Quantum thermodynamics at critical points during melting and solidification processes

We systematically explore and show the existence of finite-temperature continuous quantum phase transition (CTQPT) at a critical point, namely, during solidification or melting such that the first-order thermal phase transition is a special case within CTQPT. Infact, CTQPT is related to chemical reaction where quantum fluctuation (due to wavefunction transformation) is caused by thermal energy and it can occur maximally for temperatures much higher than zero Kelvin. To extract the quantity related to CTQPT, we use the ionization energy theory and the energy-level spacing renormalization group method to derive the energy-level spacing entropy, renormalized Bose-Einstein distribution and the time-dependent specific heat capacity. This work unambiguously shows that the quantum phase transition applies for any finite temperatures.Comment: To be published in Indian Journal of Physics (Kolkata

Utjecaj pojačane ekspresije gena biosintetskog puta za 3-hidroksipropionsku kiselinu na njezin prinos u bakteriji Lactobacillus reuteri

3-Hydroxypropionic acid (3-HP) is a novel antimicrobial agent against foodborne pathogens like Salmonella and Staphylococcus species. Lactobacillus reuteri converts glycerol into 3-HP using a coenzyme A-dependent pathway, which is encoded by propanediol utilization operon (pdu) subjected to catabolite repression. In a catabolite repression-deregulated L. reuteri RPRB3007, quantitative PCR revealed a 2.5-fold increase in the transcripts of the genes pduP, pduW and pduL during the mid-log phase of growth. The production of 3-HP was tested in resting cells in phosphate buff er and growing batch cultures in MRS broth of various glucose/glycerol ratios. Due to the upregulation of pathway genes, specific formation rate of 3-HP in the mutant strain was found to be enhanced from 0.167 to 0.257 g per g of cell dry mass per h. Furthermore, formation of 3-HP in resting cells was limited due to the substrate inhibition by reuterin at a concentration of (30±5) mM. In batch cultures, the formation of 3-HP was not observed during the logarithmic and stationary phases of growth of wild-type and mutant strains, which was confi rmed by NMR spectroscopy. However, the cells collected in these phases were found to produce 3-HP aft er washing and converting them to resting cells. Lactate and acetate, the primary end products of glucose catabolism, might be the inhibiting elements for 3-HP formation in batch cultures. This was confirmed when lactate (25±5 mM) or acetate (20±5 mM) were added to biotransformation medium, which prevented the 3-HP formation. Moreover, the removal of sodium acetate and glucose (carbon source for lactic acid production) was found to restore 3-HP formation in the MRS broth in a similar manner to that of the phosphate buff er. Even though the genetic repression was circumvented by the up-regulation of pathway genes using a mutant strain, 3-HP formation was further limited by the substrate and catabolite inhibition.3-Hidroksipropionska kiselina je novi antimikrobni agens koji se može upotrijebiti za suzbijanje patogenih bakterija u hrani, kao što su vrste iz rodova Salmonella i Staphylococcus. Bakterija Lactobacillus reuteri iz glicerola sintetizira 3-hidroksipropionsku kiselinu biosintetskim putem ovisnim o koenzimu A, kodiranim operonom za korištenje propandiola koji je reguliran kataboličkom represijom. U mutantu L. reuteri RPRB3007 u kojem nema kataboličke represije, ispitanom pomoću metode PCR, primijećeno je 2,5 puta više transkripata gena pduP, pduW i pduL, i to tijekom logaritamske faze rasta bakterije. Proizvodnja 3-hidroksipropionske kiseline određena je u stanicama koje se ne dijele, a bile su resuspendirane u fosfatnom puferu, te u šaržnim kulturama uzgojenim u podlozi MRS s različitim omjerima glukoze i glicerola. Utvrđeno je da se u mutantu zbog pojačane ekspresije gena biosintetskog puta povećala specifična brzina nastajanja 3-hidroksipropionske kiseline, i to s 0,167 na 0,257 g po gramu suhe biomase po satu. Osim toga, sinteza je 3-hidroksipropionske kiseline u stanicama koje se ne dijele bila usporena nakon dodatka reuterina u koncentraciji od (30±5) mM. U šaržnom uzgoju nije utvrđena prisutnost 3-hidroksipropionske kiseline tijekom logaritamske i stacionarne faze rasta divljeg soja i mutanta, što je potvrđeno i NMR spektroskopijom. Međutim, nakon ispiranja i povratka u stanje mirovanja ove su stanice ponovno proizvodile 3-hidroksipropionsku kiselinu. Zaključeno je da laktat i acetat, primarni produkti katabolizma glukoze, vjerojatno inhibiraju sintezu 3-hidroksipropionske kiseline u šaržnim kulturama, što je potvrđeno činjenicom da dodatak laktata u koncentraciji od (25±5) mM ili acetata u koncentraciji od (20±5) mM podlozi sprečava sintezu 3-hidroksipropionske kiseline. Osim toga, uklanjanjem je natrijevog acetata i glukoze (izvora ugljika za proizvodnju mliječne kiseline) potaknuta proizvodnja 3-hiroksipropionske kiseline u hranjivoj podlozi MRS na sličan način kao i uporabom fosfatnog pufera. Iako je genetička represija u mutantu izbjegnuta pojačanom ekspresijom gena biosintetskog puta, proizvodnja je 3-hidroksipropionske kiseline i dalje bila ograničena supstratom i kataboličkom inhibicijom

Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network

Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model

Impact on health and provision of healthcare services during the COVID-19 lockdown in India: A multicentre cross-sectional study

Introduction The COVID-19 pandemic resulted in a national lockdown in India from midnight on 25 March 2020, with conditional relaxation by phases and zones from 20 April. We evaluated the impact of the lockdown in terms of healthcare provisions, physical health, mental health and social well-being within a multicentre cross-sectional study in India. Methods The SMART India study is an ongoing house-to-house survey conducted across 20 regions including 11 states and 1 union territory in India to study diabetes and its complications in the community. During the lockdown, we developed an online questionnaire and delivered it in English and seven popular Indian languages (Hindi, Tamil, Marathi, Telegu, Kannada, Bengali, Malayalam) to random samples of SMART-India participants in two rounds from 5 May 2020 to 24 May 2020. We used multivariable logistic regression to evaluate the overall impact on health and healthcare provision in phases 3 and 4 of lockdown in red and non-red zones and their interactions. Results A total of 2003 participants completed this multicentre survey. The bivariate relationships between the outcomes and lockdown showed significant negative associations. In the multivariable analyses, the interactions between the red zones and lockdown showed that all five dimensions of healthcare provision were negatively affected (non-affordability: OR 1.917 (95% CI 1.126 to 3.264), non-accessibility: OR 2.458 (95% CI 1.549 to 3.902), inadequacy: OR 3.015 (95% CI 1.616 to 5.625), inappropriateness: OR 2.225 (95% CI 1.200 to 4.126) and discontinuity of care: OR 6.756 (95% CI 3.79 to 12.042)) and associated depression and social loneliness. Conclusion The impact of COVID-19 pandemic and lockdown on health and healthcare was negative. The exaggeration of income inequality during lockdown can be expected to extend the negative impacts beyond the lockdown

How is COVID-19 altering the manufacturing landscape? A literature review of imminent challenges and management interventions

Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance

The ORNATE India project: Building research capacity and capability to tackle the burden of diabetic retinopathy-related blindness in India

The ORNATE India project is an interdisciplinary, multifaceted United Kingdom (UK)–India collaborative study aimed to build research capacity and capability in India and the UK to tackle the burden of diabetes-related visual impairment. For 51 months (October 2017–December 2021), this project built collaboration between six institutions in the UK and seven in India, including the Government of Kerala. Diabetic retinopathy (DR) screening models were evaluated in the public system in Kerala. An epidemiological study of diabetes and its complications was conducted through 20 centers across India covering 10 states and one union territory. The statistical analysis is not yet complete. In the UK, risk models for diabetes and its complications and artificial intelligence-aided tools are being developed. These were complemented by joint studies on various aspects of diabetes between collaborators in the UK and India. This interdisciplinary team enabled increased capability in several workstreams, resulting in an increased number of publications, development of cost-effective risk models, algorithms for risk-based screening, and policy for state-wide implementation of sustainable DR screening and treatment programs in primary care in Kerala. The increase in research capacity included multiple disciplines from field workers, administrators, project managers, project leads, screeners, graders, optometrists, nurses, general practitioners, and research associates in various disciplines. Cross-fertilization of these disciplines enabled the development of several collaborations external to this project. This collaborative project has made a significant impact on research capacity development in both India and the UK

Data Security Enhancement in 4G Vehicular Networks Based on Reinforcement Learning for Satellite Edge Computing

The vehicular network provides the dedicated short-range communication (DSRC) with IEEE 802.11p standard. The VANET model comprises of cellular vehicle-to-everything communication with wireless communication technology. Vehicular Edge Computing exhibits the promising technology to provide promising Intelligent Transport System Services. Smart application and urban computing. Satellite edge computing model is adopted in vehicular networks to provide services to the VANET communication for the management of computational resources for the end-users to provide access to low latency services for maximal execution of service. The satellite edge computing model implemented with the 4G vehicular communication network model subjected to data security issues. This paper presented a Route Computation Deep Learning Model (RCDL) to improve security in VANET communication with 4G technology. The RCDL model uses the route establishment model with the optimal route selection. The compute route is transmitted with the cryptographic scheme model for the selection of optimal route identified from the satellite edge computing model. The proposed RCDL scheme uses the deep learning-based reinforcement learning scheme for the attack prevention in the VANET environment employed with the 4G technology communication model. The simulation results expressed that proposed RCDL model achieves the higher PDR value of 98% which is ~6% higher than the existing model. The estimation of end-to-end delay is minimal for the RCDL scheme and improves the VANET communication