Abstract: Despite the fact that Knowledge Sharing (KS) is very important, we found only little discussion about the reasons why people have the willingness to share knowledge at such platform even though there is no immediate benefit to the persons who contribute knowledge in it. The aim of this study is to develop an integrative understanding of the factors that support or inhibit individuals- knowledge sharing intentions in virtual communities and to find whether habit would generate people-s willingness to be involved. We apply Social Capital Theory (SCT), and we also add two dimensions for discussion: member incentive and habitual domain (HD). This research assembles the questionnaire from individuals who have experienced knowledge sharing in virtual communities, and applies survey and Structural Equation Model (SEM) to analyze the results from the questionnaires. Finally, results confirm that individuals are willing to share knowledge in virtual communities: (1) if they consider reciprocity, centrality, and have longer tenure in their field, and enjoy helping. (2) if they have the habit of sharing knowledge. This study is useful for the developers of virtual communities to insight into knowledge sharing in cyberspace.
Abstract: This study aims to examine the factors affecting
knowledge sharing behavior in knowledge-based electronic communities (e-communities) because quantity and quality of
knowledge shared among the members play a critical role in the community-s sustainability. Past research has suggested three
perspectives that may affect the quantity and quality of knowledge
shared: economics, social psychology, and social ecology. In this
study, we strongly believe that an economic perspective may be suitable to validate factors influencing newly registered members-
knowledge contribution at the beginning of relationship development.
Accordingly, this study proposes a model to validate the factors influencing members- knowledge sharing based on Transaction Cost
Theory. By doing so, we may empirically test our hypotheses in various types of e-communities to determine the generalizability of our research models.
Abstract: The purpose of this study is to derive parameters
estimating for the Lyman–Kutcher–Burman (LKB) normal tissue
complication probability (NTCP) model using analysis of scintigraphy
assessments and quality of life (QoL) measurement questionnaires for
the parotid gland (xerostomia). In total, 31 patients with
head-and-neck (HN) cancer were enrolled. Salivary excretion factor
(SEF) and EORTC QLQ-H&N35 questionnaires datasets are used for
the NTCP modeling to describe the incidence of grade 4 xerostomia.
Assuming that n= 1, NTCP fitted parameters are given as TD50= 43.6
Gy, m= 0.18 in SEF analysis, and as TD50= 44.1 Gy, m= 0.11 in QoL
measurements, respectively. SEF and QoL datasets can validate the
Quantitative Analyses of Normal Tissue Effects in the Clinic
(QUANTEC) guidelines well, resulting in NPV-s of 100% for the both
datasets and suggests that the QUANTEC 25/20Gy gland-spared
guidelines are suitable for clinical used for the HN cohort to
effectively avoid xerostomia.
Abstract: The main goal in this paper is to quantify the quality of
different techniques for radiation treatment plans, a back-propagation
artificial neural network (ANN) combined with biomedicine theory
was used to model thirteen dosimetric parameters and to calculate
two dosimetric indices. The correlations between dosimetric indices
and quality of life were extracted as the features and used in the ANN
model to make decisions in the clinic. The simulation results show
that a trained multilayer back-propagation neural network model can
help a doctor accept or reject a plan efficiently. In addition, the
models are flexible and whenever a new treatment technique enters
the market, the feature variables simply need to be imported and the
model re-trained for it to be ready for use.
Abstract: To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.