bluuebunny/crossref_metadata_2025 huggingface dataset:
A Modular Method for GPR Hyperbolic Feature Detection and Quantitative Parameter Inversion of Underground Pipelines
Authors: Zhu Chengke , Ye Hongxia
Published: April 2023

Ground penetrating radar (GPR) is widely used to inspect underground pipelines because it is non-destructive. When the scan line of GPR is perpendicular to the pipe, it will exhibit hyperbolic features in GPR B-scan images, which have no intuitive relationship with the geometric and physical parameters of the pipeline, making the interpretation of GPR images difficult. This paper proposes a modular detection and quantitative inversion method for the hyperbolic features in GPR B-scan images, which is divided into two steps. In the first step, the YOLOv7 object detection network is used to automatically detect the hyperbolic features in GPR images. In the second step, a two-stage curve fitting method is proposed based on the characteristics of the detection model. It uses a few key point annotations of the hyperbolic pattern and some parameters of the GPR system to quantitatively invert the depth and radius of pipes. Using the same hardware and data set, YOLOv7 achieves an 11.1% improvement in detection accuracy and an 18.2% improvement in speed compared to YOLOv5. The relative errors of the proposed method for the depth and radius of the synthetic data in homogeneous media are 0.6% and 4.4%, respectively, and 4.8% and 15% in non-homogeneous media. The relative error of the depth inversion of the measured data TU1208 is less than 10%. The results show that the method can effectively invert the depth and radius of underground pipelines and reduce the difficulty of GPR data interpretation.
Using Night Lights from Space to Assess Areas Impacted by the 2023 Turkey Earthquake
Authors: Levin Noam
Published: April 2023

The 6 February 2023 earthquakes that hit south-eastern Turkey were amongst the deadliest in the past century. Here, we report the ability to map and quantify areas impacted by these earthquakes using changes in nighttime lights, as mapped by NASA’s VIIRS/DNB sensor. We show the correspondence between the 7.8 magnitude earthquake and impacted areas, located in cities and towns, mostly along the fault line, in areas where macroseismic intensity values were higher than 7. We verified the darkening of night lights as recorded by VIIRS using the new SDGSAT-1 Glimmer multispectral nighttime sensor, as well as by comparing changes in nighttime lights with reports on damaged buildings. The ability to rapidly map impacted areas from space using nighttime lights is of key importance for prioritizing and directing emergency and rescue services globally.
Probabilistic Fingermark Quality Assessment with Quality Region Localisation
Authors: Oblak Tim , Haraksim Rudolf , Beslay Laurent , Peer Peter
Published: April 2023

The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.
Automated Machine Learning Strategies for Multi-Parameter Optimisation of a Caesium-Based Portable Zero-Field Magnetometer
Authors: Dawson Rach , O’Dwyer Carolyn , Irwin Edward , Mrozowski Marcin S. , Hunter Dominic , Ingleby Stuart , Riis Erling , Griffin Paul F.
Published: April 2023

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (T/Hz), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM’s operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500 fT/Hz to <109fT/Hz. The flexibility and efficiency of the ML approaches can be utilised to benchmark SERF OPM sensor hardware improvements, such as cell geometry, alkali species and sensor topologies.
Scan Matching-Based Particle Filter for LIDAR-Only Localization
Authors: Adurthi Nagavenkat
Published: April 2023

This paper deals with the development of a localization methodology for autonomous vehicles using only a 3D LIDAR sensor. In the context of this paper, localizing a vehicle in a known 3D global map of the environment is equivalent to finding the vehicle’s global 3D pose (position and orientation), in addition to other vehicle states, within this map. Once localized, the problem of tracking uses the sequential LIDAR scans to continuously estimate the states of the vehicle. While the proposed scan matching-based particle filters can be used for both localization and tracking, in this paper, we emphasize only the localization problem. Particle filters are a well-known solution for robot/vehicle localization, but they become computationally prohibitive as the states and the number of particles increases. Further, computing the likelihood of a LIDAR scan for each particle is in itself a computationally expensive task, thus limiting the number of particles that can be used for real-time performance. To this end, a hybrid approach is proposed that combines the advantages of a particle filter with a global-local scan matching method to better inform the resampling stage of the particle filter. We also use a pre-computed likelihood grid to speed up the computation of LIDAR scan likelihoods. Using simulation data of real-world LIDAR scans from the KITTI datasets, we show the efficacy of the proposed approach.
Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections
Authors: Huang Shang , Trad Daniel
Published: April 2023

Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures and offer high-resolution images of the complex subsurface. Nevertheless, there are some limitations to the chosen initial model, aperture illumination and computation efficiency. RTM has a strong dependency on the initial velocity model. The RTM result image will perform poorly if the input background velocity model is inaccurate. One solution is to apply least-squares reverse-time migration (LSRTM), which updates the reflectivity and suppresses artifacts through iterations. However, the output resolution still depends heavily on the input and accuracy of the velocity model, even more than for standard RTM. For the aperture limitation, RTM with multiple reflections (RTMM) is instrumental in improving the illumination but will generate crosstalks because of the interference between different orders of multiples. We proposed a method based on a convolutional neural network (CNN) that behaves like a filter applying the inverse of the Hessian. This approach can learn patterns representing the relation between the reflectivity obtained through RTMM and the true reflectivity obtained from velocity models through a residual U-Net with an identity mapping. Once trained, this neural network can be used to enhance the quality of RTMM images. Numerical experiments show that RTMM-CNN can recover major structures and thin layers with higher resolution and improved accuracy compared with the RTM-CNN method. Additionally, the proposed method demonstrates a significant degree of generalizability across diverse geology models, encompassing complex thin layers, salt bodies, folds, and faults. Moreover, The computational efficiency of the method is demonstrated by its lower computational cost compared with LSRTM.
Novel Siloxane Derivatives as Membrane Precursors for Lactate Oxidase Immobilization
Authors: Vokhmyanina Darya V. , Sharapova Olesya E. , Buryanovataya Ksenia E. , Karyakin Arkady A.
Published: April 2023

We report new enzyme-containing siloxane membranes for biosensor elaboration. Lactate oxidase immobilization from water–organic mixtures with a high concentration of organic solvent (90%) leads to advanced lactate biosensors. The use of the new alkoxysilane monomers—(3-aminopropyl)trimethoxysilane (APTMS) and trimethoxy[3-(methylamino)propyl]silane (MAPS)—as the base for enzyme-containing membrane construction resulted in a biosensor with up to a two times higher sensitivity (0.5 A·M−1·cm−2) compared to the biosensor based on (3-aminopropyl)triethoxysilane (APTES) we reported previously. The validity of the elaborated lactate biosensor for blood serum analysis was shown using standard human serum samples. The developed lactate biosensors were validated through analysis of human blood serum.
Benchmarking 2D Multi-Object Detection and Tracking Algorithms in Autonomous Vehicle Driving Scenarios
Authors: Gragnaniello Diego , Greco Antonio , Saggese Alessia , Vento Mario , Vicinanza Antonio
Published: April 2023

Self-driving vehicles must be controlled by navigation algorithms that ensure safe driving for passengers, pedestrians and other vehicle drivers. One of the key factors to achieve this goal is the availability of effective multi-object detection and tracking algorithms, which allow to estimate position, orientation and speed of pedestrians and other vehicles on the road. The experimental analyses conducted so far have not thoroughly evaluated the effectiveness of these methods in road driving scenarios. To this aim, we propose in this paper a benchmark of modern multi-object detection and tracking methods applied to image sequences acquired by a camera installed on board the vehicle, namely, on the videos available in the BDD100K dataset. The proposed experimental framework allows to evaluate 22 different combinations of multi-object detection and tracking methods using metrics that highlight the positive contribution and limitations of each module of the considered algorithms. The analysis of the experimental results points out that the best method currently available is the combination of ConvNext and QDTrack, but also that the multi-object tracking methods applied on road images must be substantially improved. Thanks to our analysis, we conclude that the evaluation metrics should be extended by considering specific aspects of the autonomous driving scenarios, such as multi-class problem formulation and distance from the targets, and that the effectiveness of the methods must be evaluated by simulating the impact of the errors on driving safety.
Speech Recognition of Accented Mandarin Based on Improved Conformer
Authors: Yang Xing-Yao , Zhang Shao-Dong , Xiao Rui , Yu Jiong , Li Zi-Yang
Published: April 2023

The convolution module in Conformer is capable of providing translationally invariant convolution in time and space. This is often used in Mandarin recognition tasks to address the diversity of speech signals by treating the time-frequency maps of speech signals as images. However, convolutional networks are more effective in local feature modeling, while dialect recognition tasks require the extraction of a long sequence of contextual information features; therefore, the SE-Conformer-TCN is proposed in this paper. By embedding the squeeze-excitation block into the Conformer, the interdependence between the features of channels can be explicitly modeled to enhance the model’s ability to select interrelated channels, thus increasing the weight of effective speech spectrogram features and decreasing the weight of ineffective or less effective feature maps. The multi-head self-attention and temporal convolutional network is built in parallel, in which the dilated causal convolutions module can cover the input time series by increasing the expansion factor and convolutional kernel to capture the location information implied between the sequences and enhance the model’s access to location information. Experiments on four public datasets demonstrate that the proposed model has a higher performance for the recognition of Mandarin with an accent, and the sentence error rate is reduced by 2.1% compared to the Conformer, with only 4.9% character error rate.
Study on the Psychological States of Olfactory Stimuli Using Electroencephalography and Heart Rate Variability
Authors: Laohakangvalvit Tipporn , Sripian Peeraya , Nakagawa Yuri , Feng Chen , Tazawa Toshiaki , Sakai Saaya , Sugaya Midori
Published: April 2023

In the modern information society, people are constantly exposed to stress due to complex work environments and various interpersonal relationships. Aromatherapy is attracting attention as one of the methods for relieving stress using aroma. A method to quantitatively evaluate such an effect is necessary to clarify the effect of aroma on the human psychological state. In this study, we propose a method of using two biological indexes, electroencephalogram (EEG) and heart rate variability (HRV), to evaluate human psychological states during the inhalation of aroma. The purpose is to investigate the relationship between biological indexes and the psychological effect of aromas. First, we conducted an aroma presentation experiment using seven different olfactory stimuli while collecting data from EEG and pulse sensors. Next, we extracted the EEG and HRV indexes from the experimental data and analyzed them with respect to the olfactory stimuli. Our study found that olfactory stimuli have a strong effect on psychological states during aroma stimuli and that the human response to olfactory stimuli is immediate but gradually adapts to a more neutral state. The EEG and HRV indexes showed significant differences between aromas and unpleasant odors especially for male participants in their 20–30s, while the delta wave and RMSSD indexes showed potential for generalizing the method to evaluate psychological states influenced by olfactory stimuli across genders and generations. The results suggest the possibility of using EEG and HRV indexes to evaluate psychological states toward olfactory stimuli such as aroma. In addition, we visualized the psychological states affected by the olfactory stimuli on an emotion map, suggesting an appropriate range of EEG frequency bands for evaluating psychological states applied to the olfactory stimuli. The novelty of this research lies in our proposed method to provide a more detailed picture of the psychological responses to olfactory stimuli using the integration of biological indexes and emotion map, which contributes to the areas such as marketing and product design by providing insights into the emotional responses of consumers to different olfactory products.
An Improved Time Delay Measurement Method for the Long-Distance Underwater Environment
Authors: Wu Ruisheng , Wang Yuzhe , Huang Lidong , Zou Zhuoyang , Wu Bin
Published: April 2023

With the development of underwater navigation and underwater communication, it remains difficult to obtain time delay measurements after propagating long distance. This paper proposes an improved high-accuracy time delay measuring method for long distance underwater channel propagation. First, by sending an encoded signal, the signal acquisition is carried out at the receiving end. Then, to improve signal to noise ratio (SNR), bandpass filtering is carried out on the receiving end. Next, considering the random changes in the underwater sound propagation channel, a strategy is proposed to select the optimal time window for cross-correlation. Then, new regulations are proposed to calculate the cross-correlation results. To verify the effectiveness of the algorithm, we compared it with other algorithms under low SNR conditions using Bellhop simulation data. Finally, the accurate time delay is obtained. With underwater experiments over different distances, the method proposed by the paper achieves high accuracy. The error is about 10−3 s. The proposed method makes a contribution to underwater navigation and communication.
Design of a Microwave Heating and Permittivity Measurement System Based on Oblique Aperture Ridge Waveguide
Authors: Gou Mingyi , Chen Qian , Dong Penghao , Liu Changjun , Huang Kama
Published: April 2023

In this paper, an oblique aperture ridge waveguide operating at 2450 MHz is proposed, and, using the ridge waveguide, a permittivity measurement system is constructed which can measure the permittivity of materials during microwave heating. The system calculates the amplitudes of the scattering parameters by using the forward, reflected and transmitted powers of the power meters, and it reconstructs the permittivity of the material by combining the scattering parameters with an artificial neural network. The system is used to measure the complex permittivity of mixed solutions of methanol and ethanol with different ratios at room temperature, and the permittivity of methanol and ethanol with increasing temperature, from room temperature to 50 °C. The measured results are in good agreement with the reference data. The system allows simultaneous measurement of the permittivity with microwave heating and provides real-time, rapid changes in the permittivity during heating, avoiding thermal runaway and providing a reference for applications of microwave energy in the chemical industry.
Intelligent Control and Digital Twins for Industry 4.0
Authors: Tepljakov Aleksei
Published: April 2023

One of the prominent features of the Fourth Industrial Revolution—frequently referred to as Industry 4 [...]
Measurements, Predictions, and Control in Microgrids and Power Electronic Systems
Authors: Baier Carlos R. , Hernández Jesus C. , Wheeler Patrick
Published: April 2023

The systems used to distribute electricity are currently undergoing a series of changes that are aiding in the development of smart grids
Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications
Authors: Ghosh Prosenjit Kumar , Sundaravadivel Prabha
Published: April 2023

The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions.
Automation and Optimization of Rat Heart Decellularization Using a Vibrating Fluid Column
Authors: Bonciog Dumitru-Daniel , Lascu Mihaela-Ruxandra , Mâțiu-Iovan Liliana , Ordodi Valentin Laurențiu
Published: April 2023

This paper presents the validation of a software application to optimize the discoloration process in simulated hearts and to automate and determine the final moment of decellularization in rat hearts using a vibrating fluid column. The implemented algorithm specifically for the automated verification of a simulated heart’s discoloration process was optimized in this study. Initially, we used a latex balloon containing enough dye to reach the opacity of a heart. The complete discoloration process corresponds to complete decellularization. The developed software automatically detects the complete discoloration of a simulated heart. Finally, the process stops automatically. Another goal was to optimize the Langendorff-type experimental apparatus, which is pressure-controlled and equipped with a vibrating fluid column that shortens the decellularization time by mechanically acting directly on cell membranes. Control experiments were performed with the designed experimental device and the vibrating liquid column using different decellularization protocols for hearts taken from rats. In this work, we used a commonly utilized solution based on sodium dodecyl sulfate. Ultraviolet spectrophotometry was used to measure the evolution of the dye concentration in the simulated hearts and, similarly, to determine the concentrations of deoxyribonucleic acid (DNA) and proteins in the rat hearts.
Compact Back-End Electronics with Temperature Compensation and Efficient Data Management for In Situ SiPM-Based Radiation Detection
Authors: Dixon Nile E. J. , Monk Stephen D. , Graham James , Cheneler David
Published: April 2023

A compact back-end interface for silicon photomultipliers (SiPMs) implementing Zener diode-based temperature compensation has been developed for the remote detection of beta and gamma radiation. Remote detection is facilitated by the development of an efficient data management system utilising MySQL database storage for recording periodic spectra data for wireless access over a private Wi-Fi network. A trapezoidal peak shaping algorithm has been implemented on an FPGA for the continuous conversation of pulses from the SiPM, signifying the detection of a radiological particle, into spectra. This system has been designed to fit within a 46 mm cylindrical diameter for in situ characterization, and can be attached to one or more SiPMs used in conjunction with a range of scintillators. LED blink tests have been used to optimise the trapezoidal shaper coefficients to maximise the resolution of the recorded spectra. Experiments with an array of SiPMs integrated with a NaI(Tl) scintillator exposed to sealed sources of Co-60, Cs-137, Na-22 and Am-241 have shown that the detector achieves a peak efficiency of 27.09 ± 0.13% for a gamma peak at 59.54 keV produced by Am-241, and a minimum energy resolution (Delta E/E) of 4.27 ± 1.16% for the 1332.5 keV gamma peak from Co-60.
Understanding the Links between Diversity and Creativity as Assessed in the Boroughs of London
Authors: Pac-Salas David , Sepulveda Leandro , Baez Melian Juan Miguel , Minguijon Jaime
Published: April 2023

This paper analyses the links between creativity and diversity in the different boroughs of London. Based on rich data from the UK Census of Population of 2011 and other sources, we specifically analysed the correlations between creativity and diversity within the London boroughs. The main results of this study indicate that there is no direct correlation between creativity and diversity. Some significant correlations have been observed, however, between variables that shape such indices. Namely, the “creative class” tend to live in more diverse, more heterogeneous neighbourhoods (alongside people from many different countries) and they are more prepared to tolerate such diverse environments. The study also shows that diversity of geographical origin (measured by country of origin) is a more relevant factor for boosting creativity than variables such as religious diversity. This article contributes to the theoretical field of research exploring the impact of diversity on creative people and cities.
An Agile Model-Based Software Engineering Approach Illustrated through the Development of a Health Technology System
Authors: Huss Moe , Herber Daniel R. , Borky John M.
Published: April 2023

Model-Based Software Engineering (MBSE) is an architecture-based software development approach. Agile, on the other hand, is a light system development approach that originated in software development. To bring together the benefits of both approaches, this article proposes an integrated Agile MBSE approach that adopts a specific instance of the Agile approach (i.e., Scrum) in combination with a specific instance of an MBSE approach (i.e., Model-Based System Architecture Process—“MBSAP”) to create an Agile MBSE approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP). The proposed approach was validated through a pilot study that developed a health technology system over one year, successfully producing the desired software product. This work focuses on determining whether the proposed sMBSAP approach can deliver the desired Product Increments with the support of an MBSE process. The interaction of the Product Development Team with the MBSE tool, the generation of the system model, and the delivery of the Product Increments were observed. The preliminary results showed that the proposed approach contributed to achieving the desired system development outcomes and, at the same time, generated complete system architecture artifacts that would not have been developed if Agile had been used alone. Therefore, the main contribution of this research lies in introducing a practical and operational method for merging Agile and MBSE. In parallel, the results suggest that sMBSAP is a middle ground that is more aligned with federal and state regulations, as it addresses the technical debt concerns. Future work will analyze the results of a quasi-experiment on this approach focused on measuring system development performance through common metrics.
Soil Microbial Composition and Soil Health of Reverse-Osmosis-Concentrate and Brackish-Groundwater Irrigated Soils in Southern New Mexico
Authors: Ben Ali Akram R. , Holguin Francisco F. Omar , Shukla Manoj K.
Published: April 2023

The phospholipid fatty acid method was used to determine the shifts in microbial biomass due to irrigation with reverse-osmosis (RO) concentrate (or highly saline reject water) and brackish groundwater (BGW). In this greenhouse study, RO concentrate and BGW were applied to irrigate pecan trees for 8 months for two consecutive seasons. The objectives of the study were to (i) evaluate how irrigation with RO concentrate and BGW impacts soil microbial composition in pecan rhizospheres using microbial phospholipid fatty acid (PLFA) biomarkers as indicators, and (ii) evaluate its implications on soil health. Three treatments of RO concentrate (EC = 8.0 dS/m), BGW (EC = 4.0 dS/m), and the city of Las Cruces’s water (EC = 0.8 dS/m) as a control were used to irrigate pecan trees. EC, pH, and organic matter (OM%) content of the soil samples were measured, and PLFA biomarkers for the microbial community were determined. Na-, Cl-, and K-ion concentrations were 26.16, 32.54, and 5.93 meq/L in 2017 and 25.44, 24.26, and 5.49 meq/L in 2018, respectively, in RO irrigation pots. For two seasons, gram-positive bacteria were dominant, while gram-negative bacteria were not detected in the second season. PLFA biomarkers of fungi were found among all three treatments in the first season; however, they appeared only with BGW in the second season. Actinomycetes were recorded in the first season while they were not seen in the second season. Increases in soil salinity and microbial shifts could have important implications for soil health. Irrigating with RO and BGW shifted the soil microbial composition; therefore, long-term irrigation with BGW and RO concentrate would be deleterious for pecan production and soil health.
Hydrothermal Liquefaction of Pinewood Sawdust: Influence of Reaction Atmosphere
Authors: Wang Haoyu , Jiang Yipei , Park Evan , Han Xue , Zeng Yimin , Xu Chunbao
Published: April 2023

Hydrothermal liquefaction (HTL) is a thermochemical process for production of biocrude oils, commonly from wet biomass under inert atmosphere (N2). Influence of reaction atmosphere on HTL of pinewood sawdust was investigated in this work, at 300 °C for 60 min with the presence of KOH or H2SO4 catalyst under N2, H2, and O2 atmosphere, respectively. Very interestingly, the reaction atmosphere showed significant influence on both products distribution and properties of the biocrude oils. Generally, H2 atmosphere enhanced biomass degradation in the presence of either KOH or H2SO4 catalyst, producing the highest biocrude oil yield, lowest solid residue yield, and the best oil quality in terms of total acid number (TAN), viscosity and average molecular weights (Mn, Mw). Whereas the HTL in O2 atmosphere showed the poorest performance in terms of yields and properties of biocrude oils. The highest quality of biocrude oil was produced using KOH catalyst in H2 atmosphere with the maximum biocrude yield (approx. 34 wt.%) and the highest energy recovery (ER) in biocrude (ER = 73.14%). The measured properties of the oil are as follows: TAN = 40.2 mg KOH/g, viscosity = 51.2 cp, Mn = 470 g/mol, Mw = 767 g/mol. In addition, the biocrude oils produced in H2 atmosphere contain more light oil (naphtha) fraction (23.9 wt.% with KOH and 16.5 wt.% with H2SO4) with lower boiling points, while those generated in O2 atmosphere have more carboxylic acid compounds.
Analysis of Eco-Innovations in Peruvian Accommodation Establishments
Authors: Cántaro Márquez Alicia Lencia , Miranda Vásquez Greysi Fiorela , Ángeles Barrantes Daysy
Published: April 2023

Technical environmental innovations (TEIs), also called eco-innovations, are key tools within environmental management systems owing to their potential contribution in reducing environmental degradation. Thus, the importance of the use of TEIs in the hospitality industry was discussed in this study. A qualitative-method approach was used. To that end, six hotel managers from Peruvian accommodation establishments were interviewed. It was found that the use of environmental process innovations reduces operating costs and environmental discharges. However, management indicators are required to measure the real impact on the environment. Despite the benefits of TEIs, their use has not yet become widespread in the Peruvian hospitality industry owing to internal barriers, such as the company’s size and investment capacity, and external barriers, such as the absence of a policy framework established by public management entities and the lack of suppliers.
Design of an Optimal Adoptive Fault Ride through Scheme for Overcurrent Protection of Grid-Forming Inverter-Based Resources under Symmetrical Faults
Authors: Islam Saif Ul , Kim Soobae
Published: April 2023

As the integration of inverter-based resources (IBRs) is rapidly increasing in regard to the existing power system, switching from grid-following (GFL) to grid-forming (GFM) inverter control is the solution to maintain grid resilience. However, additional overcurrent protection, especially during fault transition, is required due to limited overcurrent capability and the high magnitude of spikes during fault recovery in IBRs, specifically in the GFM control mode. Furthermore, the power system stability should not be compromised by the employment of additional fault ride through (FRT) schemes. This article presents the design and implementation of an adoptive fault ride through (FRT) scheme for grid-forming inverters under symmetrical fault conditions. The proposed adoptive FRT scheme is comprised of two cascaded power electronic-based circuits, i.e., fault current ride through and a spikes reactor. This adoptive FRT scheme optimizes the fault variables during the fault time and suppresses the fault clearing spikes, without affecting system stability. A three-bus inverter-based grid-forming model is used in MATLAB/Simulink for the implementation of the proposed scheme. Further, a conventionally used FRT scheme, which includes fault current reactors, is simulated in the same test environment for justification of the proposed adoptive scheme. The adoptive FRT scheme is simulated for both time domain and frequency domain to analyze the response of harmonic distortion with the suppression of the fault current. Moreover, the proposed scheme is also simulated under the GFL mode of IBRs to justify the reliability of the scheme. The overall simulation results and performance evaluation indices authenticate the optimal, fault tolerant, harmonic, and spike-free behavior of the proposed scheme at both the AC and DC side of the grid-forming inverters.
The Improvement of Rice Straw Anaerobic Co-Digestion with Swine Wastewater by Solar/Fe(II)/PS Pretreatment
Authors: Liu Pengcheng , Pan Yunxia
Published: April 2023

Rice straw (RS) is among the agricultural waste products with the highest methane production potential in the world, but the refractory complex structure and high carbon-to-nitrogen ratio of RS cause low methane conversion efficiency and limit its widespread application in anaerobic digestion. In this study, Solar/Fe (II)/persulfate (PS) pretreatment of RS was investigated to improve microbial accessibility, and anaerobic co-digestion combined pretreated RS and swine wastewater (SW) were evaluated to improve the efficiency of anaerobic digestion. The results showed that the Solar/Fe (II)/PS pretreatment could disrupt the structure of RS and promote the reduction of sugar content, increasing microbial accessibility to RS. When all the components of the pretreated RS (including the use of the solution remaining from the pretreatment) were anaerobically co-digested with SW, the cumulative biogas production and cumulative methane production reached 252.10 mL/g·VS and 163.71 mL/g·VS, 19.18% and 36.97% higher than the anaerobic co-digestion of untreated RS and SW, respectively. The anaerobic co-digestion of the Solar/Fe (II)/PS-pretreated RS with SW is a promising approach to achieving the utilization of RS components and maximizing methane yields, providing a cost-effective and pollution-free method for the production of high-quality bioenergy from agricultural waste.
Diet and Nutritional Status of West African and Caribbean Adults in the United Kingdom: Perspectives to Inform Community-Based Approaches to Healthy Eating
Authors: Senior Lauren , Maynard Maria J. , Apekey Tanefa A.
Published: April 2023

Reliable data on the diet and nutritional status of African and Caribbean adults in the UK is essential for the implementation of targeted interventions to prevent diseases, and to ensure health equity. This review provides an overview of diet and nutritional status, and research and intervention priorities for West African and Caribbean adults in the UK. Cross-sectional studies that compared the two groups with their counterparts in their country of origin and/or in Europe were identified from PubMed, SCOPUS, Cochrane and CINAHL electronic databases and manual searches of bibliographies. Six eligible studies were reviewed. Reported nutrient composition focused predominantly on energy, and macronutrients, with limited information on micronutrients. Carbohydrates and fats were the main sources of energy. Low quantities of non-starch polysaccharides fibre and protein were reported. Participants from rural areas in Ghana had lower average BMI and smaller waist circumference compared to their counterparts in urban Ghana and Europe. Ghanaians in Europe consumed more alcohol and were less physically active than those in the home country. The studies measured limited nutrient composition and health-related outcomes. Studies that measure a wide range of anthropometric and clinical outcomes and key micronutrients, and use objective measures of health-related risk behaviours are needed.
ESG and Corporate Performance: Evidence from Agriculture and Forestry Listed Companies
Authors: Zeng Lishi , Jiang Xuemei
Published: April 2023

Agriculture and forestry are fundamental industries. With the development of the ESG concept, stakeholders are increasingly concerned about the relationship between ESG and agricultural and forestry corporate performance. This paper examines 156 listed agricultural and forestry companies to explore the impact of ESG on corporate performance, both theoretically and empirically, using two-stage least squares. Heterogeneity is explored from the perspective of three sub-dimensions of ESG and industry comparison, respectively. Finally, the impact mechanism of ESG is analyzed from three perspectives: government, market, and company. Results indicate that (1) ESG and corporate performance are significantly and positively correlated, and higher ESG ratings are beneficial to corporate performance improvement. (2) Compared with E performance, S and G performance are more conducive to promoting corporate performance growth. (3) There is no significant difference in the effect of ESG on corporate performance between listed companies in agriculture and forestry. (4) Tax incentives and the regional marketization degree have a negative moderating effect, but the proportion of female executives plays a positive moderating role. These findings provide useful insights for listed companies in agriculture and forestry to improve ESG performance and, consequently, corporate performance, and also promote listed companies to play a greater leading role in green development.
Can Digitalization Foster Sustainable Financial Inclusion? Opportunities for Both Banks and Vulnerable Groups
Authors: Chu Ying , Ye Shujun , Li Hongchang , Strauss Jack , Zhao Chen
Published: April 2023

Financial inclusion is a crucial link in achieving the Sustainable Development Goals (SDGs). Strengthening the capability of financial institutions to provide inclusive financial services can help to narrow the inequality gap and increase access to opportunities. Digitalization, with its competitive advantages and rapid growth, may be a powerful tool to foster financial inclusion and sustainable development. This paper examines the effects of bank digitalization on sustainable financial inclusion and explores two underlying incentive mechanisms in banks: profit driven and risk aversion. We construct a basic model and a mechanism model and exploit a nonlinear attempt, heterogeneous estimation as well as supplementary variable and instrument variable methods for a robustness test. The results of the basic model demonstrate that bank digitalization has significant positive effects on financial inclusion and the current financial inclusive effects are sustainable. The mechanism models designed as the mediation effect panel model suggest that digitalization enables banks to expand the business probability frontier of profit-driven behavior and pursuit of credit portfolio diversity in risk aversion behavior, thereby promoting sustainable financial inclusion. As a result of digitalization, vulnerable groups can benefit from sustainable financial inclusion, while financial inclusion feeds back into banks’ sustainable development. This paper conforms to the trend of the development of digitalization and provides theoretical and empirical support for banks to build digitalization and realize sustainable financial inclusion, which contributes to the “triple-win” financial ecology for improving banks’ performance, increasing the rights of vulnerable groups and promoting sustainable development throughout society.
Research on the Threshold Effect of Internet Development on Regional Inclusive Finance in China
Authors: Zhang Chenjing , Li Qiaoge , Mao Di , Wang Mancang
Published: April 2023

The study aims to investigate how the internet has affected China’s financial inclusion from the standpoint of developing internet technologies. Firstly, using the coefficient of variation method and the principal component analysis method, the financial inclusion index (IFI) and the internet development index (INT) were built from multiple dimensions based on the 2006–2016 provincial panel data of China. Then, the fixed-effect panel threshold model, the fixed-effect estimate, and the 2SLS estimate were used to empirically test the impact of internet development on inclusive finance in China. We found that China’s financial inclusion was significantly and positively affected by internet development. Additionally, this effect was nonlinear, and there was a threshold effect on the proportion of internet users. The development of the internet had a significant positive effect on financial inclusion when the internet user proportion (ISP) was higher than 19%, and the effect on IFI became stronger when ISP rose above 53%. This study complements earlier research, in which internet finance is usually perceived as a composite notion, by thoroughly examining the effects of internet information technology on the growth of financial inclusion. Based on our findings, we further put forward policy recommendations for the sustainable development of inclusive finance in terms of the intelligent integration and collaboration of internet communication technologies. Financial inclusion is critical for achieving sustainability because it provides access to affordable financial services to underserved individuals and businesses, and brings them into the formal financial sector, thereby improving their livelihoods while reducing poverty and inequality.
The Avaritia: Entrepreneurship Practice to Understand the Problem of Information Control through Gamification
Authors: Park Sungjin , Kim Sangkyun
Published: April 2023

Information control creates inequality in society, and thus, widens the wealth gap. This study aimed to develop entrepreneurship education gamification to understand problems of information control and developed a gamification called “The Avaritia”. To verify the effectiveness of the game, pre/post-questionnaire responses were verified. The results indicate that The Avaritia helped us understand the social problems of information control and had a positive effect on the cognitive change of learners. The results of this study suggest the need for entrepreneurship education using gamification and emphasize the importance of social entrepreneurship.
Uncertainty in Determination of the Soil-Water Characteristic Curve Based on the van Genuchten Model
Authors: Wang Shijun , Yang Jing , Wang Xing , Zhang Zhong , Zhai Qian
Published: April 2023

The van Genuchten (1980) (VG) model is one of the most popular and widely used equations to achieve the best fitting of experimental data of the soil-water characteristic curve (SWCC) test. As the best-fitted SWCC is determined from regression analysis, there are various possible solutions for the best-fitted curve (or the determined fitting parameters). The residual error, which is obtained from regression analyses, can be considered as an index indicating the incapability of the SWCC equation in representing the measured SWCC experimental data. As a result, there is always uncertainty associated with determined SWCC from the regression analysis. The residual error from the regression analysis can be used for the quantification of the uncertainty of determined SWCC. In this paper, Taylor’s expansion is adopted, and only the first-order term is considered for the VG model. Subsequently, the variances of the fitting parameters in the VG model are estimated from the residual error. Consequently, the uncertainty of SWCC using the VG model is quantified by using the upper and lower bounds with a 95% confidence level. The artificial soil, composed of mixed sand and kaolin, was prepared for the SWCC measurement. This paper shows that the proposed method can be used to quantify the uncertainty of SWCC from different specimens (a total of 12 specimens).
Assessment of Outdoor Pedestrian Ventilation Performance While Controlling Building Array Scale and Density
Authors: Li Riyi , Zhang Yufeng , Cui Yumeng
Published: April 2023

In hot and humid regions of China, people experience great discomfort. Good ventilation improves human comfort by facilitating the discharge of heat in a region. None of the previous studies considered which scale is appropriate for the outdoor ventilation of building arrays, and the ventilation performance differs with the array size. Moreover, the building density has an upper limit in Chinese cities, and many studies overestimate this density. Based on these considerations, the neighborhood block is proposed to represent the scale of building arrays with the combination of the urban planning scale and climatic scale. Using this scale, the building density and representative building array configurations for hot and humid regions of China were determined. The outdoor ventilation of these building arrays at the pedestrian height was then studied via computational fluid dynamics simulations. The results show that, in the neighborhood block, an increase in the building height and length is beneficial for the mean velocity, whereas an increase in the building distance is not, and a staggered layout has a negative effect on ventilation. Furthermore, the semi-enclosed layouts are no better than the enclosed layouts in terms of ventilation and sunlight. Some contributions contradict the existing studies because of the selection of different array scales and densities, which prove their significance.