Construction, observation and knowledge abstraction for go endgames on small boards.

A Go endgame database consists of optimal game values and moves for every legal arrangement of no more than S pieces on an N by N board. This paper describes methods for constructing such databases when 1 < N ≤ 5 and S = N 2 . When cycles of plies with lengths greater than 4 are encountered, two rules, one allowing cycles and the other disallowing them, are implemented. Observations and knowledge are obtained for these endgames, which may elucidate the fundamental properties of the popular game Go. First, the optimal game values are different when N is even and odd, regardless of whether the repetition of positions is allowed. When N is odd, the first player can occupy the whole board, while this is not the case when N is even. Second, allowing cycles makes the first and second players equal in strength when N is even, whereas the first player always dominates when N is odd. Using the state-of-the-art open-source deep learning Go engine KataGo to correctly solve a given position as an indicator, factors affecting level of difficulty are found, including the distributions of the optimal game values among all legal plies and the cardinality and values of the true optimal plies. A simple formula is designed that works on more than 10% of the positions so that positions with a given level of difficulty can be found with a high probability.

Toward Trusted IoT by General Proof-of-Work.

Internet of Things (IoT) is used to describe devices with sensors that connect and exchange data with other devices or systems on the Internet or other communication networks. Actually, the data not only represent the concrete things connected but also describe the abstract matters related. Therefore, it is expected to support trust on IoT since blockchain was invented so that trusted IoT could be possible or, recently, even metaverse could be imaginable. However, IoT systems are usually composed of a lot of device nodes with limited computing power. The built-in unsolved performance and energy-consumption problems in blockchain become more critical in IoT. The other problems such as finality, privacy, or scalability introduce even more complexity so that trusted IoT is still far from realization, let alone the metaverse. With general Proof of Work (GPoW), the energy consumption of Bitcoin can be reduced to less than 1 billionth and proof of PowerTimestamp (PoPT) can be constructed so that a global even ordering can be reached to conduct synchronization on distributed systems in real-time. Therefore, trusted IoT is possible. We reintroduce GPoW with more mathematic proofs so that PoPT can be optimal and describe how PoPT can be realized with simulation results, mining examples and synchronization scenario toward trusted IoT.

A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: emphasis on security and clinical rule supporting.

BACKGROUND: Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD. OBJECTIVE: The objective of this research was to build an international PD database implemented with security and clinical rule supporting functions, which made the data-sharing easier and improve the accuracy of data. METHODS: The proposed system is a cloud-based application. To fulfill its requirements, the system comprises four subsystems: a data management subsystem, a clinical rule supporting subsystem, a short message notification subsystem, and an information security subsystem. After completing the surgery, the physicians input the data retrospectively, which are analyzed to study factors associated with post-PD common complications (delayed gastric emptying and pancreatic fistula) to validate the clinical value of this system. RESULTS: Currently, this database contains data from nearly 500 subjects. Five medical centers in Taiwan and two cancer centers in Mongolia are participating in this study. A data mining model of the decision tree analysis showed that elderly patients (>76 years) with pylorus-preserving PD (PPPD) have higher proportion of delayed gastric emptying. About the pancreatic fistula, the data mining model of the decision tree analysis revealed that cases with non-pancreaticogastrostomy (PG) reconstruction - body mass index (BMI)>29.65 or PG reconstruction - BMI>23.7 - non-classic PD have higher proportion of pancreatic fistula after PD. CONCLUSIONS: The proposed system allows medical staff to collect and store clinical data in a cloud, sharing the data with other physicians in a secure manner to achieve collaboration in research.

A physician order category-based clinical guideline comparison system.

Today, in order to provide high-quality medical services and to extend resources and reduce costs, many large hospitals have adopted clinical guidelines as a structured way to manage medical activities. However, customization of clinical guidelines in order to treat a large number of patients is a major challenge. In this paper, we present a physician order category-based clinical guideline comparison system. The system uses a preprocessor software to convert the clinical guidelines from a Microsoft Word document into XML format, and it can also compare clinical guidelines over the conceptual view such as the physician order category. The system has already been used to compare the HCC surgical clinical guidelines of Taiwan and Mongolia-resulting in some differences being found, for which possible causes were discussed. Therefore, it can be seen that our research provides a practical and convenient way in which to compare clinical guidelines based on physician order category-thereby saving time and enabling physicians to quickly resolve discrepancies and make necessary adjustments to clinical guidelines.

A data-mining framework for transnational healthcare system.

Medical resources are important and necessary in health care. Recently, the development of methods for improving the efficiency of medical resource utilization is an emerging problem. Despite evidence supporting the use of order sets in hospitals, only a small number of health information systems have successfully equipped physicians with analysis of complex order sequences from clinical pathway and clinical guideline. This paper presents a data-mining framework for transnational healthcare system to find alternative practices, including transfusion, pre-admission tests, and evaluation of liver diseases. However, individual countries vary with respect to geographical location, living habits, and culture, so disease risks and treatment methods also vary across countries. To realize the difference, a service-oriented architecture and cloud-computing technology are applied to analyze these medical data. The validity of the proposed system is demonstrated in including Taiwan and Mongolia, to ensure the feasibility of our approach.

Redactable signatures for signed CDA Documents.

The Clinical Document Architecture, introduced by Health Level Seven, is a XML-based standard intending to specify the encoding, structure, and semantics of clinical documents for exchange. Since the clinical document is in XML form, its authenticity and integrity could be guaranteed by the use of the XML signature published by W3C. While a clinical document wants to conceal some personal or private information, the document needs to be redacted. It makes the signed signature of the original clinical document not be verified. The redactable signature is thus proposed to enable verification for the redacted document. Only a little research does the implementation of the redactable signature, and there still not exists an appropriate scheme for the clinical document. This paper will investigate the existing web-technologies and find a compact and applicable model to implement a suitable redactable signature for the clinical document viewer.

Efficient simulation of the spatial transmission dynamics of influenza.

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we present a significant improvement to the efficiency of an individual-based stochastic disease simulation framework commonly used in multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations. The ability to perform efficient simulation allows us to run a batch of simulations and take account of their average in real time. The averaged data are stable and can be used to differentiate spreading patterns that are not readily seen by only conducting a few runs.

Efficient simulation of the spatial transmission dynamics of influenza.

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially-resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we describe a significant improvement in the efficiency of an individual-based stochastic disease simulation framework that has been used for multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations.