bluuebunny/crossref_metadata_2025 huggingface dataset:
Morphological and Molecular Diversity among Pin Nematodes of the Genus Paratylenchus (Nematoda: Paratylenchidae) from Florida and Other Localities and Molecular Phylogeny of the Genus
Authors: Álvarez-Ortega Sergio , Subbotin Sergei A. , Wang Koon-Hui , Stanley Jason D. , Vau Silvia , Crow William , Inserra Renato N.
Published: July 2023

Pin nematodes (Paratylenchus spp.) are root parasites found worldwide. They have different life cycles and feeding habits and can damage a wide range of plants. A remarkable diversity of pin nematode species was found in soil samples from Florida and other states of the USA, Canada, and Spain. Using integrative taxonomy, two new species (Paratylenchus hawaiiensis sp. n. and P. roboris sp. n.), six valid species (Paratylenchulus acti, P. aquaticus, P. goldeni, P. paralatescens, P. minutus (=P. shenzhenensis syn. n.), and P. straeleni), and two undescribed species were identified from Florida; P. goldeni, P. hamatus, P. hamicaudatus, P. holdemani, and P. pedrami were found in California, P. minutus in Hawaii, P. goldeni in Oregon and Washington, and one new species, Paratylenchus borealis sp. n., in Alaska. Outside the USA, Paratylenchus projectus was detected in samples from Canada and Spain as well as P. holdemani and Paratylenchus sp. from Spain. The pin nematode species from Belgium and Russia identified in former studies as Paratylenchus sp. F was herein described as a new species with the name of P. borealis sp. n., using a population from Alaska. Previously reported molecular type A of P. aquaticus from Hawaii was reclassified as P. hawaiiensis sp. n., using a population from Florida. Paratylenchus roboris sp. n. from Florida has obese sedentary females with a stylet 63–71 µm long. The results of the molecular analysis of P. shenzhenensis from Florida and China indicated that it was conspecific with P. minutus from Hawaii and considered here as its junior synonym. New 26 D2–D3 expansion segments of 28S rRNA, 17 ITS rRNA, and 20 COI gene sequences were obtained in this study. Phylogenetic relationships of Paratylenchus are reconstructed using the D2–D3 of 28S rRNA, ITS rRNA, and COI gene sequences. Congruence of molecular and morphological evolution and species identification problems are discussed. Obese females were found in two major clades of Paratylenchus. The problem of reference materials is discussed, and it is proposed to make more efforts to collect topotype materials of known Paratylenchus species for molecular study.
Structural and Optical Characterizations of Polymethyl Methacrylate Films with the Incorporation of Ultrafine SiO2/TiO2 Composites Utilized as Self-Cleaning Surfaces
Authors: Songpanit Maneerat , Boonyarattanakalin Kanokthip , Limwichean Saksorn , Lertvanithphol Tossaporn , Horprathum Mati , Pecharapa Wisanu , Mekprasart Wanichaya
Published: July 2023

The structural and optical characterizations of nanocomposite films of polymethyl methacrylate (PMMA) and SiO2/TiO2 composites prepared via the spin-coating technique were investigated using different SiO2:TiO2 ratios. The SiO2/TiO2 nanocomposites were synthesized using the sonochemical process with Si:Ti precursor ratios of 1:0.1, 1:0.5, 1:1, 1:2, 1:4, and 0:1. All characterizations of ultrafine SiO2/TiO2 particles were loaded at 1 wt.% in a PMMA matrix for the fabrication of transparent SiO2/TiO2/PMMA composite films. The phase structure and morphology of SiO2/TiO2/PMMA composite films were monitored using X-ray diffraction, optical microscopy, and field-emission scanning electron microscopy. A surface roughness analysis of SiO2/TiO2/PMMA nanocomposite films was conducted using atomic force microscopy. For optical characterization, transmission properties with different incident angles of SiO2/TiO2/PMMA composite films were analyzed with UV-vis spectrophotometry. The water contact angles of SiO2/TiO2/PMMA composite films were analyzed to identify hydrophilic properties on film surfaces. Photocatalytic reactions in SiO2TiO2 composite films under UV irradiation were evaluated using rhodamine B dye degradation. The optimal condition of SiO2/TiO2/PMMA nanocomposite films was obtained at a 1:1 SiO2:TiO2 ratio in self-cleaning applications, resulting from good particle dispersion and the presence of the TiO2 phase in the composite.
Estimating APC Model Parameters for Dynamic Intervals Determined Using Change-Point Detection in Continuous Processes in the Petrochemical Industry
Authors: Yu Yoseb , Lee Minyeob , Lee Chaekyu , Cheon Yewon , Baek Seungyun , Kim Youngmin , Kim Kyungmin , Jung Heechan , Lim Dohyeon , Byun Hyogeun , Jeong Jongpil
Published: July 2023

Several papers have proven that advanced process controller (APC) systems can save more energy in the process than proportional-integral-differential (PID) controller systems. Therefore, implementing an APC system is ultimately beneficial for saving energy in the plant. In a typical APC system deployment, the APC model parameters are calculated from dynamic data intervals obtained through the plant test. However, depending on the proficiency of the APC engineer, the results of the plant test and the APC model parameters are implemented differently. To minimize the influence of the APC engineer and calculate universal APC model parameters, a technique is needed to obtain dynamic data without a plant test. In this study, we utilize time-series data from a real petrochemical plant to determine dynamic intervals and estimate APC model parameters, which have not been investigated in previous studies. This involves extracting the data of the dynamic intervals with the smallest mean absolute error (MAE) by utilizing statistical techniques such as pruned exact linear time, linear kernel, and radial basis function kernel of change-point detection (CPD). After that, we fix the hyper parameters at the minimum MAE value and estimate the APC model parameters by training with the data from the dynamic intervals. The estimated APC model parameters are applied to the APC program to compare the APC model fitting rate and verify the accuracy of the APC model parameters in the dynamic intervals obtained through CPD. The final validation of the model fitting rates demonstrates that the identification of the dynamic intervals and the estimation of the APC model parameters through CPD show high accuracy. We show that it is possible to estimate APC model parameters from dynamic intervals determined by CPD without a plant test.
Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform
Authors: Zhang Yong , He Guangjun , Li Guangjian
Published: July 2023

Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy process identifies the fluctuations in electrical signals that occur during distribution intervals. The linear relationship between two peak wavelets throughout the intervals are verified across successive distribution phases. In this paper, non-recurrent validation for these fluctuations is considered based on the limits found between the power drop and failure. This modification is used for preventing surge-based faults due to external signals. The inference process hinders the distribution of new devices and re-assigns them based on availability and the peak load experienced. Therefore, the device from which the inference outputs are taken is non-linear, and the frequently employed wavelet transforms are recommended for replacement or diagnosis. This method improves the fault detection process and ensures minimal distribution failures.
Comparison of Four Purification Methods on Serum Extracellular Vesicle Recovery, Size Distribution, and Proteomics
Authors: Jimenez Dianny Elizabeth , Tahir Muhammad , Faheem Muhammad , Alves Wellington Bruno dos Santos , Correa Barbara de Lucena , Andrade Gabriel Rocha de , Larsen Martin R. , Oliveira Getulio Pereira de , Pereira Rinaldo Wellerson
Published: July 2023

In recent decades, the role played by extracellular vesicles in physiological and pathological processes has attracted attention. Extracellular vesicles are released by different types of cells and carry molecules that could become biomarkers for the diagnosis of diseases. Extracellular vesicles are also moldable tools for the controlled release of bioactive substances in clinical and therapeutic applications. However, one of the significant challenges when studying these exciting and versatile vesicles is the purification process, which presents significant difficulties in terms of lack of purity, yield, and reproducibility, reflected in unreliable data. Therefore, our objective in the present study was to compare the proteomic profile of serum-derived EVs purified using ExoQuick™ (Systems Biosciences), Total Isolation Kit (Life Technologies), Ultracentrifugation, and Ultrafiltration. Each technique utilized for purification has shown different concentrations and populations of purified particles. The results showed marked differences in distribution, size, and protein content, demonstrating the need to develop reproducible and reliable protocols to isolate extracellular vesicles for their clinical application.
Cheng Yu’s Response to the Moral Crisis and the Modern Fate of Confucian China
Authors: Yao Dadui
Published: July 2023

Cheng Yu is a significant yet underexplored figure in modern Chinese history. His ideas on Confucianism were closely linked to three pivotal moments of crisis in modern Chinese history: The First Sino-Japanese War, the May Fourth Movement, and the Second Sino-Japanese War. The First Sino-Japanese War led to the Qing government’s Hundred Days Reform, initiated by Kang Youwei in 1898. When the Reform failed, Cheng, the secretary of Timothy Richard, assisted Kang in his escape and became Kang’s disciple. In 1906, he traveled to Japan to investigate the development of industries, manufacturing, and hospitals, but his primary interest lay in Japan’s educational model. Cheng believed that China’s education lacked moral advancement compared to Japan’s, and he deemed it necessary to promote practical learning and moral education. Following the May Fourth Movement in 1919, he vigorously advocated moral instruction and hoped the government would establish Confucianism as the state religion. He believed it was the only way the government could preserve the country’s culture and save China from imminent destruction. Cheng’s ideas of Confucianism were emblematic of his era. In 1938, he attempted to reform Confucianism and promote Confucian moral education in the school system by cultivating the Chinese people’s cultural confidence and national identity. Cheng’s solution to China’s moral crisis was a response to the challenging question of the modern fate of Confucian China.
DSSFN: A Dual-Stream Self-Attention Fusion Network for Effective Hyperspectral Image Classification
Authors: Yang Zian , Zheng Nairong , Wang Feng
Published: July 2023

Hyperspectral images possess a continuous and analogous spectral nature, enabling the classification of distinctive information by analyzing the subtle variations between adjacent spectra. Meanwhile, a hyperspectral dataset includes redundant and noisy information in addition to larger dimensions, which is the primary barrier preventing its use for land cover categorization. Despite the excellent feature extraction capability exhibited by convolutional neural networks, its efficacy is restricted by the constrained receptive field and the inability to acquire long-range features due to the limited size of the convolutional kernels. We construct a dual-stream self-attention fusion network (DSSFN) that combines spectral and spatial information in order to achieve the deep mining of global information via a self-attention mechanism. In addition, dimensionality reduction is required to reduce redundant data and eliminate noisy bands, hence enhancing the performance of hyperspectral classification. A unique band selection algorithm is proposed in this study. This algorithm, which is based on a sliding window grouped normalized matching filter for nearby bands (SWGMF), can minimize the dimensionality of the data while preserving the corresponding spectral information. Comprehensive experiments are carried out on four well-known hyperspectral datasets, where the proposed DSSFN achieves higher classification results in terms of overall accuracy (OA), average accuracy (AA), and kappa than previous approaches. A variety of trials verify the superiority and huge potential of DSSFN.
Characterization of Nitrogen Dioxide Variability Using Ground-Based and Satellite Remote Sensing and In Situ Measurements in the Tiber Valley (Lazio, Italy)
Authors: Bassani Cristiana , Vichi Francesca , Esposito Giulio , Falasca Serena , Di Bernardino Annalisa , Battistelli Francesca , Casadio Stefano , Iannarelli Anna Maria , Ianniello Antonietta
Published: July 2023

The spatial–temporal distributions of nitrogen dioxide (NO2) in a rural area of Tiber valley were evaluated over one year (March 2022–February 2023) using remote sensing and in situ measurements. Surface concentration monitoring was conducted using a Pandora-2s spectrometer and a chemiluminescence analyzer operated at the Liberti Observatory (CNR-IIA). In spring, when the growing season and the agricultural activities increase, NO2 peaks were detectable by the Pandora but not by the in situ analyzer. The tropospheric Pandora and TROPOMI VCD products showed similar temporal patterns as those of the analyzer at the Observatory. High TROPOMI VCD levels in spring were detected at the Observatory and at six sites selected as representative of rural, residential, and industrial environments. WRF simulations found that high pollution events, observed by the Pandora and analyzer, occurred in calm wind conditions, favouring the accumulation of NO2 locally emitted. The complementary dataset provided by remote sensing and in situ techniques efficiently captured the spatial–temporal NO2 variability in a rural site exposed to low emission sources, thus supporting future decisional policies and actions.
Development of a UAV Based Framework for CH4 Monitoring in Sludge Treatment Centres
Authors: Abeywickrama Hiniduma Gamage Kavindi , Bajón-Fernández Yadira , Srinamasivayam Bharanitharan , Turner Duncan , Rivas Casado Mónica
Published: July 2023

With the increasing trend in the global average temperature, the UK’s water industry has committed to achieve Net Zero by 2030 and part of this includes cutting CH4 emissions from sludge treatment facilities. Currently, emissions are estimated following the carbon accounting workbook guidelines and using default emission factors. However, this method might not be a true representation of emissions as these vary depending on many factors. The use of unmanned aerial vehicles (UAVs) has proved cost effective for environmental monitoring tasks requiring high spatial resolution information. Within the context of CH4 emissions and in the last decade, the technology has been curtailed by sensor weight and size. Recent advances in sensor technology have enabled the development of a fit-for purpose UAV CH4 sensor (U10) which uses Tuneable Diode Laser Absorption Spectroscopy. This study intends to develop a framework for CH4 data collection strategies from sludge treatment centres using UAV-U10 technology and asset level CH4 enhancement estimations based on geostatistical interpolation techniques and the mass balance approach. The framework presented here enables the characterization of spatial and temporal variations in CH4 concentrations. It promotes asset level CH4 enhancement estimation based on on-site measurements.
Sparse Direct Position Determination Based on TDOA Information in Correlation-Domain
Authors: Jiang Hang , Li Jianfeng , Zhu Kehui , Li Yingying
Published: July 2023

The sparse direct position determination (DPD) method requires reconstructing the emitter position with prior knowledge. However, in non-cooperative localization scenarios, it is difficult to reconstruct the transmitted signal with the unknown signal form and propagation model. In this paper, a sparse DPD method based on time-difference-of-arrival (TDOA) information in correlation-domain is proposed. Different from the traditional sparse DPD method, the received signal is converted into correlation-domain, and the proposed dictionary matrix is generated by the quantized delay difference, which solves the pseudo-positioning problem. Compared to the conventional multi-signal classification (MUSIC) method, multi-frequency fusion (MFF) method, and two-step positioning algorithm, the proposed algorithm achieves higher positioning accuracy. The feasibility of the algorithm has been verified by both simulation and real-world measured tests.
Spire RO Thermal Profiles for Climate Studies: Initial Comparisons of the Measurements from Spire, NOAA-20 ATMS, Radiosonde, and COSMIC-2
Authors: Jing Xin , Ho Shu-Peng , Shao Xi , Liu Tung-Chang , Chen Yong , Zhou Xinjia
Published: July 2023

Global Navigation Satellite System (GNSS) Radio Occultation (RO) data play an essential role in improving numerical weather prediction (NWP) and monitoring climate change. The NOAA Commercial RO Purchase Program (CDP) purchased RO data provided by Spire Global Inc. To ensure the data quality from Spire Global Inc. is consistent with other RO missions, we need to quantify their accuracy and retrieval uncertainty carefully. In this work, Spire Wet Profile (wet temperature profile) data from 7 September 2021 to 31 October 2022, processed by the University Corporation for Atmospheric Research (UCAR), and COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere, and Climate-2/Formosa Satellite Mission 7) data are evaluated through comparison with NOAA-20 Advanced Technology Microwave Sounder (ATMS) microwave sounder measurements and collocated RS41 radiosonde measurements. Through the Community Radiative Transfer Model (CRTM) simulation, we convert the Spire and COSMIC-2 RO retrievals to ATMS brightness temperature (BT) at sounding channels CH07 to CH14 (temperature channels), with weighting function peak heights from 8 km to 35 km, and CH19 to CH22 (water vapor channels), with weighting function peak heights ranging from 3.2 km to 6.7 km, and compare the simulations with the collocated NOAA-20 ATMS measurements over ocean. Using ATMS observations as references, Spire and COSMIC-2 BTs agree well with ATMS within 0.07 K for CH07-14 and 0.20 K for CH19-22. The trends between Spire and COSMIC-2 are consistent within 0.07 K/year over the oceans for ATMS CH07-CH13 and CH19-22, indicating that Spire/COSMIC-2 wet profiles are, in general, compatible with each other over oceans. The RO retrievals and RS41 radiosonde observation (RAOB) comparison shows that above 0.2 km altitude, RS41 RAOB matches Spire/COSMIC-2 temperature profiles well with a temperature difference of <0.13 K, and the trends between Spire and COSMIC-2 are consistent within 0.08 K/year over land, indicating that Spire/COSMIC-2 wet profiles are overall compatible with each other through RS41 RAOB measurements over land. In addition, the consistency of Spire and COSMIC-2 based on different latitude intervals, local times, and signal-to-noise ratios (SNRs) through ATMS was evaluated. The results show that the performance of Spire is comparable to COSMIC-2, even though COSMIC-2 has a higher SNR. The high quality of RO profiles from Spire is expected to improve short- and medium-range global numerical weather predictions and help construct consistent climate temperature records.
Comparison of Freshwater Content and Variability in the Arctic Ocean Using Observations and Model Simulations
Authors: Hoffman Emma L. , Subrahmanyam Bulusu , Trott Corinne B. , Hall Sarah B.
Published: July 2023

Freshwater content (FWC), generally characterized in the Arctic Ocean by salinities lower than 34.8 psu, has shifted in both quantity and distribution in recent decades in the Arctic Ocean. This has been largely driven by changes in the volume and salinity of freshwater sources and the direction and magnitude of major currents. In this study, we analyze the variability in FWC and other physical oceanographic variables from 1993 to 2021 in the Arctic Ocean and Beaufort Gyre (BG) using in situ and remote sensing observations and five ocean models and reanalysis products. Generally, ocean models and reanalysis products underestimate FWC in the BG when compared with observations. Modeled FWC and sea surface height (SSH) in the BG are well correlated during the time period and are similar to correlations of the observational data of these variables. ORAS5 compares best to EN4 salinity over the entire study period, although GLORYS12 agrees well pre-2007 and SODA post-2007. Outside the BG, consistency between modeled SSH, FWC, and limited observations varies between models. These comparisons help identify discrepancies in ocean model and reanalysis products while highlighting areas where future improvements are necessary to further our understanding of Arctic FWC. As observations are scarce in the Arctic, these products and their accuracy are important to studying this dynamic and vulnerable ocean.
A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
Authors: Amato Eleonora , Corradino Claudia , Torrisi Federica , Del Negro Ciro
Published: July 2023

The latest generation of high-spatial-resolution satellites produces measurements of high-temperature volcanic features at global scale, which are valuable to monitor volcanic activity. Recent advances in technology and increased computational resources have resulted in an extraordinary amount of monitoring data, which can no longer be so readily examined. Here, we present an automatic detection algorithm based on a deep convolutional neural network (CNN) that uses infrared satellite data to automatically determine the presence of volcanic thermal activity. We exploit the potentiality of the transfer learning technique to retrain a pre-trained SqueezeNet CNN to a new domain. We fine-tune the weights of the network over a new dataset opportunely created with images related to thermal anomalies of different active volcanoes around the world. Furthermore, an ensemble approach is employed to enhance accuracy and robustness when compared to using individual models. We chose a balanced training dataset with two classes, one containing volcanic thermal anomalies (erupting volcanoes) and the other containing no thermal anomalies (non-erupting volcanoes), to differentiate between volcanic scenes with eruptive and non-eruptive activity. We used satellite images acquired in the infrared bands by ESA Sentinel-2 Multispectral Instrument (MSI) and NASA & USGS Landsat 8 Operational Land Imager and Thermal InfraRed Sensor (OLI/TIRS). This deep learning approach makes the model capable of identifying the appearance of a volcanic thermal anomaly in the images belonging to the volcanic domain with an overall accuracy of 98.3%, recognizing the scene with active flows and erupting vents (i.e., eruptive activity) and the volcanoes at rest. This model is generalizable, and has the capability to analyze every image captured by these satellites over volcanoes around the world.
Real-Time Wavefront Sensing at High Resolution with an Electrically Tunable Lens
Authors: Oliva-García Ricardo , Cairós Carlos , Trujillo-Sevilla Juan M. , Velasco-Ocaña Miriam , Rodríguez-Ramos José Manuel
Published: July 2023

We have designed, assembled, and evaluated a compact instrument capable of capturing the wavefront phase in real time, across various scenarios. Our approach simplifies the optical setup and configuration, which reduces the conventional capture and computation time when compared to other methods that use two defocused images. We evaluated the feasibility of using an electrically tunable lens in our camera by addressing its issues and optimizing its performance. Additionally, we conducted a comparison study between our approach and a Shack–Hartmann sensor. The camera was tested on multiple targets, such as deformable mirrors, lenses with aberrations, and a liquid lens in movement. Working at the highest resolution of the CMOS sensor with a small effective pixel size enables us to achieve the maximum level of detail in lateral resolution, leading to increased sensitivity to high-spatial-frequency signals.
Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream
Authors: Tang Sichao , Lv Hengyi , Zhao Yuchen , Feng Yang , Liu Hailong , Bi Guoling
Published: July 2023

Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevitably present in the camera’s output results. This noise can degrade the camera’s perception of events and the performance of algorithms for processing event streams. Moreover, since the output of event cameras is in the form of address-event representation, efficient denoising methods for traditional frame images are no longer applicable in this case. Most existing denoising methods for event cameras target background activity noise and sometimes remove real events as noise. Furthermore, these methods are ineffective in handling noise generated by high-frequency flickering light sources and changes in diffused light reflection. To address these issues, we propose an event stream denoising method based on salient region recognition in this paper. This method can effectively remove conventional background activity noise as well as irregular noise caused by diffuse reflection and flickering light source changes without significantly losing real events. Additionally, we introduce an evaluation metric that can be used to assess the noise removal efficacy and the preservation of real events for various denoising methods.
QuantLaneNet: A 640-FPS and 34-GOPS/W FPGA-Based CNN Accelerator for Lane Detection
Authors: Lam Duc Khai , Du Cam Vinh , Pham Hoai Luan
Published: July 2023

Lane detection is one of the most fundamental problems in the rapidly developing field of autonomous vehicles. With the dramatic growth of deep learning in recent years, many models have achieved a high accuracy for this task. However, most existing deep-learning methods for lane detection face two main problems. First, most early studies usually follow a segmentation approach, which requires much post-processing to extract the necessary geometric information about the lane lines. Second, many models fail to reach real-time speed due to the high complexity of model architecture. To offer a solution to these problems, this paper proposes a lightweight convolutional neural network that requires only two small arrays for minimum post-processing, instead of segmentation maps for the task of lane detection. This proposed network utilizes a simple lane representation format for its output. The proposed model can achieve 93.53% accuracy on the TuSimple dataset. A hardware accelerator is proposed and implemented on the Virtex-7 VC707 FPGA platform to optimize processing time and power consumption. Several techniques, including data quantization to reduce data width down to 8-bit, exploring various loop-unrolling strategies for different convolution layers, and pipelined computation across layers, are optimized in the proposed hardware accelerator architecture. This implementation can process at 640 FPS while consuming only 10.309 W, equating to a computation throughput of 345.6 GOPS and energy efficiency of 33.52 GOPS/W.
Three-Dimensional Shape and Deformation Measurements Based on Fringe Projection Profilometry and Fluorescent Digital Image Correlation via a 3 Charge Coupled Device Camera
Authors: Sun Wei , Xu Zhongda , Li Xin , Chen Zhenning , Tang Xinqiao
Published: July 2023

We propose a novel hybrid FPP-DIC technique to measure an object’s shape and deformation in 3D simultaneously by using a single 3CCD color camera, which captures the blue fringe patterns and red fluorescent speckles within the same image. Firstly, red fluorescent speckles were painted on the surface of the specimen. Subsequently, 12 computer-generated blue fringe patterns with a black background were projected onto the surface of the specimen using a DLP projector. Finally, both the reference and deformed images with three different frequencies and four shifted phases were captured using a 3CCD camera. This technique employed a three-chip configuration in which red–green–blue chips were discretely integrated in the 3CCD color camera sensor, rendering independent capture of RGB information possible. Measurement of out-of-plane displacement was carried out through the implementation of Fringe Projection Profilometry (FPP), whereas the in-plane displacement was evaluated using a 2D Digital Image Correlation (DIC) method by leveraging a telecentric-lens-based optical system. In comparison to the traditional FPP-DIC hybrid methodology, the present approach showed a lower incidence of crosstalk between the fringe patterns and speckle patterns while also offering a corrective for the coupling of the in-plane displacement and out-of-plane displacement. Experimental results for the in-plane cantilever beam and out-of-plane disk comparisons with the traditional 3D-DIC method indicated that the maximum discrepancy obtained between FPP-DIC and 3D-DIC was 0.7 μm and 0.034 mm with different magnifications, respectively, validating the effectiveness and precision of the novel proposed FPP-DIC method.
Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations
Authors: Saeed Saqib , Altamimi Salha A. , Alkayyal Norah A. , Alshehri Ebtisam , Alabbad Dina A.
Published: July 2023

This systematic literature review explores the digital transformation (DT) and cybersecurity implications for achieving business resilience. DT involves transitioning organizational processes to IT solutions, which can result in significant changes across various aspects of an organization. However, emerging technologies such as artificial intelligence, big data and analytics, blockchain, and cloud computing drive digital transformation worldwide while increasing cybersecurity risks for businesses undergoing this process. This literature survey article highlights the importance of comprehensive knowledge of cybersecurity threats during DT implementation to prevent interruptions due to malicious activities or unauthorized access by attackers aiming at sensitive information alteration, destruction, or extortion from users. Cybersecurity is essential to DT as it protects digital assets from cyber threats. We conducted a systematic literature review using the PRISMA methodology in this research. Our literature review found that DT has increased efficiency and productivity but poses new challenges related to cybersecurity risks, such as data breaches and cyber-attacks. We conclude by discussing future vulnerabilities associated with DT implementation and provide recommendations on how organizations can mitigate these risks through effective cybersecurity measures. The paper recommends a staged cybersecurity readiness framework for business organizations to be prepared to pursue digital transformation.
Wearable Optical Fiber Sensors in Medical Monitoring Applications: A Review
Authors: Zhang Xuhui , Wang Chunyang , Zheng Tong , Wu Haibin , Wu Qing , Wang Yunzheng
Published: July 2023

Wearable optical fiber sensors have great potential for development in medical monitoring. With the increasing demand for compactness, comfort, accuracy, and other features in new medical monitoring devices, the development of wearable optical fiber sensors is increasingly meeting these requirements. This paper reviews the latest evolution of wearable optical fiber sensors in the medical field. Three types of wearable optical fiber sensors are analyzed: wearable optical fiber sensors based on Fiber Bragg grating, wearable optical fiber sensors based on light intensity changes, and wearable optical fiber sensors based on Fabry–Perot interferometry. The innovation of wearable optical fiber sensors in respiration and joint monitoring is introduced in detail, and the main principles of three kinds of wearable optical fiber sensors are summarized. In addition, we discuss their advantages, limitations, directions to improve accuracy and the challenges they face. We also look forward to future development prospects, such as the combination of wireless networks which will change how medical services are provided. Wearable optical fiber sensors offer a viable technology for prospective continuous medical surveillance and will change future medical benefits.
Intelligent IoT Platform for Multiple PV Plant Monitoring
Authors: Utama Ida Bagus Krishna Yoga , Pamungkas Radityo Fajar , Faridh Muhammad Miftah , Jang Yeong Min
Published: July 2023

Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day’s power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.
Social Media-Enabled Sustainable Communities: A Case of Indian Elected Women Representatives (EWRs)
Authors: B. M. Darshan , Suresh Kalyani
Published: July 2023

The impact of social media on communities is an ongoing topic of research. This quantitative study specifically investigates how elected women representatives (EWRs) are using social media for participatory development communication. The study employs a questionnaire to collect data from 200 EWRs and utilizes various statistical tests, including descriptive statistics, convergent validity and composite reliability analysis, structural equation modeling, and correlation analysis. The results suggest that social media can positively influence social participation, with the sense of platform and social equity having a significant impact. However, safety and security, as well as social interaction, do not show a positive impact on social participation. Additionally, social participation positively impacts social satisfaction. These findings have important implications for organizations interested in promoting participatory development communication among EWRs using social media platforms.
Comprehensive Evaluation of the High-Quality Development of the Ecological and Economic Belt along the Yellow River in Ningxia
Authors: Li Chaochao , Peng Wenfa , Shen Xiaojing , Gu Jingchao , Zhang Yadong , Li Mingyang
Published: July 2023

The measurement of high-quality economic development and regional research plays a crucial role in achieving ecologically led high-quality development. This paper aims to establish a comprehensive evaluation index system for the high-quality level of development in Ningxia’s Yellow River ecological and economic zone, focusing on five dimensions: innovation, coordination, green, openness, and shared. By employing a factor analysis, this study estimates the level of high-quality development along the Yellow River Ecological Economic Belt in Ningxia for the year 2020. A multi-index panel data factor analysis and entropy weight TOPSIS method are employed to analyze the spatiotemporal evolution characteristics of high-quality development across 10 counties from 2014 to 2020. The empirical findings reveal that, in 2020, the overall level of high-quality development in the region remained relatively low. Among the contributing factors, shared and coordinated development demonstrated the highest impact on high-quality development, while open, green, and innovative development showed limited influence. Only Xingqing District, Litong District, and Helan County in the 10 counties have a composite factor score of greater than 0 on the level of economic development, while the other 7 counties have a composite score of less than 0. The study also identified a significant spatial heterogeneity in the quality of high-quality development along Ningxia’s Yellow River Ecological Economic Belt. Through a cluster analysis, the regions along the Yellow River Ecological Economic Belt in Ningxia are classified into categories of high, medium, and low levels of quality development. Over the period from 2014 to 2020, each county exhibited a steady increase in high-quality development, while the differences in development levels among the 10 counties gradually diminished. Based on these findings, practical suggestions are provided to guide Ningxia in leading the path of high-quality development through ecological civilization construction along the Yellow River ecological economy.
Healthy or Environmentally Friendly? Meat Consumption Practices of Green Consumers in Vietnam and Switzerland
Authors: Markoni Evelyn , Ha Thanh Mai , Götze Franziska , Häberli Isabel , Ngo Minh Hai , Huwiler Reto Martin , Delley Mathilde , Nguyen Anh Duc , Bui Thi Lam , Le Nhu Thinh , Pham Bao Duong , Brunner Thomas A.
Published: July 2023

High meat consumption is a phenomenon in both developed countries such as Switzerland and emerging countries such as Vietnam. This high meat consumption is associated with environmental, social, and health consequences. Drawing upon social practice theory, this study explores the influence of social practices on the meat consumption of green consumers, as a growing number of consumers in both countries want to eat healthy and sustainably but still have different needs and face different barriers. Data were collected from online group discussions. For green consumers, meat consumption was found to convey certain meanings and depends, among other things, on the information available. The consumption decision in Vietnam is strongly influenced by health and food safety, whereas negative environmental consequences are important in Switzerland. Social and cultural aspects also play a major part in the decision to eat or abstain from meat in both countries. Meat is a non-negotiable part of any special occasion meal in Vietnam and is often eaten at social gatherings in Switzerland. We argue that meat consumption is linked to social status in both countries, but family influence is stronger in Vietnam than in Switzerland. Interventions, such as policy measures that are adapted to regional, cultural, and consumer group specificities and focus on social practices rather than individual behavior, are a promising means to promote meat reduction.
Using a Phosphorus Flow Diagram as a Boundary Object to Inform Stakeholder Engagement
Authors: Merck Ashton W. , Grieger Khara D. , Deviney Alison , Marshall Anna-Maria
Published: July 2023

Phosphorus (P) is essential for life on Earth, yet its current management is unsustainable. Stakeholder engagement is urgently needed to help ensure that scientific and technical solutions to improve P sustainability meet the needs of diverse groups, yet there are comparatively few studies that provide insights into stakeholder views, perceptions, or concerns. In this opinion, we use a mass flow diagram of P as a boundary object to understand the complex challenges of sustainable P management. In particular, we map US stakeholder groups onto the mass flow diagram to incorporate human factors into mass flows at a national scale. Our approach is grounded in well-established social–scientific methodologies, such as stakeholder mapping and social network analysis, but is applied in a novel way that can be generalized to other mass flows and geographic areas. We then suggest ways that researchers can use the annotated flow diagram to identify both knowledge gaps and research gaps in stakeholder engagement, especially in interdisciplinary or convergence research contexts.
CiteSpace-Based Visualization Analysis on the Trombe Wall in Solar Buildings
Authors: Yin Qing , Liu Hengyu , Zhou Tianfu
Published: July 2023

The Trombe Wall is the main solar collector component in solar buildings, and it has attracted much attention due to its ability to maximize solar energy utilization and reduce buildings’ energy consumption. Numerous research studies have been conducted. Bibliometrics and CiteSpace visual analysis software are used in this paper to summarize and find that the research process for the Trombe Wall can be divided into three periods: the preliminary development period, the ice-breaking period, and the comprehensive development period. Then, we visually analyze information, such as countries, institutions, authors, journals, references, and keywords, from 537 selected articles in the Web of Science (WOS) database during the period 1991–2023. The results show that current research in this area primarily focuses on “thermal comfort”, “BLC”, “PCM-Trombe Wall”, “PV-Trombe Wall”, and “BIPV system”. On this basis, potential development trends in this field and some future research issues that need to be addressed are proposed. Furthermore, the study identifies potential development trends in this area. By providing a comprehensive understanding of the current research status, research frontiers, research hotspots, and research trends in this area, this study provides valuable theoretical guidance for subsequent research.
Legal System of Soil Pollution Remediation in China and Its Regulation and Guidance to Soil Pollution Remediation
Authors: Liu Zhuyi , Yin Yuhan , Zhang Yun , Shi Shangeng
Published: July 2023

Faced with increasingly serious soil pollution problems, China has passed the Chinese Soil Pollution Prevention and Control Law (hereinafter referred to as the Law on the Prevention and Control of Soil Pollution) and established a series of soil pollution remediation systems. However, there are still some shortcomings in the current legal system for soil pollution remediation, which cannot fully solve the problems faced in soil remediation activities. The research focuses on the legal system for soil pollution remediation. This study analyzes the development process of relevant systems, focusing on the analysis of the Soil Pollution Prevention and Control Law, including the basic content and process of soil pollution prevention and control work. On this basis, this study summarizes the legal structure and practice of soil pollution control in China and analyzes the problems existing in the implementation of China’s soil pollution remediation legal system, such as imperfect remediation standards and insufficient public participation. Then, the relevant policies of EU countries are summarized and analyzed. Combined with the actual situation of China, suggestions are put forward to improve the legal system of soil pollution remediation in China and strengthen soil remediation.
Analysis of Energy Laboratory Safety Management in China Based on the System-Theoretic Accident Model and Processes/System Theoretic Process Analysis STAMP/STPA Model
Authors: Zhong Shuheng , Du Jinping , Jiang Xidi
Published: July 2023

Laboratory safety in colleges and universities has received wider attention as a critical annual inspection by the Ministry of Education. The laboratory environment is complex and diverse, with many hazard factors. To effectively prevent the occurrence of laboratory emergencies in universities, the STAMP/STPA model is used to analyze the safety of energy laboratory safety management in a resource-based university. Between 2021 and 2022, we carried out laboratory safety inspection and field observation for a mining resource university in China, and identified 16 unsafe control actions in the field of energy laboratory safety management in the university, and identified ten critical causal factors leading to unsafe control actions. Combining the actual situation of the mining resource university, the short-term countermeasures and long-term countermeasures to improve laboratory safety management are proposed to guarantee the university’s laboratory safety management. Moreover, the research results have suggestions for the construction and development of laboratory safety management at similar universities.
State-Owned Equity Participation and Corporations’ ESG Performance in China: The Mediating Role of Top Management Incentives
Authors: Qian Ting , Yang Caoyuan
Published: July 2023

This study examined the unique circumstances surrounding state-owned equity participation in enterprises in China. Specifically, this study examined the impact of state-owned equity participation on the environmental, social, and governance (ESG) performance of enterprises. Focusing on A-share listed firms on the Shanghai and Shenzhen Stock Exchanges, and using data from 2013 to 2021, the results of our empirical testing showed that state-owned equity participation could significantly improve the ESG performance of enterprises, with this conclusion remaining reliable after a series of robustness tests. Top management incentives were a mediating mechanism for state-owned equity participation in enhancing ESG performance. This study also found that when state-owned equity participated in large enterprises, or companies with a high degree of digital transformation, the effect on the ESG performance was greater than in small or medium-sized enterprises, or enterprises with a low level of digital transformation. The findings of this study add to the current body of research on the factors influencing corporate ESG performance, and the impact of state-owned equity on corporate non-financial performance.
The Dual Strategy for Textile and Fashion Production Using Clothing Waste
Authors: Lee Hyewon
Published: July 2023

This study aims to utilize a dual physical and digital strategy for the completion of a process that achieves two goals: the treatment of large amounts of clothing waste and the development of materials and products. This study expands the author’s previous research on the feasibility of using clothing waste as a textile material and the development of weaving methods. The processes of material analysis, design, material development, and product production for clothing waste were connected by the dual strategy. The project was conducted by three groups of designers for ten months and evaluated by ten experts. A total of eighteen digital products were developed, including three physical products and one digital twin. Digital and physical models were dressed and subjected to objective and in-depth evaluations by experts. The experts determined that the match rate between the physical products and digital twins was over 90% and that each process step was conducted appropriately. The process was also deemed applicable for 50% of the industrial sector and 80% of the education sector. Therefore, this study connected the quantitative disposal of garment waste to the qualitative design and production of new material, introducing a new process strategy to maintain sustainability in the fashion industry.