bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of KOSEN Weather Station and Provision of Weather Information to Farmers
Authors: Kim Jeyeon , Minagawa Daichi , Saito Daiki , Hoshina Shinichiro , Kanda Kazuya
Published: March 2022

In recent years, environmental information monitoring in the agricultural field has become an important issue. There is an increasing demand for meteorological information in local areas such as a rice field, a greenhouse, etc., owned by an agricultural worker. Conventional research has been actively conducted on weather stations in local areas. However, weather stations that are inexpensive, highly accurate, and have achieved stable measurements indoors and outdoors for long periods of time (over a year) are not reported. In addition, there is a lack of research that simultaneously acquires weather information, stores weather information, and provides weather information to farmers. These three functions are important in the agricultural field. In this paper, we discuss the development of a meteorological observation device, the construction of a cloud server for storing meteorological information, and the provision of information to users. First, we develop the novel meteorological observation device (KOSEN-Weather Station), which applies a simple Aßmann’s aspiration psychrometer for highly accurate temperature and humidity measurements. To evaluate the reliability of KOSEN-WS, we compare the weather information measured by KOSEN-WS with that of WXT520. As a result, it is shown that KOSEN-WS is viable. Then, KOSEN-WS is installed in the field, and the stability and durability of KOSEN-WS are examined. As a result, the KOSEN-WS has been operating stably over 19 months and provides weather information to users. Then, it is shown that the KOSEN-WS is able to operate continuously under the environment of −16.5 °C to 44.9 °C. Next, for the storage of meteorological information, we construct the cloud server. Then, a webpage is created to provide easy-to-understand weather information to farmers. Furthermore, to prevent damage to crops, if the current temperature is lower than the set temperature, or if the current temperature is higher than the set temperature, an alert is sent to the farmers. As a result, the system is highly evaluated by agricultural workers and JA staff. From the above results, the effectiveness of this system is shown.
Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
Authors: Haseeb Khalid , Rehman Amjad , Saba Tanzila , Bahaj Saeed Ali , Lloret Jaime
Published: March 2022

Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.
A Data Warehouse-Based System for Service Customization Recommendations in Product-Service Systems
Authors: Esheiba Laila , Helal Iman M. A. , Elgammal Amal , El-Sharkawi Mohamed E.
Published: March 2022

Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.
Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras
Authors: Kalake Lesole , Dong Yanqiu , Wan Wanggen , Hou Li
Published: March 2022

Multi-object tracking in video surveillance is subjected to illumination variation, blurring, motion, and similarity variations during the identification process in real-world practice. The previously proposed applications have difficulties in learning the appearances and differentiating the objects from sundry detections. They mostly rely heavily on local features and tend to lose vital global structured features such as contour features. This contributes to their inability to accurately detect, classify or distinguish the fooling images. In this paper, we propose a paradigm aimed at eliminating these tracking difficulties by enhancing the detection quality rate through the combination of a convolutional neural network (CNN) and a histogram of oriented gradient (HOG) descriptor. We trained the algorithm with an input of 120 × 32 images size and cleaned and converted them into binary for reducing the numbers of false positives. In testing, we eliminated the background on frames size and applied morphological operations and Laplacian of Gaussian model (LOG) mixture after blobs. The images further underwent feature extraction and computation with the HOG descriptor to simplify the structural information of the objects in the captured video images. We stored the appearance features in an array and passed them into the network (CNN) for further processing. We have applied and evaluated our algorithm for real-time multiple object tracking on various city streets using EPFL multi-camera pedestrian datasets. The experimental results illustrate that our proposed technique improves the detection rate and data associations. Our algorithm outperformed the online state-of-the-art approach by recording the highest in precisions and specificity rates.
Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
Authors: Esposito Marco , Palma Lorenzo , Belli Alberto , Sabbatini Luisiana , Pierleoni Paola
Published: March 2022

Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.
Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis
Authors: Wang Chengjia , Yang Guang , Papanastasiou Giorgos
Published: March 2022

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an unsupervised deep learning registration methodology that can accurately model affine and non-rigid transformations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent representations, and involves two factorised transformation networks (one per each encoder-decoder channel) and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named “FIRE”) shows improved performances against the reference standard baseline method (i.e., Symmetric Normalization implemented using the ANTs toolbox) on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments. We focus on explaining model-data components to enhance model explainability in medical image registration. On computational time experiments, we show that the FIRE model performs on a memory-saving mode, as it can inherently learn topology-preserving image registration directly in the training phase. We therefore demonstrate an efficient and versatile registration technique that can have merit in multi-modal image registrations in the clinical setting.
The Campo de Dalias GNSS Network Unveils the Interaction between Roll-Back and Indentation Tectonics in the Gibraltar Arc
Authors: Galindo-Zaldivar Jesús , Gil Antonio J. , Tendero-Salmerón Víctor , Borque María J. , Ercilla Gemma , González-Castillo Lourdes , Sánchez-Alzola Alberto , Lacy María C. , Estrada Ferran , Avilés Manuel , Alfaro Pedro , Madarieta-Txurruka Asier , Chacón Fernando
Published: March 2022

The Gibraltar Arc includes the Betic and Rif Cordilleras surrounding the Alboran Sea; it is formed at the northwest–southeast Eurasia–Nubia convergent plate boundary in the westernmost Mediterranean. Since 2006, the Campo de Dalias GNSS network has monitored active tectonic deformation of the most seismically active area on the north coast of the Alboran Sea. Our results show that the residual deformation rates with respect to Eurasia range from 1.7 to 3.0 mm/year; roughly homogenous west-southwestward displacements of the northern sites occur, while the southern sites evidence irregular displacements towards the west and northwest. This deformation pattern supports simultaneous east-northeast–west-southwest extension, accommodated by normal and oblique faults, and north-northwest–south-southeast shortening that develops east-northeast–west-southwest folds. Moreover, the GNSS results point to dextral creep of the main northwest–southeast Balanegra Fault. These GNNS results thus reveal, for the first time, present-day interaction of the roll-back tectonics of the Rif–Gibraltar–Betic slab in the western part of the Gibraltar Arc with the indentation tectonics affecting the eastern and southern areas, providing new insights for improving tectonic models of arcuate orogens.
Rapid DNA Sequencing Technology Based on the Sanger Method for Bacterial Identification
Authors: Furutani Shunsuke , Furutani Nozomi , Kawai Yasuyuki , Nakayama Akifumi , Nagai Hidenori
Published: March 2022

Antimicrobial resistance, a global health concern, has been increasing due to inappropriate use of antibacterial agents. To facilitate early treatment of sepsis, rapid bacterial identification is imperative to determine appropriate antibacterial agent for better therapeutic outcomes. In this study, we developed a rapid PCR method, rapid cycle sequencing, and microchip electrophoresis, which are the three elemental technologies for DNA sequencing based on the Sanger sequencing method, for bacterial identification. We achieved PCR amplification within 13 min and cycle sequencing within 14 min using a rapid thermal cycle system applying microfluidic technology. Furthermore, DNA analysis was completed in 14 min by constructing an algorithm for analyzing and performing microchip electrophoresis. Thus, the three elemental Sanger-based DNA sequencing steps were accomplished within 41 min. Development of a rapid purification process subsequent to PCR and cycle sequence using a microchip would help realize the identification of causative bacterial agents within one hour, and facilitate early treatment of sepsis.
Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation
Authors: Putri Wenny Ramadha , Liu Shen-Hsuan , Aslam Muhammad Saqlain , Li Yung-Hui , Chang Chin-Chen , Wang Jia-Ching
Published: March 2022

Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with high-quality manual labels. The larger the amount of data, the better the algorithm performs. In this paper, we propose a self-supervised framework utilizing the pix2pix conditional adversarial network for generating unlimited diversified iris images. Then, the generated iris images are used to train the iris segmentation network to achieve state-of-the-art performance. We also propose an algorithm to generate iris masks based on 11 tunable parameters, which can be generated randomly. Such a framework can generate an unlimited amount of photo-realistic training data for down-stream tasks. Experimental results demonstrate that the proposed framework achieved promising results in all commonly used metrics. The proposed framework can be easily generalized to any object segmentation task with a simple fine-tuning of the mask generation algorithm.
Polish Court Ruling Classification Using Deep Neural Networks
Authors: Kostrzewa Łukasz , Nowak Robert
Published: March 2022

In this work, the problem of classifying Polish court rulings based on their text is presented. We use natural language processing methods and classifiers based on convolutional and recurrent neural networks. We prepared a dataset of 144,784 authentic, anonymized Polish court rulings. We analyze various general language embedding matrices and multiple neural network architectures with different parameters. Results show that such models can classify documents with very high accuracy (>99%). We also include an analysis of wrongly predicted examples. Performance analysis shows that our method is fast and could be used in practice on typical server hardware with 2 Processors (Central Processing Units, CPUs) or with a CPU and a Graphics processing unit (GPU).
Environmental Policy Design and Implementation: Toward a Sustainable Society
Authors: Hejnowicz Adam P. , Thorn Jessica P. R.
Published: March 2022

“No matter how complex global problems may seem, it is we ourselves who have given rise to them [...]
The Impact of Temperature and Precipitation Change on the Production of Grapes in the Czech Republic
Authors: Veselá Kamila , Severová Lucie , Svoboda Roman
Published: March 2022

The warming of the planet and ongoing climate change are now a scientifically proven fact. These phenomena have an impact on nature and many human activities, but logically affect agriculture the most. History has confirmed that the production of grapes (the extent and quality) is significantly affected by climate change. The main goal of this study was to evaluate the impact of climate change through changes in average precipitation and average temperatures on the quantity of grape production in the Czech Republic. A partial goal was then to predict the future development of grape production depending on the expected total precipitation and average temperatures. The effect of changes in average temperatures and total precipitation was evaluated using multiple linear regression methods. The multiple regression model did not reveal a dependence of the total precipitation and average temperatures on the development of the value of vine production due to the statistical insignificance of the effect of average temperatures on the value of vine production. However, when abstracting the effect of average temperatures on the value of vine production, the research confirmed the effect of the change in total precipitation on the value of vine production. The analysis identified the effect of changes in total precipitation and temperatures on the production of grapes in the Czech Republic.
The Impacts of Fishermen’s Resilience towards Climate Change on Their Well-Being
Authors: Shaffril Hayrol Azril Mohamed , Abu Samah Asnarulkhadi , Samsuddin Samsul Farid
Published: March 2022

This study aims to examine the mediating effect of climate resilience on the relationship between socio-economic, social relationship, social environment, and sense of community with fishermen’s subjective well-being (life satisfaction, positive feeling, and negative feeling). This quantitative study performed a multi-stage sampling and selected 400 fishermen as respondents. For analysis purpose, this study relied on Partial Least Square Structural Equation Modelling (PLS-SEM). The structural model concluded that socio-economic, social relationship, social environment, and sense of community explained 55.4% variance in resilience. The mediating analysis confirmed the resiliency mediating effect on all twelve hypothesized relationships. A number of recommendations related to extending the areas of the study, to focus specifically on the small-scale fishermen, and to consider the inclusion of several others’ additional mediating effects were highlighted.
Impact of Green Finance and Environmental Regulations on the Green Innovation Efficiency in China
Authors: Zhao Tong , Zhou Haihua , Jiang Jinde , Yan Wenyan
Published: March 2022

Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a very important role in regional sustainable development. Data from 31 provinces and cities in China from 2011 to 2020 were used to select the proportion of energy saving and environmental protection costs in GDP as the green financial value, and the proportion of industrial pollution control input in GDP as the environmental regulation index. Green innovation efficiency is measured from two aspects of input and output by DEA method, and carried out for 31 provinces and cities in three regions. Using the DEA-Malmquist index to measure regional green innovation efficiency, the results show that the green innovation efficiency in three regions basically presents an upward trend, but the upward trend of green innovation efficiency is different between the three regions. A Tobit regression model is constructed to explore the impact of green finance and environmental regulations on the green innovation efficiency in these three regions. Research indicates that environmental regulations, the proportion of output value of tertiary industry in GDP, industrial structure, and foreign direct investment have significant impacts on the green innovation efficiency in all regions. Green finance, industrial structure, and power consumption have a significant impact on the green innovation efficiency in eastern China. Industrial structure has a significant impact on green innovation efficiency in central China, while power consumption and industrial structure have a significant impact on green innovation efficiency in western China. Therefore, each region needs to improve the standard of environmental regulation innovation, and introduce and use foreign investment in a scientific and reasonable way so as to promote the improvement of industrial infrastructure.
Defensive Strongholds and Fortified Castles in Poland—From the Art of Fortifications to Tourist Attractions
Authors: Kulpiński Jacek , Prukop Beata , Rut Paweł , Rejman Aneta , Świder Paweł , Cynarski Wojciech J.
Published: March 2022

The scientific problem undertaken is the importance of castles for the functioning of cultural tourism in the opinion of the inhabitants of Central Europe. What is the use of medieval monuments for the art of fortifications today? The main method of research is a diagnostic survey carried out with the use of a survey on a group of N, important according to the statistics in the number of n = 614 respondents. Statistical analyzes were performed using Statistica version 13.3. On the basis of the presented research results, it can be concluded that the interests of the respondents are very broad and varied. Taking into account the relatively large group of respondents, the research results can be considered reliable. An important goal was supplementing the knowledge, meeting the needs of learning about history, and getting acquainted with the prevailing historical tradition in castles of Europe. The questions presented here accent the interests of castles for the functioning of cultural tourism in the opinion of the inhabitants of Central Europe.
Soil Amendment with Arbuscular Mycorrhizal Fungi and Biochar Improves Salinity Tolerance, Growth, and Lipid Metabolism of Common Wheat (Triticum aestivum L.)
Authors: Ndiate Ndiaye Ibra , Zaman Qamar uz , Francis Imade Nosakahre , Dada Oyeyemi Adigun , Rehman Abdul , Asif Muhammad , Goffner Deborah , Kane Aboubacry , Liqun Cai , Haider Fasih Ullah
Published: March 2022

Salt stress in soils impacts grain crop yield. Soil amendment with biochar and arbuscular mycorrhizal alone has been analyzed to improve the growth of several crops under salinity stress. However, the combined application of biochar and arbuscular mycorrhizal fungi for the remediation of salinity and improvement of crop productivity in wheat are rarely discussed and have remained unclear. Therefore, this experiment was performed to investigate the effect with biochar (150 g biochar per each treated pot containing 3 kg soil) and/or arbuscular mycorrhizal fungi (20 g AMF inoculum containing 80% mycorrhizal roots, 100–160 spores, and extraradical hyphae per each treated pot) on the productivity of wheat (Triticum aestivum L.) under four salt stress gradients; 0, 50, 100, and 150 mM NaCl. The results show salinity significantly reduced plant height (9.9% to 22.9%), shoot fresh weight (35.6% to 64.4%), enzymatic activities (34.1% to 39.3%), and photosynthetic pigments—i.e., total chlorophyll contents (75.0%) and carotenoids contents (56.2%) of plants—as compared with control. Under exclusive biochar application, the plants were moderately tolerant to salinity stress, which was evident in their growth, moderately reduced fatty acid content, partially impaired enzymatic activity, and photosynthetic pigments, while under the exclusive AMF application, the wheat plants were relatively sensitive to salinity stress, resulting in impaired growth rate, decreased unsaturated fatty acid composition, enzymatic activity, and photosynthetic pigments. Conversely, under the co-application of biochar and AMF, wheat plants partially increased plant height (14.1%), shoot fresh biomass (75.7%), root fresh biomass (24.9%), partially increased enzymatic activity (49.5%), and unimpaired photosynthetic pigments (30.2% to 54.8%) of wheat under salinity stress. Current findings concluded that exclusive incorporation of biochar, and the synergistic application of AMF and biochar, could be utilized as a promising way to reduce the deleterious effects of salinity stress in wheat production.
Crack Propagation Assessment of Time-Dependent Concrete Degradation of Prestressed Concrete Sleepers
Authors: Li Dan , You Ruilin , Kaewunruen Sakdirat
Published: March 2022

As prestressed concrete sleepers are continuously exposed to various environmental and loading conditions, it is increasingly crucial to analyse their current and future serviceability performance. In practice, the main cause of cracking in prestressed concrete sleepers is usually induced by impact loads. The most heavily influenced sections are the midspan and rail-seat area of sleepers. This paper investigates the effects of time-dependent concrete strength degradation on the capacity of prestressed concrete sleepers. The factors affecting concrete strength degradation are analysed in order to evaluate the crack behaviour of prestressed concrete sleepers. A finite element modelling approach is developed for prestressed concrete sleepers, which is used to assess the effects of structural behaviour in railway sleepers. The sleeper model has been calibrated and validated. This research firstly discusses time-dependent behaviour using load–crack length responses. It is shown that various cracking modes cause an overall increase in the maximum cracking length as prestressed concrete sleepers age. This paper demonstrates that initial cracking loads and ultimate crack lengths have significant change in first 20 years. After 40 years of service life, the crack resistance of prestressed concrete sleepers becomes very weak which is only 61.32% of the new sleeper. In long term, the initial cracking load keeps reducing, and the crack propagation rate becomes sharp. The presented methodology and results can greatly assist in decision-making for the repair or replacement of prestressed concrete sleepers and aid in the design of new prestressed concrete sleepers considering their future performance.
Understanding the Determinants of Pro-Environmental Behavior among South Africans: Evidence from a Structural Equation Model
Authors: Rampedi Isaac Tebogo , Ifegbesan Ayodeji Peter
Published: March 2022

This paper explores the relationship between pro-environmental behaviour (PEB) and environmental awareness, knowledge, attitudes, risk perceptions and activism in South Africa. To achieve this goal, the 2010–2012 data from the International Social Survey Program “Environmental III” was analysed by means of descriptive and inferential statistics, including the employment of the structural equation model. Significant differences were found between pro-environmental behaviour and other environmental variables such as environmental concern, awareness, willingness to sacrifice, and others in terms of the educational background, place of living, ethnic identity and provinces where respondents lived. Thus, to increase PEB amongst citizens would require the introduction and support of development programmes that enhance access to more education and environmental awareness across all population groups.
Application of Decoupling Approach to Evaluate Electricity Consumption, Agriculture, GDP, Crude Oil Production, and CO2 Emission Nexus in Support of Economic Instrument in Nigeria
Authors: Sane Mathy , Hajek Miroslav , Phiri Joseph , Babangida Jamilu Said , Nwaogu Chukwudi
Published: March 2022

The paper appraised the nexus between electricity consumption, agriculture, GDP, oil production, and carbon dioxide (CO2) emissions in Nigeria using a decoupling approach. The result showed that agriculture, electricity, and GDP were predictive variables for CO2 emissions in the Granger causality analysis. The relationship between GDP and CO2 emissions also indicated that the amount of CO2 released tends to rise as the economy’s output and industrial sectors grow, making GDP and CO2 emissions increasingly relevant indicators as a driver of CO2 emissions. Modern agriculture is reliant on large-scale use of fossil fuels and fertilizer production, as well as GHG emissions from crop and livestock production. However, increasing per capita real production can help to enhance quality of the environment, and speed up the uptake of renewable energy which can consequently help to ameliorate global warming. As a result of this study’s policy implications, policies in the agricultural sector that could combat CO2 emissions, including deforestation, land clearing, fertilization with highly environmentally destructive chemicals, neglected integration of agroforestry, and social forestry practices, can help reduce CO2 emissions in the agricultural sector. In addition, the study recommends that the financial markets’ monetary policy should regulate the GDP to charges to compensate for their various sectors’ contributions to CO2 emissions. This investigation might help policymakers in Nigeria to define the CO2 emission monetary and fiscal strategies. In addition, more alternative energy sources such as biofuels, hydropower, solar energy, and other renewable resources should be embraced in Nigeria as sustainable substitutes for fossil fuels.
Soil Remediation of Subtropical Garden Grasses and Shrubs Using High-Performance Ester Materials
Authors: Gou Hao , Liao Jin , Du Fan , Tang Changliang , Lin Yali , Li Dingjuan , Zhang Yulei , Ning Yangyang , Ye Zihui , Xu Zheyao , Zhou Cuiying , Liu Zhen
Published: March 2022

Soil erosion due to rainstorms is a serious problem in subtropical gardens in South China. Soil conservation and the restoration of degraded landscapes are important research topics at home and abroad. Because of the sluggish growth of plants under traditional cultivation techniques, they are incapable of effectively protecting the soil. Therefore, the rapid and high-quality soil conservation of subtropical landscapes remains an urgent problem to be overcome. The purpose of this study is to improve the red soil and ground environment for the growth of grasses and shrubs through high-performance ester materials. Our objective was to find a solution for the high impact of soil loss on subtropical landscapes. In this study, we used the ecological restoration of soil as the starting point and selected a typical subtropical garden in South China as the field test point. We carried out soil erosion resistance testing using high-performance ester materials. The anti-erosion abilities of slopes under various working conditions are discussed. During the growth period, the soil indexes were monitored for a long time, and the growth of grasses and shrubs was compared. The obtained monitoring data were analyzed with mathematical statistics. We found that the addition of high-performance ester materials significantly reduced soil loss by 52.60%. High-performance ester materials have a good hydrothermal regulation function, which can promote the germination and later growth of sloping plants. The decrease in ground internal density promotes the extension of plant roots. High-performance ester materials can improve soil permeability and activity and promote vegetation growth. In terms of turf thickness and overall growth as well as shrubs crown width and height, high-performance ester materials have a beneficial effect on promoting plant growth. Soil remediation using high-performance ester materials has good economic value, high water-holding capacity, adaptability, and convenience. In this study, we determined a solution for the high impact of soil loss on subtropical landscapes. The soil remediation of a subtropical garden using high-performance ester materials was successful. The practice of landscape soil remediation engineering presented in this paper can provide a reference for typical landscape soil remediation in subtropical zones.
Material Treatment in the Pulsation Reactor—From Flame Spray Pyrolysis to Industrial Scale
Authors: Heidinger Stefan , Spranger Felix , Dostál Jakub , Zhang Chunliang , Klaus Christian
Published: March 2022

Current challenges in the areas of health care, environmental protection, and, especially, the mobility transition have introduced a wide range of applications for specialized high-performance materials. Hence, this paper presents a novel approach for designing materials with flame spray pyrolysis on a lab scale and transferring the synthesis to the pulsation reactor for mass production while preserving the advantageous material properties of small particle sizes and highly specific surface areas. A proof of concept is delivered for zirconia and silica via empirical studies. Furthermore, an interdisciplinary approach is introduced to model the processes in a pulsation reactor in general and for single material particles specifically. Finally, facilities for laboratory investigations and pulsation reactor testing in an industrial environment are presented.
Sustainable Production of Tomato Plants (Solanum lycopersicum L.) under Low-Quality Irrigation Water as Affected by Bio-Nanofertilizers of Selenium and Copper
Authors: Saffan Mohamed M. , Koriem Mohamed A. , El-Henawy Ahmed , El-Mahdy Shimaa , El-Ramady Hassan , Elbehiry Fathy , Omara Alaa El-Dein , Bayoumi Yousry , Badgar Khandsuren , Prokisch József
Published: March 2022

Under the global water crisis, utilizing low-quality water sources in agriculture for irrigation has offered an effective solution to address the shortage of water. Using an excess of low-quality water sources may cause serious risks to the environment, which threaten crop safety and human health. Three kinds of irrigation water (0.413, 1.44, and 2.84 dS m−1) were selected under foliar-applied bio-nanofertilizers of selenium (100 mg L−1) and copper (100 mg L−1) in individual and/or combined application. The nanofertilizers were tested on the production of tomato under greenhouse. After harvesting, the quality of tomato yield and soil biology was evaluated. Using saline water for irrigation caused many main features in this study such as increasing the accumulation of salts, soil organic matter, and CaCO3 in soil by 84.6, 32.3, and 18.4%, respectively, compared to control. The highest tomato yield (2.07 kg plant−1) and soluble solids content (9.24%) were recorded after irrigation with low water quality (2.84 dS m−1) and nano-Cu fertilization. The plant enzymatic antioxidants and soil biological activity were decreased in general due to the salinity stress of irrigation water. After 30 days from transplanting, all studied soil biological parameters (soil microbial counts and enzymes) were higher than the same parameters at harvesting (80 days) under different categories of water quality. The values of all soil biological parameters were decreased by increasing water salinity. This study was carried out to answer the question of whether the combined nanofertilizers of selenium and copper can promote tomato production under saline water irrigation. Further investigations are still needed concerning different applied doses of these nanofertilizers.
Impact of Land Cover Changes on the Availability of Water Resources in the Regional Natural Park Serranía de Las Quinchas
Authors: Ortegón Yuddy Alejandra Castro , Acosta-Prado Julio César , Acosta Castellanos Pedro Mauricio
Published: March 2022

Protected areas offer environmental goods and services to local communities, who have based their aptitude on the availability of water resources with practices associated with the legacy of their ancestors. The purpose of this paper is to evaluate the impact of changes in land cover on the availability of water resources in the Serrania de Las Quinchas Regional Natural Park (PNRSQ), located in the department of Boyacá, Colombia. The SWAT tool and the Corine Land Cover methodology were used between 2008 and 2017. In addition, data of hydrometeorological tests were used to determine the water behavior of the basin together with the Digital Elevation Model (DEM). The results show that after the declaration of the area as a protection zone in 2008, there have been changes in the land cover producing a greater availability of water resources and the partial restoration of the study area. Additionally, hydrological modeling allowed knows the behavior of the basin under different conditions. The resulting information allows decision-makers to evaluate the best options to guarantee water resources and generate strategies that allow communities to reinvent their way of production and adapt to ecosystem conditions without affecting their ecological functioning.
Homotopy Coherent Representations
Authors: Porter Tim , Stasheff Jim
Published: March 2022

Homotopy coherence has a considerable history, albeit also by other names. For this volume highlighting symmetries, the appropriate use is homotopy coherence of representations, at one time known as representations up to homotopy/homotopy coherent representations. We present a brief semi-historical survey, providing some links that may not be common knowledge.
Singular Lagrangians, Constrained Hamiltonian Systems and Gauge Invariance: An Example of the Dirac–Bergmann Algorithm
Authors: Brown J. David
Published: March 2022

The Dirac–Bergmann algorithm is a recipe for converting a theory with a singular Lagrangian into a constrained Hamiltonian system. Constrained Hamiltonian systems include gauge theories—general relativity, electromagnetism, Yang–Mills, string theory, etc. The Dirac–Bergmann algorithm is elegant but at the same time rather complicated. It consists of a large number of logical steps linked together by a subtle chain of reasoning. Examples of the Dirac–Bergmann algorithm found in the literature are designed to isolate and illustrate just one or two of those logical steps. In this paper, I analyze a finite-dimensional system that exhibits all of the major steps in the algorithm. The system includes primary and secondary constraints, first and second class constraints, restrictions on Lagrange multipliers, and both physical and gauge degrees of freedom. This relatively simple system provides a platform for discussing the Dirac conjecture, constructing Dirac brackets, and applying gauge conditions.
Numerical Equilibrium Configurations and Quadrupole Moments of Post-Merger Differentially Rotating Relativistic Stars
Authors: Franceschetti Kevin , Del Zanna Luca , Soldateschi Jacopo , Bucciantini Niccolò
Published: March 2022

Numerical simulations of binary neutron star mergers invariably show that, when a long-lived remnant forms, its rotation profile is never a simple decaying function of the radius but rather exhibits a maximum rotation rate shifted away from the center. This is in contrast to the usual differential rotation profile employed for the numerical modeling of axisymmetric equilibria of relativistic stars. Two families of rotation rate functions that mimic post-merger profiles were proposed by Uryū et al. (2017). In this work we implement Uryū’s profiles into the XNS code by Bucciantini and Del Zanna (2011) and we present novel equilibrium sequences of differentially rotating neutron stars. These are constructed by using three different equations of state, in order to study the dependence of mass, radius, angular momentum, and other important physical quantities, especially the quadrupole deformation and metric quadrupole moment, from the rotation properties.
Impact of AGB Stars on the Chemical Evolution of Neutron-Capture Elements
Authors: Cescutti Gabriele , Matteucci Francesca
Published: March 2022

In this review, we discuss the impact of s-process nucleosynthesis in asymptotic giant branch stars on the enrichment of heavy elements. We review the main steps made on this subject in the last 40 years and discuss the importance of modelling the evolution of the abundances of such elements in our Milky Way. From the comparison between model results and observations, we can impose strong constraints on stellar nucleosynthesis, as well as on the evolution of the Milky Way.
A Novel Method to Reduce ELISA Serial Dilution Assay Workload Applied to SARS-CoV-2 and Seasonal HCoVs
Authors: Pattinson David , Jester Peter , Guan Lizheng , Yamayoshi Seiya , Chiba Shiho , Presler Robert , Rao Hongyu , Iwatsuki-Horimoto Kiyoko , Ikeda Nobuhiro , Hagihara Masao , Uchida Tomoyuki , Mitamura Keiko , Halfmann Peter , Neumann Gabriele , Kawaoka Yoshihiro
Published: March 2022

Assays using ELISA measurements on serially diluted serum samples have been heavily used to measure serum reactivity to SARS-CoV-2 antigens and are widely used in virology and elsewhere in biology. We test a method using Bayesian hierarchical modelling to reduce the workload of these assays and measure reactivity of SARS-CoV-2 and HCoV antigens to human serum samples collected before and during the COVID-19 pandemic. Inflection titers for SARS-CoV-2 full-length spike protein (S1S2), spike protein receptor-binding domain (RBD), and nucleoprotein (N) inferred from 3 spread-out dilutions correlated with those inferred from 8 consecutive dilutions with an R2 value of 0.97 or higher. We confirm existing findings showing a small proportion of pre-pandemic human serum samples contain cross-reactive antibodies to SARS-CoV-2 S1S2 and N, and that SARS-CoV-2 infection increases serum reactivity to the beta-HCoVs OC43 and HKU1 S1S2. In serial dilution assays, large savings in resources and/or increases in throughput can be achieved by reducing the number of dilutions measured and using Bayesian hierarchical modelling to infer inflection or endpoint titers. We have released software for conducting these types of analysis.
COVID-19 Vaccination Limits Systemic Danger Signals in SARS-CoV-2 Infected Patients
Authors: Angioni Roberta , Sasset Lolita , Cioccarelli Chiara , Sánchez-Rodríguez Ricardo , Bertoldi Nicole , Putaggio Cristina C. , Viola Antonella , Cattelan Annamaria , Molon Barbara
Published: March 2022

Vaccination with an mRNA COVID-19 vaccine determines not only a consistent reduction in the risk of SARS-CoV-2 infection but also contributes to disease attenuation in infected people. Of note, hyperinflammation and damage-associated molecular patterns (DAMPs) have been clearly associated with severe illness and poor prognosis in COVID-19 patients. In this report, we revealed a significant reduction in the levels of IL-1ß and DAMPs molecules, as S100A8 and High Mobility Group Protein B1 (HMGB1), in vaccinated patients as compared to non-vaccinated ones. COVID-19 vaccination indeed prevents severe clinical manifestations in patients and limits the release of systemic danger signals in SARS-CoV-2 infected people.
West Nile Virus Lineage 2 Spreads Westwards in Europe and Overwinters in North-Eastern Spain (2017–2020)
Authors: Aguilera-Sepúlveda Pilar , Napp Sebastián , Llorente Francisco , Solano-Manrique Carlos , Molina-López Rafael , Obón Elena , Solé Alba , Jiménez-Clavero Miguel Ángel , Fernández-Pinero Jovita , Busquets Núria
Published: March 2022

West Nile virus lineage 2 (WNV-L2) emerged in Europe in 2004; since then, it has spread across the continent, causing outbreaks in humans and animals. During 2017 and 2020, WNV-L2 was detected and isolated from four northern goshawks in two provinces of Catalonia (north-eastern Spain). In order to characterise the first Spanish WNV-L2 isolates and elucidate the potential overwintering of the virus in this Mediterranean region, complete genome sequencing, phylogenetic analyses, and a study of phenotypic characterisation were performed. Our results showed that these Spanish isolates belonged to the central-southern WNV-L2 clade. In more detail, they were related to the Lombardy cluster that emerged in Italy in 2013 and has been able to spread westwards, causing outbreaks in France (2018) and Spain (2017 and 2020). Phenotypic characterisation performed in vitro showed that these isolates presented characteristics corresponding to strains of moderate to high virulence. All these findings evidence that these WNV-L2 strains have been able to circulate and overwinter in the region, and are pathogenic, at least in northern goshawks, which seem to be very susceptible to WNV infection and may be good indicators of WNV-L2 circulation. Due to the increasing number of human and animal cases in Europe in the last years, this zoonotic flavivirus should be kept under extensive surveillance, following a One-Health approach.
Applications of Heat Exchanger in Solar Desalination: Current Issues and Future Challenges
Authors: Elsheikh Ammar H. , Panchal Hitesh N. , Sengottain Shanmugan , A. Alsaleh Naser , Ahmadein Mahmoud
Published: March 2022

Solar desalination is a process to convert saline water into potable water by the application of solar energy. The enhancement of the distillate output of the solar desalination is low, so it is not considered as a method to produce potable water. A heat exchanger is an important device used for heat transfer applications. The present review article illustrates the application of a heat exchanger with a solar desalination system to enhance the distillate output. In the current review, it is found that the heat exchanger is an important device to improve the distillate productivity of the solar desalination system. Finally, the future work and future challenges of using a heat exchanger with a solar desalination system are presented.
Study on the SBA-15 Silica and ETS-10 Titanosilicate as Efficient Adsorbents for Cu(II) Removal from Aqueous Solution
Authors: Humelnicu Doina , Zinicovscaia Inga , Humelnicu Ionel , Ignat Maria , Yushin Nikita , Grozdov Dmitrii
Published: March 2022

The efficiency of Cu(II) removal from aqueous solution by two adsorbents, silica SBA-15 and titanosilicate ETS-10, was investigated. Effects of various experimental parameters such as: contact time, pH, initial copper concentration, adsorbent dosage, temperature were investigated in order to determine the maximum adsorption capacity of the adsorbents. The maximum adsorption capacity of silica SBA-15 was achieved at pH 5.0, and of titanosilicate ETS-10 at pH 6.0. The Freundlich, Langmuir, and Temkin isotherm models were applied in order to describe the equilibrium adsorption of Cu(II) by the studied adsorbents. Equilibrium data fitted well to the Langmuir model with a higher adsorption capacity of ETS-10 (172.53 mg·g−1) towards Cu(II) than SBA-15 (52.71 mg·g−1). Pseudo-first- and pseudo-second-order, Elovich, and Weber–Morris intraparticle diffusion models were used for description of the experimental kinetic data. It was found that the pseudo-first-order and pseudo-second-order kinetic models were the best applicable models to describe the adsorption kinetic data. Thermodynamic parameters that characterize the process indicated that the adsorption of Cu(II) onto the two adsorbents is spontaneous and endothermic.