bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feature enhancement network architecture for low-light image
Authors: Jin Zihao , Liu Zhiyong 
Published: August 2024

Abstract

Limitations such as shooting equipment and environmental conditions can significantly impact the quality of captured images. Image enhancement, a crucial task in computer vision, aims to improve the overall or local characteristics of an image, particularly its brightness and contrast, to enhance the visual effect. The Retinex model has proven effective for low-light image enhancement. In this work, we propose a novel feature enhancement network architecture that combines the Retinex model with deep learning. The network comprises three main components: a decomposition network for image decomposition, an enhancement network for the luminance component obtained from decomposition, and a denoising network for the reflection component. The network’s learning process relies primarily on key constraints, including the consistency of the reflection component and the smoothness of the luminance component post-image decomposition. We conducted experiments on synthetic datasets involving real environments and processed images, and the results demonstrate that our method strikes a good balance between reducing parameters and maintaining high-quality image enhancement compared to other algorithms.
Electronic Transport and Magnetic Properties of CrTe Epitaxial Thin Films with Room Temperature Ferromagnetism
Authors: Luo Fu-Sheng , Wang Zhao-Cai , Tang F. , Fang Y. , Ye Mao , Zheng Ren-Kui 
Published: August 2024

Abstract

In this paper, we report the electronic transport and room-temperature ferromagnetism with Curie temperature ~ 330 K in 25-nm NiAs-type CrTe thin films epitaxially grown on SrTiO3 (111) substrates using molecular beam epitaxy. Upon cooling from 350 K, the CrTe film undergoes paramagnetic-to-ferromagnetic (PM-to-FM) and ferromagnetic-to-antiferromagnetic (AFM) phase transitions. In the ground state (2 K), the spins basically form antiferromagnetic ordering between Cr1 and Cr2 layers, however, still with a weak remanent magnetization of 123 emu/cc (~ 0.23 µB/f.u.), implying slight spin canting within Cr sub-lattice direction. For the 100, 500, and 1000 Oe magnetic field, the field-cooled in-plane remanent magnetization at 2 K exceeds the field-cooled out-of-plane one by factors of 42, 12.4, and 14.8, respectively, indicating strong magnetic anisotropy. Associated with the PM-to-FM phase transition, the CrTe film shows an insulator-to-metal electronic phase transition and negative magnetoresistance, which can be qualitatively understood within the framework of the electronic phase separation model.
Anti-DEFA5 Monoclonal Antibody Clones 1A8 and 4F5 Immunoreactive Bioassay for Diagnosing Inflammatory Bowel Disease
Authors: Thangaiyan Rabi , Sakwe Amos M. , Hawkins Alexander T. , Washington Mary K. , Ballard Billy R. , Izban Michael G. , Chirwa Sanika S. , Hildreth James E.K. , Shanker Anil , Blum David L. , M'Koma Amosy E. 
Published: August 2024

Abstract

Background
Robust evidence suggests that the aberrant expression of α defensin 5 protein (DEFA5) in colon inflammatory bowel diseases (IBDs) underlies the distinct pathogenesis of Crohn’s colitis, can be exploited as a reliable diagnostic biomarker to differential diagnosis of Crohn’s colitis (CC) from Ulcerative colitis (UC) in otherwise indeterminate colitis (IC). We evaluated the specificity of the commercially available anti-DEFA5 antibodies and showed further validation of their appropriateness for a given application is required.
Methods We established two mouse monoclonal DEFA5 antibody clones 1A8 and 4F5 by immunizing the mice with purified recombinant protein and validated the specificity, selectivity and cross reactivity in recognizing the endogenous and recombinant DEFA5 protein, especially for Immunohistochemistry, Western blot, Immunoprecipitation, or enzyme-linked immunosorbent assay.
Results Clones 1A8 and 4F5 recognized effectively the endogenous DEFA5 in active human diverticulitis (DV), UC, CC or IC disease samples, including transiently transfected HEK293T cells expressing DEFA5 with high degree of specificity and minimal non-confounding cross reactivity.
Conclusions 1A8 and 4F5 clones are worth studying in larger IBD cohorts to fully address whether DEFA5 expression may be used as a diagnostic biomarker to discrimination of the diagnosis of UC from CC or IC into authentic CC or UC or a colitis with different pathological characteristics.
The Role of Geography in Taxonomy and Conservation of Species Related to Banisteriopsis Malifolia (Malpighiaceae)
Authors: Souza Raniely Miranda , Maciel Jefferson Rodrigues , Saavedra-Saenz Yurani Carolina , Oliveira Regina Célia 
Published: August 2024

Abstract

Banisteriopsis includes species that are difficult to define, and there is a hypothesis that sympatric species are more similar than those with wide distributions. The objective of this study was to test the circumscription of species related to B. malifolia to examine the relationship between geographical distribution, sympatry, and allopatry with the morphological variation of taxa, as well as to assess the degree of threat to the taxa. The results surprisingly point to greater morphological variation in allopatric species than in sympatric ones, the taxa are better explained within the current circumscription and no corroborate the new status for Banisteriopsis malifolia var, appressa recently proposed. We confirm the dramatic occurrence of threatened species in this group.
Psychological Interventions for Internalized Weight Stigma: A Systematic Review of Feasibility, Acceptability, and Preliminary Efficacy
Authors: D’Adamo Laura , Shonrock Abigail T. , Monocello Lawrence , Goldberg Jake , Yaeger Lauren H. , Jebeile Hiba , Pearl Rebecca , Wilfley Denise E. 
Published: August 2024

Abstract

Background
Internalized weight stigma (IWS) is highly prevalent and associated with deleterious mental and physical health outcomes. Initiatives are needed to address IWS and promote effective coping and resilience among individuals who are exposed to weight stigma. We conducted a systematic review of psychological interventions for IWS and examined their feasibility, acceptability, and preliminary efficacy at reducing IWS and related negative physiological and psychological health outcomes.
Methods
Eight databases were searched. Inclusion criteria included: (1) psychological intervention; (2) published in English; and (3) included internalized weight stigma as an outcome. Exclusion criteria included: (1) commentary or review; and (2) not a psychological intervention. A systematic narrative review framework was used to synthesize results.
Results
Of 161 articles screened, 20 were included. Included interventions demonstrated high feasibility, acceptability, and engagement overall. Sixteen of 20 included studies observed significant reductions in IWS that were maintained over follow-up periods, yet data on whether interventions produced greater reductions than control conditions were mixed. Studies observed significant improvements in numerous physical and mental health outcomes.
Conclusions
Findings indicate that existing interventions are feasible, acceptable, and may provide meaningful improvements in IWS and associated health outcomes, highlighting the potential for psychological interventions to promote improved health and wellbeing in individuals with IWS. Additional research using rigorous study designs (e.g., randomized controlled trials) is needed to further evaluate the efficacy of interventions for IWS.
Design of internal model controller based on robustness/performance tradeoff tuning for robot arm with time delay
Authors: LIU Li-ye 
Published: August 2024

Abstract

A framework of dealing with the issue of the long delay time is established for the control system of the Cloud Robot Arm (CRA). To be convenient for the dynamic process analysis and the controller design of CRA control system, the each joint of the robot arm plant is regard as the independent system, considering the influence of the time delay of the cloud networking. Then, a controller design method of the Internal Model Control -Proportional Integral Derivative (IMC-PID) based on the tradeoff tuning method between the robustness/performance is proposed to handle the load toque disturbance for the CRA control system, guaranteeing the dynamic performance. The simulation results reflect the effectiveness of the proposed method.
Lightweight Code Coverage Analysis for Deep Learning Framework Testing
Authors: Li Senyi , Xiao Xiong , Yang Bing , Li Junqiang , Yu Hongfang , Luo Long , Zhang Xiaosong , Sun Gang 
Published: August 2024

Abstract

Coverage analysis is increasingly important for enhancing deep learning (DL) framework testing. However, current code coverage analyzers face challenges with low compatibility and high overhead when applied to DL frameworks. Our findings indicate that state-of-the-art Python analyzers, such as coverage.py and SlipCover, introduce an overhead of at least 1 \(\times\) . In the context of C coverage, the widely-used analyzer gcc is compatible only with TensorFlow and imposes a substantial overhead of 130,166$\times$. These issues with compatibility and overhead significantly limit the utility of coverage analysis in many applications, including testing and fuzzing, and prevent its adoption in deployment scenarios.In this paper, we propose DCOV, a lightweight coverage analyzer designed explicitly for DL frameworks to address the above challenges. DCOV leverages run-time instrumentation for Python code and compile-time instrumentation for C code. Additionally, we decouple instrumentation and coverage analysis functions by an interface-based instrumentation design, allowing Python and C probes to share the same coverage analysis algorithm. Furthermore, DCOV minimizes execution and analysis overheads through several strategies, including coverage granularity optimization, parallel processing and bit counting algorithm optimization.Our empirical evaluation demonstrates DCOV's applicability to popular deep learning frameworks, such as TensorFlow and PyTorch, with a minimal overhead of approximately 18 milliseconds per single run. In addition, by adopting parallel processing and bit counting algorithm optimization, DCOV reduces analysis overhead by 99%. Moreover, by integrating DCOV with existing DL framework testing systems, we observe a 2.8$\times$ speedup and uncover two previously unknown bugs in TensorFlow (one of them being accepted).
A lmod1a mutation causes megacystis microcolon intestinal hypoperistalsis in a CRISPR/Cas9-modified zebrafish model
Authors: Kalim Alvin Santoso , Nagata Kouji , Toriigahara Yukihiro , Shirai Takeshi , Kirino Kosuke , Xiu-Ying Zhang , Kondo Takuya , Kawakubo Naonori , Miyata Junko , Matsuura Toshiharu , Tajiri Tatsuro 
Published: August 2024

Abstract

Purpose
Megacystis microcolon intestinal hypoperistalsis syndrome (MMIHS) is defined as a congenital visceral myopathy with genetic mutations. However, the etiology and pathophysiology are not fully understood. We aimed to generate a gene leiomodin-1a (lmod1a) modification technique to establish a zebrafish model of MMIHS.
Methods
We targeted lmod1a in zebrafish using CRISPR/Cas9. After confirming the genotype, we measured the expression levels of the target gene and protein associated with MMIHS. A gut transit assay and spatiotemporal mapping were conducted to analyze the intestinal function.
Results
Genetic confirmation showed a 5-basepair deletion in exon 1 of lmod1a, which caused a premature stop codon. We observed significant mRNA downregulation of lmod1a, myh11, myod1 and acta2 and the protein expression of Lmod1 and Acta2 in the mutant group. A functional analysis of the lmod1a mutant zebrafish showed that its intestinal peristalsis was fewer, slower, and shorter in comparison to the wild type.
Conclusion
This study showed that targeted deletion of lmod1a in zebrafish resulted in depletion of MMIHS-related genes and proteins, resulting in intestinal hypoperistalsis. This model may have the potential to be utilized in future therapeutic approaches, such as drug discovery screening and gene repair therapy for MMIHS.
Secure Medical Image Transmission and Storagein IoT Cloud Using GAN - RBM with Real-TimeAnalysis
Authors: K Saranya , A Valarmathi 
Published: August 2024

Abstract

As IoT devices become increasingly integrated into healthcare systems, thesecurity and privacy of medical image data transmitted and stored in cloudenvironments are paramount. This paper introduces a novel approach that harnessesvanilla Generative Adversarial Networks (GANs) and chaotic RestrictedBoltzmann Machines (RBMs) to tackle security challenges in medical imagetransmission and storage. GANs generate privacy-preserving representationsof medical images, while RBMs facilitate secure compression, encryption, andanomaly detection. The suggested GAN-RBM (GAN-RBM) integrates GANsand RBMs into IoT systems to provide the safe and effective transfer of medicalimages over networks that may not be secure. Additionally, it offers strong protectionfor image data saved in the cloud. Therefore, these massive amounts ofmedical data are securely stored, processed, and handled using cloud computingtechnology, which is protected from numerous assaults. Real-time (IntraCranialHaemorrhage -ICH) datasets are collected from hospitals for MRI, and CT imagesare utilized for experimentation. Performance metrics such as encryption/decryptionspeed, compression ratio, and anomaly detection accuracy are evaluated todemonstrate the effectiveness of the proposed approach in safeguarding patient privacy, ensuring data integrity, and detecting unauthorized access or tamperingattempts. Encrypting a 140MB image takes 950 seconds and decrypting 990seconds. For 200 epochs, the proposed model has a compression ratio of 97.34%and GANs-RBM outperformed all other machine-learning models with a 94.17%anomaly detection score.
Design a Multimodal Biometric Based Protection System by Generation of a Revocable Cryptographic Key Using Separately Extracted Feature Fusion-based Convolutional Neural Network With Bat Optimization
Authors: S Manjusha Nair , Dharan Smitha 
Published: August 2024

Abstract

An authentication and access control method based on the automatic and immediate verification of a person's bodily traits is known as biometric security. Recently, Biometric Identification Systems (BIS) demands have risen significantly. However, it is challenging to offer robust security quickly because most conventional single and multimodal biometric authentication systems utilize data size, static parameters, and keys. As a result, it has various issues, including noise in the data, unacceptable error rates, and spoof attacks. To overcome these issues, this paper designs a separately extracted feature fusion based convolutional neural network with bat optimization (SEFF-CNN-BO). Additionally, the Attribute-Based Encryption (ABE) technique is employed to securely share the generated cryptographic key. Moreover, Bat Optimization and user input string-based permutation followed by SHA-256 hash value generation is applied to the extracted biometric features in the key generation phase to ensure key revocability and to enhance the security of the biometric system. This SEFF-CNN-BO system has combined biological features from the iris, face, and fingerprint for individual identification. The performance of SEFF-CNN-BO was calculated and evaluated in terms of precision, accuracy, recall, specificity, sensitivity, F-Score, etc. Compared to previous models, the developed SEFF-CNN-BO model attained 99.56% accuracy and 99.64% recall respectively. A comprehensive analysis of the proposed model against different security attacks, key revocability, and evaluation of the strength of the generated cryptographic keys is done using various metrics available in NIST statistical test suite. Analysis shows that the method is secure against all known attacks and also the key is 100% revocable.
Emotion Classification In-Context in Spanish
Authors: Thapa Bipul , Cofre Gabriel 
Published: August 2024

Abstract

This paper investigates the classification of customer feedback into emotions and examines document clustering to uncover themes within the feedback in Spanish using advanced NLP techniques , ML models, and Deep Learning embed-dings. We evaluate Logistic Regression, custom Neural Networks (cNN), and Support Vector Machine (SVM) models. Our experiments reveal that Logistic Regression and cNN achieve average accuracies of 97.13% and 98%, respectively, on the training set, and 85.17% and 86.12% on the test set. Various SVM kernels with two principal components (PCAs) resulted in training accuracies of 64.11%, 66.51%, 67.22%, and 46.29% for the linear , polynomial, RBF, and sigmoid kernels, respectively , and corresponding test accuracies of 62.20%, 62.20%, 64.11%, and 44.02%. A grid search identified the RBF kernel with C = 0.5 and γ = 10 as having the highest training and test accuracies of 66.51% and 64.59%. Using a 3-PCA SVM approach, training accuracies for the linear, polynomial, RBF, and sigmoid kernels were 66.63%, 66.39%, 70.93%, and 61.60%, and test accuracies were 62.68%, 63.64%, 70.33%, and * Equal contribution † 60.77%. The RBF kernel with C = 10 and γ = 1 achieved the highest training and test accuracies of 69.86% and 70.81%. These findings highlight the complexity of emotion classification in Spanish and demonstrate the potential for high accuracy using these models.
Unveiling the spatio-temporal characteristics and driving factors of water infrastructure financialization in China
Authors: Wang Zhen , Wu Jianbin , Wang Min , Cheng Zhe 
Published: August 2024

ABSTRACT
Financialization is an emerging and crucial means to address the challenge of funding the water infrastructure development shortage. However, the spatio-temporal characteristics and influencing mechanisms of the state-based water financialization remain unclear. To this end, this study constructed an indicator of the water infrastructure financialization(WIF), using China as a typical case, and thoroughly investigated the spatio-temporal characteristics and driving factors of the WIF in China from 2008 to 2020 based on spatial analysis methods. The results show that the current level of WIF in China is not high, and the construction of water infrastructure mainly relies on the traditional model led by the government. The spatial distribution shows a pattern of gradient decline from Henan and Zhejiang as the center to the surrounding areas. The uneven development of WIF has been improved under the effect of policies. The urbanization and demand for water infrastructure are the main driving factors affecting regional water financialization. The interaction of the driving factors has a more obvious impact on WIF. This study enriches the global knowledge system of infrastructure financialization and provides a reference value for other developing countries regarding water infrastructure financing.
Surface water dynamics of Lake Chad Basin (Sahelian Africa) based on daily temporal resolution Earth observation time series
Authors: Fokeng Reeves M. , Bachofer Felix , Sogno Patrick , Klein Igor , Uereyen Soner , Kuenzer Claudia 
Published: August 2024

ABSTRACT

Water availability is vital for the sustenance of livelihoods in the Lake Chad Basin. However, the daily and seasonal dynamics of open water bodies are not well understood. This study aims to (1) analyze the daily and seasonal dynamics of water bodies, (2) estimate changes in surface water area extent including trends and change points, and (3) assess the connection between surface water extent and seasonal rainfall variation. To achieve this, we used the Global WaterPack and ERA5-Land daily aggregated datasets. We employed time series decomposition, trends analysis, and temporal lag correlation in our analysis. The results showed strong seasonal patterns of natural lakes compared to reservoirs/dams. Between 2003 and 2022, Lake Chad averaged 2,475.64 km2. The Northern pool of Lake Chad exhibited significant fluctuations, remaining below 600 km² between 2005 and 2012, from 2016 to 2019), with less than 350 km2 lasting only for a few days annually. The Southern pool averaged between 2,200 and 2,400 km2, except during drought years (2006–2007), specifically between the days of the year to approximately 66, and days 301–365/6. In Lake Fitri, the yearly maximum and minimum water extents were observed between days 1–59 and 305–365/6, and between days 60 and 304, respectively.
The impacts of climate change on meteorological drought in the Central Highlands region of Vietnam
Authors: Quoc Lap Tran 
Published: August 2024

ABSTRACT

This study aimed to analyse the characteristics of drought under the impacts of climate change in the Central Highlands of Vietnam. The standardized precipitation indices at 3-, 6-, 9- and 12-month time scales were calculated based on the average monthly precipitation at 11 meteorological stations between 1985–2014. The drought characteristics were analysed based on the modified Mann-Kendall test, Theil-Sen methods and the theory of runs. Analysing the impact of climate change on drought events, four CMIP6 GCM models, namely, CNRM-CM6-1-HR, MRI-ESM2-0, BCC-CSM2-MR and MIROC6, under the SSP2-4.5 and SSP5-8.5 scenarios were used to obtain the average monthly precipitation for the period 2031-2060 to compute the SPI3, SPI6, SPI9 and SPI12. The results indicate that the drought frequency under the SSP5-8.5 scenario ranges from moderate to severe. The BCC-CSM2-MR, CNRM-CM6-1-HR and MIROC6 models showed an increase in drought frequency from 2 to 4% for SPI3 and SPI6 and from 4 to 6% for SPI9 and SPI12. Under the SSP2-4.5 scenario, the frequency of severe droughts also increases by 2-4% compared with that in the 1985-2014 baseline periods for SPI6, SPI9 and SPI12. Consequently, moderate to severe drought episodes are predicted to occur more frequently in the research area.
Discharge and sediment load modeling using rating curve-based missing data management
Authors: Haque Marjena Beantha , Karmakar Shyamal , Datta Srijon , Sajid Ayub Parvez , Mamun M M Abdullah Al , Hoque Md Enamul , Hossain M Mozaffar , Alam Md. Shafiul 
Published: August 2024

ABSTRACT

Hydrological models are vital for water management to determine in-stream flow, irrigational water, domestic water supply, and biodiversity conservation. This study formulates a hydrological model with a novel approach for streamflow and sediment load in the QGIS-supported Soil and Water Assessment Tool for the Halda River catchment, a unique ecological habitat for natural carp spawning and freshwater sources. The daily simulation uses an innovative stage–discharge relationship technique from available 15-day interval flow data. The model evaluation parameters R2 values 0.80 and 0.62, and NS values 0.81 and 0.61 for calibration and validation of streamflow suggested excellent agreement in the seasonal cycle and most of the monsoon peak flow. The streamflow/precipitation ratio indicates a significant influence of groundwater through infiltration. The baseflow shows a decreasing trend. The sediment load based on suspended sediment concentration at a downstream location is 1,625 tons/day. On the contrary, the model prediction is 30 times lower. The scattered sediment load data support the model estimate by considering relatively lower intervention or land use change in its upstream. This model provides a baseline for daily flow and sediment load for scenario modeling (e.g., climate change, land use change) for environmental flow estimation of the fish habitat, freshwater supply, irrigation, and salinity intrusion.