bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Physicochemical and In Vivo Biological Properties of Polymeric Nanocapsules Based on Chitosan and Poly(N-vinyl pyrrolidone-alt-itaconic anhydride)
Authors: Dellali Kheira Zanoune , Dellali Mohammed , Raţă Delia Mihaela , Cadinoiu Anca Niculina , Atanase Leonard Ionut , Popa Marcel , Spataru Mihaela-Claudia , Solcan Carmen
Published: April 2022

Drug delivery is an important field of nanomedicine, and its aim is to deliver specific active substances to a precise site of action in order to produce a desired pharmacological effect. In the present study nanocapsules were obtained by a process of interfacial condensation between chitosan (dissolved in the aqueous phase) and poly(N-vinyl pyrrolidone-alt-itaconic anhydride), a highly reactive copolymer capable of easily opening the anhydride ring under the action of amine groups of chitosan. The formed amide bonds led to the formation of a hydrogel membrane. The morphology of the obtained nanocapsules, their behavior in aqueous solution of physiological pH, and their ability to encapsulate and release a model drug can be modulated by the parameters of the synthesis process, such as the molar ratio between functional groups of polymers and the ratio of the phases in which the polymers are solubilized. Although a priori both polymers are biocompatible, this paper reports the results of a very detailed in vivo study conducted on experimental animals which have received the obtained nanocapsules by three administration routes—intraperitoneal, subcutaneous, and oral. The organs taken from the animals’ kidney, liver, spleen, and lung and analyzed histologically demonstrated the ability of nanocapsules to stimulate the monocytic macrophage system without producing inflammatory changes. Moreover, their in vivo behavior has been shown to depend not only on the route of administration but also on the interaction with the cells of the organs with which they come into contact. The results clearly argue the biocompatibility of nanocapsules and hence the possibility of their safe use in biomedical applications.
Cultural Factors Influencing Mental Health Stigma: Perceptions of Mental Illness (POMI) in Pakistani Emerging Adults
Authors: Ahmad Salman Shaheen , Koncsol Stephen W.
Published: April 2022

Pakistan has a fast-growing, young, and highly religious population. Mental health literacy and care in Pakistan do not meet the population’s needs, and mental health stigma (MHS) is cited as the cause. Explanations for MHS across cultures include collectivism, and sociocultural-religious/spiritual beliefs and values surrounding mental illness and those who experience it. MHS interventions and campaigns that aim to improve help-seeking behaviors require insight into the emic perspectives of each target population. Although these perspectives have been elusive for Pakistanis, they are more available today due to growing interest in studying and improving Pakistani mental health. This cross-sectional study of 92 Pakistani emerging adults explored whether collectivism was associated with stigmatizing attitudes toward mental illness. This study also piloted the Perceptions of Mental Illness (POMI) questionnaire, a 44-item true/false survey customized to the Pakistani context, to assess how mental health knowledge, perceptions, exposure, and help-seeking preferences related to stigmatizing attitudes. Results indicated that the POMI provided unique insights into Pakistani beliefs and attitudes that relate to both stigmatizing attitudes and collectivism. With further development, the POMI may be used to guide the design of mental health awareness programs in Pakistan, ultimately helping to reduce MHS and increase help seeking when needed.
Relationship between Religiosity and Subjective Well-Being among Middle-Aged Korean Women: Focused on Roles of Existential Consciousness and Savoring Beliefs
Authors: Beak Eunjin , Chung Sung-Jin , Suh Kyung-Hyun
Published: April 2022

This study identified the relationship between religiosity and the subjective well-being of middle-aged Korean women, and examined a double mediating effect model of existential consciousness and savoring beliefs on this relationship. The participants of this study were 285 middle-aged Korean women, aged between 40–60 years. The PROCESS Macro 3.5 Model 6 was used to analyze the sequential double mediating effects. The results revealed that existential consciousness and savoring beliefs were positively correlated with the subjective well-being of middle-aged women, whereas their intrinsic religiosity was not significantly correlated with subjective well-being because it was positively correlated with negative emotions, as well as with life satisfaction and positive emotions. In a sequential double mediation model for the subjective well-being of middle-aged women, the direct effect of intrinsic religiosity on savoring beliefs was negatively significant after adjusting for indirect effects through existential consciousness. The sequential indirect effect of existential consciousness and savoring beliefs on the intrinsic religiosity and subjective well-being of middle-aged women was significant. Without these indirect effects, intrinsic religiosity negatively influenced middle-aged women’s subjective well-being in this model. These results suggest that existential consciousness plays an important role in the subjective well-being of middle-aged women.
Activated Carbons from Hydrothermal Carbonization and Chemical Activation of Olive Stones: Application in Sulfamethoxazole Adsorption
Authors: Diaz Elena , Sanchis Ines , Coronella Charles J. , Mohedano Angel F.
Published: April 2022

This work focuses on the production of activated carbons by hydrothermal carbonization of olive stones at 220 °C, followed by chemical activation with KOH, FeCl3 and H3PO4 of the hydrochar obtained. In addition, N-doped hydrochars were also obtained by performing the hydrothermal carbonization process with the addition of (NH4)2SO4. All hydrochars, N-doped and non-doped, showed low BET surface areas (4–18 m2 g−1). Activated hydrochars prepared using H3PO4 or KOH as activating agents presented BET surface areas of 1115 and 2122 m2 g−1, respectively, and those prepared from N-doped hydrochar showed BET surface area values between 1116 and 2048 m2 g−1 with an important contribution of mesoporosity (0.55–1.24 cm3 g−1). The preparation procedure also derived inactivated hydrochars with predominantly acidic or basic groups on their surface. The resulting materials were tested in the adsorption of sulfamethoxazole in water. The adsorption capacity depended on both the porous texture and the electrostatic interactions between the adsorbent and the adsorbate. The adsorption equilibrium data (20 °C) fitted fairly well to the Langmuir equation, and even better to the Freundlich equation, resulting in the non-doped hydrochar activated with the KOH as the best adsorbent.
LiDAR Filtering in 3D Object Detection Based on Improved RANSAC
Authors: Wang Bingxu , Lan Jinhui , Gao Jiangjiang
Published: April 2022

At present, the LiDAR ground filtering technology is very mature. There are fewer applications in 3D-object detection due to the limitations of filtering accuracy and efficiency. If the ground can be removed quickly and accurately, the 3D-object detection algorithm can detect objects more accurately and quickly. In order to meet the application requirements of 3D-object detection, inspired by Universal-RANSAC, we analyze the detailed steps of RANSAC and propose a precise and efficient RANSAC-based ground filtering method. The principle of GroupSAC is analyzed, and the sampled points are grouped by attributes to make it easier to sample the correct point. Based on this principle, we devise a method for limiting sampled points that is applicable to point clouds. We describe preemptive RANSAC in detail. Its breadth-first strategy is adopted to obtain the optimal plane without complex iterations. We use the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and the KITTI dataset for testing. Experiments show that our method has higher filtering accuracy and efficiency compared with the currently widely used methods. We explore the application of ground filtering methods in 3D-object detection, and the experimental results show that our method can improve the object detection accuracy without affecting the efficiency.
A Robot Pose Estimation Optimized Visual SLAM Algorithm Based on CO-HDC Instance Segmentation Network for Dynamic Scenes
Authors: Chen Jinjie , Xie Fei , Huang Lei , Yang Jiquan , Liu Xixiang , Shi Jianjun
Published: April 2022

In order to improve the accuracy of visual SLAM algorithms in a dynamic scene, instance segmentation is widely used to eliminate dynamic feature points. However, the existing segmentation technology has low accuracy, especially for the contour of the object, and the amount of calculation of instance segmentation is large, limiting the speed of visual SLAM based on instance segmentation. Therefore, this paper proposes a contour optimization hybrid dilated convolutional neural network (CO-HDC) algorithm, which can perform a lightweight calculation on the basis of improving the accuracy of contour segmentation. Firstly, a hybrid dilated convolutional neural network (HDC) is used to increase the receptive field, which is defined as the size of the region in the input that produces the feature. Secondly, the contour quality evaluation (CQE) algorithm is proposed to enhance the contour, retaining the highest quality contour and solving the problem of distinguishing dynamic feature points from static feature points at the contour. Finally, in order to match the mapping speed of visual SLAM, the Beetle Antennae Search Douglas–Peucker (BAS-DP) algorithm is proposed to lighten the contour extraction. The experimental results have demonstrated that the proposed visual SLAM based on the CO-HDC algorithm performs well in the field of pose estimation and map construction on the TUM dataset. Compared with ORB-SLAM2, the Root Mean Squared Error (Rmse) of the proposed method in absolute trajectory error is about 30 times smaller and is only 0.02 m.
Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model
Authors: Zhao Shijie , Zheng Wei , Li Zhaowei , Zhu Huizhong , Xu Aigong
Published: April 2022

This paper mainly studies the improvement of the efficiency and out-of-domain reliability of gravity matching navigation for underwater vehicles. To overcome the traversal low-efficiency problem of the traditional terrain contour matching (TERCOM) algorithm and improve the positioning reliability of its out-of-domain mismatches, a novel soft-margin local semicircular-domain re-searching model (SLSR) is proposed by integrating the soft-margin circular grid matching (SCGM) mechanism and the local semicircular grid re-matching (LSGR) mechanism. SCGM uses three times the inertial navigation cumulative error and adds the unit grid resolution as the soft margin boundary to generate the soft-margin circular domain, which contributes to reducing in-domain matching grid points and enhancing the matching efficiency of algorithms. Then the optimal matching position in this soft-margin circular domain is obtained by using the optimization principle of matching indices. LSGR is triggered when the optimal matching position of SCGM is located near the soft-margin circular-domain boundary. It employs this optimal matching point as the center and the unit inertial navigation error as the radius to recreate the semicircular local re-searching matched grid domain (termed as semicircular domain). Moreover, the optimal matching point in this semicircular domain is obtained by the matching index optimization principle, and then it is compared and updated to obtain the final best matching position of SLSR. The simulation results show that SCGM and LSGR of the proposed SLSR method can effectively improve the matching efficiency and out-of-domain matching reliability of underwater navigation, respectively. Under the same testing conditions for the tracking starting points from three gravity regions, the number of out-of-domain mismatches of SLSR, compared with TERCOM, are lower up to 92.68%, 90.24% and 98.62%, while the average matching accuracies are relatively improved by 88.37%, 85.48% and 83.66%, which verifies the validity and feasibility of the proposed SLSR model on improving the efficiency and out-of-domain reliability of underwater gravity matching navigation.
Multipath Error Modeling Methodology for GNSS Integrity Monitoring Using a Global Optimization Strategy
Authors: Fang Xinqi , Song Dan , Shi Chuang , Fan Lei , Hu Ziye
Published: April 2022

Valid multipath error model is the prerequisite for high-performance GNSS integrity monitoring. It is indispensable to civil aviation and other Safety-of-Life (SoL) users. The model must perfectly bound multipath error while preventing the constructed model from being too conservative. Nevertheless, no sound methodologies to meet both the requirements have been introduced in previous literatures, and subsequently, practices always require iterative manual trade-offs. To improve the efficiency of multipath modeling, we propose a new automatic multipath error modeling methodology. It quantifies the above requirements in the objective function of multiobjective genetic algorithm (GA) so that multipath modeling can be managed automatically. Moreover, through introducing a new model that is based on two inflation factors, conservatism of modeling results can be significantly reduced. Experiments based on a 4-month dataset of BDS-3 Medium Earth Orbit (MEO) satellites show that constructed multipath models effectively bound actual error in each elevation bin. In addition, the new model form with two inflation factors brings average CDF difference reduction of 67.4% at B1I and 50.6% at B3I, which means significantly mitigation in terms of conservatism.
Transport and Application Layer DDoS Attacks Detection to IoT Devices by Using Machine Learning and Deep Learning Models
Authors: Almaraz-Rivera Josue Genaro , Perez-Diaz Jesus Arturo , Cantoral-Ceballos Jose Antonio
Published: April 2022

From smart homes to industrial environments, the IoT is an ally to easing daily activities, where some of them are critical. More and more devices are connected to and through the Internet, which, given the large amount of different manufacturers, may lead to a lack of security standards. Denial of service attacks (DDoS, DoS) represent the most common and critical attack against and from these networks, and in the third quarter of 2021, there was an increase of 31% (compared to the same period of 2020) in the total number of advanced DDoS targeted attacks. This work uses the Bot-IoT dataset, addressing its class imbalance problem, to build a novel Intrusion Detection System based on Machine Learning and Deep Learning models. In order to evaluate how the records timestamps affect the predictions, we used three different feature sets for binary and multiclass classifications; this helped us avoid feature dependencies, as produced by the Argus flow data generator, whilst achieving an average accuracy >99%. Then, we conducted comprehensive experimentation, including time performance evaluation, matching and exceeding the results of the current state-of-the-art for identifying denial of service attacks, where the Decision Tree and Multi-layer Perceptron models were the best performing methods to identify DDoS and DoS attacks over IoT networks.
Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback
Authors: Ding Kangjia , Zhang Bochao , Ling Zongquan , Chen Jing , Guo Liquan , Xiong Daxi , Wang Jiping
Published: April 2022

Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score (p < 0.05). The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients’ motor function clinically.
Development and Experimental Studies of an Identification Method of Kinematic Parameters for Industrial Robots without External Measuring Instruments
Authors: Gubankov Anton , Yukhimets Dmitry
Published: April 2022

This paper proposes a method for the identification of kinematic parameters of multilink series industrial robots, which does not require the use of complex and expensive equipment for high-precision external measurements of the position and orientation of an end effector in a Cartesian coordinate system. This method, by means of simple and affordable tools, enables us to achieve a significantly increased accuracy of movement of end effectors in serial robots performing various technological operations. The proposed method is experimentally verified and can be applied directly in production lines.
Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction
Authors: Bilotta Stefano , Nesi Paolo
Published: April 2022

CO2 emissions from burning fossil fuels make a relevant contribution to atmospheric changes and climate disruptions. In cities, the contribution by traffic of CO2 is very relevant, and the general CO2 estimation can be computed (i) on the basis of the fuel transformation in energy using several factors and efficiency aspects of engines and (ii) by taking into account the weight moved, distance, time, and emissions factor of each specific vehicle. Those approaches are unsuitable for understanding the impact of vehicles on CO2 in cities since vehicles produce CO2 depending on their specific efficiency, producer, fuel, weight, driver style, road conditions, seasons, etc. Thanks to today’s technologies, it is possible to collect real-time traffic data to obtain useful information that can be used to monitor changes in carbon emissions. The research presented in this paper studied the cause of CO2 emissions in the air with respect to different traffic conditions. In particular, we propose a model and approach to assess CO2 emissions on the basis of traffic flow data taking into account uncongested and congested conditions. These traffic situations contribute differently to the amount of CO2 in the atmosphere, providing a different emissions factor. The solution was validated in urban conditions of Florence city, where the amount of CO2 is measured by sensors at a few points where more than 100 traffic flow sensors are present (data accessible on the Snap4City platform). The solution allowed for the estimation of CO2 from traffic flow, estimating the changes in the emissions factor on the basis of the seasons and in terms of precision. The identified model and solution allowed the city’s distribution of CO2 to be computed.
Research on Coverage Optimization in a WSN Based on an Improved COOT Bird Algorithm
Authors: Huang Yihui , Zhang Jing , Wei Wei , Qin Tao , Fan Yuancheng , Luo Xuemei , Yang Jing
Published: April 2022

To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems.
Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF
Authors: Sun Wen , Wang Zhenyuan , Wang Junnian , Wang Xiangyu , Liu Lili
Published: April 2022

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE.
Simulated and Experimental Verification for a Terahertz Specific Finite Rate of Innovation Signal Processing Method
Authors: Ramirez Barker Xavier E. , Stanchev Rayko I. , Hernandez Serrano Arturo I. , Pickwell-MacPherson Emma
Published: April 2022

Recently, finite rate of innovation methods have been successfully applied to achieve low sampling rates in many areas, such as for ultrasound and radio signals. However, to the best of our knowledge, there are no journal publications applying this to real terahertz signals. In this work, we mathematically describe a finite rate of innovation method applied specifically to terahertz signals both experimentally and in simulation. To demonstrate our method, we applied it to randomized simulated signals with and without the presence of noise and to simple experimental measurements. We found excellent agreement between the simulated signals and those recreated based on results from our method, with this success also being replicated experimentally. These results were obtained at relatively low sampling rates, compared to standard methods, which is a key advantage to using a finite rate of innovation method as it allows for faster data acquisition and signal processing.
DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization
Authors: Zhao Mingle , Zhou Dingfu , Song Xibin , Chen Xiuwan , Zhang Liangjun
Published: April 2022

Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g., the depth code) derived from deep neural networks has been employed in the visual-only or visual-inertial simultaneous localization and mapping (SLAM) systems, which achieve promising performances on both camera motion and local dense geometry estimations from monocular images. However, the existing visual-inertial SLAM systems combined with depth codes are either built on a filter-based SLAM framework, which can only update poses and maps in a relatively small local time window, or based on a loosely-coupled framework, while the prior geometric constraints from the depth estimation network have not been employed for boosting the state estimation. To well address these drawbacks, we propose DiT-SLAM, a novel real-time Dense visual-inertial SLAM with implicit depth representation and Tightly-coupled graph optimization. Most importantly, the poses, sparse maps, and low-dimensional depth codes are optimized with the tightly-coupled graph by considering the visual, inertial, and depth residuals simultaneously. Meanwhile, we propose a light-weight monocular depth estimation and completion network, which is combined with attention mechanisms and the conditional variational auto-encoder (CVAE) to predict the uncertainty-aware dense depth maps from more low-dimensional codes. Furthermore, a robust point sampling strategy introducing the spatial distribution of 2D feature points is also proposed to provide geometric constraints in the tightly-coupled optimization, especially for textureless or featureless cases in indoor environments. We evaluate our system on open benchmarks. The proposed methods achieve better performances on both the dense depth estimation and the trajectory estimation compared to the baseline and other systems.
Evaluating Changes in Mental Workload in Indoor and Outdoor Ultra-Distance Cycling
Authors: Irvine Dominic , Jobson Simon A. , Wilson John P.
Published: April 2022

Whilst increasing mental workload has been shown to have a detrimental effect on cycling performance and more generally to increase the risk of harm, no studies have measured how mental workload changes as a function of ultra-distance cycling, indoors or outdoors. Our objective was to measure the difference in mental workload, as indicated by changes in EEG theta power, components of HRV and psychomotor vigilance and as reported using the ‘NASA Task Load Index questionnaire’, before and after a 5 h indoor ride and outdoor ride completed at 65% of functional threshold power. Results of the NASA-TLX indicated the mental demand of outdoor cycling to be significantly less than that of indoor cycling. There were significant differences in the PVT results between the pre and the post outdoor ride average and median response times. The slowest 10% PVT responses were significantly slower pre than post the indoor ride. There were significant differences in HRV between pre and post outdoor and indoor rides, specifically, in the average RR intervals, RMSSD (ms2), LFPower (ms2), NN50. There were modest changes in indicators of mental workload during an ultra-distance cycle ride. As such, mental workload during ultra-distance cycling is unlikely to be a contributory factor to decreases in performance or to an increased likelihood of accident and injury.
Multi-Scenario Simulation and Prediction of Regional Habitat Quality Based on a System Dynamic and Patch-Generating Land-Use Simulation Coupling Model—A Case Study of Jilin Province
Authors: Zhao Boxuan , Li Shujie , Liu Zhaoshun
Published: April 2022

Effectively evaluating and predicting the change trend in regional habitat quality and its response to land-use change is of great significance for promoting regional high-quality and sustainable development. In this study, Jilin Province is taken as an example, and the SD–PLUS coupling model and the habitat quality module of the InVEST model are used to simulate the habitat quality of Jilin Province in 2030. In addition, the population and urbanization rates under the SSPs path are taken as the scenario framework, and the habitat quality changes from 1995 to 2030 are discussed. The results showed that the kappa coefficient of the simulation results was 0.93, and the FoM coefficient was 0.102. The simulation effect was good. The land-use simulation results under the four scenarios were quite different, and the change areas were mainly concentrated in the northwest, southeast, and central urban areas of Jilin Province. From 1995 to 2015, the habitat quality showed spatial distribution characteristics of “high in the southeast and low in the northwest”, and the habitat quality continued to deteriorate. By 2030, the ecological priority scenario (S3) is expected to have the maximum average value of habitat quality. Under this scenario, the scale of ecological land, such as forest, increases, and the scale of construction land is controlled. The minimum habitat quality appeared in the situation of rapid expansion (S2), and the increase in low-quality habitat areas was highly consistent with the expansion trend in urban construction land. In the coordinated development scenario, the growth rate of construction land reached 52.26%, but the average value of habitat quality decreased by only 0.0002, which basically found the balance point between economic development and ecological protection. S4 had a high reference value for the future development of Jilin Province.
Research on Collaborative Innovation Mode of Enterprise Group from the Perspective of Comprehensive Innovation Management
Authors: Feng Wei , Zhao Ling , Chen Yue
Published: April 2022

At present, collaborative innovation has become an integral part of corporate group strategy. However, there are few collaborative innovation research pieces focusing on corporate groups. This article takes Tus-Holdings, a model enterprise in the field of science and technology services, as the research object, uses case study methods, and systematically analyzes the corporate group’s strategy, customers, R&D, management, finance, talent, and other factors from the strategic, business, and support levels under the framework of total innovation management research on the collaborative innovation model of management and the form of cooperative surplus. The research found that the collaborative innovation model is an important support for enterprise groups to build a comprehensive, collaborative innovation system; the internal collaborative innovation model of enterprise groups shows nonlinearity and diversity; collaborative surplus performance is closely related to the collaborative innovation mode, and different collaborative innovation modes will produce a different collaborative surplus. These research results have important theoretical value and practical significance for modern enterprise groups to correctly implement collaborative innovation strategy and improve the efficiency of collaborative innovation.
Heavy Metals/Metalloids in Soil of a Uranium Tailings Pond in Northwest China: Distribution and Relationship with Soil Physicochemical Properties and Radionuclides
Authors: Mao Yu , Yong Jinlong , Liu Qian , Wu Baoshan , Chen Henglei , Hu Youhua , Feng Guangwen
Published: April 2022

Uranium tailings ponds have a potential impact on the soil ecological environment and human health. In this study, the measurement and spatial distribution characteristics of soil physicochemical properties (pH, EC, TN, TOC, and TP) and heavy metals/metalloids (Cd, Pb, Zn, Cr, and As) in two different profiles (0–5 cm, 5–15 cm) were completed and visualized in a decommissioned uranium tailings pond in Northwest China. The results showed that almost all measured values in the study area were within the background values of China and other countries or regions around the world. The visual spatial distribution map showed that the spatial distribution characteristics of the EC, TP content, Pb content, and Cr content of the soil in the tailings pond and its adjacent area increased with the increase in depth of the vertical profile. The visual correlation heatmap analysis found that, in general, there were significant positive correlations among heavy metals and radionuclides and significant negative correlations among heavy metals, radionuclides, and physicochemical properties. The cluster tree divided environmental factors into two clusters; pH, TP, 40K, Cd, and Zn formed one cluster, which could be related to the similar structures and physicochemical properties of Cd and Zn, and Pb, Cr, 232Th, TN, EC, TOC, As, 238U, and 226Ra formed another cluster of lithophile elements with similar geochemical properties. Based on the analysis results, the uranium tailings pond is in good operation, and no migration and diffusion of heavy metals/metalloids to the surrounding soil ecological environment was found.
The Spatial Evolution Law and Water Inrush Mechanism of Mining-Induced Overburden in Shallow and Short Coal Seam Group
Authors: Pan Weidong , Jiang Peng , Li Boyang , Li Jianghua , Yang Yinchao
Published: April 2022

In order to grasp the overburden evolution law of the shallow and short coal seam group, based on the key bed theory, a mechanical analysis of the longitudinal expansion of mining-induced overburden fractures in the coal seam group was carried out, and the spatial evolution characteristics of mining-induced overburden fractures were simulated by the numerical simulation method. The results show that in the process of working face advancement, with the continuous instability and failure of the overburden, the size and shape of the fracture network are also changed. The repeated mining of the lower coal seam further causes the secondary activation of the upper overburden, which makes the roof fractures of the partially compacted goaf violently move again. The “channel source” and “space source” continue to carry out the process of “generation-expansion-compression-generation-expansion”, in combination with pore fracture elastic theory. The water inrush characteristics of the whole coal seam are divided into three “solid-liquid” coupling stages: the original gap seepage stage, the initial water discharge stage of mining fissures and the water inrush stage of fractured rock mass. The steady value of water inflow and its variation characteristics with time are predicted by using the formula of deep well flow in a confined aquifer.
Situation Analysis and the Potential for Circularity of the Wastewater Sector in Lithuania
Authors: Kruopienė Jolita , Žiukaitė Miglė
Published: April 2022

The concept of circular economy is one of the priority areas for economic development on which the current environmental policy of the European Union is based. Most existing European wastewater treatment plants were built and operated according to the concept of linear economy where wastewater and sewage sludge were considered as waste only. In fact, there is a wide range of possibilities to use them as a resource to recover materials and energy. Implementation of the concept of circular economy in a relatively small country requires a great deal of additional research and monitoring to assess the available potential. In this paper, data on municipal wastewater in agglomerations larger than population equivalent of 2000 were analyzed, and the situation is presented through indicators covering prevention, removal, reuse, recycling, and recovery. The novelty of this article is that it evaluates the circularity of Lithuanian wastewater treatment plants and their potential for the future. Thus far, the wastewater sector in the country has been found to largely comply with legal obligations, but very few solutions of circularity were found to be implemented.
Fashion E-Tail and the Impact of Returns: Mapping Processes and the Consumer Journey towards More Sustainable Practices
Authors: Bozzi Carolina , Neves Marco , Mont’Alvão Claudia
Published: April 2022

The purpose of this study is to trace the processes behind the elaboration of the product page and map the shopping journey to identify ways to reduce returns. This is a qualitative study conducted in three stages: exploratory interviews with users, semi-structured interviews with e-commerce and logistics specialists, and directed storytelling sessions with users. Our findings indicate that the e-commerce specialists are not fully aware of the impacts caused by the high return rates, and product presentation pages are therefore not elaborated to provide users with all the information necessary to make accurate purchases. Sellers should improve product presentation pages to increase product knowledge by providing tools to enhance quality mental imagery. Additionally, sellers should inform consumers of the impact of their shipping options and returning habits. There are ways to optimize logistics processes to reduce the environmental impact. Prior research has addressed these issues separately. Besides, have addressed mental imagery to increase sales. This study brings a holistic approach and brings mental imagery as a tool to provide users with more information about a product.
Blockchain Technologies and Digitalization in Function of Student Work Evaluation
Authors: Bjelobaba Goran , Paunovic Marija , Savic Ana , Stefanovic Hana , Doganjic Jelena , Miladinovic Bogavac Zivanka
Published: April 2022

Following COVID-19, new accreditation standards include digitization, entrepreneurship, social inclusion and the circular economy. Blockchain can help to simplify difficult accreditation processes that ensure worldwide teaching, learning, practice and business communication excellence. The paper proposes a Collaborative Learning and Student Work Evaluation (CLSWE) model based on blockchain technologies (BCTs) encompassing selected concepts from the scientific research peer-review process. BCTs are used to develop a safe platform for storing and exchanging data about students’ projects and evaluations. The CLSWE model offers the possibility of improving cooperation between higher-education institutions and companies that seek the “employable skills” of proactive students. Before implementing the CLSWE model, a questionnaire was conducted to survey lecturers about their attitudes related to the potential application of BCTs. The results of the surveys are encouraging and reveal a desire and willingness to introduce BCTs in education. A project scheme with the main functionalities of the model and a description of the roles of the prominent participants was designed. A platform with a database created in the MySQL language for the testing model was built. This research also contributes to higher education literature in terms of the sustainability of the education process and collaborative learning with BCTs.
Challenges and Perspectives in Innovative Projects Focused on Sustainable Industry 4.0—A Case Study on Polish Project Teams
Authors: Podgórska Marzena
Published: April 2022

Contemporary project teams are increasingly used to solve problems that are at the crossroads of many disciplines and areas dedicated to Industry 4.0, which is a watershed in the implementation of Sustainable Development Goals (SDGs). Industry 4.0 can serve as a platform for the alignment of SDGs with the ongoing digital transformation. This involves specific challenges for teams, but also allows perspectives that may create innovative and high-quality results. In order to meet these challenges while taking advantage of the opportunities offered by interdisciplinary cooperation, project teams, including the team leader, should have specific competencies. With this in mind, the aim of this article is to identify the challenges and perspectives related to working in interdisciplinary Sustainable Industry 4.0 project teams and to define the competencies necessary to act as a member and leader of these teams. Implementation of this aim will be possible by answering two research questions: (1) What requirements and opportunities are involved with interdisciplinary work amongst members of Sustainable Industry 4.0 project teams; and (2) What are the competencies necessary of members and leaders of such teams to meet these requirements and take advantage of the opportunities for such cooperation? An exploratory case study was conducted among members of interdisciplinary project teams at one of the leading technical universities in Poland. Qualitative data were obtained from many sources: interviews, internal documentation of analyzed projects and managerial notes. The obtained results allow us to state that the most important challenges and perspectives related to the work of interdisciplinary Sustainable Industry 4.0 teams include coordination of individual parts of the project, integrative leadership, establishing a common language, broad views on the issues raised and building a team consisting of specialists with the required competencies. The competencies of the project team that are important for working in the analyzed environment include strategic perspective, communication skills and persuasion, while for leaders, competencies must include the ability to coordinate work, resource management, empowering and motivation.
Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology
Authors: Mon Yi-Jen
Published: April 2022

The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance.
Estimating the Lifetime of Solar Photovoltaic Modules in Australia
Authors: Tan Verity , Dias Pablo R. , Chang Nathan , Deng Rong
Published: April 2022

Determining the lifetime of solar photovoltaic modules is integral to planning future installations and ensuring effective end-of-life management. The lifetime of photovoltaic modules is most commonly considered to be 25 years based on performance guarantees of 80% power output after 25 years of operation; however, influences including climatic conditions, social behaviour, fiscal policy, and technological improvements have the potential to prompt early replacement. Therefore, this work aims to estimate the operating lifetime of photovoltaic panels more accurately in Australia by considering a variety of technical, economic, and social reasons for decommissioning. Based on a range of sources including government organisations, other policymakers, regulators and advisors, energy suppliers, researchers, recyclers, and manufacturers, three lifetime models—power decrease, damage and technical failures, and economic motivation—were developed and then weighted in three scenarios to form overall views of panel lifetime in Australia. In addition, it was concluded that the module lifetime will vary considerably between countries due to differences in market factors. Therefore, these results specifically address Australia as most of the input data were sourced from Australian industry reports and Australian photovoltaic systems and interpreted within the context of the Australian photovoltaic market. However, the methodology of estimating lifetime based on both technical and non-technical factors can be applied to other scenarios by using country-specific data. With the popularity of photovoltaic technology beginning in the early 2010s and given the practical lifetimes of 15–20 years found in this work, Australia will need to act swiftly within the next three years to responsibly manage the looming solar panel waste.
A Complete Characterization of Bidegreed Split Graphs with Four Distinct α-Eigenvalues
Authors: Su Guifu , Song Guanbang , Yin Jun , Du Junfeng
Published: April 2022

It is a well-known fact that a graph of diameter d has at least d+1 eigenvalues. A graph is d-extremal (resp. dα-extremal) if it has diameter d and exactly d+1 distinct eigenvalues (resp. α-eigenvalues), and a graph is split if its vertex set can be partitioned into a clique and a stable set. Such graphs have a diameter of at most three. If all vertex degrees in a split graph are either d˜ or d^, then we say it is (d˜,d^)-bidegreed. In this paper, we present a complete classification of the connected bidegreed 3α-extremal split graphs using the association of split graphs with combinatorial designs. This result is a natural generalization of Theorem 4.6 proved by Goldberg et al. and Proposition 3.8 proved by Song et al., respectively.
Haemodynamic Signatures of Temporal Integration of Visual Mirror Symmetry
Authors: Bellagarda Cayla A. , Dickinson J. Edwin , Bell Jason , Badcock David R.
Published: April 2022

EEG, fMRI and TMS studies have implicated the extra-striate cortex, including the Lateral Occipital Cortex (LOC), in the processing of visual mirror symmetries. Recent research has found that the sustained posterior negativity (SPN), a symmetry specific electrophysiological response identified in the region of the LOC, is generated when temporally displaced asymmetric components are integrated into a symmetric whole. We aim to expand on this finding using dynamic dot-patterns with systematically increased intra-pair temporal delay to map the limits of temporal integration of visual mirror symmetry. To achieve this, we used functional near-infrared spectroscopy (fNIRS) which measures the changes in the haemodynamic response to stimulation using near infrared light. We show that a symmetry specific haemodynamic response can be identified following temporal integration of otherwise meaningless dot-patterns, and the magnitude of this response scales with the duration of temporal delay. These results contribute to our understanding of when and where mirror symmetry is processed in the visual system. Furthermore, we highlight fNIRS as a promising but so far underutilised method of studying the haemodynamics of mid-level visual processes in the brain.