bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing standards for the implementation of stepped care in child and youth mental health service settings: protocol for a multi-method, delphi-based study
Authors: Young Bryan , Mughal Sarah , Churchill AnnMarie , Rash Joshua , Tee Karen , Salmon Amy , Shah Jai
Published: December 2024

Introduction

Canadian youth mental health (YMH) systems have the potential to urgently tackle the mental health treatment gap currently impacting young people, and stepped care (SC) is one model that can address this need. The adoption of SC models can guide the development of better-connected YMH systems by simplifying transitions and care pathways. To do so requires robust standards that are co-created across stakeholder groups, including with lived experience experts, to ensure the effective implementation of SC models.

Methods and analysis

This study aims to establish standards for implementing SC in Canadian child and YMH service settings by convening and developing a learning alliance (LA) of 65 individuals, translating guiding principles to standards via consensus methods (Delphi study), and operationalising and applying draft standards to three test ecosystems. Members of the LA will be recruited via snowball and purposive recruitment techniques to complete an e-Delphi study over three to four rounds until consensus is achieved. Participants will rank their agreement with including specific clause items in the final standard, and will be given opportunities to provide feedback and suggest revisions during each round. Comments will be analysed, scored and coded accordingly. Once consensus has been achieved, members of the LA will consider the application of these implementation standards in three settings that could benefit from SC. The protocol for this study was registered at Open Science Framework (https://doi.org/10.17605/OSF.IO/J5UNW).

Ethics and dissemination

The protocol has been approved by the Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal—Mental Health and Neuroscience subcommittee. As part of the ethics approval, informed consent forms for all Delphi participants were created and distributed to participants ahead of the Delphi. This includes parental consent forms for all LA members participating in the study who are under the age of 18. On completion, the project will ultimately support the implementation of SC in diverse service systems and guide the development of a robust and connected mental health delivery system in Canada. The final standard will be shared with relevant government bodies and health planners and disseminated via academic and other platforms.
Unravelling the potential relationship between the climate crisis and the health of migrant children in LAC: perceptions from migrant parents and healthcare professionals in Chile
Authors: Carreno Calderon Alejandra , Bluckaz Alice , Cabieses Baltica
Published: December 2024

Introduction

Latin America and the Caribbean (LAC) is one of the regions most affected by the climate crisis, which is connected to international migration through a complex nexus. During the last years, migratory flows on the continent have increasingly included children and adolescents who are migrating through non-authorised crossing points. The existing literature shows how inequities negatively affect migrant children and the role that healthcare systems can play to mitigate them.

Objective

Based on a qualitative study, the paper aims to analyse the role of the climate crisis on the healthcare needs of migrant children from LAC who are currently living in Chile, from the point of view of parents from five main countries of immigration in Chile and healthcare professionals.

Method

An exploratory study was conducted in Arica, Antofagasta and Santiago. In-depth interviews with 20 migrant parents and 20 healthcare professionals were carried out. The interviews were transcribed verbatim and a thematic analysis was performed.

Results

Three findings emerged from this study: (1) food insecurity affects LAC migrant children in their country of origin and during their migratory trajectories to Chile, (2) natural disasters and environmental degradation in the countries of origin are not the only drivers of migration for LAC families but also prevent returns, even when they remain undocumented and (3) LAC migrant children are exposed to urban pollution and contaminants in informal settlements due to difficulties in accessing formal housing, among others.

Conclusions

The climate crisis must be integrated into the study of migrant health in LAC, considering the current context of multiple political, health and economic crises in the region. Healthcare professionals and communities play a central role in creating interventions to build sustainable and resilient universal healthcare systems.
Biofeedback rehabilitation in children with encopresis due to retentive constipation using simple tools: a real-world study in a French paediatric centre
Authors: Campeotto Florence , Abt Servane , Enaud Raphael , Avril Servane , Abi-Nader Elie , Neuraz Antoine , Ronconi Monica Silvia
Published: December 2024

This study aimed to describe the effectiveness of biofeedback (BFB) rehabilitation in children with retentive encopresis (RE). A retrospective, single-institution study was conducted in children with BFB sessions for RE between 2017 and 2020. Manometry data and associated envy scores were analysed. BFB was a success (recovery from RE or ≥50% reduction in faecal soiling) in 37/44 (84%) children at the last session and in 32/44 (73%) 6 months later. The median age of children was higher in the success group at 6 months (p=0.013). BFB could then be clinically effective in children with RE.
Lactylation-driven TNFR2 expression in regulatory T cells promotes the progression of malignant pleural effusion
Authors: Xue Qianqian , Peng Wenbei , Zhang Siyu , Wei Xiaoshan , Ye Linlin , Wang Zihao , Xiang Xuan , Liu Yao , Wang Haolei , Zhou Qiong
Published: December 2024

Background

Although tumor necrosis factor receptor 2 (TNFR2) has been recognized as an attractive next-generation candidate target for cancer immunotherapy, the factors that regulate the gene expression and their mechanistic effects on tumor-infiltrating regulatory T cells (Treg cells) remain poorly understood.

Methods

Single-cell RNA sequencing analysis was employed to analyze the phenotypic and functional differences between TNFR2+Treg cells and TNFR2−Treg cells. Malignant pleural effusion (MPE) from humans and mouse was used to investigate the potential mechanisms by which lactate regulates TNFR2 expression.

Results

Treg cells with high TNFR2 expression exhibited elevated levels of immune checkpoint molecules. Additionally, the high expression of TNFR2 on Treg cells was positively correlated with a poor prognosis in MPE patients. Moreover, we revealed that lactate upregulated TNFR2 expression on Treg cells, thereby enhancing their immunosuppressive function in MPE. Mechanistically, lactate modulated the gene transcription of transcription factor nuclear factor-κB p65 (NF-κB p65) through histone H3K18 lactylation (H3K18la), subsequently upregulating the gene expression of TNFR2 and expediting the progression of MPE. Notably, lactate metabolism blockade combined with immune checkpoint blockade (ICB) therapy effectively enhanced the efficacy of ICB therapy, prolonged the survival time of MPE mice, and improved immunosuppression in the microenvironment of MPE.

Conclusions

The study explains the mechanism that regulates TNFR2 expression on Treg cells and its function in MPE progression, providing novel insights into the epigenetic regulation of tumor development and metabolic strategies for MPE treatment by targeting lactate metabolism in Treg cells.
The “$$B\rightarrow K\pi $$ puzzle”: a new perspective
Authors: Szabelski Adam
Published: December 2024

A phenomenological analysis based on the published branching fractions and CP asymmetry observables of the

$$B\rightarrow K\pi $$

B
→
K
π

,

$$B\rightarrow \pi \pi $$

B
→
π
π

and

$$B\rightarrow KK$$

B
→
K
K

dataset is performed. The amplitude decomposition by the topological diagrams and the SU(3) irreducible representation is used. The result of the global fit can be interpreted as a solution to the “

$$B\rightarrow K\pi $$

B
→
K
π

puzzle”. The obtained precision enables to test a SU(3)-based sum rule showing

$$6\sigma $$

6
σ

discrepancy with the expectation value in the Standard Model scenario. To improve the obtained results the main effort should concentrate on the

$$B^0\rightarrow K^0\pi ^0$$

B
0

→

K
0

π
0

measurements.
A Personalized Recommendation Approach for Blended English Teaching Database Services Based on Content Retrieval
Authors: Jia Shi
Published: December 2024

The blended English teaching database may include non-relational databases and relational databases. The data structure, query method and storage mechanism of each database are different, which increases the difficulty of data integration and analysis, thereby reducing the recommendation validity of personalized recommendations for blended English teaching database services. To this end, a personalized recommendation method for blended English teaching database services based on content retrieval is proposed. Analyze the unique attributes of non-relational and relational databases in the blended English teaching database, accurately define the complex relationships between tables in different databases, and then build a mapping relationship diagram model of the blended English teaching database to achieve the optimization of the database structure graphical conversion. Through cluster analysis of the behavioral data of the service objects of the blended English teaching database, the user’s preference characteristics are captured, and these preference data are efficiently organized in a labeled manner, achieving accurate matching of user preferences. Entropy value vectorization technology is used to efficiently process the service data text in the blended English teaching database. Through content retrieval technology, the similarity between graph data and retrieval data is calculated, and interference factors in the data are effectively eliminated. On this basis, a personalized recommendation model based on content retrieval is constructed to provide users with more accurate and efficient recommendation services. Experimental results show that the proposed method shows significant advantages in improving recommendation effects.
Intelligent Music Recommendation System: Using Algorithms to Improve the Accuracy of Personalized Music Experience
Authors: Wan Liruxin , Lu Jing
Published: December 2024

To ensure that users have a good time when using music services and that Intelligent Music Recommendation Systems (IMRS) work well, it is important to study effective methods of music recommendation and to build a reliable platform for online recommendations that work in real time. For the purpose of improving upon current music recommendation systems, which suffer from poor generalizability, inadequate classification efficiency, and inadequate signal feature capture. We propose the Deep Sleep Optimization (DSO) algorithm as a deep confidence network-based music recommendation system and utilize simulated testing to prove its efficacy. As a result, the IMRS-DSO was created to enhance the deep confidence network in order to create a music recommendation algorithm that makes use of massive amounts of data and intelligent optimization techniques. The method relies on perusing the system’s database for information about each song, including its genre, artist, and tracks. By analyzing these characteristics, the recommendation engine can find songs that are similar to the music the utilizes has entered and then provide suggestions for like songs. Music recommendation accuracy, memory, and coverage are entirely improved through the predicted strategy, which effectively meets the real-time requirements. Collaborative filtering (COL) is a system that can automatically uncover users’ future interests and is often used for highly personalized music suggestions. Improves the flaws of conventional music recommendation systems’ inadequate generalization, inadequate signal feature capture, and inadequate classification efficiency.
Dance Action Recognition Model Based on Spatial Frequency Domain Features of Contour Images
Authors: Lei Qiao
Published: December 2024

Aiming at solving the problems of low recognition rate, low recognition efficiency and poor recognition effect in the current dance motion recognition methods that are affected by the surrounding environment, this study proposes a dance action recognition model based on the spatial frequency domain features of contour image. This study uses texture information to extract the dance action contour image, solve the feature vector of the contour image by the hypercomplex Fourier transform, and adopts the phase spectrum and energy spectrum transformation to smooth the contour image, so as to generate a saliency map, finally completes the extraction and preprocessing of dance action contour image. This paper distinguishes the high-frequency and low-frequency parts of dance action through the method of discrete cosine transform, calculates the number of pixels contained in the dance action images to be recognized, and extracts the spatial frequency domain features of contour image of dance action, builds the human posture model. This model realizes the dance action recognition by using the classifier to process the above-extracted feature vector and its label. The experimental result shows that the dance action recognition effect of this research model is good, and its recognition rate is high in different dance action types, and can effectively meet the needs of dance action recognition.
An Automatic Recommendation Method for English Language Education Resources Based on Particle Swarm Algorithm
Authors: Zhang Dan
Published: December 2024

This study addresses the challenge of maintaining an up-to-date and diverse university English resource library by proposing an advanced method that leverages deep learning and particle swarm optimization (PSO). Existing systems often struggle to efficiently integrate new, varied educational materials while balancing user satisfaction with educational effectiveness. To bridge this gap, we introduce a PSO-based multi-objective recommendation framework that incrementally updates the resource library, ensuring it remains both relevant and comprehensive. Our method begins with automated data collection from sources such as academic papers, news, and educational materials, followed by rigorous data cleaning and standardization using natural language processing (NLP) techniques. At the core of our system is a fine-tuned GPT-3 model, optimized to identify and categorize resources accurately. The multi-objective PSO algorithm then balances user satisfaction with educational value, meeting both immediate learning needs and long-term goals. Continuous user feedback further refines our approach, keeping recommendations aligned with evolving educational demands. Experimental results demonstrate that our approach surpasses existing methods in precision, coverage, diversity, and novelty, establishing it as a robust solution for AI-driven educational resource management.
Progress of Chinese Medicine in Regulating Microglial Polarization against Alzheimer’s Disease
Authors: Yang Fengge , Gao Wei , Wang Junting , Li Xue , Li Honglin
Published: December 2024

Alzheimer’s disease (AD), the predominant form of dementia, is a neurodegenerative disorder of the central nervous system (CNS) characterized by a subtle onset and a spectrum of cognitive and functional declines. The clinical manifestation of AD encompasses memory deficits, cognitive deterioration, and behavioral disturbances, culminating in a severe impairment of daily living skills. Despite its high prevalence, accounting for 60–70% of all dementia cases, there remains an absence of curative therapeutics. Microglia (MG), the resident immune cells of the CNS, exhibit a bifurcated role in AD pathogenesis. Functioning in a neuroprotective capacity, MGs express scavenger receptors, facilitating the clearance of [Formula: see text]-amyloid protein (A[Formula: see text]) and cellular debris. Conversely, aberrant activation of MGs can lead to the secretion of pro-inflammatory cytokines, thereby propagating neuroinflammatory responses that are detrimental to neuronal integrity. The dynamics of MG activation and the ensuing neuroinflammation are pivotal in the evolution of AD. Chinese medicine (CM), a treasure trove of traditional Chinese cultural practices, has demonstrated significant potential in the therapeutic management of AD. Over the past triennium, CM has garnered considerable research attention for its multifaceted approaches to AD, including the regulation of MG polarization. This review synthesizes current knowledge on the origins, polarization dynamics, and mechanistic interplay of MG with AD pathology. It further explores the nexus between MG polarization and cardinal pathological hallmarks of AD, such as A[Formula: see text] plaque deposition, hyperphosphorylation of tau, synaptic plasticity impairments, neuroinflammation, and brain–gut-axis dysregulation. The review also encapsulates the therapeutic strategies of CM, which encompass monomers, formulae, and acupuncture. These strategies modulate MG polarization in the context of AD treatment, thereby providing a robust theoretical framework in which to conduct future investigative endeavors in both the clinical and preclinical realms.
Pharmacological Effects of a Ginseng-Containing Chinese Medicine Formula in Treating Hepatocellular Carcinoma Based on Comprehensive Bioinformatics and Experimental Validation
Authors: Gao Tianqi , Zhou Ruisheng , Huang Dan , Wu Dailin , Gao Yong , Yuan Yi , Li Jing , Huang Shangyi , Xian Yanfang , Tang Ying , Lin Zhixiu , Zhou Daihan , Wang Shutang
Published: December 2024

Ginseng-containing Shentao Ruangan granules (STR) have been a well-known Chinese medicine prescription for the treatment of hepatocellular carcinoma (HCC) in China for decades. This study aimed to establish an in silico experimental framework to decipher the underlying mechanism of STR in the treatment of HCC. Microarray analysis, network pharmacology, RNA-sequencing (RNA-seq), bioinformatics analysis, and in vivo and in vitro experiments were used as integrated approaches to uncover the effects and mechanisms of action of STR. The introduction of STR significantly suppresses the proliferation and metastasis of HepG2 and Huh7 cells. STR treatment notably suppressed the growth of transplanted Huh7 tumors. Furthermore, STR administration reduced the expression of various epithelial-to-mesenchymal transition (EMT)-related proteins including N-cadherin, vimentin, and [Formula: see text]-catenin. By employing a systems biology approach, 21 common genes were identified across RNA-seq data, TCGA-HCC dataset, and network pharmacology analysis. Finally, of these genes nine were found to be associated with both OS and PFS in patients with HCC within the TCGA cohort. Validation of candidate genes by qPCR and WB identified a significant downregulation in the expression of pGSK3[Formula: see text] and RELA protein with increasing concentrations of STR. These results elucidated the mechanism by which STR inhibits tumor growth and EMT of HCC may be related to the GSK3[Formula: see text]/RELA pathway.
TOWARD ROBUST SPEECH EMOTION RECOGNITION AND CLASSIFICATION USING NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING MODEL
Authors: ALAHMARI SAAD , AL-SHATHRY NAJLA I. , ELTAHIR MAJDY M. , ALZAIDI MUHAMMAD SWAILEH A. , ALGHAMDI AYMAN AHMAD , MAHMUD AHMED
Published: December 2024

Speech Emotion Recognition (SER) plays a significant role in human–machine interaction applications. Over the last decade, many SER systems have been anticipated. However, the performance of the SER system remains a challenge owing to the noise, high system complexity and ineffective feature discrimination. SER is challenging and vital, and feature extraction is critical in SER performance. Deep Learning (DL)-based techniques emerge as proficient solutions for SER due to their competence in learning unlabeled data, superior capability of feature representation, capability to handle larger datasets and ability to handle complex features. Different DL techniques, like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Deep Neural Network (DNN) and so on, are successfully presented for automated SER. The study proposes a Robust SER and Classification using the Natural Language Processing with DL (RSERC-NLPDL) model. The presented RSERC-NLPDL technique intends to identify the emotions in the speech signals. In the RSERC-NLPDL technique, pre-processing is initially performed to transform the input speech signal into a valid format. Besides, the RSERC-NLPDL technique extracts a set of features comprising Mel-Frequency Cepstral Coefficients (MFCCs), Zero-Crossing Rate (ZCR), Harmonic-to-Noise Rate (HNR) and Teager Energy Operator (TEO). Next, selecting features can be carried out using Fractal Seagull Optimization Algorithm (FSOA). The Temporal Convolutional Autoencoder (TCAE) model is applied to identify speech emotions, and its hyperparameters are selected using fractal Sand Cat Swarm Optimization (SCSO) algorithm. The simulation analysis of the RSERC-NLPDL method is tested using a speech database. The investigational analysis of the RSERC-NLPDL technique showed superior accuracy outcomes of 94.32% and 95.25% under EMODB and RAVDESS datasets over other models in distinct measures.
HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUM
Authors: ALLAFI RANDA , ALROWAIS FADWA , ALJEBREEN MOHAMMED , NEGM NOHA , SALAMA AHMED S. , MARZOUK RADWA
Published: December 2024

Human activity recognition (HAR) employs a broad range of sensors that generate massive volumes of data. Traditional server-based and cloud computing methods require all sensor data to be sent to servers or clouds for processing, which leads to high latency and bandwidth costs. The long-term data transfer between servers and sensors maximizes the cost of latency and bandwidth. Real-time processing is, nevertheless, highly required for human action identification. By bringing processing and quick data storage to the sensors instead of depending on a central database, edge computing is rapidly emerging as a solution to this issue. Artificial intelligence is responsible for most HAR, which demands a lot of processing power and calculation. Artificial intelligence (AI) needs more computation which is not allowed by edge computing. So Edge intelligence, which allows AI to operate at the network edge for actual-time applications, has been made possible by the advent of binarized neural networks. To provide less latency and less memory for human activity identification at the edge network, we construct a hybrid deep learning-based binarized neural network (HDL-Binary Dilated DenseNet) in this research. Fractal HAR optimization algorithms could be applied to these algorithms. For example, fractal-HAR optimization techniques might be used to provide less latency and less memory human activity identification at the edge network. Using three sensors-based human activity detection datasets such as Radar HAR dataset, UCI HAR dataset and UniMib-SHAR dataset, we implemented the Hybrid Binary Dilated Dense Net. It is then assessed using four criteria. Comparatively, the Hybrid Binary Dilated DenseNet performs better with 99.6% radar HAR dataset which is highest than other models like CNN-BiLSTM and GoogLeNet.
SMART SURVEILLANCE: ADVANCED DEEP LEARNING-BASED VEHICLE DETECTION AND TRACKING MODEL ON UAV IMAGERY
Authors: OTHMAN KAMAL M. , ALZABEN NADA , ALRUWAIS NUHA , MARAY MOHAMMED , DAREM ABDULBASIT A. , MOHAMED ABDULLAH
Published: December 2024

Unmanned aerial vehicles (UAVs) can monitor traffic in different scenarios like surveillance, control, and security. The object detection method depends on UAVs equipped with vision sensors, which have received significant attention in domains such as intelligent transportation systems (ITSs) and UAVs, which can monitor road traffic across some distance and offer vital data for following intelligent traffic supervision tasks, namely traffic situational awareness, detecting sudden accidents, and calculating traffic flow. Nevertheless, most vehicle targets exhibit specific features and lesser sizes that challenge accurate vehicle recognition in UAV overhead view. Employing innovative computer vision (CV) models, vehicle recognition and tracking in UAV images contains detecting and following vehicles in aerial footage taken by UAVs. This procedure leverages deep learning (DL) approaches for perfectly detecting vehicles and a robust tracking method for monitoring their actions through the frames, offering vital information for traffic management, surveillance, and urban planning. Therefore, this study designs an Advanced DL-based Vehicle Detection and Tracking on UAV Imagery (ADLVDT-UAVI) approach. The drive of the ADLVDT-UAVI technique is to detect and classify distinct vehicles in the UAV images correctly as Brain-Like Computing technique for Traffic Flow Optimization in Smart Cities. In this approach, Gaussian filtering (GF) primarily eliminates the noise. Besides, the ADLVDT-UAVI technique utilizes a squeeze-and-excitation capsule network (SE-CapsNet) for feature vector derivation. Meanwhile, the hyperparameter selection process involves using the Fractals coati optimization algorithm (COA). Finally, the self-attention bi-directional long short-term memory (SA-BiLSTM) approach is utilized to classify detected vehicles. To validate the improved results of the ADLVDT-UAVI approach, a wide range of experiments is performed under VEDAI and ISPRS Postdam datasets. The experimental validation of the ADLVDT-UAVI approach portrayed the superior accuracy outcome of 98.35% and 98.96% compared to recent models.
ANALYZING THE RELATIONSHIP BETWEEN BRAIN AND HEART ACTIVITY ACROSS VARIOUS COLLABORATIVE TASKS
Authors: KREJCAR ONDREJ , MARESOVA PETRA , ABDULLAH JAMALUDDIN , NAMAZI HAMIDREZA
Published: December 2024

Understanding the correlation between the brain’s activity and the physiological responses of other organs under varying conditions is a crucial area of research that holds significant potential for advancing our knowledge of human physiology. In this study, we focused on investigating the interaction between the heart and brain by employing advanced complexity analysis techniques, specifically examining the fractal dimension and approximate entropy of electroencephalogram (EEG) and R-R interval time series. The analysis was conducted on data collected from 12 subjects who were observed under three distinct conditions: baseline (normal resting state) and two collaborative activities performed both with and without the presence of noise. Our findings revealed that the complexity patterns of EEG and R-R signals showed similar trends in alterations across all conditions, suggesting a strong coupling between the brain’s and heart’s responses. This observed coupling highlights the potential for a coordinated physiological interaction between these two critical systems. Furthermore, our approach, which successfully decoded the heart–brain correlation, offers a promising framework for extending this analysis to explore correlations between the brain and other organs, thereby contributing to a deeper understanding of the complex networks that underlie human health and adaptive physiological responses.
A COMPLEXITY-BASED EXAMINATION OF VARIATIONS IN BRAIN ACTIVITY BETWEEN INDIVIDUALS WITH INTELLECTUAL AND DEVELOPMENTAL DISORDERS (IDD) AND HEALTHY CONTROLS DURING REST AND EXTERNAL STIMULATION PERIODS
Authors: PAKNIYAT NAJMEH , SELAMAT ALI , KREJCAR ONDREJ , ANSARI SHADAB AHAMAD , NAMAZI HAMIDREZA
Published: December 2024

Investigating the disparities in brain activity between healthy individuals and those with intellectual and developmental disorders (IDD) is pivotal in biomedical research. In this study, we delved into these differences by employing a complexity-based analysis of electroencephalogram (EEG) signals. Specifically, we computed the fractal dimension (FD), approximate entropy, and sample entropy (SampEn) of EEG signals for both control (healthy) subjects and IDD individuals during rest and stimulation with music. Our findings revealed that IDD subjects exhibited higher complexity in both rest and stimulation scenarios compared to control subjects, indicative of heightened brain activity complexity among IDD individuals. This analysis not only sheds light on the variations in brain activity associated with IDD, but also underscores its potential applicability in assessing brain activity alterations across various disorders compared to healthy counterparts.
An Effective Handwritten Character Recognition Framework for South Indian Languages Using Adaptive Deep Learning Network
Authors: Banavatu Triveni , Parthasarathy Govindaswamy
Published: December 2024

The conversion of handwritten text into machine-readable format is termed as Handwritten Character Recognition (HCR). The differences in size, design, and alignment angle of the Telugu and Kannada alphabets have difficulty in recognizing handwritten documents in these languages across various real-world applications. Newly developed machine learning and deep learning models provide a significant improvements in the handwritten text recognition. These innovative methods offer promising enhancements in the accuracy and efficiency of character recognition within handwritten documents. However, effective recognition of digits is not an easy task due to people’s varying writing styles in the input sample. To overcome such limitations, we explore a novel approach specifically designed to boost the performance of HCR in South Indian languages such as Kannada and Telugu. Initially, handwritten images are gathered using traditional data sources. These collected images are then given into the recognition phase. Here, an Adaptive Dilated convolution-based Deep Network (ADC-DeepNet) is developed for character identification purposes. In ADC-DeepNet, the ShuffleNetV2 blends with the Bidirectional Long Short-Term Memory (Bi-LSTM) to produce accurate results. This fusion provides effective character recognition. Here, the Iterative Concept of Lyrebird Optimization (ICLO) is newly proposed to optimize the variables from ADC-DeepNet to improve the character recognition efficacy. The efficiency of the HCR system is evaluated among several recent techniques with some performance measures. Finally, the outcome showed that the accuracy of the proposed approach is 95.6, and other models like CNN, ResNet, Convolutional Autoencoder, and DeepNet gave the accuracy of 88.8, 91.5, 90.6, and 93.3, respectively. Thus, the findings of the experiment show that the developed ADC-DeepNet model can effectively identify the handwritten characters in south Indian languages.
Stability of determining a Dirichlet–Laplace–Beltrami operator from its boundary spectral data
Authors: Choulli Mourad , Yamamoto Masahiro
Published: December 2024

We establish stability inequalities for the problem of determining a Dirichlet–Laplace–Beltrami operator from its boundary spectral data. We study the case of complete spectral data as well as the case of partial spectral data.
Sectorial Analysis of Foreign Direct Investment and Trade Openness on Carbon Emissions: A Threshold Regression Approach
Authors: Mishra Mihir , Das Debadatta , Laurinavicius Algimantas , Laurinavicius Antanas , Chang Bisharat Hussain
Published: December 2024

Prior studies focused on the relationship between foreign direct investment (FDI), carbon emissions, and trade openness; however, previous investigations have yet to explicitly look at carbon emissions using data at the sector level. Using the threshold regression technique and the concentration of carbon emissions as the main indicator, this research adds to the body of knowledge by examining the sector-level impacts of FDI and trade openness on carbon emissions. Our results show that the effect depends on the selected thresholds, highlighting the effects of FDI and trade openness on carbon emissions in the industrial area. The influence coefficient of FDI on carbon emissions in industries particular to a specific sector varies over time. Conversely, carbon emissions are affected by trade openness both favorably and unfavorably. More specifically, in less carbon-intensive industries the, lack of carbon emissions results from greater international trade openness. Within industrial sectors, carbon emissions are heavily influenced by variables including GDP per capita, employment levels, autonomous technical invention, and the intensity of economic movement.
Characterizing Structure and Electrochemical Properties of Advanced Si/C Anode Materials
Authors: Rathore Divya , Abraham Jeffin James , Mendel-Elias Eytan , Li Zhifei , Zaker Nafiseh , Amirkhiz Babak Shalchi , Johnson Michel , Hamam Ines , Leontowich Adam F. G. , Bond Toby , Dahn J. R.
Published: December 2024

The increasing commercial interest in silicon-based anode materials for Li-ion batteries has driven the development of advanced structural designs to address the challenges of poor cycling stability. This study examines the structure of commercial silicon/carbon composite materials where nano silicon clusters are embedded within a carbon matrix. The size of silicon and carbon nanoclusters is determined by comparing experimental X-ray diffraction patterns with calculated patterns based on the Debye scattering formalism, as implemented in the program DEBUSSY. The size, morphology, surface areas, and porosities of the carbon matrix and composite are measured, along with their resulting tap and true densities. Their electrochemical performance is also assessed to determine operando stack growth and cycling stability. By restricting silicon cluster sizes to sub-nanometer dimensions within a porous carbon matrix, a low specific surface area can be achieved along with a specific capacity of ∼2000 mAh g−1. Additionally, this approach results in high tap density values close to 1 g cc−1, reduces reversible stack growth, and minimizes irreversible stack growth caused by particle cracking during volume changes, thereby significantly enhancing the overall stability and performance of the anode material.
Impact of Metal Oxide Nanoparticles on SEI and on Sodium Deposition - Dissolution
Authors: Simon E. Faktorovich , Marrache R. , Peled E.
Published: December 2024

Sodium metal batteries (SMBs) are emerging as a next-generation rechargeable technology due to sodium’s abundance, cost-effectiveness, and high theoretical specific capacity. However, challenges such as uneven sodium plating, dendrite formation, and low coulombic efficiency (CE) limit their performance. This study explores the impact of various additives to carbonate based electrolyte, including metal oxide nanoparticles (Al2O3, TiO2) and vinylene carbonate (VC), on the electrochemical behavior of SMBs, focusing on sodium deposition morphology, solid electrolyte interphase (SEI) composition, and capacity losses. The VC additive contributed to a thicker SEI, characterized by an inorganic-rich inner layer and polymer-rich outer layer, resulting in the highest CE across current rates. In contrast, Al2O3 samples demonstrated intermediate performance, with NaOH on the SEI surface and reduced polymer content compared to VC. Additionally, we show that capacity losses from isolated “dead” sodium represent the largest contributor to total capacity losses, with cells containing VC and 1% Al2O3 exhibiting the lowest total capacity losses. Moreover, the study demonstrates the uneven distribution of sodium deposition on the aluminum CC, especially its tendency to favor regions with lower fluorine concentration in the SEI. The findings from this study are important for optimizing SMB design, stability, and cycling efficiency.
Heparin and Bivalirudin in Percutaneous Coronary Intervention for Acute Coronary Syndromes: A Review Article
Authors: Wang Guiping , Qi Kaijie , Li Xuyang , Zuo Shuping , Zhang Ruolin , Zhao Yanan , Sun Suya , Zhang Juanjuan , Liu Xiaokun
Published: December 2024

Acute coronary syndrome (ACS) is one of the most common leading global causes of mortality, encompassing ST‐segment elevation myocardial infarction (STEMI), non‐ST‐segment elevation myocardial infarction (NSTEMI), and unstable angina (UA). Percutaneous coronary intervention (PCI) has become a pivotal therapeutic approach for ACS, underscoring the importance of anticoagulation strategies. Among the commonly employed anticoagulants in PCI, heparin and bivalirudin take precedence, with heparin serving as the archetypal choice. Nevertheless, the determination of an optimal anticoagulation regimen remains a point of contention in contemporary clinical practice. To address the differences in anticoagulants during PCI, we meticulously conducted a literature review through PubMed and Web of Science, employing search terms such as “heparin,” “bivalirudin,” “percutaneous coronary intervention,” and “acute coronary syndrome.” For patients with PIC brought on by STEMI, NSTEMI, and stable or UA pectoris, the review focused on randomized controlled trials to assess and compare the efficacy and safety of heparin and bivalirudin as anticoagulant options. This systematic review is aimed at furnishing valuable insights into the ongoing debate surrounding the choice of anticoagulation regimens in PCI. By scrutinizing clinical evidence derived from relevant trials, we seek to inform and guide healthcare practitioners in making informed decisions based on the unique requirements of patients with various ACS presentations.
A Missed Opportunity to Institutionalise Health Equity: A Qualitative Study on COVID‐19 Vaccination for Socially Marginalised Groups in Emilia‐Romagna (Italy)
Authors: Mammana Leonardo , Da Mosto Delia , Anastasia Alessandra , Cavagnis Sara , Chesini Giulia , Quargnolo Mattia , Tarditi Davide , Zanutto Giorgia , Zannoner Anna , Bodini Chiara
Published: December 2024

Health equity for people who have been socially marginalised (PSM) is still a major challenge for health systems, further exacerbated by the COVID‐19 syndemic and vaccination campaign. Nevertheless, the relevance of these events may open windows of opportunity to reorient health policies from an equity perspective. Based on this premise, a qualitative study was conducted in the Emilia‐Romagna region (Italy) during the COVID‐19 vaccination campaign, with the aim to identify the policies and practices adopted by local health authorities in order to ensure vaccination of PSM. The study involved key informants working in dedicated outpatient clinics for PSM or in primary healthcare (PHC) departments. A short checklist was used to assess local practices, followed by semistructured interviews to further understand areas such as (a) strategies to promote accessibility/access, (b) approaches for vaccination delivery, (c) information system and data collection, (d) planning and governance and (f) opportunities and future perspectives. The findings show that the regional COVID‐19 vaccination campaign was based on a tailored approach, promoting multimethod strategies to increase accessibility for PSM. Limitations of the study are the lack of direct experience of PSM and the lack of correlation between the identified strategies and vaccination rates. However, the study confirms the existence of systematic shortcomings and barriers in accessing PHC services, which contribute to the process of social marginalisation that is responsible for health inequalities among PSM. In this respect, the COVID‐19 vaccination campaign ensured broader access to a specific intervention without addressing the structural and organisational determinants of health inequalities among PSM, thus representing a failed opportunity to institute health equity. Our findings suggest the need to strengthen political commitment and promote effective participatory health policies capable of identifying and overcoming structural barriers in order to achieve a structural change towards greater health equity.
Application of Mill Chatter Component Extraction and Root Cause Diagnosis in the Industrial Cold Rolling Mill
Authors: Lu Xing , Sun Jie , Cheng Qiyou , Zhang Dianhua
Published: December 2024

The third‐octave mode mill chatter is a prominent rejection in the efficiency and quality of the strip. The third‐octave mode chatter frequency is in the range of 150–250 Hz. Frequency analysis of vibration acceleration signal or original rolling process parameters is an effective method to detect third‐octave mode chatter characteristics, and cold rolling mill chatter identification by frequency analysis is quite desired. However, original industrial data are contaminated by noise, making it difficult to obtain the principal component of 150–250 Hz when chatter occurs. Besides, it is necessary to find out the root rolling condition parameter causing chatter. To meet the early warning of the third‐octave mode chatter in the production line, a new adaptive cuckoo search algorithm variational mode decomposition (CSA‐VMD) and transfer entropy–based root cause diagnosis models are proposed. The verification results showed that the CSA‐VMD method can extract the third‐octave mode chatter component and avoid the mode mixing problem and endpoint effect. Early mill chatter warning should be provided when the tension fluctuation exceeds 20% in the uniform speed rolling phase.
The optimization technique for a hybrid renewable energy system based on solar-hydrogen generation
Authors: Shklyarskiy Yaroslav , Skvortsov Ivan , Sutikno Tole , Manap Mustafa
Published: December 2024

This paper provides an in-depth analysis of the challenges associated with the commonly used optimization methods for determining the configuration of hybrid renewable energy systems that rely on solar generation and hydrogen technologies. It highlights the key research directions in this field and includes a brief overview of the number of publications related to the subject. The standard topology employed in the implementation of these studied complexes is clearly defined and described. The paper also presents a comprehensive examination of various modern optimization methods and approaches employed to determine the optimal structure of these complexes. The paper thoroughly discusses the advantages and disadvantages of these methods, while also proposing future development directions that suggest specific adjustments to existing approaches. These adjustments aim to enhance decision-making efficiency and maximize economic benefits. Overall, the paper emphasizes the significance of research in renewable energy sources and hydrogen technologies, underlining their relevance and potential for sustainable energy solutions. In a concise manner, this paper addresses identified problems, highlights research directions, analyzes optimization methods, proposes improvements, and argues for the importance of this research field.
Association of Social Isolation and Plasma Metabolites With the Risk of Venous Thromboembolism
Authors: Xiang Hao , Gan Xiaoqin , Zhang Yanjun , Zhang Yuanyuan , Ye Ziliang , Yang Sisi , Huang Yu , Wu Yiting , Zhang Yiwei , Qin Xianhui
Published: December 2024

BACKGROUND:

To explore the association of social isolation with venous thromboembolism (VTE) risk and the mediating role of metabolomics on this association.

METHODS:

Overall, 236 026 participants free of VTE and with complete metabolomics data at baseline from the UK Biobank were included. Social isolation was constructed based on the frequency of friend/family visits, weekly group activities, and living alone. The study outcome was incident VTE, including deep vein thrombosis or pulmonary embolism. Least absolute shrinkage and selection operator regression was applied to create a social isolation–associated metabolic signature. We estimated hazard ratios of study outcomes in relation to social isolation/metabolic signature and calculated the proportion of social isolation–VTE association mediated by the metabolic signature.

RESULTS:

During a median follow-up of 12.5 years, 6681 VTE cases were recorded. Social isolation was positively associated with VTE risk (versus without social isolation; hazard ratio, 1.17 [95% CI, 1.09–1.27]). A metabolic signature for social isolation was constructed using 28 metabolites. This metabolic signature showed significant association with VTE risk (per SD increment; hazard ratio, 1.16 [95% CI, 1.13–1.19]) and mediated 11.0% (95% CI, 7.0–19.8) of the increased VTE risk associated with social isolation. 11 metabolites in the metabolic signature also significantly mediated the association between social isolation and VTE risk, with mediation proportions ranging from 0.5% to 2.6%. Similar results were found for incident deep vein thrombosis and pulmonary embolism, respectively.

CONCLUSIONS:

Social isolation was positively associated with VTE risk. The metabolic signature for social isolation, especially the 11 major metabolites included, significantly mediated the association between social isolation and VTE risk.
Predictors and Outcomes of Hematoma Expansion and Neurological Decline in Intracerebral Hemorrhage: A Multisite Mobile Stroke Unit Study
Authors: Gulati Asish K. , Ma Tianwen , Rolle Isaiah , Peterson Ryan , Pirlog Bianca , Chen Justine , Heckathorn Megan , Ratcliff Jonathan , Bianchi Nicolas , Fleming Carol , Frankel Michael , Grotta James , Bowry Ritvij , Parker Stephanie , Hicks William , Navalkele Digvijaya
Published: December 2024

Background

Hemorrhagic stroke, particularly intracerebral hemorrhage, is one of the leading causes of permanent disability worldwide. Early hematoma expansion (HE) is a key factor in neurological deterioration (ND) and functional outcomes post intracerebral hemorrhage. This observational cohort study examines the predictors and outcomes of HE and ND in spontaneous intracerebral hemorrhage patients using data from 3 Mobile Stroke Units (MSUs) in the United States.

Methods

Patients diagnosed with spontaneous intracerebral hemorrhage within 4 hours of last known well were included. Data collection involved initial and follow‐up non‐contrast (computerized tomography) CT scans from MSUs and emergency departments, from which the rate of HE and ND were determined. The interrater reliability for radiographic signs of HE was also assessed. Predictive factors were analyzed using multiple logistic regression models.

Results

Of the 55 patients who met the inclusion criteria, 27% experienced HE within the first 4 hours from last known well with the majority of HE occurring within the first 60 to120 minutes. Significant predictors of HE were a history of diabetes and blend sign on initial MSU CT scans. Predictors of ND included initial hematoma volume and the presence of hydrocephalus. Patients with HE and ND had a higher discharge modified‐Rankin Scale score and mortality rates. Interrater agreement on radiographic signs of HE varied, with moderate agreement on some signs and little to none on others.

Conclusion

Highest HE was seen within 60 to120 minutes from last known well for patients between MSU and emergency department transport. The blend sign and a history of diabetes emerged as key predictors of HE, whereas initial hematoma volume and hydrocephalus were significant for ND. Interventions to prevent HE and ND should be tested on the MSU in future studies.