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This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

In this study, we introduce and investigate a new class of split inverse problems, comprising a multidimensional parameter of evolution, which we call the multidimensional split variational inequality problem with multiple output sets. To demonstrate its applicability, we formulate the equilibrium flow of multidimensional traffic network models for an arbitrary number of locations. We define a multidimensional split Wardrop condition with multiple output sets and establish its equivalence with the formulated equilibrium flow of multidimensional traffic network models. We then establish the existence and uniqueness of equilibria for our proposed model. In addition, we propose a method for solving the introduced problem. We then validate our results using some numerical experiments. [ABSTRACT FROM AUTHOR]

Our body is comprised of trillions of bacteria, virus, fungi etc. The term "microbiome" refers to them all together. In contrast to human health, some of the bacteria are linked to illnesses that are critical to the immune system, heart, and weight. In fact, most of the microbes in our intestines are found in a pocket of our large intestine called the caecum. They are referred to as a gut microbiome. In fact, there are more bacterial cells in our body that are human cells. Thereare roughly 40 trillion of bacterial cells in our body while the number of human cells are nearly 30 trillion. The gut microbiome refers to all of the microbes present in our intestine, which acts as another organ to live with microbes with millions of years. In the present review the the composition of gut microbiome and their function towards human health is discussed. Microbes have played a very important role in human body. From birth and throughout life, the gut microbiota influences the body by regulating the immune system, central nervous system, and other physiological processes as well as food digestion. The gut microbiota and its benefits for human health are the main topics of this review. [ABSTRACT FROM AUTHOR]

Microorganisms create biofilms, which are surface adherent community structures. These biofilms are essential to the infection process mediated by microbes. Antibiotic resistance is another thing that biofilm spreads, which is a big worry these days. Diverse bacteria use diverse mechanisms to create biofilms, and these mechanisms often depend on the environment in which they grow as well as strainspecific characteristics. Many chemical compounds are discovered to be useful in investigating the biofilm management method. The usefulness of nanoparticles in preventing biofilm-mediated disease is the subject of the current review. Using nanoscale particles to fight microbial biofilm is one possible way to treat these persistent diseases. Recently, antibacterial agents have been delivered employing innovative nanotechnology-based antimicrobial activity in order to destroy planktonic bacteria and their biofilm structures. In the sphere of medicine, this technique is now considered developing. Antimicrobial-loaded nanoparticles alone or in combination with other materials could increase the bacterial activity of nanomaterials to prevent the formation of biofilms. These particles are reactive substances that readily penetrate the matrix, serving as a barrier to numerous antibodies. One type of nanoparticle, called AgNPs, exhibited antibacterial action by rupturing the integrity of the bacterial cell membrane, which resulted in the release of cellular content and eventual death. Additionally, polymeric-based formulations like hydrogel, polymeric microspheres, nanospheres, and smart olimer, as well as lipid-based nanoparticles like liposomes and solid lipid nanoparticles, have been used in the biofilm treatment. Additionally, research is ongoing with various metals like copper, zinc, and their oxides. Here, we talked about the safety issues and the promise of metal oxide nanoparticles. The pathogens are effectively killed by NPs without endangering other cells or having any negative effects on living cells. [ABSTRACT FROM AUTHOR]

There are a large number of time domain, frequency domain and time-frequency signal processing methods available for univariate feature extraction. However, there is no consensus in SHM on which feature, or feature sets, are best suited for the identification, localisation and prognosis of damage. This paper attempts to address this problem by providing a comprehensive benchmark of feature selection & reduction methods applied to an extensive set of univariate features. These univariate features are extracted using multiple statistical, temporal and spectral methods from the benchmark S101 and Z24 bridge datasets. These datasets contain labelled accelerometer recordings from full scale bridges as they are progressively subjected to multiple damage scenarios. To identify the minimal set of features that best distinguishes between the multiple damage states, a supervised machine learning approach is used in combination with multiple feature selection methods. The ability of these reduced feature sets to distinguish between damage states is benchmarked using the prediction performance of the classification models, with the training and test sets obtained through stratified k-fold cross validation. The results obtained show that reduced sets of univariate features, extracted from a single accelerometer sensor, are capable of accurately distinguishing between multiple classes of healthy and damaged states. This work provides a benchmark for SHM practitioners and researchers alike for the choice, comparison and validation of feature extraction and feature selection methods across a wide range of systems. [ABSTRACT FROM AUTHOR]

Peatlands store up to 2320 Mt of carbon (C) on only ~20% of the land area in Ireland; however, approximately 90% of this area has been drained and is emitting up to 10 Mt C per year. Gross primary productivity (GPP) is a one of the key components of the peatland carbon cycle, and detailed knowledge of the spatial and temporal extent of GPP under changing management practices is imperative to improve our predictions of peatland ecology and biogeochemistry. This research assesses the relationship between remote sensing and ground-based estimates of GPP for a near-natural peatland in Ireland using eddy covariance (EC) techniques and high-resolution Sen-tinel 2A satellite imagery. Hybrid models were developed using multiple linear regression along with six widely used conventional indices and a light use efficiency model. Estimates of GPP using NDVI, EVI, and NDWI2 hybrid models performed well using literature-based light use efficiency parameters and showed a significant correlation from 89 to 96% with EC-derived GPP. This study also reports additional site-specific light use efficiency parameters for dry and hydrologically normal years on the basis of light response curve methods (LRC). Overall, this research has demonstrated the potential of combining EC techniques with satellite-derived models to better understand and monitor key drivers and patterns of GPP for raised bog ecosystems under different climate scenarios and has also provided light use efficiency parameters values for dry and wetter conditions that can be used for the estimation of GPP using LUE models across various site and scales. [ABSTRACT FROM AUTHOR]

Background: The cornerstone of management of phyllodes tumor is surgery. No standard of care exists regarding adjuvant therapy; however, local recurrence is a predominant pattern of failure. The aims of this study were to evaluate the clinical characteristics and treatment patterns of phyllodes tumor and to compare local recurrence and disease-free survival rates of patients with borderline and malignant phyllodes tumor treated with or without adjuvant radiotherapy. Materials and Methods: We analyzed the demographic data, treatment details, and recurrence patterns of all patients with nonmetastatic phyllodes tumors of the breast (n = 34) treated with a multimodality approach who presented to our institute from January 2015 to December 2020. Results: The median age at presentation was 41.2 years, and the median tumor size was 12.5 cm. All patients underwent definitive surgical procedures in the form of wide local excision or mastectomy. No recurrence was noted in the patients with a benign histology. After a median follow-up period of 38 months, the local recurrence rate was 27% for the patients with borderline and malignant histology treated with adjuvant radiotherapy (n = 11) versus 47% for those (n = 17) who did not receive adjuvant radiotherapy. The 3-year local recurrence-free survival rate was 72% in the adjuvant radiotherapy group, versus 51% in the surgery only group. Conclusion: The results of the current study confirm the excellent prognosis of patients with benign phyllodes tumor undergoing surgery alone. Local recurrence was the predominant mode of failure in the patients with borderline and malignant histology. Locoregional control was improved with the addition of postoperative radiotherapy in the patients with borderline and malignant histology irrespective of margin status. [ABSTRACT FROM AUTHOR]

Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector. [ABSTRACT FROM AUTHOR]

The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model--cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. DOI: 10.1061/(ASCE)0733-947X(2009)135:9(658) CE Database subject headings: Traffic flow; Traffic models; Seasonal variations; Forecasting; Intersections; Data collections.

Methane (CH4), a potent greenhouse gas, traps heat in the atmosphere and significantly contributes to global warming. Atmospheric CH4 comes from various natural and anthropogenic sources. CH4 emissions from the decomposition of organic material by bacteria in natural wetlands, other land types, agriculture, and waste management constitute the major component of global emissions. Although there is no clear evidence that CH4 emissions from wetlands and other natural sources have increased substantially in the last decade, uncertainties remain regarding sources and their spatial extent causing discrepancies between emission estimates from inventories/models and estimates inferred by an ensemble of atmospheric inversions. Here we show that satellite-based CH4 total column measurements along with surface albedo from Sentinel-5 Precursor (S-5p) show unique sensitivity to certain land types. Consequently, the areal extent of six land types (marsh, swamp, forest, grassland, cropland, and barren-land) could be identified with high overall accuracy by analysing S-5p data over Canada utilising our classification-segmentation algorithm. Monthly and yearly inventory maps were created, which can be used to validate or complement global models where data from other sources are missing and may help in further constraining the methane budget. [ABSTRACT FROM AUTHOR]

The seasonal autoregressive integrated moving average (SARIMA) model is one of the popular univariate time-series models in the field of short-term traffic flow forecasting. The parameters of the SARIMA model are commonly estimated using classical (maximum likelihood estimate and/or least-squares estimate) methods. In this paper, instead of using classical inference the Bayesian method is employed to estimate the parameters of the SARIMA model considered for modeling. In Bayesian analysis the Markov chain Monte Carlo method is used to solve the posterior integration problem in high dimension. Each of the estimated parameters from the Bayesian method has a probability density function conditional to the observed traffic volumes. The forecasts from the Bayesian model can better match the traffic behavior of extreme peaks and rapid fluctuation. Similar to the estimated parameters, each forecast has a probability density curve with the maximum probable value as the point forecast. Individual probability density curves provide a time-varying prediction interval unlike the constant prediction interval from the classical inference. The time-series data used for fitting the SARIMA model are obtained from a certain junction in the city center of Dublin. DOI: 10.1061(ASCE)0733-947X(2007)133:3(180) CE Database subject headings: Traffic flow; Predictions; Auto-regressive moving-average model; Bayesian analysis; Forecasting.

Recently, the structural information about non-coking and coking coal has attracted pronounced interest for sustainable utilization of poor coking coal in different industries specifically for steel industry like Tata Steel. In the present work, demineralization techniques via acid treatment (HCl-HF) are effective to remove mineral phase from coal. The difference in carbon chemistry of non-coking (CDPCI), weakly coking (Curragh SS), and prime coking coals (Illawarra and Jamadoba) has been studying through advanced characterization methods like petrography, TGA-DTG, FTIR spectroscopy, X-ray diffraction, Raman, 13C solid-state NMR, FESEM, and TEM. These advanced characterization techniques offer comparative structural information of different coal through the distribution of individual coal maceral and mineral grains with their rank (petrographic study), functional group study along aliphatic-aromatic structural relationship measurement (FTIR), crystalline/graphitic nature of carbon (XRD & Raman) with oxygenated carbon skeletal structural information (Solid State 13C NMR), aromatic-aliphatic cross-linking behavior (FESEM) and their orientation toward lattice fringe structure (TEM). [ABSTRACT FROM AUTHOR]

Purpose: We describe the development and the psychometric properties of an instrument to assess self-perceived EOL care competencies for healthcare professionals: The End-of-Life Care Questionnaire (EOL-Q). Methods: The EOL-Q consists of 28 questions assessing knowledge, attitudes and behaviors with subscale items addressing seven domains of care: decision-making, communication, continuity of care, emotional support for patients/families, symptom management, spiritual support for patients/families, and support for clinicians. The EOL-Q was used to assess competencies of 1,197 healthcare professionals from multiple work units at a large medical center. Cronbach's alpha coefficients were calculated for the survey and subscales. A factor analysis was also conducted. Results: Internal consistency reliability was for was high for the total scale (0.93) and for the subscales addressing knowledge, behaviors, decision-making, communication, emotional support and symptom management (0.84-0.92); and moderate (>0.68) for the attitudes and continuity of care subscales. The factor analysis demonstrated robust consolidation of the communication and continuity of care subscales (eigenvalue 9.47), decision-making subscale (eigenvalue 3.38), symptom management subscale (eigenvalue 1.51), and emotional and spiritual support subscales (eigenvalue 1.13). Conclusion: Analysis of the psychometric properties of the EOL-Q care across settings supports its reliability and validity as a measure of self-perceived EOL care competencies in the domains of communication and continuity of care, decision-making, symptom management, and emotional and spiritual support. The EOL-Q displays promise as a tool for use in a variety of educational, research, and program development initiatives in EOL care. [ABSTRACT FROM AUTHOR]

Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge – before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state. [ABSTRACT FROM AUTHOR]

• As the largest, most consistent provider of care, nurses play a pivotal role in improving care and patient outcomes at the frontline. • The Nursing Quality Improvement in Practice tool can be used to determine current levels of QI competence (e.g. knowledge, skills, and attitudes) and engagement of nurses in QI. • Strategies for increasing QI competence and engagement of nurses must be created and deployed in order to improve quality and safety. Nurses play a pivotal role in improving patient care. To maximize nurses' impact on quality, nurses must have quality improvement (QI) competence and engage fully in QI initiatives. To describe QI competence (knowledge, skills, and attitudes) among frontline nurses and leaders; and compare variations in competence among nursing roles, experience, and specialty areas. A total of 681 nurses at one heath system fully completed the Nursing Quality Improvement Practice tool electronically. Half of the respondents reported QI engagement (53.6%). Mean knowledge scores were 5.08 (SD 1.16, 7 items). Skill proficiency was low (M = 2.82, SD = 1.03; range 1–6) although QI attitudes were favorable (M = 3.76, SD = 0.63; range 1–5). Significant differences in skills and attitudes were identified by role. QI competence among nurses employed in various specialty areas were similar. Strategies for increasing QI competence and engagement of nurses must be created and deployed in order to improve quality and safety. [ABSTRACT FROM AUTHOR]

A 28‐year hydrological record on four intermittent wetlands (turloughs) in a hydraulically linked karst area in the west of Ireland was used to assess ecohydrological metrics for different vegetation communities. A methodology using a combination of continuous water level monitoring and high resolution topographic surveying was used to develop a detailed hydrological model of the karst network, from which water levels at any point within the turloughs can be defined at any time during the 28‐year period (1989 to 2017). The flood conditions experienced across the spatial distributions for different vegetation communities (as mapped by a field survey) have then been collated and presented as statistical distributions for flood duration, flood depth, flood frequency and mean temperature/global radiation at the time of year in spring when the flood waters start to recede. Analysis of these four turloughs has revealed distinct differences between vegetation communities, from Eleocharis acicularis communities at the turlough base typically experiencing 6 to 7 months of inundation per year compared to the limestone pavement community at the top fringes of the turloughs only flooded from 1 to 2 months per year. An approach that used Sentinel‐2 satellite data to provide an assessment of whether there have been changes in the spatial distribution of the communities is also presented. Such metrics can be evaluated alongside other variables such as water quality (particularly nutrients), soil type and land‐use, in order to understand the habitat requirements for such plant communities and their associated ecological systems. [ABSTRACT FROM AUTHOR]