Search results for: robust diagnosis

Authors: Mohammad Moezzibadi, Isabelle Charpentier, Adrien Wanko, Robert Mosé

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The calibration of hydrodynamic parameters for subsurface constructed wetlands (CWs) is a sensitive process since highly non-linear equations are involved in unsaturated flow modeling. CW systems are engineered systems designed to favour natural treatment processes involving wetland vegetation, soil, and their microbial flora. Their significant efficiency at reducing the ecological impact of urban runoff has been recently proved in the field. Numerical flow modeling in a vertical variably saturated CW is here carried out by implementing the Richards model by means of a mixed hybrid finite element method (MHFEM), particularly well adapted to the simulation of heterogeneous media, and the van Genuchten-Mualem parametrization. For validation purposes, MHFEM results were compared to those of HYDRUS (a software based on a finite element discretization). As van Genuchten-Mualem soil hydrodynamic parameters depend on water content, their estimation is subject to considerable experimental and numerical studies. In particular, the sensitivity analysis performed with respect to the van Genuchten-Mualem parameters reveals a predominant influence of the shape parameters α, n and the saturated conductivity of the filter on the piezometric heads, during saturation and desaturation. Modeling issues arise when the soil reaches oven-dry conditions. A particular attention should also be brought to boundary condition modeling (surface ponding or evaporation) to be able to tackle different sequences of rainfall-runoff events. For proper parameter identification, large field datasets would be needed. As these are usually not available, notably due to the randomness of the storm events, we thus propose a simple, robust and low-cost numerical method for the inverse modeling of the soil hydrodynamic properties. Among the methods, the variational data assimilation technique introduced by Le Dimet and Talagrand is applied. To that end, a variational data assimilation technique is implemented by applying automatic differentiation (AD) to augment computer codes with derivative computations. Note that very little effort is needed to obtain the differentiated code using the on-line Tapenade AD engine. Field data are collected for a three-layered CW located in Strasbourg (Alsace, France) at the water edge of the urban water stream Ostwaldergraben, during several months. Identification experiments are conducted by comparing measured and computed piezometric head by means of the least square objective function. The temporal variability of hydrodynamic parameter is then assessed and analyzed.

Keywords: automatic differentiation, constructed wetland, inverse method, mixed hybrid FEM, sensitivity analysis

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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Authors: Mjellma Rexhepi, Blerta Rexhepi Kelmendi, Blana Krasniqi, Shaip Krasniqi

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Rheumatoid arthritis is a chronic disease that causes inflammation of the joints which can be so severe that can cause not only deformities but also impairment of function that limits movement. This also contributes to the pain that accompanies this disease. This remains a problematic and challenging disease of modern medicine because treatment is still symptomatic. The main purpose of drug treatment is to reduce the activity of the disease, achieve remission, avoid disability and death. The etiology of the disease is idiopathic, but can also be linked to genetic, nongenetic factors such as hormonal, environmental or infectious. Current scientific evidence shows that vitamin D plays an important role in immune regulation mechanisms. Lack of this vitamin has been linked to loss of immune tolerance and the appearance of autoimmune processes, including rheumatoid arthritis. The purpose of the work was to define Vitamin D in patients hospitalized with rheumatoid arthritis in University Clinical Center of Kosova, as a basis of their connection with lifestyle and physical inactivity. The sample for the work was selected from patients with criteria met for rheumatoid arthritis who were hospitalized at the tertiary level of health care in Kosova. During the work have been investigated 100 consecutive patients fulfilling diagnostic criteria for rheumatoid arthritis, whereas in addition to the general characteristics are also determined the values of vitamin D at the beginning of hospitalization. The average age of the sample analyzed was 50.9±5.7 years old, with an average duration of rheumatoid arthritis disease 7.8±3.4 years. At the beginning of hospitalization, before treatment was initiated, the average value of vitamin D was 15.86±3.43, which according to current reference values is classified into the category of insufficient values. Correlating the duration of the disease, from the time of diagnosis to the day of hospitalization, on one side and the level of vitamin D on the other side, the negative correlation of a lower degree derived (r =-0.1). Physical activity affects the concentration of vitamin D in the blood through increased metabolism of fat and the release of vitamin D and its metabolites from adipose tissue. To now it is evident that physical activity is also accompanied by higher levels of vitamin D. In patients with rheumatoid arthritis, vitamin D levels were low compared to normal. Future works should be oriented toward investigating in detail the bone structure, quality of life and pain in patients with rheumatoid arthritis. More detailed scientific projects, with larger numbers of participants, should be designed for the future to clarify more possible mechanisms as factors related to this phenomenon such as inactivity, lifestyle and the duration of the disease, as well as the importance of keeping vitamin D values at normal limits.

Keywords: hospitalization, lifestyle, rheumatoid arthritis, vitamin D

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Authors: Jun Won Kim

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Objective: Theta-phase gamma-amplitude coupling (TGC) was used as a novel evidence-based tool to reflect the dysfunctional cortico-thalamic interaction in patients with schizophrenia. However, to our best knowledge, no studies have reported the diagnostic utility of the TGC in the resting-state electroencephalographic (EEG) of neuroleptic-naive patients with schizophrenia compared to healthy controls. Thus, the purpose of this EEG study was to understand the underlying mechanisms in patients with schizophrenia by comparing the TGC at rest between two groups and to evaluate the diagnostic utility of TGC. Method: The subjects included 90 patients with schizophrenia and 90 healthy controls. All patients were diagnosed with schizophrenia according to the criteria of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) by two independent psychiatrists using semi-structured clinical interviews. Because patients were either drug-naïve (first episode) or had not been taking psychoactive drugs for one month before the study, we could exclude the influence of medications. Five frequency bands were defined for spectral analyses: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30-80 Hz). The spectral power of the EEG data was calculated with fast Fourier Transformation using the 'spectrogram.m' function of the signal processing toolbox in Matlab. An analysis of covariance (ANCOVA) was performed to compare the TGC results between the groups, which were adjusted using a Bonferroni correction (P < 0.05/19 = 0.0026). Receiver operator characteristic (ROC) analysis was conducted to examine the discriminating ability of the TGC data for schizophrenia diagnosis. Results: The patients with schizophrenia showed a significant increase in the resting-state TGC at all electrodes. The delta, theta, slow alpha, fast alpha, and beta powers showed low accuracies of 62.2%, 58.4%, 56.9%, 60.9%, and 59.0%, respectively, in discriminating the patients with schizophrenia from the healthy controls. The ROC analysis performed on the TGC data generated the most accurate result among the EEG measures, displaying an overall classification accuracy of 92.5%. Conclusion: As TGC includes phase, which contains information about neuronal interactions from the EEG recording, TGC is expected to be useful for understanding the mechanisms the dysfunctional cortico-thalamic interaction in patients with schizophrenia. The resting-state TGC value was increased in the patients with schizophrenia compared to that in the healthy controls and had a higher discriminating ability than the other parameters. These findings may be related to the compensatory hyper-arousal patterns of the dysfunctional default-mode network (DMN) in schizophrenia. Further research exploring the association between TGC and medical or psychiatric conditions that may confound EEG signals will help clarify the potential utility of TGC.

Keywords: quantitative electroencephalography (QEEG), theta-phase gamma-amplitude coupling (TGC), schizophrenia, diagnostic utility

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Authors: Lakmini Wijesooriya, Vicki Chalker, Jessica Day, Priyantha Perera, N. P. Sunil-Chandra

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M. pneumoniae has been identified as an etiology for exacerbation of asthma (EQA), although viruses play a major role in EOA. M. pneumoniae infection is treated empirically with macrolides, and its antibiotic sensitivity is not detected routinely. Characterization of the organism by genotyping and determination of macrolide resistance is important epidemiologically as it guides the empiric antibiotic treatment. To date, there is no such characterization of M. pneumoniae performed in Sri Lanka. The present study describes the characterization of M. pneumoniae detected from a child with EOA following a screening of 100 children with EOA. Of the hundred children with EOA, M. pneumoniae was identified only in one child by Real-Time polymerase chain reaction (PCR) test for identifying the community-acquired respiratory distress syndrome (CARDS) toxin nucleotide sequences. The M. pneumoniae identified from this patient underwent detection of macrolide resistance via conventional PCR, amplifying and sequencing the region of the 23S rDNA gene that contains single nucleotide polymorphisms that confer resistance. Genotyping of the isolate was performed via nested Multilocus Sequence Typing (MLST) in which eight (8) housekeeping genes (ppa, pgm, gyrB, gmk, glyA, atpA, arcC, and adk) were amplified via nested PCR followed by gene sequencing and analysis. As per MLST analysis, the M. pneumoniae was identified as sequence type 14 (ST14), and no mutations that confer resistance were detected. Resistance to macrolides in M. pneumoniae is an increasing problem globally. Establishing surveillance systems is the key to informing local prescriptions. In the absence of local surveillance data, antibiotics are started empirically. If the relevant microbiological samples are not obtained before antibiotic therapy, as in most occasions in children, the course of antibiotic is completed without a microbiological diagnosis. This happens more frequently in therapy for M. pneumoniae which is treated with a macrolide in most patients. Hence, it is important to understand the macrolide sensitivity of M. pneumoniae in the setting. The M. pneumoniae detected in the present study was macrolide sensitive. Further studies are needed to examine a larger dataset in Sri Lanka to determine macrolide resistance levels to inform the use of macrolides in children with EOA. The MLST type varies in different geographical settings, and it also provides a clue to the existence of macrolide resistance. The present study enhances the database of the global distribution of different genotypes of M. pneumoniae as this is the first such characterization performed with the increased number of samples to determine macrolide resistance level in Sri Lanka. M. pneumoniae detected from a child with exacerbation of asthma in Sri Lanka was characterized as ST14 by MLST and no mutations that confer resistance were detected.

Keywords: mycoplasma pneumoniae, Sri Lanka, characterization, macrolide resistance

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Authors: Ibrahim Umar, Ashiru Rajab, Sumayya Chindo, Emmanuel Olashore

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INTRODUCTION: Kano is the most populous state in Nigeria and one of the two states with the highest TB burden in the country. The state notifies an average of 8,000+ TB cases quarterly and has the highest yearly notification of all the states in Nigeria from 2020 to 2022. The contribution of the state TB program to the National TB notification varies from 9% to 10% quarterly between the first quarter of 2022 and second quarter of 2023. The Kano State TB Situation Room is an innovative platform for timely data collection, collation and analysis for informed decision in health system. During the 2023 second National TB Testing week (NTBTW) Kano TB program aimed at early TB detection, prevention and treatment. The state TB Situation room provided avenue to the state for coordination and surveillance through real time data reporting, review, analysis and use during the NTBTW. OBJECTIVES: To assess the role of innovative information and communication technology platform for real-time TB reporting during second National TB Testing week in Nigeria 2023. To showcase the NTBTW data cascade analysis using TSR as innovative ICT platform. METHODOLOGY: The State TB deployed a real-time virtual dashboard for NTBTW reporting, analysis and feedback. A data room team was set up who received realtime data using google link. Data received was analyzed using power BI analytic tool with statistical alpha level of significance of <0.05. RESULTS: At the end of the week-long activity and using the real-time dashboard with onsite mentorship of the field workers, the state TB program was able to screen a total of 52,054 people were screened for TB from 72,112 individuals eligible for screening (72% screening rate). A total of 9,910 presumptive TB clients were identified and evaluated for TB leading to diagnosis of 445 TB patients with TB (5% yield from presumptives) and placement of 435 TB patients on treatment (98% percentage enrolment). CONCLUSION: The TB Situation Room (TBSR) has been a great asset to Kano State TB Control Program in meeting up with the growing demand for timely data reporting in TB and other global health responses. The use of real time surveillance data during the 2023 NTBTW has in no small measure improved the TB response and feedback in Kano State. Scaling up this intervention to other disease areas, states and nations is a positive step in the right direction towards global TB eradication.

Keywords: tuberculosis (tb), national tb testing week (ntbtw), tb situation rom (tsr), information communication technology (ict)

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Authors: Regina A. Tayong, Reza Barati

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A stable, fast and robust three-phase, 2D IMPES simulator has been developed for assessing the influence of; breaker concentration on yield stress of filter cake and broken gel viscosity, varying polymer concentration/yield stress along the fracture face, fracture conductivity, fracture length, capillary pressure changes and formation damage on fracturing fluid cleanup in tight gas reservoirs. This model has been validated as against field data reported in the literature for the same reservoir. A 2-D, two-phase (gas/water) fracture propagation model is used to model our invasion zone and create the initial conditions for our clean-up model by distributing 200 bbls of water around the fracture. A 2-D, three-phase IMPES simulator, incorporating a yield-power-law-rheology has been developed in MATLAB to characterize fluid flow through a hydraulically fractured grid. The variation in polymer concentration along the fracture is computed from a material balance equation relating the initial polymer concentration to total volume of injected fluid and fracture volume. All governing equations and the methods employed have been adequately reported to permit easy replication of results. The effect of increasing capillary pressure in the formation simulated in this study resulted in a 10.4% decrease in cumulative production after 100 days of fluid recovery. Increasing the breaker concentration from 5-15 gal/Mgal on the yield stress and fluid viscosity of a 200 lb/Mgal guar fluid resulted in a 10.83% increase in cumulative gas production. For tight gas formations (k=0.05 md), fluid recovery increases with increasing shut-in time, increasing fracture conductivity and fracture length, irrespective of the yield stress of the fracturing fluid. Mechanical induced formation damage combined with hydraulic damage tends to be the most significant. Several correlations have been developed relating pressure distribution and polymer concentration to distance along the fracture face and average polymer concentration variation with injection time. The gradient in yield stress distribution along the fracture face becomes steeper with increasing polymer concentration. The rate at which the yield stress (τ_o) is increasing is found to be proportional to the square of the volume of fluid lost to the formation. Finally, an improvement on previous results was achieved through simulating yield stress variation along the fracture face rather than assuming constant values because fluid loss to the formation and the polymer concentration distribution along the fracture face decreases as we move away from the injection well. The novelty of this three-phase flow model lies in its ability to (i) Simulate yield stress variation with fluid loss volume along the fracture face for different initial guar concentrations. (ii) Simulate increasing breaker activity on yield stress and broken gel viscosity and the effect of (i) and (ii) on cumulative gas production within reasonable computational time.

Keywords: formation damage, hydraulic fracturing, polymer cleanup, multiphase flow numerical simulation

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Authors: Preet Shah, Om Shrivastav

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Introduction : In India, Leishmania donovani is the only parasite causing Leishmaniasis. The parasite infects the reticuloendothelial system and is found in the bone marrow, spleen and liver. Treatment of choice is amphotericin-B with sodium stibogluconate being an alternative. Miltefosine is useful in refractory cases. In our case, Leishmaniasis occurred in a person residing in western India (which is quite rare) and it failed to respond to two different drugs (again an uncommon feature) before it finally responded to a third one. Description: A 50 year old lady, a resident of western India, with no history of recent travel, presented with an ulcer on the left side of the nose since 8 months. She was apparently alright 8 months back, when she noticed a small ulcerated lesion on the left ala of the nose which was immediately biopsied. The biopsy revealed amastigotes of Leishmania for which she was administered intra-lesional sodium stibogluconate for 1 month (4 doses every 8 days).Despite this, there was no regression of the ulcer and hence she presented to us for further management. On examination, her vital parameters were normal. Barring an ulcer on the left side of the nose, rest of the examination findings were unremarkable. Complete blood count was normal. Ultrasound abdomen showed hepatomegaly. PET-CT scan showed increased metabolic activity in left ala of nose, hepatosplenomegaly and increased metabolic activity in spleen and bone marrow. Bone marrow biopsy was done which showed hypercellular marrow with erythroid preponderance. Considering a diagnosis of leishmaniasis which had so far been unresponsive to sodium stibogluconate, she was started on liposomal amphotericin-B. At the time of admission, her creatinine level was normal, but it started rising with the administration of liposomal amphotericin-B, hence the dose was reduced. Despite this, creatinine levels did not improve, and she started developing hypokalemia and hypomagnesemia as side effects of the drug, hence further reductions in the dosage were made. Despite a total of 3 weeks of liposomal amphotericin-B, there was no improvement in the ulcer. As had so far failed to respond to sodium stibogluconate and liposomal amphotericin-B, it was decided to start her on miltefosine. She received the miltefosine for a total of 12 weeks. At the end of this duration, there was a marked regression of the cutaneous lesion. Conclusion: Refractoriness to amphotericin-B in leishmaniasis may be seen in up to 5 % cases. Here, an alternative drug such as miltefosine is useful and hence we decided to use it, to which she responded adequately. Furthermore, although leishmaniasis is common in the eastern part of India, it is a relatively unknown entity in the western part of the country with the occurrence being very rare. Because of these 2 reasons, we consider our case to be a unique one.

Keywords: amphotericin-b, leishmaniasis, miltefosine, tropical diseases

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: P. Uma Devi, S. Sujith, K. Rahul, T. S. Dipu, V. Anil Kumar , Vidya Menon

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Introduction: Candida is an important cause of bloodstream infections (BSIs), causing significant mortality and morbidity. The epidemiology of Candida infection is also changing, mainly in relation to the number of episodes caused by species Candida non-albicans. However, in India, the true burden of candidemia is not clear. Thus, this study was conducted to evaluate the clinical characteristics, species distribution, antifungal susceptibility and outcome of candidemia at our hospital. Methodology: Between January 2012 and April 2014, adult patients with at least one positive blood culture for Candida species were identified through the microbiology laboratory database (for each patient only the first episode of candidemia was recorded). Patient data was collected by retrospective chart review of clinical characteristics including demographic data, risk factors; species distribution, resistance to antifungals and survival. Results: A total of 165 episodes of Candida BSI were identified, with 115 episodes occurring in adult patients. Most of the episodes occurred in males (69.6%). Nearly 82.6% patients were between 41 to 80 years and majority of the patients were in the intensive care unit (65.2%) at the time of diagnosis. On admission, 26.1% and 18.3% patients had pneumonia and urinary tract infection, respectively. Majority of the candidemia episodes were found in the general medicine department (23.5%) followed by gastrointestinal surgery (13.9%) and medical oncology & haematology (13%). Risk factors identified were prior hospitalization within one year (83.5%), antibiotic therapy within the last one month (64.3%), indwelling urinary catheter (63.5%), central venous catheter use (59.1%), diabetes mellitus (53%), severe sepsis (45.2%), mechanical ventilation (43.5%) and surgery (36.5%). C. tropicalis (30.4%) was the leading cause of infection followed by C. parapsilosis (28.7%) and C. albicans (13%). Other non-albicans species isolated included C. haemulonii (7.8%), C. glabrata (7%), C. famata (4.3%) and C. krusei (1.7%). Antifungal susceptibility to fluconazole was 87.9% (C. parapsilosis), 100% (C. tropicalis) and 93.3% (C. albicans). Mortality was noted in 51 patients (44.3%). Early mortality (within 7 days) was noted in 32 patients while late mortality (between 7 and 30 days) was noted in 19 patients. Conclusion: In recent years, candidemia has been flourishing in critically ill patients. Comparison of data from our own hospital from 2005 shows a doubling of the incidence. Rapid changes in the rate of infection, potential risk factors, and emergence of non-albicans Candida demand continued surveillance of this serious BSI. High index of suspicion and sensitive diagnostics are essential to improve outcomes in resource limited settings with emergence of non-albicans Candida.

Keywords: antifungal susceptibility, candida albicans, candidemia, non-albicans candida

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Authors: Abi Miller, Eleanor Wilson, Claire Diver

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Background: UK Head and Neck Cancer incidence and prevalence were rising related to better treatment outcomes and changed demographics. More people of working-age now survive Head and Neck Cancer. For individuals, work provides income, purpose, and social connection. For society, work increases economic productivity and reduces welfare spending. In the UK, a cancer diagnosis is classed as a disability and more disabled people leave the workplace than non-disabled people. Limited evidence exists on return-to-work after Head and Neck Cancer, with no UK qualitative studies. Head and Neck Cancer survivors appear to return to work less when compared to other cancer survivors. This study aimed to explore the effects of Head and Neck Cancer disability on survivors’ return-to-work experience. Methodologies: This was an exploratory qualitative study using a critical realist approach to carry out semi-structured one-off interviews with Head and Neck Cancer survivors who had returned to work. Interviews were informed by an interview guide and carried out remotely by Microsoft Teams or telephone. Interviews were transcribed verbatim, pseudonyms allocated, and transcripts anonymized. Data were interpreted using Reflexive Thematic Analysis. Findings: Thirteen Head and Neck Cancer survivors aged between 41 -63 years participated in interviews. Three major themes were derived from the data: changed identity and meaning of work after Head and Neck Cancer, challenging and supportive work experiences and impact of healthcare professionals on return-to-work. Participants described visible physical appearance changes, speech and eating challenges, mental health difficulties and psycho-social shifts following Head and Neck Cancer. These factors affected workplace re-integration, ability to carry out work duties, and work relationships. Most participants experienced challenging work experiences, including stigmatizing workplace interactions and poor communication from managers or colleagues, which further affected participant confidence and mental health. Many participants experienced job change or loss, related both to Head and Neck Cancer and living through a pandemic. A minority of participants experienced strategies like phased return, which supported workplace re-integration. All participants, bar one, wanted conversations with healthcare professionals about return-to-work but perceived these conversations as absent. Conclusion: All participants found returning to work after Head and Neck Cancer to be a challenging experience. This appears to be impacted by participant physical, psychological, and functional disability following Head and Neck Cancer, work interaction and work context.

Keywords: disability, experience, head and neck cancer, qualitative, return-to-work

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Authors: Aparecida De Fatima Dutra, Freddie Avant, Wilma Cordova

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People living with HIV/AIDS (PLWHA) have benefited from advances in treatment. Medical costs are a challenge for some, but the real challenge is the stigma and discrimination PLWHA continue to face, even though the disease has festered for the last four decades. Few studies regarding stigma and discrimination give voice to those affected by these practices. This study provides a voice to PLWHA in Brazil and in the US as to how they perceive stigma and discrimination, as well as services they access. The methodology of this study was designed based on phenomenological research, which is a research that aims to identify what individuals facing the same situation have to share about their experiences. Qualitative research using in- depth interviews was used in order to gather participants’ perceptions about services they access, and stigma and discrimination they experience as PLWHA (hypothesis). The target population was a minority group of 13 Afro-descendant men, mean age of 48.3, residents in East Texas, United States and Salvador, Brazil. Our findings indicate that in both countries, overall, participants have reasonable access to medication and qualified services, except for some specialties, such as dentistry. With regard to stigma and discrimination the majority of participants have not disclosed their diagnosis. They state they prefer not to disclose for fear of being ostracized and rejected. Participants who did reveal their status indicate that stigma and discrimination is a daily occurrence. These experiences tend to occur within their own families, neighborhoods, and in public health agencies where HIV/AIDS is not the focus. Participants who did offer suggestions for social change indicated they would have to reveal their status even if it means being stigmatized and discriminated against. Other factors contributing to this discrimination include skin color and poverty. This study concludes that even after decades since the spread of this epidemic, nothing has changed regarding stigma and discrimination towards PLWHA. Lack of awareness, empathy and education continue to be a major challenge, not only at a local level but across the globe. In conclusion, as documented in previous studies while stigma and discrimination towards this population prevail, negative attitudes will continue to jeopardize all individuals from receiving equal access to prevention, treatment and care. It is crucial to face stigma and discrimination not only as individual experiences, but as social practices that violate and restrict human rights and that as a result, reinforce inequality and social exclusion. Policies should be at the forefront to eliminate the stigma and discrimination PLWHA experience. Health professionals and societies must take a stand in order to promote mindfulness about the negative effect of oppression towards individuals living with HIV/AIDS and the potential global impact of these practices.

Keywords: discrimination, HIV/AIDS, human rights, stigma

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Authors: A. D'Alessandro, F. Ubertini, A. L. Materazzi

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In the field of civil engineering, Structural Health Monitoring is a topic of growing interest. Effective monitoring instruments permit the control of the working conditions of structures and infrastructures, through the identification of behavioral anomalies due to incipient damages, especially in areas of high environmental hazards as earthquakes. While traditional sensors can be applied only in a limited number of points, providing a partial information for a structural diagnosis, novel transducers may allow a diffuse sensing. Thanks to the new tools and materials provided by nanotechnology, new types of multifunctional sensors are developing in the scientific panorama. In particular, cement-matrix composite materials capable of diagnosing their own state of strain and tension, could be originated by the addition of specific conductive nanofillers. Because of the nature of the material they are made of, these new cementitious nano-modified transducers can be inserted within the concrete elements, transforming the same structures in sets of widespread sensors. This paper is aimed at presenting the results of a research about a new self-sensing nanocomposite and about the implementation of smart sensors for Structural Health Monitoring. The developed nanocomposite has been obtained by inserting multi walled carbon nanotubes within a cementitious matrix. The insertion of such conductive carbon nanofillers provides the base material with piezoresistive characteristics and peculiar sensitivity to mechanical modifications. The self-sensing ability is achieved by correlating the variation of the external stress or strain with the variation of some electrical properties, such as the electrical resistance or conductivity. Through the measurement of such electrical characteristics, the performance and the working conditions of an element or a structure can be monitored. Among conductive carbon nanofillers, carbon nanotubes seem to be particularly promising for the realization of self-sensing cement-matrix materials. Some issues related to the nanofiller dispersion or to the influence of the nano-inclusions amount in the cement matrix need to be carefully investigated: the strain sensitivity of the resulting sensors is influenced by such factors. This work analyzes the dispersion of the carbon nanofillers, the physical properties of the fresh dough, the electrical properties of the hardened composites and the sensing properties of the realized sensors. The experimental campaign focuses specifically on their dynamic characterization and their applicability to the monitoring of full-scale elements. The results of the electromechanical tests with both slow varying and dynamic loads show that the developed nanocomposite sensors can be effectively used for the health monitoring of structures.

Keywords: carbon nanotubes, self-sensing nanocomposites, smart cement-matrix sensors, structural health monitoring

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Authors: Gagan Brar, Munruchi Kaur

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Macrocybe gigantea was collected from the wild, growing as pure white, fleshy, robust fruit bodies in caespitose clusters. Initially, the few ladies collecting these fruiting bodies for cooking revealed their edibility status, which was later confirmed through classical and molecular taxonomy. The culture of this potential wild edible taxa was raised with an aim of domesticating it. Various solid and liquid media were evaluated for their vegetative growth, in which Malt Extract Agar was found to be the best solid medium and Glucose Peptone medium as the best liquid medium. The effect of different temperatures as well as pH was also evaluated for the vegetative growth of M. gigantea, and it was found that it shows maximum vegetative growth at 30° and pH 5. For spawn preparation, various grains viz. Wheat grains, Jowar grains, Bajra grains and Maize grains were evaluated, and it was found that wheat grains boiled for 30 minutes gave the maximum mycelial growth. Mother spawn was thus prepared on wheat grains boiled for 30 minutes. For raising the fruiting bodies, different locally available agro-wastes were tried, and it was found that paddy straw gives the best growth. Both wilds as well as cultivated M. gigantea were compared through HPLC to evaluate the different nutritional and nutraceutical values. For the evaluation of different sugars in wild and cultivated M. gigantea, 15 sugars were taken for analysis. Among these Melezitose, Trehalose, Glucose, Xylose and Mannitol were found in the wild collection of M. gigantea; in the cultivated sample, Melezitose, Trehalose, Xylose and Dulcitol were detected. Among the 20 different amino acids, 18 amino acids were found, except Asparagine and Glutamine in both wild as well as cultivated samples. Among the 37 tested fatty acids, only 6 fatty acids, namely Palmitic acid, Stearic acid, Cis-9 Oleic acid, Linoleic acid, Gamma-Linolenic acid and Tricosanoic acid, were found in both wild and cultivated samples, although the concentration of these fatty acids was more in the cultivated sample. From the various vitamins tested, Vitamin C, D and E were present in both wild and cultivated samples. Both wild as well as cultivated samples were evaluated for the presence of phenols; for this purpose, eleven phenols were taken as standards in HPLC analysis, and it was found that Gallic acid, Resorcinol, Ferulic acid and Pyrogallol were present in the wild mushroom sample whereas in the cultivated sample Ferulic acid, Caffeic Acid, Vanillic acid and Vanillin are present. The flavonoid analysis revealed the presence of Rutin, Naringin and Quercetin in wild M. gigantea, while 5 Naringin, Catechol, Myrecetin, Gossypin and Quercetin were found in cultivated one. From the comparative chromatographic profiling of both wild as well as cultivated M. gigantea, it is concluded that no nutrient loss was found during its cultivation. An increase in percentage of secondary metabolites (i.e., phenols and flavonoids) was found in cultivated one as compared to wild M. gigantea. Thus, from future perspective cultivated species of M. gigantea can be recommended for the commercial purpose as a good food supplement.

Keywords: culture, edible, fruit bodies, wild

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Authors: Panaya Sudta, Wanchalerm Patanacharoenwong, Prachya Bumrungkun

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As With the evolution of smart grids, ensuring the reliability and efficiency of electrical systems in smart cities has become crucial. This paper proposes a distinct approach that combines advanced machine learning techniques to accurately predict electrical failures and address voltage reliability issues. This approach aims to improve the accuracy and efficiency of reliability evaluations in smart cities. The aim of this research is to develop a comprehensive predictive model that accurately predicts electrical failures and voltage reliability in smart cities. This model integrates RFM analysis, K-means clustering, and LSTM networks to achieve this objective. The research utilizes RFM analysis, traditionally used in customer value assessment, to categorize and analyze electrical components based on their failure recency, frequency, and monetary impact. K-means clustering is employed to segment electrical components into distinct groups with similar characteristics and failure patterns. LSTM networks are used to capture the temporal dependencies and patterns in customer data. This integration of RFM, K-means, and LSTM results in a robust predictive tool for electrical failures and voltage reliability. The proposed model has been tested and validated on diverse electrical utility datasets. The results show a significant improvement in prediction accuracy and reliability compared to traditional methods, achieving an accuracy of 92.78% and an F1-score of 0.83. This research contributes to the proactive maintenance and optimization of electrical infrastructures in smart cities. It also enhances overall energy management and sustainability. The integration of advanced machine learning techniques in the predictive model demonstrates the potential for transforming the landscape of electrical system management within smart cities. The research utilizes diverse electrical utility datasets to develop and validate the predictive model. RFM analysis, K-means clustering, and LSTM networks are applied to these datasets to analyze and predict electrical failures and voltage reliability. The research addresses the question of how accurately electrical failures and voltage reliability can be predicted in smart cities. It also investigates the effectiveness of integrating RFM analysis, K-means clustering, and LSTM networks in achieving this goal. The proposed approach presents a distinct, efficient, and effective solution for predicting and mitigating electrical failures and voltage issues in smart cities. It significantly improves prediction accuracy and reliability compared to traditional methods. This advancement contributes to the proactive maintenance and optimization of electrical infrastructures, overall energy management, and sustainability in smart cities.

Keywords: electrical state prediction, smart grids, data-driven method, long short-term memory, RFM, k-means, machine learning

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Authors: Marlin Mubarak

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Background: Endometriosis is a complex disabling disease affecting young females in the reproductive period mainly. The aim of this project is to generate a diagnostic model to predict severity and stage of endometriosis prior to Laparoscopic surgery. This will help to improve the pre-operative diagnostic accuracy of stage 3 & 4 endometriosis and as a result, refer relevant women to a specialist centre for complex Laparoscopic surgery. The model is based on the British Society of Gynaecological Endoscopy (BSGE) pain questionnaire, clinical examination and ultrasound scan. Design: This is a prospective, observational, study, in which women completed the BSGE pain questionnaire, a BSGE requirement. Also, as part of the routine preoperative assessment patient had a routine ultrasound scan and when recto-vaginal and deep infiltrating endometriosis was suspected an MRI was performed. Setting: Luton & Dunstable University Hospital. Patients: Symptomatic women (n = 56) scheduled for laparoscopy due to pelvic pain. The age ranged between 17 – 52 years of age (mean 33.8 years, SD 8.7 years). Interventions: None outside the recognised and established endometriosis centre protocol set up by BSGE. Main Outcome Measure(s): Sensitivity and specificity of endometriosis diagnosis predicted by symptoms based on BSGE pain questionnaire, clinical examinations and imaging. Findings: The prevalence of diagnosed endometriosis was calculated to be 76.8% and the prevalence of advanced stage was 55.4%. Deep infiltrating endometriosis in various locations was diagnosed in 32/56 women (57.1%) and some had DIE involving several locations. Logistic regression analysis was performed on 36 clinical variables to create a simple clinical prediction model. After creating the scoring system using variables with P < 0.05, the model was applied to the whole dataset. The sensitivity was 83.87% and specificity 96%. The positive likelihood ratio was 20.97 and the negative likelihood ratio was 0.17, indicating that the model has a good predictive value and could be useful in predicting advanced stage endometriosis. Conclusions: This is a hypothesis-generating project with one operator, but future proposed research would provide validation of the model and establish its usefulness in the general setting. Predictive tools based on such model could help organise the appropriate investigation in clinical practice, reduce risks associated with surgery and improve outcome. It could be of value for future research to standardise the assessment of women presenting with pelvic pain. The model needs further testing in a general setting to assess if the initial results are reproducible.

Keywords: deep endometriosis, endometriosis, minimally invasive, MRI, ultrasound.

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Authors: Ramin Mehdiabadi, Neda Menbari, Mohammad Nazir Menbari

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As a pressing public health concern, breast cancer stands as the predominant oncological diagnosis and principal cause of cancer-related mortality among women globally, accounting for 11.7% of new cancer incidences and 6.9% of cancer-related deaths. The annual figures indicate that approximately 230,480 women are diagnosed with breast cancer in the United States alone, with 39,520 succumbing to the disease. While developed economies have reported a deceleration in both incidence and mortality rates across various forms of cancer, including breast cancer, emerging and low-income economies manifest a contrary escalation, largely attributable to lifestyle-mediated risk factors such as tobacco usage, physical inactivity, and high caloric intake. Breast cancer is distinctly characterized by molecular heterogeneity, manifesting in specific subtypes delineated by biomarkers—Estrogen Receptors (ER), Progesterone Receptors (PR), and Human Epidermal Growth Factor Receptor 2 (HER2). These subtypes, comprising Luminal A, Luminal B, HER2-enriched, triple-negative/basal-like, and normal-like, necessitate nuanced, subtype-specific therapeutic regimens, thereby challenging the applicability of generalized treatment protocols. Within this molecular complexity, the transcription factor Forkhead Box M1 (FoxM1) has garnered attention as a significant driver of cellular proliferation, tumorigenesis, metastatic progression, and treatment resistance in a spectrum of human malignancies, including breast cancer. Concurrently, microRNAs (miRs), specifically miR-216b-5p, have been identified as post-transcriptional gene expression regulators and potential tumor suppressors. The overarching objective of this academic investigation is to explicate the multifaceted interrelationship between FoxM1 and miR-216b-5p across the disparate molecular subtypes of breast cancer. Employing a methodologically rigorous, interdisciplinary research design that incorporates cutting-edge molecular biology techniques, sophisticated bioinformatics analytics, and exhaustive meta-analyses of extant clinical data, this scholarly endeavor aims to unveil novel biomarker-specific therapeutic pathways. By doing so, this research is positioned to make a seminal contribution to the advancement of personalized, efficacious, and minimally toxic treatment paradigms, thus profoundly impacting the global efforts to ameliorate the burden of breast cancer.

Keywords: breast cancer, fox m1, microRNAs, mir-216b-5p, gene expression

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Authors: Jonathan Sahu, Jill Aylott

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Individuals with Autism, Intellectual and Developmental disabilities (AIDD) are one of the most vulnerable groups in society, hampered not only by their own limitations to understand and interact with the wider society, but also societal limitations in perception and understanding. Communication to express their needs and wishes is fundamental to enable such individuals to live and prosper in society. This research project was designed as an organisational case study, in a large secondary health care hospital within the National Health Service (NHS), to assess the quality of care provided to people with AIDD and to review the role of advocacy to reduce health inequalities in these individuals. Methods: The research methodology adopted was as an “insider researcher”. Data collection included both quantitative and qualitative data i.e. a mixed method approach. A semi-structured interview schedule was designed and used to obtain qualitative and quantitative primary data from a wide range of interdisciplinary frontline health care workers to assess their understanding and awareness of systems, processes and evidence based practice to offer a quality service to people with AIDD. Secondary data were obtained from sources within the organisation, in keeping with “Case Study” as a primary method, and organisational performance data were then compared against national benchmarking standards. Further data sources were accessed to help evaluate the effectiveness of different types of advocacy that were present in the organisation. This was gauged by measures of user and carer experience in the form of retrospective survey analysis, incidents and complaints. Results: Secondary data demonstrate near compliance of the Organisation with the current national benchmarking standard (Monitor Compliance Framework). However, primary data demonstrate poor knowledge of the Mental Capacity Act 2005, poor knowledge of organisational systems, processes and evidence based practice applied for people with AIDD. In addition there was poor knowledge and awareness of frontline health care workers of advocacy and advocacy schemes for this group. Conclusions: A significant amount of work needs to be undertaken to improve the quality of care delivered to individuals with AIDD. An operational strategy promoting the widespread dissemination of information may not be the best approach to deliver quality care and optimal patient experience and patient advocacy. In addition, a more robust set of standards, with appropriate metrics, needs to be developed to assess organisational performance which will stand the test of professional and public scrutiny.

Keywords: advocacy, autism, health inequalities, intellectual developmental disabilities, quality of care

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Authors: Lina Psarra, Manogj Sundaresan, Purjeet Sutar

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In response to the global imperative for sustainable solutions, this article proposes the development of a comprehensive circularity index applicable to a wide range of products across various industries. The absence of a consensus on using a universal metric to assess circularity performance presents a significant challenge in prioritizing and effectively managing sustainable initiatives. This circularity index serves as a quantitative measure to evaluate the adherence of products, processes, and systems to the principles of a circular economy. Unlike traditional distinct metrics such as recycling rates or material efficiency, this index considers the entire lifecycle of a product in one single metric, also incorporating additional factors such as reusability, scarcity of materials, reparability, and recyclability. Through a systematic approach and by reviewing existing metrics and past methodologies, this work aims to address this gap by formulating a circularity index that can be applied to diverse product portfolio and assist in comparing the circularity of products on a scale of 0%-100%. Project objectives include developing a formula, designing and implementing a pilot tool based on the developed Product Circularity Index (PCI), evaluating the effectiveness of the formula and tool using real product data, and assessing the feasibility of integration into various sustainability initiatives. The research methodology involves an iterative process of comprehensive research, analysis, and refinement where key steps include defining circularity parameters, collecting relevant product data, applying the developed formula, and testing the tool in a pilot phase to gather insights and make necessary adjustments. Major findings of the study indicate that the PCI provides a robust framework for evaluating product circularity across various dimensions. The Excel-based pilot tool demonstrated high accuracy and reliability in measuring circularity, and the database proved instrumental in supporting comprehensive assessments. The PCI facilitated the identification of key areas for improvement, enabling more informed decision-making towards circularity and benchmarking across different products, essentially assisting towards better resource management. In conclusion, the development of the Product Circularity Index represents a significant advancement in global sustainability efforts. By providing a standardized metric, the PCI empowers companies and stakeholders to systematically assess product circularity, track progress, identify improvement areas, and make informed decisions about resource management. This project contributes to the broader discourse on sustainable development by offering a practical approach to enhance circularity within industrial systems, thus paving the way towards a more resilient and sustainable future.

Keywords: circular economy, circular metrics, circularity assessment, circularity tool, sustainable product design, product circularity index

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Authors: Justine Jun

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This study aims to reveal migrant women English instructors' workplace learning experiences in Canadian post-secondary institutions in Ontario. Although many scholars have conducted research studies on internationally educated teachers and their professional and employment challenges, few studies have recorded migrant women English language instructors’ professional learning and support experiences in post-secondary English language programs in Canada. This study employs a qualitative research paradigm. Mezirow’s Transformative Learning Theory is an essential lens for the researcher to explain, analyze, and interpret the research data. It is a collaborative research project. The researcher and participants cooperatively create photographic or other artwork data responding to the research questions. Photovoice and arts-informed data collection methodology are the main methods. Research participants engage in the study as co-researchers and inquire about their own workplace learning experiences, actively utilizing their critical self-reflective and dialogic skills. Co-researchers individually select the forms of artwork they prefer to engage with to represent their transformative workplace learning experiences about the Canadian workplace cultures that they underwent while working with colleagues and administrators in the workplace. Once the co-researchers generate their cultural artifacts as research data, they collaboratively interpret their artworks with the researcher and other volunteer co-researchers. Co-researchers jointly investigate the themes emerging from the artworks. They also interpret the meanings of their own and others’ workplace learning experiences embedded in the artworks through interactive one-on-one or group interviews. The following are the research questions that the migrant women English instructor participants examine and answer: (1) What have they learned about their workplace culture and how do they explain their learning experiences?; (2) How transformative have their learning experiences been at work?; (3) How have their colleagues and administrators influenced their transformative learning?; (4) What kind of support have they received? What supports have been valuable to them and what changes would they like to see?; (5) What have their learning experiences transformed?; (6) What has this arts-informed research process transformed? The study findings implicate English language instructor support currently practiced in post-secondary English language programs in Ontario, Canada, especially for migrant women English instructors. This research is a doctoral empirical study in progress. This research has the urgency to address the research problem that few studies have investigated migrant English instructors’ professional learning and support issues in the workplace, precisely that of English instructors working with adult learners in Canada. While appropriate social and professional support for migrant English instructors is required throughout the country, the present workplace realities in Ontario's English language programs need to be heard soon. For that purpose, the conceptualization of this study is crucial. It makes the investigation of under-represented instructors’ under-researched social phenomena, workplace learning and support, viable and rigorous. This paper demonstrates the robust theorization of English instructors’ workplace experiences using Mezirow’s Transformative Learning Theory in the English language teacher education field.

Keywords: English teacher education, professional learning, transformative learning theory, workplace learning

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Authors: Tagel Gebrehiwot, Carolina Castilla

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Food insecurity and child malnutrition are among the most critical issues in Ethiopia. Accordingly, different reform programs have been carried to improve household food security. The Food Security Program (FSP) (among others) was introduced to combat the persistent food insecurity problem in the country. The FSP combines a safety net component called the Productive Safety Net Program (PSNP) started in 2005. The goal of PSNP is to offer multi-annual transfers, such as food, cash or a combination of both to chronically food insecure households to break the cycle of food aid. Food or cash transfers are the main elements of PSNP. The case for cash transfers builds on the Sen’s analysis of ‘entitlement to food’, where he argues that restoring access to food by improving demand is a more effective and sustainable response to food insecurity than food aid. Cash-based schemes offer a greater choice of use of the transfer and can allow a greater diversity of food choice. It has been proven that dietary diversity is positively associated with the key pillars of food security. Thus, dietary diversity is considered as a measure of household’s capacity to access a variety of food groups. Studies of dietary diversity among Ethiopian rural households are somewhat rare and there is still a dearth of evidence on the impact of PSNP on household dietary diversity. In this paper, we examine the impact of the Ethiopia’s PSNP on household dietary diversity and child nutrition using panel household surveys. We employed different methodologies for identification. We exploit the exogenous increase in kebeles’ PSNP budget to identify the effect of the change in the amount of money households received in transfers between 2012 and 2014 on the change in dietary diversity. We use three different approaches to identify this effect: two-stage least squares, reduced form IV, and generalized propensity score matching using a continuous treatment. The results indicate the increase in PSNP transfers between 2012 and 2014 had no effect on household dietary diversity. Estimates for different household dietary indicators reveal that the effect of the change in the cash transfer received by the household is statistically and economically insignificant. This finding is robust to different identification strategies and the inclusion of control variables that determine eligibility to become a PSNP beneficiary. To identify the effect of PSNP participation on children height-for-age and stunting we use a difference-in-difference approach. We use children between 2 and 5 in 2012 as a baseline because by then they have achieved long-term failure to grow. The treatment group comprises children ages 2 to 5 in 2014 in PSNP participant households. While changes in height-for-age take time, two years of additional transfers among children who were not born or under the age of 2-3 in 2012 have the potential to make a considerable impact on reducing the prevalence of stunting. The results indicate that participation in PSNP had no effect on child nutrition measured as height-for-age or probability of beings stunted, suggesting that PSNP should be designed in a more nutrition-sensitive way.

Keywords: continuous treatment, dietary diversity, impact, nutrition security

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Authors: Khadija Al-Hosni, Moon Fai Chan, Mohammed Al-Azri

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Background: Several studies suggest that most school-age adolescents are poorly informed on cancer warning signs and risk factors. Providing adolescents with sufficient knowledge would increase their awareness in adulthood and improve seeking behaviors later. Significant: The results will provide a clear vision in assisting key decision-makers in formulating policies on the students' awareness programs towards cancer. So, the likelihood of avoiding cancer in the future will be increased or even promote early diagnosis. Objectives: to evaluate the effectiveness of an education program designed to increase awareness of cancer signs and symptoms risk factors, improve the behavior of seeking help among school students in Oman, and address the barriers to obtaining medical help. Methods: A randomized controlled trial with two groups was conducted in Oman. A total of 1716 students (n=886/control, n= 830/education), aged 15-17 years, at 10th and 11th grade from 12 governmental schools 3 in governorates from 20-February-2022 to 12-May-2022. Basic demographic data were collected, and the Cancer Awareness Measure (CAM) was used as the primary outcome. Data were collected at baseline (T0) and 4 weeks after (T1). The intervention group received an education program about cancer's cause and its signs and symptoms. In contrast, the control group did not receive any education related to this issue during the study period. Non-parametric tests were used to compare the outcomes between groups. Results: At T0, the lamp was the most recognized cancer warning sign in control (55.0%) and intervention (55.2%) groups. However, there were no significant changes at T1 for all signs in the control group. In contrast, all sign outcomes were improved significantly (p<0.001) in the intervention group, the highest response was unexplained pain (93.3%). Smoking was the most recognized risk factor in both groups: (82.8% for control; 84.1% for intervention) at T0. However, there was no significant change in T1 for the control group, but there was for the intervention group (p<0.001), the highest identification was smoking cigarettes (96.5%). Too scared was the largest barrier to seeking medical help by students in the control group at T0 (63.0%) and T1 (62.8%). However, there were no significant changes in all barriers in this group. Otherwise, being too embarrassed (60.2%) was the largest barrier to seeking medical help for students in the intervention group at T0 and too scared (58.6%) at T1. Although there were reductions in all barriers, significant differences were found in six of ten only (p<0.001). Conclusion: The intervention was effective in improving students' awareness of cancer symptoms, warning signs (p<0.001), and risk factors (p<0.001 reduced the most addressed barriers to seeking medical help (p<0.001) in comparison to the control group. The Ministry of Education in Oman could integrate awareness of cancer within the curriculum, and more interventions are needed on the sociological part to overcome the barriers that interfere with seeking medical help.

Keywords: adolescents, awareness, cancer, education, intervention, student

Procedia PDF Downloads 86

Authors: Stefano Zaninotto, Geoffrey Farrugia, Johan Debattista, Jason Gauci

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The need to reduce fuel and noise during taxi operations in the airports with a scenario of constantly increasing air traffic has resulted in an effort by the aerospace industry to move towards electric taxiing. In fact, this is one of the problems that is currently being addressed by SESAR JU and two main solutions are being proposed. With the first solution, electric motors are installed in the main (or nose) landing gear of the aircraft. With the second solution, manned or unmanned electric tow trucks are used to tow aircraft from the gate to the runway (or vice-versa). The presence of the tow trucks results in an increase in vehicle traffic inside the airport. Therefore, it is important to design the system in a way that the workload of Air Traffic Control (ATC) is not increased and the system assists ATC in managing all ground operations. The aim of this work is to develop an electric taxiing system, based on the use of autonomous tow trucks, which optimizes aircraft ground operations while keeping ATC in the loop. This system will consist of two components: an optimization tool and a Graphical User Interface (GUI). The optimization tool will be responsible for determining the optimal path for arriving and departing aircraft; allocating a tow truck to each taxiing aircraft; detecting conflicts between aircraft and/or tow trucks; and proposing solutions to resolve any conflicts. There are two main optimization strategies proposed in the literature. With centralized optimization, a central authority coordinates and makes the decision for all ground movements, in order to find a global optimum. With the second strategy, called decentralized optimization or multi-agent system, the decision authority is distributed among several agents. These agents could be the aircraft, the tow trucks, and taxiway or runway intersections. This approach finds local optima; however, it scales better with the number of ground movements and is more robust to external disturbances (such as taxi delays or unscheduled events). The strategy proposed in this work is a hybrid system combining aspects of these two approaches. The GUI will provide information on the movement and status of each aircraft and tow truck, and alert ATC about any impending conflicts. It will also enable ATC to give taxi clearances and to modify the routes proposed by the system. The complete system will be tested via computer simulation of various taxi scenarios at multiple airports, including Malta International Airport, a major international airport, and a fictitious airport. These tests will involve actual Air Traffic Controllers in order to evaluate the GUI and assess the impact of the system on ATC workload and situation awareness. It is expected that the proposed system will increase the efficiency of taxi operations while reducing their environmental impact. Furthermore, it is envisaged that the system will facilitate various controller tasks and improve ATC situation awareness.

Keywords: air traffic control, electric taxiing, autonomous tow trucks, graphical user interface, ground operations, multi-agent, route optimization

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Authors: Netasha Shaikh, Bryony Wood, Demitra Tsivos, Michael Knight, Risto Kauppinen, Elizabeth Coulthard

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Background: Effective treatment of dementia will require early diagnosis, before significant brain damage has accumulated. Memory loss is an early symptom of Alzheimer’s disease (AD). The hippocampus, a brain area critical for memory, degenerates early in the course of AD. The hippocampus comprises several subfields. In contrast to healthy aging where CA3 and dentate gyrus are the hippocampal subfields with most prominent atrophy, in AD the CA1 and subiculum are thought to be affected early. Conventional clinical structural neuroimaging is not sufficiently sensitive to identify preferential atrophy in individual subfields. Here, we will explore the sensitivity of new magnetic resonance imaging (MRI) sequences designed to interrogate medial temporal regions as an early marker of Alzheimer’s. As it is likely a combination of tests may predict early Alzheimer’s disease (AD) better than any single test, we look at the potential efficacy of such imaging alone and in combination with standard and novel cognitive tasks of hippocampal dependent memory. Methods: 20 patients with mild cognitive impairment (MCI), 20 with mild-moderate AD and 20 age-matched healthy elderly controls (HC) are being recruited to undergo 3T MRI (with sequences designed to allow volumetric analysis of hippocampal subfields) and a battery of cognitive tasks (including Paired Associative Learning from CANTAB, Hopkins Verbal Learning Test and a novel hippocampal-dependent abstract word memory task). AD participants and healthy controls are being tested just once whereas patients with MCI will be tested twice a year apart. We will compare subfield size between groups and correlate subfield size with cognitive performance on our tasks. In the MCI group, we will explore the relationship between subfield volume, cognitive test performance and deterioration in clinical condition over a year. Results: Preliminary data (currently on 16 participants: 2 AD; 4 MCI; 9 HC) have revealed subfield size differences between subject groups. Patients with AD perform with less accuracy on tasks of hippocampal-dependent memory, and MCI patient performance and reaction times also differ from healthy controls. With further testing, we hope to delineate how subfield-specific atrophy corresponds with changes in cognitive function, and characterise how this progresses over the time course of the disease. Conclusion: Novel sequences on a MRI scanner such as those in route in clinical use can be used to delineate hippocampal subfields in patients with and without dementia. Preliminary data suggest that such subfield analysis, perhaps in combination with cognitive tasks, may be an early marker of AD.

Keywords: Alzheimer's disease, dementia, memory, cognition, hippocampus

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Authors: J. Fox, J. Randhawa, M. Chan, L. Campbell, A. Weakely, D. J. Harvey, S. Tomaszewski Farias

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Early identification of individuals at risk for conversion to dementia provides an opportunity for preventative treatment. Many older adults (30-60%) report specific subjective cognitive decline (SCD); however, previous research is inconsistent in terms of what types of complaints predict future cognitive decline. The purpose of this study is to identify which specific complaints from the Everyday Cognition Scales (ECog) scales, a measure of self-reported concerns for everyday abilities across six cognitive domains, are associated with: 1) conversion from a clinical diagnosis of normal to either MCI or dementia (categorical variable) and 2) progressive cognitive decline in memory and executive function (continuous variables). 415 cognitively normal older adults were monitored annually for an average of 5 years. Cox proportional hazards models were used to assess associations between self-reported ECog items and progression to impairment (MCI or dementia). A total of 114 individuals progressed to impairment; the mean time to progression was 4.9 years (SD=3.4 years, range=0.8-13.8). Follow-up models were run controlling for depression. A subset of individuals (n=352) underwent repeat cognitive assessments for an average of 5.3 years. For those individuals, mixed effects models with random intercepts and slopes were used to assess associations between ECog items and change in neuropsychological measures of episodic memory or executive function. Prior to controlling for depression, subjective concerns on five of the eight Everyday Memory items, three of the nine Everyday Language items, one of the seven Everyday Visuospatial items, two of the five Everyday Planning items, and one of the six Everyday Organization items were associated with subsequent diagnostic conversion (HR=1.25 to 1.59, p=0.003 to 0.03). However, after controlling for depression, only two specific complaints of remembering appointments, meetings, and engagements and understanding spoken directions and instructions were associated with subsequent diagnostic conversion. Episodic memory in individuals reporting no concern on ECog items did not significantly change over time (p>0.4). More complaints on seven of the eight Everyday Memory items, three of the nine Everyday Language items, and three of the seven Everyday Visuospatial items were associated with a decline in episodic memory (Interaction estimate=-0.055 to 0.001, p=0.003 to 0.04). Executive function in those reporting no concern on ECog items declined slightly (p <0.001 to 0.06). More complaints on three of the eight Everyday Memory items and three of the nine Everyday Language items were associated with a decline in executive function (Interaction estimate=-0.021 to -0.012, p=0.002 to 0.04). These findings suggest that specific complaints across several cognitive domains are associated with diagnostic conversion. Specific complaints in the domains of Everyday Memory and Language are associated with a decline in both episodic memory and executive function. Increased monitoring and treatment of individuals with these specific SCD may be warranted.

Keywords: alzheimer’s disease, dementia, memory complaints, mild cognitive impairment, risk factors, subjective cognitive decline

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Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

Abstract:

In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

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Authors: Denise Levy, Gian M. A. A. Sordi

Abstract:

This web based project focuses on continuing corporate education and improving workers' skills in Brazilian radioactive facilities throughout the country. The potential of Information and Communication Technologies (ICTs) shall contribute to improve the global communication in this very large country, where it is a strong challenge to ensure high quality professional information to as many people as possible. The main objective of this system is to provide Brazilian radioactive facilities a complete web-based repository - in Portuguese - for research, consultation and information, offering conditions for learning and improving professional and personal skills. UNIPRORAD is a web based system to offer unified programs and inter-related information about radiological protection programs. The content includes the best practices for radioactive facilities in order to meet both national standards and international recommendations published by different organizations over the past decades: International Commission on Radiological Protection (ICRP), International Atomic Energy Agency (IAEA) and National Nuclear Energy Commission (CNEN). The website counts on concepts, definitions and theory about optimization and ionizing radiation monitoring procedures. Moreover, the content presents further discussions related to some national and international recommendations, such as potential exposure, which is currently one of the most important research fields in radiological protection. Only two publications of ICRP develop expressively the issue and there is still a lack of knowledge of fail probabilities, for there are still uncertainties to find effective paths to quantify probabilistically the occurrence of potential exposures and the probabilities to reach a certain level of dose. To respond to this challenge, this project discusses and introduces potential exposures in a more quantitative way than national and international recommendations. Articulating ICRP and AIEA valid recommendations and official reports, in addition to scientific papers published in major international congresses, the website discusses and suggests a number of effective actions towards safety which can be incorporated into labor practice. The WEB platform was created according to corporate public needs, taking into account the development of a robust but flexible system, which can be easily adapted to future demands. ICTs provide a vast array of new communication capabilities and allow to spread information to as many people as possible at low costs and high quality communication. This initiative shall provide opportunities for employees to increase professional skills, stimulating development in this large country where it is an enormous challenge to ensure effective and updated information to geographically distant facilities, minimizing costs and optimizing results.

Keywords: distance learning, information and communication technology, nuclear science, radioactive facilities

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Authors: Parisa Mansour

Abstract:

Introduction: Early diagnosis of coronavirus disease (COVID-19) is extremely important to isolate and treat patients in time, thus preventing the spread of the disease, improving prognosis and reducing mortality. High-resolution computed tomography (HRCT) chest imaging and artificial intelligence (AI)-based analysis of HRCT chest images can play a central role in the treatment of patients with COVID-19. Objective: To investigate different chest HRCT findings in different stages of COVID-19 pneumonia and to evaluate the potential role of artificial intelligence in the quantitative assessment of lung parenchymal involvement in COVID-19 pneumonia. Materials and Methods: This retrospective observational study was conducted between May 1, 2020 and August 13, 2020. The study included 2169 patients with COVID-19 who underwent chest HRCT. HRCT images showed the presence and distribution of lesions such as: ground glass opacity (GGO), compaction, and any special patterns such as septal thickening, inverted halo, mark, etc. HRCT findings of the breast at different stages of the disease (early: andlt) 5 days, intermediate: 6-10 days and late stage: >10 days). A CT severity score (CTSS) was calculated based on the extent of lung involvement on HRCT, which was then correlated with clinical disease severity. Use of artificial intelligence; Analysis of CT pneumonia and quot; An algorithm was used to quantify the extent of pulmonary involvement by calculating the percentage of pulmonary opacity (PO) and gross opacity (PHO). Depending on the type of variables, statistically significant tests such as chi-square, analysis of variance (ANOVA) and post hoc tests were applied when appropriate. Results: Radiological findings were observed in HRCT chest in 1438 patients. A typical pattern of COVID-19 pneumonia, i.e., bilateral peripheral GGO with or without consolidation, was observed in 846 patients. About 294 asymptomatic patients were radiologically positive. Chest HRCT in the early stages of the disease mostly showed GGO. The late stage was indicated by such features as retinal enlargement, thickening and the presence of fibrous bands. Approximately 91.3% of cases with a CTSS = 7 were asymptomatic or clinically mild, while 81.2% of cases with a score = 15 were clinically severe. Mean PO and PHO (30.1 ± 28.0 and 8.4 ± 10.4, respectively) were significantly higher in the clinically severe categories. Conclusion: Because COVID-19 pneumonia progresses rapidly, radiologists and physicians should become familiar with typical TC chest findings to treat patients early, ultimately improving prognosis and reducing mortality. Artificial intelligence can be a valuable tool in treating patients with COVID-19.

Keywords: chest, HRCT, covid-19, artificial intelligence, chest HRCT

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Authors: Thaminda Liyanage, Anuga Liyanage, Chamila Kurukulasuriya, Sidath Bandara

Abstract:

Background: The burden of chronic kidney disease (CKD) is growing rapidly around the world, particularly in Asia. Over the last two decades Sri Lanka has experienced an epidemic of CKD with ever growing number of patients pursuing medical care due to CKD and its complications, specially in the “Mahaweli” river basin in north central region of the island nation. This was apparently a new form of CKD which was not attributable to conventional risk factors such as diabetes mellitus, hypertension or infection and widely termed as “CKD-unknown” or “CKDu”. In the past decade a number of small scale studies were conducted to determine the aetiology, prevalence and complications of CKDu in North Central region. These hospital-based studies did not provide an accurate estimate of the problem as merely 10% or less of the people with CKD are aware of their diagnosis even in developed countries with better access to medical care. Interestingly, similar observations were made on the changing epidemiology of obesity in the region but no formal study was conducted to date to determine the magnitude of obesity burden. Moreover, if increasing obesity in the region is associated with CKD epidemic is yet to be explored. Methods: We will conduct an area wide cross sectional survey among all adult residents of the “Mahaweli” development project area 5, in the North Central Province of Sri Lanka. We will collect relevant medical history, anthropometric measurements, blood and urine for hematological and biochemical analysis. We expect a participation rate of 75%-85% of all eligible participants. Participation in the study is voluntary, there will be no incentives provided for participation. Every analysis will be conducted in a central laboratory and data will be stored securely. We will calculate the prevalence of obesity and chronic kidney disease, overall and by stage using total number of participants as the denominator and report per 1000 population. The association of obesity and CKD will be assessed with regression models and will be adjusted for potential confounding factors and stratified by potential effect modifiers where appropriate. Results: This study will provide accurate information on the prevalence of obesity and CKD in the region. Furthermore, this will explore the association between obesity and CKD, although causation may not be confirmed. Conclusion: Obesity and CKD are increasingly recognized as major public health problems in Sri Lanka. Clearly, documenting the magnitude of the problem is the essential first step. Our study will provide this vital information enabling the government to plan a coordinated response to tackle both obesity and CKD in the region.

Keywords: BMI, Chronic Kidney Disease, obesity, Sri Lanka

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