bluuebunny/crossref_metadata_2025 huggingface dataset:
A Concurrent Framework for Constrained Inverse Kinematics of Minimally Invasive Surgical Robots
Authors: Colan Jacinto , Davila Ana , Fozilov Khusniddin , Hasegawa Yasuhisa
Published: March 2023

Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot’s motion and the accuracy of its movements. In particular, the inverse kinematics (IK) problem is critical for robot-assisted minimally invasive surgery (RMIS), where satisfying the remote center of motion (RCM) constraint is essential to prevent tissue damage at the incision point. Several IK strategies have been proposed for RMIS, including classical inverse Jacobian IK and optimization-based approaches. However, these methods have limitations and perform differently depending on the kinematic configuration. To address these challenges, we propose a novel concurrent IK framework that combines the strengths of both approaches and explicitly incorporates RCM constraints and joint limits into the optimization process. In this paper, we present the design and implementation of concurrent inverse kinematics solvers, as well as experimental validation in both simulation and real-world scenarios. Concurrent IK solvers outperform single-method solvers, achieving a 100% solve rate and reducing the IK solving time by up to 85% for an endoscope positioning task and 37% for a tool pose control task. In particular, the combination of an iterative inverse Jacobian method with a hierarchical quadratic programming method showed the highest average solve rate and lowest computation time in real-world experiments. Our results demonstrate that concurrent IK solving provides a novel and effective solution to the constrained IK problem in RMIS applications.
An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection
Authors: Abu Bakar Rana , Huang Xin , Javed Muhammad Saqib , Hussain Shafiq , Majeed Muhammad Faran
Published: March 2023

Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.
Autonomous Planning of Discontinuous Terrain-Dependent Crawling for Space Dobby Robots
Authors: Jiang Jiabo , Wei Cheng , Yu Yunfeng , Sun Shengxin
Published: March 2023

Complex space missions require more space robotic extravehicular operations required to crawl on spacecraft surfaces with discontinuous features at the graspable point, greatly increasing the difficulty of space robot motion manipulation. Therefore, this paper proposes an autonomous planning method for space dobby robots based on dynamic potential fields. This method can realize the autonomous crawling of space dobby robots in discontinuous environments while considering the task objectives and the self-collision problem of robotic arms when crawling. In this method, a hybrid event–time trigger with event triggering as the main trigger is proposed by combining the working characteristics of space dobby robots and improving the gait timing trigger; the dynamic potential field function is designed to adjust the space robot robotic arm grasping point adaptively according to the space robot state. Simulation results verify the effectiveness of the proposed autonomous planning method.
Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking
Authors: Hasanujjaman Muhammad , Chowdhury Mostafa Zaman , Jang Yeong Min
Published: March 2023

Complete autonomous systems such as self-driving cars to ensure the high reliability and safety of humans need the most efficient combination of four-dimensional (4D) detection, exact localization, and artificial intelligent (AI) networking to establish a fully automated smart transportation system. At present, multiple integrated sensors such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), and car cameras are frequently used for object detection and localization in the conventional autonomous transportation system. Moreover, the global positioning system (GPS) is used for the positioning of autonomous vehicles (AV). These individual systems’ detection, localization, and positioning efficiency are insufficient for AV systems. In addition, they do not have any reliable networking system for self-driving cars carrying us and goods on the road. Although the sensor fusion technology of car sensors came up with good efficiency for detection and location, the proposed convolutional neural networking approach will assist to achieve a higher accuracy of 4D detection, precise localization, and real-time positioning. Moreover, this work will establish a strong AI network for AV far monitoring and data transmission systems. The proposed networking system efficiency remains the same on under-sky highways as well in various tunnel roads where GPS does not work properly. For the first time, modified traffic surveillance cameras have been exploited in this conceptual paper as an external image source for AV and anchor sensing nodes to complete AI networking transportation systems. This work approaches a model that solves AVs’ fundamental detection, localization, positioning, and networking challenges with advanced image processing, sensor fusion, feathers matching, and AI networking technology. This paper also provides an experienced AI driver concept for a smart transportation system with deep learning technology.
Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement
Authors: Wan Shuting , Dong Fan , Zhang Xiong , Wu Wenbo , Li Jialu
Published: March 2023

A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed. First, the parallel Mel filter is used to reduce the dimension of the fault voiceprint signal to obtain the Mel time spectrum. Then, the Mixup data enhancement algorithm is used to reorganize the generated small number of samples, effectively expanding the number of samples. Finally, CNN is used to classify and identify the transformer fault types. The diagnosis accuracy of this method for a typical unbalanced fault of a power transformer can reach 99%, which is superior to other similar algorithms. The results show that this method can effectively improve the generalization ability of the model and has good classification performance.
Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors
Authors: Thangavel Kathiravan , Spiller Dario , Sabatini Roberto , Amici Stefania , Longepe Nicolas , Servidia Pablo , Marzocca Pier , Fayek Haytham , Ansalone Luigi
Published: March 2023

Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.
Time-Series Representation Learning in Topology Prediction for Passive Optical Network of Telecom Operators
Authors: Zhao Haoran , Fang Yuchen , Zhao Yuxiang , Tian Zheng , Zhang Weinan , Feng Xidong , Yu Li , Li Wei , Fan Hulei , Mu Tiema
Published: March 2023

The passive optical network (PON) is widely used in optical fiber communication thanks to its low cost and low resource consumption. However, the passiveness brings about a critical problem that it requires manual work to identify the topology structure, which is costly and prone to bringing noise to the topology logs. In this paper, we provide a base solution firstly introducing neural networks for such problems, and based on that solution we propose a complete methodology (PT-Predictor) for predicting PON topology through representation learning on its optical power data. Specifically, we design useful model ensembles (GCE-Scorer) to extract the features of optical power with noise-tolerant training techniques integrated. We further implement a data-based aggregation algorithm (MaxMeanVoter) and a novel Transformer-based voter (TransVoter) to predict the topology. Compared with previous model-free methods, PT-Predictor is able to improve prediction accuracy by 23.1% in scenarios where data provided by telecom operators is sufficient, and by 14.8% in scenarios where data is temporarily insufficient. Besides, we identify a class of scenarios where PON topology does not follow a strict tree structure, and thus topology prediction cannot be effectively performed by relying on optical power data alone, which will be studied in our future work.
Wearable Activity Trackers Objectively Measure Incidental Physical Activity in Older Adults Undergoing Aortic Valve Replacement
Authors: Straiton Nicola , Hollings Matthew , Gullick Janice , Gallagher Robyn
Published: March 2023

Background: For older adults with severe aortic stenosis (AS) undergoing aortic valve replacement (AVR), recovery of physical function is important, yet few studies objectively measure it in real-world environments. This exploratory study explored the acceptability and feasibility of using wearable trackers to measure incidental physical activity (PA) in AS patients before and after AVR. Methods: Fifteen adults with severe AS wore an activity tracker at baseline, and ten at one month follow-up. Functional capacity (six-minute walk test, 6MWT) and HRQoL (SF 12) were also assessed. Results: At baseline, AS participants (n = 15, 53.3% female, mean age 82.3 ± 7.0 years) wore the tracker for four consecutive days more than 85% of the total prescribed time, this improved at follow-up. Before AVR, participants demonstrated a wide range of incidental PA (step count median 3437 per day), and functional capacity (6MWT median 272 m). Post-AVR, participants with the lowest incidental PA, functional capacity, and HRQoL at baseline had the greatest improvements within each measure; however, improvements in one measure did not translate to improvements in another. Conclusion: The majority of older AS participants wore the activity trackers for the required time period before and after AVR, and the data attained were useful for understanding AS patients’ physical function.
A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area
Authors: Zheng Qicun , Yue Haixia , Liu Dacheng , Jia Xiaoxue
Published: March 2023

This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit. The SMA is solved by means of a new method composed of three successive phases, namely, grid space construction, candidate strip generation and strip selection. First, the grid space construction is proposed to discretize the irregular area into a set of points in a specific plane rectangular coordinate system and calculate the total profit of a solution of SMA. Then, the candidate strip generation is designed to produce numerous candidate strips based on the grid space of the first phase. At last, in the strip selection, the optimal schedule for all the SAR satellites is developed based on the result of the candidate strip generation. In addition, this paper proposes a normalized grid space construction algorithm, a candidate strip generation algorithm and a tabu search algorithm with variable neighborhoods for the three successive phases, respectively. To verify the effectiveness of the proposed method in this paper, we perform simulation experiments on several scenarios and compare our method with the other seven methods. Compared to the best of the other seven methods, our proposed method can improve profit by 6.38% using the same resources.
Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch
Authors: Tan Tan-Hsu , Shih Jyun-Yu , Liu Shing-Hong , Alkhaleefah Mohammad , Chang Yang-Lang , Gochoo Munkhjargal
Published: March 2023

Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people’s activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.
Real-Time Fire Smoke Detection Method Combining a Self-Attention Mechanism and Radial Multi-Scale Feature Connection
Authors: Jin Chuan , Zheng Anqi , Wu Zhaoying , Tong Changqing
Published: March 2023

Fire remains a pressing issue that requires urgent attention. Due to its uncontrollable and unpredictable nature, it can easily trigger chain reactions and increase the difficulty of extinguishing, posing a significant threat to people’s lives and property. The effectiveness of traditional photoelectric- or ionization-based detectors is inhibited when detecting fire smoke due to the variable shape, characteristics, and scale of the detected objects and the small size of the fire source in the early stages. Additionally, the uneven distribution of fire and smoke and the complexity and variety of the surroundings in which they occur contribute to inconspicuous pixel-level-based feature information, making identification difficult. We propose a real-time fire smoke detection algorithm based on multi-scale feature information and an attention mechanism. Firstly, the feature information layers extracted from the network are fused into a radial connection to enhance the semantic and location information of the features. Secondly, to address the challenge of recognizing harsh fire sources, we designed a permutation self-attention mechanism to concentrate on features in channel and spatial directions to gather contextual information as accurately as possible. Thirdly, we constructed a new feature extraction module to increase the detection efficiency of the network while retaining feature information. Finally, we propose a cross-grid sample matching approach and a weighted decay loss function to handle the issue of imbalanced samples. Our model achieves the best detection results compared to standard detection methods using a handcrafted fire smoke detection dataset, with APval reaching 62.5%, APSval reaching 58.5%, and FPS reaching 113.6.
Separation of Metal and Cathode Materials from Waste Lithium Iron Phosphate Battery by Electrostatic Process
Authors: Zhu Huabing , Bai Yuxuan , Zu Lei , Bi Haijun , Wen Jian
Published: March 2023

The improper disposal of retired lithium batteries will cause environmental pollution and a waste of resources. In this study, a waste lithium iron phosphate battery was used as a raw material, and cathode and metal materials in the battery were separated and recovered by mechanical crushing and electrostatic separation technology. The effects on material electrostatic separation of separation parameters such as the crushing particle size, the voltage of the static electrode, and the rotating speed of the grounding rotor were all studied combined with trajectory simulation and separation experiments. The results show that the crushing particle size of the material has the most significant impact on the separation effect, and the material separation effect primarily occurs in the range of 0.2–2.0 mm particle sizes. When the voltage of the static electrode is 30 kV, the rotating speed of the grounded rotor is 60 r/min, and the particle size is 0.4–0.8 mm, and the recovery rates for aluminum, copper, and lithium iron phosphate reach 93.2%, 91.1%, and 97.1%, respectively. In the recovery process for waste lithium batteries, using electrostatic separation technology instead of high-temperature roasting or chemical leaching can effectively improve the separation efficiency and reduce secondary pollution.
Preliminary Approach for the Development of Sustainable University Campuses: A Case Study Based on the Mitigation of Greenhouse Gas Emissions
Authors: Araújo Ivo , Nunes Leonel J. R. , Curado António
Published: March 2023

University campuses consume a significant amount of energy. Given the high volume of people who commute to and from campuses, the resources consumed, such as water and energy, and the amount of waste that must be managed, they can be compared to small towns. To address this issue, university managers and decision-makers have implemented various technical measures to improve water and energy efficiency and waste management. These measures aim to increase campus sustainability and enhance the well-being of the academic community. One popular measure is the installation of autonomous energy production systems, such as photovoltaic (PV) systems, which replace external energy sources and reduce GHG emissions. For example, a PV system installed on a university campus has been found to supply 19% of the campus’s electricity needs and replace 21 tCO2·yr−1. However, adopting organizational measures to manage the use of produced energy and increasing school community’s environmental awareness about energy efficiency is crucial in order to change behavior and improve campus sustainability.
A Comprehensive Evaluation of Walkability in Historical Cities: The Case of Xi’an and Kyoto
Authors: Yuan Kun , Abe Hirokazu , Otsuka Noriko , Yasufuku Kensuke , Takahashi Akira
Published: March 2023

Walkability is an important indicator of sustainable urban development, especially in fragmented historical blocks affected by modern development efforts. Xi’an, China, and Kyoto, Japan, which are among the oldest cities in Asia, have similar historical backgrounds and a grid-based street network that differs from European historical cities. They also have their own distinctive development characteristics. This study uses a novel quantitative approach to identify areas for improvement in the historical built environment of the two cities that, as part of an overall effort to create a pedestrian-friendly urban center, will promote and facilitate walking activities. The proposed method combines macro- and micro-aspects to identify factors that can either improve or hinder the walking environment. It was found that to ensure a walkable environment in its historical blocks, Xi’an will need to consider the potential negative impact on walkability as it pursues increased modernization. The built environment and the infrastructure of its streets need to be improved, and its historical streetscape and vegetation characteristics need to be maintained. As for Kyoto, to ensure a favorable walking environment, the focus should be on improving the vitality of its streets.
Analyzing Critical Success Factors of Lean 4.0 Implementation in Small and Medium Enterprises for Sustainable Manufacturing Supply Chain for Industry 4.0 Using PLS-SEM
Authors: Qureshi Karishma M. , Mewada Bhavesh G. , Buniya Mohanad Kamil , Qureshi Mohamed Rafik Noor Mohamed
Published: March 2023

Lean 4.0 (L4.0) is a transformed form of traditional lean to suit Industry 4.0’s (I4.0) requirements. The L4.0 has a great deal of potential to match the I4.0’s challenges in terms of speed, dynamics, and efficacy once it has been digitalized. The study aims to identify and model the L4.0 CSFs for successful lean implementations in SMEs to suit I4.0 needs. The thorough analysis of the literature led to the identification of the L4.0 CSFs. The expert panel from SMEs was involved in selecting the relevant 22 L4.0 CSFs that suited the needs of manufacturing SMEs. Based on the feedback of the expert panel, a questionnaire survey was carried out. Further, collected responses were analyzed using an exploratory factor analysis (EFA). The EFA results identified four major groups: ‘worker-enabled technologies’, ‘IT-enabled technologies’, ‘management’, and ‘L4.0’ related CSFs. Furthermore, the study uses partial least square structural equation modeling (PLS-SEM) to simulate the L4.0 CSFs and identify the influence of each CSF toward successful lean implementation. The PLS-SEM results confirm that the lean CSFs have positive effects on successful lean implementation. The present research contributes to enhancing the knowledge and L4.0 practices of manufacturing SMEs. The results show that successful implementation will promote ‘productivity improvements (PI)’, ‘waste reduction (WR)’, ‘competitive advantage (CA)’, and ‘sustainable manufacturing system (SMS)’.
An Experimental Comparison of the Performance of Various Evacuated Tube Solar Collector Designs

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Authors: Said Sana , Mellouli Sofiene , Alqahtani Talal , Algarni Salem , Ajjel Ridha , Ghachem Kaouther , Kolsi Lioua
Published: March 2023

An experimental study was carried out to assess the thermal performance of a few evacuated tube solar collectors (ETSCs) for water heating. The thermal performance of two kinds of ETSC (heat pipe ETSC and direct-flow ETSC) was investigated using an indoor experimental apparatus in lab testing conditions with a solar simulator. Several experimental tests were carried out for the heat pipe ETSC system under different operating conditions, such as the solar intensity (300, 500, and 1000 W/m2) and the tilt angle (0°, 30° and 90°) of the ETSC and the water flow rate (0.6, 1.2, and 2.4 LPM). Moreover, four configurations of direct-flow ETSC (U tube, double U tubes, coaxial tubes, and helical tube) were examined and compared to the conventional heat pipe ETSC. The results of the experiment proved that the ETSC system presents a great performance at higher solar irradiance and lower water flow rates, and the experiments indicated that with a 30° tilt angle, the ETSC reaches the maximum thermal efficiency of 36%. Furthermore, compared to the conventional heat pipe ETSC and the other proposed configurations of direct-flow ETSCs, the helical tube-based ETSC has a better thermal efficiency, 69%, and can be considered a greater potential heat exchanger that can be integrated in ETSCs. To the best of our knowledge, it is the first time this helical tube type been integrated into the ETSC and tested under these conditions.

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Optimal Design of an Eco-Friendly Transportation Network under Uncertain Parameters
Authors: Alshamrani Ahmad , Sengupta Dipanjana , Das Amrit , Bera Uttam Kumar , Hezam Ibrahim M. , Nayeem Moddassir Khan , Aqlan Faisal
Published: March 2023

The choice of attributes in the multi-attribute decision-making process becomes frequently uncertain because of the diverse degree of preference for alternatives. These are assessed utilizing human decisions and linguistic terms that can be utilized for a more adaptable and delicate assessment. The present article illustrates a multi-attribute decision-making (MADM) process, named the exponential technique for order of preference by similarity to an ideal solution (Exp-TOPSIS), considering the selection of attributes with existing uncertainty. Another three notable multi-attribute decision-making (MADM) processes, termed as multi-attribute utility theory (MAUT), elimination and choice expressing reality method (ELECTRE), and the technique for order of preference by similarity to an ideal solution (TOPSIS) are utilized to present a comparison with the proposed methodology by proposing a mathematical model for a solid transportation problem intending to minimize carbon emissions under an uncertain environment. The uncertainty theory, which depends on human conviction degree, is utilized to define the uncertain parameters of the model related to the problem. Applying the proposed one and the other three multi-attribute decision-making processes, the best emission factors are observed to mitigate the carbon emissions from the transport sectors. In this context, the proposed method has some advantages over the existing techniques in selecting the emission factors. All four MADM approaches with different weights have been tested to choose the best five attributes among nine options to be utilized in the mathematical model to minimize the total carbon emission ejection from transportation. In every case, the obtained result states that the proposed Exp-TOPSIS gives the minimum carbon emissions in a range of 2100–2500 units. LINGO 13.0 solver is used to address the deterministic solid transportation problem, and finally, this study presents some investigations on the selection of carbon emission factors and future utilization of the proposed multi-attribute decision-making process.
Role of Chironomus plumosus (Diptera, Chironomidae) Population in the Central Zone of the Shallow Lake Trasimeno (Italy)
Authors: Pallottini Matteo , Pagliarini Sarah , Catasti Marianna , La Porta Gianandrea , Selvaggi Roberta , Gaino Elda , Spacone Leonardo , Di Giulio Alessandro Maria , Ali Arshad , Goretti Enzo
Published: March 2023

Adult swarms of non-biting pestiferous midges (Chironomidae: Diptera), primarily Chironomus plumosus, prevail over and around Lake Trasimeno (Italy) during the summer season. The current field survey (2018–2021, 33 sampling occasions) was carried out in the central area of the lake. It revealed a macrobenthic community consisting of Chironomidae (40.72%) and Oligochaeta (59.23%). Chironomus plumosus was the dominant chironomid species (98.84%). A previous survey (2000–2002, 11 sampling occasions) had highlighted a similar community, although C. plumosus had a lower density, comprising a maximum of 295.0 ind. m−2. This density was five times lower than the maximum densities of 2018–2021. A survey (2018–2021, 58 sampling occasions) conducted in the littoral zone revealed much greater chironomid biodiversity, with C. plumosus abundance of only 24.35% among all chironomids. This species showed an average density (88.1 ind. m−2) five times lower than its density (467.9 ind. m−2) in the central zone (2018–2021). Therefore, the central area of the lake, constituting about 90% of its total surface area, is the main region for the origin of C. plumosus adults and, consequently, during the summer months, it is the primary source of the annoying swarms that affect residents and tourists of the lake vicinity.
Optimization of Operating Parameters for Two-Phase Anaerobic Digestion Treating Slaughterhouse Wastewater for Biogas Production: Focus on Hydrolytic–Acidogenic Phase
Authors: Tsegaye Dejene , Khan Mohammed Mazharuddin , Leta Seyoum
Published: March 2023

In a two-phase anaerobic digestion process, enhanced biogas production and organic pollutant removal depend on the stability and performance of the hydrolytic–acidogenic and methanogenic phases. Additionally, the hydrolytic–acidogenic phase is a rate-limiting step, which calls for the further optimization of operating parameters. The objective of this study was to optimize the operating parameters of the hydrolytic–acidogenic reactor (HR) in the two-phase anaerobic digestion treating slaughterhouse wastewater. The experiment was carried using bench-scale sequential bioreactors. The hydrolytic–acidogenic reactor operating parameters were optimized for six different hydraulic retention times (HRTs) (6–1 day) and organic loading rates (OLRs) (894.41 ± 32.56–5366.43 ± 83.80 mg COD/L*day). The degree of hydrolysis and acidification were mainly influenced by lower HRT (higher OLR), and the highest values of hydrolysis and acidification were 63.92% and 53.26% at an HRT of 3 days, respectively. The findings indicated that, at steady state, the concentrations of soluble chemical oxygen demand (SCOD) and total volatile fatty acids (TVFAs) decrease as HRT decreases and OLR increases from HRTs of 3 to 1 day and 894.41–1788.81 mg COD/L*day, respectively, and increase as the HRT decreases from 6 to 4 days. The concentration of NH4+-N ranges from 278.67 to 369.46 mg/L, which is not in the range that disturbs the performance and stability of the hydrolytic acidogenic reactor. It was concluded that an HRT of 3 days and an ORL of 1788.81 mg COD/L*day were selected as optimal operating conditions for the high performance and stability of the two-phase anaerobic digestion of slaughterhouse wastewater in the hydrolytic–acidogenic reactor at a mesophilic temperature. The findings of this study can be applicable for other agro-process industry wastewater types with similar characteristics and biowaste for value addition and sustainable biowaste management and safe discharge.
Spatial and Temporal Dynamics of Drought and Waterlogging in Karst Mountains in Southwest China
Authors: Dai Li , Zhao Yuhan , Yin Changying , Mao Chunyan , Zhang Ping , Zhou Fang , Yu Xianyun
Published: March 2023

Under the synergetic effect of land use and climate change, natural disasters occur frequently in the karst region of southwest China. This study used the daily precipitation data from 33 meteorological stations across 61 years (1960–2020), utilized the MK test and the Z index to calculate the levels of drought and waterlogging (DW) at multiple times (month and year) and spatial (province, sub-divisions, station) scales, and investigated the spatiotemporal patterns and their associated factors in DW in the karst mountains of Guizhou, southwest China. The results showed that: (1) DW occurred frequently and increasingly during the study period in Guizhou, with seven mutations of annual DW. (2) There were more droughts (especially heavy droughts) based on annual data, but more waterlogging (especially heavy waterlogging) based on monthly data. Drought occurred most frequently in summer, while waterlogging was most frequent in spring, followed by winter. (3) The normalized difference drought and waterlogging index (NDDWI) was created in this study to exhibit combined DW phenomena, which could be improved in the future to better present the compound hazards. The spatiotemporal patterns of DW in Guizhou were complicated and associated with terrain, geology, climate change, vegetation, land use, etc.
Novel COVID-19 Outbreak and Global Uncertainty in the Top-10 Affected Countries: Evidence from Wavelet Coherence Approach
Authors: Rehman Mohd Ziaur , Khan Shabeer , Abbas Ghulam , Alhashim Mohammed
Published: March 2023

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability.
Research on the Influence of E-Learning Quality on the Intention to Continue E-Learning: Evidence from SEM and fsQCA
Authors: Zheng Hao , Qian Yu , Wang Zongran , Wu Yonghe
Published: March 2023

This study explores how Chinese college students perceive the quality of e-learning and its impact on their adoption of online learning. The study develops an impact model of e-learning quality on continuous learning intention based on the stimulus-organism-response framework, technology acceptance model, and information system success model. The model is validated using a structural equation model (SEM) and fuzzy set qualitative comparative analysis (fsQCA) with data collected from 253 college students at a university in Eastern China. The SEM analysis shows that perceived ease of use and perceived usefulness positively influence e-learning continuance intention, while system quality and personalization have a positive impact on perceived ease of use, and learning community positively impacts perceived usefulness. The fsQCA analysis shows that college students’ willingness to continue e-learning is not solely dependent on a single factor of e-learning service quality but is also influenced by the interaction between various factors. Therefore, e-learning providers should take into account both external stimuli and internal perception factors when designing e-learning services. The findings of this study have practical implications for improving e-learning quality and enhancing the online learning experience. E-learning providers should consider the importance of system quality, personalization, and learning community in improving perceived ease of use and usefulness, which in turn can increase students’ intention to continue e-learning. The study also highlights the importance of considering the interaction between various factors that influence e-learning adoption rather than relying solely on individual factors.
Analysis of Landscape Patterns Changes and Driving Factors of the Guangdong Chaoan Fenghuangdancong Tea Cultural System in China
Authors: Guo Xuan , Min Qingwen
Published: March 2023

Guangdong Chaoan Fenghuangdancong Tea (GCFT) Cultural System is the second batch of China’s Nationally Important Agricultural Heritage Systems (China-NIAHS), identified by the Ministry of Agriculture and Rural Affairs in 2014 as having rich biodiversity, valuable knowledge of indigenous technology, and unique ecological and cultural landscape. Under the dual background of rapid urbanization and agricultural industry structure transformation, China-NIAHS-GCFT is facing the reality of structural changes in land use/cover and landscape patterns. Therefore, it is important to systematically portray land use/land cover (LULC) changes in China-NIAHS-GCFT sites and clarify the spatial pattern differences due to the impact of China-NIAHS-GCFT recognition on tea garden areas and the tea industry. This study was conducted in Chaozhou City, Guangdong Province, where GCFT is located, to compare and analyze the LULC characteristics of the core area of the heritage site (Chaoan, Chaozhou) and the control area (Raoping, Chaozhou) before and after recognition. We assessed the spatial variation in tea garden area and the intrinsic driving mechanisms of the change by integrating social factors, such as China-NIAHS-GCFT recognition, and natural factors, such as elevation, precipitation, and temperature. The results show that: (1) Around 2010, the change in LULC of the core and control areas progressed from slight changes to dramatic changes, mainly shifting from natural to anthropogenic landscapes. The decrease in the cropland and grassland and the increase in built-up land in the core area were obviously larger than those in the control area. (2) Before and after GCFT was recognized as China-NIAHS in 2014, the changing pattern of tea garden shifts from “basically stable and small growth” to a trend of “substantial expansion”. Specifically, the recognition brought about tea garden area expansion and tea industry development in the core area, especially Fenghuang. Meanwhile, a radiating effect extends to the control area, especially the townships adjacent to Fenghuang. (3) Similar natural climatic conditions of temperature and precipitation in the two regions provide a basic growing environment for tea trees; however, elevation was the key natural resource condition affecting the distribution of tea gardens. The elevation conditions of the core area are more suitable for growth of tea trees compared to the control area.
A Combined Evaluation Method of Corporate Social Responsibility Based on the Difference and Similarity: A Case Study of Transportation Industry in China
Authors: Lv Liang , Fan Lidong , Meng Bin , Abedin Mohammad Zoynul , Feng Haoyue
Published: March 2023

Currently, it is becoming more essential for a company to operate in an economically, socially and environmentally sustainable manner. This study aims to evaluate corporate social responsibility (CSR) in China’s transportation industry by applying indicator weighting approaches. This study evaluated 68 transportation companies with 82 associated CSR indicators. Firstly, this paper established an indicator system, including seven criteria layers that conform to the characteristics of the Chinese transportation industry. Secondly, by integrating the G1 method, standard deviation method and CRITIC (criteria importance though intercriteria correlation) approach, a combined weighting method, CWMDS (combined weighting method based on the combination of difference and similarity), was constructed, which embodies the two goals of “horizontal similarity and vertical difference”. The experimental results show that CWMDS achieves better results in many aspects. The empirical results show that the air transportation subindustry was the best in CSR performance while the high-speed transportation subindustry was the worst. Finally, this paper put forward some policy suggestions to promote China’s transportation enterprises to fulfill their social responsibility.
Effect of Water Level Reduction on the Littoral Zone in Terms of Its Efficiency in Lake Protection
Authors: Ławniczak-Malińska Agnieszka
Published: March 2023

Decreased water levels due to climate change cause many negative effects on lake ecosystems. The aim of this study was to (a) assess the effect of the reduction of water levels on nutrient availability in the sediment in the littoral zone; (b) evaluate the effect of changes in water level on biomass productivity and nutrient concentrations in the aboveground biomass of four emergent species: Phragmites australis (Cav.) Trin. ex Steud., Typha angustifolia L., Carex acutiformis L., Glyceria maxima (C. Hartm.) Holmb; and (c) assess the efficiency of the littoral zone in the reduction of nutrient pollution. The study hypothesis was that water level reduction has a positive effect on the plant biomass of high productive species. The study was carried out in the littoral zone of Tomickie Lake, situated in the western part of Poland. This lake is located in the protected area—the buffer zone of Wielkopolska National Park, and at the international level—Natura 2000. Six transects, perpendicular to the shoreline, were selected at two subzones—permanently and seasonally flooded. Analyses of nutrient concentrations in sediments and plant species were performed. The results show the higher productivity of reeds in the zone where water occurs seasonally at the site through the year, which reached 1193 g dry weight/m2. The decline of the water level may lead to the increased growth of highly productive species as emergent vegetation with a broad ecological scale in terms of nutrient concentrations and changes of water depth, i.e., Phragmites australis (Cav.) Trin. ex Steud. Species that prefer growth in the deeper part of the lake will be characterized by lower productivity, despite the high availability of nutrients. Changes in the availability of nutrients may cause the intensification of lake overgrowth by very productive species, which may affect biodiversity, which is particularly high in protected areas.
Enzymatic-Induced Calcite Precipitation (EICP) Method for Improving Hydraulic Erosion Resistance of Surface Sand Layer: A Laboratory Investigation
Authors: Zomorodian Seyed Mohammad Ali , Nikbakht Sodabeh , Ghaffari Hamideh , O’Kelly Brendan C.
Published: March 2023

As a bio-inspired calcite precipitation method, bio-grouting via enzymatic-induced calcite precipitation (EICP) uses free urease enzyme to catalyze the urea hydrolysis reaction. This soil stabilization approach is relatively new and insufficiently investigated, especially for applications involving surface layer stabilization of sandy soil deposits for increasing hydraulic erosion resistance. This paper presents a laboratory investigation on the surface erosion resistance improvements for compacted medium-gradation quartz sand specimens mediated using 10 different EICP treatment protocols. They involved single- and two-cycle injections of the urease enzyme (activity of 2400 U/L) and 0.5, 0.75, or 1.0-M urea–CaCl2 cementation solution reagents. The urease enzyme was extracted from watermelon seeds. Erosion rates were determined for various hydraulic shear stresses applied using the erosion function apparatus. The spatial distribution and morphology of precipitated calcite within the pore-void spaces of the crustal sand layer were investigated with a scanning electron microscope. Compared to untreated sand, all 10 investigated EICP treatment protocols produced substantially improved erosion resistance, especially for the higher cementation solution concentration (1.0 M). Of these 10 EICP protocols, a single cycle of enzyme–1.0-M-cementation solutions injections was identified as the more pragmatic option for achieving near-optimum erosion resistance improvements. Highest and lowest amounts (18.8 and 5.0 wt%) of precipitated calcite corresponded to the best and worst performing EICP-treated specimens, although the calcite’s spatial distribution in treated specimens is another important factor.