Irradiated glass and thermoluminescence yield: Dosimetric utility reviewed

With numbers of efforts being made towards harnessing the thermoluminescence yield of doped glass media for dosimetric applications, predominantly in the radiotherapeutic regime, review is provided of the background to this, tracing developments leading to the present day. Included are an examination of the relative strengths of the various TLD currently on offer and that of glass fabrications, commercial Ge-doped optical fibre as well as novel fibres fabricated from Ge-doped glass. The demands that modern radiotherapeutic dose delivery systems are placing upon these passive forms of dosimetry are reviewed together with the various responses arising from current efforts. Also reviewed are the basis of the luminescence yield, citing the defect types occurring in silica, even in the absence of extrinsic dopants

Radionuclide concentrations in medicinal florae and committed effective dose through Ayurvedic medicines

Purpose: Ayurveda is one of the oldest systems of medicines in the world being practiced widely in the Indian subcontinent for more than 3000 years, and still remains as one of the important traditional health care systems. The Ayurvedic drugs are derived primarily from various parts of the plants, like root, leaf, flower, fruit or plant as a whole. Plants uptake minerals and other nutrients from the soil through their root system. Along with other minerals radionuclides present in the growing media also reach to the plant parts following the same pathway. Realizing the probable health hazards via the intake of Ayurvedic drugs, it is important to assess the concentration of natural radionuclides in commonly used medicinal plants. Materials and methods: NaI(Tl) scintillator-based gamma-ray spectrometry has been used to determine the activity concentrations of primordial radionuclides (226Ra, 232Th and 40K) in the most commonly used medicinal plant parts as ingredients of Ayurvedic medicines in India. Results and discussion: The average specific activity (Bqkg−1) of 226Ra, 232Th and 40K was found to be 43 ± 18, 36 ± 15 and 230 ± 46, respectively. The estimated annual committed effective doses due to the intake of common Ayurvedic medicines at prescribed dosage was found to be 39 ± 16 µSv y−1, which is quite low as compared with the radiation dose limit of 1 mSvy−1 from all natural sources, reported by the International Commission on Radiological Protection (ICRP-60). Conclusions: It is found categorically that intake of Ayurvedic medicines at normal dosage poses no radiological hazard to the individual. Present results are significant in the wake of myths that many hazardous materials including radioisotopes are present at higher levels. Obtained results also serve as a reference information for the distribution of radionuclides in medicinal plant species

Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model

SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400th patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time

Excitation functions of deuteron-induced nuclear reactions on erbium in the energy range of 4–24 MeV

Excitation functions for the natEr(d,x)163,165,166,167,168,170Tm and natEr(d,x)171Er nuclear reactions were measured from the respective thresholds up to 24 MeV by using a stacked-foil activation technique combined with HPGe γ-ray spectrometry. Measured data show a partial agreement when compared with the available literature data, theoretical data extracted from the TENDL-2019 library, and predicted data by the model code EMPIRE-3.2.2. Estimated physical thick target yields of the reaction products show close values to the directly measured ones available in the literature. The deduced yield curves indicate that a typical irradiation (Ed = 15 MeV, Id = 100 µA, tirr = 72 h) of enriched 167Er target by a low energy deuteron cyclotron is suitable to obtain more than a hundred GBq activity of 167Tm with negligible impurity from 168Tm. Measured data of 170Er(d,p)171Er reaction have great significance to improve the predicting capability of the model code

The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Human speech indirectly represents the mental state or emotion of others. The use of Artificial Intelligence (AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech. In this study, we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). About 2800 audio files were extracted from the Toronto emotional speech set (TESS) database for this study. A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data. A total of seven types of emotions; Angry, Disgust, Fear, Happy, Neutral, Pleasant-surprise, and Sad were used in this study. Energy, Fundamental frequency, and Mel Frequency Cepstral Coefficient (MFCC) have been used to extract the emotion features, and these features resulted in 97.5% accuracy in the mixed LSTM+CNN model. This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech. It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing

Modeling on population growth and its adaptation: A comparative analysis between Bangladesh and India

The biggest challenge in the world is population growth and determining how society and the state adapt to it as it directly affects the fundamental human rights such as food, clothing, housing, education, medical care, etc. The population estimates of any country play an important role in making the right decision about socio-economic and population development projects. Unpredictable population growth can be a curse. The purpose of this research article is to compare the accuracy process and proximity of three mathematical model such as Malthusian or exponential growth model, Logistic growth model and Least Square model to make predictions about the population growth of Bangladesh and India at the end of 21st century. Based on the results, it has been observed that the population is expected to be 429.32(in million) in Bangladesh and 3768.53 (in million) in India by exponential model, 211.70(in million) in Bangladesh and 1712.94(in million) in India by logistic model and 309.28 (in million) in Bangladesh and 2686.30 (in million) in India by least square method at the end of 2100. It was found that the projection data from 2000 to 2020 using the Logistic Growth Model was very close to the actual data. From that point of view, it can be predicted that the population will be 212 million in Bangladesh and 1713 million in India at the end of the 21st century. Although transgender people are recognized as the third sex but their accurate statistics data is not available. The work also provides a comparative scenario of how the state has adapted to the growing population in the past and how they will adapt in the future

Investigation on the effect of 238U replacement with 232Th in small modular reactor (SMR) fuel matrix

Effect of 238U replacement with 232Th in small modular reactor fuel matrix was studied. Four different 235U enrichment levels (10, 13.8, 16.5 and 19.8 wt%) were used in a pairwise manner for UO2 and (ThO2 + 235U) fuels. The calculation was performed using Monte Carlo N-particle code integrated with CINDER90 for burn-up calculations in a homogeneous fuel assembly. The results show that enrichment level <17 wt% for thorium fuel produced virtually no plutonium isotopes but became visible only at 19.8 wt% enrichment. The number of neutrons produced per fission () for ThO2 + 235U was less than that of UO2 because its averaged contribution from 235U and 233U was smaller compared to the similar contribution from 239Pu, 241Pu and 235U. Large amount of 239Pu and actinides were produced from UO2 fuel due to the impact of 238U. The reactivity of thorium at the beginning of cycle (BOC) was smaller compared to uranium but higher at end of cycle (EOC) resulting to higher excess reactivity in all thorium fuel. Production of little plutonium isotopes by thorium fuel suggests that it would make a good proliferation resistance fuel and could be used in any W-SMR to incinerate stockpiled plutonium

Elevated concentrations of terrestrial radionuclides in sand: An essential raw material used in Bangladeshi dwellings

Sand is one of the main materials used in the construction industry. Elevated concentrations of terrestrial radionuclides in sand may cause great harm to dwellers. Present study determines concentrations of terrestrial radionuclides in sand that is widely used as a raw material in the construction industry in Bangladesh, via conventional HPGe γ-ray spectrometry. The average activity concentrations of 226Ra, 232Th and 40K in the analysed samples were found as 36.8 ± 3.9, 58.9 ± 6.0 and 755 ± 91 Bqkg−1 in Base sand, 68.1 ± 5.3, 185.7 ± 9.9 and 1032 ± 104 Bqkg−1 in Sylhet sand, and 38.1 ± 3.6, 74.7 ± 5.9 and 636 ± 77 Bqkg−1 in Aster sand. All data show relatively higher values than the world average of 35, 30, 400 Bqkg−1, respectively. Key hazard parameters were estimated to realize the radiation effects on human health due to the use of sand as construction and building materials. The estimated parameters for Sylhet sand show relatively higher values than the population-weighted world average values, prescribed by regulatory bodies. Results show that the Sylhet sand may pose a significant radiation hazard to the dwellers via prolonged exposure, and necessary precautionary steps need to be taken to ensure safe dwellings while using this sand for construction and decorative purposes

Production cross sections of thulium radioisotopes for alpha-particle induced reactions on holmium

The production cross sections of 165–168Tm radioisotopes produced in α-particle induced reactions on 165Ho were measured from 49.6 MeV down to the respective thresholds using the stacked-foil activation technique and HPGe gamma-ray spectrometry. The measured excitation functions were compared with previous measurements as well as the TENDL-2019 library. We also calculated the thick target yields for all the assessed radioisotopes by integration of the measured excitation functions and found that they show good agreement with the directly measured thick target yields except for 168Tm production

Assessing Service Quality Using SERVQUAL Model: An Empirical Study on Some Private Universities in Bangladesh

The increased competitive academic environment pushes higher institutions to improve their service quality for meeting the market demands. It is thus necessary to assess the factors that satisfy students and make them loyal to the university. This study has focused on assessing service quality, using the SERVQUAL Model to measure students’ satisfaction with private universities in Bangladesh. In the study, the primary data collection method through a questionnaire with the 5-point Likert scale was adopted to collect data from 229 students who are currently studying in different private universities in Chattogram, Bangladesh. Structural equation modeling (SEM) has been performed to analyze the data through the AMOS 22 statistical package. The findings report that a comfortable lecture room under the tangible dimension, providing service on time under the reliability dimension, the capacity of administrative staff to solve immediate problems under the responsiveness dimension, proficient lecturers for teaching and research under the assurance dimension, and focus of university management on students under the empathy dimension have a great influence on student satisfaction. The study concludes that managers and authorities of private universities must focus on ensuring better service quality as student satisfaction largely affects the sustainability and recurrence development of the institutions