Modification of TiO_2 Nanoparticles with Organodiboron Molecules Inducing Stable Surface Ti^(3+) Complex

As one of the most promising semiconductor oxide materials, titanium dioxide (TiO_2) absorbs ultraviolet (UV) light but not visible light. To address this limitation, the introduction of Ti^(3+) defects represents a common strategy to render TiO_2 visible-light-responsive. Unfortunately, current hurdles in Ti^(3+) generation technologies impeded the widespread application of Ti^(3+) modified materials. Herein, we demonstrate a simple and mechanistically distinct approach to generating abundant surface-Ti^(3+) sites without leaving behind oxygen vacancy and sacrificing one-off electron donors. In particular, upon adsorption of organodiboron reagents onto TiO_2 nanoparticles, spontaneous electron injection from the dibron-bound O^(2-) site to adjacent Ti^(4+) site leads to an extremely stable blue surface Ti^(3+)‒O^(-•) complex. Notably, this defect generation protocol is also applicable to other semiconductor oxides including ZnO, SnO_2, Nb_2O_5 and In_2O_3. Furthermore, the as-prepared photoelectronic device using this strategy affords 10^3 fold higher visible light response, and the fabricated perovskite solar cell shows an enhanced performance

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals

We investigate how various coarse-graining methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find, that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval $\Delta$ this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when $\Delta1$ a crossover appears at small scales and moves to intermediate and large scales with increasing $\Delta$. For very rough coarse-graining ($\Delta>3$) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales, thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry methods. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.Comment: 19 pages, 13 figure

Contribution of morpho-physiological attributes in determining the yield of mungbean

Field experiments were conducted in 2006 and 2007 under subtropical conditions to investigate the variations in growth and reproductive characters, and yield attributes for selection of important source and sinks characters using correlation and path coefficient analyses in 45 mungbean genotypes. Large genetic variability existed in source characters viz., leaf area index (LAI) (1.22 to 3.80) and sink characters viz., number of racemes plant-1 (6.30 to 22.9), flowers plant-1 (18.1 to 51.9) and pods plant-1 (9.6 to 22.1). Genotypic correlation study revealed that among the traits investigated, LAI was the most important source that determined total dry mass (TDM) yield, and reproductive characters like number of racemes, flowers and pods plant-1 were the most important sinks that determined seed yield. Contrarily, reproductive efficiency (RE, % pod set to opened flowers) did not show significant relationship with pod number and seed yield, indicating that selection of high yield based on RE may be misleading. Path coefficient analysis further revealed that number of flowers, pods and 100-seed weight constituted central important sinks which exerted direct positive influence on seed yield. The results indicated that pod yield could be increased by increased raceme and flower production, while seed yield could be increased by increasing pod production. High yielding genotypes, in general, possessed higher earlier mentioned source (LAI) and sink (flower and pod number) characters which resulted in higher seed yield in mungbean. This information could be exploited in the future plant breeding programmes.Key words: Source-sink, correlation, path analysis, mungbean

Ethanol-Induced Hepatic and Renal Histopathological Changes in BALB/c mice

This study was to investigate the histopathologic changes of different concentrations of ethanol on the mice liver and kidney. Forty albino mice of the Mus musculus species, BALB/c strain mice underwent this study and were divided into four groups; control, %20, %40 and %60 of ethanol administration groups. The mice of each group (%20, %40 and %60 of ethanol) were orally administered with 1ml of ethanol 4days/week for 3 weeks. Hematoxylin and eosin staining indicated development of mild to severe lesions in kidney and liver which included; In %20 of ethanol administration group there was mild lesion development; hydropic swelling in liver and swelling of kidney parenchyma while in %40 of ethanol administration group developed moderate changes; hydropic swelling of hepatocytes and kidney tubules with hyaline degeneration and in %60 of ethanol administration group produced severe lesion; focal macro and micro abscess in liver parenchyma and focal neutrophil infiltration within renal parenchyma and hyaline cast within renal tubules. Based on our study, it can be concluded that ethanol intoxication leads to a various disorders of the liver and kidney which arrange from mild to severe injury which was depended on the concentration of ethanol. Keywords: Ethanol, Mice, Kidney, Liver, H&E stain

Postpartum Sexual Function; Conflict in Marriage Stability: A Systematic Review

Background: One of the most important issues affecting the stability of marriage is sexual function, so its problem can lead to divorce or separation of the couple. Pregnancy and delivery as one the most important periods of women's life can have significant effects on sexual function. This study reviews the postpartum sexual function and its related factors in Iran.Methods: This study is a systematic review of the sexual function after childbirth in Iran. By using of valid keywords and searching in databases such as Google scholar, SID, Magiran, Medlib, Irandoc, Iranmedex, the total number of 15 articles between 2005 and 2012 years have been evaluated. Results were reported quantitatively and qualitatively.Results: Total Sample was 4109 women, with an average of 274 samples per study. Plenty of studies in Tehran was 46% and other cities was 54%. The majority of studies showed no relation between mode of delivery and sexual function (P=0.14), but there were significant relation between lactation and postpartum sexual function (P<0.05) as, breastfeeding decreased sexual function. Also sexual function score has decreased with increasing parity.Conclusion: According to the effects of lactation and parity on women sexual function, therefore high risk for divorce, sex education after childbirth, especially in the first six months after delivery, maybe helpful in prevention of sexual dysfunction after delivery

Informal learning recognition through a cloud ecosystem

Learning and teaching processes, like all human activities, can be mediated through the use of tools. Information and communication technologies are now widespread within education. Their use in the daily life of teachers and learners affords engagement with educational activities at any place and time and not necessarily linked to an institution or a certificate. In the absence of formal certification, learning under these circumstances is known as informal learning. Despite the lack of certification, learning with technology in this way presents opportunities to gather information about and present new ways of exploiting an individual’s learning. Cloud technologies provide ways to achieve this through new architectures, methodologies, and workflows that facilitate semantic tagging, recognition, and acknowledgment of informal learning activities. The transparency and accessibility of cloud services mean that institutions and learners can exploit existing knowledge to their mutual benefit. The TRAILER project facilitates this aim by providing a technological framework using cloud services, a workflow, and a methodology. The services facilitate the exchange of information and knowledge associated with informal learning activities ranging from the use of social software through widgets, computer gaming, and remote laboratory experiments. Data from these activities are shared among institutions, learners, and workers. The project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.

Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis

Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models’ capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model’s efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs

Multifocal Extensive Spinal Tuberculosis with Retropharyngeal Abscess

An unusual case of a young boy presenting with spinal tuberculosis involving cervical & thoracic vertebrae, along with retropharyngeal abscess is reported. The patient presented with progressive quadriparesis, fever, night sweat and cervical lymphadenopathy. The lab studies confirmed tuberculosis and patient received anti-tubercular chemotherapy. After development of quadriparesis, spinal surgery was done. The post operative course was uneventful and the patient is on gradual neurological recovery. DOI: http://dx.doi.org/10.3329/bsmmuj.v4i2.8646 BSMMU J 2011; 4(2):128-13