Search results for: learning outcomes assessment

Authors: Mariola Głowacka

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Aim: The aim of this study was to present the assessment of dyspnea and anxiety in seniors staying in the hospice in the context of the nurse's tasks. Materials and methods: The presented research was carried out at the "Hospicjum Płockie" Association of St. Urszula Ledóchowska in Płock, in a stationary ward, for adults. The research group consisted of 100 people, women, and men. In the study described in this paper, the method of diagnostic survey, the method of estimation and analysis of patient records were used, and the research tools were the numerical scale of the NRS assessment, the modified Borg scale to assess dyspnea, the Trait Anxiety scale to test the intensity of anxiety and the sociodemographic assessment of the respondent. Results: Among the patients, the greatest number were people without dyspnoea (38 people) and with average levels of dyspnoea (26 people). People with lung cancer had a higher level of breathlessness than people with other cancers. Half of the patients included in the study felt anxiety at a low level. On average, men had a higher level of anxiety than women. Conclusion: 1) Patients staying in the hospice require comprehensive nursing care due to the underlying disease, comorbidities, and a wide range of medications taken, which aggravate the feeling of dyspnea and anxiety. 2) The study showed that in patients staying in the hospice, the level of dyspnea was of varying severity. The greatest number of people were without dyspnea (38) and patients with a low level of dyspnea (34). There were 12 people experiencing an average level of dyspnea and a high level of dyspnea 15. 3) The main factor influencing the severity of dyspnea in patients was the location of cancer. There was no significant relationship between the intensity of dyspnea and the age, gender of the patient, and time from diagnosis. 4) The study showed that in patients staying in the hospice, the level of anxiety was of varying severity. Most people experience a low level of anxiety (51). There were 16 people with a high level of anxiety, while there were 33 people experiencing anxiety at an average level. 5) The patient's gender was the main factor influencing the increase in anxiety intensity. Men had higher levels of anxiety than women. There was no significant correlation between the intensity of anxiety and the age of the respondents, as well as the type of cancer and time since diagnosis. 6) The intensity of dyspnea depended on the type of cancer the subjects had. People with lung cancer had a higher level of breathlessness than those with breast cancer and bowel cancer. It was not found that the anxiety increased depending on the type of cancer and comorbidities of the examined person.

Keywords: cancer, shortness of breath, anxiety, senior, hospice

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Authors: Neal Rupani

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Background: A surgical trainee has limited learning opportunities in the operating room in order to gain an ever-increasing standard of surgical skill, competency, and proficiency. These opportunities continue to decline due to numerous factors such as the European Working Time Directive and increasing requirement for service provision. It is therefore imperative to obtain the highest educational value from each educational opportunity. A measure that has yet to be validated in England on surgical trainees called the Operating Room Educational Environment Measure (OREEM) has been developed to identify and evaluate each component of the educational environment with a view to steer future change in optimising educational events in theatre. Aims: The aims of the study are to assess the reliability of the OREEM within England and to evaluate the surgical trainee’s objective perspective of the current operating room educational environment within one region within England. Methods: Using a quantitative study approach, data was collected over one month from surgical trainees within Health Education Thames Valley (Oxford) using an online questionnaire consisting of demographic data, the OREEM, a global satisfaction score. Results: 140 surgical trainees were invited to the study, with an online response of 54 participants (response rate = 38.6%). The OREEM was shown to have good internal consistency (α = 0.906, variables = 40) and unidimensionality, along with all four of its subgroups. The mean OREEM score was 79.16%. The areas highlighted for improvement predominantly focused on improving learning opportunities (average subscale score = 72.9%) and conducting pre- and post-operative teaching (average score = 70.4%). The trainee perception is most satisfactory for the level of supervision and workload (average subscale score = 82.87%). There was no differences found between gender (U = 191.5, p = 0.535) or type of hospital (U = 258.0, p = 0.099), but the learning environment was favoured towards senior trainees (U = 223.5, p = 0.017). There was strong correlation between OREEM and the global satisfaction score (r = 0.755, p<0.001). Conclusions: The OREEM was shown to be reliable in measuring the educational environment in the operating room. This can be used to identify potentially modifiable components for improvement and as an audit tool to ensure high standards are being met. The current perception of the education environment in Health Education Thames Valley is satisfactory, and modifiable internal and external factors such as reducing service provision requirements, empowering trainees to plan lists, creating a team-working ethic between all personnel, and using tools that maximise learning from each operation have been identified to improve learning in the future. There is a favourable attitude to use of such improvement tools, especially for those currently dissatisfied.

Keywords: education environment, surgery, post-graduate education, OREEM

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Authors: Ritesh K. Shukla

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Degradation of biological samples in terms of macromolecules (DNA, RNA, and protein) are the major challenges in the forensic investigation which misleads the result interpretation. Currently, there are no precise methods available to circumvent this problem. Therefore, at the preliminary level, some methods are urgently needed to solve this issue. In this order, Comet assay is one of the most versatile, rapid and sensitive molecular biology technique to assess the DNA degradation. This technique helps to assess DNA degradation even at very low amount of sample. Moreover, the expedient part of this method does not require any additional process of DNA extraction and isolation during DNA degradation assessment. Samples directly embedded on agarose pre-coated microscopic slide and electrophoresis perform on the same slide after lysis step. After electrophoresis microscopic slide stained by DNA binding dye and observed under fluorescent microscope equipped with Komet software. With the help of this technique extent of DNA degradation can be assessed which can help to screen the sample before DNA fingerprinting, whether it is appropriate for DNA analysis or not. This technique not only helps to assess degradation of DNA but many other challenges in forensic investigation such as time since deposition estimation of biological fluids, repair of genetic material from degraded biological sample and early time since death estimation could also be resolved. With the help of this study, an attempt was made to explore the application of well-known molecular biology technique that is Comet assay in the field of forensic science. This assay will open avenue in the field of forensic research and development.

Keywords: comet assay, DNA degradation, forensic, molecular biology

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Authors: Teklay Gebrecherkos, Mahmud Abdulkadir

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Abstract: The emergence and subsequent rapid worldwide spread of the COVID-19 pandemic have posed a global crisis, with a tremendously increasing burden of infection, morbidity, and mortality risks. Recent studies have suggested that severe cases of COVID-19 are characterized by massive biochemical, hematological, and inflammatory alterations whose synergistic effect is estimated to progress to multiple organ damage and failure. In this regard, biochemical monitoring of COVID-19 patients, based on comprehensive laboratory assessments and findings, is expected to play a crucial role in effective clinical management and improving the survival rates of patients. However, biochemical markers that can be informative of COVID-19 patient risk stratification and predictor of clinical outcomes are currently scarcely available. The study aims to investigate the profiles of common biochemical markers and their influence on the severity of the COVID-19 infection in Tigray, Ethiopia. Methods: A laboratory-based cross-sectional study was conducted from July to August 2020 at Quiha College of Engineering, Mekelle University COVID-19 isolation and treatment center. Sociodemographic and clinical data were collected using a structured questionnaire. Whole blood was collected from each study participant, and serum samples were separated after being delivered to the laboratory. Hematological biomarkers were analyzed using FACS count, while organ tests and serum electrolytes were analyzed using ion-selective electrode methods using a Cobas-6000 series machine. Data was analyzed using SPSS Vs 20. Results: A total of 120 SARS-CoV-2 patients were enrolled during the study. The participants ranged between 18 and 91 years, with a mean age of 52 (±108.8). The majority (40%) of participants were between the ages of 60 and above. Patients with multiple comorbidities developed severe COVID-19, though not statistically significant (p=0.34). Mann-Whitney U test analysis showed that biochemical tests such as neuropile count (p=0.003), AST levels (p=0.050), serum creatinine (p=0.000), and serum sodium (p=0.015) were significantly correlated with severe COVID-19 disease as compared to non-severe disease. Conclusion: The severity of COVID-19 was associated with higher age, organ tests AST and creatinine, serum Na+, and elevated total neutrophile count. Thus, further study needs to be conducted to evaluate the alterations of biochemical biomarkers and their impact on COVID-19.

Keywords: COVID-19, biomarkers, mortality, Tigray, Ethiopia

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Authors: Jen-Te Pai, Jao-Heng Liu, Shin-En Pai

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The impact of climate change has intensified in recent years, causing Taiwan to face higher frequency and serious natural disasters. Therefore, it is imperative for industrial parks manufacturers to promote adaptation policies in response to climate change. On the other hand, with the rise of the international anti-terrorism situation, once a terrorist attack occurs, it will attract domestic and international media attention, especially the strategic and economic status of the science park. Thus, it is necessary to formulate adaptation and mitigation strategies under climate change and social economic disasters. After reviewed the literature about climate change, urban disaster prevention, vulnerability assessment, and risk communication, the study selected 62 industrial parks compiled by the Industrial Bureau of the Ministry of Economic Affairs of Taiwan as the research object. This study explored the vulnerability and disaster prevention and disaster relief functional assessment of these industrial parks facing of natural and socio-economic disasters. Furthermore, this study explored planned adaptation of industrial parks management section and autonomous adaptation of corporate institutions in the park. The conclusion of this study is that Taiwan industrial parks with a higher vulnerability to natural and socio-economic disasters should employ positive adaptive behaviours.

Keywords: adaptive behaviours, analytic network process, vulnerability, industrial parks

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Authors: Duangduen L. Bhanthumnavin, Duchduen E. Bhanthumnavin

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Widening gap between new academic knowledge in all areas and habit of exploring and exploiting this precious information by students causes an alarm and need for urgent prevention. At present, all advanced nations are committed to WHO’s Sustainable Development Goals (SDGs), which require some objective achievements by the year 2030 and further. The responsibility has been enforced on university lecturers, in addition to the higher education learning outcomes (HELO). The two groups of goals (SDGs and HELO) can be realized if most university instructors are capable of inculcating some important psychological characteristics and behavioral change in the new generations. Thus, this study aimed at pinpointing the significant factors for additional teaching skills of instructors regardless of the area of study. University lecturers from various parts of Thailand, with the total of 540 persons, participated in this cross-sectional study. Based on interactionism model of behavior antecedents, it covers psychological situational factors, as well as their interaction. Most measuring instruments were summated rating with 10 or more items, each accompanied by a six-point rating scale. All these measures were constructed with acceptable standards. Most of the respondents were volunteers who gave their written responses in a meeting room or conference hall. By applying Multiple Regression Analysis in the total sample as well as in the subsamples of these university instructors, about 70 to 73 predictive percentages with 4 to 6 significant predictors were found. The major dependent variable was instructor’s teaching behavior for inculcating the psycho-moral strength for academic exploration and knowledge application. By performing ANOVA, the less-active instructors were identified as the ones with lower education (Master’s level or lower), the minimal research producers, and the ones with less in-service trainings. The preventive factors for these three groups of instructors were intention to increase the students’ psychological development as well as moral development in their regular teaching classes. In addition, social support from their supervisors and coworkers was also necessary. Recommendations for further research and training are offered and welcomed.

Keywords: psychological inculcation, at-risk instructors, preventive measures, undergraduate teaching

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Authors: Li-Ting Kung, Hui-Zhu Chen, Hsin-Tzu Lee, Wan-Yin Hsu

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Patients with spinal cord injury combined with diabetic nephropathy may under a lot of painful conditions due to complications related to the illness itself or treatments, such as recurrent pressure ulcers, autonomic and peripheral neuropathy, as well as dialysis, for long term. This case report illustrated the nursing experience of transferring a spine cord injure patient who received hemodialysis due to adverse lifestyle-induced diabetic nephropathy to the hospice ward. Nursing care was provided in this patient from July 25th to August 30th, 2015. The tool of 'Gordon’s 11-item functional health assessment' and clinical observation, interviews as well as physical examination were used as data collections. Based on results of health assessment as above, the patient’s health problems were identified as the following: impaired skin integrity, chronic pain, and hopeless. Besides to relieve the symptom of pain due to disease or the treatment of hemodialysis and provide wound care, the first author also played a role to assist the patient to achieve his goal of receiving the hospice palliative care. Finally, with much effort of nurses to communicate with medical teams between the surgical and hospice wards, the patient was transferred to the hospice ward to have fulfilled his last wish of having a good death. We hope this nursing experience can be applied to other similar cases in the future.

Keywords: diabetic nephropathy, hospice care, palliative care, spinal cord injury

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Asmaa Shehata

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Prior research indicates that second language (L2) learners encounter difficulty in the distinguishing novel L2 contrasting sounds that are not contrastive in their native languages. L2 orthographic information, however, is found to play a positive role in the acquisition of non-native phoneme contrasts. While most studies have mainly involved a familiar written script (i.e., the Roman script), the influence of a foreign, unfamiliar script is still unknown. Therefore, the present study asks: Does unfamiliar L2 script play a role in creating distinct phonological representations of novel contrasting phonemes? It is predicted that subjects’ performance in the unfamiliar orthography group will outperform their counterparts’ performance in the control group. Thus, training that entails orthographic inputs can yield a significant improvement in L2 adult learners’ identification and lexical encoding of novel L2 consonant contrasts. Results are discussed in terms of their implications for the type of input introduced to L2 learners to improve their language learning.

Keywords: Arabic, consonant contrasts, foreign script, lexical encoding, orthography, word learning

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Authors: Wan-Ting Kuo, Yi-Wen Liu, Hsiao-Sheng Liu

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Annual incidence of bladder cancer increases in the world and occurs frequently in the male. Most common type is transitional cell carcinoma (TCC) which is treated by transurethral resection followed by intravesical administration of agents. In clinical treatment of bladder cancer, chemotherapeutic drugs-induced apoptosis is always used in patients. However, cancers usually develop resistance to chemotherapeutic drugs and often lead to aggressive tumors with worse clinical outcomes. Approximate 70% TCC recurs and 30% recurrent tumors progress to high-grade invasive tumors, indicating that new therapeutic agents are urgently needed to improve the successful rate of overall treatment. Nonapoptotic program cell death may assist to overcome worse clinical outcomes. Autophagy which is one of the nonapoptotic pathways provides another option for bladder cancer patients. Autophagy is reported as a potent anticancer therapy in some cancers. First of all, we established a mouse orthotopic bladder tumor formation model in order to create a similar tumor microenvironment. IVIS system and micro-ultrasound were utilized to noninvasively monitor tumor formation. In addition, we carried out intravesical treatment in our animal model to be consistent with human clinical treatment. In our study, we carried out intravesical instillation of the autophagy inducer in mouse orthotopic bladder tumor to observe tumor formation by noninvasive IVIS system and micro-ultrasound. Our results showed that bladder tumor formation is suppressed by the autophagy inducer, and there are no significant side effects in the physiology of mice. Furthermore, the autophagy inducer upregulated autophagy in bladder tissues of the treated mice was confirmed by Western blot, immunohistochemistry, and immunofluorescence. In conclusion, we reveal that a novel autophagy inducer with low side effects suppresses bladder tumor formation in our mouse orthotopic bladder tumor model, and it provides another therapeutic approach in bladder cancer patients.

Keywords: bladder cancer, transitional cell carcinoma, orthotopic bladder tumor formation model, autophagy

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Authors: Colin O’Hare, Zili Zho, Thomas Sneddon

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The Australian superannuation system has received high praise for its participation rates and level of funding in retirement yet it is only 25 years old. In recent years, with increasing longevity and persistent lower rates of investment return, how adequate will the funds accumulated through a superannuation system be? In this paper we take Australia as a case study and build a stochastic model of accumulation and decummulation of funds and determine the expected number of years a fund may last an individual in retirement.

Keywords: component, mortality, stochastic models, superannuation

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Authors: Tran Thi Thuy Oanh, Nguyen Ngoc Bao Tran

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In the view of Cognitive Linguistics (CL), knowledge and experience of things and events are used by human beings in expressing concepts, especially in their daily life. The human conceptual system is considered to be fundamentally metaphorical in nature. It is also said that the way we think, what we experience, and what we do everyday is very much a matter of language. In fact, language is an integral factor of cognition in that CL is a family of broadly compatible theoretical approaches sharing the fundamental assumption. The relationship between language and thought, of course, has been addressed by many scholars. CL, however, strongly emphasizes specific features of this relation. By experiencing, we receive knowledge of lives. The partial things are ideal domains, we make use of all aspects of this domain in metaphorically understanding abstract targets. The paper refered to applying this theory on pragmatics lessons for major English students at University of Foreign Language Studies - The University of Da Nang, Viet Nam. We conducted the study with two third – year students groups studying English pragmatics lessons. To clarify this study, the data from these two classes were collected for analyzing linguistic perspectives in the view of CL and traditional concepts. Descriptive, analytic, synthetic, comparative, and contrastive methods were employed to analyze data from 50 students undergoing English pragmatics lessons. The two groups were taught how to transfer the meanings of expressions in daily life with the view of CL and one group used the traditional view for that. The research indicated that both ways had a significant influence on students' English translating and interpreting abilities. However, the traditional way had little effect on students' understanding, but the CL view had a considerable impact. The study compared CL and traditional teaching approaches to identify benefits and challenges associated with incorporating CL into the curriculum. It seeks to extend CL concepts by analyzing metaphorical expressions in daily conversations, offering insights into how CL can enhance language learning. The findings shed light on the effectiveness of applying CL in teaching and learning English pragmatics. They highlight the advantages of using metaphorical expressions from daily life to facilitate understanding and explore how CL can enhance cognitive processes in language learning in general and teaching English pragmatics to third-year students at the UFLS - UDN, Vietnam in personal. The study contributes to the theoretical understanding of the relationship between language, cognition, and learning. By emphasizing the metaphorical nature of human conceptual systems, it offers insights into how CL can enrich language teaching practices and enhance students' comprehension of abstract concepts.

Keywords: cognitive linguisitcs, lakoff and johnson, pragmatics, UFLS

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Authors: Man-Yun Liu, Emily Chia-Yu Su

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Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

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Authors: Dzhavidan Zeinalov, Bugra Sen, Firangiz Aslanova

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Most text-to-speech models cannot operate well in low-resource languages and require a great amount of high-quality training data to be considered good enough. Yet, with the improvements made in ASR systems, it is now much easier than ever to collect data for the design of custom text-to-speech models. In this work, our work on using the ASR model to collect data to build a viable text-to-speech system for one of the leading financial institutions of Azerbaijan will be outlined. NVIDIA’s implementation of the Tacotron 2 model was utilized along with the HiFiGAN vocoder. As for the training, the model was first trained with high-quality audio data collected from the Internet, then fine-tuned on the bank’s single speaker call center data. The results were then evaluated by 50 different listeners and got a mean opinion score of 4.17, displaying that our method is indeed viable. With this, we have successfully designed the first text-to-speech model in Azerbaijani and publicly shared 12 hours of audiobook data for everyone to use.

Keywords: Azerbaijani language, HiFiGAN, Tacotron 2, text-to-speech, transfer learning, whisper

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Afrah Aladwani, Alexander Mullen, Mohammad AlRashidi, Omamah Alfarisi, Faisal Alterkit, Abdulwahab Aladwani, Asit Kumar, Emad Eldosouky

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Introduction: Trastuzumab is a HER-2 targeted humanized monoclonal antibody that significantly improves the therapeutic outcomes of metastatic and non-metastatic breast cancer. However, it is associated with increased risk of cardiotoxicity that ranges from mild decline in the cardiac ejection fraction to permanent cardiomyopathy. Concerns have been raised in treating eligible older patients. This study compares trastuzumab outcomes between two age cohorts in the Kuwait Cancer Control Centre (KCCC). Methods: In a prospective comparative observational study, 93 HER-2 positive breast cancer patients undergoing different chemotherapy protocols + trastuzumab were included and divided into two cohorts based on their age (˂60 and ≥60 years old). The baseline left ventricular ejection fraction (LVEF) was assessed and monitored every three months during trastuzumab treatment. Event of cardiotoxicity was defined as ≥10% decline in the LVEF from the baseline. The lower accepted normal limit of the LVEF was 50%. Results: The median baseline LVEF was 65% in both age cohorts (IQR 8% and 9% for older and younger patients respectively). Whereas, the median LVEF post-trastuzumab treatment was 51% and 55% in older and younger patients respectively (IQR 8%; p-value = 0.22), despite the fact that older patients had significantly lower exposure to anthracyclines compared to younger patients (60% and 84.1% respectively; p-value ˂0.001). 86.7% and 55.6% of older and younger patients, respectively, developed ≥10% decline in their LVEF from the baseline. Among those, only 29% of older and 27% of younger patients reached a LVEF value below 50% (p-value = 0.88). Statistically, age was the only factor that significantly correlated with trastuzumab induced cardiotoxicity (OR 4; p-value ˂0.012), but it did not increase the requirement for permanent discontinuation of treatment. A baseline LVEF value below 60% contributed to developing a post-treatment value below normal ranges (50%). Conclusion: Breast cancer patients aged 60 years and above in Kuwait were at 4-fold higher risk of developing ≥10% decline in their LVEF from the baseline than younger patients during trastuzumab treatment. Surprisingly, previous exposure to anthracyclines and multiple comorbidities were not associated with significant increased risk of cardiotoxicity.

Keywords: breast cancer, elderly, Trastuzumab, cardiotoxicity

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Authors: Mehmet Akif Bütüner, İlhan Koşalay

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Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. While the control systems operating in these power plants ensure that the system operates at the desired operating point, it is also responsible for stopping the relevant unit safely in case of any malfunction. While these control systems are expected not to miss signals that require stopping, on the other hand, it is desired not to cause unnecessary stops. In traditional control systems including modern systems with SCADA infrastructure, alarm conditions to create warnings or trip conditions to put relevant unit out of service automatically are usually generated with predefined limits regardless of different operating conditions. This approach results in alarm/trip conditions to be less likely to detect minimal changes which may result in serious malfunction scenarios in near future. With the methods proposed in this research, routine behavior of the oil circulation of hydraulic governor of a HEPP will be modeled with machine learning methods using historical data obtained from SCADA system. Using the created model and recently gathered data from control system, oil pressure of hydraulic accumulators will be estimated. Comparison of this estimation with the measurements made and recorded instantly by the SCADA system will help to foresee failure before becoming worse and determine remaining useful life. By using model outputs, maintenance works will be made more planned, so that undesired stops are prevented, and in case of any malfunction, the system will be stopped or several alarms are triggered before the problem grows.

Keywords: hydroelectric, governor, anomaly detection, machine learning, regression

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Authors: Marzouk Benali, Jawad Jeaidi, Behrang Mansoornejad, Olumoye Ajao, Banafsheh Gilani, Nima Ghavidel Mehr

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Canadian forest industry is seeking to identify and implement transformational strategies for enhanced financial performance through the emerging bioeconomy or more specifically through the concept of the biorefinery. For example, processing forest residues or surplus of biomass available on the mill sites for the production of biofuels, biochemicals and/or biomaterials is one of the attractive strategies along with traditional wood and paper products and cogenerated energy. There are many possible process-product biorefinery pathways, each associated with specific product portfolios with different levels of risk. Thus, it is not obvious which unique strategy forest industry should select and implement. Therefore, there is a need for analytical and design tools that enable evaluating biorefinery strategies based on a set of criteria considering a perspective of sustainability over the short and long terms, while selecting the existing core products as well as selecting the new product portfolio. In addition, it is critical to assess the manufacturing flexibility to internalize the risk from market price volatility of each targeted bio-based product in the product portfolio, prior to invest heavily in any biorefinery strategy. The proposed paper will focus on introducing a systematic methodology for designing integrated biorefineries using process systems engineering tools as well as a multi-criteria decision making framework to put forward the most effective biorefinery strategies that fulfill the needs of the forest industry. Topics to be covered will include market analysis, techno-economic assessment, cost accounting, energy integration analysis, life cycle assessment and supply chain analysis. This will be followed by describing the vision as well as the key features and functionalities of the I-BIOREF software platform, developed by CanmetENERGY of Natural Resources Canada. Two industrial case studies will be presented to support the robustness and flexibility of I-BIOREF software platform: i) An integrated Canadian Kraft pulp mill with lignin recovery process (namely, LignoBoost™); ii) A standalone biorefinery based on ethanol-organosolv process.

Keywords: biorefinery strategies, bioproducts, co-production, multi-criteria decision making, tool

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Authors: M. J. Baum, B. Gibbes, A. Grinham, S. Albert, D. Gale, P. Fisher

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Alongside the rapid expansion of Seawater Reverse Osmosis technologies there is a concurrent increase in the production of hypersaline brine by-products. To minimize environmental impact, these by-products are commonly disposed into open-coastal environments via submerged diffuser systems as inclined dense jet outfalls. Despite the widespread implementation of this process, diffuser designs are typically based on small-scale laboratory experiments under idealistic quiescent conditions. Studies concerning diffuser performance in the field are limited. A set of experiments were conducted to assess the near field characteristics of brine disposal at the Gold Coast Desalination Plant offshore multiport diffuser. The aim of the field experiments was to determine the trajectory and dilution characteristics of the plume under various discharge configurations with production ranging 66 – 100% of plant operative capacity. The field monitoring system employed an unprecedented static array of temperature and electrical conductivity sensors in a three-dimensional grid surrounding a single diffuser port. Complimenting these measurements, Acoustic Doppler Current Profilers were also deployed to record current variability over the depth of the water column and wave characteristics. Recorded data suggested the open-coastal environment was highly active over the experimental duration with ambient velocities ranging 0.0 – 0.5 m∙s^-1, with considerable variability over the depth of the water column observed. Variations in background electrical conductivity corresponding to salinity fluctuations of ± 1.7 g∙kg^-1 were also observed. Increases in salinity were detected during plant operation and appeared to be most pronounced 10 – 30 m from the diffuser, consistent with trajectory predictions described by existing literature. Plume trajectories and respective dilutions extrapolated from salinity data are compared with empirical scaling arguments. Discharge properties were found to adequately correlate with modelling projections. Temporal and spatial variation of background processes and their subsequent influence upon discharge outcomes are discussed with a view to incorporating the influence of waves and ambient currents in the design of brine outfalls into the future.

Keywords: brine disposal, desalination, field study, negatively buoyant discharge

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Abigail Bentley, Audrey Prost, Nayreen Daruwalla, Apoorwa Gupta, David Osrin

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BACKGROUND: Intimate partner violence (IPV) can impact a woman’s physical, reproductive and mental health, including common mental disorders such as anxiety and depression. However, people other than an intimate partner may also perpetrate violence against women in the family, particularly in India. This study aims to investigate the relationship between experiences of violence perpetrated by the husband and other members of the wider household and symptoms of common mental disorders in women residing in informal settlement (slum) areas of Mumbai. METHODS: Experiences of violence were assessed through a detailed cross-sectional survey of 598 women, including questions about specific acts of emotional, economic, physical and sexual violence across different time points in the woman’s life and the main perpetrator of each act. Symptoms of common mental disorders were assessed using the 12-item General Health Questionnaire (GHQ-12). The GHQ-12 scores were divided into four groups and the relationship between experiences of each type of violence in the last 12 months and GHQ-12 score group was analyzed using ordinal logistic regression, adjusted for the woman’s age and clustering. RESULTS: 482 (81%) women consented to interview. On average, they were 28.5 years old, had completed 7 years of education and had been married 9 years. 88% were Muslim and 47% lived in joint and 53% in nuclear families. 44% of women had experienced at least one act of violence in their lifetime (33% emotional, 22% economic, 23% physical, 12% sexual). 7% had a high GHQ-12 score (6 or above). For violence experiences in the last 12 months, the odds of being in the highest GHQ-12 score group versus the lower groups combined were 13.1 for emotional violence, 6.5 for economic, 5.7 for physical and 6.3 for sexual (p<0.001 for all outcomes). DISCUSSION: The high level of violence reported across the lifetime could be due to the detailed assessment of violent acts at multiple time points and the inclusion of perpetrators within the family other than the husband. Each type of violence was associated with greater odds of a higher GHQ-12 score and therefore more symptoms of common mental disorders. Emotional violence was far more strongly associated with symptoms of common mental disorders than physical or sexual violence. However, it is not possible to attribute causal directionality to the association. Further work to investigate the relationship between differing severity of violence experiences and women’s mental health and the components of emotional violence that make it so strongly associated with symptoms of common mental disorders would be beneficial.

Keywords: common mental disorders, family violence, India, informal settlements, mental health, violence against women

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Evelyn Fabian

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The focus of this discussion centres on the emerging issues in Early Childhood Care and development in Nigeria. Early childhood care is the bedrock of Nigeria’s educational system. However, there are critical issues that had not been addressed and it is frustrating the entire educational process. Thus, this paper will show the inter-connectedness between these issues such as poor funding, trained skillful teachers that would supervise the learning process of the kids, unconducive learning environment and lack of relevant facilities. For a clear grasp of these issues, the researcher visited 36 early childhood centres distributed across the 36 spates of Nigeria. The findings which were expressed in simple percentages revealed a near total absence or government neglect of these critical areas. The findings equally showed a misplaced priority in the government allocation of funds to early child care education and development. The study concludes that this mismatch in the training of these categories of pupils, government should expedite action in addressing these emerging issues in early childhood care and development in Nigeria.

Keywords: early childhood, ECCE, education, emerging issues

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Authors: Bheeshma B., Mathivanan N., Manoj Deepak M., Prabhu Thangaraju, K. Venkatachalam

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This study involves the effect of autologous blood injection for patients who had degeneration of the origin of extensor carpi radialis brevis which was confirmed radio logically and by ultrasound examination and failed cortisone injections to the lateral epicondylitis. In this prospective longitudinal series involves pre-injection assessment of grip strength, pain, and function, using the patient-rated tennis elbow evaluation. In this study, blood from the contralateral limb is taken and injected into the affected limb with the help of ultrasound guidance and then the patient wore a customized wrist support for five days, after which they were commenced with stretching, strengthening, and massage programme with an occupational therapist. In these patients assessment was done after six months and then finally at 12 months after injection, using the patient-rated tennis elbow evaluation. 50 patients completed the study, showing significant improvement in pain; the worst pain decreased by two to five points out of a 10-point visual analogue for pain. Self-perceived function improved by 11–25 points out of 100. Women showed significant increase in grip, but men did not. Our study thus concludes that autologous blood injection show significant improvement in pain and function in patients with chronic lateral epicondylitis, who did not have relief with cortisone injection.

Keywords: lateral epicondylitis, autologous blood injection, conservative treatment, plasma-rich proteins (PRPs)

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Authors: Jarek Krajewski, Andre Wittenborn, Martin Sauerland

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This abstract describes a promising approach for estimating post-traumatic stress disorder (PTSD) based on prosodic speech characteristics. It illustrates the validity of this method by briefly discussing results from an Arabic refugee sample (N= 47, 32 m, 15 f). A well-established standardized self-report scale “Reaction of Adolescents to Traumatic Stress” (RATS) was used to determine the ground truth level of PTSD. The speech material was prompted by telling about autobiographical related sadness inducing experiences (sampling rate 16 kHz, 8 bit resolution). In order to investigate PTSD-induced speech changes, a self-developed set of 136 prosodic speech features was extracted from the .wav files. This set was adapted to capture traumatization related speech phenomena. An artificial neural network (ANN) machine learning model was applied to determine the PTSD level and reached a correlation of r = .37. These results indicate that our classifiers can achieve similar results to those seen in speech-based stress research.

Keywords: speech prosody, PTSD, machine learning, feature extraction

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: E. Lacasa, J. L. Santolaya, I. Millán

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The progress toward sustainability implies maintaining and preferably improving both, human and ecosystem well-being, according to a triple bottom line that includes the environmental, economic and social dimensions. The life cycle assessment (LCA) is a method applicable to all production sectors that aims to quantify the environmental pressures and the benefits related to goods and services, as well as the trade-offs and the scope for improving areas of the production process. While using LCA to measure the environmental dimension of sustainability is widespread, similar approaches for the economic and the social dimensions still have limited application worldwide and there is a need for consistent and robust methods and indicators. This paper focuses on the milk production process and presents the analysis of the flows exchanged by an industrial installation through accounting all the energy and material inputs and the associated emissions and waste outputs at this stage of its life cycle. The functional unit is one litre of milk produced. Different metrics and indicators are used to assess the three dimensions of sustainability. Metrics considered useful to assess the production activities are the total water and energy consumptions and the milk production volume of each cow. The global warming, the value added and the working hours are indicators used to measure each sustainability dimension. The study is performed with two types of feeding of the cows, which includes a change in percentages of components as well. Nutritional composition of the milk obtained is almost kept. It is observed that environmental and social improvements involve high economic costs.

Keywords: milk production, sustainability, indicators, life cycle assessment

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Authors: Shima Nabinejad, Holger Schüttrumpf

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Farmers who are living in flood-prone areas such as coasts are exposed to storm surges increased due to climate change. Crop cultivation is the most important economic activity of farmers, and in the time of flooding, agricultural lands are subject to inundation. Additionally, overflow saline water causes more severe damage outcomes than riverine flooding. Agricultural crops are more vulnerable to salinity than other land uses for which the economic damages may continue for a number of years even after flooding and affect farmers’ decision-making for the following year. Therefore, it is essential to assess what extent the agricultural areas are flooded and how much the associated flood damage to each individual farmer is. To address these questions, we integrated farmers’ decision-making at farm-scale with flood risk management. The integrated model includes identification of hazard scenarios, failure analysis of structural measures, derivation of hydraulic parameters for the inundated areas and analysis of the economic damages experienced by each farmer. The present study has two aims; firstly, it attempts to investigate the flooded cropland and potential crop damages for the whole area. Secondly, it compares them among farmers’ field for three flood scenarios, which differ in breach locations of the flood protection structure. To achieve its goal, the spatial distribution of fields and cultivated crops of farmers were fed into the flood risk model, and a 100-year storm surge hydrograph was selected as the flood event. The study area was Pellworm Island that is located in the German Wadden Sea National Park and surrounded by North Sea. Due to high salt content in seawater of North Sea, crops cultivated in the agricultural areas of Pellworm Island are 100% destroyed by storm surges which were taken into account in developing of depth-damage curve for analysis of consequences. As a result, inundated croplands and economic damages to crops were estimated in the whole Island which was further compared for six selected farmers under three flood scenarios. The results demonstrate the significance and the flexibility of the proposed model in flood risk assessment of flood-prone areas by integrating flood risk management and decision-making.

Keywords: crop damages, flood risk analysis, individual farmer, inundated cropland, Pellworm Island, storm surges

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: Julie Mariam Abraham, G. J. Melendez-Torres

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Background. Maternal mortality is disproportionately higher in low- and middle- income countries (LMICs) compared to other parts of the world. At the current pace of progress, the Sustainable Development Goals for maternal mortality rate will not be achieved by 2030. A variety of factors influence the increased risk of maternal complications in LMICs. These are exacerbated by socio-economic and political factors, including poverty, illiteracy, and gender inequality. This paper aims to use realist synthesis to identify the contexts, mechanisms, and outcomes (CMOs) of maternal health interventions conducted in LMICs to inform evidence-based practice for future maternal health interventions. Methods. In May 2022, we searched four electronic databases for systematic reviews of maternal health interventions in LMICs published in the last five years. We used open and axial coding of CMOs to develop an explanatory framework for intervention effectiveness. Results. After eligibility screening and full-text analysis, 44 papers were included. The intervention strategies and measured outcomes varied within reviews. Healthcare system level contextual factors were the most frequently reported, and infrastructural capacity was the most reported context. The most prevalent mechanism was increased knowledge and awareness. Discussion. Health system infrastructure must be considered in interventions to ensure effective implementation and sustainability. Healthcare-seeking behaviours are embedded within social and cultural norms, environmental conditions, family influences, and provider attitudes. Therefore, effective engagement with communities and families is important to create new norms surrounding pregnancy and delivery. Future research should explore community mobilisation and involvement to enable tailored interventions with optimal contextual fit.

Keywords: maternal mortality, service delivery and organisation, realist synthesis, sustainable development goals, overview of reviews

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