bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation on Enhanced Heat Transfer Using Sonochemically Synthesized Stable Zno-Eg@Dw Nanofluids in Horizontal Calibrated Circular Flow Passage
Authors: Ahmed Waqar , Zaman Chowdhury Zaira , Kazi Salim Newaz , Johan Mohd. Rafie Bin , Badruddin Irfan Anjum , Soudagar Manzoore Elahi M , Kamangar Sarfaraz , Mujtaba Muhammad Abbas , Gul Mustabshirha , Khan T.M. Yunus
Published: April 2021

In this research, Zinc Oxide-Ethylene @ glycol distilled water based nanofluid was synthesized using the sonochemical method. The convective heat transfer properties of as synthesized nanofluid were observed for a closed single circular tube pipe in turbulent flow regimes. The prepared nanofluids were characterized by ultra violet spectroscopy (UV–VIS), UV–VIS absorbance, X-Ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Transmission electron microscopy (TEM) and stability analysis. Five calibrated k-type thermocouples were mounted on the surface of the test section. Analytical data related to heat transfer properties of the synthesized nanofluid for the heat exchanger, incorporated with the closed circular tube test section were collected. The addition of ZnO solid nanoparticles in the EG@DW mixture enhanced the value of thermal conductivity and other thermophysical characteristics of the nanofluids. Maximum thermal conductivity was observed at 45 °C for using 0.1 wt.% of ZnO nanoparticles EG@DW nanofluid. Increasing the wt.% of ZnO solid nanoparticles in the EG@DW mixture had increased the thermal conductivity subsequently with change in temperature from 20 to 45 °C. Furthermore, Nusselt numbers of ZnO-EG@DW-based nanofluid was estimated for the various concentration of ZnO present in EG@DW-based fluid. The presence of ZnO solid nanoparticles into the EG@DW base fluid escalate the Nusselt (Nu) number by 49.5%, 40.79%, 37% and 23.06% for 0.1, 0.075, 0.05 and 0.025 wt.% concentrations, respectively, at room temperature. Varying wt.% of ZnO (0.1, 0.075, 0.05 and 0.025) nanoparticles had shown improved heat transfer (h) properties compared to the base fluid alone. The absolute average heat transfer of ZnO-EG@DW nanofluid using the highest concentration of 0.1 wt.% was improved compared to the EG@DW mixture. The magnitude of absolute average heat transfer was increased from 600 W/m2k for the EG@DW mixture to 1200 W/m2k for ZnO-EG@DW nanofluid. Similarly, the heat transfer improvement for the other three wt.% (0.075, 0.05 and 0.025) was noticed as 600–1160, 600–950 and 600–900 W/m2k, respectively, which is greater than base fluid.
Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters
Authors: Ching David S. , Safta Cosmin , Reichardt Thomas A.
Published: April 2021

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.
Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting
Authors: Jayalakshmi N. Yogambal , Shankar R. , Subramaniam Umashankar , Baranilingesan I. , Karthick Alagar , Stalin Balasubramaniam , Rahim Robbi , Ghosh Aritra
Published: April 2021

Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.
Inspection Data Collection Tool for Field Testing of Photovoltaic Modules in the Atacama Desert
Authors: Vásquez Pía , Devoto Ignacia , Ferrada Pablo , Taquichiri Abel , Portillo Carlos , Palma-Behnke Rodrigo
Published: April 2021

The Atacama Desert receives the highest levels of solar irradiance in the world with an annual average of 2500 kWh/m2 for the global horizontal irradiance and 3500 kWh/m2 for the direct normal irradiance. One of the challenges is the large portion of ultraviolet light. This part of the spectrum be detrimental for the encapsulant materials, reducing their lifetime. To develop a module adapted to the Atacama Desert conditions, it is imperative to have standardized information from first-hand about the typical faults experienced by photovoltaic modules operating in the desert. This work reports on the design and implementation of the Inspection Data Collection Tool to evaluate crystalline silicon-based moules operating in desert climates. The tool brings together novel features of compatibility with current standards, efficient mobile-type instrumentation (equipment and tools), clear procedures/protocols for non-expert users and low development costs. A total of 95 modules were inspected to characterize failure/degradation issues. Three components of the solar modules were assessed: front cover glass, ethylene-vinyl acetate encapsulant and solar cells. Seven abnormalities were analyzed: Soiling, front cover glass discoloration, encapsulant delamination, hotspots, partial shading, cell fracture and faulty soldering. Soiling was the most common issue, showing correlation between dust deposition and location.
Route Profile Dependent Tram Regenerative Braking Algorithm with Reduced Impact on the Supply Network
Authors: Radaš Ivan , Župan Ivan , Šunde Viktor , Ban Željko
Published: April 2021

Electric trams are one of the standard forms of public transport. They are characterized by large amounts of electric current and electric current gradient from the power grid, especially during acceleration. For this reason, a regenerative braking system is considered with the aim of reducing electric current peaks and increasing energy efficiency by reducing the total energy consumption of the power grid. A supercapacitor module is used as a storage device for storing and utilizing the braking energy. The supercapacitor module and the power grid constitute a hybrid energy system, for which a control algorithm has been developed. The control algorithm takes into account the influence of the elevation profile and the slope of the vehicle route in storing and using the braking energy. The operation of the algorithm was simulated and analyzed using the MATLAB/Simulink software package for tram lines with different elevation profiles.
Design and Implementation of Maiden Dual-Level Controller for Ameliorating Frequency Control in a Hybrid Microgrid
Authors: Latif Abdul , Hussain S. M. Suhail , Das Dulal Chandra , Ustun Taha Selim
Published: April 2021

It is known that keeping the power balance between generation and demand is crucial in containing the system frequency within acceptable limits. This is especially important for renewable based distributed hybrid microgrid (DHμG) systems where deviations are more likely to occur. In order to address these issues, this article develops a prominent dual-level “proportional-integral-one plus double derivative {PI−(1 + DD)} controller” as a new controller for frequency control (FC) of DHμG system. The proposed control approach has been tested in DHμG system that consists of wind, tide and biodiesel generators as well as hybrid plug-in electric vehicle and an electric heater. The performance of the modified controller is tested by comparing it with standard proportional-integral (PI) and classical PID (CPID) controllers considering two test scenarios. Further, a recently developed mine blast technique (MBA) is utilized to optimize the parameters of the newly designed {PI − (1 + DD)} controller. The controller’s performance results are compared with cases where particle swarm optimization (PSO) and firefly (FF) techniques are used as benchmarks. The superiority of the MBA-{PI − (1 + DD)} controller in comparison to other two strategies is illustrated by comparing performance parameters such as maximum frequency overshoot, maximum frequency undershoot and stabilization time. The displayed comparative objective function (J) and JFOD index also shows the supremacy of the proposed controller. With this MBA optimized {PI − (1 + DD)} controller, frequency deviations can be kept within acceptable limits even with high renewable energy penetration.
An Event-Specific Real-Time PCR Method for Measuring Transgenic Lysozyme Goat Content in Trace Samples
Authors: Xu Wenting , Cui Jinjie , Liu Biao , Yang Litao
Published: April 2021

Lysozymes are used in sterilisation, antisepsis, dairy additives, inflammation, and cancer. One transgenic goat line expressing high levels of human lysozyme (hLZ) in goat milk has been developed in China. Herein, we established an event-specific real-time polymerase chain reaction (real-time PCR) method to detect the transgenic hLZ goat line. The developed method has high specificity, sensitivity and accuracy, and a wide quantitative dynamic range. The limit of detection and limit of quantification was 5 and 10 copies per reaction, respectively. The practical sample analysis results showed that the method could identify and quantify transgenic lysozyme content in trace samples in routine lab analyses. Furthermore, the potential applicability in risk assessment, such as molecular characterisation and gene horizontal transfer, was confirmed. We believe that this method is suitable for the detection of transgenic hLZ goat line and its derivate.
Mathematical Modelling of Ultrasound-Assisted Extraction Kinetics of Bioactive Compounds from Artichoke By-Products
Authors: Reche Cristina , Rosselló Carmen , Umaña Mónica M. , Eim Valeria , Simal Susana
Published: April 2021

Valorization of an artichoke by-product, rich in bioactive compounds, by ultrasound-assisted extraction, is proposed. The extraction yield curves of total phenolic content (TPC) and chlorogenic acid content (CAC) in 20% ethanol (v/v) with agitation (100 rpm) and ultrasound (200 and 335 W/L) were determined at 25, 40, and 60 °C. A mathematical model considering simultaneous diffusion and convection is proposed to simulate the extraction curves and to quantify both temperature and ultrasound power density effects in terms of the model parameters variation. The effective diffusion coefficient exhibited temperature dependence (72% increase for TPC from 25 °C to 60 °C), whereas the external mass transfer coefficient and the equilibrium extraction yield depended on both temperature (72% and 90% increases for TPC from 25 to 60 °C) and ultrasound power density (26 and 51% increases for TPC from 0 (agitation) to 335 W/L). The model allowed the accurate curves simulation, the average mean relative error being 5.3 ± 2.6%. Thus, the need of considering two resistances in series to satisfactorily simulate the extraction yield curves could be related to the diffusion of the bioactive compound from inside the vegetable cells toward the intercellular volume and from there, to the liquid phase.
Shared Neurodevelopmental Perturbations Can Lead to Intellectual Disability in Individuals with Distinct Rare Chromosome Duplications
Authors: Corrêa Thiago , Santos-Rebouças Cíntia B. , Mayndra Maytza , Schinzel Albert , Riegel Mariluce
Published: April 2021

Chromosomal duplications are associated with a large group of human diseases that arise mainly from dosage imbalance of genes within the rearrangements. Phenotypes range widely but are often associated with global development delay, intellectual disability, autism spectrum disorders, and multiple congenital abnormalities. How different contiguous genes from a duplicated genomic region interact and dynamically affect the expression of each other remains unclear in most cases. Here, we report a genomic comparative delineation of genes located in duplicated chromosomal regions 8q24.13q24.3, 18p11.32p11.21, and Xq22.3q27.2 in three patients followed up at our genetics service who has the intellectual disability (ID) as a common phenotype. We integrated several genomic data levels by identification of gene content within the duplications, protein-protein interactions, and functional analysis on specific tissues. We found functional relationships among genes from three different duplicated chromosomal regions, reflecting interactions of protein-coding genes and their involvement in common cellular subnetworks. Furthermore, the sharing of common significant biological processes associated with ID has been demonstrated between proteins from the different chromosomal regions. Finally, we elaborated a shared model of pathways directly or indirectly related to the central nervous system (CNS), which could perturb cognitive function and lead to ID in the three duplication conditions.
Oxidative Properties of Polystyrene Nanoparticles with Different Diameters in Human Peripheral Blood Mononuclear Cells (In Vitro Study)
Authors: Kik Kinga , Bukowska Bożena , Krokosz Anita , Sicińska Paulina
Published: April 2021

With the ongoing commercialization, human exposure to plastic nanoparticles will dramatically increase, and evaluation of their potential toxicity is essential. There is an ongoing discussion on the human health effects induced by plastic particles. For this reason, in our work, we assessed the effect of polystyrene nanoparticles (PS-NPs) of various diameters (29, 44 and 72 nm) on selected parameters of oxidative stress and the viability of human peripheral blood mononuclear cells (PBMCs) in the in vitro system. Cells were incubated with PS-NPs for 24 h in the concentration range of 0.001 to 100 µg/mL and then labeled: formation of reactive oxygen species (ROS) (including hydroxyl radical), protein and lipid oxidation and cell viability. We showed that PS-NPs disturbed the redox balance in PBMCs. They increased ROS levels and induced lipid and protein oxidation, and, finally, the tested nanoparticles induced a decrease in PBMCs viability. The earliest changes in the PBMCs were observed in cells incubated with the smallest PS-NPs, at a concentration of 0.01 μg/mL. A comparison of the action of the studied nanoparticles showed that PS-NPs (29 nm) exhibited a stronger oxidative potential in PBMCs. We concluded that the toxicity and oxidative properties of the PS-NPs examined depended to significant degree on their diameter.
Targeting S1PRs as a Therapeutic Strategy for Inflammatory Bone Loss Diseases—Beyond Regulating S1P Signaling
Authors: Yu Hong
Published: April 2021

As G protein coupled receptors, sphingosine-1-phosphate receptors (S1PRs) have recently gained attention for their role in modulating inflammatory bone loss diseases. Notably, in murine studies inhibiting S1PR2 by its specific inhibitor, JTE013, alleviated osteoporosis induced by RANKL and attenuated periodontal alveolar bone loss induced by oral bacterial inflammation. Treatment with a multiple S1PRs modulator, FTY720, also suppressed ovariectomy-induced osteoporosis, collagen or adjuvant-induced arthritis, and apical periodontitis in mice. However, most previous studies and reviews have focused mainly on how S1PRs manipulate S1P signaling pathways, subsequently affecting various diseases. In this review, we summarize the underlying mechanisms associated with JTE013 and FTY720 in modulating inflammatory cytokine release, cell chemotaxis, and osteoclastogenesis, subsequently influencing inflammatory bone loss diseases. Studies from our group and from other labs indicate that S1PRs not only control S1P signaling, they also regulate signaling pathways induced by other stimuli, including bacteria, lipopolysaccharide (LPS), bile acid, receptor activator of nuclear factor κB ligand (RANKL), IL-6, and vitamin D. JTE013 and FTY720 alleviate inflammatory bone loss by decreasing the production of inflammatory cytokines and chemokines, reducing chemotaxis of inflammatory cells from blood circulation to bone and soft tissues, and suppressing RANKL-induced osteoclast formation.
Development of a Robust High-Throughput Screening Platform for Inhibitors of the Striatal-Enriched Tyrosine Phosphatase (STEP)
Authors: Lambert Lester J , Grotegut Stefan , Celeridad Maria , Gosalia Palak , Backer Laurent JS De , Bobkov Andrey A , Salaniwal Sumeet , Chung Thomas DY , Zeng Fu-Yue , Pass Ian , Lombroso Paul J , Cosford Nicholas DP , Tautz Lutz
Published: April 2021

Many human diseases are the result of abnormal expression or activation of protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs). Not surprisingly, more than 30 tyrosine kinase inhibitors (TKIs) are currently in clinical use and provide unique treatment options for many patients. PTPs on the other hand have long been regarded as “undruggable” and only recently have gained increased attention in drug discovery. Striatal-enriched tyrosine phosphatase (STEP) is a neuron-specific PTP that is overactive in Alzheimer’s disease (AD) and other neurodegenerative and neuropsychiatric disorders, including Parkinson’s disease, schizophrenia, and fragile X syndrome. An emergent model suggests that the increase in STEP activity interferes with synaptic function and contributes to the characteristic cognitive and behavioral deficits present in these diseases. Prior efforts to generate STEP inhibitors with properties that warrant clinical development have largely failed. To identify novel STEP inhibitor scaffolds, we developed a biophysical, label-free high-throughput screening (HTS) platform based on the protein thermal shift (PTS) technology. In contrast to conventional HTS using STEP enzymatic assays, we found the PTS platform highly robust and capable of identifying true hits with confirmed STEP inhibitory activity and selectivity. This new platform promises to greatly advance STEP drug discovery and should be applicable to other PTP targets.
Lipid Droplet Motility Increases Following Viral Immune Stimulation
Authors: Monson Ebony A. , Whelan Donna R. , Helbig Karla J.
Published: April 2021

Lipid droplets (LDs) have traditionally been thought of as solely lipid storage compartments for cells; however, in the last decade, they have emerged as critical organelles in health and disease. LDs are highly dynamic within cells, and their movement is critical in organelle–organelle interactions. Their dynamics are known to change during cellular stress or nutrient deprivation; however, their movement during pathogen infections, especially at very early timepoints, is under-researched. This study aimed to track LD dynamics in vitro, in an astrocytic model of infection. Cells were either stimulated with a dsRNA viral mimic, poly I:C, or infected with the RNA virus, Zika virus. Individual LDs within infected cells were analysed to determine displacement and speed, and average LD characteristics for multiple individual cells calculated. Both LD displacement and mean speed were significantly enhanced in stimulated cells over a time course of infection with an increase seen as early as 2 h post-infection. With the emerging role for LDs during innate host responses, understanding their dynamics is critical to elucidate how these organelles influence the outcome of viral infection.
A Sex Perspective in Neurodegenerative Diseases: microRNAs as Possible Peripheral Biomarkers
Authors: Piscopo Paola , Bellenghi Maria , Manzini Valeria , Crestini Alessio , Pontecorvi Giada , Corbo Massimo , Ortona Elena , Carè Alessandra , Confaloni Annamaria
Published: April 2021

Sex is a significant variable in the prevalence and incidence of neurological disorders. Sex differences exist in neurodegenerative disorders (NDs), where sex dimorphisms play important roles in the development and progression of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the last few years, some sex specific biomarkers for the identification of NDs have been described and recent studies have suggested that microRNA (miRNA) could be included among these, as influenced by the hormonal and genetic background. Failing to consider the possible differences between males and females in miRNA evaluation could introduce a sex bias in studies by not considering some of these sex-related biomarkers. In this review, we recapitulate what is known about the sex-specific differences in peripheral miRNA levels in neurodegenerative diseases. Several studies have reported sex-linked disparities, and from the literature analysis miR-206 particularly has been shown to have a sex-specific involvement. Hopefully, in the near future, patient stratification will provide important additional clues in diagnosis, prognosis, and tailoring of the best therapeutic approaches for each patient. Sex-specific biomarkers, such as miRNAs, could represent a useful tool for characterizing subgroups of patients.
Deep Learning in Virtual Screening: Recent Applications and Developments
Authors: Kimber Talia B. , Chen Yonghui , Volkamer Andrea
Published: April 2021

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.
Transcriptome Analysis of Pre-Storage 1-MCP and High CO2-Treated ‘Madoka’ Peach Fruit Explains the Reduction in Chilling Injury and Improvement of Storage Period by Delaying Ripening
Authors: Choi Han Ryul , Jeong Min Jae , Baek Min Woo , Choi Jong Hang , Lee Hee Cheol , Jeong Cheon Soon , Tilahun Shimeles
Published: April 2021

Cold storage of peach fruit at low temperatures may induce chilling injury (CI). Pre-storage 1-MCP and high CO2 treatments were reported among the methods to ameliorate CI and reduce softening of peach fruit. However, molecular data indicating the changes associated with pre-storage 1-MCP and high CO2 treatments during cold storage of peach fruit are insufficient. In this study, a comparative analysis of the difference in gene expression and physico-chemical properties of fruit at commercial harvest vs. stored fruit for 12 days at 0 °C (cold-stored (CS), pre-storage 1-MCP+CS, and pre-storage high CO2+CS) were used to evaluate the variation among treatments. Several genes were differentially expressed in 1-MCP+CS- and CO2+CS-treated fruits as compared to CS. Moreover, the physico-chemical and sensory data indicated that 1-MCP+CS and CO2+CS suppressed CI and delayed ripening than the CS, which could lead to a longer storage period. We also identified the list of genes that were expressed commonly and exclusively in the fruit treated by 1-MCP+CS and CO2+CS and compared them to the fruit quality parameters. An attempt was also made to identify and categorize genes related to softening, physiological changes, and other ripening-related changes. Furthermore, the transcript levels of 12 selected representative genes from the differentially expressed genes (DEGs) in the transcriptome analysis were confirmed via quantitative real-time PCR (qRT-PCR). These results add information on the molecular mechanisms of the pre-storage treatments during cold storage of peach fruit. Understanding the genetic response of susceptible cultivars such as ‘Madoka’ to CI-reducing pre-storage treatments would help breeders release CI-resistant cultivars and could help postharvest technologists to develop more CI-reducing technologies.
Insights into the Pathophysiology of Psychiatric Symptoms in Central Nervous System Disorders: Implications for Early and Differential Diagnosis
Authors: Menculini Giulia , Chipi Elena , Paolini Paoletti Federico , Gaetani Lorenzo , Nigro Pasquale , Simoni Simone , Mancini Andrea , Tambasco Nicola , Di Filippo Massimiliano , Tortorella Alfonso , Parnetti Lucilla
Published: April 2021

Different psychopathological manifestations, such as affective, psychotic, obsessive-compulsive symptoms, and impulse control disturbances, may occur in most central nervous system (CNS) disorders including neurodegenerative and neuroinflammatory diseases. Psychiatric symptoms often represent the clinical onset of such disorders, thus potentially leading to misdiagnosis, delay in treatment, and a worse outcome. In this review, psychiatric symptoms observed along the course of several neurological diseases, namely Alzheimer’s disease, fronto-temporal dementia, Parkinson’s disease, Huntington’s disease, and multiple sclerosis, are discussed, as well as the involved brain circuits and molecular/synaptic alterations. Special attention has been paid to the emerging role of fluid biomarkers in early detection of these neurodegenerative diseases. The frequent occurrence of psychiatric symptoms in neurological diseases, even as the first clinical manifestations, should prompt neurologists and psychiatrists to share a common clinico-biological background and a coordinated diagnostic approach.
Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests
Authors: Kashyap Bhuwan , Kumar Ratnesh
Published: April 2021

Reducing agricultural losses is an effective way to sustainably increase agricultural output efficiency to meet our present and future needs for food, fiber, fodder, and fuel. Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and deploying efficient measures. This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops. Key sensing methodologies for plant pathogen (or phytopathogen), as well as herbivorous insects/pests are presented, where the working principles are described, and key recent works discussed. The detection methods overviewed for phytopathogen-related stress identification include nucleic acid-based methods, immunological methods, imaging-based techniques, spectroscopic methods, phytohormone biosensing methods, monitoring methods for plant volatiles, and active remote sensing technologies. Whereas the pest-related sensing techniques include machine-vision-based methods, pest acoustic-emission sensors, and volatile organic compound-based stress monitoring methods. Additionally, Comparisons have been made between different sensing techniques as well as recently reported works, where the strengths and limitations are identified. Finally, the prospective future directions for monitoring biotic stress in crops are discussed.
Transfer of Land Use Rights in Rural China and Farmers’ Utility: How to Select an Optimal Payment Mode of Land Increment Income
Authors: Yan Lei , Hong Kairong , Li Hui
Published: April 2021

Background: The distribution of farmers’ increment income is the key to the transfer of land use rights. This research aims to detect the optimal payment mode for the distribution of land increment income obtained by farmers in land rights transfer. Methods: The research relied on case analysis, mathematical analysis, and numerical simulation. Results: According to China’s existing payment modes for the increment income of rural collectively owned operating construction land (RCOCL), we summarized these payment modes into three: namely, lump-sum currency payment, a mixed payment of pension and lump-sum currency, and a mixed payment of dividend and lump-sum currency. If the land transfer price of RCOCL is lower than a specific value, the lump-sum currency payment will be optimal for farmers. Suppose the land transfer price is higher than this value. If the enterprise’s profit margin is higher than the pension rate of return, the mixed payment of dividend and lump-sum currency will be optimal; if not, the mixed payment of pension and lump-sum currency will be optimal. Conclusions: Differences in regions, enterprise attributes, and farmers’ characteristics will make the optimal proportion of pension or stock capital in land increment income (OPPSC) different. Generally, OPPSC is often between 40% and 60%.
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
Authors: Studer Stefan , Bui Thanh Binh , Drescher Christian , Hanuschkin Alexander , Winkler Ludwig , Peters Steven , Müller Klaus-Robert
Published: April 2021

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.
Expression of Anti-Lipopolysaccharide Factor Isoform 3 in Chlamydomonas reinhardtii Showing High Antimicrobial Activity
Authors: Li Anguo , Huang Ruihao , Wang Chaogang , Hu Qunju , Li Hui , Li Xiao
Published: April 2021

Antimicrobial peptides are a class of proteins with antibacterial functions. In this study, the anti-lipopolysaccharide factor isoform 3 gene (ALFPm3), encoding an antimicrobial peptide from Penaeus monodon with a super activity was expressed in Chlamydomonas reinhardtii, which would develop a microalga strain that can be used for the antimicrobial peptide production. To construct the expression cluster, namely pH2A-Pm3, the codon optimized ALFPm3 gene was fused with the ble reporter by 2A peptide and inserted into pH124 vector. The glass-bead method was performed to transform pH2A-Pm3 into C. reinhardtii CC-849. In addition to 8 μg/mL zeocin resistance selection, the C. reinhardtii transformants were further confirmed by genomic PCR and RT-PCR. Western blot analysis showed that the C. reinhardtii-derived ALFPm3 (cALFPm3) was successfully expressed in C. reinhardtii transformants and accounted for 0.35% of the total soluble protein (TSP). Furthermore, the results of antibacterial assay revealed that the cALFPm3 could significantly inhibit the growth of a variety of bacteria, including both Gram-negative bacteria and Gram-positive bacteria at a concentration of 0.77 μM. Especially, the inhibition could last longer than 24 h, which performed better than ampicillin. Hence, this study successfully developed a transgenic C. reinhardtii strain, which can produce the active ALFPm3 driven from P. monodon, providing a potential strategy to use C. reinhardtii as the cell factory to produce antimicrobial peptides.
Botulinum Toxin Treatment for Thoracic Outlet Syndrome Induced by Subclavius Muscle Hypertrophy
Authors: Cavallieri Francesco , Galletti Stefano , Fioravanti Valentina , Menozzi Elisa , Contardi Sara , Valzania Franco
Published: April 2021

Thoracic outlet syndrome (TOS) is frequently caused by bone abnormalities and congenital or acquired soft-tissue alterations. Among these, isolated Subclavius Muscle (SM) hypertrophy represents a rare condition that could lead to a reduction in costoclavicular space and brachial plexus compression. A 47-year-old forest ranger with a history of gun shooting during animal hunting and training sessions of skeet shooting for 20 years developed TOS due to ultrasonography-detected isolated SM hypertrophy, successfully treated with an ultrasound-guided Botulinum Toxin (BTX)-A injection. In our patient, ultrasonography of the brachial plexus has allowed SM hypertrophy to be recognized and to perform BTX-A injection just in the muscle, with a complete resolution of the symptoms.
Factors Associated with Medication Non-Adherence among Patients with Lifestyle-Related Non-Communicable Diseases
Authors: Nakajima Rie , Watanabe Fumiyuki , Kamei Miwako
Published: April 2021

This cross-sectional study explored the association between medication non-adherence and its factors in patients with non-communicable diseases (NCDs) using an online structured questionnaire emailed to 30,000 people (aged over 20 years who lived in Japan at the time of the survey). The questions concerned respondents’ characteristics, medication non-adherence, health beliefs, lifestyles, and trouble taking medication. Factors related to non-adherence were analyzed among patients with lifestyle-related NCDs categorized into two age groups: 20–59, and >60 years. Unintentional (p < 0.001) and intentional (p < 0.001) non-adherence were more common among patients aged 20–59 than in older adults. NCD patients aged 20–59 experienced significantly more trouble taking medication than older adults. Multiple regression analysis showed that for patients aged 20–59 with NCDs, unintentional non-adherence was significantly and positively associated with current smoking habits (β = 0.280, p < 0.001), while intentional non-adherence was significantly and positively associated with alcohol consumption (β = 0.147, p = 0.020) and current smoking habits (β = 0.172, p = 0.007). In patients aged 20–59, unhealthy eating habits (β = −0.136, p = 0.034) and lack of exercise (β = −0.151, p = 0.020) were negatively associated with intentional non-adherence. In conclusion, factors affecting medication non-adherence in patients with lifestyle-related diseases are related to health awareness, lifestyle, and medication barriers.
Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data
Authors: He Mingjun , He Shuangyan , Zhang Xiaodong , Zhou Feng , Li Peiliang
Published: April 2021

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.
DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
Authors: Wang Peng , Niu Yanxiong , Xiong Rui , Ma Fu , Zhang Chunxi
Published: April 2021

Remote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer aggregation network with updated deformable convolution layers as the backbone to extract object features. The detection and classification of objects was based on the center-point network without non-maximum suppression. Secondly, the dynamic gradient adjustment embedded into the classification loss function was put forward to harmonize the quantity imbalance between easy and hard examples, as well as between positive and negative examples. Furthermore, the complete intersection over union (CIoU) loss function was selected as the objective function of bounding box regression, which achieves better convergence speed and accuracy. Finally, in order to validate the effectiveness and precision of the dynamic gradient adjustment network (DGANet), we conducted a series of experiments in remote sensing public datasets UCAS-AOD and LEVIR. The comparison experiments demonstrate that the DGANet achieves a more accurate detection result in optical remote sensing images.
A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks
Authors: Tian Mengqiu , Jiao Wanguo , Chen Yaqian
Published: April 2021

In wireless rechargeable sensor networks, mobile vehicles (MVs) combining energy replenishment and data collection are studied extensively. To reduce data overflow, most recent work has utilized more vehicles to assist the MV to collect buffered data. However, the practical network environment and the limitations of the vehicle in the data collection are not considered. UAV-enabled data collection is immune to complex road environments in remote areas and has higher speed and less traveling cost, which can overcome the lack of the vehicle in data collection. In this paper, a novel framework joining the MV and UAV is proposed to prolong the network lifetime and reduce data overflow. The network lifetime is correlated with the charging order; therefore, we first propose a charging algorithm to find the optimal charging order. During the charging period of the MV, the charging time may be longer than the collecting time. An optimal selection strategy of neighboring clusters, which could send data to the MV, was found to reduce data overflow. Then, to further reduce data overflow, an algorithm is also proposed to schedule the UAV to assist the MV to collect buffered data. Finally, simulation results verified that the proposed algorithms can maximize network lifetime and minimize the data loss simultaneously.
A Projector-Based Augmented Reality Navigation System for Computer-Assisted Surgery
Authors: Gao Yuan , Zhao Yuyun , Xie Le , Zheng Guoyan
Published: April 2021

In the medical field, guidance to follow the surgical plan is crucial. Image overlay projection is a solution to link the surgical plan with the patient. It realizes augmented reality (AR) by projecting computer-generated image on the surface of the target through a projector, which can visualize additional information to the scene. By overlaying anatomical information or surgical plans on the surgery area, projection helps to enhance the surgeon’s understanding of the anatomical structure, and intuitively visualizes the surgical target and key structures of the operation, and avoid the surgeon’s sight diversion between monitor and patient. However, it still remains a challenge to project the surgical navigation information on the target precisely and efficiently. In this study, we propose a projector-based surgical navigation system. Through the gray code-based calibration method, the projector can be calibrated with a camera and then be integrated with an optical spatial locator, so that the navigation information of the operation can be accurately projected onto the target area. We validated the projection accuracy of the system through back projection, with average projection error of 3.37 pixels in x direction and 1.51 pixels in y direction, and model projection with an average position error of 1.03 ± 0.43 mm, and carried out puncture experiments using the system with correct rate of 99%, and qualitatively analyzed the system’s performance through the questionnaire. The results demonstrate the efficacy of our proposed AR system.
Towards a Combined Use of Geophysics and Remote Sensing Techniques for the Characterization of a Singular Building: “El Torreón” (the Tower) at Ulaca Oppidum (Solosancho, Ávila, Spain)
Authors: Maté-González Miguel Ángel , Sáez Blázquez Cristina , Carrasco García Pedro , Rodríguez-Hernández Jesús , Fernández Hernández Jesús , Vallés Iriso Javier , Torres Yolanda , Troitiño Torralba Libertad , Courtenay Lloyd A. , González-Aguilera Diego , López-Cuervo Serafín , Aguirre de Mata Julián , Velasco Gómez Jesús , Piras Marco , Filippo Andrea di , Yravedra José , Fernández Fernández Maximiliano , Chapa Teresa , Ruiz Zapatero Gonzalo , Álvarez-Sanchís Jesús R.
Published: April 2021

This research focuses on the study of the ruins of a large building known as “El Torreón” (the Tower), belonging to the Ulaca oppidum (Solosancho, Province of Ávila, Spain). Different remote sensing and geophysical approaches have been used to fulfil this objective, providing a better understanding of the building’s functionality in this town, which belongs to the Late Iron Age (ca. 300–50 BCE). In this sense, the outer limits of the ruins have been identified using photogrammetry and convergent drone flights. An additional drone flight was conducted in the surrounding area to find additional data that could be used for more global interpretations. Magnetometry was used to analyze the underground bedrock structure and ground penetrating radar (GPR) was employed to evaluate the internal layout of the ruins. The combination of these digital methodologies (surface and underground) has provided a new perspective for the improved interpretation of “El Torreón” and its characteristics. Research of this type presents additional guidelines for better understanding of the role of this structure with regards to other buildings in the Ulaca oppidum. The results of these studies will additionally allow archaeologists to better plan future interventions while presenting new data that can be used for the interpretation of this archaeological complex on a larger scale.
Conformity of the NASADEM_HGT and ALOS AW3D30 DEM with the Altitude from the Brazilian Geodetic Reference Stations: A Case Study from Brazilian Cerrado
Authors: Bettiol Giovana Maranhão , Ferreira Manuel Eduardo , Motta Luiz Pacheco , Cremon Édipo Henrique , Sano Edson Eyji
Published: April 2021

The Brazilian Cerrado (tropical savanna) is the second largest biome in South America and the main region in the country for agricultural production. Altitude is crucial information for decision-makers and planners since it is directly related to temperature that conditions, for example, the climatic risk of rainfed crop plantations. This study analyzes the conformity of two freely available digital elevation models (DEMs), the NASADEM Merged Digital Elevation Model Global 1 arc second (NASADEM_HGT) version 1 and the Advanced Land Observing Satellite Global Digital Surface Model (ALOS AW3D30), version 3.1, with the altitudes provided by 1695 reference stations of the Brazilian Geodetic System. Both models were evaluated based on the parameters recommended in the Brazilian Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD), which defines error tolerances according to eight different scales (from 1:1000 to 1:250,000) and classes A (most strict tolerance, for example, 0.17 m for 1:1000 scale), B, C, and D (least strict tolerance, for example, 50 m for 1:250,000 scale). Considering the class A, the NASADEM_HGT meets 1:250,000 and lower scales, while AW3D30 meets 1:100,000 and lower scales; for class B, NASADEM_HGT meets 1:100,000 scale and AW3D30 meets 1:50,000. AW3D30 presented lower values of root mean square error, standard deviation, and bias, indicating that it presents higher accuracy in relation to the NASADEM_HGT. Within eight of Cerrado’s municipalities with the highest grain production, the differences between average altitudes, measured by the Cohen’s effect size, were statistically insignificant. The results obtained by the PEC-PCD for the Cerrado biome indicate that both models can be employed in different DEM-dependent applications over this biome.