Search results for: residual life prediction

Authors: Pamela Del Rosario, Elisabetta Palumbo, Marzia Traverso

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The building sector plays an important role in addressing pressing environmental issues such as climate change and resource scarcity. The energy performance of buildings is considerably affected by the external envelope. In fact, a considerable proportion of the building energy demand is due to energy losses through the windows. Nevertheless, according to literature, to pay attention only to the contribution of windows to the building energy performance, i.e., their influence on energy use during building operation, could result in a partial evaluation. Hence, it is important to consider not only the building energy performance but also the environmental performance of windows, and this not only during the operational stage but along its complete life cycle. Life Cycle Assessment (LCA) according to ISO 14040:2006 and ISO 14044:2006+A1:2018 is one of the most adopted and robust methods to evaluate the environmental performance of products throughout their complete life cycle. This life-cycle based approach avoids the shift of environmental impacts of a life cycle stage to another, allowing to allocate them to the stage in which they originated and to adopt measures that optimize the environmental performance of the product. Moreover, the LCA method is widely implemented in the construction sector to assess whole buildings as well as construction products and materials. LCA is regulated by the European Standards EN 15978:2011, at the building level, and EN 15804:2012+A2:2019, at the level of construction products and materials. In this work, the environmental performance of wooden windows was assessed by implementing the LCA method and adopting primary data. More specifically, the emphasis is given to embedded and operational impacts. Furthermore, correlations are made between these environmental impacts and aspects such as type of wood and window transmittance. In the particular case of the operational impacts, special attention is set on the definition of suitable maintenance scenarios that consider the potential climate influence on the environmental impacts. For this purpose, a literature review was conducted, and expert consultation was carried out. The study underlined the variability of the embedded environmental impacts of wooden windows by considering different wood types and transmittance values. The results also highlighted the need to define appropriate maintenance scenarios for precise assessment results. It was found that both the service life and the window maintenance requirements in terms of treatment and its frequency are highly dependent not only on the wood type and its treatment during the manufacturing process but also on the weather conditions of the place where the window is installed. In particular, it became evident that maintenance-related environmental impacts were the highest for climate regions with the lowest temperatures and the greatest amount of precipitation.

Keywords: embedded impacts, environmental performance, life cycle assessment, LCA, maintenance stage, operational impacts, wooden windows

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Authors: Shubham Tandon, Rajni Goel

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The prime objective of our review paper is to study the quantitative psychology impact on our daily life. Quantitative techniques have been studied with the aim of discovering solutions in an advanced way. To get the unbiased and correct results, statistics and other useful mathematical aspects have been reviewed. So, many psychologists use quantitative techniques while working in the area of psychology with the aim of discovering solutions in an advanced way. This ensures their accurate outcomes as those will make use of precise criteria in knowing the minds and conditions of any person. Also, proper experimentation and observational tools are taken care of to avoid some possibilities of invalid data.

Keywords: quantitative psychology, psychologists, statistics, person, results, minds

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Authors: Tennyson Samraj

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The intent of this paper is threefold: (1) to propose the Epicurean tenet that beliefs associated with God are to be detached from the transcendent God, as the basis to end religious conflicts; (2) to project John Hick’s advice that no one has monopoly over religious claims, as the basis for religious tolerance and (3) to present the common sense approach to respect religion without disrespecting science. Religious claims create societal tension on two matters: conflict between believers and conflict with the sciences. Anyone interested in the two fundamental questions related to consciousness and cosmology as to how and why the universe exists will have to deal with science and religion. However, while science addresses the question of how the universe came into existence and how it works, religion addresses the question of why the universe exists. If religion is a quest to understand why the universe exists, then we must address the question as to when religion is considered meaningful and when is it considered detrimental. Is there a relationship between why we choose to live and why the universe exists? Science and Religion are partners in defining our life in the context of the universe. Science without Religion limits itself to knowing ‘how’ the universe came into existence without questioning ‘why’; Religion without Science limits itself of knowing ‘why’ the universe exists without knowing ‘how.’ Is it possible to detach beliefs about God from God? When religious claims are understood in the context of the questions that necessitates the answers, religious claims can be understood as being separate from the transcendent God. This paper purports that this Epicurean tenet provides the impetus to address the questions that necessitate religious claims. This helps us to explain the relevance of why we believe in what we believe; define the relationship between the self, soul and the sacred; and establish the connection between this life and the after-life in the context of life-beyond-this-planet.

Keywords: religion, epicurus, John Hick, relevance of religion

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Authors: Mohamad Shariff Nurasikin, Paul Crawford, Nicola Wright

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People with any mental disorder can get benefit from the spiritual aspects of life for recovery, particularly in searching for the meaning of life and engaging in meaningful activities. However, little is known about such effects in the population of bipolar disorder. The concepts of spirituality are highly contestable, as they are too broad and removed from the original religious understanding. The concepts are more notable as encompassing multi-dimensional aspects of people’s lives such as social, emotional, and psychological. Viewing that Western or secular worldview dominates most of the literature in spirituality, it is time to explore the concept of spirituality from the Eastern and religious worldview, such as the Malaysian view. Thus, the aim of this study is to provide a conceptual understanding of people with bipolar disorder with a religious affiliation in Malaysia. This study employs a Grounded Theory and explores the narratives from the interviews of 25 participants. The narratives strongly suggest the salient resources or can be referred to as various forms of capital, as in the capital theory, namely, religious, social, psychological, and medicinal. More important is how these capitals are the enablers for recovery in mental health and well-being, where the participants in the sample engage in a more meaningful life and positive adaptations. This study also extends the Bourdieusian spiritual capital, in which the salient resources are termed as the capital bundle. More significant is how the capital bundles are working contiguously in building and accumulating the spiritual capital. This process is conducive to recovery within the social life of people with bipolar disorder or perhaps other mental disorders.

Keywords: bipolar, Bourdeau, recovery, spiritual

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: M. Pienaar, J. W. Odendaal, J. C. Smit, J. Joubert

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Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

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Authors: Barnali Thakuria, Labananda Choudhury

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Gender equality and women’s empowerment is one of the components of eight Millennium Development Goal (MDG).Literacy and employment are the parameters which reflect the empowerment of women. But in a developing country like India, literacy and working status among the females are not satisfactory. Both literacy and employment technically can be measured by Literate Life Expectancy (LLE) and Working Life Expectancy (WLE).One can also combine both the factors literacy and working to get a better new measure. The proposed indicator can be called literate-working life expectancy (LWLE). LLE gives an average number of years a person lives in a literate state under current mortality and literacy conditions while WLE defined as average number of years a person lives in a working state if current mortality and working condition prevails. Similarly, LWLE gives number of expected years by a person living under both literate and working state. The situation of females cannot be figured out without comparing both the sexes. In the present paper an attempt has been made to estimate LLE and WLE in India along with some selected states from various zones of India namely Assam from the North-East, Gujarat from the West, Kerala from the South, Rajasthan from the North, Uttar Pradesh from the Central and West Bengal from the East respectively for both the sexes based on 2011 census. Furthermore, we have also developed a formula for a new indicator namely Literate-Working Life Expectancy (LWLE) and the proposed index has been applied in India and the selected states mentioned above for both males and females. Data has been extracted from SRS(Sample Registration System) based Abridged Life Table and Census of India. The computation of LLE follows the method developed by Lutz while WLE has followed the method developed by Saw Swee Hock. By combining both the factors literacy and employment, the new indicator LWLE also follows the method like LLE and WLE. Contrasted results have been found in different parts of India. The result shows that LLE at birth is highest(lowest) in the state Kerala(Uttar Pradesh) with 61.66 (39.51) years among the males. A similar situation is also observed among the females with 62.58 years and 25.11 years respectively. But male WLE at birth is highest (lowest) in Rajasthan(Kerala) with 37.11 (32.64) years. Highest female WLE at birth is also observed in Rajasthan with 23.51 years and the lowest is concentrated in Uttar Pradesh with 11.76 years. It is also found that Kerala’s performance is exceptionally good in terms of LWLE at birth while the lowest LWLE at birth prevails in the state Uttar Pradesh among the males. Female LWLE at birth is highest(lowest) in Kerala(Uttar Pradesh) with 19.73(4.77)years. The corresponding value of the index increases as the number of factors involved in the life expectancy decrease. It is found that women are lagging behind in terms of both literacy and employment. Findings of the study will help the planners to take necessary steps to improve the position of women.

Keywords: life expectancy, literacy, literate life expectancy, working life expectancy

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Authors: Julien Romieux, Fabio Verdicchio

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Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: cooperative signal processing, signal representation and approximation, power management, wireless sensor networks

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Authors: M. E. Silva, I. Brás

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The aim of this work was to study the viability of recycling the residual yeast and diatomaceous earth (RYDE) slurry generated by the beer brewing industry by composting with animal manures, as well as to evaluate the quality of the composts obtained. Two pilot composting trials were carried out with different mixes: cow manure/RYDE slurry (Pile CM) and sheep manure/RYDE slurry (Pile SM). For all piles, wood chips were applied as bulking agent. The process was monitored by evaluating standard physical and chemical parameters. The compost quality was assessed by the heavy metals content and phytotoxicity. Both piles reached a thermophilic phase in the first day, however having different trends. The pH showed a slight alkaline character. The C/N reached values lower than 19 at the end of composting process. Generally, all the piles exhibited absence of heavy metals. However, the pile SM exhibited phytotoxicity. This study showed that RYDE slurry can be valorized by composting with cow manure.

Keywords: beer brewing wastes, compost, valorization, quality

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Authors: I. Dumand, C. Clesse, M. Decker, C. Savini, J. Lighezzolo-Alnot

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In the landscape of French psychiatry, long-term institutionalization of patients with severe and disabling chronics disorders is common. Faced with the failures of classical reinsertion, sometimes these users are hurriedly considered as 'insortables'. However, this representation is often swayed by the current behavior of the patient observed through the clinical observation. Unfortunately, it seems that this way of proceeding can not integrate the potentialities of the institutionalized patients and their possible evolution. Therefore, in order not to make hasty conclusions about the life perspectives of these individuals, it seems essential to associate with clinical observation a psycho social rehabilitation diagnosis. Multidisciplinary, it combine all the aspects that make up the life of the subject (the life aspirations, psycho social determinants, family support, cognitive potential, symptoms ...). In this paper, we will rank these different aspects necessary prerequisites to the realization of a psycho social rehabilitation diagnosis. Then, we will specifically speak of the issue of psychological evaluation. By adopting an integrative approach combining neuro psychological tools (Grober and Buschke, Stroop, WCST, AIPSS, WAIS, Eyes test ...) and projective tools interpreted under a psycho dynamic angle (Rorschach, TAT ..) we think that we can grasp the patient in his globality. Thus, during this process we will justify the interest of combining a cognitive and a psycho affective approach, we will identify the different items assessed and their future implications on the everyday life of the users. Finally, we show that this diagnosis can give a chance to reintegration to 30% of patients considered as ''insortables''. In conclusion, we will highlight the importance of this process dear to the community psychology emphasizing in the same time the interests of this approach in terms of empowerment, recovery and quality of life.

Keywords: assessment, potentiality, psychosocial rehabilitation diagnosis, tools

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Authors: J. Leige Lopes, L. Aparecida Bastos, R. Monteiro da Silva

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The objective of this paper is to study the work of children and adolescents and the vicious circle of poverty from the perspective of Guinar Myrdal’s Theory of Circular Cumulative Causation. The objective is to show that if a person starts working in the juvenile phase of life they will be classified as poor or extremely poor when they are adult, which can to be observed in the case of Brazil, more specifically in the north and northeast. To do this, the methodology used was statistical and econometric analysis by applying a probit model. The main results show that: if people reside in the northeastern region of Brazil, and if they have a low educational level and if they start their professional life before the age 18, they will increase the likelihood that they will be poor or extremely poor. There is a consensus in the literature that one of the causes of the intergenerational transmission of poverty is related to child labor, this because when one starts their professional life while still in the toddler or adolescence stages of life, they end up sacrificing their studies. Because of their low level of education, children or adolescents are forced to perform low-paid functions and abandon school, becoming in the future, people who will be classified as poor or extremely poor. As a result of poverty, parents may be forced to send their children out to work when they are young, so that in the future they will also become poor adults, a process that is characterized as the "vicious circle of poverty."

Keywords: children, adolescents, Gunnar Myrdal, poverty, vicious circle

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Authors: Mohieddine Benghersallah, Lakhdar Boulanouar, Salim Belhadi

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Currently, higher production rate with required quality and low cost is the basic principle in the competitive manufacturing industry. This is mainly achieved by using high cutting speed and feed rates. Elevated temperatures in the cutting zone under these conditions shorten tool life and adversely affect the dimensional accuracy and surface integrity of component. Thus it is necessary to find optimum cutting conditions (cutting speed, feed rate, machining environment, tool material and geometry) that can produce components in accordance with the project and having a relatively high production rate. Response surface methodology is a collection of mathematical and statistical techniques that are useful for modelling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. The work presented in this paper examines the effects of cutting parameters (cutting speed, feed rate and depth of cut) on to the surface roughness through the mathematical model developed by using the data gathered from a series of milling experiments performed.

Keywords: Statistical analysis (RSM), Bearing steel, Coating inserts, Tool life, Surface Roughness, End milling.

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Authors: Mohammad Javad Behzadnia, Amir Bahador Boroumand

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Introduction: Limited facility situations require allocating the most available resources for most casualties. Accordingly, Traumatic Brain Injury (TBI) is the one that may need to transport the patient as soon as possible. In a mass casualty event, deciding when the facilities are restricted is hard. The Extended Glasgow Outcome Score (GOSE) has been introduced to assess the global outcome after brain injuries. Therefore, we aimed to evaluate the prognostic factors associated with GOSE. Materials and Methods: In a multicenter cross-sectional study conducted on 144 patients with TBI admitted to trauma emergency centers. All the patients with isolated TBI who were mentally and physically healthy before the trauma entered the study. The patient’s information was evaluated, including demographic characteristics, duration of hospital stays, mechanical ventilation on admission laboratory measurements, and on-admission vital signs. We recorded the patients’ TBI-related symptoms and brain computed tomography (CT) scan findings. Results: GOSE assessments showed an increasing trend by the comparison of on-discharge (7.47 ± 1.30), within a month (7.51 ± 1.30), and within three months (7.58 ± 1.21) evaluations (P < 0.001). On discharge, GOSE was positively correlated with Glasgow Coma Scale (GCS) (r = 0.729, P < 0.001) and motor GCS (r = 0.812, P < 0.001), and inversely with age (r = −0.261, P = 0.002), hospitalization period (r = −0.678, P < 0.001), pulse rate (r = −0.256, P = 0.002) and white blood cell (WBC). Among imaging signs and trauma-related symptoms in univariate analysis, intracranial hemorrhage (ICH), interventricular hemorrhage (IVH) (P = 0.006), subarachnoid hemorrhage (SAH) (P = 0.06; marginally at P < 0.1), subdural hemorrhage (SDH) (P = 0.032), and epidural hemorrhage (EDH) (P = 0.037) were significantly associated with GOSE at discharge in multivariable analysis. Conclusion: Our study showed some predictive factors that could help to decide which casualty should transport earlier to a trauma center. According to the current study findings, GCS, pulse rate, WBC, and among imaging signs and trauma-related symptoms, ICH, IVH, SAH, SDH, and EDH are significant independent predictors of GOSE at discharge in TBI patients.

Keywords: field, Glasgow outcome score, prediction, traumatic brain injury.

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Authors: Yohanes Kristianto

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The objective of this paper is to compose a sustainable supply chain that integrates product, process and networks design. An integrated life cycle assessment and techno-economic optimization is proposed that might deliver more economically feasible operations, minimizes environmental impacts and maximizes social contributions. Closed loop economy of the supply chain is achieved by reusing waste to be raw material of final products. Societal benefit is given by the supply chain by absorbing waste as source of raw material and opening new work opportunities. A case study of ethanol supply chain from rice straws is considered. The modeling results show that optimization within the scope of LCA is capable of minimizing both CO₂ emissions and energy and utility consumptions and thus enhancing raw materials utilization. Furthermore, the supply chain is capable of contributing to local economy through jobs creation. While the model is quite comprehensive, the future research recommendation on energy integration and global sustainability is proposed.

Keywords: life cycle assessment, techno-economic optimization, sustainable supply chains, closed loop economy

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Authors: Krestina V. Alsaraeva, Victor E. Gromov, Sergey V. Konovalov, Anna A. Atroshkina

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Processing of Al-19.4Si alloy by high intensive electron beam has been carried out and multiple increase in fatigue life of the material has been revealed. Investigations of structure and surface modified layer destruction of Al-19.4Si alloy subjected to multicycle fatigue tests to fracture have been carried out by methods of scanning electron microscopy. The factors responsible for the increase of fatigue life of Al-19.4Si alloy have been revealed and analyzed.

Keywords: Al-19.4Si alloy, high intensive electron beam, multicycle fatigue, structure

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Authors: Jessica Bain, Greg Thoma, Marty Matlock, Jeyam Subbiah, Ebenezer Kwofie

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The environmental impact and particulate matter emissions (PM) associated with the production and packaging of 1 kg of almonds were evaluated using life cycle assessment (LCA). The assessment began at the point of ready to harvest with a system boundary was a cradle-to-gate assessment of almond packaging in California. The assessment included three scenarios of off-ground harvesting of almonds. The three general off-ground harvesting scenarios with variations include the harvested almonds solar dried on a paper tarp in the orchard, the harvested almonds solar dried on the floor in a separate lot, and the harvested almonds dried mechanically. The life cycle inventory (LCI) data for almond production were based on previously published literature and data provided by Almond Board of California (ABC). The ReCiPe 2016 method was used to calculate the midpoint impacts. Using consequential LCA model, the global warming potential (GWP) for the three harvesting scenarios are 2.90, 2.86, and 3.09 kg CO2 eq/ kg of packaged almond for scenarios 1, 2a, and 3a, respectively. The global warming potential for conventional harvesting method was 2.89 kg CO2 eq/ kg of packaged almond. The particulate matter emissions for each scenario per hectare for each off-ground harvesting scenario is 77.14, 9.56, 66.86, and 8.75 for conventional harvesting and scenarios 1, 2, and 3, respectively. The most significant contributions to the overall emissions were from almond production. The farm gate almond production had a global warming potential of 2.12 kg CO2 eq/ kg of packaged almond, approximately 73% of the overall emissions. Based on comparisons between the GWP and PM emissions, scenario 2a was the best tradeoff between GHG and PM production.

Keywords: life cycle assessment, low moisture foods, sustainability, LCA

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Authors: Alexandre M. Brighenti

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One of the most damaging species in sunflower crops in Brazil is the hairy beggarticks (Bidens pilosa L.). The large number of seeds, the various vegetative cycles during the year, the staggered germination and the scarcity of selective and effective herbicides to control this weed in sunflower are some of attributes that hinder the effectiveness in controlling hairy beggarticks populations. The experiment was carried out with the objectives of evaluating the control of hairy beggarticks plants in sunflower crops, and to assess sunflower tolerance to residual herbicides. The treatments were as follows: S-metolachlor (1,200 and 2,400 g ai ha^-1), flumioxazin (60 and 120 g ai ha^-1), sulfentrazone (150 and 300 g ai ha^-1) and two controls (weedy and weed-free check). Phytotoxicity on sunflower plants, percentage of control and density of hairy beggarticks plants, sunflower stand and plant height, head diameter, oil content and sunflower yield were evaluated. The herbicides flumioxazin and sulfentrazone were the most efficient in hairy beggarticks control. S-metolachlor provided acceptable control levels. S-metolachlor (1,200 g ha^-1), flumioxazin (60 g ha^-1) and sulfentrazone (150 g ha^-1) were the most selective doses for sunflower crop.

Keywords: flumioxazin, Helianthus annuus, S-metolachlor, sulfentrazone, weeds

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Authors: Yang Shen, Yongpan Cheng, Jinliang Xu

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The effective blade cooling is of great significance for improving the performance of turbine. The mist cooling emerges as the promising way compared with the transitional single-phase cooling. In the mist cooling, the injected droplet will evaporate rapidly, and cool down the blade surface due to the absorbed latent heat, hence the lifetime for evaporating droplet becomes critical for design of cooling passages for the blade. So far there have been extensive studies on the droplet evaporation, but usually the isothermal model is applied for most of the studies. Actually the surface cooling effect can affect the droplet evaporation greatly, it can prolong the droplet evaporation lifetime significantly. In our study, a new theoretical model for sessile droplet evaporation with surface cooling effect is built up in toroidal coordinate. Three evaporation modes are analyzed during the evaporation lifetime, include “Constant Contact Radius”(CCR) mode、“Constant Contact Angle”(CCA) mode and “stick-slip”(SS) mode. The dimensionless number E0 is introduced to indicate the strength of the evaporative cooling, it is defined based on the thermal properties of the liquid and the atmosphere. Our model can predict accurately the lifetime of evaporation by validating with available experimental data. Then the temporal variation of droplet volume, contact angle and contact radius are presented under CCR, CCA and SS mode, the following conclusions are obtained. 1) The larger the dimensionless number E0, the longer the lifetime of three evaporation cases is; 2) The droplet volume over time still follows “2/3 power law” in the CCA mode, as in the isothermal model without the cooling effect; 3) In the “SS” mode, the large transition contact angle can reduce the evaporation time in CCR mode, and increase the time in CCA mode, the overall lifetime will be increased; 4) The correction factor for predicting instantaneous volume of the droplet is derived to predict the droplet life time accurately. These findings may be of great significance to explore the dynamics and heat transfer of sessile droplet evaporation.

Keywords: blade cooling, droplet evaporation, lifetime, theoretical analysis

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Authors: Isa Ahmadi, Ramin Khamedi

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In this paper, the elasto-plastic and cyclic torsion of a shaft is studied using a finite element method. The Prager kinematic hardening theory of plasticity with the Ramberg and Osgood stress-strain equation is used to evaluate the cyclic loading behavior of the shaft under the torsional loading. The material of shaft is assumed to follow the non-linear strain hardening property based on the Prager model. The finite element method with C1 continuity is developed and used for solution of the governing equations of the problem. The successive substitution iterative method is used to calculate the distribution of stresses and plastic strains in the shaft due to cyclic loads. The shear stress, effective stress, residual stress and elastic and plastic shear strain distribution are presented in the numerical results.

Keywords: cyclic loading, finite element analysis, Prager kinematic hardening model, torsion of shaft

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

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Authors: Chun-Qing Li, Hassan Baji, Wei Yang

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Corrosion of steel in reinforced concrete structures is a major problem worldwide. The volume expansion of corrosion products causes concrete cover cracking, which could lead to delamination of concrete cover. The time to cover cracking plays a key role to the assessment of serviceability of reinforced concrete structures subjected to corrosion. Many analytical, numerical, and empirical models have been developed to predict the time to cracking initiation due to corrosion. In this study, a numerical model based on finite element modeling of corrosion-induced cracking process is used. In order to predict the service life based on time to cover initiation, the numerical approach is coupled with a probabilistic procedure. In this procedure, all the influential factors affecting time to cover cracking are modeled as random variables. The results show that the time to cover cracking is highly variables. It is also shown that rust product expansion ratio and the size of more porous concrete zone around the rebar are the most influential factors in predicting service life of corrosion-affected structures.

Keywords: corrosion, crack width, probabilistic, service life

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Authors: Ghoul Rachid Brahim

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Introduction: Back pain is a real public health problem with a significant socio-economic impact. It is the consequence of a degeneration of the lumbar intervertebral disc (IVD). This often asymptomatic pathology is compatible with an active life. As soon as it becomes symptomatic, conservative treatment is recommended in the majority of cases. The physical or functional disability is resistant to well-monitored conservative treatment, which justifies a surgical alternative which imposes a well-studied reflection on the objectives to be achieved. Objective: Evaluate the indication and short and medium term contribution of surgery in the management of painful degenerative lumbar disc disease. To prove the effectiveness of surgical treatment in the management of painful lumbar degenerative disc disease. Materials and methods: This is a prospective descriptive mono-centric study without comparison group, comprising a series of 104 patients suffering from lumbar painful degenerative disc disease treated surgically. Retrospective analysis of data collected prospectively. Comparison between pre and postoperative clinical status, by pain self-assessment scores and on the impact on pre and postoperative quality of life (3, 6 to 12 months). Results: This study showed that patients who received surgical treatment had great improvements in symptoms, function and several health-related quality of life in the first year after surgery. Conclusions: The surgery had a significantly positive impact on patients' pain, disability and quality of life. Overall, 97% of the patients were satisfied.

Keywords: degenerative disc disease, intervertebral disc, several health-related quality, lumbar painful

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Authors: Athena Pedro, Zandile Batweni, Laura Bradfield, Michael Dare, Ashley Nyman

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The rapid growth and development of an infant in the first 1000 days of life means that this time period provides the greatest opportunity for a positive developmental impact on a child’s life socially, emotionally, cognitively and physically. Current research is being focused on children in the first 1000 days, but there is a lack of research and understanding of mothers and their experiences during this crucial time period. Thus, it is imperative that more research is done to help better understand the experiences of mothers during the first 1000 days of their child’s life, as well as gain more insight into mothers’ knowledge regarding this time period. The first 1000 days of life, from conception to two years, is a critical period, and the child’s attachment to his or her mother or primary caregiver during this period is crucial for a multitude of future outcomes. The aim of this study was to explore mothers’ understanding and experience of the first 1000 days of their child’s life, specifically looking at attachment in the context of Bowlby and Ainsworths’ attachment theory. Using a qualitative methodological framework, data were collected through semi-structured individual interviews with 12 first-time mothers from low-income communities in Cape Town. Thematic analysis of the data revealed that mothers articulated the importance of attachment within the first 1000 days of life and shared experiences of how they bond and form attachment with their babies. Furthermore, these mothers expressed their belief in the long-term effects of early attachment of responsive positive parenting as well as the lasting effects of poor attachment and non-responsive parenting. This study has implications for new mothers and healthcare staff working with mothers of new-born babies, as well as for future contextual research. By gaining insight into the mothers’ experiences, policies and intervention efforts can be formulated in order to assist mothers during this time, which ultimately promote the healthy development of the nation’s children and future adult generation. If researchers are also able to understand the extent of mothers’ general knowledge regarding the first 1000 days and attachment, then there will be a better understanding of where there may be gaps in knowledge and thus, recommendations for effective and relevant intervention efforts may be provided. These interventions may increase knowledge and awareness of new mothers and health care workers at clinics and other service providers, creating a high impact on positive outcome. Thus, improving the developmental trajectory for many young babies allows them the opportunity to pursue optimal development by reaching their full potential.

Keywords: attachment, experience, first 1000 days, knowledge, mothers

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Suzanne L. Stewart, Mikaela D. Gabriel

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Background: Indigenous People face multiple barriers to successful life transitions, including housing, employment, education, and health. Current statistical trends paint devastating life transitions for Indigenous Peoples, but colonization and its intergenerational impacts are typically lacking as the crucial context in which these trends occur. This presentation will illustrate the massive impact of colonization on Indigenous Peoples; its intergenerational transmission, and how it impacts Indigenous clients seeking mental health treatment today. Methods: A qualitative, narrative inquiry methodology was used to honour Indigenous storytelling and knowledge transmission. Indigenous Elders, outreach workers, and homeless clients were interviewed and narratively analyzed for in-depth trends and themes. Impact: This research provides a wealth of in-depth information as to the life transition needs of Indigenous clients, identify the systemic impacts of colonization to the health and wellbeing of Indigenous People, and strategies for mental health treatment.

Keywords: indigenous trauma, indigenous peoples of canada, intergenerational trauma, colonial trauma and treatment

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Authors: E. Abdi

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The purpose of this theoretical literature review is to study the relevant academic literature on lack of cardiorespiratory fitness and its effects on children. The total of thirty eight relevant documents were identified and considered for this review which nineteen of those were original research articles published in peer reviewed journals. The other nineteen articles were statistical documents. This document is structured to examine 4 effects in deficiency of cardiorespiratory fitness in school aged children: (a) obesity, (b) inadequate fitness level, (c) unhealthy life style, and (d) academics. The categories provide a theoretical framework for future studies. The results are broken down into 6 sections: (a) academics,( b) healthy life style, (c) low cost, (d) obesity, (e) Relative Age Effect (RAE), and (f) race/poverty. The study discusses that regular physical fitness assists children and adolescents to develop healthy physical activity behaviors which can be sustained throughout adult life. Conclusion suggests that advocacy for increasing physical activity and decreasing sedentary behaviors at school and home are necessary.

Keywords: cardiorespiratory, endurance, physical activity, physical fitness

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Authors: Esra Zengin, Sinan Akkar

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Reliable and accurate prediction of nonlinear structural response requires specification of appropriate earthquake ground motions to be used in nonlinear time history analysis. The current research has mainly focused on selection and manipulation of real earthquake records that can be seen as the most critical step in the performance based seismic design and assessment of the structures. Utilizing amplitude scaled ground motions that matches with the target spectra is commonly used technique for the estimation of nonlinear structural response. Representative ground motion ensembles are selected to match target spectrum such as scenario-based spectrum derived from ground motion prediction equations, Uniform Hazard Spectrum (UHS), Conditional Mean Spectrum (CMS) or Conditional Spectrum (CS). Different sets of criteria exist among those developed methodologies to select and scale ground motions with the objective of obtaining robust estimation of the structural performance. This study presents ground motion selection and scaling procedure that considers the spectral variability at target demand with the level of ground motion dispersion. The proposed methodology provides a set of ground motions whose response spectra match target median and corresponding variance within a specified period interval. The efficient and simple algorithm is used to assemble the ground motion sets. The scaling stage is based on the minimization of the error between scaled median and the target spectra where the dispersion of the earthquake shaking is preserved along the period interval. The impact of the spectral variability on nonlinear response distribution is investigated at the level of inelastic single degree of freedom systems. In order to see the effect of different selection and scaling methodologies on fragility curve estimations, results are compared with those obtained by CMS-based scaling methodology. The variability in fragility curves due to the consideration of dispersion in ground motion selection process is also examined.

Keywords: ground motion selection, scaling, uncertainty, fragility curve

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Andreas Aceranti, Sergio Romanò, Simonetta Vernocchi, Silvia Arnaboldi, Emilio Mazza

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As a result of the Sars-Cov2 pandemic, the incidence of thymism disorders has significantly increased and, often, patients are reluctant to want to take drugs aimed at stabilizing mood. In order to provide an alternative approach to drug therapies, we have prepared a study in order to evaluate the possibility of improving the quality of life of these subjects through osteopathic treatment. Patients were divided into visceral and fascial manual treatment with the aim of increasing serotonin levels and stimulating the vagus nerve through validated techniques. The results were evaluated through the administration of targeted questionnaires in order to assess quality of life, mood, sleep and intestinal functioning. At a first endpoint we found, in patients undergoing fascial treatment, an increase in quality of life and sleep: in fact, they report a decrease in the number of nocturnal awakenings; a reduction in falling asleep times and greater rest upon waking. In contrast, patients undergoing visceral treatment, as well as those included in the control group, did not show significant improvements. Patients in the fascial group have, in fact, reported an improvement in thymism and subjective quality of life with a generalized improvement in function. Although the study is still ongoing, based on the results of the first endpoint we can hypothesize that fascial stimulation of the vagus nerve with manual and osteopathic techniques may be a valid alternative to pharmacological treatments in mood and sleep disorders.

Keywords: ostheopathy, insomnia, noctural awakening, thymism

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Authors: Gulay Aras, Isil Kutluturk Karagoz, Z. Candan Algun

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Background: Hypermetropia in school-age is a pathology that responds to treatment. In the literature, there has been no study of exercise practice in hypermetropia treatment. Objective: The purpose of this study was to investigate the effects of eye exercises and convergence exercise on visual acuity in school-age children with hypermetropia. Methods: Forty volunteer school-age children with hypermetropia (30 girls, 30 boys, between 7-17 years of age) were included in the study. Sociodemographic information and clinical characteristics were evaluated. 40 participants were randomly divided into two groups: eye exercises and convergence exercises. Home exercise protocols were given to all groups for six weeks, and regular phone calls were made once a week. Individuals performed eye exercises 10 times, convergence exercises 5 min. for two sessions per day for six weeks. The right and left eyes of all the subjects participating in the study were assessed separately by the eye doctor with a Snellen chart. The participants' quality of life was assessed using Pediatric Quality of Life Inventory Version 4.0. Physical health total score (PHTS) and scale total score (STS), which were obtained by evaluating Psychosocial health total score (PSHTS) school, emotional and social functioning, were calculated separately in the scores. At the end of the exercise program, the assessment tests applied at the beginning of the study were reapplied to all individuals. Results: There was no statistically significant difference between the pre- and post-Snellen chart measurements and quality of life in the eye exercises group (p > 0,05). There was a statistically significant difference in visual acuity of right and left eyes (p=0,004, p=0,014) and quality of life in PHTS, PSHTS and STS in the convergence exercise group (p=0,001, p=0,017, p=0,001). Conclusions: In school-age children, convergence exercises were found to be effective on visual acuity and health-related quality of life. Convergence exercises are recommended for the treatment of school-aged children with hypermetropia.

Keywords: convergence exercise, eye exercises, hypermetropia, school-age children

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