First-principle calculations to investigate structural, electronic, mechanical, optical, and thermodynamic features of promising (La, In)-doped AlSb for optoelectronic applications.

CONTEXT: A remarkable change in lattice parameters and bulk modulus is achieved by the suitable addition of Al (Al1-x Lax Sb) and In (Al1-x Inx Sb) atoms in the AlSb compound. Electronic responses like band structure, the total partial density of states, and the elemental density of states are thoroughly investigated. The computed values indicate that the binary compound AlSb is an indirect band gap and an optically inactive response. After increasing the doping concentrations (0.25, 0.5, 0.75) of La and In in AlSb, the band gap changes from indirect to direct nature. Hence, Al1-0.75 La0.25 Sb, Al1-0.50 La0.50 Sb, Al1-0.75 In0.25Sb, and Al1-0.50 In0.50Sb become optically active. The illustrious roles of Al-3p and In-4d states on the band gap and nonlinear responses of these compounds are extensively explored by the comparison between the computed results of ultra-soft and norm converging pseudopotentials. The excess specific heat (CV), enthalpy of mixing (Hm), and phonon dispersion curves resulting from the concentrations "x" are estimated in order to investigate the thermodynamic stability responses of the pristine and doped AlSb. The obtained CV and thermal coefficient statistics for Al1-x Lax Sb and Al1-x Inx Sb may be useful for a good mapping of experimental results and examining these compounds' enharmonic responses. There is a valuable change in optical characteristics like dielectric functional, absorption, conductivity, and refractive index due to the addition of (La, In) impurities in AlSb. It is further observed that Al1-0.75 La0.25 Sb, Al1-0.50 La0.50 Sb, Al1-0.75 In0.25Sb, and Al1-0.50 In0.50Sb are significantly mechanically stable compared to pristine AlSb. The above results suggest that Al1-x Lax Sb and Al1-x Inx Sb are high-performance optical materials and can be promising potential candidates for optoelectronic applications. METHODS: The structural, electronic, mechanical, vibrational, and optical responses of the pure and doped Al1-0.75 La0.25 Sb, Al1-0.50 La0.50 Sb, Al1-0.75 In0.25Sb, and Al1-0.50 In0.50Sb are investigated, using Heydscuseria-Ernzerhof screened hybrid functional (HSEO6) and generalized gradient approximation (GGA) with norm-converging and ultra-soft pseudopotential techniques in the density functional theory.

Role of Procalcitonin and C-reactive Protein as Predictors of Sepsis and in Managing Sepsis in Postoperative Patients: A Systematic Review.

Systemic inflammatory response syndrome (SIRS) and sepsis are inflammatory responses to infection or trauma, causing symptoms and adverse outcomes such as organ shutdown and death. Different scoring systems can help in the diagnosis of SIRS and sepsis. Several biomarkers such as C-reactive protein (CRP), procalcitonin (PCT), and white blood cells (WBCs) can serve as predictors of sepsis. Surgery, trauma, and burns are the non-inflammatory causes of SIRS and sepsis. In postoperative patients, both inflammatory and non-inflammatory causes of immune response may co-exist. The role of inflammatory biomarkers in identifying sepsis development, deciding to use antibiotics, and discharging patients needs further exploration and clarity. We searched medical databases such as PubMed/Medline, PMC, ScienceDirect, Cochrane Library, and Google Scholar for relevant medical literature. The identified papers were screened, eligibility criteria were applied, and 15 research papers were identified. The finalized papers explored the roles of CRP and PCT in postoperative patients. Both CRP and PCT are raised in a postoperative patient, and then, gradually, the levels decrease. However, in case of an infection, these levels continue to rise and signify an infection, which may progress to sepsis. The cut-off values can guide decision-making about when to start antibiotics and discharge the patient. PCT was found to be more reliable in identifying the infection and preventing the development of sepsis. Further research is needed to identify the exact cut-off values that can help in decision-making.

The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR).

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.

Comparing the Efficacy and Safety of Denosumab with Bisphosphonates in Increasing Bone Mineral Density in Patients with Prostate Cancer and Breast Cancer on Antihormonal Treatment.

Osteoporosis is a common condition prevalent in both sexes that can be primary and secondary. Secondary osteoporosis may occur in cancer patients undergoing antihormonal treatment, leading to an increased risk of fractures. Androgen deprivation therapy (ADT) in patients with prostate cancer and aromatase inhibitors (AI) in patients with breast cancer can drastically increase the risk of osteoporosis. Bisphosphonates are one of the key medications in managing these patients and are widely prescribed. A monoclonal antibody called denosumab, which is a relatively new treatment option, is also used in this population group. To conduct a detailed comparison of these groups, we performed a thorough literature search using Pubmed and Google Scholar to extract data in the form of research papers/clinical trials. A total of 18 research papers were extracted using Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and other inclusion and exclusion criteria. Seven of these papers were based on randomized controlled trials (RCTs) comparing denosumab with either placebo or bisphosphonates in patients with breast cancer and prostate cancer. Two meta-analyses comparing the safety and efficacy of both these drugs in this population group were also included. Denosumab was found to significantly increase bone mineral density (BMD) for up to two years and showed better results than bisphosphonates, while both had a comparable safety profile. More trials should be conducted in patients with prostate cancer or breast cancer on ADT or AI therapy, respectively, for longer durations to assess the long-term safety of these drugs in this population.

Influence of carrier media physical properties on start-up of moving attached growth systems.

Five carrier media with different shapes (spherical and cylindrical), sizes, voidage and protected surface areas (112-610 m2/m3) were studied in a pilot scale moving bed biofilm reactor (MBBR). This study aimed at assessing start-up duration using biofilm formation rates. Results indicated that the spherical media required shorter periods to achieve stable biofilm formation rates associated with chemical oxygen demand (COD) (15-17 days), compared to cylindrical high surface area media (23-24 days). Protected surface area presented weaker correlations with the biofilm formation rate for COD (R2 = 0.83) and ammonia removal (R2 = 0.76). However, good correlations were observed with a combination of the media physical factors: dimensionality (Di), voidage (Voi), and hydraulic efficiency (HE) strongly correlated with biofilm formation rates for heterotrophic (R2 = 0.95) and nitrifying bacteria (R2 = 0.92). This study proposes that the media physical properties can contribute to shortening start-up, contributing to improved removal rates and fast commissioning of MBBRs.

Transient Accumulation of NO2- and N2O during Denitrification Explained by Assuming Cell Diversification by Stochastic Transcription of Denitrification Genes.

Denitrifying bacteria accumulate [Formula: see text], NO, and N2O, the amounts depending on transcriptional regulation of core denitrification genes in response to O2-limiting conditions. The genes include nar, nir, nor and nosZ, encoding [Formula: see text]-, [Formula: see text]-, NO- and N2O reductase, respectively. We previously constructed a dynamic model to simulate growth and respiration in batch cultures of Paracoccus denitrificans. The observed denitrification kinetics were adequately simulated by assuming a stochastic initiation of nir-transcription in each cell with an extremely low probability (0.5% h-1), leading to product- and substrate-induced transcription of nir and nor, respectively, via NO. Thus, the model predicted cell diversification: after O2 depletion, only a small fraction was able to grow by reducing [Formula: see text]. Here we have extended the model to simulate batch cultivation with [Formula: see text], i.e., [Formula: see text], NO, N2O, and N2 kinetics, measured in a novel experiment including frequent measurements of [Formula: see text]. Pa. denitrificans reduced practically all [Formula: see text] to [Formula: see text] before initiating gas production. The [Formula: see text] production is adequately simulated by assuming stochastic nar-transcription, as that for nirS, but with a higher probability (0.035 h-1) and initiating at a higher O2 concentration. Our model assumes that all cells express nosZ, thus predicting that a majority of cells have only N2O-reductase (A), while a minority (B) has [Formula: see text]-, NO- and N2O-reductase. Population B has a higher cell-specific respiration rate than A because the latter can only use N2O produced by B. Thus, the ratio [Formula: see text] is low immediately after O2 depletion, but increases throughout the anoxic phase because B grows faster than A. As a result, the model predicts initially low but gradually increasing N2O concentration throughout the anoxic phase, as observed. The modelled cell diversification neatly explains the observed denitrification kinetics and transient intermediate accumulations. The result has major implications for understanding the relationship between genotype and phenotype in denitrification research.

Homeostatic control of nitric oxide (NO) at nanomolar concentrations in denitrifying bacteria - modelling and experimental determination of NO reductase kinetics in vivo in Paracoccus denitrificans.

Homeostatic control of nitric oxide (NO) at nanomolar concentrations appears common among denitrifying bacteria, often ascribed to synchronized expression of nitrite and nitric oxide reductase (Nir and Nor). We questioned whether this is sufficient: using the reported substrate affinities for cytochrome cd1 nitrite reductase (cNor), our model of batch cultures of Paracoccus denitrificans predicted NO concentrations orders of magnitude higher than measured. We rejected a hypothesis that the homeostatic control is due to a negative feedback by NO on the activity of NirS because the inclusion of such feedback resulted in too slow anaerobic growth and N2 production. We proceeded by determining the kinetic parameters for cNor in vivo by a carefully designed experiment, allowing the estimation of NO concentration at the cell surface while anoxic cultures depleted low headspace doses of NO. With the new parameters for cNor kinetics in vivo {v = vmax /[1 + K2 /(NO) + K1 × K2 /(NO)(2) ]; vmax = 3.56 fmol NO cell(-1) h(-1) , K1 < 1 nM, and K2 = 34 nM}, the model predicted NO concentrations close to that measured. Thus, enzyme kinetics alone can explain the observed NO homeostasis. Determinations of enzyme kinetic parameters in vivo are not trivial but evidently required to understand and model NO kinetics in denitrifying organisms in soils and aquatic environments.

Low probability of initiating nirS transcription explains observed gas kinetics and growth of bacteria switching from aerobic respiration to denitrification.

In response to impending anoxic conditions, denitrifying bacteria sustain respiratory metabolism by producing enzymes for reducing nitrogen oxyanions/-oxides (NOx) to N2 (denitrification). Since denitrifying bacteria are non-fermentative, the initial production of denitrification proteome depends on energy from aerobic respiration. Thus, if a cell fails to synthesise a minimum of denitrification proteome before O2 is completely exhausted, it will be unable to produce it later due to energy-limitation. Such entrapment in anoxia is recently claimed to be a major phenomenon in batch cultures of the model organism Paracoccus denitrificans on the basis of measured e(-)-flow rates to O2 and NOx. Here we constructed a dynamic model and explicitly simulated actual kinetics of recruitment of the cells to denitrification to directly and more accurately estimate the recruited fraction (Fden). Transcription of nirS is pivotal for denitrification, for it triggers a cascade of events leading to the synthesis of a full-fledged denitrification proteome. The model is based on the hypothesis that nirS has a low probability (rden, h(-1)) of initial transcription, but once initiated, the transcription is greatly enhanced through positive feedback by NO, resulting in the recruitment of the transcribing cell to denitrification. We assume that the recruitment is initiated as [O2] falls below a critical threshold and terminates (assuming energy-limitation) as [O2] exhausts. With rden = 0.005 h(-1), the model robustly simulates observed denitrification kinetics for a range of culture conditions. The resulting Fden (fraction of the cells recruited to denitrification) falls within 0.038-0.161. In contrast, if the recruitment of the entire population is assumed, the simulated denitrification kinetics deviate grossly from those observed. The phenomenon can be understood as a 'bet-hedging strategy': switching to denitrification is a gain if anoxic spell lasts long but is a waste of energy if anoxia turns out to be a 'false alarm'.

A model to assess dust explosion occurrence probability.

Dust handling poses a potential explosion hazard in many industrial facilities. The consequences of a dust explosion are often severe and similar to a gas explosion; however, its occurrence is conditional to the presence of five elements: combustible dust, ignition source, oxidant, mixing and confinement. Dust explosion researchers have conducted experiments to study the characteristics of these elements and generate data on explosibility. These experiments are often costly but the generated data has a significant scope in estimating the probability of a dust explosion occurrence. This paper attempts to use existing information (experimental data) to develop a predictive model to assess the probability of a dust explosion occurrence in a given environment. The pro-posed model considers six key parameters of a dust explosion: dust particle diameter (PD), minimum ignition energy (MIE), minimum explosible concentration (MEC), minimum ignition temperature (MIT), limiting oxygen concentration (LOC) and explosion pressure (Pmax). A conditional probabilistic approach has been developed and embedded in the proposed model to generate a nomograph for assessing dust explosion occurrence. The generated nomograph provides a quick assessment technique to map the occurrence probability of a dust explosion for a given environment defined with the six parameters.