bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Second-Order Fixed-Time Sliding Mode Controller with a Disturbance Observer for Electronic Throttle Valves
Authors: Feng Yinkai , Long Yun , Yao Chong , Song Enzhe
Published: September 2023

In order to enhance the precision and speed of control for electronic throttle valves (ETVs) in the face of disturbance and parameter uncertainties, an adaptive second-order fixed-time sliding mode (ASOFxTSM) controller is developed, along with disturbance observer compensation techniques. Initially, a control-oriented model specifically considering lumped disturbances within the ETV is established. Secondly, to address the contradiction between fast response and heavy chattering of conventional fixed-time sliding mode, a hierarchical sliding surface approach is introduced. This approach proficiently alleviates chattering effects while preserving the fixed convergence properties of the controller. Furthermore, to enhance the anti-disturbance performance of the ETV control system, an innovative fixed-time sliding mode observer is incorporated to estimate lumped disturbances and apply them as a feed-forward compensation term to the ASOFxTSM controller output. Building upon this, a parameter adaptive mechanism is introduced to optimize control gains. Subsequently, a rigorous stability proof is conducted, accompanied by the derivation of the expression for system convergence time. Finally, a comparison is drawn between the proposed controller and fixed-time sliding mode and super-twisting controllers through simulations and experiments. The results demonstrate the superiority of the proposed method in terms of chattering suppression, rapid dynamic response, and disturbance rejection capability.
The Use of Mobile Sensors by Children: A Review of Two Decades of Environmental Education Projects
Authors: Silva Maria João , Gouveia Cristina , Gomes Cristina Azevedo
Published: September 2023

Over the past twenty years, the use of electronic mobile sensors by children and youngsters has played a significant role in environmental education projects in Portugal. This paper describes a research synthesis of a set of case studies (environmental education projects) on the use of sensors as epistemic mediators, evidencing the technological, environmental, social, and didactical dimensions of environmental education projects over the last two decades in Portugal. The triggers of the identified changes include: (i) the evolution of sensors, information and communication platforms, and mobile devices; (ii) the increasing relevance of environmental citizenship and participation; (iii) the recognition of the role of multisensory situated information and quantitative information in environmental citizenship; (iv) the cause–effect relation between didactical strategies and environmental education goals; (v) the potential of sensory and epistemic learners’ practices in the environment to produce learning outcomes and new knowledge. To support the use of senses and sensors in environmental education projects, the SEAM model was created based on the developed research synthesis.
A Phase Recovery Technique Using the Genetic Algorithm for Aberration Correction in a Coherent Imaging System
Authors: Zhang Yu , Zhang Hongwen , Yuan Guoqin
Published: September 2023

For traditional imaging systems, high imaging quality and system miniaturization are often contradictory. In order to meet the requirements of high imaging quality and system miniaturization, this paper proposes a method to correct the aberration of coherent imaging optical systems. The method is based on the idea of phase recovery and the imaging principle of a coherent imaging system to recover the aberrations at the exit pupil of the system. According to the recovered aberrations, conjugate filters are constructed to correct the image quality in the frequency domain. The imaging quality of the system is improved without changing the original optical path, and the simplicity of the system is guaranteed. To solve the pupil frequency domain aberration more accurately, this paper adopts the dual competition and parallel recombination strategy based on the genetic algorithm and introduces the disaster model. The improved genetic algorithm can effectively restrain the appearance of the “precocity” phenomenon. Finally, the paraxial imaging optical path is simulated and verified by experiments. The results show that, after aberration correction, the image sharpness is improved and the edge information is richer, which verifies the feasibility of the coherent imaging system image quality enhancement method proposed in this paper.
Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis
Authors: Anker Marvin , Yousefi-Darani Abdolrahim , Zettel Viktoria , Paquet-Durand Olivier , Hitzmann Bernd , Krupitzer Christian
Published: September 2023

Sourdough can improve bakery products’ shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.
A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils
Authors: Pypin Arseniy A. , Shik Anna V. , Stepanova Irina A. , Doroshenko Irina A. , Podrugina Tatyana A. , Beklemishev Mikhail K.
Published: September 2023

Optical “fingerprints” are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining “static” spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20–55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy.
WeMo: A Prototype of a Wearable Mobility Device Adapting to User’s Natural Posture Changes
Authors: Chen Yang , Kuwahara Takashi , Nishimura Yuki , Suzuki Kenji
Published: September 2023

Mobility is fundamental for human beings. In the current society, many personal mobility solutions have been invented to enable more time-efficient mobility, such as self-balancing vehicles, electric unicycles, and electric scooters. Personal mobility devices can provide flexibility to transportation. However, most personal mobility devices need to be carried by their users in the case that they climb stairs and steps. Therefore, many researchers have focused on developing stair-climbing vehicles, but due to the complicated mechanism, these devices are usually huge and heavy. To realize a new type of personal mobility device with more flexibility, we proposed a novel concept of a personal mobility device design that combines the agile mobility of a wheel type mechanism but does not limit a human’s natural stair climbing ability. In this study, we introduced a compact personal mobility device, namely WeMo, under the concept of “wearing mobility”, which extends humans’ mobility in daily life. The developed hardware realizes “walking mode” and “driving mode”. Users can move with the motorized driven wheels of the device during driving mode, and users can walk on their feet without any interference from the device during walking mode. In this manuscript, the detailed design of the hardware and control strategy were explained first.Then, we conducted fundamental user tests and discussed the ability of the developed device from test results. Finally, the conclusions and future work were provided.
CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism
Authors: Li Xia , Zhang Hui , Yang Hao , Li Tie-Qiang
Published: September 2023

Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network’s receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN’s average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.
High-Sensitivity Temperature Sensor Based on Fiber Fabry-Pérot Interferometer with UV Glue-Filled Hollow Capillary Fiber
Authors: Zheng Yiwen , Chen Yongzhang , Zhang Qiufang , Tang Qianhao , Zhu Yixin , Yu Yongqin , Du Chenlin , Ruan Shuangchen
Published: September 2023

Optical fiber Fabry-Pérot (FP) interferometer sensors have long been the focus of researchers in sensing applications because of their simple light path, low cost, compact size and convenient manufacturing methods. A miniature and highly sensitive optic fiber temperature sensor using an ultraviolet glue-filled FP cavity in a hollow capillary fiber is proposed. The sensor is fabricated by fusion splicing a single-mode fiber with a hollow capillary fiber, which is filled with ultraviolet glue to form a FP cavity. The sensor has a good linear response in the temperature testing and high-temperature sensitivity, which can be increased with the length of the FP cavity. The experimental results show that the temperature sensitivity reaches 1.174 nm/°C with a high linear response in the range of 30–60 °C. In addition, this sensor is insensitive to pressure and can be highly suitable for real-time water temperature monitoring for ocean research. The proposed ultraviolet glue-filled structure has the advantages of easy fabrication, high-temperature sensitivity, low cost and an arbitrary length of capillary, which has broad application prospects for marine survey technology, biological diagnostics and environmental monitoring.
Surface Acoustic Wave Immunosensor for Detection of Botulinum Neurotoxin
Authors: Grabka Michał , Jasek Krzysztof , Witkiewicz Zygfryd
Published: September 2023

A Love-type acoustic wave sensor (AT-cut quartz substrate, SiO2 guiding layer) with a center frequency of approximately 120 MHz was used to detect a simulant of pathogenic botulinum neurotoxin type A—recombinant of BoNT-A light chain—in liquid samples. The sensor was prepared by immobilizing monoclonal antibodies specific for botulinum neurotoxin via a thiol monolayer deposited on a gold substrate. Studies have shown that the sensor enables selective analyte detection within a few minutes. In addition, the sensor can be used several times (regeneration of the sensor is possible using a low pH buffer). Nevertheless, the detectability of the analyte is relatively low compared to other analytical techniques that can be used for rapid detection of botulinum neurotoxin. The obtained results confirm the operation of the proposed sensor and give hope for further development of this label-free technique for detecting botulinum neurotoxin.
Trends in Single-Molecule Total Internal Reflection Fluorescence Imaging and Their Biological Applications with Lab-on-a-Chip Technology
Authors: Colson Louis , Kwon Youngeun , Nam Soobin , Bhandari Avinashi , Maya Nolberto Martinez , Lu Ying , Cho Yongmin
Published: September 2023

Single-molecule imaging technologies, especially those based on fluorescence, have been developed to probe both the equilibrium and dynamic properties of biomolecules at the single-molecular and quantitative levels. In this review, we provide an overview of the state-of-the-art advancements in single-molecule fluorescence imaging techniques. We systematically explore the advanced implementations of in vitro single-molecule imaging techniques using total internal reflection fluorescence (TIRF) microscopy, which is widely accessible. This includes discussions on sample preparation, passivation techniques, data collection and analysis, and biological applications. Furthermore, we delve into the compatibility of microfluidic technology for single-molecule fluorescence imaging, highlighting its potential benefits and challenges. Finally, we summarize the current challenges and prospects of fluorescence-based single-molecule imaging techniques, paving the way for further advancements in this rapidly evolving field.
The Use of Sensors to Prevent, Predict Transition to Chronic and Personalize Treatment of Low Back Pain: A Systematic Review
Authors: Herrero Pablo , Ríos-Asín Izarbe , Lapuente-Hernández Diego , Pérez Luis , Calvo Sandra , Gil-Calvo Marina
Published: September 2023

Non-specific low back pain (NSLBP) is a highly prevalent condition that implies substantial expenses and affects quality of life in terms of occupational and recreational activities, physical and psychological health, and general well-being. The diagnosis and treatment are challenging processes due to the unknown underlying causes of the condition. Recently, sensors have been included in clinical practice to implement its management. In this review, we furthered knowledge about the potential benefits of sensors such as force platforms, video systems, electromyography, or inertial measure systems in the assessment process of NSLBP. We concluded that sensors could identify specific characteristics of this population like impaired range of movement, decreased stability, or disturbed back muscular activation. Sensors could provide sufferers with earlier diagnosis, prevention strategies to avoid chronic transition, and more efficient treatment approaches. Nevertheless, the review has limitations that need to be considered in the interpretation of results.
A Neurophysiological Evaluation of Cognitive Load during Augmented Reality Interactions in Various Industrial Maintenance and Assembly Tasks
Authors: Alessa Faisal M. , Alhaag Mohammed H. , Al-harkan Ibrahim M. , Ramadan Mohamed Z. , Alqahtani Fahad M.
Published: September 2023

Augmented reality (AR) has been shown to improve productivity in industry, but its adverse effects (e.g., headaches, eye strain, nausea, and mental workload) on users warrant further investigation. The objective of this study is to investigate the effects of different instruction methods (i.e., HoloLens AR-based and paper-based instructions) and task complexity (low and high-demanding tasks) on cognitive workloads and performance. Twenty-eight healthy males with a mean age of 32.12 (SD 2.45) years were recruited in this study and were randomly divided into two groups. The first group performed the experiment using AR-based instruction, and the second group used paper-based instruction. Performance was measured using total task time (TTT). The cognitive workload was measured using the power of electroencephalograph (EEG) features and the NASA task load index (NASA TLX). The results showed that using AR instructions resulted in a reduction in maintenance times and an increase in mental workload compared to paper instructions, particularly for the more demanding tasks. With AR instruction, 0.45% and 14.94% less time was spent on low- and high-demand tasks, respectively, as compared to paper instructions. According to the EEG features, employing AR to guide employees during highly demanding maintenance tasks increased information processing, which could be linked with an increased germane cognitive load. Increased germane cognitive load means participants can better facilitate long-term knowledge and skill acquisition. These results suggested that AR is superior and recommended for highly demanding maintenance tasks since it speeds up maintenance times and increases the possibility that information is stored in long-term memory and encrypted for recalls.
Optical Properties of Light-Scattering Standards for CCD Photometry
Authors: Bondariev Denys , Bezugla Natalia , Komada Paweł , Stelmakh Nataliia , Bezuglyi Mykhailo
Published: September 2023

This paper analyzes the light-scattering standards currently used for calibration (verification) and systematic research in photo and spectrophotometry tools. The application specificities in studying the diffuse reflected and transmitted light during biomedical CCD photometry are considered. The advantages of a new class of photometers with non-spherical reflectors as ellipsoids of revolution truncated along the focal planes with the internal mirror surface are presented. The ellipsoid first focal plane is combined with the surface of the under-study media, and the second is optically coupled to the CCD image sensor plane. The principles of zone analysis of spatial distribution reproduced in photometric images on a CCD sensor are substantiated. The illuminance levels of photometric image areas in reflected and transmitted light from the thickness of the standard for the wavelength of laser radiation of 650 nm of different power was experimentally investigated. Polynomial dependences were obtained, and regression coefficients of the illuminance of the external and middle rings in photometric images for the reflected and transmitted light on the laser power were determined.
Temperature-Compensated Solution Concentration Measurements Using Photonic Crystal Fiber-Tip Sensors
Authors: Cano-Velázquez Mildred S. , Hendriks Arthur L. , Picelli Luca , van Veldhoven Rene P. J. , Fiore Andrea
Published: September 2023

We demonstrate fiber optic sensors with temperature compensation for the accurate measurement of ethanol concentration in aqueous solutions. The device consists of two photonic crystal (PhC) fiber-tip sensors: one measures the ethanol concentration via refractive index (RI) changes and the other one is isolated from the liquid for the independent measurement of temperature. The probes utilize an optimized PhC design providing a Lorentzian-like, polarization-independent response, enabling a very low imprecision (pm-level) in the wavelength determination. By combining the information from the two probes, it is possible to compensate for the effect that the temperature has on the concentration measurement, obtaining more accurate estimations of the ethanol concentration in a broad range of temperatures. We demonstrate the simultaneous and single-point measurements of temperature and ethanol concentration in water, with sensitivities of 19 pm/°C and ∼53 pm/%, in the ranges of 25 °C to 55 °C and 0 to 50% (at 25 °C), respectively. Moreover, a maximum error of 1.1% in the concentration measurement, with a standard deviation of ≤0.8%, was obtained in the entire temperature range after compensating for the effect of temperature. A limit of detection as low as 0.08% was demonstrated for the concentration measurement in temperature-stable conditions.
Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion
Authors: Zhang Zhenlong , Wang Zhe , Luo Ying , Zhang Jiaqian , Tian Duan , Zhang Yongde
Published: September 2023

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.
Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants
Authors: Khan Arooj , Shafi Imran , Khawaja Sajid Gul , de la Torre Díez Isabel , Flores Miguel Angel López , Galvlán Juan Castañedo , Ashraf Imran
Published: September 2023

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.
Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising
Authors: Zhang Shuo , Liu Chunyu , Zhang Yuxin , Liu Shuai , Wang Xun
Published: September 2023

Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective.
Re-Making Clothing, Re-Making Worlds: On Crip Fashion Hacking
Authors: Barry Ben , Nesbitt Philippa , De Villa Alexis , McMullin Kristina , Dumitra Jonathan
Published: September 2023

This article explores how Disabled people’s fashion hacking practices re-make worlds by expanding fashion design processes, fostering relationships, and welcoming-in desire for Disability. We share research from the second phase of our project, Cripping Masculinity, where we developed fashion hacking workshops with D/disabled, D/deaf and Mad men and masculine non-binary people. In these workshops, participants worked in collaboration with fashion researchers and students to alter, embellish, and recreate their existing garments to support their physical, emotional, and spiritual needs. We explore how our workshops heeded the principles of Disability Justice by centring flexibility of time, collective access, interdependence, and desire for intersectional Disabled embodiments. By exploring the relationships formed and clothing made in these workshops, we articulate a framework for crip fashion hacking that reclaims design from the values of the market-driven fashion industry and towards the principles of Disability Justice. This article is written as a dialogue between members of the research team, the conversational style highlights our relationship-making process and praxis. We invite educators, designers, and/or researchers to draw upon crip fashion hacking to re-make worlds by desiring with and for communities who are marginalized by dominant systems.
Design and Development of a Conceptual Solar Energy Laboratory for District Heating Applications
Authors: Chung Jaewook , Sukumaran Sreenath , Hlebnikov Aleksandr , Volkova Anna
Published: September 2023

The decarbonization of the district heating (DH) sector is receiving attention worldwide. Solar energy and heat pump technologies are widely considered in existing and new DH networks. There is a need to understand the influence of solar energy on district heating experimentally. However, only a few university laboratories are focused on district heating aspects. Further, the concept of such laboratories is not adequately disseminated in the scientific literature. The main objective of this paper is to develop a conceptual design of a solar energy laboratory with a focus on district heating systems. The proposed concept forms part of the preliminary study carried out by a research group at the Tallinn University of Technology. First, a brief literature review on solar energy laboratory development is provided. Then, the conceptual design of such a laboratory is presented, along with a case study. Regardless of project size, the main components of a district heating-based solar energy laboratory are solar collectors, thermal energy storage (TES) tanks, and a control system. The proposed laboratory is expected to serve multiple roles, such as a practical laboratory to provide interdisciplinary courses for students, a research and experimental platform for researchers, and a cradle to achieve the campus green initiative. It is roughly estimated that the thermal energy output from the proposed laboratory would meet around 25% of the heat demand of the institutional building during the summer season (May, June, July, and August). It is expected that the present study will be a reference material for the development of innovative energy laboratories in educational institutions.
Sustainable Waste Management in the Production of Medicinal and Aromatic Plants—A Systematic Review
Authors: Marcelino Sara , Gaspar Pedro Dinis , Paço Arminda
Published: September 2023

Without a Sustainable Waste Management (SWM) system, the growing demand for Medicinal and Aromatic Plants (MAPs) can also lead to a considerable increase in the waste generated by the industry. Since MAP residues have a notable potential to be valorised, the implementation of Circular Economy (CE) solutions can play a central role in converting waste into economic opportunities, while fostering a sustainable planet. CE helps to mitigate environmental and social risks caused by the accumulation of biomass by turning waste into valuable products. A systematic review was conducted, aiming to identify potential applications for the valorisation of MAP residues under a sustainable approach. A total number of 47 studies were analysed, providing a novel compilation of possibilities for decision makers in the MAP industry to develop new products for crop management or new businesses in food, cosmetic, pharmaceutical, chemical, paper, or building industries. Researchers in this field have focused more on the industrial value of MAP residues than on the empirical assessment of environmental and economic benefits. Further investigation should be undertaken to present empirical applications and to develop a decision support system to assess the sustainable performance of valorisation options.
Effect of Transient Tide-Level Change on the Sliding Distance of a Breakwater Caisson during Storms
Authors: Kim Dong-Hyawn , Choi Jung-Wook
Published: September 2023

When a storm begins at low tide, the tide will increase with time. However, when a storm begins at high tide, the tide will decrease during the storm. Therefore, to achieve more accurate estimations of sliding distance, both the tide-level change and the tide level itself should be considered. In this study, a new approach to taking the change in tide level into account when calculating the sliding distance of gravity-type breakwaters during storms is proposed. Conducting the numerical analysis, we found that, when typhoons begin at low tide, the sliding distance of the breakwater increases when considering the change in tide level, and conversely, when they begin at high tide, the change in tide level results in the sliding distance being shortened. Therefore, considering the change in tide level with time can result in a more accurate and realistic estimation of the sliding distance of the breakwater being attained. This is expected to contribute greatly to the development of deformation-based performance design methods for breakwaters.
Environmental Regulations and Carbon Emissions: The Role of Renewable Energy Research and Development Expenditures
Authors: Tao Yinying , Destek Mehmet Akif , Pata Ugur Korkut , Khan Zeeshan
Published: September 2023

This present research offers fresh insights regarding the causality nexus between environmental regulations and a sustainable environment. Also, this study considered the importance of renewable energy research and development (RERD), technological innovation (TI), and economic growth (GDP). Using the U.S. extended dataset covering the period 1990–2020; this research employed the wavelet methods (wavelet power spectrum and wavelet coherence) to observe the causal connections between mentioned variables based on the time-frequency domain. The empirical results from the wavelet power spectrum asserted that carbon emissions (CO2), RERD, and TI are vulnerable during the study period, while GDP and environmental policies are stable. Additionally, the wavelet coherence approach unveils relationships both in-phase and anti-phase. A causal connection is evident between CO2 and other variables. Particularly, a unidirectional causality is found from TI to CO2 emissions, while a bidirectional causal association exists between GDP and CO2, and environmental policy stringency, and CO2. Moreover, a bidirectional causality exists between RERD and CO2, but this association is insignificant. Based on findings, this research suggests enhancing RERD investment, strengthening environmental regulations, and promoting green technological innovation to attain a sustainable environment.
Effects of Residents’ Empowerment on Citizenship Behavior and Support for Convention Development: Moderation of Innovativeness
Authors: Choi Sooyoung , Kazakova Alisa , Choi Kijung , Choi Yong-Seok , Kim Insin
Published: September 2023

This study investigated how residents’ empowerment influences their engagement in the sustainability of the convention industry in their community, specifically, their citizenship behavior (CB) toward convention visitors and support for convention development in their community. Additionally, the current study examined the moderator of a convention center’s innovativeness. The sample used for data analysis was 415 residents from Seoul and Busan, South Korea. The results of structural equation modeling revealed that residents’ empowerment enhances their participation in CB toward visitors and support. The multi-group comparison analysis suggested that a convention center’s innovativeness moderates the effect of residents’ empowerment on their CB toward visitors but not on convention development support. These results highlight the important role of empowering residents in convention host communities in psychological, sociological, and political ways in promoting community engagement and support for convention development. Local authorities and convention center management should ensure that residents have sufficient knowledge about the benefits of convention development and involve them in decision-making processes.
Experimental Investigation on Anisotropy of Rocks Using Digital Drilling Technology
Authors: Liu Xinxing , Ma Xudong , He Mingming , Ma Chunchi
Published: September 2023

Accurate determination of rock anisotropy is of great significance for analyzing the safety and stability of engineering rock mass. In this paper, uniaxial compression tests are carried out on four kinds of rocks: slate, gneiss, sandstone and shale, to obtain the uniaxial compressive strength of each rock in the different directions. Digital drilling tests are carried out on four kinds of rocks to study the anisotropy of drilling parameters. According to the working principle of the drill bit, its force balance analysis model is established, and the concept of cutting strength ratio is proposed. Using the drilling parameters (drilling depth, drilling time, torque and thrust, etc.) in the different directions for each rock, the interrelationships between them are analyzed. The anisotropy index of rock is defined according to the ratio of cutting strength in different directions of drill parameters, and a new method for judging rock anisotropy is proposed. The results show that the thrust and torque in all directions of the rock increase with the drilling depth. The torque in all directions of the rock has a positive linear relationship with the thrust. The ranking of the anisotropy degree for the four types of rocks is as follows: gneiss > slate > shale > sandstone. The anisotropy results have been validated by an alternative method utilizing uniaxial compressive strength. The determination results are verified by the uniaxial compressive strength of the rocks, and the degree of anisotropy of the four rocks is consistent with the determination results. This method can help engineers analyze the anisotropy of rock, and provide a new idea for studying the integrity and stability of rock mass.
How Does Corporate Innovation Affect Sustainable Business Investment?
Authors: Kim Jinsu , Lee Hyunchul
Published: September 2023

This study examines the impact of corporate innovation on sustainable business investments of companies listed on the Korea exchange from 2011 to 2019. To this end, our study applies Hennessy’s investment model, which presents the relationship between corporate investment and Tobin’s mean Q in a probabilistic space. We find evidence of a positive relationship between corporate investment and Tobin’s average Q. Greater corporate growth opportunities lead to greater business investments, whereas the expected recovery ratio of debt capital has a negative relationship with corporate investments. The innovation performance variable is positively associated with the investments. Our results are suggestive of business investments being determined by investment outcomes, rather than the financial resource inputs for corporate innovation. Our study holds significance not only in the academic dimension, but also in policymaking. Since corporate growth is the outcome of corporate investments, the government may establish and implement economic policies that induce such investments.
Robust Fuzzy Logic MPPT Using Gradient-Based Optimization for PEMFC Power System
Authors: Rezk Hegazy , Aly Mokhtar , Ghoniem Rania
Published: September 2023

In this study, the design of fuzzy logic control (FLC) systems for proton exchange membrane fuel cells (PEMFCs) maximum power point tracking (MPPT) is improved. The improvement is made possible by using a gradient-based optimizer (GBO), which maximizes the FLC systems’ freedom and flexibility while enabling accurate and speedy tracking. During optimization, the parameters of the FLC membership functions are considered choice variables, and the error integral is assigned to be the objective function. The proposed GBO-FLC method’s results are contrasted with those of other computational methods. The results demonstrated that the proposed GBO-FLC beats the other strategies regarding mean, median, variance, and standard deviation. A thorough comparison between the regular FLC and the upgraded FLC was conducted using a variety of scenarios with varied temperatures and water content. The results demonstrate that the suggested FLC-based GBO design provides a dependable MPPT solution in PEMFCs. The advancement of FLC systems through optimizing power generation in fuel cells is made possible by this work, opening the door for more effective and reliable alternative energy sources.
A Review of Multilevel Inverter Topologies for Grid-Connected Sustainable Solar Photovoltaic Systems
Authors: Nyamathulla Shaik , Chittathuru Dhanamjayulu
Published: September 2023

Solar energy is one of the most suggested sustainable energy sources due to its availability in nature, developments in power electronics, and global environmental concerns. A solar photovoltaic system is one example of a grid-connected application using multilevel inverters (MLIs). In grid-connected PV systems, the inverter’s design must be carefully considered to improve efficiency. The switched capacitor (SC) MLI is an appealing inverter over its alternatives for a variety of applications due to its inductor-less or transformer-less operation, enhanced voltage output, improved voltage regulation inside the capacitor itself, low cost, reduced circuit components, small size, and less electromagnetic interference. The reduced component counts are required to enhance efficiency, to increase power density, and to minimize device stress. This review presents a thorough analysis of MLIs and a classification of the existing MLI topologies, along with their merits and demerits. It also provides a detailed survey of reduced switch count multilevel inverter (RSC-MLI) topologies, including their designs, typical features, limitations, and criteria for selection. This paper also covers the survey of SC-MLI topologies with a qualitative assessment to aid in the direction of future research. Finally, this review will help engineers and researchers by providing a detailed look at the total number of power semiconductor switches, DC sources, passive elements, total standing voltage, reliability analysis, applications, challenges, and recommendations.
Global Industrial Chain Resilience Research: Theory and Measurement
Authors: Ma Li , Li Xiumin , Pan Yu
Published: September 2023

Global industrial chain resilience refers to the capability of industrial chains, on a global scale, to maintain or restore their normal operations and value-creating ability in the face of various risks and uncertainties. This resilience is crucial for addressing crises, promoting economic growth, and upholding national security. However, there is currently a lack of unified standards and methods for measuring and enhancing global industrial chain resilience. This study constructs a global industrial chain production model in a multi-country and multi-stage open economy context. It utilizes data from the 1990–2021 Eora MRIO (Multi-Regional Input–Output) dataset to analyze the formation, measurement, and influencing factors of global industrial chain resilience. The research findings indicate that since 2010, the disparity in industrial chain resilience between different countries has gradually widened. Manufacturing plays a pivotal role in maintaining industrial chain stability. Additionally, factors such as input costs and technological levels have been found to positively impact the enhancement of global industrial chain resilience. Therefore, this study provides theoretical and empirical support for exploring and improving global industrial chain resilience, offering valuable guidance for policymakers and entrepreneurs.
Estimation of the Cadmium Nephrotoxicity Threshold from Loss of Glomerular Filtration Rate and Albuminuria
Authors: Satarug Soisungwan , Vesey David A. , Khamphaya Tanaporn , Pouyfung Phisit , Gobe Glenda C. , Yimthiang Supabhorn
Published: September 2023

Cadmium (Cd) is a pervasive, toxic environmental pollutant that preferentially accumulates in the tubular epithelium of the kidney. Current evidence suggests that the cumulative burden of Cd here leads to the progressive loss of the glomerular filtration rate (GFR). In this study, we have quantified changes in estimated GFR (eGFR) and albumin excretion (Ealb) according to the levels of blood Cd ([Cd]b) and excretion of Cd (ECd) after adjustment for confounders. ECd and Ealb were normalized to creatinine clearance (Ccr) as ECd/Ccr and Ealb/Ccr. Among 482 residents of Cd-polluted and non-polluted regions of Thailand, 8.1% had low eGFR and 16.9% had albuminuria (Ealb/Ccr) × 100 ≥ 20 mg/L filtrate. In the low Cd burden group, (ECd/Ccr) × 100 < 1.44 µg/L filtrate, eGFR did not correlate with ECd/Ccr (β = 0.007) while an inverse association with ECd/Ccr was found in the medium (β = −0.230) and high burden groups (β = −0.349). Prevalence odds ratios (POR) for low eGFR were increased in the medium (POR 8.26) and high Cd burden groups (POR 3.64). Also, eGFR explained a significant proportion of Ealb/Ccr variation among those with middle (η2 0.093) and high [Cd]b tertiles (η2 0.132) but did not with low tertiles (η2 0.001). With an adjustment of eGFR, age and BMI, the POR values for albuminuria were increased in the middle (POR 2.36) and high [Cd]b tertiles (POR 2.74) and those with diabetes (POR 6.02) and hypertension (2.05). These data indicate that (ECd/Ccr) × 100 of 1.44 µg/L filtrate (0.01–0.02 µg/g creatinine) may serve as a Cd threshold level based on which protective exposure guidelines should be formulated.
Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning
Authors: Ruan Wenhong , Wang He , Liu Chang , Guo Zongkuan
Published: September 2023

In the 2030s, a new era of gravitational wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji, and TianQin, will open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for the redshifted total mass, mass ratio, coalescence time, and luminosity distance of an MBHB in about twenty seconds. Our model can serve as an effective data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contain multiple MBHB signals.