bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection and Characterization of Gastric Cancer Using Cascade Deep Learning Model in Endoscopic Images
Authors: Teramoto Atsushi , Shibata Tomoyuki , Yamada Hyuga , Hirooka Yoshiki , Saito Kuniaki , Fujita Hiroshi
Published: August 2022

Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, segmentation of invasive areas of gastric cancer was performed directly from endoscopic images and the detection sensitivity per case was 0.98. This method has challenges of false positives and computational costs because segmentation was applied to all healthy images that were captured during the examination. In this study, we propose a cascaded deep learning model to perform categorization of endoscopic images and identification of the invasive region to solve the above challenges. Endoscopic images are first classified as normal, showing early gastric cancer and showing advanced gastric cancer using a convolutional neural network. Segmentation on the extent of gastric cancer invasion is performed for the images classified as showing cancer using two separate U-Net models. In an experiment, 1208 endoscopic images collected from healthy subjects, 533 images collected from patients with early stage gastric cancer, and 637 images from patients with advanced gastric cancer were used for evaluation. The sensitivity and specificity of the proposed approach in the detection of gastric cancer via image classification were 97.0% and 99.4%, respectively. Furthermore, both detection sensitivity and specificity reached 100% in a case-based evaluation. The extent of invasion was also identified at an acceptable level, suggesting that the proposed method may be considered useful for the classification of endoscopic images and identification of the extent of cancer invasion.
A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data
Authors: Gerolami Justin , Wong Justin Jong Mun , Zhang Ricky , Chen Tong , Imtiaz Tashifa , Smith Miranda , Jamaspishvili Tamara , Koti Madhuri , Glasgow Janice Irene , Mousavi Parvin , Renwick Neil , Tyryshkin Kathrin
Published: August 2022

Complex high-dimensional datasets that are challenging to analyze are frequently produced through ‘-omics’ profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in ‘-omics’ data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in ‘-omics’ data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected genes in a model to classify the three subtypes and compared the results to models built using all genes and all differentially expressed genes. Genes identified with the molecular feature selection tool performed better than the other models in this study in all comparison metrics: accuracy, precision, recall, and F1-score using a significantly smaller set of genes. In addition, we developed a simple graphical user interface for the molecular feature selection tool, which is available for free download. This user-friendly interface is a valuable tool for the identification of potential biomarkers in gene expression datasets and is an asset for biomarker discovery studies.
STANDARD M10 SARS-CoV-2 Assay for Rapid Detection of SARS-CoV-2: Comparison of Four Real-Time PCR Assays
Authors: Jeong Seri , Lee Nuri , Lee Su Kyung , Cho Eun-Jung , Hyun Jungwon , Park Min-Jeong , Song Wonkeun , Kim Hyun Soo
Published: August 2022

The demand for assays that can rapidly and accurately detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains high. We evaluated the performance of two rapid real-time reverse transcription polymerase chain reaction (RT-qPCR) assays (STANDARD M10 SARS-CoV-2 and Xpert Xpress SARS-CoV-2) against conventional RT-qPCR assays (STANDARD M nCoV and Allplex SARS-CoV-2) for detecting SARS-CoV-2. A total of 225 swab samples were collected and tested using the four assays. The STANDARD M10 SARS-CoV-2 assay showed 97.4% positive percent agreement (PPA) and 100.0% negative percent agreement (NPA) compared to the STANDARD M nCoV assay and Allplex SARS-CoV-2 assay. STANDARD M10 exhibited high performance except in samples with low viral loads (cycle threshold (Ct) > 30). Xpert Xpress showed PPA and NPA of 100.0% compared to the two conventional RT-qPCR assays. The kappa coefficient (Κ) showed nearly almost perfect agreement between each assay and conventional RT-qPCR assays. The correlations of Ct values between the two rapid RT-qPCR and conventional RT-qPCR assays were >0.8, indicating strong correlations. All included assays could detect SARS-CoV-2 variants, such as the Alpha, Beta, and Gamma variants. The recently developed STANDARD M10 has a shorter turnaround time and random-access detection on automated devices, thereby facilitating efficient testing in emergency settings.
GamaComet: A Deep Learning-Based Tool for the Detection and Classification of DNA Damage from Buccal Mucosa Comet Assay Images
Authors: Afiahayati  , Anarossi Edgar , Yanuaryska Ryna Dwi , Mulyana Sri
Published: August 2022

Comet assay is a simple and precise method to analyze DNA damage. Nowadays, many research studies have demonstrated the effectiveness of buccal mucosa cells usage in comet assays. However, several software tools do not perform well for detecting and classifying comets from a comet assay image of buccal mucosa cells because the cell has a lot more noise. Therefore, a specific software tool is required for fully automated comet detection and classification from buccal mucosa cell swabs. This research proposes a deep learning-based fully automated framework using Faster R-CNN to detect and classify comets in a comet assay image taken from buccal mucosa swab. To train the Faster R-CNN model, buccal mucosa samples were collected from 24 patients in Indonesia. We acquired 275 comet assay images containing 519 comets. Furthermore, two strategies were used to overcome the lack of dataset problems during the model training, namely transfer learning and data augmentation. We implemented the proposed Faster R-CNN model as a web-based tool, GamaComet, that can be accessed freely for academic purposes. To test the GamaComet, buccal mucosa samples were collected from seven patients in Indonesia. We acquired 43 comet assay images containing 73 comets. GamaComet can give an accuracy of 81.34% for the detection task and an accuracy of 66.67% for the classification task. Furthermore, we also compared the performance of GamaComet with an existing free software tool for comet detection, OpenComet. The experiment results showed that GamaComet performed significantly better than OpenComet that could only give an accuracy of 11.5% for the comet detection task. Downstream analysis can be well conducted based on the detection and classification results from GamaComet. The analysis showed that patients owning comet assay images containing comets with class 3 and class 4 had a smoking habit, meaning they had more cells with a high level of DNA damage. Although GamaComet had a good performance, the performance for the classification task could still be improved. Therefore, it will be one of the future works for the research development of GamaComet.
High Consumption of Discretionary Beverages in Young Australian Adults Aged 18–30 Years: A Cross-Sectional Study
Authors: Duncan Alana , Rangan Anna , Ho Pui Ying , Chan Virginia , Davies Alyse J. , Wellard-Cole Lyndal , Allman-Farinelli Margaret
Published: August 2022

Despite health advice and campaigns, discretionary beverages remain a source of added sweeteners (sugar and intense sweeteners) and fat in the dietary intakes of many young adults. This study aimed to determine discretionary beverage consumption amongst 18 to 30-year-olds residing in New South Wales, Australia. Data were collected in 2017/2018 during the MYMeals study in which 1044 participants recorded their food and beverage consumption over a three-day period, using the purpose-designed Eat and Track (EaT) app. Discretionary beverages included all water-based and milk-based drinks with added sugar, intense sweeteners or fats and excluded alcoholic beverages. Descriptive statistics were used to analyse the proportion of consumers for different types of beverages, and contribution to overall energy and nutrient intakes. ANCOVA analyses compared the energy and nutrient intakes of consumers and non-consumers, adjusted for gender and age group. Sixty-two percent of participants with complete data (n = 1001) were classified as consumers of discretionary beverages. The most consumed beverages were soft drinks (39.0%) and flavoured tea/coffee (23.1%). The greatest proportion of nutrients contributed by discretionary beverages was total sugars (27.2% of total per consumers). In comparison to non-consumers, consumers of discretionary beverages had higher mean daily intakes of energy (kJ) (8736 versus 7294), and higher percentage energy (%E) from total sugars (16.5 versus 13.3) (p < 0.001) and saturated fat (12.5 versus 12.0) (p < 0.05) but lower protein (18.5 versus 20.5) (p < 0.001). The consumption of non-alcoholic discretionary beverages continues to be a source of significant energy and total sugars among young adults.
Persistent Charging System for Crazyflie Platform
Authors: Nguyen Ngoc Phi , Lee Bo Hye , Xuan-Mung Nguyen , Ha Le Nhu Ngoc Thanh , Jeong Han Sol , Lee Seok Tae , Hong Sung Kyung
Published: August 2022

Nowadays, quadcopters are used widely in different applications, but their flight time is limited during operation. In this paper, a precision landing method based on a Kalman filter is proposed for an autonomous indoor persistent drone system that aims to increase the flight time of quadcopters. First, a local positioning system is used for tracking performance. Second, instead of using this local positioning system during the landing phase, a multi-ranger sensor is proposed to increase the accuracy of horizontal errors. Next, based on the relative position provided by the multi-ranger sensor, a Kalman filter technique is applied to estimate the relative velocity of the system, which is then applied to control the position of the quadcopter during the landing phase. Finally, a charging state machine law is proposed to charge the battery of three quadcopters sequentially. The experimental results demonstrate that the proposed concept based on a multi-ranger sensor can enhance the accuracy of the landing phase in comparison with the conventional method.
Quantumness and Dequantumness Power of Quantum Channels
Authors: Song Hongting , Li Nan
Published: August 2022

Focusing on the dynamics of quantumness in ensembles, we propose a framework to qualitatively and quantitatively characterize quantum channels from the perspective of the amount of quantumness in ensembles that a quantum channel can induce or reduce. Along this line, the quantumness power and dequantumness power are introduced. In particular, once a quantum dynamics described by time-varying quantum channels reduces the quantumness for any input ensembles all the time, we call it a completely dequantumness channel, whose relationship with Markovianity is analyzed through several examples.
How Can Foreign Direct Investment Trigger Green Growth? The Mediating and Moderating Role of the Energy Transition
Authors: Caetano Rafaela Vital , Marques António Cardoso , Afonso Tiago Lopes
Published: August 2022

Developed countries have the resources/technologies to combat pollution even at the expense of economic growth. Developing countries are in a less fortunate position. Foreign Direct Investment (FDI) can be a tool for developed countries to transfer polluting industries, which increases pollution in host countries. However, as FDI might reduce pollution by reducing energy consumption, the pollutant effect might also be influenced. Therefore, this study examines the mediating effect of energy consumption on the impact of FDI on pollution, and the role of FDI to attain Green Growth via energy transition. The main findings indicate that FDI impacts pollution through energy consumption and that energy transition plays a vital role in reducing this mediating effect. Developing countries appear to use non-renewable energy to fill energy demand. In both groups of countries, FDI is a driver of Green Growth. However, developing countries require larger efforts to achieve Green Growth through the energy transition.
Research on the Introduction of a Robotic Process Automation (RPA) System in Small Accounting Firms in Taiwan
Authors: Hsiung Hsing-Hua , Wang Juo-Lien
Published: August 2022

This study explores the characteristics that influence the success factors of accounting firms in the introduction of a RPA system. The RPA system success factors used in this paper are based on the measurements of the Technology Acceptance Model (TAM) and the Information System Success Model (ISS). In this paper, a questionnaire survey method was used, and a total of 140 questionnaires were distributed to 70 small accounting firms in Taiwan. The results of this study showed that three characteristic factors—male, higher familiarity with the system and high CEO support—were significantly positively correlated with the success factors of the RPA system in accounting firms. Due to resource constraints, small- and medium-sized accounting firms have gone through a more difficult journey of digital transformation than the Big Four. Therefore, research on digital transformation with small- and medium-sized firms as samples is a topic worthy of attention. The research conclusions of this paper can provide a reference for the accounting digital transformation strategies of the accounting industry and educational institutions.
Enhancing Home Education in Italian Context: Teachers’ Perception of a Hybrid Inclusive Classroom
Authors: Benigno Vincenza , Caruso Giovanni Paolo , Dagnino Francesca Maria , Dalla Mutta Edoardo , Fante Chiara
Published: August 2022

Homebound Education in Italy is based on one-to-one teaching conducted by teachers who visit the sick student at home. This modality does not include interactions between homebound students and classmates, thus inhibiting the educational aspect of peer relationships. With the outbreak of the COVID-19 pandemic and the consequent dispositions of the Ministry of Education regarding remote education and integrated digital didactic (DID), new opportunities became available for homebound students. In this research, we applied and tested in the context of homebound education a model of hybrid inclusive classroom developed in a previous project ((TRIS), addressing homebound students that could not permanently attend school and followed lessons remotely. The present study involved two homebound students affected by chronic and acute diseases. During the 2020/21 school year, the model was proposed to the two school councils (22 teachers in all) and the trial monitored; at the end of the school year, semi-structured interviews were conducted with teachers and transcriptions analyzed using a deductive approach based on the model. Results confirm some findings of the TRIS project, while new aspects emerged linked to the specific context. Overall, the implemented hybrid classroom seems to have positively affected both the learnings and students’ inclusion.
Psychometric Validation of the Portuguese Version of the Modern Homonegativity Scale among Portuguese College Students
Authors: García-Berbén Ana Belén , Pereira Henrique , Lara-Garrido Adrián S. , Álvarez-Bernardo Gloria , Esgalhado Graça
Published: August 2022

The studies focused on analysing attitudes toward homosexuality show that the manifestation of homonegativity has evolved into more modern forms. We therefore propose using instruments that capture subtle aspects in discrimination against gay and lesbian people. The objective of this study is twofold. First, we aim to adapt and validate the Portuguese version of the Modern Homonegativity Scale. Second, we set out to analyse the modern homonegativity shown by Portuguese university students. The scale includes two parallel subscales (MHS-Gay Men and MHS-Lesbian Women), each with 12 items. Six hundred and forty-one Portuguese college students between 18 and 27 years of age participated in the study (Mage = 21.23; SD = 1.88). The results demonstrate the unidimensionality of the scale and a high degree of internal consistency, along with satisfactory fit indices. Those people who identified as male and heterosexual showed greater modern homonegativity. We conclude that the Portuguese version of the MHS is valid and reliable for evaluating modern homonegativity in Portugal.
Modeling and Simulation Analysis of Speed-Regulating Valve Flow Fluctuations under Differential Pressure Steps
Authors: Li Jianying , Zhang Qizheng , Zhang Yang , Peng Chen , Yang Yu
Published: August 2022

As an important component of the movement speed adjustment components of the executive elements in a hydraulic system, the speed-control valve should have good dynamic response ability and dynamic and static flow stability. In the working state, due to the fluctuation of oil supply pressure, load variation, valve spool movement, force variation, etc., the pressure difference between the inlet and outlet of the speed-regulation valve is prone to a step response. Due to the nonlinear relationship between the flow and pressure, the phenomenon of instantaneous fluctuations in the controlled flow occurs, which in turn leads to the unstable phenomenon of the speed of the execution element. Based on the working principle of the internal core components of a speed-control valve with a pressure compensation function, this study establishes a dynamic mathematical model of the valve, which is analyzed using AMEsim software. Aiming at the key problem of sudden changes in the differential pressure steps, the characteristics of a speed-regulation valve are analyzed in relation to the differential pressure increment, spring stiffness, spring preload, and initial opening of the pressure compensator and its viscous damping coefficient. The influence of the above five variables on the flow fluctuation is studied and a reasonable parameter adjustment range is given. The simulation and analysis results provide strong theoretical support for the performance and parameter optimization configuration of the speed-regulation valve.
An Intelligent Identification Approach Using VMD-CMDE and PSO-DBN for Bearing Faults
Authors: Yang Erbin , Wang Yingchao , Wang Peng , Guan Zheming , Deng Wu
Published: August 2022

In order to improve the fault diagnosis accuracy of bearings, an intelligent fault diagnosis method based on Variational Mode Decomposition (VMD), Composite Multi-scale Dispersion Entropy (CMDE), and Deep Belief Network (DBN) with Particle Swarm Optimization (PSO) algorithm—namely VMD-CMDE-PSO-DBN—is proposed in this paper. The number of modal components decomposed by VMD is determined by the observation center frequency, reconstructed according to the kurtosis, and the composite multi-scale dispersion entropy of the reconstructed signal is calculated to form the training samples and test samples of pattern recognition. Considering that the artificial setting of DBN node parameters cannot achieve the best recognition rate, PSO is used to optimize the parameters of DBN model, and the optimized DBN model is used to identify faults. Through experimental comparison and analysis, we propose that the VMD-CMDE-PSO-DBN method has certain application value in intelligent fault diagnosis.
MfdcModel: A Novel Classification Model for Classification of Benign and Malignant Breast Tumors in Ultrasound Images
Authors: Liu Wei , Guo Minghui , Liu Peizhong , Du Yongzhao
Published: August 2022

Automatic classification of benign and malignant breast ultrasound images is an important and challenging task to improve the efficiency and accuracy of clinical diagnosis of breast tumors and reduce the rate of missed and misdiagnosis. The task often requires a large amount of data to train. However, it is difficult to obtain medical images, which contradicts the large amount of data needed to obtain good diagnostic models for training. In this paper, a novel classification model for the classification of breast tumors is proposed to improve the performance of diagnosis models trained by small datasets. The method integrates three features from medical features extracted from segmented images, features selected from the pre-trained ResNet101 output by principal component analysis (PCA), and texture features. Among the medical features that are used to train the naive Bayes (NB) classifier, and the PCA-selected features are used to train the support vector machine (SVM) classifier. Subsequently, the final results of boosting are obtained by weighting the classifiers. A five-fold cross-validation experiment yields an average accuracy of 89.17%, an average precision of 90.00%, and an average AUC value of 0.95. According to the experimental results, the proposed method has better classification accuracy compared to the accuracy obtained by other models trained on only small datasets. This approach can serve as a reliable second opinion for radiologists, and it can also provide useful advice for junior radiologists who do not have sufficient clinical experience.
Novel Scratch Programming Blocks for Web Scraping
Authors: Park Youngki , Shin Youhyun
Published: August 2022

Although Scratch is the most widely used block-based educational programming language, it is not easy for students to create various types of Scratch programs based on real-life data because it does not provide web scraping capabilities. In this paper, we present novel Scratch blocks for web scraping. Using these blocks, students can not only scrape the contents of HTML elements in a web page by using CSS selectors but also automate their keyboard and mouse in a number of ways, such as by using XPaths, the coordinates of the mouse, input strings, keys, or hot keys. We also present file access blocks that allow students to easily store and retrieve the scraped data in the form of key–value pairs. We conducted two lectures for a total of 15 primary/secondary school (K-12) teachers, allowing them to make ten web scraping example applications. As a result of a survey of the teachers, the proposed web scraping blocks achieved high scores for all evaluation measures.
Energy Efficient Underlaid D2D Communication for 5G Applications
Authors: Nagapuri Lalitha , Prabu A. V. , Penchala Suresh , Salah Bashir , Saleem Waqas , Kumar G. Sateesh , Aziz Amira Sayed A.
Published: August 2022

With the increasing importance of reduced-energy wireless communication in 5G networks, maximizing the energy efficiency of device-to-device (D2D) communication has gained a lot of research interest. But so far, the methods have only been able to address the single-cell scenario. This paper proposes an algorithm to improve the non-convex problem in underlaid D2D communication for multiple band scenarios. In 5G wireless communications, device to device is one of the methods used for achieving better energy efficiency. It also reduces the throughput latency. The optimization problem is formulated with a derivative algorithm and proposed modified derivative algorithm. Both the algorithms are compared, and this comparison shows that the modified derivative algorithm is more efficient than the derivative algorithm.
Secure Performance Analysis of Aerial RIS-NOMA-Aided Systems: Deep Neural Network Approach
Authors: Dang Huu-Phuc , Van Nguyen Minh-Sang , Do Dinh-Thuan , Nguyen Minh-Hoa , Pham Minh-Triet , Kim Anh-Tuan
Published: August 2022

The next generation of wireless systems has benefits in terms of spectrum and energy inefficiencies by exploiting two promising techniques including Non-Orthogonal Multiple Access (NOMA) and Reconfigurable Intelligent Surfaces (RIS). The scenario of two legitimate users existing together with an eavesdropper is worth examining in terms of secure matter while enabling machine learning tools at the base station for expected improvement. The base station deals with a highly complicated algorithm to adjust parameters against the attack of eavesdroppers and to better improve the secure performance of mobile users. This paper suggests a better solution to allow the base station to predict performance at destinations to adjust necessary parameters such as power allocation coefficients properly. To this end, we propose a deep neural network (DNN)-based approach which also leverages the benefits of aerial RIS to achieve predictable performance and significant secure performance improvement could be enhanced. We first derive the formulations for security outage probability (SOP) in closed-form expressions and analyze the strictly positive secrecy capacity (SPSC), which are crucial performance metrics to determine how the systems are against the existence of eavesdroppers. Such eavesdroppers intend to overhear signal transmission dedicated to intended users and incur degraded system performance. The numerical simulations are expected to evaluate how the machine learning tool works with the traditional computation of system performance metrics which is able to be verified by comparing with the Monte-Carlo method. Our numerical simulations demonstrate that the design of a higher number of meta-surface elements at the RIS, as well as a higher signal-to-noise ratio (SNR) levels at the base station, are key parameters to achieving improved security performance for users. For detailed guidelines of the RIS-NOMA aided system, we provide a table of parameters samples resulting in secure performance as expected.
A Vehicle Lateral Motion Control Based on Tire Cornering Stiffness Estimation Using In-Wheel Motors
Authors: Park Giseo
Published: August 2022

In this paper, a study on vehicle lateral motion control using an in-wheel motor (IWM) based on tire cornering stiffness estimation is presented. The main purpose of this paper is to develop a lateral motion control that can be implemented considering practical issues in real-world vehicle applications such as driver comfort, high availability, and stable control accuracy. The proposed lateral motion controller for yaw rate tracking is intended to improve vehicle cornering agility. In this paper, we develop a model-based controller with a feedforward control term to accomplish this. In particular, a change in tire cornering stiffness according to the size of the tire slip angle is reflected to improve control accuracy. Finally, the Weighted Least Square (WLS) allocation method optimally distributes the IWM torque to each wheel. Simulation studies confirm that some evaluation factors are improved in terms of cornering performance compared to conventional control algorithms.
Statistical Channel Model and Systematic Random Linear Network Coding Based QoS Oriented and Energy Efficient UWSN Routing Protocol
Authors: Basavaraju Pramod Halebeedu , Lokesh Gururaj Harinahalli , Mohan Gowtham , Jhanjhi Noor Zaman , Flammini Francesco
Published: August 2022

Considering the significance of an energy efficient, delay tolerant and reliable communication protocol for underwater acoustic wireless sensor network (UWSN), this paper proposes a novel systematic random linear network coding (SRLNC) based transmission system examined over a robust statistical UWSN channel model. The derived statistical channel model deals with both the small-scale fading primarily caused by scattering and small wavelength changes and large-scale fading introduced due to node dislocation in the underwater acoustic medium. The proposed SRLNC transmission-based routing approach has been applied over the proposed underwater acoustic (statistical) channel model, and respective performance assessment has been conducted in terms of throughput, energy efficiency, delay and computational complexity by varying network condition parameters. The contributions such as low coefficient vector and Galois filed, low redundant message requirements, computationally efficient pre-coding scheme, iterative buffer flush and enhanced FEC based decoding make the SRLNC based routing protocol sufficiently robust to enable reliable, energy-efficient and delay resilient communication over UWSN. The proposed SRLNC based UWSN routing protocol and its efficacy over dynamic channel conditions affirm that it can be a potential solution for QoS-oriented mission critical underwater communication purposes.
Built-In Functional Testing of Analog In-Memory Accelerators for Deep Neural Networks
Authors: Mishra  Abhishek Kumar , Das Anup Kumar , Kandasamy Nagarajan
Published: August 2022

The paper develops a methodology for the online built-in self-testing of deep neural network (DNN) accelerators to validate the correct operation with respect to their functional specifications. The DNN of interest is realized in the hardware to perform in-memory computing using non-volatile memory cells as computational units. Assuming a functional fault model, we develop methods to generate pseudorandom and structured test patterns to detect hardware faults. We also develop a test-sequencing strategy that combines these different classes of tests to achieve high fault coverage. The testing methodology is applied to a broad class of DNNs trained to classify images from the MNIST, Fashion-MNIST, and CIFAR-10 datasets. The goal is to expose hardware faults which may lead to the incorrect classification of images. We achieve an average fault coverage of 94% for these different architectures, some of which are large and complex.
Integrated Scheduling Algorithm of Two Workshops Based on Process End Time Driven and Processing Area Priority
Authors: Zhan Xiaojuan , Xie Zhiqiang , Yao Dengju
Published: August 2022

In order to effectively allocate processes of Aa single complex product to two workshops with the same equipment, an integrated scheduling algorithm of two workshops based on process end time driven and processing area priority is proposed. To optimize the total processing time, a process scheduling strategy based on process end time driven is proposed. By scheduling multiple devices at the same time, the parallelism of process execution is improved, in order to reduce the total processing time and improve the utilization rate of equipment. In order to optimize the process migration times between two workshops, a workshop assignment strategy based on process processing area priority is proposed, which comprehensively considers the influence of the immediate preceding process, neighbor process and friend process of the schedulable process on workshop assignment and makes the process in the same processing area be processed in the same workshop as much as possible, so as to reduce the process migration times. The example analysis shows that the proposed algorithm is superior to the existing two workshops integrated scheduling algorithms in terms of reducing the total processing time, reducing the process migration times, and improving the utilization rate of equipment.
Functionally Graded Piezoelectric Energy Harvester: A Numerical Study
Authors: Kumar Anuruddh , Ansari Mohamed Nainar Mohamed , Ibrahim Sobhy M. , Thomas Paramanandam , Vaish Rahul
Published: August 2022

The performance of linear energy harvesters is primarily confined to a very narrow operating frequency bandwidth around its natural frequency. Even a slight deviation of the excitation frequency from the fundamental frequency of the system tremendously reduces the harvester’s performance. In order to minimize this shortcoming, the presented study considers the piezoelectric energy harvester with magnets introducing non-linearity in the system. The simple harmonic balance method is used to solve the non-linearity and for computing the voltage output and power in the frequency domain. In addition, the study also incorporates the functionally graded piezoelectric materials because of their superior properties. The distance between magnets (d0) has been varied from 0.4 mm to 10 mm along with grading index (n) in the range of 0 to ∞. Finally, voltage and power across the resistance were computed. The effective harvesting frequency range for d0 = 0.4 mm and n = 1 is observed in the range of 20 Hz to 85 Hz, while it was only between 35 Hz and 65 Hz for d0 = 10 mm, yielding a 216% increase in the frequency bandwidth. Under different case studies, the peak output power varied from 2 mW (d0 = 0.4 mm and n = ∞) to 6 mW (d0 = 10 mm and n = 0).
Efficient Simulation of Sandbody Architecture Using Probability Simulation—A Case Study in Cretaceous Condensate Gas Reservoir in Yakela Area, Tahe Oilfield, China
Authors: Chen Shuyang , Pan Lin , Wang Xiao , Liang Honggang , Dong Tian
Published: August 2022

The Cretaceous condensate gas reservoir in Yakela is in a fan delta system in which the river channel swings frequently and the contact relationships between sandbodies are complicated both vertically and horizontally. Therefore, making the sandbody architecture clear is becoming the most urgent demand in locating the remaining oil. However, conventional well correlations and fine interpretation do not apply in this area due to the large-spacing of wells and the lack of reliable seismic data. In this paper, we analyzed the vertical characteristics of sandbody architecture including the type and thickness of architectural elements and their contact relationships based on well data, then simulated the lateral and planar distribution probabilities via a database containing a large number of dimension parameters from relevant architectural elements using Monte Carlo simulation. This simulation provides reasonable and efficient estimation of inter-well sandbody distribution. The workflow and data we present can be applied to similar clastic reservoir modeling and simulations, especially for areas with insufficient well and seismic data.
Effects of Compression Ratios on Combustion and Emission Characteristics of SI Engine Fueled with Hydrogen-Enriched Biogas Mixture
Authors: Nguyen Quang Trung , Le Minh Duc
Published: August 2022

The effects of hydrogen-enriched biogas on combustion and emissions of a dual-fuel spark-ignition engine with different hydrogen concentration ratios were studied numerically. A 1-cylinder spark ignition was used to perform a numerical simulation. To reveal the influence of the compression ratios on combustion and emissions of a gaseous engine, the crankshaft of the engine was modified to generate different compression ratios of 8.5, 9.0, 9.4, 10.0, and 10.4. The biogas contained 60 and 40% methane (CH4) and carbon dioxide (CO2), respectively, while the hydrogen fractions used to enrich biogas were 10, 20, and 30% of the mixture by volume. The ignition timing is fixed at 350 CA°. The results indicate that the in-cylinder pressure, combustion temperature, and combustion burning speed increase gradually with increasing hydrogen concentration due to the combustion characteristics of hydrogen in blends. As increasing the compression ratio, NOx emissions increase proportionally, while CO2 emissions decrease gradually. Almost no combustion process occurs as operating the compression ratio below 8.5 when using pure biogas. However, adding 20% of hydrogen fraction could improve the combustion process significantly even at a low compression ratio.
Biogas from Nitrogen-Rich Biomass as an Alternative to Animal Manure Co-Substrate in Anaerobic Co-Digestion Processes
Authors: Isabel Parralejo Alcobendas Ana , Luis Royano Barroso , Juan Cabanillas Patilla , Jerónimo González Cortés
Published: August 2022

Nitrogen-rich biomass can be suitable for utilization as a substrate in anaerobic co-digestion (AC-D) instead of animal manure. This biomass combined with other substrates could replace animal waste in certain cases in which animal waste cannot be used to obtain methane gas. Methane is the majority component of the biogas produced in AC-D used as an energy source. In this research, a comparative study has been developed between leguminous plant biomass and pig manure in AC-D in a semicontinuous regime at different Organic Load Rate (OLR) values (1.2–1.8 g VS LD−1 d−1). The most elevated methane yield (494 NL CH4 kg VS−1) belongs to assays developed with nitrogen-rich biomass at 1.4 g VS LD−1 d−1. Methane-yield results of nitrogen-rich biomass are higher than pig manure results for all OLR studied values. The digestate obtained in the AC-D is a fertilizer of interest due to its nitrogen content and ability to save energy by replacing mineral fertilizers.
Electricity Spot Price Modeling and Forecasting in European Markets
Authors: Tehrani Shadi , Juan Jesús , Caro Eduardo
Published: August 2022

In many competitive electricity markets around the world, the dynamic behavior of hourly electricity prices is subject to significant uncertainty and volatility due to electricity demand, availability of generation sources, fuel costs, and power plant availability. This work is devoted to describing and comparing the dynamics of electricity prices for some markets in Europe, selecting the five countries representing the largest economies in Western Europe (France, Germany, Italy, Spain, and the United Kingdom). Additionally, Denmark is included in the study to assess whether the size of the country is a determinant of price behavior. The six datasets of hourly price series, which exhibits a strong daily seasonality, are modelled using the most relevant well-known statistical models for time series analysis: ARIMA models and different versions of GARCH models. The comparison of the estimated models’ parameters, the analysis of outliers’ rate of appearance and the evaluation of out-of-sample one-day-ahead forecast let us draw some insightful similarities and dissimilarities between the analyzed countries.
Localized Heating to Improve the Thermal Efficiency of Membrane Distillation Systems
Authors: Criscuoli Alessandra , Carnevale Maria Concetta
Published: August 2022

Membrane distillation (MD) is a thermal-based membrane operation with high potential for the treatment of aqueous streams. However, its implementation is limited and only few examples of MD pilots can be found in desalination. One of the reasons behind this is that MD requires thermal energy for promoting the evaporation of water, which implies higher energy consumption with respect to pressure-driven membrane operations, like reverse osmosis (RO). Recently, among the different methods investigated to improve the thermal efficiency of MD, attempts for obtaining a localized heating of the feed, close to the membrane surface, were carried out. This work reviews experimental activities on the topic, dealing with both modified membranes, used under solar irradiation or coupled to an electric source, and specifically designed heated modules. The main results are reported and points of action for further optimization are identified. In particular, although at an early stage, this type of approach led to improvements in membrane flux and to a reduction of energy consumption with respect to conventional MD. Nevertheless, long tests to ensure a stable performance time, the optimization of operating conditions, the development of methods to control fouling issues, and the identification of the best module design, together with the scale-up of membranes/modules developed, represent the main research efforts needed for future implementation of localized heating strategy.
Global Warming Potential of New Gaseous Refrigerants Used in Chillers in HVAC Systems
Authors: Szczęśniak Sylwia , Stefaniak Łukasz
Published: August 2022

Due to the global warming and resulting problems, attention has been paid to greenhouse gases released into the atmosphere since the 1980s and 1990s. For this reason, the Montreal Protocol and the Kyoto Protocol have tightened regulations on the use of gaseous refrigerants in both HVAC systems and industrial refrigeration. Gradually, new generations of gaseous refrigerants, that theoretically have much less negative environmental impact than their predecessors, are introduced into the market. The key parameter describing environmental impact is the GWP index, which is most often defined on a time horizon of 100 years. The long-term use of new generations of gaseous refrigerants in HVAC systems reduces CO2 emissions into the atmosphere; however, given that new generation gases often have a short lifetime, it seems that the adopted assessment may not be applicable. The aim of the article was to show how emissions of CO2 equivalent to the atmosphere differs in the short and long time horizon. The article presents the results of calculations of equivalent CO2 emissions to the atmosphere caused by the operation of compressor cooling devices used in HVAC systems, where cooling is done with the use of water or a water-glycol solution. The analysis was carried out for 28 commonly used devices on the world market. The analyzed devices work with refrigerants: R513A, R454B, R290, R1234ze, R32, R134a, R410A. The equivalent emissions values for GWP 100 and GWP 20 were analyzed in relation to the unit power of the devices depends on refrigerant mass and number of fans. The study showed that in the case of new generation refrigerants with a very short lifetime, the use of GWP 100 indicators is misleading and does not fully reflect the effects of environmental impact, especially in the area of refrigeration equipment application. The article shows that the unit value of the cooling load related to the number of fans or the unit would be helpful in assessing the environmental impact of a cooling device.
Measurement of Forces in a Mining Plough Chain during Operation
Authors: Szot Mariusz , Herezy Łukasz , Szade Piotr , Frąc Witold
Published: August 2022

Pulling chains are one of the most critical components of a plough longwall system. Their failure is hard to predict and results in costly and lengthy downtimes. Measurements of the actual forces acting on a plough chain are needed as they can provide important information about the nature of their operation and contribute to preventing their wear and failure by supplementing data gathered by a plough control station. In this work, a strain gauge system for the measurement of the forces in a plough chain is designed, manufactured and tested under real conditions for the first time. Constructed using a cylindrical strain gauge and widely available electronic components, the finished sensor was calibrated on a testing machine and used to measure forces in a 42 × 137 plough chain at LW Bogdanka. Measurements conducted under real conditions show that forces in the chain during steady ploughing does not exceed 400 kN. However, the forces peaked to up to 728.6 kN at certain points in the operation. The implications of such events are discussed. The device allowed for successful measurements of forces in difficult conditions of a mining plough longwall and provides new information on their course and characteristics, which is very valuable for plough operators and chain-life predictions. This work suggests the further integration of force sensors into longwall plough system.
Efficient Identification of Jiles–Atherton Model Parameters Using Space-Filling Designs and Genetic Algorithms
Authors: Khemani Varun , Azarian Michael H. , Pecht Michael G.
Published: August 2022

The Jiles–Atherton model is widespread in the hysteresis description of ferromagnetic, ferroelectric, magneto strictive, and piezoelectric materials. However, the determination of model parameters is not straightforward because the model involves numerical integration and the solving of ordinary differential equations, both of which are error prone. As a result, stochastic optimization techniques have been used to explore the vast ranges of these parameters in an effort to identify the parameter values that minimize the error differential between experimental and modelled hysteresis curves. Because of the time-consuming nature of these optimization techniques, this paper explores the design space of the parameters using a space-filling design. This design provides a narrower range of parameters to look at with optimization algorithms, thereby reducing the time required to identify the optimal Jiles–Atherton model parameters. This procedure can also be carried out without using expensive hysteresis measurement devices, provided the desired transformer’s secondary voltage is known.
Is Monkeypox Being Underdiagnosed in Countries with More Stigmatizing Attitudes towards Men Who Have Sex with Men? A Simple Ecological Analysis
Authors: Kenyon Chris
Published: August 2022

It is not known why the recent outbreak of monkeypox (MPX) has been more extensive in certain European countries than others. Previous studies have found that European countries with more stigmatizing attitudes to homosexuality have more undiagnosed HIV infections in men who have sex with men (MSM). We hypothesized that MPX in MSM may be underdiagnosed in European countries with more stigmatizing attitudes to homosexuality and less access to sexually transmitted infection (STI) testing for MSM. To test this hypothesis, we used Spearman’s correlation to assess if the national incidence of MPX in European countries was negatively associated with the intensity of screening for STIs and a composite indicator of Lesbian Gay Bisexual Transgender Intersex (LGBTI) rights (the Rainbow Index). We found that the national cumulative incidence of MPX was positively correlated with the intensity of chlamydia/gonorrhoea screening (rho 0.68, p-value < 0.0001), syphilis screening (rho 0.62, p-value < 0.0001), and the Rainbow Index (rho 0.65, p-value < 0.0001). Our analysis thus suggests caution is required in interpreting the relatively lower incidence of MPX reported from several Eastern European countries. A key limitation of this analysis is that the incidence of MPX was calculated in the whole population and not limited to the MSM population.