bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Sensorless Hybrid Control Method of Permanent Magnet Synchronous Motor Based on I/F Startup
Authors: Ning Bowen , Zhao Yiheng , Cheng Shimin
Published: January 2023

To realize permanent magnet synchronous motor (PMSM) in the full speed domain without speed sensor operation, a hybrid control method combining I/F startup and extended Kalman filter (EKF) is proposed in this paper. This method employs I/F startup to transition at low speed, effectively resolving the issue that the position estimation method based on the back electromotive force (EMF) model fails at zero speed and low speed, and converts to EKF for speed closed-loop vector control at medium and high speed. Moreover, a new feedback regulation mechanism as a solution to the problem of smooth switching between the two methods is proposed. First, the power angle is determined based on the relationship between the given I/F frequency and the estimated EKF position angle. Using the information of power angle, the damping torque of the system is increased to reduce velocity fluctuations during I/F startup. In addition, the balance point of current and position error angle is adjusted using the closed-loop information of position error angle to reduce the torque abrupt change before and after switching, thereby making the motor switching process to EKF speed closed-loop control more stable. Finally, simulation results are used to verify the effectiveness of the proposed scheme.
ROMI: A Real-Time Optical Digit Recognition Embedded System for Monitoring Patients in Intensive Care Units
Authors: Jeon Sanghoon , Ko Byuk Sung , Son Sang Hyuk
Published: January 2023

With advances in the Internet of Things, patients in intensive care units are constantly monitored to expedite emergencies. Due to the COVID-19 pandemic, non-face-to-face monitoring has been required for the safety of patients and medical staff. A control center monitors the vital signs of patients in ICUs. However, some medical devices, such as ventilators and infusion pumps, operate in a standalone fashion without communication capabilities, requiring medical staff to check them manually. One promising solution is to use a robotic system with a camera. We propose a real-time optical digit recognition embedded system called ROMI. ROMI is a mobile robot that monitors patients by recognizing digits displayed on LCD screens of medical devices in real time. ROMI consists of three main functions for recognizing digits: digit localization, digit classification, and digit annotation. We developed ROMI by using Matlab Simulink, and the maximum digit recognition performance was 0.989 mAP on alexnet. The developed system was deployed on NVIDIA GPU embedded platforms: Jetson Nano, Jetson Xavier NX, and Jetson AGX Xavier. We also created a benchmark by evaluating the runtime performance by considering ten pre-trained CNN models and three NVIDIA GPU platforms. We expect that ROMI will support medical staff with non-face-to-face monitoring in ICUs, enabling more effective and prompt patient care.
Anomaly Detection and Repairing for Improving Air Quality Monitoring
Authors: Rollo Federica , Bachechi Chiara , Po Laura
Published: January 2023

Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants’ concentrations can support the identification of urban areas with poor air quality and raise citizens’ awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms’ performances.
Multi-Sensor Respiratory–Swallow Telecare System for Safe Feeding in Different Trunk Inclinations: System Development and Clinical Application
Authors: Shieh Wann-Yun , Wang Chin-Man , Ju Yan-Ying , Cheng Hsin-Yi Kathy
Published: January 2023

Proper positioning is especially important to ensure feeding and eating safely. With many nursing facilities restricting visitations and close contact during the coronavirus pandemic, there is an urgent need for remote respiratory–swallow monitoring. This study aimed to develop a semiautomatic feeding telecare system that provides instant feedback and warnings on-site and remotely. It also aimed to analyze the effects of trunk positions on respiratory–swallow coordination. A signal collector with multiple integrated sensors for real-time respiratory–swallow monitoring and warning was developed. A repeated measures design was implemented to evaluate the effects of trunk inclination angles on the swallow-related functions. Significant differences in inclination angles were discovered for swallowing apnea (p = 0.045) and total excursion time of thyroid cartilage (p = 0.037), and pairwise comparisons indicated that these differences were mostly present at 5° to 45°. Alerts were triggered successfully when undesired respiratory patterns or piecemeal occurred. The results indicated that a care recipient can swallow more easily when sitting upright (5°) than when leaning backward (45°). This telecare system provides on-site and remote respiratory–swallow monitoring and alerting for residents in care facilities and can serve as a pipeline for the early screening of swallowing dysfunction.
Research Advances on Fiber-Optic SPR Sensors with Temperature Self-Compensation
Authors: Zhao Hongxia , Wang Feng , Han Zhaojia , Cheng Peihong , Ding Zhiqun
Published: January 2023

The fiber-optic surface plasmon resonance sensor has very promising applications in environmental monitoring, biochemical sensing, and medical diagnosis, due to the superiority of high sensitivity and novel label-free microstructure. However, the influence of ambient temperature is inevitable in practical sensing applications, and even the higher the sensitivity, the greater the influence. Therefore, how to eliminate temperature interference in the sensing process has become one of the hot issues of this research field in recent years, and some accomplishments have been achieved. This paper mainly reviews the research results on temperature self-compensating fiber-optic surface plasmon sensors. Firstly, it introduces the mechanism of a temperature self-compensating fiber-optic surface plasmon resonance sensor. Then, the latest development of temperature self-compensated sensor is reviewed from the perspective of various fiber-optic sensing structures. Finally, this paper discusses the most recent applications and development prospects of temperature self-compensated fiber-optic surface plasmon resonance sensors.
A Lab-on-a-Chip Approach for the Detection of the Quarantine Potato Cyst Nematode Globodera pallida
Authors: Camacho Maria João , Albuquerque Débora C. , de Andrade Eugénia , Martins Verónica C. , Inácio Maria L. , Mota Manuel , Freitas Paulo P.
Published: January 2023

The potato cyst nematode (PCN), Globodera pallida, has acquired significant importance throughout Europe due to its widespread prevalence and negative effects on potato production. Thus, rapid and reliable diagnosis of PCN is critical during surveillance programs and for the implementation of control measures. The development of innovative technologies to overcome the limitations of current methodologies in achieving early detection is needed. Lab-on-a-chip devices can swiftly and accurately detect the presence of certain nucleotide sequences with high sensitivity and convert the presence of biological components into an understandable electrical signal by combining biosensors with microfluidics-based biochemical analysis. In this study, a specific DNA-probe sequence and PCR primers were designed to be used in a magnetoresistive biosensing platform to amplify the internal transcribed spacer region of the ribosomal DNA of G. pallida. Magnetic nanoparticles were used as the labelling agents of asymmetric PCR product through biotin–streptavidin interaction. Upon target hybridization to sensor immobilized oligo probes, the fringe field created by the magnetic nanoparticles produces a variation in the sensor’s electrical resistance. The detection signal corresponds to the concentration of target molecules present in the sample. The results demonstrate the suitability of the magnetic biosensor to detect PCR target product and the specificity of the probe, which consistently distinguishes G. pallida (DV/V > 1%) from other cyst nematodes (DV/V < 1%), even when DNA mixtures were tested at different concentrations. This shows the magnetic biosensor’s potential as a bioanalytical device for field applications and border phytosanitary inspections.
Backward Acoustic Waves in Piezoelectric Plates: Possible Application as Base for Liquid Sensors
Authors: Smirnov Andrey , Zaitsev Boris , Nedospasov Ilya , Nazarov Gleb , Kuznetsova Iren
Published: January 2023

Backward acoustic waves are characterized by oppositely directed phase and group velocities. These waves can exist in isotropic and piezoelectric plates. They can be detected using a set of interdigital transducers with different spatial periods located on the same piezoelectric substrate. In this paper, the effect of a nonviscous and nonconductive liquid on the characteristics of a first-order backward antisymmetric wave in a YX plate of lithium niobate is studied theoretically and experimentally. It is shown that the presence of liquid does not lead to the transformation or disappearance of this wave. It is shown that these waves are close to the cutoff frequency and are characterized by the presence of a point with zero group velocity. The design of a liquid sensor based on these waves is proposed.
Efficient Approach for Anomaly Detection in IoT Using System Calls
Authors: Shamim Nouman , Asim Muhammad , Baker Thar , Awad Ali Ismail
Published: January 2023

The Internet of Things (IoT) has shown rapid growth and wide adoption in recent years. However, IoT devices are not designed to address modern security challenges. The weak security of these devices has been exploited by malicious actors and has led to several serious cyber-attacks. In this context, anomaly detection approaches are considered very effective owing to their ability to detect existing and novel attacks while requiring data only from normal execution. Because of the limited resources of IoT devices, conventional security solutions are not feasible. This emphasizes the need to develop new approaches that are specifically tailored to IoT devices. In this study, we propose a host-based anomaly detection approach that uses system call data and a Markov chain to represent normal behavior. This approach addresses the challenges that existing approaches face in this area, mainly the segmentation of the syscall trace into suitable smaller units and the use of a fixed threshold to differentiate between normal and malicious syscall sequences. Our proposed approach provides a mechanism for segmenting syscall traces into the program’s execution paths and dynamically determines the threshold for anomaly detection. The proposed approach was evaluated against various attacks using two well-known public datasets provided by the University of New South Mexico (UNM) and one custom dataset (PiData) developed in the laboratory. We also compared the performance and characteristics of our proposed approach with those of recently published related work. The proposed approach has a very low false positive rate (0.86%), high accuracy (100%), and a high F1 score (100%) that is, a combined performance measure of precision and recall.
TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles
Authors: Kamath B Nikhil , Fernandes Roshan , Rodrigues Anisha P. , Mahmud Mufti , Vijaya P. , Gadekallu Thippa Reddy , Kaiser M. Shamim
Published: January 2023

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.
A Self-Decoupling Technique to Realize Dense Packing of Antenna Elements in MIMO Arrays for Wideband Sub-6 GHz Communication Systems
Authors: Khan Shahid , Marwat Safdar Nawaz Khan , Khan Muhammad Amir , Ahmed Salman , Gohar Neelam , Alim Mohammad Ehsanul , Algarni Abeer D. , Elmannai Hela
Published: January 2023

A self-decoupled technique is described that enables the radiating elements in the antenna array to be densely packed for multiple-input multiple-output (MIMO) wireless communications systems. High isolation between the adjacent antenna elements is obtained by fixing the radiating elements in an orthogonal configuration with respects to each other. Current from the adjacent ports cancels their impact which results in low mutual coupling. The additional benefit of this configuration is realizing a densely packed array. The ground plane of each radiating element on the array board itself are isolated to mitigate surface wave propagations to suppress mutual coupling between the antenna elements. The radiating elements are based on a modified edge-fed circular patch antenna that includes a curved slot line and open-circuited stub to widen the array’s impedance bandwidth with no impact on the antenna’s footprint size. The proposed technique was verified with the design of an antenna array of matrix size 4 × 4 centered at 3.5 GHz. The array had a measured impedance bandwidth of 4 GHz from 1.5 GHz to 5.5 GHz, which corresponds to a fractional bandwidth of 114%, peak gain of 3 dBi and radiation efficiency of 84%. Its average diversity gain and envelope correlation coefficient (ECC) over its operating band are 9.6 dB and <0.016, respectively. The minimum isolation achieved between the radiating elements is better than 15 dB. The dimensions of the array are 0.4 × 0.4 × 0.039λ_g^3. The proposed array has characteristics suitable for sub-6 GHz wireless communication systems
MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors
Authors: Ogundokun Roseline Oluwaseun , Misra Sanjay , Akinrotimi Akinyemi Omololu , Ogul Hasan
Published: January 2023

Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
Liquid Chromatography Fingerprint Analysis of Released Compounds in Plasma Samples of Stroke Patients after Thrombolytic Treatment
Authors: Castellanos Mar , Fernández-Couto Dolores , Da Silva-Candal Andrés , Feal-Painceiras Maria J. , Rodríguez-Yáñez Manuel , Gubern-Mérida Carme , Sanchez Juan M.
Published: January 2023

Plasma samples obtained from stroke patients treated with recombinant tissue-type plasminogen activator (rt-PA) and not treated with rt-PA were evaluated with different HPLC methodologies to obtain information about the possible release of small molecules as a result of the thrombolytic treatment. Plasma samples, without derivatization and derivatized with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC), were evaluated with a HPLC gradient method, which consisted of a mobile phase of 10 mM ammonium acetate buffered solution (pH = 5.3) and acetonitrile. Three different detection methods were applied: UV, fluorescence, and ESI-MS. The results obtained showed that a group of new highly hydrophilic compounds appeared in most samples analyzed from treated patients, just after the administration of rt-PA. These compounds appeared shortly after the administration of the drug and were detected during the first 24 h after treatment, disappearing from plasma after this time. These new compounds were not detected either in controls or in non-treated stroke patients, which suggests that they were released into the plasma as a consequence of the thrombolytic effect of the drug. Our results suggest that these new compounds might be free glycans. The use of AQC as a derivatizing reagent has demonstrated that the new compounds detected cannot contain primary or secondary amine groups in their structure. The molecular mass determined by ESI-MS (821 Da) suggests that if these compounds are free glycans they might be a high-mannose type.
Bioassay-Guided Isolation and Identification of Antibacterial Components against Escherichia coli from Industrial Hemp Leaves
Authors: Fu Yafen , Zhu Siyuan , Duan Shengwen , Liu Liangliang
Published: January 2023

Industrial hemp leaves have raised much interest in nutraceuticals and functional foods areas. To expand its application ranges, the antibacterial activities of industrial hemp leaf extract on Escherichia coli, Staphylococcus aureus, and Bacillus cereus were evaluated and the active components were screened. As a result, the industrial hemp leaf extract was found to have strong bacteriostatic effects on E. coli and S. aureus. Bioassay-guided fractionation and isolation from fractions active against E. coli were conducted. Two compounds, cannabidivarinic acid and cannabidiolic acid, were firstly recognized by analytical HPLC by comparing the retention times and UV spectra with standards and later isolated using preparative HPLC. Moreover, the antibacterial mechanisms of cannabidivarinic acid and cannabidiolic acid were investigated by testing the alkaline phosphatase activity, β-galactosidase activity, conductivity, proteins leakage, nucleic acid leakage, and scanning electron microscope observation. The results demonstrated that cannabidivarinic acid and cannabidiolic acid could destroy the cell wall and membrane of E. coli, resulting in the inhibition of enzyme activity and leakage of contents. They could damage the bacteria cell envelope as well. Presented results pointed out cannabidivarinic acid and cannabidiolic acid as promising natural bacteriostatic agents for the food, pharmaceutical, and cosmetic industry.
Large Chestnut Trees Did Not Respond to Annual Fertiliser Applications, Requiring a Long-Term Approach to Establishing Effective Fertilisation Plans
Authors: Arrobas Margarida , Silva João , Busato Mariah Ruaro , Ferreira Ana Carolina , Raimundo Soraia , Pereira Abel , Finatto Taciane , de Mello Nilvania Aparecida , Correia Carlos Manuel , Rodrigues Manuel Ângelo
Published: January 2023

Due to the high value of the fruit, the European chestnut (Castanea sativa Mill.), usually grown in agroforestry systems, has been planted as a single species in orchards managed with increasingly intensive cropping practices, such as the regular use of fertilisers. This justifies research into establishing fertilisation programmes oriented towards ecological intensification. In this study, the results of fruit production, plant nutritional status and soil properties are reported from a field trial in which three NPK fertilisers (20:7:10, 13:11:21 and 7:14:14) and a control treatment were used. Chestnut yields did not vary significantly between treatments, although the mean values of the control showed a clear downward trend. N supplied by the fertilisers seems to have been the most important factor in the difference between the fertilised and control treatments, since leaf N concentrations were lower in the control and often below the lower limit of the sufficiency range. Soil inorganic N levels in the autumn, and tissue N concentrations of the herbaceous vegetation developing beneath the trees, indicated risks of N loss to the environment and highlighted the importance of this vegetation remaining during the winter. The chestnuts’ poor response to fertiliser applications was attributed to the buffering effect of the large perennial structure of the trees on the distribution of nutrients to the growing plant parts. In large trees, it seems appropriate to base the annual fertilisation plan on leaf nutrient concentration. Thus, farmers probably should avoid spending money on fertilizer applications as long as leaf nutrient concentrations do not approach the lower limits of sufficiency ranges.
Methane and Nitrous Oxide Emissions from a Temperate Peatland under Simulated Enhanced Nitrogen Deposition
Authors: Meng Xue , Zhu Zhiguo , Xue Jing , Wang Chunguang , Sun Xiaoxin
Published: January 2023

Nitrogen (N) deposition has increased in recent years and is significantly affected by global change and human activities. Wetlands are atmospheric CH4 and N2O sources and may be affected by changes in N deposition. To reveal the effects of increased N deposition on peatland greenhouse gas exchange, we observed the CH4 and N2O emissions from controlled microcosms collected from a temperate peatland in the Xiaoxing’an mountains, Northeast China. We found that the moss biomass did not change, but the total herb biomass increased by 94% and 181% with 5 and 10-times-higher N deposition, respectively. However, there were no significant changes in CH4 emissions from the microcosms with N addition. The unchanged CH4 emissions were mainly caused by the opposite effect of increased nitrate and ammonium concentrations on soil CH4 production and the increased plant biomass on CH4 emission. We also found that the manipulated microcosms with 5 and 10-times-higher N deposition had 8 and 20-times-higher seasonal average N2O emissions than the control microcosms, respectively. The increased N2O emissions were mainly caused by short-term (≤7 d) pulse emissions after N addition. The pulse N2O emission peaks were up to 1879.7 and 3836.5 μg m−2 h−1 from the microcosms with 5 and 10-times-higher N deposition, respectively. Nitrate and ammonium concentrations increasing in the soil pore water were the reason for the N2O emissions enhanced by N addition. Our results indicate that the increase in N deposition had no effects on the CH4 emissions but increased the N2O emissions of the temperate peatland. Moreover, pulse emissions are very important for evaluating the effect of N addition on N2O emissions.
Assessment of Saudi Arabia’s Classification and Selection Criteria for Heritage Sites: A Case Study of Barzan Heritage Area in Hail City
Authors: Albaqawy Ghazy Abdullah , Alnaim Mohammed Mashary , Bay Mohammed Abdulfattah , Touahmia Mabrouk
Published: January 2023

Cultural heritage has numerous developmental prospects that might contribute to the sustainable development and quality of life of communities. However, cultural heritage products have a complex relationship with many sectors. This paper examines Saudi criteria for the classification and selection of cultural heritage and presents a case study on the implementation of these criteria. It aims to develop local assessment criteria for cultural heritage values and conservation in Saudi Arabia. A theoretical analysis has been conducted to evaluate the criteria for the classification and selection of architectural heritage value and the relationship between whole heritage values. The paper discusses the criteria’s limitations and impacts and identifies misunderstandings of cultural heritage values in Saudi Arabia. The findings of this study indicate that the mechanism of implementing a list can affect both tangible and intangible cultural heritage in different ways and at different levels. The criteria should include all cultural heritage values within a context rather than a single building or object. Thus, one of the main objectives of the criteria is to recognize and highlight the importance of a site through its values. The case study of the Barzan District illustrates the factual impact of current legislation and organizational structures on heritage sites.
Comparative Analysis of the Genetic Diversity of Faba Bean (Vicia faba L.)
Authors: Avramidou Eleni , Ganopoulos Ioannis , Mylona Photini , Abraham Eleni M. , Nianiou-Obeidat Irini , Osathanunkul Maslin , Madesis Panagiotis
Published: January 2023

Faba bean (Vicia faba L.) is an important grain legume with high protein content (approximately 25–30%) and high nutritional value. It is broadly cultivated in temperate areas both for human consumption and as animal feed. According to FAOSTAT (2020), the total cultivated area of faba bean reached approximately 2.5 million ha, yielding more than 4.5 million tons. The characterization of the genetic diversity in faba bean is an important parameter for genetic and biodiversity studies, germplasm characterization, and for introducing genetic variability in plant breeding. The present study aims to assess the genetic diversity among 53 Greek, varied faba bean populations provided by the Hellenic Agricultural Organization “DEMETER” seed bank. To determine the genetic diversity of the studied populations, six SCoT DNA markers were used. A total of 114 loci were obtained with 37.95% being polymorphic and 62.05% monomorphic within or between populations. SCoT markers are a useful tool for the detection of genetic diversity among faba bean populations and encourage targeted crossing strategies. The present study is the first step towards the development of an efficient breeding program.
Analyzing Critical Success Factors for Sustainable Cloud-Based Mobile Learning (CBML) in Crisp and Fuzzy Environment
Authors: Naveed Quadri Noorulhasan , Qahmash Adel Ibrahim , Qureshi Mohamed Rafik N. , Ahmad Naim , Abdul Rasheed Mohammed Aref , Akhtaruzzaman Md
Published: January 2023

Mobile Learning (M-Learning), driven by technological digital advancement, is one of the essential formats of online learning, providing flexibility to learners. Cloud-based mobile learning (CBML) provides value additions by providing an economic alternative to E-learning. Revolutionary changes in smartphone design and features have enhanced the user experience, thus encouraging mobile learning. During the COVID-19 pandemic, E-Learning and M-Learning allowed continuing education to occur. These methods continue to offer more opportunities to learners than constrained face-to-face classroom learning. There are many main critical success factors (CSFs) and subfactors that play an influential role in sustainable M-Learning success. The current study focuses on the assessment and ranking of various main factors and subfactors of CBML. Analytic hierarchy process-group decision-making (AHP-GDM)- and fuzzy analytic hierarchy process (FAHP)-based methodologies were used to evaluate and model the main factors and subfactors of CBML in crisp and fuzzy environments. Higher education institutes must strive to address these main factors and subfactors if they are to fulfill their vision and mission in the teaching–learning system while adopting sustainable M-Learning.
The Multidimensional Measurement Method of Urban Sprawl and Its Empirical Analysis in Shanghai Metropolitan Area
Authors: Shi Yishao , Zhou Liangliang , Guo Xiatong , Li Jiaqi
Published: January 2023

Urban sprawl concerns the high-quality and sustainable development of large cities. Due to the ambiguous definition, diversity of measurement indices and complexity of the driving mechanism of urban sprawl, the research results are rich but controversial. How does one carry out multidimensional measurement of urban sprawl? How does one reveal the spatio-temporal evolution characteristics of urban sprawl dynamically? First, according to the three common characteristics of urban sprawl (discontinuity of land use, low population density and inefficiency of land use), we, respectively, measure the urban sprawl of Shanghai metropolitan area by single index and comprehensive indices based on multi-source geospatial data. Next, using geographic information system (GIS) method, the temporal and spatial characteristics of urban sprawl in Shanghai are quantitatively and dynamically analyzed. The results show that (1) land use continuity reveals that fringe expansion is the main mode of urban sprawl, population density exhibits an upwards trend, and land use benefit shows that the sprawl increased first, then decreased and increased again, i.e., “N” type trend. The results of the above three comprehensive superpositions indicate that the urban sprawl in Shanghai changed from severe in 1995 to mild in 2010 and in 2020. (2) From 1990 to 2020, urban sprawl in Shanghai showed a trend of decreasing first, then increasing and decreasing again, which is consistent with an evolutionary trend of newly increased construction land. The larger the sprawl area was, the lower the land use efficiency of the sprawl area was. (3) The main directions of urban sprawl were southeast and southwest, and Songjiang District and Pudong New Area were the main sprawl areas. The peak value of urban sprawl mainly occurred at 20–30 km and was located in the area between the outer ring and the suburban ring. (4) Through time series analysis, we found that the effective supply of housing significantly affected the intensity and scale of urban sprawl but not the speed of urban sprawl in Shanghai metropolitan area. These findings are helpful to reasonably evaluate the real picture of urban sprawl in Shanghai metropolitan areas and provide reference for the formulation of urban sprawl governance policies.
SCDNet: Self-Calibrating Depth Network with Soft-Edge Reconstruction for Low-Light Image Enhancement
Authors: Qu Peixin , Tian Zhen , Zhou Ling , Li Jielin , Li Guohou , Zhao Chenping
Published: January 2023

Captured low-light images typically suffer from low brightness, low contrast, and blurred details due to the scattering and absorption of light and limited lighting. To deal with these issues, we propose a self-calibrating depth network with soft-edge reconstruction for low-light image enhancement. Concretely, we first employ the soft edge reconstruction module to reconstruct the soft edge of the input image and extract the texture and detail information of the image. Afterward, we explore the convergence properties of each input via the self-calibration module to significantly improve the computational effectiveness of the method and gradually correct the inputs at each subsequent level. Finally, the low-light image is iteratively enhanced by an iterative light enhancement curve to obtain a high-quality image. Extensive experiments demonstrate that our SCDNet visually enhances the brightness and contrast, restores the actual color, and makes the image more in line with the characteristics of the human eye vision system. Meanwhile, our SCDNet outperforms the compared methods in some qualitative and quantitative metrics.
Influence of Pipe-Jacking Reaction on Earth Pressure of Back Wall of Pilot Tunnel of Subway Station
Authors: Bai Qian , Zhao Wen , Wang Xin , Ma Huili , Zhao Zhen
Published: January 2023

The study of the distribution form and calculation method of the earth pressure on the back wall of a pilot tunnel under a jacking reaction is very important to ensure the safety and smooth construction of the pipe jacking. Based on a metro station in Shenyang, this paper firstly investigates the effects of the changes in the jacking reaction parameters, such as the loading intensity, loading position, and loading area on the earth pressure and the displacement of the back wall in a pilot tunnel through numerical simulation, and then proposes a formula for calculating the soil reaction on the back wall and verifies it by comparison with the FEA (finite element method) results. The results show that the earth pressure distribution pattern of the back wall is similar to the normal distribution curve under the action of the jacking reaction. The horizontal displacement and earth pressure of the back wall will gradually increase with the increase in the jacking reaction. The horizontal displacement of the back wall is greatest when the load is applied to the middle wall, followed by the top wall and the bottom wall. The maximum horizontal displacement is reduced by approximately 24.25% when the loading position changes from the middle to the bottom. As the loading area increases, the maximum horizontal displacement of the back wall decreases, in the order of 11.8% and 14.45% relative to the previous level. The earth pressure of the back wall also decreases, in the order of 17.92% and 22.76% relative to the previous level. The equations presented are applicable to the calculation of the soil reaction in the limit state.
Evaluation of Land Carrying Capacity of 31 Provinces in China Based on a Natural–Societal-Supply–Demand Framework
Authors: Hu Huijuan , Han Ling
Published: January 2023

Land carrying capacity (LCC) refers to the ability of land resources to support human activities, and has become an important tool for research into the man–land relationship. Due to the 31 provinces of China possessing huge differences in resource and social development, it is necessary to understand the key factors and differences in LCC in each province. This paper provided a framework for the Natural–Societal-Supply–Demand of LCC, and analyzed the LCC of the 31 provinces of China via the cross relationship between the four subsystems. In total, 22 indicators were selected, and the weight of the indicators was calculated via the gray correlation coefficient. Through this framework, the LCC is endowed by natural resources and is improved by social development; the comprehensive LCC of the 31 provinces was evaluated using geographic information data and statistical data, and the main causes of overload were analyzed by using the obstacle model. The results show that (1) The natural resources of most provinces in China cannot support the current population; (2) Social development has significantly improved the LCC; and (3) The shortage of natural resources is the key factor in LCC overloading in most provinces. Insufficient food supply, insufficient carbon sequestration, the shortage of construction land, and insufficient water supply are the main causes of overload in China.
Natural Biomass-Derived Porous Carbon from Water Hyacinth Used as Composite Cathode for Lithium Sulfur Batteries
Authors: Nurhilal Otong , Hidayat Sahrul , Sumiarsa Dadan , Risdiana Risdiana
Published: January 2023

We have successfully prepared porous carbon from water hyacinth plants using several steps, i.e., carbonization, activation, and calcination processes. Water hyacinth porous carbon is an alternative as a carbon material due to the ease and low cost of the manufacturing process, abundant raw materials in nature, and its contribution to solving environmental problems. Utilization of water hyacinth weed plants as raw materials for porous carbon will provide added value to water hyacinth. In this research, porous carbon is used as the host material of sulfur in the electrodes of lithium sulfur batteries. The N2 adsorption desorption characterization showed a porous carbon surface area of around 642 m2/g and a total pore volume of 0.713 cm3/g. The sulfur content of the composite electrode of C/S 1:2.5 (%w/w) was 60.6%. The four-line probe (FLP) testing showed electrical conductivity of porous carbon of around 3.93 × 10−2 S/cm and the electrical conductivity of the composite electrode was around 5.4 × 10−4 S/cm. Furthermore, the composite electrodes were applied as cathodes of lithium sulfur batteries, which have thicknesses around 200 µm and sulfur loading of 3.57 mg/cm2. The highest discharge capacity of the battery was 312 mAh/g and the Coulombic efficiency was around 70%.
The Concept of Ocean Sustainability in High School: Measuring the Ocean Literacy of Vocational High School Students in Indonesia
Authors: Chang Cheng-Chieh , Tsai Liang-Ting , Meliana Dwi
Published: January 2023

In this study, we used propositional concept maps with different ocean-related conceptual sentences to assess the ocean literacy of maritime vocational high school students in Grades 10–12 in Indonesia. Indonesia is the world’s largest archipelagic country, and many aspects of people’s lives, including their jobs, are related to the ocean. Therefore, it is crucial to assess the ocean literacy and related knowledge of students, especially maritime vocational high school students. Thus, in this study, an open-ended questionnaire was employed to acquire data from 255 students at the National Vocational High School 1 Temon in Yogyakarta, Indonesia. A total of 215 males and 40 females participated in this study. In addition to descriptive analysis, we used a one-way analysis of variance and a t-test for statistical inference. The results showed that 11th and 12th graders scored significantly higher in ocean literacy than 10th graders, and female students significantly outperformed male students. Furthermore, student learning was teacher-centered. In this paper, we summarize the results and provide suggestions and references concerning ocean literacy for teachers and high school students.
Green Logistics, Green Human Capital, and Circular Economy: The Mediating Role of Sustainable Production
Authors: Cheng Ya , Masukujjaman Mohammad , Sobhani Farid Ahammad , Hamayun Muhammad , Alam Syed Shah
Published: January 2023

Many countries worldwide have adopted a sustainable development model to strike a balance between economic progress, environmental preservation, and social protection, and one of the most effective solutions for promoting sustainable development has been the circular economy (CE). Given each country’s resource restrictions, businesses must implement green manufacturing practices to establish a circular economy. Therefore, this study intends to assess the role of green logistics (GL) and green human capital (GHC) in promoting a circular economy. Additionally, the mediating role of sustainable production (SP) in the interplays between the constructs was examined. Data for this research was collected from 211 garment manufacturing firms in Bangladesh, using a standardized questionnaire. The partial least square structural equation modeling (PLS-SEM) technique was employed for inferential statistical analysis. The findings revealed that GL and GHC are significant drivers of CE. Additionally, GL and GHC were observed to have a positive linkage with the SP of firms. The findings uncovered that SP positively impacts circular economy implementation. Furthermore, SP was found to significantly mediate the linkages between GL and CE, as well as between GHC and CE. Ours is one of a limited number of research projects that address the role of GL and GHC in implementing CE through SP. Hence, this study added critical insights to the extant theory and practice while reporting several theoretical and managerial implications.
Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand
Authors: Lun Niang Sian , Chaudhary Siddharth , Ninsawat Sarawut
Published: January 2023

Urbanization and expansion in each city of emerging countries have become an essential function of Earth’s surface, with the majority of people migrating from rural to urban regions. The various urban category characteristics have emphasized the great importance of understanding and creating suitable land evaluations in the future. The overall objective of this study is to classify the urban zone utilizing building height which is estimated using Sentinel-1 synthetic aperture radar (SAR) and various satellite-based indexes of Sentinel-2A. The first objective of this research is to estimate the building height of the Sentinel-1 SAR in Nonthaburi, Thailand. A new indicator, vertical-vertical-horizontal polarization (VVH), which can provide a better performance, is produced from the dual-polarization information, vertical-vertical (VV), and vertical-horizontal (VH). Then, the building height model was developed using indicator VVH and the reference building height data. The root means square error (RMSE) between the estimated and reference height is 1.413 m. Then, the second objective is to classify three classes of urban types, which are composed of residential buildings, commercial buildings, and other buildings, including vegetation, waterbodies, car parks, and so on. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built up the index (NDBI) are extracted from the Sentinel-2A data. To classify the urban types, a three-machine learning classifier, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were developed. The classification uses randomly trained data from each 500 m focus study which are divided into a 100 × 100 m grid. Different models are examined using different variables, for example, classification using only building height and only spectral indices. The indices and estimated building height were used to classify the urban types. Not only the average of various satellite-based indices and building height of 100 × 100 m grid was used, but also the minimum, maximum, mean, and standard deviation were calculated from NDVI, NDWI, NDBI, and building height. There are a total of 16 variables used in the model. Eventually, the principal components analysis (PCA) was used to reduce the variables and get better performance of the models. SVM showed better accuracy than the other two, RF and KNN. The accuracies of SVM, RF, and KNN are 0.86, 0.75, and 0.76, respectively.
Intensification of Rice-Fallow Agroecosystem of South Asia with Oilseeds and Pulses: Impacts on System Productivity, Soil Carbon Dynamics and Energetics
Authors: Naik Sushanta Kumar , Mali Santosh Sambhaji , Jha Bal Krishna , Kumar Rakesh , Mondal Surajit , Mishra Janki Sharan , Singh Arun Kumar , Biswas Ashis Kumar , Choudhary Arbind Kumar , Choudhary Jaipal Singh , Hans Hansraj , Das Anup , Babu Subhash , Layek Jayanta , Upadhyaya Ashutosh , Bhatt Bhagwati Prasad , Chaudhari Suresh Kumar
Published: January 2023

Rice-fallow systems in South Asian countries play a pivotal role in increasing agricultural production. However, the productivity of these system is largely challenged by deteriorating soil health and limited residual soil moistures in dry-periods, precluding the possibility of winter and/or spring season crops after rice harvest. This investigation explores the possibilities of including winter and/or spring crops through conservation agriculture (CA)-based management practices and evaluates its effect on soil carbon dynamic, system productivity, energy and carbon budgeting. Field experiments were conducted at a farmer’s field in participatory modes at Chene Village, Jharkhand, India, and had five treatments comprising (1) fallow-land [FL]; (2) transplanted puddle rice (TPR)-fallow (winter)-fallow (summer), a typical cultivation practice of this region [RF]; (3) TPR–conventional-till mustard–conventional-till blackgram [CP]; (4) CA with zero-till transplanted rice (ZTTR)-ZT mustard–ZT blackgram [CA1]; (5) CA with zero-till direct seeded rice (ZTDSR)-ZT mustard–ZT blackgram [CA2]. Results revealed that CA2 comprising full CA practice of growing direct seeded rice, mustard and blackgram under ZT increased total soil organic carbon (TSOC) of 0–0.6 m soil layer by 14.3% and 10.3% over RF and CP, respectively. The C-sequestration rate was higher in CA treatments and varied from 0.55 to 0.64 Mg C ha−1 year−1. The system rice equivalent yield in CA2 during the initial four years was lower than CP but it was 23.7% higher over CP in fifth year. The CA-based system (CA2) enhanced the water productivity of rice, mustard and blackgram by 7.0%, 23.6% and 14.1% over CP, respectively. The CA2 treatment, having higher system productivity, better C-sequestration potential, lower C-footprint, higher energy and water productivity has very good potential for sustaining soil health and crop yield of rice-fallow systems.