Registration of brain tumor images using hyper-elastic regularization

In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean strain energy density model as a regularizer in deformable registration framework. Implementations on synthetic and patient data provide promising results, which might have relevant use in clinical problems

Cellular automata segmentation of brain tumors on post contrast MR images

In this paper, we re-examine the cellular automata(CA) al- gorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmenta- tion method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Val- idation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type

Oddity in nonrelativistic, strong gravity

We consider the presence of odd powers of the speed of light $c$ in the covariant nonrelativistic expansion of General Relativity (GR). The term of order $c$ in the relativistic metric is a vector potential that contributes at leading order in this expansion and describes strong gravitational effects outside the (post-)Newtonian regime. The nonrelativistic theory of the leading order potentials contains the full non-linear dynamics of the stationary sector of GR.Comment: 24 pages + appendices. Version accepted for publication by EPJC. Subsection 4.4 on possible phenomenological applications adde

Controller design for integrating processes with Coefficient Diagram Method

Bu çalışmanın amacı, transfer fonksiyonunda integratör bulunan zaman gecikmeli sistemlerin kontrolünde klasik PID kontrolörlerin sınırlılıklarını göstermektir. Bu nedenle, bu tür sistemler için daha iyi bir davranış elde etmek amacıyla Katsayı Diyagram Metodu (KDM) olarak adlandırılan bir polinomsal yaklaşımın kullanılması önerilmiştir. KDM ile kontrolör tasarımı eşdeğer zaman sabiti, kararlılık indeksi ve karalılık sınır indeksi gibi uygun davranış kriterlerine karşı kapalı çevrim sisteminin karakteristik polinomunun katsayılarını seçmeye dayalıdır. Yapılan tasarım örneği KDM’in hem referans basamak girişin takibi ve hem de bozucu işaretin söndürülmesi için davranışta önemli bir iyileşme sağladığını göstermiştir. Ayrıca kontrol en kısa yerleşme süresini ve parametre değişimlerine karşı en dayanıklı davranışı sağlamıştır. Anahtar Kelimeler: Katsayı Diyagram Metodu, zaman gecikmesi, integratörlü sistemler, dayanıklılık.The objective of this paper is to illustrate the limitations of classical PID controllers in controlling time delay systems with integrating transfer functions. Generally, the control of integrating processes is more difficult than the classical stable open-loop processes. Especially, integrating processes existing time delay make difficult the control operation. Numerous PID strategies have been proposed for these systems recently. Therefore, using a polynomial approach, Coefficient Diagram Method (CDM) has been proposed in order to obtain a better performance for these systems. The controller design by CDM is based on the choice of the coefficients of the characteristic polynomial of the closed loop system according to the convenient performance criteria such as equivalent time constant, stability index, and stability limit index. The studies on this method illustrated that the CDM provides a significantly improved performance both for the reference step input tracking and for the disturbance rejection. Also the control system provides the smallest settling time and the most robust performance to the parameter changes. An example are presented for an integrating process with time delay to illustrate the effectiveness of the proposed method and compared it with existing ones. It is shown that CDM design is more stable and robust whilst giving the desired time domain performance.Keywords: Coefficient Diagram Method, time delay, integrating processes, robustness

On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes

In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modeling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.Comment: 27 pages, 10 figure

Bis(acetato-κO)bis­[2-(pyridin-2-yl)ethanol-κ2 N,O]copper(II)

The title compound, [Cu(CH3COO)2(C7H9NO)2], is a monomeric complex with an octa­hedral geometry. The CuII atom is located on an inversion center and is coordinated by acetate and 2-(pyridin-2-yl)ethanol ligands. The acetate group is coordinated in a monodentate manner, while the 2-(pyridin-2-yl)ethanol is coordinated as a bidentate ligand involving the endocyclic N atom and the hy­droxy O atom of the ligand side chain. An intra­molecular hydrogen bond is observed between the hy­droxy O atom and the non-coordinated acetate O atom. No classical inter­molecular hydrogen-bond contacts were observed. However, the crystal packing is effected by C—H⋯O inter­actions, which link the mononuclear entities into layers parallel to the bc plane

EFFECTS OF ORTHODONTIC ADHESIVE MATERIALS ON S. MUTANS AND LACTOBACILLI LEVELS IN HUMAN SALIVA

ABSTRAC

Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays

Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify different dental diseases has tremendously increased. Although numerous ML models have been developed for the interpretation of panoramic X-rays, there has not been an end-to-end model developed that can identify problematic teeth with dental enumeration and associated diagnoses at the same time. To develop such a model, we structure the three distinct types of annotated data hierarchically following the FDI system, the first labeled with only quadrant, the second labeled with quadrant-enumeration, and the third fully labeled with quadrant-enumeration-diagnosis. To learn from all three hierarchies jointly, we introduce a novel diffusion-based hierarchical multi-label object detection framework by adapting a diffusion-based method that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Specifically, to take advantage of the hierarchically annotated data, our method utilizes a novel noisy box manipulation technique by adapting the denoising process in the diffusion network with the inference from the previously trained model in hierarchical order. We also utilize a multi-label object detection method to learn efficiently from partial annotations and to give all the needed information about each abnormal tooth for treatment planning. Experimental results show that our method significantly outperforms state-of-the-art object detection methods, including RetinaNet, Faster R-CNN, DETR, and DiffusionDet for the analysis of panoramic X-rays, demonstrating the great potential of our method for hierarchically and partially annotated datasets. The code and the data are available at: https://github.com/ibrahimethemhamamci/HierarchicalDet.Comment: MICCAI 202