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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]

This article reports the fluorometric detection of toxic hexavalent chromium Cr (VI)) in wastewater and Cr (VI) contaminated living cells using in-situ grown carbon quantum dots into the goethite (α-FeOOH) nano-matrix. The synthesized nano-hybrid shows enormous potential in determining the chromium contamination levels in various types of water samples. This selective fluorometric probe is enormously sensitive (LOD 81 nM) toward hexavalent chromium, which makes it a dedicated chromium sensor. Moreover, the sensing mechanism has been assessed using Stern–Volmer's equation and fluorescence lifetime experiments showing the simultaneous occurrence of photoinduced electron transfer and the inner filter effect. This chromium sensor has also been employed to assess the contamination level in real-life industrial wastewater. The performance of this probe in a real-life wastewater sample is quite commendable. Further, this biocompatible fluorometric probe has been used to demonstrate the in-vitro sensing of Cr (VI) in HeLa cells. The rapid detection mechanism of hexavalent chromium in living cells has been validated using theoretical docking simulations. Henceforth, this fluorometric sensor material could open new avenues not only in wastewater monitoring but also in biomedical applications. [ABSTRACT FROM AUTHOR]

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]

Background: As utilization of individual antenatal care (I-ANC) has increased throughout sub-Saharan Africa, questions have arisen about whether individual versus group-based care might yield better outcomes. We implemented a trial of group-based antenatal care (G-ANC) to determine its impact on birth preparedness and complication readiness (BPCR) among pregnant women in Ghana. Methods: We conducted a cluster randomized controlled trial comparing G-ANC to routine antenatal care in 14 health facilities in the Eastern Region of Ghana. We recruited women in their first trimester to participate in eight two-hour interactive group sessions throughout their pregnancies. Meetings were facilitated by midwives trained in G-ANC methods, and clinical assessments were conducted in addition to group discussions and activities. Data were collected at five timepoints, and results are presented comparing baseline (T0) to 34 weeks' gestation to 3 weeks post-delivery (T1) for danger sign recognition, an 11-point additive scale of BPCR, as well as individual items comprising the scale. Results: 1285 participants completed T0 and T1 assessments (N = 668 I-ANC, N = 617, G-ANC). At T1, G-ANC participants were able to identify significantly more pregnancy danger signs than I-ANC participants (mean increase from 1.8 to 3.4 in G-ANC vs. 1.7 to 2.2 in I-ANC, p < 0.0001). Overall BPCR scores were significantly greater in the G-ANC group than the I-ANC group. The elements of BPCR that showed the greatest increases included arranging for emergency transport (I-ANC increased from 1.5 to 11.5% vs. G-ANC increasing from 2 to 41% (p < 0.0001)) and saving money for transportation (19–32% in the I-ANC group vs. 19–73% in the G-ANC group (p < 0.0001)). Identifying someone to accompany the woman to the facility rose from 1 to 3% in the I-ANC group vs. 2–20% in the G-ANC group (p < 0.001). Conclusions: G-ANC significantly increased BPCR among women in rural Eastern Region of Ghana when compared to routine antenatal care. Given the success of this intervention, future efforts that prioritize the implementation of G-ANC are warranted. Trial registration: ClinicalTrials.gov Identifier: NCT04033003 (25/07/2019). Protocol available: Protocol Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508671/. [ABSTRACT FROM AUTHOR]

Hotel housekeepers have challenging working conditions, putting them at risk for poor health such as hypertension. Despite their risks, few studies have measured their blood pressure (BP). The purpose of this study was to explore hotel housekeepers' blood pressure and the associations between work and BP. Methods: A community engagement approach was used to recruit study participants. Data sources included questionnaires, and BP measurement. Results: Over 25% of the 39 hotel housekeepers reported hypertension diagnosis and/or antihypertensive medication used. Across the job domains, job satisfaction was correlated with diastolic BP, and workload was correlated with systolic BP. There were difference in blood pressure reading, diagnosis and job domains between workers affiliated with union and those with no union affiliation. Discussion: Hypertension is a major concern among this worker group and warrants further investigation. Studies targeting union and nonunion workers are needed, given their differences. Researchers will likely benefit from a community engagement approach with hotel housekeepers. [ABSTRACT FROM AUTHOR]

Lathyrus sativus L. (Grass pea) is the source for cheap and nutritious food choice in drought and famine susceptible zones in greater part of North India and Africa. The non-protein amino acid β-N-oxalyl-L-α,β-diaminopropionic acid (β-ODAP) has been known for decades for its potent neurotoxic effect, causing irreversible neurodegenerative disease “neurolathyrism”, present in both seed and leaf of Lathyrus sativus L. and other species in varying proportions. It is crucial to establish a rapid as well as reliable detection methodology for β-ODAP content in various Lathyrus plants. Currently available HPLC based methods involve multi-step derivatization of the sample. To overcome this, we have developed β-ODAP analysis method by HPLC without any prior derivatization. This method is statistically significant in the range of 2 to 100μg/ml and exhibited linear response with r2 > 0.99. Limit of detection and quantitation of the later method was determined to be 5.56 μg/ml and 16.86 μg/ml, respectively. In addition to this, a TLC based method has also been developed. The limit of detection of β-ODAP is 0.6μg and for its substrate, L-1,2-diaminopropionic acid is 5μg. Both HPLC and TLC methods were validated by conducting in-vitro bioconversion test to detect the presence of biocatalyst in plant extract. This method is economical, rapid and simple. [ABSTRACT FROM AUTHOR]

In recent years, cycling has been recognised by policy makers and practitioners as a sustainable alternative to motorised travel. However, a lack of perceived safety associated with cycling may have inhibited its growth as a popular mode of transportation in urban environments. A questionnaire-based survey, conducted in Dublin city, found cyclists to perceive cycling in the city as less safe than driving. The survey responses were analysed in an ordered logistic regression framework to identify the determinants of their perceived safety in order to recommend possible policy interventions for improvement of these perceptions. A cyclist safety index is also proposed, which allows the perceptions of cyclists relative to driving to be quantified for consideration in transport network design and adaptation for cyclists. [ABSTRACT FROM AUTHOR]

Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique. [ABSTRACT FROM AUTHOR]

Non-recurrent congestion or incidents are detrimental to the operability and efficiency of busy urban transport networks. There exist multiple automatic incident detection algorithms (AIDAs) to remotely detect the occurrence of an incident in highway or freeway scenarios; however, very little research has been performed to automatically detect incidents in signalized urban arterials. This limited research attention has mostly been focused on developing new urban arterial specific algorithms, rather than identifying alternative methods to synthesize existing freeway-based algorithms for urban conditions. The main hindrance to such synthesis is that the traffic patterns on the signalized urban arterials are significantly different from the same on highways/freeways due to the presence of traffic intersections. This article introduces a new strategy of customizing the existing AIDAs (freeway based or otherwise) to significantly improve their adaptability to signalized urban arterial transport networks. The new strategy focuses on preprocessing the traffic information before being used as input to a freeway/highway-based AIDA to lessen the effect of traffic signals and to imitate the input patterns in highway/freeway-based incident conditions. The effectiveness of this new strategy has been established with the help of four existing AIDAs. The proposed strategy is a simple solution to implement existing algorithms to signalized urban networks without any further instrumentation or operational cost. [ABSTRACT FROM AUTHOR]

Short-term traffic-flow forecasting, the process of predicting future traffic conditions based on historical and real-time observations, is an essential aspect of intelligent transportation systems. The existing well-known algorithms used for short-term traffic flow include time-series analysis-based techniques, of which the seasonal auto-regressive moving average model is one of the most precise methods used in this field. The effectiveness of short-term traffic flow in an urban transport network can be fully realised only in its multivariate form where traffic flow is predicted at multiple sites simultaneously. In this paper, this concept in explored utilising an additive seasonal vector auto-regressive moving average model to predict traffic flow in the short-term future considering the spatial dependency among multiple sites. The dynamic linear model representation of the auto-regressive moving average model is used to reduce the number of latent variables. The parameters of the model are estimated in a Bayesian inference framework employing a Markov chain Monte Carlo sampling method. The efficiency of the proposed prediction algorithm is evaluated by modelling real-time traffic-flow observations available from a certain junction in the city centre of Dublin, Ireland. [ABSTRACT FROM AUTHOR]

In this study, the market potential of car sharing has been evaluated using multiple alternative scenarios which examine the geographic, financial and environmental factors influencing car sharing adoption. The scenarios are applied to the available and collected travel information of the Irish population to estimate the potential impact of introducing car sharing in Ireland. The analysis identified that car owners who travel predominantly on alternative modes, could make significant cost and CO 2 savings through car sharing. A reduction of yearly CO 2 emissions of 86 kt is readily achievable through car sharing, with reductions up to 895 kt possible with appropriate policy and financial support. These figures are comparable to other measures proposed under the Irish National Climate Change Strategy. [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]

Predictions of fundamental traffic variables in the short-term or near-term future are vital for any successful dynamic traffic management application. Univariate short-term traffic flow prediction algorithms are popular in literature. However, to facilitate the operationalities of advanced adaptive traffic management systems, there is a necessity of developing multivariate traffic condition prediction algorithms. A new multivariate short-term traffic flow and speed prediction methodology is proposed in this paper where the traffic flow and speed observations from uncongested (or linear) and congested (or nonlinear) regimes are regime-adjusted to ensure consistent system dynamics. The prediction methodology is developed by using artificial neural networks (ANN) algorithms in conjunction with adaptive learning rules. These learning rules demonstrate significantly improved accuracy and simultaneous reduction in computation times. Additionally, the paper attempts to identify the most suitable adaptive learning rule from a chosen pool of rules. The validation of the prediction methodology is performed by using traffic data from multiple locations in the United Kingdom (U.K.). The results indicate that the proposed multivariate forecasting algorithm is effective and computationally parsimonious to simultaneously predict traffic flow and speed in freeway or highway networks. [ABSTRACT FROM AUTHOR]

Abstract: Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available. [Copyright &y& Elsevier]

The existing well-known short-term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily 'trend' of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time-of-the-day. A case study has been performed at two busy junctions at the city-centre of Dublin to validate the effectiveness of the strategy. [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. [ABSTRACT FROM AUTHOR]

Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100–500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring. [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. [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]

We report a case of biphasic synovial sarcoma of the mediastinum, a very rare tumor, in a 12-year-old boy with left-sided chest pain of 3 years duration at presentation. Chest X-ray showed left-sided opacity with loss of cardiac silhouette and the mediastinum deviated to the opposite side. Computed tomography (CT) of thorax showed left-sided posterior mediastinal mass with left-sided pleural effusion and pleural thickening. CT guided fine needle aspiration cytology (FNAC) from the mass reported it as spindle cell variant of adenocarcinoma. Ultrasonography (USG) of the whole abdomen revealed no abnormality. The mediastinal tumor was resected by left thoracotomy and histopathological report confirmed it to be a biphasic synovial sarcoma with capsule invasion at places. [ABSTRACT FROM AUTHOR]

Conjunctival melanoma is a rare neoplasm of the eye. It constitutes 2% of all ocular malignancy. Most of the ocular melanoma arises from uveal tissue and primary melanoma arising from conjunctiva is rare. Its incidence is 0.2-0.8 per million in the white population. It is identified most frequently in the perilimbal interpalpebral bulbar conjunctiva. It can arise, de novo (12%), from an existing nevus (20% to 30%), or, more frequently, from an area of primary acquired melanosis (75%). Here we report a case of conjunctival melanoma in a 27 year old male patient presented with increase in the size of a black spot in conjunctiva of left eye which was excised by cryotherapy. The histology revealed conjunctival melanoma with free margin and the patient was put on follow-up. After a period of twenty-one months, patient developed metastasis to the preauricular lymph node which was histologically confirmed. He was treated with total parotidectomy and supra-omohyoid neck dissection. Again after four months metastasis was found in ipsilateral parotid region which was treated by modified radical neck dissection followed by irradiation. Five months after that the patient presented with metastatic post-auricular lymph-node and malignant pleural effusion. Disease progression occurred despite cisplatin based chemotherapy and ultimately patient succumbed to cerebral metastasis. The overall survival was thirty seven month. This case report suggests that conjunctival melanoma is a highly aggressive malignancy with unsatisfactory amenability to loco-regional and systemic therapy. [ABSTRACT FROM AUTHOR]

• Vehicle, cyclist & pedestrian count data from 44 neighboring or collocated stations were analyzed. • Pre & post-pandemic datasets showed pattern changes due to Covid-19 restrictions were mode-specific. • Land-use restrictions and altered trip purposes impacted daily and weekly travel patterns. • Cross-correlation, clustering, and Bayesian change-point analyses showed congruent inferences. • Timeline of pandemic restrictions could be matched using statistical analyses on traffic counts. The spread of Covid-19 and implementation of various restrictions changed how and why people travel. The present study analyzed three different modes of transportation, to understand the impact of Covid-19 mobility and land-use restrictions on chosen neighbourhoods in Dublin City, Ireland. Classification analysis, Spatial correlation analysis, and Bayesian change point analysis had been conducted using vehicle, cyclist & pedestrian count data from 44 neighbouring or collocated stations to explore the statistical changes in the traffic system characteristics. Apart from reduction in traffic counts, the other impacts were modal shift, rise in cyclist numbers, and similarity between weekday & weekend patterns observed. Analyses could identify that changes in the statistical aspects of traffic system are congruent with the changes in lockdown measures. Overall, this study presented a set of tools to identify the existence and degree of changes in traffic patterns over time due to any mobility or land-use policy changes. [ABSTRACT FROM AUTHOR]

Due to the inaccessibility of many wetlands, there is a growing recognition that remote sensing techniques can be a viable and cost-effective alternative to field-based ecosystem monitoring. Wetlands encompass a diverse array of habitats, for example, fens, bogs, marshes, and swamps. In this study, we have concentrated on natural raised bogs, found in the Irish midlands. The objective of the study was to investigate the use of multispectral satellite imagery for delineating the extent of raised bogs and then monitoring their ecological composition in order to help with Ireland's obligations under the EU Habitats Directive. This was carried out using open-source Sentinel-2 data. An initial study to delineate the boundary of the bogs using the combination of edge detection techniques was performed. Once the bog boundary was defined, the spectra from the delineated area were studied. Various vegetation indices along with soil moisture information and DEM were used as features to train the classification algorithm. An ensemble classifier Bagged Tree (BT) as a supervised pixel-based learner has been used for mapping the ecotopes. In order to acknowledge the spatial distribution of the ecotopes, we partitioned the bog into ecotope-objects via graph cut segmentation also known as MAP (maximum a posteriori) estimation. The results have been verified for 5 different bog-sites using field-derived ecotope maps which has shown that the addition of spatial knowledge enhances the overall accuracy. The study further extends to investigate the scope whether it is possible to transfer the knowledge contained in the classification algorithm from one bog-wetland to other wetlands. If this is possible it would mean that other bogs could be automatically and remotely mapped. Various bog-indices highlighting special areas in the bogs under consideration were created. For bogs located in close proximity, the knowledge transfer is carried out successfully for the active regions (i.e. the major peat forming areas) using a binary-linear discriminant. The study limits to closely located bogs due to unavoidable changes in environmental conditions which in turn changes the intensity values of ecotopes in far-away locations. Such a methodology that is just being developed may be able to significantly reduce the amount of field work required by ecologists on each wetland using freely available satellite data. [ABSTRACT FROM AUTHOR]

Background: Although the majority of Ghanaian women receive antenatal care (ANC), many exhibit low health literacy by misinterpreting and incorrectly operationalizing ANC messages, leading to poor maternal and newborn health outcomes. Prior research in low-resource settings has found group antenatal care (G-ANC) feasible for women and providers. This study aims to determine the effect of G-ANC on increasing maternal health literacy. We hypothesized that pregnant women randomized into G-ANC would exhibit a greater increase in maternal health literacy than women in routine, individual ANC. Methods: A 5-year cluster randomized controlled trial was conducted in 14 rural and peri-urban health facilities in the Eastern Region of Ghana. Facilities were paired based on patient volume and average gestational age at ANC enrollment and then randomized into intervention (G-ANC) vs. control (routine, individual ANC); 1761 pregnant women were recruited. Data collection occurred at baseline (T0) and post-birth (T2) using the Maternal Health Literacy scale, a 12-item composite scale to assess maternal health literacy. Logistic regression compared changes in health literacy from T0 to T2. Results: Overall, women in both the intervention and control groups improved their health literacy scores over time (p < 0.0001). Women in the intervention group scored significantly higher on 3 individual items and on overall composite scores (p < 0.0001) and were more likely to attend 8 or more ANC visits. Conclusion: While health literacy scores improved for all women attending ANC, women randomized into G-ANC exhibited greater improvement in overall health literacy post-birth compared to those receiving routine individual care. Life-saving information provided during ANC must be presented in an understandable format to prevent women and newborns from dying of preventable causes. Trial Registry: Ethical approval for the study was obtained from the Institutional Review Boards of the University of Michigan (HUM#00161464) and the Ghana Health Service (GHS-ERC: 016/04/19). [ABSTRACT FROM AUTHOR]

Background: Healthy Lifetime, a theoretically driven, personalized health coaching program delivered electronically, including face-to-face videoconferencing, was developed to intervene in early aging to stave off functional decline and minimize the onset/exacerbation of chronic conditions. Objective: To determine the efficacy of a theoretically driven, personalized health coaching program in participants 50 years and older with one or more chronic conditions using a randomized, controlled, pragmatic clinical trial methodology. Methods: Participants were randomly assigned to the HL (n = 59) or a usual care (n = 63) group. The HL group received health coaching from a trained nurse over eight weeks. Outcomes were measured at baseline, eight weeks, and 20 weeks (after the 12-week no-treatment phase). Regression modeling with fixed-effect repeated measures was used to account for the longitudinal data collection. Results: For the HL group, health habits increased at 8 weeks (3.1 units; SE = 1.0; p =.0005; effect size =.15). This difference was sustained at 20 weeks (2.4 units, SE = 0.2; p =.0005). Independent self-care agency improved at 8 weeks in individuals with high blood pressure (13.5 units; SE = 4.37; p =.0023; effect size =.3). However, that difference was not sustained at 20 weeks (p =.47). No significant improvements were shown in the usual care group at 8 weeks or 20 weeks. Conclusions: HL participants significantly improved their health habits at 8 weeks and sustained this improvement at week 20 (after a 12-week no-treatment phase) vs. the usual care group. Changing health habits alone has been shown to reduce all-cause morbidity and mortality in chronic disease. The high-functioning, community-dwelling older adults with chronic diseases we studied is an important target population for primary care practices to intervene early in aging to stave off the complications of chronic disease and functional decline. Trial registration: ClinicalTrials.gov (record NCT05070923, 07/10/2021). [ABSTRACT FROM AUTHOR]

Cycling as a mode of transport avoids the negative external costs of driving such as air pollution, carbon dioxide emissions and noise and can also reduce the public health costs associated with physical inactivity. However, increased cycling may also have disadvantages such as increased exposure to air pollution and risk of traffic collisions. A number of studies have developed methods to quantify these health and environmental impacts and have shown that the overall impact of increased cycling is positive. However, while minimising travel costs is traditionally the main objective of transport planners, these studies have not included travel cost impacts in their analyses. In this study, the impacts of a modal shift towards cycling are quantified, taking into account health, environmental and travel cost impacts. It was found that the health and environmental impacts of increased cycling in Dublin, Ireland, would be strongly positive, mainly due to the health benefits of physical activity. When travel costs are also included in the analysis, the central estimate of net impact remains positive but the uncertainty increases considerably. This underscores the importance of the transport and health sectors working together to maximise the social welfare resulting from transport projects. [ABSTRACT FROM AUTHOR]

Abstract Land Use Regression (LUR) models was developed using fixed site and mobile station data in Dublin Ireland. The uncertainty associated with short term, mobile station data was accounted for using a weighted function, considering the differences in record duration of data across the study. A systematic buffer distance decay curve-based approach was also considered in this study to identify appropriate predictors and their associated buffer distance. The analysis was performed in Dublin city for fine particulate matter (PM 2.5) where 10 fixed monitoring stations data were available, with average daily concentration data records of 1–7 years in length. 105 mobile station data were also collected at several locations in the city for durations of 12 h to 3 days. The performance of a developed multiple linear regression based LUR model was evaluated by using the leave-one-out-cross-validation procedure. The model explained variances (R2) of the LUR model were found to be 0.219 for winter and 0.688 for summer months. One of the primary reasons for poor performance of the LUR model was that the relationship between the predictors and the mean PM 2.5 concentration were found to be non-linear which cannot be modelled by using multiple linear regression approach. To address this, a weighted support vector regression (WSVR) approach was also considered that can account for non-linearity while developing the LUR model. The model performance in terms of R2 was found to improve to 0.912 for winter and 0.916 for summer using this method. The developed WSVR model can be used to predict PM 2.5 concentration at any ungauged locations in the study area with considerable accuracy, which can be the basis for future epidemiological studies. Highlights • Weighted Support Vector Regression based nonlinear land use regression were developed. • The model effectively capture spatial variation of PM2.5 air pollution in Dublin. • Combination of fixed and mobile station data were used in the analysis. • Predictors were identified based on systematic buffer distance decay curve approach. [ABSTRACT FROM AUTHOR]

Abstract The vehicular emission modelling software COPERT is extensively used in generating emission levels for National Emissions Inventory in Europe and in some other countries internationally. This paper aims to study the uncertainties associated with emission estimates generated from COPERT(v5.1) outputs through sensitivity analysis of the model input parameters. The input parameters considered in this study are temperature, speed, relative humidity, trip length, and driving style (mileage share). Many of these parameters are not directly measured or country-wide average values are investigated in this study. The uncertainties of the emission estimates were obtained through varying the parameter values, within realistic limits, either individually or considering factor interactions by varying two or more parameters simultaneously. The uncertainties of the emission estimates of certain pollutants (CO, VOC and NMVOC) can be significant from −58% to +76%. Some gases, such as CO 2 show almost no sensitivity to input parameter variations. The results indicate that the COPERT outputs are most sensitive to variations in trip length and speed, both of which are not directly measured. Considering the percentage of urban driving share has a huge effect on the national emission estimates, inventories were separately prepared for the Greater Dublin Area (GDA), the largest urban area of Ireland with highest population and vehicle densities. GDA has 48% and 50.37% shares of CO 2 and NOx emissions respectively, compared to the national levels. The consequence of over- or underestimation of emission inventories for Ireland was calculated to be approximately +/− €45million. Considering the major implications of NEI for climate change and other related impacts it is crucial to report the pollutant emission levels appropriately stating uncertainty levels. The findings of this study provide national experts with a methodology to estimate uncertainties of COPERT outputs and to identify the key sources of these uncertainties where policy and emission reduction strategies should be targeted. Graphical abstract Image 100836 Highlights • Vehicular emission levels modelled using COPERT varies significantly based on assumptions. • Sensitivity modelling of COPERT input parameters, values of which are not widely measured. • Individual & interactive effects of temperature, speed, driving style etc. on emission variations. • Uncertainties of CO, VOC, NMVOC, N 2 O levels are greater than uncertainties of CO 2 , NOx, & PM levels. • Damage costs of pollutants under/overestimated by approximately 43.9 mil€. [ABSTRACT FROM AUTHOR]

The transportation sector is the greatest contributor to air pollution. With the booming demand for transportation, reducing the pollution has become one of the main concerns of researchers. EPA emission standards are designed to protect air quality and human health. Diesel Euro 5 NOxhas become a matter of disquiet since it has been found that NOxemissions are significantly exceeding the standard limit. This paper presents a study to estimate the disparity in real-world NOxemission levels resulted from all diesel Euro 5 passenger cars (PC) and light commercial vehicles (LCV) that are present in Ireland. NOxemission levels calculated based on laboratory test results, on-road measurements and the COPERT 4 model were compared. Additionally, NOxemission levels from the defective Volkswagen models have been calculated to quantify the effect of the Volkswagen scandal on Ireland. Impacts of excess NOxemissions on health and cost have also been presented. [ABSTRACT FROM PUBLISHER]

Multifocal osteosarcoma (MFOS), osteosarcoma involving multiple sites, is a rare variant of osteosarcoma. When the lesions appear within 6 months of initial presentation of the tumor, it is known as synchronous MFOS. Synchronous MFOS has an incidence of 1%-3% only. Moreover, the histological variant of small cell osteosarcoma is even rarer. A case of 14-year-old male with synchronous MFOS of small cell type involving frontal and mandibular bone simultaneously is being reported here. It poses a dilemma to both the clinician and the pathologist to diagnose whether it represents multiple primary tumor or metastatic disease. [ABSTRACT FROM AUTHOR]

Several chemotherapeutic agents applied to human beings for past few decades for different ailments, have been found to possess potent antitubercular activity. Two such agents, methdilazine (Md) and promethazine (Pz) were used to select mycobacterial mutants resistant to themselves at different levels and tested to determine if such mutants simultaneously developed cross-resistance to known antitubercular drugs. Mutants were produced by application of a heavy inoculum on Lowenstein-Jensen medium containing Md (or Pz) at concentrations higher than their respective minimum inhibitory concentrations (MICs). These were then tested to find out if such mutants have changed their resistogramme pattern with respect to the test antitubercular agents. Certain first-step Md-mutants became simultaneously resistant to ethambutol and pyrazinamide; while the step-2 Md-mutants revealed further increase in resistance to these agents along with resistance to isoniazid, rifampicin and streptomycin as well. In the study with Pz it was noted that many mutants showed distinctly higher levels of resistance to all the test drugs, particularly to isoniazid, pyrazinamide and streptomycin. The levels of MIC were distinctly high in many mutants. These observations on cross-resistances seem to be best explained on the basis of a reduction in cell-membrane permeability acting in a non-specific manner. The role of such a cross-resistance may possibly be accounting for an overall increase in the MICs of many drugs against several groups of microorganisms including Mycobacterium tuberculosis and other species of Mycobacterium during the last five decades. [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]

The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets. [ABSTRACT FROM AUTHOR]

Several chemotherapeutic agents applied to human beings for past few decades for different ailments, have been found to possess potent antitubercular activity. Two such agents, methdilazine (Md) and promethazine (Pz) were used to select mycobacterial mutants resistant to themselves at different levels and tested to determine if such mutants simultaneously developed cross-resistance to known antitubercular drugs. Mutants were produced by application of a heavy inoculum on Lowenstein-Jensen medium containing Md (or Pz) at concentrations higher than their respective minimum inhibitory concentrations (MICs). These were then tested to find out if such mutants have changed their resistogramme pattern with respect to the test antitubercular agents. Certain first-step Md-mutants became simultaneously resistant to ethambutol and pyrazinamide; while the step-2 Md-mutants revealed further increase in resistance to these agents along with resistance to isoniazid, rifampicin and streptomycin as well. In the study with Pz it was noted that many mutants showed distinctly higher levels of resistance to all the test drugs, particularly to isoniazid, pyrazinamide and streptomycin. The levels of MIC were distinctly high in many mutants. These observations on cross-resistances seem to be best explained on the basis of a reduction in cell-membrane permeability acting in a non-specific manner. The role of such a cross-resistance may possibly be accounting for an overall increase in the MICs of many drugs against several groups of microorganisms including Mycobacterium tuberculosis and other species of Mycobacterium during the last five decades. [ABSTRACT FROM AUTHOR]

In the past several years, active travel (walking and cycling) has increasingly been recognized as an effective means of improving public health by increasing physical activity and by avoiding the negative externalities of motorized transport. The impacts of increased active travel on mortality and morbidity rates have been quantified through a range of methodologies. In this study, the existing publications in this field of research have been reviewed to compare and contrast the methodologies adapted and to identify the key considerations and the best practices. The publications were classified in terms of the health summary outcomes and exposure variables considered, the model structures used in the studies and the impact of these choices on the results. Increased physical activity was identified as the most important determinant of the health impacts of active travel but different ways of quantifying these health impacts can lead to substantial differences in the scale of the impact. Further research is required into the relationship between increased physical activity and health effects in order to reach consensus on the most reliable modelling approach for this important determinant of benefits. Critical discussions on other exposure variables have also been provided to ascertain best practices. Additionally, a logical flow of the modelling processes (and their variations) has also been illustrated which can be followed for developing future studies into the health impacts of active travel. [ABSTRACT FROM AUTHOR]

Heavy-Duty Vehicles (HDVs) pose a significant challenge in meeting emissions reduction targets. However, studies on estimating emissions from Irish HDV fleet are rare. This paper estimates fuel consumption and CO 2 emissions from tractor-trailers that occupy nearly half of Ireland's HDV fleet using the European Commission recommended VECTO tool. To improve the estimation accuracy, driving cycles specific to Irish road conditions were developed utilising real naturalistic driving data from trucks. The Irish driving cycles significantly differ from the VECTO inbuilt driving cycles in percentage time spent in different gears, fuel consumption, and CO 2 emissions. CO 2 emissions estimated with Irish driving cycles range from 733 to 1961 g/km, while with VECTO driving cycles, the values ranged from 728 to 1865 g/km. Further, the study evaluated the sensitivity towards payload, road gradient, and idling percentages. The study insights would provide an essential evidence base for policymakers in Ireland. [Display omitted] • Developed Irish urban, regional, and freeway driving cycles. • Employed the VECTO tool to assess CO 2 emissions from Irish heavy-duty vehicle fleet. • Focused on tractor-trailers with a major share in the Irish fleet. • Evaluated the effect of idling, gradient, and payload on CO 2 emissions. • Fuel consumption is underestimated by up to 7.1% when the road gradient is ignored. [ABSTRACT FROM AUTHOR]

Abstract: Non-motorized commuting such as, walking and cycling to work has been recognized as essential in attaining sustainability in urban mobility. Owing to this recognition, in recent years there has been a surge of interest among policy makers and practitioners in promoting non-motorized commuting in Ireland. This paper presents an investigative study to explain the non-motorized mode share of commuter journeys in terms of relevant socioeconomic, transportation and household specific factors in five major cities of Ireland. The non-motorized modes were analyzed using the latest available Irish census data (2006). An overall analysis of the entire study region was conducted along with the development of models specific to each city, gender, distance (under and over 5km) and to the choice between non-motorized modes (walking and cycling) to gain a deeper understanding of the determinants which influence the choice of non-motorized travel mode for commuter journeys. Gender, car ownership and journey distance were revealed by analysis to have the largest effect on the use of non-motorized transport (NMT). Major Irish cities show similar behavior regarding NMT use and nation-wide policy development can be successful, provided some region or city specific differences are incorporated during policy implementation. The developed models are important tools in understanding the effectiveness of the policy interventions in promoting non-motorized travel for utilitarian purposes across the major cities of Ireland. [Copyright &y& Elsevier]

To make visual data a part of quantitative assessment for infrastructure maintenance management, it is important to develop computer-aided methods that demonstrate efficient performance in the presence of variability in damage forms, lighting conditions, viewing angles, and image resolutions taking into account the luminous and chromatic complexities of visual data. This article presents a semi-automatic, enhanced texture segmentation approach to detect and classify surface damage on infrastructure elements and successfully applies them to a range of images of surface damage. The approach involves statistical analysis of spatially neighboring pixels in various color spaces by defining a feature vector that includes measures related to pixel intensity values over a specified color range and statistics derived from the Grey Level Co-occurrence Matrix calculated on a quantized grey-level scale. Parameter optimized non-linear Support Vector Machines are used to classify the feature vector. A Custom-Weighted Iterative model and a 4-Dimensional Input Space model are introduced. Receiver Operating Characteristics are employed to assess and enhance the detection efficiency under various damage conditions. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, cycling has been recognized and is being promoted as a sustainable mode of travel. The perception of cycling as an unsafe mode of travel is a significant obstacle in increasing the mode share of bicycles in a city. Hence, it is important to identify and analyze the factors which influence the safety experiences of the cyclists in an urban signalized multi-modal transportation network. Previous researches in the area of perceived safety of cyclists primarily considered the influence of network infrastructure and operation specific variables and are often limited to specific locations within the network. This study explores the factors that are expected to be important in influencing the perception of safety among cyclists but were never studied in the past. These factors include the safety behavior of existing cyclists, the users of other travel modes and their attitude toward cyclists, facilities and network infrastructures applicable to cycling as well as to other modes in all parts of an urban transportation network. A survey of existing cyclists in Dublin City was conducted to gain an insight into the different aspects related to the safety experience of cyclists. Ordered Logistic Regression (OLR) and Principal Component Analysis (PCA) were used in the analysis of survey responses. This study has revealed that respondents perceive cycling as less safe than driving in Dublin City. The new findings have shown that the compliance of cyclists with the rules of the road increase their safety experience, while the reckless and careless attitudes of drivers are exceptionally detrimental to their perceived safety. The policy implications of the results of analysis are discussed with the intention of building on the reputation of cycling as a viable mode of transportation among all network users. [Copyright &y& Elsevier]

• Drivers' efficiency can be modelled using telematics data from HDV fleet. • Efficiency frontier analysis distinguishes between efficient and inefficient drivers. • Average speed, idling and harsh braking impact fuel consumption in HDVs. Fuel economy in freight transport is key in attaining economic and environmental efficiency and sustainability. Fuel consumption in Heavy-Duty Vehicle (HDV) is dependent on a set of driver-behaviour parameters and a set of external factors including terrain, loading, vehicle type, and goods etc. Therefore, this study aims at identifying the company-specific critical driving behaviour factors contributing to fuel consumption at HDV fleet using General Estimation Equation analysis. Further, a methodology is proposed to identify fuel-efficient and inefficient drivers using Data Envelopment Analysis and efficiency frontiers. As an illustrative example, a timber haulage company was analysed in which monthly driver behaviour performance data was collected using a telematics system. The results identified average speed, braking, and idling to be key factors influencing fuel consumption. Individual driver's performance in a month is marked as efficient or inefficient considering the set of critical driving-behaviour parameters, and inefficient drivers can improve their performance by following the proposed personalised incentive scheme. [ABSTRACT FROM AUTHOR]

Monitoring wetlands is necessary in order to understand and protect their ecohydrological balance. In Ireland, traditionally wetland-monitoring is carried out by manual field visits which can be very time-consuming. To automate the process, this study extends the ability of remote sensing-based monitoring of wetlands by combining RGB image processing, machine learning algorithms, and satellite data analysis to create seasonal maps of vegetation communities within the wetlands. The methodology matches multispectral and broad coverage of open-source Sentinel-2 (S2) imagery with the high spatial granularity of Unmanned Aerial Vehicles (UAV) or drone images. Single sensor drone imagery was captured, colour corrected and classified using random forest (RF) classifier for a subset of the wetland. The classified imagery was upsampled to satellite imagery scale to create training data for vegetation-segmentation in the entire wetland. The process was repeated for multiple seasons, and an annual map was created utilising the majority voting. The proposed framework has been evaluated on various wetlands across Ireland, with results presented herein for an ombrotrophic peatland complex, Clara Bog. The accuracy of the maps was checked utilising a set of area-based performance metric. The application of this method thereby reduces the number of field surveys typically required to assess the long-term ecological change of such wetland habitats. The performance of the proposed method demonstrates that the technique is a robust, quick, and cost-effective way to map wetland habitats seasonally and to explore their ecohydrological synergies. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of this study was to examine the factors influencing job satisfaction of nurses, physicians, and advanced practice providers in ambulatory oncology settings. BACKGROUND: Job satisfaction is essential to clinician well-being and quality of care. METHODS: In 2017, clinicians from 29 ambulatory medical oncology practices completed anonymous paper questionnaires that examined job satisfaction, clinician-to-clinician communication, and perceptions of patient safety. Linear regression, adjusted for clustered observations, examined the relationship between job satisfaction, clinician communication, and patient safety perceptions. RESULTS: Of 280 respondents (response rate of 68%), 85% reported that they were satisfied or very satisfied with their current position. Patient safety and accuracy of clinician communication were positively and significantly associated with job satisfaction. CONCLUSIONS: Although most surveyed clinicians were satisfied, 15% were dissatisfied and reported communication and safety concerns. Leadership efforts to strengthen clinician communication actions and develop positive safety cultures are promising strategies to promote clinician well-being and high-quality cancer care. [ABSTRACT FROM AUTHOR]

Real-time estimates of natural frequencies, mode shapes and damping of a structural system can be interpreted to its structural health. In this regard, real-time estimation of damping ratios for full-scale structures can be useful by itself or in conjunction with real-time estimates of natural frequencies. Such estimates also allow for continuous health monitoring. This paper demonstrates an approach of assessing real-time damping in full scale bridges and demonstrates this on one of the iconic steel bridges in Ireland, the Daly's "Shaky" bridge. This is the only suspension bridge in the city of Cork, Ireland and renowned in popular culture for its lively behaviour. From existing vibration data evaluated from an image processing technique, the damping estimates of the Daly's bridge are evaluated based on an automated enhanced frequency domain decomposition (AE-FDD) technique. The method provides accurate estimates of natural frequencies and mode shapes and additionally yields the damping ratio corresponding to each vibration (and/or torsional) mode. This technique of real-time damping estimation can be easily adapted for other full-scale structures in an automated real-time framework. [ABSTRACT FROM AUTHOR]

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice. [ABSTRACT FROM AUTHOR]

There has been a recent surge of research interest in quantifying the health and environmental impacts of increased cycling in urban environments. Although there is general agreement that the benefits of increased cycling outweigh the risks, most of the methodologies developed have had limited value for evaluating real-world transport policies. This is because they are based on hypothetical scenarios where increased cycling takes place but give no consideration to the courses of action which may help policymakers to achieve the scenarios. A useful extension to these methodologies would be one which allowed a user to find the optimal infrastructure design and/or policies which would maximize total societal benefit, taking into account the health and environmental impacts of cycling. In this study, a Network Design Problem is formulated for systematically designing cycling network layouts in order to maximize the net benefits to the network users and society. The problem is formulated as a mathematical program with equilibrium constraints (MPEC) and a solution approach based on a genetic algorithm (GA) is provided to solve the problem. The problem formulation and solution algorithm are tested using a numerical example. The GA algorithm was shown to efficiently converge to an optimal or near-optimal solution for the cycle network design. The proposed optimization framework may be adopted by transport authorities and/or urban planners as a decision support tool to help them to systematically identify the best design for a cycle network which balances the benefits and risks to all stakeholders. [ABSTRACT FROM AUTHOR]

In India, video monitoring of urban roads in order to estimate traffic condition parameters (volume, speed and density) is difficult due to the heterogeneous nature of the traffic and poor lane discipline. As an alternative, a methodology to estimate levels of traffic congestion on urban roads using image processing of monitored video recordings was developed. Four levels of congestion, from completely congested to free-flow conditions, were identified utilising surrogates such as area occupancy or average velocity along with additional attributes such as the number of corner features extracted using image processing. The algorithm was adjusted for changes in lighting and camera angles. The performance of the framework was evaluated on videos recorded from a major road in Chennai, India. The findings indicated that the proposed framework is well-suited for real-time identification of traffic states and has potential as a cost-effective tool for updating traveller information systems in similar road transport networks. [ABSTRACT FROM AUTHOR]

PURPOSE: We know little about how increased technological sophistication of clinical practices affects safety of chemotherapy delivery in the outpatient setting. This study investigated to what degree electronic health records (EHRs), satisfaction with technology, and quality of clinician-to-clinician communication enable a safety culture. METHODS: We measured actions consistent with a safety culture, satisfaction with practice technology, and quality of clinician communication using validated instruments among 297 oncology nurses and prescribers in a statewide collaborative. We constructed an index to reflect practice reliance on EHRs (1 = "all paper" to 5 = "all electronic"). Linear regression models (with robust SEs to account for clustering) examined relationships between independent variables of interest and safety. Models were adjusted for clinician age. RESULTS: The survey response rate was 68% (76% for nurses and 59% for prescribers). The mean (standard deviation) safety score was 5.3 (1.1), with a practice-level range of 4.9 to 5.4. Prescribers reported fewer safety actions than nurses. Higher satisfaction with technology and higher-quality clinician communication were significantly associated with increased safety actions, whereas increased reliance on EHRs was significantly associated with lower safety actions. CONCLUSION: Practices vary in their performance of patient safety actions. Supporting clinicians to integrate technology and strengthen communication are promising intervention targets. The inverse relationship between reliance on EHRs and safety suggests that technology may not facilitate clinicians' ability to attend to patient safety. Efforts to improve cancer care quality should focus on more seamless integration of EHRs into routine care delivery and emphasize increasing the capacity of all care clinicians to communicate effectively and coordinate efforts when administering high-risk treatments in ambulatory settings. [ABSTRACT FROM AUTHOR]

Abstract: Underwater inspections stand to gain from using stereo imaging systems to collect three‐dimensional measurements. Although many stereo‐matching algorithms have been devised to solve the correspondence problem, that is, find the same points in multiple images, these algorithms often perform poorly when applied to images of underwater scenes due to the poor visibility and the complex underwater light field. This article presents a new stereo‐matching algorithm, called PaLPaBEL (Pyramidal Loopy Propagated BELief) that is designed to operate on challenging imagery. At its core, PaLPaBEL is a semiglobal method based on a loopy belief propagation message passing algorithm applied on a Markov random field. A pyramidal scheme is adopted that enables wide disparity ranges and high‐resolution images to be handled efficiently. For performance evaluation, PaLPaBEL is applied to underwater stereo images captured under various visibility conditions in a laboratory setting, and to synthetic imagery created in a virtual underwater environment. The technique is also demonstrated on stereo images obtained from a real‐world inspection. The successful results indicate that PaLPaBEL is well suited for underwater application and has value as a tool for the cost‐effective inspection of marine structures. [ABSTRACT FROM AUTHOR]

Image processing-based methods, capable of detecting and quantifying cracks, surface defects or recovering 3D shape information are increasingly being recognised as a valuable tool for inspecting underwater structures. It is of great practical importance for inspectors to know the effectiveness of such techniques when applied in conditions. This paper considers an underwater environment characterised by poor visibility chiefly governed by the lighting and turbidity levels, along with a range of geometry and damage conditions of calibrated specimens. The paper addresses the relationship between underwater visibility and the performance of image-based methods through the development and calibration of a first open-source underwater lighting and turbidity image repository (ULTIR). ULTIR contains a large collection of images of submerged specimens that have been photographed under controlled lighting and turbidity levels featuring various forms of geometry and damage. ULTIR aims to facilitate inspectors when rationalising the use of image processing methods as part of an underwater inspection campaign and to enable researchers to efficiently evaluate the performance of image-based methods under realistic operating conditions. Stakeholders in underwater infrastructure can benefit through this first large, standardised, well-annotated, and freely available database of images and associated metadata. [ABSTRACT FROM AUTHOR]

Emissions from real-world driving are often higher than estimated emission levels based on Euro emissions standards and type-approval tests. Consequently, the air quality in cities has not improved as originally anticipated from fleet renewal and stringent emission regulations for newer vehicles. This paper presents the real-world emission factors (EFs) for in-use cars, taxis, vans, and buses obtained using on-road remote sensing in Dublin city, Ireland. EFs from both petrol and diesel Euro 6 vehicles were significantly improved compared to the EFs of older emission standard vehicles. The analyses indicated that the presence of a few high emitting older vehicles skewed the overall distribution of EFs. Detailed description of variations in EFs for individual pollutants based on changes in emission standards, location, and other driving characteristics were studied and reported, which provide justification for implementing policies targeting fleet renewal. • First study to report real-world emission factors for in-use vehicles in Ireland. • This study considers cars, taxis, vans, and buses of varying makes and models. • Decreasing trends of NO EFs for all diesel vehicles with higher Euro standards. • Strong temperature dependence of NO x emissions for Euro 4 and Euro 5 diesel cars. • Remote sensing measurements can be used to inform vehicle emission control policies. [ABSTRACT FROM AUTHOR]