Search results for: regression training

Authors: Innocent Pangapanga, Eric Mungatana

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Tropical cyclones–related floods (TCRFs) in Malawi have devastating effects on smallholder agriculture, thereby threatening the food security agenda, which is already constrained by poor agricultural innovations, low use of improved varieties, and unaffordable inorganic fertilizers, and fragmenting landholding sizes. Accordingly, households have engineered and indigenously implemented sustainable landscape management (SLM) innovations to contain the adverse effects of TCRFs on farm productivity. This study, therefore, interrogated the efficacy of SLM adoption on farm productivity under varying TCRFs, while controlling for the potential selection bias and unobservable heterogeneity through the application of the Endogenous Switching Regression Model. In this study, we further investigated factors driving SLM adoption. Substantively, we found TCRFs reducing farm productivity by 31 percent, on the one hand, and influencing the adoption of SLM innovations by 27 percent, on the other hand. The study also observed that households that interacted SLM with TCRFs were more likely to enhance farm productivity by 24 percent than their counterparts. Interestingly, the study results further demonstrated that multiple adoptions of SLM-related innovations, including intercropping, agroforestry, and organic manure, enhanced farm productivity by 126 percent, suggesting promoting SLM adoption as a package to appropriately inform existing sustainable development goals’ agricultural productivity initiatives under intensifying TCRFs in the country.

Keywords: tropical cyclones–related floods, sustainable landscape management innovations, farm productivity, endogeneity, endogenous switching regression model, panel data, smallholder agriculture

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Authors: Atef M. Ghaleb, Mohamed Z. Ramadan, Khalid Saad Aljaloud

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The aim of this study is to assesses the lifting capabilities of persons experiencing hypoxia. It also examines the behavior of the physiological response induced through the lifting process related to changing in the hypoxia and lifting frequency variables. For this purpose, the study performed two consecutive tests by using; (1) training and acclimatization; and (2) an actual collection of data. A total of 10 male students from King Saud University, Kingdom of Saudi Arabia, were recruited in the study. A two-way repeated measures design, with two independent variables (ambient oxygen (15%, 18% and 21%)) and lifting frequency (1 lift/min and 4 lifts/min) and four dependent variables i.e., maximum acceptable weight of lift (MAWL), Electromyography (EMG) of four muscle groups (anterior deltoid, trapezius, biceps brachii, and erector spinae), rating of perceived exertion (RPE), and rating of oxygen feeling (ROF) were used in this study. The results show that lifting frequency has significantly impacted the MAWL and muscles’ activities. The oxygen content had a significant effect on the RPE and ROE. The study has revealed that acclimatization and training sessions significantly reduce the effect of the hypoxia on the human physiological parameters during the manual materials handling tasks.

Keywords: lifting capabilities, muscle activities, oxygen content, perceived exertion

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Arazan Jane V., Badiang, Ronesito Jr. R., Clavesillas Cristine Joy H., Belleza Saramie S.

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This study examined the coaching style among the PE Teachers in terms of Autonomy, Supportive style, and Controlling Style. On the other hand, gives opportunities to an athlete to be independent, task-oriented, and acknowledge their feelings and perspective of each individual. A controlling coaching style is also portrayed by the rises and falls over an athlete's training development; when this variance is identified, it might harm training. The selection of the respondents of the study will use a random sample of High School PE teachers of the Division of Davao del Norte with a total of 78 High School PE teachers, which can be broken down into 70 High School PE Teachers for Quantitative data for the survey questionnaire and 8 PE Teachers for Qualitative data (IDI). In the quantitative phase, a set of survey questionnaires will be used to gather data from the participants—the extent of the Implementation Questionnaire. The tool will be a researcher-made questionnaire based on the Coaching Styles of selected High School PE teachers of Davao Del Norte. In the qualitative phase, an interview guide questionnaire will be used. Focus group discussions will be conducted to determine themes and patterns or participants' experiences and insights. The researchers conclude that the degree of coaching style among PE Teachers from the Division of Davao del Norte is high, as seen by the findings of this study, and that coaching style among these teachers is highly noticeable.

Keywords: supportive autonomy style, controlling style, live experiences, exemplified

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Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

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This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

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Authors: Kenneth O. Otasowie, Matthew Ikuabe, Clinton Aigbavboa, Ayodeji Oke

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Subcontracting has become a fundamental feature in the construction industry, particularly as most projects in South Africa (SA) are executed by subcontractors. However, the sector in SA contributes to the high level of occupational hazards and injuries recorded, despite Health and Safety (H&S) regulations being enforced in the industry. Hence, this study aims to evaluate the barriers to health and safety practices by subcontractors in SA Construction Industry. A survey design was adopted. A total number of one hundred and forty-four (144) questionnaires were administered to quantity surveyors, construction managers, construction project managers, project managers, architects, and civil and structural engineers, who are owners or work in small and medium enterprises in Guateng Province, SA and eighty-three (83) were returned and found suitable for analysis. Collected data were analysed using percentage, mean item score, standard deviation, and one-sample t-test. The findings show that lack of skilled workers, lack of safety training, and insufficient safety awareness are the most significant barriers to health and safety practices in SA Construction Industry. Therefore, the study recommends the improvement in skills of staff and adequate training for the safe execution of work be provided to all employees and supervisors in these subcontracting firms. These will mitigate the rate of accident occurrence on construction sites.

Keywords: barriers, health and safety, subcontractors, South Africa

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Authors: Wan-Ling Chang, Hsiu-Ju Huang, Jui-Hsiu Chang

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In 2004, the Ministry of Foreign Affairs Taiwan launched ‘An Arrangement on Working Holiday Scheme’ with 15 countries including New Zealand, Japan, Canada, Germany, South Korea, Britain, Australia and others. The aim of the scheme is to allow young people to work and study English or other foreign languages. Each year, there are 30,000 Taiwanese youths applied for participating in the working holiday schemes. However, frequent accidents could cause huge medical expenses and post-delivery fee, which are usually unaffordable for most families. Therefore, this study explored the relationship among perceived risk toward working holiday, insurance product knowledge, brand image and insurance purchase intention for Taiwanese youths who plan to apply for working holiday. A survey questionnaire was distributed for data collection. A total of 316 questionnaires were collected for data analyzed. Data were analyzed using descriptive statistics, independent samples T-test, one-way ANOVA, correlation analysis, regression analysis and hierarchical regression methods of analysis and hypothesis testing. The results of this research indicate that perceived risk has a negative influence on insurance purchase intention. On the opposite, product knowledge has brand image has a positive influence on the insurance purchase intention. According to the mentioned results, practical implications were further addressed for insurance companies when developing a future marketing plan.

Keywords: insurance product knowledges, insurance purchase intention, perceived risk, working holiday

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Authors: Mwehe Mathenge, Ben G. J. S. Sonneveld, Jacqueline E. W. Broerse

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Smallholder farmers are important drivers of agriculture productivity, food security, and poverty reduction in Sub-Saharan Africa. However, they are faced with myriad challenges in their efforts at participating in agribusiness markets. How the geographic explicit factors existing at the local level interact to impede smallholder farmers' decision to participates (or not) in agribusiness markets is not well understood. Deconstructing the spatial complexity of the local environment could provide a deeper insight into how geographically explicit determinants promote or impede resource-poor smallholder farmers from participating in agribusiness. This paper’s objective was to identify, map, and analyze local spatial autocorrelation in factors that impede poor smallholders from participating in agribusiness markets. Data were collected using geocoded researcher-administered survey questionnaires from 392 households in Western Kenya. Three spatial statistics methods in geographic information system (GIS) were used to analyze data -Global Moran’s I, Cluster and Outliers Analysis (Anselin Local Moran’s I), and geographically weighted regression. The results of Global Moran’s I reveal the presence of spatial patterns in the dataset that was not caused by spatial randomness of data. Subsequently, Anselin Local Moran’s I result identified spatially and statistically significant local spatial clustering (hot spots and cold spots) in factors hindering smallholder participation. Finally, the geographically weighted regression results unearthed those specific geographic explicit factors impeding market participation in the study area. The results confirm that geographically explicit factors are indispensable in influencing the smallholder farming decisions, and policymakers should take cognizance of them. Additionally, this research demonstrated how geospatial explicit analysis conducted at the local level, using geographically disaggregated data, could help in identifying households and localities where the most impoverished and resource-poor smallholder households reside. In designing spatially targeted interventions, policymakers could benefit from geospatial analysis methods in understanding complex geographic factors and processes that interact to influence smallholder farmers' decision-making processes and choices.

Keywords: agribusiness markets, GIS, smallholder farmers, spatial statistics, disaggregated spatial data

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Authors: Amirmohsen Behjat, Aleck Ostry, Christina Miewald, Bernie Pauly

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Farmers’ Markets have become one of the important healthy food suppliers in both rural communities and urban settings. Farmers’ markets are evolving and their number has rapidly increased in the past decade. Despite this drastic increase, the distribution of the farmers’ markets is not even across different areas. The main goal of this study is to explore the socioeconomic, geographic, and demographic variables which affect the establishment of farmers’ market in rural communities in British Columbia (BC). Thus, the data on available farmers’ markets in rural areas were collected from BC Association of Farmers’ Markets and spatially joined to BC map at Dissemination Area (DA) level using ArcGIS software to link the farmers’ market to the respective communities that they serve. Then, in order to investigate this issue and understand which rural communities farmer’ markets tend to operate, a binary logistic regression analysis was performed with the availability of farmer’ markets at DA-level as dependent variable and Deprivation Index (DI), Metro Influence Zone (MIZ) and population as independent variables. The results indicated that DI and MIZ variables are not statistically significant whereas the population is the only which had a significant contribution in predicting the availability of farmers’ markets in rural BC. Moreover, this study found that farmers’ markets usually do not operate in rural food deserts where other healthy food providers such as supermarkets and grocery stores are non-existent. In conclusion, the presence of farmers markets is not associated with socioeconomic and geographic characteristics of rural communities in BC, but farmers’ markets tend to operate in more populated rural communities in BC.

Keywords: farmers’ markets, socioeconomic and demographic variables, metro influence zone, logistic regression, ArcGIS

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Authors: Fereshteh Zarei, Mahdi Kohandel

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The aim of this study was investigate and compare plyometric and vibration exercises on muscle explosive power in non-athlete female students. For this purpose, 45 female students from non-athletes selected target then divided in to the three groups, two experimental and one control groups. From all groups were getting pre-tested. Experimental A did whole-body vibration exercises involved standing on one of machine vibration with frequency 30 Hz, amplitude 10 mm and in 5 different postures. Training for each position was 40 seconds with 60 seconds rest between it, and each season 5 seconds was added to duration of each body condition, until time up to 2 minutes for each postures. Exercises were done three times a week for 2 month. Experimental group B did plyometric exercises that include jumping, such as horizontal, vertical, and skipping .They included 10 times repeat for 5 set in each season. Intensity with increasing repetitions and sets were added. At this time, asked from control group that keep a daily activity and avoided strength training, explosive power and. after do exercises by groups we measured factors again. One-way analysis of variance and paired t statistical methods were used to analyze the data. There was significant difference in the amount of explosive power between the control and vibration groups (p=0/048) there was significant difference between the control and plyometric groups (019/0 = p). But between vibration and plyometric groups didn't observe significant difference in the amount of explosive power.

Keywords: vibration, plyometric, exercises, explosive power, non-athlete

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Authors: C. Villamizar, M. Serrato

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In the pursuit of athletic performance, the role of physical training which is determined by a number of charges or taxes on physiological stress and musculoskeletal systems of the human body generated by the intensity and duration is fundamental. Given the physical demands of these activities both training and competitive must take into account the optimal relationship with a straining process recovery post favoring the process of overcompensation which aims to facilitate the return and rising energy potential and protein synthesis also of different tissues. Allowing muscle function returns to baseline or pre-exercise states. If this recovery process is not performed or is not allowed in a proper way, will result in an increased state of fatigue. Active recovery, is one of the strategies implemented in the sport for a return to pre-exercise physiological states. However, there are some adverse assumptions regarding the negative effects, as is the possibility of increasing the degradation of muscle glycogen and thus delaying the synthesis thereof. For them, it is necessary to investigate what would be the effects generated application made at different times after the effort. The aim of this study was to determine the effects of active recovery post effort made at three different times: immediately, at 12 and 24 hours on biochemical markers creatine kinase in youth soccer player’s categories. A randomized controlled trial with allocation to three groups was performed: A. active recovery immediately after the effort; B. active recovery performed at 12 hours after the effort; C. active recovery made at 24 hours after the effort. This study included 27 subjects belonging to a Colombian soccer team of the second division. Vital signs, weight, height, BMI, the percentage of muscle mass, fat mass percentage, personal medical history, and family were valued. The velocity, explosive force and Creatin Kinase (CK) in blood were tested before and after interventions. SAFT 90 protocol (Soccer Field specific Aerobic Test) was applied to participants for generating fatigue. CK samples were taken one hour before the application of the fatigue test, one hour after the fatigue protocol and 48 of the initial CK sample. Mean age was 18.5 ± 1.1 years old. Improvements in jumping and speed recovery the 3 groups (p < 0.05), but no statistically significant differences between groups was observed after recuperation. In all participants, there was a significant increment of CK when applied SAFT 90 in all the groups (median 103.1-111.1). The CK measurement after 48 hours reflects a recovery in all groups, however the group C, a decline below baseline levels of -55.5 (-96.3 /-20.4) which is a significant find. Other research has shown that CK does not return quickly to their baseline, but our study shows that active recovery favors the clearance of CK and also to perform recovery 24 hours after the effort generates higher clearance of this biomarker.

Keywords: active recuperation, creatine phosphokinase, post training, young soccer players

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Authors: Florian Tille, Heide Weishaar, Bernhard Gibis, Susanne Schnitzer

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Introduction: The quality of patient-doctor communication and of written health information is central to organizational health literacy (HL). Whether patients understand their doctors’ explanations and textual material on health, however, is understudied. This study identifies the overall levels of patient understanding of health information and its associations with patients’ social characteristics in outpatient health care in Germany. Materials & Methods: This analysis draws on data collected via a 2017 national health survey with a sample of 6,105 adults. Quality of communication was measured for consultations with general practitioners (GPs) and specialists (SPs) via the Ask Me 3 program questions, and through a question on written health material. Correlations with social characteristics were explored employing bivariate and multivariate logistic regression analyses. Results: Over 90% of all respondents reported that they had understood their doctors’ explanations during the last consultation. Failed understanding was strongly correlated with patients’ very poor health (Odds Ratio [OR]: 5.19; 95% confidence interval [CI]: 2.23–12.10; ref. excellent/very good health), current health problem (OR: 6.54, CI: 1.70–25.12; ref. preventive examination) and age 65 years and above (OR: 2.97, CI: 1.10–8.00; ref. 18 to 34 years). Fewer patients answered they understood written material well (86.7% for las visit at GP, 89.7% at SP). Understanding written material poorly was highly associated with basic education (OR: 4.20, CI: 2.76–6.39; ref. higher education) and 65 years old and above (OR: 2.66, CI: 1.43–4.96). Discussion: Overall ratings of oral patient-doctor communication and written communication of health information are high. Yet, a considerable share of patients reports not-understanding their doctors and poor understanding of the written health-related material. Interventions that can contribute to improving organizational HL in outpatient care in Germany include HL training for doctors, reducing system barriers to easily-accessible health information for patients and combining oral and written health communication means. Conclusion: This work adds to the study of organizational HL in Germany. To increase patient understanding of health-relevant information and thereby possibly reduce health disparities, meeting the communication needs especially of persons in different age groups, with basic education and in very poor health is suggested.

Keywords: health survey, organizational health literacy, patient-doctor communication, social characteristics, outpatient care, Ask Me 3

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Authors: Milica Ž. Karadžić, Lidija R. Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Z. Kovačević, Anamarija I. Mandić, Katarina Penov-Gaši, Andrea R. Nikolić, Aleksandar M. Oklješa

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This study considered selection of the in silico molecular descriptors and the models for newly synthesized steroid derivatives description and their characterization using chemometric techniques. Multiple linear regression (MLR) models were established and gave the best molecular descriptors for quantitative structure-retention relationship (QSRR) modeling of the retention of the investigated molecules. MLR models were without multicollinearity among the selected molecular descriptors according to the variance inflation factor (VIF) values. Used molecular descriptors were ranked using generalized pair correlation method (GPCM). In this method, the significant difference between independent variables can be noticed regardless almost equal correlation between dependent variable. Generated MLR models were statistically and cross-validated and the best models were kept. Models were ranked using sum of ranking differences (SRD) method. According to this method, the most consistent QSRR model can be found and similarity or dissimilarity between the models could be noticed. In this study, SRD was performed using average values of experimentally observed data as a golden standard. Chemometric analysis was conducted in order to characterize newly synthesized steroid derivatives for further investigation regarding their potential biological activity and further synthesis. This article is based upon work from COST Action (CM1105), supported by COST (European Cooperation in Science and Technology).

Keywords: generalized pair correlation method, molecular descriptors, regression analysis, steroids, sum of ranking differences

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Authors: Christopher Coley, Srividya Sheshadri, Rao R. Bhavani

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Empowering marginalized populations, especially women, with greater economic, social, and other leadership roles has been shown to have a profound effect on entire communities. There are discernible links between sustainable development, poverty reduction, and skill training for empowerment; however, one of the major challenges with implementing empowerment programs is to establish an understanding within the community that investing in women’s education carries the potential of high return for everyone. Effective strategies that can both empower women, and overcome the complex social issues normally faced, need to be developed and shared across stakeholders. Amrita University’s AMMACHI Labs, a research lab engaged in women empowerment through Technical Vocational Education and Training (TVET), has launched a new initiative, WE: Sanitation, a project aiming to train women to build their own toilets and promote healthy sanitation practices in rural villages across India. While in some cases, the community has come together and toilets are being built, there has been resistance by the community, especially men, in many places. This paper will explore the experiences of field workers and the initial results of the WE: Sanitation project, including observations on the trends of community dynamics, raise important questions for the direction of development work in general, and especially for sanitation projects in rural India.

Keywords: community-based development, gender dynamics, Indian sanitation, women empowerment, TVET

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Authors: Lin Shang Neng

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Due to rapid changes in the societal environment, career development and planning have become increasingly crucial, leading more individuals to seek the assistance of career consultations. However, the training process for career consultants often emphasizes the application of assessment tools and guidance in job-seeking behavior. The abilities of case conceptualization and consulting skills require further in-service supervision. This study aims to inquire about the supervised experiences of employment specialists at the Employment Service Center of the Taiwan Ministry of Labor or career consultants who held private clinics for at least three years. The research participants were continuously supervised by the Adlerian approach twice a month for at least one year, helping them integrate the whole picture of the client through Lifestyle Assessment (the qualitative way, specific diagnosis) and other Adlerian assessment tools (the quantitative way, general diagnosis.) The supervisor was familiar with Adlerian Psychology and certified by the North American Society of Adlerian Psychology. The research method involves semi-structured interviews and qualitative analysis. For the ethical considerations, the participants were invited to interview after the supervision sessions finished. The findings of this research were discussed with possible implications, like how they applied Adlerian Psychology to their career consultations, especially to case conceptualizations and consulting skills. Recommendations for further research and training for career consultants are also discussed.

Keywords: supervision, Adlerian psychology, case conceptualization, career consultant

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Authors: Fen-Ling Kuo, Hsin-Chieh Lee, Han-Yun Hsiao, Jui-Chi Lin

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Background: Children with cerebral palsy (CP) usually suffered from mild to maximum upper limb dysfunction such as having difficulty in reaching and picking up objects, which profoundly affects their participation in activities of daily living (ADLs). Robot-assisted rehabilitation provides intensive physical training in improving sensorimotor function of the hand. Many researchers have extensively studied the effects of robot-assisted therapy (RT) for the paretic upper limb in patients with stroke in recent years. However, few studies have examined the effect of RT on hand function in children with CP. The purpose of this study is to investigate the effectiveness of Gloreha Sinfonia, a robotic device with a dynamic arm support system mainly focus on distal upper-limb training, on improvements of hand function and ADLs in children with CP. Methods: Seven children with moderate CP were recruited in this case series study. RT using Gloreha Sinfonia was performed 2 sessions per week, 60 min per session for 6 consecutive weeks, with 12 times in total. Outcome measures included the Fugl-Meyer Assessment-upper extremity (FMA-UE), the Box and Block Test, the electromyography activity of the extensor digitorum communis muscle (EDC) and brachioradialis (BR), a grip dynamometer for motor evaluation, and the ABILHAND-Kids for measuring manual ability to manage daily activities, were performed at baseline, after 12 sessions (end of treatment) and at the 1-month follow-up. Results: After 6 weeks of robot-assisted treatment of hand function, there were significant increases in FMA-UE shoulder/elbow scores (p=0.002), FMA-UE wrist/hand scores (p=0.002), and FMA-UE total scores (p=0.002). There were also significant improvements in the BR mean value (p = 0.015) and electrical agonist-antagonist muscle ratio (p=0.041) in grasping a 1-inch cube task. These gains were maintained for a month after the end of the intervention. Conclusion: RT using Gloreha Sinfonia for hand function training may contribute toward the improvement of upper extremity function and efficacy in recruiting BR muscle in children with CP. The results were maintained at one month after intervention.

Keywords: activities of daily living, cerebral palsy, hand function, robotic rehabilitation

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: N. Ipiña, R. Basagoiti, O. Jimenez, I. Arriaran

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Higher education students are increasingly enrolling in online courses, they are, at the same time, generating data about their learning process in the courses. Data collected in those technology enhanced learning spaces can be used to identify patterns and therefore, offer recommendations/personalized courses to future online students. Moreover, recommendations are considered key aspects for personalization in online learning. Taking into account the above mentioned context, the aim of this paper is to explore the perception of higher education students and teachers towards receiving recommendations in online courses. The study was carried out with 322 students and 10 teachers from two different faculties (Engineering and Education) from Mondragon University. Online questionnaires and face to face interviews were used to gather data from the participants. Results from the questionnaires show that most of the students would like to receive recommendations in their online courses as a guide in their learning process. Findings from the interviews also show that teachers see recommendations useful for their students’ learning process. However, teachers believe that specific pedagogical training is required. Conclusions can also be drawn as regards the importance of personalization in technology enhanced learning. These findings have significant implications for those who train online teachers due to the fact that pedagogy should be the driven force and further training on the topic could be required. Therefore, further research is needed to better understand the impact of recommendations on online students’ learning process and draw some conclusion on pedagogical concerns.

Keywords: higher education, perceptions, recommendations, online courses

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Authors: Lola T. Alimkhodjaeva, Lola T. Zakirova, Soniya S. Ziyavidenova

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Background: Breast cancer (BC) is still one of the urgent oncology problems. The essential obstacle to the full anti-tumor therapy implementation is drug resistance development. Taking into account the fact that chemotherapy is main antitumor treatment in BC patients, the important task is to improve treatment results. Certain success in overcoming this situation has been associated with the use of methods of extracorporeal blood treatment (ECBT), plasmapheresis. Materials and Methods: We examined 129 women with resistant BC stages 3-4, aged between 56 to 62 years who had previously received 2 courses of CAF chemotherapy. All patients additionally underwent 2 courses of CAF chemotherapy but against the background ECBT with ultrasonic exposure. We studied the following parameters: 1. The highlights of peripheral blood before and after therapy. 2. The state of cellular immunity and identification of activation markers CD23 +, CD25 +, CD38 +, CD95 + on lymphocytes was performed using monoclonal antibodies. Evaluation of humoral immunity was determined by the level of main classes of immunoglobulins IgG, IgA, IgM in serum. 3. The degree of tumor regression was assessed by WHO recommended 4 gradations. (complete - 100%, partial - more than 50% of initial size, process stabilization–regression is less than 50% of initial size and tumor advance progressing). 4. Medical pathomorphism in the tumor was determined by Lavnikova. 5. The study of immediate and remote results, up to 3 years and more. Results and Discussion: After performing extracorporeal blood treatment anemia occurred in 38.9%, leukopenia in 36.8%, thrombocytopenia in 34.6%, hypolymphemia in 26.8%. Studies of immunoglobulin fractions in blood serum were able to establish a certain relationship between the classes of immunoglobulin A, G, M and their functions. The results showed that after treatment the values of main immunoglobulins in patients’ serum approximated to normal. Analysis of expression of activation markers CD25 + cells bearing receptors for IL-2 (IL-2Rα chain) and CD95 + lymphocytes that were mediated physiological apoptosis showed the tendency to increase, which apparently was due to activation of cellular immunity cytokines allocated by ultrasonic treatment. To carry out ECBT on the background of ultrasonic treatment improved the parameters of the immune system, which were expressed in stimulation of cellular immunity and correcting imbalances in humoral immunity. The key indicator of conducted treatment efficiency is the immediate result measured by the degree of tumor regression. After ECBT performance the complete regression was 10.3%, partial response - 55.5%, process stabilization - 34.5%, tumor advance progressing no observed. Morphological investigations of tumor determined therapeutic pathomorphism grade 2 in 15%, in 25% - grade 3 and therapeutic pathomorphism grade 4 in 60% of patients. One of the main criteria for the effect of conducted treatment is to study the remission terms in the postoperative period (up to 3 years or more). The remission terms up to 3 years with ECBT was 34.5%, 5-year survival was 54%. Carried out research suggests that a comprehensive study of immunological and clinical course of breast cancer allows the differentiated approach to the choice of methods for effective treatment.

Keywords: breast cancer, immunoglobulins, extracorporeal blood treatment, chemotherapy

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Authors: Rajarshi Sharma, Rashidul Alam, Visavino Seleyi, Yuvila Sangtam

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The Intensity-Duration-Frequency (IDF) relationship of rainfall amounts is one of the most commonly used tools in water resources engineering for planning, design and operation of water resources project, or for various engineering projects against design floods. The establishment of such relationships was reported as early as in 1932 (Bernard). Since then many sets of relationships have been constructed for several parts of the globe. The objective of this research is to derive IDF relationship of rainfall for western watershed of Guwahati, Assam. These relationships are useful in the design of urban drainage works, e.g. storm sewers, culverts and other hydraulic structures. In the study, rainfall depth for 10 years viz. 2001 to 2010 has been collected from the Regional Meteorological Centre Borjhar, Guwahati. Firstly, the data has been used to construct the mass curve for duration of more than 7 hours rainfall to calculate the maximum intensity and to form the intensity duration curves. Gumbel’s frequency analysis technique has been used to calculate the probable maximum rainfall intensities for a period of 2 yr, 5 yr, 10 yr, 50 yr, 100 yr from the maximum intensity. Finally, regression analysis has been used to develop the intensity-duration-frequency (IDF) curve. Thus, from the analysis the values for the constants ‘a’,‘b’ &‘c’ have been found out. The values of ‘a’ for which the sum of the squared deviation is minimum has been found out to be 40 and when the corresponding value of ‘c’ and ‘b’ for the minimum squared deviation of ‘a’ are 0.744 and 1981.527 respectively. The results obtained showed that in all the cases the correlation coefficient is very high indicating the goodness of fit of the formulae to estimate IDF curves in the region of interest.

Keywords: intensity-duration-frequency relationship, mass curve, regression analysis, correlation coefficient

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Authors: Thomas J. Bell III

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This paper explores the value of assimilating certification training within a traditional course curriculum. This innovative approach is believed to increase stakeholder value within the Computer Information System program at Texas Wesleyan University. Stakeholder value is obtained from increased job marketability and critical thinking skills that create employment-ready graduates. This paper views value as first developing the capability to earn an industry-recognized certification, which provides the student with more job placement compatibility while allowing the use of critical thinking skills in a liberal arts business program. Graduates with industry-based credentials are often given preference in the hiring process, particularly in the information technology sector. And without a pioneering curriculum that better prepares students for an ever-changing employment market, its educational value is dubiously questioned. Since certifications are trending in the hiring process, academic programs should explore the viability of incorporating certification training into teaching pedagogy and courses curriculum. This study will examine the use of the balanced scorecard across four performance dimensions (financial, customer, internal process, and innovation) to measure the stakeholder value of certification training within a traditional course curriculum. The balanced scorecard as a strategic management tool may provide insight for leveraging resource prioritization and decisions needed to achieve various curriculum objectives and long-term value while meeting multiple stakeholders' needs, such as students, universities, faculty, and administrators. The research methodology will consist of quantitative analysis that includes (1) surveying over one-hundred students in the CIS program to learn what factor(s) contributed to their certification exam success or failure, (2) interviewing representatives from the Texas Workforce Commission to identify the employment needs and trends in the North Texas (Dallas/Fort Worth) area, (3) reviewing notable Workforce Innovation and Opportunity Act publications on training trends across several local business sectors, and (4) analyzing control variables to identify specific correlations between industry alignment and job placement to determine if a correlation exists. These findings may provide helpful insight into impactful pedagogical teaching techniques and curriculum that positively contribute to certification credentialing success. And should these industry-certified students land industry-related jobs that correlate with their certification credential value, arguably, stakeholder value has been realized.

Keywords: certification exam teaching pedagogy, exam preparation, testing techniques, exam study tips, passing certification exams, embedding industry certification and curriculum alignment, balanced scorecard performance evaluation

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Authors: Çağla Aras, Kübra Bi̇nay, Aysberg Şamil önlü, Mine Baydan Aran, Dicle Aras

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The purpose of the present research was to investigate the acute effects of 30-min hatha yoga and 30-min resistance-based aerobic exercise (RBAE) on computerized dynamic posturography (CDP) and functional head impulse test (fHIT) scores in adult female yoga practitioners. To reach this aim, ten participants executed CDP and fHIT three times in total: at rest, after yoga, and after RBAE. The yoga practice lasted a total of 30 minutes, including 25 min of asanas and 5 minutes of savasana. RBAE lasted a total of 30 minutes with an intensity of 70-75% of the heart rate reserve method. When the results were examined, no change was observed in any parameters of the fHIT scores due to resting or exercise implementation. On the contrary, some changes were observed in CDP test results depending on the type of exercise. The post-RBAE somatosensory and visual systems values were higher than resting (p<0.05). The composite balance score derived after RBAE was found to be improved when compared to post-yoga and resting values (p<0.01). Lastly, the post-RBAE vestibular system score was found to be statistically significantly higher than the post-Yoga values. In addition, it was observed that body composition parameters, especially decreasing BW, LBM, PBF, MBF and TBW, were associated with improved postural stability values. According to the results, it can be stated that neither hatha yoga nor resistance-based aerobic exercise has an acute effect on functional vestibulo-ocular reflex. In addition, although there was no change in balance level after yoga, it was observed that RBAE performed at 70-75% of the heart rate reserve and for 30 minutes had positive acute effects on postural stability and balance.

Keywords: hatha yoga, resistance training, aerobic training, high intensity training, computerized dynamic posturography, functional head impulse test

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Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

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Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

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Authors: Riyona Chetty, Raisuyah Bhagwan, Nalini Govender

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Background: During rehabilitation, the prosthetist establishes prosthetic and/or orthotic realistic goals and programmes as well as clinical outcome measures. They are well-positioned to improve the amputee’s physical and psychosocial well-being. Objective: This study aims to explore the ways in which the prosthetist may be able to improve the holistic well-being of the amputee. Methods: Data was collected using a focus group discussion with 16 prosthetists at a medical facility in KwaZulu-Natal, South Africa. Results: The findings indicate that amputees are encouraged to consider physical activities to improve their health. However, a major challenge experienced by the prosthetists was their lack of adequate psychosocial expertise, which affected their ability to offer emotional support. Additional factors such as language barriers, rotational systems, and unrealistic expectations further obstructed optimal service delivery. Conclusion: The prosthetists are adequately skilled in manufacturing the ideal prosthesis and encouraging physical exercise to promote the amputee’s physical health. However, their lack of psychosocial training limits them in providing emotional support during rehabilitation. It is recommended that prosthetists are provided with professional training to provide emotional support as part of holistic healthcare. Clinical relevance: The intention of this study was to provide pertinent recommendations for prosthetists, enabling them to provide holistic quality care to their patients.

Keywords: psychological, social, well-being, amputee, prosthetist

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Authors: Neil Jordan M. Uy, Fe Pelilia V. Hernandez

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A counselor is always regarded as a professional who embodies the willingness to help others through the process of counseling. He is knowledgeable and skillful of the different theories, tools, and techniques that are useful in aiding the client to cope with their dilemmas. The negative experiences of the clients that are shared during the counseling session can affect the professional counselor. Compassion fatigue, a professional impairment, is characterized by the decline of one’s productivity and the feeling of anxiety and stress brought about as the counselor empathizes, listens, and cares for others. This descriptive type of research aimed to explore variables that are predictors of compassion fatigue utilizing three research instruments; Demographic Profile Sheet, Professional Quality of Life Scale, and Neo-Pi-R. The 52 respondents of this study were counselors from the different Dominican schools in the Philippines. Generally, the counselors have low level of compassion fatigue across personal characteristics (age, gender, years of service, highest educational attainment, and professional status) and personality traits (extraversion, agreeableness, conscientiousness, openness, and neuroticism). ANOVA validated the findings of this that among the personal characteristics and personality traits, extraversion with f-value of 3.944 and p-value of 0.026, and conscientiousness, with f-value of 4.125 and p-value of 0.022 were found to have significant difference in the level of compassion fatigue. A very significant difference was observed with neuroticism with f-value of 6.878 and p-value 0.002. Among the personal characteristics and personal characteristics, only neuroticism was found to predict compassion fatigue. The computed r2 value of 0.204 using multiple regression analysis suggests that 20.4 percent of compassion fatigue can be predicted by neuroticism. The predicting power of neuroticism can be computed from the regression model Y=0.156x+26.464; where x is the number of neuroticism.

Keywords: big five personality traits, compassion fatigue, counselors, professional quality of life scale

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Authors: C. C. Chima, C. J. M. Anumaka

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This study evaluates environmental disclosures on the financial performance of quoted industrial goods manufacturing sectors in Nigeria. The study employed a quasi-experimental research design to establish the relationship that exists between the environmental disclosure index and financial performance indices (return on assets - ROA, return on equity - ROE, and earnings per share - EPS). A purposeful sampling technique was employed to select five (5) industrial goods manufacturing sectors quoted on the Nigerian Stock Exchange. Secondary data covering 2011 to 2020 financial years were extracted from annual reports of the study sectors using a content analysis method. The data were analyzed using SPSS, Version 23. Panel Ordinary Least Squares (OLS) regression method was employed in estimating the unknown parameters in the study’s regression model after conducting diagnostic and preliminary tests to ascertain that the data set are reliable and not misleading. Empirical results show that there is an insignificant negative relationship between the environmental disclosure index (EDI) and the performance indices (ROA, ROE, and EPS) of the industrial goods manufacturing sectors in Nigeria. The study recommends that: only relevant information which increases the performance indices should appear on the disclosure checklist; environmental disclosure practices should be country-specific; and company executives in Nigeria should increase and monitor the level of investment (resources, time, and energy) in order to ensure that environmental disclosure has a significant impact on financial performance.

Keywords: earnings per share, environmental disclosures, return on assets, return on equity

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Authors: Lazaro E. Nnko, Japhet J. Kashaigili, Gerald C. Monela, Pantaleo K. T. Munishi

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Agroforestry systems and practices are perceived to improve livelihood and sustainable management of natural resources. However, their adoption in various regions differs with the biophysical conditions and societal characteristics. This study was conducted in Kilombero District to investigate the factors influencing the adoption of different agroforestry systems and practices in agro-ecosystems and farming systems. A household survey, key informant interviews, and focus group discussion was used for data collection in three villages. Descriptive statistics and multinomial logistic regression in SPSS were applied for analysis. Results show that Igima and Ngajengwa villages had home garden practices dominated, as revealed by 63.3% and 66.7%, respectively, while Mbingu village had mixed intercropping practice with 56.67%. Agrosilvopasture systems were dominant in Igima and Ngajengwa villages with 56.7% and 66.7%, respectively, while in Mbingu village, the dominant system was agrosilviculture with 66.7%. The results from multinomial logistic regression show that different explanatory variable was statistical significance as predictors of the adoption of agroforestry systems and practices. Residence type and sex were the most dominant factor influencing the adoption of agroforestry systems. Duration of stay in the village, availability of extension education, residence, and sex were the dominant factor influencing the adoption of agroforestry practices. The most important and statistically significant factors among these were residence type and sex. The study concludes that agroforestry will be more successful if the local priorities, which include social-economic need characteristics of the society, will be considered in designing systems and practices. The socio-economic need of the community should be addressed in the process of expanding the adoption of agroforestry systems and practices.

Keywords: agroforestry adoption, agroforestry systems, agroforestry practices, agroforestry, Kilombero

Procedia PDF Downloads 107