bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO
Authors: Wang Zhuang , Liu Cheng , Hu Qihou , Dong Yunsheng , Liu Haoran , Xing Chengzhi , Tan Wei
Published: May 2021

Persistent heavy haze episodes have repeatedly shrouded North China in recent years. Besides anthropogenic emissions, natural dust also contributes to the aerosols in this region. Through continuous observation by a dual-wavelength Raman lidar, the primary aerosol types and their contributions to air pollution in North China were determined. The following three aerosol types can be classified: natural dust, anthropogenic aerosols, and the mixture of anthropogenic aerosols and dust (polluted dust). The classification results are basically consistent with the classification results from the cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) satellite measurements. The relative bias of the lidar ratio between the Raman lidar and CALIPSO is less than 25% over 90% of the cases, indicating that the CALIPSO lidar ratio selection algorithm is reasonable. The classification results show that approximately 45% of aerosols below 1.8 km are contributed by polluted dust during our one year observations. The contribution of dust increased with height, from 6% at 500 m to 28% at 1,800 m, while the contribution of anthropogenic aerosols decreased from 49% to 25%. In addition, polluted dust is the major aerosol subtype below 1.0 km in spring (over 60%) and autumn (over 70%). Anthropogenic aerosols contribute more than 75% of air pollution in summer. In winter, anthropogenic aerosols prevailed (over 80%) in the lower layer, while polluted dust (around 60%) dominated the upper layer. Our results identified the primarily aerosol types to assess the contributions of anthropogenic and natural sources to air pollution in North China, and highlight that natural dust plays a crucial role in lower-layer air pollution in spring and autumn, while controlling anthropogenic aerosols will significantly improve air quality in winter.
Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
Authors: Wu Zechuan , Li Mingze , Wang Bin , Quan Ying , Liu Jianyang
Published: May 2021

Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment.
Detection of Antibodies against Hepatitis A Virus (HAV) by a Surface Plasmon Resonance (SPR) Biosensor: A New Diagnosis Tool Based on the Major HAV Capsid Protein VP1 (SPR-HAVP1)
Authors: Santos Gabriel Menezes Costa dos , Alves Carlos Roberto , Pinto Marcelo Alves , Amado Leon Luciane Almeida , Souza-Silva Franklin
Published: May 2021

Hepatitis A (HA) is an acute human infectious disease caused by a positive single-stranded RNA virus (HAV). It is mainly acquired through the fecal–oral route and is primarily spread by contact between people and exposure to contaminated water and food. Recently, large outbreaks of HA have been reported by low and moderate endemicity countries, emphasizing its importance in public health and the need for rapid and large-scale diagnostic tests to support public health decisions on HA. This work proposes a new tool for HAV diagnosis based on the association of surface plasmonic resonance with major capsid protein VP1 (SPR-HAVP1 assay), detecting IgM antibodies for HAV in human serum samples. Structural analyses of VP1 B-lymphocyte epitopes showed continuous and discontinuous epitopes. The discontinuous epitopes were identified in the N-terminal region of the VP1 protein. Both epitope types in the VP1 protein were shown by the reactivity of VP1 in native and denaturing conditions to IgM anti-HAV, which was favorable to tests of VP1 in the SPR assays. SPR-HAVP1 assays showed good performance in the detection of IgM polyclonal antibody anti-HAV. These assays were performed using a COOH5 sensor chip functionalized with VP1 protein. The sensorgram record showed a significant difference between positive and negative serum samples, which was confirmed by analysis of variation of initial and final dissociation values through time (ΔRUd/t). The data gathered here are unequivocal evidence that the SPR-HAVP1 strategy can be applied to detect IgM antibodies in human serum positive to the HAV. This is a new tool to be explored to diagnose human HAV infections.
Mathematical Analysis and Micro-Spacing Implementation of Acoustic Sensor Based on Bio-Inspired Intermembrane Bridge Structure
Authors: Shen Xiang , Zhao Liye , Xu Jiawen , Yao Xuwei
Published: May 2021

A biomimetic study on the auditory localization mechanism of Ormia ochracea was performed to improve the localization ability of small acoustic systems. We also present a microscale implementation of an acoustic localization device inspired by the auditory organ of the parasitic O. ochracea. The device consists of a pair of circular membranes coupled together with an elastic beam. The coupling serves to amplify the difference in magnitude and phase between the two membranes’ responses as the incident angle of the sound changes, allowing directional information to be deduced from the coupled device response. The research results show that the intermembrane bridge structure improves the sound source localization and directional weak acoustic signal acquisition of sound detectors. The recognition rate of the phase difference and amplitude ratio was greatly improved. The theoretical resolution of the incident angle of the sound source can reach 2° at a phase difference recognition rate of 5°. The sound source’s optimal identification frequency range for the coupling device based on the intermembrane bridge bionic structure is 300 Hz to 1500 Hz.
Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic–Swarm Optimization for the Inverse Kinematic Solution
Authors: Soleimani Amiri Mohammad , Ramli Rizauddin
Published: May 2021

It is necessary to control the movement of a complex multi-joint structure such as a robotic arm in order to reach a target position accurately in various applications. In this paper, a hybrid optimal Genetic–Swarm solution for the Inverse Kinematic (IK) solution of a robotic arm is presented. Each joint is controlled by Proportional–Integral–Derivative (PID) controller optimized with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called Genetic–Swarm Optimization (GSO). GSO solves the IK of each joint while the dynamic model is determined by the Lagrangian. The tuning of the PID is defined as an optimization problem and is solved by PSO for the simulated model in a virtual environment. A Graphical User Interface has been developed as a front-end application. Based on the combination of hybrid optimal GSO and PID control, it is ascertained that the system works efficiently. Finally, we compare the hybrid optimal GSO with conventional optimization methods by statistic analysis.
Design Guidelines for Database-Driven Internet of Things-Enabled Dynamic Spectrum Access
Authors: Guimarães Dayan A. , Pereira Elivander J. T. , Alberti Antônio M. , Moreira Jonas V.
Published: May 2021

The radio-frequency spectrum shortage, which is primarily caused by the fixed allocation policy, is one of the main bottlenecks to the deployment of existing wireless communication networks, and to the development of new ones. The dynamic spectrum access policy is foreseen as the solution to this problem, since it allows shared spectrum usage by primary licensed and secondary unlicensed networks. In order to turn this policy into reality, the secondary network must be capable of acquiring reliable, real-time information on available bands within the service area, which can be achieved by means of spectrum sensing, spectrum occupancy databases, or a combination of them. This Review presents guidelines related to the design of a framework that can be adopted to foster dynamic spectrum access policies. The framework applies special-purpose Internet of Things (IoT) devices that perform spectrum sensing, subsequently feeding a spectrum occupancy database, which in turn will be used by the secondary network to gather information on location-dependent spectrum availability. The guidelines address technological enablers capable of making the framework feasible, reliable and secure.
A Triggering Mechanism for Cyber-Attacks in Naval Sensors and Systems
Authors: Leite Junior Walmor Cristino , de Moraes Claudio Coreixas , de Albuquerque Carlos E. P. , Machado Raphael Carlos Santos , de Sá Alan Oliveira
Published: May 2021

In the maritime sector, the integration of radar systems, Automatic Identification System (AIS) and Electronic Chart Display and Information System (ECDIS) through digital technologies enables several benefits to maritime operations, but also make ships prone to cyberattacks. In this context, this work investigates the feasibility of an attacker using a radar system or AIS as open door to remotely send commands to a cyber threat hosted on a ship, even if the ship’s systems are air gapped—i.e., are not connected to other networks. The received commands are intended to trigger a cyber threat located in the ship. Although the literature covers several analyzes on cyber risks and vulnerabilities in naval systems, it lacks exploiting mechanisms capable of acknowledging attack commands received through radar and AIS. To this end, this work proposes a triggering mechanism that uses a template matching technique to detect specific patterns transmitted by the attacker to the ship’s radar or AIS. The results show the effectiveness of the proposed technique as a tool to acknowledge the received attack commands and activate a malicious code previously installed on the ship. In the case of attacks on a radar system, the accuracy achieved by the proposed method is 0.90. In the case of attacks on an AIS/ECDIS setup it presents an accuracy of 0.93. In both cases the proposed mechanism maintains the due safety against accidental attack activations.
Spectral Efficiency Improvement of 5G Massive MIMO Systems for High-Altitude Platform Stations by Using Triangular Lattice Arrays
Authors: Dicandia Francesco Alessio , Genovesi Simone
Published: May 2021

The beneficial effects of adopting a triangular lattice on phased arrays with regular and periodic grids for high-altitude platform station (HAPS) systems are presented in the scenario of massive MIMO communications operating within the 5G NR n257 and n258 frequency bands. Assessment of a planar array with 64 elements (8 × 8) is provided for both a triangular lattice and a square one in terms of array gain, average sidelobe level (ASLL), and mutual coupling. Particular attention is devoted to illustrating the impact of the antenna array lattice at the system level by evaluating its significant merits, such as its spectral efficiency (SE) and signal-to-interference ratio (SIR). The better performance exhibited by the triangular lattice array in comparison to the square one makes it appealing for the 5G massive MIMO paradigm.
Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors
Authors: Shin Sungtae , Yoon Han Ul , Yoo Byungseok
Published: May 2021

Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).
A UAV-Based Air Quality Evaluation Method for Determining Fugitive Emissions from a Quarry during the Railroad Life Cycle
Authors: KIM Min-kyeong , Jang Yelim , Heo Jaeseok , Park Duckshin
Published: May 2021

Gravel is used in railway infrastructure to reduce environmental impacts and noise, but gravel on tracks must be replaced continuously because it deforms due to wear and weathering. It is therefore necessary to review the entire railroad life cycle. In this study, an unmanned aerial vehicle (UAV) was used to measure resuspended dust over a wide area. The dust was generated from transport movements in relation to the operation of a quarry, which represents the first stage of the railway life cycle. The dust was measured at Gangwon-do quarry using a Sniffer4D module, which can provide measurements at 1 s intervals through a light scattering method and has high reliability (R2 = 0.95 for PM2.5, R2 = 0.88 for PM10). The hourly generation of fugitive dust was calculated as 2937.5 g/h for PM2.5 and 4293.2 g/h for PM10. The social cost of dust generation was calculated as KRW 36.59 billion. The amount of dust generated per hour at the quarry was ~12 times greater than that generated by the operation of a regulator as a maintenance vehicle, with the largest amount of fugitive dust generated by the washing-type vehicle. This is the first study to measure the amount of fugitive dust generated in real time at 1 s intervals by monitoring the first stage of the railroad life cycle over a wide area using a Sniffer4D module attached to a UAV. This method can be replicated for use in various studies.
Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot
Authors: Kao Sho-Tsung , Ho Ming-Tzu
Published: May 2021

The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball’s position and velocity. The ball’s future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball.
V-Spline: An Adaptive Smoothing Spline for Trajectory Reconstruction
Authors: Cao Zhanglong , Bryant David , Molteno Timothy C.A. , Fox Colin , Parry Matthew
Published: May 2021

Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline—which we name the V-spline—that incorporates position and velocity information and a penalty term that controls acceleration. We introduce an adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is proposed, and, in simulation studies, the V-spline shows superior performance to existing methods. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle.
Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection
Authors: Yan Yung-Jhe , Cheng Nai-Lun , Jan Chia-Ing , Tsai Ming-Hsui , Chiou Jin-Chern , Ou-Yang Mang
Published: May 2021

This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.
Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China
Authors: Han Aru , Lu Xiaoling , Qing Song , Bao Yongbin , Bao Yuhai , Ma Qing , Liu Xingpeng , Zhang Jiquan
Published: May 2021

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.
Generating Instructive Questions from Multiple Articles to Guide Reading in E-Bibliotherapy
Authors: Xin Yunxing , Cao Lei , Wang Xin , He Xiaohao , Feng Ling
Published: May 2021

E-Bibliotherapy deals with adolescent psychological stress by manually or automatically recommending multiple reading articles around their stressful events, using electronic devices as a medium. To make E-Bibliotherapy really useful, generating instructive questions before their reading is an important step. Such a question shall (a) attract teens’ attention; (b) convey the essential message of the reading materials so as to improve teens’ active comprehension; and most importantly (c) highlight teens’ stress to enable them to generate emotional resonance and thus willingness to pursue the reading. Therefore in this paper, we propose to generate instructive questions from the multiple recommended articles to guide teens to read. Four solutions based on the neural encoder-decoder model are presented to tackle the task. For model training and testing, we construct a novel large-scale QA dataset named TeenQA, which is specific to adolescent stress. Due to the extensibility of question expressions, we incorporate three groups of automatic evaluation metrics as well as one group of human evaluation metrics to examine the quality of the generated questions. The experimental results show that the proposed Encoder-Decoder with Summary on Contexts with Feature-rich embeddings (ED-SoCF) solution can generate good questions for guiding reading, achieving comparable performance on some semantic similarity metrics with that of humans.
A Systematic Review of Location Aware Schemes in the Internet of Things
Authors: Khan Muneeb A. , Saboor Abdul , Kim Hyun-chul , Park Heemin
Published: May 2021

The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications’ performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.
Gender Diversity in Research and Innovation Projects: The Proportion of Women in the Context of Higher Education
Authors: Restrepo Natalia , Unceta Alfonso , Barandiaran Xabier
Published: May 2021

Europe has gradually consolidated its integration of the gender dimension in the field of research and innovation. Institutional structures have shown that the development of gender diversity policies have improved the conditions of equality between women and men. However, the representation of women in the workforce is still an everyday and universal concern. This article analyses the position of women in academic research. For this purpose, the evolution of the participation of women in research and innovation projects in the context of higher education is observed, taking the University of the Basque Country, Spain, as a case study. In order to evaluate the situation of women in research and innovation projects, the authors analyse a database with 75,864 records of projects collected between 2007 and 2018. The analysis confirms that a more balanced participation has been achieved between women and men in research and innovation teams and in project management structures. However, it also shows that gender diversity should continue to be a priority, in addition to its integration in science and technology financing programmes. Knowing this reality can be useful to promote the intensive development of public policies and to contribute towards the effort to improve the statistics of women’s participation in science.
A Study of Multiregional Economic Correlation Analysis Based on Big Data—Taking the Regional Economy of Cities in Shaanxi Province, China, as an Example
Authors: Tuo Shouheng , He Hong
Published: May 2021

To enhance the sustainability of the regional economy, this study attempts to integrate historical big data of multiregional and multi-industry economic indicators, aiming to explore and discover the correlations among regions, industries, or cross-regional economic indicators. In this paper, two correlation analysis models (the 2-order correlation model and the elastic-net regularized generalized linear model) are used to conduct a correlation analysis study of multiregional and multi-industry economies, and 20 years of historical data from 9 prefecture-level cities in Shaanxi (778 indicators in total) are analyzed empirically. The results show that the proposed method can mine complex correlations from economic big data.
Multi-Criteria Decision Analysis Using Life Cycle Assessment and Life Cycle Costing in Circular Building Design: A Case Study for Wall Partitioning Systems in the Circular Retrofit Lab
Authors: Rajagopalan Neethi , Brancart Stijn , De Regel Sofie , Paduart Anne , Temmerman Niels De , Debacker Wim
Published: May 2021

The Circular Economy (CE) paradigm has been gaining momentum. However, the tools and methods used to design, measure and implement circularity are not immediately suitable for decision making and practice by key stakeholders. This article details a qualitative and a quantitative method to evaluate characteristics such as circularity, adaptability and reuse of building elements amongst others in order to provide decision-makers, such as building clients, architects, investors and policy makers, an objective way to assess the benefits and constraints of circular buildings and elements. The study implements the method in the case study, the Circular Retrofit Lab in Belgium, and uses a multi-criteria decision approach to evaluate qualitative parameters and life cycle assessment and life cycle costing to quantitatively evaluate the circular solutions proposed in this study. As such, the paper shows how a multi-criteria decision approach can be applied to evaluate circular building solutions in the context of practical architectural projects, in this case assessing the suitability of three interior wall systems for applications with different turnover rates. The study shows that the overall performance of the evaluated wall systems varies largely from one expected user scenario to the other.
Analyzing Sustainability Reports of Global, Public Corporations by Industrial Sectors and National Origins
Authors: Kim Hyun-Duck
Published: May 2021

Due to the demand by stakeholder groups of global public corporations for greater transparency in business operations, corporations have continuously tried to embody the concept of sustainability in their business strategies and operations. That is, they have collectively published sustainability reports to state their progress toward achieving sustainability goals. However, understanding of the thematic and conceptual structures of environmental and sustainability reports of the global public corporations is still limited. In this study, the author identified key thematic attributes through text data mining analysis: (a) sustainability, (b) energy, (c) approach, (d) environmental, and (e) people for industrial sectors. Additionally, themes of (a) business, (b) employees, (c) financial, (d) energy, and (e) suppliers appeared most frequently and were the top five compounding themes for overall national origins. In fact, the majority of findings pointed out that these themes and concepts have limited environmental and climatic relevance, as they only align with certain goals, such as UN SDGs 12, 13, and 14. That is, understanding key factors of their sustainability reports is crucial toward overcoming the challenges of achieving SDGs. Furthermore, these findings in accordance with industrial sectors and national origins of the global public corporations can help to derive a more in-depth understanding of current reports on environmental and sustainability-driven business operations.
Reimagining “Collaborative Exploration”—A Signature Pedagogy for Sustainability in Early Childhood Education and Care
Authors: Ødegaard Elin Eriksen
Published: May 2021

The purpose of this article is to identify the components and features of a signature pedagogy for sustainability in early childhood education and care to respond to the call for tradition and innovation in early childhood education. Collaborative exploration is proposed as a pedagogical strategy, a relevant mode of action for sustainable practice. This is a conceptual article that recalls the origins of early childhood pedagogy and uses an exemplary empirical narrative from a recent study to illustrate collaborative exploration in an early childhood educational setting. The outlining of the key features of collaborative exploration is furthermore inspired by dialogism. This article provides an argument against mainstream understandings of pedagogical strategies for early childhood education, which are often based on instrumental program approaches, emphasizing the transmission of information in a traditional classroom setting. It is argued that practices of collaborative exploration are embodied in a way that is aligned with the tradition of child-centered early years pedagogy. Moreover, they are crucial to ensuring that all participating children are given responsive support to become members of ecologically, socially and culturally sustainable educational practices, strengthening children’s resilience and agency and inclusive education. The article’s value lies in its potential to support teachers’ thinking and practice in recognizing and articulating collaborative exploration as a signature pedagogy.
Aeroponics Root Chamber Temperature Conditioning Design for Smart Mini-Tuber Potato Seed Cultivation
Authors: Kuncoro C. Bambang Dwi , Sutandi Tandi , Adristi Cornelia , Kuan Yean-Der
Published: May 2021

The aeroponic plant root environment has a significant role in producing high-quality seed tuber potatoes. However, in lowland and tropical regions, the aeroponic system cannot yield high-quality potato seed because the average environment temperature year-round is high. In a high-temperature environment, the potato plant roots cannot optimally absorb the nutrient solution for healthy plant growth. This paper presents the method used to maintain the aeroponics root chamber temperature conditions. An air conditioning system was adopted to supply air with the optimal temperature range for mini-tuber potato seed cultivation. The vapor compression refrigeration type was applied in the air conditioning system. The root chamber temperature is controlled and monitored using an Arduino Uno board system. The mini-tuber potato seed cultivation field experiment results show the proposed method can maintain the aeroponic root chamber temperature. The root chamber temperature treatment operated in the 10 °C–20 °C range. This temperature range improved the potato seed tuber yield. The potato seed tuber yield potential is observed from the stolon number produced by the mini-tuber potato plants cultivated in the root chamber with the conditioned temperature. The field experiment reveals that the stolon number produced by potato seeds cultivated in the root chamber with conditioned temperature was up to 77% greater than the number of potato seeds cultivated in the root chamber with the unconditioned temperature.
Using FADN Data to Estimate CO2 Abatement Costs from Italian Arable Crops
Authors: Bazzani Guido M. , Vitali Giuliano , Cardillo Concetta , Canavari Maurizio
Published: May 2021

The assessment of economic and environmental sustainability of agricultural systems represents a critical issue, which has been addressed in this work with a multi-objective programming model to explore the abatement costs (AC) of CO2 for a set of representative contexts of Italian arable land agriculture. The study was based on the FADN-compliant Italian database RICA and estimates the abatement costs of CO2 emissions in a short time horizon, using linear multi-objective programming and compromise programming. RICA data were used to quantify technical parameters of the model, adopting an innovative concept of a cropping scheme to simulate land-use adaptation. The study shows a quite diversified situation regarding income and emission levels per hectare across the Italian region and farm classes. A reduction of CO2 emissions higher than 5 kg/ha at an AC lower than 1 EUR/kg is affordable only in seven regions, among which Abruzzo, Lombardy, and Puglia show the highest potential. Comparing the estimated abatement costs for CO2 emissions with the corresponding European Trade System prices highlights a difference of 1 order of magnitude, proving that emission reductions for Italian arable crops still require research and innovation to lower adaptation costs.
Development of a Low-Cost Biomass Furnace for Greenhouse Heating
Authors: Ali Asif , Iqbal Tahir , Cheema Muhammad Jehanzeb Masud , Afzal Arslan , Yasin Muhammad , Haq Zia ul , Malik Arshad Mahmood , Khan Khalid Saifullah
Published: May 2021

The energy crisis and increasing fossil fuel prices due to increasing demands, controlled supplies, and global political unrest have adversely affected agricultural productivity and farm profitability across the globe and Pakistan is not an exception. To cope with this issue of energy deficiency in agriculture, the best alternate strategy is to take advantage of biomass and solid waste potential. In low-income countries such as Pakistan, the greenhouse heating system mostly relies on fossil fuels such as diesel, gasoline, and LPG. Farmers are reluctant to adopt greenhouse farming due to the continuously rising prices of the fossil fuels. To reduce reliance on fossil fuel energy, the objective of this study was to utilize biomass from crop residues to develop an efficient and economical biomass furnace that could heat greenhouses to protect the crop from seasonal temperature effects. Modifications made to the biomass furnace, such as the incorporation of insulation around the walls of the furnace, providing turbulators in fire tubes, and a secondary heat exchanger (heat recovery system) in the chimney, have increased the thermal efficiency of the biomass furnace by about 21.7%. A drastic reduction in hazardous elements of flue gases was observed due to the addition of a water scrubber smoke filter in the exit line of the flue. The efficiency of the biomass furnace ranged from 50.42% to 54.18%, whereas the heating efficiency of the diesel-fired heater was 71.19%. On the basis of the equal heating value of the fuels, the unit material and operating costs of the biomass furnace for wood, cotton stalks, corn cobs, and cow dung were USD 2.04, 1.86, 1.78, and 2.00 respectively against USD 4.67/h for the diesel heater. The capital and operating costs of the biomass furnace were about 50% and 43.7% of the diesel heater respectively, resulting in a seasonal saving of about 1573 USD. The produced smoke was tested as environmental friendly under the prescribed limits of the National Environmental Quality Standards (NEQS), which shows potential for its large-scale adoption and wider applications.
Automated Valuation Modelling: Analysing Mortgage Behavioural Life Profile Models Using Machine Learning Techniques
Authors: Nica Ionuț , Alexandru Daniela Blană , Crăciunescu Simona Liliana Paramon , Ionescu Ștefan
Published: May 2021

The main purpose of this research is to study the predictive power of behavioural life profile models for mortgages using machine learning techniques and emerging languages from the same data sets. Based on the results, banks can determine whether the predictive power of the model can be improved regarding estimates of probability of redemption, and probability of internal transfer beyond traditional techniques. Model training will take place using algorithms based on machine learning such as: random forests, extreme gradient, boosting, light gradient boosting, Adaboost, and ExtraTrees. To perform simulations on fast learning and permit testing of hypotheses, the IBM cloud environment and the Watson proven analytical environment will be used, in order to maximize the value derived from the investment and determine the decision on the implementation and modelling strategy for business disciplines. Therefore, these factors could provide a solid basis for the sustainable development of the mortgage market, and the approach in this research is a starting point for identifying the best decisions taken by banking institutions to contribute to the sustainable development of mortgage lending.
A Novel Service Provision Mode for Sustainable Development of the Telecom Industry
Authors: Wang Xueyan , Wu Hong , Lu Lu
Published: May 2021

The deepening integration of telecommunication technology into other industries has giving birth to a variety of new applications, such as self-driving, telemedicine, and intelligent manufacturing. Whether the telecom service is traditional or new, users put forward personalized and multidimensional requirements for performance, which results in a conflict between their requirements for customized service and the failure of telecom enterprises to meet every requirement. This contradiction directly influences the sustainability and stable development of the telecom industry. To address this problem, this paper carried out systematic research into collaboration and adaptation between business-model and technological innovation. From the view of business-model innovation, this paper proposes a novel service provision mode named Multidimensional Customization of Telecom Services for Each User to analyze the value of the business model and the factors that influence it. From the view of technological innovation, it will design an implementation scheme corresponding to the business model, and verify its advantages in network-carrying capacity and significance to enterprises’ business value through a simulation experiment. The research shows that the novel mode is beneficial both to telecom users and telecom enterprises. In addition, it addresses this novel mode’s impact on environmental sustainability and the regulation of the telecom industry, as well as the limitations of this research and future research directions.
Compulsory Education Teachers’ Perceptions of Resources, Extracurricular Activities and Inclusive Pedagogical Training in Spain
Authors: Leiva-Olivencia Juan José , López-Berlanga Maria Carmen , Miñán Espigares Antonio , Villegas Lirola Francisco
Published: May 2021

Inclusive education is an issue of great interest and social and pedagogical significance in the quality of the education system. Its impact on the context, reality and training of teachers is a decisive impulse to build an open mind in relation to diversity as a characteristic element of education and today’s society. The objective of this study was to explore the perceptions of compulsory education teachers (primary and secondary) about teacher training, resources, and after-school activities in the care of students with specific educational support needs associated with disability in 12 Autonomous Communities of Spain. A survey has been carried out, for which an ad hoc questionnaire was built, involving 2457 docents. A descriptive and inferential analysis has been carried out by means of an average comparison between each issue and the different intrapersonal factors. Specifically, two types of tests have been used, using the SPSS version 25 program for analysis: testing independent samples (Levene test and t-test for equal means) and one-way ANOVA according to the type of independent variable considered. Among the results is the need to increase teacher training in inclusive education, the existence of divergences on the material, and spatial resources available for diversity care. Similarly, the relevance of after-school activities was identified as initiatives and spaces for the visibility of diversity and culture of inclusion in schools.
Modeling the Fourth Dimension of Architectural Heritage: Enabling Processes for a Sustainable Conservation
Authors: Mammoli Raissa , Mariotti Chiara , Quattrini Ramona
Published: May 2021

This study focuses on modeling the fourth dimension of historic architectures with an HBIM approach and special regard to stratigraphic analysis. The goal is to push the limits of current technology to understand the history of buildings, with impacts on protecting their authenticity; it is pursued with a practitioners-oriented methodology able to make aware models of their phases. The target audience are experts in the field of heritage conservation, while the outcome is to support long-term strategies for the sustainable management of heritage. Contents follow this structure: (1) Introduction: this section frames the benefits of affirming heritage’s physical authenticity and managing risks; it clarifies assumptions and the research aim; (2) State of the Art: this highlights the topic relevance, which is not yet fully resolved, focusing on semantics, critical-interpretative data control, and on the automation of some crucial results; (3) Materials and Methods: this describes the integrated workflow, including the photogrammetric acquisition, modeling, and data enrichment, the semi-automatic Harris matrix construction, and the optimization of laser data; (4) Results: this presents the results of modelling stratigraphic units, enriching them with information according to a semantics coherent with the conservation process, to govern the temporal relations while automating key outputs; (5) Discussion: this section refines the implemented solutions and introduce future works.
Communities of Innovation for Climate Change Adaptation and Disaster Risk Reduction: Niche Creation and Anticipation
Authors: López-Gunn Elena , Swinkels Julian , Anzaldúa Gerardo , Bea Manuel , Colaço Maria Conceição , Deksne Māra , Lalaj Nensi , McDonald Hugh , Rica Marta
Published: May 2021

This paper presents the concept of communities of innovation for climate change adaptation and disaster risk reduction. The paper discusses the added value of these types of communities to help address uncertain futures from the impact of climate change, which are highly context dependent. The paper frames these communities of innovation as part of innovation ecosystems in order to reflect on their key elements and added value. We argue that climate change and disaster risk management responses need to meet the needs of those experiencing problems with those that can offer solutions in distinct localities, including those that could fund or finance potential innovative solutions. Developing communities of innovation with the specific task of anticipating and creating niche solutions has been gaining traction in the EU. Some developed under the H2020 BRIGAID project are analysed here for other emergent COIs, as future-oriented communities tasked with the challenge to reduce disaster risks and enhance the climate resilience in their own spaces. This paper reflects on the experience of participants in these communities and their reflections and experience on whether these offer a useful form of organisation to anticipate future challenges, create niche solutions, and bring innovations to the market. The paper concludes with how communities of innovation can contribute to niche design and narratives of change to help achieve social and environmental resilience to reframe and help transition and transform current systems into more resilient, future-oriented communities.
Impacts of COVID-19 on Agricultural Production Branches: An Investigation of Anxiety Disorders among Farmers
Authors: Cevher Celal , Altunkaynak Bulent , Gürü Meltem
Published: May 2021

The aim of our study was to determine the level of anxiety among farmers in different agricultural branches in Turkey during the COVID-19 outbreak and to examine its association with socioeconomic concerns and social support variables. Based on a survey of 2125 Turkish farm enterprises, this study examined effects on agricultural production during the implementation of COVID-19 restrictions among agricultural branches. The Generalized Anxiety Disorder Scale and Oslo Social Support Scale were used in the study. Survey data were collected from farmers by phone. Age, place of residence, income status, agricultural branches, land size, the use of trucks, animal husbandry, access to technical support from agricultural organizations, access to support from neighbors, and social support level were found to have a significant effect on anxiety level (p < 0.05). The anxiety levels of farmers engaged in animal husbandry and vegetable farming were found to be higher than those in other agricultural branches. The lowest anxiety level was observed in farmers engaged in cereal production. Taking these results into consideration is important for preventing problems in agricultural production. If anxiety levels are not improved, it is predicted that farmers in agricultural production branches with high levels of anxiety will move towards branches with lower levels of anxiety.