Background To prospectively investigate the role of Fast spin-echo T 2 -weighted (FSE T 2 -w) and diffusion-weighted imaging (DWI) in magnetic resonance imaging (MRI) for detecting spine bone marrow changes in postmenopausal women with osteoporosis (OP). A total of 101 postmenopausal women, mean age of 60.97 ± 7.41 (range 52–68) years old, who underwent dual-energy X-ray absorptiometry of the spine, were invited to this study and divided into three bone density (normal, osteopenic, and osteoporotic) groups based on T-score. After that MRI scan with both FSE T 2 -w and DWI of the vertebral body was done to calculate the signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC). Finally, MRI findings were compared in patients, between three groups and correlated with bone marrow density. Results The osteoporotic group showed significantly lower mean ADC values, compared to osteopenic and normal groups (0.58 ± 0.02 vs. 0.36 ± 0.05 vs. 0.24 ± 0.06 × 10 –3 mm ² /s, p < 0.001). According to these results, a significant positive correlation was found between T-scores and ADC values ( r = 0.652, p < 0.001). The mean SNR in FSE T 2 -w images for normal, osteopenic, and osteoporotic groups was calculated 5.61 ± 0.32, 5.48 ± 0.55, and 6.63 ± 0.67, respectively. No significant correlation was found between the mean SNR and T-score for all groups ( r = − 0.304, p > 0.05). Conclusions DWI can be used as a noninvasive, quantitative, and valuable technique for OP evaluation. While, routine MRI needs more investigation to be demonstrated as a reliable diagnostic indicator for OP.
In the process of this work, the Pulsed Laser Deposition (PLD) technique was used to deposit nanoparticles of pure titanium oxide (TiO2) onto a glass substrate at temperatures ranging from 100 to 400 degrees Celsius. This experiment made use of a Nd: YAG laser that had its frequency-doubled. The laser had a wavelength of 532 nanometers and an average laser strength of 800 millijoules. To explore the optical properties, transmittance spectrometry measurements were carried out for both visible and ultraviolet wavelengths. These measurements were carried out. The results of the optical transmittance test showed that it was more than 80 percent, which indicates that it is suitable for use in applications involving solar cells. The research was conducted on a number of optical constants, including the refractive index, the absorption coefficient, and the attenuation coefficient, and the values of these optical constants were determined. The value of the refractive index was found to be 2.49 when measured at a temperature of 400 degrees Celsius and a wavelength of 550 nanometers. Additionally, it was feasible to calculate the density of the titanium dioxide coating, which came out to be 3.6881 grams per cubic centimeter after the calculation was complete. The use of a numerical equation was utilized to ascertain the connection that exists between density and base temperature. It is an empirical equation that may be employed in the process of calculating the density of the components that are being used, and it has the potential to do so successfully. This equation is one of a kind since it was produced via the use of a theoretical computer program, and it is specific to the results that were obtained from the research.
Existing implementations of file systems often seem to be made on an ad hoc and implicit basis. This paper aims to enhance the organization of files and retrieval of files by modifying the traditional hierarchical file system to improve built-in query support and bulk metadata updates supported at the file system level. We introduce tags in a hierarchy of file collections and use links to allow file retrieval from multiple paths as files exist in multiple directories simultaneously. By using a series of modest changes to the hierarchical file system, we propose a novel Linked Tree Tags (LTTs) model. These changes include using multiple tags instead of names, collections instead of directories, exposing a query language at the Application Programming Interface (API) level, and allowing controlled file links. We assess our model's expressive capability and demonstrate that LTTs overcome traditional file systems' limits and provide users with the to manage their files easily.
Advances in Web 2.0 technologies have led to the widespread assimilation of electronic commerce platforms as an innovative shopping method and an alternative to traditional shopping. However, due to pro-technology bias, scholars focus more on adopting technology, and slightly less attention has been given to the impact of electronic word of mouth (eWOM) on customers' intention to use social commerce. This study addresses the gap by examining the intention through exploring the effect of eWOM on males' and females' intentions and identifying the mediation of perceived crowding. To this end, we adopted a dual-stage multi-group structural equation modeling and artificial neural network (SEM-ANN) approach. We successfully extended the eWOM concept by integrating negative and positive factors and perceived crowding. The results reveal the causal and non-compensatory relationships between the constructs. The variables supported by the SEM analysis are adopted as the ANN model's input neurons. According to the natural significance obtained from the ANN approach, males' intentions to accept social commerce are related mainly to helping the company, followed by core functionalities. In contrast, females are highly influenced by technical aspects and mishandling. The ANN model predicts customers' intentions to use social commerce with an accuracy of 97%. We discuss the theoretical and practical implications of increasing customers' intention toward social commerce channels among consumers based on our findings.
Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.
The drawbacks of the "take-or-pay" conception led to the search for alternatives to mitigate its effects. This paper proposes an intelligent multistage control system consisting of two-phased to control the energy sources' operation in a connected-mode microgrid consisting of renewable energy sources and a diesel generator. The first phase is the forecasting process to predict the day's required fuel amount, thus dispensing with the take-or-pay method that forces the purchase of redundant quantities of fuel periodically. This stage depends on the deep neural network long short-term memory method. The second phase is the energy management to find optimum energy source scheduling relying on a model-free strategy using reinforcement learning to achieve minimum energy cost consumption. The proposed methodology is verified using improved PSO. Simulations and theoretical calculations reveal that the proposed scheme is very successful at decreasing energy consumption costs, in addition to meeting the preferences of users.
Remote sensing image registration can benefit from a machine learning method based on the likelihood of predicting semantic spatial position distributions. Semantic segmentation of images has been revolutionized due to the accessibility of high-resolution remote sensing images and the advancement of machine learning techniques. This system captures the semantic distribution location of the matching reference picture, which ML mapped using learning-based algorithms. The affine invariant is utilized to determine the semantic template’s barycenter position and the pixel’s center, which changes the semantic border alignment problem into a point-to-point matching issue for the machine learning-based semantic pattern matching (ML-SPM) model. The first step examines how various factors such as template radius, training label filling form, or loss function combination affect matching accuracy. In this second step, the matching of sub-images (MSI) images is compared using heatmaps created from the expected similarity between the images’ cropped sub-images. Images having radiometric discrepancies are matched with excellent accuracy by the approach. SAR-optical image matching has never been easier, and now even large-scale sceneries can be registered using this approach, which is a significant advance over previous methods. Optical satellite imaging or multi-sensor stereogrammetry can be combined with both forms of data to enhance geolocation.
The landscape of the Mesopotamian floodplain is mainly structured by channel processes, including the formation of levees, meanders, scrollbars, oxbow lakes, crevasse splays, distributary channels, inter-distributary bays, and marshes. Moreover, several human-made features also form and shape this landscape, such as canals, roads, trenches, farms, and settlement sites ranging in size from villages to cities. A significant part of the Mesopotamian floodplain is covered by marshes, especially the southern region. These marshlands have thrived for thousands of years and are well known for their sustainable biodiversity and ecosystem. However, after the deliberate draining of the marshes in the 1990s, the areas have become dry and only small areas of shallow water and narrow strips of vegetation remain. Several kinds of archaeological landscape features have appeared on the surface and can be clearly identified in both ground surveys and with the use of remote sensing tools. This paper aims to determine the type and nature of the preserved archaeological features that appear in the landscape of the dried marshes and whether they are different from other features elsewhere in the Mesopotamian floodplain. An intensive ground survey was carried out in a selected area of the dried marshland, resulting in the identification of six types of archaeological features: settlement sites, rivers, canals, farms, grooves, and roads (hollow ways). These features used to be covered by bodies of deep water and dense zones of vegetation (reeds and papyrus).
Oxysterols are cholesterol metabolites generated in the liver and other peripheral tissues as a mechanism of removing excess cholesterol. Oxysterols have a wide range of biological functions, including the regulation of sphingolipid metabolism, platelet aggregation, and apoptosis. However, it has been found that metabolites derived from cholesterol play essential functions in cancer development and immunological suppression. In this regard, research indicates that 27-hydroxycholesterol (27-HC) might act as an estrogen, promoting the growth of estrogen receptor (ER) positive breast cancer cells. The capacity of cholesterol to dynamically modulate signaling molecules inside the membrane and particular metabolites serving as signaling molecules are two possible contributory processes. 27-HC is a significant metabolite produced mainly through the CYP27A1 (Cytochrome P450 27A1) enzyme. 27-HC maintains cholesterol balance biologically by promoting cholesterol efflux via the liver X receptor (LXR) and suppressing de novo cholesterol production through the Insulin-induced Genes (INSIGs). It has been demonstrated that 27-HC is able to function as a selective ER regulator. Moreover, enhanced 27-HC production is in favor of the growth of end-stage malignancies in the brain, thyroid organs, and colon, as shown in breast cancer, probably due to pro-survival and pro-inflammatory signaling induced by unbalanced levels of oxysterols. However, the actual role of 27-HC in cancer promotion and progression remains debatable, and many studies are warranted to be performed to unravel the precise function of these molecules. This review article will summarize the latest evidence on the deleterious or beneficial functions of 27-HC in various types of cancer, such as breast cancer, prostate cancer, colon cancer, gastric cancer, ovarian cancer, endometrial cancer, lung cancer, melanoma, glioblastoma, thyroid cancer, adrenocortical cancer, and hepatocellular carcinoma.
This work studies optical absorption in the zinc-blende boron-containing quantum dot (QD) structures. Eight structures are studied; two of them are the ternary BInP/GaP and BInP/BP. The others are BGaAsP/BP, BAlAsP/BAs, BInAsP/InP, BGaInAs/GaAs, BGaInP/BP, and BInAsP/GaP. The emission wavelengths of the structures cover a broad spectrum range from UV to near-infrared. The structures with BAs and BP barriers emit at 227,292nm. The structures BInAsP/InP and BGaInAs/GaAs have peak absorptions at 870nm and 920nm wavelengths, while ternary and quaternary structures with GaP barrier are at 720 and 1200nm. The structures with GaP barrier have importance in silicon device technology. The absorption peaks are arranged where the smallest energy difference between the transition subbands correspond to a higher absorption peak and are associated with a wide bandgap energy difference between the barrier and QD. For boron increment by 0.005 in the QD region, the peak absorption of BInP/GaP and BInP/BP in the TE mode have a wider red-shift (170 nm) in the peak wavelength. BGaAsP/BP has an absorption peak four orders higher than BAlAsP/BP. For of BInAsP/InP QD structure, the absorption spectrum is increased by more than four times under 0.0001-mole fraction increment of boron.
The present investigation was designed to study the prevalence of cryptosporidiosis in the colorectal cancer patients compared to the healthy subjects. The present descriptive case-control study was performed on 174 subjects including 87 healthy people and 87 patients with colorectal cancer attending to general hospitals in Lorestan Province, Western Iran, during October 2019–August 2020. A fresh stool specimen was collected from each subject in a sterile labeled container. The collected stool samples were concentrated using the sucrose flotation method and then prepared for Ziehl-Neelsen staining for microscopic examination. All samples were also tested using the Nested-PCR assays by amplifying the 18S rRNA gene for the presence of Cryptosporidium DNA. Demographic and possible risk factors such as age, gender, residence, agriculture activity, history of contact with livestock, consumption unwashed fruits/vegetables, and hand washing before eating were investigated in all the studied subjects using a questionnaire. Of the 87 patients with colorectal cancer, 37 (42.5%) had Cryptosporidium infection. A significant difference (p < 0.001) in the prevalence of Cryptosporidium spp. infections among the participants in the case and control (11, 12.6%) groups was observed. We found that cryptosporidiosis was not linked with age, gender, hand washing, agriculture activity, and history of contact with livestock in the colorectal patients. However, residence in urban areas was significantly associated with the prevalence of cryptosporidiosis. The 18 s rRNA gene of Cryptosporidium in 48 samples was successfully amplified by the Nested-PCR. Based on the obtained findings, Cryptosporidium spp. infections were observed significantly more frequently in the patients with colorectal cancer in comparison with the healthy individuals. It is suggested to carry out similar studies in various parts of Iran with larger sample sizes and further parasitological tests.
Aggressive, unexpected, and catastrophic changes in the environment-induced or impacted by the cultivation of land, crops, and cattle are known as agricultural disasters. In agriculture, the volume of data unpredictability, processing, and data management standards for interoperability are significant concerns. While natural catastrophes are still a considerable problem, the enormous amount of data available has opened up new avenues for coping. Accordingly, big data analytics has profoundly changed the way people respond to disasters in the agriculture sector. In this paper, the Data handling model using big data analytics (DHM-BDA)explores the role of big data in managing agricultural disasters and highlights the technical status of delivering practical and efficient disaster management solutions. DHM-BDA is used to address the essential sources of big data that include climatic causes and associated successes and developing technological problems in different disaster management phases. In addition, it aids in the monitoring, mitigation, alleviation, and acceptance of agricultural catastrophes and the process of recovery and rebuilding. The simulation findings have been executed, and the suggested model enhances the prediction ratio of 98.9%, decision-making level of 97.8%, data management of 96.5%, production ratio of 95.6%, and risk reduction ratio of 97.1% compared to other existing approaches.
Sleep scoring is one of the primary tasks for the classification of sleep stages in Electroencephalogram (EEG) signals. Manual visual scoring of sleep stages is time-consuming as well as being dependent on the experience of a highly qualified sleep expert. This paper aims to address these issues by developing a new method to automatically classify sleep stages in EEG signals. In this research, a robust method has been presented based on the clustering approach, coupled with probability distribution features, to identify six sleep stages with the use of EEG signals. Using this method, each 30-second EEG signal is firstly segmented into small epochs and then each epoch is divided into 60 sub-segments. Each sub-segment is decomposed into five levels by using a discrete wavelet transform (DWT) to obtain the approximation and detailed coefficient. The wavelet coefficient of each level is clustered using the k-means algorithm. Subsequently, features are extracted based on the probability distribution for each wavelet coefficient. The extracted features then are forwarded to the least squares support vector machine classifier (LS-SVM) to identify sleep stages. Comparisons with several existing methods are also made in this study. The proposed method for the classification of the sleep stages achieves an average accuracy rate of 97.4%. It can be an effective tool for sleep stages classification and can be useful for doctors and neurologists for diagnosing sleep disorders.
Pancreatic and islet cell transplantation are considered surgical therapeutic modalities for type 1 diabetes mellitus with or without end-stage renal disease. The pancreatic transplant can be performed alone or with the kidney transplant simultaneously or at different times. It contributed to an improved quality of life in those patients. Pancreatic transplantation and islet cell transplantation provide different degrees of insulin independence. Although the latter needs less monitoring, yet, it is more expensive and tedious. The experiences in the Middle East and North African countries for both procedures are young but mature. They need more scheduled national and/or regional programs to provide diverse options for their citizens.
In this article, the new exact solitary wave solutions for the generalized nonlinear Schrödinger equation with parabolic nonlinear (NL) law employing the improved tanh(Γ(ϖ))-coth(Γ(ϖ)) function technique and the combined tan(Γ(ϖ))-cot(Γ(ϖ)) function technique are obtained. The offered techniques are novel and also for the first time in this study are used. Different collections of hyperbolic and trigonometric function solutions acquired rely on a map between the considered equation and an auxiliary ODE. The several hyperbolic and trigonometric forms of solutions based on diverse restrictions between parameters involved in equations and integration constants that appear in the solution are obtained. A few significant ones among the reported solutions are pictured to perceive the physical utility and peculiarity of the considered model utilizing mathematical software. The main subject of this work is that one can visualize and update the knowledge to overcome the most common techniques and defeat to solve the ODEs and PDEs. The concluded solutions are demonstrated where are valid by using Maple software and also found those are correct. The proposed methodology for solving the metamaterilas model are designed where is effectual, unpretentious, expedient, and manageable. Finally, the existence of the obtained solutions for some conditions is also analyzed.
Interest is increasing in certain parts of the world in replacing synthetic dyes with dyes from natural sources, particularly from plants. Although textile dyers have used various groups of natural dyes, microscopists generally have restricted their use to anthocyanins. Recently, however, another class of plant-based dyes has found some favor, the betacyanins. Betacyanins are a group of red and violet betalain dyes found only in certain plants of the order Caryophyalles and in Basidiomycetes mushrooms. Although the chemical structures of betacyanins are known, little use has been made of that information to understand or predict their behavior with biomedical specimens. We investigated two common, widely distributed betacyanin-containing plants, edible beets (Beta vulgaris) and wild pokeweed (Phytolacca americana). Aqueous alcoholic extracts were made from beet root and pokeweed berries, adjusted to pH 4.1 or 5.3 and used together with Harris’ hematoxylin to stain histological sections. We used a methanolic extract of pokeweed berries, pH 3.0, to stain cultured mycological specimens. Both extracts produced satisfactory staining that was equivalent to that of eosin Y, although the colors were more muted with the beet root extract. Epithelial cytoplasm, muscle, collagen and erythrocytes were well demonstrated. Betanin is the predominant component of beet root extract; it possesses one delocalized positive charge and three carboxylic acid substituents. The dyes are weak acids and the carboxylate anions are more diffuse than for eosin Y; this produces weaker bonding to tissue cations. The principal colored component of pokeweed berries, prebetanin, possesses a sulfonic acid group as well as carboxylic acids, which favors acid dyeing and more intense coloration. Both dyes show potential for hydrogen bonding and to a much lesser extent for some types of van der Waals forces. Complex formation with metals such as aluminum to create a nuclear stain is not likely with beet root dyes nor is it possible with pokeweed dyes. Betacyanins are suitable for staining microscopy preparations in place of other red acid dyes such as eosin. Of the two dyes tested here, prebetanin from pokeweed berries was superior to betanin from red beet roots. These berries are widely distributed and readily collected; the extraction procedure is simple and does not require expensive solvents.
Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). Three different scenarios are examined, such as scenario1 (SC1): used to predict daily power generation, scenario 2 (SC2): used to predict power generation for monthly prediction and scenario 3 (SC3): used to predict hydropower generation (HPG) seasonally. The statistical analysis and pre-processing techniques were applied to the raw data before developing the models. Five statistical indexes are employed to evaluate the performances of various models developed. The results indicate that the proposed models can be used to predict HPG efficiently and could be an effective method for energy decision-makers. The sensitivity analyses found the most effective models for predicting HPG for three scenarios using graphical distribution data (Taylor diagram). Regarding the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models for ANN and SVM. The results presented that the value of 95PPU for all models falls into the range between 80% and 100%. As for the d-factor, all values in all scenarios are less than one.
Electroencephalography (EEG) is a complex signal that may require several years of training, advanced signal processing, and feature extraction methodologies to interpret correctly. Recently, many methods have been used to extract and classify EEG data. This study reviews 62 papers that used EEG signals to detect driver drowsiness, published between January 2018 and 2022. We extract trends and highlight interesting approaches from this large body of literature to inform future research and formulate recommendations. To find relevant papers published in scientific journals, conferences, and electronic preprint repositories, researchers searched major databases covering the domains of science and engineering. For each investigation, many data items about (1) the data, (2) the channels used, (3) the extraction and classification procedure, and (4) the outcomes were extracted. These items were then analyzed one by one to uncover trends. Our analysis reveals that the amount of EEG data used across studies varies. We saw that more than half the studies used simulation driving experimental. About 21% of the studies used support vector machine (SVM), while 19% used convolutional neural networks (CNN). Overall, we can conclude that drowsiness and fatigue impair driving performance, resulting in drivers who are more exposed to risky situations.
This paper shows how to use the fractional Sumudu homotopy perturbation technique (SHP) with the Caputo fractional operator (CF) to solve time fractional linear and nonlinear partial differential equations. The Sumudu transform (ST) and the homotopy perturbation technique (HP) are combined in this approach. In the Caputo definition, the fractional derivative is defined. In general, the method is straightforward to execute and yields good results. There are some examples offered to demonstrate the technique's validity and use.
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