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Anti-f is produced by exposure to the compound antigen ce (f) on red blood cells (RBCs), expressed when both c and e are present on the same protein (cisposition). Although anti-f was discovered in 1953, there are few cases reported worldwide because the presence of anti-f is often masked by anti-c or anti-e and is not generally found as a single antibody. In the present case, anti-f was identified by using three-cell screening and 11-cell identification panels. The identification of anti-f was further supported by additional testing, including (1) Rh antigen typing; (2) antibody identification panels (enzyme-treated panel [ficin] and an in-house–constructed Rh panel); (3) look-back and phenotyping of donor RBC units, which were responsible for alloimmunization; and (4) molecular testing of the patient’s RBCs.

Across workplaces, slip-and-fall accidents occur frequently due to reduced friction at the shoe-floor interaction. However, these events are typically ignored, and the importance of appropriate footwear is frequently overlooked. Mechanical-based slip and fall risk assessment devices are used to evaluate the frictional measurements at the shoe and floor contact. The available devices are however lab-based and have difficulty in replicating the realistic slipping biomechanics. Additionally, the current devices are expensive to be used in lower-middle-income nations. In this study, a biomimetic and cost-effective slip testing device was presented. The performance of the manufactured slip tester was thoroughly validated and examined utilising five formal shoes on three different flooring surfaces in different contaminant conditions. The findings showed that the slip tester effectively distinguished between the combinations of shoes, contaminant, and floorings. It was discovered that the slip tester's performance effectively distinguished between different shoe tread patterns across several slipping combinations.

The high-temperature corrosion is a crucial issue of stainless-steel components such as boilers and superheaters in power plant industries when it operates at elevated temperatures due to impurities in the environment, which deteriorate the properties of the materials. The current research work goal is to investigate the cyclic corrosion characteristics of the bare and Ni–20%Cr, a high-velocity oxy-fuel sprayed coating on AISI 321 steel in Na2SO4–60%V2O5salt atmosphere at 900 °C. Thermogravimetric study was used to examine the corrosion kinetics by weight change technique. Surface morphology and compositions of the coating and oxide scale morphology were characterized. X-ray diffraction analysis divulges that Fe2O3is the primary scale in the uncoated specimen. Corrosion attack across the depth and scale thickness is analyzed by a cross-sectional study. More oxide scales spalling could be observed in the uncoated specimen during the corrosion study due to nonprotective Fe2O3formation. Compared with the bare specimen, the coated steel provides superior corrosion resistance at 900 °C. This is ascribed to the occurrence of NiO, NiCr2O4, and Cr2O3scales in the coated specimen, which provides sufficient protection to the specimen.

The risk of prostate cancer among transgender women undergoing medical and surgical gender-affirming interventions remains unclear, though up to a fivefold decreased risk has been reported in comparison to cisgender men. In this study, we conducted a comparative analysis of the risk of prostate cancer among transgender women (TW) using data from TriNetX, a large database, versus SEER. Our findings indicate that, overall, transgender women exhibited a 2.56-fold lower risk of prostate cancer compared to cisgender men. Specifically, among TW on hormone therapy between ages 50–64, we observed a 2.06-fold decrease in risk. Contrary to the previous perception of prostate cancer being rare in transgender women, our study suggests that it may not be as uncommon as previously believed.

Purpose: The purpose of this study is to recognise the role of information and communication technology (ICT) tools in different sectors like Education, Health Care, Business, FMCG and Entertainment in the phase of social distancing. This study also attempts to provide a quantitative review of the scholarly literature on this topic. Design/methodology/approach: A comprehensive literature evaluation was undertaken using a database encompassing 150 English-language papers with publication dates ranging from 2019 to 2021. The research profile and thematic analysis are presented through a comprehensive content analysis, resulting in four themes. The study reviews various research articles and reports related to social distancing and opens a discussion on the growing importance of ICT tools during this COVID-19 era. Findings: ICT acts as a surviving tool for the economy by creating a virtual environment and helping people to stay socially connected during this pandemic. There is a lack of empirical evidence to support the facts so further research is required. Research limitations/implications: There are two drawbacks to the current study. Firstly, this study established a rigorous review methodology in which the researchers opted to exclude any grey literature, non-peer-reviewed articles, books, notes and book chapters from consideration. These sources could have had pertinent literature. Secondly, even after protocol's rigour and numerous rounds of checks by a team of academicians and researchers, an anomaly may have sneaked into the evaluation. Originality/value: The current study contributes to the growing literature on ICT tools particularly in this phase of social distancing. This paper highlights the need for future research in this area supported by different statistics. [ABSTRACT FROM AUTHOR]

The optimal distribution network (DN) setup in the modern framework baffles the DN planner, as various technoeconomic impacts need to be analyzed. The prevailing studies urge multiobjective problem formulation to quantify the wide range of technoeconomic performance indices; however, no adequate method is posited to measure the relative influence coefficient of these indices in DN planning. This article proposes a novel method based on the Shannon entropy formula to determine the relative influence level of indices in multiobjective optimally distributed generation unit(s) placement problems. Numerical results have been presented to validate the efficacy of the proposed approach over the previously published strategies.

Slips and falls are prevalent across the globe and pose major threat to the safety of public. Significant frictional performance of a footwear is essential to mitigate the traumatic injuries caused due to unintentional slips and falls. Several studies in the past have evaluated the traction performance of the footwear across common slippery conditions, but only a few have considered testing the worn shoes. In this study, nine systematically altered tread designs were implemented to study the effects of tread geometries across its unworn and worn conditions. The outsoles were experimentally tested for their friction performance and also simulated for wet slippery conditions using a novel computational fluid dynamics based framework. Outsoles with a tread width of 6 mm and tread gaps of 2 and 3 mm showed higher friction coefficients over dry surfaces. After first wear phase, outsoles with 2 and 3 mm gaps exhibited higher friction outcomes as compared to higher tread gaps (i.e., 4 mm). Outsoles with tread width of 2 mm and tread gaps of 2 and 3 mm showed retention of treads, to some extent, and also exhibited the highest friction values even after third wear phase. Considering all the parameters (i.e., tread gap, tread width, friction values and wear phases), outsole with tread width and gap of 4 mm exhibited considerably higher friction values. The results from this study are anticipated to provide better understanding of the effect of footwear tread parameters on their frictional performance. Furthermore, the procedures used in this study would also help the footwear manufacturers to determine the footwear replacement thresholds to mitigate the slip and fall risks.

This is the first prospective multi-institutional study of PFUI. We found no difference in stricture rate between patients treated with primary realignment and SPT with delayed urethroplasty.

Logistic distribution (LogDis) is frequently used in many different applications, such as logistic regression, logit models, classification, neural networks, physical sciences, sports modeling, finance and health and disease studies. For instance, the distribution function of the LogDis has the same functional form as the derivative of the Fermi function that can be used to set the relative weight of various electron energies in their contributions to electron transport. The LogDis has wider tails than a normal distribution (NorDis), so it is more consistent with the underlying data and provides better insight into the likelihood of extreme events. For this reason the United States Chess Federation has switched its formula for calculating chess ratings from the NorDis to the LogDis. The outcomes of many real-life experiments are sequences of record-breaking data sets, where only observations that exceed (or only those that fall below) the current extreme value are recorded. The practice demonstrated that the widely used estimators of the scale and location parameters of logistic record values, such as the best linear unbiased estimators (BLUEs), have some defects. This paper investigates the shrinkage estimators of the location and scale parameters for logistic record values using prior information about their BLUEs. Theoretical and computational justifications for the accuracy and precision of the proposed shrinkage estimators are investigated via their bias and mean square error (MSE), which provide sufficient conditions for improving the proposed shrinkage estimators to get unbiased estimators with minimum MSE. The performance of the proposed shrinkage estimators is compared with the performances of the BLUEs. The results demonstrate that the resulting shrinkage estimators are shown to be remarkably efficient.

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.

Harmony search algorithm (HSA), inspired by the behaviors of music improvisation process, is a widely used metaheuristic to solve global optimization problems arises in various fields. The reason of its popularity is its simplicity of algorithm structure and good performance. However, the conventional harmony search algorithm (HSA) experiences prone towards the local optima, tedious task of tuning parameters, and premature convergence. To overcome all these drawbacks of conventional HSA and further improve the precision of numerical results, a new variant of the HSA called modified harmony search algorithm (MHSA) is proposed in the present study. This MHSA utilizes the valuable information stored in the harmony memory and modifies the search strategy to make an efficient search procedure by adopting a new formulation to the pitch adjustment process, randomization process, harmony memory considering rate (HMCR), and pitch adjustment rate (PAR). The experimental validation and comparative performance study with conventional HSA, variants of HSA such as adaptive harmony search with best based search strategy (ABHS), enhanced self-adaptive global-best harmony search (ESGHS), novel self-adaptive harmony search (NSHS), parameter adaptive harmony search (PAHS), Gaussian global-best harmony search algorithm (GGHS) and other metaheuristics such as sine cosine algorithm (SCA), grey wolf optimizer (GWO), comprehensive learning particle swarm optimization (CLPSO), gbest-guided artificial bee colony (GABC), and covariance matrix adaptation evolution strategy (CMA-ES) is conducted on a set of 23 well-known benchmark problems. In addition to this benchmarking, the proposed MHSA is also used to solve three structural engineering design problem. The statistical test and convergence behaviour analysis are used to analyze the quality of search and significance of improved accuracy. The comparison illustrates the superior search efficiency of the proposed MHSA than other algorithms as a global optimizer.

Millions of burn injuries are reported every year, with severe cases needing skin graft implantation. To date, the highest achievable expansion produced by split-thickness skin grafting has been reported to be just three times, which is insufficient for covering large burn sites with limited donor site skin. In this work, the authors studied the expansion potential of skin grafts with rotating-triangle (RT)-shaped auxetic incision patterns, which are known to exhibit a highly negative Poisson’s effect. Ten two-layer skin graft models with RT angles varying from 0 to 135° were developed and tested under uniaxial and biaxial tensile loads. Poisson’s effect, meshing ratios (MRs) and induced stresses were quantified across all models and loads up to the ultimate tensile stress. Very high expansions (MR > 45), varying widely across uniaxial and biaxial loads and without rupture, were observed for low RT angles. However, for RT angles greater than 45°, the expansions significantly decreased, with the lowest MR reported at 135°. Beyond an RT angle of 90°, the induced stresses were very high, with potential for skin rupture. Such expansion potentials and stress estimations with skin grafts have not been reported previously and would be indispensable for initiating groundbreaking advances in burn surgery research.

The main purpose of this study is to examine barriers towards e-commerce platforms in developing countries using specific analytic techniques. The present empirical analysis has its main objectives of detailed study of barriers that keep deferring them to adopt an e-commerce platform. To achieve this, the barriers were selected from an extensive literature review and expert advice from this field. Then the responses were studied using a fuzzy DEMATEL approach. The causal relationship graphs provide detailed structural interdependencies of the barriers. In this exploratory study benchmarking has come out to be the most influential barrier that needs to be practiced while adopting an e-commerce platform. Furthermore, a lack of top management commitment has also been a significant barrier in the organization while initiating an online real-time business. The result also showed that higher turnover and poor planning are the most influenced barrier among the other parameter, the sensitivity analysis of the method is conducted to validate the robustness of the results.

The paper analyzes a power generating system model consisting of two sub-systems in series. Sub-system-1 consists of four generating units in parallel and sub-system -2 consists of two transformer units in parallel. Three generating units are sufficient to supply the required power and the fourth is added as a redundant unit to enhance the reliability of the system. Similarly, one transformer unit is sufficient to run the system satisfactorily and the second is added to increase the system reliability further. The expressions for several system characteristics useful to system manager, engineers, training supervisors have been obtained using supplementary variable technique.

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

To our knowledge there are no studies that evaluate urethroplasty practice patterns among genitourinary reconstructive surgeons.

Anion exchange membranes (AEMs) play an essential role in artificial photosynthesis devices, which photoelectrochemically convert CO2and water into useful products. AEMs allow the transport of charge carriers between electrodes while minimizing the transport of CO2reduction products (e.g., ethanol). Fundamental transport studies in AEMs relevant to artificial photosynthesis are uncommon. Herein, we describe the preparation of an imidazolium-functionalized poly(phenylene oxide) membrane. Membrane transport properties were controlled by systematic variation of the degree of imidazolium functionalization, which induced changes in the membrane water volume fraction. Ethanol permeability and ionic conductivity increased with the membrane water volume fraction. Consequently, the membranes with a relatively high ionic conductivity exhibited a relatively high ethanol permeability, presenting a trade-off in the transport properties desirable for artificial photosynthesis applications. This work seeks to enable the optimization of AEMs for artificial photosynthesis through the systematic study of membrane structure (water volume fraction) and its relevance to alcohol transport and electrolyte ion conductivity.

Grey wolf optimiser (GWO) is a relatively simple and efficient nature-inspired optimisation algorithm which has shown its competitive performance compared to other population-based meta-heuristics. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership behaviour of grey wolves in nature. To combat the issue of premature convergence and local optima stagnation, an enhanced version of GWO is proposed in this paper. The proposed algorithm is named accelerated grey wolf optimiser (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, set of 23 well-known classical benchmark problems are used. The results and comparison through various metrics show the reliability and efficiency of the A-GWO.

ABSTRACTReal-world optimisation problems that are not endowed in mathematical characteristics like differentiability, convexity etc. require non-traditional optimisation approaches that explore the promising regions of the search space stochastically to achieve the optima of the problem. Grey Wolf Optimizer (GWO) is one of the efficient and recently developed approaches in the area of Swarm Intelligence to solve real-world optimisation problems over continuous space. However, in some cases, due to the insufficient diversity, GWO still suffers from the problem of stagnation in local optimums. Therefore, this article presents the novel algorithm OCS-GWO that enhances the performance of original GWO by introducing the opposition-based learning to approximate the closer search candidate solution to the global optima and chaotic local search for the exploitation of the search regions efficiently. In OCS-GWO, a chaotic local search is used for balancing the exploration and exploitation operators that are the underlying features of any stochastic search algorithm. The performance of the proposed algorithm OCS-GWO has been evaluated on a set of 23 standard benchmark test problems and on three engineering application problems – gear train, cantilever beam and speed reducer design problems. The experimental results on test problems and engineering applications confirm the efficiency and reliability of the proposed algorithm over original GWO.

Retention time alignment across large number of runs is important for reproducibility of SWATH-MS runs. We developed a hybrid RT alignment approach to establish correspondence among MS2-peaks. After testing on a gold-standard validation dataset, we were also able to align peaks from large-scale human plasma runs, which have elution-order-swapped peaks, with 98% accuracy compared to 76% by the existing method.

Background:Organ donation committees were established in 2008 by NHS Blood and Transplant to improve the rates of organ donation in the UK.Aims:The aims of this study were three-fold: to review the role and structure of organ donation committees in England; to assess how effective organ donation committees are at driving improvements; and to make recommendations regarding the future role, responsibilities and structure of organ donation committees.Methods:This study adopted a cross-sectional mixed-methods research approach using questionnaires and semi-structured interviews.Results:There was a divergence between how each committee functioned and how effective they were in achieving their aims.Discussion:There were seven key findings which related to the effectiveness of organ donation committees. These included, but were not limited to, a lack of consistency in how the role of an organ donation committee was viewed by members of NHS Blood and Transplant; and that the influence a chair has within their respective NHS Trust is key to an organ donation committee being effective in achieving its aims.Conclusion:A framework and several recommendations were produced and aimed to help improve the effectiveness of organ donation committees.

An indoor wireless network comprises of multiple devices connected wirelessly in a small geographical area, giving mobility to the connected devices. RF signal associated with wireless networks suffers from many propagation losses. It becomes essential to estimate losses at different locations, in the intended coverage area, to attain the desired performance seamlessly. It helps in the optimal device placement in the wireless networks. In the application, in addition to the propagation losses, there is a need to estimate the loss induced by the presence of smoke and fire, individually, in an indoor environment. This paper first deals with the design of a heatmap generating tool, in MATLAB, which gives the approximate signal strength at different locations on a given floor for a given transmitter. Then the paper discusses the formation of plasma in fast flaming fires and presents the path loss due to it. This path loss is incorporated in the heatmap generating tool and a heatmap is generated for our office ground floor for the case of a flaming fire. The paper also describes a testbed, setup in our office premises, to study the scattering loss caused by smoke particles.

ABSTRACTGrey Wolf Optimiser (GWO) is a recently developed optimisation approach to solve complex non-linear optimisation problems. It is relatively simple and leadership-hierarchy based approach in the class of Swarm Intelligence based algorithms. For solving complex real-world non-linear optimisation problems, the search equation provided in GWO is not of sufficient explorative behaviour. Therefore, in the present paper, an attempt has been made to increase the exploration capability along with the exploitation of a search space by proposing an improved version of classical GWO. The proposed algorithm is named as Cauchy-GWO. In Cauchy-GWO Cauchy operator has been integrated in which first two new wolves are generated with the help of Cauchy distributed random numbers and then another new wolf is generated by taking the convex combination of these new wolves. The performance of Cauchy-GWO is exhibited on standard IEEE CEC 2014 benchmark problem set. Statistical analysis of the results on CEC 2014 benchmark set and popular evaluation criteria, Performance Index (PI) proves that Cauchy-GWO outperforms GWO in terms of error values defined in IEEE CEC 2014 benchmarks collection. Later on in the paper, GWO and Cauchy-GWO algorithms have been used to solve three well-known engineering application problems and two problems of reliability. From the analysis conducted in the present paper, it can be concluded that the proposed algorithm, Cauchy-GWO is reliable and efficient algorithm to solve continuous benchmark test problems, as well as real-life applications problems.

Indo-Cyanine Green Fluorescence is an emerging technology with more frequent use in laparoscopic and robotic surgery. It relies on near-infrared (NIR) fluorescence to demonstrate tissue perfusion with demarcation of tissue planes and vascular pedicles. The aim of the study is to evaluate the role of this technology in laparoscopic adrenalectomy (LA). 55 patients underwent laparoscopic adrenalectomy using NIR Fluorescence enabled laparoscope. All cases received a standard initial dose of 5-mg dye to aid tissue visualization. Surgery proceeded with “fluorescence mode” demonstrating real-time NIR images superimposed on standard white-light imaging. The timing, number of doses were dictated by the operating surgeon, which were recorded and correlated with intra-operative fluorescence visualization. 54 patients underwent successful LA, with one conversion in a case of large pheochromocytoma due to difficult hemostasis. The lag between ICG administration and visualization of adrenal fluorescence varied between 30 and 75 s. The total duration of adrenal parenchymal fluorescence after a single dose did not exceed 15 min in our series. Average total administered dose was 14.4 mg. We suffered no mortality. There were no adverse effects due to the dye. 5 patients suffered Grade I complications, with one patient suffering Grade II and IV complication each, as per Clavien–Dindo Classification. Final histopathology demonstrated pheochromocytoma, adrenocortical adenoma, adrenocortical carcinoma, cushing’s adenoma, aldosteronoma, and myelolipoma. We describe our initial positive experience with ICG fluorescence in LA, with a detailed description of dye administration in our study. The technology offers real-time differentiation of tissues and identification of vascular structures, providing immediate guidance during surgery. Further evaluation of its role in adrenocortical malignancy is warranted. NIR fluorescence is a safe, useful addition in laparoscopic adrenalectomy which will undergo further refinement over time.

Daily activities such as walking and running possess abundant amount of energy, which is usually wasted and have the potential to be harvested. Especially, the energy generated from footsteps, if tapped appropriately, may be useful for charging electronic devices such as cell phones, wearables, and medical devices. In this study, the kinetic energy from footsteps was captured using a novel six-layered compartmental insole design with embedded piezoelectric transducers. This system was fabricated using additive manufacturing techniques and evaluated for its capabilities to charge a battery while walking and running. The charging performance was estimated initially for 6000 walking steps and extended to approximately 67,000 simulated running steps to fully charge the battery. A range of transducer arrangements were tested, indicating better power generation from the parallel combinations compared to series. Across gait activities, running was observed to lead to better coordination between the piezoelectric transducers and generation of higher power, compared to in walking. While very high amount of electrical power was not realized, the device performance was found to be satisfactory to charge low power rated wearable lifestyle electronic or medical devices. The findings from this work is anticipated to provide novel insights for power harvesting from human gait.

Slips and falls across the workplaces account for several fatal and non-fatal traumatic injuries worldwide. Consequences of these accidents include deterioration of the overall quality of living and increased economical strains through hospitalization and compensation costs. Unintentional slips usually occur due to a sudden decrease in the friction of shoes and the floorings. Due to low purchasing parity in the low- or middle-income countries (such as India), awareness related to the footwear’s slip resistance performance often goes neglected. Due to this, the public commonly choose to buy low-cost footwear. Also, footwear traction performance deteriorates with progressive wearing. Therefore, assessing the performance of footwear in new and worn-out conditions across common slippery situations is essential. In this work, ten Indian-manufactured formal shoes were assessed for its slip-resistant performance across three common floorings in dry- and water-contaminated conditions using a novel slip testing device. The results indicated that most of the selected low-cost footwear showed high slip risks across wet flooring conditions. Shoes with absence or worn-out treads at the heel region led to the availability of much lower friction at the shoe–floor interface. Shoes with soft outsoles, when worn, exhibited large worn areas and high slipping risks as compared to the ones with hard outsoles (R2= 0.92). The findings from this study are anticipated to lead to better understanding of the slip risks associated with low-cost formal footwear in India, as well as provide guidelines for their selection and timely replacement.

An aneurysm is a medical condition where a section of the artery bulges out under high pressure. Patients suffering from aneurysm rupture have a mortality rate of around 20% and a morbidity rate of up to 40%. The present imaging methods, such as MRI and CT scans, only offer geometrical information on the aneurysm and cannot predict the risk of rupture associated with its progression. To address this gap, a novel computational modeling framework was developed to describe aneurysm growth and analyze the rupture risk under varying pressure loading conditions. The aneurysms were modeled at the vulnerable posterior cerebral artery (PCA) and posterior communicating artery (PCoA), extracted using image segmentation. Five different aneurysm diameters and two wall thicknesses were considered to simulate different phases of aneurysm progression. The realistic pressure loadings on the posterior cerebral arteries were described using three pressures (diastolic, systolic, and hypertensive), and the stress distributions across all models were evaluated to estimate the rupture risk. For PCA, the value of max. von-Mises stress varied between 5.334 MPa and 13.324 MPa for different models with wall thickness of 0.05 mm and from 2.579 MPa to 7.582 MPa for 0.1 mm wall thickness models. For PCoA, the value of max. von-Mises stress ranged from 2.073 MPa to 11.383 MPa for artery-aneurysm models with 0.075 mm thickness and from 2.817 MPa to 10.779 MPa for artery-aneurysm models with 0.15 mm thickness. It was found that the stress values on the aneurysm walls significantly varies with change in blood pressure and aneurysm diameter. An aneurysm with a large diameter and thin wall was also observed to pose a significant risk of rupture, particularly at high blood pressures. These results are expected to provide valuable information to the medical practitioners and help in the prediction of rupture risks using image analysis of aneurysm size and in making timely treatment decisions.

Medical emergencies such as insulin-induced hypoglycaemia, anaphylactic reactions, seizures and opioid induced respiratory depression, need to be treated as quickly as possible in order to prevent untoward life-threatening circumstances. The currently available therapeutic interventions mainly involve administration of drugs through the parenteral route. However, the sophisticated administration related requirements of parenteral dosage forms, acts as a major concern for successful treatment of a medical emergency in non-clinical settings. The intranasal (IN) route, owing to its ease of administration, non-invasiveness, and quick onset of action, can be a potential alternative to the parenteral route in fulfilling the unmet needs of patients. Numerous attributes limit the nasal delivery of a drug, such as the nasal anatomy and physiology, physicochemical characteristics of the drug, and formulation related parameters. Despite the challenges involved in IN delivery, many breakthrough formulations introduced in to the market, have demonstrated equivalent biopharmaceutical performance to the parenteral route. This review critically discusses various IN marketed products which are currently in practice for treating medical emergencies in non-medical settings. In particular, the review discusses dosage form design and excipient roles in developing drug products to be used to attain rapid rise in plasma concentration for producing a fast onset of action similar to that of the parenteral intramuscular mode of drug administration.

Refractory tracheoesophageal fistula (TEF) are difficult to treat condition with high morbidity and mortality associated with surgical re- exploration. Various endoscopic modalities have offered minimally invasive therapeutic options. Tissue adhesive agents with ablation have achieved closure of TEF with varying success. We aimed to adopt a novel method by using modified polydioxanone (PDS) suture mesh with fibrin glue in a child who had previously failed surgical correction twice and various endoscopic modalities.

The 3rd Generation Partnership Project (3GPP) has implemented the Authentication and Key Agreement (AKA) protocol in 5G communication networks to ensure user equipment privacy. Due to the high density and concurrent communication, the primary goal is to provide an efficient authentication for massive enhanced Machine Type Communication (eMTC) devices. However, certain security flaws have been discovered in the 5G-AKA protocol, and no scheme has yet been developed that meets the needs of a group of eMTC devices, such as signaling congestion avoidance, key forward/backward secrecy (KFS/KBS) establishment, resistance against malicious attacks, and session key secrecy. Furthermore, the current group-based 5G communication network techniques do not require the group membership update mechanism at each device joining or leaving the group. To address these issues, we present a lightweight and secure technique for assembling a secure ecosystem of eMTC devices in a 5G network. The protocol employs the incremental hash mechanism to complete group member joining/leaving activities. For a thorough assessment of LS-AKA, the Random Oracle Model (ROM) is used to do formal security proofs, and informal security analysis shows that it is resistant to malicious attacks. In addition, the performance and simulation results of the existing and suggested protocols in terms of signaling, communication, and computing overhead are analysed. According to the assessment results, the LS-AKA protocol improves the privacy and confidentiality of the 5G-enabled smart environment.

The diabetic prevalent population is increasing day by day, which further increases the cases of diabetic ulceration, loss of sensation in the plantar region of the foot, and amputations in severe patients. These complications can be managed up to some extent by using modified shoes, offloading orthosis, etc. In recent advances, the use of additive manufacturing (i.e., 3D printing) to fabricate orthosis for diabetic patients is pioneering. The method of 3D printing of the foot orthosis for the diabetic foot management can be used along with the traditional methods. This review article will provide extensive literature on the pathophysiology of diabetes, the related complications and management techniques, and the use of additive manufacturing in the orthosis fabrication methods and its progress.

Inguinal hernia is a common surgical condition encountered of pediatric age group. Inguinal hernia may get incarcerated and if strangulated, it requires prompt intervention. Rarely incarcerated bowel can perforate leading to formation of entero-cutaneous fistula. Littre's hernia is the protrusion of a Meckel diverticulum through a potential abdominal opening.

The fundamental tenet of consumerism revolves around the belief that the burgeoning consumption of goods is favorable for the economy. Since the dawn of the Industrial Revolution, humanity has witnessed an exponential upsurge in consumerism. It has been related both to the increase in the population size as well as an increase in our demands due to constant changes in lifestyle. Multiple sources have corroborated the fact that if this consumption behavior continues unabated, we will soon face an acute shortage of resources of all kinds. Both consumer behavior patterns, such as addictive consumption and conspicuous consumption, can be attributed to this. Amongst the solutions available, ‘Demarketing’ is one. It is a type of marketing when a brand wants to discourage you from buying its product. The paper is descriptive in nature and is based on secondary data, which will be collected from journals, blogs, websites, magazines, books, etc. The paper intends to explore the theme of demarketing vis-à-vis the materialistic purchase behavior of a modern-day consumer and green demarketing strategies that companies are adopting by way of sustainable marketing.

Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii) interpret the discovered communities by identifying the relative importance of different covariates in them. Recent methods have almost exclusively focused on the first point above. However, the quantitative improvements offered by them are often due to complex black-box models like deep neural networks at the expense of interpretability. Approaches that focus on the second point are either domain specific or have poor performance in practice. This article proposes interpretable, domain-independent statistical models for networks with node covariates that additionally offer good quantitative performance. The proposed models equip Stochastic Block Models with Restricted Boltzmann Machines to provide interpretable insights about the communities and they support both pure and mixed community memberships. Besides providing interpretability, our approach’s main strength is that it does not explicitly assume a causal direction between community memberships and node covariates, making it applicable in diverse domains. We derive efficient inference procedures for our models, which can, in some cases, run in linear time in the number of nodes and edges. Experiments on several synthetic and real-world networks demonstrate that our models achieve close to state-of-the-art performance on community detection and link prediction tasks while also providing interpretations for the discovered communities.

Urinary incontinence after treatment for prostate cancer is common, and the burden of disease is substantial with an increasing number of prostate cancer survivors. We aim to review recent advances in this field with a focus on therapeutic surgical interventions and their outcomes.

The stock market is complex in nature and it is very difficult to predict. Investors have many factors that affect the stock values. The stock market plays an important role in the financial aspect of the country’s growth. The demand to predict stock values is very high, hence is the need for stock market analysis. This article is basically focused on always taking risks to invest his money in the stock market to gain profit. There are various machine-learning techniques available to predict the stock market. There are on predicting the stock market values. In the current scenario, the stock market forecasting is done using machine learning and artificial intelligence which makes the prediction process easier and based on the values of the current stock rate by training on the previous values. Basically, stock price prediction is based on time series data means every new data are dependent or based on previous data value. The dataset used for this is Dell daily stock for the period 17 Aug 2016–21 May 2021 which was used in this article. There are different kinds of models that can help in predicting the stock market. A simple machine-learning model cannot be applied to time series data, that is why we studied many models such as LSTM and ARIMA model, which are best for time series data. In addition, at the end, we saw that ARIMA is one of the best models for predicting the stock market values for short time series. This model is based on previous values. This model gives the more accurate and best results as compared to another one.

Blow Molding is one of the most versatile and economical process available for molding hollow materials. When polyethylene is stretched, it exhibits strain-hardening properties, which are temperature, pressure, velocity and strain-rate dependent. In this paper, preform is made by extrusion and forced between two halves by pressurisation. This process includes isothermal and transient flow of newtonian fluid in complex geometries simultaneous with structuring and solidification. A time dependent problem is defined and setting material properties and boundaries condition for a 2D axisymmetric bottle blow molding. Numerical data available in POLYDATA for a time–dependent problem using ANSYS POLYFLOW were applied.Results display in form contours associated with different variables at different time steps and good agreement with the bottle thickness profile is observed.