bluuebunny/crossref_metadata_2025 huggingface dataset:
A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
Authors: Ezeife Christie I. , Karlapalepu Hemni
Published: October 2023

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems.
Research on Load Spectrum Reconstruction Method of Exhaust System Mounting Bracket of a Hybrid Tractor Based on MOPSO-Wavelet Decomposition Technique
Authors: Sun Liming , Liu Mengnan , Wang Zhipeng , Wang Chuqiao , Luo Fuqiang
Published: October 2023

To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing.
A Novel PCR-Based Functional Marker of Rice Blast Resistance Gene Pi25
Authors: Fan Yeyang , Zhang Zhenhua , Huang Derun , Huang Tingxu , Wang Hongfei , Zhuang Jieyun , Zhu Yujun
Published: October 2023

Rice blast is arguably the most devastating fungal disease of rice. Utilization of resistance genes to breed resistant cultivars is one of the most economical and environmentally friendly approaches to combat the disease. Pi25, a major resistance gene conferring broad-spectrum resistance to both leaf and neck blast, is an ideal gene resource to improve the resistance of rice varieties to blast. Recently, several allele-specific markers were developed. However, they were deficiently efficient due to either an additional process of restriction enzyme digestion for cleaved amplified polymorphic sequence (CAPS) markers or the risk of false-positive error in identifying susceptible Tetep allele (Pi25TTP) for PCR-based markers. In this study, based on a conserved single nucleotide polymorphism (SNP) between resistant and susceptible alleles, a tetra-primer amplification refractory mutation system (ARMS)-PCR marker was developed. The new marker, namely Pi25-2687R3, could effectively distinguish the resistant Gumei 2 (GM2) allele (Pi25GM2) and the susceptible allele Pi25TTP. Moreover, a perfect consistency of genotyping was exhibited between Pi25-2687R3 and published CAPS marker CAP3/Hpy 99I. A more accurate genotyping was also displayed compared to the previous PCR-based SNP marker Pi25-2566. Our finding proved that Pi25-2687R3 could achieve the same result as CAP3/Hpy 99I with less workload and cost and could promote the accuracy in the identification of genotypes superior to Pi25-2566. This study provided a quick and reliable functional marker for discriminating Pi25 alleles, which would be a valuable tool for genotypic assay and rice molecular breeding of blast resistance.
AMMI Analysis of Genotype × Environment Interaction on Sugar Beet (Beta vulgaris L.) Yield, Sugar Content and Production in Romania
Authors: Oroian Camelia , Ugruțan Florin , Mureșan Iulia Cristina , Oroian Ioan , Odagiu Antonia , Petrescu-Mag Ioan Valentin , Burduhos Petru
Published: October 2023

The overall yield and sugar content of sugar beet (Beta vulgaris L.) were determined by the genotype and its interaction with the environment. This study aimed to analyze the interaction of 23 genotypes with different environmental conditions during two growing seasons. To estimate the variance of genotypes, environment, and genotype function of the environment, the R 3.5.1 software package was used. In addition, the multivariate stability method was used to explain the G (genotype) × E (environment) interaction based on the GGE (Genotype plus Genotype-by-Environment) and AMMI (additive main effects and multiplicative interaction) biplots. The AMMI ASV (AMMI stability value) and biplot analysis revealed that only two genotypes (G10 and G11) showed higher values for yield and sugar content and production compared to the other genotypes. The AMMI ASV analysis also showed that the environment significantly influenced the sugar beet yield, sugar content, and sugar production, which were the descriptors for production in this study.
Concealed Conduit Routing in Building Slabs
Authors: Ueng Shyh-Kuang , Chang Chun-Chieh
Published: October 2023

Concealed pipes are vital facilities for transporting water, air, electricity, and natural gas in modern buildings. These pipes are constructed inside slabs of buildings, and thus conventional piping algorithms, dedicated to arranging exposed pipes in open spaces or on object surfaces, are not suitable for laying out their paths. In this article, an innovative method is presented for designing the concealed conduits of modern buildings. In the proposed piping procedure, the target building is regarded as a framework composed of beams, columns, and slabs. These substructures are encoded in a weighted graph, which serves as the top-level representation of the workspace. Then, these substructures are split into voxels and constitute the bottom-level representation of the workspace. Each concealed pipe is constructed by using a two-stage piping scheme to comply with the representation of the workspace. In the first stage, the slabs containing the terminals of the pipe are located in the top-level representation, and the shortest path connecting these slabs is calculated using Dijkstra’s algorithm. In the second stage, a feasible space is generated by collecting selective voxels in these slabs first. Then, the pipe path is routed inside the feasible space by a shortest-path-finding computation. Next, the pipe surface is generated and represented by using triangle meshes. Finally, the bottom-level representation is modified and the routing process is repeated to lay out the remaining concealed pipes. The experimental results show that the proposed piping procedure efficiently arranges concealed pipes inside buildings of various topologies and internal layouts. As it benefits from the two-level representation and the two-stage routing method, the piping process consumes reasonable computational costs. The paths of the resultant pipes are optimized, and their positions meet the geometrical constraints and safety regulations.
Neural Radiation Fields in a Tidal Flat Environment
Authors: Ge Huilin , Zhu Zhiyu , Qiu Haiyang , Zhang Youwen
Published: October 2023

Tidal flats are critical ecosystems, playing a vital role in biodiversity conservation and ecological balance. Collecting tidal flat environmental information using unmanned aerial vehicles (UAVs) and subsequently utilizing 3D reconstruction techniques for their detection and protection holds significance in providing comprehensive and detailed tidal flat information, including terrain, slope, and other parameters. It also enables scientific decision-making for the preservation of tidal flat ecosystems and the monitoring of factors such as rising sea levels. Moreover, the latest advancements in neural radiance fields (Nerf) have provided valuable insights and novel perspectives for our work. We face the following challenges: (1) the performance of a single network is limited due to the vast area to cover; (2) regions far from the camera center may exhibit suboptimal rendering results; and (3) changes in lighting conditions present challenges for the achievement of precise reconstruction. To tackle these challenges, we partitioned the tidal flat scene into distinct submodules, carefully preserving overlapping regions between each submodule for collaborative optimization. The luminance of each image is quantified by the appearance embedding vector produced by every captured image. Subsequently, this corresponding vector serves as an input to the model, enhancing its performance across varying lighting conditions. We also introduce an ellipsoidal sphere transformation that brings distant image elements into the sphere’s interior, enhancing the algorithm’s capacity to represent remote image information. Our algorithm is validated using tidal plane images collected from UAVs and compared with traditional Nerf based on two metrics: peak signal-to-noise ratio (PSNR) and learned perceptual image patch similarity (LPIPS). Our method enhances the PSNR value by 2.28 and reduces the LPIPS value by 0.11. The results further demonstrate that our approach significantly enhances Nerf’s performance in tidal flat environments. Utilizing Nerf for the 3D reconstruction of tidal flats, we bypass the need for explicit representation and geometric priors. This innovative approach yields superior novel view synthesis and enhances geometric perception, resulting in high-quality reconstructions. Our method not only provides valuable data but also offers profound insights for environmental monitoring and management.
Evaluation of Noise Levels in a University Dental Clinic
Authors: Antoniadou Maria , Tziovara Panagiota , Konstantopoulou Sophia
Published: October 2023

Noise levels in a dental office can be produced by different specialty instruments. Exposure to high levels of noise (unwanted sounds) may cause auditory and non-auditory health problems in dentists. The aim of this study was to (a) measure the noise levels within different clinics and laboratories of the Department of Dentistry, School of Health Sciences of the National and Kapodistrian University of Athens, (b) promote information sharing on this serious health issue among stakeholders, and (c) collect data to organize preventive measures for students and personnel (faculty members, collaborators, administrative, and technical staff). Since the study did not apply to acoustics and acoustic measurements, a digital sound level meter and noise-integrating dosimeters with an analogue electronic transducer were used to collect data from certain postgraduate (PG) and undergraduate (UG) clinics and laboratories (LAB) during peak working periods and with a duration of 1 h per clinic/lab. Both personal (dosimeters) and static (area monitoring) noise exposure assessments were evaluated, resulting in various teaching-related activities in dental clinics. At all locations, the maximum exposure limit value of 87 dB(A) was not exceeded. However, chairside personal measurements during ultrasonic work revealed that the lower exposure action value of 80 dB(A) was exceeded. PG clinics were noisier than UG. LAB training settings, even with the new equipment, were close to the upper exposure limit due to the simultaneous use of airotors. In this context, targeted research and investigations into measures are proposed to safeguard the health and safety of students during their duties at the dental school.
Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
Authors: Valido Manuel Rodríguez , Bendicho Peña Fabiani , Martín Reyes Miguel , Rodríguez-Juncá Alicia
Published: October 2023

The use of underwater recording is widely implemented across different marine ecology studies as a substitute for more invasive techniques. This is the case of the Deep Scattering Layer (DSL), a biomass-rich layer in the ocean located between 400 and 600 m deep. The data processing of underwater videos has usually been carried out manually or targets organisms above a certain size. Marine snow, or macroscopic amorphous aggregates, plays a major role in nutrient cycles and in the supply of organic material for organisms living in the deeper layers of the ocean. Marine snow, therefore, should be taken into account when estimating biomass abundance in the water column. The main objective of this project is to develop a new software application for the automatic detection and analysis of biomass abundance relative to time in underwater videos, taking into consideration small items. The application software is based on a pipeline and client-server architecture, developed in Python and using open source libraries. The software was trained with underwater videos of the DSL recorded with low-cost equipment. A usability study carried out with end-users shows satisfaction with the user-friendly interface and the expected results. The software application developed is capable of automatically detecting small items captured by underwater videos. In addition, it can be easily adapted to a web application.
Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
Authors: Mohamed Abdul Cader Akmal Jahan , Banks Jasmine , Chandran Vinod
Published: October 2023

Contact-based biometric applications primarily use prints from a finger or a palm for a single instance in different applications. For access control, there is an enrollment process using one or more templates which are compared with verification images. In forensics applications, randomly located, partial, and often degraded prints acquired from a crime scene are compared with the images captured from suspects or existing fingerprint databases, like AFIS. In both scenarios, if we need to use handprints which include segments from the finger and palm, what would be the solution? The motivation behind this is the concept of one single algorithm for one hand. Using an algorithm that can incorporate both prints in a common processing framework can be an alternative which will have advantages like scaling to larger existing databases. This work proposes a method that uses minutiae or minutiae-like features, Delaunay triangulation and graph matching with invariant feature representation to overcome the effects of rotation and scaling. Since palm prints have a large surface area with degradation, they tend to have many false minutiae compared to fingerprints, and the existing palm print algorithms fail to tackle this. The proposed algorithm constructs Delaunay triangulated graphs (DTG) using minutiae where Delaunay triangles form from minutiae, and initiate a collection of base triangles for opening the matching process. Several matches may be observed for a single triangle match when two images are compared. Therefore, the set of initially matched triangles may not be a true set of matched triangles. Each matched triangle is then used to extend as a sub-graph, adding more nodes to it until a maximum graph size is reached. When a significant region of the template image is matched with the test image, the highest possible order of this graph will be obtained. To prove the robustness of the algorithm to geometrical variations and working ability with extremely degraded (similar to latent prints) conditions, it is demonstrated with a subset of partial-quality and extremely-low-quality images from the FVC (fingerprint) and the THUPALMLAB (palm print) databases with and without geometrical variations. The algorithm is useful when partial matches between template and test are expected, and alignment or geometrical normalization is not accurately possible in pre-processing. It will also work for cross-comparisons between images that are not known a priori.
Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
Authors: Luo Yongyu , Luo Zhongqiang
Published: October 2023

Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domestic and international research efforts. Then, conventional approaches for infrared and visible picture fusion, such as the multi-scale decomposition method and the sparse representation method, are thoroughly introduced. The advancement of deep learning in recent years has greatly aided the field of picture fusion. The outcomes of the fusion have a wide range of potential applications due to the neural networks’ strong feature extraction and reconstruction skills. As a result, this research also evaluates deep learning techniques. After that, some common objective evaluation indexes are provided, and the performance evaluation of infrared and visible image fusion is introduced. The common datasets in the areas of infrared and visible image fusion are also sorted out at the same time. Datasets play a significant role in the advancement of infrared and visible image fusion and are an essential component of infrared and visible image fusion testing. The application of infrared and visible image fusion in many domains is then simply studied with practical examples, particularly in developing fields, used to show its application. Finally, the prospect of the current infrared and visible image fusion field is presented, and the full text is summarized.
A Systematic Literature Review on Using the Encoder-Decoder Models for Image Captioning in English and Arabic Languages
Authors: Alsayed Ashwaq , Arif Muhammad , Qadah Thamir M. , Alotaibi Saud
Published: October 2023

With the explosion of visual content on the Internet, creating captions for images has become a necessary task and an exciting topic for many researchers. Furthermore, image captioning is becoming increasingly important as the number of people utilizing social media platforms grows. While there is extensive research on English image captioning (EIC), studies focusing on image captioning in other languages, especially Arabic, are limited. There has also yet to be an attempt to survey Arabic image captioning (AIC) systematically. This research aims to systematically survey encoder-decoder EIC while considering the following aspects: visual model, language model, loss functions, datasets, evaluation metrics, model comparison, and adaptability to the Arabic language. A systematic review of the literature on EIC and AIC approaches published in the past nine years (2015–2023) from well-known databases (Google Scholar, ScienceDirect, IEEE Xplore) is undertaken. We have identified 52 primary English and Arabic studies relevant to our objectives (The number of articles on Arabic captioning is 11, and the rest are for the English language). The literature review shows that applying the English-specific models to the Arabic language is possible, with the use of a high-quality Arabic database and following the appropriate preprocessing. Moreover, we discuss some limitations and ideas to solve them as a future direction.
Effect of Block-Based Python Programming Environment on Programming Learning
Authors: Kim Yongcheon , Kim Jamee , Lee Wongyu
Published: October 2023

The advancement of computing technology has led to many changes in a variety of fields, and the importance of programming education has been emphasized in many countries worldwide. Despite the importance of programming education, the cognitive burden of text programming for beginners has not been reduced. The goal of this study was to implement an environment where a text programming language is used in a block-based programming environment and to determine at which school level this learning environment affects positive perceptions of programming. To achieve this goal, we conducted programming classes targeting 128 middle school, high school, and university students for 14 weeks and analyzed the effects of the factors of “understanding of programming instructions”, “usage confidence”, and “usefulness” on “positive perceptions of programming”. The results of the analysis by school level show that “usefulness” influenced positivity toward programming for middle school students, “usefulness” and “understanding of programming instruction” for high school students, and “understanding of programming instruction” and “usage confidence” for university students. Therefore, the significance of this study confirms the need to construct the learning environment differently depending on school level, even for beginners.
Error Modeling and Parameter Calibration Method for Industrial Robots Based on 6-DOF Position and Orientation
Authors: Lao Dabao , Quan Yongbin , Wang Fang , Liu Yukun
Published: October 2023

The positional accuracy and orientation accuracy of industrial robots are crucial technical indicators for determining their applicability in industrial scenarios. However, the majority of current calibration methods for industrial robots only consider positional errors, neglecting the significance of orientation accuracy. This paper presents a more accurate error model and parameter calibration method for industrial robots based on six degrees-of-freedom position and orientation to identify the actual structural parameters. Firstly, based on the modified Denavit–Hartenberg parameters, the transformation errors of the tool coordinate system and measurement coordinate frame were introduced to establish a geometric parameter error model with positional and orientation accuracy as the optimization objectives. Secondly, to address the drawback of falling into local optima when identifying geometric parameters simultaneously, a geometric parameter cross-identification method based on the Levenberg–Marquardt algorithm is proposed. Lastly, the linear relationship between the parameters was analyzed, and a scheme for not calibrating some geometric parameters under specific conditions was given. Simulation results demonstrated that, under the premise of existing transformation errors, the proposed geometric parameter error model can accurately identify the actual structural parameters of industrial robots. After calibration, the positional error at the robot’s flange end decreased from 1.9536 mm to 0.0122 mm, and the orientation error decreased from 1.46 × 10−2 rad to 1.31 × 10−4 rad. Furthermore, compared to identifying the geometric parameters simultaneously, the proposed cross-identification method has a wider convergence range.
Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection
Authors: Yi Xiaojian , Huang Peizheng , Che Shangjie
Published: October 2023

Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.
Improving Nitrogen Removal Efficiency in SBR Reactors by Controlling Operational Phases
Authors: Quan Tran Ha , Gogina Elena , Makisha Nikolay , Gulshin Igor , Makisha Elena
Published: October 2023

Wastewater treatment facilities using the activated sludge process are the most widely used solutions worldwide, regardless of the region. According to statistics, the most common option for Vietnam’s conditions is the sequenced batch reactor, which is used at a third of wastewater treatment plants (WWTP). Thus, studies of treatment processes in SBRs that seek high-quality treatment may have a wide range of further applications in practice. Another reason is that wastewater treated to meet Vietnamese standards has a significant negative impact on water resources. The research was carried out in relation to Hoaxuan wastewater treatment plants, where pilot models were installed that worked on influent wastewater. Studies of the nitrification process in SBRs (cycle duration 4 h) have shown that the highest efficiency of removal of pollutants (organic pollution by 90%, ammonium nitrogen by 80%) is achieved at a pH value close to 8.0. The increase in pH also had a positive effect on the increase in the specific rate of nitrification. At the second stage of the research, the process of denitrification in SBRs (cycle duration of 6.5 h) was considered. The results demonstrated that treatment quality corresponded to the standards for the Russian Federation, which are stricter than Vietnamese standards (3.0 mg/L vs. 30 mg/L of biochemical oxygen demand; 0.4 mg/L vs. 5 mg/L of ammonium nitrogen). For the second part of the study, a specific denitrification rate was determined in order to establish its dependence on the main parameters of the SBR model performance.
The Design of a Video Reflection Removal Method Based on Illumination Compensation and Image Completion Fusion
Authors: Ding Shaohong , Xu Yi , Kong Xiangcun , Shi Shuyue , Ni Juan
Published: October 2023

Our objective is to develop a video reflection removal algorithm that is both easy to compute and effective. Unlike previous methods that depend on machine learning, our approach proposes a local image reflection removal technique that combines image completion and lighting compensation. To achieve this, we utilized the MSER image region feature point matching method to reduce image processing time and the spatial area of layer separation regions. In order to improve the adaptability of our method, we implemented a local image reflection removal technique that utilizes image completion and lighting compensation to interpolate layers and update motion field data in real-time. Our approach is both simple and efficient, allowing us to quickly obtain reflection-free video sequences under a variety of lighting conditions. This enabled us to achieve real-time detection effects through video restoration. This experiment has confirmed the efficacy of our method and demonstrated its comparable performance to advanced methods.
Vitamin K Status of Patients Undergoing Hemodialysis: Insights from a Cross-Sectional Study
Authors: Palamar Marcel , Grosu Iulia , Schiller Adalbert , Petrica Ligia , Bodea Madalina , Sircuta Alexandru , Rusan Cornel , Tanasescu Daniela Maria , Bob Flaviu
Published: October 2023

(1) Background: Vitamin K deficiency is a common feature of chronic kidney disease (CKD), leading to impaired bone quality and an increased risk of vascular calcifications. A method to indirectly assess the vitamin K status is measuring the blood level of vitamin K-dependent proteins (VKDP): osteocalcin (OC) and matrix GLA protein (MGP). The aim of this study is to correlate the level of total OC and inactive MGP (dp-uc MGP) with markers of CKD mineral bone disorder (CKD-MBD). (2) Methods: We conducted a single-center cross-sectional study that included 45 CKD G5D patients and measured their blood biochemistry, complete blood count and total osteocalcin and dp-uc MGP contents. (3) Results: We found a strong, statistically significant correlation of the total OC with the markers of CKD-MBD, such as: iPTH, serum calcium and serum phosphorus, and a strong, indirect statistically significant correlation with abdominal circumference. There was also a statistically significant correlation of dp-uc MGP with the markers of inflammation (CRP). Higher levels of dp-uc MGP were found in the patients treated with vitamin K antagonists, non-calcium-based phosphate binders and the vitamin D receptor activator, paricalcitol. (4) Conclusions: In our study, we found that when it is measured indirectly using VKDP levels, vitamin K deficiency is associated with CKD-MBD. Certain widely used medications such as phosphate binders reduce vitamin K absorption, supplementary vitamin D increases vitamin K requirements, and also vitamin K antagonists influence the blood level of VKDPs.
A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey
Authors: Reja R. K. , Amin Ruhul , Tasneem Zinat , Abhi Sarafat Hussain , Bhatti Uzair Aslam , Sarker Subrata Kumar , Ain Qurat ul , Ghadi Yazeed Yasin
Published: October 2023

The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered.
Prediction of PM2.5 Concentration Using Spatiotemporal Data with Machine Learning Models
Authors: Ma Xin , Chen Tengfei , Ge Rubing , Xv Fan , Cui Caocao , Li Junpeng
Published: October 2023

Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m3. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications.
Study on the Optical–Physical Properties of Aerosol Layers in Africa Based on a Laser Satellite
Authors: Zhang Miao , Mu Pengyu , Chen Xin , Wu Di , Lu Fengxian , Qi Pengcheng , Li Larry Bailian , Gong Wei
Published: October 2023

Atmospheric aerosols have important effects on the environment and human health. In this study, we analyzed the atmospheric aerosol layers’ optical and physical properties over Africa utilizing CALIPSO level 2 products from 2007 to 2019. Interannual and seasonal variations in aerosol optical parameters were studied: the lowest aerosol layer (AODL), the base height of the lowest aerosol layer (BL), the top height of the lowest aerosol layer (HL), the top height of the highest aerosol layer (HH), the volumetric depolarization ratio of the lowest aerosol layer (DRL), the color ratio of the lowest aerosol layer (CRL), the total AOD of all aerosol layers (AODT), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (TL), and the AOD proportion of the lowest aerosol layer (PAODL). The annual mean AODT was slightly higher in southern Africa than in northern Africa. HL and HH had strongly positive correlations with landform elevations. However, HL and HH were greater in northern Africa than in southern Africa from March to August. The reason could be that northern Africa is dominated by deserts with high temperatures and intense atmospheric vertical convections leading to dust layers existing in the upper air. PAODL values were lower in northern Africa (daytime: 71%; nighttime: 61%) than in southern Africa (daytime: 78%; nighttime: 69%), revealing that aerosol stratifications were more frequent in northern Africa than in southern Africa. DRL values were higher in northern Africa (daytime: 0.16; nighttime: 0.11) than in southern Africa (daytime: 0.07; nighttime: 0.05) indicating the predominance of non-spherical dust particles in northern Africa. This work can provide an important understanding of regional aerosol layers’ optical and physical properties to scientists and local environmental protection agencies.
Spatial Dependence Analysis of Weekly Moving Cumulative Rainfall for Flood Risk Assessment
Authors: Chomphuwiset Prapawan , Phoophiwfa Tossapol , Kannika Wanlop , Seenoi Palakorn , Suraphee Sujitta , Park Jeong-Soo , Busababodhin Piyapatr
Published: October 2023

Climate change has intensified the frequency and severity of extreme weather events, necessitating a nuanced understanding of flood patterns for effective risk management. This study examines flood risk in the Chi watershed, Thailand, using Weekly Moving Cumulative Rainfall (WMCR) data from 1990 to 2021. We employ extreme value copula analysis to assess spatial dependence between meteorological stations in the watershed. Nine bivariate generalized extreme value (BGEV) models were evaluated using the Akaike Information Criterion (AIC) and the Likelihood Ratio test (LRT) to ensure model robustness. The BGEV model revealed higher tail dependence among stations near the bay of the watershed. We also calculated the flood recurrence period to estimate flood events’ frequency and potential severity. Stations ST5 (Khon Kaen), ST6 (Tha Phra Khon Kaen), and ST8 (Maha Sarakham) were identified as potential hotspots, with higher probabilities of experiencing extreme rainfall of approximately 200 (mm.) during the rainy season. These findings provide valuable insights for flood management and mitigation strategies in the Chi watershed and offer a methodological framework adaptable to other regions facing similar challenges.
Endogenic Phenolic Compounds of Barley as Potential Biomarkers Related to Grain Mycotoxin Production and Cultivar Selection
Authors: Badea Ana , Tucker James R. , Sabra Ali , Netticadan Thomas , Blackwell Barbara , Yu Liping , Kodikara Chamali , Wijekoon Champa
Published: October 2023

Barley (Hordeum vulgare L.) is the fourth largest cereal crop in the world. One of the most devastating diseases in barley worldwide is Fusarium head blight (FHB) caused by Fusarium graminearum Schwabe. Several mycotoxins are produced by FHB infection, and deoxynivalenol (DON) is one of them responsible for the deterioration of grain quality. The current limited number of reliable molecular markers makes the development of FHB-resistant cultivars rather difficult and laborious. Moreover, there is a limited number of designed specific biomarkers that could distinguish the FHB resistance and mycotoxin accumulation in barley cultivars. This study investigated the phenolic compounds of ten different Canadian barley cultivars, grown in artificially FHB-infected and non-infected field trials. The enzyme-linked immunosorbent assay (ELISA) was used to assess the presence of DON in the harvested infected grains of each tested variety. High-performance liquid chromatography (HPLC) analysis was performed using both infected and non-infected samples. We identified differences among cultivars tested in non-infected samples through quantitative analysis of free and bound phenolic compounds. The resistant cultivars showed higher amounts of major bound phenolic compounds compared to the susceptible check CDC Bold. Additionally, the FHB-infected cultivars produced significantly higher amounts of sinapic acid (SIN) () and catechin (CAT) in the soluble free form of phenolics in barley compared to the non-infected subjects. This study suggests that phenolic compounds in barley could allow barley breeders to precisely identify and develop FHB-resistant barley germplasm and cultivars.
Prioritized Identification of Fearful Eyes during the Attentional Blink Is Not Automatic
Authors: Li Shuaixia , Hao Bin , Dang Wei , He Weiqi , Luo Wenbo
Published: October 2023

The eye region conveys considerable information regarding an individual’s emotions, motivations, and intentions during interpersonal communication. Evidence suggests that the eye regions of an individual expressing emotions can capture attention more rapidly than the eye regions of an individual in a neutral affective state. However, how attentional resources affect the processing of emotions conveyed by the eye regions remains unclear. Accordingly, the present study employed a dual-target rapid serial visual presentation task: happy, neutral, or fearful eye regions were presented as the second target, with a temporal lag between two targets of 232 or 696 ms. Participants completed two tasks successively: Task 1 was to identify which species the upright eye region they had seen belonged to, and Task 2 was to identify what emotion was conveyed in the upright eye region. The behavioral results showed that the accuracy for fearful eye regions was lower than that for neutral eye regions under the condition of limited attentional resources; however, accuracy differences across the three types of eye regions did not reach significance under the condition of adequate attentional resources. These findings indicate that preferential processing of fearful expressions is not automatic but is modulated by available attentional resources.
Multimodal Physical Exercise Affects Visuo-Spatial Working Memory: Preliminary Evidence from a Descriptive Study on Tai-Chi Practitioners and Runners
Authors: Troisi Lopez Emahnuel , Liparoti Marianna , Passarello Noemi , Lucidi Fabio , Mandolesi Laura
Published: October 2023

Recent evidence has shown a relationship between physical exercise (PE) and cognitive functioning. However, it is unknown if unimodal and multimodal modalities of PE affect cognitive abilities in different ways. To fill this gap, we analyzed the effects of unimodal PE (running) and multimodal PE (Tai Chi) on specific cognitive abilities. A sample of 33 participants (mean age = 52.6 ± 7.2) divided into eleven runners, eleven Tai Chi practitioners, and eleven age-matched sedentary individuals were subjected to a neuropsychological tests battery to assess shifting and problem solving abilities (Rule Shift Cards, BADS-RS, and Key Search tasks), verbal fluency (semantic and phonemic verbal fluency tasks), verbal memory (Rey’s 15 words test), visuo-spatial working memory (Corsi test), and global cognitive functioning (clock-drawing test). The results showed significantly higher BADS-RS scores in runners and Tai Chi practitioners in comparison to the sedentary participants, thus evidencing improved shifting abilities for active individuals. Interestingly, post hoc analysis showed significantly higher span scores of Corsi test only in Tai Chi practitioners as compared to sedentary participants, suggesting how multimodal PE facilitates the visuo-spatial working memory processes. Although preliminary, our descriptive study indicates that the type of PE could modulate specific cognitive domains, even if the practice of motor activity favors a global cognitive improvement.
Effect of Macro Polyolefin Fibers on Bond Strength of Tension Lap Splices in RC Beams
Authors: Al-Hussein Abdullah , Majeed Fareed H. , Abbas Abdulnasser M.
Published: October 2023

The effect of macro synthetic polyolefin fibers on the bond strength of tension lap splices in reinforced concrete (RC) beams is investigated in this study. The bond between the reinforcement and concrete plays a vital role in the strength of RC beams. The presence of polyolefin fibers in the lap splice zone confines the concrete and enhances the bond strength of the steel bars. The use of synthetic fibers is preferable to steel ones since steel suffers from corrosion over time. Tests were conducted on 12 full-scale beam specimens to determine the effect of fiber volume fraction (Vf), bar diameter (db) and concrete cover-to-bar diameter (c/db) on the response. Four volume fractions (Vf = 0, 0.5, 1 and 1.5%) of polyolefin fibers and three bar sizes (db = 16, 20 and 25 mm) with the corresponding (c/db = 2.31, 1.75 and 1.30) were considered to evaluate the bond strength. The test results demonstrated that the polyolefin fibers noticeably enhanced the bond strength and ductility of spliced tension bars. Experimental results were compared with those obtained from two theoretical methods including ACI Committee 318 design provisions. The results showed that the equation proposed by the ACI Committee overestimates the bond strength.
The Impact of Measurable Findings from Pre- and Post-Occupancy Evaluations of Indoor Environmental Quality in the Primary Workspace
Authors: Bae Suyeon , Martin Caren S.
Published: October 2023

This study examined the influence of quantitative and qualitative findings from both a pre-design occupancy evaluation (PDOE) and a post-occupancy evaluation (POE) on the expanded design team’s (designers, client, and researchers) approach to updating the interior work environment. The design team’s implementation of mid-course design solution modifications to the primary workspaces of an engineering group’s environment both after the PDOE and the POE yielded many significant outcomes. The POE was conducted using the same instrument as the PDOE one year after the employees had moved back into their renovated space on a corporate campus. The study’s results were used as a diagnostic tool to improve indoor environmental quality (IEQ) conditions for employees and provided benchmarks for the remaining (85%) campus workplace improvements. The influence and importance of IEQ criteria on employee satisfaction, work performance, and health were demonstrated and contribute to the body of knowledge regarding the influence of the built environment on building occupants. A paired inquiry (PDOE plus POE) helps ensure that human, financial, and environmental resources are appropriated with a higher likelihood of success over POE data alone in terms of meeting current and future employees’ needs and corporate goals.
Circular, Local, Open: A Recipe for Sustainable Building Construction
Authors: Kouvara Asimina , Priavolou Christina , Ott Denise , Scherer Philipp , van Zyl-Bulitta Verena Helen
Published: October 2023

In response to the construction sector’s contribution to the climate crisis and exacerbation of social inequalities, we explore sustainable alternatives in building construction, informed by the illustrative case study of the Polycare construction system. First, through a Life-Cycle Assessment (LCA) method, we show that the ecological footprint of circularity-oriented buildings based on polymer concrete is significantly lower than that of conventional cement concrete buildings. Despite the drawbacks of polymer concrete, its high-performance properties and the possibility to integrate secondary materials in its recipe can result in a reduced carbon footprint. When coupled with design-embedded modularity that facilitates circular processes (e.g., the disassembly and reuse of building components), buildings similar to those in the case study demonstrate potential for transitioning towards comprehensive sustainable building practices. Further, we discuss how this sustainability potential could be enhanced, drawing from interviews with Polycare’s stakeholders and key literature findings. In this direction, we provide a set of proposals anchored in the argument that threefold “circularity, localisation, and openness” is vital for sustainable and affordable alternatives, with openness being a crucial element for fostering innovation, adaptability, and scalability in building processes.
Visualization Analysis of Emergency Exit Signs Literature Based on CiteSpace
Authors: Chen Hongtao , Zhi Jinyi , Xiang Ze-Rui , Zou Rui , Ding Tiecheng
Published: October 2023

Emergency exit signs are a mandatory and essential element for the prevention and planning of evacuation in all types of buildings. In recent decades, some achievements have been made in emergency exit signs research, but there is a lack of literature reviews on the subject. This study focused on exploring the research status and development trends in emergency exit signs using a visualization analysis of bibliometrics. The findings of this paper are as follows: First, through co-authorship analysis, we identified countries, institutions, and authors that have made outstanding contributions in the research area. Second, through co-citation analysis, we revealed important journals, documents, and authors in the research field. Third, through keyword co-occurrence analysis, we found research focuses include sign effectiveness, research methods, and research content. And the research frontiers include virtual reality, visibility, and emergency evacuation. The study can serve as a reference for relevant researchers studying emergency exit signs.