Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the ...finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum (<inline-formula> <tex-math notation="LaTeX">6.83\times 10 ^{-4} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">2.06\times 10 ^{-4} </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">4.48\times 10 ^{-6} </tex-math></inline-formula>) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.
Abstract
We propose a mesoscopic Brownian magneto heat pump made of a single charged Brownian particle that is steered by an external magnetic field. The particle is subjected to two thermal noises ...from two different heat sources. When confined, the particle performs gyrating motion around a potential energy minimum. We show that such a magneto-gyrator can be operated as both a heat engine and a refrigerator. The maximum power delivered by the engine and the performance of the refrigerator, namely the rate of heat transferred per unit external work, can be tuned and optimised by the applied magnetic field. Further tunability of the key properties of the engine, such as the direction of gyration and the torque exerted by the engine on the confining potential, is obtained by varying the strength and direction of the applied magnetic field. In principle, our predictions can be tested by experiments with colloidal particles and complex plasmas.
Abstract Background Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being ...classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. Methods A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. Results Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). Conclusions DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.
Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and ...inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance.
This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort.
We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade.
Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.
Templating for Total Hip Arthroplasty in the Modern Age Vigdorchik, Jonathan M; Sharma, Abhinav K; Jerabek, Seth A ...
Journal of the American Academy of Orthopaedic Surgeons,
2021-Mar-01, Volume:
29, Issue:
5
Journal Article
Peer reviewed
Preoperative templating provides several benefits to the patient, surgeon, and hospital. Appropriate implant selection and sizing optimizes surgical workflow and leads to efficient care-delivery ...systems. Accurate templating establishes intraoperative targets for component position and reduces complications such as leg length inequality, impingement, wear, dislocation, and fracture, all of which lead to decreased patient satisfaction. Recent technological advances in preoperative imaging include a better understanding of patient-specific pelvic motion allowing the surgeon to preoperatively address the risk of lumbar pathology with adjustments in component placement and bearing choice. The introduction of two-dimensional to three-dimensional (3D) radiographs, biplanar low-dose radiographs, and computed tomography scans with 3D reconstructions have all allowed for a more comprehensive preoperative planning in 3D. This article will review the fundamentals of templating before total hip arthroplasty with an emphasis on how to incorporate and implement patient-specific pelvic motion and 3D templating into practice.
Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinson’s disease (PD). ...Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in PD. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN–STN coherence in PD. STN–STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD.
Whole genome sequencing (WGS) holds great potential for the management and control of tuberculosis. Accurate analysis of samples with low mycobacterial burden, which are characterized by low (40%) ...levels of contamination, is challenging. We created the MAGMA (Maximum Accessible Genome for Mtb Analysis) bioinformatics pipeline for analysis of clinical Mtb samples. High accuracy variant calling is achieved by using a long seedlength during read mapping to filter out contaminants, variant quality score recalibration with machine learning to identify genuine genomic variants, and joint variant calling for low Mtb coverage genomes. MAGMA automatically generates a standardized and comprehensive output of drug resistance information and resistance classification based on the WHO catalogue of Mtb mutations. MAGMA automatically generates phylogenetic trees with drug resistance annotations and trees that visualize the presence of clusters. Drug resistance and phylogeny outputs from sequencing data of 79 primary liquid cultures were compared between the MAGMA and MTBseq pipelines. The MTBseq pipeline reported only a proportion of the variants in candidate drug resistance genes that were reported by MAGMA. Notable differences were in structural variants, variants in highly conserved rrs and rrl genes, and variants in candidate resistance genes for bedaquiline, clofazmine, and delamanid. Phylogeny results were similar between pipelines but only MAGMA visualized clusters. The MAGMA pipeline could facilitate the integration of WGS into clinical care as it generates clinically relevant data on drug resistance and phylogeny in an automated, standardized, and reproducible manner.
Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular ...amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ
, Aβ
, t-tau, cleaved-tau (c-tau), p-tau
, p-tau
, p-tau
, and p-tau
). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).
We study the motion of a Brownian particle subjected to Lorentz force due to an external magnetic field. Each spatial degree of freedom of the particle is coupled to a different thermostat. We show ...that the magnetic field results in correlation between different velocity components in the stationary state. Integrating the velocity autocorrelation matrix, we obtain the diffusion matrix that enters the Fokker-Planck equation for the probability density. The eigenvectors of the diffusion matrix do not align with the temperature axes. As a consequence the Brownian particle performs spatially correlated diffusion. We further show that in the presence of an isotropic confining potential, an unusual, flux-free steady state emerges which is characterized by a non-Boltzmann density distribution, which can be rotated by reversing the magnetic field. The nontrivial steady state properties of our system result from the Lorentz force induced coupling of the spatial degrees of freedom which cease to exist in equilibrium corresponding to a single-temperature system.
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