 on an R.T.C. France Solar cell. The proposed new optimization models are more efficient in dealing with the natural characteristics of the MCSSC. The simulation results show that the MTDM gives more accurate solutions as a model to the MCSSC compared with the results reported in the literature. From the viewpoint of soft computing paradigms, the EHO outperforms CLPSO in terms of the solution quality and convergence rates.
Optimum Layout of Low Power LC-Based Digitally Controlled Oscillator for Bluetooth Low Energy in a 4G/5G LTE System
Authors: Kim Min-Su , Yoo Sang-Sun
Published: January 2021

This paper presents an optimum layout method of a low-power, digitally controlled oscillator (DCO) for a Bluetooth low-energy (BLE) transceiver in a 4G/5G LTE system. For the optimal LC-based DCO layout, three different layouts, including different gm cell locations and an Al metal layer, were implemented, and performance was compared and verified for BLE application. The implemented neck DCO (NDCO), where the gm cell is located in the neck of the main inductor, showed superior performance compared to other layouts in terms of low phase noise and low power consumption. The designed NDCO had a low phase noise of −116.1 dBc/Hz at 1 MHz with a 0.5 mW power consumption. The supply voltage and oscillation frequency range were 0.8 V and 4.7–5.7 GHz, respectively, and the NDCO designed with the optimal layout had a good figure-of-merit of −192.6 dBc/Hz.
Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions Ahead of a TBM Using Data-Driven Approach
Authors: Sebbeh-Newton Sylvanus , Ayawah Prosper E.A. , Azure Jessica W.A. , Kaba Azupuri G.A. , Ahmad Fauziah , Zainol Zurinahni , Zabidi Hareyani
Published: January 2021

Pre-tunneling exploration for rock mass classification is a common practice in tunneling projects. This study proposes a data-driven approach that allows for rock mass classification. Two machine learning (ML) classification models, namely random forest (RF) and extremely randomized tree (ERT), are employed to classify the rock mass conditions encountered in the Pahang-Selangor Raw Water Tunnel in Malaysia using tunnel boring machine (TBM) operating parameters. Due to imbalance of rock classes distribution, an oversampling technique was used to obtain a balanced training dataset for unbiased learning of the ML models. A five-fold cross-validation approach was used to tune the model hyperparameters and validation-set approach was used for the model evaluation. ERT achieved an overall accuracy of 95%, while RF achieved 94% accuracy, in rightly classifying rock mass conditions. The result shows that the proposed approach has the potential to identify and correctly classify ground conditions of a TBM, which allows for early problem detection and on-the-fly support system selection based on the identified ground condition. This study, which is part of an ongoing effort towards developing reliable models that could be incorporated into TBMs, shows the potential of data-driven approaches for on-the-fly classification of ground conditions ahead of a TBM and could allow for the early detection of potential construction problems.
Critical Comparison of Spherical Microindentation, Small Punch Test, and Uniaxial Tensile Testing for Selective Laser Melted Inconel 718
Authors: Courtright Zachary S. , Leclerc Nicolas P. , Kim Hyung Nun , Kalidindi Surya R.
Published: January 2021

Standardized mechanical tests have become one of the central bottlenecks in the efficient and cost-effective exploration of the process space in advanced manufacturing processes such as additive manufacturing (AM). This paper presents a critical comparison of the relative advantages and disadvantages between emergent high-throughput mechanical test protocols, specifically, spherical microindentation and small punch test, and standardized tension tests. The critical comparison considers the effectiveness and viability of the testing protocols to rapidly screen stress-strain data and mechanical properties of candidate AM-processed metal specimens. These comparisons were performed on samples of Inconel 718 produced by selective laser melting (SLM). Modulus, yield strength, and ultimate tensile strength were evaluated, and the combination of high-throughput mechanical test protocols displayed results consistent with standard tension tests. This study shows that high-throughput mechanical test protocols can successfully produce reliable stress-strain data using significantly smaller material volume and reduced labor compared to the standardized tension tests.
Geochemical Assessment and Mobility of Undesired Elements in the Sludge of the Phosphate Industry of Gafsa-Metlaoui Basin, (Southern Tunisia)
Authors: Smida Olfa , Souissi Radhia , Salem Marzougui , Souissi Fouad
Published: January 2021

The raw phosphates in the Gafsa-Metlaoui phosphate basin are valorized by wet processes that are performed in the laundries of the Gafsa Phosphates Company (CPG, Gafsa, Tunisia) to reach market grades (>28% P2O5). This enrichment process allows the increase of P2O5 content by the elimination of the coarse (>2 mm) and fine (<71 µm) fractions. Mineralogical analysis has shown that all the investigated materials (raw phosphate, marketable phosphate, coarse waste, and fine waste) from the laundries of M’Dhilla-Zone L and Redeyef are both composed of carbonate fluorapatite, carbonates, quartz, gypsum, clays, and clinoptilolite. Chemical analysis shows that Cr, Cd, Zn, Pb, and U are concentrated in the fine wastes and associated with the clay–phosphate fraction. The rare earth elements are more concentrated in both raw and marketable phosphates. Drilling and sludge-water analysis, along with leaching tests conducted on the fine wastes, showed that, due to phosphate industry, cadmium, fluorine, and sulfate contributing to the pollution of water resources in the region, pollution is more conspicuous at M’Dhilla.
Amoxicillin in Water: Insights into Relative Reactivity, Byproduct Formation, and Toxicological Interactions during Chlorination
Authors: Siciliano Antonietta , Guida Marco , Libralato Giovanni , Saviano Lorenzo , Luongo Giovanni , Previtera Lucio , Di Fabio Giovanni , Zarrelli Armando
Published: January 2021

In recent years, many studies have highlighted the consistent finding of amoxicillin in waters destined for wastewater treatment plants, in addition to superficial waters of rivers and lakes in both Europe and North America. In this paper, the amoxicillin degradation pathway was investigated by simulating the chlorination process normally used in a wastewater treatment plant to reduce similar emerging pollutants at three different pH values. The structures of 16 isolated degradation byproducts (DPs), one of which was isolated for the first time, were separated on a C-18 column via a gradient HPLC method. Combining mass spectrometry and nuclear magnetic resonance, we then compared commercial standards and justified a proposed formation mechanism beginning from the parent drug. Microbial growth inhibition bioassays with Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus were performed to determine the potential loss of antibacterial activity in isolated degradation byproducts. An increase of antibacterial activity in the DPs was observed compared to the parent compound.
Recent Advancements of UF-Based Separation for Selective Enrichment of Proteins and Bioactive Peptides—A Review
Authors: Ratnaningsih Enny , Reynard Reynard , Khoiruddin Khoiruddin , Wenten I Gede , Boopathy Ramaraj
Published: January 2021

Proteins are one of the primary building blocks that have significant functional properties to be applied in food and pharmaceutical industries. Proteins could be beneficial in their concentrated products or isolates, of which membrane-based filtration methods such as ultrafiltration (UF) encompass application in broad spectra of protein sources. More importantly, selective enrichment by UF is of immense interest due to the presence of antinutrients that may dominate their perspicuous bioactivities. UF process is primarily obstructed by concentration polarization and fouling; in turn, a trade-off between productivity and selectivity emerges, especially when pure isolates are an ultimate goal. Several factors such as operating conditions and membrane equipment could leverage those pervasive contributions; therefore, UF protocols should be optimized for each unique protein mixture and mode of configuration. For instance, employing charged UF membranes or combining UF membranes with electrodialysis enables efficient separation of proteins with a similar molecular weight, which is hard to achieve by the conventional UF membrane. Meanwhile, some proposed strategies, such as utilizing ultrasonic waves, tuning operating conditions, and modifying membrane surfaces, can effectively mitigate fouling issues. A plethora of advancements in UF, from their membrane material modification to the arrangement of new configurations, contribute to the quest to actualize promising potentials of protein separation by UF, and they are reviewed in this paper.
Nonlinear Control of Multibody Flexible Mechanisms: A Model-Free Approach
Authors: Boscariol Paolo , Scalera Lorenzo , Gasparetto Alessandro
Published: January 2021

In this paper a novel nonlinear controller for position and vibration control of flexible-link mechanisms is introduced. The proposed control strategy is model-free and does not require the measurement of the elastic deformation of the mechanism, since the control relies only on the knowledge of the angular position of the actuator and on its time derivative, which can be measured simply with a quadrature encoder. The conditions for the closed-loop stability are evaluated using Lyapunov theory. The performance of the proposed technique is evaluated on a four-bar flexible-link mechanism. Superior vibration damping and more accurate trajectory tracking is obtained in comparison with a PD controller and a fractional order controller, which relies on the same set of measurement as the proposed nonlinear controller.
Authorization Mechanism Based on Blockchain Technology for Protecting Museum-Digital Property Rights
Authors: Wang Yun-Ciao , Chen Chin-Ling , Deng Yong-Yuan
Published: January 2021

In addition to the exhibition, collection, research, and educational functions of the museum, the development of a future museum includes the trend of leisure and sightseeing. Although the museum is a non-profit organization, if it can provide digital exhibits and collections under the premises of “intellectual property rights” and “cultural assets protection”, and licensing and adding value in various fields, it can generate revenue from digital licensing and handle the expenses of museum operations. This will be a new trend in the sustainable development of museum operations. Especially since the outbreak of COVID-19 at the beginning of this year (2020), the American Alliance of Museums (AAM) recently stated that nearly a third of the museums in the United States may be permanently closed since museum operations are facing “extreme financial difficulties.” This research is aimed at museums using the business model of “digital authorization”. It proposes an authorization mechanism based on blockchain technology protecting the museums’ digital rights in the business model and the application of cryptography. The signature and time stamp mechanism achieve non-repudiation and timeless mechanism, which combines blockchain and smart contracts to achieve verifiability, un-forgery, decentralization, and traceability, as well as the non-repudiation of the issue of cash flow with signatures and digital certificates, for the digital rights of museums in business. The business model proposes achievable sustainable development. Museums not only achieve the goal of promoting social education, but also solve their financial problems.
Two-Stage Production System Pondering upon Corporate Social Responsibility in Food Supply Chain: A Case Study
Authors: Chen Ten-Suz , Huang Yung-Fu , Weng Ming-Wei , Do Manh-Hoang
Published: January 2021

Corporate social responsibility (CSR) has witnessed remarkable attention in academic studies as well as being widely conducted in different industries globally. This specific case was chosen as one of the biggest dairy companies that may be represented for Vietnam dairy supply chain management. This research aims to integrate CSR initiatives into food supply chain management to clarify the optimal replenishment policy, paying close attention to the relationship between midstream manufacturers and final customers. The classical economic production quantity model has been employed, relying on the two-stage assembly production system. The three parameters that contribute to the total profit formulation that have been considered consist of the social charity amount for per unit selling, the unit wholesale price of the manufacturer, and the return rate of used goods from the customer. The study has stressed that there is a significant impact from implementing CSR initiatives on the enterprise’s inventory policy that leads to enhance the firm’s financial performance.
SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch
Authors: Li Qing , Lin Yingcheng , He Wei
Published: January 2021

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.
A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
Authors: Phan Quoc Thang , Wu Yuan Kang , Phan Quoc Dung
Published: January 2021

In recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefore, numerous artificial intelligent methods such as machine learning and deep learning have been considered as solutions for accurate wind power forecasts. In addition to deterministic forecasting, the probabilistic forecasting becomes more important, because it indicates the level of uncertainty. In this paper, a hybrid forecasting model considering different Numerical Weather Prediction (NWP) models and the XGBoost training model is proposed for short-term wind power forecasting. The proposed forecasting algorithm includes data preprocessing, in which an autoencoder model is used to reduce the dimension of 20 NWP ensembles. The performance of the proposed method is investigated using historical wind power measurements and NWP results by the Taiwan Central Weather Bureau (CWB); the NWP includes spot wind speeds from WRFD, RWRF, and ensemble wind speeds from WEPS. Based on the forecasting results, the proposed model produces better performance and forecasting accuracy among other forecasting models, which reveals the importance of data preprocessing using autoencoders and the use of deep learning models in deterministic or probabilistic forecasts.
Numerical Study on Factors Affecting the Induction of Apoptotic Temperatures of Tumor in the Multi-Layer Skin Structure Using Monte Carlo Method
Authors: Kim Donghyuk , Kang Sukkyung , Kim Hyunjung
Published: January 2021

The incidence of skin cancer is increasing with the recent increase in UV exposure. The treatment of skin cancer generally proceeds through an excision of the tumor area, which causes bleeding into the affected area and surrounding tissues, and there is a possibility that secondary infection may occur. Photothermal therapy is drawing attention as an alternative treatment to overcome this limitation. In this study, a numerical analysis was performed on skin cancer tumors located between the reticular dermis and the skin surface by applying the Monte Carlo method. The numerical analysis derives a quantitative correlation using an effective apoptosis ratio with respect to the intensity of the laser that produces the optimal photothermal therapy effect and the volume fraction of gold nanorods (GNRs) injected into a tumor. Through this study, it is confirmed that the optimal treatment effect exists for the depth and length of the various tumors, the intensity of the laser, and the volume fraction of GNRs to minimize the thermal damage to the surrounding normal tissues while maximizing the apoptosis in the tumor. It is expected that it can be used as an optimal condition for better treatment while performing photothermal therapy in the future.
Effect of the El Niño Decaying Pace on the East Asian Summer Monsoon Circulation Pattern during Post-El Niño Summers
Authors: Jiang Wenping , Li Gen , Wang Gongjie
Published: January 2021

El Niño events vary from case to case with different decaying paces. In this study, we demonstrate that the different El Niño decaying paces have distinct impacts on the East Asian monsoon circulation pattern during post-El Niño summers. For fast decaying (FD) El Niño summers, a large-scale anomalous anticyclone dominates over East Asia and the North Pacific from subtropical to mid-latitude; whereas, the East Asian monsoon circulation display a dipole pattern with anomalous northern cyclone and southern anticyclone for slow decaying (SD) El Niño summers. The difference in anomalous East Asian monsoon circulation patterns was closely associated with the sea surface temperature (SST) anomaly patterns in the tropics. In FD El Niño summers, the cold SST anomalies in the tropical central-eastern Pacific and warm SST anomalies in the Maritime Continent induce the anticyclone anomalies over the Northwest Pacific. In contrast, the warm Kelvin wave anchored over the tropical Indian Ocean during SD El Niño summers plays a crucial role in sustaining the anticyclone anomalies over the Northwest Pacific. In particular, the opposite atmospheric circulation anomaly patterns over Northeast Asia and the mid-latitude North Pacific are mainly modulated by the stationary Rossby wave trains triggered by the opposite SST anomalies in the tropical eastern Pacific during FD and SD El Niño summers. Finally, the effect of distinct summer monsoon circulation patterns associated with the El Niño decay pace on the summer climate over East Asia are also discussed.
Concurrent Influence of Different Natural Sources on the Particulate Matter in the Central Mediterranean Region during a Wildfire Season
Authors: Castagna Jessica , Senatore Alfonso , Bencardino Mariantonia , Mendicino Giuseppe
Published: January 2021

Wildfire occurrence and severity in the Mediterranean region during the summer season is increasing, being favoured by climate change-induced conditions (i.e., drought, heatwaves). Moreover, additional natural sources frequently impact this region, particularly Saharan dust intrusions. This study focuses on the combined effect of wildfires and Saharan dust on the air quality of the central Mediterranean Basin (CMB) during 2017, an exceptional year for forested burned areas in southern Italy. The annual behaviors of PM2.5, PM10, CO, benzene, and benzo(a)pirene measurements that were recorded at a rural regional-background station located in southern Italy, highlighted a concentration increase during summer. Both Saharan dust and wildfire events were identified while using Navy Aerosol Analysis and Prediction System (NAAPS) model maps, together with high-resolution Weather Research and Forecast—Hybrid Single-Particle Lagrangian Integrated Trajectory (WRF-HYSPLIT) back-trajectories. Additionally, Visible Infrared Imaging Radiometer Suite (VIIRS) satellite detections were considered to establish the enrichment of air masses by wildfire emissions. Finally, the occurrence of these natural sources, and their influence on particulate matter, were examined. In this case study, both PM2.5 and PM10 exceedances occurred predominantly in conjunction with wildfire events, while Saharan dust events mainly increased PM10 concentration when overlapping with wildfire effects.
Spatiotemporal Estimation of the Olive and Vine Cultivations’ Growing Degree Days in the Balkans Region
Authors: Charalampopoulos Ioannis , Polychroni Iliana , Psomiadis Emmanouil , Nastos Panagiotis
Published: January 2021

Olive and vine cultivations are two of the most important crops in Europe, yielding high quality and value food products. The climate change over the Balkans may elevate the agroecological pressure for the established crops and shift their cultivations areas. One of the widely-used agroclimatic indices is the growing degree days (GDD) which accumulates the necessary thermal units for the selected crops. Despite the advances on the agroclimatic research, there are few available methods for spatiotemporal estimation of this useful index. So, this research is focused on the construction of simple and reliable equations for the calculation and projection of olive and vine cultivations’ GDD over the Balkans. The models’ input parameters are the time, the altitude, the distance from the seashore, and the latitude. Its assembly is made by the extracted spatial data, combined with the Agri4Cast dataset for the period of 1980 to 2018 incorporating the regional climate change trend. The results indicate that the most influential parameter is the time, followed by the latitude, for both cultivations. According to the projections, as quantified by GDD, a vast sprawl of olive and vine cultivation areas will have been formed to the northern parts of the studied area. To be more precise, the viticulture could expand spatially by 28.8% (of the Balkans area) by 2040, and by 15.1% to 2060, when the olive cultivations’ area could sprawl 23.9% by 2040 and 20.3% by 2060.
Response of Pluripotent Stem Cells to Environmental Stress and Its Application for Directed Differentiation
Authors: Kaitsuka Taku , Hakim Farzana
Published: January 2021

Pluripotent stem cells have unique characteristics compared to somatic cells. In this review, we summarize the response to environmental stresses (hypoxic, oxidative, thermal, and mechanical stresses) in embryonic stem cells (ESCs) and their applications in the differentiation methods directed to specific lineages. Those stresses lead to activation of each specific transcription factor followed by the induction of downstream genes, and one of them regulates lineage specification. In short, hypoxic stress promotes the differentiation of ESCs to mesodermal lineages via HIF-1α activation. Concerning mechanical stress, high stiffness tends to promote mesodermal differentiation, while low stiffness promotes ectodermal differentiation via the modulation of YAP1. Furthermore, each step in the same lineage differentiation favors each appropriate stiffness of culture plate; for example, definitive endoderm favors high stiffness, while pancreatic progenitor favors low stiffness during pancreatic differentiation of human ESCs. Overall, treatments utilizing those stresses have no genotoxic or carcinogenic effects except oxidative stress; therefore, the differentiated cells are safe and could be useful for cell replacement therapy. In particular, the effect of mechanical stress on differentiation is becoming attractive for the field of regenerative medicine. Therefore, the development of a stress-mediated differentiation protocol is an important matter for the future.
Application of Autologous Peripheral Blood Mononuclear Cells into the Area of Spinal Cord Injury in a Subacute Period: A Feasibility Study in Pigs
Authors: Shulman Iliya , Ogurcov Sergei , Kostennikov Alexander , Rogozin Alexander , Garanina Ekaterina , Masgutova Galina , Sergeev Mikhail , Rizvanov Albert , Mukhamedshina Yana
Published: January 2021

Peripheral blood presents an available source of cells for both fundamental research and clinical use. In our study, we have evaluated the therapeutic potential of peripheral blood mononuclear cells (PBMCs) excluding the preliminary sorting or mobilization of peripheral blood stem cells. We have evaluated the regenerative potential of PBMCs embedded into a fibrin matrix (FM) in a model of pig spinal cord injury. The distribution of transplanted PBMCs in the injured spinal cord was evaluated; PBMCs were shown to penetrate into the deep layers of the spinal cord and concentrate mainly in the grey matter. The results of the current study revealed an increase in the tissue integrity in the area adjacent to the epicenter of injury and the partially restored conduction along posterior columns of the spinal cord in animals after FM+PBMC application. The multiplex analysis of blood serum and cerebrospinal fluid showed the cytokine imbalance to occur without significantly shifting toward pro-inflammatory or anti-inflammatory cytokine cascades.
Prepubertal Periodontitis in a Patient with Combined Classical and Periodontal Ehlers–Danlos Syndrome
Authors: Stock Friedrich , Hanisch Marcel , Lechner Sarah , Biskup Saskia , Bohring Axel , Zschocke Johannes , Kapferer-Seebacher Ines
Published: January 2021

We report an extremely rare case of combined classical and periodontal Ehlers−Danlos syndrome (EDS) with early severe periodontitis and a generalized lack of attached gingiva. A German family with classical EDS was investigated by physical and dental evaluation and exome and Sanger sequencing. Due to the specific periodontal phenotype in the affected child, an additional diagnosis of periodontal EDS was suspected. Physical and genetic examination of two affected and three unaffected family members revealed a family diagnosis of classical EDS with a heterozygous mutation in COL5A1 (c.1502del; p.Pro501Leufs*57). Additional to the major clinical criteria for classical EDS—generalized joint hypermobility, hyperelastic skin, and atrophic scarring —the child aged four years presented with generalized alveolar bone loss up to 80%, early loss of two lower incisors, severe gingival recession, and generalized lack of attached gingiva. Due to these clinical findings, an additional diagnosis of periodontal EDS was suspected. Further genetic analysis revealed the novel missense mutation c.658T>G (p.Cys220Gly) in C1R in a heterozygous state. Early severe periodontitis in association with generalized lack of attached gingiva is pathognomonic for periodontal EDS and led to the right clinical and genetic diagnosis in the present case.
Connexins, Innexins, and Pannexins: From Biology to Clinical Targets
Authors: Aasen Trond
Published: January 2021

In 1964, Loewenstein and Kanno [...]
The Effects of Working Memory Training on Brain Activity
Authors: Nęcka Edward , Gruszka Aleksandra , Hampshire Adam , Sarzyńska-Wawer Justyna , Anicai Andreea-Elena , Orzechowski Jarosław , Nowak Michał , Wójcik Natalia , Sandrone Stefano , Soreq Eyal
Published: January 2021

This study aimed to investigate if two weeks of working memory (WM) training on a progressive N-back task can generate changes in the activity of the underlying WM neural network. Forty-six healthy volunteers (23 training and 23 controls) were asked to perform the N-back task during three fMRI scanning sessions: (1) before training, (2) after the half of training sessions, and (3) at the end. Between the scanning sessions, the experimental group underwent a 10-session training of working memory with the use of an adaptive version of the N-back task, while the control group did not train anything. The N-back task in the scanning sessions was relatively easy (n = 2) in order to ensure high accuracy and a lack of between-group differences at the behavioral level. Such training-induced differences in neural efficiency were expected. Behavioral analyses revealed improved performance of both groups on the N-back task. However, these improvements resulted from the test-retest effect, not the training outside scanner. Performance on the non-trained stop-signal task did not demonstrate any transfer effect. Imaging analysis showed changes in activation in several significant clusters, with overlapping regions of interest in the frontal and parietal lobes. However, patterns of between-session changes of activation did not show any effect of training. The only finding that can be linked with training consists in strengthening the correlation between task performance accuracy and activation of the parietal regions of the neural network subserving working memory (left superior parietal lobule and right supramarginal gyrus posterior). These results suggest that the effects of WM training consist in learning that, in order to ensure high accuracy in the criterion task, activation of the parietal regions implicated in working memory updating must rise.
Dendritic Cells: Behind the Scenes of T-Cell Infiltration into the Tumor Microenvironment
Authors: Lucarini Valeria , Melaiu Ombretta , Tempora Patrizia , D’Amico Silvia , Locatelli Franco , Fruci Doriana
Published: January 2021

Tumor-infiltrating CD8+ T cells have been shown to play a crucial role in controlling tumor progression. However, the recruitment and activation of these immune cells at the tumor site are strictly dependent on several factors, including the presence of dendritic cells (DCs), the main orchestrators of the antitumor immune responses. Among the various DC subsets, the role of cDC1s has been demonstrated in several preclinical experimental mouse models. In addition, the high density of tumor-infiltrating cDC1s has been associated with improved survival in many cancer patients. The ability of cDC1s to modulate antitumor activity depends on their interaction with other immune populations, such as NK cells. This evidence has led to the development of new strategies aimed at increasing the abundance and activity of cDC1s in tumors, thus providing attractive new avenues to enhance antitumor immunity for both established and novel anticancer immunotherapies. In this review, we provide an overview of the various subsets of DCs, focusing in particular on the role of cDC1s, their ability to interact with other intratumoral immune cells, and their prognostic significance on solid tumors. Finally, we outline key therapeutic strategies that promote the immunogenic functions of DCs in cancer immunotherapy.