Combined Stereotactic Body Radiotherapy and Immunotherapy Versus Transarterial Chemoembolization in Locally Advanced Hepatocellular Carcinoma: A Propensity Score Matching Analysis

Immunotherapy has achieved modest clinical activity in HCC patients. Propensity score matching analysis was conducted to compare the efficacy and safety of combined stereotactic SBRT-IO versus TACE in patients with locally advanced HCC in a tertiary center of Hong Kong. Patients with locally advanced HCC who were medically inoperable for, refractory to, or refused to curative surgical interventions were eligible. The primary outcome was PFS; the secondary outcomes were OS, ORR as per mRECIST version 1.1, and TRAEs. Matching pair analysis was performed to compare the clinical outcomes. A total of 226 patients were eligible. Approximately 16 patients in the SBRT-IO group were matched with 48 patients treated with TACE. The median tumor size was 10 cm (range: 2.9–19.6 cm) and 20.3% of the patients had portal vein invasion. The 12- and 24-month PFS were significantly better in the SBRT-IO group (93.3% vs 16.7% and 77.8% vs 2.1%, respectively, p <0.001); the 12- and 24-month OS were also better in the SBRT-IO arm (93.8% vs 31.3% and 80.4% vs 8.3%, respectively, p <0.001). The ORR was 87.5% (CR: 50%, PR: 37.5%) in SBRT-IO arm compared to 16.7% (CR: 2.4%, PR: 14.3%) in those receiving TACE alone (p <0.001). There were fewer ≥grade 3 TRAE (60.4% vs 18.8%, p = 0.004) and treatment discontinuations (25% vs 12.5%, p = 0.295) due to adverse events in the SBRT-IO arm. SBRT-IO had significant superior survival and less treatment toxicity than TACE in patients with locally advanced HCC. Our results provide rationale for studying this combination therapy in prospective randomized trials.

Rewilding Cognition: Complex Dynamics in Open Experimental Systems

Insight problems are sometimes designed to encourage an incorrect and misleading interpretation that veils a simple answer. The socks problem is one such problem: Given black socks and brown socks in a drawer mixed in a ratio of four to five, how many socks will you have to take out to make sure that you have a pair of the same color? The ratio information is misleading since, with only two colors, pulling three socks will guarantee a matching pair. Recently, offered a distinction between first- and second-order problem-solving: The former proceeds with and through a physical model of the problem, while the latter proceeds in the absence of such interactions with the world, in other words on the basis of mental processes alone. Vallée-Tourangeau and March also proposed a thought experiment, suggesting that the ratio information in the socks problem might be quickly abandoned in a first-order environment, that is, one where participants observe the results of drawing socks out of a bag rather than imagining themselves doing so. We tested this prediction by randomly allocating participants to a low- (second-order) or high- (first-order) interactivity condition. Marginally more participants announced the correct answer within a 5-minute period in the high than in the low condition, although the difference was not significant. Detailed analysis of the video recording revealed the challenges of operationalizing a second-order condition, as participants engaged in dialogical interactions with the experimenter. In addition, the manner in which the high-interactivity condition was designed appeared to encourage the physical reification of the misleading ratio, thus anchoring that information more firmly rather than defusing it through interactivity. We close the paper with some reflections on wide, or systemic, cognition in experimental research on creative problem-solving.

A method based on TODIM technique for multi-criteria two-sided matching and its application in person-position matching

It is impossible for agents on both sides to achieve complete rationality in the decision-making process of two-sided matching (TSM). The TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method considering the psychological behavior of decision-makers is well applied in the multiple criteria decision making (MCDM) problems. The TSM is a MCDM problem. Therefore, in this paper, a method based on TODIM technique is introduced to solve the TSM problem, in which the intuitionistic linguistic numbers are utilized to describe the mutual evaluation between candidates and hiring managers. The focus of this paper is to develop a method for the multi-criteria TSM problem under intuitionistic linguistic environment. First, the evaluation matrices of each agent with respect to each criterion are provided by agents on the opposite side, and the weight assigned to each criterion is determined according to the importance of the evaluation criterion to the matching agent. Then, the dominance measurement of each agent over another one can be calculated based on the intuitionistic linguistic TODIM method. Next, a bi-objective optimization model which aims to maximize the overall satisfaction degree of agents on both sides is constructed to attain the optimal matching pair. Furthermore, the feasibility of the solution method is verified by a case study of person-position matching (PPM), and the matching result demonstrates that the proposed method is effective in dealing with multi-criteria PPM problem. Finally, the sensitivity of parameters and some comparative studies are discussed.

Map Merging with Suppositional Box for Multi-Robot Indoor Mapping

For the map building of unknown indoor environment, compared with single robot, multi-robot collaborative mapping has higher efficiency. Map merging is one of the fundamental problems in multi-robot collaborative mapping. However, in the process of grid map merging, image processing methods such as feature matching, as a basic method, are challenged by low feature matching rate. Driven by this challenge, a novel map merging method based on suppositional box that is constructed by right-angled points and vertical lines is proposed. The paper firstly extracts right-angled points of suppositional box selected from the vertical point which is the intersection of the vertical line. Secondly, based on the common edge characteristics between the right-angled points, suppositional box in the map is constructed. Then the transformation matrix is obtained according to the matching pair of suppositional boxes. Finally, for matching errors based on the length of pairs, Kalman filter is used to optimize the transformation matrix. Experimental results show that this method can effectively merge map in different scenes and the successful matching rate is greater than that of other features.

Effect of Perception Differences in Money Communication Between Parent-Adolescents on Financial Autonomy: An Experimental Study Using Financial Education Workshops

This study evaluates the effect of parent-child money communication on financial autonomy of the adolescents by considering the gender of the parent as a controlled variable by utilizing pre- and post- survey based experimental research design. The sample consisted of 300 female parents and their children under adolescence stage of life. Assuming that claim is often made by parents regarding their frequent money communication with their children, their children were asked to rate their perception towards parent’s money communication with them. Later, their female parent (mother) were invited for financial education workshops series and asked to complete pre-survey before they attended the first financial education workshop. The follow-up survey was done for female parents and their adolescent children six months after completion of the financial education workshop series. In both the surveys, 300 responses were collected from female parents and adolescents on nineteen pairs of money communication, wherein parents were not told that their children were also asked to rate the matching pair of each item of parent money communication scale and vice versa. The financial autonomy was measured by using pre- and post- surveys, wherein only adolescents participated in the surveys. The results of paired t-test provides noticeable conclusion that financial education given to the parent positively enhances money communication among parent-adolescent by reducing the disparity in the responses collected from the parents and adolescents on each matched pairs separately and collectively and this reduced disparity leads to enhance the financial autonomy of the adolescents. The findings may help policy makers and financial educators to design and implement such workshops which may open lines of “money communication” between parents and children. Key words: financial education workshops, parent-adolescent money communication, financial autonomy.

Using Certainty Factor Method to Handle Uncertain Condition in Hepatitis Diagnosis

The research aimed to develop a web-based application using the certainty factor. The use of this certainty factor method allowed processing the data based on the degree of confidence from the experts and the users. The users inputted their symptoms each with the level of confidence. The inference engine drew some conclusions based on the matching process between the input and the rules in the knowledge-based. For every matching pair, the system would calculate the certainty factor. The knowledge-based was developed through discussion with three specialist physicians and literature in some previous studies. The evaluation of the system involved three specialists for validation testing and 51 respondents for BlackBox testing. The final result is displayed in the form of a percentage for each hepatitis type, explanation of first aid for hepatitis, and referral hospital for hepatitis patients. The result shows that the error rate in the diagnosis process is under 36%. Most of the respondents think that the quality of the system is good overall.

Bridge-surface panoramic-image generation for automated bridge-inspection using deepmatching

Visual inspection is important for the efficient maintenance of bridge structures and has recently been supplemented with the use of image-processing techniques that can localize and quantify damages using images captured from bridges. A series of overlapping bridge images can be combined for constructing a panoramic bridge-surface image in which the locations and sizes of the damages can be noted. Despite the excellent performance of image-processing techniques, generating panoramic images from a series of bridge-surface images is challenging as bridge-surface images may not possess distinct patterns or patterns that can act as reference feature points for stitching adjacent images. To address this issue, this paper presents a general method for stitching bridge-surface images using Deepmatching, which determines a pixel-wise correspondence between an image pair in comparison with conventional feature-wise matching methods. To employ Deepmatching for panoramic-image generation, (1) image matching pair search using 2D Delaunay triangulation, (2) parametric model for optimal image stitching were developed, and (3) field validation was conducted in this study. First, possible image matching pairs are organized using the two-dimensional Delaunay triangulation, and then Deepmatching is used to determine the matching points between possible image pairs. The developed parametric model refines the valid image matching pair, which is used for obtaining optimal global homographies for panoramic-image generation. For the validation of the proposed method, a lab-scale experiment on a flat concrete wall and a field experiment on a concrete bridge were conducted. The experimental validation demonstrates that the proposed method successfully identifies dense matching points between image pairs and generates a panoramic image while minimizing the occurrence of ghosting and drift.

Do People Remember What Is Prototypical? The Role of Accent–Religion Intersectionality for Individual and Category Memory

Evidence suggests that accents can be typically more powerful in activating ethnicity categorization than appearance. Concurrently, some social categories, such as ethnicity, can be linked with other categories, such as religion. We investigate how people categorize those who belong to a (mis)matching pair of categories? In the present study, we investigated Germans’ categorization of women either wearing a headscarf (Muslim religious symbol), or not, and speaking either standard German or German with an Arabic accent. The “Who Said What?” paradigm and multinomial modelling yielded that category memory, indicative of subtyping, was best for nonprototypical targets (i.e., headscarf and standard German accent, no headscarf and Arabic accent). In contrast, in-group targets (no headscarf and standard German accent) were individually remembered better than all other targets, whereas nonprototypical targets (no-headscarf and Arabic accent) were not remembered individually at all. These findings are discussed in terms of intersectionality and category prototypicality.

Systematic review of matching techniques used in model-driven methodologies

In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems. There are a number of different approaches to model matching, with most of them focusing on different goals, i.e., the accuracy of the matching process, or the generality of the algorithm. Moreover, there exist algorithms that use the textual representations of the models during the matching process. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. The search process was conducted based on a well-defined methodology. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. We present the state-of-the-art of model matching, highlighting the differences between different matching techniques, mainly focusing on text-based and graph-based algorithms. Finally, the main open questions, challenges, and possible future directions in the field of model matching are discussed, also including topics like benchmarking, performance and scalability, and conflict handling.

Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data.