bluuebunny/crossref_metadata_2025_split huggingface dataset:
CycleGAN-based image translation from MRI to CT scans
Authors: Cai Yingchao , Li Mengxiao , Liu Shiqi , Zhou Changhao 
Published: December 2023

Abstract
In recent years, the application of deep learning techniques to medical image analysis has shown promising results in improving diagnosis and treatment of diseases. One such technique is CycleGAN, a variant of Generative Adversarial Networks (GANs) that enables unpaired image-to-image translation. This paper presents a CycleGAN-based approach for transforming CT and MRI scans, which can provide doctors with more diagnostic information and assist in the prediction and diagnosis of tumors. Our experiments are based on brain scan images collected from the Kaggle dataset, with no paired information available. The generator and discriminator models of the CycleGAN are trained with the Adam optimizer and a cycle consistency loss weight (λ) of 10. The total training time is about 12 days, and the model is tested for 75 epochs with a fixed learning rate of 0.0002. The results demonstrate the effectiveness of the proposed method, achieving high-quality image translation from MRI to CT scans. The advantages of CycleGAN in medical image analysis include its ability to handle unpaired data, perform cross-domain image translation, ensure cycle consistency, and generate diverse outputs. Future work can further explore the use of CycleGAN for other medical image analysis tasks and investigate how to optimize the model performance.
The application of deep learning in computer vision: Facial emotion recognition based on convolutional neural network
Authors: Liu Weiting , Xia Minghong 
Published: December 2023

Abstract
Emotion recognition is an essential task in many fields, including affective computing, psychology, and human-computer interaction. The ability to accurately recognize emotions from facial expressions can help in the development of more personalized and responsive systems. The objective of this paper is to investigate the effectiveness of a Convolutional Neural Network (CNN) model in classifying emotions from facial expressions. We used the images classified into seven different categories from FER2013 dataset. The model is implemented using TensorFlow Keras, which consists of four CNN layers followed by two fully connected (FCN) layers and an output layer. In between each layer we use batch normalization, max pooling, and dropout to reduce calculation cost and prevent overfitting. Our model achieved an accuracy of 66.85% and a precision accuracy of 77.18% on the training set after 20 epochs. The recall and the F-1 score is 56.93% and 0.6545 respectively. This study demonstrates that the proposed CNN model can effectively recognize emotions in facial images. The availability of the FER2013 dataset provides researchers with an opportunity to further explore this area of research. The proposed model can be useful in various applications, such as affective computing, human-computer interaction, and psychology. Future work could involve tuning the hyperparameters of the model or using different datasets to explore the generalizability of the proposed model. Overall, our study provides insights into the effectiveness of CNNs for emotion recognition and highlights the potential for further research in this area.
Overview on reinforcement learning of multi-agent game
Authors: Zou Wenrui 
Published: December 2023

Abstract
Game intelligence is an emerging hot topic in the field of artificial intelligence in recent years, and multi-agent learning is a frontier topic in the field of the intelligent game, which has a huge development prospect in all fields. This paper introduces the origin of reinforcement learning (RL) from the law of effect in animal experimental psychology and the optimization theory of optimal control. Then, the author describes the systematic composition of multi-agent reinforcement learning (MARL), and summarizes the classification of its research methods. The existing problems of MARL are discussed from three aspects: non-stationarity of the environment, partial observability, and the dimensional explosion problem. Finally, an outlook on the future is given based on the current development status of MARL and the important and difficult issues in the research field.
CNN combined with attention module for facial expression recognition
Authors: Yang Tiancheng 
Published: December 2023

Abstract
In contemporary times, the proliferation of information technology has prompted an increased interest among scholars towards artificial intelligence, particularly its constituent algorithms, such as deep learning, multilayer perceptron and convolutional neural networks. Of specific interest is the analysis of facial expressions, which has emerged as a popular research topic. However, classifying facial expressions presents a significant challenge due to variations in expressions associated with different emotions, as well as similarities between the various emotions. The task of expression classification is further complicated by the abundance of facial features that must be considered. In this paper, the basic Convolutional Neural Network (CNN) model was first trained on the FRE-2013 face expression dataset and optimized by adding an attention module to learn and analyse the key features of faces, thereby improving the classification accuracy of the model. In short, this model achieved an accuracy of 71.65% and an F1-Score of 0.66 while the model without the attention module only achieved a model accuracy of 69.86% and an F1-Score of 0.65, an improvement of about 2%. The analysis demonstrated that by adding the attention mechanism, some important features e.g. the eyes and mouth, are given more weight, thus improving the classification accuracy.
Edge-centric energy efficient LSTM with federated learning model based computation offloading for IoT applications
Authors: Selvamani P , Anitha R 
Published: December 2023

Abstract
A more recent innovation to support cloud computing is edge computing that can address the deficiency of the existing centralised cloud computing paradigm and bring compute and storage resources closer to devices. Edge computing isn’t always the same as conventional cloud computing. Edge computing isn’t always the same as conventional cloud computing. It is a new computational model that performs computation at the network edge. Its crucial idea is to bring computing in the way of the information’s source. Task scheduling is the process allocating incoming requests (tasks) using a specific method to make the best use of the resources for the needed process. Users of services must submit their requests online because cloud computing is the technology used to deliver services through the internet. This paper aims to provide an Edge centric LSTM model with Federated learning for Data storage and model training that will take place on powerful edge servers in distributed ML methods. a massively distributed tasks requiring Processing on-site, coordinating the task remotely and execution are carried out in collaboration with number of multiple edge nodes and the distant cloud infrastructure. As a result, improved computation offloading and networking tradeoffs are possible to achieve low latency, large bandwidth take place on powerful edge servers.
Research on mass image classification based on CNN deep learning
Authors: Dai Hengde 
Published: December 2023

Abstract
Image classification is one of the most important fundamental problems in computer vision research. It involves many basic problems in the field of computer vision, such as feature extraction, object detection and so on. Therefore, the study of image classification is helpful to understand the basic knowledge in the field of computer vision. Since CNN model made a breakthrough, deep learning has been widely used in image classification field. This paper aims to apply the deep learning method to solve the problem of image classification, study and understand image classification problems to help further study and study in the future. By learning 50000 well classified images, this research tries to find the most suitable algorithm and model to achieve the optimal classification, realizing 20 classification and 100 classifications for 10000 unclassified images with the highest accuracy possible.
Foam stability and thermo-mechanical properties of micro/nano filler loaded castor oil based flexible polyurethane foam
Authors: Shaik Aabid Hussain , Patel Chahil , Rahaman Ariful , Chakraborty Samarshi , Kumar Sanjay , Agashe Snehalata , Rahaman Mostafizur , Periyasami Govindasami , Chandan Mohammed Rehaan 
Published: December 2023

Abstract
Use of fillers in polymers is to improve thermo-mechanical properties of the resulting material. Fillers are also used in polymeric foam as cell openers. Flexible polyurethane (PU) foams undergo major loss in structural stability when synthetic polyol is replaced with castor oil in the formulation as an alternate polyol. This study probes the effect of various micro and nano-fillers on PU foams prepared using blend polyol containing castor oil and synthetic polyol at a ratio of 1:1. Physical and cellular properties such as foam height, cell diameter, strut thickness and cell number density were evaluated to probe the structural stability of the foam. All foams prepared were found stable while it was found that the densities of the PU foams synthesized were greater than that of the conventional PU foams. Addition of fillers found to enhance thermal and mechanical properties of the foam. Moreover, all foam samples were found to observe thermal stability over and above 258 °C. Minimum glass transition temperature was recorded for 15% HG samples (i.e., −35.5 °C). Highest tensile strength was observed for 15% Si samples whereas, highest elongation was observed for 10% NC.
Wafer-scale uniformity of Dolan-bridge and bridgeless Manhattan-style Josephson junctions for superconducting quantum processors
Authors: Muthusubramanian Nandini , Finkel Matvey , Duivestein Pim , Zachariadis Christos , van der Meer Sean L M , Veen Hendrik M , Beekman Marc W , Stavenga Thijs , Bruno Alessandro , DiCarlo Leonardo 
Published: December 2023

Abstract
We investigate die-level and wafer-scale uniformity of Dolan-bridge and bridgeless Manhattan-style Josephson junctions, using multiple substrates with and without through-silicon vias (TSVs). Dolan junctions fabricated on planar substrates have the highest yield and lowest room-temperature conductance spread, equivalent to

∼

100

M
H
z

in transmon frequency. In TSV-integrated substrates, Dolan junctions suffer most in both yield and disorder, making Manhattan junctions preferable. Manhattan junctions show pronounced conductance decrease from wafer center to edge, which we qualitatively capture using a geometric model of spatially-dependent resist shadowing during junction electrode evaporation. Analysis of actual junction overlap areas using scanning electron micrographs supports the model, and further points to a remnant spatial dependence possibly due to contact resistance.
High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization
Authors: Ramya R , Kumar P 
Published: December 2023

Abstract
Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.
Observation of the performance enhancement of a non-degenerate JPA versus degenerate JPA in a QTMS radar simulation
Authors: Norouzi Milad , Hosseiny Seyed Mohammad , Seyed-Yazdi Jamileh , Irannezhad Fatemeh 
Published: December 2023

Abstract
Small differences in the types of Josephson parametric amplifiers (JPAs) can cause significant changes in quantum two-mode squeezed (QTMS) radars. In this work, taking into account these differences, we examine two types of JPA in terms of pumping in the quantum radar, including degenerate JPA (DJPA) and non-degenerate JPA (NDJPA) under the same conditions, leading to results very significant for the design of a QTMS radar. This study shows that QTMS radar performance is better than that of the DJPA scenario when using the NDJPA scenario. In addition, we calculated the signal-to-noise (SNR) ratio and receiver operating characteristic (ROC) for two scenarios, both of which show the superiority of the NDJPA scenario. The SNR of the QTMS radar in the NDJPA case is about 1.7 dB higher than in the DJPA case. In addition, the channel number of detection probability in the NDJPA scenario is lower than that in the DJPA scenario, which indicates better ROC performance. Finally, using an NDJPA in a quantum radar can achieve a performance improvement over a quantum radar using a DJPA.
Atypical Myasthenia gravis in an older person resulting in delayed diagnosis: a case report
Authors: Adamu Maimuna , Saka Felix 
Published: December 2023

Abstract
Myasthenia gravis (MG) is a rare autoimmune neuromuscular disorder characterised by varying degrees of skeletal muscle weakness. Diagnosing MG represents a challenge for clinicians due to the diversity of disease manifestations. We present an unusual case of this disease in an older gentleman in which the patient reported swollen tongue and lips which was incorrectly diagnosed and treated as angioedema. Months later, he developed progressive dysphagia, dysarthria and weight loss. The diagnosis was further delayed by the identification of a hiatal hernia and Zenker’s diverticulum after extensive investigation for dysphagia. A total of 15 months on prompted by persistent symptoms, the patient was found to have MG by positive antibody test and Electromyogram. Treatment was initiated which led to a rapid and significant relief of his symptoms.
‘I don’t think they really link together, do they?’ An ethnography of multi-professional involvement in advance care planning in nursing homes
Authors: Andrews Nicola , Myall Michelle 
Published: December 2023

Abstract

Background
Given the globally ageing population, care homes have an important role in delivering palliative and end-of-life care. Advance care planning (ACP) is promoted to improve the quality of end-of-life care in this setting. While many professionals can be involved in ACP, little is known about what influences multi-professional involvement and how multi-professional working impacts the ACP process in the UK. This study investigated multi-professional practice in relation to ACP in nursing homes.

Design and methods
An ethnography was undertaken in two UK nursing homes using multiple methods of data collection: observations, interviews and document review. Participants included the following: nursing home residents (n = 6), relatives (n = 4), nursing home staff (n = 19), and visiting health and social care professionals (n = 7). Analysis integrated thematic analysis, mapping of resident ACP trajectories and documentary analysis.

Findings
This paper suggests that multi-professional and relatives’ involvement in ACP was disjointed. Continuity and coordination were disrupted by misalignment of visiting professional and nursing home organisational structures. Findings show a ‘knotworking’ approach to teamwork and power imbalance between nursing home staff and visiting professionals, such as general practitioners. While residents wished their relatives to be involved in their ACP, this was not formally recognised, and limited support existed to facilitate their involvement.

Conclusion
The structure and organisation of multi-professional and relatives’ involvement in ACP led to fragmentation of the process. This marginalised the voice of both the resident and nursing home staff, thereby limiting ACP as a tool to enhance quality of end-of-life care.
Influence of polarized light, disruptive visual patterns, and chemical cues on oviposition in the aquatic midge, Chironomus riparius (Diptera: Chironomidae)
Authors: Walsh Wesley , Duffner Mitch , Pasternak Anna , Brodie Bekka S , Johnson Kelly S 
Published: December 2023

Abstract
Chironomid nonbiting midges are common in many waterbodies, occurring at high densities in sediment and also when flying adults emerge. Although important for food webs and ecosystem processes, the mass emergences of chironomid adults make some species nuisance pests. As part of an effort to develop “push–pull” strategies for managing midge populations, we investigated the importance of visual and chemical cues used by gravid females when selecting sites for oviposition. Field and laboratory oviposition choice tests with Chironomus riparius (Meigen) were used to assess the attractiveness of dark container colors and polarized light for females seeking water for egg laying. Females were not sensitive to increased intensity of polarized light, but they laid fewer eggs in containers with white color above the water’s edge but black below the surface. A disruptive pattern of vertical black and white stripes at the water’s edge reduced egg laying to a similar degree as white. To investigate the importance of olfactory or gustatory cues in oviposition decisions, we tested 3 potential chemical repellents (damaged larvae simulating predator presence, azadiractin, and picaridin) and 5 potential attractants (a tannin–molasses mixture, leaf detritus, live Hydrilla plants, periphyton, and fermented alfalfa infusions used to bait mosquito oviposition traps). Chemical cues appeared to play a weak role, if any, in oviposition choices. Onlyazadiractin (0.02 and 0.11 ppm) reduced the number of egg ropes deposited, but the mechanism may have been from mechanical trapping and drowning, not deterrence.
Genetic identification, pathogenicity and patulin production of Penicillium species from apple blue mold in China
Authors: Shen Youming , Ma Wenyu , Ma Ning , Li Mengyao , Zhang Jianyi , Xu Guofeng 
Published: December 2023

Abstract

Objectives
Blue mold is a common postharvest disease that leads to severe rot and patulin (PAT) contamination in fruits, which is a key factor endangering the storage and quality of apple products. However, the Penicillium species from apple blue mold in China and their pathogenicity and PAT production ability have not been well studied.

Materials and Methods
In this study, 62 strains of pathogenic Penicillium species were isolated from blue mold apples collected from different regions of China. The ITS, BenA, and CaM genes were sequenced for molecular identification.

Results
Penicillium strains were identified as three species, 57 P. expansum, 3 P. crustosum, and 2 P. polonicum. Twelve variants of the ITS sequence, 61 variants of BenA and 67 variants of CiaM genes were identified between Penicillium species. Fungal morphology was observed on potato dextrose agar (PDA). After 10 d of cultivation, P. expansum cultures were detected with PAT concentrations ranging from 457.98 to 1333.85 mg/kg, and two P. polonicum strains were 6.02 mg/kg and 6.30 mg/kg. The three P. crustosum strains could not produce PAT on PDA. In apple inoculation, P. expansum strains showed significantly faster infection rates than P. crustosum and P. polonicum strains. All P. expansum strains can produce PAT during infection, and the concentrations in the rot apple tissues were 2.31–88.67 mg/kg. The P. crustosum and P. polonicum strains could not produce PAT during infection.

Conclusions
This study provides data and information regarding the morphology, virulence and PAT production of Penicillium pathogens that will improve the understanding of apple blue mold and postharvest disease control.
Optimization of a DNA extraction protocol from fingerprints for the analysis of nuclear STR and mitochondrial DNA genetic profiles
Authors: Loarce Yolanda , Rubio Pilar , Sánchez-Sanz Almudena , Higuera Ariadna , Rodríguez-Pascual José A , Blanco Julián , Gutiérrez-Redomero Esperanza , González Juan M 
Published: December 2023

Abstract

Most of the cells found in fingerprints belong to the cornified barrier of the epidermis (stratum corneum), lack nuclei and cytoplasmic organelles, and are filled with keratin. Nuclear and mitochondrial DNA is degraded and embedded in the keratin mesh, a very resistant protein that is difficult to remove during DNA extraction. In this work, we studied the possible negative effect of keratin on Polymerase Chain Reaction (PCR) reactions and the influence of keratinase and proteinase K on the extraction of DNA from fingerprints. The role of glycogen in the DNA yield during the precipitation step and the importance of washing the obtained DNA with 70% ethanol were also studied. DNA was extracted from 96 fingerprints corresponding to recent prints and stored for 0, 1, 5, and 18 months from six individuals. No differences were observed in the concentration of extracted DNA or in the number of nuclear Short Tandem Repeat (STR) alleles in the genetic profiles of fingerprints stored during different times. However, sex differences were observed in both the concentration of DNA obtained and the number of nuclear STR alleles detected, being lower in females than in males. In 80% of the fingerprints genetic profiles were obtained with at least half of the STR nuclear markers and, in 50% of the fingerprints genetic profiles were obtained with more than 90% of the markers, which would allow an unambiguous identification of the donor. In all fingerprints where mitochondrial DNA was analyzed, complete sequencing of the HV1 and HV2 regions was possible, which increases the accuracy of the results obtained. The optimized protocol allowed obtaining a complete STR nuclear genetic profile of a 20-year-old palmprint.

Key points