bluuebunny/crossref_metadata_2025 huggingface dataset:
Harmonizing Definitions and Methods to Estimate Deforestation at the Lacandona Tropical Region in Southern Mexico
Authors: Fernández-Montes de Oca Ana , Ghilardi Adrián , Kauffer Edith , Gallardo-Cruz José Alberto , Núñez Juan Manuel , Sánchez-Cordero Víctor
Published: May 2022

Deforestation is a major factor reducing natural habitats, leading to tropical ecosystems and biodiversity loss worldwide. The Lacandona region in southern Mexico holds one of the largest fragments of tropical rainforest in North America. We evaluated the deforestation of the Lacandona region harmonizing concepts and methodologies. An international (FAO definition), governmental (national definition), and regional definition of deforestation with applications at different scales were analyzed and harmonized with two classification methods (likelihood and spectral angle mapper (SAM)). We used 2015 and 2018 Landsat 8 images, and likelihood and SAM classifications were applied for FAO and regional definitions of deforestation. Overall, the best evaluated classifier in quantity was likelihood for 2015 and 2018 (kappa: 0.87 and 0.70, overall accuracy: 91.8 and 80.4%, and quantity disagreement: 4.1 and 10 %, respectively). The allocation disagreement only showed exchange between classes. Nevertheless, they did not show differences between classifiers, although 2015 had less disagreement than 2018: exchange, 4.1% for likelihood and SAM; shift: 0% for likelihood and SAM. Maps based on the regional definition of deforestation showed that the likelihood classification detected 11,441 ha less deforestation than SAM (40,538 and 51,979 ha, respectively). The FAO definition of deforestation showed that likelihood classification detected 11,914 ha less deforestation than SAM classification (37,152 and 49,066 ha, respectively). Further, the likelihood classification showed 3387 ha more of deforestation according to the regional definition than the FAO definition of deforestation (40,538 and 37,152 ha, respectively). SAM classification showed that the regional definition showed 2913 ha more deforestation than the FAO definition (51,979 and 49,066, respectively). We concluded that implementation of governmental programs in the Lacandona region requires estimations based on a careful selection of deforestation definitions and methods.
Using Satellite NDVI Time-Series to Monitor Grazing Effects on Vegetation Productivity and Phenology in Heterogeneous Mediterranean Forests
Authors: Balata Duarte , Gama Ivo , Domingos Tiago , Proença Vânia
Published: May 2022

The reintroduction of livestock grazing to regulate biomass load is being tested for large-scale restoration in Mediterranean landscapes affected by rural abandonment. Concurrently, there is a need to develop cost-effective methods to monitor such interventions. Here, we investigate if satellite data can be used to monitor the response of vegetation phenology and productivity to grazing disturbance in a heterogenous forest mosaic with herbaceous, shrub, and tree cover. We identify which vegetation seasonal metrics respond most to grazing disturbances and are relevant to monitoring efforts. The study follows a BACI (Before-After-Control-Impact) design applied to a grazing intervention in a Pyrenean oak forest (Quercus pyrenaica) in central Portugal. Using NDVI time-series from Sentinel-2 imagery for the period between June 2016 and June 2021, we observed that each type of vegetation exhibited a distinct phenology curve. Herbaceous vegetation was the most responsive to moderate grazing disturbances with respect to changes in phenology and productivity metrics, namely an anticipation of seasonal events. Results for shrubs and trees suggest a decline in peak productivity in grazed areas but no changes in phenology patterns. The techniques demonstrated in this study are relevant to a broad range of use cases in the large-scale monitoring of fine-grained heterogeneous landscapes.
Parameter Estimation for Sea Clutter Pareto Distribution Model Based on Variable Interval
Authors: Fan Yifei , Chen Duo , Tao Mingliang , Su Jia , Wang Ling
Published: May 2022

The generalized Pareto (GP) distribution model is often used to describe the amplitude statistical feature of sea clutter. Generally, the parameters of GP distribution are estimated by moments estimators. However, when the sea state is high, the appearance of sea spikes will increase the echo of the anomalous scattering units, which leads to a decrease in the parameter estimation accuracy and target detection performance. To improve the parameter estimation accuracy, this paper proposes a novel parameter estimation method based on variable intervals. Considering the local properties of sea clutter, we take a variable interval of the entire sea clutter series for parameter estimation, where the interval position is selected according to the sea state conditions. For contrast, the bipercentile parameter estimation and truncate moment estimation are also introduced. Finally, the experiment based on the real measured X-band sea clutter datasets indicates that the proposed method outperforms the state-of-the-art moments estimators.
Investigating the Magnetotelluric Responses in Electrical Anisotropic Media
Authors: Luo Tianya , Hu Xiangyun , Chen Longwei , Xu Guilin
Published: May 2022

When interpreting magnetotelluric (MT) data, because of the inherent anisotropy of the earth, considering electrical anisotropy is crucial. Accordingly, using the edge-based finite element method, we calculated the responses of MT data for electrical isotropic and anisotropic models, and subsequently used the anisotropy index and polar plot to depict MT responses. High values of the anisotropy index were mainly yielded at the boundary domains of anomalous bodies for isotropy cases because the conductive differences among isotropic anomalous bodies or among anomalous bodies and background earth can be regarded as macro-anisotropy. However, they only appeared across anomalous bodies in the anisotropy cases. The anisotropy index can directly differentiate isotropy from anisotropy but exhibits difficulty in reflecting the azimuth of the principal conductivities. For the isotropy cases, polar plots are approximately circular and become curves with a big ratio of the major axis to minor axis, such as an 8-shaped curve for the anisotropic earth. Furthermore, the polar plot can reveal the directions of principal conductivities. However, distorted by anomalous bodies, polar plots with a large ratio of the major axis to minor axis occur in isotropic domains around the anomalous bodies, which may lead to the misinterpretation of these domains as anisotropic earth. Therefore, combining the anisotropy index with a polar plot facilitates the identification of the electrical anisotropy.
Application of the Doppler Spectrum of the Backscattering Microwave Signal for Monitoring of Ice Cover: A Theoretical View
Authors: Karaev Vladimir , Titchenko Yury , Panfilova Maria , Ryabkova Maria , Meshkov Eugeny , Ponur Kirill
Published: May 2022

In the radar remote sensing of sea ice, the main informative parameter is the backscattering radar cross section (RCS), which does not always make it possible to unambiguously determine the kind of scattering surface (ice/sea waves) and therefore leads to errors in estimating the area of the ice cover. This paper provides a discussion of the possibility of using the Doppler spectrum of the reflected microwave signal to solve this problem. For the first time, a semi-empirical model of the Doppler spectrum of a radar microwave signal reflected by an ice cover was developed for a radar with a wide antenna beam mounted on a moving carrier at small incidence angles of electromagnetic waves (0°–19°). To describe the Doppler spectrum of the reflected microwave signal, the following parameters were used: shift and width of the Doppler spectrum, as well as skewness and kurtosis coefficients. Research was conducted on the influence of the main parameters of the measurement scheme (movement velocity, width of antenna beam, sounding direction, incidence angle) and the sea ice concentration (SIC) on the parameters of the Doppler spectrum. It was shown that, in order to determine the kind of scattering surface, it is necessary to use a wide or knife-like (by the incidence angle) antenna. Calculations confirmed the assumption that, when measured from a moving carrier, the Doppler spectrum is a reliable indicator of the transition from one kind of scattering surface to another. The advantage of using the coefficients of skewness and kurtosis in the analysis is that it is not necessary to keep the radar velocity unchanged during the measurement process.
Classification of Eurasian Watermilfoil (Myriophyllum spicatum) Using Drone-Enabled Multispectral Imagery Analysis
Authors: Brooks Colin , Grimm Amanda , Marcarelli Amy M. , Marion Nicholas P. , Shuchman Robert , Sayers Michael
Published: May 2022

Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance SAV study and management, especially if these approaches can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present an uncrewed aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM), primarily using a six-band Tetracam multispectral camera, flown over sites in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. We analyzed water chemistry and light data and found our sites clustered into sites with higher and lower water clarity, although all sites had relatively high water clarity. The overall average accuracy achieved was 76.7%, with 78.7% producer’s and 77.6% user’s accuracy for the EWM. These accuracies were higher than previously reported from other studies that used remote sensing to map SAV. Our study found that two tested scale parameters did not lead to significantly different classification accuracies between sites with higher and lower water clarity. The EWM classification methodology described here should be applicable to other SAV species, especially if they have growth patterns that lead to high amounts of biomass relative to other species in the upper water column, which can be detected with the type of red-edge and infrared sensors deployed for this study.
Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield
Authors: Chen Shuo , Liu Weihang , Feng Puyu , Ye Tao , Ma Yuchi , Zhang Zhou
Published: May 2022

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.
Hyperspectral Image Denoising via Group Sparsity Regularized Hybrid Spatio-Spectral Total Variation
Authors: Zhang Pengdan , Ning Jifeng
Published: May 2022

In this paper, we propose a new hyperspectral image (HSI) denoising model with the group sparsity regularized hybrid spatio-spectral total variation (GHSSTV) and low-rank tensor decomposition, which is based on the analysis of structural sparsity of HSIs. First, the global correlations among all modes are explored by the Tucker decomposition, which applies low-rank constraints to the clean HSIs. To avoid over-smoothing, we propose GHSSTV regularization to ensure the group sparsity not only in the first-order gradient domain but also in the second-order ones along the spatio-spectral dimensions. Then, the sparse noise in HSI can be detected by the ℓ1 norm. Furthermore, strong Gaussian noise is simulated by the Frobenius norm. The alternating direction multiplier method (ADMM) algorithm is employed to effectively solve the GHSSTV model. Finally, experimental results from a series of simulations and real-world data suggest a superior performance of the GHSSTV method in HSIs denoising.
Analysing Process and Probability of Built-Up Expansion Using Machine Learning and Fuzzy Logic in English Bazar, West Bengal
Authors: Das Tanmoy , Shahfahad  , Naikoo Mohd Waseem , Talukdar Swapan , Parvez Ayesha , Rahman Atiqur , Pal Swades , Asgher Md Sarfaraz , Islam Abu Reza Md. Towfiqul , Mosavi Amir
Published: May 2022

The study sought to investigate the process of built-up expansion and the probability of built-up expansion in the English Bazar Block of West Bengal, India, using multitemporal Landsat satellite images and an integrated machine learning algorithm and fuzzy logic model. The land use and land cover (LULC) classification were prepared using a support vector machine (SVM) classifier for 2001, 2011, and 2021. The landscape fragmentation technique using the landscape fragmentation tool (extension for ArcGIS software) and frequency approach were proposed to model the process of built-up expansion. To create the built-up expansion probability model, the dominance, diversity, and connectivity index of the built-up areas for each year were created and then integrated with fuzzy logic. The results showed that, during 2001–2021, the built-up areas increased by 21.67%, while vegetation and water bodies decreased by 9.28 and 4.63%, respectively. The accuracy of the LULC maps for 2001, 2011, and 2021 was 90.05, 93.67, and 96.24%, respectively. According to the built-up expansion model, 9.62% of the new built-up areas was created in recent decades. The built-up expansion probability model predicted that 21.46% of regions would be converted into built-up areas. This study will assist decision-makers in proposing management strategies for systematic urban growth that do not damage the environment.
Enhancing Wire-Rope Damage Signals Based on a Radial Magnetic Concentrator Bridge Circuit
Authors: Tian Jie , Wang Wei , Wang Hongyao , Bai Qiang , Zhou Zeyang , Li Pengbo
Published: May 2022

Hall-effect sensors are used for the non-destructive testing of wire ropes owing to their low power consumption and high operation frequency. The high-speed operation of wire ropes causes vibration inclination at different frequencies, which makes it difficult to detect the ropes. Considering that the radial signal in the magnetic flux leakage (MFL) detection method can respond to damages to the maximum extent possible, this study proposes a radial magnetic concentrator suitable for the non-destructive testing of wire ropes based on theoretical analysis and transient magnetic field simulations. The concentrator improves the radial magnetic circuit, polymerizes the leakage of the magnetic field in the detection device, and the leakage of the magnetic field of the defect converges at the sensor position of the circumferential array to improve the signal-to-noise ratio of the Hall-effect sensor. In addition, the MFL field is homogenized through the structure of the magnetic concentrator when the wire rope is tilted, which weakens the influence of the vibration tilt of the wire rope on the test results. Finally, the experiments show that the amplitude of the wire-rope damage signal is effectively improved by using the proposed radial magnetic concentration technology, hence being convenient for defect analyses.
4-Dimethylaminoantipyrine as a Broad Electrochemical Indicator for Immunosensors Platform
Authors: Melo Francielli C. C. , Alves Renata P. , Valle Anderson L. , Santos Fabiana de A. A. , Dias Ana Carolina S. , Goulart Isabela M. B. , Oliveira Eduardo G. A. , Oliveira Guedmiller S. , Rodrigues Luciano P. , Goulart Luiz R.
Published: May 2022

Here, we describe 4-dimethylaminoantipyrine (4-DMAA)-mediated interfacing as a broad biochemical indicator to stabilize and promote the higher response of electrodes for immunological detection. We hypothesized that the improved biological interactions of 4-DMAA with electrodes and biological samples may be due to the interaction properties of the benzene and pyrazole chemical groups with graphite and proteins, respectively. In order to demonstrate that 4-DMAA could be used as a general indicator in electrochemical immunoassays, we used peptides as probes for the diagnosis of four neglected tropical infectious diseases Tegumentary leishmaniasis, Visceral leishmaniasis, Strongyloidiasis, and Leprosy on commercial graphite screen-printed electrodes. 4-DMAA oxidation was used to indicate specific biological recognition between the epitope-based peptide and serum immunoglobulin G (IgG) from infected patients. We demonstrated that 4-DMAA should be incorporated into the electrodes prior to serum application, which avoids interference with its sensitivity and specificity. In addition, 4-DMAA oxidizes at a low anodic potential, and the oxidation peak is useful for detecting proteins in biological fluids. In summary, we have successfully demonstrated the broad application of 4-DMAA as a general indicator for the specific diagnosis of four infectious diseases in electrochemical immunosensors. Such a strategy is quite advantageous for indirect detection of proteins that lack electrochemical activities or are spatially inaccessible on the electrode surface. This new indicator opens a new avenue for monitoring biological recognition, especially for immunosensors.
Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context
Authors: Monti Lorenzo , Tse Rita , Tang Su-Kit , Mirri Silvia , Delnevo Giovanni , Maniezzo Vittorio , Salomoni Paola
Published: May 2022

Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23.
Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments
Authors: Hashmi Khurram Azeem , Pagani Alain , Liwicki Marcus , Stricker Didier , Afzal Muhammad Zeshan
Published: May 2022

In recent years, due to the advancements in machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvements in deep learning, traditional approaches, such as sliding windows and manual feature selection techniques, have been replaced with deep learning techniques. However, object detection algorithms face a problem when performed in low light, challenging weather, and crowded scenes, similar to any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE, and achieve a mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.
Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis
Authors: Saleh Hager , Mostafa Sherif , Alharbi Abdullah , El-Sappagh Shaker , Alkhalifah Tamim
Published: May 2022

Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people’s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content’s sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model’s performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models.
Identification of Industrial Heritage and a Theoretical Framework for an Industrial Heritage Inventory System in Pakistan
Authors: Iqbal Naveed , Akbar Syed Hamid , Van Cleempoel Koenraad
Published: May 2022

Heritage studies and historical preservation in Pakistan, which have gained momentum in the past few decades, have been focused primarily on residential, commercial, and religious architecture dating to the British and Mughal eras, leaving aside an important layer of economic and urban history related to the industrialization of major cities. Most industrial buildings are left to decay or are demolished to make space for new development. Based on a literature review of Pakistan’s industrial heritage and site surveys conducted in Pakistan between 2019 and 2021, several industrial facilities were identified that have social, cultural, and economic potential for reuse or retooling to address the problem of inadequate public amenities in urban areas. There is, however, no documentation regarding such sites that is available for use by potential stakeholders. An inventory system is proposed to establish an overview of the industrial heritage in Pakistan (types of sites, current properties, spatial characteristics, significance) and to facilitate the recognition and management of these sites. An “industrial site inventory record” (ISIR) form is proposed to record information about industrial buildings and sites. In addition, an “industrial site statement of significance” (ISSOS) is proposed for use in recording the description, heritage value, and important architectural elements of significant industrial buildings and sites. The proposed inventory system is a step toward the recognition of industrial heritage and its potential for adaptive reuse and contribution to urban regeneration.
Implications of the Relocation Type and Frequency for Shared Autonomous Bike Service: Comparison between the Inner and Complete City Scenarios for Magdeburg as a Case Study
Authors: Haj Salah Imen , Mukku Vasu Dev , Kania Malte , Assmann Tom , Zadek Hartmut
Published: May 2022

Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and transportation sector nowadays. Researchers, universities, and companies are working intensively to provide novel mobility options that can be environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising using self-driving bikes. Shared autonomous cargo bike fleets are likely to increase the livability and sustainability of the city as the use of cargo bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is still needed to develop this new concept. To address this research gap, this paper examines the on-demand shared-use autonomous bikes service (OSABS) from a fleet management perspective. In fact, such a system requires good management strategies in order to ensure its efficiency. Through an agent-based simulation of a case study in Magdeburg, we investigate various parameters that can influence the performance and the service quality of OSABS such as the rebalancing frequency and the relocation type. Tests were performed for two different operational areas: the inner city and the complete city of Magdeburg. We conclude with different management insights for an optimized functioning of the system.
Integrated Zoning Protection of Urban Remains from Perspective of Sustainable Development—A Case Study of Changchun
Authors: Zhang Jiawang , Wang Jianguo , Tao Jingmei , Tang Siqi , Zhao Wutao
Published: May 2022

In recent years, the development of urbanization in China has entered a new stage with “quality” as its core. In the process of sustainable urban development, urban planning and construction relics are precious historical warnings and educational resources, and in the stage of accelerated urbanization, social transformation, and industrial upgrading, urban sites face severe pressure and challenges for conservation. This paper presents a summary analysis of various international charters concerning historical and cultural heritage in recent times, and analyzes the urban spatial structure, urban functional zoning, and the differences and evolutionary characteristics of urban construction sites between the pseudo-Manchukuo Xinjing plan and other regions of the world in the same period from a global perspective. The city of Changchun is also used as an example to systematically analyze the existing relics in Changchun using a historical-geographical perspective and spatial analysis methods. The results show that, firstly, the conservation objects of Chinese relics are being enriched and the scope of protection is being expanded, but that there is a lack of protection and utilization of urban heritage and its surrounding environment. Secondly, that the road network system and spatial structure of the pseudo-Manchukuo Xinjing city planning are basically preserved, that the urban green space and functional zoning have been changed, and that the architectural relics show a spatial distribution trend of north-north-east. Thirdly, that the urban functional zoning has been used to construct an urban relics protection area in which three suggestions for the protection of urban relics have been put forward: to establish a holistic protection system for urban planning functional areas; to establish a “district-axis-point” trinity protection model to promote the effective function recovery of urban planning and construction functional areas; and to continue the effective functions of the city. This provides reference for the study of modern urban planning ideas and solutions for current sustainable urban development, upgrading of public service facilities, and green low-carbon urban transformation.
Strategy to Enhance Emergency Interconnected Operation of Water Distribution System
Authors: Jun Hwandon , Gim Arin , Jung Donghwi , Lee Seungyub
Published: May 2022

This study identified the causes of insufficient emergency interconnected operation (EIO) performance, such as pressure-related problems and connection problems caused by elevation differences between blocks, the characteristics and locations of emergency interconnection pipes (EIPs), and pumps. Then, it tested four strategies to improve the EIO performance, including increasing the EIP diameter or installing additional EIPs, pressure reducing valves (PRVs), or pumps. The advanced pressure-driven analysis model was applied to quantify the EIO performance improvement achieved using these strategies. Further, these strategies were tested in a real water distribution system. To solve the low-pressure problem, the EIP diameter was increased and an additional pump was installed; the former did not significantly improve, whereas the latter improved supply by 20–30%. To solve the high-pressure problem, PRVs were installed to maintain the EIO performance effectively. To solve connection problems, new EIPs were installed. Although this improved the supply performance, the installation of pumps was recommended to overcome elevation differences. The proposed strategies should contribute to the allocation of facilities such as EIPs, pumps, and PRVs for realizing effective EIO.
An Artificial-Immune-System-Based Algorithm Enhanced with Deep Reinforcement Learning for Solving Returnable Transport Item Problems
Authors: Achamrah Fatima Ezzahra , Riane Fouad , Sahin Evren , Limbourg Sabine
Published: May 2022

This paper proposes a new approach, i.e., virtual pooling, for optimising returnable transport item (RTI) flows in a two-level closed-loop supply chain. The supply chain comprises a set of suppliers delivering their products loaded on RTIs to a set of customers. RTIs are of various types. The objective is to model a deterministic, multi-supplier, multi-customer inventory routing problem with pickup and delivery of multi-RTI. The model includes inventory-level constraints, the availability of empty RTIs to suppliers, and the minimisation of the total cost, including inventory holding, screening, maintenance, transportation, sharing, and purchasing costs for new RTIs. Furthermore, suppliers with common customers coordinate to virtually pool their inventory of empty RTIs held by customers so that, when loaded RTIs are delivered to customers, each may benefit from this visit to pick up the empty RTI, regardless of the ownership. To handle the combinatorial complexity of the model, a new artificial-immune-system-based algorithm coupled with deep reinforcement learning is proposed. The algorithm combines artificial immune systems’ strong global search ability and a strong self-adaptability ability into a goal-driven performance enhanced by deep reinforcement learning, all tailored to the suggested mathematical model. Computational experiments on randomly generated instances highlight the performance of the proposed approach. From a managerial point of view, the results stress that this new approach allows for economies of scale and cost reduction at the level of all involved parties to about 40%. In addition, a sensitivity analysis on the unit cost of transportation and the procurement of new RTIs is conducted, highlighting the benefits and limits of the proposed model compared to dedicated and physical pooling modes.
Spatiotemporal Differences and Ecological Risk Assessment of Heavy Metal Pollution of Roadside Plant Leaves in Baoji City, China
Authors: Zhang Junhui , Guan Yunjiu , Lin Qing , Wang Yaxin , Wu Bowen , Liu Xin , Wang Bo , Xia Dunsheng
Published: May 2022

The concentration of heavy metals in plants’ leaves can effectively indicate the spatiotemporal differences of environmental pollution, providing a scientific basis for the monitoring of urban air quality. The concentration of Ni, Cu, Cd, Pb and Zn in the leaves of five different species (Ophiopogon japonicus, Ligustrum vicaryi, Platanus acerifolia, Sophora japonicaand Cedrusdeodara) were measured, which were from I, II, III, IV (0.05 m, 0.25 m, 1 m, 4 m) at different times (May and November) in the green belt of Baoji city. The degree of heavy metal pollution and potential ec ological risk were analyzed. The results revealed that the concentration of Zn, Cu and Pb in roadside plant leaves was relatively high. In May, the heavy metal concentration was the highest in the leaves of C.deodara, whereas this was the case for S. japonica in November. Arbors were more effective at capturing particles from the atmosphere than low plants. At the same height, areas with high levels of heavy metal pollution in May were basically the same as that in November, and areas with high levels of pollution were affected by traffic and industry. The pollution index and the comprehensive index of potential ecological risk of element Cd were the highest, indicating that the potential harm of Cd to the environment should receive more attention from the Government.
Application of the Constraint Negotiation Theory to the Plant-Based Meat Alternatives Food Service Business: An Exploration of Perceived Value and Negotiation–Constraint–Visit Intention Relationships
Authors: Jang Ha-Won , Cho Meehee
Published: May 2022

Plant-based meat alternatives (PBMAs) are attracting attention worldwide for positive reasons, such as animal welfare, environmental protection, and health promotion. Despite this trend and the importance of PBMAs, few quantitative studies have analyzed them from a consumer behavior perspective, which is necessary to understand the factors that influence the consumption of PBMAs. Therefore, this study focused on the value of PBMAs for consumers. Data from the final sample of 315 Korean consumers aged 18 years or older were analyzed using structural equation model analysis and multigroup analysis on the AMOS program. We applied the constraint reduction model, derived from the constraint negotiation theory, and identified the causal relationship between the value of PBMAs, constraint factors (intrapersonal and structural), negotiation factors (time, information, and cost), and restaurant visit intention. As speculated, all the hypotheses were supported. The value of PBMAs had a positive effect on visit intention and negotiation. Negotiations had a positive effect on visit intention and lowered constraints. However, constraints negatively affected visit intention. Additionally, a multigroup analysis between vegetarians and non-vegetarians was performed. Moderating effects were confirmed in the relationship between the value of PBMAs and negotiations, and between negotiations and constraints. This study thus contributes to the scant quantitative research in the field of consumer behavior toward PBMAs and is the first to apply the constraint negotiation theory to PBMA consumption research in the field of food service and hospitality industries.
Regenerative Agriculture and Its Potential to Improve Farmscape Function
Authors: O’Donoghue Tom , Minasny Budiman , McBratney Alex
Published: May 2022

Recent reviews have identified major themes within regenerative agriculture—soil health, biodiversity, and socioeconomic disparities—but have so far been unable to clarify a definition based on practice and/or outcomes. In recent years, the concept has seen a rapid increase in farming, popular, and corporate interest, the scope of which now sees regenerative agriculture best viewed as a movement. To define and guide further practical and academic work in this respect, the authors have returned to the literature to explore the movement’s origins, intentions, and potential through three phases of work: early academic, current popular, and current academic. A consistent intention from early to current supporters sees the regeneration, or rebuilding, of agricultural resources, soil, water, biota, human, and energy as necessary to achieve a sustainable agriculture. This intention aligns well with international impetus to improve ecosystem function. The yet to be confirmed definition, an intention for iterative design, and emerging consumer and ecosystem service markets present several potential avenues to deliver these intentions. To assist, the authors propose the Farmscape Function framework, to monitor the impact of change in our agricultural resources over time, and a mechanism to support further data-based innovation. These tools and the movement’s intentions position regenerative agriculture as a state for rather than type of agriculture.
Deep Learning Models for COVID-19 Detection
Authors: Serte Sertan , Dirik Mehmet Alp , Al-Turjman Fadi
Published: May 2022

Healthcare is one of the crucial aspects of the Internet of things. Connected machine learning-based systems provide faster healthcare services. Doctors and radiologists can also use these systems for collaboration to provide better help to patients. The recently emerged Coronavirus (COVID-19) is known to have strong infectious ability. Reverse transcription-polymerase chain reaction (RT-PCR) is recognised as being one of the primary diagnostic tools. However, RT-PCR tests might not be accurate. In contrast, doctors can employ artificial intelligence techniques on X-ray and CT scans for analysis. Artificial intelligent methods need a large number of images; however, this might not be possible during a pandemic. In this paper, a novel data-efficient deep network is proposed for the identification of COVID-19 on CT images. This method increases the small number of available CT scans by generating synthetic versions of CT scans using the generative adversarial network (GAN). Then, we estimate the parameters of convolutional and fully connected layers of the deep networks using synthetic and augmented data. The method shows that the GAN-based deep learning model provides higher performance than classic deep learning models for COVID-19 detection. The performance evaluation is performed on COVID19-CT and Mosmed datasets. The best performing models are ResNet-18 and MobileNetV2 on COVID19-CT and Mosmed, respectively. The area under curve values of ResNet-18 and MobileNetV2 are 0.89% and 0.84%, respectively.
Related Party Transactions and Earnings Management: The Moderating Effect of ESG Performance
Authors: Gavana Giovanna , Gottardo Pietro , Moisello Anna Maria
Published: May 2022

The purpose of this paper is to investigate the effect of ESG performance on the possible association between related party transactions (RPTs) and different types of earnings management (EM). We study a sample of Italian non-financial listed firms over the 2014–2019 period, controlling for the fixed effects of the company’s sector of operation and the year. We investigate the effect of Environmental, Social and Governance (ESG) performance scores on the association between related party transactions and real earnings management (REM), as well as accrual-based earnings management (AEM). We provide evidence that firms might use RPTs in association with downward AEM or as a substitute of REM perpetrated by sales anticipations and discretionary expenses reductions, as well as an autonomous form of earnings manipulation. Our empirical evidence shows a significant moderating effect of ESG performance on earnings management. In particular, social as well as governance performance significantly moderate the association between RPTs and downward AEM; environmental performance moderates the possible use of RPTs in association with cashflow-based REM. This is the first study that analyzes the effect of ESG performance on the possible association between related party transactions and earnings management.
Multi-Indicator and Geospatial Based Approaches for Assessing Variation of Land Quality in Arid Agroecosystems
Authors: Abuzaid Ahmed S , Mazrou Yasser S. A. , El Baroudy Ahmed A , Ding Zheli , Shokr Mohamed S.
Published: May 2022

Novel spatial models for appraising arable land resources using data processing techniques can increase insight into agroecosystem services. Hence, the principal component analysis (PCA), hierarchal cluster analysis (HCA), analytical hierarchy process (AHP), fuzzy logic, and geographic information system (GIS) were integrated to zone and map agricultural land quality in an arid desert area (Matrouh Governorate, Egypt). Satellite imageries, field surveys, and soil analyses were employed to define eighteen indicators for terrain, soil, and vegetation qualities, which were then reduced through PCA to a minimum data set (MDS). The original and MDS were weighted by AHP through experts’ opinions. Within GIS, the raster layers were generated, standardized using fuzzy membership functions (linear and non-linear), and assembled using arithmetic mean and weighted sum algorithms to produce eight land quality index maps. The soil properties (pH, salinity, organic matter, and sand), slope, surface roughness, and vegetation could adequately express the land quality. Accordingly, the HCA could classify the area into eight spatial zones with significant heterogeneity. Selecting salt-tolerant crops, applying leaching fraction, adopting sulfur and organic applications, performing land leveling, and using micro-irrigation are the most recommended practices. Highly significant (p < 0.01) positive correlations occurred among all the developed indices. Nevertheless, the coefficient of variation (CV) and sensitivity index (SI) confirmed the better performance of the index developed from the non-linearly scored MDS and weighted sum model. It could achieve the highest discrimination in land qualities (CV > 35%) and was the most sensitive (SI = 3.88) to potential changes. The MDS within this index could sufficiently represent TDS (R2 = 0.88 and Kappa statistics = 0.62), reducing time, effort, and cost for estimating the land performance. The proposed approach would provide guidelines for sustainable land-use planning in the studied area and similar regions.
The Impact of Integration of Industry 4.0 and Internal Organizational Forces on Sustaining Competitive Advantages and Achieving Strategic Objectives
Authors: Ramadan Muawia , Amer Tariq , Salah Bashir , Ruzayqat Mohammed
Published: May 2022

Adopting and implementing the Industry 4.0 strategy to increase the overall performance of the organization became one of the main aims of organizations. However, ignoring the linkages between implementing strategic decisions and organizational internal factors/forces can endanger and shrink its performance, competitive advantages, and thus its strategic success. In this context, many companies failed to achieve the expected benefits of adopting the Industry 4.0 strategy. Therefore, the gained advantages of adopting the Industry 4.0 strategy should be sustained through perfect and comprehensive integration between Industry 4.0 concepts and the accompanying upgrades and changes in the organizational internal factors/forces. This will capitalize on organizations’ internal strengths and avoid weaknesses or turn them into strengths. In this paper, a conceptual model is proposed to investigate the relation between Industry 4.0 and internal organizational forces and examine their impacts on the sustainable competitive advantages of the organization. In the hypothesized model, three innovation capabilities (i.e., technological, economic, and commercial innovation) have been used to mediate the relation between the internal forces and the sustainable competitive advantages in parallel with Industry 4.0 adoption. The model and the proposed hypotheses have been simulated and tested using partial least squares structural equations modeling software called SmartPLS. The sample size used is 125 responses from different manufacturing fields. The results demonstrate the significant role that the internal organizational forces play in maintaining and sustaining the organization’s competitive advantages in combination with Industry 4.0.
The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis
Authors: Herceg Sina , Kaaya Ismail , Ascencio-Vásquez Julián , Fischer Marie , Weiß Karl-Anders , Schebek Liselotte
Published: May 2022

The environmental footprint of photovoltaic electricity is usually assessed using nominated power or life cycle energy output. If performance degradation is considered, a linear reduction in lifetime energy output is assumed. However, research has shown that the decrease in energy output over time does not necessarily follow a linear degradation pattern but can vary at different points in the module’s lifetime. Further, photovoltaic modules follow different degradation patterns in different climate zones. In this study, we address the influence of different degradation aspects on the greenhouse gas (GHG) emissions of PV electricity. Firstly, we apply different non-linear degradation scenarios to evaluate the GHG emissions and show that the differences in GHG emissions in comparison to a linear degradation can be up to 6.0%. Secondly, we use the ERA5 dataset generated by the ECMWF to calculate location-dependent degradation rates and apply them to estimate the location-specific GHG emissions. Due to the reduction in lifetime energy output, there is a direct correlation between the calculated degradation rate and GHG emissions. Thirdly, we assess the impact of climate change on degradation rates and on the respective GHG emissions of photovoltaic electricity using different climate change scenarios. In a best-case scenario, the GHG emissions are estimated to increase by around 5% until the year 2100 and by around 105% by 2100 for a worst-case scenario.
The Role of Knowledge Creation and Transfer in Family Firm Succession
Authors: Pipatanantakurn Kalin , Ractham Vichita Vathanophas
Published: May 2022

The purpose of the study is to investigate the role that knowledge creation and knowledge transfer processes play in family firm intergenerational succession in Thailand. An exploratory qualitative case study approach is used. Interviews were conducted with successors and predecessors of small, medium and large Thai firms that have undergone leadership succession within the past five years (30 firms, for n = 60 interviews). Data were analyzed using a qualitative content analysis approach. There were 16 different knowledge approaches identified that are undertaken by the successor. These processes are commonplace to firms, including formal and informal, internal and external processes of knowledge creation and transfer. Most of these occur at different stages of preparation for succession (pre-succession, transition and succession stages). While some knowledge approaches are used across firms, others are specific to small or large firms. These knowledge approaches and stages were used to develop a knowledge process model for family firm succession. The research develops an original model of the knowledge processes associated with family firm succession. This model, which incorporates a staged succession model with the knowledge processes identified, explains how and why knowledge creation and transfer occur during the succession process.