bluuebunny/crossref_metadata_2025 huggingface dataset:
Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
Authors: Ramli Roziana , Hasikin Khairunnisa , Idris Mohd Yamani Idna , A. Karim Noor Khairiah , Wahab Ainuddin Wahid Abdul
Published: November 2021

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).
Reinforcement Learning for Options Trading
Authors: Wen Wen , Yuan Yuyu , Yang Jincui
Published: November 2021

Reinforcement learning has been applied to various types of financial assets trading, such as stocks, futures, and cryptocurrencies. Options, as a novel kind of derivative, have their characteristics. Because there are too many option contracts for one underlying asset and their price behavior is different. Besides, the validity period of an option contract is relatively short. To apply reinforcement learning to options trading, we propose the options trading reinforcement learning (OTRL) framework. We use options’ underlying asset data to train the reinforcement learning model. Candle data in different time intervals are utilized, respectively. The protective closing strategy is added to the model to prevent unbearable losses. Our experiments demonstrate that the most stable algorithm for obtaining high returns is proximal policy optimization (PPO) with the protective closing strategy. The deep Q network (DQN) can exceed the buy and hold strategy in options trading, as can soft actor critic (SAC). The OTRL framework is verified effectively.
Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times
Authors: Alnahhal Mohammed , Ahrens Diane , Salah Bashir
Published: November 2021

This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.
A Proposal for Clothing Size Recommendation System Using Chinese Online Shopping Malls: The New Era of Data
Authors: Yuan Ying , Park Myung-Ja , Huh Jun-Ho
Published: November 2021

Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system has been implemented focusing mainly on size extraction and recommendation functions along with a UI (user interface). For the former function, data are necessary to extract customers’ sizes and, for instance, the system to be used in China adopts their Chinese standard body size GB/T (Chinese national standard) considering that there are a variety of body types in their substantial population. The system shows the most similar size dataset among the body size GB/T dataset to the customer once he/she inputs his/her height and weight. Each GB/T data was entered after categorizing it according to the proportion between height and weight. For the latter function, size recommendation, size coding was performed first for all the clothes by the shop owner by entering individual size data. The clothes providing the most suitable fit for the customer are recommended by the selection of that which has the smallest deviation between coded clothes size and the customer body data after performing a series of comparative calculations. To validate the effectiveness of the extraction, a method that checks whether the difference between extracted size and the body size that has been measured remains within the error range of 4cm was used. The result showed there to be an approximate 88% matching rate for women and a slightly lower accuracy of 80% for men. Moreover, the error rate was relatively smaller for the upper half clothing such as shirts, jackets, and blouses or one-piece dresses. Such a result may have been generated since the GB/T data were actually the average data entered 10 years prior without categorizing nationalities, ages, and body types in detail. This research emphasized the necessity of a database containing a more segmented human body size data, which can be effective for extracting and recommending sizes more accurately as the latest ones continue to accumulate.
The Glutamate Receptor Plays a Role in Defense against Botrytis cinerea through Electrical Signaling in Tomato
Authors: Feng Shuxian , Pan Caizhe , Ding Shuting , Ma Qiaomei , Hu Chaoyi , Wang Ping , Shi Kai
Published: November 2021

Plant glutamate-like receptor genes (GLRs) are homologous to mammalian ionotropic glutamate receptors genes (iGluRs). Although GLRs have been implicated in plant defenses to biotic stress, the relationship between GLR-mediated plant immunity against fungal pathogens and electrical signals remains poorly understood. Here, we found that pretreatment with a GLR inhibitor, 6,7-dinitriquinoxaline-2,3-dione (DNQX), increased the susceptibility of tomato plants to the necrotrophic fungal pathogen Botrytis cinerea. Assessment of the glr3.3, glr3.5 and glr3.3/glr3.5 double-mutants upon B. cinerea infection showed that tomato GLR3.3 and GLR3.5 are essential for plant immunity against B. cinerea, wherein GLR3.3 plays the main role. Analysis of the membrane potential changes induced by glutamate (Glu) or glycine (Gly) revealed that amplitude was significantly reduced by knocking out GLR3.3 in tomato. While treatment with Glu or Gly significantly increased immunity against B. cinerea in wild-type plants, this effect was significantly attenuated in glr3.3 mutants. Thus, our data demonstrate that GLR3.3- and GLR3.5-mediated plant immunity against B. cinerea is associated with electrical signals in tomato plants.
Interactions between Two Deformable Droplets in Tandem Fixed in a Gas Flow Field of a Gas Well
Authors: Wang Zhibin , Sun Tianli , Yang Zhongwei , Zhu Guo , Shi Hongyan
Published: November 2021

Knowing the droplet-deformation conditions, the droplet-breakup conditions, and the drag force in the interaction between two droplets with a high Reynolds number is of importance for tracking droplet movement in the annular flow field of a gas well. The interactions between two droplets with a high Reynolds number in a tandem arrangement fixed in flowing gas was investigated. The volume of fluid (VOF) method was used to model the droplets’ surface structure. Two different body forces were exerted on both droplets to hold them suspended at a fixed location, which eliminated the effect of droplet acceleration or deceleration on the drag and decreased the amount of computation required. The exerted body forces were calculated using the Newton iteration procedure. The interactions between the two droplets were analyzed by comparison with the simulation results of a single isolated droplet. The effect of the separation distance on the drag force was investigated by changing the separation spacing. The simulation results showed that for droplets with a small separating space between them, the dynamics of the downstream droplet were influenced significantly by the upstream droplet. The drag coefficient of the downstream droplet decreased considerably to a small, even negative, value, especially for droplets with higher Weber numbers and smaller initial separating spaces between them, while the drag force of the upstream droplet was influenced only slightly. In addition, a formula for predicting the final drag coefficient of the downstream droplet was devised.
Mesenchymal Stem Cell-Derived Exosomes: The New Frontier for the Treatment of Intervertebral Disc Degeneration
Authors: Tilotta Veronica , Vadalà Gianluca , Ambrosio Luca , Russo Fabrizio , Cicione Claudia , Di Giacomo Giuseppina , Papalia Rocco , Denaro Vincenzo
Published: November 2021

Low back pain (LBP) is one of the most frequent symptoms associated with intervertebral disc degeneration (IDD) and affects more than 80% of the population, with strong psychosocial and economic impacts. The main cause of IDD is a reduction in the proteoglycan content within the nucleus pulposus (NP), eventually leading to the loss of disc hydration, microarchitecture, biochemical and mechanical properties. The use of mesenchymal stem cells (MSCs) has recently arisen as a promising therapy for IDD. According to numerous reports, MSCs mediate their regenerative and immunomodulatory effects mainly through paracrine mechanisms. Recent studies have suggested that extracellular vesicles (EVs) extracted from MSCs may be a promising alternative to cell therapy in regenerative medicine. EVs, including exosomes and microvesicles, are secreted by almost all cell types and have a fundamental role in intercellular communication. Early results have demonstrated the therapeutic potential of MSCs-derived EVs for the treatment of IDD through the promotion of tissue regeneration, cell proliferation, reduction in apoptosis and modulation of the inflammatory response. The aim of this review is to focus on the biological properties, function, and regulatory properties of different signaling pathways of MSCs-derived exosomes, highlighting their potential applicability as an alternative cell-free therapy for IDD.
Roughness Digital Characterization and Influence on Wear of Retrieved Knee Components
Authors: Affatato Saverio , Ruggiero Alessandro , Logozzo Silvia , Valigi Maria Cristina
Published: November 2021

Tribological performance of knee components are strongly related to the surface characteristics. Primarily, the roughness and its 3D distribution on the surfaces affect the joint performance. One of the main limitations related to the tribological study of knee prostheses is that most of the research studies report in vitro or in silico results, as knee retrievals are difficult to find or are too damaged to be analyzed. This paper is focused on the roughness characterization of retrieved metal femoral components of total knee replacements (TKR) by means of a rugosimeter and involving digital methods to reconstruct the 3D topography of the studied surfaces. The aim of this study is to investigate how changes and distribution of roughness are correlated between the medial vs. the lateral part and how the resulting digital topography can give insights about the wear behavior.
Experimental Study on the Temporal and Morphological Characteristics of Dynamic Tensile Fractures in Igneous Rocks
Authors: Xu Xuan , Chi Li-Yuan , Yang Jun , Yu Qi
Published: November 2021

In order to investigate the temporal and morphological characteristics of dynamic tensile fractures, experiments on Brazilian specimens machined from igneous rocks (Breccia and Andesite) are carried out with the split Hopkinson pressure bar (SHPB). Detailed observation of the fracture processes of the Brazilian specimens is captured by high-speed camera at a frame rate of 100,000 frames per second. The rate-dependent effect of the dynamic tensile strength of the two igneous rocks is fitted and predicted by the incubation time criterion. Digital image correlation (DIC) is used to calculate the full-field tensile strain distributions on the specimen surface during the loading stage preceding fracture, and this hysteresis of dynamic fracture relative to stress level is interpreted by introducing the concept of incubation time. After the main crack appears, image processing technology is exploited to extract the pixel information of cracks in the high-speed images. Then, FracPaQ quantifies the morphology of the fragmentized process by filling the binarization of cracks with fracture traces. After coordination of the statistical information from these fracture traces, the rose diagram representing their angles and length weights can visually represent the fragmentized characteristics of the Brazilian specimen. Specifically, length-angle distributions of fracture traces at various moments are consistent with the Gaussian function, and the curve fitting parameters reflect differences in the fracture behaviors between the two igneous rocks. In conclusion, the dynamic fracture characteristics of two igneous rocks in dynamic splitting processes are quantified statistically, which can provide references for relevant research.
Static and Dynamic Analysis of Non-Pneumatic Tires Based on Experimental and Numerical Methods
Authors: Genovese Andrea , Garofano Dario , Sakhnevych Aleksandr , Timpone Francesco , Farroni Flavio
Published: November 2021

Since the beginning of their production, pneumatic tires have experienced tremendous improvements in structure and materials, becoming the dominant design in the world tires market. Nevertheless, relying upon pressurized air, they are affected by maintenance and security issues that can lead to fatal accidents. Therefore, tire-makers are investigating new tire designs, called Airless or Non-Pneumatic, with the aim of removing air-related problems. The research about such tires is still at an early stage, especially if compared to the one conducted on the pneumatic ones. In this paper, the development of a methodology capable of studying the mechanical behavior of a Non-Pneumatic Tire (NPT) by means of experimental data and numerical approach is illustrated. The experimental activities consisted of a scanner acquisition of the NPT and a footprint analysis for the calculation of the radial stiffness and contact patch pressure distribution. Moreover, the Digital Image Correlation (DIC) technique was applied to carry out a more specific study about the spoke’s deformation. From the acquired 3D model, a calculation of the NPT vertical deflection with finite element analysis (FEA) was performed—validating the model and then submitting it to a steady state analysis—that allows the simulation of a steady state rolling tire with the possibility to replicate different values of slip ratio. The results of the experimental activities are in good agreement with the ones obtained with FEA, further validating the developed methodology.
Project Management Information Systems (PMISs): A Statistical-Based Analysis for the Evaluation of Software Packages Features
Authors: Micale Rosa , La Fata Concetta Manuela , Lombardo Alberto , La Scalia Giada
Published: November 2021

Project Managers (PMs) working in competitive markets are finding Project Management Information Systems (PMISs) useful for planning, organizing and controlling projects of varying complexity. A wide variety of PMIS software is available, suitable for projects differing in scope and user needs. This paper identifies the most useful features found in PMISs. An extensive literature review and analysis of commercial software is made to identify the main features of PMISs. Afterwards, the list is reduced by a panel of project management experts, and a statistical analysis is performed on data acquired by means of two different surveys. The relative importance of listed features is properly computed, and the interactions between the respondent’s profiles and PMIS features are also investigated by cluster and respondents’ analyses. The paper provides information for researchers and practitioners interested in PMISs packages and their applications. Furthermore, the analyses may help practitioners when choosing a PMIS, and also for developers of PMISs software in understanding user needs.
Trends in In Silico Approaches to the Prediction of Biologically Active Peptides in Meat and Meat Products as an Important Factor for Preventing Food-Related Chronic Diseases
Authors: Kęska Paulina , Gustaw Waldemar , Stadnik Joanna
Published: November 2021

The increasing awareness of modern consumers regarding the nutritional and health value of food has changed their preferences, as well their requirements, for food products, including meat and meat products. Expanding the knowledge on the impact of food on human health is currently one of the most important research areas for scientists worldwide, and it is also of interest to consumers who want to consciously compose their daily diets. New research methods, such as in silico techniques, offer solutions to these new challenges. These research methods are preferred over food evaluation, e.g., from meat, because of their advantages, such as low costs, shorter analysis times, and general availability (e.g., online databases), and are often used to design in vitro and, subsequently, in vivo tests. This review focuses on the possible use of in silico computerized methods to assess the potential of food as a source of these health-relevant biomolecules by using examples from the literature on meat and meat products. This review also provides information and important suggestions for analyzing peptides in terms of assessing their best sources, and screening those resistant to digestive factors and that show biological activity. The information provided in this review could contribute to the development of new sources of foods as biomolecules important for preventing or treating food-related chronic diseases, such as obesity, hypertension, and diabetes.
Accounting for Interelement Interferences in Atomic Emission Spectroscopy: A Nonlinear Theory
Authors: Popova Anna N. , Sukhomlinov Vladimir S. , Mustafaev Aleksandr S.
Published: November 2021

The article describes a nonlinear theory of how the presence of third elements affects the results of analyzing the elemental composition of substances by means of atomic emission spectroscopy. The theory is based on the assumption that there is an arbitrary relationship between the intensity of the analytical line of the analyte and the concentration of impurities and alloying elements. The theory has been tested on a simulation problem using commercially available equipment (the SPAS-05 spark spectrometer). By comparing the proposed algorithm with the traditional one, which assumes that there is a linear relationship between the intensity of the analytical line of the analyte and the intensities of the spectral lines (or concentrations) in the substance, it was revealed that there is a severalfold decrease in the deviations of nominal impurity concentrations from the measured ones. The results of this study allow for reducing the number of analytical procedures used in analyzing materials that have different compositions and the same matrix element. For instance, it becomes possible to determine the composition of iron-based alloys (low-alloy and carbon steels; high-speed steels; high-alloy, and heat-resistant steels) using one calibration curve within the framework of a universal analytical method.
Targeted Protein Profiling of In Vivo NIPP-Treated Tissues Using DigiWest Technology
Authors: Ruoff Felix , Henes Melanie , Templin Markus , Enderle Markus , Bösmüller Hans , Wallwiener Diethelm , Brucker Sara Y. , Schenke-Layland Katja , Weiss Martin
Published: November 2021

Non-invasive physical plasma (NIPP) is a novel therapeutic tool, currently being evaluated for the treatment of cancer and precancerous lesions in gynecology and other disciplines. Additionally, patients with cervical intraepithelial neoplasia (CIN) may benefit from NIPP treatment due to its non-invasive, side-effect-free, and tissue-sparing character. However, the molecular impact of in vivo NIPP treatment needs to be further investigated. For this purpose, usually only very small tissue biopsies are available after NIPP treatment. Here, we adapted DigiWest technology, a high-throughput bead-based Western blot, for the analysis of formalin-fixed paraffin-embedded (FFPE) cervical punch biopsies with a minimal sample amount. We investigated the molecular effects of NIPP treatment directly after (0 h) and 24 h after in vivo application. Results were compared to in vitro NIPP-treated human malignant cervical cells. NIPP effects were primarily based on an inhibitory impact on the cell cycle and cell growth factors. DigiWest technology was suitable for detailed protein profiling of small, primary FFPE biopsies.
Object Localization and Tracking System Using Multiple Ultrasonic Sensors with Newton–Raphson Optimization and Kalman Filtering Techniques
Authors: Juan Chung-Wei , Hu Jwu-Sheng
Published: November 2021

In this paper, an object localization and tracking system is implemented with an ultrasonic sensing technique and improved algorithms. The system is composed of one ultrasonic transmitter and five receivers, which uses the principle of ultrasonic ranging measurement to locate the target object. This system has several stages of locating and tracking the target object. First, a simple voice activity detection (VAD) algorithm is used to detect the ultrasonic echo signal of each receiving channel, and then a demodulation method with a low-pass filter is used to extract the signal envelope. The time-of-flight (TOF) estimation algorithm is then applied to the signal envelope for range measurement. Due to the variations of position, direction, material, size, and other factors of the detected object and the signal attenuation during the ultrasonic propagation process, the shape of the echo waveform is easily distorted, and TOF estimation is often inaccurate and unstable. In order to improve the accuracy and stability of TOF estimation, a new method of TOF estimation by fitting the general (GN) model and the double exponential (DE) model on the suitable envelope region using Newton–Raphson (NR) optimization with Levenberg–Marquardt (LM) modification (NRLM) is proposed. The final stage is the object localization and tracking. An extended Kalman filter (EKF) is designed, which inherently considers the interference and outlier problems of range measurement, and effectively reduces the interference to target localization under critical measurement conditions. The performance of the proposed system is evaluated by the experimental evaluation of conditions, such as stationary pen localization, stationary finger localization, and moving finger tracking. The experimental results verify the performance of the system and show that the system has a considerable degree of accuracy and stability for object localization and tracking.
KOERI’s Tsunami Warning System in the Eastern Mediterranean and Its Connected Seas: A Decade of Achievements and Challenges
Authors: Necmioğlu Öcal , Turhan Fatih , Özer Sözdinler Ceren , Yılmazer Mehmet , Güneş Yavuz , Cambaz Musavver Didem , Altuncu Poyraz Selda , Ergün Tuğçe , Kalafat Doğan , Özener Haluk
Published: November 2021

A tsunami warning system providing services in the Eastern Mediterranean, Aegean, Marmara and Black Seas under the UNESCO Intergovernmental Oceanographic Commission (IOC)—Intergovernmental Coordination Group (ICG) for the Tsunami Early Warning and Mitigation System in the North-Eastern Atlantic, the Mediterranean and Connected Seas (NEAMTWS) framework was established in Turkey by the Kandilli Observatory and Earthquake Research Institute (KOERI) (Özel et al., 2011). KOERI’s Regional Earthquake and Tsunami Monitoring Center (RETMC) was established on the foundations of the legacy KOERI National Earthquake Monitoring Center (NEMC) by adding observation, analysis and operational capability related to tsunami early warnings after an extensive preparatory period during 2009 and 2011. The center initiated its test-mode 7/24 operational status as a national tsunami warning center in 2011, and after a one year period it became operational as a candidate tsunami warning center for NEAMTWS on 1 July 2012, together with CENALT (Centre d’Alerte aux Tsunamis—France) and followed by the NOA (National Observatory of Athens—Greece) on 28 August 2012, INGV (Instituto Nazionale di Geofisica e Vulcanologia—Italy) on 1 October 2014 and IPMA (Instituto Português do Mar e da Atmosfera—Portugal) on 1 February 2018, completing full coverage of the tsunami-prone regions monitored by NEAMTWS. In this paper, an overview of the progress and continuous improvement of KOERI’s tsunami early warning system will be presented, together with lessons learned from important tsunamigenic events, such as the 20 July 2017 Bodrum–Kos Mw 6.6 and 30 October 2020 Samos–Izmir Mw 6.9 earthquakes. Gaps preventing the completion of an effective tsunami warning cycle and areas for future improvement are also addressed.
Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
Authors: De Beelde Brecht , Plets David , Joseph Wout
Published: November 2021

With the deployment of data-driven assembly and production factories, challenges arise in sensor data acquisition and gathering. Different wireless technologies are currently used for transferring data, each with different advantages and constraints. In this paper, we present a hybrid network architecture for providing Quality of Service (QoS) in an industrial environment where guaranteed minimal data rates and maximal latency are of utmost importance for controlling devices and processes. The location of the access points (APs) is determined during the initial network-planning action, together with physical parameters such as frequency, transmit power, and modulation and coding schemes. Instead of performing network-planning just once before the network rollout, the network is monitored continuously by adding telemetry data to the frame header of all data streams, and the network is automatically reconfigured in real-time if the requirements are not met. By not using maximum transmit powers during the initial roll-out, more APs are needed, but coverage is guaranteed when new obstructions such as metallic racks or machinery are added. It is found that decreasing the transmit power by 6 dB gives the best trade-off between the number of required APs and network robustness. The proposed architecture is validated via simulations and via a proof-of-concept setup.
Predicting Climate Change Impact on Water Productivity of Irrigated Rice in Malaysia Using FAO-AquaCrop Model
Authors: Houma Abdusslam A. , Kamal Md Rowshon , Mojid Md Abdul , Zawawi Mohamed Azwan Mohamed , Rehan Balqis Mohamed
Published: November 2021

Water productivity (WP) is a key indicator of agricultural water management, since it affects the quantity of water used for crop yield in various management scenarios. This study evaluated the WP of irrigated rice due to a changing climate in the Northwest Selangor Rice Irrigation Scheme (NSRIS) by using field experimental data and the FAO-AquaCrop Model. Pertinent soil, water, climate, and crop data were acquired by executing a field investigation during the off-season (dry season, January–April) and main season (wet season, July–October) in 2017. The AquaCrop 6.0 model was calibrated and validated using the measured data. A Climate-smart Decision Support System (CSDSS) with an ensemble of 10 Global Climate Models (GCMs) was used to downscale climate variables under RCP4.5, RCP6.0, and RCP8.5 emission scenarios during baseline (1976 to 2005) and future (2020 to 2099) periods. The AquaCrop model fairly predicted rice yields under field conditions with root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE) and index of agreement (d) between the observed and estimated yields of 0.173, 0.157, −0.31 to 5.4 and 0.78, respectively for the off-season; and 0.167, 0.127, −5.6 to 2.3 and 0.73, respectively for the main season. It predicted a 10% decrease in actual crop evapotranspiration (ETc) in both crop seasons in the future. The WP of rice based on total water input (WPIrr+RF), applied irrigation (WPIrr), and actual crop evapotranspiration (WPETc) will likely increase by 14–24%, 14–19%, and 17–29%, respectively under the three RCP emission scenarios in the off-season. The likely increase in WP for the corresponding base is 13–22%, 15–24%, and 14–25% in the main season. Various agronomic management options linked to WP will most likely become important in making crucial decisions to cope with the risk of impacts on climate change.
Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis
Authors: Kamyab Marjan , Liu Guohua , Adjeisah Michael
Published: November 2021

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.
Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
Authors: Bae Jaesung , Jee Hongsub , Park Yongseob , Lee Jaehyeong
Published: November 2021

Shingled photovoltaic (PV) modules with increased output have attracted growing interest compared to conventional PV modules. However, the area per unit solar cell of shingled PV modules is smaller because these modules are manufactured by dividing and bonding solar cells, which means that shingled PV modules can easily have inferior shading characteristics. Therefore, analysis of the extent to which the shadow affects the output loss is essential, and the circuit needs to be designed accordingly. In this study, the loss resulting from the shading of the shingled string used to manufacture the shingled module was analyzed using simulation. A divided cell was modeled using a double-diode model, and a shingled string was formed by connecting the cell in series. The shading pattern was simulated according to the shading ratio of the vertical and horizontal patterns, and in the case of the shingled string, greater losses occurred in the vertical direction than the horizontal direction. In addition, it was modularized and compared with a conventional PV module and a shingled PV module. The results confirmed that the shingled PV module delivered higher shading output than the conventional PV module in less shade, and the result of the shading characteristic simulation of the shingled PV module was confirmed to be accurate within an error of 1%.
Fuzzy Quality Evaluation Model Constructed by Process Quality Index
Authors: Yu Chun-Min , Wu Chih-Feng , Chen Kuen-Suan , Hsu Chang-Hsien
Published: November 2021

Many studies have pointed out that the-smaller-the-better quality characteristics (QC) can be found in many important components of machine tools, such as roundness, verticality, and surface roughness of axes, bearings, and gears. This paper applied a process quality index that is capable of measuring the level of process quality. Meanwhile, a model of fuzzy quality evaluation was developed by the process quality index as having a one-to-one mathematical relationship with the process yield. In addition to assessing the level of process quality, the model can also be employed as a basis for determining whether to improve the process quality at the same time. This model can cope with the problem of small sample sizes arising from the need for enterprises’ quick response, which means that the accuracy of the evaluation can still be maintained in the case of small sample sizes. Moreover, this fuzzy quality evaluation model is built on the confidence interval, enabling a decline in the probability of misjudgment incurred by sampling errors.
Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
Authors: Gazdar Achraf , Hidri Lotfi , Ben Youssef Belgacem , Kefi Meriam
Published: November 2021

Video streaming services are one of the most resource-consuming applications on the Internet. Thus, minimizing the consumed resources at runtime in general and the server/network bandwidth in particular are still challenging for researchers. Currently, most streaming techniques used on the Internet open one stream per client request, which makes the consumed bandwidth increases linearly. Hence, many broadcasting/streaming protocols have been proposed in the literature to minimize the streaming bandwidth. These protocols can be divided into two main categories, namely, reactive and proactive broadcasting protocols. While the first category is recommended for streaming unpopular videos, the second category is recommended for streaming popular videos. In this context, in this paper we propose an enhanced version of the reactive protocol Slotted Stream Tapping (SST) called Share All SST (SASST), which we prove to further reduce the streaming bandwidth with regard to SST. We also propose a new proactive protocol named the New Optimal Proactive Protocol (NOPP) based on an optimal scheduling of video segments on streaming-channel. SASST and NOPP are to be used in cloud and CDN (content delivery network) networks where the IP multicast or multicast HTTP on QUIC could be enabled, as their key principle is to allow the sharing of ongoing streams among clients requesting the same video content. Thus, clients and servers are often services running on virtual machines or in containers belonging to the same cloud or CDN infrastructure.
Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
Authors: Hong Guo-Jhang , Li Dong-Lin , Pare Shreya , Saxena Amit , Prasad Mukesh , Lin Chin-Teng
Published: November 2021

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
Study on Seismic Response in Deeply Deposited Saturated Liquefiable Soil Reinforced by Using Subarea Long-Short Gravel Piles
Authors: Liu Junding , Li Rongjian , Zhang Shibin , Bai Weishi , Li Ze
Published: November 2021

To avoid large deformation, resulting from liquefaction, in inclined and deeply deposited liquefiable soil, it is necessary to design economical and reasonable reinforcement schemes. A reinforcement scheme employing subarea long-short gravel piles was proposed, and it was successfully applied in the embankment construction of the Aksu-kashgar highway. To reveal its underlying mechanism and effect on the seismic performance of the highway, the dynamic responses of natural foundation and two kinds of reinforced foundations were analyzed and compared under this scheme, using the program FEMEPDYN. Results showed that both the seismic subsidence and the excess pore pressure ratios were far less in the foundation reinforced with isometric gravel piles and in the foundation reinforced with subarea long-short gravel piles, compared with that in natural foundation. Therefore, the potential hazards of liquefaction were overcome in these two kinds of reinforced foundations. Furthermore, it was obvious that the shielding region only formed within the foundation reinforced with subarea long-short gravel piles. With the shielding effect, the proposed reinforcement scheme employing subarea long-short gravel piles not only eliminated liquefaction in deeply deposited liquefiable soil, but it also demonstrated an outstanding advantage in that the total length of gravel piles used was greatly reduced compared to the total length in the isometric gravel piles scheme and the interphase long-short gravel piles.
Quantum Field Theory Representation in Quantum Computation
Authors: Lori Nicolás , Neves José , Machado José
Published: November 2021

Recently, from the deduction of the result MIP* = RE in quantum computation, it was obtained that Quantum Field Theory (QFT) allows for different forms of computation in quantum computers that Quantum Mechanics (QM) does not allow. Thus, there must exist forms of computation in the QFT representation of the Universe that the QM representation does not allow. We explain in a simple manner how the QFT representation allows for different forms of computation by describing the differences between QFT and QM, and obtain why the future of quantum computation will require the use of QFT.
Assessment of Respiratory System Resistance during High-Frequency Oscillatory Ventilation Based on In Vitro Experiment
Authors: Matejka Jan , Rozanek Martin , Rafl Jakub
Published: November 2021

High-frequency oscillatory ventilation (HFOV) is a type of mechanical ventilation with a protective potential characterized by a small tidal volume. Unfortunately, HFOV has limited monitoring of ventilation parameters and mechanical parameters of the respiratory system, which makes it difficult to adjust the continuous distension pressure (CDP) according to the individual patient’s airway status. Airway resistance Raw is one of the important parameters describing the mechanics of the respiratory system. The aim of the presented study was to verify in vitro whether the resistance of the respiratory system Rrs can be reliably determined during HFOV to evaluate Raw in pediatric and adult patients. An experiment was performed with a 3100B high-frequency oscillator, a physical model of the respiratory system, and a pressure and flow measurement system. The physical model with different combinations of resistance and compliance was ventilated during the experiment. The resistance Rrs was calculated from the impedance of the physical model, which was determined from the spectral density of the pressure at airway opening and the spectral cross-density of the gas flow and pressure at airway opening. Rrs of the model increased with an added resistor and did not change significantly with a change in compliance. The method is feasible for monitoring respiratory system resistance during HFOV and has the potential to optimize CDP settings during HFOV in clinical practice.
Effect of Pulsed Electric Field (PEF) on Bacterial Viability and Whey Protein in the Processing of Raw Milk
Authors: Šalaševičius Aivaras , Uždavinytė Dovilė , Visockis Mindaugas , Ruzgys Paulius , Šatkauskas Saulius
Published: November 2021

There is growing concern regarding the nutritional value of processed food products. Although thermal pasteurization, used in food processing, is a safe method and is widely applied in the food industry, food products lack quality and nutritional value because of the high temperatures used during pasteurization. In this study, the effect of pulsed electric field (PEF) processing on whey protein content and bacterial viability in raw milk was evaluated by changing the PEF strength and number of pulses. For comparison, traditional pasteurization techniques, such as low-temperature long-time (LTLT), ultra-high temperature (UHT), and microfiltration (MF), were also tested for total whey protein content, bacterial activity, and coliforms. We found that, after treatment with PEF, a significant decrease in total bacterial viability of 2.43 log and coliforms of 0.9 log was achieved, although undenatured whey protein content was not affected at 4.98 mg/mL. While traditional pasteurization techniques showed total bacterial inactivation, they were detrimental for whey protein content: β-lactoglobulin was not detected using HPLC in samples treated with UHT. LTLT treatment led to a significant decrease of 75% in β-lactoglobulin concentration; β-lactoglobulin content in milk samples treated with MF was the lowest compared to LTLT and UHT pasteurization, and ~10% and 27% reduction was observed.
Power of the Temporary: Social Art in Spaces of Transitional Living
Authors: Wood Jody
Published: November 2021

This communication paper addresses the role of ephemeral and temporary artistic interventions into the systemic problem of homelessness and the question of sustainability in social art practice. I approach these issues through my work with homeless service agencies that are shaped by rules and procedures intended to increase predictability, whereas, as an artist, my work resists such rigidity by carving out space for spontaneity, vulnerability, and renewal. The dilemma of sustaining socially engaged art long-term raises particular questions within the context of institutions such as these. Can a project be successful as a temporary intervention within systems of predictability? If a project does become sustainable in the long-term, is there a way it can retain a level of energy incited by newness and unexpectedness? I discuss these issues in the context of two of my long-term projects, Beauty in Transition (2013–2016) and Choreographing Care (2016–2021), both working within homeless service agencies. Beauty in Transition was a pop-up mobile hair salon offering free haircare for transitional housing residents. Choreographing Care, a project supporting homeless service staff, started as a socially engaged art project and was adopted into an emergency shelter in Charlotte, North Carolina, U.S.A as an organizational initiative. The ideas I discuss in this paper are supported and inspired by disciplines of research including care ethics of Gilligan, social behavioral science of Goffman, and approaches to participation discussed by Helguera and Kaprow.
Storm-Surge Induced Water Level Changes in the Odra River Mouth Area (Southern Baltic Coast)
Authors: Kowalewska-Kalkowska Halina
Published: November 2021

The Odra River mouth area is a region of the Southern Baltic coastal zone especially prone to the influence of storm surges. In the present study, the height and extent of the Baltic storm surges, and temporal offsets of the respective maximum water level occurrences in the Odra River mouth area were explored using cross-correlation, cluster analysis and principal component analysis. The analyses were based on hourly water level readings retrieved from water gauging stations located along the lower Odra reaches and at the coasts of the Szczecin Lagoon and the Pomeranian Bay during storm surge years 2008/2009–2019/2020. The analysis of mutual relationships between water levels during storm surges indicated that the extent of marine influence on the lower Odra River and within the Szczecin Lagoon was variable during the studied surge events, and dependent on meteorological conditions (the strongest during the sustained occurrence of wind blowing from the northern sector), discharge from the Odra River catchment (the strongest at low discharge), ice conditions on the lower Odra (suppressing the storm surge propagation upstream), and general sea level in the Pomeranian Bay (stronger at high sea levels). The strongest correlation between sea levels at Świnoujście and water levels in the Szczecin Lagoon and the lower Odra was found at a 6–7 h offset. The extent of storm surges usually reached 100 km up the lower Odra channels, less frequently reaching 130 km away from the sea.
Quantifying the Impact of Future Climate Change on Runoff in the Amur River Basin Using a Distributed Hydrological Model and CMIP6 GCM Projections
Authors: Wen Ke , Gao Bing , Li Mingliang
Published: November 2021

The Amur River is one of the top ten longest rivers in the world, and its hydrological response to future climate change has been rarely investigated. In this study, the outputs of four GCMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were corrected and downscaled to drive a distributed hydrological model. Then, the spatial variations of runoff changes under the future climate conditions in the Amur River Basin were quantified. The results suggest that runoffs will tend to increase in the future period (2021–2070) compared with the baseline period (1961–2010), particularly in August and September. Differences were also found among different GCMs and scenarios. The ensemble mean of the GCMs suggests that the basin-averaged annual precipitation will increase by 14.6% and 15.2% under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. The increase in the annual runoff under the SSP2-4.5 scenario (22.5%) is projected to be larger than that under the SSP5-8.5 scenario (19.2%) at the lower reach of the main channel. Future climate changes also tend to enhance the flood peak and flood volume. The findings of this study bring new understandings of the hydrological response to future climate changes and are helpful for water resource management in Eurasia.