bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on a Variable Pressure Driving Method for Soft Robots Based on the Electromagnetic Effect
Authors: Zhang Zhongyuan , Zhang Lei , Guan Mingjing , Zhang Shuai , Jiao Tengfei
Published: July 2023

This study proposes a novel variable air pressure supply structure based on the electromagnetic effect. This structure can be implemented in various soft robots driven by air pressure, including pneumatic artificial muscles, pneumatic soft grippers, and other soft robots. The structure’s main body comprises a hollow circular tube, a magnetic piston arranged in the tube, and an electromagnetic solenoid nested outside the tube. The electromagnetic solenoid is designed with special winding and power supply access modes, generating either an attractive force or a repulsive force on the magnetic piston. This solenoid conforms with the magnetic piston expectation in the tube by changing the polarity direction. The interior of the whole structure is a closed space. The gas is conveyed to the soft robot by the gas guide hoses at the two ends of the structure, and the expansion energy of the compressed gas is fully utilized. Then, the gas supply pressure is controlled to drive the robot. The mathematical model of the structure is established based on the analysis of the electromagnetic force and gas pressure on the piston. The simulation results show that the structure’s inherent vibration characteristics under various parameters align with expectations. The real-time automatic optimization of the controller parameters is realized by optimizing the incremental proportional-integral-derivative (PID) controller based on a neural network. The simulation results show that the structure can meet the application requirements. The experimental results show that the proposed gas supply structure can provide a continuous pressure supply curve with any frequency in a specific amplitude range and has an excellent tracking effect on the sinusoidal-like pressure curve.
Evaluating Urban Building Damage of 2023 Kahramanmaras, Turkey Earthquake Sequence Using SAR Change Detection
Authors: Wang Xiuhua , Feng Guangcai , He Lijia , An Qi , Xiong Zhiqiang , Lu Hao , Wang Wenxin , Li Ning , Zhao Yinggang , Wang Yuedong , Wang Yuexin
Published: July 2023

On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.
Potential of a Miniature Spectral Analyzer for District-Scale Monitoring of Multiple Gaseous Air Pollutants
Authors: Fathy Alaa , Gnambodoe-Capochichi Martine , Sabry Yasser M. , Anwar Momen , Ghoname Amr O. , Saeed Ahmed , Leprince-Wang Yamin , Khalil Diaa , Bourouina Tarik
Published: July 2023

Gas sensors that can measure multiple pollutants simultaneously are highly desirable for on-site air pollution monitoring at various scales, both indoor and outdoor. Herein, we introduce a low-cost multi-parameter gas analyzer capable of monitoring multiple gaseous pollutants simultaneously, thus allowing for true analytical measurement. It is a spectral sensor consisting of a Fourier-transform infrared (FTIR) gas analyzer based on a mid-infrared (MIR) spectrometer. The sensor is as small as 7 × 5 × 2.5 cm3. It was deployed in an open-path configuration within a district-scale climatic chamber (Sense City, Marne-la-Vallée, France) with a volume of 20 × 20 × 8 m3. The setup included a transmitter and a receiver separated by 38 m to enable representative measurements of the entire district domain. We used a car inside the climatic chamber, turning the engine on and off to create time sequences of a pollution source. The results showed that carbon dioxide (CO2) and water vapor (H2O) were accurately monitored using the spectral sensor, with agreement with the reference analyzers used to record the pollution levels near the car exhaust. Furthermore, the lower detection limits of CO, NO2 and NO were assessed, demonstrating the capability of the sensor to detect these pollutants. Additionally, a preliminary evaluation of the potential of the spectral sensor to screen multiple volatile organic compounds (VOCs) was conducted at the laboratory scale. Overall, the results demonstrated the potential of the proposed multi-parameter spectral gas sensor in on-site gaseous pollution monitoring.
A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application
Authors: Christakos P. , Petrellis N. , Mousouliotis P. , Keramidas G. , Antonopoulos C. P. , Voros N.
Published: July 2023

A high-performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. The popular Ensemble of Regression Trees (ERTs) machine learning method has been employed for the alignment of 68 facial landmarks. Open-source implementation of ERTs for facial shape alignment has been ported to different platforms and adapted for the acceleration of the frame processing speed using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without sacrificing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Hence, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other machine learning applications that require large data transfers to their kernels with low reusability.
Semantic Information Recovery in Wireless Networks
Authors: Beck Edgar , Bockelmann Carsten , Dekorsy Armin
Published: July 2023

Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon’s classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and, thus, enables savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We consider this task as an end-to-end Information Bottleneck problem, enabling compression while preserving relevant information. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario; SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems.
Fabrication and Characterization of Three-Dimensional Microelectromechanical System Coaxial Socket Device for Semiconductor Package Testing
Authors: Kim Tae-Kyun , Yook Jong-Gwan , Kim Joo-Yong , Cho Yong-Ho , Lee Uh-Hyeon
Published: July 2023

With the continuous reduction in size and increase in density of semiconductor devices, there is a growing demand for contact solutions that enable high-speed testing in automotive, 5G, and artificial intelligence-based devices. Although existing solutions, such as spring pins and rubber sockets, have been effective in various applications, there is still a need for new solutions that accommodate fine-pitch, high-speed, and high-density requirements. This study proposes a novel three-dimensional microelectromechanical system spring structure coaxial socket for semiconductor chip package testing. The socket design incorporates impedance matching for high-speed testing and addresses the challenges of fine-pitch and high-density applications. Mechanical tests are conducted to evaluate the durability of the structure and electrical tests are performed to verify electrical characteristics by utilizing a vector network analyzer up to 60 GHz. Our results have revealed promising performance and will help in further optimizing the design for potential production in the field and industry.
Adaptive Output Feedback Control for Nonholonomic Chained Systems with Integral Input State Stability Inverse Dynamics
Authors: Li Yang , Xu Linxing , Wang Xiuli , Wang Cunsong
Published: July 2023

This paper investigates a class of nonholonomic chained systems with integral input-to-state stable (iISS) inverse dynamics subject to unknown virtual control directions and parameter uncertainty included in drift terms. First, the system is divided into two interconnected subsystems according to the system’s structure. Second, one controller is designed using a switch strategy for state finite escape. Then, another controller and adaptive law are designed by combining a reduced-order state observer and backstepping method after input-state scaling. Finally, simulation results validate the feasibility of the proposed control algorithm.
Reusable SERS Substrates Based on Gold Nanoparticles for Peptide Detection
Authors: Qi Zhang , Akhmetzhanov Timur , Pavlova Arina , Smirnov Evgeny
Published: July 2023

Raman spectroscopy is a powerful analytical technique widely used for quantitative and qualitative analysis. However, the development of inexpensive, reproducible, and reusable enhancing substrates remains a challenge for material scientists and analytical chemists. In this study, we address this challenge by demonstrating the deposition of core–shell nanoparticles consisting of a gold core and a thin inert SiO2 shell within a confined space, resulting in the formation of a highly efficient Raman-enhancing structure. Nanoparticles were characterized by UV–vis spectroscopy, dynamic light scattering, and total reflectance X-ray fluorescence spectroscopy, whereas the prepared substrates were characterized by scanning electron microscopy and Raman spectroscopy with a model molecule, malachite green. The relationship between Raman intensity and the loading of malachite green dye exhibited linearity, indicating the uniform spatial distribution of hotspots across the substrate. The limit of detection was determined as 2.9 μM of malachite green when 10 uL was distributed over a ca. 25 mm2 surface area. Moreover, the same substrate, after thorough washing in ethanol, was successfully employed for the detection of bovine serum albumin at a concentration level of 55 μg mL−1, demonstrating its reusability and versatility. Our findings highlight the potential of these substrates for various applications in biomedical research, clinical diagnosis, and beyond.
Laser Activation for Highly Boron-Doped Passivated Contacts
Authors: Sharbaf Kalaghichi Saman , Hoß Jan , Zapf-Gottwick Renate , Werner Jürgen H.
Published: July 2023

Passivated, selective contacts in silicon solar cells consist of a double layer of highly doped polycrystalline silicon (poly Si) and thin interfacial silicon dioxide (SiO2). This design concept allows for the highest efficiencies. Here, we report on a selective laser activation process, resulting in highly doped p++-type poly Si on top of the SiO2. In this double-layer structure, the p++-poly Si layer serves as a layer for transporting the generated holes from the bulk to a metal contact and, therefore, needs to be highly conductive for holes. High boron-doping of the poly Si layers is one approach to establish the desired high conductivity. In a laser activation step, a laser pulse melts the poly Si layer, and subsequent rapid cooling of the Si melt enables electrically active boron concentrations exceeding the solid solubility limit. In addition to the high conductivity, the high active boron concentration in the poly Si layer allows maskless patterning of p++-poly Si/SiO2 layers by providing an etch stop layer in the Si etchant solution, which results in a locally structured p++-poly Si/SiO2 after the etching process. The challenge in the laser activation technique is not to destroy the thin SiO2, which necessitates fine tuning of the laser process. In order to find the optimal processing window, we test laser pulse energy densities (Hp) in a broad range of 0.7 J/cm2 ≤ Hp ≤ 5 J/cm2 on poly Si layers with two different thicknesses dpoly Si,1 = 155 nm and dpoly Si,2 = 264 nm. Finally, the processing window 2.8 J/cm2≤ Hp ≤ 4 J/cm2 leads to the highest sheet conductance (Gsh) without destroying the SiO2 for both poly Si layer thicknesses. For both tested poly Si layers, the majority of the symmetric lifetime samples processed using these Hp achieve a good passivation quality with a high implied open circuit voltage (iVOC) and a low saturation current density (J0). The best sample achieves iVOC = 722 mV and J0 = 6.7 fA/cm2 per side. This low surface recombination current density, together with the accompanying measurements of the doping profiles, suggests that the SiO2 is not damaged during the laser process. We also observe that the passivation quality is independent of the tested poly Si layer thicknesses. The findings of this study show that laser-activated p++-poly Si/SiO2 are not only suitable for integration into advanced passivated contact solar cells, but also offer the possibility of maskless patterning of these stacks, substantially simplifying such solar cell production.
Investigating Risks to the Implementation of the Great Equatorial Landbridge (GELB) Highway Project across Africa
Authors: Kazungu Raphael Konde , Sharifi Ayyoob
Published: July 2023

Transboundary Trade Corridors (TTC) are becoming increasingly important for achieving national, regional, and global development objectives. However, the cross-boundary nature of these projects involves dealing with diverse contexts that span across different countries and involve multiple stakeholders with varying interests. These circumstances exacerbate the risks and uncertainties that arise during their implementation, intensifying the challenges involved in making decisions about how to proceed with their execution. Insufficient evaluations of development projects have been identified as a contributing factor to unforeseen risks, which in turn can result in the underperformance and failure of transportation infrastructure projects. This ultimately acts as an impediment to achieving sustainable development goals. Further, rapid deployment of post risk-event corrective measures can exacerbate, for instance, macro-economic crisis and civil unrest. The Great Equatorial Land Bridge is a TTC planned to traverse five countries: Cameroon, Central African Republic, South-Sudan, Ethiopia, and Kenya. Despite the anticipated advantages for globalization, political cooperation, and regional integration, the risks that must be addressed to successfully implement this project are still uncertain. We, therefore, use a Multicriteria Decision-Making (MCDM) framework to investigate risks to its successful implementation. The analysis results highlight that effectively managing economic, political, and geographic risks is crucial for the successful implementation of the project. Policy-makers, contractors and multiple stakeholders will benefit from this study’s depiction of the risks and their relative importance. Results can also inform actions toward sustainable development of the project.
Land Application of Biosolids-Derived Biochar in Australia: A Review
Authors: Sinha Payel , Marchuk Serhiy , Harris Peter , Antille Diogenes L. , McCabe Bernadette K.
Published: July 2023

Thermal treatment in Australia is gaining interest due to legislative changes, waste reduction goals, and the need to address contaminants’ risks in biosolids used for agriculture. The resulting biochar product has the potential to be beneficially recycled as a soil amendment. On-farm management practices were reviewed to identify barriers that need to be overcome to increase recycling and examine the role of pyrolysis and gasification in effectively improving the quality and safety of biochar intended for land application. Key findings revealed the following: (1) thermal treatment can effectively eliminate persistent organic pollutants, microplastics, and pathogens, and (2) more than 90% of the total heavy metals content in biosolids may become immobilized when these are converted to biochar, thus reducing their bioavailability following land application. While the reported research on the short-term effects of biosolids-derived biochar suggests promising agronomic results, there is a dearth of information on long-term effects. Other knowledge gaps include the optimization of land application rates, understanding of the rate of breakdown, and the fate of contaminants in soil and water, including heavy metal mobility and redistribution in the environment by processes such as erosion and runoff following land application. An improved understanding of nutrients and contaminants dynamics in soils receiving biosolids-derived biochar is a pre-requisite for their safe use in Australian agriculture, and therefore, it is highlighted as a priority area for future research.
Determinants of the Purchase of Secondhand Products: An Approach by the Theory of Planned Behaviour
Authors: Rodrigues Maria , Proença João F. , Macedo Rita
Published: July 2023

In the context of sustainable consumption, this study aims to understand the determinants of the intention to purchase secondhand products, exploring the direct and indirect effect of each in the constructs of the Theory of Planned Behaviour (TPB). To the traditional variables of TPB, Attitude, subjective norm and perception of behavioural control, were added the price, the need for exclusivity, frugality, environmental concern and environmental knowledge. Quantitative approaches were used, data were collected through a questionnaire and 805 responses were analyzed through PLS-SEM. The results revalidated the TPB variables, frugality, price and environmental concern, as determinants of the intention to purchase secondhand products. Price and environmental concern were the greatest influence on purchase intention. The results also reveal differences between younger and older people in relation to secondhand purchasing and sustainable consumption. Environmental concern is only significant in the purchase intention of younger people, because in older people the price prevails as more influential. This study expands TPB, introducing secondhand and sustainability determinants and analyzing the direct and indirect effects of behavioural constructs applied to purchase intention of secondhand products. Firms and brands need to change from linearity to circular production and follow the intentions of consumers to buy secondhand products, especially young consumers.
A Robust Stochastic Programming Model for the Well Location Problem: The Case of The Brazilian Northeast Region
Authors: da Cunha Nunes Dayanna Rodrigues , da Silva Júnior Orivalde Soares , de Mello Bandeira Renata Albergaria , Vieira Yesus Emmanuel Medeiros
Published: July 2023

Slow-onset disasters, such as drought, are usually more destructive in the long term since they affect the productive capacity of a community, thereby preventing it from recovering using its resources. This requires the leaders and planners of drought areas to establish the best strategies for effective drought management. In this direction, the present work develops a robust stochastic programming approach for the problem of locating artesian wells for the relief of drought-affected populations under uncertainty. Our model considers different demand scenarios and proposes a novel perspective which considers both social and hydrogeological aspects for the location choice, aiming to maximize the affected area’s satisfaction through its prioritization using a composite drought risk index as well as to maximize the probability of success in water prospecting. We present a case study of our robust stochastic optimization approach for the Brazilian Semiarid Region using demand points from the database of Operação Carro-Pipa. Our findings show that a robust solution has a better expected value for the objective function considering all scenarios, so it can help decision makers to plan facility location and demand allocation under demand uncertainty, pointing out the best solution according to their degree of risk aversion.
Evaluation of Waste Bottle Crates in the Form of Pyro-Oil and Fine Granules as Bitumen Rejuvenators and Modifiers
Authors: AL-Taheri Saleh A. , Awed Ahmed M. , Gabr Alaa R. , El-Badawy Sherif M.
Published: July 2023

This research study aims to investigate the feasibility of incorporating high-density polyethylene waste (HDPEW) into bitumen applications. Two conventional conditions of bitumen, namely, aged bitumen (AB) and virgin bitumen (VB), are rejuvenated and modified, respectively, using post-consumer HDPEW sourced out of bottle crates. The outcome (Pyro oil, PO-HDPEW) of the pyrolysis thermochemical process is used by 10, 20, and 30% to rejuvenate AB, while the fine-ground granules (FG) (FG-HDPEW) are used by 2, 3, 4, and 5% to modify the VB with different percentages. Physical and rheological characterization testing, including penetration, softening point temperature, and rotational viscosity (RV), is conducted to evaluate the performance of the HDPEW-rejuvenated and -modified binders and optimize both rejuvenator and modifier percentages. In addition, physical and chemical tests, including scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier transform infrared spectroscopy (FTIR) are conducted to analyze the composition, distribution of surface contaminants, and the molecular structure of the bitumen, based on their respective wavelengths. Moreover, advanced mechanical and rheological tests, including dynamic shear rheometer (DSR), multiple stress creep and recovery (MSCR), and linear amplitude sweep (LAS) tests, are conducted to investigate the susceptibility of the rejuvenated and modified bitumen with HDPEW to rutting and fatigue cracking. The testing results demonstrate that the addition of PO-HDPEW to AB and FG-HDPEW modification of VB can enhance the physical, chemical, mechanical, and rheological properties of bitumen; however, this study recommends further research on the aging performance of the PO-HDPEW-rejuvenated bitumen. This research provides insights into using HDPEW as a cost-effective and eco-friendly rejuvenator and modifier on bitumen properties, which can aid in the longevity and performance of pavements.
The Role of Digital Technology within the Business Model of Sustainable Entrepreneurship
Authors: Fuerst Sascha , Sanchez-Dominguez Odille , Rodriguez-Montes Miguel Angel
Published: July 2023

The adoption and usage of digital technologies is considered one of the most promising transformations for sustainability, and sustainable entrepreneurship is considered key for providing solutions for the grand social and environmental challenges. The purpose of our study is to shed light on the role of digital technologies in value creation, value delivery, and value capture within the business models of sustainable entrepreneurship. This is important for understanding how digital technologies leverage sustainability. In fact, we have little knowledge on the impact of digital technologies within the business models of sustainable entrepreneurship. We studied the adoption and usage of digital technologies within the business models of 10 born-sustainable new ventures in Mexico applying a comparative case study approach. We found that context matters. Extant research claims that digital technologies promote social inclusion, increase connectivity, and lead to broader stakeholder integration. Despite these claims, this is the most challenging part for entrepreneurs. Our main findings, therefore, emphasize the constraints of the implementation of digital technologies within sustainable business models. This adds to the academic discourse a cross-geographic perspective from the Global South.
Impact of Urbanization on Eco-Efficiency of Tourism Destinations
Authors: Zhang Jing , Ba Duoxun , Dong Suocheng , Xia Bing
Published: July 2023

Under the dual background of climate change and post-epidemic economic recovery, the study of the eco-efficiency of tourism destinations in the process of urbanization is critical to promoting the green and healthy development of tourism. This paper selects tourism destinations in 30 provinces of China in 2000–2019 as the research object, calculates the economic efficiency and eco-efficiency of China’s tourism destinations by constructing the Super-SBM (Slacks-Based Measure) model and visualizes the spatial distribution pattern and evolution trend of economic efficiency and eco-efficiency of China’s tourism destinations through spatial hotspot and center of gravity analysis. The coupling model is used to find the coupling relationship between the efficiency of China’s tourism destinations (economic efficiency and eco-efficiency) and urbanization. Finally, Tobit panel regression is used to find out how urbanization affects the eco-efficiency of tourism destinations. The results show that: (1) the eco-efficiency of tourism destinations in China is higher than the economic efficiency, as well as showing a downward trend. (2) The economic efficiency of tourism destinations in western China has increased while the eco-efficiency has declined. (3) China’s tourism destinations are undergoing the process of transformation and restructuring, and have not yet reached the decoupling standard. (4) On the whole, the improvement in urbanization is conducive to promoting the economic and environmentally sustainable development of tourism destinations. The main driving indicators are the living standards for urban residents, urban resources and environment, the industrial structure, and the role of the government. This study attempts to find a balance between the economic benefits and ecological impacts of tourism destinations and alleviate the real demand for the rapid economic recovery of tourism destinations in the post-epidemic era and the tension between human activities and the ecological environment. The research results are expected to provide a path for the healthy development of tourism destinations in the process of China’s new urbanization and provide a reference for tourism destinations in developing countries similar to China.
A Content Poisoning Attack Detection and Prevention System in Vehicular Named Data Networking
Authors: Magsi Arif Hussain , Yovita Leanna Vidya , Ghulam Ali , Muhammad Ghulam , Ali Zulfiqar
Published: July 2023

Named data networking (NDN) is gaining momentum in vehicular ad hoc networks (VANETs) thanks to its robust network architecture. However, vehicular NDN (VNDN) faces numerous challenges, including security, privacy, routing, and caching. Specifically, the attackers can jeopardize vehicles’ cache memory with a Content Poisoning Attack (CPA). The CPA is the most difficult to identify because the attacker disseminates malicious content with a valid name. In addition, NDN employs request–response-based content dissemination, which is inefficient in supporting push-based content forwarding in VANET. Meanwhile, VNDN lacks a secure reputation management system. To this end, our contribution is three-fold. We initially propose a threshold-based content caching mechanism for CPA detection and prevention. This mechanism allows or rejects host vehicles to serve content based on their reputation. Secondly, we incorporate a blockchain system that ensures the privacy of every vehicle at roadside units (RSUs). Finally, we extend the scope of NDN from pull-based content retrieval to push-based content dissemination. The experimental evaluation results reveal that our proposed CPA detection mechanism achieves a 100% accuracy in identifying and preventing attackers. The attacker vehicles achieved a 0% cache hit ratio in our proposed mechanism. On the other hand, our blockchain results identified tempered blocks with 100% accuracy and prevented them from storing in the blockchain network. Thus, our proposed solution can identify and prevent CPA with 100% accuracy and effectively filters out tempered blocks. Our proposed research contribution enables the vehicles to store and serve trusted content in VNDN.
Pedagogy in Teaching through English Medium Instruction—Academics’ Cases in a Chinese University
Authors: Sun Wanting , Han Jinghe , Curry Christina , Carroll Kay
Published: July 2023

As a result of two decades of development, the trajectory of English medium instruction (EMI) research has moved from the identification of problems towards a focus on pedagogy. In response to studies in the literature claiming that the dominant approach in EMI pedagogy is the direct transmission of knowledge, and calls for research investigating classroom discourse in EMI teaching, this study explores the pedagogical features enacted by bilingual EMI academics in a Chinese university’s EMI program. The participants were four academics who were teaching a subject in parallel across both EMI and CMI student cohorts. The research employs Paulo Freire’s framework of dialogic teaching as the theoretical lens and focused on investigating moments in which the EMI lecturers ‘dialogued’, engaged, and/or interacted with students in their EMI classes. This research found that, in spite of their varied disciplines, the lecturers mostly implemented expository teaching in their EMI and CMI classes in general. The efforts of individual academics in relation to intellectual equality, the wellbeing of students, and the encouragement of critical thinking through dialogue and interaction were identified. However, due to the academics positioning themselves as experts in subject knowledge, the tendency in their teaching was characterized as monologic rather than dialogic. This research contests a major theme in the literature, which is that the academics’ English (EMI teaching), in reference to their first language (L1), is not the major contributor to their pedagogical approach.
Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System
Authors: Bonthu Bhulakshmi , Mohan Subaji
Published: July 2023

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.
Impact of Tax Administration on ESG Performance—A Quasi-Natural Experiment Based on China’s Golden Tax Project III
Authors: Meng Liyuan , Zhang Yuchen
Published: July 2023

With the growing importance of sustainable development, ESG is receiving attention from governments, firms, and investors. This study uses the reform of China’s tax inspection system—Golden Tax Project III—as a quasi-natural experiment to examine the impact of the enhanced tax administration on firms’ ESG performance with a sample of Chinese A-share listed companies from 2008 to 2020. The findings, which were identified via a time-varying difference-in-differences (time-varying DID) approach, indicate that Golden Tax Project III significantly reduces firms’ ESG performance and has the most significant impact on the environmental dimension and the minor impact on the social dimension. The negative impact of Golden Tax Project III on ESG performance is more substantial among non-state firms and firms in regions with higher levels of marketization than other firms. The mechanism test shows that Golden Tax Project III negatively affects ESG performance by increasing the tax burden on firms. The moderating effect test shows that tax incentives can effectively mitigate the dampening effect of strengthened tax administration on ESG performance. This study enriches the literature on the factors influencing firms’ ESG performance to a certain extent; it helps the government use taxation policies to inspire firms to improve ESG performance, contributing to sustainable development.
Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand
Authors: Liu Dong , Xiao Feng , Luo Jian , Yang Fan
Published: July 2023

Due to the inherent uncertainties of the bus system, bus bunching remains a challenging problem that degrades bus service reliability and causes passenger dissatisfaction. This paper introduces a novel deep reinforcement learning framework specifically designed to address the bus bunching problem by implementing dynamic holding control in a multi-agent system. We formulate the bus holding problem as a decentralized, partially observable Markov decision process and develop an event-driven simulator to emulate real-world bus operations. An approach based on deep Q-learning with parameter sharing is proposed to train the agents. We conducted extensive experiments to evaluate the proposed framework against multiple baseline strategies. The proposed approach has proven to be adaptable to the uncertainties in bus operations. The results highlight the significant advantages of the deep reinforcement learning framework across various performance metrics, including reduced passenger waiting time, more balanced bus load distribution, decreased occupancy variability, and shorter travel time. The findings demonstrate the potential of the proposed method for practical application in real-world bus systems, offering promising solutions to mitigate bus bunching and enhance overall service quality.
The Conceptualisation of User-App Interactivity in Augmented Reality-Mediated Learning: Implications for Literacy Education
Authors: Wang Tingjia , Tan Lynde
Published: July 2023

Augmented reality (AR) is transforming users’ multisensory experiences and heightening the level of engagement with multimodal learning. Scholarly attention is urgently needed to conceptualise and examine user–app interactivity in educational contexts. Drawing on the systemic functional–multimodal discourse analysis approach, this article aims to explore key user roles prompted by AR apps and examine educational functions that these user roles fulfil in AR-mediated learning. Based on our analysis of 14 AR apps selected for a 3-day workshop with six Australian primary school teachers, we identified four categories of user roles that facilitated different literacy activity types during AR-mediated learning. To design effective learning experiences, this article argues that teachers need to consider the resonance between students’ AR experiences and their prior engagement with other forms of digital texts when planning for scaffolding strategies.
Native vs. Unique Fruit Popularity: Exploring the Sustainable Fruit Consumption in Poland—Research Report
Authors: Gruszecka-Kosowska Agnieszka , Mazur-Włodarczyk Katarzyna , Wódkowska Agata
Published: July 2023

As nutritional statistical data in Poland have provided very general information on this topic, the purpose of this online questionnaire survey was to investigate the trend of fruit consumption through the prism of consumers’ awareness of the sustainability of fruit production (ecological cultivation and production—certified organic farming). Our research is based on a questionnaire prepared by the authors; it was supported by the desk research method. This study used descriptive analysis involving a summary of historical data and diagnostic analyses focusing on the causes of past events. Pivot tables created in Microsoft Excel, the chi-2 test, and content analysis were used in the analysis of the data. One of the main conclusions was that the aspect of sustainability was less significant than the convenience of purchasing fruits. The research also showed an imbalance in fruit consumption, which indicated the need to promote the positive aspects of fruit consumption, as well as the need to increase social education in terms of current knowledge about the beneficial aspects of a more balanced diet.
Field Performance Monitoring of Energy-Generating High-Transparency Agrivoltaic Glass Windows
Authors: Vasiliev Mikhail , Rosenberg Victor , Lyford Jamie , Goodfield David
Published: July 2023

Currently, there are strong and sustained growth trends observed in multi-disciplinary industrial technologies such as building-integrated photovoltaics and agrivoltaics, where renewable energy production is featured in building envelopes of varying degrees of transparency. Novel glass products can provide a combination of thermal energy savings and solar energy harvesting, enabled by either patterned-semiconductor thin-film energy converters on glass substrates, or by using luminescent concentrator-type approaches to achieve high transparency. Significant progress has been demonstrated recently in building integrated solar windows featuring visible light transmission of up to 70%, with electric power outputs of up to Pmax ~ 30–33 Wp/m2. Several slightly different designs were tested during 2021–2023 in a greenhouse installation at Murdoch University in Perth, Western Australia; their long-term energy harvesting performance differences were found to be on the scale of ~10% in wall-mounted locations. Solar greenhouse generated electricity at rates of up to 19 kWh/day, offsetting nearly 40% of energy costs. The objective of this paper is to report on the field performance of these PV windows in the context of agrivoltaics and to provide some detail of the performance differences measured in several solar window designs related to their glazing structure materials. Methods for the identification and quantification of long-term field performance differences and energy generation trends in solar windows of marginally different design types are reported. The paper also aims to outline the practical application potential of these transparent construction materials in built environments, focusing on the measured renewable energy figures and seasonal trends observed during the long-term study.
A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment
Authors: Lin Faa-Jeng , Chang Chao-Fu , Huang Yu-Cheng , Su Tzu-Ming
Published: July 2023

This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and the charging/discharging control strategy of a battery energy storage system (BESS). Then, a deep reinforcement learning method, the deep deterministic policy gradient (DDPG), is utilized to develop the power dispatch of a microgrid to minimize the total energy expense while considering power constraints, load uncertainties and electricity price. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the deep reinforcement learning method. Furthermore, a comparison of the proposed method with the experience-based energy management system (EMS), Newton particle swarm optimization (Newton-PSO) and the deep Q-learning network (DQN) is provided to evaluate the obtained solutions. In this study, the average deviation of the LSTM forecast accuracy is less than 5%. In addition, the daily operating cost of the proposed method obtains a 3.8% to 7.4% lower electricity cost compared to that of the other methods. Finally, a detailed emulation in the OPAL-RT environment is carried out to validate the effectiveness of the proposed method.
Predicting Dose-Dependent Carcinogenicity of Chemical Mixtures Using a Novel Hybrid Neural Network Framework and Mathematical Approach
Authors: Limbu Sarita , Dakshanamurthy Sivanesan
Published: July 2023

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as per- and polyfluorinated substances (PFASs), which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a hybrid neural network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures, enabling the creation of classification models for binary (carcinogenic or noncarcinogenic) and multiclass classification (categorical carcinogenicity) and regression (carcinogenic potency). Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including random forest, bootstrap aggregating, adaptive boosting, support vector regressor, gradient boosting, kernel ridge, decision tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and noncarcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieved average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multiple mixtures such as PFAS mixtures and co-occurring chemicals.
No Secondary Treatment Failure during Incobotulinumtoxin—A Long-Term Treatment Demonstrated by the Drawing of Disease Severity
Authors: Hefter Harald , Brauns Raphaela , Ürer Beyza , Rosenthal Dietmar , Albrecht Philipp , Samadzadeh Sara
Published: July 2023

The aim of this study was to detect clinical hints regarding the development of secondary treatment failure (STF) in patients with focal dystonia who were exclusively treated with incobotulinumtoxin/A (incoBoNT/A). In total, 33 outpatients (26 with idiopathic cervical dystonia, 4 with Meige syndrome and 3 with other cranial dystonia) who were treated with repeated injections of incoBoNT/A for a mean period of 6.4 years without interruptions were recruited to draw the course of their disease severity (CoD) from the onset of symptoms to the onset of BoNT therapy (CoDB graph) and from the onset of BoNT therapy to recruitment (CoDA graph). At the time of recruitment, the patients assessed the change in severity as a percentage of the severity at the onset of BoNT therapy. Blood samples were taken to test the presence of neutralizing antibodies (NABs) using the mouse hemidiaphragm assay (MHDA). Patients reported an improvement of about 70% with respect to the mean. None of the patients tested positive for MHDA. Three different types of CoDB and three different types of CoDA graphs could be distinguished. The patients with different CoDB graphs reported different long-term outcomes, but there was no significant difference in long-term outcomes between patients with different CoDA graphs. None of the patients produced a CoDA graph with an initial improvement and a secondary worsening as a hint for the development of STF. A primary non-response was not observed in any of the patients. During long-term treatment with BoNT/A, NABs and/or STF may develop. However, in the present study on patients with incoBoNT/A long-term monotherapy, no hints for the development of NABs or STF could be detected, underlining the low antigenicity of incoBoNT/A.