If the ratio m/p tends to zero, where m is the number of factors m and p the number of observable variables, then the inverse diagonal element of the inverted observable covariance matrix (σ pjj) -1 tends to the corresponding unique variance ψ jj for almost all of these (Guttman, 1956). If the smallest singular value of the loadings matrix from Common Factor Analysis tends to infinity as p increases, then m/p tends to zero. The same condition is necessary and sufficient for (σ pjj) -1 to tend to ψ jj for all of these. Several related conditions are discussed. © 2006 The Psychometric Society.
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This study aims to understand how alternative tourism can contribute to the destination image of Palestine, given its negative image in the media. It proposes a framework for various destination image aspects and applies this framework in the context of alternative tourism in Palestine. It seeks to explore the key image formation factors, the perceived images of Palestine, and the post-visit behaviours of tourists who had engaged in alternative tourism in Palestine. This research contributes in fulfilling intriguing gaps in the Palestinian destination’s image literature, as well as the alternative tourism field that has emerged manifestly in Palestine. This study is exploratory in nature applying qualitative methodology by using open-ended questions in email interviews, and the interviews were analysed using thematic analysis. The empirical results proved that tourists who had visited Palestine and engaged in alternative tourism, had positive destination images, opposite to the ones portrayed in the media that show Palestine as a dangerous place to visit. Finally, this research provides academic and managerial implications.
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Particle image velocimetry has been widely used in various sectors from the automotive to aviation, research, and development, energy, medical, turbines, reactors, electronics, education, refrigeration for flow characterization and investigation. In this study, articles examined in open literature containing the particle image velocimetry techniques are reviewed in terms of components, lasers, cameras, lenses, tracers, computers, synchronizers, and seeders. The results of the evaluation are categorized and explained within the tables and figures. It is anticipated that this paper will be a starting point for researchers willing to study in this area and industrial companies willing to include PIV experimenting in their portfolios. In addition, the study shows in detail the advantages and disadvantages of past and current technologies, which technologies in existing PIV laboratories can be renewed, and which components are used in the PIV laboratories to be installed.
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Background: To assess the prevalence of chronic lymphedema and trismus in patients > 6 months after head and neck cancer (HNC) treatment, and to explore how the severity of these conditions correlates with body image and quality of life. Methods: The cross-sectional sample included 59 patients, treated for HNC between six months to three years ago. Physical measurements were performed to assess the presence of external lymphedema and trismus (<36 mm). Furthermore, participants completed two questionnaires regarding body image (BIS) and quality of life (UW-QoL V4). Results: Lymphedema prevalence was 94.1% (95% CI 0.86–0.98), with a median severity score of 9 (range 0–24). Trismus prevalence in this sample was 1.2%. The median BIS score was 2, indicating a positive body image. The UW-QoL score showed a good QOL with a median of 100. Only the domain of saliva and overall related health had a lower median of 70 and 60, respectively. There was no correlation between lymphedema and body image (r = 0.08, p = 0.544). Patients with higher lymphedema scores reported poorer speech with a moderate correlation (r = −0.39, p = 0.003). Conclusion: Lymphedema is a highly prevalent, but moderately severe late side-effect of HNC with a limited impact on quality of life domains except for speech, in our cohort.
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Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
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The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
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We show how to estimate a Cronbach's alpha reliability coefficient in Stata after running a principal component or factor analysis. Alpha evaluates to what extent items measure the same underlying content when the items are combined into a scale or used for latent variable. Stata allows for testing the reliability coefficient (alpha) of a scale only when all items receive homogenous weights. We present a user-written program that computes reliability coefficients when implementation of principal component or factor analysis shows heterogeneous item loadings. We use data on management practices from Bloom and Van Reenen (2010) to explain how to implement and interpret the adjusted internal consistency measure using afa.
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Objective: To examine the underlying factor structure and psychometric properties of the Assessment of Self-management in Anxiety and Depression (ASAD) questionnaire, which was specifically designed for patients with (chronic) anxiety and depressive disorders. Moreover, this study assesses whether the number of items in the ASAD can be reduced without significantly reducing its precision. Methods: The ASAD questionnaire was completed by 171 participants across two samples: one sample comprised patients with residual anxiety or depressive symptoms, while the other consisted of patients who have been formally diagnosed with a chronic anxiety or depressive disorder. All participants had previously undergone treatment. Both exploratory (EFA) and confirmatory factor analyses (CFA) were conducted. Internal consistency and test–retest reliability were also assessed. Results: Both EFA and CFA indicated three solid factors: Seeking support, Daily life strategies and Taking ownership [Comparative Fit Index = 0.80, Tucker Lewis Index = 0.78, Root Mean Square Error of Approximation = 0.09 (CI 0.08–1.00), Standardized Root Mean Square Residual = 0.09 ($2 = 439.35, df = 168)]. The ASAD was thus reduced from 45 items to 21 items, which resulted in the ASAD-Short Form (SF). All sub-scales had a high level of internal consistency (> a = 0.75) and test–retest reliability (ICC > 0.75). Discussion: The first statistical evaluation of the ASAD indicated a high level of internal consistency and test–retest reliability, and identified three distinctive factors. This could aid patients and professionals’ assessment of types of self-management used by the patient. Given that this study indicated that the 21-item ASAD-SF is appropriate, this version should be further explored and validated among a sample of patients with (chronic or partially remitted) anxiety and depressive disorders. Alongside this, to increase generalizability, more studies are required to examine the English version of the ASAD within other settings and countries.
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This paper reports on an experiment comparing students’ results on image-rich numeracy problems and on equivalent word problems. Given the well reported problematic nature of word problems, the hypothesis is that students score better on image-rich numeracy problems than on comparable word problems. To test the hypothesis a randomized controlled trial was conducted with 31,842 students from primary, secondary, and vocational education. The trial consisted of 21 numeracy problems in two versions: word problems and image-rich problems. The hypothesis was confirmed for the problems used in this experiment. With the insights gained we intend to improve the assessment of students’ abilities in solving quantitative problems from daily life. Numeracy, word problem, image-rich problem, randomized controlled trial, assessment
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Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.
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