bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimates of Rayleigh distribution parameters in statistical methods of quality control
Authors: Grigoriev S N , Emelianov P N , Masterenko D A , Ped’ S E 
Published: September 2023

Abstract
The paper considers the problem of estimating of a two-parameter generalization of Rayleigh distribution and finding distributions of estimates. This problem arose from the needs of statistical sampling inspection of dimensions obtained in the production process with certain features of the part basing and cylindrical surface processing. Parameter estimates are found using order statistics. Computational procedures are constructed in the Matlab environment, which make it possible to construct distributions of estimates in the form of tables. The correctness of the calculations was verified using stochastic simulation and the construction of empirical distribution functions of estimates. The results obtained can be used to build operational characteristics of control plans and select control plans with the required producer’s and customer’s risks, as well as to build control charts for statistical control of the manufacturing process.
Traffic lights recognition in foggy weather based on YOLOv5
Authors: Zhang Huiwen 
Published: September 2023

Abstract
Autonomous driving technology has gradually matured. The main application is unmanned vehicles, which have begun to test on the road. However, there is a critical technical problem with mitigating interfering factors in efficient traffic light detection and recognition. Due to the size of traffic lights and the complex road conditions, the detection accuracy is not up to satisfactory. This paper proposes a Traffic Light Recognition system based on YOLOv5, which has high speed and accuracy. The system also tests foggy data which gain from image processing. The experimental results show that the method achieved 0.9943 in normal conditions and 0.9937 in foggy conditions. Overall, the efficiency of YOLOv5 meets the practical application requirements.
A new computationally efficient method to tune BERT networks – transfer learning
Authors: Wang Zian 
Published: September 2023

Abstract
BERT is a pre-trained language model that achieves state-of-the-art performance on natural language processing (NLP) tasks. Once it was published, it quickly became one of the most popular models in the NLP field. The official recommended method for applying BERT to downstream tasks is fine-tuning. However, we argue that transfer learning is also a very practical approach to applying BERT, and this approach has its own advantages compared to fine-tuning. In this paper, we explore the advantages of the transfer learning approach through the method that uses transfer learning and fine-tuning approaches separately to apply BERT to the same eight GLUE benchmark tasks and compare the training time spent and model performance scores, such as accuracy or F1 score, of the two approaches. Finally, this paper finds that among all eight GLUE tasks and on small training sets, transfer learning saves 30% to 50% of the time compared to a fine-tuning approach to train the models on the data of downstream tasks and can achieve very similar performance. Besides, for the same amount of time, transfer learning can obtain higher performance scores than fine-tuning on larger training sets. In conclusion, on small training sets, transfer learning has a huge advantage in time consumption under the premise of approximate performance, and it also performs better on large training sets under the same length of time, compared to fine-tuning.
Dynamic gesture recognition based on compressed video for UAV control
Authors: Yang Yuchen 
Published: September 2023

Abstract
As a multifunctional aircraft with small size, low cost and easy control, UAVs can be used in many fields such as gardening, plant protection, mapping, logistics, military, etc. For a more convenient human-machine interaction mode, the user interacts with the UAVs in the form of dynamic gestures. The current traditional approach is to train convolutional neural networks with images as direct inputs to the system for the purpose of controlling UAVs. However, this approach leaves the temporal representation information between images missing, resulting in poor training results or over-reliance on computing resources. Therefore, this paper proposes an efficient processing strategy, that is, for simple gesture tasks, the optimized frame extraction algorithm is adopted to process picture-based gesture recognition; for complex gesture tasks, compressed video is used as system input to complete video-based gesture recognition. Based on the training results in action recognition datasets, UCF-101 and HMDB-51, the efficiency of compressed video in gesture recognition tasks has been verified, which can be applied to UAV dynamic gesture control.
Neural network modelling of vehicle fuel consumption and NOx emissions based on data clustering
Authors: Yang Xinhao 
Published: September 2023

Abstract
Since automobiles are becoming increasingly popular, fuel consumption has become a crucial factor to consider when purchasing a vehicle. Car emissions, one of the causes of global warming, have become a big worry as the planet warms. As a result, the fuel efficiency and exhaust emissions of automobiles are tracked in this article using neural networks based on DBSCAN clustering. This study uses DBSCAN to cluster and model NOx emissions and vehicle fuel consumption. Using DBSCAN, the vehicle speed, acceleration, fuel use, and NOx emission data are first grouped and pre-processed into many data clusters. Then, each data cluster develops a corresponding feedforward neural network model to simulate NOx emission. The experimental results show that the method suggested in this study substantially improves the model’s dynamic reaction time and modeling accuracy.
Performance comparison of representative methods for few-shot speech gender analysis
Authors: Li Songling , Pan Haoyu , Zhang Haoming , Zhang Junzhan 
Published: September 2023

Abstract
As one of the basic tasks of the famous and important speech emotion recognition system, gender recognition based on speech analysis has attracted a lot of research interests in recent year. This paper considers the model performance of different machine learning methods on gender recognition in few-shot learning. In this article, we use three types of methods, namely Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Random forest. Among them, in the experiments of the SVM method, we report the linear SVM and nonlinear SVM models separately. Different experiments were performed with respect to the number of features and number of iterations and the results and possible issues were evaluated.
Prediction and classification of skin melanoma cancer using active hybrid machine learning technique
Authors: Saravanan S , Sathishkumar V E , Rajalakshmi N.R , Sukumar R , Muthukumaran V. 
Published: September 2023

Abstract
Melanoma is a dangerous skin cancer that may spread if not detected early. Thus, the medical industry has evolved with automated diagnostic tools that may assist physicians and even regular people diagnose an illness. Here, we provide a combined method for identifying melanomas on the skin. For our proposed approach, we make use of three forecasts. This problem was solved by training a neural network and two-learning machine classifiers on data describing the borders, textures, and colours of skin lesions. These tactics are combined for maximum effectiveness by majority voting. Tests show that using all three methods simultaneously improves precision.
Music classification with convolutional and artificial neural network
Authors: Li Zuge , Peng Haitao , Tan Siwei , Zhu Fangyu 
Published: September 2023

Abstract
Music genre classification focus on efficiently finding expected music with a similar genre through numerous melodies, which could better satisfy the tastes and expectations of the users when listening to music. This paper proposes a new method to classify different kinds of music with Artificial Neural networks (ANN) and Convolutional Neural Networks (CNNs). First, Mel Frequency Cepstral Coefficients (MFCC) are used to preprocess the Mel-frequency cepstrum (MFC). Then, we upgrade Anupam’s CNN model. Since the extracted features only by MFC are not suitable for CNN to learn as a small dataset like this. Multiple features are then exacted for each audio file. The two most correlated features on the datasets are adopted as the input of an ANN. To verify the proposed method’s effectiveness, we compare our method with other state-of-the-art methods on the GTZAN dataset. The experimental results show that we can get higher accuracy compared to Anupam. If using only one MFCC feature, Conv-Conv-Pool, a sub-structure that we add two convolutional layers before each max pooling layer, performs better than Conv-Pool, and Conv-Pool performs better than ANN. However, by concatenating another correlated feature, spectral centroid means, which is a measure used in digital signal processing to characterize a spectrum, a simple ANN can have much higher accuracy than the one utilizing only a single MFCC feature with an accuracy of about 94.1%.
Galaxy morphology classification with densenet
Authors: Wang Guangze 
Published: September 2023

Abstract
The classification of galaxies has always been an essential topic in astronomy, which can help to understand how galaxies form and evolve. This paper uses an effective deep-learning architecture, DenseNet-201, to classify galaxy morphology. Because galaxies are only concentrated in the center of the images, we preprocess the data in the way of reframing the images from the size of 256 × 256 × 3 to 224 × 224 × 3 which can eliminate all random noises like any other sub-object. The proposed method, DenseNet, connects all layers to each other. By using the DenseBlock+Transition structure, it realizes feature reuse and reduces the number of features, which could improve computational efficiency. We compare DenseNet-201 with VGG16 and MobileNetV2. VGG16 is very neat and contains multiple Conv->Conv->Max_pool structures, and the essential part of MobileNet is depthwise separable convolution. The whole network appears to be stacks of deeply detachable convolution modules. Applying DenseNet-201 to the dataset, Galaxy10 DECals, we achieved 84.1% accuracy of classification, higher than VGG16 of 79% and MobileNetV2 of 78%.
Mining requests in Brazil’s indigenous lands finally removed, but the battle continues
Authors: Verstegen Judith A , Rorato Ana C 
Published: September 2023

Abstract
Mining causes intense socio-environmental impacts and threatens Indigenous peoples in Brazil, exposing them to violence, contagious diseases, mercury contamination, and loss of livelihoods. Recent collaborative efforts by society achieved positive advances against mining in Indigenous Lands (ILs). Notably, the National Mining Agency (ANM) has revoked thousands of mining requests that encroached upon ILs for decades, marking a historic but underpublicized milestone. However, in recent months, the National Congress has approved a series of counter-attacks against Indigenous rights. Despite these advancements, it is imperative for society to sustain pressure in combating illegal mining in ILs and the ongoing attacks by ruralist and mining groups, who have a long history of undermining Indigenous rights.
Low-dimensional van der Waals materials for linear-polarization-sensitive photodetection: materials, polarizing strategies and applications
Authors: Ma Yuhang , Yi Huaxin , Liang Huanrong , Wang Wan , Zheng Zhaoqiang , Yao Jiandong , Yang Guowei 
Published: September 2023

Abstract
Detecting light from a wealth of physical degrees of freedom (e.g. wavelength, intensity, polarization state, phase, etc) enables the acquirement of more comprehensive information. In the past two decades, low-dimensional van der Waals materials (vdWMs) have established themselves as transformative building blocks toward lensless polarization optoelectronics, which is highly beneficial for optoelectronic system miniaturization. This review provides a comprehensive overview on the recent development of low-dimensional vdWM polarized photodetectors. To begin with, the exploitation of pristine 1D/2D vdWMs with immanent in-plane anisotropy and related heterostructures for filterless polarization-sensitive photodetectors is introduced. Then, we have systematically epitomized the various strategies to induce polarization photosensitivity and enhance the degree of anisotropy for low-dimensional vdWM photodetectors, including quantum tailoring, construction of core–shell structures, rolling engineering, ferroelectric regulation, strain engineering, etc, with emphasis on the fundamental physical principles. Following that, the ingenious optoelectronic applications based on the low-dimensional vdWM polarized photodetectors, including multiplexing optical communications and enhanced-contrast imaging, have been presented. In the end, the current challenges along with the future prospects of this burgeoning research field have been underscored. On the whole, the review depicts a fascinating landscape for the next-generation high-integration multifunctional optoelectronic systems.
274 Regular dog walking is associated with reduced likelihood of unexplained falls, fear of falling and mobility problems in later life
Authors: Gallagher E , Briggs R , Kenny R A 
Published: September 2023

Abstract

Background
It has been suggested that regular dog walking may confer important health benefits in later life, including reducing the risk of falls, but little work has been done to date examining this. The aim of this study therefore was to assess whether community-dwelling older people who regularly walk their dog had reduced likelihood of falls, fear of falling and mobility problems.

Methods
At TILDA Wave 5, self-report was elicited from participants aged ≥60 years (n = 4,161; Mean age 71 years; 54% female) for dog ownership and frequency of dog walking. Regular dog walking was ≥4 days/week. Incidence of unexplained falls (not caused by a slip/trip) in the last 2 years was also by self-report. Fear of falling was defined as answering ‘yes’ to: ‘Are you afraid of falling?’ Mobility was measured with the Timed-Up-and-Go (TUG). Abnormal TUG was ≥14 seconds. Logistic Regression models reporting odds ratios assessed the association between regular dog walking and outcomes of interest, adjusting for age, sex, marital status, educational attainment, alcohol excess.

Results
15% of participants (629/4,151) were regular dog walkers. Dog walkers had a significantly faster TUG than non-dog walkers (10.3 [95% Confidence Interval [CI] 10.1–10.5]) vs 11.7 (95% CI 11.1–12.2) seconds). Regular dog walkers had a lower likelihood of unexplained falls (OR 0.59 (95% CI 0.37–0.95; p = 0.028), fear of falling (OR 0.78 [95% CI 0.63–0.96]; p = 0.022) and mobility problems (0.62 [95% CI 0.43–0.89]; p = 0.009) in fully adjusted models.

Conclusion
Cross-sectionally, regular dog walkers were less likely to report unexplained falls and fear of falling and had better mobility (by TUG) than non-dog walkers, after robustly controlling for covariates. While longitudinal data confirming the direction of the association is required, older people should be encouraged to continue regular dog walking where feasible.
43 Nursing Home Residents presenting to the Emergency Department; what can multidisciplinary, integrated frailty teams offer?
Authors: Cronin L , Leonard A , Pillay I 
Published: September 2023

Abstract

Background
Comprehensive Geriatric Assessment (CGA) in the Emergency Department (ED) may provide opportunities to improve care of Nursing Home Residents (NHR). The aim was to develop an abbreviated CGA (aCGA) for NHRs presenting to the ED and identify their multidisciplinary care needs.

Methods
The existing Frailty at the Front Door (FFD) CGA was simplified. Over a three-month period, consecutive patients over the age of 75 years in an age friendly hospital accredited emergency department, were selected by address from iPMS and underwent an aCGA. Age, gender, Clinical Frailty Score (CFS), 4AT, excessive polypharmacy, high risk medications usage, 4QT swallow screen, MST nutrition screen, need for physiotherapy and/or occupational therapy input, use of intravenous (IV) antibiotic therapy and the number of those experiencing their last 1,000 days was entered prospectively onto an Excel database. Data was analysed using descriptive statistics.

Results
104 NHR presented to the ED, 59 of which (57%) underwent an ‘a-CGA’. Co-ordinated care was then delivered through the FFD. The mean (SD) age was 85 years (+/−6.5). Male to female ratio was 2:1. The mean CFS was 6.6(+/−0.5) 0.39% (n = 23) were at risk of delirium (4AT ≥ 2), 80% (n = 47) had excessive poly-pharmacy or were prescribed high risk medications, 63% (n = 37) presented with dysphagia, 50% (n = 30) were at risk for malnutrition (MST ≥ 2), 39% (n = 23) required trans-disciplinary assessment of function/mobility, 52% (n = 31) required IV antibiotics and 20% (n = 12) were experiencing their last 1,000 days.

Conclusion
The aCGA effectively identified frailty syndromes, triggering early input from the ED based interdisciplinary team. A significant number of NHRs were in their last 1,000 days. Administering IV antibiotics in nursing homes with on-site access to the wider multi-disciplinary team would address the needs of many NHRs who currently have no option other than admission to hospital. For those in their last 1,000 days particularly, this would represent a more person-centred approach.
55 Dysphagia identification practices in residential long-term care settings in the Republic of Ireland: a cross-sectional electronic survey
Authors: Artiles C E , Mooney M , Regan J , Donnellan C 
Published: September 2023

Abstract

Background
Dysphagia is common in older adults with neurological and/or neurodegenerative conditions requiring early identification to avoid serious complications, including malnutrition, dehydration and aspiration pneumonia. The aim of this study was to describe dysphagia identification practices in Residential Long-Term Care Settings (RLTCS) in the Republic of Ireland.

Methods
A cross sectional electronic survey was designed using Qualtrics XM Platform that included 40 questions divided in 5 sections. The survey was circulated via email to RLTCS to be completed by either a clinical nurse or nurse manager in each facility. Descriptive data analysis was conducted on survey responses using RStudio.

Results
There were 45 surveys completed out of N = 429 electronically circulated (10% response rate). All RLTCS that completed the survey reported to routinely implement dysphagia identification practices. Observation of signs and symptoms of dysphagia at mealtimes (100%), review of clinical records to identify dysphagia risk factors and comorbidities (93%) and resident self-report of swallowing difficulties (91%) were the most common dysphagia identification practices. Poor oral motor function (89%) and history of aspiration pneumonia (87%) were reported as the main dysphagia risk factors. A swallow screening test was less commonly used to identify dysphagia (42%). Coughing or choking on food or fluids, inability to swallow medication, oral or pharyngeal residue, delayed or absent initiation of swallow, leaking of food or fluids and difficulty with coordination of breathing and swallowing were identified by all respondents as key criteria for risk of dysphagia. Almost all participants reported that residents at risk of dysphagia were routinely referred to speech and language therapy services for further assessment.

Conclusion
A range of practices were identified in RLTCS that participated in the survey. Further research approaches are required to provide a more in-depth perspective of dysphagia identification practices and protocols used in RLTCS for timely and accurate identification of dysphagia to avoid associated complications.
60 Surgical inpatient delirium: a re-audit of the documentation of the 4AT score
Authors: Koay W J , Wilkinson G , Cohalan D , Burke P , Vallely S , Byrne C , O'Scanaill P 
Published: September 2023

Abstract

Background
Postoperative delirium and perioperative neurocognitive disorder are the commonest postoperative complications in patients over 65. This has implications for brain health, hospital length of stay and incidences of morbidity and mortality. Our aim was to perform a re-audit of the documentation of 4AT scores for surgical inpatients &gt;65 following educational sessions across key ward-based staff (medical, nursing, and allied-health).

Methods
Using the guidelines for Perioperative Care for People Living with Frailty undergoing Elective and Emergency Surgery from the Centre of Perioperative Care (CPOC) and the British-Geriatric-Society (BGS) as a standard, a re-audit of surgical inpatients &gt;65 was performed on four surgical wards over two weeks. Data was collected on 4AT documentation on admission, day one and day three post-operatively, type-of-surgery, anaesthesia and risk factors for delirium (dementia, polypharmacy, opioid & benzodiazepine use, alcohol excess, ≥2 medical co-morbidities, number of ward moves and surgery &gt;2 hours).

Results
Of the 55 surgical patients, the average age was 76 (65–93). 32.7% of patients presented for hip fracture surgery (n = 18), 40% abdominal/pelvic surgery (n = 22), 15% other surgeries (n = 15). 35% of patients (n = 19) had baseline 4AT documented preoperatively compared to 25%(n = 9) in the initial audit. 4AT score documentation during admission was 47.3%(n = 26) compared to 33%(n = 11). ≥2 surgical risk factors for delirium were found in all patients. The incidence of delirium was 14.5% with eight patients having a 4AT score &gt; 4. Patients who developed delirium had 3–8 risk factors. Four of eight patients with delirium had pre-existing cognitive impairment and/or dementia.

Conclusion
There was an increase in frequency of 4AT assessments as a delirium screening tool in our cohort following successful collaboration between medical, surgical, anaesthesia, nursing and allied-health staff. Regular educational sessions to maintain awareness and screening of delirium peri-operatively in surgical patients &gt;65 will be a focus for continued quality improvement.