bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Effect of Tillage Depth on the Working Performance of Tractor-Moldboard Plow System under Various Field Environments
Authors: Kim Yeon-Soo , Lee Sang-Dae , Baek Seung-Min , Baek Seung-Yun , Jeon Hyeon-Ho , Lee Jun-Ho , Kim Wan-Soo , Shim Jong-Yeal , Kim Yong-Joo
Published: April 2022

The purpose of this study was to analyze the tillage depth effect on the tractor-moldboard plow systems in various soil environments and tillage depths using a field load measurement system. A field load measurement system can measure the engine load, draft force, travel speed, wheel axle load, and tillage depth in real-time. In addition, measurement tests of soil properties in the soil layer were preceded to analyze the effect of field environments. The presented results show that moldboard plow at the same tillage depth had a wide range of influences on the tractor’s working load and performance under various environments. As the draft force due to soil–tool interaction occurred in the range of 5.6–17.7 kN depending on the field environment, the overall mean engine torque and rear axle torque were up to 2.14 times and 1.67 times higher in hard and clayey soil, respectively, than in soft soil environments. In addition, the results showed tractive efficiency of 0.56–0.73 and were analyzed to have a lugging ability of 67.8% with a 44% maximum torque rise. The engine power requirement in hardpan was similar within 3.6–9.6%, but the power demand of the rear axle differed by up to 18.4%.
A Personalized Task Allocation Strategy in Mobile Crowdsensing for Minimizing Total Cost
Authors: Gao Hengfei , Zhao Hongwei
Published: April 2022

Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by sorting the cost of users to complete the tasks, where the cost is measured by the expense of time or distance. In this paper, we argue that the task allocation process is actually a dynamic combinational optimization problem because the previous allocated task will influence the initial state of the user to finish the next task, and the user’s preference will also influence the actual cost. To this end, we propose a personalized task allocation strategy for minimizing total cost, where the cost for a user to finish a task is measured by both the moving distance and the user’s preference for the task, then instead of statically allocating the tasks, the allocation problem is formulated as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP). Furthermore, we transform the multiple-TSP to the single-TSP by proving the equivalency, and two solutions are presented to solve the single-TSP. One is a greedy algorithm, which is proved to have a bound to the optimal solution. The other is a genetic algorithm, which spends more calculation time while achieving a lower total cost. Finally, we have conducted a number of simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The simulation results could match the results of theoretical analysis.
Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations
Authors: Jakovlev Sergej , Eglynas Tomas , Voznak Miroslav , Jusis Mindaugas , Partila Pavol , Tovarek Jaromir , Jankunas Valdas
Published: April 2022

Due to the mechanical nature of container handling operations, as well as natural factors, container and handling infrastructure suffers various types of damage during use, especially within the tight and enclosed environments of a ship’s hull. In this operational environment, it is critical to detect any sort of physical impacts between the vertical cell guides of the ship’s hull and the container. Currently, an inspection of impacts and evaluation of any consequences is performed manually, via visual inspection processes. This process is time-consuming and relies on the technical expertise of the personnel involved. In this paper, we propose a five-step impact-detection methodology (IDM), intended to detect only the most significant impact events based on acceleration data. We conducted real measurements in a container terminal using a sensory device placed on the spreader of the quay crane. The proposed solution identified an average of 12.8 container impacts with the vertical cell guides during common handling operations. In addition, the results indicate that the presented IDM can be used to recognize repeated impacts in the same space of each bay of the ship, and can be used as a decision support tool for predictive maintenance systems.
Food Habits: Insights from Food Diaries via Computational Recurrence Measures
Authors: Pai Amruta , Sabharwal Ashutosh
Published: April 2022

Humans are creatures of habit, and hence one would expect habitual components in our diet. However, there is scant research characterizing habitual behavior in food consumption quantitatively. Longitudinal food diaries contributed by app users are a promising resource to study habitual behavior in food selection. We developed computational measures that leverage recurrence in food choices to describe the habitual component. The relative frequency and span of individual food choices are computed and used to identify recurrent choices. We proposed metrics to quantify the recurrence at both food-item and meal levels. We obtained the following insights by employing our measures on a public dataset of food diaries from MyFitnessPal users. Food-item recurrence is higher than meal recurrence. While food-item recurrence increases with the average number of food-items chosen per meal, meal recurrence decreases. Recurrence is the strongest at breakfast, weakest at dinner, and higher on weekdays than on weekends. Individuals with relatively high recurrence on weekdays also have relatively high recurrence on weekends. Our quantitatively observed trends are intuitive and aligned with common notions surrounding habitual food consumption. As a potential impact of the research, profiling habitual behaviors using the proposed recurrent consumption measures may reveal unique opportunities for accessible and sustainable dietary interventions.
An Aptasensor Based on a Flexible Screen-Printed Silver Electrode for the Rapid Detection of Chlorpyrifos
Authors: Inam A. K. M. Sarwar , Angeli Martina Aurora Costa , Douaki Ali , Shkodra Bajramshahe , Lugli Paolo , Petti Luisa
Published: April 2022

In this work, we propose a novel disposable flexible and screen-printed electrochemical aptamer-based sensor (aptasensor) for the rapid detection of chlorpyrifos (CPF). To optimize the process, various characterization procedures were employed, including Fourier transform infrared spectroscopy (FT-IR), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV). Initially, the aptasensor was optimized in terms of electrolyte pH, aptamer concentration, and incubation time for chlorpyrifos. Under optimal conditions, the aptasensor showed a wide linear range from 1 to 105 ng/mL with a calculated limit of detection as low as 0.097 ng/mL and sensitivity of 600.9 µA/ng. Additionally, the selectivity of the aptasensor was assessed by identifying any interference from other pesticides, which were found to be negligible (with a maximum standard deviation of 0.31 mA). Further, the stability of the sample was assessed over time, where the reported device showed high stability over a period of two weeks at 4 °C. As the last step, the ability of the aptasensor to detect chlorpyrifos in actual samples was evaluated by testing it on banana and grape extracts. As a result, the device demonstrated sufficient recovery rates, which indicate that it can find application in the food industry.
UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
Authors: Abdel-Malek Mai A. , Sayed Muhammad M. , Azab Mohamed
Published: April 2022

Next Generation cellular networks are expected to offer better service quality, secure and reliable service provisioning, and more cooperative operation even in unexpected stressful situations. Service provider cooperation can facilitate reliable service provisioning and extended coverage in disasters situations or partial network failures. However, the current 4G and 5G standards do not offer security and privacy-friendly support for inter-operator agility and service mobility, a key enabler for such cooperation. The situation becomes more critical in presence of attackers, where establishing trust relationships becomes very complicated. This paper presents a novel UAV-assisted user-agility support framework that enables trustworthy seamless service migration in a zero-trust environment. The proposed framework facilitates temporal authentication-authority delegation and proxying to enable preservice, all-party mutual authentication. The framework is implemented and tested on top of the srsRAN open-source 4G/5G software stack. Experiments showed that the presented framework managed to facilitate effective and efficient trustworthy service migration between heterogeneous service provider networks.
Design, Fabrication and Analysis of Magnetorheological Soft Gripper
Authors: Bernat Jakub , Gajewski Piotr , Kapela Rafał , Marcinkowska Agnieszka , Superczyńska Paulina
Published: April 2022

The magnetorheological elastomer is promising material for applications in soft robotics. Its properties like reactive to external magnetic field and softness allow to construct an attractive devices. This work presents a construction of soft gripper assembled with magnetorheological elastomers. The work describes the detailed molding process of magnetorheological elastomers. Further, the electromechanical properties of magnetorheological elastomers are shown using a simple beam. Finally, the soft gripper is constructed and analyzed with the series of experiments.
State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere
Authors: Chang Yu-Chi , Lin Chia-Hsien , Dmitriev Alexei V. , Hsieh Mon-Chai , Hsu Hao-Wei , Lin Yu-Ciang , Mendoza Merlin M. , Huang Guan-Han , Tsai Lung-Chih , Li Yung-Hui , Tsogtbaatar Enkhtuya
Published: April 2022

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-DenseNet24), deep watershed transform (DWT), Mask R-CNN, and spatial attention-UNet (SA-UNet) for the recovery of ionograms. The performance of the models is evaluated by intersection over union (IoU). We collect and manually label 6131 ionograms, which are acquired from a low-latitude ionosonde in Taiwan. These ionograms are contaminated by strong quasi-static noise, with an average signal-to-noise ratio (SNR) equal to 1.4. Applying the five models to these noisy ionograms, we show that the models can recover useful signals with IoU > 0.6. The highest accuracy is achieved by SA-UNet. For signals with less than 15% of samples in the data set, they can be recovered by Mask R-CNN to some degree (IoU > 0.2). In addition to the number of samples, we identify and examine the effects of three factors: (1) SNR, (2) shape of signal, (3) overlapping of signals on the recovery accuracy of different models. Our results indicate that FC-DenseNet24, DWT, Mask R-CNN and SA-UNet are capable of identifying signals from very noisy ionograms (SNR < 1.4), overlapping signals can be well identified by DWT, Mask R-CNN and SA-UNet, and that more elongated signals are better identified by all models.
Deep Learning-Based Indoor Localization Using Multi-View BLE Signal
Authors: Koutris Aristotelis , Siozos Theodoros , Kopsinis Yannis , Pikrakis Aggelos , Merk Timon , Mahlig Matthias , Papaharalabos Stylianos , Karlsson Peter
Published: April 2022

In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.
Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology
Authors: Zhang Shuheng , Zeng Hanguo , Ji Wei , Yi Kun , Yang Shuangfeng , Mao Peisheng , Wang Zhanjun , Yu Hongqian , Li Manli
Published: April 2022

Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.
SMART SKY EYE System for Preliminary Structural Safety Assessment of Buildings Using Unmanned Aerial Vehicles
Authors: Bae Jaehoon , Lee Jonghoon , Jang Arum , Ju Young K. , Park Min Jae
Published: April 2022

The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been increasingly used for safety surveillance in the construction industry and civil engineering fields. This paper presents an aerial image-based approach using UAVs to inspect cracks and deformations in buildings. A state-of-the-art safety evaluation method termed SMART SKY EYE (Smart building safety assessment system using UAV) is introduced; this system utilizes an unmanned airplane equipped with a thermal camera and programmed with various surveying efficiency improvement methods, such as thermography, machine-learning algorithms, and 3D point cloud modeling. Using this method, crack maps, crack depths, and the deformations of structures can be obtained. Error rates are compared between the proposed and conventional methods.
Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent
Authors: Chhetri Tek Raj , Kurteva Anelia , DeLong Rance J. , Hilscher Rainer , Korte Kai , Fensel Anna
Published: April 2022

The enforcement of the GDPR in May 2018 has led to a paradigm shift in data protection. Organizations face significant challenges, such as demonstrating compliance (or auditability) and automated compliance verification due to the complex and dynamic nature of consent, as well as the scale at which compliance verification must be performed. Furthermore, the GDPR’s promotion of data protection by design and industrial interoperability requirements has created new technical challenges, as they require significant changes in the design and implementation of systems that handle personal data. We present a scalable data protection by design tool for automated compliance verification and auditability based on informed consent that is modeled with a knowledge graph. Automated compliance verification is made possible by implementing a regulation-to-code process that translates GDPR regulations into well-defined technical and organizational measures and, ultimately, software code. We demonstrate the effectiveness of the tool in the insurance and smart cities domains. We highlight ways in which our tool can be adapted to other domains.
Feasibility of SERS-Active Porous Ag Substrates for the Effective Detection of Pyrene in Water
Authors: Capaccio Angela , Sasso Antonio , Rusciano Giulia
Published: April 2022

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants that are typically released into the environment during the incomplete combustion of fossil fuels. Due to their relevant carcinogenicity, mutagenicity, and teratogenicity, it is urgent to develop sensitive and cost-effective strategies for monitoring them, especially in aqueous environments. Surface-enhanced Raman spectroscopy (SERS) can potentially be used as a reliable approach for this purpose, as it constitutes a valid alternative to traditional techniques, such as liquid and gas chromatography. Nevertheless, the development of an SERS-based platform for detection PAHs has so far been hindered by the poor adsorption of PAHs onto silver- and gold-based SERS-active substrates. To overcome this limitation, several research efforts have been directed towards the development of functionalized SERS substrates for the improvement of PAH adsorption. However, these strategies suffer from the interference that functionalizing molecules can produce in SERS detection. Herein, we demonstrate the feasibility of label-free detection of pyrene by using a highly porous 3D-SERS substrate produced by an inductively coupled plasma (ICP). Thanks to the coral-like nanopattern exhibited by our substrate, clear signals ascribable to pyrene molecules can be observed with a limit of detection of 23 nM. The observed performance can be attributed to the nanoporous character of our substrate, which combines a high density of hotspots and a certain capability of trapping molecules and favoring their adhesion to the Ag nanopattern. The obtained results demonstrate the potential of our substrates as a large-area, label-free SERS-based platform for chemical sensing and environmental control applications.
Chemical Profiling and Characterization of Different Cultivars of Cannabis sativa L. Inflorescences by SPME-GC-MS and UPLC-MS
Authors: Cicaloni Vittoria , Salvini Laura , Vitalini Sara , Garzoli Stefania
Published: April 2022

The chemical profile of the Cannabis sativa L. female inflorescences is rather complex being characterized by a large number of molecules belonging to different chemical classes. Considering the numerous applications in various fields, including the medical and pharmaceutical sectors, that have seen a large use of Cannabis genus in recent years, a precise characterization of the matrices is essential. In this regard, the application of adequate and suitable sampling and analysis techniques becomes important in order to provide an identification of the metabolites characterizing the profile of the sample under examination. The goal of this work is to provide additional information on the chemical composition of the inflorescences of five C. sativa different cultivars grown in Emilia Romagna (Italy) through the application of sophisticated analysis techniques such as Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry and Ultra-Performance Liquid Chromatography-Mass Spectrometry (SPME-GC-MS and UPLC-MS). The obtained data highlighted the presence of a high number of volatile and non-volatile compounds, thus allowing a comparative evaluation of the different samples. Furthermore, an in-depth statistical survey by Principal Components Analysis (PCA) and HeatMap, Hierarchical luster Analysis (HCA) and Partial Least Squares Discriminant Analysis (PLS-DA-VIP), was conducted to consider any correlations between the investigated cultivars. The findings of this study may help to provide more information on the C. sativa inflorescences useful for potential applications of their metabolites in scientific research.
QuEChERS Method Combined with Gas- and Liquid-Chromatography High Resolution Mass Spectrometry to Screen and Confirm 237 Pesticides and Metabolites in Cottonseed Hull
Authors: Tong Kaixuan , Xie Yujie , Huang Siqi , Liu Yongcheng , Wu Xingqiang , Fan Chunlin , Chen Hui , Lu Meiling , Wang Wenwen
Published: April 2022

Cottonseed hull is a livestock feed with large daily consumption. If pesticide residues exceed the standard, it is easy for them to be introduced into the human body through the food chain, with potential harm to consumer health. A method for multi-residue analysis of 237 pesticides and their metabolites in cottonseed hull was developed by gas-chromatography and liquid-chromatography time-of-flight mass spectrometry (GC-QTOF/MS and LC-QTOF/MS). After being hydrated, a sample was extracted with 1% acetic acid in acetonitrile, then purified in a clean-up tube containing 400 mg MgSO4, 100 mg PSA, and 100 mg C18. The results showed that this method has a significant effect in removing co-extracts from the oily matrix. The screening detection limit (SDL) was in the range of 0.2–20 μg/kg, and the limit of quantification (LOQ) was in the range of 0.2–20 μg/kg. The recovery was verified at the spiked levels of 1-, 2-, and 10-times LOQ (n = 6), and the 237 pesticides were successfully verified. The percentages of pesticides with recovery in the range of 70–120% were 91.6%, 92.8%, and 94.5%, respectively, and the relative standard deviations (RSDs) of all pesticides were less than 20%. This method was successfully applied to the detection of real samples. Finally, this study effectively reduced the matrix effect of cottonseed hull, which provided necessary data support for the analysis of pesticide residues in oil crops.
Children’s Participation in the Design of Smart Solutions: A Literature Review
Authors: Pellegrino Maria Angela , Roumelioti Eftychia , D’Angelo Mauro , Gennari Rosella
Published: April 2022

Smart solutions are widespread and diversified. Smart cities and smart objects are example of smart solutions. Their design usually follows certain patterns so that they can detect events and react accordingly. As future citizens, children are expected to interact with them in their daily lives. It is thus crucial to provide children with the tools for understanding, creating, and possibly programming them—in short, designing them. This paper presents a literature review of workshops involving children in designing smart solutions. The review coded a total of 25 papers that met the inclusion criteria. The discussion draws a research agenda using the workshops organized by the authors, the Roobopoli workshops for smart cities and the SNaP workshops for smart objects, as reference. Whereas smart cities and smart objects are usually addressed separately, this paper binds and compares them in order to investigate what, in different settings, enables children to be part of the design of smart solutions.
Dependability Modeling of Software Systems with UML and DAM: A Guide for Real-Time Practitioners
Authors: Bernardi Simona , Merseguer José , Petriu Dorina C.
Published: April 2022

The modeling of system non-functional properties is a broad field. Among these properties, dependability is an important one for real-time and embedded systems. On the other hand, UML offers the profiling mechanism to address specific modeling domains. In particular, the DAM (dependability analysis and modeling) profile provides a modeling framework for dependability in the model-driven paradigm. This work is for practitioners to understand the basics of dependability modeling, using DAM. In this sense, the paper digests the literature to understand the concept of the UML profile, the MARTE profile and to obtain a practical guide on dependability modeling using DAM. The modeling approach is illustrated through a case study taken from the literature.
Uncertainty in the Calibration Transfer of Solar Irradiance Scale: From Absolute Cavity Radiometers to Standard Pyrheliometers
Authors: Balenzategui José Lorenzo , Molero María , Silva José Pedro , Fabero Fernando , Cuenca José , Mejuto Eduardo , De Lucas Javier
Published: April 2022

In this work, the method for calculation of uncertainty of pyrheliometers’ responsivity during their outdoor calibration process in the laboratory is exposed. It is applied first for calibration of standard pyrheliometers by comparison to cavity radiometers, and after for calibration of an end-user pyrheliometer against that standard pyrheliometer. The dissemination of the WRR irradiance scale is illustrated in practice and the increasing uncertainty in the traceability chain is quantified. The way of getting traceability to both WRR scale and to SI units in the current situation, where the shift between these radiometric scales is pending to be solved, is also explained. However, the impact of this gap between scales seems to be more important for calibrations of reference Class A pyrheliometers than in the final determination of DNI irradiance, because in this case, the cumulative uncertainty is large enough as to not significantly be affected for the difference. The way to take into account different correction terms in the measurement model function, and how to compute the corresponding uncertainty, is explained too. The influence of temperature of some pyrheliometers during calibration process and the potential impact on the DNI irradiance calculated with these instruments is exemplified.
Arsenic-Induced Oxidative Stress and Antioxidant Defense in Plants
Authors: Nahar Kamrun , Rhaman Mohammad Saidur , Parvin Khursheda , Bardhan Kirti , Marques Deyvid Novaes , García-Caparrós Pedro , Hasanuzzaman Mirza
Published: April 2022

The non-essential metalloid arsenic (As) is widely distributed in soil and underground water of many countries. Arsenic contamination is a concern because it creates threat to food security in terms of crop productivity and food safety. Plants exposed to As show morpho-physiological, growth and developmental disorder which altogether result in loss of productivity. At physiological level, As-induced altered biochemistry in chloroplast, mitochondria, peroxisome, endoplasmic reticulum, cell wall, plasma membrane causes reactive oxygen species (ROS) overgeneration which damage cell through disintegrating the structure of lipids, proteins, and DNA. Therefore, plants tolerance to ROS-induced oxidative stress is a vital strategy for enhancing As tolerance in plants. Plants having enhanced antioxidant defense system show greater tolerance to As toxicity. Depending upon plant diversity (As hyperaccumulator/non-hyperaccumulator or As tolerant/susceptible) the mechanisms of As accumulation, absorption or toxicity response may differ. There can be various crop management practices such as exogenous application of nutrients, hormones, antioxidants, osmolytes, signaling molecules, different chelating agents, microbial inoculants, organic amendments etc. can be effective against As toxicity in plants. There is information gap in understanding the mechanism of As-induced response (damage or tolerance response) in plants. This review presents the mechanism of As uptake and accumulation in plants, physiological responses under As stress, As-induced ROS generation and antioxidant defense system response, various approaches for enhancing As tolerance in plants from the available literatures which will make understanding the to date knowledge, knowledge gap and future guideline to be worked out for the development of As tolerant plant cultivars.
Analysis of the Development of Industrial Symbiosis in Emerging and Frontier Market Countries: Barriers and Drivers
Authors: Boom-Cárcamo Efrain , Peñabaena-Niebles Rita
Published: April 2022

Industrial symbiosis (IS) allows the use of the resources of a productive chain, based on collaboration between companies, finding ways to use the waste of one as inputs or raw materials for the other entity. IS seeks to generate environmental sustainability, maximize resources, and generate social, environmental, and economic benefits based on the physical exchanges of waste, residues, and materials, which generate various advantages for companies and environmental benefits for society. Over the years, research has been conducted worldwide on the implementation of IS in business settings and case studies related to IS in countries with strong economies; however, no papers mapping studies on IS that are focused on emerging and frontier market countries have been identified, and academic literature on research in these countries is also scarce. In this research, an in-depth review of the literature on IS cases in emerging and frontier market countries was conducted to provide future researchers with information on the similarities, weaknesses, strengths, and elements to consider in addressing the topic and closing research gaps in the area. In addition, a mapping was made of the evolution of studies on IS according to country, economic activity, distribution by journal, year of publication, methods used, barriers and drivers in the case studies, and the importance of this topic in the current academic context. In Asian and developing countries, the integration of companies and economic activities takes place in industrial parks, and they have legislation and government regulations that support IS. On the other hand, in the United States and Africa, integrating various sources such as energy, water, coal, and waste in industrial environments is at an early stage of development, and opportunities are being identified to promote IS between companies. This research interests a broad audience, including investors, regulators, policymakers, and researchers interested in fostering IS in emerging and frontier market countries as a mechanism for industrial and economic development.
Predicting Advertisement Revenue of Social-Media-Driven Content Websites: Toward More Efficient and Sustainable Social Media Posting
Authors: Li Szu-Chuang , Chen Yu-Ching , Chen Yi-Wen , Huang Yennun
Published: April 2022

Social media platforms such as Facebook have been a crucial web traffic source for content providers. Content providers build websites and apps to publish their content and attract as many readers as possible. More readers mean more influence and revenue through advertisement. As Internet users spend more and more time on social media platforms, content websites also create social media presence, such as Facebook pages, to generate more traffic and thus revenue from advertisements. With so much content competing for limited real estate on social media users’ timelines, social media platforms begin to rank the contents by user engagements of previous posts. Posting content to social media that receives little user interaction will hurt the content providers’ future presence on social media. Content websites need to consider business sustainability when utilizing social media, to ensure that they can respond to short-term financial needs without compromising their ability to meet their future needs. The present study aims to achieve this goal by building a model to predict the advertisement revenue, which is highly correlated with user engagements, of an intended social media post. The study examined combinations of classification methods and data resampling techniques. A content provider can choose the combination that suits their needs by comparing the confusion matrices. For example, the XGBoost model with undersampled data can reduce the total post number by 87%, while still making sure that 49% of the high-performance posts will be posted. If the content provider wants to make sure more high-performance posts are posted, then they can choose the DNN(Deep Neural Network) model with undersampled data to post 66% of high-performance posts, while reducing the number of total posts by 69%. The study shows that predictive models could be helpful for content providers to balance their needs between short-term revenue income and long-term social media presence.
What Is the Role of Tourism Management and Marketing toward Sustainable Tourism? A Bibliometric Analysis Approach
Authors: Pahrudin Pahrudin , Liu Li-Wei , Li Shao-Yu
Published: April 2022

Sustainability is one of the current much-discussed issues in the world, including in tourism sectors. However, there are few studies on tourism management and marketing toward sustainable tourism. This study aims to analyze the literature on tourism management and marketing toward sustainable tourism by using a bibliometric analysis approach based on the Web of Science (WOS) database. A sample of 528 articles was analyzed in this study. The data were processed and the results were graphically presented by using the R program. The results found the occurrence of publications by year, publication source information and authors, journals, countries, institutions, thematic maps, and current trends of topics in tourism management and marketing toward sustainable tourism. Furthermore, the role of tourism management and marketing toward sustainable tourism were discussed in this study. The bibliometric analysis helps to identify a future research agenda in sustainable tourism.
Multi-Risk Source Oil Spill Risk Assessment Based on a Fuzzy Inference System
Authors: Jiang Yao , Zhao Xu , Wang Yaochi , Wang Jinyu
Published: April 2022

Oil is one of the most important sources of energy, about 25 percent of which comes from offshore sources. As a result, the transportation of oil tankers, and the construction of offshore oil platforms and subsea pipelines have increased, to facilitate offshore oil exploitation. However, most oil spill risk assessments analyze the impact of one risk source, and rarely consider multiple risk sources in the study area. This paper focuses on three risk sources that may cause oil spills in a certain area, and establishes an oil spill risk assessment model through a fuzzy inference system. Oil spill probabilities for different risk sources are calculated through the model. According to the definition of oil spill risk, the risk probability of multiple risk sources in the study area is obtained, which can provide technical support for regional oil spill emergency capacity and emergency resource allocation.
Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
Authors: Natividade Jorge , Cruz Carlos Oliveira , Silva Cristina Matos
Published: April 2022

The construction sector and the operation and maintenance of buildings largely contribute to energy consumption and emission of greenhouse gases (GHGs) in the European Union (EU). Therefore, it is of utmost importance to improve the energy performance of buildings. Yet, this frequently involves high short-term investments, which may not be compatible with owners’ budgetary constraints. In this research we analyze the importance of Energy Performance Contracting (EnPC) for the improvement of energy efficiency in buildings. These models allow bypassing budgetary restrictions of owners (public and private ones) and bring private capital to finance energy efficiency measures. The paper analyses different models of contracting Energy Service Companies (ESCOs), from traditional models to alternative models, and exposes the versatility of the new contracting models and the associated risks. Several applications of energy performance contracts implemented in European countries are presented to identify the main characteristics that lead to successful contracts. The paper also includes the discussion of energy performance contracts applied to a public building (a school) that seeks to reduce its annual energy consumption, by testing the use of three types of energy performance contracts. The results show that there is potential in the use of EnPC but it is critical to select the most adequate model, especially when defining the contract duration, to balance both owners’ and companies’ interests.
Mechanical Behavior of Low-Density Polyethylene Waste Modified Hot Mix Asphalt
Authors: Rincón-Estepa Jessica Adaluz , González-Salcedo Esthefanny Victoria , Rondón-Quintana Hugo Alexander , Reyes-Lizcano Fredy Alberto , Bastidas-Martínez Juan Gabriel
Published: April 2022

The use of polymeric waste in the modification of asphalt binders for the construction of road pavements is a technique studied several years ago. However, the use of these materials involves high temperatures close to 190 °C, which generate large environmental impacts due to their emissions. In this study, an asphalt cement (AC) with low-density polyethylene (LDPE) residue contents of 5%, 7%, and 10% with respect to the mass of the AC was modified by the wet method. The modification was carried out using a temperature of 150 °C with the aim of preventing the oxidation of the AC and reducing the emissions generated at high temperatures. Based on the physical-rheological properties of the modified asphalt binder, it was found that 5% LDPE produces the best performance. Subsequently, a hot-mix asphalt type HMA-19 control without a modified asphalt binder and another with a modified asphalt binder were manufactured in order to evaluate the mechanical behavior by means of the Marshall test, an indirect tensile strength (ITS) test, resilient modulus (RM) testing, resistance to fatigue testing, permanent deformation testing, and the Cantabro test. Additionally, the asphalt mixtures were tested under the conditions of short-term aging (STOA, Short-Term Oven Aging), long-term aging (STOA + LTOA, Long-Term Oven Aging), and partial saturation with water (STOA + LTOA + water). Based on the results, an ANOVA analysis of variance was performed to assess whether the changes in the mechanical response of the modified mixture are statistically significant with respect to the control mixture. As a general conclusion, it is reported that mixtures with LDPE can be used for thick layers in high-temperature climates in order to control rutting.
Comment on Bettignies et al. The Scale-Dependent Behaviour of Cities: A Cross-Cities Multiscale Driver Analysis of Urban Energy Use. Sustainability 2019, 11, 3246
Authors: Arbabi Hadi , Meyers Gregory , Tan Ling-Min , Mayfield Martin
Published: April 2022

Bettignies et al. examine power-law relationships between drivers of energy use and urban features at city and infra-city levels for ten different cities in six countries across four continents, featuring a wide distribution of urban indicators from various data sources. The authors employ univariate linear regression models using selected log-transformed indicators to investigate whether the intensity of energy use scales with urban indicators such as population size, density, and income. Bettignies et al. suggest that based on their findings, the urban energy-use drivers are in fact scale-dependent, and that their results reveal a substantial heterogeneity across and within cities. They reference this as why more consideration needs to be paid to local factors when devising urban policy. On this note, we argue that Bettignies et al. appear to have not only misunderstood the urban scaling literature they have cited, but have also employed flawed methodological design in their analysis that ultimately leaves their conclusions unsubstantiated.
Analyzing Safety Concerns of (e-) Bikes and Cycling Behaviors at Intersections in Urban Area
Authors: Wang Jian , Chen Ye , Chen Dawei
Published: April 2022

Extensive effort has been devoted to examining the causal relationship between contributing factors and injury severities. Given the important role of riders’ behaviors in traffic conflicts, this paper aims to analyze the causal effects of traffic conflicts resulting from riders’ behaviors at intersections. The authors collected video data on 152 traffic conflicts caused by riders’ dangerous behaviors in Jiangning District, China. This paper proposes a Bayesian-structural equation modeling (BSEM) approach. Based on the obtained BSEM path coefficient diagram, the factor loadings and path coefficients are analyzed to unveil the potential influence of factors, including personal features, dangerous behavior tendency, temporal and spatial characteristics of dangerous behavior, and the external environment. The results show that compared to human factors, environmental factors have a less direct impact on the severity of traffic conflicts; instead, they have an indirect positive impact on traffic conflicts by affecting behaviors. That is, if riders judge that road conditions are not suitable to conduct dangerous behaviors, they become more cautious in view of current road conditions and time revenue. Furthermore, dangerous cycling behaviors that continue to encroach on the time and space of motorized vehicles are prone to be more dangerous. The dangerous behaviors that continuously encroach on the time and space of motor vehicles (e.g., disobeying traffic signals and riding in a motorway) are significant predictors of serious conflicts. Considering the heterogeneity of riding behavior, these findings could be applied to develop effective education and intervention programs for preventing riders’ high-risk behaviors and improving the traffic environment.
Responses in Soil Carbon and Nitrogen Fractionation after Prescribed Burning in the Montseny Biosphere Reserve (NE Iberian Peninsula)
Authors: Chowdhury Sangita , Manjón-Cabeza José , Ibáñez Mercedes , Mestre Christian , Broncano Maria José , Mosquera-Losada María Rosa , Plaixats Josefina , Sebastià M.-Teresa
Published: April 2022

Prescribed fire is one of the most widely-used management tools to recover encroached rangelands. Fire has been reported to cause changes in the soil physical and chemical properties. However, the legacy effects of former plant species on soil responses to fire remains unknown. The legacy effect of the former extant plant species on soil carbon (C) and nitrogen (N) fractionation distribution after prescribed burning in topsoil (0–5 cm and 5–10 cm) was investigated in Mediterranean shrublands in Montseny. We sampled soils under five vegetation patch types: Cytisus scoparius L., Calluna vulgaris L., Erica arborea L., Pteridium aquilinum L., and Cladonia biocrusts, pre- and post-burning. Multivariate analysis on soil C and N fractions showed that soils under the legume Cytisus and the biocrust were the most differentiated. Vegetation patch types tended to respond differently to burning, soils under Cytisus, Cladonia and Calluna showing the strongest response. Total C and N, and C and N in sand decreased after burning in the 0–5 cm soil layer. Conversely, C in silt, as well as N in clay and silt, increased with soil depth after burning. This study will be helpful for understanding ecological legacy effects and their possible consequences when planning prescribed burning.
The Slowdown in China’s Energy Consumption Growth in the “New Normal” Stage: From Both National and Regional Perspectives
Authors: Cao Lizhan , Wang Hui
Published: April 2022

A series of systematic changes have occurred in the areas of growth rate, economic structure, and growth engine in China’s economic “new normal” stage. This study aims to evaluate how these systematic changes affect the slowdown in China’s energy consumption growth at both national and regional levels. We propose a nested index decomposition analysis (NIDA) model to uncover both the production- and demand-side factors. Development patterns are also defined in terms of energy consumption deceleration. Results show that the national energy consumption deceleration is mainly attributed to economic slowdown rather than improvements in economic structure (including energy mix, industrial structure, regional structure, and demand structure) and energy efficiency, implying that China’s current development pattern is unsustainable because the energy consumption deceleration is gained mainly at the expense of economic expansion. From a regional perspective, the developed regions are on an unsustainable path toward energy consumption deceleration because of relatively limited potential for structural updates and efficiency gains; while most of the less developed regions are on sustainable or unbalanced development paths. Policy recommendations are provided for both national and regional energy consumption deceleration.
Modeling and Evaluation of Market Incentives for Battery Electric Vehicles
Authors: Ogunkunbi Gabriel Ayobami , Al-Zibaree Havraz Khedhir Younis , Meszaros Ferenc
Published: April 2022

Electric mobility has great potential to help achieve global climate change mitigation goals. Within the domain of electric vehicles (EVs), battery electric vehicles (BEVs) have the largest advantage over conventional internal combustion engine technology. However, their penetration depends on many factors. Several studies have investigated the different policy incentives supporting the EV market, but less analytical research has been conducted to evaluate the different incentives. This study investigates the wide range of incentives that were adopted across 15 countries between 2010 and 2018, and their effectiveness in the market amidst other factors, using a generalized linear model. The results of the statistical analysis identified the availability of local BEV production facility, income, accessibility to fast charging infrastructure, and value-added tax exemptions as positive and statistically significant factors to the market share of BEVs across the years, while positive significant correlations were not identified for the other factors and incentives investigated. This emphasizes the impact of economic performance, technological certainty, and the presence of charging infrastructure with fast charging solutions in BEV adoption decision making, and could be an indication to policymakers of the limited impact of other factors when considered over a length of time.