Cost-Sensitive Decision Tree with Multiple Resource Constraints

Resource constraints are commonly found in classification tasks. For example, there could be a budget limit on implementation and a deadline for finishing the classification task. Applying the top-down approach for tree induction in this situation may have significant drawbacks. In particular, it is difficult, especially in an early stage of tree induction, to assess an attribute’s contribution to improving the total implementation cost and its impact on attribute selection in later stages because of the deadline constraint. To address this problem, we propose an innovative algorithm, namely, the Cost-Sensitive Associative Tree (CAT) algorithm. Essentially, the algorithm first extracts and retains association classification rules from the training data which satisfy resource constraints, and then uses the rules to construct the final decision tree. The approach has advantages over the traditional top-down approach, first because only feasible classification rules are considered in the tree induction and, second, because their costs and resource use are known. In contrast, in the top-down approach, the information is not available for selecting splitting attributes. The experiment results show that the CAT algorithm significantly outperforms the top-down approach and adapts very well to available resources.Cost-sensitive learning, mining methods and algorithms, decision trees

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Poly[diaqua­bis(2,2′-bipyridine)tris­(μ4-2,2′-bipyridine-4,4′-dicarboxyl­ato)dineodymium(III)]

In the crystal structure of the title mixed-ligand coordination polymer, [Nd2(C12H6N2O4)3(C10H8N2)2(H2O)2]n, the NdIII ion is in an octa­hedral coordination environment formed by one water mol­ecule, one chelating 2,2′-bipyridine ligand, and five monodentate carboxyl­ate groups. The local coordination polyhedron around the NdIII ion is a bicapped trigonal prism. Two NdIII centers are bridged by four carboxyl­ate groups to form an Nd2 dimeric unit; these are further connected by 2,2′-bipyridine-4,4′-dicarboxyl­ate linkers, resulting in a layered coordination network

Chemoradiation for Olfactory Neuroblastoma

AbstractOlfactory neuroblastoma is a rare intranasal tumor, and the standard treatment for this disease remains controversial. Some clinicians contend that a combination of surgery and radiotherapy is the most efficacious approach, which frequently has a good prognosis. Chemotherapy is often reserved for those patients with tumor recurrence and distant metastasis. Regarding such metastasis, it is well-known that cervical metastasis indicates poor prognosis. We presented a 36-year-old woman who was diagnosed with a neuroblastoma with neck lymph node metastasis who did not undergo surgery. We treated her with chemotherapy followed by concurrent chemoradiation, and the result showed good response without sequela. We also reviewed the literature regarding the chemotherapy of olfactory neuroblastoma. In conclusion, olfactory neuroblastoma is highly sensitive to chemotherapy. However, long-term surveillanceof patients should be maintained in the event of local recurrence and distant metastasis

Blind Source Separation of Hemodynamics from Magnetic Resonance Perfusion Brain Images Using Independent Factor Analysis

Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature

5-Iodo­pyrimidin-2-amine

The mol­ecule of the title compound, C4H4IN3, has crystallographic mirror plane symmetry. In the crystal, the mol­ecules are connected through N—H⋯N hydrogen bonds into polymeric tapes extended along the a axis, which are typical of 2-amino­pyrimidines. Each mol­ecule acts as a double donor and a double acceptor in the hydrogen bonding