COMPUTER UTILIZATION AS A BALANCED OPPONENT IN DAM-DAM-AN

Dam-dam-an is a traditional game played by two players using a board with the size of 8x8 as a media. Each player gets 16 pieces that can be moved one step forward or leaped over opponent‟s piece. The goal of this game is to eliminate all of our pieces as soon as possible. It is exciting to have a challenging opponent, yet it is difficult to find a skillful one. A way to solve this problem is using intelligent computer. This paper presents the result of our research about the computer utilization as balanced opponent in dam-dam-an game using Alpha-Beta Pruning. Alpha-Beta Pruning is a best-step searching algorithm which works by considering and assessing every possibility while excluding the less useful steps. The implementation is developed using C# programming language based on Windows Runtime. Some features were added to make this game more exciting, particularly intelligence level selection (easy, medium, and hard), human or computer opponent selection, undo function, pausing menu, save-load the game, and setting the turning time. This game was validated by twenty respondents which were categorized based on their skill. Each respondent played against the computer thrice and the results were recorded. The results showed that the number of winning between the computer and each player are almost the same. It shows that the computer opponent may prove itself to be a challenging opponent for human player

Faculty Publications and Creative Works 2001

One of the ways in which we recognize our faculty at the University of New Mexico is through Faculty Publications & Creative Works. An annual publication, it highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 2001 calendar year. Faculty Publications & Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new ideas. In support of this, UNM faculty during 2001 produced over 2,299* works, including 1,685 scholarly papers and articles, 69 books, 269 book chapters, 184 reviews, 86 creative works and 6 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Smart and Pervasive Healthcare

Smart and pervasive healthcare aims at facilitating better healthcare access, provision, and delivery by overcoming spatial and temporal barriers. It represents a shift toward understanding what patients and clinicians really need when placed within a specific context, where traditional face-to-face encounters may not be possible or sufficient. As such, technological innovation is a necessary facilitating conduit. This book is a collection of chapters written by prominent researchers and academics worldwide that provide insights into the design and adoption of new platforms in smart and pervasive healthcare. With the COVID-19 pandemic necessitating changes to the traditional model of healthcare access and its delivery around the world, this book is a timely contribution

Interplay between inflammation and calcification in cardiovascular diseases

Cardiovascular calcification has been linked to all-cause mortality and is a broadly adopted predictor of cardiovascular (CV) events. Rather than a mere by-product of the changing disease environment, calcification impacts actively the disease progression and pathogenesis as it predominates both in early- and late-stages, through mediating tissue biomechanical destabilisation and directly impacting tissue inflammation. However, its clinical contribution to the fate of the disease remains to be elucidated. Emerging body of evidence from both basic and clinical research has demonstrated the significance of the innate immune system in cardiovascular diseases (CVDs). Here, inflammation and calcification are engaged in a vicious cycle particularly at early-stages, whereas in advanced-lesions, large calcifications linked with suppressed inflammation and plaque stability. However, this interaction during disease progression remains largely elusive. The aim of this thesis is to investigate the interplay between inflammation and calcification in advanced atherosclerosis and calcific aortic valve disease (CAVD). Study I explores gene and protein expression signatures and biological pathways of advanced CAVD lesions in order to characterise the underlining mechanisms associated with the disease pathology. Multi-omics integration of overlapping transcriptome/proteome molecules with miRNAs, identified a unique CAVD-related protein-protein 3D layered interaction network. After addition of a metabolite layer, Alzheimer's disease (AD) was identified in the core of the gene-disease network. This study suggests a novel molecular CAVD network potentially linked to amyloid-like structures formation. Study II characterises osteomodulin (OMD) in the context of atherosclerosis, chronic kidney disease (CKD) and CAVD. Plasma OMD levels were correlated with markers of inflammation and bone turnover, with the protein being present in the calcified arterial media of patients with CKD stage 5. Circulating OMD levels were also associated with cardiac valve calcification in the same patients and its positive signal was detected in calcified valve leaflets by immunohistochemistry. In patients with carotid atherosclerosis, plasma OMD levels were increased in association with plaque calcification as assessed by computed tomography. Transcriptomic and proteomic data analysis showed that OMD expression was upregulated in atherosclerotic compared to non-atherosclerotic control arteries, and particularly in highly calcified plaques, where its expression correlated positively with markers of vascular smooth muscle cells (VSMCs) and osteoblasts. In vivo, OMD was enriched in VSMCs around calcified nodules in aortic media of nephrectomised rats and in plaques from ApoE-/- mice on warfarin. In vitro experiments revealed that exogenous administration of recombinant human OMD protein repressed the calcification process of VSMCs treated with phosphate by maintaining the VSMC contractile phenotype along with enriched extracellular matrix (ECM) organisation, thereby attenuating VSMC osteoblastic transformation. Study III analyses OMD expression in human carotid plaques and particularly its link with future CV events. Transcriptomic analysis revealed that OMD levels were increased in plaques from asymptomatic patients compared to symptomatic ones, with high levels being associated with fewer CV events in a follow-up analysis. Study IV investigates the link between mast cell (MC) activation and key features of human plaque vulnerability, and the role of MC in VSMC-mediated calcification. Integrative analyses from a large biobank of human plaques showed that MC activation is inversely associated with macrocalcification and positively with morphological parameters of plaque vulnerability. Bioinformatic analyses revealed associations of MCs with NK cells and other immune cells in plaques. Mechanistic in vitro experiments showed that calcification attenuated MC activation, while both active and resting MCs induced VSMC calcification and triggered their dedifferentiation towards a pro-inflammatory- and osteochondrocyte-like phenotype. Overall, this thesis demonstrates that the underlying mechanisms of CVD related to inflammation and calcification can be comprehensively characterised by integration of largescale multi-omics datasets along with cellular and molecular assays on one side, and disease specific biomarkers and advanced diagnostic imaging tools on the other. In summary, these studies not only indicate that advanced-calcification is a stabilising factor for plaque and disease progression but also, unveil novel insights into the cardiovascular calcification pathobiology, and offer promising biomarkers and new therapeutic avenues for further exploration

Deprescribing tool for STOPPFall (screening tool of older persons prescriptions in older adults with high fall risk) items

Background: Health care professionals are often reluctant to deprescribe fall-risk-increasing drugs (FRIDs). Lack of knowledge and skills form a significant barrier. To support clinicians in the management of FRIDs and to facilitate the deprescribing process, a deprescribing tool was developed by a European expert group for STOPPFall (Screening Tool of Older Persons Prescriptions in older adults with high fall risk) items. Methods: STOPPFall was created using an expert Delphi consensus process in 2019 and in 2020, 24 panellists from EuGMS SIG on Pharmacology and Task and Finish on FRIDs completed deprescribing tool questionnaire. To develop the questionnaire, a Medline literature search was performed. The panellists were asked to indicate for every medication class a possible need for stepwise withdrawal and strategy for withdrawal. They were asked in which situations withdrawal should be performed. Furthermore, panellists were requested to indicate those symptoms patients should be monitored for after deprescribing and a possible need for follow-ups. Results: Practical deprescribing guidance was developed for STOPPFall medication classes. For each medication class, a decision tree algorithm was developed including steps from medication review to symptom monitoring after medication withdrawal. Conclusion: STOPPFall was combined with a practical deprescribing tool designed to optimize medication review. This practical guide can help overcome current reluctance towards deprescribing in clinical practice by providing an up-to-date and straightforward source of expert knowledge

Association between number of medications and mortality in geriatric inpatients : a Danish nationwide register-based cohort study

Purpose: To explore the association between the number of medications and mortality in geriatric inpatients taking activities of daily living and comorbidities into account. Methods: A nationwide population-based cohort study was performed including all patients aged C65 years admitted to geriatric departments in Denmark during 2005-2014. The outcome of interest was mortality. Activities of daily living using Barthel-Index (BI) were measured at admission. National health registers were used to link data on an individual level extracting data on medications, and hospital diseases. Patients were followed to the end of study (31.12.2015), death, or emigration, which ever occurred first. Kaplan-Meier survival curves were used to estimate crude survival proportions. Univariable and multivariable analyses were performed using Cox regression. The multivariable analysis adjusted for age, marital status, period of hospital admission, BMI, and BI (model 1), and further adding either number of diseases (model 2) or Charlson comorbidity index (model 3). Results: We included 74603 patients (62.8% women), with a median age of 83 (interquartile range [IQR] 77-88) years. Patients used a median of 6 (IQR 4-9) medications. Increasing number of medications was associated with increased overall, 30-days, and 1-year mortality in all 3 multivariable models for both men and women. For each extra medication the mortality increased by 3% in women and 4% in men in the fully adjusted model. Conclusion: Increasing number of medications was associated with mortality in this nationwide cohort of geriatric inpatients. Our findings highlight the importance of polypharmacy in older patients with comorbidities