bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis
Authors: Yang Zhengli , Liu Chao , Nie Ruihua , Zhang Wanchang , Zhang Leili , Zhang Zhijie , Li Weile , Liu Gang , Dai Xiaoai , Zhang Donghui , Zhang Min , Miao Shuangxi , Fu Xiao , Ren Zhiming , Lu Heng
Published: August 2022

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility.
Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach
Authors: Bebie Maria , Cavalaris Chris , Kyparissis Aris
Published: August 2022

Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression model (VI-MLR) based on vegetation indices (EVI, NMDI) was used. In the second approach, the reflectance data of all Sentinel-2 bands for several dates during the growth periods were used as input parameters in three machine learning model algorithms, i.e., random forest (RF), k-nearest neighbors (KNN), and boosting regressions (BR). Modeling results were examined against yield data collected by a combine harvester equipped with a yield mapping system. VI-MLR showed a moderate performance with R2 = 0.532 and RMSE = 847 kg ha−1. All machine learning approaches enhanced model accuracy when all images during the growing periods were used, especially RF and KNN (R2 > 0.91, RMSE < 360 kg ha−1). Additionally, RF and KNN accuracy remained high (R2 > 0.87, RMSE < 455 kg ha−1) when images from the start of the growing period until March, i.e., three months before harvest, were used, indicating the high suitability of machine learning on Sentinel-2 data for early yield prediction of durum wheat, information considered essential for precision agriculture applications.
Robust OCC System Optimized for Low-Frame-Rate Receivers
Authors: Dobre Robert-Alexandru , Preda Radu-Ovidiu , Badea Radu-Alexandru
Published: August 2022

Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions for the broader public because video cameras are available on almost any smartphone nowadays. Unfortunately, most OCC systems that have been proposed until now require either expensive and specialized high-frame-rate cameras as receivers, which are unavailable on smartphones, or they rely on the rolling shutter effect, being sensitive to camera movement and pointing direction, they produce light flicker when low-frame-rate cameras are used, or they must discern between more than two light intensity values, affecting the robustness of the decoding process. This paper presents in detail the design of an OCC system that overcomes these limitations, being designed for receivers capturing 120 frames per second and being easily adaptable for any other frame rate. The system does not rely on the rolling shutter effect, thus making it insensitive to camera movement during frame acquisition and less demanding about camera resolution. It can work with reflected light, requiring neither a direct line of sight to the light source nor high resolution image sensors. The proposed communication is invariant to the moment when the transmitter and the receiver are started as the communication is self-synchronized, without any other exchange of information between the transmitter and the receiver, without producing light flicker, and requires only two levels of brightness to be detected (light on and light off). The proposed system overcomes the challenge of not producing light flicker even when it is adapted to work with very low-frame-rate receivers. This paper presents the statistical analysis of the communication performance and discusses its implementation in an indoor localization system.
Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis
Authors: Arpan Ishu , Shah Vrutangkumar V. , McNames James , Harker Graham , Carlson-Kuhta Patricia , Spain Rebecca , El-Gohary Mahmoud , Mancini Martina , Horak Fay B.
Published: August 2022

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.
Euclidean Graphs as Crack Pattern Descriptors for Automated Crack Analysis in Digital Images
Authors: Strini Alberto , Schiavi Luca
Published: August 2022

Typical crack detection processes in digital images produce a binary-segmented image that constitutes the basis for all of the following analyses. Binary images are, however, an unsatisfactory data format for advanced crack analysis algorithms due to their sparse nature and lack of significant data structuring. Therefore, this work instead proposes a new approach based on Euclidean graphs as functional crack pattern descriptors for all post-detection analyses. Conveying both geometrical and topological information in an integrated representation, Euclidean graphs are an ideal structure for efficient crack path description, as they precisely locate the cracks on the original image and capture salient crack skeleton features. Several Euclidean graph-based algorithms for autonomous crack refining, correlation and analysis are described, with significant advantages in both their capabilities and implementation convenience over the traditional, binary image-based approach. Moreover, Euclidean graphs allow the autonomous selection of specific cracks or crack parts based on objective criteria. Well-known performance metrics, namely precision, recall, intersection over union and F1-score, have been adapted for use with Euclidean graphs. The automated generation of Euclidean graphs from binary-segmented images is also reported, enabling the application of this technique to most existing detection methods (e.g., threshold-based or neural network-based) for cracks and other curvilinear features in digital images.
Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices
Authors: Wu Hongde , O’Connor Noel E. , Bruton Jennifer , Hall Amy , Liu Mingming
Published: August 2022

In this paper, we investigate different scenarios of anomaly detection on decentralised Internet of Things (IoT) applications. Specifically, an anomaly detector is devised to detect different types of anomalies for an IoT data management system, based on the decentralised alternating direction method of multipliers (ADMM), which was proposed in our previous work. The anomaly detector only requires limited information from the IoT system, and can be operated using both a mathematical-rule-based approach and the deep learning approach proposed in the paper. Our experimental results show that detection based on mathematical approach is simple to implement, but it also comes with lower detection accuracy (78.88%). In contrast, the deep-learning-enabled approach can easily achieve a higher detection accuracy (96.28%) in the real world working environment.
An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
Authors: Yang Wang , He Yi , Yao Sheng , Zhang Lifeng , Cao Shengpeng , Wen Zhiqing
Published: August 2022

Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram’s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method’s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.
Detection of a New Large Free Core Nutation Phase Jump
Authors: Malkin  Zinovy , Belda Santiago , Modiri  Sadegh
Published: August 2022

We announce the detection of a new large jump in the phase of the free core nutation (FCN). This is only the second such large FCN phase jump in more than thirty years of FCN monitoring by means of a very long baseline interferometry (VLBI) technique. The new event was revealed and confirmed by analyzing two FCN models derived from a long-time series of VLBI observations. The jump started in 2021 and is expected to last until the late fall of 2022. The amplitude of the phase jump is expected to be approximately 3 rad, which is as much as 1.5 times larger than the first phase jump in 1999–2000. A connection of the new FCN phase jump with the recent geomagnetic jerk started in 2020 is suggested.
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions
Authors: Duan Qiang , Hu Shijing , Deng Ruijun , Lu Zhihui
Published: August 2022

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community’s interest in this emerging field.
Foreword: Narrative Convictions in “Revolting” Times
Authors: Fine Michelle
Published: August 2022

Convictions: (1)strongly held beliefs, firmly felt and enacted(2)consequence of being criminalized [...]
Impact of Business Model Innovation on Sustainable Performance of Processed Marine Food Product SMEs in Thailand—A PLS-SEM Approach
Authors: Madhavan Meena , Sharafuddin Mohammed Ali , Chaichana Thanapong
Published: August 2022

This study aims to develop four conceptual higher order models for assessing the causal relationship between the environmental turbulence (ET), sustainable competitive advantage (SCA), business model innovation (BMI), and sustainable performance (SP) of small and medium sized enterprises (SMEs). The conceptual models were developed through literature review and tested with 91 entrepreneurs and managers from processed marine food product SMEs in Thailand. The higher order models were tested with partial least square structural equation modeling using seminr package in R. The results reveal that SCA mediates the relationship between BMI and SP. Further, the study found a serial mediation effect of BMI and SCA in the relationship between ET and SP. Thus, this study is novel in its approach of using ET as an antecedent and moderator and SCA as a mediator in assessing the relationship between BMI and SP. The study also found that the effects of ET are balanced when the SMEs incorporate BMI, which further leads to the achievement of SCA and SP. Thus, the findings extend an increasingly complex literature in the assessment of SCA’s role in SMEs’ SP. Further, the scale and the models can be used to assess how the SMEs respond to ET and modify their BMI to attain SCA and SP.
Economic and Environmental Assessment of Biomass Power Plants in Southern Italy
Authors: La Scalia Giada , Adelfio Luca , La Fata Concetta Manuela , Micale Rosa
Published: August 2022

In 2019, Europe adopted the New Green Deal as a strategic plan to become a competitive, resource-efficient, and driven economy by reducing its gas emissions and carbon footprint. Due the COVID-19 pandemic, this strategic plan was recently updated to expedite the green transition of European industries. Therefore, the present paper deals with the problem of deciding an appropriate size for a biomass plant that directly produces electric energy by means of two different conversion processes: combustion and gasification. After an initial estimation of the energy potential in western Sicily, GIS data of biomass growth were used to identify the appropriate size for the power plants under investigation. The economic feasibility of biomass utilization was evaluated over a capacity range of 10 to 30 MW, considering total capital investments, revenues from energy sales, and total operating costs. Moreover, the effect of variations on incentive prices was analyzed by means of a sensitivity analysis. Comparing the different plant solutions considered, the environmental sustainability was also analyzed using the life cycle assessment (LCA) approach. The results showed that the combustion solution had a higher profitability and a lower environmental impact for each plant size. The obtained results also demonstrated that providing power from residual biomass in small agricultural communities would significantly reduce their environmental impacts while improving the economic feasibility of their waste management practices.
Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication
Authors: Gang Qiao , Muhammad Aman , Khan Zahid Ullah , Khan Muhammad Shahbaz , Ahmed Fawad , Ahmad Jawad
Published: August 2022

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless sensor networks (MI-UWSNs). The accurate localization of sensor nodes in MI communication can effectively be utilized for industrial IoT applications, e.g., underwater gas and oil pipeline monitoring, and in other important underwater IoT applications, e.g., smart monitoring of sea animals, etc. The most-feasible technology for medium- and short-range communication in IIoT-based UWSNs is MI communication. To improve underwater communication, this paper presents a machine learning-based prediction of localization estimation accuracy of randomly deployed sensor Rx nodes through anchor Tx nodes in the MI-UWSNs. For the training of ML models, extensive simulations have been performed to create two separate datasets for the two configurations of excitation current provided to the Tri-directional (TD) coils, i.e., configuration1-case1_configuration2-case1 (c1c1_c2c1) and configuration1-case2_configuration2-case2 (c1c2_c2c2). Two ML models have been created for each case. The accuracies of both models lie between 95% and 97%. The prediction results have been validated by both the test dataset and verified simulation results. The other important contribution of this paper is the development of a novel assembling technique of a MI-TD coil to achieve an approximate omnidirectional magnetic flux around the communicating coils, which, in turn, will improve the localization accuracy of the Rx nodes in IIoT-based MI-UWSNs.
Current Status and Future Perspectives of Carbon Pricing Research in Austria
Authors: Santos Luan , Steininger Karl , Cordeiro Marcelle Candido , Vogel Johanna
Published: August 2022

In the context of the European Green Deal, the European Commission and the European Parliament proposed to intensify Europe’s ambition for its 2030 climate targets. In the case of Austria, the current government set itself the goal of being carbon neutral by 2040. The Federal Chancellery of Austria also presented a plan to introduce a carbon price for emissions from the non-EU ETS; a task force is underway to launch a formal proposal in 2022, and thus it is relevant to investigate the present stage of scientific research on carbon pricing in Austria. Therefore, the present paper examines and evaluates scientific publications using bibliometric techniques, combined with a systematic literature review (SLR). Our results show that even though the current government presented a plan to introduce a carbon price, there are still uncertainties about the design of the carbon pricing instrument, the institutional and regulatory framework, revenue recycling policies, impacts on sectoral GDP, competitiveness indicators and international trade. Furthermore, the transport sector was the largest contributor to the increase in total emissions in recent years in Austria; therefore, it requires special attention in terms of mitigating and adaptive measures.
Reuse of Powders and Recycled Aggregates from Mixed Construction and Demolition Waste in Alkali-Activated Materials and Precast Concrete Units
Authors: Robayo-Salazar Rafael , Valencia-Saavedra William , Mejía de Gutiérrez Ruby
Published: August 2022

This article describes the recycling of coarse and fine fractions and powder from construction and demolition waste (CDW) using alkaline activation technology (geopolymerization). The CDW sample used corresponds to a mixture (mixed waste) of concrete (Co), ceramics (Ce) and masonry (M). Co, Ce and M (CDW-Mixed) powders were used as geopolymer precursors. As an alkaline activator, a mixture of sodium hydroxide (NaOH) and sodium silicate (Na2SiO3) was used. From CDW-Mixed, a hybrid cement added with 10% ordinary Portland cement (OPC) was synthesized to promote curing at room temperature (25 °C). From the alkali-activated hybrid cement and the incorporation of mixed recycled aggregates (gravel and sand), applications of mortars, concretes, fiber-reinforced materials and prefabricated units, such as solid blocks, perforated (hollow) blocks and pavers, were produced. The results of the physical–mechanical characterization validate the application potential of these CDW-based materials in the construction sector. Compressive strengths of up to 40.5 MPa for mortar and 36.9 MPa for concrete were obtained after 90 days of curing at room temperature ≈ 25 °C. Similarly, a life cycle analysis (LCA) associated with raw materials demonstrated the environmental sustainability (44% lower carbon footprint) of mixed alkali-activated CDWs compared to conventional materials based on OPC.
Developing the Urban Blue-Green Infrastructure as a Tool for Urban Air Quality Management
Authors: Badach Joanna , Szczepański Jakub , Bonenberg Wojciech , Gębicki Jacek , Nyka Lucyna
Published: August 2022

Urban structure is an important factor that shapes the process of urban ventilation and pollution dispersion. With proper planning of the urban spatial layout, city breathability can be effectively regulated, contributing to urban air quality improvement. This paper investigates the development and current management of urban systems of green and open spaces in four Polish cities: Gdańsk, Warsaw, Poznań and Wrocław, with a particular focus on the planning aspects of urban ventilation and air quality management. The initial GIS-based comparison of historical plans and the current spatial layouts of the cities show that these systems, consciously shaped at the beginning of the twentieth century, remain clearly identifiable. However, in some locations, the continuance of these systems was interrupted by later investments. The next step was to develop GIS procedures to effectively map the spatial distribution of selected urban form indicators that are related to urban ventilation, especially the frontal area index. The results made it possible to determine the main features of the current ventilation systems and to identify some of the local problem areas. The last phase of the study was to conduct a local-scale analysis of these problem areas. With this study, the applicability of various analysis and simulation tools for the purpose of improving city breathability by appropriate integrated planning and design decisions was demonstrated. The presented approach, taking into account the city- and micro-scale interactions, should be used in current planning practice to preserve the historically developed ventilation systems.
Digital Balanced Scorecard System as a Supporting Strategy for Digital Transformation
Authors: Fabac Robert
Published: August 2022

Reports of the high percentage of failed digital transformation ventures motivate the need to find a comprehensive framework with regulatory attributes to support these initiatives. Due to its structure, consistent strategy decomposition capabilities, and strategic map architecture, the assumption is that a customized version of the Balanced Scorecard can ensure the better overall success of digital transformation projects. The qualitative analysis methodology was applied to previous research, and this study identified critical issues and challenges related to the strategy and overall endeavor of digital transformation. Based on the methodology of the traditional Balanced Scorecard, a draft version of the Digital Balanced Scorecard was formulated. The Digital Balanced Scorecard is a comprehensive, primarily prescriptive model that is focused directly on the challenges, opportunities, and obstacles of transformation. The proposed BSC model can consistently interpret a digital strategy and assist organization leaders in successfully formulating and coordinating all necessary activities and projects to apply technologies. The Digital BSC provides the projection of financial results and improvements in sustainability after transformation. The proposed solution to support digital transformation can accelerate an organization’s development, improve efficiency, and strengthen efforts to achieve an organization’s sustainability goals.
Optimization of Aircraft Taxiing Strategies to Reduce the Impacts of Landing and Take-Off Cycle at Airports
Authors: Di Mascio Paola , Corazza Maria Vittoria , Rosa Nicolò Rocco , Moretti Laura
Published: August 2022

The increasing attention of opinion towards climate change has prompted public authorities to provide plans for the containment of emissions to reduce the environmental impact of human activities. The transport sector is one of the main ones responsible for greenhouse emissions and is under investigation to counter its burdens. Therefore, it is essential to identify a strategy that allows for reducing the environmental impact produced by aircraft on the landing and take-off cycle and its operating costs. In this study, four different taxiing strategies are implemented in an existing Italian airport. The results show advantageous scenarios through single-engine taxiing, reduced taxi time through improved surface traffic management, and onboard systems. On the other hand, operating towing solutions with internal combustion cause excessive production of pollutants, especially HC, CO, NOX, and particulate matter. Finally, towing with an electrically powered external vehicle provides good results for pollutants and the maximum reduction in fuel consumption, but it implies externalities on taxiing time. Compared to the current conditions, the best solutions ensure significant reductions in pollutants throughout the landing and take-off cycle (−3.2% for NOx and −44.2% for HC) and economic savings (−13.4% of fuel consumption).
Influence of Micro- and Macrostructure of Atomised Water Jets on Ammonia Absorption Efficiency
Authors: Wąsik Wiktor , Majder-Łopatka Małgorzata , Rogula-Kozłowska Wioletta
Published: August 2022

Ammonia has a very wide range of applications. Its worldwide production exceeds 230 million tonnes per year. Due to its properties, ammonia causes a serious threat to human life and health when released uncontrolled into the environment. Research carried out in the word shows that this substance may be effectively neutralised by absorption in water. The aim of research described in this paper is to determine the influence of key parameters of the micro- and macrostructure of water streams on the course of the ammonia absorption process. During the studies, different types of water nozzles were used, with similar efficiency and supply pressure, but characterised by different parameters of the micro- and macrostructure of the produced stream. The experiments were divided into two stages. In the first one, the macro- and microparameters of the streams were measured, while in the second one, the changes in ammonia concentration were established during delivering spray jet generation by different nozzles. Among the basic parameters of the macrostructure, the spray angle and liquid distribution in the jet (spray intensity) were determined, while for the microstructure, the droplet size distribution and mean droplet diameters were measured. Ammonia concentration was measured by means of a photoionisation detector (PID). In order to evaluate the absorption efficiency of different water spray jets, the apparent absorption rate (kp) and the half-time of concentration reduction (t½) in the kinetic range were established. The study has confirmed that atomised water jets are an effective method for neutralising ammonia released into the environment. The research has a practical aspect and shows that the structure of atomised water streams influence the course of the absorption process. Increasing the spray angle in a conical stream leads to an improvement in the quality of water atomisation and helps increase ammonia absorption. Moreover, it was also observed that for the absorption of spatial ammonia clouds, use should be made of nozzles generating streams with full spray cones and high uniformity of spray and dispersion.
Effect of PbO and B2O3 on the Physical, Structural, and Radiation Shielding Properties of PbO-TeO2-MgO-Na2O-B2O3 Glasses
Authors: Al-Ghamdi Hanan , Sayyed M. I. , Kumar Ashok , Yasmin Sabina , Elbashir B. O. , Almuqrin Aljawhara H.
Published: August 2022

The process of melt quenching is utilized in the preparation of the PbO-TeO2-MgO-Na2O-B2O3 glasses. The effect of PbO and B2O3 on the physical, structural, and radiation shielding properties of present glasses have been presented in this study. As the lead concentration rises, both the density and the molecular weight rise, climbing from 3.283 to 3.923 g/cm3 and from 105.638 to 128.675 g, respectively. The utilization of PbO as an alternative to B2O3 contributes to an increase in the overall number of at-oms, which in turn contributes to an increase in the molar volume. The XRD spectra show that the samples are amorphous. The different bending and stretching vibrations of the bonds present in the samples are shown by the FTIR spectra. The mass attenuation coefficient (MAC), linear attenuation coefficients (LAC), half-value layers (HVL), and effective atomic number (Zeff) were calculated using Phy-X software within the energy range 0.284–2.506 MeV. These obtained verdicts advocate that pre-pared Pb4 glass containing the highest concentration of PbO showed supreme shielding ability comparing the rest of the pre-pared glasses. According to these results, it can be said that PbO and B2O3 are the weighty additive composites for glass composition in the interest of radiation shielding.
Socio-Economic Impacts and Challenges of the Coronavirus Pandemic (COVID-19): An Updated Review
Authors: Delardas Orestis , Kechagias Konstantinos S. , Pontikos Pantelis N. , Giannos Panagiotis
Published: August 2022

The coronavirus disease 2019 (COVID-19) pandemic has shaken up the socio-economic order on a global scale with interventions designed to curb the spread of the disease bearing multiple and reinforcing impacts on several aspects of economic and social lives. The effects of COVID-19 were diverse and often spilled over different or interdependent industries. Economies were hit top-down and bottom-up while businesses and individuals alike endured significant changes that altered national and international supply and demand trends for products and services. The primary and secondary sectors were especially influenced by supply shortages while services and education were largely demand-driven. Monetary policies were specifically targeted to ease these disruptions while protective measures for employees in many cases constrained business competitiveness. The present study provided a cross-sectoral (primary, secondary, tertiary, and quaternary sectors) outline of the implications and challenges since the start of the crisis, centralising important information and offering a view of the current socio-economic situation.
Optimizing H-BIM Workflow for Interventions on Historical Building Elements
Authors: Guerra de Oliveira Sara , Biancardo Salvatore Antonio , Tibaut Andrej
Published: August 2022

Intervention projects for historical buildings depend on the quality of multidisciplinary data sets; their collection, structure, and semantics. Building information model (BIM) based workflows for historical buildings accumulate some of the data sets in a shared information model that contains the building’s geometry assemblies with associated attributes (such as material). A BIM model of any building can be a source of data for different engineering assessments, for example, solar and wind exposure and seismic vulnerability, but for historic buildings it is particularly important for interventions like conservation, rehabilitation, and improvements such as refurbishment and retrofitting. When the BIM model is abstracted to a semantic model, enabling the use of semantic technologies such as reasoning and querying, semantic links can be established to other historical contexts. The semantic technologies help historic building experts to aggregate data into meaningful form. Ontologies provide them with an accurate knowledge representation of the concepts, relationships, and rules related to the historic building. In the paper, we are proposing an improved workflow for the transformation of a heritage BIM model to a semantic model. In the BIM part the workflow demonstrates how the fully parametric modelling of historical building components is relevant, for example, in terms of reusability and adaptation to a different context. In the semantic model part, ontology reuse, reasoning, and querying mechanisms are applied to validate the usability of the proposed workflow. The presented work will improve knowledge-sharing and reuse among stakeholders involved in historic building projects.
Association between Internal Control and Sustainability: A Literature Review Based on the SOX Act Framework
Authors: Su Wunhong , Zhang Liuzhen , Ge Chao , Chen Shuai
Published: August 2022

With the integration of sustainable development into all aspects of the economy, politics, society, culture, and ecology, the effectiveness and innovation of enterprises in sustainability have become global research issues. Internal control affects the current operation and management as the main means for enterprises to maintain normal production and operation and prevent risks. Therefore, it is closely related to the future development of enterprises. This study uses 84 papers from Web of Science to systematically trace the determinants of enterprise sustainability using Vosviewer software and the Sarbanes–Oxley Act (SOX) as a manifestation of internal control to analyze the role and controversies of internal control in the process of enterprise sustainable strategic planning. The results show that internal control has, on the one hand, positive effects on enterprise sustainability by improving the quality of financial information, derived effects, and spillover effects. However, on the other hand, internal control can be detrimental to enterprise sustainability by increasing compliance costs and legal liabilities. This study points out opportunities and directions for improving enterprise internal control regulation and empirical research in response to such results. Finally, this study provides implications for enterprises seeking to achieve a sustainable level of development regarding the proper implementation of internal controls, as well as avenues for further research.
Regional Differences in the Emission-Reduction Effect of Environmental Regulation Based on the Perspective of Embodied Carbon Spatial Transfer Formed by Inter-Regional Trade
Authors: Chen Zhijian , Zhang Li , Zhang Yujie , Zhao Yun , Zhong Zhangqi
Published: August 2022

On the basis of the latest input–output data, this paper estimates the amount of embodied carbon emissions in inter-regional trade by constructing a multiregional input–output model to evaluate how environmental regulation stringency influences its spatial transfer. We found that environmental regulation stringency had significant positive correlation with transferring out embodied carbon emissions in trade at the national level, and a significant negative correlation with transferring in embodied carbon emissions in trade. In East and Central China, effective environmental regulation observably improves the issue of carbon emissions caused by trade, while in the western region, environmental regulation stringency had significant positive correlation with transferring in and out embodied carbon emissions in inter-regional trade. For that reason, we further use the geographically weighted regression model (GWR) to assess the spatial evolution characteristics of the intensity of environmental regulation on the transfer of embodied carbon emissions in trade; thereby, the above results are verified and show that environmental regulation has failed to play its due role.
To What Extent Does Environmental Regulation Influence Emission Reduction? Evidence from Local and Neighboring Locations in China
Authors: Song Jing , Li Mengyuan , Wang Shaosong , Ye Tao
Published: August 2022

Setting environmental regulation policy is an important practice to reach the goal of ecological environmental protection. By establishing fixed effects and spatial spillover models, this paper examines the relationship between the environmental regulation of carbon emissions and the impact on spatial spillovers. The results of our analysis show that: 1. environmental regulation has significant inhibitory effects on carbon emissions, which is beneficial to carbon dioxide emission reduction, and 2. environmental regulation has a significant inhibitory impact on local carbon emissions but increases carbon emissions in neighboring locations. Therefore, in order to achieve the aim of carbon emission reduction, appropriate environmental regulation policies should be established, which, in turn, would provide better coordination of the carbon emission relationship between different regions. Meanwhile, environmental regulation plays an important role in protecting the environment. To strengthen environmental governance and promote the coordinated development of regional carbon emission reduction, we need to implement a top-level design of environmental regulation and build a market-oriented environmental regulation system.
Remote Sensing Land Use Evolution in Earthquake-Stricken Regions of Wenchuan County, China
Authors: Kang Junmei , Wang Zhihua , Cheng Hongbin , Wang Jun , Liu Xiaoliang
Published: August 2022

Earthquakes and their secondary geological disasters have a certain impact on the land cover, which leads to the degradation of the ecological environment and the stability of the ecosystem. At present, there are few studies on the spatial–temporal evolution characteristics of land-use change in earthquake-stricken regions, especially the lack of quantitative evaluation of the impact of earthquakes on land use at the micro-scale. The “5·12” Wenchuan ms8.0 earthquake caused serious damage to the surface resources in the disaster area. The study on the spatial–temporal evolution characteristics of land-use change in the disaster area can provide a reference for the remote sensing dynamic monitoring of the ecological environment. Therefore, based on geographical big data, this paper used a land-use comprehensive degree index, land-use transfer matrix, and landscape ecological index to explore and analyze the spatial–temporal evolution characteristics of land use in Wenchuan County before and after the earthquake. The results showed that the types of cropland, forest, built-up, and bare land changed greatly before and after the earthquake. During the earthquake recovery period, the comprehensive index of land use in the study area basically showed an increasing trend. Under the effect of artificial measures and natural restoration, land use was continuously improved, and vegetation was restored well. After 2008, the Patch Density (PD) and Landscape Shape Index (LSI) values of most landscape types decreased, and the Aggregation Index (AI) values increased, indicating that the ecological environment of the whole region showed a benign development in the post-earthquake period. The results not only contribute to the establishment of scientific ecological environment management in earthquake-stricken regions but also contribute to the formulation of long-term ecological environment monitoring and ecological restoration planning according to the law of land-use change.
Extended Assessment of Sprinkler Irrigation Uniformity in Greenhouses Using GIS and Hydraulic Modeling
Authors: Barberena Iñigo , Campo-Bescós Miguel Ángel , Casalí Javier
Published: August 2022

Traditionally, distribution uniformity has been obtained by using rain gauges, which makes it a very expensive process. This paper sought to create a simulation strategy using QGIS and EPANET, both free software, that allowed the simulation of the water application results of all the emitters of an irrigation installation. In this way, it was possible to obtain the geospatial representation of the applied water and finally to know the distribution uniformity in the whole installation. The simulation finally fulfilled its objective and was compared with a study of distribution uniformity with rain gauges. The biggest difference between the measured and simulated data was a difference of 5.76% among the sectors. The simulated uniformity was very similar to the measured uniformity, which allowed us to affirm that the proposed simulation methodology was adequate. We believe that the methodology proposed in this article could be very useful in improving the management of sprinkler irrigation systems, particularly those in which distribution uniformity is of special importance. These improvements in management can also result in savings in water and other inputs, which are becoming increasingly important in the current context of climate change and the reduction in the impact of agriculture on the environment. Finally, similar studies could be carried out with the same tools for other pressurized irrigation systems, such as sprinkler irrigation outside greenhouses and drip irrigation.
Exploring Factors Influencing Renewable Energy Diffusion in Commercial Buildings in Nigeria: A Grounded Theory Approach
Authors: Unuigbe Maria , Zulu Sambo Lyson , Johnston David
Published: August 2022

The adoption and integration of renewable energy technologies (RETs) into buildings is key to making the necessary transition to low-carbon and resilient built environments. However, such technologies have struggled to gain a firm foothold in countries within the sub-Saharan African (SSA) region. This is particularly the case in Nigeria, which suffers from severe energy poverty, despite its significant RE and conventional energy potential. In Nigeria, a significant proportion of the energy demand for offices is provided by self-powered off-grid fossil-fuel generators. The country is also one of the primary settings for increased construction activity. This, combined with its susceptibility to the effects of climate change, presents significant concerns relating to the resilience of its built environment. However, there has not yet been a comprehensive empirical study addressing this, as previous studies have been limited in their insight and perspectives. This study adopted a grounded theory method (GTM) aligned with Charmaz’s approach, to gain in-depth participant-driven insights into factors influencing sustainable energy use in commercial buildings, focusing on solar photovoltaics (PVs). This led to the development of a theory of the sustainability transition process of construction professionals (CPs). It provides relevant, reliable, and relatable points of reference that would be beneficial to policymakers in developing plans for actionable interventions for PV and broader sustainable measures toward green energy transition. Furthermore, it highlights the value of employing GTMs in construction management research beyond the developing context. This paper contributes theoretically, empirically, and methodologically to facilitate a better understanding of the situations (context) grounded in empirical data.
Accomplishing Sustainability in Manufacturing System for Small and Medium-Sized Enterprises (SMEs) through Lean Implementation
Authors: Qureshi Karishma M. , Mewada Bhavesh. G. , Alghamdi Saleh Y. , Almakayeel Naif , Qureshi Mohamed Rafik N. , Mansour Mohamed
Published: August 2022

Business enterprises such as small and medium-sized enterprises (SMEs) play a significant role in economic development but struggle for sustainability. A business enterprise such as a manufacturing unit tries many technological innovations and strategic initiatives to accomplish sustainability in the manufacturing system. Lean manufacturing implementation is one such initiative that helps SMEs manufacture value-added products with increased profitability and waste minimization. However, lean implementation in SMEs is challenging. Hence, it is essential to follow a systematic framework and control the critical success factors (CSFs) in attempting lean implementation. The purpose of this research is to find, evaluate, and rank the CSFs of lean implementation of SMEs so that they may be controlled to accomplish successful lean implementation. The CSFs of lean implementation found by an in-depth assessment of the literature are modeled using the interpretative structural modeling (ISM) approach. MICMAC analysis is also used in classifying and understanding the significance of each lean implementation CSF. ISM and MICMAC provide the relationship modeling to reveal the inter-relationships of each lean implementation CSF. Subsequently, the ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) has been applied to rank the CSFs of lean implementations. The results show that sustainability in a manufacturing system, financial capability, and employee involvement hold significant importance in lean implementations in manufacturing SMEs. Practicing managers may benefit from revisiting their lean implementation plans and respective aligned strategies. They will also be in a position to identify and focus on the scarce resources required for the subsequent lean implementations.