Synthesis, characterization and evaluation of biological activities of manganese-doped zinc oxide nanoparticles

Purpose: To synthesize, characterize and investigate the antimicrobial properties of pure and manganese-doped zinc oxide nanoparticles.Method: Un-doped and manganese-doped zinc oxide (Mn-doped ZnO) nanoparticles were prepared using co-precipitation method. The synthesized Mn-doped ZnO  nanoparticles were characterized using energy-dispersive x-ray spectroscopy  (EDX), scanning electron microscopy (SEM), and x-ray diffraction (XRD)  spectroscopic techniques. Their band gap energies were measured with ultraviolet-visible (UVVis) spectroscopy, while their antioxidant properties were evaluated by ferric reducing antioxidant power (FRAP), DPPH radical-scavenging, ferric  thiocyanate (FTC) and total phenolic content (TPC) assays. The antimicrobial  activities of the nanoparticles against different bacterial strains were determined using agar diffusion method.Result: Results from XRD, SEM, EDX and UV-Vis analyses demonstrated  successful synthesis of undoped and Mn-doped ZnO nanoparticles as seen in their hexagonal, wurtzite structures. The un-doped and Mn-doped ZnO nanoparticles had average grain sizes of 16.72 nm and 17.5 nm, and band gap energies of 3.585 eV and 2.737 eV, respectively. Significant antibacterial activity was manifested by Mndoped ZnO against E. coli, S. aureus, Klebsiella and B. subtilis, with zones of inhibition (ZOIs) of 13 ± 0.09 mm, 14 ± 0.01 mm, 18 ± 0.07 mm and 20 ± 0.10 mm, respectively. The Mn-doped ZnO nanoparticles also exhibited effective and significant antioxidant potential relative to butylated hydroxytoluene (BHT) and un-doped ZnO nanoparticles.Conclusion: Mn-doped ZnO nanoparticles demonstrate significant antimicrobial and antioxidant activities. Thus, the preparation is a good candidate for further development into therapeutic formulations.Keywords: Mn-doped ZnO, Nanoparticles, Properties, Antioxidant, Antibacteria

Efficacy of Balloon Kyphoplasty in Compression Fractures of the Thoracolumbar Spine

Objective:  To study the Efficacy of Balloon Kyphoplasty in compression fractures of the thoracolumbar spine. Material and Methods:  This study was conducted on 95 patients with thoracolumbar wedge fractures from 2017 to 2022. Complete neurological examination and CT and MRI scans of the spine of all patients were done. All patients have been treated with a balloon kyphoplasty procedure. Data was collected on VAS score, SF-36 score, kyphotic angle and percentage of vertebral body destruction both preoperatively and post-operatively. Statistical analysis was done by using paired sample t-test. Results:  The mean age was 57 years. Males were 58.9% and females 41.0%. Osteoporosis was the cause of fracture in 90.5% and trauma in 9.4% of patients. VAS improved from 7.42 ± 1.24 to post-procedure 3.24 ± 1.51, P < 0.0001. SF-36 improved from 35.31 ± 17.4 to post-procedure 49.23 ± 19.2, P < 0.0001. Kyphosis angle restoration from 18.42 ± 7.41 to post-procedure 10.61 ± 6.32, P value < 0.0001. Percentage loss of vertebral height from 32.91% to postoperatively 17.64% (SD-17.2 and P < 0.0001). 10.5% of patients developed cement leakage and there is no leakage in 89.4%. The adjacent level fracture occurred in 4 patients. Conclusion:  Balloon Kyphoplasty is an effective procedure for thoracolumbar wedge fractures. It improves pain, activities of daily living, kyphosis angle improvement, and restoration of vertebral height

Smart Relay Selection Scheme Based on Fuzzy Logic with Optimal Power Allocation and Adaptive Data Rate Assignment

In this paper fuzzy logic-based algorithm with improved process of relay selection is presented which not only allocate optimal power for transmission but also help in choosing adaptive data rate. This algorithm utilizes channel gain, cooperative gain and signal to noise ratio with two cases considered in this paper: In case-I nodes do not have their geographical location information while in case-II nodes are having their geographical location information. From Monte Carlo simulations, it can be observed that both cases improve the selection process along with data rate assignment and power allocation, but case-II is the most reliable with almost zero probability of error at the cost of computational complexity which is 10 times more than case-I

Efficacy of Posterior and Posterolateral Approach for Decompression and Fusion for Thoracolumbar Tuberculosis

Objective:  To study the efficacy of the posterior and posterolateral approach in thoracolumbar tuberculosis. Material and Methods:  60 patients with thoracolumbar TB spine were enrolled in the study from 2015 to 2021. These patients had single-level disease with low back pain and neurological compromise in lower limbs. The diagnosis was made on an MRI of the spine and elevated ESR levels. All patients were started with antituberculous treatment. The pain was measured on the VAS score, and neurological status was assessed on the ASIA score. Kyphosis angle was calculated on a lateral x-ray of the spine. All patients were operated on by posterior and posterolateral approaches with decompression and fusion. At follow-up, fusion was assessed on every visit by x-ray along with neurological status and pain score. Results:  The mean age of patients was 45.8 years (25 to 66 years). 59.5% patients achieved radiological fusion on follow-up x-rays. There was a significant improvement in VAS score preoperatively mean and SD6.38 ± 1.24to postoperatively 4.45 ± 1.09. The mean and SD of kyphosis in patients preoperatively was 22.3 ± 3.06 to post-operative 22.3 ± 3.06 with a p-value < 0.05 which shows significant improvement. Conclusion:  Posterior and posterolateral decompression and fusion of thoracolumbar tuberculosis is a good surgical approach in respect of neurological outcome, correction of kyphosis, and pain improvement

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Artificial Intelligence-Based Techniques for Rainfall Forecasting in Thailand: A Comprehensive Review

Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting is crucial for Thailand’s agricultural sector. Agriculture depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development. Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades. AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features. This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting. For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are analyzed. The authors focus on Thailand and the worldwide applications of AITs for rainfall forecasting and determine the best methods for Thailand. This will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons. The investigation concludes that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand

Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques

Handling missing values is a critical component of the data processing in hydrological modeling. The key objective of this research is to assess statistical techniques (STs) and artificial intelligence-based techniques (AITs) for imputing missing daily rainfall values and recommend a methodology applicable to the mountainous terrain of northern Thailand. In this study, 30 years of daily rainfall data was collected from 20 rainfall stations in northern Thailand and randomly 25–35 % of data was deleted from four target stations based on Spearman correlation coefficient between the target and neighboring stations. Imputation models were developed on training and testing datasets and statistically evaluated by mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient (r). This study used STs, including arithmetic averaging (AA), multiple linear regression (MLR), normal-ratio (NR), nonlinear iterative partial least squares (NIPALS) algorithm, and linear interpolation was used. • STs results were compared with AITs, including long-short-term-memory recurrent neural network (LSTM-RNN), M5 model tree (M5-MT), multilayer perceptron neural networks (MLPNN), support vector regression with polynomial and radial basis function SVR-poly and SVR-RBF. • The findings revealed that MLR imputation model achieved an average MAE of 0.98, RMSE of 4.52, and R2 was about 79.6 % at all target stations. On the other hand, for the M5-MT model, the average MAE was 0.91, RMSE was about 4.52, and R2 was around 79.8 % compared to other STs and AITs. M5-MT was most prominent among AITs. Notably, the MLR technique stood out as a recommended approach due to its ability to deliver good estimation results while offering a transparent mechanism and not necessitating prior knowledge for model creation

Everyday Life Information Seeking Patterns of Resident Female University Students in Pakistan

Purpose: Everyday life information seeking (ELIS) is essential for the mastery of life and plays a central role in the daily problem-solving activities of all human beings. This study aimed to investigate the everyday life information seeking of female university students residing at hostels in Lahore, Pakistan. Design/methodology/approach: A quantitative research approach using a survey method was adopted to identify the dimensions of ELIS and to fill the paucity of research on the topic. Two-hundred and forty-eight female students living in various university and private hostels in Lahore, Pakistan, were surveyed, and the collected data was analyzed using Statistics Package for Social Sciences (SPSS) version 21. Findings: Results of the study revealed that hostel living expanded the social circle of students, as it offered exposure to diverse multicultural social groups. Female students living in hostels required information related to their academic needs, safety concerns, social life, and self-help issues. They relied mainly on mobiles phones, the internet, social media, and inter-personal relationships for everyday information. Cultural differences, natural hesitation, language barriers, time concerns, and difficulty in identifying reliable information were the primary obstacles in meeting everyday life information needs. Originality/value: The findings of this study can be used as a premise for developing interventions and information services for female students that allow them to live comfortably in a safe environment in hostels and achieve their educational goals. The study results may also provide useful insights for university administrations to establish libraries in hostels to better cater to their female residents’ information needs