bluuebunny/crossref_metadata_2025 huggingface dataset:
Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index
Authors: Huang Yabo , Meng Mengmeng , Hou Zhuoyan , Wu Lin , Guo Zhengwei , Shen Xiajiong , Zheng Wenkui , Li Ning
Published: June 2023

Accurate land cover classification (LCC) is essential for studying global change. Synthetic aperture radar (SAR) has been used for LCC due to its advantage of weather independence. In particular, the dual-polarization (dual-pol) SAR data have a wider coverage and are easier to obtain, which provides an unprecedented opportunity for LCC. However, the dual-pol SAR data have a weak discrimination ability due to limited polarization information. Moreover, the complex imaging mechanism leads to the speckle noise of SAR images, which also decreases the accuracy of SAR LCC. To address the above issues, an improved dual-pol radar vegetation index based on multiple components (DpRVIm) and a new LCC method are proposed for dual-pol SAR data. Firstly, in the DpRVIm, the scattering information of polarization and terrain factors were considered to improve the separability of ground objects for dual-pol data. Then, the Jeffries-Matusita (J-M) distance and one-dimensional convolutional neural network (1DCNN) algorithm were used to analyze the effect of difference dual-pol radar vegetation indexes on LCC. Finally, in order to reduce the influence of the speckle noise, a two-stage LCC method, the 1DCNN-MRF, based on the 1DCNN and Markov random field (MRF) was designed considering the spatial information of ground objects. In this study, the HH-HV model data of the Gaofen-3 satellite in the Dongting Lake area were used, and the results showed that: (1) Through the combination of the backscatter coefficient and dual-pol radar vegetation indexes based on the polarization decomposition technique, the accuracy of LCC can be improved compared with the single backscatter coefficient. (2) The DpRVIm was more conducive to improving the accuracy of LCC than the classic dual-pol radar vegetation index (DpRVI) and radar vegetation index (RVI), especially for farmland and forest. (3) Compared with the classic machine learning methods K-nearest neighbor (KNN), random forest (RF), and the 1DCNN, the designed 1DCNN-MRF achieved the highest accuracy, with an overall accuracy (OA) score of 81.76% and a Kappa coefficient (Kappa) score of 0.74. This study indicated the application potential of the polarization decomposition technique and DEM in enhancing the separability of different land cover types in SAR LCC. Furthermore, it demonstrated that the combination of deep learning networks and MRF is suitable to suppress the influence of speckle noise.
Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference
Authors: Du Libin , Li Huming , Wang Lei , Lin Xu , Lv Zhichao
Published: June 2023

Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential.
Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation
Authors: Bu Lijing , Dai Dong , Zhang Zhengpeng , Yang Yin , Deng Mingjun
Published: June 2023

Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.
Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction
Authors: Zhu Wen , Lee Soo-Jin
Published: June 2023

We present an adaptive method for fine-tuning hyperparameters in edge-preserving regularization for PET image reconstruction. For edge-preserving regularization, in addition to the smoothing parameter that balances data fidelity and regularization, one or more control parameters are typically incorporated to adjust the sensitivity of edge preservation by modifying the shape of the penalty function. Although there have been efforts to develop automated methods for tuning the hyperparameters in regularized PET reconstruction, the majority of these methods primarily focus on the smoothing parameter. However, it is challenging to obtain high-quality images without appropriately selecting the control parameters that adjust the edge preservation sensitivity. In this work, we propose a method to precisely tune the hyperparameters, which are initially set with a fixed value for the entire image, either manually or using an automated approach. Our core strategy involves adaptively adjusting the control parameter at each pixel, taking into account the degree of patch similarities calculated from the previous iteration within the pixel’s neighborhood that is being updated. This approach allows our new method to integrate with a wide range of existing parameter-tuning techniques for edge-preserving regularization. Experimental results demonstrate that our proposed method effectively enhances the overall reconstruction accuracy across multiple image quality metrics, including peak signal-to-noise ratio, structural similarity, visual information fidelity, mean absolute error, root-mean-square error, and mean percentage error.
A Secure and Smart Home Automation System with Speech Recognition and Power Measurement Capabilities
Authors: Irugalbandara Chandra , Naseem Abdul Salam , Perera Sasmitha , Kiruthikan Sithamparanathan , Logeeshan Velmanickam
Published: June 2023

The advancement in the internet of things (IoT) technologies has made it possible to control and monitor electronic devices at home with just the touch of a button. This has made people lead much more comfortable lifestyles. Elderly people and those with disabilities have especially benefited from voice-assisted home automation systems that allow them to control their devices with simple voice commands. However, the widespread use of cloud-based services in these systems, such as those offered by Google and Amazon, has made them vulnerable to cyber-attacks. To ensure the proper functioning of these systems, a stable internet connection and a secure environment free from cyber-attacks are required. However, the quality of the internet is often low in developing countries, which makes it difficult to access the services these systems offer. Additionally, the lack of localization in voice assistants prevents people from using voice-assisted home automation systems in these countries. To address these challenges, this research proposes an offline home automation system. Since the internet and cloud services are not required for an offline system, it can perform its essential functions, while ensuring protection against cyber-attacks and can provide quick responses. It offers additional features, such as power usage tracking and the optimization of linked devices.
LSTMAtU-Net: A Precipitation Nowcasting Model Based on ECSA Module
Authors: Geng Huantong , Ge Xiaoyan , Xie Boyang , Min Jinzhong , Zhuang Xiaoran
Published: June 2023

Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0–2 h. Existing methods use radar echo maps and the Z–R relationship to directly predict future rainfall rates through deep learning methods, which are not physically constrained, but suffer from severe loss of predicted image details. This paper proposes a new model framework to effectively solve this problem, namely LSTMAtU-Net. It is based on the U-Net architecture, equipped with a Convolutional LSTM (ConvLSTM) unit with the vertical flow direction and depthwise-separable convolution, and we propose a new component, the Efficient Channel and Space Attention (ECSA) module. The ConvLSTM unit with the vertical flow direction memorizes temporal changes by extracting features from different levels of the convolutional layers, while the ECSA module innovatively integrates different structural information of each layer of U-Net into the channelwise attention mechanism to learn channel and spatial information, thereby enhancing attention to the details of precipitation images. The experimental results showed that the performance of the model on the test dataset was better than other examined models and improved the accuracy of medium- and high-intensity precipitation nowcasting.
Defect Detection Algorithm for Wing Skin with Stiffener Based on Phased-Array Ultrasonic Imaging
Authors: Wu Chuangui , Xu GuiLi , Shan Yimeng , Fan Xin , Zhang Xiaohui , Liu Yaxing
Published: June 2023

In response to the real-time imaging detection requirements of structural defects in the R region of rib-stiffened wing skin, a defect detection algorithm based on phased-array ultrasonic imaging for wing skin with stiffener is proposed. We select the full-matrix–full-focusing algorithm with the best imaging quality as the prototype for the required detection algorithm. To address the problem of poor real-time performance of the algorithm, a sparsity-based full-focusing algorithm with symmetry redundancy imaging mode is proposed. To address noise artifacts, an adaptive beamforming method and an equal-acoustic-path echo dynamic removal scheme are proposed to adaptively suppress noise artifacts. Finally, within 0.5 s of imaging time, the algorithm achieves a detection sensitivity of 1 mm and a resolution of 0.5 mm within a single-frame imaging range of 30 mm × 30 mm. The defect detection algorithm proposed in this paper combines phased-array ultrasonic technology and post-processing imaging technology to improve the real-time performance and noise artifact suppression of ultrasound imaging algorithms based on engineering applications. Compared with traditional single-element ultrasonic detection technology, phased-array detection technology based on post-processing algorithms has better defect detection and imaging characterization performance and is suitable for R-region structural detection scenarios.
Real-Time Detection of Intruders Using an Acoustic Sensor and Internet-of-Things Computing
Authors: Al-Khalli Najeeb , Alateeq Saud , Almansour Mohammed , Alhassoun Yousef , Ibrahim Ahmed B. , Alshebeili Saleh A.
Published: June 2023

Modern home automation systems include features that enhance security, such as cameras and radars. This paper proposes an innovative home security system that can detect burglars by analyzing acoustic signals and instantly notifying the authorized person(s). The system architecture incorporates the concept of the Internet of Things (IoT), resulting in a network and a user-friendly system. The proposed system uses an adaptive detection algorithm, namely the “short-time-average through long-time-average” algorithm. The proposed algorithm is implemented by an IoT device (Arduino Duo) to detect people’s acoustical activities for the purpose of home/office security. The performance of the proposed system is evaluated using 10 acoustic signals representing actual events and background noise. The acoustic signals were generated by the sounds of keys shaking, the falling of a small object, the shrinking of a plastic bag, speaking, footsteps, etc. The effects of different algorithms’ parameters on the performance of the proposed system have been thoroughly investigated.
HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using Computed Tomography Images and Text
Authors: Li Ruiyang , Yang Fujun , Liu Xianjie , Shi Hongwei
Published: June 2023

Prosthetic joint infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients’ numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based Feature Fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4% and an area under the curve (AUC) of 95.9%, outperforming recent multimodal approaches by 2.9% in ACC and 2.2% in AUC, with a parameter count of only 68 M. Notably, the interpretability results highlighted our model’s strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.
A Flexible Traffic Signal Coordinated Control Approach and System on Complicated Transportation Control Infrastructure
Authors: Chen Songhang , Shang Chunlin , Zhu Fenghua
Published: June 2023

The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach.
Developing a Novel Terahertz Fabry–Perot Microcavity Biosensor by Incorporating Porous Film for Yeast Sensing
Authors: Kim Hwan Sik , Jun Seung Won , Ahn Yeong Hwan
Published: June 2023

We present a novel terahertz (THz) Fabry–Perot (FP) microcavity biosensor that uses a porous polytetrafluoroethylene (PTFE) supporting film to improve microorganism detection. The THz FP microcavity confines and enhances fields in the middle of the cavity, where the target microbial film is placed with the aid of a PTFE film having a dielectric constant close to unity in the THz range. The resonant frequency shift increased linearly with increasing amount of yeasts, without showing saturation behavior under our experimental conditions. These results agree well with finite-difference time-domain (FDTD) simulations. The sensor’s sensitivity was 11.7 GHz/μm, close to the optimal condition of 12.5 GHz/μm, when yeast was placed at the cavity’s center, but no frequency shift was observed when the yeast was coated on the mirror side. We derived an explicit relation for the frequency shift as a function of the index, amount, and location of the substances that is consistent with the electric field distribution across the cavity. We also produced THz transmission images of yeast-coated PTFE, mapping the frequency shift of the FP resonance and revealing the spatial distribution of yeast.
Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent
Authors: Nicola Marcel , Nicola Claudiu-Ionel , Ionete Cosmin , Șendrescu Dorin , Roman Monica
Published: June 2023

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance Rs and combined inertia of rotor and load J) and changes in load torque TL can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.
Multiple Precaching Vehicle Selection Scheme Based on Set Ranking in Intermittently Connected Vehicular Networks
Authors: Nam Youngju , Bang Jaejeong , Choi Hyunseok , Shin Yongje , Oh Seungmin , Lee Euisin
Published: June 2023

In vehicular networks, vehicles download vehicular information for various applications, including safety, convenience, entertainment, and social interaction, from the corresponding content servers via stationary roadside units. Since sufficient RSUs might be difficult to deploy due to rough geographical conditions or high deployment costs, vehicular networks can feature uncovered outage zones between two neighboring RSUs. In these outage zones, vehicles cannot download content, and thus the vehicle networks are defined as intermittently connected vehicular networks. In intermittently connected vehicular networks, the download delay and traffic overhead on the backhaul links are increased due to the large size of the content requested by vehicle users and the long distances between RSUs. Using the mobility information of vehicles, several schemes have been proposed to solve this issue by precaching and relaying content via multiple relaying vehicles in the outage zone. However, because they involved the individual ranking of vehicles for precaching and allocated all of the available precaching amounts to the top-ranking vehicles, they decreased the success rate of content requests and the fairness of vehicle precaching. To overcome the problem of these previous schemes, this paper proposes a multiple precaching vehicle selection (MPVS) scheme that efficiently selects a content-precaching vehicle group with multiple precaching vehicles to precache relayed content in outage zones. To achieve this, we first designed numerical models to decide the necessity and the amount of precaching and to calculate the available precaching amounts of vehicles. Next, MPVS calculates all available vehicle sets and ranks each set based on the available precaching amount. Then, the content-precaching vehicle group is identified from the sets by considering both set rankings and vehicle communication overheads. MPVS also provides a content downloading process through the content-precaching vehicle group in the outage zone. Simulation results conducted in various environments with a content request model and a highway mobility model verified that MPVS was superior to a representative previous scheme.
An Approach for 3D Modeling of the Regular Relief Surface Topography Formed by a Ball Burnishing Process Using 2D Images and Measured Profilograms
Authors: Slavov Stoyan , Van Lyubomir Si Bao , Dimitrov Diyan , Nikolov Boris
Published: June 2023

Advanced in the present paper is an innovative approach for three-dimensional modeling of the regular relief topography formed via a ball burnishing process. The proposed methodology involves capturing a greyscale image of and profile measuring the surface topography in two perpendicular directions using a stylus method. A specially developed algorithm further identifies the best match between the measured profile segment and a row or column from the captured topography image by carrying out a signal correlation assessment based on an appropriate similarity metric. To ensure accurate scaling, the image pixel grey levels are scaled with a factor calculated as being the larger ratio between the ultimate heights of the measured profilograms and the more perfectly matched image row/column. Nine different similarity metrics were tested to determine the best performing model. The developed approach was evaluated for eight distinct types of fully and partially regular reliefs, and the results reveal that the best-scaled 3D topography models are produced for the fully regular reliefs with much greater heights. Following a thorough analysis of the results obtained, at the end of the paper, we draw some conclusions and discuss potential future work.
Use of a Dielectric Sensor for Salinity Determination on an Extensive Green Roof Substrate
Authors: Kargas Georgios , Ntoulas Nikolaos , Tsapatsouli Andreas
Published: June 2023

The irrigation of extensive green roofs with recycled or saline water could contribute to the conservation of valuable drinking water supplies. In such cases, the continuous monitoring of substrate electrical conductivity (ECsw) is of immense importance for the sustainable growth of the plants growing on the green roof. The present study aimed to estimate the ECsw (pore water EC) of an extensive green roof substrate in lysimeters with the use of the WET-2 dielectric sensor. Half of the 48 lysimeters that simulated extensive green roofs had a substrate depth of 7.5 cm, while the other half had a 15 cm substrate depth. The warm season turfgrass Paspalum vaginatum ‘Platinum TE’ was established at the lysimeters, and during the summer period, it was irrigated every two days at a rate of 14 mm with NaCl solutions of various electrical conductivities (ECi): (a) 3 dS m−1, (b) 6 dS m−1, and (c) 12 dS m−1, while potable water of 0.3 dS m−1 ECi served as the control. The relation between bulk electrical conductivity, σb, and bulk dielectric permittivity, εb, of the substrate was observed to be linear for all ECi levels up to σb values of 2–2.5 dS m−1. The ECsw was predicted by employing the salinity index method which was modified to be applied to the particular case of a green roof substrate. Knowing the salinity index and organic portion (%, v/v) for a given green roof substrate, we could calculate the ECsw. It was found that the use of the salinity index method predicts reliably the ECsw up to 10–11 dS m−1, while the method overestimates ECsw at very low levels of electrical conductivity.
Low-Complexity Wideband Interference Mitigation for UWB ToA Estimation
Authors: Hechenberger Stefan , Tertinek Stefan , Arthaber Holger
Published: June 2023

Reliable time of arrival (ToA) estimation in dense multipath (DM) environments is a difficult task, especially when strong interference is present. The increasing number of multiple services in a shared spectrum comes with the demand for interference mitigation techniques. Multiple receiver elements, even in low-energy devices, allow for interference mitigation by processing coherent signals, but computational complexity has to be kept at a minimum. We propose a low-complexity, linearly constrained minimum variance (LCMV) interference mitigation approach in combination with a detection-based ToA estimator. The performance of the method within a realistic multipath and interference environment is evaluated based on measurements and simulations. A statistical analysis of the ToA estimation error is provided in terms of the mean absolute error (MAE), and the results are compared to those of a band-stop filter-based interference blocking approach. While the focus is on receivers with only two elements, an extension to multiple elements is discussed as well. Results show that the influence of strong interference can be drastically reduced, even when the interference bandwidth exceeds 60% of the signal bandwidth. Moreover, the algorithm is robust to uncertainties in the angle of arrival (AoA) of the desired signal. Based on these results, the proposed mitigation method is well suited when the interference bandwidth is large and when computational power is a critical resource.
Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method
Authors: Li Jingwen , Cai Yanting , Gong Xu , Jiang Jianwu , Lu Yanling , Meng Xiaode , Zhang Li
Published: June 2023

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.
Assessing the Efficiency of Ion Exchange Resins for the Recovery of Scandium from Sulfuric Acid Leaching Solutions
Authors: Toli Aikaterini , Mikeli Eleni , Marinos Danai , Balomenos Efthymios , Panias Dimitrios
Published: June 2023

Scandium, a valuable element with restricted production sources mainly situated in China and Russia, is typically obtained as a by-product during the production of various materials. As the demand for scandium grows in the expanding aluminum and fuel cell industries, and with significant investments in rare earth mining in the USA and Australia, there is a need to explore alternative recovery sources. This research investigates the recovery of scandium from an acid pregnant leaching solution using ion exchange resins. The pregnant leaching solution was obtained after the leaching of bauxite residue with sulfuric acid. Commercial resins with different functional groups were tested for their performance in scandium extraction. In addition, the co-adsorption of impurities, such as iron and titanium, was studied. The feed solution consisted of 12.7 mg/L Sc and main impurities of 272 mg/L Fe and 33.6 mg/L Ti and was pretreated before the ion exchange process by acidification with sulfuric acid and iron powder addition to suppress silica gel formation and minimize the Fe(III) content in the solution accordingly. Among the tested resins, a D2EHPA-impregnated resin had high selectivity for Sc towards Ti, while a monophosphonic resin was also a promising option since it had a higher capacity for Sc but co-extracted Ti. These findings offer promising opportunities for the recovery of scandium from acid leaching solutions and could contribute to addressing the growing demand for this valuable element.
Corporate Governance and Financial Reporting Quality: The Mediation Role of IFRS
Authors: Gardi Bayar , Aga Mehmet , Abdullah Nabaz Nawzad
Published: June 2023

This study investigates how corporate governance affects the financial reporting quality of selected banks in Iraq, focusing on the role of IFRS adoption. The research collected data from 298 questionnaires distributed among diverse private banks, including IS Bank, Vakif Bank, RT Bank, Cihan Bank, Bank of Iraq, and TD Bank. Sobel analysis was used to analyze the mediation between variables. The results demonstrate that IFRS adoption plays a positive mediating role in the relationship between corporate governance and financial reporting quality in private banks. The study highlights the practical benefits of implementing strong corporate governance practices and adopting IFRS, such as improved reporting quality, regulatory compliance, better decision-making, and enhanced reputation. Private banks in Iraq can utilize these findings to enhance their financial performance and reputation by shaping their accounting and governance policies. The research paper provides original insights into the positive impact of corporate governance on financial reporting quality while considering the mediating influence of IFRS adoption, making it a valuable contribution to the research community.
Continuous Removal of Dyes from Wastewater Using Banana-Peel Bioadsorbent: A Low-Cost Alternative for Wastewater Treatment
Authors: Yhon Jennifer , Mendoza Jeamilette , Osorio Efren , Domínguez María Paz
Published: June 2023

Methylene blue is a widely used dye that can have severe negative impacts on the environment and human health. Adsorption is an effective method for removing it from wastewater, but the high cost of traditional adsorbents has motivated the research into low-cost alternatives derived from waste biomass. Designing a dye-removal process requires the knowledge of breakthrough curves. This paper assesses the use of banana peel as an adsorbent in a continuous process for the removal of methylene blue. The adsorption efficiency of lab-scale continuous systems using a stock solution of 0.5 g L−1 methylene blue was analyzed. The best performance was found at pH 6, with a particle size of 0.08–0.3 mm and a fixed bed height of 7.5 cm. The total adsorption capacity was 22.11 mg/g based on experimental data and 25.40 mg/g based on mathematical modeling (Thomas model). The saturation time was 53 h. According to the results, the process conditions and adsorbent characteristics have a critical role in the design of continuous adsorption systems. However, further research is needed to generalize conclusions about the process and include additional experimental data under different operating conditions.
Digital Twin: An Added Value for Digital CONWIP in the Context of Industry 4.0
Authors: Benhamou Latifa , Lamouri Samir , Burlat Patrick , Giard Vincent
Published: June 2023

Despite technological progress and a large amount of research on Industry 4.0, digital transformation remains a complex process that most manufacturers are hesitant to invest in. Interest in digital Kanban, for example, remains low compared with traditional Kanban, which is widely used. This applies to the other card-based production control systems, including CONstant Work-In-Process (CONWIP), which is the focus of this paper. In an industrial context where digitization and Industry 4.0 are the main trends, one may wonder why traditional CONWIP is preferred to digital CONWIP. Following a praxeological approach (i.e., study of practice and instrumentation), this article explores the strengths and weaknesses of the CONWIP practice, in both its paper and electronic versions, while taking into account the human dimension. The aim is to motivate potential CONWIP users to implement it in its digital mode and to show them how a Digital Twin-based solution can overcome the managerial problems that arise with digitization while enabling improved performance. As an illustration, experience feedback from several companies using Digital Twin with CONWIP is provided.
Selection of Superior Senna macranthera Seeds, Carbon Stock, and Seedling Survival, and Costs for Habitat Restoration
Authors: Araújo Joyce de Oliveira , Pinheiro Daniel Teixeira , Queiroz Geovana Brito , Soares Júlia Martins , Hoshide Aaron Kinyu , Morais Junior Vicente Toledo Machado de , Rocha Samuel José Silva Soares da , Santos Dias Denise Cunha Fernandes dos
Published: June 2023

Conservation and recovery of degraded areas generate great demand for seeds of native tree species. The development and/or improvement of efficient techniques for the evaluation of forest-seed quality is important for the production and establishment of high-quality seedlings for restoration. In this study, the tissue density of radiographic images of Senna macranthera seeds was related to their physiological quality. Moreover, biomass, carbon stock, seedling survival, and X-ray technique costs were estimated for S. macranthera. Collected seeds were analyzed using digital radiography to measure relative and integrated density. The physical integrity of seed tissues was visually evaluated. Seeds were then germination tested to assess seedling development-related traits. Semiautomated radiography allowed for visualizing internal seed structures and observing their density and physical-integrity differences as well as physiological quality. Moreover, seed lots with lower relative and integrated densities had more physical damage and/or malformation, thus producing less vigorous seedlings. The average carbon stock was 21.42 kg per tree. The seed selection cost was USD 0.0132/seed at an 81% germination rate. The annual cost of planting S. macranthera seedlings was USD 7500 per hectare during the establishment year and averaged USD 1562 per year for replanting lost transplants over the eight years after initial planting. Applying these techniques may enhance the seedling production of this species, contributing to reforestation programs in Brazil.
The Impact of YouTube in Tourism Destinations: A Methodological Proposal to Qualitatively Measure Image Positioning—Case: Saudi Arabia
Authors: Jara-Amézaga Christian
Published: June 2023

The purpose of this research is, through a methodological proposal, to determine the representation of the image of a destination generated by the travel youtubers of the gastronomic segment. This is the case of Saudi Arabia and its gastronomic image. To identify the gastronomic image of Saudi Arabia, the theories of social representation, agenda setting, and framing will be used to analyze the videos of two important gastronomic youtubers and their effects on their audiences. Overall, this study supports the idea that new communication channels can be used to verify the effectiveness, adapting the methodologies used in traditional media as well as taking advantage of processing software for qualitative and quantitative data such as Atlasti. The proposed study can contribute to methodological development in the field, and the insights gained by its application can influence tourism marketing and media planning in tourism organizations. The results show several positioning attributes detected in the public frame as amazing, hospitality, delicious, and culture as well as in the media frame were found attributes such as delicious, fresh, camel, liver, lamb, ghee, spices, rice, etc.
Research on Evolutionary Game and Simulation of Information Sharing in Prefabricated Building Supply Chain
Authors: Zhang Rumeng , Li Lihong
Published: June 2023

Enterprises in the prefabricated building supply chain (PBSC) only share information according to their interests, which is bound to cause conflicts of interest and reduce the efficiency of supply chain operations. To promote information sharing (IS) in PBSC, it is necessary to construct an evolutionary game model that fits the realistic network. In this paper, based on the integration of existing research, 13 influencing factors of IS in PBSC are analyzed comprehensively from the perspective of information ecology theory. In addition, due to the complexity and uncertainty of the PBSC, enterprise interaction and supply chain network structure affect the IS decision. Therefore, this paper builds an evolutionary game model of IS in PBSC under a scale-free network, and conducts numerical simulation analysis with MATLAB 2017 software to analyze the evolution law of enterprise IS under different situations. The results show that (1) when the network scale is large, the density of information sharers generally increases, and the speed of network evolution to a steady state generally slows down; (2) eight factors can promote the increase in information sharers’ density, and five factors can inhibit it, but factors have no significant effect on the speed of network evolution to reach the steady state. Based on the simulation results, this paper proposes countermeasures and suggestions such as strengthening the support of the policy environment and social environment, setting up the demonstration benchmark of leading construction enterprises, establishing a directional information resource database, and improving information technologies and risk management systems to provide the scientific basis for government supervision and enterprise decision making.
Prevalence of Enterovirus in Water Consumed in Rural Areas in a State in the Midwest Region of Brazil
Authors: Bordoni Graziela Picciola , Barbosa Lucas Candido Gonçalves , Oliveira Thais Reis , Lima Fernando Santos , Goes Viviane Monteiro , Estrela Mariely Cordeiro , de Souza Priscila Zanette , de Oliveira Santos Mônica , de Souza Guilherme Rocha Lino , Vieira José Daniel Gonçalves , Scalize Paulo Sérgio , Carneiro Lilian Carla
Published: June 2023

Individuals in rural communities often obtain water from surface and groundwater sources, where the microbial quality is often unknown. Enteric viruses are among the main pathogenic microorganisms responsible for waterborne disease outbreaks. Thus, the objective of this work was to search for enterovirus in water samples from 25 rural communities. For this, 160 water samples were collected. Detection and quantification of the enterovirus (EV) were performed through molecular tests using the two main amplification reagents for qPCR. The prevalence of EV was identified in 4.4% (7/160) of the samples when Sybr Green® was used, all in groundwater sources. Additionally, EV was found in 9.7% of shallow tubular wells, 3.8% of deep tubular wells, 4.3% of shallow dug wells and 5.9% of spring water. When using TaqMan®, there was no amplification of the EV cDNA. Conclusions: Sybr Green®, being a more accessible reagent, has a greater predilection for molecular tests, but the study showed that Taqman® could suffer less interference from environmental samples, resulting in more reliable values of viral quantification. In this context, the detection of EV in groundwater can help in monitoring the virus in this source, in addition to helping managers of these communities in decision making.
Reflective Glimpses of Culture in EFL Online Classes during COVID-19 Pandemic in Oman
Authors: Salih Abdelrahman Abdalla , Omar Lamis Ismail
Published: June 2023

The onset of the COVID-19 pandemic has made online learning the most on-demand fashion that almost the whole world aspires to navigate. Much of the literature on coronavirus-triggered online learning focuses on technological challenges, features of online learning platforms, teacher training, and professional development. However, an equally significant aspect that awaits researchers’ attention is interculturality in the online language classroom, particularly the presence of students’ cultural values in the virtual classroom environment in relation to teaching and learning. This study examines aspects of cultural values that either facilitate or impede learning and the strategies teachers adopt in their encounters with instances of culture in the classroom. Using the qualitative approach, two groups of 75 English as a Foreign Language (EFL) undergraduate learners and 17 expatriate teachers of English from an Omani Higher Learning Institution responded to the open-ended survey questions. Participants’ responses and observations were thoroughly analyzed and grouped into various themes and domains to facilitate uncovering the presence of culture in EFL online classrooms. Results reveal cultural values that resisted online learning and hindered class interactive spaces as well as cultural values considered normal in onsite classes. The study highlights the significance of understanding the role of students and teachers in negotiating cultural constraints and the possible strategies to overcome them including engagement, direct communication, and institutional empowerment of teachers’ role in pedagogical effectiveness.
Estimation of Natural Ventilation Rates in an Office Room with 145 mm-Diameter Circular Openings Using the Occupant-Generated Tracer-Gas Method
Authors: Seol Hyeonji , Arztmann Daniel , Kim Naree , Balderrama Alvaro
Published: June 2023

Natural ventilation in a building is an effective way to achieve acceptable indoor air quality. Ventilation dilutes contaminants such as bioeffluents generated by occupants, substances emitted from building materials, and the water vapor generated by occupants’ activities. In a building that requires heating and cooling, adequate ventilation is crucial to minimize energy consumption while maintaining healthy indoor air quality. However, measuring the actual magnitude of the natural ventilation rate, including infiltration through the building envelope and airflow through the building openings, is not always feasible. Although international and national standards suggested the required ventilation rates to maintain acceptable indoor air quality in buildings, they did not offer action plans to achieve or evaluate those design ventilation rates in buildings in use. In this study, the occupant-generated carbon dioxide (CO2) tracer gas decay method was applied to estimate the ventilation rates in an office room in Seoul, South Korea, from summer to winter. Using the method, real-time ventilation rates can be calculated by monitoring indoor and outdoor CO2 concentrations without injecting a tracer gas. For natural ventilation in the test room, 145 mm-diameter circular openings on the fixed glass were used. As a result, first, the indoor CO2 concentrations were used as an indicator to evaluate how much the indoor air quality deteriorated when all the windows were closed in an occupied office room compared to the international standards for indoor air quality. Moreover, we found out that the estimated ventilation rates varied depending on various environmental conditions, even with the same openings for natural ventilation. Considering the indoor and outdoor temperature differences and outdoor wind speeds as the main factors influencing the ventilation rates, we analyzed how they affected the ventilation rates in the different seasons of South Korea. When the wind speeds were calm, less than 2 m/s, the temperature difference played as a factor that influenced the estimated ventilation rates. On the other hand, when the temperature differences were low, less than 3 °C, the wind speed was the primary factor. This study raises awareness about the risk of poor indoor air quality in office rooms that could lead to health problems or unpleasant working environments. This study presents an example of estimating the ventilation rates in an existing building. By using the presented method, the ventilation rate in an existing building can be simply estimated while using the building as usual, and appropriate ventilation strategies for the building can be determined to maintain the desired indoor air quality.
Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses
Authors: Baptista José , Jesus Beatriz , Cerveira Adelaide , Pires Eduardo J. Solteiro
Published: June 2023

The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.