bluuebunny/crossref_metadata_2025 huggingface dataset:
Counterfactual Anonymous Quantum Teleportation in the Presence of Adversarial Attacks and Channel Noise
Authors: Nang Paing Saw , Setiawan Jason William , Tariq Shehbaz , Talha Rahim Muhammad , Lee Kyesan , Shin Hyundong
Published: October 2022

Hiding the identity of involved participants in the network, known as anonymity, is a crucial issue in some cryptographic applications such as electronic voting systems, auctions, digital signatures, and Byzantine agreements. This paper proposes a new anonymous quantum teleportation protocol based on counterfactual communication where no information-carrying particles pass through the channel. It is achieved by the distribution of a counterfactual entanglement among the participants in the network followed by the establishment of an anonymous entanglement between the sender and the receiver. Afterwards, the sender can anonymously teleport a quantum state to the receiver by utilizing the anonymous entanglement. However, the practicality of the anonymous quantum network mainly calls for two performance measures—robustness against adversarial attacks and noisy environments. Motivated by these demands, firstly, we prove the security of our proposed protocol and show that it achieves both the sender and receiver’s anonymity in the presence of active adversaries and untrusted parties. Along with anonymity, we also ensure the correctness of the protocol and the privacy of the teleported qubit. Finally, we analyze the robustness of our proposed protocol under the presence of channel noise and compare its fidelity with those of the conventional protocols. The main advantage of our proposed protocol is that it can provide useful anonymous quantum resources for teleportation under noisy environment with a higher security compared to previous protocols.
Micro-/Nano-Fiber Sensors and Optical Integration Devices
Authors: Li Jin
Published: October 2022

Because of their strong surface evanescent field, micro-/nanofibers have been used to develop optical sensors and modulation devices with a high performance and integration [...]
Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications
Authors: Shumba Angela-Tafadzwa , Montanaro Teodoro , Sergi Ilaria , Fachechi Luca , De Vittorio Massimo , Patrono Luigi
Published: October 2022

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.
Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns
Authors: Lee SeungHun , Shafqat Wafa , Kim Hyun-chul
Published: October 2022

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter.
Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
Authors: Mishra Mukesh , Sen Gupta Gourab , Gui Xiang
Published: October 2022

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.
Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors
Authors: Mirzaei Hamed , Ramezankhani Milad , Earl Emily , Tasnim Nishat , Milani Abbas S. , Hoorfar Mina
Published: October 2022

Alternative fuel sources, such as hydrogen-enriched natural gas (HENG), are highly sought after by governments globally for lowering carbon emissions. Consequently, the recognition of hydrogen as a valuable zero-emission energy carrier has increased, resulting in many countries attempting to enrich natural gas with hydrogen; however, there are rising concerns over the safe use, storage, and transport of H2 due to its characteristics such as flammability, combustion, and explosivity at low concentrations (4 vol%), requiring highly sensitive and selective sensors for safety monitoring. Microfluidic-based metal–oxide–semiconducting (MOS) gas sensors are strong tools for detecting lower levels of natural gas elements; however, their working mechanism results in a lack of real-time analysis techniques to identify the exact concentration of the present gases. Current advanced machine learning models, such as deep learning, require large datasets for training. Moreover, such models perform poorly in data distribution shifts such as instrumental variation. To address this problem, we proposed a Sparse Autoencoder-based Transfer Learning (SAE-TL) framework for estimating the hydrogen gas concentration in HENG mixtures using limited datasets from a 3D printed microfluidic detector coupled with two commercial MOS sensors. Our framework detects concentrations of simulated HENG based on time-series data collected from a cost-effective microfluidic-based detector. This modular gas detector houses metal–oxide–semiconducting (MOS) gas sensors in a microchannel with coated walls, which provides selectivity based on the diffusion pace of different gases. We achieve a dominant performance with the SAE-TL framework compared to typical ML models (94% R-squared). The framework is implementable in real-world applications for fast adaptation of the predictive models to new types of MOS sensor responses.
A Simplified Correlation Index for Fast Real-Time Pulse Shape Recognition
Authors: Cicuttin Andres , Morales Iván René , Crespo Maria Liz , Carrato Sergio , García Luis Guillermo , Molina Romina Soledad , Valinoti Bruno , Folla Kamdem Jerome
Published: October 2022

A simplified correlation index is proposed to be used in real-time pulse shape recognition systems. This index is similar to the classic Pearson’s correlation coefficient, but it can be efficiently implemented in FPGA devices with far fewer logic resources and excellent performance. Numerical simulations with synthetic data and comparisons with the Pearson’s correlation show the suitability of the proposed index in applications such as the discrimination and counting of pulses with a predefined shape. Superior performance is evident in signal-to-noise ratio scenarios close to unity. FPGA implementation of Person’s method and the proposed correlation index have been successfully tested and the main results are summarized.
Optical Polarization Division Multiplexing Transmission System Based on Simplified Twin-SSB Modulation
Authors: Zhou Ye , Ming Jun , Wang Leilei , Wu Dongyan , Zhao Li , Xiao Jiangnan
Published: October 2022

Optical twin-single sideband (Twin-SSB) modulation, due to the left sideband (LSB) and right sideband (RSB) signal carrying individual data, has become an attractive technique in fiber transmission because it satisfies the demand of the explosive increase in data traffic. This paper focuses on reducing the complexity of Twin-SSB system and further enhancing the spectral efficiency by proposing a polarization division multiplexing (PDM) Twin-SSB modulation scheme. LSB and RSB signals are extracted using de-mapping algorithm instead of optical bandpass filters (OBPFs) to reduce system complexity. To further improve spectral efficiency, PDM is employed to meet the polarization multiplexing transmission and achieve a higher transmission capacity. Based on the PDM Twin-SSB system, the LSB is 3-arr phase-shift-keying (3PSK) modulated, while RSB is quadrature phase-shift keying (QPSK) modulated. We simulated that the bit error ratio (BER) performance of LSB and RSB of X-polarization (X-Pol) and Y-polarization (Y-Pol) at 8-Gbaud, 10-Gbaud, 12-Gbaud, 14-Gbaud, and 16-Gbaud in the case of back-to-back (BTB) and 2 km standard single-mode fiber (SSMF) transmission. The simulation results verify the effectiveness and practical feasibility of the proposed PDM Twin-SSB scheme for future short-distance transmission owing to low cost, simplified structure, low algorithm complexity, and high data transmission capacity.
Fast and Non-Destructive Quail Egg Freshness Assessment Using a Thermal Camera and Deep Learning-Based Air Cell Detection Algorithms for the Revalidation of the Expiration Date of Eggs
Authors: Nakaguchi Victor Massaki , Ahamed Tofael
Published: October 2022

Freshness is one of the most important parameters for assessing the quality of avian eggs. Available techniques to estimate the degradation of albumen and enlargement of the air cell are either destructive or not suitable for high-throughput applications. The aim of this research was to introduce a new approach to evaluate the air cell of quail eggs for freshness assessment as a fast, noninvasive, and nondestructive method. A new methodology was proposed by using a thermal microcamera and deep learning object detection algorithms. To evaluate the new method, we stored 174 quail eggs and collected thermal images 30, 50, and 60 days after the labeled expiration date. These data, 522 in total, were expanded to 3610 by image augmentation techniques and then split into training and validation samples to produce models of the deep learning algorithms, referred to as “You Only Look Once” version 4 and 5 (YOLOv4 and YOLOv5) and EfficientDet. We tested the models in a new dataset composed of 60 eggs that were kept for 15 days after the labeled expiration label date. The validation of our methodology was performed by measuring the air cell area highlighted in the thermal images at the pixel level; thus, we compared the difference in the weight of eggs between the first day of storage and after 10 days under accelerated aging conditions. The statistical significance showed that the two variables (air cell and weight) were negatively correlated (R2 = 0.676). The deep learning models could predict freshness with F1 scores of 0.69, 0.89, and 0.86 for the YOLOv4, YOLOv5, and EfficientDet models, respectively. The new methodology for freshness assessment demonstrated that the best model reclassified 48.33% of our testing dataset. Therefore, those expired eggs could have their expiration date extended for another 2 weeks from the original label date.
Green Synthesis of Silver Nanoparticles Using Bellevalia Flexuosa Leaves Extract
Authors: Al-Nemrawi Nusaiba , Hameedat Fatima , El-Elimat Tamam
Published: October 2022

Silver nanoparticles (AgNPs) have broad biocidal activities, and are widely employed as an active ingredient in antiseptic, anti-viral, and anti-inflammatory preparations. Green-synthesizing AgNPs would be a rapid, cheap, and environmentally friendly method of synthesis. The methanolic extract of the leaves of Bellevalia flexuosa Boiss. (Asparagaceae) was used for the green synthesis of the AgNPs. The effects of the pH and the concentration of silver nitrate (AgNO3) on the synthesis of the AgNPs were investigated. The AgNPs produced above pH 10, and 1 mM of AgNO3 resulted in lower hydrodynamic diameters. Ultraviolet-visible spectroscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction proved the formation of the AgNPs, with a face-centered, cubed geometry. Scanning electron microscopy images showed colloidal and well-dispersed nanoparticles. In addition, the antibacterial activities of the prepared AgNPs were assessed by optical densities (ODs) against Gram-positive bacteria (Enterococcus faecalis and Staphylococcus epidermidis) and Gram-negative bacteria (Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, and Salmonella enterica). The broths of Gram-negative and Gram-positive bacteria that contained AgNPs, showed lower OD values compared to the controls. In conclusion, AgNPs were prepared using B. flexuosa methanolic extract, and showed antibacterial activity against the tested bacterial strains.
Meeting Unmet Needs for Stroke Rehabilitation in Rural Public Health: Explorative Economic Evaluation of Upper Limb Robotics-Based Technologies through a Capabilities Lens
Authors: Brusco Natasha , Voogt Andrea , Nott Melissa , Callaway Libby , Mansoubi Mae , Layton Natasha
Published: October 2022

Rehabilitation technologies are rapidly evolving, presenting promising interventions for people with neurological impairments. Access to technology, however, is greater in metropolitan than rural areas. Applying a capabilities approach to this access issue foregrounds healthcare recipients’ rights and personhood within the discourse on resource allocation. Within this context, this study aimed to investigate the economic viability of robotics-based therapy (RBT) in rural Victoria, Australia. A regional health network developed a model of care to provide equitable access to RBT following stroke. This explorative economic evaluation examined both the clinical and economic impact of RBT program implementation across six program iterations compared to 1:1 out-patient rehabilitation. While clinical outcomes were equivalent, the per patient RBT cost ranged from AUD 2681 (Program 1) to AUD 1957 (Program 6), while the per patient cost of usual care 1:1 out-patient rehabilitation, was AUD 2584. Excluding Program 1, the health service cost of usual care 1:1 out-patient rehabilitation was consistently higher, indicating that an established RBT program may be cost-effective, specifically providing less cost for the same effect. This research demonstrates the economic feasibility of delivering RBT in a regional public health stroke service. More broadly, it provided a reduction in the capability gap between rural and metropolitan stroke survivors by tackling an access disadvantage.
Exploring Citizens Perception of the Police Role and Function in a Post-Colonial Nation
Authors: Mathura Michael
Published: October 2022

Before attempting to develop productive and harmonious working relationships between citizens and the police in a post-colonial society such as Trinidad and Tobago (T&T), it is imperative to initially gain a more precise understanding of the role and function of the police. This qualitative study suggested that the current role and function of officers is parallel to the colonial model of policing, where officers operated in a paramilitary manner. This model of policing was concerned with law enforcement and public order duties, which was highlighted as counterproductive for police and public relations. The model was also popular for police treatment based on citizens socioeconomic status. The results of this study suggest that police officers should implement a Service Oriented Policing approach (SOP), which could allow police officers to become proactively involved with communities and citizens, build stronger and increasingly productive relationships and be more effective and efficient as an institution.
Channel Evolution under the Control of Base-Level Cycle Change and the Influence on the Sustainable Development of the Remaining Oil—A Case in Jiang Ling Depression, Jiang Han Basin, China
Authors: Zhu Wei , Shen Mingsu , Dai Shixin , Liu Kuanning , Qi Yongdi
Published: October 2022

The extension of river channels is one of the key factors in determining the remaining oil distribution. Different sedimentary facies and bedding types of oil layers will form specific characteristics of remaining oil distribution after water injection development. Using massive drilling, core, logging, seismic, and production data, on the basis of sequence stratigraphy base-level cycle change, the river records and development history are restored, and the fine connectivity of reservoirs and the configuration relationship of production wells are studied. The following conclusions are drawn: (1) A sequence stratigraphic division scheme is established. In the established sequence framework, the types and characteristics of reservoir sand bodies are analyzed. The 2nd and 6th members of Yu yang formation can be divided into 2 long-term base level cycles, 5 medium-term base level cycles, and 17 short-term base level cycles. The evolution of the second and sixth members of the Yu yang formation shows a pattern of base level rising, falling and rising again; (2) the vertical sedimentary evolution sequence is underwater distributary channel distributary channel meandering channel distributary channel flood plain. The types of channel sand bodies developed from little overlap to more vertical or lateral overlap and then gradually changed to isolated type; (3) according to the structural location and development sequence, different types of reservoirs are identified. Combined with the statistics of the drilled data of Yu yang formation k2y4 in Fu I fault block, it is found that the connectivity rate of oil layer thickness (the ratio of oil layer connectivity thickness to total thickness of sand layer) within the oil-bearing area is 84.4%, and the connectivity rate of the number of layers (8) is 60%. The connectivity condition is relatively good.
Analysis of Spatiotemporal Characteristics and Influencing Factors for the Aid Events of COVID-19 Based on GDELT
Authors: Yao Yunxing , Zhang Yinbao , Liu Jianzhong , Li Yanpei , Li Xiaopei
Published: October 2022

The uncertainty of COVID-19 and the spatial inequality of anti-pandemic materials have made international aid an important means for many countries to cope with this global public health crisis. It is of far-reaching significance to analyze the spatiotemporal characteristics and influencing factors of international aid events for the global joint fight against COVID-19 and the sustainability of global public health business. The data on aid events from 23 January 2020 to 31 October 2021, were from the GDELT database. China, the United States, the United Kingdom, and Canada were selected as the study objects because they provided more aid. Their spatiotemporal characteristics of main aid flows, the response characteristics of the aid requests, and the characteristics of verbal aid to cash in were studied using spatial statistical analysis methods. The influencing factors of aid allocation also were studied by regression analysis. The results found that: the international aid flow of each country was consistent in spatial distribution, mainly to countries with severe pandemics and neighboring countries. However, there were differences in the recipients. China mainly aided developing countries, while the United States, the United Kingdom, and Canada mainly aided developed countries. Relatively speaking, China was more responsive to aid requests and more aggressive in cashing in on verbal aid. The countries considered the impact of their economic interests when they planned to aid. At the same time, there were obvious “bandwagon effect” and “small country tendency” on the aid events.
Family-Size Effect on Intergenerational Income Mobility under China’s Family Planning Policy: Testing the Quantity–Quality Trade-Off
Authors: Mu Xinxin , Chen Shenghu
Published: October 2022

The causal link between family size and human capital investment in children is critical for family planning policy. However, empirical studies aiming to test the quantity–quality trade-off are far from sufficient. This paper tried to estimate the family-size effect on intergenerational income mobility using China Family Panel Survey (CFPS) data. The empirical model of intergenerational income mobility with respective to family size was formulated, and the fertility rates allowed by family planning policy were used as an instrument variable for family size. It was found that intergenerational income elasticity tended to decrease with an increase in family size. The impact of family size on intergenerational income elasticity was sensitive to the income rank positions, and nonlinearity in intergenerational transmission of income under unequal family was observed. A quantity–quality trade-off analysis was applied to further test the family-size effect. Pronounced family-size effects were observed in low-income regions with tight budget constraints and in regions with less-developed credit markets, followed by an obvious quantity–quality trade-off. The sex difference in intergenerational transmission of income may be attributed to the existence of the “preference to sons over daughters” phenomenon. The present work provides a theoretical basis for shaping family planning policies toward sustainability.
Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0
Authors: Tobon-Valencia Estefania , Lamouri Samir , Pellerin Robert , Moeuf Alexandre
Published: October 2022

The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as a starting point toward digitization. The current state of this process reveals a lack of tools to support decision making and the need to increase the reliability of input data and to make the process more agile. Industry 4.0 technologies and process modeling could increase agility in the planning process. However, the digital transformation of medium-term planning activities in SMEs has not been studied. To fill this gap, a group of six experts was consulted. The novelty of this study was to identify the Industry 4.0 technologies that could improve medium-term planning and integrate them into a standardized MPS process model. This model is an ultimate point of digitization that cannot be achieved immediately by any SME, but only after several cycles of planning, deployment, and improvement. Therefore, this research also provides a method to help SMEs determine how to start the digitization of their MPS process.
Integrative Review of Absorptive Capacity’s Role in Fostering Organizational Resilience and Research Agenda
Authors: Khin Khin Oo Nay Chi , Rakthin Sirisuhk
Published: October 2022

Organizational resilience (OR) has been studied as an important construct for maintaining an organization’s sustainability in today’s dynamic business world. However, the exact way to systematically achieve OR in real organizational settings is still unknown. In this paper, the scholars elucidate how OR can be fostered by developing knowledge absorptive capacity based on the knowledge-based view and dynamic capability theory. The paper highlights the significance of knowledge resources for a firm’s survival nowadays and provides conceptual clarity of how a firm’s ACAP could reinforce fostering OR. Thereby, this review fills the knowledge gaps of previous studies. Based on the review corpus, scholars also address other prominent antecedents for nurturing OR, such as leadership styles, dynamic capabilities, organizational learning, unlearning, networks, and social capital. Lastly, a conceptual model was developed for future organizational studies. In addition to the aforementioned contributions, the study’s novelty also lies in the review method, which is systematically conducted in an integrated manner by combining a bibliometric analysis and a scoping review. Furthermore, the study analyzes a more expansive database that includes 823 documents and covers documents published more recently, from 1992 to 2021.
Spatial Peer Effect of Enterprises’ Digital Transformation: Empirical Evidence from Spatial Autoregressive Models
Authors: Pan Xiaozhen , Xu Gengxi , Zhu Nina
Published: October 2022

This study uses the spatial autoregressive model for panel data to empirically test the spatial peer effect of enterprises’ digital transformation by using a sample of Chinese listed companies during 2012–2021. We find that there is a significant spatial peer effect in the digital transformation of Chinese companies, and the level of digital transformation of a company increase with the level of digital transformation of its spatial peer companies. Moreover, heterogeneity analysis shows that the spatial peer effect of digital transformation can be effectively played only under a higher digital environment, higher marketization environment, and state-owned equity nature of companies. The findings suggest that it should focus on building a benchmark company for digital transformation, vigorously enhance the digital and marketization environment in the region and provide more policy support for the digital transformation of non-state-owned enterprises.
Space-Time Evolution Characteristics of Deformation and Failure of Surrounding Rock in Deep Soft Rock Roadway
Authors: Wang Xinfeng , Zhang Yiying , Zhang Qiao , Wei Youyu , Liu Wengang , Jiang Tian
Published: October 2022

In view of the problems of large deformation of surrounding rock, high in-situ stress, and serious soft fracture of rock stratum in deep soft rock roadway, the instability deformation failure mode of deep soft rock roadway is analyzed theoretically. The FLAC3D software is used to establish a three-dimensional numerical model of surrounding rock damage under load, and to study the displacement, stress, and plastic expansion process of damage and failure evolution in the surrounding rock of the roadway. The mechanical response mechanism of deep soft rock roadway surrounding rock bending deformation, elastoplastic transformation, and unloading failure is verified by MATLAB numerical analysis, and the space-time evolution characteristics of soft rock deformation and failure are revealed. The results show that the surrounding rock of deep soft rock roadway has many failure modes, such as obvious displacement and deformation, high stress concentration, and intensified plastic transformation in the surrounding rock. The vertical stress in the surrounding rock is concentrated at the direct top and bottom, and the horizontal stress is concentrated at the roadway side and bottom; plastic deformation and failure first appeared at the roadway side, and then extended to other parts. The research conclusion provides an important reference for surrounding rock control and roof management of high-stress soft rock roadway under deep excavation disturbance.
Barriers and Opportunities: Specialty Cultivated Mushroom Production in the United States
Authors: Moxley Alexandria , Ebel Roland , Cripps Cathy L. , Austin Caroline Graham , Stein Mary , Winder Meaghan
Published: October 2022

Producing and consuming specialty cultivated mushrooms (SCMs), cultivated mushrooms outside of the Agaricus genus, has the potential to positively impact sustainable food systems. Few studies have examined consumer perceptions of SCMs and industry-wide trends of SCM production in the United States (US), despite the USD 66.1 million in SCM sales in the US during 2020. This study looked at the barriers to and opportunities for cultivating, marketing, and consuming SCMs in the US by conducting a producer survey with SCM facilities in the US (n = 63). Survey results found diversification across products and practices within the SCM industry and on an individual business level. The most common place SCM growers sold their products was farmers’ markets (n = 63). The majority (53%) of growers (n = 60) used diverse (four or more) approaches to advertise their products. The majority of SCM growers (57%) indicated they had participated in a community outreach event in the past five years to help promote their SCM products (n = 63). Findings indicate there are opportunities for greater SCM business owner diversity. Our results indicate that production of SCMs may support economic, environmentally, and socio-culturally sustainable food systems and that there is further room for increased sustainability across the industry.
Cadmium (Cd) and Copper (Cu) Exposure and Bioaccumulation Arrays in Farm Ruminants: Impact of Forage Ecotypes, Ecological Sites and Body Organs
Authors: Hussain Muhammad Iftikhar , Naeem Majida , Khan Zafar Iqbal , Akhtar Shahzad , Nadeem Muhammad , Alnuwaiser Maha Abdallah , Ahmad Kafeel , Vicente Oscar , Yang Hsi-Hsien
Published: October 2022

Copper (Cu) and cadmium (Cd) metal distribution in soil–plant ecosystems and their public health risk impact on ruminants (cows, buffalo, and sheep) are explored in the present investigation. Five different forage crops were selected, and the foraging responses of three types of ruminants (cows, buffalo, and sheep) at three ecological sites were evaluated. The soil of these three ecological sites was metal polluted (due to wastewater irrigation) and was studied to evaluate the metal contamination and pollution load index. For the assessment of Cd and Cu, soil, vegetation, blood, hair, and feces samples were collected and analysed using an atomic absorption spectrophotometer. High consumption of fodder crops (Sorghum bicolor Kuntze, Sesbania bispinosa (Jacq.) W. Wight, Cynodon dactylon (L.) Pers., Suaeda fruticosa (L.) Forssk., and Tribulus terrestris L.) by cows and buffalo at site-III resulted in an increase in daily Cu and Cd intake. The pollution load index was higher at site-II and site-III, indicating a severe health risk scenario for local inhabitants. Cd and Cu were at their maximum levels in fodder crops. A significant increase in the concentrations of Cd and Cu was found in the blood, hair, and feces of cows and buffalo at site-III. Ecological indicators such as the bioaccumulation factor, the pollution load index, and the enrichment factor were found to be higher in buffalo than cows. The Cd level in forages was highest at the site-III Cd level and in the order of S. bispinosa > S. fruticosa > T. terresteris > C. dactylon > S. bicolor. Although these levels were lower than the permissible maximum limit, they were generally higher in the forage crops. Exposure of local inhabitants to the consumption of milk and meat from these cattle showed the serious health risks consequences. This situation can be properly managed by general monitoring of soil and vegetation pollution, avoiding metal contamination in the soil and food chain components, and using treated waste water and other alternate water sources for forage irrigation.
Green vs. Screen: Exploring the Outcomes of an In-Person and Virtual Nature-Based Environmental Education Intervention for Low-Income Children
Authors: Sprague Nadav L. , Sachs Ashby L. , Ekenga Christine C.
Published: October 2022

The onset of the COVID-19 pandemic in 2020 forced a rapid transition to virtual learning. During the pandemic, many nature-based environmental education (NBEE) interventions shifted to virtual formats. In this study, we compare the impacts of a virtual NBEE intervention with its in-person NBEE counterpart. Between January and May 2021, a total of 49 low-income children (ages 9 to 13) from St. Louis, MO USA participated in this study. There were 37 children that participated in the virtual NBEE intervention and 12 students in the in-person NBEE intervention. Study participants completed a pre-/post-test survey that included items related to exposure to nature, perceived neighborhood safety, and self-reported quality of life. Children who participated in the in-person intervention reported higher post-intervention levels of nature exposure, perceived neighborhood safety, self-confidence, and self-efficacy than children who participated in the virtual intervention. The study’s findings have implications for the implementation of virtual learning during future public health emergencies and environmental disasters, including extreme climate events.
Genetic Algorithms-Based Optimum PV Site Selection Minimizing Visual Disturbance
Authors: Nagkoulis Nikolaos , Loukogeorgaki Eva , Ghislanzoni Michela
Published: October 2022

In this paper, an integrated methodology is developed to determine optimum areas for Photovoltaic (PV) installations that minimize the relevant visual disturbance and satisfy spatial constraints associated with land use, as well as environmental and techno-economic siting factors. The visual disturbance due to PV installations is quantified by introducing and calculating the “Social Disturbance” (SDIS) indicator, whereas optimum locations are determined for predefined values of two siting preferences (maximum allowable PV locations—grid station distance and minimum allowable total coverage area of PV installations). Thematic maps of appropriate selected exclusion criteria are produced, followed by a cumulative weighted viewshed analysis, where the SDIS indicator is calculated. Optimum solutions are then determined by developing and employing a Genetic Algorithms (GAs) optimization process. The methodology is applied for the municipality of La Palma Del Condado in Spain for 100 different combinations of the two siting preferences. The optimization results are also employed to create a flexible and easy-to-use web-GIS application, facilitating policy-makers to choose the set of solutions that better fulfils their preferences. The GAs algorithm offers the ability to determine distinguishable, but compact, regions of optimum locations in the region, whereas the results indicate the strong dependence of the optimum areas upon the two siting preferences.
Analysis of Spatial Differences and the Influencing Factors in Eco-Efficiency of Urban Agglomerations in China
Authors: Liu Danyu , Zhang Ke
Published: October 2022

In the context of climate change, studying the ecological efficiency (EE) of urban agglomerations is of great significance in promoting sustainable development. First, night light data are used as the expected output to build an evaluation index system based on the five major urban agglomerations, namely, the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, the middle reaches of the Yangtze River, and Chengdu–Chongqing urban agglomerations. Second, the super-efficient Epsilon-based (super-EBM) model and the input–output redundancy rates are used to measure the EE of the five major urban agglomerations from 2006 to 2018. Then, their spatial differences are explored with the help of the Gini coefficient. Finally, the spatial differences in the EE drivers of urban agglomerations are analyzed using Geodetector. The results reveal the following. (1) The EE of the five major urban agglomerations present the decline fluctuation trend of “∧”. However, this trend has slowed down. From the perspective of urban agglomeration, Beijing–Tianjin–Hebei > The Pearl River Delta > Chengdu–Chongqing > Yangtze River Delta > the middle reaches of the Yangtze River. The lowest efficiency of the Yangtze River’s middle reaches has “high investment, low output, and high pollution” characteristics. (2) The EE of the five major urban agglomerations had weak synergistic development and noticeable spatial differences. The primary sources are inter-group differences and hypervariable density. (3) From the perspective of influencing, the difference in technological innovation levels (TEC) is the single leading factor in the differences in the EE space of urban agglomerations. In addition, the interaction combination of industrial structure upgrades (IDS) and traffic infrastructure (TRAF) is a crucial combination driver. However, the core influencing factors of spatial differences in EE in five urban agglomerations are heterogeneous. Among them, the nature-influencing factors of the EE space differences in the Beijing–Tianjin–Hebei and the Chengdu–Chongqing urban agglomerations are environmental regulations (ER). Meanwhile, the influencing factor in the Yangtze River Delta urban agglomeration is the development of urbanization (URB). Moreover, the prominent factor in the middle reaches of the Yangtze River and the Pearl River Delta urban agglomerations is foreign direct investment (FDI). On this basis, this study aims to promote ecological civilization construction in urban agglomerations and optimize regional integrated spatial patterns.
Towards an Understanding of FinTech Users’ Adoption: Intention and e-Loyalty Post-COVID-19 from a Developing Country Perspective
Authors: Alkhwaldi Abeer F. , Alharasis Esraa Esam , Shehadeh Maha , Abu-AlSondos Ibrahim A. , Oudat Mohammad Salem , Bani Atta Anas Ahmad
Published: October 2022

Earlier literature has shown that the implementation of FinTech innovations is not only determined by banks, financial institutions, or government support, but also by the perception and experiences of FinTech users. FinTech research has shown encouraging findings from scholars in developed countries. However, little is known about the users’ acceptance and use of FinTech in Jordan. The aim of this study is to investigate the determinants of users’ intentions and e-Loyalty toward FinTech adoption in Jordan post the COVID-19 era. A conceptual framework was developed by integrating the four original constructs of the unified theory of acceptance and use of technology (UTAUT), namely performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), with three additional factors: personal innovativeness (PI), financial literacy (FL), and uncertainty avoidance (UA). In addition, the proposed model considered the e-Loyalty of FinTech users as a consequence of having a good FinTech experience. A quantitative approach using a cross-sectional online questionnaire was applied to collect data from 423 FinTech users. Data were analyzed utilizing structural equation modeling (SEM) based on AMOS 26.0 software package. The findings revealed that UA has a moderating effect on the relationship between FC and users’ intentions. Also, PI has a significant impact on PE and EE. While PE, SI, and FC are factors that enhance behavioral intentions. In return, it builds users’ e-Loyalty toward FinTech services and is deemed a new normal behavior. This study may help FinTech service providers and policymakers better understand the, currently relatively low, usage rate of FinTech, and how it contributes to the development of strategies that boost the acceptance and e-Loyalty of FinTech by Jordanian users after the COVID-19 era, where FinTech is still considered an innovation.
Examining the Correlation among Economic Development, Foreign Direct Investment, and CO2 Emissions by Utilizing the VECM Model—Empirical Study in Vietnam
Authors: Tran Trang Mai , Phan Thao Huong , Tran Thao Viet , Le Anh Tram Thi
Published: October 2022

From 1990 to 2019, Vietnam entered a period of economic growth, attracting foreign investment. However, during this period, CO2 emissions also increased significantly. This paper will examine the short- and long-term relations of Vietnam’s relationship among FDI, economic growth, and CO2 emissions based on the data on CO2, GDP, and FDI of Vietnam in 1990–2019. This article uses the VECM model to test the effect among variables. Research results identify an impact and causal correlation among CO2 emissions and economic growth and foreign direct investment in Vietnam. Research results show a long-term relationship between CO2 emissions, GDP, and FDI. The authors did not find a short-run relationship between the above variables. Assessing impacts of CO2 emissions on economic growth and foreign direct investment will help policymakers make reasonable policies to balance the increase in foreign investment capital and economic growth and reduce CO2 to achieve the commitment in COP26.
Assessment of Weather Research and Forecasting (WRF) Physical Schemes Parameterization to Predict Moderate to Extreme Rainfall in Poorly Gauged Basin
Authors: Zaidi Syeda Maria , Gisen Jacqueline Isabella Anak , Eltahan Mohamed , Yu Qian , Misbari Syarifuddin , Ngien Su Kong
Published: October 2022

Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting.
Does High-Speed Railway Promote the Level of Human Capital? An Empirical Analysis Based on Three Urban Agglomerations in China
Authors: Xu Yafei , Ou Guoli
Published: October 2022

This paper analyzed the impact of high-speed railway (HSR) construction on human capital and its mechanism by establishing high-speed railway-city level panel data from 2003 to 2019 and constructing a multiperiod difference- in-differences (DID) DID model in China. The results showed that HSR has significantly promoted the level of human capital in Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Pearl River Delta urban agglomeration in China, with the overall effect value of 20.8%. The conclusion that HSR promotes the level of human capital is still valid through endogenous treatment of the instrumental variable method and robustness test. Further analysis showed that the impact of HSR construction on the level of human capital in urban agglomerations is mainly through the labor mobility and education investment potential, with the effect values of 3.65% and 4.44%, respectively. The findings of this paper highlight the importance of exploiting the external utility of HSR, improving the level of regional human capital, and promoting high-quality economic development of urban agglomerations.