Aircraft Landing Time Prediction with Deep Learning on Trajectory Images

Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway. In this study, a trajectory image-based deep learning method is proposed to predict ALTs for the aircraft entering the research airspace that covers the Terminal Maneuvering Area (TMA). Specifically, the trajectories of all airborne arrival aircraft within the temporal capture window are used to generate an image with the target aircraft trajectory labeled as red and all background aircraft trajectory labeled as blue. The trajectory images contain various information, including the aircraft position, speed, heading, relative distances, and arrival traffic flows. It enables us to use state-of-the-art deep convolution neural networks for ALT modeling. We also use real-time runway usage obtained from the trajectory data and the external information such as aircraft types and weather conditions as additional inputs. Moreover, a convolution neural network (CNN) based module is designed for automatic holding-related featurizing, which takes the trajectory images, the leading aircraft holding status, and their time and speed gap at the research airspace boundary as its inputs. Its output is further fed into the final end-to-end ALT prediction. The proposed ALT prediction approach is applied to Singapore Changi Airport (ICAO Code: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B) data from November 1 to November 30, 2022. Experimental results show that by integrating the holding featurization, we can reduce the mean absolute error (MAE) from 82.23 seconds to 43.96 seconds, and achieve an average accuracy of 96.1\%, with 79.4\% of the predictions errors being less than 60 seconds.Comment: In 2023 13th SESAR Innovation Days (SIDS2023

Decoding University Hierarchy and Prestige in China through Domestic Ph.D. Hiring Network

The academic job market for fresh Ph.D. students to pursue postdoctoral and junior faculty positions plays a crucial role in shaping the future orientations, developments, and status of the global academic system. In this work, we focus on the domestic Ph.D. hiring network among universities in China by exploring the doctoral education and academic employment of nearly 28,000 scientists across all Ph.D.-granting Chinese universities over three decades. We employ the minimum violation rankings algorithm to decode the rankings for universities based on the Ph.D. hiring network, which offers a deep understanding of the structure and dynamics within the network. Our results uncover a consistent, highly structured hierarchy within this hiring network, indicating the imbalances wherein a limited number of universities serve as the main sources of fresh Ph.D. across diverse disciplines. Furthermore, over time, it has become increasingly challenging for Chinese Ph.D. graduates to secure positions at institutions more prestigious than their alma maters. This study quantitatively captures the evolving structure of talent circulation in the domestic environment, providing valuable insights to enhance the organization, diversity, and talent distribution in China's academic enterprise

Genetic diversity and character association analysis based on pomological traits in olive (Olea europaea L.)

Thirteen exotic genotypes of olive (Olea europaea L.) were studied for the genetic variability, correlation and path coefficient analysis for fruit quality, yield and yield contributing traits at experimental farm of ICAR-CITH, Srinagar during 2009-2013. Maximum variability was recorded for fruit yield and oil content, however, low differ-ences between the phenotypic and genotypic coefficients of variations indicated low environmental influences on the expression of these characters. High heritability coupled with high genetic advance was obtained with fruit yield per plant, acidity, fruit pulp weight, fruit weight and stone weight. Fruit weight (r=0.329), stone weight (r=0.405) and oil content (r=0.841) were the most important traits, which possessed significant positive association with fruit yield per plant. Path coefficient analysis revealed that among the different yield contributing characters oil content (0.875), fruit weight (0.797) followed by acidity (0.501), peroxides value ( 0.199) and fruit length (0.054) influenced fruit yield per plant directly. The direct effects of these characters on fruit yield were found positive and considerably very high.The selection based on fruit weight, stone weight oil content and yield per plant will be effective for enhancing the fruit and oil yieldand making future olive breeding strategies

Association Studies of Regional Scientific and Technology Talent Coupled with the High-Tech Industry

Based on the theory of coupled systems, we use gray relational analysis to build a complex system that regional technology talent coupled with regional high-tech industry. We examine coupling relations of regional technology talent with the high-tech industry and analyze the law of the coupling of them. Results indicated (1) the degree of coupling of the regional technology talent and the high-tech industry system is relatively high, which has much close relationship. (2) China’s central and western provinces are mostly classified to low-level coupling and antagonistic stage, the degree of coupling of each regional technology talent and high-tech industry interact is significantly different, and we found that regional distribution has corresponding relationship with the level of economic development.Key words: Technology talent; High-tech industry; Coupling degree; Couplin

Talent hat, cross-border mobility, and career development in China

This study aims to investigate the influence of cross-border recruitment program in China, which confers scientists with a 'talent hat' including a startup package comprising significant bonuses, pay, and funding, on their future performance and career development. By curating a unique dataset from China's 10-year talent recruitment program, we employed multiple matching designs to quantify the effects of the cross-border recruitment with 'talent hat' on early career STEM scholars. Our findings indicate that the cross-border talents perform better than their comparable contenders who move without talent hats and those who do not move, given equivalent scientific performance before relocation. Moreover, we observed that scholars in experimental fields derive greater benefits from the talent program than those in non-experimental fields. Finally, we investigated how the changes in scientific environment of scientists affect their future performance. We found that talents who reassembled their collaboration network with new collaborators in new institutions after job replacement experienced significant improvements in their academic performance. However, shifting research directions entails risks, which results in a subsequent decrease of future productivity and citation impact following the relocation. This study has significant implications for young scientists, research institutions, and governments concerning cultivating cross-border talents

Non-syndromic enlarged vestibular aqueduct caused by novel compound mutations of the SLC26A4 gene: a case report and literature review

Enlarged vestibular aqueduct is an autosomal genetic disease mainly caused by mutations in the SLC26A4 gene and includes non-syndromic and syndromic types. This study aimed to identify genetic defects in a Chinese patient with non-syndromic enlarged vestibular aqueduct (NSEVA) and to investigate the impact of variants on the severity of non-syndromic enlarged vestibular aqueduct. A male patient with NSEVA, aged approximately 6 years, was recruited for this study. The clinical characteristics and results of auxiliary examinations, including laboratory and imaging examinations, were collected, and 127 common hereditary deafness genes were detected by chip capture high-throughput sequencing. Protein structure predictions, the potential impact of mutations, and multiple sequence alignments were analyzed in silico. Compound heterozygote mutations c.1523_1528delinsAC (p.Thr508Asnfs*3) and c.422T>C (p.Phe141Ser) in the SLC26A4 gene were identified. The novel frameshift mutation c.1523_1528delinsAC produces a severely truncated pendrin protein, and c.422T>C has been suggested to be a disease-causing mutation. Therefore, this study demonstrates that the novel mutation c.1523_1528delinsAC in compound heterozygosity with c.422T>C in the SLC26A4 gene is likely to be the cause of NSEVA. Cochlear implants are the preferred treatment modality for patients with NSEVA and severe-to-profound sensorineural hearing loss Genetic counseling and prenatal diagnosis are essential for early diagnosis. These findings expand the mutational spectrum of SLC26A4 and improve our understanding of the molecular mechanisms underlying NSEVA

Astragaloside IV, a Novel Antioxidant, Prevents Glucose-Induced Podocyte Apoptosis In Vitro and In Vivo

Glucose-induced reactive oxygen species (ROS) production initiates podocyte apoptosis, which represents a novel early mechanism leading to diabetic nephropathy (DN). Here, we tested the hypothesis that Astragaloside IV(AS-IV) exerts antioxidant and antiapoptotic effects on podocytes under diabetic conditions. Apoptosis, albuminuria, ROS generation, caspase-3 activity and cleavage, as well as Bax and Bcl-2 mRNA and protein expression were measured in vitro and in vivo. Cultured podocytes were exposed to high glucose (HG) with 50, 100 and 200 µg/ml of AS-IV for 24 h. AS-IV significantly attenuated HG-induced podocyte apoptosis and ROS production. This antiapoptotic effect was associated with restoration of Bax and Bcl-2 expression, as well as inhibition of caspase-3 activation and overexpression. In streptozotocin (STZ)-induced diabetic rats, severe hyperglycemia and albuminuria were developed. Increased apoptosis, Bax expression, caspase-3 activity and cleavage while decreased Bcl-2 expression were detected in diabetic rats. However, pretreatment with AS-IV (2.5, 5, 10 mg·kg−1·d−1) for 14 weeks ameliorated podocyte apoptosis, caspase-3 activation, renal histopathology, podocyte foot process effacement, albuminuria and oxidative stress. Expression of Bax and Bcl-2 mRNA and protein in kidney cortex was partially restored by AS-IV pretreatment. These findings suggested AS-IV, a novel antioxidant, to prevent Glucose-Induced podocyte apoptosis partly through restoring the balance of Bax and Bcl-2 expression and inhibiting caspase-3 activation

Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models

In the financial industry, credit scoring is a fundamental element, shaping access to credit and determining the terms of loans for individuals and businesses alike. Traditional credit scoring methods, however, often grapple with challenges such as narrow knowledge scope and isolated evaluation of credit tasks. Our work posits that Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks. To systematically explore LLMs for credit scoring, we propose the first open-source comprehensive framework. We curate a novel benchmark covering 9 datasets with 14K samples, tailored for credit assessment and a critical examination of potential biases within LLMs, and the novel instruction tuning data with over 45k samples. We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks. We evaluate CALM, and existing state-of-art (SOTA) open source and close source LLMs on the build benchmark. Our empirical results illuminate the capability of LLMs to not only match but surpass conventional models, pointing towards a future where credit scoring can be more inclusive, comprehensive, and unbiased. We contribute to the industry's transformation by sharing our pioneering instruction-tuning datasets, credit and risk assessment LLM, and benchmarks with the research community and the financial industry

Production of Gadolinium-loaded Liquid Scintillator for the Daya Bay Reactor Neutrino Experiment

We report on the production and characterization of liquid scintillators for the detection of electron antineutrinos by the Daya Bay Reactor Neutrino Experiment. One hundred eighty-five tons of gadolinium-loaded (0.1% by mass) liquid scintillator (Gd-LS) and two hundred tons of unloaded liquid scintillator (LS) were successfully produced from a linear-alkylbenzene (LAB) solvent in six months. The scintillator properties, the production and purification systems, and the quality assurance and control (QA/QC) procedures are described.Comment: 15 pages, 11 figures. Submitted to Nuclear Instruments and Methods in Physics Research Section