bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of High-Frequency Vibration Parameters for Airborne Terahertz SAR Using Chirplet Decomposition and LS Sequential Estimators
Authors: Hao Zhaoxin , Sun Jinping , Li Qing , Shan Tao
Published: July 2022

Due to the short wavelength of the terahertz wave, airborne terahertz synthetic aperture radar (THz-SAR) suffers from echo phase errors caused by the high-frequency vibration of the platform. These errors will result in defocusing and the emergence of ghost targets, which will degrade the quality of the image. Therefore, it is necessary to compensate for phase errors in order to bring the image into focus. This paper proposes a multi-component high-frequency vibration parameter estimation method based on chirplet decomposition and least squares (LS) sequential estimators, which differs from other methods that can only be applied to simple harmonic vibrations. In particular, we first obtain the instantaneous chirp rate (ICR) of the signal by chirplet decomposition. Then, we employ the LS sequential estimators in conjunction with separable regression technique (SRT) to estimate vibration parameters. The estimated parameters are subsequently used to re-establish the ICR components for each vibration component and these parameters are further re-estimated to improve their accuracy. Based on the estimated parameters, phase compensation functions can be constructed to suppress the defocusing and ghost targets in airborne THz-SAR imaging. Simulated results on point targets and distributed imaging scenes demonstrate that the proposed method is accurate and reliable even at low signal-to-noise ratios (SNRs).
Deep Learning Aided Time–Frequency Analysis Filter Framework for Suppressing Ionosphere Clutter
Authors: Ji Xiaowei , Li Jiaming , Yang Qiang , Wang Linwei , Suo Ying , Wu Xiaochuan
Published: July 2022

In a heterogeneous environment, the ionosphere is dynamically changing in the Earth’s middle latitude, and backscatter from fast-moving irregularities in the plasma can cause ionosphere clutter to extend. Suppressing varying ionosphere clutter and exploring obscured targets are challenging tasks for high frequency surface wave radar (HFSWR). For responding to these challenges, this research presents a multi-channel deep learning time–frequency feature filter framework (DL-TFF). Firstly, we observed the behavior of the ionosphere clutter for a long period of time before selecting the representative ionosphere clutter. Secondly, different transform techniques are applied to provide a time–frequency representation of the non-stationary echo signals, and representation results of different echo components are collected as a training set for feature learning. Thirdly, we design a multi-channel time–frequency feature learning network (MTF), which is responsible for mining discriminative time–frequency information between targets and different types of ionosphere clutter. Experimental results on real HFSWR data sets have demonstrated that DL-TFF can remove varying ionosphere clutter and simultaneously reveal covered targets. Moreover, its suppression effectiveness is more ideal than the previous classical method.
Built-Up Area Mapping for the Greater Bay Area in China from Spaceborne SAR Data Based on the PSDNet and Spatial Statistical Features
Authors: Zhang Wei , Lu Shengtao , Xiang Deliang , Su Yi
Published: July 2022

Built-up areas (BAs) information acquisition is essential to urban planning and sustainable development in the Greater Bay Area in China. In this paper, a pseudo-Siamese dense convolutional network, namely PSDNet, is proposed to automatically extract BAs from the spaceborne synthetic aperture radar (SAR) data in the Greater Bay Area, which considers the spatial statistical features and speckle features in SAR images. The local indicators of spatial association, including Moran’s, Geary’s, and Getis’ together with the speckle divergence feature, are calculated for the SAR data, which can indicate the potential BAs. The amplitude SAR images and the corresponding features are then regarded as the inputs for PSDNet. In this framework, a pseudo-Siamese network can independently learn the BAs discrimination ability from the SAR original amplitude image and the features. The DenseNet is adopted as the backbone network of each channel, which can improve the efficiency while extracting the deep features of the BAs. Moreover, it also has the ability to extract the BAs with multi-scale sizes by using a multi-scale decoder. The Sentinel-1 (S1) SAR data for the Greater Bay Area in China are used for the experimental validation. Our method of BA extraction can achieve above 90% accuracy, which is similar to the current urban extraction product, demonstrating that our method can achieve BA mapping for spaceborne SAR data.
Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China
Authors: Ma Yongjie , Zhao Qingzhi , Wu Kan , Yao Wanqiang , Liu Yang , Li Zufeng , Shi Yun
Published: July 2022

Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China.
Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot
Authors: Ge Shaodi , Fan Chongyi , Wang Jian , Huang Xiaotao
Published: July 2022

Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method.
Mapping the Distribution of Summer Precipitation Types over China Based on Radar Observations
Authors: Tang Jing , Chen Sheng , Li Zhi , Gao Liang
Published: July 2022

In this study, the spatiotemporal distribution and characteristics of different precipitation types (stratiform, convective, and snow) over China are analyzed using the radar mosaic images during the summer season over 4 years (from 2018 to 2021). The convective precipitation occurs most frequently along the eastern coast regions. In June, the strong convection center is located in Southern China and moves northward to Eastern China in July, while the lowest frequency occurs in August. Stratiform precipitation dominates summer precipitation over China and mainly distributes in inland regions, with the highest frequency in August. Snowfall primarily presents in the mountains and plateau regions of Western China with the frequency of occurrence around 20%. The snowfall area in July is significantly smaller than that in June and August. The convective, stratiform, and snowfall show strong diurnal variation in terms of solar standard time (LST) especially for snowfall. The convective precipitation demonstrates a bimodal pattern, with the highest peak in the afternoon (15–16 LST) and the secondary peak in the early morning (04–07 LST). Stratiform precipitation is mainly active from the afternoon to the next morning (14–05 LST). Snowfall is significantly more common in the nighttime (around 12%) than in the daytime (around 4%). The occurrence ratio of snowfall at midnight in July is significantly higher than that in June and August. It is expected that this study on summer precipitation over China can be used as a reference to hydrometeorological research and also to improve the understanding of radar precipitation research over China.
Single Cell Protein Production Using Different Fruit Waste: A Review
Authors: Thiviya Punniamoorthy , Gamage Ashoka , Kapilan Ranganathan , Merah Othmane , Madhujith Terrence
Published: July 2022

The single cell protein (SCP) technique has become a popular technology in recent days, which addresses two major issues: increasing world protein deficiency with increasing world population and the generation of substantial industrial wastes with an increased production rate. Global fruit production has increased over the decades. The non-edible parts of fruits are discarded as wastes into the environment, which may result in severe environmental issues. These fruit wastes are rich in fermentable sugars and other essential nutrients, which can be effectively utilized by microorganisms as an energy source to produce microbial protein. Taking this into consideration, this review explores the use of fruit wastes as a substrate for SCP production. Many studies reported that the wastes from various fruits such as orange, sweet orange, mango, banana, pomegranate, pineapple, grapes, watermelon, papaya, and many others are potential substrates for SCP production. These SCPs can be used as a protein supplement in human foods or animal feeds. This paper discusses various aspects in regard to the potential of fruit wastes as a substrate for SCP production.
Trauma Chronicity and the Long-Term Needs of Childhood Sexual Trauma Survivors
Authors: Schuyler Ashley C. , Catania Joseph A.
Published: July 2022

Research has linked childhood sexual trauma (CST) with adverse adult outcomes (AAOs) that span physical, psychological, and social domains of functioning. Differences in conceptualizing and measuring CST, however, have inhibited the examination of trauma-related variables hypothesized to impact adult outcomes. We used National Sexual Health Survey (NSHS; 1995–1996) data to examine trauma chronicity (i.e., duration) and AAOs (domains: physical and mental health, close relationships, and achievement). The NSHS (N = 6537, 18–70 years) assessed duration using perpetrator-specific CST reports. Adjusting for background characteristics, we examined CST duration in relation to AAOs and CST-related help-seeking. Approximately 8% of participants reported CST. Chronic (vs. single-exposure) CST survivors were at substantially higher risk of experiencing AAOs [i.e., mean AAOs and specific AAOs (e.g., physical and emotional health problems, divorce/separation, and poverty)]. CST had direct effects on sexual dysfunction and satisfaction, and on relationship stressors which may impact sexual relationship quality. Although 62% of CST survivors did not seek help, those with more chronic CST histories reported a higher prevalence of trauma-related help-seeking. Our work underscores the importance of examining CST chronicity in relation to long-term developmental outcomes. Chronicity assessment may be an important screening tool in the therapeutic context and in broader community screening efforts.
Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm
Authors: Niu Yunyun , Yang Zehua , Wen Rong , Xiao Jianhua , Zhang Shuai
Published: July 2022

The green open vehicle routing problem with time windows has been widely studied to plan routes with minimal emissions in third-party logistics. Due to the NP-hardness, the performance of the general heuristics significantly degrades when dealing with large-scale instances. In this paper, we propose a membrane-inspired hybrid algorithm to solve the problem. The proposed algorithm has a three-level structure of cell-like nested membranes, where tabu search, genetic operators, and neighbourhood search are incorporated. In particular, the elementary membranes (level-3) provide extra attractors to the tabu search in their adjacent level-2 membranes. The genetic algorithm in the skin membrane (level-1) is designed to retain the desirable gene segments of tentative solutions, especially using its crossover operator. The tabu search in the level-2 membranes helps the genetic algorithm circumvent the local optimum. Two sets of real-life instances, one of a Chinese logistics company, Jingdong, and the other of Beijing city, are tested to evaluate our method. The experimental results reveal that the proposed algorithm is considerably superior to the baselines for solving the large-scale green open vehicle routing problem with time windows.
Assessing the Economic Contribution of Ocean-Based Activities Using the Pacific Coast of British Columbia as a Case Study
Authors: Teh Lydia C. L. , Cheung William W. L. , Sumaila Rashid
Published: July 2022

Global obligations to achieve sustainable oceans by 2030 require countries to commit to solutions that balance ocean use and protection. To do so necessitates baseline understanding of the ocean’s contribution to socio-economic well-being, which we do by measuring the economic activity of ocean-related sectors. Economic assessments tend to be data intensive and are typically reliant on professional economists, yet they are increasingly relevant to non-economists who engage in ocean management and communication, where they are integral in facilitating trade-off analysis of future ocean change. Thus, there is a need to make ocean economic assessment more accessible to nonspecialists. We fill this need by providing a pragmatic framework for conducting an economic assessment using British Columbia’s ocean sector as a case study. Our results show the impact of the province’s ocean sectors on four economic indicators and indicate that the ocean contributed almost $5 billion (or about 2%) to provincial gross domestic product (GDP) and generated about 106,120 jobs (over 4% of the province’s total) in 2015. Of these, the marine transport sector made the highest overall contribution followed by cruise lines, with GDP impacts of 66% and 13%, respectively. It should be noted that this estimated economic value is not representative of the full value of the ocean as it excludes oil and gas, research and education, and other activities that do not meet our criteria for inclusion, and it does not account for cultural and ecological values. Nonetheless the study highlights the substantial number of economic benefits generated by the blue economy. More significantly, the framework provides a simplified procedure for quantifying economic benefits, and can be applied by nonspecialists to perform rapid economic assessments in a variety of contexts.
Industry 4.0 in Financial Services: Mobile Money Taxes, Revenue Mobilisation, Financial Inclusion, and the Realisation of Sustainable Development Goals (SDGs) in Africa
Authors: Mpofu Favourate Y.
Published: July 2022

The use of mobile phones is a global phenomenon that provides developing countries with novel opportunities to enhance economic growth and facilitate improvement in the welfare of citizens. Governments have introduced mobile money taxes to improve tax revenue generation. This has been met with criticism by the public, media, and businesses on the basis that they hinder financial inclusion, constrain economic growth, and impede the attainment of some of the 2030 sustainable development goals, such as reduction in poverty, minimising inequality, building strong institutions, and providing decent work. Through a comprehensive critical review of literature, this study discusses mobile money taxes and their effects on revenue mobilisation, financial inclusion, and the attainment of the 2030 sustainable development goals. The findings reveal mixed opinions. While some scholars argued that mobile money taxes were instrumental in improving revenue generation, tax compliance, and reducing tax administration and compliance costs, some suggested otherwise, pointing out their negative impact. The unfavourable externalities include reduced financial inclusion, affordability challenges, reduction in usage of mobile money platforms, increased poverty and inequality, and ultimately the non-achievement of SDGs. The study contributes to the theoretical literature on the body of taxation and financial inclusion. It also gives insights to policymakers regarding likely implications of mobile money taxes.
The Importance of Fab Labs in the Development of New Products toward Mass Customization
Authors: Carqueijó Sérgio , Ramos Delfina , Gonçalves Joaquim , Carvalho Sandro , Murmura Federica , Bravi Laura , Doiro Manuel , Santos Gilberto , Zgodavová Kristína
Published: July 2022

The idea of developing Fab Labs (Fabrication Laboratories) was originated by Neil Gershenfeld of the Massachusetts Institute of Technology (MIT) Center for Bits and Atoms in 2006, where it signaled the start of a new era that is changing the world economy by breaking down the boundaries between the digital and physical worlds. The Portuguese Fab Labs are analyzed and a subsequent comparison with others European countries and in the USA was made. This comparison is based on aspects of the profile, the knowledge, the services, and the users. The survey was made by questionnaire, where the Portuguese version of it was adapted from another one disseminated at European level and in the USA, created in connection with a doctoral thesis in Italy. There are 25 active Fab Labs in Portugal, of which 16 responses were obtained and considered valid, so they represent the sample of our study. The results show that the Portuguese Fab Labs are in an embryonic phase with few associated or registered users. Portuguese Fab Labs have areas of work and investment capacity in machinery and technology similar to those of other European countries. However, in terms of turnover, there is a big difference between Portugal and some of the European countries, with American Fab Labs having completely different realities from the European ones. This work is relevant because it compares the Fab Labs of developed countries with those of Portugal. To overcome the difference in good practices existing in other countries, the Portuguese Fab Labs need: (1) better publicity, as well as more support for volunteer workers at Fab Labs, so that more ideas will appear and therefore more products; (2) the facilitation of the use of Fab Labs to have more volunteer workers, who must receive experimental courses, in order to make the best use of the available equipment; (3) evolution, from the current subtractive manufacturing to the additive manufacturing looking for innovation; (4) improved quality, ergonomics, and safety in the design of their own products; (5) and on the part of those responsible, therecognition, dissemination, and celebration of the best ideas that have turned into good products, in order to spread good practices.
An Impact Path Analysis of Russo–Ukrainian Conflict on the World and Policy Response Based on the Input–Output Network
Authors: Li Weidong , Wang Anjian , Zhong Weiqiong , Wang Chunhui
Published: July 2022

With the outbreak of the Russo–Ukrainian conflict, serious economic and financial sanctions have been initiated against Russia by many nations led by the United States and Europe. In the age of economic globalization, no countries can stand or fall alone. Which countries and industries will the economic shocks caused by the sanctions affect? How will the shocks propagate through the global economic system? In this paper, we adopt the input–output analysis and complex network methods to explore an impact path analysis of the Russo–Ukrainian conflict on the world from the regional, industrial, and critical path perspectives. The results show that (1) Russian economic development tends to depend more on the interaction among domestic industries, so it has a certain compressive capacity against sanctions. (2) There is a high economic interdependence between Russia and China, Germany, the United States, France, and South Korea. Sanctions against Russia will cause quite direct and serve economic shocks on these countries alongside Russia. (3) Industries such as Mining and quarrying, energy production, Coke and refined petroleum products, Chemical and chemical products, and Construction in Russia that are acting as either the center of transforming resources, as important suppliers or consumers for adjacent industries, or with weak symmetry and strong clustering, should be particularly analyzed. (4) Key industries in Russia play an important role as consumers of German machinery and equipment; the United States’ professional, scientific, and technical activities; and as suppliers for Chinese coke and refined petroleum products and the Japanese construction industry. Finally, corresponding policy suggestions are put forward.
Sustainable Biosynthesis of Esterase Enzymes of Desired Characteristics of Catalysis for Pharmaceutical and Food Industry Employing Specific Strains of Microorganisms
Authors: Dahiya Divakar , Singh Nigam Poonam
Published: July 2022

Reactions catalysed by sustainably produced enzymes can contribute to the bioeconomy supporting several industries. Low-value compounds can be transformed into added-value products or high-resolution chemicals could be prepared in reactions catalysed by biocatalyst esterase enzymes. These enzymes can be synthesised by purposely isolated or genetically modified strains of microorganisms. Enzymes belonging to the hydrolase family catalyse the formation and hydrolysis of ester bonds to produce the desired esterified molecule. The synthesis of homo-chiral compounds can be accomplished either by chemical or biocatalytic processes, the latter being preferred with the use of microbial esterases. For varied applications, esterases with high stability and retained activity at lower and higher temperatures have been produced with strains isolated from extreme environments. For sustainable production of enzymes, higher productivity has been achieved by employing fast-growing Escherichia coli after incorporating plasmids of required characteristics from specific isolates. This is a review of the isolated and engineered strains used in the biosynthesis of esterase of the desired property, with the objective of a sustainable supply of enzymes, to produce products of industrial importance contributing to the economy.
Evaluation of Agricultural Machinery Using Multi-Criteria Analysis Methods
Authors: Puška Adis , Nedeljković Miroslav , Šarkoćević Živče , Golubović Zoran , Ristić Vladica , Stojanović Ilija
Published: July 2022

To achieve the highest possible agricultural production, it is necessary to procure the appropriate agricultural machinery. A tractor is the most useful machine in agriculture that performs various functions. Therefore, the selection of a tractor is one of the key decisions in the agriculture-production process. This study aims to evaluate heavy tractors for agricultural production in Bosnia and Herzegovina. Since this is a selection between different tractors, which are evaluated using several criteria, the methods of multi-criteria analysis (MCDA) were used in this study. Five different methods were used to determine the weight of the criteria, of which a modified standard-deviation method is a new method used in practice, while the tractor ranking was performed using the CRADIS (compromise ranking of alternatives from distance to ideal solution) method. The results showed that the best-ranked tractor is A4, while the most deviations from the ranking occur when the entropy method is used. The contribution of this study is in the systematization of the methods for the objective determination of the criteria weights and the development of new methods to facilitate decision-making in agriculture and other industries.
Is the Timber Construction Sector Prepared for E-Commerce via Instagram®? A Perspective from Brazil
Authors: De Araujo Victor , Švajlenka Jozef , Vasconcelos Juliano , Santos Herisson , Serra Sheyla , Almeida Filho Fernando , Paliari José , Lahr Francisco Rocco , Christoforo André
Published: July 2022

Timber construction is a contemporary solution where sales are performed through in-store trade. Timber house e-commerce may stimulate global markets, and in times of intense remote routines, it is useful to improve the performance of the wood and construction sectors. Timber housing producers and dealers are the research object under analysis to study the electronic market of this Brazilian sector on Instagram®. With just over four hundred companies, nearly 80% of this sector already utilizes this virtual social network. We validated the outcomes through a significant sampling of all 315 companies with corporate profiles to identify their competences, goals, and failures. The profiles prioritize the elucidation of products and basic features, not yet being ready for the virtual sales of timber houses. The study makes it clear that our results typify a sector marked by a developing electronic market. Inserting a description of product prices and detailed specifications may change this status. From failures and competences, a classification for social profiles was proposed to measure the progress and virtual participation of each individual.
A Systematic Review of the Extent to Which BIM Is Integrated into Operation and Maintenance
Authors: Abideen Dania K. , Yunusa-Kaltungo Akilu , Manu Patrick , Cheung Clara
Published: July 2022

Although a considerable amount of research has addressed the use of building information modelling (BIM) in facilities management (FM) within the past years, there is limited systematic review on investigating the potentials of BIM within the operation and maintenance (O&M) life cycle phase. Yet, this phase could account for approximately 60% of the total life cycle costs of assets. The purpose of this paper is to conduct a systematic literature review on the application of BIM in the O&M phase to identify current research trends, research gaps and future directions. This study achieves the aforementioned purpose by adopting the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P). It employs qualitative and quantitative techniques to analyse the articles from a combination of three multi-disciplinary research databases, namely—Scopus, Web of Science and Engineering Village, which also comprises Compendex, InSpec, GeoRef and GeoBase sub-databases. After an in-depth review of 196 selected journal articles, this study systemically presents: (1) bibliometric analyses of the literature in BIM and O&M; (2) research patterns and trends; (3) drivers and barriers to BIM–O&M integration; and (4) research gaps mapped into a standard project management lifecycle to guide future research directions. The content analysis reveals that BIM has mainly been integrated under seven functions: information management (IM), advanced technology (AT), maintenance and asset management (MAM), indoor management (IM), performance assessment (PA), visualisation (V), and lean management (LM). Findings of the study reveal that the UK, USA and China are the top ranked countries with regards to research outputs on BIM–O&M integrations. The majority of BIM integrations within O&M falls under the information management function, whereas the least research output was recorded under lean management. In addition, the majority of studies focused on institutional and infrastructure facility types, but residential, industrial and commercial buildings were underrepresented, despite their disproportionate physical dominance within most societies. Findings also show that potential drivers and barriers for BIM integrations within O&M can be classified into three main categories—technical, organisational, and legal/contractual. It is then concluded that the application of BIM within O&M is still emerging, which further emphasises the need for more studies that address value realization in the context of BIM in O&M, with particular focus on the specific O&M principles for different building types.
Out of Sight, Out of Mind: Using Post-Kerbside Organics Treatment Systems to Engage Australian Communities with Pro-Environmental Household Food Waste Behaviours
Authors: Landells Esther , Naweed Anjum , Pearson David H. , Karunasena Gamithri G. , Oakden Samuel
Published: July 2022

Dealing with the wicked problem of global food waste and loss is a complex and challenging area. In Australia, increased political will has landed the diversion of domestic food waste from landfill squarely at the feet of local government (councils), often requiring significant change to kerbside collections systems. This paper discusses how post-kerbside household food waste treatment systems can encourage pro-environmental behaviours. To achieve this, current food waste literature is examined against kerbside domestic waste collection measurable outcomes (diversion rates, system uptake and contamination rates). The hypothesis is that specific interventions can establish, or rebuild, community trust, responsibility and pro-environmental behaviours around food waste avoidance and diversion. Two post-kerbside systems—commercial composting and anaerobic digestion—provided the framework. Two themes emerged from the study: (1) the benefits of connecting the community with the interactions of household food waste inputs with post-treatment outputs (compost, soil conditioners, digestates and biogases); and (2) providing engaged communities with pathways for sustainable, pro-environmental actions whilst normalizing correct kerbside food waste recycling for the less engaged (habitual behaviours, knowledge and cooperation). The paper contributes to understanding how councils can connect their communities with the issues of household food waste.
Knowledge Level and Consumption Behavior of Native Plants, Meats, and Drinking Waters with High Fluoride Concentrations about the Relation to the Potential Health Risk of Fluoride in Lamphun Province Thailand: A Case Study
Authors: Tewarangsri Wittaya , Chaiwong Tippawan , Phan Kongkea , Lapinee Chaipat , Chaiwong Sanhawat
Published: July 2022

Fluoride exposure from natural, agricultural, and industrial sources has harmed people living in fluoride-affected areas. Fluoride accumulates in the human body after being exposed to it through the food chain. The population consisted of 371 community health volunteers who were surveyed and chosen based on personal fluoride information. Only 39 residents were chosen to be interviewed and take part in the trial, which involved drinking fluoride-containing groundwater (>1.5 part per million: ppm) and urine testing that revealed urine fluoride level (>0.7 ppm). In addition, 47 biological samples and eight commercially bottled water specimens were examined. The information was gathered in four ways: (1) a questionnaire-based survey of fluoride knowledge, (2) food consumption behavior with locally grown vegetables, fruits, poultry, and meat, and commercially bottled water produced by groundwater in fluoride-affected areas, (3) a semi-food frequency questionnaire, and (4) fluoride content measurements using an ion-selective electrode. According to the analyses, the participants ranged in age from 51 to 60 years, with approximately 60.38% of them female and born and raised in polluted areas. The majority of subjects had a low level of fluoride knowledge (65.23%). The respondents’ primary source of drinking water (100.00%) was commercially bottled water; they chewed camellia sinensis 11.56% of the time (1 to 5 years) and they drank tea 9.16% of the time (during 1 to 5 years). Sus scrofa domesticus was responsible for the intake of vegetables and fruits, whereas Brassica chinensis, Jusl var para-chinensis (Bailey), and Tsen and Lee were responsible for the intake of poultry and animal flesh. They were all purchased at a local farm. The hazard quotient was greater than one, and the fluoride concentration (ppm) ranged between 75.00% (0.29–5.20), 57.14% (0.01–0.46), 88.89% (0.07–0.91), 100.00% (0.43–3.07), 100.00% (0.58–0.77), 42.86% (0.12–0.62 ppm.), 60.00% (0.11–1.44), and 33.33% (0.10–0.80) in drinking water, fruit, young and mature plants. Fluoride ingestion may pose a health concern. Under the 95th percentile condition, 74.47% consumed water with a high fluoride level, vegetables and fruits, and poultry and meats.
Economic Growth, CO2 Emissions Quota and Optimal Allocation under Uncertainty
Authors: Hsiao Chiu-Ming
Published: July 2022

This study attempts to link greenhouse gas emissions and economic development, and under the premise of considering economic development, proposes an optimal quota of greenhouse gas emissions. Based on the environmental Kuznets curve hypothesis, the logarithmic value of greenhouse gas emissions is an inverted U-shaped function of the logarithmic value of GDP per capita. The empirical results showed that most countries in the world support the Kuznets curve hypothesis. Moreover, using data collected from Our World in Data, the optimal allocation of a greenhouse gas emissions quota can be found by minimizing the uncertainty risk subject to a prespecified global economic growth rate. For government policymakers, they may apply the framework in this study to determine an optimal allocation of greenhouse gas emissions for each sector that will ensure the intended level of domestic economic growth.
XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems
Authors: Le Thi-Thu-Huong , Oktian Yustus Eko , Kim Howon
Published: July 2022

The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT provides promising opportunities for growth, the massive sensor IoT data collected are easily attacked by cyber criminals. Hence, IIoT requires different high security levels to protect the network. An Intrusion Detection System (IDS) is one of the crucial security solutions, which aims to detect the network’s abnormal behavior and monitor safe network traffic to avoid attacks. In particular, the effectiveness of the Machine Learning (ML)-based IDS approach to building a secure IDS application is attracting the security research community in both the general cyber network and the specific IIoT network. However, most available IIoT datasets contain multiclass output data with imbalanced distributions. This is the main reason for the reduction in the detection accuracy of attacks of the ML-based IDS model. This research proposes an IDS for IIoT imbalanced datasets by applying the eXtremely Gradient Boosting (XGBoost) model to overcome this issue. Two modern IIoT imbalanced datasets were used to assess our proposed method’s effectiveness and robustness, X-IIoTDS and TON_IoT. The XGBoost model achieved excellent attack detection with F1 scores of 99.9% and 99.87% on the two datasets. This result demonstrated that the proposed approach improved the detection attack performance in imbalanced multiclass IIoT datasets and was superior to existing IDS frameworks.
A Novel Optimization for GPU Mining Using Overclocking and Undervolting
Authors: Shuaib Mohammed , Badotra Sumit , Khalid Muhammad Irfan , Algarni Abeer D. , Ullah Syed Sajid , Bourouis Sami , Iqbal Jawaid , Bharany Salil , Gundaboina Lokesh
Published: July 2022

Cryptography and associated technologies have existed for a long time. This field is advancing at a remarkable speed. Since the inception of its initial application, blockchain has come a long way. Bitcoin is a cryptocurrency based on blockchain, also known as distributed ledger technology (DLT). The most well-known cryptocurrency for everyday use is Bitcoin, which debuted in 2008. Its success ushered in a digital revolution, and it currently provides security, decentralization, and a reliable data transport and storage mechanism to various industries and companies. Governments and developing enterprises seeking a competitive edge have expressed interest in Bitcoin and other cryptocurrencies due to the rapid growth of this recent technology. For computer experts and individuals looking for a method to supplement their income, cryptocurrency mining has become a big source of anxiety. Mining is a way of resolving mathematical problems based on the processing capacity and speed of the computers employed to solve them in return for the digital currency incentives. Herein, we have illustrated benefits of utilizing GPUs (graphical processing units) for cryptocurrency mining and compare two methods, namely overclocking and undervolting, which are the superior techniques when it comes to GPU optimization. The techniques we have used in this paper will not only help the miners to gain profits while mining cryptocurrency but also solve a major flaw; in order to mitigate the energy and resources that are consumed by the mining hardware, we have designed the mining hardware to simultaneously run longer and consume much less electricity. We have also compared our techniques with other popular techniques that are already in existence with respect to GPU mining.
Tourism Sustainability and COVID-19 Pandemic: Is There a Positive Side?
Authors: Seabra Cláudia , Bhatt Ketan
Published: July 2022

The COVID-19 pandemic has wreaked havoc on the tourism industry like never before, resulting in massive losses of revenue and jobs around the world. Accordingly, the pandemic exacerbated the already existing sustainability challenges of the tourism industry. However, there is also a positive side of the pandemic which is often overlooked by international scholarship. Thus, the present study aims to review the extant literature in the area of COVID-19 and the tourism industry’s sustainability and resilience in future crises. Through a synthesis of secondary data, it was possible to bring attention to the negative as well as the positive effects of COVID-19 on the global tourism industry. This article contributed to a better understanding of the positive side of the pandemic in terms of rethinking, resetting, and redefining the industry in a more sustainable way. The study lays out a conceptual framework for tourism managers and destination planners to identify the pandemic as an opportunity and adopt sustainable solutions to deal with the post-pandemic challenges, thereby developing more sustainable and resilient tourism businesses and destinations.
Key Initiatives for Digital Transformation, Green New Deal and Recovery after COVID-19 within the Construction Industry in Korea
Authors: Kim Siyeon , Lee Meesung , Yu Ilhan , Son JeongWook
Published: July 2022

The low productivity of the construction industry has been identified as a chronic problem, and this has been exacerbated by recent environmental factors, including COVID-19 and climate change. As a response to these environmental changes, policies related to recovery after COVID-19, digital transformation, and the Green New Deal have been established worldwide, and related projects have been launched. These three issues may seem to have different goals but they are interrelated, and guidelines for the direction of the construction industry should be outlined. In this study, key initiatives that need to be prioritized to successfully achieve the goals of the three issues are identified. Based on these, strategic directions and mid- to long-term measures to address environmental changes are suggested. This study surveys the perceptions of construction experts to establish the key initiatives via the analytic hierarchy process method, and their expected utility was evaluated. This study is significant in that it analyzes how construction experts perceive environmental changes and contributes to the development of the construction industry by identifying key initiatives that should be undertaken.
An Investigation of Preschool Level Out-of-Class Education Activities in Finland, Estonia, Ireland, and Turkey within the Framework of 21st Century Skills
Authors: Özgem Kezban , Akçıl Umut
Published: July 2022

The skills and competencies to be acquired in the 21st century are collected under these topics: learning and innovation skills; life and career skills; information, media and technology skills. The changes taking place in the world, in information technologies and global transformation, have promoted the development of different approaches, models, skills, and various learning theories in education. One of the prominent features shaping this period is the acquisition of these desired skills from the pre-school period, and this acquisition occurs through providing children with different experiences and offering rich environments and materials to children. In this respect, the activities carried outdoors have gained as much significance as in-class activities. This study aims to compare the 21st-century skills-based outdoor educational activities in Finland, Estonia, Ireland, and Turkey. In this respect, the horizontal and diagnostic approach used in comparative education studies is applied in combination. Document analysis is used in this research to gather data on the objectives of the countries regarding preschool out-of-class education as well as out-of-class education environments; the activities, modules, and assessment methods used were obtained from Finland, Estonia, Ireland, and Turkey’s Ministry of Education websites, countries’ laws on education, official pre-school education reports, education systems, articles, and online databases, etc. Among the countries examined, Finland, Estonia, and Ireland have learning modules related to out-of-class learning activities, which indicates that these countries have more options and thus a greater variety of out-of-class activities and environments. It also indicates that out-of-class education activities are carried out more extensively in these countries. It has been concluded that Ireland has more out-of-class learning activities in number and a number of the activities address a higher number of developmental areas. However, it has also been revealed that there is no module, program, or booklet for out-of-class learning activities in Turkey. From this point of view, the recommendation is for Turkish education to create a preschool education program module with out-of-class activities to adapt the Turkish preschool education program to the imperatives of contemporary learning outcomes.
Bibliometric Method for Manufacturing Servitization: A Review and Future Research Directions
Authors: Chen Yong , Wu Zhengjie , Yi Wenchao , Wang Bingjia , Yao Jianhua , Pei Zhi , Chen Jiaoliao
Published: July 2022

To gain sustainable development, it is a trend that manufacturing companies are change the value chain from manufacturing-centric to service-centric. Therefore, the capability of the manufacturing service is as significant as the production ability of enterprises, which reflects the supply chain management (SCM), flexible production, production efficiency, and other indicators of the enterprises. It is the first paper to discuss the sustainability of service-oriented manufacturing using bibliometric analysis. It derives a detailed review and future outlook on the development of manufacturing servitization, indicating the research directions for future development, and provides a valuable reference for researchers in related directions. The bibliometric analysis discusses countries or regions, research areas, authors, keywords, institutions, and journals based on the literature data from the Web of Science (WoS). The results show that research on manufacturing services has gradually received attention since its inception and has become popular since 2008. The papers published from 2008 to 2021 account for 77.62%. The USA is the most studied country on this topic, followed by China and the UK. The International Journal of Production Research regarding the most quantity of articles, and Beihang University is the most influential institution in this field. The largest amount of articles published in the area of “business and economics”, amounting to 1565 articles. In recent years, the main research areas included “Industry 4.0”, “cloud manufacturing (CMfg)”, “Internet of Things (IoT)”, “big data” and “services innovation”. Finally, “digital and intelligent manufacturing” and “product-service systems” are potential research directions for the future.
Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model
Authors: Alsulami Abdulaziz A. , Abu Al-Haija Qasem , Alqahtani Ali , Alsini Raed
Published: July 2022

Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.