Search results for: glycemic variability

Authors: Ahmed Inal, Samir Rouidi, Samir Bachouche

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The present study is focused on the distribution and composition of seafloor marine litter associated to trawlable fishing areas along Algerian coast. The sampling was done with a GOC73 bottom trawl during four (04) demersal resource assessment cruises, respectively, in 2016, 2019, 2021 and 2022, carried out on board BELKACEM GRINE R/V. A total of 254 fishing hauls were sampled for the assessment of marine litter. Hauls were performed between 22 and 600 m of depth, the duration was between 30 and 60 min. All sampling was conducted during daylight. After the haul, marine litter was sorted and split from the catch. Then, according to the basis of the MEDITS protocol, litters were sorted into six different categories (plastic, rubber, metal, wood, glass and natural fiber). Thereafter, all marine litter were counted and weighed separately to the nearest 0.5 g. The results shows that the maximums of marine litter densities in the seafloor of the trawling fishing areas along Algerian coast are, respectively, 1996 item/km2 in 2016, 5164 item/km2 in 2019, 2173 item/km2 in 2021 and 7319 item/km2 in 2022. Thus, the plastic is the most abundant litter, it represent, respectively, 46% of marine litter in 2016, 67% in 2019, 69% in 2021 and 74% in 2022. Regarding the weight of the marine litter, it varies between 0.00 and 103 kg in 2016, between 0.04 and 81 kg in 2019, between 0.00 and 68 Kg in 2021 and between 0.00 and 318 kg in 2022. Thus, the maximum rate of marine litter compared to the total catch approximate, respectively, 66% in 2016, 90% in 2019, 65% in 2021 and 91% in 2022. In fact, the average loss in catch is estimated, respectively, at 7.4% in 2016, 8.4% in 2019, 5.7% in 2021 and 6.4% in 2022. However, the bathymetric and geographical variability had a significant impact on both density and weight of marine litter. Marine litter monitoring program is necessary for offering more solution proposals.

Keywords: composition, distribution, seabed, marine litter, algerian coast

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Authors: Jose Ruiz, Jose Velasquez, Holger Lovon

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Peruvian university buildings are critical structures for which very little research about its seismic vulnerability is available. This paper develops a probabilistic methodology that predicts seismic loss for university buildings with simulated fragility functions. Two university buildings located in the city of Cusco were analyzed. Fragility functions were developed considering seismic and structural parameters uncertainty. The fragility functions were generated with the Latin Hypercube technique, an improved Montecarlo-based method, which optimizes the sampling of structural parameters and provides at least 100 reliable samples for every level of seismic demand. Concrete compressive strength, maximum concrete strain and yield stress of the reinforcing steel were considered as the key structural parameters. The seismic demand is defined by synthetic records which are compatible with the elastic Peruvian design spectrum. Acceleration records are scaled based on the peak ground acceleration on rigid soil (PGA) which goes from 0.05g to 1.00g. A total of 2000 structural models were considered to account for both structural and seismic variability. These functions represent the overall building behavior because they give rational information regarding damage ratios for defined levels of seismic demand. The university buildings show an expected Mean Damage Factor of 8.80% and 19.05%, respectively, for the 0.22g-PGA scenario, which was amplified by the soil type coefficient and resulted in 0.26g-PGA. These ratios were computed considering a seismic demand related to 10% of probability of exceedance in 50 years which is a requirement in the Peruvian seismic code. These results show an acceptable seismic performance for both buildings.

Keywords: fragility functions, university buildings, loss assessment, Montecarlo simulation, latin hypercube

Procedia PDF Downloads 116

Authors: M. A. Alqahtani, D. P. Coleman, N. D. Pugh, L. D. M. Nokes

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In diagnostic ultrasound, the echo graphic B-scan texture is an important area of investigation since it can be analyzed to characterize the histological state of internal tissues. An important factor requiring consideration when evaluating ultrasonic tissue texture is the depth. The effect of attenuation with depth of ultrasound, the size of the region of interest, gain, and dynamic range are important variables to consider as they can influence the analysis of texture features. These sources of variability have to be considered carefully when evaluating image texture as different settings might influence the resultant image. The aim of this study is to investigate the effect of depth on the texture features in-vivo using a 3D ultrasound probe. The left leg medial head of the gastrocnemius muscle of 10 healthy subjects were scanned. Two regions A and B were defined at different depth within the gastrocnemius muscle boundary. The size of both ROI’s was 280*20 pixels and the distance between region A and B was kept constant at 5 mm. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The paired t –test was used to test the depth effect for the normally distributed data and the Wilcoxon–Mann-Whitney test was used for the non-normally distributed data. The gray level, variance, and run length matrix were significantly lowered when the depth increased. The other texture parameters showed similar values at different depth. All the texture parameters showed no significant difference between depths A and B (p > 0.05) except for gray level, variance and run length matrix (p < 0.05). This indicates that gray level, variance, and run length matrix are depth dependent.

Keywords: ultrasound image, texture parameters, computational biology, biomedical engineering

Procedia PDF Downloads 267

Authors: Chanada Aonsri, Wichai Eungpinichpong

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Stereotypical behavior is one of the learning and social skills development problems that affect children with autism. Previous studies found that traditional Thai massage (TTM) could reduce stereotypical behaviors in autistic children. However, the effects of TTM delivered by the parents of autistic children have not been explored. This pilot study investigated the effects of TTM by parents on stereotypical behaviors in children with autism. A one-group pretest-posttest design was applied for 15 children, aged 4-16 years, with their parents' permissions. They participated in the study at the Special Education program of the Special Education Center of Khon Kaen University, Thailand. After being trained in a specialized TTM for children, the parents delivered 50-minute TTM to children once a day, twice a week for eight weeks. The severity of autism and autistic behaviors were measured using the Childhood Autism Rating Scale (CARS), and the Autism Treatment Evaluation Checklist (ATEC), respectively. The functions of autonomic nervous systems were measured using Heart Rate Variability (HRV) to indicated physical and mental disorders such as stress. The data at baseline and the 8th week were analyzed using either an independent t-test or Wilcoxon signed-rank test. The study found that 16 sessions of TTM significantly improved measured data for autism in all children including the CARS (p<0.001), ATEC, speech/language/communication (p<0.001), sociability (p<0.001), sensory/cognitive awareness (p<0.001), health/physical/behavior (p < 0.001), and HRV (p<0.001). The results indicated that TTM performed by parents could be useful as an adjunct therapy for autistic children as it can reduce stereotypical behaviors and stress.

Keywords: traditional Thai massage, stereotypical behaviors, Autistic children, parent

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Authors: Hossein Hassani, Ali Rezaei

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In recent years, the study of soil contamination problems surrounding mines and smelting plants has attracted some serious attention of the environmental experts. These elements due to the non- chemical disintegration and nature are counted as environmental stable and durable contaminants. Variability of these contaminants in the soil and the time and financial limitation for the favorable environmental application, in order to reduce the risk of their irreparable negative consequences on environment, caused to apply the favorable grading of these contaminant for the further success of the risk management processes. In this study, we use the contaminants factor risk indices, average concentration, enrichment factor and geoaccumulation indices for evaluating the metal contaminant of including Pb, Ni, Se, Mo and Zn in the soil of Sarcheshmeh copper mine area. For this purpose, 120 surface soil samples up to the depth of 30 cm have been provided from the study area. And the metals have been analyzed using ICP-MS method. Comparison of the heavy and potentially toxic elements concentration in the soil samples with the world average value of the uncontaminated soil and shale average indicates that the value of Zn, Pb, Ni, Se and Mo is higher than the world average value and only the Ni element shows the lower value than the shale average. Expert opinions on the relative importance of each indicators were used to assign a final weighting of the metals and the heavy metals were ranked using the TOPSIS approach. This allows us to carry out efficient environmental proceedings, leading to the reduction of environmental ricks form the contaminants. According to the results, Ni, Pb, Mo, Zn, and Se have the highest rate of risk contamination in the soil samples of the study area.

Keywords: contamination coefficient, geoaccumulation factor, TOPSIS techniques, Sarcheshmeh copper complex

Procedia PDF Downloads 253

Authors: Mamadou Dione, Eric Matzner-lober, Philippe Alexandre

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The Energy Transition Act defined by the French State has precise implications on Renewable Energies, in particular on its remuneration mechanism. Until then, a purchase obligation contract permitted the sale of wind-generated electricity at a fixed rate. Tomorrow, it will be necessary to sell this electricity on the Market (at variable rates) before obtaining additional compensation intended to reduce the risk. This sale on the market requires to announce in advance (about 48 hours before) the production that will be delivered on the network, so to be able to predict (in the short term) this production. The fundamental problem remains the variability of the Wind accentuated by the geographical situation. The objective of the project is to provide, every day, short-term forecasts (48-hour horizon) of wind production using weather data. The predictions of the GFS model and those of the ECMWF model are used as explanatory variables. The variable to be predicted is the production of a wind farm. We do two approaches: a direct approach that predicts wind generation directly from weather data, and an integrated approach that estimâtes wind from weather data and converts it into wind power by power curves. We used machine learning techniques to predict this production. The models tested are random forests, CART + Bagging, CART + Boosting, SVM (Support Vector Machine). The application is made on a wind farm of 22MW (11 wind turbines) of the Compagnie du Vent (that became Engie Green France). Our results are very conclusive compared to the literature.

Keywords: forecast aggregation, machine learning, spatio-temporal dynamics modeling, wind power forcast

Procedia PDF Downloads 194

Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

Procedia PDF Downloads 39

Authors: Asma Shaheen, Javed Iqbal

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The water resources are depleting due to an intrusion of industrial pollution. There are clusters of industries including leather tanning, textiles, batteries, and chemical causing contamination. These industries use bulk quantity of water and discharge it with toxic effluents. The penetration of heavy metals through irrigation from industrial effluent has toxic effect on soil and groundwater. There was strong positive significant correlation between all the heavy metals in three media of industrial effluent, soil and groundwater (P < 0.001). The metal to the metal association was supported by dendrograms using cluster analysis. The geospatial variability was assessed by using geographically weighted regression (GWR) and pollution model to identify the simulation of carcinogenic elements in soil and groundwater. The principal component analysis identified the metals source, 48.8% variation in factor 1 have significant loading for sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), chromium (Cr), nickel (Ni), lead (Pb) and zinc (Zn) of tannery effluent-based process. In soil and groundwater, the metals have significant loading in factor 1 representing more than half of the total variation with 51.3 % and 53.6 % respectively which showed that pollutants in soil and water were driven by industrial effluent. The cumulative eigen values for the three media were also found to be greater than 1 representing significant clustering of related heavy metals. The results showed that heavy metals from industrial processes are seeping up toxic trace metals in the soil and groundwater. The poisonous pollutants from heavy metals turned the fresh resources of groundwater into unusable water. The availability of fresh water for irrigation and domestic use is being alarming.

Keywords: groundwater, geostatistical, heavy metals, industrial effluent

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Authors: Ali Mortazavi

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The main objective of this research project is to delve into a multitude of geomechanical risks associated with various mining methods employed within the underground mining industry. Controlling geotechnical design parameters and operational factors affecting the selection of suitable mining techniques for a given underground mining condition will be considered from a risk assessment point of view. Important geomechanical challenges will be investigated as appropriate and relevant to the commonly used underground mining methods. Given the complicated nature of rock mass in-situ and complicated boundary conditions and operational complexities associated with various underground mining methods, the selection of a safe and economic mining operation is of paramount significance. Rock failure at varying scales within the underground mining openings is always a threat to mining operations and causes human and capital losses worldwide. Geotechnical design is a major design component of all underground mines and basically dominates the safety of an underground mine. With regard to uncertainties that exist in rock characterization prior to mine development, there are always risks associated with inappropriate design as a function of mining conditions and the selected mining method. Uncertainty often results from the inherent variability of rock masse, which in turn is a function of both geological materials and rock mass in-situ conditions. The focus of this research is on developing a methodology which enables a geomechanical risk assessment of given underground mining conditions. The outcome of this research is a geotechnical risk analysis algorithm, which can be used as an aid in selecting the appropriate mining method as a function of mine design parameters (e.g., rock in-situ properties, design method, governing boundary conditions such as in-situ stress and groundwater, etc.).

Keywords: geomechanical risk assessment, rock mechanics, underground mining, rock engineering

Procedia PDF Downloads 120

Authors: Y. Wang, S. Zhang, X. Chen

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The longitudinal compressive strength of fibre reinforced plastic (FRP) possess a large stochastic variability, which limits efficient application of composite structures. This study aims to address how the random fibre packing affects the uncertainty of FRP compressive strength. An novel approach is proposed to generate random fibre packing status by a combination of Latin hypercube sampling and random sequential expansion. 3D nonlinear finite element model is built which incorporates both the matrix plasticity and fibre geometrical instability. The matrix is modeled by isotropic ideal elasto-plastic solid elements, and the fibres are modeled by linear-elastic rebar elements. Composite with a series of different nominal fibre volume fractions are studied. Premature fibre waviness at different magnitude and direction is introduced in the finite element model. Compressive tests on uni-directional CFRP (carbon fibre reinforced plastic) are conducted following the ASTM D6641. By a comparison of 3D FE models and compressive tests, it is clearly shown that the stochastic variation of compressive strength is partly caused by the random fibre packing, and normal or lognormal distribution tends to be a good fit the probabilistic compressive strength. Furthermore, it is also observed that different random fibre packing could trigger two different fibre micro-buckling modes while subjected to longitudinal compression: out-of-plane buckling and twisted buckling. The out-of-plane buckling mode results much larger compressive strength, and this is the major reason why the random fibre packing results a large uncertainty in the FRP compressive strength. This study would contribute to new approaches to the quality control of FRP considering higher compressive strength or lower uncertainty.

Keywords: compressive strength, FRP, micro-buckling, random fibre packing

Procedia PDF Downloads 254

Authors: Yajun Qiao, Yaner Yan, Ning Li, Shuqing An

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In order to relieve the pressure on a land resource from a huge population, reclamation has occurred in many coastal wetlands. Plants can maintain their elemental composition within normal limits despite the variations of external conditions. Reclamation may affect carbon (C), nitrogen (N) and phosphorus (P) stoichiometry in the plant to some extent by altering physical and chemical properties of soil in a coastal wetland. We reported the seasonal dynamic of C, N and P stoichiometry in root, stem and leaf of Suaeda salsa (L.) Pall. and in soil between reclamation plots and natural plots. Our results of three-way ANOVA indicated that sampling season always had significant effect on C, N, P concentrations and their ratios; organ had no significant effect on N, P concentration and N:P; plot type had no significant effect on N concentration and C:N. Sampling season explained the most variability of tissue N and P contents, C:N, C:P and N:P, while it’s organ for C using the restricted maximum likelihood (REML) method. By independent sample T-test, we found that reclamation affect more on C, N and P stoichiometry of stem than that of root or leaf on the whole. While there was no difference between reclamation plots and natural plots for soil in four seasons. For three organs, C concentration had peak values in autumn and minimum values in spring while N concentration had peak values in spring and minimum values in autumn. For P concentration, three organs all had peak values in spring; however, the root had minimum value in winter, the stem had that in autumn, and leaf had that in summer. The seasonal dynamic of C, N and P stoichiometry in a leaf of Suaeda salsa were much steadier than that in root or stem under the drive of reclamation.

Keywords: nitrogen, phosphorus, reclamation, seasonal dynamic, Suaeda salsa

Procedia PDF Downloads 333

Authors: Masoud Sheidai

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Biologists with a special field of population genetics and phylogeny have different research tasks such as populations’ genetic variability and divergence, species relatedness, the evolution of genetic and morphological characters, and identification of DNA SNPs with adaptive potential. To tackle these problems and reach a concise conclusion, they must use the proper and efficient statistical and bioinformatic methods as well as suitable genetic and morphological characteristics. In recent years application of different bioinformatic and statistical methods, which are based on various well-documented assumptions, are the proper analytical tools in the hands of researchers. The species delineation is usually carried out with the use of different clustering methods like K-means clustering based on proper distance measures according to the studied features of organisms. A well-defined species are assumed to be separated from the other taxa by molecular barcodes. The species relationships are studied by using molecular markers, which are analyzed by different analytical methods like multidimensional scaling (MDS) and principal coordinate analysis (PCoA). The species population structuring and genetic divergence are usually investigated by PCoA and PCA methods and a network diagram. These are based on bootstrapping of data. The Association of different genes and DNA sequences to ecological and geographical variables is determined by LFMM (Latent factor mixed model) and redundancy analysis (RDA), which are based on Bayesian and distance methods. Molecular and morphological differentiating characters in the studied species may be identified by linear discriminant analysis (DA) and discriminant analysis of principal components (DAPC). We shall illustrate these methods and related conclusions by giving examples from different edible and medicinal plant species.

Keywords: GWAS analysis, K-Means clustering, LFMM, multidimensional scaling, redundancy analysis

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Authors: Nishi Bhuvandas, P. V. Timbadiya, P. L. Patel, P. D. Porey

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Recent perceived climate variability raises concerns with unprecedented hydrological phenomena and extremes. Distribution and circulation of the waters of the Earth become increasingly difficult to determine because of additional uncertainty related to anthropogenic emissions. According to the sixth Intergovernmental Panel on Climate Change (IPCC) Technical Paper on Climate Change and water, changes in the large-scale hydrological cycle have been related to an increase in the observed temperature over several decades. Although many previous research carried on effect of change in climate on hydrology provides a general picture of possible hydrological global change, new tools and frameworks for modelling hydrological series with nonstationary characteristics at finer scales, are required for assessing climate change impacts. Of the downscaling techniques, dynamic downscaling is usually based on the use of Regional Climate Models (RCMs), which generate finer resolution output based on atmospheric physics over a region using General Circulation Model (GCM) fields as boundary conditions. However, RCMs are not expected to capture the observed spatial precipitation extremes at a fine cell scale or at a basin scale. Statistical downscaling derives a statistical or empirical relationship between the variables simulated by the GCMs, called predictors, and station-scale hydrologic variables, called predictands. The main focus of the paper is on the need for using statistical downscaling techniques for projection of local hydrometeorological variables under climate change scenarios. The projections can be then served as a means of input source to various hydrologic models to obtain streamflow, evapotranspiration, soil moisture and other hydrological variables of interest.

Keywords: climate change, downscaling, GCM, RCM

Procedia PDF Downloads 380

Authors: Oscar Covarrubias, Diane Ghanem, Christopher Murdock, Babar Shafiq

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Introduction: ChatGPT is an advanced language AI tool designed to understand and generate human-like text. The aim of this study is to assess the ability of ChatGPT-4 to re-write orthopaedic trauma patient education materials at the recommended 6th-grade level. Methods: Two independent reviewers accessed ChatGPT-4 (chat.openai.com) and gave identical instructions to simplify the readability of provided text to a 6th-grade level. All trauma-related articles by the Orthopaedic Trauma Association (OTA) and American Academy of Orthopaedic Surgeons (AAOS) were sequentially provided. The academic grade level was determined using the Flesh-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE). Paired t-tests and Wilcox-rank sum tests were used to compare the FKGL and FRE between the ChatGPT-4 revised and original text. Inter-rater correlation coefficient (ICC) was used to assess variability in ChatGPT-4 generated text between the two reviewers. Results: ChatGPT-4 significantly reduced FKGL and increased FRE scores in the OTA (FKGL: 5.7±0.5 compared to the original 8.2±1.1, FRE: 76.4±5.7 compared to the original 65.5±6.6, p < 0.001) and AAOS articles (FKGL: 5.8±0.8 compared to the original 8.9±0.8, FRE: 76±5.5 compared to the original 56.7±5.9, p < 0.001). On average, 14.6% of OTA and 28.6% of AAOS articles required at least two revisions by ChatGPT-4 to achieve a 6th-grade reading level. ICC demonstrated poor reliability for FKGL (OTA 0.24, AAOS 0.45) and moderate reliability for FRE (OTA 0.61, AAOS 0.73). Conclusion: This study provides a novel, simple and efficient method using language AI to optimize the readability of patient education content which may only require the surgeon’s final proofreading. This method would likely be as effective for other medical specialties.

Keywords: artificial intelligence, AI, chatGPT, patient education, readability, trauma education

Procedia PDF Downloads 50

Authors: Adandé Belarmain Fandohan

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Climate change has become a major concern globally, particularly in rural communities that have to find rapid coping solutions. Several vulnerability assessment approaches have been developed in the last decades. This comes along with a higher risk for different methods to result in different conclusions, thereby making comparisons difficult and decision-making non-consistent across areas. The effect of methods and computational approaches on estimates of people’s vulnerability was assessed using data collected from the Gambia. Twenty-four indicators reflecting vulnerability components: (exposure, sensitivity, and adaptive capacity) were selected for this purpose. Data were collected through household surveys and key informant interviews. One hundred and fifteen respondents were surveyed across six communities and two administrative districts. Results were compared over three computational approaches: the maximum value transformation normalization, the z-score transformation normalization, and simple averaging. Regardless of the approaches used, communities that have high exposure to climate change and extreme events were the most vulnerable. Furthermore, the vulnerability was strongly related to the socio-economic characteristics of farmers. The survey evidenced variability in vulnerability among communities and administrative districts. Comparing output across approaches, overall, people in the study area were found to be highly vulnerable using the simple average and maximum value transformation, whereas they were only moderately vulnerable using the z-score transformation approach. It is suggested that assessment approach-induced discrepancies be accounted for in international debates to harmonize/standardize assessment approaches to the end of making outputs comparable across regions. This will also likely increase the relevance of decision-making for adaptation policies.

Keywords: maximum value transformation, simple averaging, vulnerability assessment, West Africa, z-score transformation

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Authors: Paridhi Agarwal, Kusum M. George

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A cynic is someone upset about the future prematurely. In today’s highly competitive workplace, cynicism has become a prominent concern. It is a controversial issue that brings about psychological disengagement and antagonism towards the management. In organizational sciences, scientific investigation of this negative work behavior is lacking, and so there is no universal definition so far. But most commonly, Organizational Cynicism (OC) has been characterized as an unfavorable attitude towards the organization, encompassing a belief that the organization has low integrity, negative affect, and depreciative behavioral tendencies. Given its prevalence, this study aims to contribute to the existing body of knowledge on OC. This research examines the predictability of OC from two factors- Perceived Organizational Support (POS) and Emotional Intelligence (EI) among millennials in India as well as identify contradictions in today’s scenario. Standardized Organizational Cynicism Scale comprising of three components, Perceived Organizational Support Questionnaire and Goleman’s Emotional Intelligence Test are used on a convenient sample of 104 corporate sector employees in the age range 22-35 years. Correlation test elucidated the relationships, and regression analysis revealed the level of influence of the above variables on OC. Surprisingly, Emotional-Social Awareness had stronger relationships with all dimensions of OC in males as compared to females. It was also seen that EI and POS, together with predicted OC, but separately, only POS accounted for variability in OC, and this impact was much stronger for males, implying that there are other important factors that make females cynical at work. Thus, the over-emphasis on EI training for the millennial generation has also been challenged in this study. It can be said that there are avertible preconditions to the negative attitude- OC. This research has important managerial implications in areas of recruitment, training, and organizational environment.

Keywords: emotional intelligence, millennials, organizational cynicism, perceived organizational support.

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Authors: Mudawi M. Nour

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Background: Now a days, the multidisciplinary scientific research conception in the field of nanotechnology has witnessed development with regard to the numerous applications and synthesis of nanomaterials. Objective: The current investigation has been conducted with the main focus on the green synthesis of silver nanoparticles from the leaves of Salvadora persica and its antibacterial activity against MDR pathogens E. coli and S. aureus. Methodology: Silver nanoparticles (AgNPs) were prepared after addition of aqueous extract of Salvadora persica leaves. The UV-Vis spectrophotometer, Transmission Electron Microscopy (TEM), zeta potential and Scanning Electron Microscopy (SEM) were employed to detect the particle size and morphology, besides Fourier transform infra-red spectrometer (FTIR) analysis was performed to determine the capping and stabilizing agents in the extract. Antibacterial assay for the biogenic AgNPs was conducted against E. coli and S. aureus. Results: Color change of the mixture from yellow to dark brown is the first indication to AgNPs formation. Furthermore, 420 nm was the peak value for UV-Vis spectroscopy absorption of the mixture. Besides, TEM and SEM micrographs showed wide variability in the diameter of smaller NPs aggregated together with spherical shapes, and zeta sizer showed about 153.3 nm as an average size of nanoparticles. Microbial suppression was noticed for the tested microorganisms. Furthermore, with the help of FTIR analysis, the biomolecules that act as capping and stabilizing agents of AgNPs are proteins and phenols present in the plant extract. Conclusion: Salvadora persica leaves extract act as a reducing and stabilizing agent for the synthesis of AgNPs, keeping its ability to suppress the MDR pathogen.

Keywords: green synthesis, FTIR, MDR pathogen, salvadora persica

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Authors: Sreeja Shaw, Prosenjit Samanta, Asish Kumar Mukhopadhyay

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Cholera is still a global public health burden and is caused by Vibrio cholerae O1 and O139 serogroups. Evidence from recent outbreaks in Haiti and Yemen suggested that circulating V. cholerae O1 El Tor variant strains are continuously changing to cause more ruinous outbreaks worldwide, and most of them have emerged from the Indian subcontinents. Therefore, we studied the changing virulence characteristics along with the antibiotic resistance profile of V. cholerae O1strains isolated from seasonal outbreaks in three cholera endemic regions during 2018, Gujarat and Maharashtra in Western India (87 strains), and to compare those features with the isolates of West Bengal in Eastern India (48 strains) collected during the same period. All the strains from Western India were of Ogawa serotype, polymyxin B-sensitive, hemolytic, and contained a large fragment deletion in VSP-II genomic region similar with Yemen outbreak strains and carried more virulent Haitian genetic alleles of major virulence associated genes ctxB, tcpA, and rtxA. Conversely, 14.6% (7/48) of the strains from Eastern India were belong to the Inaba serotype, polymyxin B-resistant, non-hemolytic, harbored intact VSP-II region, classical ctxB, Haitian tcpA, and El Tor rtxA alleles. Interestingly, resistance to tetracycline and chloramphenicol was seen in isolates from both regions, which are not very common among V. cholerae O1 isolates in India. Therefore, this study indicated West Bengal as a diverse region where two different types of El Tor variant hypervirulent strains are co-existed, probably competing for their better environmental survival, which may result in severe irrepressible disease outcome in the future.

Keywords: cholera, vibrio cholerae, polymyxin B, Non-hemolytic, ctxB, tcpA, rtxA, VSP-II

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Authors: K. Shimola, M. Krishnaveni

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This paper examines vulnerability assessment of water resources in a semi-arid basin using the 4-step approach. The vulnerability assessment framework is developed to study the water resources vulnerability which includes the creation of GIS-based vulnerability maps. These maps represent the spatial variability of the vulnerability index. This paper introduces the 4-step approach to assess vulnerability that incorporates a new set of indicators. The approach is demonstrated using a framework composed of a precipitation data for (1975–2010) period, temperature data for (1965–2010) period, hydrological model outputs and the water resources GIS data base. The vulnerability assessment is a function of three components such as exposure, sensitivity and adaptive capacity. The current water resources vulnerability is assessed using GIS based spatio-temporal information. Rainfall Coefficient of Variation, monsoon onset and end date, rainy days, seasonality indices, temperature are selected for the criterion ‘exposure’. Water yield, ground water recharge, evapotranspiration (ET) are selected for the criterion ‘sensitivity’. Type of irrigation and storage structures are selected for the criterion ‘Adaptive capacity’. These indicators were mapped and integrated in GIS environment using overlay analysis. The five sub-basins, namely Arjunanadhi, Kousiganadhi, Sindapalli-Uppodai and Vallampatti Odai, fall under medium vulnerability profile, which indicates that the basin is under moderate stress of water resources. The paper also explores prioritization of sub-basinwise adaptation strategies to climate change based on the vulnerability indices.

Keywords: adaptive capacity, exposure, overlay analysis, sensitivity, vulnerability

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Authors: Aldi Nel, Cliff L. W. Jones, Justin O. G. Kemp, Peter J. Britz

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Kelp Ecklonia maxima is included in formulated abalone feeds in South Africa, but its effect on abalone growth, feed utilisation efficiency and gut-bacterial communities has not previously been investigated. An eight-month on-farm growth trial with sub-adult Haliotis midae (~43 mm shell length) fed graded levels of kelp in formulated feeds was conducted. Kelp inclusion (0.44–3.54 % of pellet dry mass) promoted faster growth (65.7 – 74.5 % total mass gain), with better feed and protein conversions (FCR: 1.4 – 1.8; PER 2.3 – 2.7), compared to abalone fed the non-supplemented feed (52.3% total mass gain; FCR: 2.1; PER 1.9; p < 0.001). The gut-bacterial communities of abalone fed kelp-supplemented feed (0.88 % of pellet dry mass) were subsequently compared with that of abalone fed a non-supplemented control diet. Abalone gut-bacterial DNA was sequenced using 16S rRNA pyrosequencing and sequences were clustered into operational taxonomic units (OTUs) at a 97 % similarity level. A supplementary 16S rRNA denaturing gradient gel electrophoresis (DGGE) analysis was conducted. The dominant OTUs differed in terms of their relative abundances, with that of an autochthonous Mollicutes strain being significantly higher (p = 0.03) in the guts of abalone fed kelp-supplemented feed. The DGGE band patterns displayed a higher within-group variability of dominant bacterial strains for abalone fed the control diet, suggesting that dietary inclusion of kelp, which is rich in fermentable polysaccharides, promotes a balanced gut-bacterial community. This may contribute to the better feed utilisation and growth in abalone fed kelp-supplemented feeds.

Keywords: abfeed, digestion, macroalgae, mariculture

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Authors: L. Sun, O. Bloom, K. Anderson

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The medial and lateral sural artery perforator flaps (MSAP and LSAP, respectively) are two recently described flaps that are less commonly used in lower limb trauma reconstructive surgeries compared to flaps such as the anterolateral thigh (ALT) flap or the gastrocnemius flap. The LSAP flap has several theoretical benefits over the MSAP, including the ability to be sensate and being more easily manoeuvred into position as a local flap for coverage of lateral knee or leg defects. It is less commonly used in part due to a lack of documented studies of the anatomical reliability of the perforator, and an unquantified average length of the pedicle used for microsurgical anastomosis (if used as a free flap) or flap rotation (if used as a pedicled flap). It has been shown to have significantly lower donor site morbidity compared to other flaps such as the ALT, due to the decreased need for intramuscular dissection and resulting in less muscle loss at the donor site. 11 cadaveric lower limbs were dissected, with a mean of 1.6 perforators per leg, with an average pedicle length of 45mm to the sural artery and 70mm to the popliteal artery. While the majority of perforating arteries lay close to the midline (average of 19mm lateral to the midline), there were patients whose artery was significantly lateral and would have been likely injured by the initial incision during an operation. Adding to the literature base of documented LSAP dissections provides a greater understanding of the anatomical basis of these perforator flaps, and the authors hope this will establish them as a more commonly used and discussed option when managing complicated lower limb trauma requiring soft tissue reconstruction.

Keywords: cadaveric, dissection, lateral, perforator flap, sural artery, surface anatomy

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

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Authors: Faisal Nouroz, Mukaramin Mukaramin

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Of the transposable elements (TEs), the retrotransposons are the most copious elements identified from many sequenced genomes. They have played a major role in genome evolution, rearrangement, and expansions based on their copy and paste mode of proliferation. They are further divided into LTR and Non-LTR retrotransposons. The purpose of the current study was to identify the LTR REs in sequenced Phoenix dactylifera genome and to study their structural diversity. A total of 150 P. dactylifera BAC sequences with > 60kb sizes were randomly retrieved from National Center for Biotechnology Information (NCBI) database and screened for the presence of LTR retrotransposons. Seven bacterial artificial chromosomes (BAC) sequences showed full-length LTR Retrotransposons with 4 Copia and 3 Gypsy families having variable copy numbers in respective families. Reverse transcriptase (RT) domain was found as the most conserved domain among Copia and Gypsy superfamilies and was used to deduce evolutionary analysis. The amino acid residues among various RT sequences showed variability in their percentages indicating post divergence evolution. Amino acid Leucine was found in highest proportions followed by Lysine, while Methionine and Tryptophan were in lowest percentages. The phylogenetic analysis based on RT domains confirmed that although having most conserved RT regions, several evolutionary events occurred causing nucleotide polymorphisms and hence clustering of Gypsy and Copia superfamilies into their respective lineages. The study will be helpful in identification and annotation of these elements in other species and genera and their distribution patterns on chromosomes by fluorescent in situ hybridization techniques.

Keywords: transposable elements, Phoenix dactylifera, retrotransposons, phylogenetic analysis

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Authors: A. A. Sadia, A. Emdad, E. Hossain

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The increasing importance of mushrooms as a source of nutrition, health benefits, and even potential cancer treatment has raised awareness of the impact of climate-sensitive variables on their cultivation. Factors like temperature, relative humidity, air quality, and substrate composition play pivotal roles in shaping mushroom growth, especially in Bangladesh. Oyster mushrooms, a commonly cultivated variety in this region, are particularly vulnerable to climate fluctuations. This research explores the climatic dynamics affecting oyster mushroom cultivation and, presents an approach to address these challenges and provides tangible solutions to fortify the agro-economy, ensure food security, and promote the sustainability of this crucial food source. Using climate and production data, this study evaluates the performance of three clustering algorithms -KMeans, OPTICS, and BIRCH- based on various quality metrics. While each algorithm demonstrates specific strengths, the findings provide insights into their effectiveness for this specific dataset. The results yield essential information, pinpointing the optimal temperature range of 13°C-22°C, the unfavorable temperature threshold of 28°C and above, and the ideal relative humidity range of 75-85% with the suitable production regions in three different seasons: Kharif-1, 2, and Robi. Additionally, a user-friendly web application is developed to support mushroom farmers in making well-informed decisions about their cultivation practices. This platform offers valuable insights into the most advantageous periods for oyster mushroom farming, with the overarching goal of enhancing the efficiency and profitability of mushroom farming.

Keywords: climate variability, mushroom cultivation, clustering techniques, food security, sustainability, web-application

Procedia PDF Downloads 39

Authors: I. Sonia d’Apolito, Bianca Sisinni, Mirko Grimaldi, Barbara Gili Fivela

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The American English contrast /ɑ-ʌ/ (cop-cup) is difficult to be produced by Italian learners since they realize L2-/ɑ-ʌ/ as L1-/ɔ-a/ respectively, due to differences in phonetic-phonological systems and also in grapheme-to-phoneme conversion rules. In this paper, we try to answer the following research questions: Can a short training improve the production of English /ɑ-ʌ/ by Italian learners? Is a perceptual training better than an articulatory (ultrasound - US) training? Thus, we compare a perceptual training with an US articulatory one to observe: 1) the effects of short trainings on L2-/ɑ-ʌ/ productions; 2) if the US articulatory training improves the pronunciation better than the perceptual training. In this pilot study, 9 Salento-Italian monolingual adults participated: 3 subjects performed a 1-hour perceptual training (ES-P); 3 subjects performed a 1-hour US training (ES-US); and 3 control subjects did not receive any training (CS). Verbal instructions about the phonetic properties of L2-/ɑ-ʌ/ and L1-/ɔ-a/ and their differences (representation on F1-F2 plane) were provided during both trainings. After these instructions, the ES-P group performed an identification training based on the High Variability Phonetic Training procedure, while the ES-US group performed the articulatory training, by means of US video of tongue gestures in L2-/ɑ-ʌ/ production and dynamic view of their own tongue movements and position using a probe under their chin. The acoustic data were analyzed and the first three formants were calculated. Independent t-tests were run to compare: 1) /ɑ-ʌ/ in pre- vs. post-test respectively; /ɑ-ʌ/ in pre- and post-test vs. L1-/a-ɔ/ respectively. Results show that in the pre-test all speakers realize L2-/ɑ-ʌ/ as L1-/ɔ-a/ respectively. Contrary to CS and ES-P groups, the ES-US group in the post-test differentiates the L2 vowels from those produced in the pre-test as well as from the L1 vowels, although only one ES-US subject produces both L2 vowels accurately. The articulatory training seems more effective than the perceptual one since it favors the production of vowels in the correct direction of L2 vowels and differently from the similar L1 vowels.

Keywords: L2 vowel production, perceptual training, articulatory training, ultrasound

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Authors: Rajeev Manhas, M. A. Bhat, A. K. Taku, Dalip Singh, Deep Shikha, Gulzar Bader

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The study was aimed to understand the distribution of various serogroups of Pasteurella multocida in bovines, small ruminants, pig, rabbit, and poultry from Jammu, Jammu and Kashmir and to characterize the isolates with respect to LPS synthesizing genes, dermonecrotic toxin gene (toxA) gene and antibiotic resistance. For isolation, the nasopharyngeal swab procedure appeared to be better than nasal swab procedure, particularly in ovine and swine. Out of 200 samples from different animals, isolation of P. multocida could be achieved from pig and sheep (5 each) and from poultry and buffalo (2 each) samples only, which accounted for 14 isolates. Upon molecular serogrouping, 3 isolates from sheep and 2 isolates from poultry were found as serogroup A, 2 isolates from buffalo were confirmed as serogroup B and 5 isolates from pig were found to belong to serogroup D. However, 2 isolates from sheep could not be typed, hence, untypable. All the 14 isolates were subjected to mPCR genotyping. A total of 10 isolates, 5 each from pig and sheep, generated an amplicon specific to genotype L6 and L6 indicates Heddleston serovars 10, 11, 12 and 15. Similarly, 2 isolates from bovines generated an amplicon of genotype L2 which indicates Heddleston serovar 2/5. However, 2 isolates from poultry generated specific amplicon with L1 signifying Heddleston serovar 1, but these isolates also produced multiple bands with primer L5. Only, one isolate of capsular type A from sheep possessed the structural gene, toxA for dermonecrotoxin. There was variability in the antimicrobial susceptibility pattern in sheep isolates, but overall the rate of tetracycline resistance was relatively high (64.28%) in our strains while all the isolates were sensitive to streptomycin. Except for the swine isolates and one toxigenic sheep isolate, the P. multocida isolates from this study were sensitive to quinolones. Although the level of resistance to commercial antibiotics was generally low, the use of tetracycline and erythromycin was not recommended.

Keywords: antibiogram, genotyping, Pasteurella multocida, serogrouping, toxA

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Authors: Patrick M. Garvey, Roger P. Pech, Daniel M. Tompkins

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Competition is a widespread interaction and natural selection will encourage the development of mechanisms that recognise and respond to dominant competitors, if this information reduces the risk of a confrontation. As olfaction is the primary sense for most mammals, our research tested whether olfactory ‘eavesdropping’ mediates alien species interactions and whether we could exploit our understanding of this behaviour to create ‘super-lures’. We used a combination of pen and field experiments to evaluate the importance of this behaviour. In pen trials, stoats (Mustela erminea) were exposed to the body odour of three dominant predators (cat / ferret / African wild dog) and these scents were found to be attractive. A subsequent field trial tested whether attraction displayed towards predator odour, particularly ferret (Mustela furo) pheromones, could be replicated with invasive predators in the wild. We found that ferret odour significantly improved detection and activity of stoats and hedgehogs (Erinaceus europaeus), while also improving detections of ship rats (Rattus rattus). Our current research aims to identify the key components of ferret odour, using chemical analysis and behavioural experiments, so that we can produce ‘scent from a can’. A lure based on a competitors’ odour would be beneficial in many circumstances including: (i) where individuals display variability in attraction to food lures, (ii) there are plentiful food resources available, (iii) new immigrants arrive into an area, (iv) long-life lures are required. Pest management can therefore benefit by exploiting behavioural responses to odours to achieve conservation goals.

Keywords: predator interactions, invasive species, eavesdropping, semiochemicals

Procedia PDF Downloads 385

Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

Procedia PDF Downloads 53

Authors: Jill Garner, Belinda Lange, Sheila Lennon, Maayken van den Berg

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Currently, there are approximately one billion people worldwide affected by a neurological condition. Many of whom are assessed and treated by a physiotherapist in a variety of settings. There is a lack of consensus in the literature related to what is clinically assessed by physiotherapists in people with neurological conditions. This study aimed to explore assessment in people with neurological conditions, including how health care setting, experience, and therapeutic approach, may influence neurological assessment. A national survey targeted Australian physiotherapists who assess adults with neurological conditions as part of their clinical practice. The survey consisted of 39 questions and was distributed to physiotherapists through the Australian Physiotherapy Association, and Chief Allied Health Officers across Australia and advertised on the National Neurological Physiotherapy Facebook page. In total, 395 respondents consented to the survey from all states within Australia. Most respondents were female (85.4%) with a mean (SD) age of 35.7 years. Respondents reported working clinically in acute, community, outpatients, and community settings. Stroke was the most assessed condition (58.0%). There is variability in domains assessed by Australian physiotherapists, with common inclusions of balance, muscle strength, gait, falls and safety, function, goal setting, range of movement, pain, coordination, activity tolerance, postural alignment and symmetry and upper limb. There is little evidence to support what physiotherapists assess in practice, in different settings, and in different states within Australia and not enough information to develop a decision tree regarding what is important for assessment in different settings. Further research is needed to explore this area and develop a consensus around best practices.

Keywords: physiotherapy, neurological, assessment, domains

Procedia PDF Downloads 74

Authors: Adey Nigatu Mersha, Ilyas Masih, Charlotte de Fraiture, Tena Alamirew

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Conflicts over water are increasing mainly as a result of water scarcity in response to higher water demand and climatic variability. There is often not enough water to meet all demands for different uses. Thus, decisions have to be made as to how the available resources can be managed and utilized. Correspondingly water allocation goals, practically national water policy goals, need to be revised accordingly as the pressure on water increases from time to time. A case study is conducted in the Upper Awash Basin, Ethiopia, to assess and evaluate prioritized comprehensive water demand management options based on the framework of integrated water resources management in account of stakeholders’ knowledge and preferences as well as practical prominence within the Upper Awash Basin. Two categories of alternative management options based on policy analysis and stakeholders' consultation were evaluated against the business-as-usual scenario by using WEAP21 model as an analytical tool. Strong effects on future (unmet) demands are observed with major socio-economic assumptions and forthcoming water development plans. Water management within the basin will get more complex with further abstraction which may lead to an irreversible damage to the ecosystem. It is further confirmed through this particular study that efforts to maintain users’ preferences alone cannot insure economically viable and environmentally sound development and vice versa. There is always a tradeoff between these factors. Hence, all of these facets must be analyzed separately, related with each other in equal footing, and ultimately taken up in decision making in order for the whole system to function properly.

Keywords: water demand, water availability, WEAP21, scenarios

Procedia PDF Downloads 257