Scaling laws governing stochastic growth and division of single bacterial cells

Uncovering the quantitative laws that govern the growth and division of single cells remains a major challenge. Using a unique combination of technologies that yields unprecedented statistical precision, we find that the sizes of individual Caulobacter crescentus cells increase exponentially in time. We also establish that they divide upon reaching a critical multiple ($\approx$1.8) of their initial sizes, rather than an absolute size. We show that when the temperature is varied, the growth and division timescales scale proportionally with each other over the physiological temperature range. Strikingly, the cell-size and division-time distributions can both be rescaled by their mean values such that the condition-specific distributions collapse to universal curves. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these complex biological processes: a single temperature-dependent scale of cellular time governs the stochastic dynamics of growth and division in balanced growth conditions.Comment: Text+Supplementar

Implementation of AES using biometric

Mobile Adhoc network is the most advanced emerging technology in the field of wireless communication. MANETs mainly have the capacity of self-forming, self-healing, enabling peer to peer communication between the nodes, without relying on any centralized network architecture. MANETs are made applicable mainly to military applications, rescue operations and home networking. Practically, MANET could be attacked by several ways using multiple methods. Research on MANET emphasizes on data security issues, as the Adhoc network does not befit security mechanism associated with static networks. This paper focuses mainly on data security techniques incorporated in MANET. Also this paper proposes an implementation of Advanced Encryption Standard using biometric key for MANETs. AES implementation includes, the design of most robust Substitution-Box implementation which defines a nonlinear behavior and mitigates malicious attacks, with an extended security definition. The key for AES is generated using most reliable, robust and precise biometric processing. In this paper, the input message is encrypted by AES powered by secured nonlinear S-box using finger print biometric feature and is decrypted using the reverse process

Consequences of COVID-19 on aviation industry: a menace to global airlines

Introduction: the aviation industry has been considered one of the major contributors to the economy of nations for several decades. Objective: as a sub-sector of the aviation industry, airlines are considered the fastest mode of transportation for passengers and cargo across the globe. Material and Method: since its invention, airlines have served millions of people to move from one country to another as well as within the country. Results: despite natural calamities and global war affairs, the airline industry has achieved immense growth in recent decades. In addition to the progress of airlines, coronavirus disease 2019 (COVID-19) has become a major hindrance to providing services to people around the world. Due to the rapid spread of deadly diseases, several airline firms have halted their air travel services in many parts of the world. Conclusion: as lockdowns and travel restrictions were enforced, this article examines the crisis of the airline industry after the onset of the COVID-19 pandemic.

Experimental Investigation on performance of silica fumes as a soil stabilizer for oil contaminated strata

Oil leakage is an environmental issue unnoticed in the present time. The problem of oil leakage and oil contamination is main concern for petroleum harvesting countries. Oil contamination in soil creates health issues in the area surrounding it. The nutrients in the soil get reduced significantly due to oil contamination which makes the land not suitable for cultivation. The oil produces hydrocarbons which makes the civil structures weak and out at risk. The most harmful effects of oil contamination are excessive settlement of structures, breakage of underground pipes, etc. In this project, we are trying to study the effects of oil contamination in the soil and also to find a sustainable solution for it. The soil is contaminated in the percentage from 0 to 20% and the tests on index and engineering properties have been conducted to find the effect of engine oil. In order to stabilize the oil contaminated soil, we use silica fumes as a stabilizing agent. The optimum percentage of silica fume is chosen based on the tests of Index and Engineering properties conducted on the soil with silica fumes. The percentage of oil where the soil properties need stabilization is known and the soil is stabilized with the optimum silica fume percentage

Assessing the Influence of Sustainable Practices on Guest Satisfaction and Loyalty in the Hotel Industry: An Empirical Investigation

This study explores the complex realm of service marketing in the lodging sector, specifically examining the dynamic and competing environment of the hotel industry. This is a trending and novel concept that the hotel industry is adapting to attract more customers. Service marketing in this context refers to the deliberate promotion and administration of intangible donations, such as guest circumstances, customer interactions, and the level of services supplied by hotels. This study investigates the distinct obstacles and advantages that hotel enterprises encounter when promoting their services, considering the intangible and qualitative characteristics of their products. An in-depth analysis is conducted on the numerous factors that influence client decisions such as pricing, location, brand public image, and internet reviews. Moreover, the research explores the importance of customer happiness and loyalty in the hotel sector, along with the impact of loyalty programs on visitor decision-making. The discussion will focus on hotel marketing tactics, which will be informed by the examination of real-world data and economic trends. In conclusion, success in the hotel industry within the hospitality sector hinges on delivering exceptional experiences, building robust customer relationships, and adapting to changing market dynamics and consumer expectations. This study enlightens the readers and the industry people on how to attain desired objectives of welcoming utmost populatio

Transient analysis of systems exhibiting inverse response and their control with CSTR as a case study

In this paper the phenomenon of inverse response from systems was investigated and its transient response thoroughly analyzed. Inverse response is shown by non-minimum phase systems and some minimum phase systems. Transient analysis of these systems is lacking in literature A case study was done for a non-linear, non-minimum phase CSTR (continuous stirred tank reactor), which was identified and a novel optimized trajectory for temperature feed was synthesized, which is a non-linear dynamic constrained optimization problem solved using ACADO for a particular chemical reaction. Different control schemes were also implemented on other systems as well, which exibhit inverse responses, including model reference adaptive control. The finding shows that PID-ZN goes with the inverse response, is unable to suppress it and requires large control effort which can have serious hardware limitations. Robustness is another area where PID is lacking with these systems. MRAC shemes were able to overcome all these issues. For CSTR also, these findings hold true thus points at using advance stratigies in process control industries for maximzing product yield

Incidence of congenital anomalies in Navodaya Medical College

Background: Congenital anomalies are defined as structural or functional anomalies including metabolic disorders, that occur during intrauterine life and can be identified antenatally, at birth or later in life. It accounts for 11% of neonatal deaths globally and accounts for 8–18% of perinatal deaths and 10–15% of neonatal deaths in India. Aims and objectives of the research were to study incidence and risk factors associated with congenital anomalies in Navodaya Medical College.Methods: The clinical study was done on 3008 patients over 1 year at Navodaya Medical College Hospital and Research Centre Thorough history, antenatal ultrasound, blood tests, new born babies were examined thoroughly by the paediatrician to detect the congenital malformation. If any internal congenital malformation were suspected further investigation like ultrasonography (USG), echocardiography (ECHO), X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) were done.Results: Out of 3008 cases, 40 babies had congenital anomalies, incidence is 1.3%, most commonly involved system is musculoskeletal system followed by cardiovascular system. Major risk factors associated are extremes of age, parity, lack of ante natal check-ups, no intake of folic acid, maternal diabetes mellitus.Conclusions: Congenital malformations though cannot be prevented totally but can be minimised and if detected early which reduces mental agony in mother and family. Prenatal counselling, periconceptional folate, anomaly scan, prenatal diagnosis reduces the incidence of neonatal and infant morbidity and mortalities in India

Deep learning techniques for physical abuse detection

Physical abuse has become a societal problem. Mostly children, women and old age people are vulnerable to it especially in cases of domestic violence or workplace aggression. Reporting it is in itself a challenge especially if there is a pre-existing relationship between the abuser and victim. In this paper we propose a deep learning technique for human action recognition and human pose identification to tackle physical abuse by detecting it in real time. 3D convolution neural network (CNN) architecture is built using 3D convolution feature extractors which extract both temporal and spatial data in the video. With multiple convolution layer and subsampling layer, the input video has been converted into feature vector. Human pose estimation is done using the detection of key points on the body. Using these points and tracking them from one frame to another gives spatial-temporal features to feed into neural network (NN). We present metrics to measure the accuracies of such systems where real time reporting and fault tolerance capabilities are of utmost importance. Weighted metrics shows accuracy of about 89.42% with precision of about 85.82% and thus shows the effectiveness of the system