Achieving Value Co-creation in IT Outsourcing

Over the past few decades, information technology outsourcing has become a widely used and researched means for enterprises to enhance their performance. As future collaboration is the most desirable outcome between enterprises and IT outsourcing vendors, the purpose of this study is to construct a new model to examine: 1) whether information technology outsourcing satisfaction impact future collaboration, 2) whether value co-creation impact outsourcing satisfaction, 3)which factors influence value co-creation, and 4) whether trust influence enterprises’ commitments. This research assumes that: 1) trust predicts enterprises’ commitments; 2) customer orientation and participation, service quality, communication culture, and enterprises’ commitment predicts value co-creation; 3) value co-creation predicts information technology outsourcing satisfaction, 4) information technology outsourcing satisfaction predicts future collaboration. This model will allow enterprises and information technology outsourcing vendors to identify factors that can increase the chances for future collaboration. To achieve this goal, this research applied structural equation modelling, using Smart Partial Least Square (SmartPLS) software, as a method to empirically validate the model through a survey analysis containing 213 CEO or senior managers’ opinions obtained through valid measurements. The actual sampling yielded 207 useful questionnaires with a valid response rate of 97%. The results of this article show that trust strongly positively influences normative commitment, continuance commitment, and affective commitment; seven factors of customer behaviours and beliefs (such as: customer orientation and participation, service quality, communication culture, normative commitment, continuance commitment, and affective commitment) positively influence value co-creation; value co-creation strongly positively influences information technology outsourcing satisfaction; and information technology outsourcing satisfaction strongly positively affects future collaboration. The implications of this study and future work are also presented

SOCIAL COGNITION AND THE EFFECT OF PRODUCT QUALITY ON ONLINE REPURCHASE INTENTION

An electronic commerce marketing channel is fully mediated by information technology, creating information asymmetry (i.e., limited information). Such asymmetry may impede consumers’ ability to effectively assess certain types of products, thus creating challenges for online sellers. Signaling theory can aid in the understanding of how extrinsic cues—signals—can be used by sellers to convey product quality information to consumers, reducing uncertainty and facilitating a purchase or exchange. This study proposes a model to investigate website quality as a potential signal of product quality and consider the moderating effects of product information asymmetries and signal credibility. The study also finds that perceived value and cognitive lock-in can predict consumer purchase intentions. Furthermore, personalized product recommendation (PPR) services offered by online retailers are found to influence consumer store loyalty. The results indicate that website quality influences consumers’ perceptions of product quality, and affects online purchase intentions. Website quality is found to have a greater influence on perceived product quality when consumers have higher information asymmetry. Signal credibility is found to strengthen the relationship between website quality and product quality perceptions for a high quality website. The implications of cognitive lock-in and product cues for increasing purchase intentions are discussed. Retailer learning reflected in higher quality PPRs is associated with both lower product screening cost and higher product evaluation cost. We also discuss which PPRs influence consumer repurchase intentions in electronic markets

The Waveform Fluctuation and the Clinical Factors of the Initial and Sustained Erythropoietic Response to Continuous Erythropoietin Receptor Activator in Hemodialysis Patients

Objectives. Erythropoiesis-stimulating agents (ESA) are the main treatment for anemia in hemodialysis (HD) patients. We evaluated factors determining the response after treatment of a new ESA (continuous erythropoietin erythropoietin receptor activator (CERA)). Methods. 61 HD patients were classified by their response at two different timings. First, patients whose hematocrit (Hct) increased 1.5% in the first week were defined as initial responders (IR, n = 16). We compared several parameters between IR and the rest of the study subjects (non-IR, n = 45). Second, patients whose Hct increased 2% in the 4th week were defined as sustained responders (SR, n = 12), and we did a similar comparison. Results. The Hct showed a waveform fluctuation. Compared with the rest, IR had significantly lower platelet counts and higher levels of ferritin, total protein, total bilirubin, and serum sodium, while SR had significantly lower levels of C-reactive protein and low-density lipoprotein (All P < 0.05). In comparison with the rest, higher Hct persisted for 10 weeks in SR but only for two separate weeks (the 1st and 7th week) in IR. Conclusions. The initial and sustained erythropoietic responses are independent from each other and are associated with different factors. Treatment focusing on these factors may improve the response

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

Ankle-Brachial Index Is a Powerful Predictor of Renal Outcome and Cardiovascular Events in Patients with Chronic Kidney Disease

Ankle-brachial index (ABI) is an accurate tool to diagnose peripheral arterial disease. The aim of this study was to evaluate whether ABI is also a good predictor of renal outcome and cardiovascular events in patients with chronic kidney disease (CKD). We enrolled 436 patients with stage 3–5 CKD who had not been undergoing dialysis. Patients were stratified into two groups according to the ABI value with a cut point of 0.9. The composite renal outcome, including doubling of serum creatinine level and commencement of dialysis, and the incidence of cardiovascular events were compared between the two groups. After a median follow-up period of 13 months, the lower ABI group had a poorer composite renal outcome (OR = 2.719, P = 0.015) and a higher incidence of cardiovascular events (OR = 3.260, P = 0.001). Our findings illustrated that ABI is a powerful predictor of cardiovascular events and renal outcome in patients with CKD

Pengembangan Suplemen Pembelajaran Fisika Gelombang Elektromagnetik Cahaya Sebagai Partikel Memanfaatkan Virtual Laboratorium

This research has been done to make a supplement for physics learning about light electromagnetic wave as a particle using virtual laboratory. The population of this research was the second year science-students at SMA Muhammadiyah 1 Metro. This development is begun by needs analysis, then identification of resource which is the background of this developmental research. The next step is, identifying the product specification then developing products which contained a tutorial book for teacher and a work sheet for student (LKS). The material and design expert test result is that those products were approved. The external test resulted by users show that the LKS was attractive, very easy to use, and useful. It also was effective to be used as a learning resource because 80% of students reached the passing grade.Telah dilakukan penelitian untuk mengembangkan suplemen pembelajaran fisika gelombang elektromagnetik cahaya sebagai partikel dengan memanfaatkan virtual laboratorium. Populasi penelitian pengembangan ini adalah siswa kelas XI IPA di SMA Muhammadiyah 1 Metro. Pengembangan ini diawali dengan analisis kebutuhan, kemudian identifikasi sumber daya yang melatar belakangi pengembangan. Langkah selanjutnya identifikasi spesifikasi produk yang dilanjutkan dengan mengembangkan produk berupa LKS untuk siswa dan buku panduan untuk guru. Hasil uji internal oleh ahli materi dan ahli desain menyatakan produk yang dikembangkan layak digunakan sebagai media pembelajaran. Hasil uji eksternal oleh pengguna menunjukkan kualitas media pembelajaran menarik, sangat mudah digunakan, dan bermanfaat serta efektif digunakan sebagai media pembelajaran dengan presentase hasil belajar sebesar 80% siswa telah memenuhi KKM

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Serologic and Molecular Biologic Methods for SARS-associated Coronavirus Infection, Taiwan

Severe acute respiratory syndrome (SARS) has raised a global alert since March 2003. After its causative agent, SARS-associated coronavirus (SARS-CoV), was confirmed, laboratory methods, including virus isolation, reverse transcriptase–polymerase chain reaction (RT-PCR), and serologic methods, have been quickly developed. In this study, we evaluated four serologic tests ( neutralization test, enzyme-linked immunosorbent assay [ELISA], immunofluorescent assay [IFA], and immunochromatographic test [ICT]) for detecting antibodies to SARS-CoV in sera of 537 probable SARS case-patients with correlation to the RT-PCR . With the neutralization test as a reference method, the sensitivity, specificity, positive predictive value, and negative predictive value were 98.2%, 98.7%, 98.7%, and 98.4% for ELISA; 99.1%, 87.8%, 88.1% and 99.1% for IFA; 33.6%, 98.2%, 95.7%, and 56.1% for ICT, respectively. We also compared the recombinant-based western blot with the whole virus–based IFA and ELISA; the data showed a high correlation between these methods, with an overall agreement of >90%. Our results provide a systematic analysis of serologic and molecular methods for evaluating SARS-CoV infection