Search results for: relaxation training

Authors: Vaikunthan Rajaratnam

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Intro: Motor imagery (MI) and mental practice (MP) can be an alternative to acquire mastery of surgical skills. One component of using this technique is the use of a mental script. The aim of this study was to design and develop a mental script for basic micro suturing training for skill acquisition using a low-fidelity rubber glove model and to describe the detailed methodology for this process. Methods: This study was based on a design and development research framework. The mental script was developed with 5 expert surgeons performing a cognitive walkthrough of the repair of a vertical opening in a rubber glove model using 8/0 nylon. This was followed by a hierarchal task analysis. A draft script was created, and face and content validity assessed with a checking-back process. The final script was validated with the recruitment of 28 participants, assessed using the Mental Imagery Questionnaire (MIQ). Results: The creation of the mental script is detailed in the full text. After assessment by the expert panel, the mental script had good face and content validity. The average overall MIQ score was 5.2 ± 1.1, demonstrating the validity of generating mental imagery from the mental script developed in this study for micro suturing in the rubber glove model. Conclusion: The methodological approach described in this study is based on an instructional design framework to teach surgical skills. This MP model is inexpensive and easily accessible, addressing the challenge of reduced opportunities to practice surgical skills. However, while motor skills are important, other non-technical expertise required by the surgeon is not addressed with this model. Thus, this model should act a surgical training augment, but not replace it.

Keywords: mental script, motor imagery, cognitive walkthrough, verbal protocol analysis, hierarchical task analysis

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Authors: Siti Rajaah Binti Sayed Sultan

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Managing the code blue event is stressful for nurses, the patient, and the patient's families. The rapid response from the first and second responders in the code blue event will improve patient outcomes and prevent tissue hypoxia that leads to brain injury and other organ failures. Providing 1 minute for the cardiac massage and 2 minutes for defibrillation will significantly improve patient outcomes. As we know, the American Heart Association came out with guidelines for managing cardiac arrest patients. The hospital must provide competent staff to manage this situation. It can be achieved when the staff is well equipped with the skill, attitude, and knowledge to manage this situation with well-planned strategies, i.e., clear guidelines for managing the code blue event, competent staff, and functional equipment. The code blue simulation (CBS) was chosen in the training program for code blue management because it can mimic real scenarios. Having the code blue simulation module will allow the staff to appreciate what they will face during the code blue event, especially since it rarely happens in that area. This CBS module training will help the staff familiarize themselves with the activities that happened during actual events and be able to operate the equipment accordingly. Being challenged and independent in managing the code blue in the early phase gives the patient a better outcome. The CBS module will help the assessor and the hospital management team with the proper tools and guidelines for managing the code blue drill accordingly. As we know, prompt action will benefit the patient and their family. It also indirectly increases the confidence and job satisfaction among the nurses, increasing the standard of care, reducing the complication and hospital burden, and enhancing cost-effective care.

Keywords: code blue simulation module, development of code blue simulation module, code blue response time, code blue drill, cardiorespiratory arrest, managing code blue

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Authors: Abderrahmane Soufi

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The building sector is the largest consumer of energy in France, accounting for 44% of French consumption. To reduce energy consumption and improve energy efficiency, France implemented an energy transition law targeting 40% energy savings by 2030 in the tertiary building sector. Building simulation tools are used to predict the energy performance of buildings but the reliability of these tools is hampered by discrepancies between the real and simulated energy performance of a building. This performance gap lies in the simplified assumptions of certain factors, such as the behavior of occupants on air conditioning and heating, which is considered deterministic when setting a fixed operating schedule and a fixed interior comfort temperature. However, the behavior of occupants on air conditioning and heating is stochastic, diverse, and complex because it can be affected by many factors. Probabilistic models are an alternative to deterministic models. These models are usually derived from statistical data and express occupant behavior by assuming a probabilistic relationship to one or more variables. In the literature, logistic regression has been used to model the behavior of occupants with regard to heating and air conditioning systems by considering univariate logistic models in residential buildings; however, few studies have developed multivariate models for higher education and vocational training buildings in a Mediterranean climate. Therefore, in this study, occupant behavior on heating and air conditioning systems was modeled using logistic regression. Occupant behavior related to the turn-on heating and air conditioning systems was studied through experimental measurements collected over a period of one year (June 2023–June 2024) in three classrooms occupied by several groups of students in engineering schools and professional training. Instrumentation was provided to collect indoor temperature and indoor relative humidity in 10-min intervals. Furthermore, the state of the heating/air conditioning system (off or on) and the set point were determined. The outdoor air temperature, relative humidity, and wind speed were collected as weather data. The number of occupants, age, and sex were also considered. Logistic regression was used for modeling an occupant turning on the heating and air conditioning systems. The results yielded a proposed model that can be used in building simulation tools to predict the energy performance of teaching buildings. Based on the first months (summer and early autumn) of the investigations, the results illustrate that the occupant behavior of the air conditioning systems is affected by the indoor relative humidity and temperature in June, July, and August and by the indoor relative humidity, temperature, and number of occupants in September and October. Occupant behavior was analyzed monthly, and univariate and multivariate models were developed.

Keywords: occupant behavior, logistic regression, behavior model, mediterranean climate, air conditioning, heating

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Somayeh Negahdari, Mohsen Ghanbarzadeh, Masoud Nikbakht, Heshmatolah Tavakol

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Asthma, obesity and overweight are the main factors causing change within the heart and respiratory airways. Asthma symptoms are normally observed during exercising. Epidemiological studies have indicated asthma symptoms occurring due to certain lifestyle habits; for example, a sedentary lifestyle. In this study, eight weeks of aerobic exercises resulted in a positive effect overall in overweight women experiencing mild chronic asthma. The quasi-experimental applied research has been done based on experimental and control groups. The experimental group (seven patients) and control group (n = 7) were graded before and after the test. According to the Borg dyspnea and fatigue Perception Index, the training intensity has determined. Participants in the study performed a sub-maximal aerobic activity schedule (45% to 80% of maximum heart rate) for two months, while the control group (n = 7) stayed away from aerobic exercise. Data evaluation and analysis of covariance compared both the pre-test and post-test with paired t-test at significance level of P≤ 0.05. After eight weeks of exercise, the results of the experimental group show a significant decrease in resting heart rate, systolic blood pressure, minute ventilation, while a significant increase in maximal oxygen uptake and tolerance activity (P ≤ 0.05). In the control group, there was no significant difference in these parameters ((P ≤ 0.05). The results indicate the aerobic activity can strengthen the respiratory muscles, while other physiological factors could result in breathing and heart recovery. Aerobic activity also resulted in favorable changes in cardiovascular parameters, and exercise tolerance of overweight women with chronic asthma.

Keywords: asthma, respiratory cardiac index, exercise tolerance, aerobic, overweight

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Authors: Leora Branfield Day, Stephanie Saunders, Leah Steinberg, Shiphra Ginsburg, Christine Soong

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Introduction: Discordance between patients expressed and documented preferences at the end of life is common. Although junior trainees frequently lead goals of care (GOC) conversations, lack of training can result in poor communication. Based on a needs assessment, we developed an interactive electronic learning module (eModule) for conducting patient-centred GOC discussions. The purpose of this study was to evaluate the impact of the eModule on residents’ attitudes towards GOC conversations. Methods: First-year internal medicine residents (n=11) from the University of Toronto selected using purposive sampling underwent semi-structured interviews before and after completing a GOC eModule. Interviews were anonymized, transcribed and open-coded using NVivo. Using a constructivist grounded theory approach, we developed a framework to understand the attitudes of residents to GOC conversations before and after viewing the module. Results: Before the module, participants described limited training and negative emotions towards GOC conversations. Many focused on code status and procedure choices (e.g., ventilation) instead of eliciting patient-centered values. Pressure to “get the DNR" led to conflicting feelings and distress. After the module, participants’ approached conversations with a greater focus on patient values and process. They felt more prepared and comfortable, recognizing the complexity of conversations and the importance of patient-centeredness. Conclusions: A novel GOC eModule allowed residents to develop a patient-centered and standardized approach to GOC conversations while improving confidence and preparedness. This resource could be an effective strategy toward attaining a critical communication competency among learners with the potential to enhance accurate GOC documentation.

Keywords: goals of care conversations, communication skills, emodule, medical education

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Authors: Kevin V. Bui, Richard A. Claytor, Elizabeth M. Priolo, Weihui Li

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Oxygen deprivation masks operate through the use of restricting valves as a means to reduce respiratory flow where flow is inversely proportional to the resistance applied. This produces the same effect as higher altitudes where lower pressure leads to reduced respiratory flow. Both increased resistance with restricting valves and reduce the pressure of higher altitudes make breathing difficultier and force breathing muscles (diaphragm and intercostal muscles) working harder. The process exercises these muscles, improves their strength and results in overall better breathing efficiency. Currently, these oxygen deprivation masks are purely mechanical devices without any electronic sensor to monitor the breathing condition, thus not be able to provide feedback on the breathing effort nor to evaluate the lung function. That is part of the reason that these masks are mainly used for high-level athletes to mimic training in higher altitude conditions, not suitable for patients or customers. The design aims to improve the current method of oxygen deprivation mask to include a larger scope of patients and customers while providing quantitative biometric data that the current design lacks. This will be accomplished by integrating sensors into the mask’s breathing valves along with data acquisition and Bluetooth modules for signal processing and transmission. Early stages of the sensor mask will measure breathing rate as a function of changing the air pressure in the mask, with later iterations providing feedback on flow rate. Data regarding breathing rate will be prudent in determining whether training or therapy is improving breathing function and quantify this improvement.

Keywords: oxygen deprivation mask, lung function, spirometer, Bluetooth

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: M. E. Joret, F. Germeys, P. Van de Craen

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Background: Previous studies have shown that ‘full’ bilingualism seems to enhance the executive functions in children, young adults and elderly people. Executive functions refer to a complex cognitive system responsible for self-controlled and planned behavior and seem to predict academic achievement. The present study aimed at investigating whether similar effects could be found in children learning their second language at school in immersion education programs. Methods: In this study, 44 children involved in immersion education for 4 to 5 years were compared to 48 children in traditional schools. All children were between 9 and 11 years old. To assess executive functions, the Simon task was used, a neuropsychological measure assessing executive functions with reaction times and accuracy on congruent and incongruent trials. To control for background measures, all children underwent the Raven’s coloured progressive matrices, to measure non-verbal intelligence and the Echelle de Vocabulaire en Images Peabody (EVIP), assessing verbal intelligence. In addition, a questionnaire was given to the parents to control for other confounding variables, such as socio-economic status (SES), home language, developmental disorders, etc. Results: There were no differences between groups concerning non-verbal intelligence and verbal intelligence. Furthermore, the immersion learners showed overall faster reaction times on both congruent and incongruent trials compared to the traditional learners, but only after 5 years of training, not before. Conclusion: These results show that the cognitive benefits found in ‘full’ bilinguals also appear in children involved in immersion education, but only after a sufficient exposure to the second language. Our results suggest that the amount of second language training needs to be sufficient before these cognitive effects may emerge.

Keywords: bilingualism, executive functions, immersion education, Simon task

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Authors: Yasin Ibrahim, Hisham Almohandes, Chia Hsu, Regina Baronia, Jesse Worsham, Sara Abdelgawad, Mansour Shawky, Mohammed Abdelfattah, Nesif Alhemiary

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Background: The matrix model (MM) is an evidence-based program for treating substance use disorders. Since first translated into Arabic in 2010, the MM has been gaining popularity in Arabic countries. However, there is no published data as pertains to its efficacy and feasibility in Arabic communities. Here we aimed at exploring providers’ perspectives on its feasibility and efficacy. Methods: Eight addiction treatment centers from four Arabic countries, namely Egypt, Kingdom of Saudi Arabia, the United Arab Emirates, and Iraq, were contacted via email. They were asked to fill in a 21-item questionnaire. Results: Matrix model continues to be utilized in 6 out of the 8 contacted programs. One center in Egypt has discontinued the MM as the providers felt it was not suitable for substance disorders other than stimulants, which are not common in Egypt. Baghdad University Medical Center has substituted MM with Colombo Program as there have been more training opportunities available for it. Data showed wide variability in regards to number of clients treated with the MM (from 300 to 2500). The Arabic version was utilized for training providers in 5 out of the 8 centers while the providers of the other 3 have been trained in the United States. All providers reported that MM made their job significantly easier, and seven providers believed that MM has favorably affected the relapse rate. In all of the six centers, MM is being utilized for many substance use disorders in addition to stimulant use disorders. Reported challenges included the acceptability of patients and their families, difficulty understanding some concepts, and high drop rates in some centers. Conclusion: Matrix model seems to be a valuable modality for the treatment of substance use disorders in Arabic countries. It has its own challenges and limitations that call for more culturally adapted versions.

Keywords: addiction, Arabic countries, developing countries, matrix model

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Estephanie S. López Contreras, Vicente Aranda Palacios, Daniela Flores Silva, Felipe Oliveros Olivares, Romina Riquelme Escobedo, Iñaki Westerhout Usabiaga

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This research explored whether the context of online instruction has influenced the development of first-year English-teaching students' communication skills, being these speaking and listening. The theoretical basis finds its niche in the need to bridge the gap in knowledge about the Chilean online educational context and the development of English communicative skills. An interpretative paradigm and a phenomenological design were implemented in this study. Twenty- two first-year students and two teachers from an English teaching training program participated in the study. The students' ages ranged from 18 to 26 years of age, and the teachers' years of experience ranged from 5 to 13 years in the program. For data collection purposes, semi- structured interviews were applied to both students and teachers. Interview questions were based on the initial conceptualization of the central phenomenon. Observations, field notes, and focus groups with the students are also part of the data collection process. Data analysis considered two-cycle methods. The first included descriptive coding for field notes, initial coding for interviews, and creating a codebook. The second cycle included axial coding for both field notes and interviews. After data analysis, the findings show that students perceived online classes as instances in which active communication cannot always occur. In addition, changes made to the curricula as a consequence of the COVID-19 pandemic have affected students' speaking and listening skills.

Keywords: attitudes, communicative skills, EFL teaching training program, online instruction, and perceptions

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Authors: Diana F. Lopes, Jorge Simoes, José Martins, Eduardo Castro

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Health professionals are the keystone of any health system, by delivering health services to the population. Given the time and cost involved in training new health professionals, the planning process of the health workforce is particularly important as it ensures a proper balance between the supply and demand of these professionals and it plays a central role on the Health 2020 policy. In the past 40 years, the planning of the health workforce in Portugal has been conducted in a reactive way lacking a prospective vision based on an integrated, comprehensive and valid analysis. This situation may compromise not only the productivity and the overall socio-economic development but the quality of the healthcare services delivered to patients. This is even more critical given the expected shortage of the health workforce in the future. Furthermore, Portugal is facing an aging context of some professional classes (physicians and nurses). In 2015, 54% of physicians in Portugal were over 50 years old, and 30% of all members were over 60 years old. This phenomenon associated to an increasing emigration of young health professionals and a change in the citizens’ illness profiles and expectations must be considered when planning resources in healthcare. The perspective of sudden retirement of large groups of professionals in a short time is also a major problem to address. Another challenge to embrace is the health workforce imbalances, in which Portugal has one of the lowest nurse to physician ratio, 1.5, below the European Region and the OECD averages (2.2 and 2.8, respectively). Within the scope of the HEALTH 2040 project – which aims to estimate the ‘Future needs of human health resources in Portugal till 2040’ – the present study intends to get a comprehensive dynamic approach of the problem, by (i) estimating the needs of physicians and nurses in Portugal, by specialties and by quinquenium till 2040; (ii) identifying the training needs of physicians and nurses, in medium and long term, till 2040, and (iii) estimating the number of students that must be admitted into medicine and nursing training systems, each year, considering the different categories of specialties. The development of such approach is significantly more critical in the context of limited budget resources and changing health care needs. In this context, this study presents the drivers of the healthcare needs’ evolution (such as the demographic and technological evolution, the future expectations of the users of the health systems) and it proposes a Bayesian methodology, combining the best available data with experts opinion, to model such evolution. Preliminary results considering different plausible scenarios are presented. The proposed methodology will be integrated in a user-friendly decision support system so it can be used by politicians, with the potential to measure the impact of health policies, both at the regional and the national level.

Keywords: bayesian estimation, health economics, health workforce planning, human health resources planning

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Authors: Birsel Canan Demirbag, Kıymet Yesilcicek Calik, Hacer Kobya Bulut

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Objective: This study was conducted to evaluate the problems of women over 65 with incontinence diagnosis. Methods: This descriptive study was conducted with women over 65 with incontinence diagnosis in four Family Health Centers in a city in Eastern Black Sea region between November 1, and December 20, 2015. 203, 107, 178, 180 women over 65 were registered in these centers and 262 had incontinence diagnosis at least once and had an ongoing complaint. 177 women were volunteers for the study. During home visits and using face-to-face survey methodology, participants were given socio-demographic characteristics survey, Sandvik severity scale, Incontinence Quality of Life Scale, Urogenital Distress Inventory and a questionnaire including challenges experienced due to incontinence developed by the researcher. Data were analyzed with SPSS program using percentages, numbers, Chi-square, Man-Whitney U and t test with 95% confidence interval and a significance level p <0.05. Findings: 67 ± 1.4 was the mean age, 2.05 ± 0.04 was parity, 44.5 ± 2.12 was menopause age, 66.3% were primary school graduates, 45.7% had deceased spouse, 44.4% lived in a large family, 67.2% had their own room, 77.8% had income, 89.2% could meet self- care, 73.2% had a diagnosis of mixed incontinence, 87.5% suffered for 6-20 years % 78.2 had diuretics, antidepressants and heart medicines, 20.5% had urinary fecal cases, 80.5% had bladder training at least once, 90.1% didn’t have bladder diary calendar/control training programs, 31.1% had hysterectomy for prolapse, 97.1'i% was treated with lower urinary tract infection at least once, 66.3% saw a doctor to get drug in the last three months, 76.2 could not go out alone, 99.2 % had at least one chronic disease, 87.6 % had constipation complain, 2.9% had chronic cough., 45.1% fell due to a sudden rise for toilet. Incontinence Impact Questionnaire Average score was (QOL) 54.3 ± 21.1, Sandvik score was 12.1 ± 2.5, Urogenital Distress Inventory was 47.7 ± 9.2. Difficulties experienced due to incontinence were 99.5% feeling of unhappiness, 67.1% constant feeling of urine smell due to failing to change briefs frequently, % 87.2 move away from social life, 89.7 unable to use pad, 99.2% feeling of disturbing households / other individuals, 87.5% feel dizziness/fall due to sudden rise, 87.4% feeling of others’ imperceptions about the situation, % 94.3 insomnia, 78.2 lack of assistance, 84.7% couldn’t afford urine protection briefs. Results: With this study, it was found out that there were a lot of unsolved issues at individual and community level affecting the life quality of women with incontinence. In accordance with this common problem in women, to facilitate daily life it is obvious that regular home care training programs at institutional level in our country will be effective.

Keywords: health problems, incontinence, incontinence quality of life questionnaire, old age, urinary urogenital distress inventory, Sandviken severity, women

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Authors: Ruiyuan Wang, Jing Han, Bruno Di Biase, Mark Antoniou

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The purpose of this study is to explore the relationship between working memory (WM) capacity and Chinese-English consecutive interpreting (CI) performance in interpreting learners with different study abroad experience (SAE). Such relationship is not well understood. This study also examines whether Chinese interpreting learners with SAE in English-speaking countries, demonstrate a better performance in inflectional morphology and agreement, notoriously unstable in Chinese speakers of English L2, in their interpreting output than learners without SAE. Fifty Chinese university students, majoring in Chinese-English Interpreting, were recruited in Australia (n=25) and China (n=25). The two groups matched in age, language proficiency, and interpreting training period. Study abroad (SA) group has been studying in an English-speaking country (Australia) for over 12 months, and none of the students recruited in China (the no study abroad = NSA group) had ever studied or lived in an English-speaking country. Data on language proficiency and training background were collected via a questionnaire. Lexical retrieval performance and working memory (WM) capacity data were collected experimentally, and finally, interpreting data was elicited via a direct CI task. Main results of the study show that WM significantly correlated with participants' CI performance independently of learning context. Moreover, SA outperformed NSA learners in terms of subject-verb number agreement. Apart from that, WM capacity was also found to correlate significantly with their morphosyntactic accuracy. This paper sheds some light on the relationship between study abroad, WM capacity, and CI performance. Exploring the effect of study abroad on interpreting trainees and how various important factors correlate may help interpreting educators bring forward more targeted teaching paradigms for participants with different learning experiences.

Keywords: study abroad experience, consecutive interpreting, working memory, inflectional agreement

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Authors: Flora Tajiki

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This study investigates the effectiveness of non-surgical and non-medical interventions in treating infertile women with severely diminished ovarian reserve (below one), low Anti-Müllerian Hormone (AMH) levels, and symptoms of early menopause. The intervention included yoga, sunlight exposure, vitamin and mineral supplementation, relaxation techniques, and daily prayers performed both before sleep and upon waking. These methods were applied to women who had shown poor response to high-dose fertility treatments, such as IVF and microinjection cycles, leading to low-quality egg production. The focus was on women with severely reduced ovarian reserve and early menopause symptoms, some of whom continued to experience relatively regular menstrual cycles despite the onset of these symptoms. This treatment was aimed at women for whom conventional fertility methods had been ineffective. The study sample consisted of 120 married women, aged 25 to 45, from the provinces of Tehran, Alborz, and western Iran, with 35 participants completing the intervention. Individual factors such as residence, education, employment status, marriage duration, family infertility history, and previous infertility treatments were examined, with income considered as a contextual variable. The results indicate that AMH may not be a definitive marker of ovarian reserve, as lifestyle modifications, such as those implemented in this study, were associated with increased AMH levels, the return of regular menstrual cycles, and successful pregnancies. No short- or long-term complications were reported during the two-year follow-up, highlighting the potential benefits of non-surgical interventions for women with early menopause symptoms and diminished ovarian reserve.

Keywords: anti-müllerian hormone, infertility, ovarian reserve, early menopause, fertility, women’s health, lifestyle modification, pregnancy

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Authors: Hiroyuki Aoki, Toru Asada, Tomomi Tanii

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The properties of a spin-cast film of a polymer material are different from those in the bulk material because the polymer chains are frozen in an un-equilibrium state due to the rapid evaporation of the solvent. However, there has been little information on the un-equilibrated conformation and dynamics in a spin-cast film at the single chain level. The real-space observation of individual chains would provide direct information to discuss the morphology and dynamics of single polymer chains. The recent development of super-resolution fluorescence microscopy methods allows the conformational analysis of single polymer chain. In the current study, the conformation of a polymer chain in a spin-cast film by the super-resolution microscopy. Poly(methyl methacrylate) (PMMA) with the molecular weight of 2.2 x 10^6 was spin-cast onto a glass substrate from toluene and chloroform. For the super-resolution fluorescence imaging, a small amount of the PMMA labeled by rhodamine spiroamide dye was added. The radius of gyration (Rg) was evaluated from the super-resolution fluorescence image of each PMMA chain. The mean-square-root of Rg was 48.7 and 54.0 nm in the spin-cast films prepared from the toluene and chloroform solutions, respectively. On the other hand, the chain dimension in a bulk state (a thermally annealed 10- μm-thick sample) was observed to be 43.1 nm. This indicates that the PMMA chain in the spin-cast film takes an expanded conformation compared to the unperturbed chain and that the chain dimension is dependent on the solvent quality. In a good solvent, the PMMA chain has an expanded conformation by the excluded volume effect. The polymer chain is frozen before the relaxation from an un-equilibrated expanded conformation to an unperturbed one by the rapid solvent evaporation.

Keywords: chain conformation, polymer thin film, spin-coating, super-resolution optical microscopy

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Authors: Sahar Rakhshan, Simonetta Geninatti Crich, Diego Alberti, Rachele Stefania

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The combination of imaging and therapeutic agents in the same smart nanoparticle is a promising option to perform a minimally invasive imaging guided therapy. In this study, Low density lipoproteins (LDL), one of the most attractive biodegradable and biocompatible nanoparticles, were used for the simultaneous delivery of Paclitaxel (PTX), a hydrophobic antitumour drug and an amphiphilic contrast agent, Gd-AAZTA-C17, in B16-F10 melanoma cell line. These cells overexpress LDL receptors, as assessed by Flow cytometry analysis. PTX and Gd-AAZTA-C17 loaded LDLs (LDL-PTX-Gd) have been prepared, characterized and their stability was assessed under 72 h incubation at 37 ◦C and compared to LDL loaded with Gd-AAZTA-C17 (LDL-Gd) and LDL-PTX. The cytotoxic effect of LDL-PTX-Gd was evaluated by MTT assay. The anti-tumour drug loaded into LDLs showed a significantly higher toxicity on B16-F10 cells with respect to the commercially available formulation Paclitaxel Kabi (PTX Kabi) used in clinical applications. It was possible to demonstrate a high uptake of LDL-Gd in B16-F10 cells. As a consequence of the high cell uptake, melanoma cells showed significantly high cytotoxic effect when incubated in the presence of PTX (LDL-PTX-Gd). Furthermore, B16-F10 have been used to perform Magnetic Resonance Imaging. By the analysis of the image signal intensity, it was possible to extrapolate the amount of internalized PTX indirectly by the decrease of relaxation times caused by Gd, proportional to its concentration. Finally, the treatment with PTX loaded LDL on B16-F10 tumour bearing mice resulted in a marked reduction of tumour growth compared to the administration of PTX Kabi alone. In conclusion, LDLs are selectively taken-up by tumour cells and can be successfully exploited for the selective delivery of Paclitaxel and imaging agents.

Keywords: low density lipoprotein, melanoma cell lines, MRI, paclitaxel, personalized medicine application, theragnostic System

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Authors: Alejandro Molina-Villegas, Silvia Fernández-Sabido, Eduardo Mendoza-Vargas, Fátima Miranda-Pestaña

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The automatic detection of indigenous languages ​​in digital texts is essential to promote their inclusion in digital media. Underrepresented languages, such as Maya, are often excluded from language detection tools like Google’s language-detection library, LANGDETECT. This study addresses these limitations by developing a hybrid language detection solution that accurately distinguishes Maya (YUA) from Spanish (ES). Two strategies are employed: the first focuses on creating a profile for the Maya language within the LANGDETECT library, while the second involves training a Naive Bayes classification model with two categories, YUA and ES. The process includes comprehensive data preprocessing steps, such as cleaning, normalization, tokenization, and n-gram counting, applied to text samples collected from various sources, including articles from La Jornada Maya, a major newspaper in Mexico and the only media outlet that includes a Maya section. After the training phase, a portion of the data is used to create the YUA profile within LANGDETECT, which achieves an accuracy rate above 95% in identifying the Maya language during testing. Additionally, the Naive Bayes classifier, trained and tested on the same database, achieves an accuracy close to 98% in distinguishing between Maya and Spanish, with further validation through F1 score, recall, and logarithmic scoring, without signs of overfitting. This strategy, which combines the LANGDETECT profile with a Naive Bayes model, highlights an adaptable framework that can be extended to other underrepresented languages in future research. This fills a gap in Natural Language Processing and supports the preservation and revitalization of these languages.

Keywords: indigenous languages, language detection, Maya language, Naive Bayes classifier, natural language processing, low-resource languages

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Authors: Jawaher F. Alsharef, Abdullah A. Ghaddaf, Mohammed S. Alomari, Abdullah A. Al Qurashi, Ahmed S. Abdulhamid, Mohammed S. Alshehri, Majed Alosaimi

Abstract:

Botulinum toxin type A (BTX-A) is the most popular therapeutic agent for muscle relaxation and pain control. Lately, BTX-A injection received great interest as a part of multimodal pain management for lower limb lengthening and deformity correction. This systematic review aimed to determine the role of BTX-A injection in pain management for during lower limb lengthening and/or deformity correction. We searched Medline, Embase, and CENTRAL. We included randomized controlled trials (RCTs) that compared the BTX-A injection to placebo for individuals undergoing lower limb lengthening and/or deformity correction. We sought to evaluate the following outcomes: pain on visual analogue scale (VAS), range of motion parameters, average opioid consumption, and adverse events. The standardized mean difference (SMD) was used to represent continuous outcomes while risk ratio (RR) was used to represent dichotomous outcomes. A total of 4 RCTs that enrolled 257 participants (337 limbs) deemed eligible. Adjuvant BTX-A injection showed a significant reduction in post-operative pain compared to placebo (SMD=–0.28, 95% CI –0.53 to –0.04). No difference was found between BTX-A injection and placebo in terms of range of motion parameters, average opioid consumption, or adverse events after surgical limb lengthening and/or deformity correction (RR= 0.77, 95% CI –0.58 to 1.03). Conclusions: Adjuvant BTX-A injection conferred a discernible reduction in post-operative pain during surgical limb lengthening and/or deformity without increasing the risk of adverse events.

Keywords: botulinum toxin type A, limb lengthening, distraction osteogenesis, deformity correction, pain management

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Authors: Irene Yi

Abstract:

Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Taonga Temwani Chibaka

Abstract:

This qualitative research study was conducted to identify the common risk factors that affect the construction industry in Malawi in the building and infrastructure (civil works) projects. The study then evaluates the possible risk responses that are done to mitigate the various risk factors that were identified. I addition the research also established the barriers to risk management implementation with lastly mapping out as where the identified risk factors fall on which stage of the project and then also map out the knowledge areas that need to be worked on the cases on Malawian construction industry in order to mitigate most of the identified risk factors. The study involved the interviewing the professionals from the construction industry in Malawi where insights and ideas were collected, analysed and interpreted. The key study findings show that risks related to clients group are perceived as most critical followed by the contractor related, consultant related and then external group related factors respectively where preventive measures are the most applied risk response technique where the aim to avoid most of the risk factors from happening. Most of the risk factors identified were internal risks and in managerial category which suggested that risk planning was to be emphasized at pre-contract stage to minimize these risks since a bigger percentage of the risk factors were mapped out at implementation stage. Furthermore, barriers to risk management were identified and the key barriers were lack of awareness; lack of knowledge; lack of formal policies in place; regarded as costly and limited time which resulted in proposing that regulating authorities to purposefully introduce intense training on risk management to make known of this new knowledge area. The study then recommends that organisation should formally implement risk management where policies should be introduced to enforce all parties to undertake this. Risk planning was regarded as paramount and this to be done from pre-contract phase so as to mitigate 80% of the risk factors. Finally, training should be done on all project management knowledge areas.

Keywords: risk management, risk factors, risks, malawi

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Mohanad Moustafa, Julia Sieberer, Rhys Davies

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Background: Orbital compartment syndrome (OCS) is a sight-threatening Ophthalmologic emergency caused by rapidly increasing intraorbital pressure. It is usually caused by a retrobulbar hemorrhage as a result of trauma. If not treated in a timely manner, permanent vision loss can occur. Lateral canthotomy and cantholysis are minor procedures that can be performed bedside with equipment available in the emergency department. The aim of the procedure is to release the attachments between the suspensory ligaments of the eye and the bony orbital wall, leading to a decrease in intraorbital pressure and preventing irreversible loss of vision. As most Ophthalmologists across the UK provide non-resident on-call service, this may lead to a delay in the treatment of OCS and stresses the need for Emergency medical staff to be able to provide this sight-saving procedure independently. Aim: To survey current training, experience, and confidence levels among Emergency Medicine doctors in performing emergency lateral canthotomy and to establish whether these variables change the following teaching from experienced ophthalmologists. RESULTS: Most EM registrars had little to no experience in performing lateral canthotomy and cantholysis. The majority of them showed a significant increase in their confidence to perform the procedure following ophthalmic-led teaching. The survey also showed that the registrars felt such training should be added to/part of the EM curriculum. Conclusion: The involvement of Ophthalmologists in the teaching of EM doctors to recognise and treat OCS independently may prevent delays in treatment and reduce the risk of permanent sight loss. This project showed potential in improving patient care and will lead to a National Survey of EM doctors across the UK.

Keywords: lateral canthotomy, retrobulbar hemorrhage, Ophthalmology, orbital compartment syndrome, sight loss, blindness

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Shaista Meghani, Rozina Karmaliani, Khairulnissa Ajani, Shireen Shahzad, Nadeem Ullah Khan

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Background: The concept of Family Presence During Resuscitation (FPDR) is gradually gaining recognition in western countries, however, it is rarely considered in South Asian countries including Pakistan. Over time, patients’ and families’ rights have gained recognition and healthcare has progressed to become more patient-family centered. Objectives: The objective of this study was to evaluate the impact of an educational program on the Knowledge, Attitude, and Practices (KAP) of healthcare professionals (HCPs) towards FPDR in Emergency Department (ED), at a tertiary care setting, in Karachi, Pakistan. Methods: This was a Pre-test and Post-test study design. A convenient universal sampling was done, and all ED nurses and physicians with more than one year of experience were eligible. The intervention included one-hour training sessions for physicians (three sessions) and nurses (eight sessions), The KAP of nurses and physicians were assessed immediately after (post-test I), and two weeks(post-test II) after the intervention using a pretested questionnaire. Results: The findings of the study revealed that the mean scores of knowledge and attitude of HCPs at both time points were statistically significant (p-value=<0.001), however, an insignificant difference was found on practice of FPDR (p-value=>0.05). Conclusion: The study findings recommend that the educational program on FPDR for HCPs needs to be offered on an ongoing basis. Moreover, training modules need to be developed for the staff, and formal guidelines need to be proposed for FPDR, through a multidisciplinary team approach.

Keywords: family presence, cardiopulmonary resuscitation, attitude, education, practices, health care professionals

Procedia PDF Downloads 191

Authors: Boularas El-Alia, Arbi Raja, Bachir Bouiadjra Sara, Rezk-Kallah Haciba, Rezkkallah Baghdad

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Objectives : To determine the handling practices of cytotoxic drugs and to describe clinical manifestations expressed by hospital personnel of Sidi Bel Abbes during the year 2014. Methods: Sectional descriptive study conducted in 3 center university hospital units (Hematology, Oncology and Urology) and Gynecology of EHS Sidi Bel Abbes. A questionnaire was administered to hospital workers regulary exposed to cytotoxic drugs. A work-place visit was performed to have an overview about working conditions. The Cytotoxic Contact Index (CCI) was calculated for each nurse on a period of 15 working days. Treatment of the results was done using SPSS software. Results: The survey reveals that 22 men and 58 women are exposed to cytotoxic drugs for an average of 7 years. Many symptoms such as ocular irritation (38,75%), throat irritation (56,25%), headache (68,75%), dizziness (43,75%), nausea (37,5%), metallic taste (30%), were reported with high frequency. Are noted in the offspring, 3 congenital anomalies,2 diaphragmatic hernia and a cleft palate. The Cytotoxic Contact Index (CCI) was higher than 3 among Oncology nurses and higher than 1 for most of the nurses of Hematology and Gynecology service. The wearing of personal protective clothing was not respected by all workers: (22/23) wear gloves and (20/23) wear a mask,(5/23) wear a cap, (2/23) wear glasses. Only 3 nurses have benefited from continuous training on handling cytotoxic drugs. Conclusion: This study shows a high occupational exposure risk to cytotoxic drugs among persons handling these drugs and the necessity to apply rigorously all measures related to personal protection awareness and training of personnel to minimize these exposure.

Keywords: cytotoxic drugs, handling, clinical manifestations, hospital staff

Procedia PDF Downloads 445

Authors: Marc Sebastián-Romagosa, Woosang Cho, Rupert Ortner, Christy Li, Christoph Guger

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Neurorehabilitation based on Brain-Computer Interfaces (BCIs) shows important rehabilitation effects for patients after stroke. Previous studies have shown improvements for patients that are in a chronic stage and/or have severe hemiparesis and are particularly challenging for conventional rehabilitation techniques. For this publication, seven stroke patients in the chronic phase with hemiparesis in the lower extremity were recruited. All of them participated in 25 BCI sessions about 3 times a week. The BCI system was based on the Motor Imagery (MI) of the paretic ankle dorsiflexion and healthy wrist dorsiflexion with Functional Electrical Stimulation (FES) and avatar feedback. Assessments were conducted to assess the changes in motor improvement before, after and during the rehabilitation training. Our primary measures used for the assessment were the 10-meters walking test (10MWT), Range of Motion (ROM) of the ankle dorsiflexion and Timed Up and Go (TUG). Results show a significant increase in the gait speed in the primary measure 10MWT fast velocity of 0.18 m/s IQR = [0.12 to 0.2], P = 0.016. The speed in the TUG was also significantly increased by 0.1 m/s IQR = [0.09 to 0.11], P = 0.031. The active ROM assessment increased 4.65º, and IQR = [ 1.67 - 7.4], after rehabilitation training, P = 0.029. These functional improvements persisted at least one month after the end of the therapy. These outcomes show the feasibility of this BCI approach for chronic stroke patients and further support the growing consensus that these types of tools might develop into a new paradigm for rehabilitation tools for stroke patients. However, the results are from only seven chronic stroke patients, so the authors believe that this approach should be further validated in broader randomized controlled studies involving more patients. MI and FES-based non-invasive BCIs are showing improvement in the gait rehabilitation of patients in the chronic stage after stroke. This could have an impact on the rehabilitation techniques used for these patients, especially when they are severely impaired and their mobility is limited.

Keywords: neuroscience, brain computer interfaces, rehabilitat, stroke

Procedia PDF Downloads 93