bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Broadband Circularly Polarized Antenna with Stable Gain Based on Square Slotted Ground
Authors: Li Mingshun , Song Wei , Sheng Xinqing 
Published: March 2023

Abstract
In this paper, a circularly polarized (CP) low cost, low profile antenna with broadband stable gain is proposed. The antenna consists of a grounded square slot with two inverted-L shaped microstrips, an asymmetrical Y-shaped microstrip radiator with a stub, and several fine tuning elements. The CP radiation is first realized by using the Y-shaped microstrip line, which incorporates a stub is to realize better impedance matching. Next, by utilizing two Inverted-L microstrips into the corners of the grounded slot, the axial ratio (AR) characteristic can be further improved. A wider AR bandwidth (ARBW) can be achieved by utilizing four rectangular strips as fine tuning elements. Simulation results show the proposed antenna demonstrates a radiation with a CP bandwidth of 50% and a stable gain with average value of 5.18 dBi.
Light Source Optimization of 3D Inner Contour Measurement System Based on Ring Beam Method
Authors: Ma Hua , Shi Zhendong , Bai Jinxi , Liu Lijia , Zhang lin 
Published: March 2023

Abstract
Ring beam method is a simple and efficient method for measuring three-dimensional internal contour. The whole three-dimensional inner contour is stitched from the contour lines of cross-sectional images collected during scanning. The contour image with uniform and moderate light intensity and fine ring band is beneficial to the calculation of contour line extraction algorithm. In practice, due to the quality of the ring beam and the modulation effect of the sample surface microstructure, the light band of the contour image has local widening, overexposure and speckle phenomena, which directly affect the contour extraction effect. The ring beam generation system is optimized here, which makes the whole system have better size adaptability and convenient adjustment. In addition, experiments results show that the measurement repeatability of the cross-sectional profile obtained after the light source optimization has been effectively improved.
The Misadjustment Error of a New Cylindrical Surface Testing Method
Authors: Xu Yuting , Guo Peiji 
Published: March 2023

Abstract
A cylindrical combination testing method is proposed in this paper. Three cylindrical mirrors are combined in pairs. By constructing a confocal optical system for interference testing to obtain combined wavefront aberration. The cylindrical surface errors are calculated by the combined wavefront aberration. The source of misalignment error is analyzed in detail and the mathematical model combined with the Chebyshev polynomial coefficients is established. That the misalignment error can be effectively removed by polynomial fitting is proved by simulation.
The Influence of Dissolved Organic Matter to the Seawater Refractive Index Using Optical Refractometer
Authors: Zhang Cong , Xiao Yuyang , Fu Yujie , Su Juan , Wu Chi , Yu Liping , Liu Qiang 
Published: March 2023

Abstract
Optic refractive index is a good proxy for in situ salinity measurement of seawater according to TEOS-10. Currently, salinity of seawater is estimated by practical salinity (SP), which is calculated by measuring electric conductivity, temperature and pressure using Conductivity-Temperature-Depth (CTD) sensors. However, the electric conductivity method only takes into account the conductive compositions to seawater salinity and ignores the contribution of non-ionic components. However, non-ionic components in seawater affect seawater salinity according to the definition of salinity of seawater, but they could not be measured by conductivity sensor. Optical refractive index is sensitive to all dissolved matters of seawater including ionic and non-ionic components. Here, we take the dissolved organic matter (DOM) extracted from Aoshan Bay seawater as the representative sample to experimentally study the contribution of the non-ionic component to the refractive index of the standard seawater. Seawater refractive indices with different DOM concentrations were measured by a home-made V-shaped groove refractometer. The experimental results show that refractive index of seawater increases linearly with increasing DOM concentration at rate of 1.19 × 10-4/1(g/kg) DOM.
Reversible coherence conversion across optical degrees-of-freedom: a tutorial
Authors: Harling Mitchell , Kelkar Varun , Abouraddy Ayman F , Toussaint Jr Kimani C 
Published: March 2023

Abstract
This work introduces the concept and experimentation of reversible optical coherence conversion, and outlines optical coherency matrix tomography as a method for measuring the full coherence of a field. To date, coherence conversion has only been demonstrated between the spatial and polarization degrees-of-freedom (DoFs). Coherence conversion offers a new control over an optical field’s DoFs—protecting against deleterious scrambling effects.
Depth resolved imaging by digital holography via sample-shifting
Authors: Jiang Zhilong , Veetil Suhas P , He Xiaoliang , Sun Aihui , Kong Yan , Liu Cheng , Zhu Jianqiang 
Published: March 2023

Abstract
Depth resolved imaging is achieved by evenly displacing the sample in the lateral direction using a standard digital holographic setup. A series of holograms are recorded in the reflection geometry while the sample is evenly moved and an algorithm is developed to selectively construct the complex amplitude of the sample at specific focal depths. The amount of information needed for reconstruction is significantly decreased by the recommended method. The suggested approach is suitable for three-dimensional imaging applications, particularly for high transmittance materials, according to experimental findings obtained using a basic holographic setup.
Electrospinning with curved nozzle spinneret for producing poly (vinyl Alcohol) nanomembrane for filtration
Authors: Menaka T , Srinivasan Andy 
Published: March 2023

Abstract
To produce a consistent and fine nanofiber with a higher yield, electrospinning with a redesigned spinneret has been used. The principle that a curved or sharp edge activates a greater electric field intensity that stretches the jet into a thinner and denser electro spinning nanoweb is the subject of this research. In the electrospinning process, a curved nozzle spinneret outperforms a circular nozzle spinneret and a standard needle type in terms of electric field intensity and dispersion. It has been discovered that a high electric field intensity causes a 30% reduction in fiber diameter, the development of a denser fiber web, and an increase in production rate up to 280 mg h−1.For testing as an air filtration material, the electro-spun nano web of Poly (vinyl alcohol) (PVA) is accumulated as a membrane and sandwiched with polypropylene nonwoven fabric. The nonwoven membrane can filter particles down to 2–3 microns, whereas the sandwiched PVA nano-fiber can filter particles into nanometers. This research could lead to the low-cost manufacture of Nano-membranes using a simple electrospinning setup.
The waste separation behaviour of primary and middle school students and its influencing factors: Evidence from Yingtan City, China
Authors: Zhang Dongyong , Chen Jingjing , Liu Lingyi , Hao Mengge , Morse Stephen 
Published: March 2023

Abstract
Waste separation at source has been proved to be an effective way to reduce the amount of municipal solid waste (MSW) which has become a major challenge to China’s ecological environment. However, waste source separation requires effort from each individual citizen. As the important drivers of change and potential influencers of the future world, younger Chinese’s waste separation behaviour is crucial to the long-term successful implementation of China’s MSW separation policy. To explore the waste separation behaviour of younger Chinese and identify the factors that may influence their behaviour so as to better encourage younger generation of Chinese to practice waste sorting in their daily lives, a questionnaire survey of 579 primary and middle school (PMS) students aged between 6 and18 years old (y/o) was carried out in Yingtan City, Jiangxi Province, China. Binary logistic regression was adopted to explore the factors that might influence the respondents’ waste separation behaviour. The results indicate that more than half PMS students in Yingtan have participated in waste separation, and junior year students perform better in waste separation practice than their seniors. Students are found to have basic knowledge of MSW classification, but they are more familiar with recyclable waste and hazardous waste than non-recyclable waste. The analysis also highlights positive relationships between PMS students’ attitude to waste separation, their willingness to do it, their environmental education and their waste separation behaviour. The level of convenience of waste sorting facilities and influences from friends and families are also positively related to the students’ waste separation practice, but families have the strongest influence. The perception of a mandatory waste separation policy would demotivate students in terms of waste separation practice, while giving rewards is considered to be the most effective approach to encouraging waste separation. Finally, management strategies for improving PMS students’ waste separation behaviour are discussed and several recommendations for improvement are made.
Assessing stormwater control measure inventories from 23 cities in the United States
Authors: Choat Benjamin , Pulido Amber , Bhaskar Aditi S , Hale Rebecca L , Zhang Harry X , Meixner Thomas , McPhillips Lauren , Hopkins Kristina , Cherrier Jennifer , Cheng Chingwen 
Published: March 2023

Abstract
Since the 1987 Clean Water Act Section 319 amendment, the US Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 US cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman’s correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g. impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the Environmental Protection Agency tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages.
Traffic-Related Air Pollution and Ultrasound Parameters of Fetal Growth in Eastern Massachusetts
Authors: Leung Michael , Modest Anna M , Hacker Michele R , Wylie Blair J , Wei Yaguang , Schwartz Joel , Iyer Hari S , Hart Jaime E , Coull Brent A , Laden Francine , Weisskopf Marc G , Papatheodorou Stefania 
Published: March 2023

Abstract
Previous studies have examined the association between prenatal nitrogen dioxide (NO2)—a traffic emissions tracer—and fetal growth based on ultrasound measures. Yet, most have used exposure assessment methods with low temporal resolution, which limits the identification of critical exposure windows given that pregnancy is relatively short. Here, we used NO2 data from an ensemble model linked to residential addresses at birth to fit distributed lag models that estimated the association between NO2 exposure (resolved weekly) and ultrasound biometric parameters in a Massachusetts-based cohort of 9,446 singleton births from 2011–2016. Ultrasound biometric parameters examined included biparietal diameter (BPD), head circumference, femur length, and abdominal circumference. All models adjusted for sociodemographic characteristics, time trends, and temperature. We found that higher NO2 was negatively associated with all ultrasound parameters. The critical window differed depending on the parameter and when it was assessed. For example, for BPD measured after week 31, the critical exposure window appeared to be weeks 15–25; 10-parts-per-billion higher NO2 sustained from conception to the time of measurement was associated with a lower mean z score of −0.11 (95% CI: −0.17, −0.05). Our findings indicate that reducing traffic emissions is one potential avenue to improving fetal and offspring health.
Comparison of Bayesian Spatiotemporal Models for Small-Area Life Expectancy: A Simulation Study
Authors: Kim Ikhan , Kang Hee-Yeon , Khang Young-Ho 
Published: March 2023

AbstractThe purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatiotemporal models. We hypothesized 6 scenarios in which all 247 districts of South Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013–2017). We generated 1,000 hypothetical data sets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between 2 Bayesian spatiotemporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cutoff value for the comparisons. The Bayesian spatiotemporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatiotemporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cutoff value that minimizes false-positive and false-negative rates. We propose 0.8 as a cutoff value for determining the statistical significance of the difference in life expectancy.
India’s tryst with the group of companies doctrine: the end or the beginning of a new dawn?
Authors: Singh Vijayendra Pratap , Jha Abhijnan , Vidyarthi Abhisar 
Published: March 2023

Abstract
Recently, in Cox & Kings Ltd. v. SAP India (P) Ltd (2022), the Supreme Court of India called into question the doctrinal ingredients and applicability of the group of companies doctrine in India. The Apex Court doubted the correctness of its consistent line of earlier decisions, wherein the group of companies doctrine had been strongly embraced as a tool to bind non-signatories to arbitration. Holding that the group of companies doctrine appears to have been based more on economics and convenience rather than law, the Supreme Court has referred several questions regarding the validity and applicability of the doctrine in India to a larger bench for consideration. The purpose of this case note is to summarize and comment on the judgment of the Supreme Court. The case note critically analyses the concerns and questions raised by the Supreme Court. In doing so, the case note concludes that there is sufficient basis for the existence and application of the group of companies doctrine under the [Indian] Arbitration and Conciliation Act, 1996.
Analysis of super-enhancer using machine learning and its application to medical biology
Authors: Hamamoto Ryuji , Takasawa Ken , Shinkai Norio , Machino Hidenori , Kouno Nobuji , Asada Ken , Komatsu Masaaki , Kaneko Syuzo 
Published: March 2023

Abstract
The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.
MetaHMEI: meta-learning for prediction of few-shot histone modifying enzyme inhibitors
Authors: Lu Qi , Zhang Ruihan , Zhou Hongyuan , Ni Dongxuan , Xiao Weilie , Li Jin 
Published: March 2023

Abstract

Motivation
Histones are the chief protein components of chromatin, and the chemical modifications on histones crucially influence the transcriptional state of related genes. Histone modifying enzyme (HME), responsible for adding or removing the chemical labels, has emerged as a very important class of drug target, with a few HME inhibitors launched as anti-cancerous drugs and tens of molecules under clinical trials. To accelerate the drug discovery process of HME inhibitors, machine learning-based predictive models have been developed to enrich the active molecules from vast chemical space. However, the number of compounds with known activity distributed largely unbalanced among different HMEs, particularly with many targets of less than a hundred active samples. In this case, it is difficult to build effective virtual screening models directly based on machine learning.

Results
To this end, we propose a new Meta-learning-based Histone Modifying Enzymes Inhibitor prediction method (MetaHMEI). Our proposed MetaHMEI first uses a self-supervised pre-training approach to obtain high-quality molecular substructure embeddings from a large unlabeled chemical dataset. Then, MetaHMEI exploits a Transformer-based encoder and meta-learning framework to build a prediction model. MetaHMEI allows the effective transfer of the prior knowledge learned from HMEs with sufficient samples to HMEs with a small number of samples, so the proposed model can produce accurate predictions for HMEs with limited data. Extensive experimental results on our collected and curated HMEs datasets show that MetaHMEI is better than other methods in the case of few-shot learning. Furthermore, we applied MetaHMEI in the virtual screening process of histone JMJD3 inhibitors and successfully obtained three small molecule inhibitors, further supporting the validity of our model.
Scrooge: a fast and memory-frugal genomic sequence aligner for CPUs, GPUs, and ASICs
Authors: Lindegger Joël , Senol Cali Damla , Alser Mohammed , Gómez-Luna Juan , Ghiasi Nika Mansouri , Mutlu Onur 
Published: March 2023

Abstract

Motivation
Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations, and/or hardware acceleration. A promising candidate for all of the above is the recently proposed GenASM algorithm. We identify and address three inefficiencies in the GenASM algorithm: it has a high amount of data movement, a large memory footprint, and does some unnecessary work.

Results
We propose Scrooge, a fast and memory-frugal genomic sequence aligner. Scrooge includes three novel algorithmic improvements which reduce the data movement, memory footprint, and the number of operations in the GenASM algorithm. We provide efficient open-source implementations of the Scrooge algorithm for CPUs and GPUs, which demonstrate the significant benefits of our algorithmic improvements. For long reads, the CPU version of Scrooge achieves a 20.1×, 1.7×, and 2.1× speedup over KSW2, Edlib, and a CPU implementation of GenASM, respectively. The GPU version of Scrooge achieves a 4.0×, 80.4×, 6.8×, 12.6×, and 5.9× speedup over the CPU version of Scrooge, KSW2, Edlib, Darwin-GPU, and a GPU implementation of GenASM, respectively. We estimate an ASIC implementation of Scrooge to use 3.6× less chip area and 2.1× less power than a GenASM ASIC while maintaining the same throughput. Further, we systematically analyze the throughput and accuracy behavior of GenASM and Scrooge under various configurations. As the best configuration of Scrooge depends on the computing platform, we make several observations that can help guide future implementations of Scrooge.

Availability and implementation
https://github.com/CMU-SAFARI/Scrooge.
Linear diophantine uncertain linguistic-based prospect theory approach for performance evaluation of islanded microgrid-system scenarios
Authors: Singh Sweta , Kanwar Neeraj , Zindani Divya 
Published: March 2023

AbstractRenewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals, i.e. ‘Affordable and clean energy’. However, experts may be faced with the challenge of selecting the best one for the electrification of an area. To avoid the challenge and realize the ultimate goal of the United Nations, the present study, therefore, proposes a novel prospect theory-based decision-making approach to help experts in opting for the best microgrid scenario. The proposed decision-making framework considers the risk appetite of the decision-maker, a quintessential aspect of the process. Linear diophantine uncertain linguistic sets are used to model the linguistic evaluations from the experts. The information from different experts is aggregated using a linear diophantine uncertain linguistic power Einstein-weighted geometric operator. Finally, the prospect-theory-based TOmada de Decisao Interativa Multicriterio approach is employed to evaluate the performance of the available microgrid scenarios and hence opt for the best microgrid scenario. The proposed framework has been used to evaluate the performance of seven possible microgrid scenarios and hence select the best one that can be implemented for rural electrification of a remote village in Assam, India. The microgrid scenario consisting of a photovoltaic–wind turbine–fuel cell–battery converter (MG3) has been revealed to be the best scenario among the seven considered microgrid scenarios. The validity of the obtained ranking results has been adjudged through a comprehensive evaluation regarding the attenuation factor and the weights of the criteria. Moreover, previous case studies have also been solved using the proposed methodology and the results reveal a good correlation between the obtained ranking results.