bluuebunny/crossref_metadata_2025_split huggingface dataset:
Image compression and reconstruction based on GUI and Huffman coding
Authors: Zhan Honghui 
Published: September 2023

Abstract
Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.
Deep learning-based facial emotion recognition exploiting self-supervised pre-training and feature pyramid
Authors: Li Jingyang , Lin Zhihui , Ruan Huihao , Wang Jiacheng 
Published: September 2023

Abstract
Facial emotion recognition (FER), a technology designed to automatically identify the emotional state of an individual based on the features of his face, is a thriving area in human-computer interaction and affective computing. Among all the FER techniques, the deep learning model, especially the convolutional neural network is more successful. In this paper, various networks are tested firstly with different optimizers based on the VGG baseline. After that, according to the results of the test, it seems that VGG19 is the best choice for the backbone network. Following that, a feature pyramid network added to the backbone model is considered to improve the model, which, however, ends up with even worse accuracy. To find another way of improvement, the attention is thrown to the unsupervised contrastive model, which is built based on the momentum contrast with VGG19 being the backbone network, culminating in an increase in the accuracy from 71.94% to 72.11% in the end.
Context-aware for predicting gestational diabetes using rule – based system
Authors: Deeba K , Sathishkumar V E , Maheshwari V , Prasanna M , Sukumar R 
Published: September 2023

Abstract
Nanotechnology seems to be an important scientific method for the diagnosis of a disorder that offers more precise and appropriate health information. Diabetes mellitus (DM) is a chronic condition frequently seen that severely affects human health. Gestational diabetes happens during gestation or gestational time. Gestational diabetes may arise from normal physical changes in the body experiences of a pregnant woman.. The placenta develops substances during pregnancy that conflict with just the enzyme insulin’s behavior. The Principle objective of this paper is to predict gestational diabetes using the rule-based system by creating a user profile with AbA1c. This research can be applied to a broader range of healthcare diagnostics to get better accuracy in the prediction
Research on meteorological temperature prediction based on machine learning
Authors: Chen Leyan 
Published: September 2023

Abstract
Predicting temperature has been a great challenge in meteorology. Accurate temperature prediction is more conducive to human production and life. This study investigated the ability of artificial neural network (ANN) and random forest (RF) methods to predict short-term monthly temperatures. The study data has been processed from the global surface temperature change estimated by NASA Goddard Institute for Space Studies. The average temperature from three months ago, the average temperature two months ago, and the average temperature one month ago were used as inputs to the application model. Both the models gave comparable results, but the superiority of the artificial neural network model over the random forest model in the present study.
Design of PID and ADRC based adaptively anti-disturbance quadrotor hovering controller
Authors: Xingjian Zhang 
Published: September 2023

Abstract
This paper reported an implementation of quadrotor controller, aimed at using ADRC-based controller cascaded with PID controller to overcome hovering difficulties and provide resistance to extreme wind conditions in the complex airflow environment common in typical urban low-flying environments. The main tasks of the implementation including analyzing the complexity of urban low air flow environments and exploring what challenges it brings to the hovering stability of lightweight quadcopters, as well as demonstrating the good performance of the cascade PID-ADRC controller in this case and compare it with a classic PID controller.
Cloud detection algorithm based on point by point refinement
Authors: Zhang Jiyin 
Published: September 2023

Abstract
In order to limit the interference of cloud noise on ground scene information, cloud detection has been a hot issue in research on remote sensing image processing. Cloud detection labels the clouds in remote sensing images at the pixel level. The majority of early cloud detection systems rely on manually created feature and threshold segmentation with limited generalizability. Remote sensing cloud detection based on deep learning has improved in accuracy and speed thanks to the quick development of convolutional neural networks, but it is still unable to satisfy practical application requirements when dealing with sceneries with variable cloud block size and sparse distribution. To this end, this study proposes a cloud detection algorithm based on point-by-point refinement based on the idea of coarse to fine. Specifically, firstly, the residual module is introduced in the U-Net network to extract more features; secondly, the point-by-point refinement module is designed to filter out the areas in the remote sensing images where the clouds are easily detected wrongly for optimization and re-prediction, and then produce finer-grained and more accurate cloud detection results. The quantitative and qualitative experiments validate the effectiveness of the proposed method.
Methods to improve the sensitivity of micro-capacitive pressure sensors
Authors: Wang Chao 
Published: September 2023

Abstract
Novel micro pressure sensors require high sensitivity, anti-interference, reliability and durability. However, due to some problems in mold making, digital signal process circuits and so on, it is difficult for the existing micro-capacitive pressure sensors to make a breakthrough in sensitivity. Firstly, this paper briefly introduces the micro-pressure capacitive sensors principle and the technical difficulties they face. After that, several schemes are listed, including digital signal process circuits, laser-induced graphene, etc. The feasibility and practicability of these methods to improve the sensitivity of micro-capacitive pressure sensors are compared and demonstrated. The following types of pressure sensors, including PDMS-based and MEMS-based pressure sensors, have their specific functions and shortcomings. The paper aims to provide some references for further research in this field, and to give some useful opinions on the practical application of micro-capacitive pressure sensors in IoT and other fields.
Reinforcement learning approach for multi-agent flexible scheduling problems
Authors: Zhou Hongjian , Gu Boyang , Jin Chenghao 
Published: September 2023

Abstract
Scheduling plays an important role in automated production. Its impact can be found in various fields such as the manufacturing industry, the service industry and the technology industry. A scheduling problem (NP-hard) is a task of finding a sequence of job assignments on a given set of machines with the goal of optimizing the objective defined. Methods such as Operation Research, Dispatching Rules, and Combinatorial Optimization have been applied to scheduling problems but no solution guarantees to find the optimal solution. The recent development of Reinforcement Learning has shown success in sequential decision-making problems. This research presents a Reinforcement Learning approach for scheduling problems. In particular, this study delivers an OpenAI gym environment with search-space reduction for JSSP and provides a heuristic-guided Q-Learning solution with state-of-the-art performance for Multi-agent Flexible Job Shop Problems.
A survey on deep learning for surgical planning
Authors: Diao Xinran 
Published: September 2023

Abstract
Deep learning-based surgical planning is currently a promising area of research. Unlike the traditional way of surgical planning through computer systems that assist physicians, deep learning methods enhance the robustness and accuracy of surgical planning systems through a data-driven approach with the great advantage it possesses in processing images. In this work, we have put together a review that presents the application of deep learning in three separate aspects of surgical planning systems, namely, surgical scene understanding, surgical scene reconstruction and automated assessment of surgical skills. This article covers multiple scopes, such as semantic segmentation, depth estimation, SLAM systems, etc. We present the relevance of these three applications for the current surgical and medical field and show how the problem can be solved by the techniques in the mentioned topics. We hope that this work will link emerging research results in the field of deep learning and surgical planning, and provide guidance to future researchers using deep learning techniques for surgical planning when it comes to understanding feasible approaches to related problems.
Application of Resnet18-Unet in separating tumors from brain MRI images
Authors: Chen Yuzhi 
Published: September 2023

Abstract
The automatic segmentation of brain tumors in magnetic resonance imaging (MRI) plays a significant role in diagnosis and treatment planning. In this research, a semantic segmentation model called Resnet18-Unet was designed to combine the Resnet18 model and the U-Net model by using the convolutional neural network’s powerful feature extraction functionality. It keeps the decoding part of U-Net and replaces the traditional VGG encoding part with Resnet18 to extract feature information more effectively. Aiming at the problem of slow training speed and difficult convergence of semantic segmentation models, dynamic learning weights are used to accelerate model convergence. The experimental results demonstrate that the Resnet18-Unet model converges faster than the U-Net model with faster convergence and better segmentation performance, and the average IoU reaches 83.17%.
A review: Wireless sensor network location
Authors: Xiao Qing 
Published: September 2023

Abstract
Node localisation is one of the important technologies in wireless sensor networks, and localisation algorithms are the core elements of localisation technology. This paper first lists the evaluation criteria of localization performance, the related terminology used in localization technology, and the classification and analysis of localization algorithms. The paper introduces various node localisation algorithms based on both ranging and non-ranging aspects, and finally presents the shortcomings of the current wireless sensor network localisation technology and provides an outlook on future research prospects.
Navigating the ecosystem flood regulation service flows from subbasin to catchment in the Han River Basin
Authors: Liu Wenping , Li Yan , Chen Shuaipeng 
Published: September 2023

Abstract
With the increasing cross-regional impact of climate change increasing in recent years, the Han River Basin, as a vital water resource supply and densely populated area in China, faces severe cross-regional flood threats and challenges. The systematic consideration of flood regulation throughout the entire upstream and downstream of the basin has become imminent. Our research aims to gain a deeper understanding of the ecosystem flood regulation service flows from upstream to downstream within subbasins and catchments scales of the Han River Basin. The results showed that the overflow path from upstream to downstream of the flood was basically consistent with the trend of the stream and the main tributaries of the Han River. It emerged that subbasins c, e, g, h and k were the key areas for the overall regulation of the upstream and downstream floods at subbasin scale. A total of 11 catchments overflow into their adjacent downstream catchments across the subbasins, a fact which is critical for catchment scale flood regulation. What’s more, there is evident interaction not only between adjacent subbasin and catchment units but also substantial exchange of service flows between non-adjacent units. Notably, catchment c’s flood regulation service flow was primarily contributed by its non-adjacent units. These findings not only contribute to filling the current knowledge gap in cross-watershed flood overflow and flood regulation service flows, but also provide support for the integrated response of upstream and downstream flood disaster risk management at the whole basin scale.
The asymptotic form of the Hagedorn temperature in planar
super Yang-Mills
Authors: Ekhammar Simon , Minahan Joseph A , Thull Charles 
Published: September 2023

Abstract
Using the supergravity dual and the plane-wave limit as a guide, we conjecture the asymptotic large coupling form of the Hagedorn temperature for planar

super Yang-Mills to order

1

/

λ

. This is two orders beyond the presently known behavior. Using the quantum spectral curve procedure of Harmark and Wilhelm, we show that our conjectured form is in excellent agreement with the numerical results.
Orbital expansion variational quantum eigensolver
Authors: Wu Yusen , Huang Zigeng , Sun Jinzhao , Yuan Xiao , Wang Jingbo B , Lv Dingshun 
Published: September 2023

Abstract
Variational quantum eigensolver (VQE) has emerged as a promising method for investigating ground state properties in quantum chemistry, materials science, and condensed matter physics. However, the conventional VQE method generally lacks systematic improvement and convergence guarantees, particularly when dealing with strongly correlated systems. In light of these challenges, we present a novel framework called orbital expansion VQE (OE-VQE) to address these limitations. The key idea is to devise an efficient convergence path by utilizing shallower quantum circuits, starting from a highly compact active space and gradually expanding it until convergence to the ground state is achieved. To validate the effectiveness of the OE-VQE framework, we conducted benchmark simulations on several small yet representative molecules, including the

H

6

chain,

H

10

ring and

N

2

. The simulation results demonstrate that our proposed convergence paths significantly enhance the performance of conventional VQE. Overall, our work sheds valuable insight into the simulation of molecules based on shallow quantum circuits, offering a promising avenue for advancing the efficiency and accuracy of VQE approaches in tackling complex molecular systems.
UAV-based height measurement and height–diameter model integrating taxonomic effects: exploring vertical structure of aboveground biomass and species diversity in a Malaysian tropical forest
Authors: Takeuchi Yayoi , Borjigin Habura , Niiyama Kaoru , Sato Tamotsu , Fukaya Keiichi , Okuda Toshinori , Hayashi Masato , Omar Hamdan , Norizan Azharizan Mohammad 
Published: September 2023

Abstract
The complex stratification of tropical forests is a key feature that directly contributes to high aboveground biomass (AGB) and species diversity. This study aimed to explore the vertical patterns of AGB and tree species diversity in the tropical forest of Pasoh Forest Reserve, Malaysia. To achieve this goal, we used a combination of field surveys and drone technology to gather data on species diversity, tree height (H), and tree diameter at breast height (D). As all trees in the 6 ha plot were tagged and identified, we used the data to classify the taxonomy and calculate species diversity indices. We used unmanned aerial vehicle-based structure-from-motion photogrammetry to develop a Digital Canopy Height Model to accurately estimate H. The collected data and previous datasets were then used to develop Bayesian height–diameter (HD) models that incorporate taxonomic effects into conventional allometric and statistical models. The best models were selected based on their performance in cross-validation and then used to estimate AGB per tree and the total AGB in the plot. Results showed that taxonomic effects at the family and genus level improved the HD models and consequent AGB estimates. The AGB was the highest in the higher layers of the forest, and AGB was largely contributed by larger trees, especially specific families such as Dipterocarpaceae, Euphorbiaceae, and Fabaceae. In contrast, species diversity was the highest in the lower layers, whereas functional diversity was the highest in the middle layers. These contrasting patterns of AGB and species diversity indicate different roles of forest stratification and layer-specific mechanisms in maintaining species diversity. This study highlights the importance of considering taxonomic effects when estimating AGB and species diversity in tropical forests. These findings underscore the need for a more comprehensive understanding of the complex stratification of tropical forests and its impact on the forest ecosystem.
17 The case for more specialist geriatric wards: redesigning health systems to meet the needs of older persons
Authors: Conlon C , Murphy R , Small C 
Published: September 2023

Abstract

Background
The National Clinical Programme for Older Persons outlines that each hospital should have a dedicated Specialist Geriatric Ward (SGW) with appropriate staffing and access to Health and Social Care Professionals. As part of a quality improvement initiative to drive the development of a new SGW, we examined what proportion of older medical admissions had care needs that would be best served in a Specialist Geriatric Ward.

Methods
We completed a retrospective cohort study of unselected medical admissions over a two week period in a level four hospital. We established criteria that would deem a patient suitable for admission to a specialist geriatric ward and examined the journey of patients who were or were not admitted under the care of Geriatric Medicine.

Results
In total over the two week period there were 307 admissions, with 144 over the age of 75 years (47%). Over half of patients had more than one bed moves during their inpatient stay. 78% were frail. 109 (75%) of patients would have been eligible for care on a specialist geriatric ward. 20% of the over 75 year olds were admitted under the care of Geriatrics. 72% of the remaining patients admitted under general medical teams would have met the criteria for care on a specialist geriatric ward. 20% of this cohort had a review by the frailty at the front door team with 23% having an inpatient geriatric medicine consult.

Conclusion
A significant proportion of older adults being admitted have care needs that would benefit from coordinated care on a specialist geriatric ward. Patients receive input from geriatric medicine through a variety of avenues but there is a need for increased access to specialist geriatric inpatient wards. This would improve patient access to the Right Care, in the Right Place at the Right Time.
66 The PROMOTe study: prebiotic supplementation improves cognition versus placebo in healthy older twins
Authors: Lochlainn M Ni , Bowyer R , Whelan K , Steves C J 
Published: September 2023

Abstract

Background
There is a growing body of evidence linking the microbiota in the human gut, to the brain and to physical function. Animal and human studies have shown that inducing changes in the microbiota can alter muscle physiology and cognitive behaviour, indicating causative pathways. The PROMOTe trial aimed to test whether the gut microbiome mediates anabolic resistance to protein in older adults. A secondary objective was to test whether modulation of the gut microbiome using a pre-biotic food supplement, improved cognition versus placebo.

Methods
This placebo-controlled double-blinded randomised controlled trial used twin pairs, recruited from the TwinsUK cohort. Those aged &gt; = 60, with low baseline protein intake, and access to a computer (due to remote trial delivery) were included. One twin received protein supplementation plus placebo and the other received protein supplementation plus a gut microbiome modulator (prebiotic). Intervention period was 12 weeks. Participants were advised to take 1 supplement sachet daily, and to undertake resistance exercises. The primary outcome was muscle strength as measured using chair-rise time. Cognition (CANTAB battery) was a secondary outcome. Linear mixed effects regression models were used to compare intervention groups (arm 1 vs arm 2; blinded). Twin clustering was considered as random effects, both family identifier and zygosity, and treatment group as fixed effect.

Results
Target sample size was 70 individuals. We screened 626 and randomised 72 participants (36 pairs). More adverse events occurred in the prebiotic group (n = 8 versus n = 2 in placebo group; p = 0.041), but compliance remained high in both groups (% adherence &gt;78% in each group; p = 0.37). There was no significant difference between arms for the primary outcome of chair rise time (coefficient 0.184; 95% CI -0.569-0.938; p = 0.631). The prebiotic intervention arm had an improved cognition factor score versus the placebo group (coefficient 0.482; 95% CI 0.823–0.141; p = 0.014).

Conclusion
Prebiotic food supplementation improves cognition versus placebo in a cohort of healthy older twins.