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Background: Osteoporosis is a disease which is characterized by low bone mass and susceptibility to fracture. Now a day, osteoporosis is considered as a potential public health issue. Year after year, thousands of people attending several hospital and clinics for taking treatment of osteoporosis in Bangladesh. But we have very limited research-based information regarding the socio-demographic and clinical profile of patients with osteoporosis. Aim of the Study: The aim of this study was to prepare a socio-demographic and clinical profile of patients with osteoporosis. Methods: This prospective observational study and was conducted in the Diabetic Association Medical College and Hospital, Faridpur, Bangladesh during the period from January 2020 to December 2021. In total 103 confirmed patients with osteoporosis were included as the study subjects for this study. Proper written consents were taken from all the participants before data collection. A predesigned questioner was used in data collection. All data were collected, processed and analyzed by using MS Office and SPSS version 23 programs as per need. Results: In this study, female participants were dominating in number and the male-female ratio was 1:2. The highest number of the participants was found from 41-60 years’ age group which was 43%. Among all the participants only 13% was illiterate and 27% were housewife; which was the highest number as a single occupation. Majority of the participants of our study were from lower class families as per the socio-economic condition. About half (46%) of our study people were with normal body-weight (BMI: 18.0-24.9). History of any bone fracture was found in 18% patients only. Among 33%, 42% and 25% participants the vitamin D level was found as< 20, 20 – 30 and > 30 ng/ml respectively. Conclusion: Osteoporosis is one of the severe health problems in the elderly population. The frequencies of this disease are alarming. With the improvement of socio- economic status, ....

In this work, we explore the effects of a quantum quench on the entanglement measures of a two-body coupled oscillator system having quartic interaction. We use the invariant operator method, under a perturbative framework, for computing the ground state of this system. We give the analytical expressions for the total and reduced density matrix of the system having non-Gaussian, quartic interaction terms. Using this reduced density matrix, we show the analytical calculation of two entanglement measures viz., Von Neumann entanglement entropy using replica trick and Renyi entanglement entropy. Further, we give a numerical estimate of these entanglement measures with respect to the dimensionless parameter $(t/\delta t$) and show its behaviour in the three regimes, i.e; late time behaviour, around the quench point and the early time behaviour. We comment on the variation of these entanglement measures for different orders of coupling strength. The variation of Renyi entropy of different orders has also been discussed., Comment: 20 pages, 4 figures, Title changed, Revised version, Accepted for publication in Physical Review D

The complete failure of the power systems infrastructure in Puerto Rico, following Hurricanes Irma and Maria in 2017, severely hampered the recovery efforts of multiple critical infrastructure systems (CIS). Understanding the relationships of infrastructure recovery efforts between power infrastructure systems and the other CIS has the potential to be a key in developing an effective recovery plan leading to resilient infrastructure systems, and thereby a more resilient community. This paper explores the critical interfaces and interdependencies in CIS recovery by examining the disruptions and recovery progress of the CIS, including the power infrastructure systems, in Puerto immediately following the events of Hurricane Maria. This research uncovers that strong CIS recovery interdependency relationships exist between the power infrastructure systems and other CIS in Puerto Rico, and these relationships contribute to the resilience of these CIS. The resultant CIS recovery associations may potentially predict the recovery progress of post-disaster CIS recovery centered on the power infrastructure systems and lay the groundwork for further interdependency analysis of CIS in post-disaster scenarios. The results may also be helpful while designing CIS for resiliency in natural disaster areas.

Computing object distance using image processing is an important research area in the field of computer vision and robot navigation applications. In this paper we have proposed a new method to compute the distance of an object using a single image. According to our observation there exists a relationship between the physical distance of an object and its pixel height. We exploit this relationship to train a system that finds a mapping between an object’s pixel height and physical distance. This mapping is then used to find the physical distance of test objects from the pixel height in the image. Experimental results demonstrate the capability of our proposed technique by estimating physical distance with accuracy as high as 98.76%.