bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring Repositioning in Home Care for Pressure Injury Prevention and Management
Authors: Gabison Sharon , Pupic Nikola , Evans Gary , Dolatabadi Elham , Dutta Tilak
Published: September 2022

Despite the widespread agreement on the need for the regular repositioning of at-risk individuals for pressure injury prevention and management, adherence to repositioning schedules remains poor in the clinical environment. The situation in the home environment is likely even worse. Our team has developed a non-contact system that can determine an individual’s position in bed (left-side lying, supine, or right-side lying) using data from a set of inexpensive load cells placed under the bed. This system was able to detect whether healthy participants were left-side lying, supine, or right-side lying with 94.2% accuracy in the lab environment. The objective of the present work was to deploy and test our system in the home environment for use with individuals who were sleeping in their own beds. Our system was able to detect the position of our nine participants with an F1 score of 0.982. Future work will include improving generalizability by training our classifier on more participants as well as using this system to evaluate adherence to two-hour repositioning schedules for pressure injury prevention or management. We plan to deploy this technology as part of a prompting system to alert a caregiver when a patient requires repositioning.
Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador
Authors: Alvear-Puertas Vanessa E. , Burbano-Prado Yadira A. , Rosero-Montalvo Paul D. , Tözün Pınar , Marcillo Fabricio , Hernandez Wilmar
Published: September 2022

Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.
On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments
Authors: Barron Alfredo , Sanchez-Gallegos Dante D. , Carrizales-Espinoza Diana , Gonzalez-Compean J. L. , Morales-Sandoval Miguel
Published: September 2022

Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.
Evaluation of the Transverse Crack Depth of Rail Bottoms Based on the Ultrasonic Guided Waves of Piezoelectric Sensor Arrays
Authors: Yang Yuan , Wang Ping , Song Tian-Lang , Jiang Yi , Zhou Wen-Tao , Xu Wei-Lei
Published: September 2022

A method based on the high-frequency ultrasonic guided waves (UGWs) of a piezoelectric sensor array is proposed to monitor the depth of transverse cracks in rail bottoms. Selecting high-frequency UGWs with a center frequency of 350 kHz can enable the monitoring of cracks with a depth of 3.3 mm. The method of arranging piezoelectric sensor arrays on the upper surface and side of the rail bottom is simulated and analyzed, which allows the comprehensive monitoring of transverse cracks at different depths in the rail bottom. The multi-value domain features of the UGW signals are further extracted, and a back propagation neural network (BPNN) is used to establish the evaluation model of the transverse crack depth for the rail bottom. The optimal evaluation model of multi-path combination is reconstructed with the minimum value of the root mean square error (RMSE) as the evaluation standard. After testing and comparison, it was found that each metric of the reconstructed model is significantly better than each individual path; the RMSE is reduced to 0.3762; the coefficient of determination R2 reached 0.9932; the number of individual evaluation values with a relative error of less than 10% and 5% accounted for 100% and 87.50% of the total number of evaluations, respectively.
PDC: Pearl Detection with a Counter Based on Deep Learning
Authors: Hou Mingxin , Dong Xuehu , Li Jun , Yu Guoyan , Deng Ruoling , Pan Xinxiang
Published: September 2022

Pearl detection with a counter (PDC) in a noncontact and high-precision manner is a challenging task in the area of commercial production. Additionally, sea pearls are considered to be quite valuable, so the traditional manual counting methods are not satisfactory, as touching may cause damage to the pearls. In this paper, we conduct a comprehensive study on nine object-detection models, and the key metrics of these models are evaluated. The results indicate that using Faster R-CNN with ResNet152, which was pretrained on the pearl dataset, mAP@0.5IoU = 100% and mAP@0.75IoU = 98.83% are achieved for pearl recognition, requiring only 15.8 ms inference time with a counter after the first loading of the model. Finally, the superiority of the proposed algorithm of Faster R-CNN ResNet152 with a counter is verified through a comparison with eight other sophisticated object detectors with a counter. The experimental results on the self-made pearl image dataset show that the total loss decreased to 0.00044. Meanwhile, the classification loss and the localization loss of the model gradually decreased to less than 0.00019 and 0.00031, respectively. The robust performance of the proposed method across the pearl dataset indicates that Faster R-CNN ResNet152 with a counter is promising for natural light or artificial light peal detection and accurate counting.
AIS Trajectories Simplification Algorithm Considering Topographic Information
Authors: Lee Wonhee , Cho Sung-Won
Published: September 2022

With the development of maritime technology and equipment, most ships are equipped with an automatic identification system (AIS) to store navigation information. Over time, the size of the data increases, rendering its storage and processing difficult. Hence, it is necessary to transform the AIS data into trajectories, and then simplify the AIS trajectories to remove unnecessary information that is not related to route shape. Moreover, topographic information must be considered because otherwise, the simplified trajectory can intersect obstacles. In this study, we propose an AIS trajectory simplification algorithm considering topographic information. The proposed algorithm simplifies the trajectories without the intersection of the trajectory and obstacle using the improved Douglas–Peucker algorithm. Polygon map random (PMR) quadtree was used to consider topographic information on the coast, and the intersection between topographic information and simplified trajectories was efficiently computed using the PMR quadtree. To verify the effectiveness of the proposed algorithm, experiments were conducted on real-world trajectories in the Korean sea. The proposed algorithm yielded simplified trajectories with no intersections of the trajectory and obstacle. In addition, the computational efficiency of the proposed algorithm with the PMR quadtree was superior to that without the PMR quadtree.
Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation
Authors: Fleischhauer Vincent , Feldheiser Aarne , Zaunseder Sebastian
Published: September 2022

Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public.
Fast Inline Microscopic Computational Imaging
Authors: Ginner Laurin , Breuss Simon , Traxler Lukas
Published: September 2022

Inline inspection is becoming an essential tool for industrial high-quality production. Unfortunately, the desired acquisition speeds and needs for high-precision imaging are often at the limit of what is physically possible, such as a large field of view at a high spatial resolution. In this paper, a novel light-field and photometry system is presented that addresses this trade off by combining microscopic imaging with special projection optics to generate a parallax effect. This inline microscopic system, together with an image processing pipeline, delivers high-resolution 3D images at high speeds, by using a lateral transport stage changing the optical perspective. Scanning speeds of up to 12 mm/s can be achieved at a depth resolution of 2.8 μm and a lateral sampling of 700 nm/pixel, suitable for inspection in high-quality manufacturing industry.
Deep Neural Network for 3D Shape Classification Based on Mesh Feature
Authors: Gao Mengran , Ruan Ningjun , Shi Junpeng , Zhou Wanli
Published: September 2022

Virtual reality, driverless cars, and robotics all make extensive use of 3D shape classification. One of the most popular ways to represent 3D data is with polygonal meshes. In particular, triangular mesh is frequently employed. A triangular mesh has more features than 3D data formats such as voxels, multi-views, and point clouds. The current challenge is to fully utilize and extract useful information from mesh data. In this paper, a 3D shape classification network based on triangular mesh and graph convolutional neural networks was suggested. The triangular face of this model was viewed as a unit. By obtaining an adjacency matrix from mesh data, graph convolutional neural networks can be utilized to process mesh data. The studies were performed on the ModelNet40 dataset with an accuracy of 91.0%, demonstrating that the classification network in this research may produce effective results.
Optimization of Au:CuO Thin Films by Plasma Surface Modification for High-Resolution LSPR Gas Sensing at Room Temperature
Authors: Proença Manuela , Rodrigues Marco S. , Meira Diana I. , Castro M. Cidalia R. , Rodrigues Pedro V. , Machado Ana V. , Alves Eduardo , Barradas Nuno P. , Borges Joel , Vaz Filipe
Published: September 2022

In this study, thin films composed of gold nanoparticles embedded in a copper oxide matrix (Au:CuO), manifesting Localized Surface Plasmon Resonance (LSPR) behavior, were produced by reactive DC magnetron sputtering and post-deposition in-air annealing. The effect of low-power Ar plasma etching on the surface properties of the plasmonic thin films was studied, envisaging its optimization as gas sensors. Thus, this work pretends to attain the maximum sensing response of the thin film system and to demonstrate its potential as a gas sensor. The results show that as Ar plasma treatment time increases, the host CuO matrix is etched while Au nanoparticles are uncovered, which leads to an enhancement of the sensitivity until a certain limit. Above such a time limit for plasma treatment, the CuO bonds are broken, and oxygen is removed from the film’s surface, resulting in a decrease in the gas sensing capabilities. Hence, the importance of the host matrix for the design of the LSPR sensor is also demonstrated. CuO not only provides stability and protection to the Au NPs but also promotes interactions between the thin film’s surface and the tested gases, thereby improving the nanocomposite film’s sensitivity. The optimized sensor sensitivity was estimated at 849 nm/RIU, which demonstrates that the Au-CuO thin films have the potential to be used as an LSPR platform for gas sensors.
Non-Destructive Impedance Monitoring of Bacterial Metabolic Activity towards Continuous Lead Biorecovery
Authors: Andrews George , Neveling Olga , De Beer Dirk Johannes , Chirwa Evans M. N. , Brink Hendrik G. , Joubert Trudi-Heleen
Published: September 2022

The adverse health effects of the presence of lead in wastewater streams are well documented, with conventional methods of lead recovery and removal suffering from disadvantages such as high energy costs, the production of toxic sludge, and low lead selectivity. Klebsiella pneumoniae and Paraclostridium bifermentans have been identified as potential lead-precipitating species for use in a lead recovery bioreactor. Electrical impedance spectroscopy (EIS) on a low-cost device is used to determine the potential for the probe-free and label-free monitoring of cell growth in a bioreactor containing these bacteria. A complex polynomial is fit for several reactive equivalent circuit components. A direct correlation is found between the extracted supercapacitance and the plated colony-forming unit count during the exponential growth phase, and a qualitative correlation is found between all elements of the measured reactance outside the exponential growth phase. Strong evidence is found that Pb(II) ions act as an anaerobic respiration co-substrate for both cells observed, with changes in plated count qualitatively mirrored in the Pb(II) concentration. Guidance is given on the implementation of EIS devices for continuous impedance monitoring.
Design and Implementation of HD Mapping, Vehicle Control, and V2I Communication for Robo-Taxi Services
Authors: Yoon Jun Yong , Jeong Jinseop , Sung Woosuk
Published: September 2022

This paper presents our autonomous driving (AD) software stack, developed to complete the main mission of the contest we entered. The main mission can be simply described as a robo-taxi service on public roads, to transport passengers to their destination autonomously. Among the key competencies required for the main mission, this paper focused on high-definition mapping, vehicle control, and vehicle-to-infrastructure (V2I) communication. V2I communication refers to the task of wireless data exchange between a roadside unit and vehicles. With the data being captured and shared, rich, timely, and non-line-of-sight-aware traffic information can be utilized for a wide range of AD applications. In the contest, V2I communication was applied for a robo-taxi service, and also for traffic light recognition. A V2I communication-enabled traffic light recognizer was developed, to achieve a nearly perfect recognition rate, and a robo-taxi service handler was developed, to perform the main mission of the contest.
Exploring Use Acceptance of Electric Bicycle-Sharing Systems: An Empirical Study Based on PLS-SEM Analysis
Authors: Pan Lijun , Xia Yangkun , Xing Lining , Song Zhihang , Xu Yunbao
Published: September 2022

The electric bicycle-sharing system (EBSS) is the fourth-generation urban shared bicycle travel system, which effectively improves the travel efficiency of urban residents and solve the problem of urban congestion. This study attempts to use an extended technology acceptance model (TAM) method to study the acceptance of EBSSs. We had introduced four potential variables, including perceived pleasure (PP), perceived environmental value (PEV), perceived cost (PC), and perceived reliability (PR), into the classic TAM to form a new EBSS-TAM. Data were obtained by using a Likert scale questionnaire from 399 citizens in China. Partial least-squares structural equation modeling (PLS-SEM) with reflective constructs was employed as the analysis method. The results showed that: (1) the EBSS-TAM can explain user behaviors regarding the use of EBSSs. PP has a positive impact on behavior attitude (BA) while having no impact on behavior intention (BI). PEV has no impact on BA and BI. PC has a negative impact on BA and has no impact on BI. PR has a positive impact on BA while having no impact on BI. Perceived ease of use (PEU) has a positive impact on PP and PEV. (2) Younger users (under 35 years old) are more likely to change from liking CBSSs to using EBSSs than older users are. Male users are more satisfied with EBSSs because of their ease of use. The users who never used CBSSs are more likely to perceive the environmental protection value of EBSSs. Some managerial implications were proposed for the EBSSs.
Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights
Authors: Papakostas Christos , Troussas Christos , Krouska Akrivi , Sgouropoulou Cleo
Published: September 2022

Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students’ spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more specifically, in spatial ability training. However, the modeling of user knowledge in order to personalize the training has been neither sufficiently explored nor exploited in this task. There is a lot of space for research in this area. In this work, we introduce a novel personalization of the learning path within an AR spatial ability training application. The aim of the research is the integration of Augmented Reality, specifically in engineering evaluation and fuzzy logic technology. During one academic semester, three engineering undergraduate courses related to the domain of spatial skills were supported by a developed adaptive training system named PARSAT. Using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of the Structure of the Observed Learning Outcomes for the design of the learning material, PARSAT offers adaptive learning activities for the students’ cognitive skills. Students’ data were gathered at the end of the academic semester, and a thorough analysis was delivered. The findings demonstrated that the proposed training method outperformed the traditional method that lacked adaptability, in terms of domain expertise and learning theories, considerably enhancing student learning outcomes.
Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review
Authors: Rashed Baidaa Mutasher , Popescu Nirvana
Published: September 2022

Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017–2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.
Each Person as an End? The Users’ Choices in the Service Delivery Process for Assistive Technology in Hungary
Authors: Menich Nóra
Published: September 2022

Based on notions from the Capability Approach, this study investigates the service delivery process for assistive technology in Hungary. The research aimed to explore whether the service delivery is person-centered, with a specific focus on the users’ possible choices. In addition to a comprehensive analysis of legislative and policy documents, qualitative data were collected in semi-structured interviews with users and professionals (n = 31) to gain a deeper understanding of personal experiences. Our findings indicate that the service delivery system is product-centered and dominated by financial considerations. The policy and legislation framework does not provide an institutional guarantee for users to be able to have their voices heard; the extent to which their opinions and preferences prevail depends on the attitude, knowledge, and goodwill of the professionals involved in the process. The realization of a person-centered approach will be hindered as long as the users’ needs are viewed from a medical point of view.
Natural and Synthetic Polymer Fillers for Applications in 3D Printing—FDM Technology Area
Authors: Sztorch Bogna , Brząkalski Dariusz , Pakuła Daria , Frydrych Miłosz , Špitalský Zdeno , Przekop Robert E.
Published: September 2022

This publication summarises the current state of knowledge and technology on the possibilities and limitations of using mineral and synthetic fillers in the field of 3D printing of thermoplastics. FDM technology can be perceived as a miniaturised variation of conventional extrusion processing (a microextrusion process). However, scaling the process down has an undoubtful drawback of significantly reducing the extrudate diameter (often by a factor of ≈20–30). Therefore, the results produced under conventional extrusion processing cannot be simply translated to processes run with the application of FDM technology. With that in mind, discussing the latest findings in composite materials preparation and application in FDM 3D printing was necessary.
Parametric Optimization of Ball-Milled Bimetallic Nanoadsorbents for the Effective Removal of Arsenic Species
Authors: Babudurai Mercyrani , Sekar Karthick , Nwakanma Onyekachi Michael , Manisekaran Ravichandran , Garza-Navarro Marco A. , Subramaniam Velumani , Cuando-Espitia Natanael , David Halaney
Published: September 2022

Arsenic (As) removal from portable water bodies using the nanotechnology-based adsorption technique offers a unique method to lower the As contamination below the World Health Organization’s (WHO) maximum contaminant level (MCL). This work promotes a systematic methodological-based adsorption study by optimizing the different parameters that affect As removal using TiO2/γ-Fe2O3 nanocomposites (T/M NCs) prepared with the green, facile, and cost-effective ball milling method. The studies using X-ray Diffraction (XRD) illustrate the structural modifications with variations in the constituting T/M ratios, with high-resolution transmission electron microscopy (HRTEM) being used for the NC morphological studies. The optical characterization studies showed that bandgap tuning between 2–2.8 eV reduced the maghemite (γ-Fe2O3) content in the NCs and the elemental analysis confirmed the desired stoichiometry of the NCs. The magnetic measurements showed that the magnetic interaction among the particles tends towards exchange coupling behavior as the weight ratio of γ-Fe2O3 content decreases in the NCs. The adsorption studies using the most efficient NCs with an optimized condition (NC dose (8 g/L), contact time (15 min), As concentration (2 ppm), and pH (4)) resulted in a more than 99% removal of As species, suggesting the excellent behavior of the synthesized nanomaterial for water treatment and making it more economical than other competing adsorption techniques and materials.
Inheritance and Allelic Relationship among Gene(s) for White Rust Resistance in Indian Mustard [Brassica juncea (L.) Czern & Coss]
Authors: Chand Subhash , Singh Naveen , Prasad Lakshman , Nanjundan Joghee , Meena Vijay Kamal , Chaudhary Rajat , Patel Manoj Kumar , Taak Yashpal , Saini Navinder , Vasudev Sujata , Yadava Devendra Kumar
Published: September 2022

White rust [Albugo candida (Pers. Ex. Lev) Kuntze] is an important oomycetes disease of Indian mustard [Brassica juncea (L.) Czern & Coss] that causes a drastic reduction in seed yield and quality when the conditions are favorable. A set of 25 Indian mustard genotypes were screened against A. candida Delhi isolate (Ac-Dli) under both controlled and natural epiphytotic conditions. Out of 25, only six genotypes (Pusa Karishma, PDZ-3, Heera, BEC-144, BIO YSR, and Donskaja) were found highly resistant in both environments. To study the inheritance of resistance, four resistant genotypes (BEC-144, BIO YSR, Pusa Karishma, and Donskaja) were crossed with three susceptible genotypes (PM-24, Pusa Vijay, and MSTWR-17-15) in a definite design. The parents, F1, F2, and BC1F1 (F1 backcrossed with susceptible parent) generations were screened under both artificially controlled and natural epiphytotic conditions and the results indicated that the resistance in BIO YSR, BEC-144, and Pusa Karishma is governed by a single dominant gene, but more than one dominant gene is responsible for resistance in Donskaja. A test of an allelism conducted on the F2 population derived by crossing resistant (BEC-144) × resistant (BIO YSR) genotypes indicated that the gene imparting resistance to Ac-Dli isolate in the resistant parents BEC-144 and BIO YSR is the same and they are allelic to each other. Therefore, in broader terms, the information generated in the present study will be useful in Indian mustard breeding programs for the development of durable resistant cultivars.
Morphological and Chemical Characterization of Particulate Matter from an Indoor Measuring Campaign
Authors: Bodor Marius , Ceoromila Alina , Bașliu Vasile
Published: September 2022

The scientifically backed conclusion that pollution with particulate matter presents an important negative effect on human health is the driver of the present study. Not only are the results presented herein a completion, and to some small extent a confirmation, of a previous study, but these findings are also a confirmation of the need to further investigate the best way for monitoring particulate matter pollution in agglomerated areas throughout the world. This need is emphasized by the moderately positive results obtained in this measuring campaign that was carried out in an indoor location of an industrial city and near a heavily circulated road. The results presented in this study were obtained by utilizing advanced methods such as optical microscopy, scanning electron microscopy (SEM), energy dispersive X-ray microanalysis (EDX), and X-ray diffraction (XRD).
Private Information Dissemination and Noise Trading: Implications for Price Efficiency and Market Liquidity
Authors: Liu Huan , Liu Weiqi , Li Yi
Published: September 2022

Information is the basis for the sustainable and stable development of financial markets. Advanced internet technology has accelerated the dissemination of information. To investigate the impacts of private information dissemination on the sustainability of the financial market, we construct a rational expectation equilibrium (REE) model. The dissemination of private information favors noise traders who receive private information and weakens the advantage of informed traders who have direct access to private information, thus reducing noise-driven volatility and increasing fundamental-driven volatility, which is not conducive to the sustainability and stability of the financial market. Private information dissemination increases information asymmetry, reduces the number of noise traders in the market, decreases market liquidity, and hurts price efficiency for both exogenous and endogenous information acquisition, which is harmful to the sustainability of the financial market. Additionally, we numerically analyze the effects of private information on noise traders, market liquidity, and price efficiency. The numerical results are consistent with the theoretical analysis. The findings highlight the potential of private information dissemination to noise traders in financial market analysis. This study contributes to the analysis of financial market sustainability.
Land Management Using Land Reserves to Alleviate Emergencies on the Example of Warsaw
Authors: Maciejewska Alina , Kuzak Łukasz , Ulanicka-Raczyńska Marianna , Moreau Kamil
Published: September 2022

Some of the most important contemporary threats to the population are global emergencies, such as pandemics, refugee waves, and climate disasters. Urban areas with high-density housing and limited access to services have been primarily affected. There is often a lack of time, resources, and space to develop primary services available near places of residence. In the face of progressing urbanisation and the expanding pandemic, it is necessary to rationally manage urban space, ensuring the use of unused, post-industrial areas, on the one hand, and minimise the negative effects of crises—the spread of a virus, the occurrence of an urban heat island, or the relocation of refugees. One beneficial solution in cities is effectively managing empty, undeveloped urban areas to develop cities in a way that is future-proof from new emergencies. In this study, we aimed to search for optimal solutions which can help to alleviate the negative effects of emergencies in the city using the decision-making model of field reserve management. Two districts of Warsaw, the capital city of Poland, were chosen for this study: one was typically urbanised, namely, Mokotów, and the other had large undeveloped areas for construction, namely, Białołęka. We analysed the land reserves of these districts. Spatial analyses of the data made it possible to identify optimal locations that constituted land reserves that can be used for essential services. Based on the analysis results, planners can effectively create a set of recommendations for local governments. Thanks to these recommendations, municipalities can manage their land reserves and eventually adapt them for the abovementioned purposes. As a result, it is expected that the effective management of land reserves using publicly available spatial data will improve preventive actions in an emergency, such as a pandemic. In addition, the proposed design solutions are universal; since they are based on the data on available field reserves and their statuses, management in emergencies will be possible.
Farmers’ Markets as Resilient Alternative Market Structures in a Sustainable Global Food System: A Small Firm Growth Perspective
Authors: Connolly Richael , Bogue Joe , Repar Lana
Published: September 2022

A sustainable food system is a key target of the global Sustainable Development Goals (SDGs). The current global food system operates on market mechanisms that prioritise profit maximisation. This paper examines how small food businesses grow and develop within grassroot economies that operate on different market mechanisms. Focusing on artisan food producers and farmers’ markets, this research highlights the potential of resilient, small-scale, diverse markets as pathways to sustainable food systems. An applied critical realist, mixed-methods study was conducted at a macro (Irish food industry), meso (farmers’ markets in the region of Munster, Ireland) and micro (artisan food producers and their businesses) level. The resulting framework provides a post-growth perspective to sustainability, proposing that farmers’ markets represent an alternative market structure to the dominant industrial market, organised on mechanisms where producers ‘Mind what they make’ and ‘Make peace with enough’. In their resilience, these markets can provide pathways for structural change. This implies a call to action to reorientate policies targeting small food businesses to move beyond the concept of firms as profit-maximizing enterprises and to instead focus on a local food policy framework that reinforces the regional ‘interstices’ within which small food businesses operate to promote diversity, resilience and sustainability in the food system.
CA-BASNet: A Building Extraction Network in High Spatial Resolution Remote Sensing Images
Authors: Huang Liang , Zhu Juanjuan , Qiu Mulan , Li Xiaoxiang , Zhu Shasha
Published: September 2022

Aiming at the problems of holes, misclassification, and rough edge segmentation in building extraction results from high spatial remote sensing images, a coordinate attention mechanism fusion network based on the BASNet network (CA-BASNet) is designed for building extraction in high spatial remote sensing images. Firstly, the deeply supervised encoder–decoder network was used to create a rough extract of buildings; secondly, to make the network pay more attention to learning building edge features, the mixed loss function composed of binary cross entropy, structural similarity and intersection-over-union was introduced into the network training process; finally, the residual optimization module of fusion coordinate attention mechanism was used for post-processing to realize the fine extraction of buildings from high spatial resolution remote sensing images. Experiments on the WHU building dataset show that the proposed network can achieve mIoU of 93.43%, mPA of 95.86%, recall of 98.79%, precision of 90.13% and F1 of 91.35%. Compared with the existing semantic segmentation networks, such as PSPNet, SegNet, DeepLapV3, SE-UNet, and UNet++, the accuracy of the proposed network and the integrity of object edge segmentation are significantly improved, which proves the effectiveness of the proposed network.
Small-Scale Hydropower and Energy Recovery Interventions: Management, Optimization Processes and Hydraulic Machines Applications
Authors: Rossi Mosè , Renzi Massimiliano , Štefan David , Muntean Sebastian
Published: September 2022

The overuse of fossil fuels has brought considerable climate change to our planet, affecting not only human life, but also the ecosystem of the Earth (e [...]
Art Market Investment Bubble during COVID-19—Case Study of the Rare Books Market in Poland
Authors: Zakonnik Łukasz , Czerwonka Piotr , Podgórski Grzegorz , Zajdel Karolina , Zajdel Radosław
Published: September 2022

The literature provides information on the impact of the pandemic on various markets. Many articles suggest the necessity of diversifying investment portfolios during financial market turmoil. The article in a unique way analyzed the rare books market in Poland during the COVID-19 period. The results of rare books auctions (the largest players with 85% of the market share were taken into account) over the 2018–2022 period were considered. The authors presented the hypothesis that COVID-19 triggered an investment bubble which may burst quite suddenly after the pandemic period. The data presented confirm the hypothesis, showing sharp increases (up to 50%) in sales revenues and a subsequent collapse. Less spectacular results were obtained when analyzing the number of books offered and sold in a given period. The authors used descriptive measures as well as statistical tests. A simple model calculating possible revenue in conjunction with the WPDI (World Pandemics Discussion Index) indicator was also presented.
Influence of Tillage and Cropping Systems on Soil Properties and Crop Performance under Semi-Arid Conditions
Authors: Boudiar Ridha , Alshallash Khalid S. , Alharbi Khadiga , Okasha Salah A. , Fenni Mohammed , Mekhlouf Abdelhamid , Fortas Bilal , Hamsi Keirieddine , Nadjem Kamel , Belagrouz Abdennour , Mansour Elsayed , Mekhlouf Mahfoud
Published: September 2022

Conservation agriculture helps to mitigate the adverse impacts of conventional practices and intensive cultivation, accordingly enhancing agricultural sustainability. Tillage management and the preceding crop impact the sustainable use of soil resources and ultimately crop growth and productivity. The present study aimed to assess the impacts of the tillage system, cropping system, and their different interactions on soil properties and agronomic performance. No-tillage (NT), reduced tillage (RT), and conventional tillage (CT) were combined with three cropping systems; continuous wheat (wheat/wheat/wheat, W/W/W), lentil/wheat/lentil (L/W/L), and oat/wheat/barley-pea (O/W/BP) during three years crop rotation. The results displayed that the conservative practices (NT and RT) recorded higher soil nutrient content (N, P, K, Ca, and Mg) than conventional tillage (CT). Moreover, NT and RT exhibited higher bulk density (BD) and lower water infiltration rates compared to CT. Besides, NT and RT tended to have higher soil moisture than CT. The cropping system W/W/W displayed higher magnesium content, while W/L/W had higher phosphorous and O/W/BP showed higher sodium values. The mono-cropping system under conventional tillage tended to have lower soil nutrient content compared to the other combinations. The highest wheat aboveground biomass, wheat grain yield, and lentil seed yield were produced by RT and CT compared to NT. Otherwise, the higher emergence of barley-pea was assigned for NT followed by CT and RT.
Stability Analysis of Pile-Supported Embankments over Soft Clay Considering Soil Failure between Piles Based on Upper Bound Theorem
Authors: Li Peng-Yu , Dou Hong-Qiang , Wang Hao , Nie Wen-Feng , Chen Fu-Quan
Published: September 2022

Most analytical methods used to analyze the stability of pile-supported embankments are based on shear and bending failure of piles. However, the centrifuge test and practical engineering show that rigid pile-supported embankments have a failure mode of soil sliding around piles. Therefore, the stability analysis method of the pile-supported embankment under the failure mode of the soil sliding around piles based on the upper bound theorem is given in this study. First, the failure mechanism is assumed to be a rigid body and slide along two logarithmic spiral surfaces in the embankment and soft soil. The rate of external work and internal energy dissipation of the failure mechanism are further obtained, wherein the internal energy dissipation rate of piles is obtained based on the limit state of soil sliding around piles. Secondly, according to the equation formed by the upper bound theorem, the optimization model of the safety factor function when the potential slip surface is passing through different pile rows is obtained. Then, the algorithm of solving the model is given and the overall safety factor of the pile-supported embankment is obtained. Compared with other methods, the advantage of this method is that it considers the influence of embankment soil property and the width perpendicular to the two-dimensional plane on the pile load and the influence of the soil internal friction angle on the slip surface. Finally, it can be concluded from parametric analysis that: with the increase of the horizontal distance from piles to the slope toe, the reinforcement effect of piles first increases and then decreases; with the decrease of the embankment height and the increase of the soft soil cohesion and internal friction angle, the overall safety factor of the embankment increases.
Where Have Carbon Emissions Gone? Evidence of Inbound Tourism in China
Authors: Hu Yingying
Published: September 2022

Tourism is emerging as an important contributor sector to carbon emissions. As inbound tourism is the main component of tourism activities, measuring and evaluating the carbon footprint of inbound tourism can help achieve low-carbon development of the global tourism industry. Based on the carbon footprint theory, this study describes the distribution and transfer path of China’s inbound tourism carbon footprint by using the China multi regional input-output model embedded in the inbound tourism satellite account. The results reveal that embodied carbon emissions (ECE) of inbound tourism is higher than direct carbon emissions, and carbon emissions intensity is close to the average economic. Therefore, it is difficult for inbound tourism to achieve the dual goals of stimulating economic growth and emission mitigation. The ECE of inbound tourism has obvious inter-provincial transfer characteristics. Inner Mongolia and Guangdong are the largest provinces with net outflows/inflows of ECE, with net outflows of 1.47 million tons and inflows of 2.66 million tons. Besides, the ECE mainly flows from the northeast and northwest regions to the southern and eastern regions. From the perspective of industry transfer, 72.2% of ECE of inbound tourism comes from the power sector.
Study of Lawn Vegetation under the Influence of Mining and Processing Enterprise Activity in the Climatic Conditions of the Arctic Zone
Authors: Goryachev Andrey , Mingaleva Tatiana , Kompanchenko Alena , Makarov Dmitriy , Masloboev Vladimir
Published: September 2022

The purpose of this study was to examine the vegetation dynamics of various lawn grass species in the city of Apatity, Murmansk region, when exposed to anthropogenic and atmospheric pollution impacts. The dust concentration in the atmospheric air was recorded, its mineralogical analysis was carried out, and the dynamics of the development of various lawn types under the anthropogenic load was studied. Cases of exceeding the MPC of dust particles in the atmospheric air were recorded. Nepheline grains were found in the dust samples, which is indicative of pollution migration from the nearby tailings storage facility. It was shown that lawns are able to grow intensively in severe climatic conditions and under the influence of mining and processing enterprises.
Detection and Assessments of Sources and Health Hazards Caused by Heavy Metals in the Dust of Urban Streets in Harbin, Northeast China
Authors: Yu Rui , Cui Zhengwu , Luo Nana , Yu Yong
Published: September 2022

To investigate heavy metals (HMs) in the dust of the urban streets and evaluate health hazards through dust pollution exposure, this research implements an analysis method called principal component analysis and a model called positive matrix factorization to investigate the associations between HMs and their plausible allocation of sources. A total number of 118 dust samples were collected from Harbin, China, which is one of the most eloquent industrial hubs and tourist destinations. The results suggest that the mean concentrations of Cd, Cr, Cu, Zn, Ni, Pb, and Mn are 1.79 ± 1.618, 67.23 ± 32.84, 57.76 ± 51.50, 328.52 ± 117.62, 27.11 ± 4.66, 83.03 ± 25.39, and 745.34 ± 153.22 mg kg−1, respectively. The erratic enrichment of Cu, Zn, Pb, and Cd is succeeded by a geo-accumulation index and the factors that are used for enrichment. Both the spatial distribution and correlation analysis imply that Cu, Zn, Pb, and Cd can be controlled by anthropogenic activities. On the contrary, Cr, Mn, and Ni can be ascribed to natural sources. The hazard quotients are less than 1, and the hazard indexes for seniors and kids are 0.129 and 0.852, respectively. So, kids had more non-carcinogenic hazards than the older individuals did. Both groups have carcinogenic risks of less than 1 × 10−6. The results indicated that street dust could not be potentially accepted as a health hazard for dwellers. Cu, Zn, Pb, Cr, Ni, and Cd existed in the street dust of the research region and have been influenced by the combination of industrial and traffic sources and domestic coal combustion, and the parent material that forms soil affects the levels of Mn. A model, called the PMF, is implemented in the study of street dust pollution sources, enhancing the reliability and accuracy of pollution source determination, and presenting some potential applications.
Dual-Channel Retailer’s Multi-Period Inventory and Pricing Policy with the Quantity-Sales Mode
Authors: Zhao Ling , Liu Jun , Xiong Minghua , Zou Anquan
Published: September 2022

This paper investigates a dual-channel retailer’s inventory and pricing integrated decision-making problem over multiple periods. The quantity-sales mode is adopted in the online channel while the unit-sales mode is used offline. The customer demand of each sales channel is stochastic, and depends on visit costs, customer return costs, sales modes and pricing levels. To achieve the long-term profit maximization, the dual-channel retailer needs to optimize the pricing levels of both channels and the ordering quantity. For this problem, we first formulate a stochastic dynamic programming model, and then prove the joint concavity and the supermodularity of the objective function. With these properties, the optimality of the base-stock policy is proved, and the optimum price policy is characterized. Specifically, if the retailer’s optimum stock is neither too low nor too high, both sales channels need to be operated simultaneously, otherwise, only operating one channel is optimal. The online price is invariant with the retailer’s stock level while the offline price is decreasing in the retailer’s stock level. Finally, numerical results show that the quantity-sales mode, dual-channel sales model, and the multi-period dynamic decisions could benefit the dual-channel retailer. In addition, influences of cost parameters on the total profit are verified numerically.