bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis and optimization of 2,5-dihydroxyterephthalic acid in a slurry reactor
Authors: Beiranvand Reza Pirdadeh , Ovaysi Saeed 
Published: March 2024

Abstract
2,5-dihydroxyterephthalic acid (DHTA) is synthesized under 10 bar pressure and 200°C temperature. Using a slurry reactor, disodium salt of hydroquinone (DSH) is contacted by carbon dioxide gas in presence of sodium acetate as catalyst. A fractional factorial design is used to screen four parameters including reactor pressure, temperature, catalyst to DSH molar ratio, and reaction time. By performing 8 experiments in the screening phase the variables ranges are modified to 175–200°C for temperature, 1.5–2.5 for catalyst to DSH molar ratio, and 3–5 hours for reaction time. Additionally, pressure is kept fixed at 10 bar throughout the remaining experiments. An additional 14 more experiments devised by Box-Behnken design scheme are performed to determine a quadratic model for the DHTA yield against the three parameters mentioned above. A mathematical optimization of the model predicts 83.385% DHTA yield at 200°C, 2.085, and 250 minutes for temperature, catalyst to DSH molar ratio, and reaction time, respectively. These parameter values are put to the test by performing one more experiment under the suggested optimum point. This results in 83% DHTA yield which is in good agreement with the model. For all experiments, the DHTA yields and the composition of byproducts were obtained using HPLC analysis. The optimized product was analyzed using XRD and FTIR analyses and the structure of the synthesized DHTA was confirmed.
Diffusion kurtosis versus diffusion-weighted MRI in differentiating clear cell renal cell carcinoma and renal angiomyolipoma with minimal fat: a comparative study
Authors: Zhu Qingqiang , Feng Xinyu , Sun Jun , Ye Jing , Zhu Wenrong , Chen Wenxin 
Published: March 2024

Abstract

Background
Apparent diffusion coefficient(ADC) assessment has also shown benefit in distinguishing renal tumor types, however, there is always concern that the ADC obtained from conventional monoexponential diffusion-weighted imaging (DWI) could not accurately reflect the real diffusivity, because of the influence of microcirculation. The aim of the current study was to produce a quantitative comparison of the potential of various diffusion parameters obtained from DWI and diffusion kurtosis imaging(DKI) in differentiating clear cell renal cell carcinoma (ccRCC) and renal angiomyolipoma with minimal fat(RAMF).
Methods
Sixty-eight patients with ccRCC and 18 patients with RAMF were retrospectively studied. For DKI and ADC, respiratory triggered echo-planar imaging sequences were acquired in axial plane (three b-values: 0, 1000, 2000s/mm2) and (one b-value: 2000s/mm2). Mean Diffusivity (MD), fractional Anisotropy (FA), mean kurtosis (MK), kurtosis anisotropy (KA), radial kurtosis (RK) and apparent diffusion coefficient (ADC) were performed. The diagnostic efficacy of various diffusion parameters for predicting ccRCC and RAMF was compared.
Results
The ADC and MD values of ccRCCs were higher than those of RAMFs (p < 0.05), while comparable FA, MK and KA values were found between ccRCCs and RAMFs (p > 0.05). Moreover, the RK values of RAMFs were higher than those of ccRCCs (p < 0.05). ROC curve analyses showed that MD values had the highest diagnostic efficacy in differentiating ccRCCs from RAMFs. For pairwise comparisons of ROC curves and diagnostic efficacy, ADC was worse than DKI analysis (p < 0.05).
Conclusion
DKI analysis performs better than ADC analysis in differentiating ccRCC and RAMF.
Artificial Intelligence for Computer Vision: Bibliometric Analysis
Authors: Oke Oluwafemi , Cavus Nadire 
Published: March 2024

Abstract
Computer Vision (CV) is a prominent area of focus in Artificial Intelligence (AI) research, with applications ranging from self-driving cars to medical imaging. A bibliometric analysis is presented in this study for the latest research in AI for CV, focusing on advancements in CV models, their evaluation, and their use in various applications from 1981 to 2023 using Web of Science Clarivate Core Collection database and a dataset of 1857 retrieved publication. VOS viewer and CiteSpace software were implemented to perform science mappings and bibliometric analysis techniques in the study. Hence, analysing citation networks, publication output, and collaboration patterns in the field to identify influential research publications, researchers, and institutions. The analysis reveals the top publications and researchers in the field, as well as the most common research topics and their relative importance. This study finds that deep learning techniques, such as convolutional neural networks (CNNs), are the dominant approach in CV research, with applications in object detection, feature extraction, and image analysis. Also, it found that USA has a wide partnership and collaborative range amongst making it the most productive country. This study also discussed few of the challenges and opportunities in AI for CV, including U-Net not generating more precise segmentation in biomedical image segmentation. The recommendation of this study for future research direction is the need for more interdisciplinary collaboration, the development of new evaluation techniques, and the exploration of novel applications of AI for CV. The bibliometric analysis results will be of interest to researchers, practitioners, and policymakers interested in AI, CV, and related fields, as well as anyone interested in the latest advancements in this exciting and rapidly evolving field.
Peripheral administration of sepiapterin replenishes brain tetrahydrobiopterin: a pharmacodynamic study
Authors: Ohashi Akiko , Nakamaru-Ogiso Eiko , Matsuoka Hiroshi , Aizawa Shin , Hasegawa Hiroyuki 
Published: March 2024

Abstract

Background
The levels of brain tetrahydrobiopterin (BH4), a coenzyme of tryptophan hydroxylase and tyrosine hydroxylase, regulate the rates of monoamine synthesis, including serotonin, dopamine, and noradrenaline. The attempt to treat congenital or acquired central nervous system (CNS) disorders caused by monoamine deficiency with synthetic BH4, 6RBH4, has failed owing to its limited permeation through the blood-brain barrier (BBB). Sepiapterin (SP), a BH4 precursor, is known to be rapidly assimilated into cellular BH4 through the BH4-salvage pathway. However, no treatment attempts with SP have been made, owing to the concerns about its ability to reach the brain parenchyma when administered peripherally. Therefore, we investigated the pharmacodynamics of SP entry into the CNS following intraperitoneal (ip) administration of SP.
Methods
To assess SP delivery, we derived an equation describing brain cell-uptake of SP as a function of its dose based on a tandem barrier model; the BBB and brain cell membranes. We estimated each of the clearances determining the directional component of the flow: influx through the BBB (CLin) and cell uptake (CLuptake) were determined in vitro, while pumping to plasma (CLout) was calculated using in vivo data from previous studies involving direct SP injection into the rat brain ventricle. To validate the model in vivo, we administered various SP doses to mice and measured the resulting increase in brain BH4 levels. To distinguish BH4 increases due to cellular uptake of SP from those due to influx of exogenous BH4, the quantification was performed 6 hours after administration, when BH4 influx had ceased.
Results
The model equation predicted a linear increase in brain BH4 with plasma SP, with a clearance CL(plasma→cells) = 5.42–10.2 µL·min-1·g-1. In vivo results showed that suprathreshold doses (> 13.3 mg/kg, mice, ip) linearly increased brain BH4 concentrations. In this context, SP reached the brain, or was taken up, with an AUCplasma = 1.3–2.45 µM·min per 1 mg/kg-body.
Conclusions
Peripheral administration of SP at a practical dose range increases brain BH4 levels in a dose-dependent manner. SP treatment could be a promising therapy to enhance monoamine synthesis in various monoamine neurotransmitter deficiencies.
Adaptive Drop Approaches to Train Spiking-YOLO Network for Traffic Flow Counting
Authors: Zhang Bin , Wu Liangshun , Liu Peilin , Juan Hu , Luo Meizhen , Peng Ling 
Published: March 2024

Abstract
Traffic flow counting is an object detection problem. YOLO (" You Only Look Once ") is a popular object detection network. Spiking-YOLO converts the YOLO network from ANN(artificial neural network) to SNN (spiking neural network), which consumes less power than YOLO. However, Spiking-YOLO is difficult to train and has poor accuracy because of overfitting. Dropout and DropConnect are two methods to regularize neural network models and have produced cutting-edge outcomes in many benchmarks. The two drop approaches are first applied to Spiking-YOLO. Considering that a higher membrane potential of a biological neuron implies a higher probability of neural activation, we propose three adaptive drop algorithms—Spiking-YOLO with adaptive Dropout, Spiking-YOLO with adaptive DropConnect, and Spiking-YOLO with group adaptive Drop, which adaptively adjusts the keep probability for training SNNs. The experimental results show that the adaptive algorithms have fast convergence speed with high detection accuracy and mAP value, and work well in the case of high-speed streaming video, making them suitable for online learning scenarios.
Life cycle assessment of ammonium sulfate recovery from urban wastewater
Authors: Gong Yuxian , Wang Xiaoyuan , Bao Xiaoan , Lam Ka Leung 
Published: March 2024

ABSTRACT

Anthropogenic nitrogen fluxes are profoundly altering the global biogeochemical nitrogen cycle. Better management of these nitrogen fluxes is essential. Recovering nitrogen from urban wastewater reduces both the energy and resources required to produce nitrogen-based fertilizer and to remove nitrogen from wastewater collected. Nitrogen can be recovered from wastewater in the form of ammonium sulfate, a common nitrogen-based fertilizer. In the urban setting, the technology can be applied to target source-separated urine or municipal wastewater. To assess the environmental sustainability of this approach, this study compared the life cycle environmental impacts of ammonium sulfate recovered from urban wastewater (through eight different recovery technology trains) and ammonium sulfate produced by six different industrial processes. The results show that wastewater-derived ammonium sulfate generally has lower potential environmental impacts than industrially produced ammonium sulfate in most of the impact categories assessed. The impact for the source-separated urine centralized recovery train is the smallest. The contribution analysis shows that energy, sulfuric acid and sodium hydroxide use are the major contributors, while the background inventory analysis shows that the results can be sensitive to the choice of region-specific background inventory. In the future, nitrogen recovery from urban wastewater is promising for the circular economy in cities.
Recent advances on the treatment of oilfield-produced water by advanced oxidation processes: A review
Authors: Zhang Huan , Gao Chunyang , Zhang Hongli , Song Naijian , Cao Qiang 
Published: March 2024

ABSTRACT

Despite the advancements in alternative fuels and energy sources, there continues to be a significant global dependence on oil production and extraction. A substantial volume of oilfield-produced water (OPW) is generated during the production and extraction processes of oil fields. Recurrent OPW treatments encountered significant challenges in addressing this particular type of wastewater. Advanced oxidation processes (AOPs) are regarded as a promising alternative approach for the degradation of recalcitrant organic compounds in the OPW. This review focuses on the characterization of OPW. The treatment of organic pollutants in wastewater using AOPs, such as ozonation, Fenton oxidation-based processes, heterogeneous photocatalysis, and persulfate oxidation, is comprehensively reviewed in terms of their efficiency for pollutant degradation. The primary challenges in this field and the future directions for development are proposed, with the aim of providing a valuable reference for achieving highly effective treatment of OPW.
Ultrasound-based Radiomics Predicts Short-term Outcomes in Hepatitis B
Virus-related Acute-on-chronic Liver Failure
Authors: Huang Xingzhi , Yuan Songsong , Xu Pan , Li Yaohui , Zhou Aiyun 
Published: March 2024

Background:
The prognosis in hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is challenging due to heterogeneity. Radiomics may
enable noninvasive outcome prediction.

Objective:
This study aimed to evaluate ultrasound-based radiomics for predicting outcomes in HBV-ACLF.

Methods:
We enrolled 264 HBV-ACLF patients, dividing them into a training cohort (n=184) and a validation cohort (n=80). From hepatic ultrasound
images, 455 radiomic features were extracted. Radiomics-based phenotypes were identified through unsupervised hierarchical clustering. A
radiomic signature was developed using a Cox-LASSO algorithm to predict 30-day mortality. Furthermore, we integrated the signature with
independent clinical predictors via multivariate Cox regression to construct a combined clinical-radiomic nomogram (CCR-nomogram). Integrated
discrimination improvement (IDI) and net reclassification improvement (NRI) assessed performance improvements achieved by adding radiomic
features to clinical data.

Results:
Both clustering and radiomic signature identified two distinct subgroups with significant differences in clinical characteristics and 30-day
prognosis. In the training cohort, the signature achieved a C-index of 0.746, replicated in validation with a C-index of 0.747. The CCR-nomogram
achieved C-indices of 0.834 and 0.819 for the training and validation cohorts. Incorporating radiomic features significantly improved the CCRnomogram
over the signature and clinical-only models, evidenced by IDI of 0.108-0.264 and NRI of 0.292-0.540 in both cohorts (all p0.05).

Conclusion:
Ultrasound-based radiomics offered prognostic information complementary to clinical data and demonstrated potential to enhance outcome
prediction in HBV-ACLF.
Effect of Liver Segments and Hepatic Fibrosis Grade on Repeatability,
Reliability, and Diagnostic Efficiency of Intravoxel Incoherent Motion
Authors: Huang Lesheng , Chen Jun , Liu Weiyin Vivian , Tian Guangjun , Wei Qian , Peng Hui , Zhang Wanchun , Li Hongyi , Peng Se , Liu Tianzhu 
Published: March 2024

Background:
Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is considered a potential marker of hepatic fibrosis (HF).

Objective:
To explore the influencing factors of repeatability and reliability in IVIM-DWI parameters of ROI-based liver segments in participants with HF
and healthy volunteers (HV) and to assess the diagnostic efficiency of these parameters in HF.

Methods:
Participants with early HF (EHF, n=59) or advanced HF (AHF, n=38) and HV (n=48) were recruited. Two examiners measured IVIM data using
mono-, bi-exponential and stretched exponential models. The results and influencing factors of repeatability and reliability of IVIM-DWI, and the
diagnostic efficiency were analyzed.

Results:
The repeatability of D* (CV: 26.62–41.47%) and DDC (CV: 18.01–34.40%) was poor, the repeatability of ADC (CV: 4.95–9.76%), D (CV:
7.09–15.52%), f (CV: 9.35–17.15%), and α (CV: 7.48–13.81%) was better; ordered logistic regression showed statistically significant results of
IVIM-derived parameters; the reliability showed no obvious trend, and ordered logistic regression showed statistically significant results of IVIMderived
parameters, groups, and partial hepatic segments (all p<0.001). IVIM-derived parameters with relatively good repeatability (CV<20%) and
reliability (ICC>0.4) were used to establish regression models for differential diagnosis. The AUC of regression models was 0.744–0.783 (EHF vs.
AHF), but no statistically significant parameters were found in the HV vs EHF comparison.

Conclusion:
IVIM-derived parameters were the most important factors affecting the repeatability and reliability, while staging of HF and hepatic segments may
be the influencing factors of reliability. IVIM-derived parameters showed medium diagnostic efficiency in distinguishing between EHF and AHF.
Identifying Predictors for Hypoplastic Aortic Arch (HAA) in Pediatric Patients
with Complex Coarctation of the Aorta (CoA)
Authors: Lyu Ying , Tian Lu , Qin Bin , Fan Xiao , He Ling 
Published: March 2024

Objective:
HAA is a significant risk factor in complex CoA patients. We conducted a retrospective study to explore the relationship between HAA and other
cardiovascular factors.

Methods:
We analyzed 103 patients diagnosed with complex CoA using CT angiography and echocardiography. Aortic diameter was measured at six levels,
and severe coarctation was defined as coarctation site to diaphragmatic level ratio (CDR) < 50%. Correlations between non-HAA and HAA groups
were assessed. Univariate and multivariate logistic regression identified HAA risk factors.

Results:
Among 103 children with complex CoA, 55 were in the non-HAA group and 48 in the HAA group. The incidence of PDA (56.3% vs. 32.7%, p <
0.05), severe coarctation (CDR < 50%, 81.3% vs. 34.5%, p < 0.01), and collateral arteries (39.6% vs. 0, p < 0.01) were higher in the HAA group
than one in the non-HAA group. The aortic arch size was positively correlated with age and negatively correlated with severe coarctation, VSD,
collateral arteries, and left heart dysfunction. Logistic regression results showed that collateral arteries were risk factors for the whole aortic arch
(proximal arch OR = 11.458; p < 0.01, distal arch OR = 4.211; p < 0.05, and isthmus OR = 11.744; p < 0.01), severe coarctation (OR = 6.653; p <
0.01), and left heart dysfunction (OR = 5.149; p < 0.01) associated with isthmus hypoplasia.

Conclusion:
This study highlights the prevalence of HAA in complex CoA patients and its associations with various cardiovascular factors. These insights
improve diagnosis and treatment approaches.
Face Recognition Using LBPH and CNN
Authors: Shukla Ratnesh Kumar , Tiwari Arvind Kumar , Ranjan Mishra Ashish 
Published: March 2024

Objective::
The purpose of this paper was to use Machine Learning (ML) techniques
to extract facial features from images. Accurate face detection and recognition has long been a
problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior
facial descriptor for face recognition. A person's face may make their identity, feelings,
and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized
authentication. Face detection and recognition help increase security; however, the most difficult
challenge is to accurately recognise faces without creating any false identities.

Methods::
The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution
Neural Network (CNN) to preprocess face images with equalized histograms.

Results::
LBPH in the proposed technique is used to extract and join the histogram values into a
single vector. The technique has been found to result in a reduction in training loss and an increase
in validation accuracy of over 96.5%. Prior algorithms have been reported with lower
accuracy when compared to LBPH using CNN.

Conclusion::
This study demonstrates how studying characteristics produces more precise results,
as the number of epochs increases. By comparing facial similarities, the vector has generated
the best result.
Uso de tecnologías CRISPR-CAS9 en microalgas aplicado a la obtención de productos biotecnológicos de interés industrial
Authors: Martinez Tapiero Dalia Yirasol , Martínez Rentería Maria Anghela , Camacho Kurmen Judith Elena 
Published: March 2024

Las microalgas se destacan por su capacidad de adaptarse a diferentes medios de cultivo, participación en captura de CO2 y productos de interés biotecnológico cómo biocombustibles, proteínas, biofertilizantes, suplementos alimenticios, pigmentos, entre otros. Se han trabajado diferentes tipos de estrés como variación del pH, deficiencia de nutrientes, estrés salino, cambios en la temperatura y alta irradiancia con el fin de aumentar la producción de metabolitos aplicado a diferentes industrias; sin embargo, la demanda de los productos biotecnológicos se ha incrementado ante las necesidades de la población, por lo que el uso de tecnologías de modificación genética ha surgido como una alternativa durante la última década gracias a la capacidad y eficiencia que muestran los métodos como mutagénesis aleatoria, supresión de genes y crispr-cas9. Se propone como objetivo conocer la aplicación del crispr-cas9 en microalgas relacionado con la obtención de productos biotecnológicos de interés industrial. Se determinó que esta tecnología aplicada en las microalgas con uso industrial incrementa la obtención de productos de interés biotecnológico como lípidos, carotenoides, proteínas y enzimas recombinantes.
Smart Economy: The Analysis of Youngpreneurs Development towards the SMEs in East Java Province
Authors: Satria Dias , Wibowo Joshi Maharani , Zahro Fatimah 
Published: March 2024

AbstractEast Java Province is one area in Indonesia with many potentials in the creative industry sector, especially SMEs. Unfortunately, SMEs growth in East Java Province is relatively slow because most of them still do not use technological tools. The reason is, SMEs is not attractive enough to the millennial generation and are still not digitized. This research was conducted to determine SMEs' potential digitalization to attract the millennial generation's interest in SMEs. The result showed three main obstacles to applying technology tools in the SMEs industry: marketing networking, branding and packaging, and product standardization. Apart from the obstacle, there are also three essential things to increase their interest in SMEs sectors, which are: SMEs community; local materials distribution; and optimization of the entrepreneurial mindset. A smart economy strategy can become a long-term economic investment to overcome every obstacle and stimulate the growth and Youngpreneur interest in the SMEs industry. Keywords:Millenial Generation, Smart Economy, SMEs
Underpricing And Herding Behaviour Ipo Stock In Covid-19 Pandemic At Indonesian Stock Exchange
Authors: Kewal Suramaya Suci , Putranto B Yohanes Andri 
Published: March 2024

AbstractThis study aims to empirically test whether there is underpricing and herding behavior in companies that have just IPO on the Indonesia Stock Exchange in 2020. The selection in 2020 is because the stock exchange experiences high trading volatility due to the crisis impact of the Covid-19 pandemic. This study uses a market adjusted return approach in calculating underpricing, and uses the Chang, Cheng, and Khorana (CCK) method to indicate the occurrence of herding behavior in IPO shares on the Indonesia Stock Exchange. The significance level used in the study was 5%. The results show that there is an underpricing phenomenon in companies whose IPOs are on the IDX and there is no evidence of herding behavior by investors on IPO shares on the IDX. Investors are advised to invest in IPO shares, especially during the pandemic and make rational considerations in making investment decisions in the capital market. Keywords: Underpricing, Herding Behavior, IPO Shares, Pandemic, Capital Market