bluuebunny/crossref_metadata_2025 huggingface dataset:
How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows
Authors: Ezzy Haitham , Charter Motti , Bonfante Antonello , Brook Anna
Published: August 2021

Small mammals, and particularly rodents, are common inhabitants of farmlands, where they play key roles in the ecosystem, but when overabundant, they can be major pests, able to reduce crop production and farmers’ incomes, with tangible effects on the achievement of Sustainable Development Goals no 2 (SDG2, Zero Hunger) of the United Nations. Farmers do not currently have a standardized, accurate method of detecting the presence, abundance, and locations of rodents in their fields, and hence do not have environmentally efficient methods of rodent control able to promote sustainable agriculture oriented to reduce the environmental impacts of cultivation. New developments in unmanned aerial system (UAS) platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection approaches at very high spatial resolutions in environmental and agricultural contexts. Object detection from remote-sensing images has been an active research topic over the last decade. With recent increases in computational resources and data availability, deep learning-based object detection methods are beginning to play an important role in advancing remote-sensing commercial and scientific applications. However, the performance of current detectors on various UAS-based datasets, including multimodal spatial and physical datasets, remains limited in terms of small object detection. In particular, the ability to quickly detect small objects from a large observed scene (at field scale) is still an open question. In this paper, we compare the efficiencies of applying one- and two-stage detector models to a single UAS-based image and a processed (via Pix4D mapper photogrammetric program) UAS-based orthophoto product to detect rodent burrows, for agriculture/environmental applications as to support farmer activities in the achievements of SDG2. Our results indicate that the use of multimodal data from low-cost UASs within a self-training YOLOv3 model can provide relatively accurate and robust detection for small objects (mAP of 0.86 and an F1-score of 93.39%), and can deliver valuable insights for field management with high spatial precision able to reduce the environmental costs of crop production in the direction of precision agriculture management.
Ship Object Detection of Remote Sensing Image Based on Visual Attention
Authors: Dong Yuxin , Chen Fukun , Han Shuang , Liu Hao
Published: August 2021

At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.
Split-Attention Networks with Self-Calibrated Convolution for Moon Impact Crater Detection from Multi-Source Data
Authors: Jia Yutong , Wan Gang , Liu Lei , Wang Jue , Wu Yitian , Xue Naiyang , Wang Ying , Yang Rixin
Published: August 2021

Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability.
Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising
Authors: Liu Wei , He Chengxun , Sun Le
Published: August 2021

During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.
Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation
Authors: Wei Hsiang-En , Grafton Miles , Bretherton Michael , Irwin Matthew , Sandoval Eduardo
Published: August 2021

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.
Analysis on the Variation of Hydro-Meteorological Variables in the Yongding River Mountain Area Driven by Multiple Factors
Authors: Niu Kaijie , Hu Qingfang , Wang Yintang , Yang Hanbo , Liang Chuan , Wang Leizhi , Li Lingjie , Li Xiting , Du Yong , Li Chengxi
Published: August 2021

In recent decades, strong human activities have not only brought about climate change including both global warming and shifts in the weather patterns but have also caused anomalous variations of hydrological elements in different basins all around the world. Studying the mechanisms and causes of these hydrological variations scientifically is the basis for the management of water resources and the implementation of ecological protection. Therefore, taking the Yongding River mountain area as a representative watershed in China, the changes of different observed and simulated hydro-meteorological variables and their possible causes are analyzed on an inter-annual scale based on ground based observations and remotely sensed data of hydrology, meteorology and underlying surface characteristics from 1956 to 2016. The results show that the annual natural runoff of Guanting hydrological station in the main stream of the Yongding River, Cetian hydrological station and Xiangshuibao hydrological station in the tributary of the Yongding River all have a significant decreasing trend and abrupt changes, and all the abrupt change points of the annual natural runoff series of the three hydrological stations appear in the early 1980s. On the inter-annual scale, the water balance model with double parameters is unable to effectively simulate the natural surface runoff after the abrupt change points. The annual average precipitation after the abrupt change points decreases by no more than 10%, compared with that before the abrupt change points. However, the precipitation from July to August, which is the main runoff-production period, decreases by more than 25%, besides the intra-annual temporal distribution of precipitation becoming uniform and a significant decrease in effective rainfall, which is the source of the runoff. Meanwhile, the NDVI in the basin show an increasing trend, while the groundwater level and land water storage decrease significantly. These factors do not lead only to the continuous reduction of the annual natural runoff in the Yongding River mountain area from 1956 to 2016, but also result in significant changes of the hydro-meteorological relationship in the basin.
Seasonal Trends in Clouds and Radiation over the Arctic Seas from Satellite Observations during 1982 to 2019
Authors: Wang Xi , Liu Jian , Yang Bingyun , Bao Yansong , Petropoulos George P. , Liu Hui , Hu Bo
Published: August 2021

A long-term dataset of 38 years (1982–2019) from the Advanced Very High Resolution Radiometer (AVHRR) satellite observations is applied to investigate the spatio-temporal seasonal trends in cloud fraction, surface downwelling longwave flux, and surface upwelling longwave flux over the Arctic seas (60~90° N) by the non-parametric methods. The results presented here provide a further contribution to understand the cloud cover and longwave surface radiation trends over the Arctic seas, and their correlations to the shrinking sea ice. Our results suggest that the cloud fraction shows a positive trend for all seasons since 2008. Both surface downwelling and upwelling longwave fluxes present significant positive trends since 1982 with higher magnitudes in autumn and winter. The spatial distribution of the trends is nearly consistent between the cloud fraction and the surface longwave radiation, except for spring over the Chukchi and Beaufort Seas. We further obtained a significant negative correlation between cloud fraction (surface downwelling/upwelling longwave fluxes) and sea-ice concentration during autumn, which is largest in magnitude for regions with substantial sea ice retreat. We found that the negative correlation between cloud fraction and sea-ice concentration is not as strong as that for the surface downwelling longwave flux. It indicates the increase in cloudiness may result in positive anomalies in surface downwelling longwave flux which is highly correlated with the sea-ice retreat in autumn.
Smartphone Sensing of Road Surface Condition and Defect Detection
Authors: Dong Dapeng , Li Zili
Published: August 2021

Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.
Fingerprint Feature Extraction for Indoor Localization
Authors: Jiang Jehn-Ruey , Subakti Hanas , Liang Hui-Sung
Published: August 2021

This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.
An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web
Authors: Rubio-Sandoval Jesus Ivan , Martinez-Rodriguez Jose L. , Lopez-Arevalo Ivan , Rios-Alvarado Ana B. , Rodriguez-Rodriguez Adolfo Josue , Vargas-Requena David Tomas
Published: August 2021

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities.
Modbus Access Control System Based on SSI over Hyperledger Fabric Blockchain
Authors: Figueroa-Lorenzo Santiago , Añorga Benito Javier , Arrizabalaga Saioa
Published: August 2021

Security is the main challenge of the Modbus IIoT protocol. The systems designed to provide security involve solutions that manage identity based on a centralized approach by introducing a single point of failure and with an ad hoc model for an organization, which handicaps the solution scalability. Our manuscript proposes a solution based on self-sovereign identity over hyperledger fabric blockchain, promoting a decentralized identity from which both authentication and authorization are performed on-chain. The implementation of the system promotes not only Modbus security, but also aims to ensure the simplicity, compatibility and interoperability claimed by Modbus.
Generative Design in Building Information Modelling (BIM): Approaches and Requirements
Authors: Ma Wei , Wang Xiangyu , Wang Jun , Xiang Xiaolei , Sun Junbo
Published: August 2021

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies.
A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation
Authors: Bu Shizhe , Meng Aiqiang , Zhou Gongjian
Published: August 2021

In bearings-only tracking systems, the pseudolinear Kalman filter (PLKF) has advantages in stability and computational complexity, but suffers from correlation problems. Existing solutions require bias compensation to reduce the correlation between the pseudomeasurement matrix and pseudolinear noise, but incomplete compensation may cause a loss of estimation accuracy. In this paper, a new pseudolinear filter is proposed under the minimum mean square error (MMSE) framework without requirement of bias compensation. The pseudolinear state-space model of bearings-only tracking is first developed. The correlation between the pseudomeasurement matrix and pseudolinear noise is thoroughly analyzed. By splitting the bearing noise term from the pseudomeasurement matrix and performing some algebraic manipulations, their cross-covariance can be calculated and incorporated into the filtering process to account for their effects on estimation. The target state estimation and its associated covariance can then be updated according to the MMSE update equation. The new pseudolinear filter has a stable performance and low computational complexity and handles the correlation problem implicitly under a unified MMSE framework, thus avoiding the severe bias problem of the PLKF. The posterior Cramer–Rao Lower Bound (PCRLB) for target state estimation is presented. Simulations are conducted to demonstrate the effectiveness of the proposed method.
Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation
Authors: Ahn Hyojung , Yeo Inchoon
Published: August 2021

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.
Low-Cost System Based on Optical Sensor to Monitor Discharge of Industrial Oil in Irrigation Ditches
Authors: Basterrechea Daniel A. , Rocher Javier , Parra Lorena , Lloret Jaime
Published: August 2021

Uncontrolled dumping linked to agricultural vehicles causes an increase in the incorporation of oils into the irrigation system. In this paper, we propose a system based on an optical sensor to monitor oil concentration in the irrigation ditches. Our prototype is based on the absorption and dispersion of light. As a light source, we use Light Emitting Diodes (LEDs) with different colours (white, yellow, blue, green, and red) and a photodetector as a sensing element. To test the sensor’s performance, we incorporate industrial oils used by a diesel or gasoline engine, with a concentration from 0 to 0.20 mLoil/cm2. The experiment was carried out at different water column heights, 0 to 20 cm. According to our results, the sensor can differentiate between the presence or absence of diesel engine oil with any LED. For gasoline engine oil, the sensor quantifies its concentration using the red light source; concentrations greater than 0.1 mLoil/cm2 cannot be distinguished. The data gathered using the red LED has an average absolute error of 0.003 mLoil/cm2 (relative error of 15.8%) for the worst case, 15 cm. Finally, the blue LED generates different signals in the photodetector according to the type of oil. We developed an algorithm that combines (i) the white LED, to monitor the presence of oil; (ii) the blue LED, to identify if the oil comes from a gasoline or diesel engine; and (iii) the red LED, to monitor the concentration of oil used by a gasoline engine.
Testing Off-the-Shelf Optical Wireless LANs for Smart City Environments
Authors: Pescosolido Loreto , Ancillotti Emilio , Passarella Andrea
Published: August 2021

Optical wireless LANs (OWLs) constitute an emerging networking paradigm for indoor scenarios’ fit to different smart cities’ fields of applications. Commercial products employing this technology have been made available on the market in recent years. In this work, we investigate, through a set of indoor communication experiments based on commercially available products, how different environmental and usage modes affect the performance of the system, addressing the presence of multiple users, the position and mobility of the mobile devices, the handover among adjacent cells and the effect of background lighting. Our finding shows that the system is quite robust with respect to the variation of operational conditions. We show that, in most conditions, the links can reliably sustain a stable throughput, achieving at least 50% of the throughput achieved with using the maximum light intensity of the transmitting lamp, while they are affected in a very mild way by factors like position and height of the mobile device, and virtually unaffected by variations in the background light.
Generalized Cardioid Distributions for Circular Data Analysis
Authors: Paula Fernanda V. , Nascimento Abraão D. C. , Amaral Getúlio J. A. , Cordeiro Gauss M.
Published: August 2021

The Cardioid (C) distribution is one of the most important models for modeling circular data. Although some of its structural properties have been derived, this distribution is not appropriate for asymmetry and multimodal phenomena in the circle, and then extensions are required. There are various general methods that can be used to produce circular distributions. This paper proposes four extensions of the C distribution based on the beta, Kumaraswamy, gamma, and Marshall–Olkin generators. We obtain a unique linear representation of their densities and some mathematical properties. Inference procedures for the parameters are also investigated. We perform two applications on real data, where the new models are compared to the C distribution and one of its extensions.
Models of Sports Management in Fitness Centres. Influence of Sex, Age and Sport Frequency. Linear Models vs. Qualitative Comparative Analysis
Authors: García-Pascual Fernando , Pérez-Campos Carlos , García Sánchez Joaquín , Soto-Rubio Ana , Aguado Berenguer Sergio
Published: August 2021

Knowing the perceptions of users of sports services has always been an object of analysis within sports management. This paper attempts to analyse what influences the satisfaction and future intentions of fitness centre customers, beyond management variables, by using two different methodologies. A sample of 389 users of a private sports centre was used. Both linear relationships between variables and the combination of sets were analysed using fuzzy set Qualitative Comparative Analysis fsQCA. It is concluded that management variables (service quality, satisfaction and perceived value) are very important for the prediction of management models, but that, according to the interaction methodology between variables, both frequency and sociodemographic variables play an important role in achieving satisfied and loyal users of the sports service. For the prediction of customers’ future intentions, within the analysed sets, it is observed that satisfaction and perceived value are the most predictive variables (raw coverage 0.66). Therefore, and as a consequence, a high number of satisfied and loyal users of the service will allow the economic viability of this service to be achieved.
Assessment of a Municipal Solid Waste Incinerator Bottom Ash as a Candidate Pozzolanic Material: Comparison of Test Methods
Authors: Faleschini Flora , Toska Klajdi , Zanini Mariano Angelo , Andreose Filippo , Settimi Alessio Giorgio , Brunelli Katya , Pellegrino Carlo
Published: August 2021

New generations of green concretes are often consuming large amounts of industrial waste, as recycled or manufactured aggregates and alternative binders substituting ordinary Portland cement. Among the recycled materials that may be used in civil engineering works, construction and demolition waste (C&DW), fly ashes, slags and municipal solid waste incinerator bottom ashes (MSWI BA) are those most diffused, but at the same, they suffer due to a large variability of their properties. However, the market increasingly asks for new materials capable of adding some specific features to construction materials, and one of the most interesting is the pozzolanic activity. Hence, this work deals with an experimental study aimed at assessing the technical feasibility of using an industrial waste comprised largely of MSWI BA, with small quantities of C&DW and electric arc furnace slag (EAFS), in green cement-based mixtures (cement paste and mortars). The aim of the work is to achieve the goal of upcycling such waste and avoiding its disposal and landfilling. Particularly, the test methods for assessing the pozzolanic activity of this waste are discussed, analyzing the efficacy of indirect methods such as the strength activity index (SAI), the conductivity test and the efficiency factor (k), together with a direct method based on lime consumption.
Analytical Models for Seawater and Boron Removal through Reverse Osmosis
Authors: Binns Michael
Published: August 2021

Regarding the purification of seawater, it is necessary to reduce both the total concentration of salt and also the concentration of boron to meet purity requirements for safe drinking water. For this purpose reverse osmosis membrane modules can be designed based on experimental data supported by computer models to determine energy efficient configurations and operating conditions. In previous studies numerical models have been suggested to predict the performance of the removal with respect to difference pressures, pH values, and temperatures. Here, an analytical model is suggested which allows for both the simplified fitting of the parameters required for predicting boron transport coefficients and also the simple equations that can be used for the design of combined seawater and boron removal systems. This modelling methodology is demonstrated through two case studies including FilmTec and Saehan membrane modules. For both cases the model is shown to be able to predict the performance with similar accuracy compared with existing finite-difference type numerical models from the literature.
Process Safety Management Quality in Industrial Corporation for Sustainable Development
Authors: Markowski Adam S. , Krasławski Andrzej , Vairo Tomaso , Fabiano Bruno
Published: August 2021

In recent years, also in connection with Covid-19 pandemics and enforced restrictions, there has been the formation of large industrial corporations gathering separate companies with similar, sometimes complementary production profiles. This evolving trend has brought usually positive economic effects; however, it has also created some integration problems that include the process safety management. The Texas City BP accident in 2005 and its tremendous human and economic losses underlined the obstacles in defining a well-structured corporation process safety management. The main causes of the above-mentioned accident were connected to an inadequate safety culture at the managerial level. Strong leadership and high standards of corporate governance are required to inspire correct safety behavior in the staff. The so-called soft skills become even more important in the Industry 4.0 arena, where the foundation of the whole system is based on an intelligent use and interpretation of data. The importance of this aspect is confirmed by several post-accidental analyses of past events. Although some research on this topic has been already done, it is worth it to dedicate some effort to identifying specific factors which influence the corporate process safety management quality, and, once identified, to assess them. This paper applies the concept of “lessons learnt” for the identification of organizational and managerial aspects worth consideration in process safety management. Based on accident and literature reviews and expert opinions, the aim is to identify the major contributing factors among leadership and safety culture, risk awareness, knowledge and competence, communication, and information and decision-making processes. To self-assess the level of commitment of the top leaders in process safety management, a checklist approach is proposed, combined with a quantitative, weighted evaluation based on the Relative Efficiency Indicator (REI). Positive value of REI may ensure the effectiveness of process safety management in major hazard industries and their appropriate adaptation to the corporation community. The proposed method, which is validated in an actual case study, underlines the importance of an appropriate education, and of a more careful selection of HSE managers.
Investigating the Image of the Bihor Tourist Destination among Romanians in the Context of Increasing Economic Indicators of Tourist Activity
Authors: Ban Olimpia , Hatos Adrian , Droj Laurențiu , Toderașcu Carmen
Published: August 2021

The concept of tourism destination image is closely related to the brand image of the destination. A good public image of a certain location is the first step in establishing or enhancing the branding of that destination. The image of the destination can be a primary, secondary or global one, the latter incorporating the first two. The sustainability of a positive image of the destination is based on both positive secondary image and positive global image. The purpose of this research is to separately analyze the two types of images for a given tourist destination (Bihor, Romania) that has registered, in recent years, a remarkable increase in the number of visitors. The increase in the number of visitors was accompanied by a substantial increase in tourism revenues, which drew attention, both for the policy makers and researchers, to investigate how the image of this popular place is perceived. The research is based on a questionnaire-based survey applied on a sample of 607 persons, residents in Romania. The research method used was the questionnaire-based survey, which was applied between May and June 2020 on online city groups. The collected data were processed with IBM Statistical package for social sciences version 25 and the results show significant differences between the two types of images (secondary image and global image), a dangerous situation in the medium and long term for destination management. The nuances in the perception of the image of a destination based on the two types of respondents (who experienced and who did not experience the destination) can be explained by the aggressive strategy of promoting the tourist destination, but this is an inefficient strategy for younger age groups. The study allows us to formulate conclusions and to propose measures to correct the situation.
Space as Sociocultural Construct: Reinterpreting the Traditional Residences in Jinqu Basin, China from the Perspective of Space Syntax
Authors: Chen Yu , Xu Keyou , Liu Pei , Jiang Ruyu , Qiu Jingyi , Ding Kangle , Fukuda Hiroatsu
Published: August 2021

The traditional residence with protogenetic spatial arrangement is regarded as a critical carrier of social logic of space, which makes it an ideal object for studying the relationship between the spatial form and social context. To this end, a comparative analysis is conducted using Depthmap Software. This study is based on space syntax theory between two groups of proxies of sharp differences in spatial organization in one geomorphic unit where the natural factors show little variations, while the human factors present a bifurcating distribution. Furthermore, the study clarifies the differences among genotypes of the domestic space system. Finally, combined with historical material, it proves the dual division of regional sociocultural factors as decisive forces shaping the traditional living space and its constructed manifestations.
Effects of Domestic Tourism on Urban-Rural Income Inequality: Evidence from China
Authors: Zeng Zhixin , Wang Xiaojun
Published: August 2021

Most studies examining the relationship between domestic tourism and urban-rural income inequality have found a positive correlation. However, the causal link between them is difficult to establish due to many potential sources of endogeneity. By including World Heritage Site (henceforth WHS) designation in the set of instruments, this paper estimates the causal effects of domestic tourism on urban-rural income inequality within 31 China’s provinces from 1998 to 2018. Our results show that developing domestic tourism can reduce urban-rural income inequality by raising income of rural residents more than twice as much as that of urban residents. Specifically, a 10% increase in domestic tourism earnings could increase the average disposable income of urban residents by 0.35% and that of rural residents by 0.94%, resulting in a 0.59% reduction in the urban-rural income ratio. According to channels analysis, domestic tourism enhances the disposable income of rural residents mainly through raising household operating income from agriculture, manufacturing, and services.
Social Innovation for a Just Sustainable Development: Integrating the Wellbeing of Future People
Authors: Vasconcellos Oliveira Rita
Published: August 2021

Social innovation has gained increased attention as a mechanism for sustainable development. As the Brundtland Commission highlights, the improvement of present conditions should not compromise future generations’ needs. So far, (social) sustainable development has mostly focused on the amelioration of contemporary people’s wellbeing, relegating its duties towards future generations to second place. Given this, I consider it necessary to (re-)direct social innovation towards the promotion of the wellbeing of future people. I propose the concept of irreplaceable goods, a notion deriving from a strong sustainability perspective, which could then be integrated into social innovation practices related to sustainable development. Focusing on guaranteeing, at least, sufficient fruition of certain goods and resources, I devise this concept as a governance tool for steering development actions towards intergenerational justice, driven by social innovation action. In this article, we firstly delineate the relations between sustainable development and social innovation, while focusing on ‘value-driven’ social innovation. Afterward, I shortly introduce strong sustainability as support for future generations’ wellbeing. Furthermore, I develop the concept of irreplaceable goods as a governance tool in social innovation practices and finalize with a discussion on the application of irreplaceable goods in the assessment of sustainable development strategies.
A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm
Authors: Dong Quande , Wang Cui , Peng Shitong , Wang Ziting , Liu Conghu
Published: August 2021

The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses a great challenge. In this regard, the present study optimized the eco-efficiency from the perspective of operating data analysis. We formulated the issue of eco-efficiency improvement into a many-objective optimization problem. Considering the complexity between the system inputs and outputs and to further reduce the computational cost, we constructed a Kriging model and made a comparison between this model and the response surface methodology based on two accuracy metrics. This surrogate model was then incorporated into the NSGA-III algorithm to obtain the Pareto-optimal front. As this Pareto-optimal front provides multiple alternative operating options, we applied the TOPSIS to select the most appropriate alternative set of operating parameters. This approach was validated using the historical operation data from the desulfurization system at a coal-fired power plant in China with a 600 MW unit. The results indicated that the optimization would cause an improvement in the efficiency of desulfurization and energy efficiency but a slight increase in the consumption of limestone slurry. This study attempted to provide an effective operating strategy to enhance the eco-efficiency performance of desulfurization systems.
Policy Considerations for African Food Systems: Towards the United Nations 2021 Food Systems Summit
Authors: Sibanda Lindiwe M. , Mwamakamba Sithembile N.
Published: August 2021

Achieving food and nutrition security and ending hunger is a complex and multi-faceted global challenge, which requires urgent attention, particularly in Africa. To eliminate hunger, the continent needs to transition to new sustainable, inclusive, and resilient food systems that deliver nutritious food and a healthy planet for all. This paper discusses challenges and opportunities highlighted during the “Food Systems Transformation to Address the SDGs” session convened by the African Research Universities Alliance (ARUA) and partners at the 8th World Sustainability Forum (WSF2020) held in September 2020. The paper reflects on how African food systems need to change to achieve the food systems related and interconnected the Sustainable Development Goals (SDGs). It also presents issues for consideration at the 2021 United Nations Food Systems Summit. Key considerations include (i) the realization that nutrition insecurity is not food insecurity, (ii) the need for Africa to actualize its potential, (iii) the need to demystify policy development processes; (iv) the need to invest in better measurements and indicators; and (v) the need to create nature-based climate-smart solutions
Regulation of RNA Interference Pathways in the Insect Vector Laodelphax striatellus by Viral Proteins of Rice Stripe Virus
Authors: Xiao Yan , Li Qiong , Wang Wei , Fu Yumei , Cui Feng
Published: August 2021

RNA interference (RNAi), especially the small interfering RNA (siRNA) and microRNA (miRNA) pathways, plays an important role in defending against viruses in plants and insects. However, how insect-transmitted phytoviruses regulate the RNAi-mediated antiviral response in vector insects has barely been uncovered. In this study, we explored the interaction between rice stripe virus (RSV) and the miRNA and siRNA pathways of the small brown planthopper, which is a vector insect. The transcript and protein levels of key genes in the two RNAi pathways did not change during the RSV infection process. When the expression of insect Ago1, Ago2, or Translin was silenced by the injection of double-stranded RNAs targeting these genes, viral replication was promoted with Ago2 silencing but inhibited with Translin silencing. Protein-protein binding assays showed that viral NS2 and RNA-dependent RNA polymerase interacted with insect Ago2 and Translin, respectively. When NS2 was knocked down, the transcript level of Ago2 increased and viral replication was inhibited. Therefore, viral NS2 behaved like an siRNA suppressor in vector insects. This protein-binding regulation of insect RNAi systems reflects a complicated and diverse coevolution of viruses with their vector insects.
Genomic Epidemiology and Evolution of Duck Hepatitis A Virus
Authors: Fehér Enikő , Jakab Szilvia , Bali Krisztina , Kaszab Eszter , Nagy Borbála , Ihász Katalin , Bálint Ádám , Palya Vilmos , Bányai Krisztián
Published: August 2021

Duck hepatitis A virus (DHAV), an avian picornavirus, causes high-mortality acute disease in ducklings. Among the three serotypes, DHAV-1 is globally distributed, whereas DHAV-2 and DHAV-3 serotypes are chiefly restricted to Southeast Asia. In this study, we analyzed the genomic evolution of DHAV-1 strains using extant GenBank records and genomic sequences of 10 DHAV-1 strains originating from a large disease outbreak in 2004–2005, in Hungary. Recombination analysis revealed intragenotype recombination within DHAV-1 as well as intergenotype recombination events involving DHAV-1 and DHAV-3 strains. The intergenotype recombination occurred in the VP0 region. Diversifying selection seems to act at sites of certain genomic regions. Calculations estimated slightly lower rates of evolution of DHAV-1 (mean rates for individual protein coding regions, 5.6286 × 10−4 to 1.1147 × 10−3 substitutions per site per year) compared to other picornaviruses. The observed evolutionary mechanisms indicate that whole-genome-based analysis of DHAV strains is needed to better understand the emergence of novel strains and their geographical dispersal.
Detection of SARS-CoV-2 Derived Small RNAs and Changes in Circulating Small RNAs Associated with COVID-19
Authors: Grehl Claudius , Schultheiß Christoph , Hoffmann Katrin , Binder Mascha , Altmann Thomas , Grosse Ivo , Kuhlmann Markus
Published: August 2021

Cleavage of double-stranded RNA is described as an evolutionary conserved host defense mechanism against viral infection. Small RNAs are the product and triggers of post transcriptional gene silencing events. Up until now, the relevance of this mechanism for SARS-CoV-2-directed immune responses remains elusive. Herein, we used high throughput sequencing to profile the plasma of active and convalescent COVID-19 patients for the presence of small circulating RNAs. The existence of SARS-CoV-2 derived small RNAs in plasma samples of mild and severe COVID-19 cases is described. Clusters of high siRNA abundance were discovered, homologous to the nsp2 3′-end and nsp4 virus sequence. Four virus-derived small RNA sequences have the size of human miRNAs, and a target search revealed candidate genes associated with ageusia and long COVID symptoms. These virus-derived small RNAs were detectable also after recovery from the disease. The additional analysis of circulating human miRNAs revealed differentially abundant miRNAs, discriminating mild from severe cases. A total of 29 miRNAs were reduced or absent in severe cases. Several of these are associated with JAK-STAT response and cytokine storm.