bluuebunny/crossref_metadata_2025 huggingface dataset:
Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh
Authors: Colosio Paolo , Tedesco Marco , Tellman Elizabeth
Published: February 2022

Monitoring floods is a major issue in water resources management and risk mitigation, especially in the Global South. Optical and radar observations, even providing a fine spatial resolution, are still limited by cloud cover interaction or insufficient temporal resolution. On the other hand, passive microwave (PMW) sensors collect information on a daily frequency with minor cloud cover interaction, but they have been historically limited in terms of spatial resolution. Here, we evaluate the capability of an enhanced spatial resolution PMW dataset (3.125 km) in monitoring spatio-temporal evolution of flood events, focusing on a major flood event that occurred in October 2005 in Bangladesh. We apply an algorithm aimed to remove the seasonal variability of surface temperature from the PMW timeseries, exploiting the difference in emissivity between dry and water-covered pixels. We assess the capability of the algorithm in capturing flood evolution and extension through the comparison with quantities obtained from optical data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and water level measurements. We also compare the enhanced product with the historical coarser resolution dataset by means of a variogram-based analysis to evaluate the improvements in terms of spatial representation. Finally, we evaluate the possibility to extract the water fraction within a single pixel by using an Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) emissivity dataset and compare the estimates with MODIS-derived water fractions. Our results show that the enhanced PMW product outperforms the coarser one when compared to flood mapped from optical data based on information content, indicating that it is possible to integrate such a product into the mapping of floods at a global scale on a daily basis.
Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning
Authors: Ørka Hans , Jutras-Perreault Marie-Claude , Candelas-Bielza Jaime , Gobakken Terje
Published: February 2022

Forest ecosystems provide a range of services and function as habitats for many species. The concept of woodland key habitats (WKH) is important for biodiversity management in forest planning standards and certification schemes. The main idea of the WKH is to preserve biodiversity hotspots in the forest landscape. Current methods used in delineating WKH rely on costly field inventories. Furthermore, it is well known that the surveyor introduces an error because of the subjective assessment. Remote sensing may reduce this error in a cost-efficient way. The current study develops automated methods using airborne laser scanning (ALS) data to delineate geomorphological WKH, i.e., rock walls and stream gorges. The methods were evaluated based on a complete field inventory of WKH in a 1600 ha area in south-eastern Norway. The delineated WKH showed high detection rates, minor omission errors, but high commissions errors. Combining the delineation into a map of potential WKH suitable to guide field surveyors resulted in detecting all field reference WKH, i.e., a detection rate of 100% and a commission error of 25%. It is concluded that a higher degree of automatization might be possible to improve results and increase the efficiency of WKH inventories.
Promotion of Interface Fusion of Solid Polymer Electrolyte and Cathode by Ultrasonic Vibration
Authors: Wang Hui , Ke Haoran , Chen Yizhe , Wang Jinhuo , Yan Fei , Cui Xiaodong
Published: February 2022

All-solid-state polymer lithium batteries have good safety, stability, and high energy densities and are employed in wireless sensors. However, the solid contact between the polymer electrolyte and the cathode leads to high interface resistance, limiting the broad application of solid-state lithium batteries. This paper proposes an ultrasonic fusion method to reduce the interface resistance between the polymer electrolyte and the cathode. The method applied a high-frequency ultrasonic vibration technique to impact the polymer electrolyte/cathode structure, melting the electrolyte at the interface and thus generating good contact at the interface. The experimental results showed that the ultrasonic fusion method decreased the interface resistance between the polymer electrolyte and the cathode by 96.2%. During the ultrasonic fusion process, high-frequency ultrasonic vibrations generated high temperatures at the interface, and the polymer electrolyte became molten, improving the contact between the electrolyte and the cathode. The ultrasonic fusion method eliminated the gaps at the interface, and the interface became more compact. Furthermore, ultrasonic vibrations made the molten electrolyte fill the holes in the cathode, and the contact area was enhanced, providing more Li+ ions transmission paths.
A Novel Integrated APCI and MPT Ionization Technique as Online Sensor for Trace Pesticides Detection
Authors: Zhao Gaosheng , Chu Fengjian , Zhou Jianguang
Published: February 2022

The misuse of pesticides poses a tremendous threat to human health. Excessive pesticide residues have been shown to cause many diseases. Many sensor detection methods have been developed, but most of them suffer from problems such as slow detection speed or narrow detection range. So, the development of rapid, direct and sensitive means of detecting trace amounts of pesticide residues is always necessary. A novel online sensor technique was developed for direct analysis of pesticides in complex matrices with no sample pretreatment. The portable sensor ion source consists of an MPT (microwave plasma torch) with desolventizing capability and an APCI (atmosphere pressure chemical ionization), which provides abundant precursor ions and a strong electric field. The performance which improves the ionization efficiency and suppresses the background signal was verified by using pesticide standard solution and pesticide pear juice solution measurements with an Orbitrap mass spectrometer. The limit of detection (LOD) and the limit of quantization (LOQ) of the method were measured by pear juice solutions that were obtained in the ranges of 0.034–0.79 μg/L and 0.14–1 μg/L. Quantitative curves were obtained ranging from 0.5 to 100 μg/L that showed excellent semi-quantitative ability with correlation coefficients of 0.985–0.997. The recoveries (%) of atrazine, imidacloprid, dimethoate, profenofos, chlorpyrifos, and dichlorvos were 96.6%, 112.7%, 88.1%, 85.5%, 89.2%, and 101.9% with the RSDs ranging from 5.89–14.87%, respectively. The results show that the method has excellent sensitivity and quantification capability for rapid and direct detection of trace pesticide.
Multi-Aspect Based Approach to Attack Detection in IoT Clouds
Authors: Desnitsky Vasily , Chechulin Andrey , Kotenko Igor
Published: February 2022

This article covers the issues of constructing tools for detecting network attacks targeting devices in IoT clouds. The detection is performed within the framework of cloud infrastructure, which receives data flows that are limited in size and content, and characterize the current network interaction of the analyzed IoT devices. The detection is based on the construction of training models and uses machine learning methods, such as AdaBoostClassifier, RandomForestClassifier, MultinomialNB, etc. The proposed combined multi-aspect approach to attack detection relies on session-based spaces, host-based spaces, and other spaces of features extracted from incoming traffic. An attack-specific ensemble of various machine learning methods is applied to improve the detection quality indicators. The performed experiments have confirmed the correctness of the constructed models and their effectiveness, expressed in terms of the precision, recall, and f1-measure indicators for each analyzed type of attack, using a series of existing samples of benign and attacking traffic.
Experimental Results of Attitude Determination Functional Algorithms Implementation in Strapdown Inertial Navigation System
Authors: Zharkov Maksim , Veremeenko Konstantin , Kuznetsov Ivan , Pronkin Andrei
Published: February 2022

Strapdown inertial navigation system (SINS) software developers are usually mainly focused on the attitude determination algorithm design and characteristics. Such an algorithm can be based on different mathematical apparatus. An approach to the derivation of attitude determination algorithm equations in strapdown inertial navigation system is proposed. This algorithm is based on direct heading, pitch, and roll calculation. The qualitative differences between the proposed algorithm and the one using a transformation matrix are noted. The main objective of the paper is direct attitude angles calculation algorithm application possibilities analysis in real SINS operating conditions. The analysis is based on a comparison of the proposed algorithm with another frequently used attitude determination algorithm based on the transformation matrix. Attitude determination algorithms performance investigation results based on car and helicopter experimental tests are presented.
Silicone Rubber Fabry-Perot Pressure Sensor Based on a Spherical Optical Fiber End Face
Authors: Jiang Changxing , Lei Xiaohua , Chen Yuru , Lv Shaojie , Liu Xianming , Zhang Peng
Published: February 2022

To improve the fringe contrast and the sensitivity of Fabry-Perot (FP) pressure sensors, a silicone rubber FP pressure sensor based on a spherical optical fiber end face is proposed. The ratio of silicone rubber ingredients and the diameter and thickness of silicone rubber diaphragm were optimized by a simulation based on experimental tests that analyzed elastic parameters, and the influence of the radius of a spherical optical fiber and the initial cavity length of the sensor on the fringe contrast was investigated and optimized. Pressure sensor samples were fabricated for pressure test and temperature cross-influence test. Gas pressure experimental results within a pressure range of 0~40 kPa show the average sensitivity of the sensor is −154.56 nm/kPa and repeatability error is less than 0.71%. Long-term pressure experimental results show it has good repeatability and stability. Temperature experimental results show its temperature cross-sensitivity is 0.143 kPa/°C. The good performance of the proposed FP pressure sensor will expand its applications in biochemical applications, especially in human body pressure monitoring.
HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features
Authors: Alkinani Monagi H. , Khan Wazir Zada , Arshad Quratulain , Raza Mudassar
Published: February 2022

Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features. HSDDD is based on three-tiered architecture. The three tiers are named as Coordination tier, Concatenation tier and Classification tier. We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN features. DCNN extracted features are fused with HOG extracted features at the Concatenation tier. Then PCA is used as a feature selection technique. PCA takes both the extracted features and removes the redundant and irrelevant information, and it improves the classification performance. After feature fusion and feature selection, the two classifiers, KNN and SVM, at the Classification tier take the selected features and classify the ten classes of distracted driving behaviors. We evaluate our proposed scheme and observe its performance by using the accuracy metrics.
Machine Vision-Based Method for Estimating Lateral Slope of Structured Roads
Authors: Yan Yunbing , Li Haiwei
Published: February 2022

Most of the studies on vehicle control and stability are based on cases of known-road lateral slope, while there are few studies on road lateral-slope estimation. In order to provide reliable information on slope parameters for subsequent studies, this paper provides a method of structured-road lateral-slope estimation based on machine vision. The relationship between the road lateral slope and the tangent slope of the lane line can be found out according to the image-perspective principle; then, the coordinates of the pre-scan point are obtained by the lane line, and the tangent slope of the lane line is used to obtain a more accurate estimation of the road lateral slope. In the implementation process, the lane-line feature information in front of the vehicle is obtained according to machine vision, the lane-line function is fitted according to an SCNN (Spatial CNN) algorithm, then the lateral slope is calculated by using the estimation formula mentioned above. Finally, the road model and vehicle model are established by Prescan software for off-line simulation. The simulation results verify the effectiveness and accuracy of the method.
A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography
Authors: Chen Jia-Wei , Huang Hsin-Kai , Fang Yu-Ting , Lin Yen-Ting , Li Shih-Zhang , Chen Bo-Wei , Lo Yu-Chun , Chen Po-Chuan , Wang Ching-Fu , Chen You-Yin
Published: February 2022

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.
High Density Resistive Array Readout System for Wearable Electronics
Authors: Lakshminarayana Shanthala , Park Younghun , Park Hyusim , Jung Sungyong
Published: February 2022

This work presents a wearable sensing system for high-density resistive array readout. The system comprising readout electronics for a high-density resistive sensor array and a rechargeable battery, was realized in a wristband. The analyzed data with the proposed system can be visualized using a custom graphical user interface (GUI) developed in a personal computer (PC) through a universal serial bus (USB) and using an Android app in smartphones via Bluetooth Low Energy (BLE), respectively. The readout electronics were implemented on a printed circuit board (PCB) and had a compact dimension of 3 cm × 3 cm. It was designed to measure the resistive sensor with a dynamic range of 1 KΩ–1 MΩ and detect a 0.1% change of the base resistance. The system operated at a 5 V supply voltage, and the overall system power consumption was 95 mW. The readout circuit employed a resistance-to-voltage (R-V) conversion topology using a 16-bit analog-to-digital converter (ADC), integrated in the Cypress Programmable System-on-Chip (PSoC®) 5LP microcontroller. The device behaves as a universal-type sensing system that can be interfaced with a wide variety of resistive sensors, including chemiresistors, piezoresistors, and thermoelectric sensors, whose resistance variations fall in the target measurement range of 1 KΩ–1 MΩ. The system performance was tested with a 60-resistor array and showed a satisfactory accuracy, with a worst-case error rate up to 2.5%. The developed sensing system shows promising results for applications in the field of the Internet of things (IoT), point-of-care testing (PoCT), and low-cost wearable devices.
Multi-Feature Single Target Robust Tracking Fused with Particle Filter
Authors: Liu Caihong , Ibrayim Mayire , Hamdulla Askar
Published: February 2022

Aiming at the problems of target model drift or loss of target tracking caused by serious deformation, occlusion, fast motion, and out of view of the target in long-term moving target tracking in complex scenes, this paper presents a robust multi-feature single-target tracking algorithm based on a particle filter. The algorithm is based on the correlation filtering framework. First, to extract more accurate target appearance features, in addition to the manual features histogram of oriented gradient features and color histogram features, the depth features from the conv3–4, conv4–4 and conv5–4 convolutional layer outputs in VGGNet-19 are also fused. Secondly, this paper designs a re-detection module of a fusion particle filter for the problem of how to return to accurate tracking after the target tracking fails, so that the algorithm in this paper can maintain high robustness during long-term tracking. Finally, in the adaptive model update stage, the adaptive learning rate update and adaptive filter update are performed to improve the accuracy of target tracking. Extensive experiments are conducted on dataset OTB-2015, dataset OTB-2013, and dataset UAV123. The experimental results show that the proposed multi-feature single-target robust tracking algorithm with fused particle filtering can effectively solve the long-time target tracking problem in complex scenes, while showing more stable and accurate tracking performance.
Halogen-Bonded Driven Tetra-Substituted Benzene Dimers and Trimers: Potential Hosts for Metal Ions
Authors: Parra Rubén D.
Published: February 2022

Cyclic dimers and trimers of tetra-substituted benzenes, ((HOOC)2-C6H2-(NHI)2), are selected as convenient model systems for investigating NI…O=C halogen bond strength and cooperativity. The four substituents in benzene are chosen so that two of them act as halogen bond acceptors (COOH) and two act as halogen bond donors (NHI), as shown in the graphical abstract below. The potential for metal ion binding by each of the halogen-bonded aggregates is also investigated using the monoatomic sodium ion, Na+. Density functional theory calculations performed using the wB97XD functional and the DGDZVP basis set confirmed the ability of halogen bonding to drive the formation of the cyclic dimers and trimers of the model system chosen for this study. Evidence of halogen bond cooperativity is seen, for example, in a 9% shortening of each NI…O=C halogen bond distance with a corresponding 53% increase in the respective critical point density value, ρNI…O=C. Cooperativity also results in a 36% increase in the magnitude of the complexation energy per halogen-bond of the trimer relative to that of the dimer. The results of this study confirm the potential for binding a single Na+ ion by either the dimer or the trimer through their respective halogen-bond networks. Binding of two metal ions was shown to be possible by the dimer. Likewise, the trimer was also found to bind three metal ions. Lastly, the overall structure of the halogen-bonded dimer or trimer endured after complexation of the Na+ ions.
High-Throughput Optimal Design of Spacers Using Triply Periodic Minimal Surfaces in BWRO
Authors: Chen Qiang , Luo Jiu , Heng Yi
Published: February 2022

The development of advanced feed spacers under different working conditions can enhance the performance of the reverse osmosis (RO) desalination process. The 3D-printed experimental results on triply periodic minimal surfaces (TPMS)-based spacers in previous literature indicate that the spacers have higher permeation flux of water compared to those of the common commercial spacers. In this paper, a hybrid modeling approach is developed and applied to predict and evaluate the performance of TPMS-based spacers. The effect of feed channels’ height and porosity on the performance of spacers in brackish water RO (BWRO) process is studied by using a high-throughput approach. The predicted pressure drop by new simulations using the TPMS-based spacers (≈0.09–0.27 bar) from inlet to outlet in a typical two-stage BWRO system is reduced by more than 89% than that of using the commercial spacer (≈2.57 bar). Using the designed advanced spacers, the average permeation flux of water increases more than 8.6% compared to that of the commercial one. With the increase in feed channel height and porosity, the performance of spacers is gradually improved. TPMS-based spacers have significant industrial application prospects.
A Video-Based Tactical Task Does Not Elicit Mental Fatigue and Does Not Impair Soccer Performance in a Subsequent Small-Sided Game
Authors: Ciocca Gianmarco , Tessitore Antonio , Mandorino Mauro , Tschan Harald
Published: February 2022

Mental fatigue can impair physical, technical, and tactical performance in sports. Since most previous research used general cognitive tasks to elicit mental fatigue, the aim of this study was to investigate whether a more sport-specific task could induce the effects of mental fatigue and impair the subsequent physical and technical performance in a soccer small-sided game. Ten soccer players performed two small-sided games on two different days in a crossover design. Before each small-sided game, they performed a video-based tactical task (30 min) and a control task (documentary watching, 30 min) in a randomized and counterbalanced order. Mental effort was measured through a visual analog scale after the tactical and control tasks. Subjective ratings of perceived exertion were assessed through the RPE questionnaire after the end of the SSG. Physical performance was assessed during the SSG through GPS technology. Results showed no differences (p > 0.05) in physical performance between the two conditions. None of the technical variables were negatively affected by the video-based tactical condition, with the number of total passes (p = 0.003; ES = 0.72 medium) and successful passes (p = 0.003; ES = 0.82 large) results even improved by the video-based tactical task. The mental effort required by the video-tactical task was significantly higher than the control task (p = 0.002; ES = 2.09 huge). However, overall RPE did not differ between conditions. The video-based tactical task did not elicit mental fatigue and did not impair subsequent physical and technical performance. The higher ecological validity of the task and the higher motivation of the participants might have contributed to the results.
Contributions of Natural Carbon Sink Capacity and Carbon Neutrality in the Context of Net-Zero Carbon Cities: A Case Study of Hangzhou
Authors: Fan Yuxin , Wei Fang
Published: February 2022

Facing the global climate change crisis, many cities have proposed the goal to achieve net-zero carbon cities. The natural carbon sink in urban space is indispensable for net-zero carbon cities, but the existing measurement system has shortcomings in the measurement elements and precision. This leads to unclear control objectives and elements of spatial planning, and the relevant planning strategies lack the support of quantitative results. We included the often-ignored natural carbon sink space and soil in the measurement scope. Taking Hangzhou as an example, we built a natural carbon sink capacity measurement system with respect to the carbon sequestration and storage capacity, measured the natural carbon sink, and evaluated its carbon neutrality’s contribution in urban space. The results showed that the carbon sink capacity of soil and small green spaces in built-up areas could affect the quantity and spatial pattern of the measurement results. Both should be included in the measurement system to improve corresponding spatial planning strategies’ reliability and feasibility. Additionally, Hangzhou’s annual natural carbon sequestration offset approximately 9.87% of the carbon emissions in the same year. With respect to the contribution to carbon neutrality, the role of natural carbon sinks in urban space was necessary, but the effect was limited. Therefore, strategies to reduce carbon emissions are integral for the net-zero carbon goal. Some spatial planning strategies to improve the urban natural carbon sink capacity are discussed. A more precise and comprehensive understanding of the urban natural carbon sink capacity can support the construction of a net-zero carbon city better.
Ecological Planning of Manufacturing Process Chains
Authors: Denkena Berend , Wichmann Marcel , Kettelmann Simon , Matthies Jonas , Reuter Leon
Published: February 2022

Production planning is a critical step for the implementation of sustainable production. It is necessary to consider energy and resource efficiency in all planning phases to promote sustainable production. In this paper, an approach for environmental impact assessment in all phases of process chain planning supported by process models is presented. The level of detail of the assessment is determined based on the level of detail of the planning phase. During the assessment, consumption of energy and resources is considered. This approach aims to align planning phases with the objective of sustainable production. In rough planning, the approach allows the selection of an ecologically favorable process chain. In detailed planning, process parameters can be selected based on their ecological sustainability. The approach can be integrated into the planning of process chains in order to consider ecological factors throughout all planning phases. The approach is evaluated by using an exemplary use case. The results indicate that rough planning under the consideration of uncertainties can form a reasonable prediction about resource efficiency for possible manufacturing routes. By systematically selecting a resource-efficient process chain, energy savings of up to 21% can be achieved for the presented use case.
Research on Digital Transformation Based on Complex Systems: Visualization of Knowledge Maps and Construction of a Theoretical Framework
Authors: Xu Xiurui , Hou Guangming , Wang Junpeng
Published: February 2022

In the digital age, the exploration of digital transformation has made remarkable progress in many fields. However, the existing theories related to digital transformation at the organizational level are relatively scattered, which makes it difficult to support the practical exploration of organizational change in the digital context. Through quantitative and visual analysis of the literature in the field of digital transformation, this study analyzes the research situation in this field from the aspects of the paper publishing trend, node literature, key scholars and regional cooperation. Through the analysis of the keywords co-occurrence network, the research frontier of digital transformation is identified, and, based on complex systems, this study discusses the research frontier from three aspects: organizational symbiosis oriented by environmental coordination, ability remodeling oriented by structural optimization and value creation oriented by functional realization. Further, based on the analysis framework and principle of organizational management systematics, this study constructs the theoretical framework of digital transformation from three aspects: core dimension, implementation mechanism and action mechanism. The systematic theoretical framework can provide reference for the development of relevant theories of digital transformation and better support the management practice of digital transformation.
A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran
Authors: Kayhomayoon Zahra , Milan Sami Ghordoyee , Arya Azar Naser , Bettinger Pete , Babaian Faezeh , Jaafari Abolfazl
Published: February 2022

Agricultural months are the critical period for the allocation of surface water and groundwater resources due to the increased demands on water supplies and decreased recharge rate. This situation urges the necessity of using conjunctive water management to fulfill the entire water demand. Here, we proposed an approach for aquifer stabilization and meeting the maximum water demand based on the available surface and groundwater resources and their limitations. In this approach, we first used the MODFLOW model to simulate the groundwater level to control the optimal withdrawal and the resulting drop. We next used a whale optimization algorithm (WOA) to develop an optimized model for the planning of conjunctive use to minimize the monthly water shortage. In the final step, we incorporated the results of the optimized conjunctive model and the available field data into the least squares-support vector machine (LS-SVM) model to predict the amounts of water shortage for each month, particularly for the agricultural months. The results showed that during the period from 2005 to 2020, the most water shortage belonged to 2018, in which only about 52% of water demand was met with the contribution of groundwater (67%) and surface water (33%). However, the groundwater level could have increased by about 0.7 m during the study period by implementing the optimized model. The results of the third part revealed that LS-SVM could predict the water shortage with better performance with a root-mean-square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe Index of 5.70 m, 3.43%, and 0.89 m, respectively. The findings of this study will enable managers to predict the water shortage in future periods to make more informed decisions for water resource allocation.
Understanding Impacts of Service Robots with the Revised Gap Model
Authors: Zhang Shengliang , Huang Chaoying , Li Xiaodong , Ren Ai
Published: February 2022

The service quality gap model, which identifies the antecedents of SERVQAUL, reveals that service quality depends on the gap between customer perceived and expected service (Gap 5), which can be caused by other gaps in the service process (Gaps 1–4). The emergence of service robots has affected the quality of services provided; however, little is known about how these impacts happen. Thus, this paper aims to explore the impacts of service robots on service quality by revising the original gap model in the context of robot service. This paper reviews and analyzes the literature related to service robots and develops a revised gap model for robot service by integrating the existing research on the impacts of service robots. By introducing the roles of robots and robot manufacturers into the original gap model, the revised gap model adds three new gaps: Gap 6 (manufacturers’ understanding gap), Gap 7 (technical gap), and Gap 8 (service coordination gap). Based on the revised gap model, the impacts of service robots on service quality are expounded (i.e., Gap 5 can be affected by not only Gaps 1–4 but also Gaps 6–8), and several propositions are introduced. This paper is the first to consider robot manufacturers as part of the service quality gap model, and the first to use a dynamic model to explore the impacts of service robots on service quality. This paper not only supplements the service robot and gap model literature but also provides service companies with a meaningful framework to improve service quality when using robots to provide service.
In Vitro Screening of New Biological Limiters against Some of the Main Soil-Borne Phytopathogens
Authors: D’Ambrosio Giovanni , Cariddi Corrado , Mannerucci Francesco , Bruno Giovanni Luigi
Published: February 2022

This study explored the role of Aphanocladium album (strain MX95), Pleurotus ostreatus (strain ALPO) and Pleurotus eryngii (strain AL142PE) as potential biological limiters. MX95, ALPO and AL142PE were screened under laboratory studies against Phytophthora nicotianae (PN), Fusarium oxysporum f. sp. radicis-lycopersici (FORL), Fusarium oxysporum f. sp. lycopersici (FOL), Fusarium solani (FS), Sclerotinia minor (SM), Sclerotinia sclerotiorum (SS), Athelia (Sclerotium) rolfsii (AR) and Verticillium dahliae (VD). The radial growth inhibition and the over-growth of potential antagonists on the target organisms were used to assess the interactions in the in vitro dual culture plate assay. The antagonistic ability of each challenge isolate was evaluated by calculating an index of the antagonism (AI) based on the interaction type in the dual cultures. MX95, reducing the growth of SS (20%) and FS (40%), displayed deadlock at mycelial contact against FOL and FORL, deadlock at distance versus VD and completely over-grew PN and SM. ALPO reduced (43 to 88%) the mycelial growth of tested pathogens except FORL and replaced PN and VD. AL142PE reducing (53 to 67%) SS, VD, FS and FOL mycelial growth and completely over-grew PN. AR showed combative ability against all the experienced biological limiters. Based on the results of the AI values, MX95 (AI = 16.5) was considered an active antagonist, while ALPO (AI = 11.5) and AL142PE (AI = 12.0) were moderately active antagonists. Strains MX95, ALPO and AL142PE were suitable as environment-friendly potential biocontrol agents to manage some of the main soil-borne agents of foot, root, soft rot and wilt diseases. These results are the first step in the assessment of the potential capacity of these organisms as biological limiters. Nevertheless, additional experiments should be performed for the translation to the field conditions in plant protection against soil-borne plant pathogens. In particular, the optimisation of dose and application time validation should be performed for a solid conclusion about the competitive ability of MX95, ALPO and AL142PE and the usefulness of potential biological limiters.
Agricultural Innovation and Sustainable Development
Authors: Blakeney Michael
Published: February 2022

Global agriculture is confronted by a number of substantial challenges, with some of them being existential [...]
Sustainable Remedy Waste to Generate SiO2 Functionalized on Graphene Oxide for Removal of U(VI) Ions
Authors: Hassanin Mohamed A. , Negm Sameh H. , Youssef Mohamed A. , Sakr Ahmed K. , Mira Hamed I. , Mohammaden Tarek F. , Al-Otaibi Jamelah S. , Hanfi Mohamed Y. , Sayyed M. I. , Cheira Mohamed F.
Published: February 2022

The Hummer process is applied to generate graphene oxide from carbon stocks’ discharged Zn-C batteries waste. SiO2 is produced from rice husks through the wet process. Subsequently, SiO2 reacted with graphene oxide to form silica/graphene oxide (SiO2/GO) as a sorbent material. XRD, BET, SEM, EDX, and FTIR were employed to characterize SiO2/GO. Factors affecting U(VI) sorption on SiO2/GO, including pH, sorption time, a dosage of SiO2/GO, U(VI) ions’ concentration, and temperature, were considered. The experimental data consequences indicated that the uptake capacity of SiO2/GO towards U(VI) is 145.0 mg/g at a pH value of 4.0. The kinetic calculations match the pseudo second-order model quite well. Moreover, the sorption isotherm is consistent with the Langmuir model. The sorption procedures occur spontaneously and randomly, as well as exothermically. Moreover, SiO2/GO has essentially regenerated with a 0.8 M H2SO4 and 1:50 S:L phase ratio after 60 min of agitation time. Lastly, the sorption and elution were employed in seven cycles to check the persistent usage of SiO2/GO.
Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring
Authors: Seo SungChul , Min Choongki , Preston Madeline , Han Sanghoon , Choi Sung-Hyuk , Kang So Young , Kim Dohyeong
Published: February 2022

Despite ample evidence that high levels of particulate matter (PM) are associated with increased emergency visits related to respiratory diseases, little has been understood about how prediction processes could be improved by incorporating real-time data from multipoint monitoring stations. While previous studies use traditional statistical models, this study explored the feasibility of deep learning algorithms to improve the accuracy of predicting daily emergency hospital visits by tracking their spatiotemporal association with PM concentrations. We compared the predictive accuracy of the models based on PM datasets collected between 1 December 2019 and 31 December 2021 from a single but more accurate air monitoring station in each district (Air Korea) and multiple but less accurate monitoring sites (Korea Testing & Research Institute; KTR) within Guro District in Seoul, South Korea. We used MLP (multilayer perceptron) to integrate PM data from multiple locations and then LSTM (long short-term memory) models to incorporate the intrinsic temporal PM trends into the learning process. The results reveal evidence that predictive accuracy is improved from 1.67 to 0.79 in RMSE when spatial variations of air pollutants from multi-point stations are incorporated in the algorithm as a 9-day time window. The findings suggest guidelines on how environmental and health policymakers can arrange limited resources for emergency care and design ambient air monitoring and prevention strategies.
Elite Sport and Sustainable Psychological Well-Being
Authors: Kremžar Jovanović Borjana , Smrdu Maja , Holnthaner Rok , Kajtna Tanja
Published: February 2022

There is conflicting evidence that sport plays a protective role in the development of psychopathological disorders and contributes to the sustainability of mental health. The aim of the present study was to determine the prevalence of mental disorders among elite Slovenian athletes. We were interested in the prevalence of depression, anxiety, suicide risk, substance abuse, and eating disorders among athletes of both sexes and among athletes of individual and team sports aged 18 years or older. A total of 97 elite athletes participated in the study. We used PAI and EDI-3 questionnaires, and the study also included a control group of 90 non-athletes, matched in gender and age to the target group of elite athletes. Testing was conducted in 2020 and 2021. The comparison between elite athletes and the normative group showed a similar expression of depressive and anxious symptomatology. The athletes have a more pronounced drive for thinness and are more expansive, self-confident, and confident of their abilities compared to the control group. A total of 14% of the athletes show self-confidence to the point of self-grandiosity. Male athletes are more likely to use alcohol and other psychoactive substances and are also more impulsive and risk-taking, while female athletes are at higher risk of developing eating disorders. The study suggests that the prevalence of mental disorders in elite athletes is as high as in the general population. There is an urgent need to sustainably ensure the psychological well-being of athletes.
Effect Analysis of Online and Offline Cognitive Internships Based on the Background of Engineering Education Accreditation
Authors: Zhang Xumei , Liu Ruyuan , Yan Wei , Wang Yan , Jiang Zhigang , Feng Zhaohui
Published: February 2022

The Engineering Education Accreditation (EEA) has been adopted in colleges and universities worldwide. As a foundational component of practical teaching, cognitive internship is a focus of EEA, with the offline mode as the common teaching method, while the online mode has been increasingly used in recent years. To meet the requirements of EEA, the effects of the online and offline cognitive internships need to be analyzed and compared. Focusing on the demand, this paper proposes a comprehensive method combining the Goal Achievement Degree (GAD) and CIPP model. The majors of the School of Automotive Transport and Engineering at Wuhan University of Science and Technology (WUST), which include the fields of vehicle engineering, traffic engineering, traffic transport, and logistics engineering, are taken as the example to conduct this research. The main findings of this paper are as follows: (1) Compared with online cognitive internship, offline internship students have a stronger ability to enhance theoretical knowledge and perceive the environment. (2) Utilizing such an online teaching method could be helpful for cultivating students’ analytical skills. (3) From the students’ perspective, the lack of funding for teaching cognitive internships, both online and offline, is the most serious problem. (4) Students perceive that the online cognitive internship helps them to understand knowledge in class and determine career planning, but it does not provide the same effect on those as the offline cognitive internship.
Optimisation of the Magnetic Circuit of a Measuring Head for Diagnostics of Steel-Polyurethane Load-Carrying Belts Using Numerical Methods
Authors: Ruta Hubert , Krakowski Tomasz , Lonkwic Paweł
Published: February 2022

The paper describes the process of a prototype head optimisation for magnetic diagnostics of steel-polyurethane load-carrying belts. The prototype, validated on a number of cranes, was subject to an improvement and optimisation attempt using numerical analysis of magnetic field distribution in the magnetic circuit, tested load-carrying belt, and environment. The analysis was carried out in the ANSYS environment using PDS—Probabilistic Design System tools (DOE—Design of Experiment). Taking the dimensions of individual elements of the magnetic circuit, material densities, and magnetic material properties as the input data, the magnetic circuit was optimised with respect to metrological properties as well as mass and size criteria. Based on the analyses carried out and the results obtained, the head design was modernised, which involved changing the geometry of elements forming the magnetic circuit. Based on observations made during tests of the prototype version of the device performed on real objects, several improvements were also proposed, consisting of the replacement of selected components with elements printed in the FDM technology. The correctness of the performed numerical analyses was verified by comparing the measured and calculated values of the total magnetic field induction in the defined plane of the magnetic circuit. The prototype versions of heads before and after modernisation were subject to comparative tests. Under laboratory conditions, both versions of heads were used to diagnose the steel-polyurethane load-carrying belts with modelled damages. The obtained test results and their statistical characteristics were analysed in detail.
Defining Firm-Level Resource Integration Effectiveness from the Perspective of Service-Dominant Logic: A Critical Factor Contributing to the Sustainability of a Firm’s Competitive Advantage and the Ecosystem It Operates
Authors: Jayasinghe Shan , Johnson Lester , Hewege Chandana , Perera Chamila
Published: February 2022

When a firm engages in repeated resource integration efforts, it develops an attribute called firm-level resource integration effectiveness (FL-RIE) over time. Due to its meta-theoretical nature, the use of this concept is limited. Thus, to make the formation of mid-range theories easier, through a systematic literature review, this study defines FL-RIE as the degree to which a firm’s resource integrating activities are successful in co-creating value, creating new resources and evaluating emerging properties of resource integration efforts. According to the definition, FL-RIE can be used to calibrate a firm’s resource integration capability in two instances. First, insights generated by FL-RIE can increase the frequency of value cocreation and new resource creation by a firm. Since increasing the frequency of value cocreation and new resource creation can ensure long-term business sustainability, FL-RIE can contribute to business sustainability. Second, insights generated by FL-RIE can improve the capability of evaluating emerging properties of resource integration efforts. Since the evaluation of emerging properties can generate feedback that can enhance resource integration capability, which can be considered a dynamic capability, FL-RIE can contribute to a firm’s sustained competitive advantage. Furthermore, since the same feedback can enhance the well-being of the other actors engaged with the firm and ensure the continuation of the ecosystem that the firm operates, FL-RIE can contribute to the sustainability of the firm and the ecosystem. The findings of this study can be used to develop a conceptual framework and a measurement scale for FL-RIE, and form several hypotheses related to strategic management and sustainability.
Spatial-Temporal Evolution of Total Factor Productivity in Logistics Industry of the Yangtze River Economic Belt, China
Authors: Mao Yu , Li Yonglin , Xu Deyi , Wu Yaqi , Cheng Jinhua
Published: February 2022

The logistics industry plays a great role in the sustainable economic development of the Yangtze River Economic Belt (YREB). This paper measures the total factor productivity (TFP) of the logistics industry by using the DEA-Malmquist index method and analyzes its spatial-temporal evolution characteristics based on panel data of 11 provinces and cities in the YREB in 2003–2017. Lastly, a spatial autocorrelation analysis was conducted in conjunction with the exploratory spatial data analysis (ESDA) model. The results show that the overall development of the logistics industry has been relatively good, with an inverted “N” shape trend over the years. Technological progress is the main reason for the growth of TFP. From a regional perspective, it shows a spatial distribution pattern of high in the east and low in the west, with an overall upward trend of TFP levels. The spatial correlation between the TFP levels of logistics in each province and city is gradually increasing, but coordinated development between regions is still limited. Finally, according to the conclusions, policy recommendations are proposed to accelerate the coordinated development of regional logistics and the innovative development of the modern logistics industry.