bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating The Adverse Health Effects of Herbal Medicine on Swiss Albino Mice: A Comprehensive Hepatotoxicity and Nephrotoxicity Analysis
Authors: Alom Md. Murshid , Zamir Md. Rausan , Islam Ariful , Islam Nazmul , Khalekuzzaman Md. , Zaman Rashed , Islam Md. Asadul 
Published: August 2024

Abstract

Herbal medicine (HM) has been utilized extensively to a wide range of illnesses for thousands of years and has significantly contributed to the health of human beings. The harmful health consequences or degree of toxicity of herbal medication are not demonstrated according to testing, manufacturing, and labeling standards of Food and Drug Administration. The aim of the study was to investigate the hepatotoxicity and nephrotoxicity of herbal medicine - Amlasia: a well‑known Hamdard herbal formulation – on swiss albino mice. For fourteen days straight, the Amlasia solution was administered orally at two separate dosages (0.5 ml per kg and 2.5 ml per kg body weight). Paracetamol was employed for a single lethal dosage of hepato-renal toxicity and allopathic medications Maxpro were utilized as standard. Enzyme levels such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), triglyceride, total protein (TP), bilirubin, albumin (ALB), blood urea nitrogen (BUN), creatinine, and urea were measured in order to evaluate liver and kidney impairment. Besides, oxidative stress markers (reduced glutathione, malondialdehyde, catalase activity) were assessed and finally, cellular toxicity level was evaluated through histopathology analysis. The outcomes for the parameters measuring liver and kidney function showed that, enzyme levels did not significantly change after Amlasia treatment. Moreover, the oxidative stress markers also indicate the non-significant difference as compared with saline control group. Finally, the histopathological analysis of the liver and kidney samples revealed normal cellular levels according to microscopic evaluation. The investigated herbal medicine did not cause hepatotoxic or nephrotoxic disorders in the experimented animals, according to biochemical parameter, oxidative stress marker and histological examination. It was concluded that, the investigated herbal medicine was therefore found to be pharmacologically safe.
Electrical and thermoelectrical conductivity of sprayed Mg x Co 3-x O 4 thin films
Authors: Soussi L. , Louardi C. , Garmim T. , Benaissa N. , Bouzidi M. , Zahot O. , Bachiri A. El , Louardi A. , ElJouad Z. , Erguig H. 
Published: August 2024

Abstract

The effect of Mg substitution on the structural, morphological and electrical properties of sprayed magnesium cobalt oxide (MgxCo3-xO4) thin films (0 ≤ x ≤ 1) have been studed using X-ray diffraction (XRD), scanning electron microscopy (SEM), compositional analysis with EDS technique and four probe method for the electric conductivity measurements. XRD evaluation exhibits that MgxCo3-xO4 thin films are polycrystalline with spinel cubic structure. The EDS study confirms the presence of Mg and Co in the substituted films with the same concentrations as in the starting solution confirming the formation of MgxCo3-xO4 thin films. The effect of temperature on the dc conductivity reveals that the electrical transport mechanism in MgxCo3-xO4 thin films is based on the three-dimensional Mott’s variable-range hopping model. The density of states, the hopping distance, and the hopping energy have been successfully evaluated.
Climate change compromises leaf units and lignin content in sun-tolerant Paubrasilia echinata plants
Authors: Cuzzuol Geraldo Rogério Faustini , Venâncio Felipe Cassa Duarte , Pezzopane José Eduardo Macedo , Toledo João Vitor 
Published: August 2024

Abstract

It is of great global interest how shade-tolerant and sun-tolerant species will respond to the Representative Concentration Pathways (RCP) scenarios of climate change predicted by the end of the 21st century. To elucidate this question, we used young plants of the shade- and sun-tolerant ecotypes of Paubrasilia echinata, a tree native to the Brazilian Atlantic Forest. Plants were grown in three climate scenario simulations: current Atlantic Forest for the southeast region of Brazil (CAF); average temperature of CAF + 2.0 ºC and 580 ppm CO2 (RCP 4.5); and average temperature of CAF + 3.5 °C and 936 ppm CO2 (RCP 8.5). Two substrate moisture conditions were established for each scenario: 40 (water deficit) and 80% (control). After 90 days of cultivation in these microclimatic conditions, we carried out growth analyses (survival, number of folioles and foliolules of their compound leaves and increase in stem elongation), non-structural carbohydrate content (sugars and starch) and cell wall polymer content (cellulose, hemicelluloses and lignin). The pessimistic climate scenario (RCP 8.5 + water deficit) inhibited photosynthesizing leaf area production (number of folioles/foliolules) of the shade-tolerant ecotype more, but without affecting survival. In this climate scenario, the shade-tolerant ecotype showed a greater capacity to accumulate sugars than the other ecotype. Also in the pessimistic climate scenario, the shade-tolerant ecotype accumulated more of this polysaccharide in the stem, while in the sun-tolerant ecotype this occurred in the root. This scenario increased the cellulose content of both ecotypes to the detriment of the lignin content of the sun-tolerant ecotype. We conclude that the rise in CO2, temperature and DPV along with low precipitation in RCP 8.5 predicted for the southeastern region of Brazil by the end of this century may compromise more the photosynthesizing leaf area production of the shade-tolerant ecotype and the wood quality of the sun-tolerant ecotype of P. echinata. However, this does not threat their survival.
O-Ring Shaped Multiresonant Wavelength Selective Plasmonic Nanoemitter Compatible with Thermal Management
Authors: Demir Ahmet , Tabaru Timuçin Emre 
Published: August 2024

Abstract

In our study, a new Metal-Insulator-Metal-Insulator (MIMI) metamaterial design was made to achieve selective infrared absorption consisting of layers arranged in the form of a metal-insulator (Ag-ITO) O-Ring shaped on a planar metal-insulator (Ag-Si) metasurface. The proposed metamaterial design has been studied for the spectral region between infrared wavelengths 800 and 12000 nm. Spectral features were analyzed using finite difference time domain (FDTD) software. This design maintains low emission in atmospheric windows in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) range. However, the MIMI structure obtained two broadband absorption peaks in the shortwave infrared (SWIR) and non-conducted infrared (NTIR) regions, which perfectly match the thermal camouflage and radiative cooling windows. Moreover, the proposed structure is polarization insensitive as it has an in-plane symmetrical design. The optical performance of the structure was optimized using numerical simulation techniques. The impedance values ​​of the nanostructure and air were compared. It was shown that they provide excellent compatibility and that the average absorption values ​​are very close to the desired value in the MWIR and LWIR regions. The designed structure offers an adjustable and highly efficient solution to manage thermal emissions in various applications and is an important innovation in the field of privacy technology.
Multivariate Time Series Analysis of Industry Development Indicators in Ethiopia
Authors: Dagnew Getachew Abate , Abate Ashenafi , Lakew Demeke , Beletew Demsew 
Published: August 2024

Abstract

Background
Industry development indicators are metrics that are used to evaluate an industry's growth and performance. This study's primary goal was to evaluate the relationship among Ethiopia's industry development indicators using a multivariate time series model based on World Bank data covering the years 1982–2021.
Method
Annual data covering the years 1982–2021 were used in a time series approach. A vector auto-regressive model was performed for interdependency modeling. Certain elements of the relationships between the variables of interest are frequently analyzed using structural vector autoregressive techniques.
Results
The results of the vector autoregressive model indicated that the current industry growth is positively related to one period lagged value of itself, industry GDP and manufacturing exports. And also, there exists a positive relationship between manufacturing exports and the first lag of itself, industry growth and manufacturing GDP. For a 1% increase in manufacturing exports, the industry growth rate increased by 0.37% in natural logarithms on average, ceteris paribus.
Conclusion
The current manufacturing growth of Ethiopia is significantly affected by the first lag of industry growth, industry GDP, manufacturing exports, and manufacturing GDP. And also, there is a unidirectional causality from industry growth to industry GDP, industry growth to manufacturing growth, manufacturing exports to industry growth, manufacturing exports to industry GDP, manufacturing growth to industry GDP, manufacturing exports to manufacturing growth, and manufacturing GDP to manufacturing growth in the short run.
A lightweight YOLOv8 algorithm for real-time flame detection in fire
Authors: Duan Shichen , Zhou Jun , Liu Chaofeng 
Published: August 2024

Abstract

This paper proposes a real-time lightweight fire flame detection algorithm based on improved YOLOv8n to promptly assess fire situations and minimize losses. The algorithm is integrated into an automated water cannon system to aid firefighting efforts. The optimization focuses on the YOLOv8n backbone, where the Bottleneck in the C2f module is replaced with FasterNet Block and EMA attention is proposed. This enhances the network's ability to capture fire features in various scenarios, improving accuracy in recognizing flames. Additionally, a lightweight Slim-Neck structure reduces computational complexity and parameters, suitable for embedded deployment. The Wise-Shape-FocalerIoU loss function further accelerates convergence. Experiments show the improved algorithm increased the precision rate by 1.2%, the mAP50 reached 99.2%, and the recall rate increased by 3.4%. Meanwhile, the amount of parameters is reduced by 19% and the GFLOPs are reduced by 1.8. This algorithm achieves lightweight processing while maintaining accuracy, providing strong technical support for fire safety.
Exploration of impact of ammonia concentration on the surface morphology, optical and wettability performance of SiO2 thin film
Authors: Pradhan Krishnapriya , Badapanda Tanmay , Ray Jasashree , Ghosh Surya Prakash 
Published: August 2024

Abstract

An effective sol-gel technique that yields conformal coatings with amorphous silica (SiO2) is the Stöber method. In the present manuscript, we have synthesis SiO2 thin film using the solgel-based Stober method and deposited on glass substrates by spin coating technique. We have examined the impact of ammonia concentration during the synthesis process of silica thin films on the structural, morphological, optical, and wetting characteristics. XRD analysis confirmed the presence of amorphous silica phase for all thin film samples. Elemental analysis illustrated the presence of Si and O element in the sample without any impurity. Moreover, the average diameter of silica nanoparticles has been obtained by utilizing Field emission scanning electron microscopy (FESEM). The variation of optical bandgap of silica thin films prepared at different ammonia concentration was investigated via UV-Vis spectrum. The wetting properties of silica thin films have been studied from the contact angle measurement concerning to variation in ammonia concentration.
Large-scale sparse signal recovery by modified HS conjugate gradient method
Authors: Li Shuang'an , Chen Fenghua 
Published: August 2024

Abstract

The \({\ell _1}\) regularization based methods for sparse signal reconstruction is a topic of considerable interest recently,which is widely employed in basis pursuit denoising, compressed sensing and other related fields.These problems can be cast as \({\ell _1}\)-regularized least-squares programs (LSPs).But it is challenging due to the non-smoothness of the regularization.Inspired by Nesterov's smoothing technique, we smoothed the regularization term.Hence this paper proposed a new modified HS conjugate gradient algorithm for solving common recovery problems in signal processing.Numerical experiment shows that our algorithm is effective and suitable for solving large-scale sparse signal recovery problems. CCS CONCEPTS Mathematics of computing ~ Mathematical analysis ~ Mathematical optimization
Intracranial Extramedullary Hematopoiesis in a Recently Recovered Monkeypox Patient: A Case Report
Authors: Stev Edward 
Published: August 2024

Abstract

Extramedullary hematopoiesis (EMH) is a compensatory response wherein blood cell production occurs outside the bone marrow, typically as a result of hematologic disorders that impair bone marrow function. Conditions such as myelofibrosis, thalassemia, and sickle cell disease often lead to EMH in organs like the liver and spleen, which serve as alternative sites for hematopoiesis. This case report highlights the importance of considering EMH in the differential diagnosis of intracranial lesions, exemplified by a 67-year-old woman who presented with headaches and generalized malaise. Her medical history included hypertension and mild anemia. Magnetic resonance imaging (MRI) revealed a cystic mass in the right inferomedial parietal lobe, which showed medial mural enhancement, edema, and mass effect, with an additional enhancing focus in the left parietal lobe. Initially suspected to be a hemangioblastoma, the resected mass was found to be a benign vascular lesion with endothelial hyperplasia, but without the characteristics of cavernous hemangioma or vascular malformation. The histopathological examination revealed foci of erythroid and myeloid precursors, and megakaryocytes, with immunohistochemical staining confirming the diagnosis of EMH. Subsequent investigations revealed no underlying hematological disorders, leading to a diagnosis of idiopathic intracranial EMH. The patient underwent intensity-modulated radiation therapy (IMRT) for the remaining lesion and showed significant improvement in symptoms at three months follow-up. This case underscores the diagnostic challenges associated with intracranial lesions and highlights the necessity of considering EMH, particularly when faced with atypical imaging and histopathological features. Future research is needed to optimize therapeutic strategies and assess long-term outcomes for intracranial EMH.
Um еstudo sobrе а formаção continuаdа dе profеssorеs como instrumеnto dе rеssignificаção dа práticа pеdаgógicа
Authors: Barros Mauricio Pereira , Silva Maria Oneide Lino da , Chаrlot Vеlеidа Аnаhi Cаpuа dа Silvа 
Published: August 2024

А prеsеntе propostа objеtivа umа rеflеxão sobrе а formаção continuаdа е suаs rеpеrcussõеs nа práticа pеdаgógicа pаrа а rеssignificаção dе umа políticа еducаtivа еm umа pеrspеctivа contеmporânеа. А formаção continuаdа é indispеnsávеl pаrа quе аs instituiçõеs dе еnsino е еquipеs dе еducаção аcompаnhеm еssаs novidаdеs е consigаm еvoluir е proporcionаr umа еducаção аindа mаis quаlificаdа е complеtа аos аlunos, promovеndo impаctos positivos еm todа а comunidаdе еscolаr. Utilizа como rеfеrеnciаl tеórico-mеtodológico o mаtеriаlismo histórico-diаlético. Pаrа dеsеnvolvimеnto do еstudo аdotou-sе umа аbordаgеm quаlitаtivа com bаsе nа pеsquisа bibliográficа е documеntаl sobrе аs concеpçõеs dе formаção no contеxto dаs políticаs еducаcionаis contеmporânеаs. É mistеr quе а еducаção continuаdа contribui dе formа significаtivа pаrа o dеsеnvolvimеnto do conhеcimеnto profissionаl do profеssor, cujo objеtivo еntrе outros, é fаcilitаr аs cаpаcidаdеs rеflеxivаs sobrе а própriа práticа docеntе еlеvаndo-а а umа consciênciа colеtivа. Como rеsultаdos conclui-sе quе а formаção continuаdа dе profеssorеs criа possibilidаdеs dе rеssignificаção dа práticа docеntе е еspаços dе fortаlеcimеnto dа rеlаção tеoriа е práticа.
Pаlаvrаs-chаvе: Formаção continuаdа; Políticаs еducаcionаis; Práticа pеdаgógicа.
Ensemble of artificial intelligence and physically based models for rainfall–runoff modeling in the upper Blue Nile Basin
Authors: Gichamo Tagesse , Nourani Vahid , Gökçekuş Hüseyin , Gelete Gebre 
Published: August 2024

ABSTRACT
This study investigated the performance of the adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), and support vector regression (SVR) models for rainfall–runoff modeling using gauged and satellite rainfall, and their fusions in the Gilgel Abay watershed, Ethiopia. Afterward, simple average ensemble (SAE), weighted average ensemble (WAE), and neural network ensemble (NNE) techniques were applied to combine the outputs of individual models under three scenarios. The performance of the models was evaluated using Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE). The results demonstrated that the ANFIS model outperformed all the other single models with validation stage NSE values of 0.864 and 0.875, and RMSE values of 23.58 and 21.84 m3/s for gauge and fusion rainfall data, respectively. Among the physical-based models, SWAT gave better modeling performance with the validation stage NSE values of 0.81 and 0.821 for gauge and fusion rainfall data, respectively. Moreover, an ensemble of artificial intelligence and physical-based models greatly improved the overall modeling performance. The NNE improved the performance of single models up to 15.7 and 21.2 5% for fusion and satellite-based rainfall modeling, respectively.
Formulation, Characterization, and Potential Therapeutic Implications of Encapsulated Recombinant Alpha-Luffin in Niosomes
Authors: Joni Hajar Abedi , Esmaeili Fariba , Landi Behnaz , Bayat Elham , Bakhshandeh Haleh , Talebkhan Yeganeh , Barkhordari Farzaneh , Sadeghi Somayeh , Nematollahi Leila , Negahdari Babak 
Published: August 2024

Objective:
The anticancer properties of recombinant α-luffin (LUF) are wellestablished.
However, the cytotoxic effects of encapsulating LUF within niosomes on the
SKBR3 breast cancer cell line have yet to be explored. Our study aimed to investigate whether
this encapsulation strategy could improve cytotoxic effects.

Methods:
Alpha-luffin was expressed, purified, and refolded. Then, this protein was utilized to
craft an optimal formulation, guided by experimental design. In this work, we have explored
various physicochemical properties, including particle size, polydispersity index, zeta potential,
morphology, entrapment efficiency, drug release and kinetics, storage stability, and FTIR spectroscopy.
Additionally, we have assessed the cellular uptake and cytotoxic effect of the optimized
niosome formulation on the SKBR3 breast cancer cell line.

Results:
The optimized niosome exhibited a mean diameter of 315±6.4 nm (DLS). Successful
encapsulation of LUF into regularly shaped, spherical niosomes was achieved, with an encapsulation
efficiency of 73.45±2.4%. Notably, Niosomal LUF (NLUF) exhibited significantly increased
cytotoxicity against SKBR3 cells.

Conclusion:
These findings suggest that niosomes loaded with LUF hold promise as a potential
treatment strategy for breast cancer.
Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset
Authors: Shahwar Tayyaba , Mallek Fatma , Ur Rehman Ateeq , Sadiq Muhammad Tariq , Hamam Habib 
Published: August 2024

Introduction:
Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects
of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we
introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting
pneumonia using 5863 X-ray scans with binary cases.

Methods:
The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These
significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit
algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a
deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices
to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10−6 and six
layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs.

Results:
The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy
gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a
variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method.

Conclusion:
This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the
speed and accuracy of the network in the clinical and healthcare sectors.
Navigating the Future of PCOS Treatment: The Precision Medicine Paradigm
Authors: Trisa Sunil Alma , Jo Caroline , P. S. Sanigha , Kolasseri Aparna Eledath , Tamizhselvi Ramasamy , Jayanthi Sivaraman 
Published: August 2024

Abstract:
Polycystic Ovary Syndrome (PCOS) is a condition affecting women of reproductive
age, characterized by a heterogeneous array of symptoms. This study aims to examine
the role of Precision and Personalized Medicine (PPM) in managing PCOS, given
the diverse manifestations of the disease and any genetic factors involved. In this review,
we have analyzed the existing literature on the heterogeneity in PCOS symptoms, efforts
to acquire PPM data for the characterization of molecular changes in PCOS, and the impact
of advances in artificial intelligence on precision medicine. PCOS symptoms present
differently in each individual, making traditional therapies ineffective. By tailoring treatment
to each individual's genetic and molecular profile, PPM offers a promising approach
to addressing the complex nature of PCOS. Understanding PCOS molecular underpinnings
requires continuous acquisition of PPM data. Advances in artificial intelligence
have greatly enhanced precision medicine's potential applications. Precision medicine
could become a standard component of PCOS care, similar to its application in treating
serious conditions like cancer and heart disease, due to its ability to address the condition's
complexity through individualized treatment approaches.