bluuebunny/crossref_metadata_2025_split huggingface dataset:
The advance of underdetermined linear regression optimization based on implicit bias
Authors: Liu Jinze 
Published: September 2023

Abstract
The gradient descent method has the characteristics of easy realization and simple structure. The traditional gradient descent method has many advantages, especially in solving convex optimization problems. In recent years, some researchers have noticed that the gradient descent algorithm is helpful to solve the problem of underdetermined linear regression optimization. Therefore, in order to explore the specific relationship between gradient descent and under-determined linear regression optimization, this paper focuses on a case with a unique finite root loss function and discusses in detail the working principles of the natural-gradient-descent, mirror-descent, and gradient-descent in this case, and proves the auxiliary effect of gradient descent on under-determined linear regression optimization theoretically. How to use the characteristics of the gradient descent algorithm to design a suitable model, and how do we use the idea of the gradient descent algorithm to find an answer for problem of under determined linear regression optimization in the practical application will be the future research work to continue to advance.
Bird audio recognition based on convolutional neural network
Authors: Li Danyu 
Published: September 2023

Abstract
Audio-based bird identification systems have proven particularly useful for biodiversity monitoring and education. Accurate understanding of the identity, geographical distribution and variation of birds is essential for the sustainable development of humans and the conservation of biodiversity. This paper establishes a bird audio recognition based on convolutional neural network model, using the programming software pycharm, with pytorch as the neural network framework, through the processing and recognition of collected bird audio, to overcome the problem of low audio recognition efficiency and poor effect.
Semantic road segmentation based on adapted Poly-YOLO
Authors: Huang Zhongming 
Published: September 2023

Abstract
With artificial intelligence continuing to change people’s everyday life in profound ways, the desire to endow vehicles with the ability to drive autonomously has emerged for years. Thus, autonomous driving has become a popular field. The autonomous driving task can be divided into three general procedures: perception, planning, and locomotion. The first and foremost part of these general procedures is the perception task. Among those perception methods, the most prevailing one is semantic segmentation, which is annotating and predicting the object located at the pixel level, meaning nearly all pixels should be classified into certain categories. However, this method provides enough accuracy while bringing a considerable computational burden. Thus, implementing real-time road semantic segmentation on autonomous driving vehicles is still a costly task. In this paper, an adapted model improved upon the Poly-YOLO baseline model is proposed, which is a well-developed object detection algorithm providing bounding polygons to enclose the target object, forming a polygon mask similar to that of semantic segmentation. This paper endeavors to enhance the model’s accuracy in detecting variously sized targets greatly and to fine-tune the model to generate more proximate enclosing polygons. The adapted model has experienced a leap in performance compared to the baseline Poly-YOLO model.
Water quality prediction based on AR and LSTM model
Authors: Wu Xinhe 
Published: September 2023

Abstract
Prediction for water quality is essential because poor water quality can result in serious health problems. Turbidity is one indicator to measure water quality. This research uses time series models, the Autoregressive (AR) model and the Long short-term memory (LSTM) model, to predict the turbidity of water based on the dataset collected from Chicago Park District with a long-term time series from 2013 to 2021. Data cleaning and data exploration are done before building the model, and the stationary test and seasonality test are executed to prepare qualified time series. Also, ACF and PACF plots are drawn to figure out the order of AR to select the best model for prediction. AR Model is a traditional time series model which can make a prediction based on the previous values of time series, and LSTM Model is an advanced Recurrent Neural Network (RNN) designed to prevent the gradient from either decaying or exploding when learning from long-term sequences. The research applies these two models to the water quality data and makes an evaluation and comparison between them. It is found that the LSTM Model has a better performance in forecasting a long-period time series than the AR Model.
A study on the model predictive control based on convolutional neural network
Authors: Wang Hongyu 
Published: September 2023

Abstract
As one of the most widely used rolling optimization methods, model predictive control (MPC) can effectively deal with constrained problems with multivariate. However, MPC relies on the accurate system dynamics model, which means that the nonlinear terms in the system model such as some irregular disturbances or noises will weaken the control effect. On the other hand, machine learning has been shown to work well on black-box systems, so the idea of using deep learning to handle nonlinear terms is feasible. This paper studies a method for applying deep learning to MPC, which is to use convolutional neural networks to fit nonlinear terms of the system model and thus remove their effects by using methods such as precise compensation. The paper also compares traditional and convolutional neural networks and the simulation results show that this method can achieve good control effect.
Strengthen motor imagery for motor disorders patients in metaverse through brain computer interfaces
Authors: Lei Jingyu 
Published: September 2023

Abstract
Both brain-computer interface (BCI) and metaverse technology are emerging research fields involving computer science, neuroscience, psychological, cognitive science, biomedical engineering, mathematics, signal processing, clinical medicine, automatic control, etc. This paper focuses on the role of both in the medical field for patients with motor disorders (MD). MD is mostly caused by neurological diseases or accidental trauma, which often results in the death of nerve cells, loss of synapses and localised neuronal conduction blockage, thereby damaging the neural network’s structural and functional integrity in the brain and disrupting the brain’s information processing processes, resulting in motor paralysis of the limbs. Approximately 3% of the world’s population is currently suffering from severe disabilities, and approximately 110-190 million people are unable to live independently. Of these, nearly 100 million suffer from motor paralysis caused by stroke, cerebrospinal cord injury and amyotrophic lateral sclerosis, which has a serious impact on their daily work and life. Scientists are already using BCI to help people with paralysis caused by neurological damage to the brain, with a lesser contribution from the metaverse. This paper draws on past BCI and biological experiments to logically reason about methodologies such as neurofeedback, motor imagery, visual prosthesis implantation and training to explore future possibilities for treating MD in the interactive world of BCI and the Metaverse.
Multiple robot motion planning on A* and RRT* algorithm
Authors: Cheng Ao , Li Jiarui , Xu Bolun 
Published: September 2023

Abstract
In recent years, multi-robot systems have been widely used in different fields. In order to ensure that each robot in the system can reach the target point and complete the task correctly, the path planning of the multi-robot system is significant. The path planning of the multi-robot system can effectively ensure that the robot selects the appropriate target and completes the task, and during the planning, it can ensure that the task completion time of the total system is the shortest or the total cost is the lowest. Experiments are carried out without considering the kinematics and physical properties of the robot itself, and it is assumed that each robot can accurately know its own position information. In this paper, we propose an A* algorithm for target trajectory planning that integrates multiple trajectories and minimizes running costs by task assignment through a linear programming model. At the same time, by adjusting the planning method of the estimated cost, the error caused by the difference between the nonlinear path and the straight line distance is reduced. After experiments, the effectiveness of the linear programming model and the difference in computation time and running time between A* and RRT* algorithms are demonstrated. What’s more, A* outperforms RRT* in completion time, while RRT* has a shorter computation time.
Research advanced in image style transfer based on deep learning
Authors: Shi Haozhuo , Wang Yingqi 
Published: September 2023

Abstract
Image style transfer aims to change a picture’s style while maintaining the integrity of the image’s original information. Image style transfer techniques based on deep learning have expanded in variety as a result of the quick advancement of artificial intelligence technology, and have made breakthroughs in both transfer accuracy and visualization effect. Focusing on image style transfer based on depth learning, this paper systematically introduces its latest research progress. Specifically, according to the difference of network structure, this paper introduces four representative style transfer methods based on CNN and GAN, including their design ideas, process based, advantages and disadvantages. In addition, this paper also qualitatively and quantitatively compares the transfer results of different algorithms on different datasets. For GAN based method, AnimeGAN has a good conversion effect in style conversion. For the CNN based method, the combination of perceptual loss and feedforward network using perceptual loss is more prominent. Finally, this paper reviews the development and breakthrough of neural network style, and sorts out and discusses the differences and shortcomings of these technologies as well as some unresolved problems.
Review of spectrum analysis in fault diagnosis for mechanical equipment
Authors: Wang Zihan , Wang Jian , Sun Yongjian 
Published: September 2023

Abstract
Various mechanical equipment play a crucial role, and their health or status may affect efficiency and safety seriously. Spectrum analysis of the corresponding signal has been widely used to diagnose the fault in the past decades. The diagnosis method based on spectrum analysis technology covers almost all aspects of mechanical fault diagnosis. However, there is a lack of review of diagnostic methods of spectrum analysis technologies in the field of mechanical equipment fault diagnosis. In order to fill this gap, this paper reviews the spectrum analysis technology in mechanical equipment diagnosis in detail. First of all, in order to let the researchers who are in contact with spectrum analysis technology for the first time quickly understand this field, the principles of spectrum are systematically sorted out, including spectrum, cepstrum, energy spectrum, power spectrum, higher-order spectrum, Hilbert spectrum, marginal spectrum, envelope spectrum, singular spectrum and so on. Furthermore, the characteristics of corresponding spectrum analysis technologies are summarized, and their advantages and disadvantages are analyzed and compared. High-quality references in recent ten years are cited for illustration to enhance persuasiveness. Finally, the prospect of spectrum analysis technology is summarized, and the future development trend of spectrum analysis technology is pointed out. It is believed that the joint diagnosis of fault severity, variable speed fault diagnosis, combined with deep learning and multiple spectrum analysis technologies should be given more attention in the future. This paper is expected to provide a comprehensive overview of mechanical fault diagnosis based on spectrum analysis theory, and help to develop corresponding spectrum analysis technologies in practical engineering.
49 Older adults’ experience of transition to the community from the Emergency Department: a qualitative evidence synthesis
Authors: Condon B , Manning M , Hayes C , Griffin A , Fitzgerald C , Galvin R , Robinson K 
Published: September 2023

Abstract

Background
Older adults comprise a growing proportion of Emergency Department (ED) attendees and are vulnerable to adverse outcomes on discharge. Interventions to reduce older adults’ risk of adverse outcomes following an ED attendance are proliferating and often focus on improving the transition from the ED to the community. To optimise the effectiveness of interventions, it is important to determine how older adults experience the transition from the ED to the community. This study systematically reviewed and synthesised qualitative studies reporting older adults’ experiences of transition to the community from the ED.

Methods
A systematic literature search of six databases was completed in March 2022 and updated in March 2023. Nine studies were included following full text review representing the experiences of over 490 older adults from five countries. Findings from included studies were synthesised as a meta-ethnography.

Results
Older adults often reported un-resolved symptoms on discharge (theme 1) which negatively affected transition home and return to daily life. Unresolved symptoms were also a major driver of ED re-attendance shortly after discharge (theme 2). Although older adults wanted a planned discharge home with organised transport (theme 4) however, this was not often achieved. Fragmented care at the point of transition home (theme 3) was a reoccurrence experience for older adults alongside inadequate healthcare provider communication/instructions on ED discharge (theme 5).

Conclusion
Study findings identified key areas for consideration in service development and intervention development for older adult’s care when transitioning from the ED to the community. ED healthcare providers should consider adapting their communication to meet the needs of older adults. Healthcare providers need to explicitly address expectations about symptom resolution during discharge planning as a collaborative process with older adults. Those developing transitional care interventions should consider older adults needs for integration of care, symptom management, clear communication from providers and desire to return to daily life.
105 Antiplatelet therapy and incident cognitive impairment or dementia—a systematic review and Meta-analysis of randomised clinical trials.
Authors: Kitt K , Murphy R , Clarke A , Reddin C , Ferguson J , Bosch J , Whiteley W , Canavan M , Judge C , O'Donnell M 
Published: September 2023

Abstract

Background
The benefit of antiplatelet therapy in the prevention of cognitive impairment or dementia is uncertain. We investigated the association between antiplatelet therapy with incident cognitive impairment or dementia in randomised clinical trials.

Methods
We searched PubMed, EMBASE, and CENTRAL for randomised clinical trials published from database inception through Feb 1st, 2023. Trials that evaluated the association of antiplatelet therapy with incident cognitive impairment or dementia were included. For single agent antiplatelet, the control group was placebo and for dual agent antiplatelet therapy, the control group was single agent monotherapy. Two authors screened and independently extracted all data, and a random-effects meta-analysis model was used to report pooled treatment effects and 95% Confidence Intervals. The primary outcome was incident cognitive impairment or dementia. Secondary outcomes included change in cognitive test scores.

Results
Eleven randomised clinical trials were eligible for inclusion (109,860 participants), all of which reported the incidence of cognitive impairment or dementia on follow-up. The mean (SD) age of trial participants was 66.2 (7.9) years and 46,150 (42%) were women. The mean duration of follow-up was 70 months. Antiplatelet therapy, compared to control, was not significantly associated with a reduced risk of cognitive impairment or dementia (11 trials; 109,860 participants) (3.49% vs 4.18% of patients over a mean trial follow-up of 5.8 years; odds ratio [OR], 0.94 [95% CI, 0.88–1.00]; absolute risk reduction, 0.2% [95% CI, −0.4% to 0.009%]; I2 = 0.0%). Antiplatelet therapy was not significantly associated with mean change in cognitive test scores.

Conclusion
In this meta-analysis of randomised clinical trials, antiplatelet therapy was not significantly associated with a lower risk of incident cognitive impairment or dementia. The confidence intervals around this outcome do not exclude a modest preventative effect, and at a population level this could have a substantial benefit. Further large trials with longer term follow up are needed.
171 Combined inflammatory biomarkers predict recurrent ipsilateral ischaemic stroke in patients with carotid atherosclerosis
Authors: Gorey S , Synnott P , Coveney S , Harbison J , Williams D J , Dolan E , Cassidy T , Barry M , O'Connell M , McDonnell C , McAuley K , Inzitari R , Marnane M , McCabe J J , Kelly P J 
Published: September 2023

Abstract

Background
Large-artery ischaemic stroke is associated with a high-risk of recurrence. Inflammation is important in the development and rupture of carotid plaque. It remains unknown if blood-biomarkers can reliably quantify vascular inflammation. We investigated whether blood-biomarkers were associated with recurrent ipsilateral Ischaemic Stroke (IS).

Methods
hsCRP and IL-6 were measured in blood-samples using immunochemiluminescence immunoassays from three cohort studies. Median follow-up was 3.9 years (IQR 1.6–5.8). The primary outcome was recurrent ipsilateral IS.

Results
Of 269 participants, 130 (48.3%) had severe CS (median age, 71 (IQR 64–78)). There were 19 ipsilateral recurrent IS in 1,046-patient-years follow-up. hsCRP was dichotomised at 2 mg/L and IL-6 at 7 pg/ml (expected-normal value for assay). 187 (69.5%) had hsCRP&lt;2 mg/L and 170 (63.2%) had IL-6 ≥ 7 pg/ml. 146 (54.3%) participants had combined-raised hsCRP≥2 mg/L and IL-6 ≥ 7 pg/ml. On unadjusted Cox analysis, hsCRP≥2 mg/L was associated with increased risk of ipsilateral recurrent IS (HR 9.25, CI 1.24–69.2, p = 0.03) while IL-6 ≥ 7 pg/ml trended towards increased risk (HR 2.36, CI 0.79–7.1, p = 0.1.) However, combined raised-hsCRP/IL-6 was associated with an increased risk of recurrent ipsilateral IS (HR 3.58, CI 1.2–10.7, p = 0.02.) However, after adjustment for age, sex, smoking and time-to-phlebotomy, these associations attenuated. When patients were divided into three risk categories defined by levels of hsCRP and IL-6 (low [both markers low], intermediate [discordant markers], high [both markers raised]0, the rate of recurrent stroke increased across the categories, p = 0.013 for trend. The c-statistic for combined-raised hsCRP/IL-6 was 0.66 (CI 0.57–0.75) indicating prognostication better than chance to predict recurrent-ipsilateral IS.

Conclusion
Using blood inflammatory proteins in combination may add value in risk prediction in patients with carotid atherosclerosis, although this concept requires independent replication and validation in larger cohorts.
196 Models of transitional care, comparison of a new innovative consultant geriatrician lead service to current practice
Authors: O’Donovan C , Dennehy K , Fitzgerald R , Plant R , Rynne R , Faez I , Horgan A , Ahern E , Jennings E 
Published: September 2023

Abstract

Background
The existence of short term (up to 6 weeks) HSE ‘transitional care funding’ has presented a potential discharge pathway for a proportion of hospital inpatients since 2015. These predominantly older, frailer patients access funding for beds that aim to support interim long term care placement or convalescence prior to discharge home. This project aimed to compare the existing ‘transitional care funded’ model of care in sites affiliated with our model 4 tertiary hospital to that of a newly trialled model where a consultant geriatrician co-ordinated patient care. This new model synchronises patient care through assessment, care planning, treatment, discharge planning and education as per best-practice.

Methods
Retrospective (January—September 2022) and prospective (September to December 2022) data collection of both care models compared Length of Stay (LOS), designation as a delayed transfer of care patient, discharge destinations (planned vs. actual), falls, allied health input, hospital readmission and death were compared between the two groups.

Results
A total of 131 patients (53 old 78 new model) were included. Average LOS in tertiary hospital was on average 30.0 versus 27.6 days. Average LOS in Transitional Care Unit (TCU) was 46 versus 25 days. A one way ANOVA was used to assess any statistical significance between the models of care across the following subheadings; death, change in Barthel Index, falls, discharge destination, readmission and sex. The one way ANOVA was statistically significant for LOS in TCU (p &lt; 0.001), change in Barthel between groups (p &lt; 0.001) and falls within TCU (p = 0.012).

Conclusion
The introduction of a new model of care at one of the TCUs has led to a significant reduction in length of stay. An opportunity to improve patient outcomes with the aim of establishing best practice have been identified. Transitional care placement influence outcomes for older persons including returning home. Our new model of care incorporates interdisciplinary teams and proactively target the complex patient’s care needs.
223 Chronic pain is frequently under-treated among older people in Ireland: data from TILDA
Authors: Swanton A , Lalor P , Lavan A , Kenny R A , Briggs R 
Published: September 2023

Abstract

Background
Chronic pain can have significant adverse effects on quality-of-life, functional status, and well-being in later life. Concerns exist regarding under-treatment of pain among older people, however. The aim of this study, therefore, is to ascertain the prevalence of chronic pain among older people in Ireland, including prescribed pharmacological treatment.

Methods
Participants ≥70 years at TILDA Wave 1 were asked ‘Are you often troubled with pain?’ and if yes, were then asked ‘How bad is it most of the time? Mild, moderate, or severe?’. Medication lists were examined by anatomical therapeutic chemical code for analgesics (N02), opioids (N02A), and non-opioid analgesics (N02B). Frailty was assessed by Fried Frailty Scale. Data was analysed descriptively. Chi-square tests assessed differences in binary variables; t-tests for continuous variables.

Results
Over 1/3 of participants (n = 2,308; mean 77 years; 53% female) reported chronic pain (839/2,308), with 10% (222/2,308) reporting severe pain. Over 60% of participants with chronic pain (517/839) reported pain made it difficult to complete day-to-day activities. Almost two thirds of participants with moderate–severe pain (407/618) were not prescribed analgesia. Participants with untreated chronic pain were more likely to be female (62% vs 52%, p &lt; 0.001), have functional impairment (22% vs 13%, p &lt; 0.001) and to be frail (14% vs 6%, p &lt; 0.001). Over 98% of participants with untreated pain had contact with healthcare (GP practice or ED) in the preceding 12 months.

Conclusion
In this population-representative analysis, chronic pain affects 1/3 older people in Ireland, with 1/10 reporting severe pain. Chronic pain often limits day-to-day activities yet is frequently under-treated or not treated at all, particularly in frail, older females, despite contact with the healthcare service. It is imperative, therefore, that we empower older people to report chronic pain to a healthcare professional and we facilitate appropriate assessment and treatment of their pain.