bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images
Authors: Teixeira Lucas O. , Pereira Rodolfo M. , Bertolini Diego , Oliveira Luiz S. , Nanni Loris , Cavalcanti George D. C. , Costa Yandre M. G.
Published: October 2021

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.
A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems
Authors: Beneyto Aleix , Puig Vicenç , Bequette B. Wayne , Vehi Josep
Published: October 2021

The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.
Internet of Things Based Contact Tracing Systems
Authors: Hu Peng , Lamontagne Philippe
Published: October 2021

The COVID-19 pandemic has significantly threatened the health and well-being of humanity. Contact tracing (CT) as an important non-pharmaceutical intervention is essential to containing the spread of such an infectious disease. However, current CT solutions are fragmented with limited use of sensing and computing technologies in a scalable framework. These issues can be well addressed with the use of the Internet of Things (IoT) technologies. Therefore, we need to overview the principle, motivation, and architecture for a generic IoT-based CT system (IoT-CTS). A novel architecture for IoT-CTS solutions is proposed with the consideration of peer-to-peer and object-to-peer contact events, as well as the discussion on key topics, such as an overview of applicable sensors for CT needs arising from the COVID-19 transmission methods. The proposed IoT-CTS architecture aims to holistically utilize essential sensing mechanisms with the analysis of widely adopted privacy-preserving techniques. With the use of generic peer-to-peer and object-to-peer sensors based on proximity and environment sensing mechanisms, the infectious cases with self-directed strategies can be effectively reduced. Some open research directions are presented in the end.
A Manycore Vision Processor for Real-Time Smart Cameras
Authors: Silva Bruno A. da , Lima Arthur M. , Arias-Garcia Janier , Huebner Michael , Yudi Jones
Published: October 2021

Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a manycore vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing elements and memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.
A 28 GHz 2 × 2 Antenna Array with 10 Beams Using Passive SPDT Switch Beamforming Network
Authors: Abdul Ghani Firas , Najafabadi Amir Mohsen Ahmadi , Saleh Heba , Kaya Yapici Murat , Tekin Ibrahim
Published: October 2021

In this paper, a dual-polarized four-port 2 × 2 series fed antenna array operating at 28 GHz with beam-switching capability is proposed. The antenna array uses a simple passive beamforming network to switch the main beam. The presented antenna design is suitable for 5G user equipment and high data rates applications by which it has a compact size with low cost and complexity. The size of the antenna is 37.2 × 37.2 mm2 including the ground plane, and it produces 10 different switched beams by using only two simple 3 dB/90∘ couplers which create the required amplitudes and phase excitations for the antenna elements. A one-port simple feeding mechanism including Peregrine PE42525 SPDT switch modules and a power divider is used to generate and measure the 10 switched beams. The antenna design is implemented on a two-layer 0.203 mm thick low-loss (tanδ = 0.0027) Rogers 4003C substrate, and it has a measured 10 dB impedance bandwidth of 4 GHz (14.3%, from 26 GHz to 30 GHz) for all ports. Measured peak isolation between any dual-polarized ports of the antenna is better than 30 dB. The antenna has an average measured realized gain of 8.9 dBi and around 10 dB side lobe level (SLL) for all beams. The antenna has 3-dB coverage of 80∘ to 90∘ in 2D space and it has a maximum of ±26∘ beam-steering angle. The antenna is designed and simulated using Ansys HFSS and fabricated using regular PCB processing.
Decision-Making Framework for Implementing Safer Human-Robot Collaboration Workstations: System Dynamics Modeling
Authors: Borges Guilherme Deola , Reis Angélica Muffato , Ariente Neto Rafael , Mattos Diego Luiz de , Cardoso André , Gonçalves Hatice , Merino Eugenio , Colim Ana , Carneiro Paula , Arezes Pedro
Published: October 2021

Human-Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.
UV-Vis Spectrophotometry and UPLC–PDA Combined with Multivariate Calibration for Kappaphycus alvarezii (Doty) Doty ex Silva Standardization Based on Phenolic Compounds
Authors: Mutiarahma Selma , Putra Venansius G. P. , Chaniago Weni , Carrera Ceferino , Anggrahini Sri , Palma Miguel , Setyaningsih Widiastuti
Published: October 2021

The algae Kappaphycus alvarezii is considered an important raw material for industrial practices, producing high economic value of various derived products. However, the quality of this commodity, which can be indicated by the level of phenolic compounds, may vary due to growth factors, including cultivation sites. An analytical UV-Vis spectrophotometry method coupled with chemometrics was proposed to standardize the red alga based on the content of phenolic compounds. The correlation between the UV-Vis spectra and UPLC–PDA results, combined with a multivariate calibration of the K. alvarezii extracts, was analyzed. The extracts were prepared using an ultrasound-based technique and subsequently subjected to UV-Vis spectral measurements at 200–800 nm and UPLC–PDA at 260 and 330 nm. Chemometric techniques and partial least squares (PLS) were applied to the acquired data to build a reliable analysis of the phenolics in the K. alvarezii extracts. The result showed that the wavelength combination of 200–450 and 600–690 nm provided a valid method for quantitative analysis of the studied phenolics that belong to hydroxybenzoic acid, hydroxycinnamic acid, and flavonoid with a coefficient of regression (R2) > 0.96 in the calibration and validation models, along with an RMSEC and RMSEP value < 8%. The method was then employed to characterize the K. alvarezii samples from 13 different cultivation areas. Principal component analysis (PCA) generated principal components that produced a clear distribution among the samples of K. alvarezii based on phenolic compounds corresponding to the geographical origin.
Clearance of Bone Substitute in Gel Form Accidentally Dispersed into the Sinus Cavity during Transcrestal Maxillary Sinus Floor Elevation: Two-Case Report
Authors: Bernardello Fabio , Lombardi Teresa , Stacchi Claudio
Published: October 2021

Sinus membrane perforation is the most frequent intraoperative complication occurring during maxillary sinus floor elevation. Although numerous techniques for perforation management are present, grafting material dissemination may still occur, representing a potential trigger factor leading to acute or chronic sinusitis. This case report describes two cases of xenogeneic bone substitute in gel form accidentally dispersed into the sinus cavity during maxillary sinus floor elevation with a transcrestal approach. In both cases, immediately postoperative radiographic imaging showed an important amount of gel graft dislodged into the sinus cavity as a consequence of hidden perforations that remained undetected during surgery. Patients were monitored for 6 months after surgery and reported no signs or symptoms related to possible sinus disease. Control radiographs showed no sinus membrane hypertrophy and/or presence of residual disseminated gel, confirming complete clearance of the accidentally dispersed graft through the ostiomeatal complex. In order to minimize postoperative complications, bone substitutes in gel form could represent an interesting alternative to granular grafts for their easier clearance from the maxillary sinus cavity in case of accidental dissemination during sinus augmentation procedures.
Precipitation Controls on Soil Biogeochemical and Microbial Community Composition in Rainfed Agricultural Systems in Tropical Drylands
Authors: Abbruzzini Thalita F. , Avitia Morena , Carrasco-Espinosa Karen , Peña Víctor , Barrón-Sandoval Alberto , Salazar Cabrera Ulises Isaac , Cruz-Ortega Rocío , Benítez Mariana , Escalante Ana E. , Rosell Julieta A. , Wegier Ana , Campo Julio
Published: October 2021

The current and expected expansion of agriculture in the drylands of Mexico, together with the decrease in precipitation occurring in the country, likely affect ecosystem processes and will bring great challenges for the suitability of rainfed agriculture for smallholder farmers. Here, we assessed metrics of the soil C, N, and P cycles, as well as soil microbial diversity, under rainfed maize and common bean cropping in arid and semiarid regions of central Mexico. The soil enzymatic vector angles of cultivated plots in both regions were above 45°, suggesting P limitation for microbial growth and crop productivity. Although changes were not observed in the intensity of this P-limitation with aridity, we found a negative effect of drought increase on the concentration of soil organic C and total N, with consequences for the C, N, and P balance in soils. Increasing aridity leads to the homogenization of microbial diversity. Considering a scenario in which decreases in mean annual precipitation would uncouple the biogeochemical cycles and homogenize soil biodiversity, the ecological implications could be an increase in the vulnerability of agricultural ecosystems to drought, with negative consequences for the suitability of rainfed agriculture in the drylands of central Mexico.
Spatial Transformation of the Pig Sector in Hungary and Slovenia: A Comparative Analysis
Authors: Csonka Arnold , Bojnec Štefan , Fertő Imre
Published: October 2021

This paper presents a comparative analysis of the spatial transformation in the Hungarian and Slovenian pig sectors at the level of local administrative units (LAU). Concentration and inequality measures were applied in the empirical analyses, along with Markov transition probability matrices, to examine the stability and/or mobility over time and the presence of clustering effects. Both countries experienced a rapid decline in pig population. This profound structural change has led to a smaller number of more concentrated pig farms and increased territorial concentration. The degree of farm and territorial concentration and inequality in Hungary has been much higher than in Slovenia, and the concentration gap between the countries has increased. Between 2000 and 2010, the degree of concentration was much higher in Hungary than in Slovenia; average herd size per holding increased by 68 percent in Hungary, and only seven percent in Slovenia. In Hungary, clustering effects were particularly significant, with the pig sector moving towards large-scale concentration. The former effect was also confirmed in the Slovenian pig sector, but significantly weakened during the period under investigation. The exploitation and policy management of spatial externalities justifies these agricultural, economic, and agri-environmental practices.
Life Cycle-Based Sustainability and Circularity Indicators for the Tourism Industry: A Literature Review
Authors: Arzoumanidis Ioannis , Walker Anna M. , Petti Luigia , Raggi Andrea
Published: October 2021

The tourism industry is one of the fastest growing sectors, even though in the last couple of years there has been a negative effect due to the COVID-19 pandemic. This growth has led to an increase in environmental, economic and social impacts. Furthermore, the transition towards a circular economy has become one of the priorities of the European Union given its orientation towards sustainable development. However, the concept of circular tourism has been poorly analysed until now. This paper aims at identifying a set of life cycle-based sustainability- and circularity-related indicators for the hospitality sector by means of a literature review and a content analysis. It presents an overview of the most relevant themes and indicators in the tourism sector, showing that carbon footprint and resource-related indicators are especially relevant. Given the complexity of the measurement of circularity and the fact that life cycle-based indicators have started to be considered in this field, the paper also proposes some criteria for indicator selection to make their application more feasible for hospitality actors.
Thermal Performance Optimization of Double and Triple Glazing Systems for Slovenian Climate Conditions
Authors: Prapotnik Brdnik Anita
Published: October 2021

Glazing elements are an important part of the thermal envelope of a building. Therefore, good thermal performance of glazing elements can improve indoor comfort and reduce annual maintenance costs and CO2 (carbon dioxide) emissions by reducing heat loss. Reducing heat loss through glazing elements during the heating season can be achieved by combining low thermal transmittance with high solar heat gain. Using standardized calculation methods and measured climate data for three Slovenian locations representing typical continental, mountainous and Mediterranean climates, this study predicts the best combination of optical properties (emissivity, transmittance and reflectance) of glass panes in double and triple glazing systems that contribute to minimal heat loss. It was found that for the double glazing system, the minimum heating and cooling demand for buildings with low solar gains or high solar gains and applied shading is achieved by an inner pane with high transmittance without low-emissivity coating, and an outer pane with low-emissivity coating with minimum possible emissivity. In Maribor and Portorož climatic zones, the panes with low emissivity coating should be used as inner panes in buildings with high solar gains. For triple glazing, the minimum heating and cooling requirements are achieved with two or three panes with low emissivity. For buildings with low solar gains, an emissivity of the coating of 0.03 is preferable, but for buildings with high solar gains, lower emissivity values should be used.
GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
Authors: Kritsis Kosmas , Kiourt Chairi , Stamouli Spyridoula , Sevetlidis Vasileios , Solomou Alexandra , Karetsos George , Katsouros Vassilis , Pavlidis George
Published: October 2021

Plant identification from images has become a rapidly developing research field in computer vision and is particularly challenging due to the morphological complexity of plants. The availability of large databases of plant images, and the research advancements in image processing, pattern recognition and machine learning, have resulted in a number of remarkably accurate and reliable image-based plant identification techniques, overcoming the time and expertise required for conventional plant identification, which is feasible only for expert botanists. In this paper, we introduce the GReek vAScular Plants (GRASP) dataset, a set of images composed of 125 classes of different species, for the automatic identification of vascular plants of Greece. In this context, we describe the methodology of data acquisition and dataset organization, along with the statistical features of the dataset. Furthermore, we present results of the application of popular deep learning architectures to the classification of the images in the dataset. Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.
Does Tax, Financial, and Government Incentives Impact Long-Term Portuguese SMEs’ Sustainable Company Performance?
Authors: Picas Sara , Reis Pedro , Pinto António , Abrantes José Luís
Published: October 2021

This article aims to assess how fiscal and financial incentives and government support conditioned the profitability of Portuguese SMEs between 2010 and 2019. The high tax and financial burdens on SMEs have consequences for sustainability and business development. Thus, the study analyzes different incentives provided by the Portuguese government to ease this burden and improve business profitability. The study uses panel data with fixed effects using five different sources of information from five internal tax grant types, three different European Union program financial subventions, and three national budget-specific expenses. The results obtained suggest that tax incentives influence the profitability of SMEs; however, government incentives do not have any impact. The QREN (financial) incentives positively decide the ROA and negatively impact the ROE, contributing to sustainable performance. Portugal 2020 incentives have a weak effect on the first years, improving in the following years. However, the incentive related to R&D is not relevant. This work aims to contribute to decision making for managers, shareholders, and government entities, allowing them to choose those measures that could increase the company’s added value, and for governments, as a tool to select incentives that will most benefit SMEs’’ profitability. This work identifies the key incentives that impact companies’ profitability.
Assessing Energy Descent Scenarios for the Ecological Transition in Spain 2020–2030
Authors: Lallana Martín , Almazán Adrián , Valero Alicia , Lareo Ángel
Published: October 2021

A global energy consumption reduction is essential to address the many dimensions of the current ecological crisis. In this paper we have compiled the reasons that justify the necessity to start this energy descent process in the countries of the global North, where the annual per capita final energy consumption was 118 GJ in 2017. Based on recent research, we approach the necessary redistribution of energy consumption at the global level and the elements that should be present in energy descent strategies. We establish an approximate threshold of minimum and maximum per capita final energy consumption, between 15.6 GJ and 31.0 GJ for the year 2050, which serves as a reference for evaluating scenarios. We continue with an analysis of two ecological transition scenarios for Spain between 2020 and 2030, Green New Deal and Degrowth. Based on a schematic calculation model defined in “Labor Scenarios in the Ecosocial Transition 2020–2030” report, we evaluate the variations in energy consumption for 86 sectors of economic activity. Results show an annual final energy consumption per capita in 2030 of 44.6 GJ and 36.8 GJ for each scenario. We conclude by analyzing the hypothetical main drivers of this sharp decline in energy consumption.
Digitalisation for Water Sustainability: Barriers to Implementing Circular Economy in Smart Water Management
Authors: Liu Qinglan , Yang Longjian , Yang Miying
Published: October 2021

“Clean water and sanitation” is listed as one of the 17 United Nations’ Sustainable Development Goals and implementing circular economy principles in the water sector has been widely regarded as an important approach in achieving this goal. In the era of Industry 4.0, research and practice in the digitalisation of the water sector to create a smart water system have attracted increasing attention. Despite the growing interest, limited research has been devoted to how digital technologies might enhance circularity. In practice, smart water systems often fail to promote circularity in such aspects as water reuse and resources recovery. This paper aims to identify the main barriers to implementing circularity in the smart water management system in Zhejiang, China. The research adopts a mixed research method that includes a literature review to identify the potential barriers from the existing studies, a case study to determine the most critical barriers in practice, and a fuzzy Delphi method to reach a consensus on the crucial barriers. The research identified 22 main barriers to implementing circular economy in smart water management. The barriers are divided into three categories: infrastructure and economic, technology, and institution and governance. The results show that the barriers related to recycling technologies, digital technology know-how, and the lack of CE awareness raise the most concern. Our findings also indicate that experts are interested in the decentralized wastewater treatment system. This research provides significant insights that practitioners, researchers, and policymakers can use in developing and implementing digital-based CE strategies to reduce water scarcity and pollution.
Environmental Impact Assessment of Different Warm Mix Asphalts
Authors: Belc Anda Ligia , Ciutina Adrian , Buzatu Raluca , Belc Florin , Costescu Ciprian
Published: October 2021

Within the last decade, much attention has been focused on determining viable techniques for producing sustainable asphalt mixtures and minimizing fuel use and greenhouse gas emissions. Thus, warm mix asphalt (WMA) has become a topic of significant interest among road specialists as it offers a potential solution for reducing the environmental impact of the asphalt mixtures due to the decreased temperatures they require for mixing and compaction compared to hot mix asphalt (HMA). The present study is focused on the Life Cycle Assessment (LCA), according to a “Cradle-to-Gate” approach, of hot mix asphalt and warm mix asphalt prepared with locally available materials and different warm mix additives such as organic additives, chemical additive, and synthetic zeolite. For the analysis of the environmental impact of the warm mix asphalts was used a dedicated software for modeling and evaluating the LCA. The WMA prepared with chemical additive or organic additive led to a decrease of the environmental impact, in the production phase, compared to HMA. The study reveals that the raw materials extraction has the greatest impact on the environment in all studied cases, followed by the actual production phase of the asphalt mixture. For WMA produced with additives there was a decrease in the global impact on the environment compared to HMA.
Supply Chain Optimization Considering Sustainability Aspects
Authors: Beheshtinia Mohammad Ali , Feizollahy Parisa , Fathi Masood
Published: October 2021

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.
Exploring the Connections of Education for Sustainable Development and Entrepreneurial Education—A Case Study of Vocational Teacher Education in Finland
Authors: Asikainen Eveliina , Tapani Annukka
Published: October 2021

Education for Sustainable Development (ESD) and Entrepreneurial Education (EE) are quite abstract and demanding concepts for teacher students. Yet, Key Sustainability Competences and Entrepreneurial Competences entail important qualities of future citizens and workers, and teacher students should become prepared to accommodate education for these competencies in their teaching practice. This paper explores teacher students’ process of sense-making of sustainable development and how becoming a teacher who practices ESD connects with entrepreneurship. EE serves as a good mirroring surface to ESD as they both have their roots in Transformative Learning (TL) but pursue transformation towards different goals. The case study follows the vocational teacher education (VTE) students’ sensemaking of Sustainable Development as a part of teacher’s work during one semester which included integrated Thematic Studies of Sustainable Development. The qualitative content analysis of students’ texts focused on signs of transformative learning and was guided by the dimensions of sustainable development and learning goals set for teacher’s sustainability competences in the VTE curriculum. The results indicate that transformative learning is possible. Furthermore, they address the importance of certain entrepreneurial capabilities in the actualization of change agency.
Evaluation and Simulation of Water Security in the Circum-Bohai Sea Region of China
Authors: Men Baohui , Han Libo , Meng Changqing
Published: October 2021

The function and necessity of water resources make them an important factor affecting economic and social development. To explore various water security issues impacting water use, the pressure-state-response model was applied in this study to construct a water security evaluation indexing system for the Circum-Bohai Sea Region (CBSR) in China. In this study, the game method was used to balance the two weighting methods, and the water security grades were calculated by the forward cloud model. Compared with the previous work, this study tried to analyze the simulated scenarios using the ELECTRE III method, determined the optimal development scenario mode based on the water security grade, and put forward water security measures and suggestions based on the results. This study put forward three scenarios of development models. Under the scenario of “pressure reduction”, it can be proposed to build suburbs and transfer urban functional areas to relieve the pressure of urban population. Under the “ideal state” scenario, water diversion projects and water storage projects can be proposed to relieve the regional water shortage. Under the scenario of “efficient response”, strengthening ecological environment construction and improving sewage treatment capacity can be proposed.
The Temporal-Spatial Pattern and Coupling Coordination of the Green Transition of Farmland Use: Evidence from Hubei Province
Authors: Ke Shangan , Wu Yueqi , Cui Haiying , Lu Xinhai , Ge Kun , Chen Danling
Published: October 2021

The green transition of farmland use is a future trend in China’s modern agriculture and green development. However, its research framework, including its evaluation system, temporal-spatial distribution, and driving mechanisms, has not been established in the existent literature. With the 17 cities in Hubei Province as an example, we evaluated the green transition of farmland use and explore the characteristics and driving mechanisms of the temporal and spatial evolution from 2000–2019. The findings were as follows: First, the green transition of farmland use in Hubei Province is in infancy, but it has great potential. Second, the growth rate of the green transition of farmland use has noticeable regional differences in the east, central, and western areas of the province. Third, the three dimensions of spatial transition, functional transition, and model transition in the green transition of farmland use have significant spatial differences in coupling and coordination, and decoupling is becoming increasingly prominent. Based on the research findings, we put forward targeted countermeasures and suggestions.
An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data
Authors: Bhatti Abdul Rauf , Bilal Awan Ahmed , Alharbi Walied , Salam Zainal , Bin Humayd Abdullah S. , R. P. Praveen , Bhattacharya Kankar
Published: October 2021

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.
Methodology for the Sustainable Development of the Italy-Croatia Cross-Border Area: Sustainable and Multimodal/Cross-Border Passenger Services
Authors: Sirotić Miljen , Žuškin Srđan , Rudan Igor , Stocchetti Andrea
Published: October 2021

The efficient evaluation of the transport sector for the implementation of sustainable mobility solutions is a crucial challenge for governments and decisionmakers for strategically reducing the negative economic, social, and environmental impacts of transport. Current research trends on transport macromanagement indicate a lack of integrative and comprehensive approaches to mitigating transport sustainability parameters. Thus, a holistic perspective on transport macromanagement is deemed necessary for supporting the transition towards a more sustainable approach to transport macroplanning. This paper contributes to the development of transport macromanagement by offering insights, tools, and strategies for contemporary sustainable transitions in the transport sector. A methodology for sustainable and multimodal/cross-border passenger services for the Italy-Croatia cross-border area, with a systematic development cycle composed of six interconnected steps for fostering stakeholder cooperation, is proposed in this paper. Furthermore, a methodological framework, a SWOT analysis, and a gap analysis on the function of defining port improvement for the action plan are elaborated in this study. The study analysis is based on the international passenger terminal ports in a passenger liner service by using a survey analysis conducted among the port authorities in the cross-border area. Moreover, the main aim of the methodology is to provide a holistic framework for the sustainable development of a complex and multilayered transport environment that encompasses a multiplicity of various stakeholders.
Paper Sensors Based on Fluorescence Changes of Carbon Nanodots for Optical Detection of Nanomaterials
Authors: Papadopoulou Evie L. , Biffi Giulia , Senthamizhan Anitha , Martín-García Beatriz , Carzino Riccardo , Krahne Roman , Athanassiou Athanassia
Published: October 2021

A paper sensor was designed in order to detect the presence of nanomaterials, such as ZnO and silica nanoparticles, as well as graphene nanoplatelets (GnP), based on fluorescence changes of carbon nanodots. Paper strips were functionalized with carbon nanodots using polyvinyl alcohol (PVA) as binder. The carbon nanodots were highly fluorescent and, hence, rendered the (cellulosic) paper stripes emissive. In the presence of silica and ZnO nanoparticles, the fluorescence emission of the carbon nanodots was quenched and the emission decay was shortened, whereas in the presence of GnP only emission quenching occurred. These different photoluminescence (PL) quenching mechanisms, which are evident from lifetime measurements, convey selectivity to the sensor. The change in fluorescence of the carbon dot-functionalized paper is also evident to the naked eye under illumination with a UV lamp, which enables easy detection of the nanomaterials. The sensor was able to detect the nanomaterials upon direct contact, either by dipping it in their aqueous dispersions, or by sweeping it over their powders. The use of the proposed optical sensor permits the detection of nanomaterials in a straightforward manner, opening new ways for the development of optical sensors for practical applications.
Nautical Tourism in Marine Protected Areas (MPAs): Evaluating an Impact of Copper Emission from Antifouling Coating
Authors: Carić Hrvoje , Cukrov Neven , Omanović Dario
Published: October 2021

Copper (Cu) has a narrow range between optimal concentrations as a micronutrient critical for phytoplankton growth and concentrations potentially toxic to living organisms. This sensitivity indicates an ecosystem vulnerability that threatens not only nature but also human health due to bioaccumulation. An important source of elevated Cu concentrations in coastal environments are biocides used as antifouling protection on ships. A pilot study conducted in the Marine Protected Area (MPA) of the Krka Estuary (Croatia) over a period of 16 months investigated the relationship between ship traffic and Cu concentrations. The aim was to contribute to more informed environmental management by assessing the associated risks. In the study presented here, Cu concentrations were monitored, analyzed, and correlated with vessel traffic. Observations revealed that the seasonal increase in maritime traffic caused by nautical tourism was associated with an increase in Cu concentrations of more than five times, posing a toxicity risk to the environment. In order to understand the distribution of copper emissions, a mapping of maritime traffic was carried out by counting transits, radar imagery, and drone photography. This approach has proven sufficient to identify the potential risks to the marine environment and human health, thus providing an effective assessment tool for marine stakeholders.
Finitely Supported Binary Relations between Infinite Atomic Sets
Authors: Alexandru Andrei , Ciobanu Gabriel
Published: October 2021

In the framework of finitely supported atomic sets, by using the notion of atomic cardinality and the T-finite support principle (a closure property for supports in some higher-order constructions), we present some finiteness properties of the finitely supported binary relations between infinite atomic sets. Of particular interest are finitely supported Dedekind-finite sets because they do not contain finitely supported, countably infinite subsets. We prove that the infinite sets ℘fs(Ak×Al), ℘fs(Ak×℘m(A)), ℘fs(℘n(A)×Ak) and ℘fs(℘n(A)×℘m(A)) do not contain uniformly supported infinite subsets. Moreover, the functions space ZAm does not contain a uniformly supported infinite subset whenever Z does not contain a uniformly supported infinite subset. All these sets are Dedekind-finite in the framework of finitely supported structures.
Electron-Acoustic (Un)Modulated Structures in a Plasma Having (r, q)-Distributed Electrons: Solitons, Super Rogue Waves, and Breathers
Authors: Albalawi Wedad , Jahangir Rabia , Masood Waqas , Alkhateeb Sadah A. , El-Tantawy Samir A.
Published: October 2021

The propagation of electron-acoustic waves (EAWs) in an unmagnetized plasma, comprising (r,q)-distributed hot electrons, cold inertial electrons, and stationary positive ions, is investigated. Both the unmodulated and modulated EAWs, such as solitary waves, rogue waves (RWs), and breathers are discussed. The Sagdeev potential approach is employed to determine the existence domain of electron acoustic solitary structures and study the perfectly symmetric planar nonlinear unmodulated structures. Moreover, the nonlinear Schrödinger equation (NLSE) is derived and its modulated solutions, including first order RWs (Peregrine soliton), higher-order RWs (super RWs), and breathers (Akhmediev breathers and Kuznetsov–Ma soliton) are presented. The effects of plasma parameters and, in particular, the effects of spectral indices r and q, of distribution functions on the characteristics of both unmodulated and modulated EAWs, are examined in detail. In a limited cases, the (r,q) distribution is compared with Maxwellian and kappa distributions. The present investigation may be beneficial to comprehend and predict the modulated and unmodulated electron acoustic structures in laboratory and space plasmas.
Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification
Authors: Baba Ali Mohammed , Midi Habshah , Adam Mohd Bakri , Abd Rahman Nur Haizum Abd
Published: October 2021

Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as ISRs−Posi and ESRs−Posi. Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, Hi2 (using robust and non-robust measures), and our proposed ISRs−Posi and ESRs−Posi plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust Hsi12 and robust Hsi22 were not very successful in detecting IOs. The Hsi12 suffered from the masking effect, and the robust Hsi22 suffered from swamping in general spatial models. Interestingly, the results showed that the proposed ESRs−Posi plot, followed by the ISRs−Posi plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.
Development of a Consumer-Based Quality Scale for Artisan Textiles: A Study with Scarves/Shawls
Authors: Seninde Denis Richard , Chambers IV Edgar , Chambers Delores H. , Chambers V Edgar
Published: October 2021

Modern textile consumers are increasingly becoming more watchful of the quality of the textiles that they purchase. This has increased the need for textile producers, especially artisan textile makers (e.g., knitters, tailors, dressmakers, seamstresses, and quilters), to improve the quality of their textile products. Information on several analytical tools that are commonly used for assessing the quality of textiles is abundant, but consumer-based tools for evaluating the quality of textiles remain limited. A consumer-based artisan textile-quality scale was developed using data collected from two focus groups (Phase 1) and a consumer quantitative study, n = 196 (Phase 2). Ten scarves and shawls were evaluated in the quantitative study and analysis of variance (ANOVA) was used to determine the differences between the mean textile ratings for all the statements. Coefficient alpha (final raw alpha = 0.87) was also used to assess if the statements were consistent in the way they measured the quality of the textiles. Pearson correlation tests were used to validate the six-statement quality scale that included statements such as overall attention to detail, the fabric is durable, and stitching is even and consistent. Artisan textile makers in the USA can use this scale to better meet the functional needs of their customers. Additionally, the process that was employed in the development of the six-statement quality scale can be used by researchers in other countries to understand better the key quality characteristics of artisan as well other textile products.
High Prevalence of SARS-CoV-2 in an Indigenous Community of the Colombian Amazon Region
Authors: Serrano-Coll Héctor , Miller Hollman , Rodríguez-Van Der Hamen Camila , Gastelbondo Bertha , Novoa Wilkhen , Oviedo Misael , Rivero Ricardo , Garay Evelin , Mattar Salim
Published: October 2021

Introduction. COVID-19 is a pathology caused by the SARS-CoV-2 virus. The World Health Organization (WHO) has reported more than 225 million cases and 4.5 million deaths worldwide. Objective: To describe the seropositivity, spatial distribution, and clinical and sociodemographic variables of SARS-CoV-2 in a community of the Colombian Amazon region. Methods. In December 2020, a cross-sectional observational study was carried out in a population located in the Colombian Amazon in the municipality of Mitú. Sociodemographic and clinical data were taken. Besides, 589 blood samples were taken, and an antibody detection was carried out with an ELISA and a recombinant protein N antigen of SARS-CoV-2. Results. A seropositivity of 57.6% was observed. The highest proportion of the infection is located in inter-municipal transport zones. The bivariate analysis did not show differences in the SARS-CoV-2 infection rate concerning the variables sex, age range, and the presence of comorbidities (p > 0.05). The bivariate and multivariate analysis showed that being symptomatic and presenting neurological manifestations of the upper respiratory tract are clinical variables associated with SARS-CoV-2 infection (p < 0.05). One of the causes of this virus’s high spread in this community could be that 53.3% of the people were asymptomatic. Conclusions. Our data showed a high burden and transmission of SARS-CoV-2 in the indigenous community. This could be linked to cultural behaviors and the high infection rate in asymptomatic patients.