bluuebunny/crossref_metadata_2025_split huggingface dataset:
PIONEER, a high-resolution single-crystal polarized neutron diffractometer
Authors: Liu Yaohua , Cao Huibo , Rosenkranz Stephan , Frost Matthew , Huegle Thomas , Lin Jiao Y. Y. , Torres Peter , Stoica Alexandru , Chakoumakos Bryan C. 
Published: July 2022

PIONEER is a high Q-resolution, single-crystal, polarized neutron diffractometer at the Second Target Station (STS), Oak Ridge National Laboratory. It will provide the unprecedented capability of measuring tiny crystals (0.001 mm3, i.e., x-ray diffraction size), ultra-thin films (10 nm thickness), and weak structural and magnetic transitions. PIONEER benefits from the increased peak brightness of STS cold-neutron sources and uses advanced Montel mirrors that are able to deliver a focused beam with a high brilliance transfer, a homogeneous profile, and a low background. Monte Carlo simulations suggest that the optimized instrument has a high theoretical peak brilliance of 2.9 × 1012 n cm−2 sr−1 Å−1 s−1 at 2.5 Å at the sample position, within a 5 × 5 mm2 region and a ±0.3° divergence range. The moderator-to-sample distance is 60 m, providing a nominal wavelength band of 4.3 Å with a wavelength resolution better than 0.2% in the wavelength range of 1.0–6.0 Å. PIONEER is capable of characterizing large-scale periodic structures up to 200 Å. With a sample-to-detector distance of 0.8 m, PIONEER accommodates various sample environments, including low/high temperature, high pressure, and high magnetic/electric field. A large cylindrical detector array (4.0 sr) with a radial collimator is planned to suppress the background scattering from sample environments. Bottom detector banks provide an additional 0.4 sr coverage or can be removed if needed to accommodate special sample environments. We present virtual experimental results to demonstrate the scientific performance of PIONEER in measuring tiny samples.
Potential use of random and linked SSR markers in establishing the true heterotic pattern in maize (
Authors: Kumar Sumit , Das Abhijit Kumar , Naliath Ritu , Kumar Ramesh , Karjagi Chikkappa G. , Sekhar Javaji C. , Vayas Mukesh , Yathish K. R. , Singh Alla , Mukri Ganapati , Rakshit Sujay 
Published: July 2022

Context Establishment of true heterotic pattern in maize germplasm can increase the efficiency of hybrid breeding. Heterosis is dependent on the genetic diversity of parents and the extent of dominance at different loci. Estimation of genetic diversity through use of molecular markers is routine practice in maize breeding. Aims The present study was designed to test whether simple sequence repeat (SSR) markers linked to yield-contributing traits are more reliable for heterotic grouping than random SSRs. Methods Diallel crosses developed among 19 inbred lines were evaluated at multi-locations. The genotypes were also grouped using polymorphic random (50) and linked (47) SSRs. Key results The crosses generated with lines belonging to different heterotic groups of linked SSR markers did not reveal any superiority over the crosses of the diallel set. By contrast, mean performance of inter-heterotic group crosses generated on the basis of random markers was superior to that of intra-heterotic crosses. Specific combining ability effects did not reveal any significant association with genetic distance of random or linked markers. Conclusions The lack of improved efficiency of linked markers over random markers can be attributed to factors including the quantitative nature of the trait, genotype × environment interactions, genetic background of germplasm in which the markers are expressed, and multiple alleles. Implications Markers linked to yield-contributing traits are no more reliable for heterotic grouping than random markers.
EGCG-coated silver nanoparticles self-assemble with selenium nanowires for treatment of drug-resistant bacterial infections by generating ROS and disrupting biofilms
Authors: Yang Chenhao , Wang Zekun , Gao Yue , Li Man , Li Yuqing , Dai Chunxue , Wang Yunsheng , Sun Dongdong 
Published: July 2022

Abstract
Bacterial infections pose a serious threat to human health, and the development of new antibiotics has not kept pace with the development of bacterial resistance. Therefore, there is an urgent need to design antibiotic-like nano-formulations that break through bacterial resistance mechanisms. In this work, we successfully synthesized a safe and effective antibacterial nano-formulation of Se@Ag@EGCG by self-assembly of epigallocatechin gallate (EGCG)-coated silver nanoparticles (Ag) on the surface of selenium nanowires (Se). The in vitro bacteriostatic results showed that 40 μg ml−1 Se@Ag@EGCG had significant antibacterial activity against drug-resistant Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) by destroying the formation of bacterial biofilm, promoting the production of high concentration reactive oxygen species and destroying bacterial cell wall. In addition, the results of in vivo antibacterial experiments showed that subcutaneous administration of 10 mg kg−1 of Se@Ag@EGCG could promote wound healing by reducing apoptosis and inflammatory responses in infected wounds. It is worth mentioning that the reduced and modified Se@Ag@EGCG by this natural product has negligible in vivo toxicity. This development strategy of nano-antibacterial materials, which breaks through the drug resistance mechanism, provides new ideas for the development of drugs for drug-resistant bacterial infections.
Small-world disordered lattices: spectral gaps and diffusive transport
Authors: Rosa Matheus I N , Ruzzene Massimo 
Published: July 2022

Abstract
We investigate the dynamic behavior of lattices with disorder introduced through non-local network connections. Inspired by the Watts–Strogatz small-world model, we employ a single parameter to determine the probability of local connections being re-wired, and to induce transitions between regular and disordered lattices. These connections are added as non-local springs to underlying periodic one-dimensional (1D) and two-dimensional (2D) square, triangular and hexagonal lattices. Eigenmode computations illustrate the emergence of spectral gaps in various representative lattices for increasing degrees of disorder. These gaps manifest themselves as frequency ranges where the modal density goes to zero, or that are populated only by localized modes. In both cases, we observe low transmission levels of vibrations across the lattice. Overall, we find that these gaps are more pronounced for lattice topologies with lower connectivity, such as the 1D lattice or the 2D hexagonal lattice. We then illustrate that the disordered lattices undergo transitions from ballistic to super-diffusive or diffusive transport for increasing levels of disorder. These properties, illustrated through numerical simulations, unveil the potential for disorder in the form of non-local connections to enable additional functionalities for metamaterials. These include the occurrence of disorder-induced spectral gaps, which is relevant to frequency filtering devices, as well as the possibility to induce diffusive-type transport which does not occur in regular periodic materials, and that may find applications in dynamic stress mitigation.
Optimization and implementation of a surface-electrode ion trap junction
Authors: Zhang Chi , Mehta Karan K , Home Jonathan P 
Published: July 2022

Abstract
We describe the design of a surface-electrode ion trap junction, which is a key element for large-scale ion trap arrays. A bi-objective optimization method is used for designing the electrodes, which maintains the total pseudo-potential curvature while minimizing the axial pseudo-potential gradient along the ion transport path. To facilitate the laser beam delivery for parallel operations in multiple trap zones, we implemented integrated optics on each arm of this X-junction trap. The layout of the trap chip for commercial foundry fabrication is presented. This work suggests routes to improving ion trap junction performance in scalable implementations. Together with integrated optical addressing, this contributes to modular trapped-ion quantum computing in interconnected two-dimensional arrays.
Geochemical evolution of basaltic flows from Dongargarh Supergroup, Bastar Craton, Central India
Authors: Haidar Tanveer , Misra Sagar , Vishwakarma Neeraj , Hari K. R. 
Published: July 2022

Abstract
Composition of basalts in magmatic arcs influenced by the subducting lithosphere, mantle wedge, dehydration of oceanic crust, and/or crustal assimilation beneath the arc. In this paper, we compiled earlier published geochemical data of Dongargarh basalts to decipher the genesis of volcanic rocks. SiO2 vs (FeO + MgO) plot of basalt suggests the volcanic rocks are tholeiitic in composition. Primitive mantle and REE normalized plots indicate either the source was enriched mantle or a possible interaction of depleted magmatic source with the Paleoarchean continental crust in the Bastar Craton. The primitive mantle normalized diagram shows a negative anomaly of Nb, Ti, and Ta indicates subduction-related magmatism. In addition to the basalt composition, variation diagrams for tectonic settings represent the continental arc-related magmatism. From the available geochemical data of basalts and earlier studies on Dongargarh volcanic, there was an oceanic ridge that was subducted beneath the continental plate. The source of Pitepani basalts was significantly enriched in HFSE and REE as compared to mid-oceanic basalts. Thus the study finds the volcanic rocks are part of enriched mantle source that formed in the subduction-related magmatism.
Channel Migration and Consequential Land Use Land Cover Changes of Subansiri River, Assam, North-Eastern India
Authors: Acharjee Shukla , Das Rajib Tarani , Shekhar Shubhendu , Singh Neelratan , Goswami Uttam , Dilini M.M.G.S. , Mawale Y.K. , Iyengar B.K. , Negi R S 
Published: July 2022

Abstract
Subansiri River is the largest tributary of the Brahmaputra River running through the Indian states of Assam and Arunachal Pradesh, and Tibet, the Autonomous Region of China. The Subansiri River is 442 km long with a drainage basin of 32,640 km2 and it contributes approximately 7.92% of the Brahmaputra’s total flow. Sequential Channel shifting has been witnessed as the most important characteristic of the Subansiri River of Assam. The detailed study on channel migration of the present course of the Subansiri River through the upper floodplain of Brahmaputra valley indicates that the area is under active erosion for a long time. Therefore, an attempt has been made to understand the relationship between the rate of channel migration and successive land use/land cover changes in its surrounding floodplain area. The Support Vector Machine (SVM) and the Artificial Neural Network (ANN) algorithms are applied on Landsat images of the years 1973, 1988, 2001, and 2017 for generating land use/land cover maps through supervised classification technique. The overall accuracy of the land use/land cover classification ranges between 81% (for the year 1988) and 84% (for the year 2017). The land use/land cover maps show an increase in the built-up area and a decrease in the agricultural area. The change has been observed vis-a-vis channel migration indicating that the migration directly affects the floodplain habitats which in turn affects the land use, Findings of this study highlight geomorphological instabilities of the study area and the vulnerability of the habitations residing near the Subansiri river.
Rice Leaf Disease Classification Using Cnn
Authors: Tejaswini Pallapothala , Singh Priyanshi , Ramchandani Monica , Rathore Yogesh Kumar , Janghel Rekh Ram 
Published: July 2022

Abstract
Rice is amongst the majorly cultivated crops in India and its leaf diseases can have a substantial impact on output and quality. The most important component is identifying rice leaf diseases, which have a direct impact on the economy and food security. Brown spot, Leaf Blast, Hispa are the most frequently occurring rice leaf diseases. To resolve this issue, we have studied various machine learning and deep learning approaches for detecting the diseases on their leaves by calculating their accuracy, recall, and precision to measure the performance. This study helps the farmers by detecting the diseases in rice leaves in order to get a healthy crop yield. The deep learning models perform well when compared with the machine learning methods. After analyzing all of the deep learning models, we found that the 5-layer convolution model had the best accuracy of 78.2 %, while others, such as VGG16, had a lower accuracy of 58.4%.
Study of effects of some metals and hydraulic retention time on performance of AMD treatment by SAPS
Authors: Patel M. D. 
Published: July 2022

Abstract
Acid mine drainage (AMD) is presently observed as one of the major water pollution problems in mining area throughout the world. Proper scientific management of AMD is essential for achieving the national goals for environmental norms. In this studyperformance of AMD treatment by laboratory successive alkalinity producing system (SAPS) by observing the effects of some metals and hydraulic retention time. In this study the experiments were conducted in laboratory successive alkalinity producing system (SAPS) with different synthetic AMDs for five Hydraulic retention times(HRTs). The successive alkalinity producing system shown the promising results during the AMD treatment. The effects of iron, aluminium and manganese were jointly investigated, whichshowed that Net alkalinity generation (NAG) increases with increase in influent metal contents. It was also observed thatNAG also showed increasing tendency with HRT during AMD treatment by SAPS. The obtained findings from this study can be applied for efficient design and operation of successive alkalinity producing system for AMD treatment process in mining industry.
GIS-based Industrial Land Suitability Analysis for locating Industrial Parks in Raipur and Nava Raipur
Authors: Baghel Ankur 
Published: July 2022

Abstract
This paper offers an integrated approach towards the preparation of a mechanism for allocation of land for the industrial parks assessing the land potential for different uses to regulate and manage the use of land and improve the surrounding environment. An Industrial land Suitability Analysis has been carried out to allocate suitable land parcels for industrial development. An Analytical Hierarchy Process (AHP) with the integration of Geographic Information System based Multi-criterion decision-making (MCDM) was adopted for weighing the parameters. With the pairwise comparison matrix, the establishment of the weights was carried out and based on obtained weights, overlaying was carried out in the GIS interface to generate outputs for the most suitable and least suitable areas for industrial land use with a synthesized suitability map. The final results showed that the prominent locations for future industrial development came mostly near roads and railway routes. Since the utilization of vacant and wastelands with preservation of agricultural fields were prime concerns while deciding on the ranking of the criteria, the suitability results showed lesser agricultural areas than in other areas.
InSAR Data Analysis using Deep Neural Networks
Authors: Agrawal Ayush Kumar , Verma Himanshu , Pasari Sumanta 
Published: July 2022

Abstract
Among all natural disasters, earthquakes cause the most severe devastation to both infrastructure as well as human lives. The pattern of surface deformation from satellite-based measures, such as interferometric synthetic aperture radar (InSAR), helps understand crustal dynamics and associated seismic hazards in tectonically active areas. In the last few years, the exponentially rising volume of InSAR data has led to the formulation of automatic crustal deformation algorithms through data-intensive machine learning models. In this study, we propose the long-short-term memory (LSTM) architecture of deep neural networks to forecast InSAR-based line-of-sight displacement. The model suitably captures the inherent temporal variations of SAR interferograms. The method implementation on a region near Central and East Java, Indonesia shows satisfactory performance (mean absolute percentage error around 2%) of the proposed LSTM model comprising four layers, each consisting of 100 nodes. We therefore conclude that deep neural network is a promising avenue for InSAR time series analysis.
Various Types of Constructed Wetland for Wastewater Treatment-A Review
Authors: Swarnakar Arvind Kumar , Bajpai Samir , Ahmad Ishtiyaq 
Published: July 2022

Abstract
As per the World Health Organization 80% of wastewater is released to the environment without satisfactory treatment. Constructed Wetlands (CWs) are one of the natural wastewater (WW) treatment methods. CWs have been recommended as a low technology, low maintenance, low operation cost, and green technology wastewater treatment system. Many types of CWs are currently in use. This paper studies these different types based on the climate, area, base materials, temperature, contaminant removal efficiency, removal mechanism and physicochemical analysis of various wastewater parameters. It is found that wetlands are successful in removing organic matter–Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD) and suspended solids and nutrients–total Nitrogen (TN) and total Phosphorus (TP). CWs need to be studied as a promising solution not only for effective treatment of wastewater but also as an economical method to improve the fertility of soil. Further, the paper discusses the scope of future research in CW to further improve the wastewater treatment technology.
Retrieval of aerosols extinction coefficient from calipso satellite observations: a case study over Bhubaneswar
Authors: Sasmita Sushree , Kumar Dudam Bharath 
Published: July 2022

AbstractIn order to examine the effects of aerosols on climate, knowledge on vertical distribution of aerosol in the lower atmosphere is essential. We analysed aerosol extinction coefficient obtained from the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) on board Lidar at campus-3 of KIIT-DU Bhubaneswar during July 2018 to June 2019. Seasonal average (± standard deviation) of extinction coefficient for the winter (December-February: DJF), summer (March-May: MAM), monsoon (June-August: JJA) and post-monsoon (September-November: SON) are found to be 0.22±0.13, 13.08±0.07, 0.27±0.05, 0.15±0.14 km-1, respectively. To differentiate the aerosol loading between the surface and elevated layer, we averaged extinction values for below and above 1km height. At surface level, the values were found to be maximum in post monsoon (0.54±0.13 km-1) and minimum (0.14±0.03 km-1) in winter. Whereas at elevated level, the values exhibit maximum in monsoon (0.45±0.45 km-1) and minimum (0.11±0.02 km-1) in post-monsoon. The vertical profile of aerosol can be used to anticipate aerosol extinction simulations over a variety of time periods and in regions where the CALIOP cannot reach.
Statistical analysis and ANN modelling of tribological performance of aluminium hybrid composites fabricated by PM method
Authors: Hussain Mohd Azmal , Parveen Aasiya , Chauhan Nathi Ram , Suhaib Mohd , Waseem Mohammad 
Published: July 2022

Abstract
This study represents the wear behaviour of the aluminium hybrid composites developed through powder metallurgy. The pure Al-matrix powder was blended with Si3N4/ZrO2 ceramic powders in a high-centrifugal ball mill and then compacted at three different compaction pressure. The sintering of the composites was done in an environment-controlled tube furnace at 500 °C for 1 h. The effect of wt% of the reinforcement, compaction pressure and applied load was investigated on the wear results performed on the pin-on-disc setup. The Analysis of variance (ANOVA) and artificial neural network (ANN) was used for the wear loss results prediction. The ANOVA results revealed that load is a highly influential parameter for wear loss followed by reinforcement and compaction pressure. Furthermore, an ANN model using feed-forward backpropagation analysis was built which found that the predicted and experimental results are close.
New horizons in late-onset essential tremor: a pre-cognitive biomarker of dementia?
Authors: Wang Xinyi , St George Rebecca J , Bai Quan , Tran Son , Alty Jane 
Published: July 2022

Abstract
Essential tremor (ET) is the most common cause of tremor in older adults. However, it is increasingly recognised that 30–50% of ET cases are misdiagnosed. Late-onset ET, when tremor begins after the age of 60, is particularly likely to be misdiagnosed and there is mounting evidence that it may be a distinct clinical entity, perhaps better termed ‘ageing-related tremor’. Compared with older adults with early-onset ET, late-onset ET is associated with weak grip strength, cognitive decline, dementia and mortality. This raises questions around whether late-onset ET is a pre-cognitive biomarker of dementia and whether modification of dementia risk factors may be particularly important in this group. On the other hand, it is possible that the clinical manifestations of late-onset ET simply reflect markers of healthy ageing, or frailty, superimposed on typical ET. These issues are important to clarify, especially in the era of specialist neurosurgical treatments for ET being increasingly offered to older adults, and these may not be suitable in people at high risk of cognitive decline. There is a pressing need for clinicians to understand late-onset ET, but this is challenging when there are so few publications specifically focussed on this subject and no specific features to guide prognosis. More rigorous clinical follow-up and precise phenotyping of the clinical manifestations of late-onset ET using accessible computer technologies may help us delineate whether late-onset ET is a separate clinical entity and aid prognostication.
Prescribing cascades: we see only what we look for, we look for only what we know
Authors: O’Mahony Denis , Rochon Paula A 
Published: July 2022

Abstract
Prescribing cascades are increasingly recognized since they were described in the mid-1990s. Cascades are more likely in older people with multimorbidity and associated polypharmacy where multiple medications can induce a variety of side effects that manifest with various non-specific symptoms that may be misidentified as new geriatric syndromes such as falls, dizziness and new-onset incontinence. Geriatricians encounter medication side effects frequently and will usually consider if an older patient presenting with new symptoms could be experiencing an adverse drug reaction or event. However, most medications prescribed to multimorbid older patients are initiated and continued by prescribers without specialist geriatric training who may not detect medication-induced morbidity. Therefore, novel approaches to the detection and management of prescribing cascades in older people are needed. Currently, the knowledge base surrounding prescribing cascades in older people is evolving towards better methods for cascade detection and secondary prevention. However, the large number of cascades described in the literature, the wide-ranging symptomatology of cascades and the rapidly increasing number of multimorbid older people at risk of cascades represent major challenges for prescribers. Furthermore, prospective prevalence studies of prescribing cascades in older people are lacking. To detect and correct prescribing cascades during routine medication review in multimorbid older people, awareness of cascades is essential. Prescribing cascade awareness in turn requires novel explicit ways of defining cascades to facilitate their rapid detection and correction during medication review. Given that prescribing cascades represent another aspect of inappropriate prescribing (IP), explicit cascades criteria should be integrated with other explicit IP criteria.