bluuebunny/crossref_metadata_2025 huggingface dataset:
An Integrated Quality Index of High-Rise Residential Buildings for All Lifecycle Stages of a Construction Facility
Authors: Lapidus Azariy , Topchiy Dmitriy , Kuzmina Tatyana , Shesterikova Yana , Bidov Tembot
Published: February 2023

The study focuses on developing a methodology for calculating an integrated quality index of administrative and engineering solutions that affect the safety of multi-storey residential buildings at each stage of their lifecycle. This method can be used to design a tool for assessing the integrated quality of multi-storey residential buildings at each stage of production. Advanced quality assessment methods were analyzed within the framework of this study. The analysis led to the conclusion that the most relevant problem is the lack of an integrated approach to the quality assessment of multi-storey residential buildings. Having studied an extensive number of research works, the authors identified the main parameters that affect the quality of a construction facility. The aims of the study were to (1) develop an integrated quality index for multi-storey residential buildings (this index is to be applicable as early as at the stage of pre-construction arrangements); (2) develop methods for calculating and evaluating the integrated quality index for high-rise residential buildings at the stage of construction arrangements; (3) improve the efficiency of administrative and engineering solutions; and (4) devise a mathematical model that will identify the numerical value of the proposed multi-factor criteria. The research results show the feasibility and expediency of introducing this methodology in residential construction as it allows for a comprehensive measurement and quality assessment of high-rise residential buildings.
Experimental Study on Interface Frictional Characteristics between Sand and Steel Pipe Jacking
Authors: Mei Zhe , Xiao Anfeng , Mei Jiahao , Hu Jinqiu , Zhang Peng
Published: February 2023

In order to study the variation law of shear frictional characteristics of the steel pipe jacking and sand interface under different working conditions, the shear stress–strain curve between five different particle sizes of sand and steel pipe jacking under different normal stress and slurry lubrication conditions was measured by using a direct shear device, and the internal friction angle, friction coefficient and cohesion of the pipe–soil interface were calculated by regression analysis. The test results show that the shear stress between sand and steel pipe jacking decreases with the increase of the average particle size of the sand, and the strain-softening phenomenon occurs. The normal stress does not change the trend of the shear stress–strain curve at the pipe–soil interface, and the peak and residual values of the shear stress increase with the increase of the normal stress. The peak and residual values of the shear stress at the pipe–soil interface under the slurry lubrication condition are smaller than those under the no slurry lubrication condition. The peak shear stress between the pipe and soil under the lubricated slurry condition decreases by about 20%. The internal friction angle and friction coefficient of the pipe–soil interface decrease with the increase of the particle size, and there is no obvious pattern between the cohesion quantity relationship and the average particle size.
Revamping Fluid Catalytic Cracking Unit, and Optimizing Catalyst to Process Heavier Feeds
Authors: Stratiev Dicho , Ivanov Mihail , Chavdarov Ivan , Argirov Georgi , Strovegli Giulia
Published: February 2023

H-Oil gas oils have a higher density and higher nitrogen content, and consequently much lower reactivity than straight-run vacuum gas oils during fluid catalytic cracking (FCC). The conversion of H-Oil gas oils observed in a laboratory catalytic cracking unit at constant operating conditions showed a 20 wt.% lower conversion rate than straight-run hydrotreated vacuum gas oil. Thus, a revamp of commercial FCC units, and the selection of a higher activity catalyst with lower coke selectivity is needed to provide the stable trouble-free operation of the unit. The performed revamp of the commercial FCC unit allowed a stable operation at a higher throughput. It also allowed an increased riser outlet temperature from 532 to 550 °C; increased maximum allowable regenerator temperature from 705 to 730 °C; decreased afterburning from 12 to 6 °C; decreased NOx emissions in the flue gas from 250 to 160 mg/Nm3; improved catalyst regeneration; decreased catalyst losses to 0.0142 kg/t feed; and improved catalyst circulation at a higher throughput. It was confirmed in the commercial FCC unit that the H-Oil light vacuum gas oil is the least reactive H-Oil gas oil during catalytic cracking.
Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
Authors: Han Deshuai , Cai Yanping , Chen WenJie , Cui Zhigao , Li Aihua
Published: February 2023

Self-adaptive software (SAS) is gaining in popularity as it can handle dynamic changes in the operational context or in itself. Time behaviors are of vital importance for SAS systems, as the self-adaptation loops bring in additional overhead time. However, early modeling and quantitative analysis of time behaviors for the SAS systems is challenging, especially under uncertainty environments. To tackle this problem, this paper proposed an approach called Timed-SAS to define, describe, analyze, and optimize the time behaviors within the SAS systems. Concretely, Timed-SAS: (1) provides a systematic definition on the deterministic time constraints, the uncertainty delay time constraints, and the time-based evaluation metrics for the SAS systems; (2) creates a set of formal modeling templates for the self-adaptation processes, the time behaviors and the uncertainty environment to consolidate design knowledge for reuse; and (3) provides a set of statistical model checking-based quantitative analysis templates to analyze and verify the self-adaptation properties and the time properties under uncertainty. To validate its effectiveness, we presented an example application and a subject-based experiment. The results demonstrated that the Timed-SAS approach can effectively reduce modeling and verification difficulties of the time behaviors, and can help to optimize the self-adaptation logic.
Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
Authors: Li Yiran , Zhao Dong , Ma Xueyi , Zhang Jianzhong , Zhao Jian
Published: February 2023

In full-field 3D displacement measurement, stereo digital image correlation (Stereo-DIC) has strong capabilities. However, as a result of difficulties with stereo camera calibration and surface merging, 360-deg panoramic displacement measurements remain a challenge. This paper proposes a panoramic displacement field measurement method in order to accurately measure the shape and panoramic displacement field of complex shaped objects with natural textures. The proposed method is based on the robust subset-based DIC algorithm and the well-known Zhang’s calibration method to reconstruct the 3D shape and estimate the full-field displacements of a complex surface from multi-view stereo camera pairs. The method is used in the determination of the scale factor of the 3D reconstructed surface and the stitching of multiple 3D reconstructed surfaces with the aid of the laser point cloud data of the object under test. Based on a discussion of the challenges faced by panoramic DIC, this paper details the proposed solution and describes the specific algorithms implemented. The paper tests the performance of the proposed method using an experimental system with a 360-deg six camera setup. The system was evaluated by measuring the rigid body motion of a cylindrical log sample with known 3D point cloud data. The results confirm that the proposed method is able to accurately measure the panoramic shape and full-field displacement of objects with complex morphologies.
Decision Framework for Predictive Maintenance Method Selection
Authors: Tiddens Wieger , Braaksma Jan , Tinga Tiedo
Published: February 2023

Many asset owners and maintainers have the ambition to better predict the future state of their equipment to make timely and better-informed maintenance decisions. Although many methods to support high-level maintenance policy selection are available, practitioners still often follow a costly trial-and-error process in selecting the most suitable predictive maintenance method. To address the lack of decision support in this process, this paper proposes a framework to support asset owners in selecting the optimal predictive maintenance method for their situation. The selection framework is developed using a design science process. After exploring common difficulties, a set of solutions is proposed for these identified problems, including a classification of the various maintenance methods, a guideline for defining the ambition level for the maintenance process, and a classification of the available data types. These elements are then integrated into a framework that assists practitioners in selecting the optimal maintenance approach. Finally, the proposed framework is successfully tested and demonstrated using four industrial case studies. It can be concluded that the proposed classifications of ambition levels, data types and types of predictive maintenance methods clarify and accelerate the complex selection process considerably.
Theoretical Investigation of Mesh Relationship and Mesh Stiffness of Internal Spur Gears with Tooth Wear
Authors: Wang Yanan , Li Keyuan , Qiao Baijie , Shen Zhixian , Chen Xuefeng
Published: February 2023

The internal gear is part of the planetary and epicyclic gear pairs in the transmission system of the helicopter. Gear tooth wear is one of the most usual gear failures. This paper establishes an analytical model to evaluate the influence of tooth wear on the mesh relationship. A new mesh relationship can be derived for internal spur gears with tooth wear by the proposed analytical model. Consequently, using the new mesh relationship, the two most important meshing excitations, mesh stiffness and unloaded static transmission error (USTE), are quantitatively evaluated for the internal gear with tooth wear. The results indicate that tooth wear mainly affects the meshing ranges of single-tooth and double-teeth in mesh stiffness, rather than its amplitude. Additionally, the amplitudes of USTE increase with the increasing wear depth. Finally, this study can offer a foundation for the dynamic modeling and fault diagnosis of internal spur gears with wear faults.
Electrochemotherapy with Bleomycin Supported by NIRF Imaging with Indocyanine Green (ICG)—In Vitro and In Vivo Case Study
Authors: Tunikowska Joanna , Rembiałkowska Nina , Michel Olga , Mączyńska Justyna , Antończyk Agnieszka , Prządka Przemysław , Kiełbowicz Zdzisław , Kulbacka Julita
Published: February 2023

Electrochemotherapy (ECT) with bleomycin has been effectively used in recent years to treat various skin tumors. Microsecond electric pulses significantly improve bleomycin (BLM) delivery and its anticancer potential. Up to now, we can determine electric field distribution in the targeted tissue, however, the distribution of the injected drug is still not well known. In this study, we propose the combination of indocyanine green (ICG) with bleomycin as a practical approach for ECT, enabling drug distribution control and detection. Normal skeletal muscle (L6) and fibrosarcoma (WEHI-164) cells were used for the viability evaluation by MTT assay after 24 and 72 h. Cells were exposed to the ESOPE protocol alone and in combination with drugs. Additionally, visualization of the uptake of ICG and ICG + BLM supported by electroporation was performed by confocal microscopy. The mast cell tumor (MCTs) was diagnosed in the feline case. The mixture of ICG + BLM was injected into the tumor, and ECT was performed under near-infrared fluorescence imaging (NIRF). The obtained results indicate the safety of the used procedure in vitro and in vivo. ICG does not affect ECT protocols in vitro. No significant cell viability decrease was noted only in the case of WEHI-164 cells post-ECT. Moreover, it does not adversely affect the procedure; in the case of in vivo surgery, it helps to control the drug distribution before and after ECT and identify the sentinel lymph node.
A Real-Time Hybrid Approach to Combat In-Browser Cryptojacking Malware
Authors: Khan Abbasi Muhammad Haris , Ullah Subhan , Ahmad Tahir , Buriro Attaullah
Published: February 2023

Cryptojacking is a type of computer piracy in which a hacker uses a victim’s computer resources, without their knowledge or consent, to mine for cryptocurrency. This is made possible by new memory-based cryptomining techniques and the growth of new web technologies such as WebAssembly, allowing mining to occur within a browser. Most of the research in the field of cryptojacking has focused on detection methods rather than prevention methods. Some of the detection methods proposed in the literature include using static and dynamic features of in-browser cryptojacking malware, along with machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and others. However, these methods can be effective in detecting known cryptojacking malware, but they may not be able to detect new or unknown variants. The existing prevention methods are shown to be effective only against web-assembly (WASM)-based cryptojacking malware and cannot handle mining service-providing scripts that use non-WASM modules. This paper proposes a novel hybrid approach for detecting and preventing web-based cryptojacking. The proposed approach performs the real-time detection and prevention of in-browser cryptojacking malware, using the blacklisting technique and statistical code analysis to identify unique features of non-WASM cryptojacking malware. The experimental results show positive performances in the ease of use and efficiency, with the detection accuracy improved from 97% to 99.6%. Moreover, the time required to prevent already known malware in real time can be decreased by 99.8%.
Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
Authors: Park Jooeun , Kim Soyeon , Park Yuri , Kim Tae-Hyun , Hwang Yuhoon
Published: February 2023

Aminated covalent organic polymer (ACOP) was synthesized through a catalyst-free Schiff base reaction involving terephthalaldehyde and melamine, and the prepared ACOP was used for the adsorption of anionic dyes. The prepared ACOP possessed a high specific surface area (582.07 m2/g) with an average pore size of 88.71 Å. Its point of zero charge was determined as pH 8.26. Anionic dye molecules, methyl orange (MO) and orange G (OG), were used to evaluate the dye adsorption efficiency of the prepared ACOP, and it was found that they were adsorbed rapidly on ACOP within 1 min. The maximum adsorption capacities (qm) of the prepared ACOP for MO and OG were 351.9 and 227.9 mg/g, respectively. Furthermore, the results of dye adsorption as a function of the initial pH and presence/absence of cationic dye (methylene blue; MB) revealed that dye adsorption on ACOP proceeded through charge–charge and π–π interactions. The presence of MB along with MO and OG enhanced the dye adsorption capacity because of the synergistic effect of the positively charged quaternized nitrogen atoms in the prepared ACOP. The dye adsorption mechanism was further investigated using Fourier transform infrared (FT-IR) analysis and X-ray photoelectron spectrometry (XPS). The ACOP adsorbent prepared herein using a facile catalyst-free reaction offers rapid adsorption with a high adsorption efficiency over a wide pH range and in the presence of cationic dye. For these reasons, it can be used for environmental remediation, especially in aqueous systems.
Craniometric Study and Anatomical Variations of Base of Skull in a Thai Population Associated with Clinical Implications
Authors: Thunyacharoen Siriwat , Mahakkanukrauh Pasuk
Published: February 2023

Knowledge of anatomical variations in the base of the skull and anatomical landmarks is crucial for clinical procedures by surgeons, ENT physicians, and radiologists. This study investigated morphometric and anatomical variations in the foramen magnum, occipital condyles, hypoglossal canals, and jugular foramina to improve knowledge of the base of the skull’s complex anatomy and consider the anatomical variations via a morphometric study. One hundred and sixty intact skulls were investigated. Morphometric measurements showed that the foramen magnum, occipital condyles, hypoglossal canals, and jugular foramina were all significantly larger in males than females and could be useful for sex determination. Increased awareness of morphological location and anatomical landmark variation can improve surgical proficiency.
Dynamic Mechanism of Dendrite Formation in Zhoukoudian, China
Authors: Liu Ningqiang , He Yinfeng , Xu Shengchao , Xiong Lei , Wei Yushuai , Li Jie , Li Peiyu , Gong Qingjie
Published: February 2023

Dendrite, a typical geological pattern, is developed along the bedding plane in limestone and dolomite strata in Jinzigou area, Zhoukoudian, Beijing, China, with a self-similar dendritic morphology. In order to reveal the dynamic mechanism of its formation, the diffusion-limited aggregation method, a model of fractal growth, was selected to simulate the microscopic dynamic mechanism of its formation. Take an L × L square lattice, with L = 200 lattices, 20,000 particles, and L = 250 lattices, 30,000 particles, for simulation. Obtain the simulation results of the dendritic pattern and the intermediate results with 8057 and 10,827 random particles released in the simulation process with 20,000 particles. The results show that the pattern is a typical fractal pattern formed in the process of fractal growth, with a fractal dimension of about 1.6. Its morphology is characterized by fractal geometry, and the dynamic mechanism of its formation is the fractal growth phenomenon generated by self-organizing criticality. The growth process is a nonlinear and non-equilibrium evolutionary process, which is dynamic and far from equilibrium. It is consistent with the fractal dimension of Zhoukoudian dendrite (1.52–1.78). Diffusion-limited aggregation (DLA) is a typical growth process in fractal growth. It leads to the growth of randomly branching structures that closely resemble various important systems in the earth sciences, providing a theoretical basis for revealing the nature of complex geological processes.
An Evaluation of Healthy Eating Scale for Patients with Pre-Diabetes Using Rasch Analysis
Authors: de Vent Kerry Anne , Do Nguyen Hoang Nguyen , Islam Fakir M. Amirul
Published: February 2023

Consumption of a healthy diet is required to manage blood glucose level in people with diabetes or pre-diabetes. The aim of this study was to test the internal validity of healthy eating habits assessed using a self-reported scale, the Rapid Eating Assessment for Participants (REAP) scale, in participants with pre-diabetes. The scale had 15 items with 4 categories: “Never”, “Rarely”, “Some days” and “Usually/most days”. Those items were administered to 119 participants from 2006 to 2008 in Victoria, Australia. Rasch analysis was applied using RUMM2030 software. Overall fit was adequate in the unaltered model (χ2 = 29.29, p = 0.500, person separation index (PSI) = 0.500), however, seven items had disordered thresholds which were remedied by collapsing “Rarely” with “Some days” (χ2 = 37.36, p = 0.166, psi = 0.550). Item 1 (“Eat 3 main meals”) remained disordered after collapsing. Two persons were removed due to their fit residuals being outside ±2.5, however, these deletions did not improve the fit, so it was decided to retain these persons in the scale. Overall, there was no significant differential item functioning (DIF) or local dependency. However, there was evidence of multidimensionality and a ceiling effect toward healthy eating in the person item threshold. The results from Rasch analysis showed that the current scale needs some improvement, such as addressing disordered thresholds, for measuring eating habits for people with pre-diabetes. A modified version with middle categories collapsed would provide a psychometrically more robust measure. Further study is needed to validate the scale as a screening tool for measuring healthy eating among people with pre-diabetes.
Influence of Technological Parameters on the Size of Benzocaine Particles in Ointments Formulated on Selected Bases
Authors: Bułaś Lucyna , Szulc-Musioł Beata , Siemiradzka Wioletta , Dolińska Barbara
Published: February 2023

Compounding formulations, including semi-solid medication forms, must meet criteria related to specific stability and quality, during a period of their use. In suspension-type ointments, one of the criteria for assessment of their correct manufacturing is particle size, which in the compounding preparation cannot exceed 90 µm. An appropriate level of particle disintegration can be achieved via a selection of technological parameters and qualitatively compatible excipients. In this study, benzocaine ointments were prepared using a levigation process. The time of its application on the particle size of API in suspension ointments was evaluated. In parallel, the effect of mixing parameters and the co-solvent used on the precipitation of active substance crystals in emulsion ointments during the storage of these formulations for 28 days was investigated. Forty suspension and emulsion ointments were prepared using selected ointment bases: Pentravan®, Lekobaza, Lekobaza LUX, Eucerin Ointment I, Nourivan™ Antiox, Fitalite™, containing 2% benzocaine. Based on the results of the stability test, four formulations were selected to study the release kinetics of benzocaine in vitro. These formulations were characterized by the rate of release consistent with the Higuchi model, and the fastest rate of release occurred from the Eucerin-based emulsion ointment.
A Novel Two-Stream Transformer-Based Framework for Multi-Modality Human Action Recognition
Authors: Shi Jing , Zhang Yuanyuan , Wang Weihang , Xing Bin , Hu Dasha , Chen Liangyin
Published: February 2023

Due to the great success of Vision Transformer (ViT) in image classification tasks, many pure Transformer architectures for human action recognition have been proposed. However, very few works have attempted to use Transformer to conduct bimodal action recognition, i.e., both skeleton and RGB modalities for action recognition. As proved in many previous works, RGB modality and skeleton modality are complementary to each other in human action recognition tasks. How to use both RGB and skeleton modalities for action recognition in a Transformer-based framework is a challenge. In this paper, we propose RGBSformer, a novel two-stream pure Transformer-based framework for human action recognition using both RGB and skeleton modalities. Using only RGB videos, we can acquire skeleton data and generate corresponding skeleton heatmaps. Then, we input skeleton heatmaps and RGB frames to Transformer at different temporal and spatial resolutions. Because the skeleton heatmaps are primary features compared to the original RGB frames, we use fewer attention layers in the skeleton stream. At the same time, two ways are proposed to fuse the information of two streams. Experiments demonstrate that the proposed framework achieves the state of the art on four benchmarks: three widely used datasets, Kinetics400, NTU RGB+D 60, and NTU RGB+D 120, and the fine-grained dataset FineGym99.
Selecting Features That Influence Vehicle Collisions in the Internet of Vehicles Based on a Multi-Objective Hybrid Bi-Directional NSGA-III
Authors: Almutairi Mubarak S. , Almutairi Khalid , Chiroma Haruna
Published: February 2023

The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions of relevant, irrelevant and redundant features. Therefore, feature selection to select only the significant features for developing vehicle collision detection alarm systems for deployment in the IoV edge is critical. However, previous studies on vehicle collision detection in the IoV have not conducted rigorous feature selection. Limited studies have mainly applied Pearson correlation coefficient (PCC) to select subset features influencing vehicle collision in the domain of IoV. However, PCC can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking, which, in turn, increases the chances of developing a model that will give a poor outcome. To close this gap, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III with a gain ratio and bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV. The proposed approach selected the minimal most significant subset features for developing a vehicle collision detection classifier with maximum accuracy for deployment in the IoV. A comparative study proves that the proposed approach performs better than the compared algorithms across hybrid-, wrapper- and filter-based feature selection methods within the family of the NSGA. Further, a comparative analysis with other evolutionary algorithms proves the superiority of the proposal. This study can help researchers in the future by avoiding the use of large-scale computing resources in acquiring data to develop vehicle collision alert systems in the IoV. This can be achieved since only the subset features discovered in this study are collected, as opposed to collecting large features, thus saving time and resources in the subsequent vehicle collision detection data collection in the IoV.
Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
Authors: Oura Daisuke , Sato Shinpe , Honma Yuto , Kuwajima Shiho , Sugimori Hiroyuki
Published: February 2023

Background: Chest X-ray (CXR) imaging is the most common examination; however, no automatic quality assurance (QA) system using deep learning (DL) has been established for CXR. This study aimed to construct a DL-based QA system and assess its usefulness. Method: Datasets were created using over 23,000 images from Chest-14 and clinical images. The QA system consisted of three classification models and one regression model. The classification method was used for the correction of image orientation, left–right reversal, and estimating the patient’s position, such as standing, sitting, and lying. The regression method was used for the correction of the image angle. ResNet-50, VGG-16, and the original convolutional neural network (CNN) were compared under five cross-fold evaluations. The overall accuracy of the QA system was tested using clinical images. The mean correction time of the QA system was measured. Result: ResNet-50 demonstrated higher performance in the classification. The original CNN was preferred in the regression. The orientation, angle, and left–right reversal of all images were fully corrected in all images. Moreover, patients’ positions were estimated with 96% accuracy. The mean correction time was approximately 0.4 s. Conclusion: The DL-based QA system quickly and accurately corrected CXR images.
An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine
Authors: Lu Jie , Guo Weidong , Liu Jinpei , Zhao Ruijie , Ding Yueyang , Shi Shaoshuai
Published: February 2023

The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding rock mass based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). The premise of the research is that the data acquired from digital drilling technology are divided into a training group and a test group; the training group continuously optimizes the algorithm for the particle swarm optimization least squares support vector machine, and then the test group is used for verification. Moreover, the fast searching abilities of the particle swarm significantly accelerate the computational power and computational accuracy of the least squares support vector machine, making it a high-speed analog search tool. Taking the Jiaozhou Bay undersea tunnel in China as an example, a comparison of the evaluation results of PSO-LSSVM and QGA-RBF (quantum genetic algorithm-radical basis function neural network) is undertaken. The results show that PSO-LSSVM matches well with the field-measured surrounding rock grade. Applying the method in an engineering context proves that it has good self-learning abilities, even when the sample size is small and the prediction accuracy is high; as such, it meets the engineering requirements. The technique has the advantages of small sample prediction, pattern recognition, and nonlinear prediction.
Design of a Hyper-Casual Futsal Mobile Game Using a Machine-Learned AI Agent-Player
Authors: An Hyeyoung , Kim Jungyoon
Published: February 2023

Mobile games continue to gain popularity, and their revenues are increasing accordingly. However, due to the inherent constraints of small screen sizes and restrictions of computing, it has been considered challenging to simulate the complex gameplay of soccer games. To this end, this paper aims to design and develop a simplified version of a five vs. five hyper-casual futsal game with only three player positions: goalkeeper, striker, and defender. It also tests a demo game to verify whether it is possible to implement an AI agent−player for each position to machine-learn and to run on a mobile device. A demo game with an AI agent−player was simulated using both PPO and SAC algorithms, and the feasibility and stability of the algorithms were compared. The results showed that each AI agent−player achieved the assigned objectives for each position and successfully machine-learned. When the algorithms were compared, the SAC algorithm showed a more stable state than the PPO algorithm when SAC directed the gameplay and interactive AI techniques. This paper shows the great potential of the application of machine-learned AI agent−players for soccer simulators on mobile platforms.
Investigation of a High-Temperature Heat Pump for Heating Purposes
Authors: Bellos Evangelos , Tsimpoukis Dimitrios , Lykas Panagiotis , Kitsopoulou Angeliki , Korres Dimitrios N. , Vrachopoulos Michail Gr. , Tzivanidis Christos
Published: February 2023

High-temperature heat pumps consist of a promising choice for substituting conventional boilers and producing hot water with temperature levels in the range of 70–80 °C. The use of high-temperature heat pumps makes it possible to exploit the existing radiators in existing buildings to achieve effective heating of the indoor space and keep thermal comfort at high standards. The goal of this work is the detailed investigation of a high-temperature heat pump, with the R1234ze(E) as the working medium for producing heating at 80 °C for space-heating purposes, in Greek climate conditions. The analysis was conducted at two locations, one in the south (Athens—Zone B) and one in the north (Thessaloniki—Zone C), in order to examine the performance of the study’s technology in the most representative locations of Greece. The thermodynamic analysis of the heat pump was performed with a developed model in Engineering Equation Solver by exploiting real data from the compressor manufacturer and by practically using a gray box model. The heating thermal loads were calculated with a dynamic analysis using TRNSYS software. According to the final results, the high-temperature heat pump was beneficial when compared to a conventional oil boiler from energy, financial, and environmental points of view. The net savings were found at EUR 6448 for Athens and EUR 13,161 for Thessaloniki; the simple payback period resulted in 8.03 years for Athens and 6.11 years for Thessaloniki, while the CO2 emissions avoidance was found at 35% for Athens and 32% for Thessaloniki. Last but not least, the lifetime CO2 emissions reduction was calculated at 20,882 kgCO2 for Athens and 35,278 kgCO2 for Thessaloniki.
Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification
Authors: Mostafa Ayman Mohamed , Aljasir Meeaad , Alruily Meshrif , Alsayat Ahmed , Ezz Mohamed
Published: February 2023

Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.
A Virtual Direct Current Control Method of LCL-DAB DC-DC Converters for Fast Transient Response and No Backflow Power
Authors: Li Mingxue , Li Zimeng , Zhao Yushun , Wang Zixiang , Zhang Chong , Feng Shuo , Yu Dongsheng
Published: February 2023

The LCL-type dual active bridge (LCL-DAB) DC-DC converter is a promising part for DC micro-grids due to its high voltage gain and low bridge current, but the issues of backflow power elimination and transient response optimization deserve attention in its operation. In this article, a virtual direct current control (VDCC) method of the LCL-DAB converter for fast transient response and no backflow power is proposed, which can eliminate the backflow power and improve the transient response against the input voltage and load disturbances. With dual-phase-shift (DPS) modulation scheme, the voltage-current characteristics are first analyzed using the phasor method. The small-signal mathematic model of the LCL-DAB converter is then established. The power characteristic is derived so the design regions of no backflow power can be graphed. On this basis, an appropriate outer phase shift ratio can be estimated to ensure a wide range of no backflow power operation. Moreover, a virtual voltage is generated to compensate in the control loop, thus the transient response against disturbances of the LCL-DAB converter can be improved under no backflow power. Simulation and prototype experimental results are presented to verify the feasibility of the proposed VDCC method.
SFCWGAN-BiTCN with Sequential Features for Malware Detection
Authors: Xuan Bona , Li Jin , Song Yafei
Published: February 2023

In the field of adversarial attacks, the generative adversarial network (GAN) has shown better performance. There have been few studies applying it to malware sample supplementation, due to the complexity of handling discrete data. More importantly, unbalanced malware family samples interfere with the analytical power of malware detection models and mislead malware classification. To address the problem of the impact of malware family imbalance on accuracy, a selection feature conditional Wasserstein generative adversarial network (SFCWGAN) and bidirectional temporal convolutional network (BiTCN) are proposed. First, we extract the features of malware Opcode and API sequences and use Word2Vec to represent features, emphasizing the semantic logic between API tuning and Opcode calling sequences. Second, the Spearman correlation coefficient and the whale optimization algorithm extreme gradient boosting (WOA-XGBoost) algorithm are combined to select features, filter out invalid features, and simplify structure. Finally, we propose a GAN-based sequence feature generation algorithm. Samples were generated using the conditional Wasserstein generative adversarial network (CWGAN) on the imbalanced malware family dataset, added to the trainset to supplement the samples, and trained on BiTCN. In comparison, in tests on the Kaggle and DataCon datasets, the model achieved detection accuracies of 99.56% and 96.93%, respectively, which were 0.18% and 2.98% higher than the models of other methods.
Experimental and 2-Step Finite Element Analysis of Cyclic Fatigue Resistance of Conventional and Heat-Treated Rotary Endodontic Nickel-Titanium Instruments
Authors: Chien Philip Yuan-Ho , Wan Boyang , Walsh Laurence James , Peters Ove Andreas
Published: February 2023

Background: To evaluate the cyclic fatigue resistance of rotary endodontic nickel-titanium instruments using both experimental and finite element methods. Methodology: Four different proprietary rotary endodontic instruments (PTG, PTU, VB, and TRN) were subjected to cyclic fatigue tests through a custom-built fixture (n = 30 for each group) and tested until failure according to the manufacturer’s recommended settings, and the number of cycles to failure (NCF) was measured. Separated fragments were measured to determine the location of the fracture event. Finite element modeling replicated this experiment in silico via a two-step method, modeling the dynamic motion via an initial insertion step, then followed by rotation. Results: TRN showed the highest NCF (2027.94 ± 452.50) with the lowest values for strain (4.49% ± 0.23%) and fragment lengths (3.26 mm ± 0.47 mm), whereas PTU showed the lowest NCF (600.90 ± 80.86) and highest values for strain (6.29% ± 0.47%) and fragment lengths (5.36 mm ± 0.79 mm). All instruments showed an increase in von Mises and maximum principal stress during the insertion step but showed no changes to the maximum stress values during the rotational step. Conclusions: The finite element analysis approach used provided useful insights into the stress evolution and stress distribution of instruments during cyclic fatigue but lacked the ability with existing inputs to predict failure from cyclic fatigue.
First Steps toward Voice User Interfaces for Web-Based Navigation of Geographic Information: A Spanish Terms Study
Authors: Blanco Teresa , Martín-Segura Sergio , de Larrinzar Juan López , Béjar Rubén , Zarazaga-Soria Francisco Javier
Published: February 2023

This work presents the first steps toward developing specific technology for voice user interfaces for geographic information systems. Despite having many general elements, such as voice recognition libraries, the current technology still lacks the ability to fully understand and process the semantics that real users apply to command geographic information systems. This paper presents the results of three connected experiments, following a mixed-methods approach. The first experiment focused on identifying the most common words used when working with maps in a web browser. The second experiment developed an understanding of the chain of commands used for map management for a specific objective. Finally, the third experiment involved the development of a prototype to validate this understanding. Using data and fieldwork, we created a minimum corpus of terms in Spanish. In addition, we identified the particularities of use and user profiles to consider in a voice user interface for geographic information systems, involving the user’s proprioception concerning the world and technology. These user profiles can be considered in future designs of human–technology interaction products. All the data collected and the source code of the prototype are provided as additional material, free to use and modify.
Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
Authors: Chen Lian-Meng , Huang Kai-Yu , Liu Yi-Jie , Zeng Yi-Hong , Li Ze-Bin , Zhou Yi-Yi , Dong Shi-Lin
Published: February 2023

Since the literature lacks an effective analysis method of collapse mechanisms and optimisation design theory for progressive collapse resistance of cable dome structure, a structural performance-based optimisation approach was proposed to improve the progressive collapse resistance for cable dome structures in this study. First, the dynamic response and collapse model of a cable dome structure were analysed after its members were removed using Ansys LS-DYNA and the full dynamic equivalent load-based instantaneous unloading method. Second, the importance coefficients of the members were calculated to determine the contribution of each member to the progressive collapse resistance of the structure. Finally, a stepwise optimisation solution was proposed by integrating a global optimisation model, which uses the mean of the importance coefficients of all members as the optimisation index, with a local optimisation model, which minimises the maximum member importance coefficient. The results indicated that different members exhibited varying levels of importance in the progressive collapse resistance of the structure, with the inner and outer hoop cables demonstrating the highest levels of importance, followed by the inner upper string of the tension hoop. The other members had low levels of importance. Compared with the cable dome structure based on the Geiger topology, the cable dome structure based on the Levy topology was more resistant to progressive collapse; such resistance decreased as the number of cable-truss frames decreased. Additionally, the local optimisation approach based on the genetic algorithm reduced the maximum member importance coefficient (i.e., that of the outer hoop cable) by 60.26%.
Workspace Analysis and Path Planning of a Novel Robot Configuration with a 9-DOF Serial-Parallel Hybrid Manipulator (SPHM)
Authors: Elsamanty Mahmoud , Faidallah Ehab M. , Hossameldin Yehia H. , Rabbo Saber Abd , Maged Shady A. , Yang Hongbo , Guo Kai
Published: February 2023

The development of serial or parallel manipulator robots is constantly increasing due to the need for faster productivity and higher accuracy. Therefore, researchers have turned to combining both mechanisms, sharing the advantage from serial to parallel or vice versa. This paper proposes a new configuration design for a serial-parallel hybrid manipulator (SPHM) using the industrial robotic KUKA Kr6 R900 and 3-DOF parallel spherical mechanism. The Kr6 R900 has six degrees of freedom (6-DOF) divided into three joints for translation (x, y, z) and another three joints for orientation (A, B, C) of the end-effector and the 3-DOF parallel spherical mechanism with three paired links. On the contrary, each limb of the parallel spherical mechanism consists of revolute–revolute–spherical joints (3-RRS). This mechanism allows translation movement along the Z-axis and orientation movements about the X- and Y- axes. The new hybrid will enrich the serial manipulator in movement flexibility and expand the workspace for serial and parallel manipulator robots. In addition, a complete conceptual design is presented in detail for the new robot configuration with a schematic and experimental setup. Then, a comprehensive mathematical model was derived and solved. The forward, inverse kinematics, and workspace analyses were derived using the graphical solution. Additionally, the new hybrid manipulator was tested for path planning. Moreover, an experimental setup was prepared to test the selected path. Finally, the new robot configuration can enlarge the workspace of both manipulators and the selected path matched to the experimental test.
Heterotrophic and Photoautotrophic Media Optimization Using Response Surface Methodology for the Novel Microalga Chlorococcum amblystomatis
Authors: Correia Nádia , Pereira Hugo , Schulze Peter S. C. , Costa Monya M. , Santo Gonçalo E. , Guerra Inês , Trovão Mafalda , Barros Ana , Cardoso Helena , Silva Joana L. , Gouveia Luísa , Varela João
Published: February 2023

The nutritional requirements of novel microalgal strains are key for their effective cultivation and metabolite content. Therefore, the optimization of heterotrophic and photoautotrophic culture media is crucial for novel Chlorococcum amblystomatis growth. Heterotrophic and photoautotrophic biomass samples were characterized to identify the differences between their heterotrophic and photoautotrophic biomass composition and their biotechnological potential. Media optimization through surface response methodology led to 44.9 and 51.2% increments in C. amblystomatis-specific growth rates under heterotrophic and photoautotrophic growth, respectively. This microalga registered high protein content (61.49–73.45% dry weight), with the highest value being observed in the optimized photoautotrophic growth medium. The lipid fraction mainly constituted polyunsaturated fatty acids, ranging from 44.47 to 51.41% for total fatty acids (TFA) in cells under heterotrophy. However, these contents became significantly higher (70.46–72.82% TFA) in cultures cultivated under photoautotrophy. An interesting carotenoids content was achieved in the cultures grown in optimized photoautotrophic medium: 5.84 mg·g−1 β-carotene, 5.27 mg·g−1 lutein, 3.66 mg·g−1 neoxanthin, and 0.75 mg·g−1 violaxanthin. Therefore, C. amblystomatis demonstrated an interesting growth performance and nutritional profile for food supplements and feed products that might contribute to meeting the world’s nutritional demand.