bluuebunny/crossref_metadata_2025 huggingface dataset:
Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations
Authors: Shan Mao , Narula Karan , Wong Yung Fei , Worrall Stewart , Khan Malik , Alexander Paul , Nebot Eduardo
Published: December 2020

Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.
A Piezoelectric Wave-Energy Converter Equipped with a Geared-Linkage-Based Frequency Up-Conversion Mechanism
Authors: Chen Shao-En , Yang Ray-Yeng , Wu Guang-Kai , Wu Chia-Che
Published: December 2020

In this paper, a piezoelectric wave-energy converter (PWEC), consisting of a buoy, a frequency up-conversion mechanism, and a piezoelectric power-generator component, is developed. The frequency up-conversion mechanism consists of a gear train and geared-linkage mechanism, which converted lower frequencies of wave motion into higher frequencies of mechanical motion. The slider had a six-period displacement compared to the wave motion and was used to excite the piezoelectric power-generation component. Therefore, the operating frequency of the piezoelectric power-generation component was six times the frequency of the wave motion. The developed, flexible piezoelectric composite films of the generator component were used to generate electrical voltage. The piezoelectric film was composed of a copper/nickel foil as the substrate, lead–zirconium–titanium (PZT) material as the piezoelectric layer, and silver material as an upper-electrode layer. The sol-gel process was used to fabricate the PZT layer. The developed PWEC was tested in the wave flume at the Tainan Hydraulics Laboratory, Taiwan (THL). The maximum height and the minimum period were set to 100 mm and 1 s, respectively. The maximum voltage of the measured value was 2.8 V. The root-mean-square (RMS) voltage was 824 mV, which was measured through connection to an external 495 kΩ resistive load. The average electric power was 1.37 μW.
A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor
Authors: Hudec Robert , Matúška Slavomír , Kamencay Patrik , Benco Miroslav
Published: December 2020

Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.
Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry
Authors: Brugés Martelo Javier , Lundgren Jan , Andersson Mattias
Published: December 2020

The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard.
Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks
Authors: Park Dongsuk , Lee Seungeui , Park SeongUk , Kwak Nojun
Published: December 2020

With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%.
Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine
Authors: Chen Yajun , Wu Zhangnan , Zhao Bo , Fan Caixia , Shi Shuwei
Published: December 2020

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.
Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs
Authors: Xiao Tuo , Cui Taiping , Islam S. M. Riazul , Chen Qianbin
Published: December 2020

With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution.
Computer-Aided Diagnosis of Alzheimer’s Disease through Weak Supervision Deep Learning Framework with Attention Mechanism
Authors: Liang Shuang , Gu Yu
Published: December 2020

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.
The Relationship between Neuromuscular Control and Physical Activity in the Formation of the Visual-Psychomotor Schemes in Preschools
Authors: Buzescu Roxana , Nechita Florentina , Cioroiu Silviu Gabriel
Published: December 2020

Background: This research has started from the empirical observation that preschoolers who practice systematic and continuous physical activities can solve the tasks they receive more accurately and in less time than those who do not do sports in an organized setting. Methods: The research was carried out in 2015 in the Laboratory of Physical Therapy and Special Motricity of the Faculty of Physical Education and Mountain Sports, Transilvania University of Brasov. The survey sample included 51 preschoolers (26 boys and 25 girls), and the study implemented “real experiment” type research with a post-test phase to find out to what extent cortical stability is dependent on practicing a form of systematic movement at the ages of 4–6 years by analyzing proprioceptive sense and neuromuscular control. Thus, we could see how a 4-to-6-year-old child’s brain responds to a given stimulus by using the ERGOSIM condition simulator, which provides real-time feedback. Results: The results of the study show significant values for the visual control of the subjects by adjusting movement. Conclusions: The practice of physical activities benefits from learning through the visual scheme, having real-time feedback, and subjects being able to maintain indices closer to the required model, on the one hand, and on the other, to return with spherical correction stimuli during a wrong move much better than those in the control group. The results suggest that systematic practice of psychomotricity can improve general development and cognition in children, and that implementing this methodology could thus be useful in educative intervention.
Recent Advances in Microfluidics-Based Chromatography—A Mini Review
Authors: Bao Bo , Wang Zhichao , Thushara Dilantha , Liyanage Achini , Gunawardena Sanja , Yang Zaiyong , Zhao Shuangliang
Published: December 2020

Microfluidics-based liquid chromatography is based on the miniaturization of the different types of liquid chromatography (LC) systems (e.g., affinity, adsorption, size exclusion, ion exchange) on a microchip to perform on-chip separation of different types of analytes. On-chip chromatography finds applications in genomics, proteomics, biomarker discovery, and environmental analysis. Microfluidics-based chromatography has good reproducibility and small sample consumption. However, the on-chip chromatography fabrication techniques are often more challenging to perform than conventional LC column preparation. Different research groups have attempted to develop different techniques to fabricate microfluidics-based LC systems. In this review, we will summarize the recent advances in microfluidics-based chromatography.
Measurements of Local Sources of Particulates with a Portable Monitor along the Coast of an Insular City
Authors: Petsas Christos , Stylianou Marinos , Zorpas Antonis , Agapiou Agapios
Published: December 2020

The air quality of modern cities is considered an important factor for the quality of life of humans and therefore is being safeguarded by various international organizations, concentrating on the mass concentration of particulate matter (PM) with an aerodynamic diameter less than 10, 2.5 and 1 μm. However, the different physical and anthropogenic processes and activities within the city contribute to the rise of fine (<1 μm) and coarse (>1 μm) particles, directly impacting human health and the environment. In order to monitor certain natural and anthropogenic events, suspecting their significant contribution to PM concentrations, seven different events taking place on the coastal front of the city of Limassol (Cyprus) were on-site monitored using a portable PM instrument; these included both natural (e.g., dust event) and anthropogenic (e.g., cement factory, meat festival, tall building construction, tire factory, traffic jam, dust road) emissions taking place in spring and summer periods. The violations of the limits that were noticed were attributed mainly to the various anthropogenic activities taking place on-site, revealing once more the need for further research and continuous monitoring of air quality.
Does Business Model Innovation Enhance the Sustainable Development of New Ventures? Understanding an Inverted-U Relationship
Authors: Mai Yuqiang , Yang Hualong , Zhang Guangyu
Published: December 2020

Improving the sustainable development of new ventures and helping them survive and develop in the entrepreneurial environment are important research issues. Business model innovation is considered as a potential way for new ventures to enhance their sustainable development capability and performance. Although previous studies have pointed out that there is a positive linear relationship between business model innovation and sustainable development, little research has found the nonlinear relationship between them, as well as the moderating effects of competitive strategy innovation and strategy flexibility. In this study, an empirical model was established to test our hypotheses and answer our research questions. Analysis of data collected from 326 questionnaires shows that an inverted-U relationship exists between business model innovation and sustainable development such that new ventures with low or high business model innovation have worse sustainable development than new ventures with appropriate business model innovation. Moreover, this paper finds that competitive strategy innovation and strategic flexibility positively moderate the inverted-U relationship between business model innovation and sustainable development of new ventures. From a theoretical perspective, this study offers a model with which to understand the impacts of business model innovation, and contributes significantly to the research on new ven- tures. From a practical perspective, this paper provides new insights into the management of new ventures and recommends several practical strategies for managers and practitioners.
Assessment of Disintegration of Compostable Bioplastic Bags by Management of Electromechanical and Static Home Composters
Authors: Cafiero Lorenzo Maria , Canditelli Margherita , Musmeci Fabio , Sagnotti Giulia , Tuffi Riccardo
Published: December 2020

Interest in small scale composting systems is currently growing, and this in turn raises the question of whether the compostable bags are as suitable as in industrial composting facilities. In this work the physical degradation percentage of compostable lightweight bioplastic bags in two types of composter was examined. The main goal was to understand whether the mild biodegrading conditions that occur in electromechanical or static home composters are sufficient to cause effective bag degradation in times consistent with the householders’ or operators’ expectations. Bags, which complied with standard EN 13432, were composted in a number of 600 L static home composters, which were run in different ways (e.g., fed only with vegetables and yard waste, optimizing the humid/bulking agent fraction, poorly managed) and a 1 m3 electromechanical composter. Six months of residence time in static home composters resulted in 90–96 wt% degradation depending on the management approach adopted, and two months in the electromechanical composter achieved 90 wt%. In the latter case, three additional months of curing treatment of the turned heaps ensured complete physical degradation. In conclusion, in terms of the level and times of physical degradation, the use of compostable bioplastic bags appeared promising and consistent with home composting practices.
The Marketization of Rural Collective Construction Land in Northeastern China: The Mechanism Exploration
Authors: Liu Hongbin , Zhou Yuepeng
Published: December 2020

The transfer of rural collective construction land into the market (RCCL marketization) is an important starting point for breaking the urban–rural dual system, realizing the sustainable use of land resources and promoting the integrated development of urban and rural areas in China. This study aims to explore the decision-making of rural households in the marketization of rural collective construction land (RCCL) by constructing a two-stage (cognition-decision) theoretical framework. Based on the household survey data collected from the pilot areas in the three northeastern provinces in China, the structural equation modelling (SEM) has been applied. The main findings are as follows: (1) the four types of exogenous latent variables, including information dissemination, management of collective economic organizations (CEOs), family characteristics, and household head characteristics, are intermediary by household cognition, which then positively affect households’ behavior and decision-making. (2) among the factors affecting household cognition, the management of CEOs exhibits the most significant impact, followed by information dissemination, family characteristics, and household head characteristics. (3) the measurable variables, including participation rights, whether there are collective operating assets, education level, and whether members have social insurance, have significant effects on the four exogenous latent variables. (4) the understanding of income distribution policy has the greatest positive impact on household cognition, while risk perception has the opposite effect, indicating an obvious “risk aversion” tendency for rural households. The findings imply that the government should improve the existing RCCL market entry system from the aspects of strengthening collective economic organization construction, land value-added income sharing mechanism, and clarifying rural land property rights, so as to reduce farmers’ decision-making risks and enhance value perception. Overall, the research presented here contributes to investigating the theoretical mechanism of household decision-making and providing empirical evidence on how to improve the marketization of rural collective construction land in China.
Valuation Models for Holiday Rentals’ Daily Rates: Price Composition Based on Booking.com
Authors: Santos José António C. , Fernández-Gámez Manuel Á. , Solano-Sánchez Miguel Ángel , Rey-Carmona Francisco José , Rio Lorena Caridad y López del
Published: December 2020

In recent years, the number of sharing economy accommodations has grown exponentially due to the Internet and peer-to-peer networks, which has made researchers increasingly interested in analysing this new type of lodging. This study sought to develop models that determine the significant variables for the daily price of staying in holiday rentals based on data extracted from Booking.com and other sources. The hedonic pricing method (HPM) was selected to conduct the research as this methodology has been widely used in real estate valuation and hotel daily rate determination; however, the HPM is still rarely used for holiday rentals. The study focused on the city of Seville, where a notable increase in holiday rentals has been observed in recent years. Variables related to the accommodation typology, including location, size and equipment, as well as seasonality, are the most influential factors in the proposed models. These results are of interest to both owners and users of holiday rentals and can help these individuals to determine if the price of a stay is what would commonly be offered in the market under normal circumstances.
Melatonin as Master Regulator in Plant Growth, Development and Stress Alleviator for Sustainable Agricultural Production: Current Status and Future Perspectives
Authors: Nawaz Khadija , Chaudhary Rimsha , Sarwar Ayesha , Ahmad Bushra , Gul Asma , Hano Christophe , Abbasi Bilal Haider , Anjum Sumaira
Published: December 2020

Melatonin, a multifunctional signaling molecule, is ubiquitously distributed in different parts of a plant and responsible for stimulating several physiochemical responses against adverse environmental conditions in various plant systems. Melatonin acts as an indoleamine neurotransmitter and is primarily considered as an antioxidant agent that can control reactive oxygen and nitrogen species in plants. Melatonin, being a signaling agent, induces several specific physiological responses in plants that might serve to enhance photosynthesis, growth, carbon fixation, rooting, seed germination and defense against several biotic and abiotic stressors. It also works as an important modulator of gene expression related to plant hormones such as in the metabolism of indole-3-acetic acid, cytokinin, ethylene, gibberellin and auxin carrier proteins. Additionally, the regulation of stress-specific genes and the activation of pathogenesis-related protein and antioxidant enzyme genes under stress conditions make it a more versatile molecule. Because of the diversity of action of melatonin, its role in plant growth, development, behavior and regulation of gene expression it is a plant’s master regulator. This review outlines the main functions of melatonin in the physiology, growth, development and regulation of higher plants. Its role as anti-stressor agent against various abiotic stressors, such as drought, salinity, temperatures, UV radiation and toxic chemicals, is also analyzed critically. Additionally, we have also identified many new aspects where melatonin may have possible roles in plants, for example, its function in improving the storage life and quality of fruits and vegetables, which can be useful in enhancing the environmentally friendly crop production and ensuring food safety.
The Role of Public Participation for Determining Sustainability Indicators for Arctic Tourism
Authors: Ólafsdóttir Rannveig
Published: December 2020

The new paradigm of sustainable development highlights the importance of enforcing defined boundaries between economies, societies, and the environment within a system. Sustainability indicators are tools that have proven to be a successful aid in defining and creating system boundaries. This paper focuses on the development of sustainability indicators for Arctic tourism, with a special emphasis on the role of public participation in their determination. It presents a stakeholder-centric approach to indicator selection by developing a framework that effectively integrates public participation in the processes of indicators’ selection and designation, and highlights the importance of combining local and expert knowledge in these processes. The results reveal that the making of sustainability indicators is an ideal platform for local voices to be heard, and thus to have a significant stake in the overall process of tourism development. The most effective way to make their voices heard in the final decision-making process is via their evaluation of the adaptability and prioritization of these indicators. The results, furthermore, stress that sustainability indicators need to be constantly re-evaluated and updated, as tourism is part of a complex and dynamic system that is constantly changing. To provide a holistic vision of the impact of economic, environmental, and social factors, as well as the causality between them in the system, sustainability indicators must be integrated from many indicators. Since the monitoring of conventional indicators is often less complicated and more cost-effective than the monitoring of integrated indicators, a better result can however be reached by combining conventional indicators with sustainability indicators.
Resilience to Climate Change in Industrial Shrimping in Bangladesh: Assessing the Comparative Role of the Stakeholders
Authors: Kais Shaikh Mohammad , Islam Md Saidul
Published: December 2020

Over the last few decades, the global shrimp aquaculture industry has grown considerably and experienced important transformations in coastal regions in the Global South. However, despite being a major contributor to GDP and export earnings of the country, the shrimp industry in Bangladesh is not yet fully developed. This important sector is often plagued by numerous environmental challenges including frequent climate disasters. To address local climate perturbations, the shrimping industry undertakes a wide range of individual, communal, and institutional level resilience activities. Drawing on primary data collected through ethnography and qualitative interviews in three shrimping communities, this paper examines the nature, effects, and efficacy of resilience strategies adopted by various stakeholders in the shrimp industry in coastal Bangladesh. This research demonstrates that there is a clearly visible resilience gradient in the shrimp aquaculture industry in Bangladesh: individual shrimp farmers and households play a pivotal role in resilience enhancement, while other stakeholders including community, state, and civil society organizations have moderate-to-little involvement in aiding resilience in the sector. The study offers a series of recommendations for resilience to climate change.
Hidden Engineers and Service Providers: Earthworms in Agricultural Land-Use Types of South Tyrol, Italy
Authors: Rüdisser Johannes , Tasser Erich , Peham Thomas , Meyer Erwin , Tappeiner Ulrike
Published: December 2020

Earthworm activities affect the provision of many ecosystem services. Land use can strongly influence earthworm communities and, hence related soil functions. We assessed earthworm biomass, abundance, and species composition on grasslands, apple orchards, and vineyards in the context of an existing sustainability assessment tool in South Tyrol, Italy. A stratified sampling campaign revealed significant differences in earthworm distribution. We found 21 to 700 individuals m−2 in grasslands and surprisingly abundant earthworm communities in apple orchards (14 to 382 individuals m−2). Results for vineyards were ambiguous with no or very low abundance in 47% of the vineyards and a maximum of 396 individuals m−2. Mesohumic endogeic species were the most abundant functional group observed (75% of the biomass in grasslands, 50% in apple orchards and vineyards). Aporrectodea caliginosa was the most abundant endogeic species, Lumbricus rubellus the dominant polyhumic endogeic species in all land-use types. We estimated a total of 34,900 t of earthworm biomass on agricultural areas in South Tyrol corresponding to a total value of EUR 872 million. Although soil quality is a complex concept that cannot be captured with a single indicator, earthworms are suitable and feasible indicators for sustainable soil use at the landscape scale.
Food Labels (Quality, Origin, and Sustainability): The Experience of Czech Producers
Authors: Chalupová Martina , Rojík Stanislav , Kotoučková Hana , Kauerová Lenka
Published: December 2020

Food labels allow producers to promote the unique selling points of their products and help consumers buy products with value-adding qualities. The aim of the present article is hence to explore Czech food producers’ expectations and experience with selected food labels. The selection was narrowed down to enterprises that had succeeded in the certification processes within the food labeling systems of European quality and origin labels (protected designation of origin (PDO), protected geographical indication (PGI), and traditional specialty guaranteed (TSG)), as well as schemes that fall under the remit of the Ministry of Agriculture of the Czech Republic, such as Klasa (Česká potravina/Czech food, Regionální potravina/regional food, and BIO). The research was made using an online questionnaire in January–February 2018, and was focused on determining possible discrepancies in expectations that the companies had before applying for quality/origin labels and their actual experience with the labelling systems, the benefits they had gained, and their overall experience with the labels. To compare the respondents’ expectations and their actual experience, both the non-parametric Wilcoxon test and t-test were used. The results show that the respondents positively evaluated the effects of marketing communication, but their expectations had been higher than what they experienced.
Cost Modeling and Evaluation of Direct Metal Laser Sintering with Integrated Dynamic Process Planning
Authors: Di Lei , Yang Yiran
Published: December 2020

Additive manufacturing technologies have been adopted in a wide range of industries such as automotive, aerospace, and consumer products. Currently, additive manufacturing is mainly used for small-scale, low volume productions due to its limitations such as high unit cost. To enhance the scalability of additive manufacturing, it is critical to evaluate and preferably reduce the cost of adopting additive manufacturing for production. The current literature on additive manufacturing cost mainly adopts empirical approaches and does not sufficiently explore the cost-saving potentials enabled by leveraging different process planning algorithms. In this article, a mathematical cost model is established to quantify the different cost components in the direct metal laser sintering process, and it is applicable for evaluating the cost performance when adopting dynamic process planning with different layer-wise process parameters. The case study results indicate that 12.73% of the total production cost could be potentially reduced when applying the proposed dynamic process planning algorithm based on the complexity level of geometries. In addition, the sensitivity analysis results suggest that the raw material price and the overhead cost are the two key cost drivers in the current additive manufacturing market.
Mathematical Properties of Variable Topological Indices
Authors: Sigarreta José M.
Published: December 2020

A topic of current interest in the study of topological indices is to find relations between some index and one or several relevant parameters and/or other indices. In this paper we study two general topological indices Aα and Bα, defined for each graph H=(V(H),E(H)) by Aα(H)=∑ij∈E(H)f(di,dj)α and Bα(H)=∑i∈V(H)h(di)α, where di denotes the degree of the vertex i and α is any real number. Many important topological indices can be obtained from Aα and Bα by choosing appropriate symmetric functions and values of α. This new framework provides new tools that allow to obtain in a unified way inequalities involving many different topological indices. In particular, we obtain new optimal bounds on the variable Zagreb indices, the variable sum-connectivity index, the variable geometric-arithmetic index and the variable inverse sum indeg index. Thus, our approach provides both new tools for the study of topological indices and new bounds for a large class of topological indices. We obtain several optimal bounds of Aα (respectively, Bα) involving Aβ (respectively, Bβ). Moreover, we provide several bounds of the variable geometric-arithmetic index in terms of the variable inverse sum indeg index, and two bounds of the variable inverse sum indeg index in terms of the variable second Zagreb and the variable sum-connectivity indices.
A Fuzzy-Based Optimization Technique for the Energy and Spectrum Efficiencies Trade-Off in Cognitive Radio-Enabled 5G Network
Authors: Khalaf Osamah Ibrahim , Ogudo Kingsley A. , Singh Manwinder
Published: December 2020

In 5G systems, enhancing the energy efficiency (EE) and spectrum efficiency (SE) is a significant task that is capable of fulfilling the demand of future wireless networks and providing Quality of Service (QoS) provisioning, such as high throughput with low power consumption. In the present work, a look-up table enabled a fuzzy-based approach to investigate the SE and EE issues in 5G networks and obtained an efficient symmetrical trade-off between them to enhance the system’s overall performance. We completed simulations in NS-2.31 software version 2.31, and MATLAB is used to visualize the results. The proposed model achieved maximum values of EE and SE of 0.92 bit/J/Hz with a sensing time τs(LUT) = 20 ms. Furthermore, optimizing the sensing time and the secondary user (SU) transmission power, yielded a maximum EE and provided a QoS provisioned cognitive radio-enabled 5G network.
m-Polar Generalization of Fuzzy T-Ordering Relations: An Approach to Group Decision Making
Authors: Zahedi Khameneh Azadeh , Kilicman Adem
Published: December 2020

Recently, T-orderings, defined based on a t-norm T and infimum operator (for infinite case) or minimum operator (for finite case), have been applied as a generalization of the notion of crisp orderings to fuzzy setting. When this concept is extending to m-polar fuzzy data, it is questioned whether the generalized definition can be expanded for any aggregation function, not necessarily the minimum operator, or not. To answer this question, the present study focuses on constructing m-polar T-orderings based on aggregation functions A, in particular, m-polar T-preorderings (which are reflexive and transitive m-polar fuzzy relations w.r.t T and A) and m-polar T-equivalences (which are symmetric m-polar T-preorderings). Moreover, the construction results for generating crisp preference relations based on m-polar T-orderings are obtained. Two algorithms for solving ranking problem in decision-making are proposed and validated by an illustrative example.
Strategic Integration Decision under Supply Chain Competition in the Presence of Online Channel
Authors: Saha Subrata , Nielsen Izabela
Published: December 2020

This study explores the pricing decisions of substitutable products for two competing supply chains in the presence of an online channel. Each supply chain consisting of a single manufacturer and an exclusive retailer and one of the manufacturers distributes products through the online channel. We examine optimal decisions under five scenarios to explore how the strategic cooperation between two manufacturers at the upstream horizontal level or with the retailer at the vertical level affects product pricing decisions and the performance of two supply chains? The results reveal that decisions for cooperation with competing manufacturers and opening an online channel are correlated. In the absence of an online channel, cooperation with their respective retailer can lead to a higher supply chain profit. However, if a manufacturer opens an online channel, then cooperation with competing manufacturers can lead to a higher supply chain profit. Under the vertical integration, total supply chain profit might be lower compared to a scenario where members in each supply chain remain independent. Consumers also need to pay more for products.
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics
Authors: Antal Gábor , Tóth Zoltán , Hegedűs Péter , Ferenc Rudolf
Published: December 2020

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.
Paternal Biomass Smoke Exposure in Rats Produces Behavioral and Cognitive Alterations in the Offspring
Authors: Sosedova Larisa M. , Vokina Vera A. , Novikov Mikhail A. , Rukavishnikov Viktor S. , Andreeva Elizaveta S. , Zhurba Olga M. , Alekseenko Anton N.
Published: December 2020

Particular concern at the present stage is the health effects of wildfires’ smoke. The aim of the study was to determine the impact of paternal biomass-smoke exposure on offspring’s behavior and cognitive abilities. Male rats were exposed to biomass smoke for four hours/day, five days/week, for four weeks. Average concentration of carbon monoxide and particulate matter of 2.5 μm PM2.5 in the chamber during exposure were 28.7 ± 5.3 mg/m3 and 1.9 ± 0.5 mg/m3, respectively. At the same time, high concentrations of furfural and acetaldehyde were detected in the air environment of the exposure chambers. Offspring was obtained by mating of experimental males with untreated females, immediately after the end of the exposure and after 60 days (long-term period). Offspring were tested by using the Morris water maze and open field at three months of age. Male and female offspring born by mating immediately after exposure demonstrated decreased exploratory behavior, locomotor activity, and spatial navigation, as well as increased anxiety levels. Locomotor and exploratory activity in rats of both sexes from progeny obtained after long-term exposure to smoke had no statistically significant differences when compared to the control; however, the females showed a high level of anxiety and impaired cognitive functions. The recovery period after biomass-smoke intoxication, comparable in duration of spermatogenesis in rats, was an important factor in reducing the risk of developing central nervous system (CNS) disorders in offspring.
The Anemonia sulcata Toxin BDS-I Protects Astrocytes Exposed to Aβ1–42 Oligomers by Restoring [Ca2+]i Transients and ER Ca2+ Signaling
Authors: Piccialli Ilaria , Tedeschi Valentina , Boscia Francesca , Ciccone Roselia , Casamassa Antonella , de Rosa Valeria , Grieco Paolo , Secondo Agnese , Pannaccione Anna
Published: December 2020

Intracellular calcium concentration ([Ca2+]i) transients in astrocytes represent a highly plastic signaling pathway underlying the communication between neurons and glial cells. However, how this important phenomenon may be compromised in Alzheimer’s disease (AD) remains unexplored. Moreover, the involvement of several K+ channels, including KV3.4 underlying the fast-inactivating currents, has been demonstrated in several AD models. Here, the effect of KV3.4 modulation by the marine toxin blood depressing substance-I (BDS-I) extracted from Anemonia sulcata has been studied on [Ca2+]i transients in rat primary cortical astrocytes exposed to Aβ1–42 oligomers. We showed that: (1) primary cortical astrocytes expressing KV3.4 channels displayed [Ca2+]i transients depending on the occurrence of membrane potential spikes, (2) BDS-I restored, in a dose-dependent way, [Ca2+]i transients in astrocytes exposed to Aβ1–42 oligomers (5 µM/48 h) by inhibiting hyperfunctional KV3.4 channels, (3) BDS-I counteracted Ca2+ overload into the endoplasmic reticulum (ER) induced by Aβ1–42 oligomers, (4) BDS-I prevented the expression of the ER stress markers including active caspase 12 and GRP78/BiP in astrocytes treated with Aβ1–42 oligomers, and (5) BDS-I prevented Aβ1–42-induced reactive oxygen species (ROS) production and cell suffering measured as mitochondrial activity and lactate dehydrogenase (LDH) release. Collectively, we proposed that the marine toxin BDS-I, by inhibiting the hyperfunctional KV3.4 channels and restoring [Ca2+]i oscillation frequency, prevented Aβ1–42-induced ER stress and cell suffering in astrocytes.
Who Decides? Toward a Typology of Transit Governance
Authors: Fischer Lauren Ames , Ray Rosalie Singerman , King David A.
Published: December 2020

This article describes a typology for formal governance structures of public transit in the United States to support inquiry into how organizational structures influence policy making processes, organizational capacity and policy outcomes. Scholarship of public transit has largely explored outcome-based research while paying less attention to how decisions are made. Despite some transport scholarship that shows how institutional characteristics influence financing, power arrangements and public discourse, there has been little recent analysis of governance within public transit systems beyond the regional role of Metropolitan Planning Organizations (MPOs). Using data from multiple sources, we assembled a database of governance structure of transit systems in the largest 40 cities in the United States. We show that the structure of transit decision making has substantial variance across and within cities, and is far from limited to MPOs. The variety of governance models and growth of local and sub-local models suggest that local context is critical for better understanding transit priorities and decision-making processes.