Search

The rapid growth of Halal travel offers a tourism opportunity for countries, but non-Muslim countries often find it difficult to meet the multiple religious needs of Muslim tourists. This study verifies Muslim tourists' requirements for basic hotel and food services and identifies the categories in which to place these attributes and which ones need resources to increase Muslim tourists' satisfaction. Applying Taiwan as a non-Muslim destination, we surveyed 216 Indonesian Muslim foreign workers and used the recently developed Gradient Boosting Decision Trees, instead of a regression method, to overcome the nonlinear and multicollinearity issues of Penalty-Reward-Contrast Analysis. Impact-Range Performance Analysis and Asymmetric Impact-Performance Analysis were then applied to examine the basic attributes of Muslim foreign workers' tourist experiences. We learned that using Gradient Boosting Decision Trees provided advantages over the regression method. Empirical results indicated that hotel-related factors are prioritized over food services when Muslim tourists visit a non-Muslim country. In addition, religious observances play an essential part in Muslim tourists' hotel choices, and conveniently accessible Halal food is a desirable travel requisite for this group. [ABSTRACT FROM AUTHOR]

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media. [ABSTRACT FROM AUTHOR]

Abstract: This study applied the non-woven material from used diaper as the carrier for bio-film process to purify the recycled water from a landscape pond at the Tainan City Municipal Culture Center (TCMCC), Taiwan. An on-site system was installed and the experiment was accomplished through three stages in 192days with different time periods of 70days, 63days, and 59days, respectively. The results showed that the non-woven media is functional for SS removal. The average SS removal of stages 1, 2, and 3 were 91%, 96%, and 95%, respectively. The highest SCOD removal efficiency of 90% occurred at stage 3. A significant color improvement of the pond water was achieved through this non-woven bio-carrier treatment system. Whole system can be without any maintenance for 139days. The result indicated that the non-woven medium system was with a great potential in treating and recycling the pond water with stable operation and satisfactory removal performance. [Copyright &y& Elsevier]

Abstract: A pilot-scale study of pharmaceutical wastewater treatment by a membrane bioreactor (MBR) process in southern Taiwan is presented in this paper. A 10 m3/day capacity MBR plant consisting of an aeration tank and a membrane bioreactor was installed to remove organic matter (measured in terms of chemical oxygen demand (COD)). The performance of the MBR was monitored for a period of 140 days. The removal of COD was on average over 95%. The effluent did not contain any suspended solids. During the 140 days of operation, manual cleaning was carried out twice and chemical cleaning was carried out once. A natural logarithmic evolution of the viscosity with TSS concentration was observed. The results of SEM and EDX demonstrated that the fouling on the membrane outer surface was mainly due to microorganisms and/or the sludge physiological properties. The results indicated that the MBR system has potential as a means of treating high-strength and fluctuating strength wastewater with consistent performance. [Copyright &y& Elsevier]

Fusarium wilt, caused by Fusarium oxysporum (Fo), is one of the most important fungal diseases worldwide. Like other plant pathogens, Fo displays specialized forms in association with its hosts. For example, F. oxysporum f. sp. niveum (Fon) is the damaging pathogen causing Fusarium wilt disease on watermelon, whereas F. oxysporum f. sp. cubense is the pathogen that infects banana. A rapid and reliable pathogen identification or disease diagnosis is essential for the integrated disease management practices in many crops. In this study, two new primer sets, Fon-1/Fon-2 and FnSc-1/FnSc-2, were developed to differentiate Fon and Fo, respectively. The PCR method using the novel primer sets has high sensitivity to detect Fon when the DNA concentration was as low as 0.01pg or when the conidia number was as few as 5. In comparison with the published primer set, the Fon-1/Fon-2 primer set, derived from the sequence of OP-M12 random primer-amplified fragment, produced a 174bp DNA fragment, and was more specific to Fon in Taiwan. In addition, with optimized PCR parameters, the molecular method using the Fon-1/Fon-2 primer set could directly detect Fon even when watermelon samples were collected in its early stage of disease development. [Copyright &y& Elsevier]

Goals of Work: This study explores the significance of patient-reported outcomes for predicting length of survival of palliative cancer patients., Patients and Methods: Patients were recruited upon admission to the inpatient palliative care unit. Weekly assessment of 180 terminal cancer patients was carried out throughout their survival time using the Medical Outcome Study 36-Item Short-Form Health Survey, the Taiwanese version of the M.D. Anderson Symptom Inventory (MDASI-T), the Karnofsky Performance Status (KPS), the Brief Pain Inventory, and the Brief Fatigue Inventory. Generalized estimating equations (GEE) were utilized to analyze whether the patient-reported outcomes predicted survival time., Main Results: Of all patients, 64 had one assessment, 51 had two, 25 had three, and 40 had four or more assessments, up to a maximum of eight. The univariate analysis showed that gender (P < 0.01), KPS (P < 0.01), the physical component summary score (P = 0.02), the MDASI-T total score (P < 0.01), composite fatigue severity (P < 0.01), and composite pain severity (P < 0.01) were significantly associated with length of survival. The multivariate analysis showed that gender (P < 0.01), KPS (P < 0.01), and the MDASI-T total score (P = 0.01) were significant predictors of survival time., Conclusions: This is the first study to explore the significance of patient-related outcomes for predicting length of survival of palliative cancer patients using the GEE method. This study confirms that overall symptom severity is a significant factor in assessing the length of survival of palliative cancer patients.