An understanding of what influences sinkhole formation and the ability to accurately predict sinkhole hazards is critical to environmental management efforts in the karst lands of southeastern Minnesota. Based on the distribution of distances to the nearest sinkhole, sinkhole density, bedrock geology and depth to bedrock in southeastern Minnesota and northwestern Iowa, a decision tree model has been developed to construct maps of sinkhole probability in Minnesota. The decision tree model was converted as cartographic models and implemented in ArcGIS to create a preliminary sinkhole probability map in Goodhue, Wabasha, Olmsted, Fillmore, and Mower Counties. This model quantifies bedrock geology, depth to bedrock, sinkhole density, and neighborhood effects in southeastern Minnesota but excludes potential controlling factors such as structural control, topographic settings, human activities and land-use. The sinkhole probability map needs to be verified and updated as more sinkholes are mapped and more information about sinkhole formation is obtained.

台灣在過去的數十年內，罹患失智症人口逐漸增多，其中的多數皆有接受了各層面的照護，舉凡藥物治療、醫護治療、復健治療以及職能治療，然其中的成效與需求之研究仍相當缺乏。故本研究採以質性與量性研究方法，以便於探索目前失智症患者家屬照護時所面臨的實際抉擇歷程與主要需求，並同時探索個案醫院內的治療效果與病患入院時狀況之關係，本研究希望藉由中部地區失智症病患照護的需求及機構之供給的角度來探索研究所能增進其醫療服務品質之處。 在質性研究方法部分，本研究以民族誌決策樹研究法來洞悉與探索家屬在面臨照護失智症病患時是否要採行機構式照護的決策歷程以及決策條件。藉由深度訪談結果粹取出的判斷準則發現，影響家屬決策之最主要考量為失智症病患者的失智程度，其餘包含道德規範、照護負擔、病患是否需要騎他的專業醫療照護以及照護中心的軟硬體環境。本研究整合考量這些判斷準則的優先順序、輕重緩急以及因果關係後將之建立決策樹，並以另外五十名家屬驗證該模型之預測力，得到預測準確率為92%。 此外，本研究再以量性方法來探索治療對於不同失智症病患的成效。結果顯示入住時狀況較好的失智症住民會以更積極的態度來接受職能治療，也因此他們擁有較大的改善或控制病情的機會，然而當住民以消極的態度接受職能治療時，則其治療效果遠不及積極治療者，也因此病情退步的機會較大，主要原因在於多數情況較差的住民具有攻擊、抗拒治療的傾向，使得照護工作變得更為艱鉅，故本研究建議家屬應重視職能治療以及與病人互動之重要性，不論是在居家照護亦或是機構式照護 / Over the past decade, the number of long-term care (LTC) residents has increased, and many have accepted treatments such as medication, rehabilitation and occupational therapy. This study employs both qualitative and quantitative techniques in order to discuss senile dementia patient care in long-term care institutions, and we use a supply and demand viewpoint to explore what services are really necessary for the patient and their family. In qualitative method, the main purpose of this stage is to use the ethnographic decision tree model to understand and explore the decision criteria of the subject. Our study found that the degree of dementia of the patient always affects the decisions made by family members – in fact, this is the most important of all criteria elicited from the interviews with family members. There are also ethical constraints, care burden, norm of filial obligation, patient need professional medical care and institutional environment, etc. which mentioned by families. We linked together the decision criteria considered most important in accounting for the decision-making sequence of family members to be the ethnographic decision tree model which predictive power is 92%. In quantitative stage, our study discussed the effectiveness of occupational therapy when given to dementia patients of different contexts. The results of this stage showed that patients of a good condition in the first stage present a more positive attitude towards participation in the occupational therapy designed by the institution; therefore, they have a greater chance of their condition improving or remaining the same. However, patients of an average condition have a more passive attitude towards taking part in any therapy; therefore, they have a greater chance of their condition deteriorating, because of their violent tendencies and their resistance to care, the task of caring for these patients is more difficult than caring for patients in the other groups. Above all, we suggest that families adopt the therapies no matter in homecare or institutionalization, as early as possible in order to improve the likelihood of being able to control the patient’s condition. It is understandable that accepting more therapies and interaction in the early stage of dementia, having higher chance to go well, however, by waiting until then they also miss the best opportunity to attempt to improve the patient’s condition, it is really not the good way we suggest to be.

[pt] A análise de complexidade clássica para problemas NP-difíceis é geralmente orientada para cenários de pior caso, considerando apenas o comportamento assintótico. No entanto, existem algoritmos práticos com execução em um tempo razoável para muitos problemas clássicos. Além disso, há evidências que apontam para algoritmos polinomiais no modelo de árvore de decisão linear para resolver esses problemas, embora não muito explorados. Neste trabalho, exploramos esses resultados teóricos anteriores. Mostramos que a solução ótima para problemas combinatórios 0-1 pode ser encontrada reduzindo esses problemas para uma Busca por Vizinho Mais Próximo sobre o conjunto de vértices de Voronoi correspondentes. Utilizamos os hiperplanos que delimitam essas regiões para gerar sistematicamente uma árvore de decisão que repetidamente divide o espaço até que possa separar todas as soluções, garantindo uma resposta ótima. Fazemos experimentos para testar os limites de tamanho para os quais podemos construir essas árvores para os casos do 0-1 knapsack, weighted minimum cut e symmetric traveling salesman. Conseguimos encontrar as árvores desses problemas com tamanhos até 10, 5 e 6, respectivamente. Obtemos também as relações de adjacência completas para os esqueletos dos politopos do knapsack e do traveling salesman até os tamanhos 10 e 7. Nossa abordagem supera consistentemente o método de enumeração e os métodos baseline para o weighted minimum cut e symmetric traveling salesman, fornecendo soluções ótimas em microssegundos. / [en] Classical complexity analysis for NP-hard problems is usually oriented to worst-case scenarios, considering only the asymptotic behavior. However, there are practical algorithms running in a reasonable time for many classic problems. Furthermore, there is evidence pointing towards polynomial algorithms in the linear decision tree model to solve these problems, although not explored much. In this work, we explore previous theoretical results. We show that the optimal solution for 0-1 combinatorial problems can be found by reducing these problems into a Nearest Neighbor Search over the set of corresponding Voronoi vertices. We use the hyperplanes delimiting these regions to systematically generate a decision tree that repeatedly splits the space until it can separate all solutions, guaranteeing an optimal answer. We run experiments to test the size limits for which we can build these trees for the cases of the 0-1 knapsack, weighted minimum cut, and symmetric traveling salesman. We manage to find the trees of these problems with sizes up to 10, 5, and 6, respectively. We also obtain the complete adjacency relations for the skeletons of the knapsack and traveling salesman polytopes up to size 10 and 7. Our approach consistently outperforms the enumeration method and the baseline methods for the weighted minimum cut and symmetric traveling salesman, providing optimal solutions within microseconds.