Constitutive laws for unidirectional composite materials

Failure predictions for a fibre-reinforced composite with unidirectional (UD) plies can only be relied upon provided the stress state is accurately known. This requires a prediction of the constitutive response to be made when the material is loaded. When failure does occur, matrix cracking is frequently the first mode of failure. Cracking results in a reduction of the material properties of the structure and can lead to other forms of damage. In this context, an elasto-plastic constitutive model that can accurately represent the full non-linear mechanical response of UD composites is developed, as well as the implementation of an improved model for matrix cracking. Unlike many existing constitutive models in the literature, the developed model captures some key features that are often neglected in constitutive modelling. These include the effect of hydrostatic pressure on both the elastic and non-elastic response. A novel yield function is formulated specifically for polymer-matrix fibre-reinforced composites, taking into account the presence of fibres in the material. The developed model is able to predict the non-linear response under complex loading combinations, given only the experimental response from two uniaxial tests. A non-associative flow rule is used to capture the pressure sensitivity of the material. The translation of subsequent yield surfaces under complex loading regimes is modelled by the inclusion of a non-linear kinematic hardening rule, which also allows for simulation of material unloading. The implementation of the model as a user defined material subroutine in a commercial finite element package is described. Regarding the modelling of matrix cracking, several methods are available in the literature. These models are reviewed and an existing model is combined with suitable failure criteria for the simulation of stiffness loss and crack accumulation in laminates. This model is then used to make predictions of crack accumulation and loss in stiffness of composite materials

PREVALENCE AND DRUG RESISTANCE IN ACINETOBACTER SP. ISOLATED FROM INTENSIVE CARE UNITS PATIENTS IN PUNJAB, INDIA

Objective: This study was designed to study the prevalence and antibiotic susceptibility patterns of Acinetobacter sp. as isolated from patients lodged in intensive care units (ICUs) of a tertiary care hospital, Ludhiana, Punjab, India.Methods: The clinical samples were simultaneously streaked on Blood agar and MacConkey agar. The identification of the bacterial isolates was carried out with the aid of Gram stain, motility test and along with a combination of other commonly employed biochemical tests. The antimicrobial susceptibility testing (AST) of all the bacterial isolates was carried out on Muller-Hinton agar through Kirby-Bauer disc diffusion method.Results: Acinetobacter sp. formed a fair allowance contributing at 42% among all ICU culture positive samples. The respiratory tract samples had a major share at 63.15% for all samples attributed to be positive for Acinetobacter sp. nosocomial etiology. The antibiotic sensitivity pattern portrayed that more than 95% of Acinetobacter sp. isolates were multiple drug resistant (MDR) whereas >50% Acinetobacter sp. showed extensive drug resistant (XDR). The last resort for such Acinetobacter sp. nosocomial infections is left to colistin and polymyxin B.Conclusion: Acinetobacter sp. is a highly prevalent microorganism among ICU patients of Ludhiana, Punjab, India, while its potential to acquire resistance toward commonly used antibiotics represents it as a grave threat to the health-care industry, therefore signifying the need for its regular monitoring in the health-care setups

Edge Computing and AI: Advancements in Industry 5.0- An Experimental Assessment

This empirical research evaluated, via experimentation, how Edge Computing and Artificial Intelligence (AI) work together in the context of Industry 5.0. With a high satisfaction rating of 88%, participants in the Edge Computing condition saw an astonishing 18% decrease in task completion times. Similarly, in the AI integration scenario, participants rated AI's value at 86%, and they saw a significant 12% reduction in task completion times and a noteworthy 7% drop in mistake rates. Significantly, with an astounding 21% gain in work completion times, the Edge Computing and AI combo had the largest performance boost. These results highlight how Edge Computing and AI may dramatically improve industrial efficiency and performance in the context of Industry 5.0, providing insightful information for businesses looking to use these technologies to streamline processes and spur innovation

Hypoxia-inducible factor (HIF): fuel for cancer progression

Hypoxia is an integral part of the tumor microenvironment, caused primarily due to rapidly multiplying tumor cells and a lack of proper blood supply. Among the major hypoxic pathways, HIF-1 transcription factor activation is one of the widely investigated pathways in the hypoxic tumor microenvironment (TME). HIF-1 is known to activate several adaptive reactions in response to oxygen deficiency in tumor cells. HIF-1 has two subunits, HIF-1β (constitutive) and HIF-1α (inducible). The HIF-1α expression is largely regulated via various cytokines (through PI3K-ACT-mTOR signals), which involves the cascading of several growth factors and oncogenic cascades. These events lead to the loss of cellular tumor suppressant activity through changes in the level of oxygen via oxygen-dependent and oxygenindependent pathways. The significant and crucial role of HIF in cancer progression and its underlying mechanisms have gained much attention lately among the translational researchers in the fields of cancer and biological sciences, which have enabled them to correlate these mechanisms with various other disease modalities. In the present review, we have summarized the key findings related to the role of HIF in the progression of tumors

A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California

textTransportation sector has been a major contributing factor to the overall emissions of most pollutants and thus their impacts on the environment. Among all transportation activities, on-road travel accounts for most part of the Greenhouse gas (GHG) emissions and fuel use. It also has a very un-desirable impact on the transportation network conditions increasing the traffic congestion levels. The main aim of transportation planning agencies is to implement the policy changes that will reduce automobile dependency and increase transit and non-motorized modes usage. However, planning agencies can come up with proactive economic, land-use and transportation policies provided they have a model which is sensitive to all the above mentioned factors to predict the vehicle fleet composition and usage of households. Moreover, the type of vehicle that a household gets (vehicle type choice) and the annual mileage (usage) associated with that vehicle is very closely related to the person in the household who uses that vehicle the most (allocation to primary driver). So, it is no longer possible to view all these decisions separately. Instead, we need to model all these decisions- vehicle type choice, usage, and allocation to primary driver simultaneously at a household level. In this study, we estimate and apply a joint household-level model of the number of vehicles owned by the household, the vehicle type choice of each vehicle, the annual mileage on each vehicle, as well as the individual assigned as the primary driver for each vehicle. A version of the proposed model system currently serves as the engine for a household vehicle composition and evolution simulator, which itself has been embedded within the larger SimAGENT (for Simulator of Activities, Greenhouse emissions, Networks, and Travel) activity-based travel and emissions forecasting system for the Southern California Association of Governments (SCAG) planning region.Civil, Architectural, and Environmental Engineerin

RUemo—The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning

The beginning of this decade brought utter international chaos with the COVID-19 pandemic and the Russia-Ukraine war (RUW). The ongoing war has been building pressure across the globe. People have been showcasing their opinions through different communication media, of which social media is the prime source. Consequently, it is important to analyze people’s emotions toward the RUW. This paper therefore aims to provide the framework for automatically classifying the distinct societal emotions on Twitter, utilizing the amalgamation of Emotion Robustly Optimized Bidirectional Encoder Representations from the Transformers Pre-training Approach (Emoroberta) and machine-learning (ML) techniques. This combination shows the originality of our proposed framework, i.e., Russia-Ukraine War emotions (RUemo), in the context of the RUW. We have utilized the Twitter dataset related to the RUW available on Kaggle.com. The RUemo framework can extract the 27 distinct emotions of Twitter users that are further classified by ML techniques. We have achieved 95% of testing accuracy for multilayer perceptron and logistic regression ML techniques for the multiclass emotion classification task. Our key finding indicates that:First, 81% of Twitter users in the survey show a neutral position toward RUW; second, there is evidence of social bots posting RUW-related tweets; third, other than Russia and Ukraine, users mentioned countries such as Slovakia and the USA; and fourth, the Twitter accounts of the Ukraine President and the US President are also mentioned by Twitter users. Overall, the majority of tweets describe the RUW in key terms related more to Ukraine than to Russia

Constitutive laws for unidirectional composite materials

Failure predictions for a fibre-reinforced composite with unidirectional (UD) plies can only be relied upon provided the stress state is accurately known. This requires a prediction of the constitutive response to be made when the material is loaded. When failure does occur, matrix cracking is frequently the first mode of failure. Cracking results in a reduction of the material properties of the structure and can lead to other forms of damage. In this context, an elasto-plastic constitutive model that can accurately represent the full non-linear mechanical response of UD composites is developed, as well as the implementation of an improved model for matrix cracking. Unlike many existing constitutive models in the literature, the developed model captures some key features that are often neglected in constitutive modelling. These include the effect of hydrostatic pressure on both the elastic and non-elastic response. A novel yield function is formulated specifically for polymer-matrix fibre-reinforced composites, taking into account the presence of fibres in the material. The developed model is able to predict the non-linear response under complex loading combinations, given only the experimental response from two uniaxial tests. A non-associative flow rule is used to capture the pressure sensitivity of the material. The translation of subsequent yield surfaces under complex loading regimes is modelled by the inclusion of a non-linear kinematic hardening rule, which also allows for simulation of material unloading. The implementation of the model as a user defined material subroutine in a commercial finite element package is described. Regarding the modelling of matrix cracking, several methods are available in the literature. These models are reviewed and an existing model is combined with suitable failure criteria for the simulation of stiffness loss and crack accumulation in laminates. This model is then used to make predictions of crack accumulation and loss in stiffness of composite materials.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Himachal Floods Crisis due to Ignoring Fragile Ecosystems

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