Search results for: inductive output tubes

Authors: Qudsiya Baig

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Empathy yields an unparalleled therapeutic value within patient physician interactions. Medical research is inundated with evidence to support that a physician’s ability to empathise with patients leads to a greater willingness to report symptoms, an improvement in diagnostic accuracy and safety, and a better adherence and satisfaction with treatment plans. Furthermore, the Institute of Medicine states that empathy leads to a more patient-centred care, which is one of the six main goals of a 21st century health system. However, there is a paradox between the theoretical significance of empathy and its presence, or lack thereof, in clinical practice. Recent studies have reported that empathy declines amongst students and physicians over time. The three most impactful contributors to this decline are: (1) disagreements over the definitions of empathy making it difficult to implement it into practice (2) poor consideration or regulation of empathy leading to burnout and thus, abandonment altogether, and (3) the lack of diversity in the curriculum and the influence of medical culture, which prioritises science over patient experience, limiting some physicians from using ‘too much’ empathy in the fear of losing clinical objectivity. These issues were investigated by conducting a fully inductive thematic narrative analysis of patient narratives in the UK to evaluate the behaviours and attitudes that patients associate with empathy. The principal enquiries underpinning this study included uncovering the factors that affected experience of empathy within provider-patient interactions and to analyse their effects on patient care. This research contributes uniquely to this discourse by examining the phenomenon of empathy directly from patients’ experiences, which were systematically extracted from a repository of online patient narratives of care titled ‘CareOpinion UK’. Narrative analysis was specifically chosen as the methodology to examine narratives from a phenomenological lens to focus on the particularity and context of each story. By enquiring beyond the superficial who-whatwhere, the study of narratives prescribed meaning to illness by highlighting the everyday reality of patients who face the exigent life circumstances created by suffering, disability, and the threat of life. The following six themes were found to be the most impactful in influencing the experience of empathy: dismissive behaviours, judgmental attitudes, undermining patients’ pain or concerns, holistic care and failures and successes of communication or language. For each theme there were overarching themes relating to either a failure to understand the patient’s perspective or a success in taking a person-centred approach. An in-depth analysis revealed that a lack of empathy was greatly associated with an emotive-cognitive imbalance, which disengaged physicians with their patients’ emotions. This study hereby concludes that competent providers require a combination of knowledge, skills, and more importantly empathic attitudes to help create a context for effective care. The crucial elements of that context involve (a) identifying empathy clues within interactions to engage with patients’ situations, (b) attributing a perspective to the patient through perspective-taking and (c) adapting behaviour and communication according to patient’s individual needs. Empathy underpins that context, as does an appreciation of narrative, and the two are interrelated.

Keywords: empathy, narratives, person-centred, perspective, perspective-taking

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Authors: Berengere Vire, Nicolas Salvetat, Yoann Lannay, Guillaume Marcellin, Siem Van Der Laan, Franck Molina, Dinah Weissmann

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Predicting suicidal behaviors is one of the most complex challenges of daily psychiatric practices. Today, suicide risk prediction using biological tools is not validated and is only based on subjective clinical reports of the at-risk individual. Therefore, there is a great need to identify biomarkers that would allow early identification of individuals at risk of suicide. Alterations of adenosine-to-inosine (A-to-I) RNA editing of neurotransmitter receptors and other proteins have been shown to be involved in etiology of different psychiatric disorders and linked to suicidal behavior. RNA editing is a co- or post-transcriptional process leading to a site-specific alteration in RNA sequences. It plays an important role in the epi transcriptomic regulation of RNA metabolism. On postmortem human brain tissue (prefrontal cortex) of depressed suicide victims, Alcediag found specific alterations of RNA editing activity on the mRNA coding for the serotonin 2C receptor (5-HT2cR). Additionally, an increase in expression levels of ADARs, the RNA editing enzymes, and modifications of RNA editing profiles of prime targets, such as phosphodiesterase 8A (PDE8A) mRNA, have also been observed. Interestingly, the PDE8A gene is located on chromosome 15q25.3, a genomic region that has recurrently been associated with the early-onset major depressive disorder (MDD). In the current study, we examined whether modifications in RNA editing profile of prime targets allow identifying disease-relevant blood biomarkers and evaluating suicide risk in patients. To address this question, we performed a clinical study to identify an RNA editing signature in blood of depressed patients with and without the history of suicide attempts. Patient’s samples were drawn in PAXgene tubes and analyzed on Alcediag’s proprietary RNA editing platform using next generation sequencing technology. In addition, gene expression analysis by quantitative PCR was performed. We generated a multivariate algorithm comprising various selected biomarkers to detect patients with a high risk to attempt suicide. We evaluated the diagnostic performance using the relative proportion of PDE8A mRNA editing at different sites and/or isoforms as well as the expression of PDE8A and the ADARs. The significance of these biomarkers for suicidality was evaluated using the area under the receiver-operating characteristic curve (AUC). The generated algorithm comprising the biomarkers was found to have strong diagnostic performances with high specificity and sensitivity. In conclusion, we developed tools to measure disease-specific biomarkers in blood samples of patients for identifying individuals at the greatest risk for future suicide attempts. This technology not only fosters patient management but is also suitable to predict the risk of drug-induced psychiatric side effects such as iatrogenic increase of suicidal ideas/behaviors.

Keywords: blood biomarker, next-generation-sequencing, RNA editing, suicide

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Authors: Ying Han, Weirong Chen, Qi Li

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An energy management method based on equivalent hydrogen consumption minimum strategy is proposed in this paper aiming at the direct-current (DC) microgrid consisting of photovoltaic cells, fuel cells, energy storage devices, converters and DC loads. The rational allocation of fuel cells and battery devices is achieved by adopting equivalent minimum hydrogen consumption strategy with the full use of power generated by photovoltaic cells. Considering the balance of the battery’s state of charge (SOC), the optimal power of the battery under different SOC conditions is obtained and the reference output power of the fuel cell is calculated. And then a droop control method based on time-varying droop coefficient is proposed to realize the automatic charge and discharge control of the battery, balance the system power and maintain the bus voltage. The proposed control strategy is verified by RT-LAB hardware-in-the-loop simulation platform. The simulation results show that the designed control algorithm can realize the rational allocation of DC micro-grid energy and improve the stability of system.

Keywords: DC microgrid, equivalent minimum hydrogen consumption strategy, energy management, time-varying droop coefficient, droop control

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Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

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Authors: Youngmok Yun, Bosung Kim, Sungho Lee, Kyeongsu Jeon, Hyunwook Hwangbo, Byounggun Choi

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A betavoltaic battery converts nuclear energy released as beta particles (β-) directly into electrical energy. Betavoltaic cells are analogous to photovoltaic cells. The beta particle’s kinetic energy enters a p-n junction and creates electron-hole pairs. Subsequently, the built-in potential of the p-n junction accelerates the electrons and ions to their respective collectors. The major challenges are electrical conversion efficiencies and exact evaluation. In this study, the performance of betavoltaic battery was evaluated. The betavoltaic cell was evaluated in the same condition as radiation from radioactive isotope using by FE-SEM(field emission scanning electron microscope). The average energy of the radiation emitted from the Ni-63 radioisotope is 17.42 keV. FE-SEM is capable of emitting an electron beam of 1-30keV. Therefore, it is possible to evaluate betavoltaic cell without radioactive isotopes. The betavoltaic battery consists of radioisotope that is physically connected on the surface of Si-based PN diode. The performance of betavoltaic battery can be estimated by the efficiency of PN diode unit cell. The current generated by scanning electron microscope with fixed accelerating voltage (17keV) was measured by using faraday cup. Electrical characterization of the p-n junction diode was performed by using Nano Probe Work Station and I-V measurement system. The output value of the betavoltaic cells developed by this research team was 0.162 μw/cm2 and the efficiency was 1.14%.

Keywords: betavoltaic, nuclear, battery, Ni-63, radio-isotope

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Authors: Iyad M. Muslih, Yehya Abdellatif, Sara Qutishat

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Renewable energy and in particular solar photovoltaic energy is emerging as a reasonable power generation source. The intermittent and unpredictable nature of solar energy can represent a serious challenge to the utility grids, specifically at relatively high penetration. To minimize the impact of PV power systems on the grid, self-consumption is encouraged. Self-consumption can be improved by matching the PV power generation with the electrical load consumption profile. This study will focus in studying different load profiles and comparing them to typical solar PV power generation at the selected sites with the purpose of analyzing the mismatch in consumption load profile for different users; residential, commercial, and industrial versus the solar photovoltaic output generation. The PV array orientation can be adjusted to reduce the mismatch effects. The orientation shift produces a corresponding shift in the energy production curve. This shift has a little effect on the mismatch for residential loads due to the fact the peak load occurs at night due to lighting loads. This minor gain does not justify the power production loss associated with the orientation shift. The orientation shift for both commercial and industrial cases lead to valuable decrease in the mismatch effects. Such a design is worth considering for reducing grid penetration. Furthermore, the proposed orientation shift yielded better results during the summer time due to the extended daylight hours.

Keywords: grid impact, HOMER, power mismatch, solar PV energy

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Authors: Hanan M. Alghamdi, Kholud E. Alsalami, Manal I. Alshaikhi, Nouf M. Alsalami, Sara A. Awad, Ruqaya A. Alrabei

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Tourism in Saudi Arabia is one of the emerging sectors with rapid growth. The Kingdom of Saudi Arabia is characterized by its wonderful and historical areas, which constitute important cultural and tourist landmarks. These landmarks attract the attention of the government of Saudi Arabia; hence the improvement of the tourism sector becomes one of the important axes of Saudi Arabia's vision 2030. There is a need to enhance the tourist experience by facilitating the tourism process for visitors to the Kingdom of Saudi Arabia. This project aims to design an application to serve domestic tourists and visitors from outside the Kingdom of Saudi Arabia. This application will contain an automated tourist generate plan service by sentiment analysis of comments in Google Map using Lexicon for method Rule-based approach. There are thirteen regions in the kingdom of Saudi Arabia. The regions supported in this application will be Makkah and Asir regions. According to the output of the sentiment analysis, the application will recommend restaurants and cafes, activities (parks, museums) and shopping (shopping centers) in the generated plan. After that, the system will show the user a drop-down list of “Mega-events in Saudi Arabia” containing a link to the site of events in the Kingdom of Saudi Arabia. and “important information for you” public decency regulations.

Keywords: tourist automated plan, sentiment analysis, comments in google map, tourism in Saudi Arabia

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Authors: Jitendra Narayan Panda, Jayashree Bijwe, Raj K. Pandey

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PAEK (Polyaryl ether ketone) being a high-performance polymer, is currently being explored for its tribo-potential by incorporating various fibers, solid lubricants. In this work, influence of amount (30 and 40 %) of short glass fibers (GF) in two composites containing PAEK (60 and 50 %) and synthetic graphite (10 %) on mechanical and tribological behaviour was studied. The composites were developed by injection molding and evaluated in adhesive wear mode (pin on disc configuration) against mild steel disc. The load and speed were selected as variable input parameters while coefficient of friction (µ), specific wear rate (K0) and PVlimit (pressure × velocity) values were selected as output parameters for performance evaluation. Although higher amount of GF lead to better mechanical properties, tribological properties were not in tune to this. Overall, µ and K0 for both composites were in the range 0.04-0.08 and 3-8x 10-16 m3/Nm respectively and decreased with increase in applied PV values till failure was observed. PVlimit was indicated by 112 and 100 MPa m/s. Such high PVlimit values are not reported for any polymer composites running in dry conditions in the literature. The mechanical properties of the C40 composite (40 % GF) proved superior to C30 composite (30 % GF). However, all tribological properties of C40 were inferior to C30. It exhibited higher µ, higher K0 and slightly lower PVlimit value. The higher % fibers proved detrimental for tribo-performance and worn surface analysis by SEM & EDAX was done on the discs & pins to understand wear mechanisms.

Keywords: PAEK composites, pin-on-disk, PV limit, friction

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Authors: Sabiha S. Marine, Nicole Flint, John Rolfe

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The Queensland state government introduced commercial gill net fishing closures in Cairns, Mackay, and Rockhampton in November 2015 to increase the recreational fishing opportunities, nature-based tourism, and economic benefits in these three regional areas. This management change is likely to improve the potential for more desirable stock structures through natural recruitment. Barramundi (Lates calcarifer) is one of the popular target fish for recreational and commercial fishers in Northern Australia. This investigation examines the effects of reduced commercial fishing from both biological and economic perspectives, particularly on the local Barramundi population of the Fitzroy River in Rockhampton, the largest river catchment flowing to the eastern coast of Australia. Data on different parameters of biological and economic aspects have been collated from secondary sources for analysis through a system simulation approach to identify the effectiveness of the commercial netting closures on recreational fishing effort, especially for the Barramundi population. The results have the potential to explain certain consequences of the netting closures in Queensland, which could serve to inform future fisheries management decisions. The study output as a whole will help in the better management of fisheries resources by evaluating recreational fishing opportunities in Queensland, where the potential for increases in recreation is high.

Keywords: Barramundi, bioeconomic model, fishery management, recreational fishing

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Authors: Takashi Shimizu, Tomoaki Hashimoto

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Controlling the flow of fluids is a challenging problem that arises in many fields. Burgers’ equation is a fundamental equation for several flow phenomena such as traffic, shock waves, and turbulence. The optimal feedback control method, so-called model predictive control, has been proposed for Burgers’ equation. However, the model predictive control method is inapplicable to systems whose all state variables are not exactly known. In practical point of view, it is unusual that all the state variables of systems are exactly known, because the state variables of systems are measured through output sensors and limited parts of them can be only available. In fact, it is usual that flow velocities of fluid systems cannot be measured for all spatial domains. Hence, any practical feedback controller for fluid systems must incorporate some type of state estimator. To apply the model predictive control to the fluid systems described by Burgers’ equation, it is needed to establish a state estimation method for Burgers’ equation with limited measurable state variables. To this purpose, we apply unscented Kalman filter for estimating the state variables of fluid systems described by Burgers’ equation. The objective of this study is to establish a state estimation method based on unscented Kalman filter for Burgers’ equation. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: observer systems, unscented Kalman filter, nonlinear systems, Burgers' equation

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Authors: Asma Perveen, M. P. Jahan

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Ni alloys have managed to cover wide range of applications such as automotive industries, oil gas industries, and aerospace industries. However, these alloys impose challenges while using conventional machining technologies. On the other hand, Micro-Electro-Discharge machining (micro-EDM) is a non-conventional machining method that uses controlled sparks energy to remove material irrespective of the materials hardness. There has been always a huge interest from the industries for developing optimum methodology and parameters in order to enhance the productivity of micro-EDM in terms of reducing machining time and tool wear for different alloys. Therefore, the aims of this study are to investigate the effects of the micro-EDM process parameters, in order to find their optimal values. The input process parameters include voltage, capacitance, and electrode rotational speed, whereas the output parameters considered are machining time, entrance diameter of hole, overcut, tool wear, and crater size. The surface morphology and element characterization are also investigated with the use of SEM and EDX analysis. The experimental result indicates the reduction of machining time with the increment of discharge energy. Discharge energy also contributes to the enlargement of entrance diameter as well as overcut. In addition, tool wears show reduction with the increase of discharge energy. Moreover, crater size is found to be increased in size along with the increment of discharge energy.

Keywords: micro holes, micro EDM, Ni Alloy, discharge energy

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Authors: Kreso Ivandic, Filip Dodigovic, Damir Stuhec

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The paper deals with the proposed 'Material' and 'Action-resistance factor' design methods in designing the embedded retaining walls. The parametric analysis of evaluating the differences of the output values mutually and compared with classic approach computation was performed. There is a challenge with the criteria for choosing the proposed calculation design methods in Eurocode 7 with respect to current technical regulations and regular engineering practice. The basic criterion for applying a particular design method is to ensure minimum an equal degree of reliability in relation to the current practice. The procedure of combining the relevant partial coefficients according to design methods was carried out. The use of mentioned partial coefficients should result in the same level of safety, regardless of load combinations, material characteristics and problem geometry. This proposed approach of the partial coefficients related to the material and/or action-resistance should aimed at building a bridge between calculations used so far and pure probability analysis. The measure to compare the results was to determine an equivalent safety factor for each analysis. The results show a visible wide span of equivalent values of the classic safety factors.

Keywords: action-resistance factor design, classic approach, embedded retaining wall, Eurocode 7, limit states, material factor design

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Authors: Nazanin Pilevari, Kamyar Mahmoodi

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The purpose of this research is to provide a model based on fuzzy inference system for evaluating empowerment of Knowledge management. The first prototype of the research was developed based on the study of literature. In the next step, experts were provided with these models and after implementing consensus-based reform, the views of Fuzzy Delphi experts and techniques, components and Index research model were finalized. Culture, structure, IT and leadership were considered as dimensions of empowerment. Then, In order to collect and extract data for fuzzy inference system based on knowledge and Experience, the experts were interviewed. The values obtained from designed fuzzy inference system, made review and assessment of the organization's empowerment of Knowledge management possible. After the design and validation of systems to measure indexes ,empowerment of Knowledge management and inputs into fuzzy inference) in the AYANDEH Bank, a questionnaire was used. In the case of this bank, the system output indicates that the status of empowerment of Knowledge management, culture, organizational structure and leadership are at the moderate level and information technology empowerment are relatively high. Based on these results, the status of knowledge management empowerment in AYANDE Bank, was moderate. Eventually, some suggestions for improving the current situation of banks were provided. According to studies of research history, the use of powerful tools in Fuzzy Inference System for assessment of Knowledge management and knowledge management empowerment such an assessment in the field of banking, are the innovation of this Research.

Keywords: knowledge management, knowledge management empowerment, fuzzy inference system, fuzzy Delphi

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Dalia Salem, Detlef Schulz

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As Germany pursues its ambitious plan towards a power system based on renewable energy sources, the necessity to establish steady, robust microgrids becomes more evident. Inside the microgrid, there is at least one grid-forming inverter responsible for generating the coupling voltage and stabilizing the system frequency within the standardized accepted limits when the microgrid is forced to operate as a stand-alone power system. Grid-forming control for distributed inverters is required to enable steady control of a low-inertia power system. In this paper, a designed droop control technique is tested at the controller of an inverter as a component of a hardware test bed to understand the microgrid behavior in two modes of operation: i) grid-connected and ii) operating in islanding mode. This droop technique includes many current and voltage inner control loops, where the Q-V and P-f droop provide the required terminal output voltage and frequency. The technique is tested first in a simulation model of the inverter in MATLAB/SIMULINK, and the results are compared to the results of the hardware laboratory test. The results of this experiment illuminate the pivotal role of the grid-forming inverter in facilitating microgrid resilience during grid disconnection events and how microgrids could provide the functionality formerly provided by synchronous machinery, such as the black start process.

Keywords: microgrid, grid-forming inverters, droop-control, islanding-operation

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: Stewart A. Keir, Faik A. Hamad

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This study aims to use both analytical and experimental methods of analysis to examine the accuracy of Computational Fluid Dynamics (CFD) models that can then be used for more complex analyses, accurately representing more elaborate flow phenomena such as internal shockwaves and boundary layers. The geometry used in the analytical study and CFD model is taken from the experimental rig. The analytical study is undertaken using isentropic and adiabatic relationships and the output of the analytical study, the 'shockwave location tool', is created. The results from the analytical study are then used to optimize the redesign an experimental rig for more favorable placement of pressure taps and gain a much better representation of the shockwaves occurring in the divergent section of the nozzle. The CFD model is then optimized through the selection of different parameters, e.g. turbulence models (Spalart-Almaras, Realizable k-epsilon & Standard k-omega) in order to develop an accurate, robust model. The results from the CFD model can then be directly compared to experimental and analytical results in order to gauge the accuracy of each method of analysis. The CFD model will be used to visualize the variation of various parameters such as velocity/Mach number, pressure and turbulence across the shock. The CFD results will be used to investigate the interaction between the shock wave and the boundary layer. The validated model can then be used to modify the nozzle designs which may offer better performance and ease of manufacture and may present feasible improvements to existing high-speed flow applications.

Keywords: CFD, nozzle, fluent, gas dynamics, shock-wave

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Authors: M. Shiva Prasad, S. R. Atchuta, T. Vijayaraghavan, S. Sakthivel

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The world is in need of efficient energy conversion technologies which are affordable, accessible, and sustainable with eco-friendly nature. Solar energy is one of the cornerstones for the world’s economic growth because of its abundancy with zero carbon pollution. Among the various solar energy conversion technologies, solar thermal technology has attracted a substantial renewed interest due to its diversity and compatibility in various applications. Solar thermal systems employ concentrators, tracking systems and heat engines for electricity generation which lead to high cost and complexity in comparison with photovoltaics; however, it is compatible with distinct thermal energy storage capability and dispatchable electricity which creates a tremendous attraction. Apart from that, employing cost-effective solar selective receiver tube in a concentrating solar thermal (CST) system improves the energy conversion efficiency and directly reduces the cost of technology. In addition, the development of solar receiver tubes by low cost methods which can offer high optical properties and corrosion resistance in an open-air atmosphere would be beneficial for low and medium temperature applications. In this regard, our work opens up an approach which has the potential to achieve cost-effective energy conversion. We have developed a highly selective tandem absorber coating through a facile wet chemical route by a combination of chemical oxidation, sol-gel, and nanoparticle coating methods. The developed tandem absorber coating has gradient refractive index nature on stainless steel (SS 304) and exhibited high optical properties (α ≤ 0.95 & ε ≤ 0.14). The first absorber layer (Cr-Mn-Fe oxides) developed by controlled oxidation of SS 304 in a chemical bath reactor. A second composite layer of ZrO₂-SiO₂ has been applied on the chemically oxidized substrate by So-gel dip coating method to serve as optical enhancing and corrosion resistant layer. Finally, an antireflective layer (MgF₂) has been deposited on the second layer, to achieve > 95% of absorption. The developed tandem layer exhibited good thermal stability up to 250 °C in open air atmospheric condition and superior corrosion resistance (withstands for > 200h in salt spray test (ASTM B117)). After the successful development of a coating with targeted properties at a laboratory scale, a prototype of the 1 m tube has been demonstrated with excellent uniformity and reproducibility. Moreover, it has been validated under standard laboratory test condition as well as in field condition with a comparison of the commercial receiver tube. The presented strategy can be widely adapted to develop highly selective coatings for a variety of CST applications ranging from hot water, solar desalination, and industrial process heat and power generation. The high-performance, cost-effective medium temperature receiver tube technology has attracted many industries, and recently the technology has been transferred to Indian industry.

Keywords: concentrated solar thermal system, solar selective coating, tandem absorber, ultralow refractive index

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Authors: Freya Harding, Anne Gatuguta, Chi Eziefula

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Introduction Research shows the quality of experiences of pregnancy, birth, and postpartum impacts the health and well-being of the mother and baby. This is recognised by the WHO in their recommendations ‘Intrapartum care for a positive childbirth experience’. The COVID-19 pandemic saw the transformation of the NHS Maternity services to prevent the transmission of COVID-19. Physical and social isolation may have affected women’s experiences of pregnancy, birth and postpartum; especially those of healthcare. Examples of such changes made to the NHS include both the reduction in volume of face-to-face consultations and restrictions to visitor time in hospitals. One notable detriment due to these changes was the absence of a partner during certain stages of birth. The aim of this study was to explore women’s experiences of pregnancy, birth, and postnatal period during the COVID-19 pandemic in the UK. Methods We collected qualitative data from women who had given birth during the COVID-19 pandemic. In-depth, semi-structured interviews were conducted with twelve participants recruited from mother and baby groups in Southeast England. Data were audio-recorded, transcribed verbatim, and analysed thematically using both inductive and deductive approaches. Ethics permission was granted from Brighton and Sussex Medical School (ER/BSMS9A83/1). Results Interviews were conducted with 12 women who gave birth between May 2020 and February 2021. Ages of the participants ranged between 28 and 42 years, most of which were white British, with one being Asian British. All participants were heterosexual and either married or co-habiting with their partner. Five participants worked in the NHS, and all participants had professional occupations. Women felt inadequately supported both socially and medically. An appropriate sense of control over their own birthing experience was lacking. Safety mechanisms, such as in-person visits from the midwife, had no suitable alternatives in place. Serious health issues were able to “slip through the net.” Mental health conditions in some of those interviewed worsened or developed. Similarly, reduced support from partners during birth and during the immediate postpartum period at the hospital, coupled with reduced ward staffing, resulted in some traumatic experiences; particularly for women who had undergone caesarean section. However, some unexpected positive effects were reported; one example being that partners were able to spend more time with their baby due to furlough schemes and working from home. Similarly, emergency care was not felt to have been compromised. Overall, six themes emerged: (1) Self-reported traumatic experiences, (2) Challenges of caring for a baby with reduced medical and social support, (3) Unexpected benefits to the parenting experience, (4) The effects of a sudden change in medical management (5) Poor communication from healthcare professionals (6) Social change; with subthemes of support accessing medical care, the workplace, family and friends, and antenatal & baby groups. Conclusions The results indicate that the healthcare system was unable to adequately deliver maternity care to facilitate positive pregnancy, birth, and postnatal experiences during the heights of the pandemic. The poor quality of such experiences has been linked an increased risk of long-term health complications in both the mother and child.

Keywords: pregnancy, birth, postpartum, postnatal, COVID-19, maternity, social support, qualitative, pandemic

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Authors: M. Benothman, M. Stear, S. Mitchel, O. Abuargob, R. Vijayan, Sateesh Kumar

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Parasitic gastroenteritis caused by gastrointestinal nematodes (GIN) is a serious pathological complication in lambs. The dispersion pattern of GIN influences their transmission dynamics. There is no proper study on this aspect in Scottish Blackface lambs in Scotland. This study undertaken on 758 naturally infected, weaned, straight bred Scottish Blackface lambs in high land pasture in East Scotland extending over three months (August, September and October) in a year, and for three successive years demonstrated that the distribution of faecal egg counts (FEC) followed negative binomial distribution, with the exception of a few samples. The inverse index of dispersion (k) ranged between 0.19 ± 0.51 and 1.09 ± 0.08. Expression of low k values resulting from aggregation in a few individuals, suggested that a small proportion of animals with heavy parasitic influx significantly influenced the level of pasture contamination and parasite transmission. There was no discernible trend in the mean faecal egg count (FEC) and mean herbage larval population (HLP) in different months and in different years. Teladorsagia was the highest pasture contaminant (85.14±14.30 L3/kdh) followed by Nematodirus (53.00±13.96), Ostertagia (28.21±10.18) and Cooperia (11.43±5.55). The results of this study would be useful in instituting gastrointestinal nematode control strategies for sheep in cool temperate agro-ecological zones.

Keywords: blackface lamb, faecal egg count, Gastrointestinal nematodes, herbage larval population, Scotland

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Authors: Esther Kelechi Soyele

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Job satisfaction is the expression of an organisation's fulfillment of work output. In order to achieve effective job satisfaction, the motivation of employees is very essential in stimulating their obligation towards their work. The study examined the motivational strategies, job satisfaction as a correlation of library service delivery in some selected tertiary institutions in southwest Nigeria. The study adopted a descriptive survey research design. A simple random sampling method was employed to select 200 library staff consisting of both library professionals and para-professionals. Two hundred (200) questionnaires were given out, but only one hundred and twenty-nine 129 (96% response rate) were used for the study. Both simple percentage and one and two way ANOVA was used for data analysis. Findings revealed that 60.4% of the respondents were males while 39.6% were female; most of the respondents’ relatively belong to the age group of 31-40 and 41-50, 93.3% were within the age range of 21-50 years, and 43.2 % were M.Sc degree holders. The result revealed a (p < 0.05) significant relationship between work motivational strategies and library service delivery. The results also revealed that motivational development program strategies and job satisfaction have (p < 0.05) a positive significant relationship with library service delivery. It was concluded that work motivation strategies are essential for job satisfaction which is very important in any organization in the attainment of its goals and objectives and helps in maintaining a high standard. The study recommended that more incentive plans that will enhance job satisfaction should be put in place to encourage employees to be more active in carrying out their job effectively.

Keywords: job satisfaction, library, library services, motivational strategies

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Authors: Ava Zarif Sanayei, Mustafa Farjad-Fard, Mohammad-Reza Mohammadian-Behbahani, Leyli Ebrahimi, Sedigheh Sina

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The fabrication and testing of a low-cost air-filled ion chamber for X-ray dosimetry is studied. The chamber is made of a metal cylinder, a central wire, a BC517 Darlington transistor, a 9V DC battery, and a voltmeter in order to have a cost-effective means to measure the dose. The output current of the dosimeter is amplified by the transistor and then fed to the large internal resistance of the voltmeter, producing a readable voltage signal. The dose-response linearity of the ion chamber is evaluated for different exposure scenarios by the X-ray tube. kVp values 70, 90, and 120, and mAs up to 20 are considered. In all experiments, a solid-state dosimeter (Solidose 400, Elimpex Medizintechnik) is used as a reference device for chamber calibration. Each case of exposure is repeated three times, the voltmeter and Solidose readings are recorded, and the mean and standard deviation values are calculated. Then, the calibration curve, derived by plotting voltmeter readings against Solidose readings, provided a linear fit result for all tube kVps of 70, 90, and 120. A 99, 98, and 100% linear relationship, respectively, for kVp values 70, 90, and 120 are demonstrated. The study shows the feasibility of achieving acceptable dose measurements with a simplified setup. Further enhancements to the proposed setup include solutions for limiting the leakage current, optimizing chamber dimensions, utilizing electronic microcontrollers for dedicated data readout, and minimizing the impact of stray electromagnetic fields on the system.

Keywords: dosimetry, ion chamber, radiation detection, X-ray

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Authors: Federica Di Marcantonio, Antoine Leblois, Wolfgang Göbel, Hervè Kerdiles

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Weather-related shocks can threaten the ability of farmers to maintain their agricultural output and food security levels. Informal coping mechanisms (i.e. migration or community risk sharing) have always played a significant role in mitigating the negative effects of weather-related shocks in Ethiopia, but they have been found to be an incomplete strategy, particularly as a response to covariate shocks. Particularly, as an alternative to the traditional risk pooling products, an innovative form of insurance known as Index-based Insurance has received a lot of attention from researchers and international organizations, leading to an increased number of pilot initiatives in many countries. Despite the potential benefit of the product in protecting the livelihoods of farmers and pastoralists against climate shocks, to date there has been an unexpectedly low uptake. Using information from current pilot projects on index-based insurance in Ethiopia, this paper discusses the determinants of uptake that have so far undermined the scaling-up of the products, by focusing in particular on weather data availability, price affordability and willingness to pay. We found that, aside from data constraint issues, high price elasticity and low willingness to pay represent impediments to the development of the market. These results, bring us to rethink the role of index insurance as products for enhancing smallholders’ response to covariate shocks, and particularly for improving their food security.

Keywords: index-based insurance, willingness to pay, satellite information, Ethiopia

Procedia PDF Downloads 386

Authors: Ouahiba Taamallah, Tarek Litim

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The study deals with the burnishing effect of AISI 52100 steel and parameters influence (Py, i and f on surface integrity. The results show that the optimal effects are closely related to the treatment parameters. With a 92% improvement in roughness, SB can be defined as a finishing operation within the machining range. Due to 85% gain in consolidation rate, this treatment constitutes an efficient process for work-hardening of material. In addition, a statistical study based on regression and Taguchi's design has made it possible to develop mathematical models to predict output responses according to the studied burnishing parameters. Response Surface Methodology RSM showed a simultaneous influence of the burnishing parameters and to observe the optimal parameters of the treatment. ANOVA Analysis of results led to validate the prediction model with a determination coefficient R2=94.60% and R2=93.41% for surface roughness and micro-hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=20 Kgf, i=5 passes and f=0.08 mm.rev-1, which favors minimum surface roughness and a maximum of micro-hardness. The result was validated by a composite desirability D_i=1 for both surface roughness and microhardness, respectively. Applying optimal parameters, burnishing showed its beneficial effects in fatigue resistance, especially for imposed loading in the low cycle fatigue of the material where the lifespan increased by 90%.

Keywords: AISI 52100 steel, burnishing, Taguchi, fatigue

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Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

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Authors: Jean Marriz M. Manzano, Marc D. Rosales, Magdaleno R. Vasquez Jr., Maria Theresa G. De Leon

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This paper discusses the effects of surface roughness on a cantilever beam vibrational energy harvester. A silicon sample was fabricated using MEMS fabrication processes. When etching silicon using deep reactive ion etching (DRIE) at large etch depths, rougher surfaces are observed as a result of increased response in process pressure, amount of coil power and increased helium backside cooling readings. To account for the effects of surface roughness on the characteristics of the cantilever beam, finite element method (FEM) modeling was performed using actual roughness data from fabricated samples. It was found that when etching about 550um of silicon, root mean square roughness parameter, Sq, varies by 1 to 3 um (at 100um thick) across a 6-inch wafer. Given this Sq variation, FEM simulations predict an 8 to148 Hz shift in the resonant frequency while having no significant effect on the output power. The significant shift in the resonant frequency implies that careful consideration of surface roughness from fabrication processes must be done when designing energy harvesters.

Keywords: deep reactive ion etching, finite element method, microelectromechanical systems, multiphysics analysis, surface roughness, vibrational energy harvester

Procedia PDF Downloads 107

Authors: Ahmed. A. Hossam Eldin, Karim H. Youssef, Kareem M. AboRas

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Recent current rapid industrial development and energy shortage are essential problems, which face most of the developing countries. Moreover, increased prices of fossil fuel and advanced energy conversion technology lead to the need for renewable energy resources. A study, modelling and simulation of an outdoor micro scale stand alone wind turbine was carried out. For model validation an experimental study was applied. In this research the aim was to clarify effects of real outdoor operating conditions and the instantaneous fluctuations of both wind direction and wind speed on the actual produced power. The results were compared with manufacturer’s data. The experiments were carried out in Borg Al-Arab, Alexandria. This location is on the north Western Coast of Alexandria. The results showed a real max output power for outdoor micro scale wind turbine, which is different from manufacturer’s value. This is due to the fact that the direction of wind speed is not the same as that of the manufacturer’s data. The measured wind speed and direction by the portable metrological weather station anemometer varied with time. The blade tail response could not change the blade direction at the same instant of the wind direction variation. Therefore, designers and users of micro scale wind turbine stand alone system cannot rely on the maker’s name plate data to reach the required power.

Keywords: micro-turbine, wind turbine, inverters, renewable energy, hybrid system

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Misbah Fatma, Anam Saiyeda

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This analysis charts the introduction and expansion of research into the filter bubble phenomena over the last 10 years using a large dataset of academic publications. This bibliometric study demonstrates how interdisciplinary filter bubble research is. The identification of key authors and organizations leading the filter bubble study sheds information on collaborative networks and knowledge transfer. Relevant papers are organized based on themes including algorithmic bias, polarisation, social media, and ethical implications through a systematic examination of the literature. In order to shed light on how these patterns have changed over time, the study plots their historical history. The study also looks at how research is distributed globally, showing geographic patterns and discrepancies in scholarly output. The results of this bibliometric analysis let us fully comprehend the development and reach of filter bubble research. This study offers insights into the ongoing discussion surrounding information personalization and its implications for societal discourse, democratic participation, and the potential risks to an informed citizenry by exposing dominant themes, interdisciplinary collaborations, and geographic patterns. In order to solve the problems caused by filter bubbles and to advance a more diverse and inclusive information environment, this analysis is essential for scholars and researchers.

Keywords: bibliometric analysis, social media, social networking, algorithmic personalization, self-selection, content moderation policies and limited access to information, recommender system and polarization

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Authors: Leila Rashidian, Abbas Ebrahimi

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The development of industrial activities, increase in fossil fuel consumption, vehicles, destruction of forests and grasslands, changes in land use, and population growth have caused to increase the amount of greenhouse gases especially CO₂ in the atmosphere in recent decades. This has led to global warming and climate change. In the present paper, we have investigated the trend of climate change according to the data simulation during the time interval of 2010-2030 in the Fars province. In this research, the daily climatic parameters such as maximum and minimum temperature, precipitation and number of sunny hours during the 1977-2008 time interval for synoptic stations of Shiraz and Abadeh and during 1995-2008 for Lar stations and also the output of HADCM₃ model in 2010-2030 time interval have been used based on the A₂ propagation scenario. The results of the model show that the average temperature will increase by about 1 degree centigrade and the amount of precipitation will increase by 23.9% compared to the observational data. In conclusion, according to the temperature increase in this province, the amount of precipitation in the form of snow will be reduced and precipitations often will occur in the form of rain. This 1-degree centigrade increase during the season will reduce production by 6 to 10% because of shortening the growing period of wheat.

Keywords: climate change, Lars WG, HADCM3, Gillan province, climatic parameters, A2 scenario

Procedia PDF Downloads 197