bluuebunny/crossref_metadata_2025 huggingface dataset:
A Continuous Conical-Mill Operation for Dry Coating of Pharmaceutical Powders: The Role of Processing Time
Authors: Roy William , Achouri Inès Esma , Hudon Sophie , Simard Jean-Sébastien , Abatzoglou Nicolas
Published: March 2022

Over the last decade, the conical mill has emerged as a potential piece of equipment to use for continuous dry coating pharmaceutical powders. In this work, silicon dioxide was used as a guest particle on two excipients, fast flow lactose (FFL) and grade PH200 microcrystalline cellulose (MCC), for dry coating by a conical mill with a modified screen that permitted batch and continuous mode operation. Samples were pre-processed in a V-blender. SEM images, particle size distribution, and EDS mapping were used to characterise the treated powders. Pre-processed samples showed some discrete coating of the host particle. After batch processing, the samples were covered with a complete coating. When processed at high impeller speed, coating of FFL was a mix of A200P and FFL fines generated by attrition. Continuous mode processed samples, which had a lower processing time, were coated discretely and showed a better coating than the pre-processed samples. Increasing guest/host mass ratio with FFL host particle had a positive impact on the quality of the coating. These results help to build the case that the processing time of the conical mill is a key parameter to the success of the conical mill as dry coating equipment in the pharmaceutical industry.
A Beautiful Failure: The Tragic—And Luminous—Life of Jim Harvey (An Experiment in Narrative Theology)
Authors: Elwell J. Sage
Published: March 2022

Jim Harvey was the artist who created the Brillo box that Andy Warhol copied and made famous. Warhol’s Brillo Boxes changed the course of art history and the entire field of aesthetics and the philosophy of art. Meanwhile, Jim Harvey died a failed second-generation Abstract Expressionist. To his death, Harvey refused to accept that his Brillo box was a work of art. However, the theory—the story—that was woven about Warhol’s Brillo Boxes transformed them from commonplace objects into multimillion-dollar masterpieces. As a counterbalance, this article appeals to narrative theology as a way to tell Jim Harvey’s story. Appealing to narrative theologian James McClendon’s notion that a biography can reveal an image—a metaphor—that serves as a prism through which that individual’s life can be seen and understood, this article suggests that the defining metaphor of Jim’s life was “artists are prophets”. Importantly, this essay is not an attempt to redeem Harvey’s artistry, although it may provoke a reconsideration of his work. Rather, it is an exploration of the tragic and luminous life of a beautiful failure.
SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images
Authors: Liu Fanfan , Zhao Wenzhe , Zhou Guangyao , Zhao Liangjin , Wei Haoran
Published: March 2022

Vehicle detection in remote sensing imagery is a challenging task because of its inherent attributes, e.g., dense parking, small sizes, various angles, etc. Prevalent vehicle detectors adopt an oriented/rotated bounding box as a basic representation, which needs to apply a distance regression of height, width, and angles of objects. These distance-regression-based detectors suffer from two challenges: (1) the periodicity of the angle causes a discontinuity of regression values, and (2) small regression deviations may also cause objects to be missed. To this end, in this paper, we propose a new vehicle modeling strategy, i.e., regarding each vehicle-rotated bounding box as a saliency area. Based on the new representation, we propose SR-Net (saliency region representation network), which transforms the vehicle detection task into a saliency object detection task. The proposed SR-Net, running in a distance (e.g., height, width, and angle)-regression-free way, can generate more accurate detection results. Experiments show that SR-Net outperforms prevalent detectors on multiple benchmark datasets. Specifically, our model yields 52.30%, 62.44%, 68.25%, and 55.81% in terms of AP on DOTA, UCAS-AOD, DLR 3K Munich, and VEDAI, respectively.
Climatic, Decadal, and Interannual Variability in the Upper Layer of the Mediterranean Sea Using Remotely Sensed and In-Situ Data
Authors: Menna Milena , Gačić Miroslav , Martellucci Riccardo , Notarstefano Giulio , Fedele Giusy , Mauri Elena , Gerin Riccardo , Poulain Pierre-Marie
Published: March 2022

The Mediterranean Sea is considered a hot spot of global warming because it has been changing faster than the global ocean, creating a strong impact on the marine environment. Recent studies agree on the increase in the sea level, in the sea surface temperature, and in the sea surface salinity in the Mediterranean Sea over the last two decades. In this research, the possible interconnection between these and other parameters that contribute to the regulatory effect of the sea on the climate are identified and discussed. Spatio-temporal variability of four oceanographic and air–sea interaction parameters (sea-level, sea surface temperature, sea surface salinity, and freshwater flux) are estimated over the last 27 years by performing the empirical orthogonal function analysis. Climatic trends, and interannual and decadal variability of the different datasets are delineated and described in the whole Mediterranean and in its sub-basins. On the climatic scale, the Mediterranean and its sub-basins behave in a coherent way, showing the seal level, temperature, salinity, and freshwater flux rise. On the interannual scale, the temporal evolution of the sea level and sea surface temperature are highly correlated, whereas freshwater flux affects the variability of sea level, temperature, and the salinity field mainly in the Western and Central Mediterranean. The decadal signal associated with the Northern Ionian Gyre circulation reversals is clearly identified in three of the four parameters considered, with different intensities and geographical extents. This signal also affects the intermediate layer of the Eastern Mediterranean, from where it is advected to the other sub-basins. Decadal signal not associated with the Northern Ionian Gyre reversals is strongly related to the variability of main sub-basin scale local structures.
Monitoring of Extreme Agricultural Drought of the Past 20 Years in Southwest China Using GLDAS Soil Moisture
Authors: Sun Xupeng , Lai Peiyu , Wang Shujing , Song Lisheng , Ma Mingguo , Han Xujun
Published: March 2022

Drought can cause severe agricultural economic losses and hinder social and economic development. To manage drought, the process of drought events needs to be described with the help of an effective drought indicator. As a comprehensive variable, soil moisture is an essential indicator for describing agricultural drought. In this work, the extreme drought events in southwest China were analysed by the Global Land Data Assimilation System (GLDAS) root zone soil moisture data set. To define the drought quantitatively, a Standardized Soil Moisture Drought Index (SSMI) was calculated using the soil moisture data, then used to get the duration, frequency, and severity of drought events in southwest China. The results showed that the frequency and intensity of drought in southwest China had an apparent upward trend before 2014 and an apparent downward trend since 2014. Moreover, there are apparent differences in the frequency and intensity of drought in various regions of southwest China. Yunnan Province is prone to spring drought events. Guangxi Province and Guizhou Province are prone to spring, autumn and winter droughts, and the intensity of autumn and winter droughts is significantly higher than that of spring droughts. The Sichuan-Chongqing border area is prone to summer drought. We found that the monthly variation of soil moisture in different provinces in southwest China is consistent, but the seasonal variation of drought is different. Meanwhile, the performance of the SSMI was compared to the commonly used drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI). The results showed that the SSMI is more sensitive to drought than both SPEI and PDSI in southwest China. The results also demonstrate that GLDAS soil moisture data can be used to study drought at a small regional scale.
Locality Constrained Low Rank Representation and Automatic Dictionary Learning for Hyperspectral Anomaly Detection
Authors: Huang Ju , Liu Kang , Li Xuelong
Published: March 2022

Hyperspectral anomaly detection (HAD) as a special target detection can automatically locate anomaly objects whose spectral information are quite different from their surroundings, without any prior information about background and anomaly. In recent years, HAD methods based on the low rank representation (LRR) model have caught much attention, and achieved good results. However, LRR is a global structure model, which inevitably ignores the local geometrical information of hyperspectral image. Furthermore, most of these methods need to construct dictionaries with clustering algorithm in advance, and they are carried out stage by stage. In this paper, we introduce a locality constrained term inspired by manifold learning topreserve the local geometrical structure during the LRR process, and incorporate the dictionary learning into the optimization process of the LRR. Our proposed method is an one-stage algorithm, which can obtain the low rank representation coefficient matrix, the dictionary matrix, and the residual matrix referring to anomaly simultaneously. One simulated and three real hyperspectral images are used as test datasets. Three metrics, including the ROC curve, AUC value, and box plot, are used to evaluate the detection performance. The visualized results demonstrate convincingly that our method can not only detect anomalies accurately, but also suppress the background information and noises effectively. The three evaluation metrics also prove that our method is superior to other typical methods.
Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis
Authors: Siłuch Marcin , Bartmiński Piotr , Zgłobicki Wojciech
Published: March 2022

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).
HybridGBN-SR: A Deep 3D/2D Genome Graph-Based Network for Hyperspectral Image Classification
Authors: Tinega Haron C. , Chen Enqing , Ma Long , Nyasaka Divinah O. , Mariita Richard M.
Published: March 2022

The successful application of deep learning approaches in remote sensing image classification requires large hyperspectral image (HSI) datasets to learn discriminative spectral–spatial features simultaneously. To date, the HSI datasets available for image classification are relatively small to train deep learning methods. This study proposes a deep 3D/2D genome graph-based network (abbreviated as HybridGBN-SR) that is computationally efficient and not prone to overfitting even with extremely few training sample data. At the feature extraction level, the HybridGBN-SR utilizes the three-dimensional (3D) and two-dimensional (2D) Genoblocks trained using very few samples while improving HSI classification accuracy. The design of a Genoblock is based on a biological genome graph. From the experimental results, the study shows that our model achieves better classification accuracy than the compared state-of-the-art methods over the three publicly available HSI benchmarking datasets such as the Indian Pines (IP), the University of Pavia (UP), and the Salinas Scene (SA). For instance, using only 5% labeled data for training in IP, and 1% in UP and SA, the overall classification accuracy of the proposed HybridGBN-SR is 97.42%, 97.85%, and 99.34%, respectively, which is better than the compared state-of-the-art methods.
Accuracy Assessment, Comparative Performance, and Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CARTOSAT 30 m, SRTM 90 m, MERIT 90 m, and TanDEM-X 90 m) Using DGPS
Authors: Preety Kumari , Prasad Anup K. , Varma Atul K. , El-Askary Hesham
Published: March 2022

Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) is examined in detail. The study area is a hard rock terrain that overall is flat-like with undulating and uneven surfaces (IIT (ISM) Campus and its environs) where the statistical analysis (corrected and uncorrected DEMs), correlation statistics and statistical tests (for elevation and slope), the impact of resampling methods, and the optimum number of GCPs for reduction of error in order to use it in further applications have been presented in detail. As the application of DGPS data at GCPs helps in the substantial reduction of bias by the removal of systematic error, it is recommended that DEMs may be corrected using DGPS before being used in any scientific studies.
Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images
Authors: Guo Yahui , Chen Shouzhi , Li Xinxi , Cunha Mario , Jayavelu Senthilnath , Cammarano Davide , Fu Yongshuo
Published: March 2022

Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.
Validation of Embedded State Estimator Modules for Decentralized Monitoring of Power Distribution Systems Using IoT Components
Authors: Junior Rosvando Marques Gonzaga , Márquez-Sánchez Sergio , Santos Jorge Herrera , de Almeida Rodrigo Maximiano Antunes , London Junior João Bosco Augusto , Rodríguez Juan Manuel Corchado
Published: March 2022

Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy.
Considerations for Radio Frequency Fingerprinting across Multiple Frequency Channels
Authors: Gutierrez del Arroyo Jose A. , Borghetti Brett J. , Temple Michael A.
Published: March 2022

Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from MCC > 0.9 (excellent) down to MCC < 0.05 (random guess), indicating that single-channel models may not maintain performance across all channels used by the transmitter in realistic operation. We proposed a training data selection technique to create multi-channel models which outperform single-channel models, improving the cross-channel average MCC from 0.657 to 0.957 and achieving frequency channel-agnostic performance. When evaluated in the presence of noise, multi-channel discriminant analysis models showed reduced performance, but multi-channel neural networks maintained or surpassed single-channel neural network model performance, indicating additional robustness of multi-channel neural networks in the presence of noise.
Energy Consumption Model of SCHC Packet Fragmentation over Sigfox LPWAN
Authors: Aguilar Sergio , Platis Antonis , Vidal Rafael , Gomez Carles
Published: March 2022

The Internet Engineering Task Force (IETF) has standardized a new framework, called Static Context Header Compression and fragmentation (SCHC), which offers adaptation layer functionality designed to support IPv6 over Low Power Wide Area Networks (LPWANs). The IETF is currently profiling SCHC, and in particular its packet fragmentation and reassembly functionality, for its optimal use over certain LPWAN technologies. Considering the energy constraints of LPWAN devices, it is crucial to determine the energy performance of SCHC packet transfer. In this paper, we present a current and energy consumption model of SCHC packet transfer over Sigfox, a flagship LPWAN technology. The model, which is based on real hardware measurements, allows to determine the impact of several parameters and fragment transmission strategies on the energy performance of SCHC packet transfer over Sigfox. Among other results, we have found that the lifetime of a device powered by a 2000 mAh battery, transmitting packets every 5 days, is 168 days for 2250-byte packets, while it increases to 1464 days for 77-byte packets.
A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms
Authors: Wu Xiaojun , Gao Zhiyuan , Yuan Sheng , Hu Qiao , Dang Zerui
Published: March 2022

Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a “UUV-task” matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.
Research on Clamping Force Control of CVT for Electric Vehicles Based on Slip Characteristics
Authors: Fu Bing , Zhu Taiping , Liu Jingang , Hu Xiaolan
Published: March 2022

The low mechanical efficiency of metal belt’s continuously variable transmission (CVT) limits its application in new energy vehicles. To further improve CVT efficiency and reduce the energy consumption of electric vehicles (EVs) with CVT, this paper proposes a pure electric CVT configuration and a clamping force control strategy. The slip characteristics of CVT are obtained through a bench test, the dynamic model of CVT slip is established, and a clamping force fuzzy control strategy is designed. The strategy is studied by simulation under extreme conditions and standard driving cycles. The simulation results show that the proposed clamping force control strategy has good adaptability. Under extreme conditions, this strategy can ensure that CVT does not undergo macro slip, while reducing the clamping force by 12.86–21.65%. Energy consumption per 100 km is 14.90 kWh in NEDC, which is 6.67% lower compared with the traditional strategy. CVT average efficiency and average transmission efficiency increased by 3.71% and 6.40%. The research results demonstrate that adjusting the CVT clamping force through fuzzy control based on the slip rate can improve the CVT efficiency and energy economy of EVs, which provides a certain reference for CVT clamping force control strategy development and the application of CVT on EVs.
Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition
Authors: Mir-Cerdà Aina , Granell Biel , Izquierdo-Llopart Anaïs , Sahuquillo Àngels , López-Sánchez José Fermín , Saurina Javier , Sentellas Sonia
Published: March 2022

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.
Extended Object Tracking with Embedded Classification
Authors: Cao Wen , Li Qiwei
Published: March 2022

This paper proposes a novel extended object tracking (EOT) approach with embedded classification. Traditionally, for extended objects, only tracking is addressed without considering classification. This has serious defects: On the one hand, some practical EOT problems require classification as an embedded subproblem; on the other hand, with the assistance of classification, the tracking performance can be improved. Therefore, we propose a systematic EOT method with embedded classification, which is desired to satisfy the practical demands and also enjoys superior tracking performance. Specifically, we first formulate the EOT problem with embedded classification by kinematic models and attribute models. Then, we explore a random-matrix-based, multiple model EOT method with embedded classification. Two strategies are creatively provided in which soft classification and hard classification are embedded, respectively. Especially for the EOT with hard classification, a sequential probability ratio-test-based classification scheme is explored due to its nice properties and adaptability to our problem. In both methods, classification assist tracking is used. The simulation results demonstrate the superiority of the proposed EOT method with embedded classification, which can not only satisfy the practical requirements for classification but can also improve the tracking performance by utilizing the assistant of classification.
Populism as New Wine in Old Bottles in the Context of Germany: ‘Symbolic Violence’ as Collective Habitus That Devalues the Human Capital of Turks
Authors: Vassilopoulou Joana , Ozbilgin Mustafa , Groutsis Dimitria , Keles Janroj
Published: March 2022

Populism in Germany is not a new phenomenon. For a long time, the alleged integration problems of Turkish workers in Germany have been at the center of the dominant discourse and academic studies. This paper demonstrates how ‘symbolic violence’ as collective habitus frames the human capital of Turks as deficient, a phenomenon which has prevailed even prior to the recent populist movements. Drawing on a company case study, interviews, and observations, our empirical investigation operationalises and expands the Bourdieusian conceptual trinity of habitus, capital, and symbolic violence through the lens of ethnicity and how it relates to populism.
Discourse on Entrepreneurial Orientation in Hidalgo State, Mexico
Authors: Hernández-Gracia Tirso Javier , Duana-Avila Danae
Published: March 2022

Entrepreneurial orientation influences the development of organizations and the need to undertake it implies assuming certain risks for entrepreneurs who must respond to the constant dynamism of the business world in search of new opportunities for success. The objective was to find the conditions for and against the entrepreneurial orientation that exists in the State of Hidalgo, through the opinion of 300 experts in entrepreneurship. A measuring instrument was used and adapted in 2019 by the National Network of MSMEs (micro, small and medium enterprises) of the Consortium of Mexican Universities (CUMEX), based on the Global Entrepreneurship Monitor and the information was collected through an online questionnaire. The main results show the need to strengthen entrepreneurial orientation, improving aspects regarding financing, government programs, the opening of the internal market and regulations, and social and cultural issues; likewise, an important finding is that the generalized perception of this phenomenon is more positive than if each dimension is analyzed in a particular way, along with the factors that compose it. This study can contribute to the generation of a better entrepreneurial culture that encourages the creation of new businesses.
Promotion and Uptake of Sustainable Consumption and Production (SCP) Practices among Kenyan MSMEs: Key Learnings
Authors: Aseto Joshua Odero , Anggraeni Kartika , Melgar Marianne Isabel Magnus , Ballón-Ossio Adriana , Sander Luca Emanuel , Grossi Francesca , Ojwang William , Gathogo Elizabeth , Njiru Caroline , Orwa Nickson
Published: March 2022

This article analyses and assesses the integration of sustainable consumption and production (SCP) practices in the horticultural production of Kenyan micro-, small-, and medium-sized enterprises (MSMEs) within the framework of the EU Switch Africa Green project ‘Green Horticulture at Lake Naivasha’ (GOALAN) in the Lake Naivasha basin, Kenya. The aim of the study is to understand what facilitates and hinders the uptake of SCP practices. Using qualitative and quantitative data collection methods, this article is based on a case study approach. The study looked into (1) 158 MSMEs (project beneficiaries and non-beneficiaries) regarding their adoption and implementation of SCP practices, as well as their encountered challenges, opportunities, and evidenced outcomes; and (2) five ‘best practice’ case studies from the project beneficiary group. Findings indicate some key factors influencing the decision of MSMEs to adopt and implement SCP practices. These factors encompass access to SCP know-how, the identification of potential benefits, access to specialized and reliable markets, access to finance, and access to infrastructure. Based on the findings, the study suggests that to promote the uptake and long-term implementation of SCP practices by horticultural MSMEs, it is key to build the capacity of MSMEs, improve financial capacity to invest in SCP measures, enhance access to markets through establishing new linkages with buyers and customers, and enable infrastructure.
Feasibility of Nickel–Aluminum Complex Hydroxides for Recovering Tungsten Ions from Aqueous Media
Authors: Ogata Fumihiko , Kawamoto Saki , Tabuchi Ayako , Toda Megumu , Otani Masashi , Nakamura Takehiro , Kawasaki Naohito
Published: March 2022

In this study, the adsorption and/or desorption capacity of tungsten ions using nickel–aluminum complex hydroxides was assessed. Nickel–aluminum complex hydroxides at various molar ratios, such as NA11 were prepared, and the adsorption capacity of tungsten ions was evaluated. Precisely, the effect of temperature, contact time, pH, and coexistence on the adsorption of tungsten ions in the water layer was demonstrated. Among the nickel–aluminum complex hydroxides at various molar ratios, the adsorption capacity onto NA11 was the highest of all adsorbents. The sulfate ions in the interlayer of NA11 was exchanged to tungsten ions, that is, the adsorption mechanism was ion exchange under our experimental conditions. Additionally, to elucidate the adsorption mechanism in detail, the elemental distribution and X-ray photoelectron spectroscopy of the NA11 surface were analyzed. Finally, the results indicated that the tungsten ions adsorbed using NA11 could be desorbed (recovered) from NA11 using sodium hydroxide solution. These results serve as useful information regarding the adsorption and recovery of tungsten ions using nickel–aluminum complex hydroxides from aqueous media.
Low-Carbon Governance, Fiscal Decentralization and Sulfur Dioxide Emissions: Evidence from a Quasi-Experiment with Chinese Heavy Pollution Enterprises
Authors: Guo Ping , Li Jin , Kuang Jinsong , Zhu Yifei , Xiao Renrui , Duan Donghao , Huang Baocong
Published: March 2022

This paper investigates the effects of enterprise environmental governance under low-carbon pilot policies in China with a difference in differences (DID) design. In examining the development of these policies, we focus on exploring their effects on sulfur dioxide emissions of heavily polluting enterprises based on prefectural city- and firm-level data. Overall, the policies significantly increased enterprise sulfur dioxide emission, and the underlying reason being that investments in carbon dioxide emissions control crowded out investment in sulfur dioxide emission control in enterprises in low-carbon pilot regions. We also find that the implementation of low-carbon pilot policies resulted in greater sulfur dioxide emission from state-owned enterprises and enterprises in western regions than from non-state-owned enterprises and those in eastern regions. It is further found that fiscal decentralization and the associated mediating effect of market segmentation promote enterprises’ carbon dioxide emissions control and inhibit their sulfur dioxide emission control. This study helps us re-examine the overall environmental effects of low-carbon policies and has implications for the revision and improvement of environmental governance policies in developing countries.
Prediction of Penetration Resistance of a Spherical Penetrometer in Clay Using Multivariate Adaptive Regression Splines Model
Authors: Sirimontree Sayan , Jearsiripongkul Thira , Lai Van Qui , Eskandarinejad Alireza , Lawongkerd Jintara , Seehavong Sorawit , Thongchom Chanachai , Nuaklong Peem , Keawsawasvong Suraparb
Published: March 2022

This paper presents the technique for solving the penetration resistance factor of a spherical penetrometer in clay under axisymmetric conditions by taking the adhesion factor, the embedded ratio, the normalized unit weight, and the undrained shear strength into account. The finite element limit analysis (FELA) is used to provide the upper bound (UB) or lower bound (LB) solutions, then the multivariate adaptive regression splines (MARS) model is used to train the optimal data between input and output database. The accuracy of MARS equations is confirmed by comparison with the finite element method and the validity of the present solutions was established through comparison to existing results. All numerical results of the penetration resistance factor have significance with three main parameters (i.e., the adhesion factor, the embedded ratio, the normalized unit weight, and the undrained shear strength). The failure mechanisms of spherical penetrometers in clay are also investigated, the contour profiles that occur around the spherical penetrometers also depend on the three parameters. In addition, the proposed technique can be used to estimate the problems that are related or more complicated in soft offshore soils.
Spatio-Temporal Extraction of Surface Waterbody and Its Response of Extreme Climate along the Upper Huaihe River
Authors: Wang Hang , Liu Zhenzhen , Zhu Jun , Chen Danjie , Qin Fen
Published: March 2022

The upper Huaihe River is the water-producing area of the Huaihe River Basin and the major grain and oil-producing area in China. The changing global climate over the recent years has increased the frequency of extreme weather in the upper reaches of the Huaihe River. Research on the responses of surface water bodies to extreme climates has become increasingly important. Based on all utilizable Landsat 4–8 T1–SR data and frequency mapping, the spatio-temporal extraction of surface water and its response to extreme climate were studied. We generated high-precision frequency maps of surface water, and a comparison of cartographic accuracy evaluation indices and spatial consistency was also carried out. The high-precision interpretation of small waterbodies constructs a surface water distribution with better continuity and integrity. Furthermore, we investigated the effect of El Niño/La Niña events on precipitation, temperature, and surface water along the upper Huaihe River, using the Mann–Kendall mutation tests. The results show: in 1987–2018, periods of abrupt changes in precipitation coincide with EI Niño/La Niña events, indicating that the precipitation was sensitive to EI Niño/La Niña events, which also strongly correlated with surface water area during wet and dry years. The effect of extreme events on seasonal water was smaller than permanent water. Surface water area showed an insignificant declining trend after 1999 and a significant drop in 2012. The phenomenon of topographic enhancement of precipitation controlled the spatial distribution of permanent water, with human activities having a substantial effect on the landscape pattern of seasonal water. Finally, discussions and applications related to the Markov Chain probability calculation theory in the paper contributed to enriching the theories on frequency mapping. The relevant results provide a theoretical basis and case support for the formulation of long-term water resources utilization and allocation policies.
An Asymmetric Analysis of the Influence That Economic Policy Uncertainty, Institutional Quality, and Corruption Level Have on India’s Digital Banking Services and Banking Stability
Authors: Syed Aamir Aijaz , Kamal Muhammad Abdul , Ullah Assad , Grima Simon
Published: March 2022

Motivated by the unprecedented high levels of recent economic policy uncertainty, the current study examines the influence of economic policy uncertainty, institutional quality, and corruption level on the Indian banking stability and the growth of digital financial services. Using the Baker et al.’s economic policy uncertainty index and nonlinear autoregressive distribution lag model on the data set of banking variables from 2004 to 2019, we infer the following findings. The unit root and the structural break tests confirm the presence of structural breaks and mixed order of integrations. Besides, the long-run nonlinear autoregressive distribution lag results substantiate a long-run asymmetric relationship between the explanatory variables (economic policy uncertainty, institutional quality, corruption level) and the outcome variables (digital banking services and banking stability). The study reveals that a 1 percent increase in the economic policy uncertainty increases nonperforming loans (proxy to measure banking stability) by 1.48 percent and decreases Z-score (proxy to measure banking stability) by −1.12 percent. Likewise, a 1 percent increase in policy uncertainty reduces the progress of digital financial services by −1.23 percent in India. In addition, the study also depicts a long-run cointegration between the explanatory and the outcome variables. Overall, the study shows significant evidence that policy uncertainty, corruption, and institutional regulation hampers Indian banking stability and digital growth. The study offers several policy implications to understand the adverse effects of economic policy uncertainty on the Indian banking sector.
Investigating the Association between Environmental Quality Characteristics and Mental Well-Being in Public Open Spaces
Authors: Karimi Negin , Sajadzadeh Hassan , Aram Farshid
Published: March 2022

The issues related to the urban environment and mental well-being have become increasingly important in recent decades. Although this association has been mainly investigated in developed countries, there is limited knowledge on whether similar results can be acquired in the urban environments of developing countries like Iran. This study intends to present a new dynamic and active approach to determine the environmental quality characteristics that influence the mental well-being of urban residents and to engage people to healthy urban public environments. In this respect, the research is directed by both qualitative and quantitative surveys in the public open spaces of Kermanshah, Iran. Firstly, the data are collected by Grounded Theory (GT) to identify significant environmental quality characteristics related to mental well-being by applying 24 semi-structured interviews. Secondly, the questionnaire survey based on Structural Equation Modeling (SEM) is applied to examine the association between each characteristic of the developed conceptual framework. The results reveal that the environmental quality characteristics of public open spaces, directly and indirectly, relate to users’ mental well-being. It should be noted that the public open spaces with unique functional and intrinsic features seem to have different impacts on mental well-being.
Determinants of COVID-19 Vaccinations among a State-Wide Year-Long Surveillance Initiative in a Conservative Southern State
Authors: Gual-Gonzalez Lídia , McCarter Maggie S. J. , Dye-Braumuller Kyndall , Self Stella , Ross Connor H. , Rodriguez-Ramos Chloe , Daguise Virginie G. , Nolan Melissa S.
Published: March 2022

By the end of 2021, the COVID-19 pandemic resulted in over 54 million cases and more than 800,000 deaths in the United States, and over 350 million cases and more than 5 million deaths worldwide. The uniqueness and gravity of this pandemic have been reflected in the public health guidelines poorly received by a growing subset of the United States population. These poorly received guidelines, including vaccine receipt, are a highly complex psychosocial issue, and have impacted the successful prevention of disease spread. Given the intricate nature of this important barrier, any single statistical analysis methodologically fails to address all convolutions. Therefore, this study utilized different analytical approaches to understand vaccine motivations and population-level trends. With 12,975 surveys from a state-wide year-long surveillance initiative, we performed three robust statistical analyses to evaluate COVID-19 vaccine hesitancy: principal component analysis, survival analysis and spatial time series analysis. The analytic goal was to utilize complementary mathematical approaches to identify overlapping themes of vaccine hesitancy and vaccine trust in a highly conservative US state. The results indicate that vaccine receipt is influenced by the source of information and the population’s trust in the science and approval process behind the vaccines. This multifaceted statistical approach allowed for methodologically rigorous results that public health professionals and policy makers can directly use to improve vaccine interventions.
Assessing Community Acceptance of Maternal Immunisation in Rural KwaZulu-Natal, South Africa: A Qualitative Investigation
Authors: Chimukuche Rujeko , Ngwenya Nothando , Seeley Janet , Nxumalo Petronella , Nxumalo Zama , Godongwana Motlatso , Radebe Nomasonto , Myburgh Nellie , Adedini Sunday , Cutland Clare
Published: March 2022

Despite the significant benefits of maternal immunisation, uptake remains low in many parts of the world. In this qualitative study, we aimed to assess the factors that influence pregnant women’s decision to engage with maternal immunisation in rural KwaZulu-Natal, South Africa. We conducted in-depth interviews with a total of 28 purposively sampled pregnant women and key informants using semi-structured topic guides. Data analysis was conducted using a modified Health Belief Model framework that included constructs of barriers to action, modifying factors of cue to action and perceived social norms. The findings show that traditional customs and institutional barriers such as low-quality health service delivery, long queues, and distance to the health facilities, immunisation vaccine stockouts and low levels of maternal knowledge influence the choice and decision to engage with maternal immunisation. Understanding health-related behaviours and addressing barriers to care is important in facilitating vaccination uptake. This study contributes to the understanding of maternal immunisation uptake in low-resource settings.
Different Irrigation Pressure and Filter on Emitter Clogging in Drip Phosphate Fertigation Systems
Authors: Liu Chunye , Wang Rui , Wang Wene , Hu Xiaotao , Wu Wanying , Liu Fulai
Published: March 2022

Irrigation pressures and filters have a significant influence on emitter clogging in drip fertigation systems. Exploring the anti-clogging performance of emitters at different irrigation pressures (80, 90, 100 kPa; 40, 50, 60 kPa) and filters (the disc filter with an aperture of 125 μm, the screen filter with an aperture of 125μm, and the screen filter with an aperture of 200 μm) can provide suitable guidance for the design and operation of drip fertigation systems. The average relative discharge (Dra), the Christiansen uniformity coefficient (CU), and the differential pressure (DP) were analyzed to study the anti-clogging performance of flat emitters (FE) and labyrinth emitters (LE). Meanwhile, the scanning electron microscope and energy dispersive spectrometer were used to observe the surface morphology and chemical composition of clogging substances in filters and emitters. Results showed that the irrigation pressure of 90 and 50 kPa did not cause a significant decrease in Dra, and the Dra of two types of emitters were all great than 95.22%. When the irrigation pressure was 80 and 40 kPa, the Dra decreased significantly (73.48–78.71%, 81.3–83.6% for FE and LE). The CU values of emitters were all greater than 90% except for the irrigation pressures of 80 and 40 kPa. The relative Dra and CU of LE were greater than those of FE, indicating that the LE had a better anti-clogging performance compared with the FE. The decrease in DP reflected an increase in the emitter clogging degree. Meanwhile, irrigation pressures and the interaction between irrigation pressures and filters imposed a significant effect on the CU at a significance level of p < 0.05, p < 0.01, respectively. The clogging process was mainly affected by physical and chemical factors, and the generated clogging substances included phosphate fertilizer, silicate, carbonate, and phosphate precipitations. In conclusion, the best operation mode of phosphate fertilizer in drip fertigation systems was to use the disc filter with an aperture of 125 μm and the LE at the irrigation pressure of 50 kPa. An appropriate irrigation pressure combined with effective filters can reduce emitter clogging while saving water and fertilizer.