bluuebunny/crossref_metadata_2025_split huggingface dataset:
NEOSTI: A Neuromorphic Electronic-Opto Spatial-Temporal Hybrid Image Sensor
Authors: Zhang Milin , Liu Tianyi , Huang Zheng , Wang Xuecheng , Shi Wanxin , Chen Hongwei 
Published: January 2025

Abstract

Image sensors in machine vision systems face significant challenges related to energy efficiency and processing capability when storing, transferring, and processing massive amounts of data. In humans, over 80% of information processed by the brain is obtained through the eyes, which are capable of detecting and synchronously processing information with extremely low overall power consumption. Inspired by the biomimetics, here we propose a Neuromorphic Electronic-Opto Spatial Temporal Imager (NEOSTI), the smallest all-in-one eye size fully integrated vision system enabling acquisition and operation in typical indoor/outdoor non-coherent environments, under both natural and artificial lighting conditions, without any extra requirement of the light source, such as laser or coherent light source. NEOSTI combines processing-pre-sensor (PPS) in optical domain, processing-in-sensor (PIS) with non-linear acquisition capability while optical to electronic converting, and processing-near-sensor (PNS) in electronic domain, enabling parallel data computing capabilities while sensing. NEOSTI also integrates a low complexity Binary Neural Network (BNN) on the chip to process image semantic information. It attains near-human performance in five static and dynamic visual processing tasks.
Naturally disengaging control to reveal habits
Authors: Oh Sarah , Collins Anne 
Published: January 2025

Abstract

Habits are an essential part of everyday decision-making. However, the mechanisms underlying habit formation and expression in humans are difficult to study in the laboratory, owing to a dearth of convenient experimental paradigms that reliably exhibit a key marker of habits – training-induced inflexibility – under ecologically valid conditions. This difficulty is often attributed to the fact that habits are identified in contrast to goal-directed (GD) control, which research participants typically engage strongly in laboratory experiments. To address this gap, we develop a new, short habit learning paradigm that incorporates several features we hypothesized would encourage participants to disengage GD control, enabling habits to exert greater influence over behavior: a hierarchical multi-step trial structure, opportunities for self-correction, and frequent switches between extensively and moderately practiced behaviors. Through a series of experiments, we demonstrate that overtraining amplifies habitual control, as evidenced by errors biased toward the overtrained context and away from the moderately-trained context at early response times, while later responses remain dominated by GD control. The reliability of this overtraining effect depended on the inclusion of task features designed to dampen GD control. In addition to providing a practical, robust, and flexible tool for studying the cognitive processes underlying habit formation and habitual control, our paradigm moves us beyond the traditional stimulus-response conception of habits, expanding the definition to include more complex, hierarchical behaviors that better reflect naturalistic human habits.
Clinical study of 20 cases of refractory Mycobacterium avium complex pulmonary disease treated with amikacin liposomal inhalation suspension
Authors: Kaneko Taichi , Hagiwara Eri , Moriuchi Asami , Tagami Yoichi , Okuda Ryo , BaBa Tomohisa , Komatsu Shigeru , Ogura Takashi 
Published: January 2025

Abstract

Backgrounds:
Several guidelines have recently indicated amikacin liposome inhalation suspension (ALIS) as a new treatment option for refractory Mycobacterium avium complex pulmonary disease (MAC-PD). There are a few reports on the efficacy of ALIS in clinical settings. We investigated adverse events and outcomes of patients who were treated with ALIS at our hospital.
Methods
The subjects were 20 patients with refractory MAC-PD observed for over six months after the introduction of ALIS treatment at our hospital from August 2021 to June 2024. Medical records of these patients were retrospectively investigated for patient background, culture conversion rate and adverse events.
Results
The median age of the patients was 71.0 years, with 16 (80%) female. Eighteen patients had Mycobacterium avium and two had Mycobacterium intracellulare. Fourteen patients were able to continue treatment over six months, while six patients discontinued treatment. The most common reasons for discontinuation were stomatitis, rash, and hypersensitivity pneumonitis. In the six-month continuation group, eight patients achieved culture conversion and six did not. One patient with systemic skin rash was successfully treated with desensitization therapy. The culture conversion rate was 57.1%, which was higher than previously reported. Among patients upto 70 years old, the rate was as high as 75.0%. Although the differences were not statistically significant, the culture conversion group tended to be younger and have a higher BMI.
Conclusions
ALIS treatment is an effective treatment that has the potential to achieve high rates of culture conversion, especially in younger patients. It is important to manage adverse events.
Alpine grassland degradation alters soil fungal communities and increases the potential risk of disease
Authors: Han Fuyao , Zhao Junsong , Liu Yutong , Yang Siwei , Liu Gang , He Xin 
Published: January 2025

Abstract

Background
Alpine grassland degradation on the Tibetan Plateau has become an important ecological and environmental problem in recent years, and grassland degradation has changed the diversity of aboveground plants and the ecology of underground soil. As an important part of the soil ecosystem, soil fungi play a positive role in soil health and plant growth by decomposing organic matter, constructing mycorrhizal symbiosis system and secreting antibiotics.
Results
In this study, high-throughput sequencing technology was used to compare soil fungal communities in natural, degraded and desertified grasslands, and to explore the effects of alpine grassland degradation on soil fungal communities and the adaptation mechanism of soil fungi to grassland degradation.
Conclusions
The study found that alpine grassland degradation altered soil fungal communities and increased the risk of potential disease.
A Rare Case of Aspergillosis Complicated by Endocarditis, Arterial Thrombosis, and Stroke in an Immunocompetent Patient: Successful Treatment with Early Intervention and Antifungal Therapy
Authors: Liu Lin-feng , Xiang Jian-wen , Pan Chen-wei , Zhou Lingli , Wang Sheng , Zhao Qi-feng , Ai Peng , Hao Yu , Gong Yu-qiang 
Published: January 2025

Abstract

Disseminated aspergillosis is a rare but highly lethal condition in immunocompetent patients. This report describes a patient's rapid progression from primary lumbar infection to infective endocarditis, arterial thrombosis, and stroke in just two days. Successful early intervention with lesion clearance and antifungal therapy highlights the importance of prompt and comprehensive treatment.
Global, Regional, and National Burden of Pulmonary Arterial Hypertension in Children and Adolescents, 1990-2021, and Projections to 2050: A Systematic Analysis for the Global Burden of Disease Study 2021
Authors: Tu Mengyun , Zhu Jieyang , Weng Jie , Wang Zhiyi , Hu Sipin 
Published: January 2025

Abstract

Background
Pulmonary arterial hypertension (PAH) in children and adolescents is a significant global health challenge, leading to a range of severe medical complications and an increased risk of premature death. In this study, we assessed the trends and cross-country disparities in the burden of PAH among children and adolescents from 1990 to 2021, and further predicted its changes to 2050.
Results
GBD 2021 estimated 5,049 incident cases, 1,972 deaths, and 170,371 DALYs of PAH in children and adolescents globally in 2021. South Asia reported the highest numbers of incident, death, and DALYs cases, while Eastern Sub-Saharan Africa had the highest age-standardized incidence rate (ASIR), and the Caribbean recorded the highest age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR). Between 1990 and 2021, the ASIR in this population showed an overall increasing trend, whereas the ASMR and ASDR demonstrated a consistent decline. The ASIR, ASMR, and ASDR were found to decrease exponentially with rising SDI levels. DALYs were disproportionately concentrated in countries with lower sociodemographic development levels. The ASIR is projected to remain globally stable, while both the ASMR and ASDR are expected to decline annually through 2050.
Conclusions
The burden of PAH in children and adolescents is concentrated in low-SDI countries. While ASIR has increased, ASMR and ASDR have steadily declined and are projected to continue decreasing through 2050. Strengthening international collaboration, improving healthcare, and targeting high-risk regions are crucial to reducing the disease burden and promoting global health equity.
Macular segmentation using an automatic deep learning and graph cut strategy
Authors: Abril-Daza J. S. , Rodriguez-Garavito C. H. , Alfonso-Vargas D. F. , Tenjo-Morales A. I. , Cuesta-Redondo J. C. 
Published: January 2025

Abstract

Age-related Macular Degeneration (AMD) is one of the main diseases affectingvisual health in the world, since it causes severe and irreversible vision loss inelderly people. According to the International Agency for the Prevention of Blind-ness (IAPB), by the year 2040, 288 million people are expected to be affectedworldwide, while in Colombia for the year 2020, approximately 234,200 caseswere estimated. With the improvement of computer vision techniques, automaticdiagnosis has become a reliable tool for screening and determining the degree ofthe disease. Many strategies have been proposed in the last decade, from the firstbased on manual characterization of the disease to current ones based on modeltraining using large databases of images annotated by specialists, with very highcomputational costs and data collection time. The present article proposes a novelmethod based on combining the two trends, using the best of each one. The dis-ease is characterized by segmenting its two fundamental features in cascade: theocular vascular network is segmented with a pre-trained deep learning model andtransformed into a graph for extraction of terminal nodes (which are known todelimit the macular zone); and the macula is segmented through a semi-automatic algorithm called graph cut that requires initial background and foreground infor-mation (which corresponds to the terminal nodes already obtained) to become anautomatic method. Finally, we present a comparison between macular segmen-tation using the proposed algorithm with parametric changes in its architectureand a deep learning model for macular segmentation with a reduced set of aug-mented images. It is confirmed that, with a limited database, the proposed graphcut-deep learning strategy is a viable way to obtain a method to extract the mainfeatures of the disease and generate a subsequent diagnosis.
A New Approach to Automatic Epilepsy Detection from EEG Signals Using Archimedean Spiral and Swin Transformer
Authors: ÜZEN Hüseyin , FIRAT Hüseyin , Alperen Salih Taha , Guldemir Hanifi , Demir Fatih , Koundal Deepika , SENGUR Abdulkadir 
Published: January 2025

Abstract

Epilepsy, a neurological disorder marked by recurrent and unpredictable seizures due to abnormal brain electrical activity, is studied using electroencephalography (EEG) which measures brain activity. The EEG signals are commonly employed to diagnose and monitor conditions like epilepsy, sleep disorders, and brain injuries. This research work introduces an effective hybrid approach based on Archimedean spiral coding (ASC) and a swin transformer-based convolutional neural network (CNN) techniques to detect epilepsy automatically using EEG signals. This proposed ASC method transforms EEG signals into a visually informative 3D matrix and employs swin transformer-based CNN architecture for classification. It yields an accuracy of 97.98% and 88.22% for sample- and subject-based ten-fold cross-validation, respectively using the public epilepsy database of 121 populations. The developed system is ready to be tested with more epilepsy patients from different races to validate the performance. It produced a better accuracy score as compared to the existing results.
Socio-Cultural Status of the Matua Community in Barisal District,Bangladesh
Authors: Ahmed Ayrin 
Published: January 2025

Abstract

The Matua community, a historically marginalized Hindu sect from the Barisal district of Bangladesh, continues to confront numerous socio-economic and cultural challenges rooted in long-standing caste- based discrimination. This study aims to examine the socio-cultural standing of the Matua people by conducting a descriptive analysis of 31 participants, with attention to factors such as education, income, cultural traditions, and political participation. The results show a notable improvement in education, as 32% of respondents have reached higher education levels. However, income inequality remains pronounced, with over 60% of the community earning less than the national average. Caste-based prejudices still obstruct social inclusion and economic progress, disproportionately impacting those with lower education and income levels. Additionally, the absence of political representation hampers the community’s efforts to address their socio-economic issues effectively. Gender inequality is another significant challenge, as Matua women face difficulties in securing professional opportunities, despite advancements in education. The study underscores the necessity for targeted strategies in education, political participation, and economic growth, along with addressing broader issues like caste discrimination and gender inequality.
Enhancing Arecanut Quality Grading: A Comparison of Custom CNNs and Transfer Learning Models
Authors: D Dhanush Ghate , Hegde Pramukh S , H Saishma , N Pallavi K , Ghate Sudeep D 
Published: January 2025

Abstract

Effective grading of arecanut is essential for ensuring product quality, maximizing market competitiveness, and satisfying consumer preferences. However, traditional methods of arecanut grading are challenging due to variations in arecanut size, shape, and appearance, resulting in subjective and inconsistent evaluations. Deep learning can enhance this process by automating grading and using sophisticated algorithms to assess both visual and non-visual attributes, thereby increasing efficiency, accuracy, and consistency. This study presents two standalone CNN-based methodologies for automated arecanut quality grading, leveraging DenseNet121 and InceptionV3 with custom layers tailored for arecanut classification. A dataset of 2,000 high-resolution images, manually curated from farms and augmented for diversity, was used for training and validation. Eight CNN architectures - DenseNet121, EfficientNetB4, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19 were evaluated. Experimental findings showed DenseNet121 and InceptionV3 achieved the highest accuracy (95.67%) and strong precision/recall scores (96%), making them the most promising models. Meanwhile, MobileNetV2 was identified as the fastest model in terms of classification speed; however, its relatively low accuracy limits its practical application in grading tasks. DenseNet121 and InceptionV3, while marginally slower at 0.015 and 0.011 seconds per image, respectively, offered a good balance between computational cost and elevated accuracy. DenseNet121 excels in feature reuse through its dense connectivity, reducing redundancy and improving performance on smaller datasets, while InceptionV3 utilizes multi-scale feature extraction to capture intricate patterns effectively. Both models demonstrate robustness under varying conditions, ensuring reliability in practical deployment scenarios. This study highlights the potential of CNNs to provide a reliable, and scalable solution for arecanut grading, benefiting farmers by expanding market opportunities.
Synergetic influence of different additives on hydrogen and methane generation from food waste
Authors: Deheri Chinmay , Jena Shakti P. , Acharya Saroj Kumar , Mishra Sarita 
Published: January 2025

Abstract

In order to co-digest food waste along with cow dung to produce hydrogen and methane, the effects were experimentally assessed in distinct batch reactors for calcium carbonate (CaCO3), copper oxide (CuO), zinc oxide (ZnO), and calcium peroxide (CaO2) as additives in this study. The maximum hydrogen generation using CaO2 was found to be 115.59 mLg− 1 total solid (TS), which was 8.6% lead by the standard specimen with no additives. In contrast, ZnO reduced lead by 10.4%. In comparison to the control sample, the generation of methane was 161.2 and 129.06. mLg− 1TS, showing a 40 and 25% increase with CaO2 and CaCO3, whereas it dropped to 62.65 and 76.23 mLg− 1 TS, depicting a 35 and 21% decrease with CuO and ZnO respectively. The addition of CaO2 and CaCO3 increased biogas generation by 32.1 and 15.1%, respectively, while the addition of CuO and ZnO decreased it by 31.4 and 24.3%. Ultimately, the digestate's physicochemical characteristics showed an improvement in organic nutrients following co-digestion, making it a useful biofertilizer for use in agriculture.
Improving The Seismic Performance Of Steel Moment Connections Using An Interlock Mechanism As A Replaceable Fuse Element: Numerical Study
Authors: Yuana Prima Sukma , Moestopo Muslinang , Kusumastuti Dyah , Suarjana Made 
Published: January 2025

Abstract

Moment-resisting frame systems have been widely used in structural buildings. However, the risk of earthquakes requires a steel structure system that is tough, reliable, easy, and fast to repair after an earthquake. Recently, there has also been a paradigm shift in the design of moment-resisting connections. Research has been developed using energy-dissipating elements due to earthquakes with replaceable connections as a fuse element. The interlock mechanism has been widely developed in recent years and even was designed with the concept of replaceability. However, the research on the connections with the interlock that has been carried out is not for seismic connection. This research was conducted to understand beam-column seismic connection behavior with an interlock as a fuse element. Analysis was done through numerical studies with monotonic and cyclic loads due to earthquakes. The simulation was conducted using ABAQUS, the parameters analyzed connection failure patterns, deformability, force-displacement hysteresis, strength and stiffness, and energy dissipation. Based on monotonic loading, the strength of the interlock connection system is higher than the SSTSF. Meanwhile, based on cyclic simulation, there is no decrease in strength exceeding 20% when the rotation is 0.04 rad. Energy dissipation in the fuse components occurs only in the fuse components, and non-fuse remains elastic. These results show that the replaceable concept can be applied to interlock connection systems. The interlock connections will be quite good for use in earthquake-resistant buildings because the analysis results show that the performance of interlock connections meets the requirements of AISC 341.
Guided Synthesis of the stable sols based on nanosized titanium(IV) oxides with the predictable particle size and antimicrobial properties
Authors: Gainanova Asiya , Glushko Aleksandr , Neznanov Alexey , Kuz’micheva Galina , Yaroshenko Margarita , Lobanova Nadezhda 
Published: January 2025

Abstract

The article discusses the targeted synthesis of sols with nanosized titanium(IV) oxides for biological and medical applications, as well as a doping agent precursor in obtaining crystals from solutions. When preparing sols without using surfactants, the synthesis conditions were changed: temperature and duration of hydrolysis, composition and concentration of titanium-containing precursors, stabilizing acid concentration. The resulting sols have different hydrodynamic diameter of titanium(IV) oxide nanoparticles (from ~2 to 2000 nm) and different stability (from 0 to 90 days). A specific set of machine learning tools was developed for well-interpretable analysis of experimental data. These tools were used in three stages of experiments to optimize three functional characteristics of the synthesized sols by varying five synthesis parameters. As a result, a universal minimalistic mechanism for the targeted synthesis of sols with the required characteristics was proposed, allowing for a reduction in the number of experiments and the study of the relationships between synthesis parameters and functional characteristics in various experiments.
Modification of P&O Method on MPPT with PI Controller to Reduce Power Oscillation during Steady-State Condition
Authors: Habibah Nisrina Rania , Nur Hasanah Rini , Abidin Zainul 
Published: January 2025

This paper describes an improvement of the Perturb and Observe (P&O) method for maximum power-point tracking (MPPT) on a photovoltaic (PV) cell generating system. The improvement is achieved using a reference voltage () value to respond to perturbation and calculates the power change based on the response. The magnitude of is determined using the reference voltage change . A large will result in a faster tracking but increasing oscillations during steady-state conditions, while a small will reduce oscillations but slowing down the MPPT response. An adaptive value is required to reduce the oscillations which have potential to degrade the efficiency of the PV cell output. A proportional-integral (PI) controller is explored in this paper in determining the most appropriate value. The results showed that oscillations around the MPP point could be reduced by 99%.