Switchable opening and closing of a liquid marble via ultrasonic levitation

Liquid marbles have promising applications in the field of microreactors, where the opening and closing of their surfaces plays a central role. We have levitated liquid water marbles using an acoustic levitator and, thereby, achieved the manipulation of the particle shell in a controlled manner. Upon increasing the sound intensity, the stable levitated liquid marble changes from a quasi-sphere to a flattened ellipsoid. Interestingly, a cavity on the particle shell can be produced on the polar areas, which can be completely healed when decreasing the sound intensity, allowing it to serve as a microreactor. The integral of the acoustic radiation pressure on the part of the particle surface protruding into air is responsible for particle migration from the center of the liquid marble to the edge. Our results demonstrate that the opening and closing of the liquid marble particle shell can be conveniently achieved via acoustic levitation, opening up a new possibility to manipulate liquid marbles coated with non-ferromagnetic particles

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with 6×3 PV array is established and experimentally tested to investigate the performance of the developed method

Novel Open-Circuit Photovoltaic Bypass Diode Fault Detection Algorithm

In this article, a novel photovoltaic (PV) bypass diode fault detection algorithm is presented. The algorithm consists of three main steps. First, the threshold voltage of the current–voltage (I–V) curve is obtained using different failure bypass diode scenarios. Second, the theoretical prediction for the faulty regions of bypass diodes is calculated using the analysis of voltage drop in the I–V curve as well as the voltage at maximum power point. Finally, the actual I–V curve under any environmental condition is measured and compared with theoretical predictions. The proposed algorithm has been experimentally evaluated using a PV string that comprises three series-connected PV modules, and subtotal of nine bypass diodes. Various experiments have been conducted under diverse bypass diodes failure conditions. The achieved detection accuracy is always greater than 99.39% and 99.74% under slow and fast solar irradiance transition, respectively

Association of systemic inflammation response index with mortality risk in older patients with hip fracture: a 10-year retrospective cohort study

ObjectiveWith a rapidly aging global population, the assessment of mortality risk following hip fracture in older adults has received increasing attention. Recently, the system inflammation response index (SIRI) has been identified as a novel prognostic marker to reflect both systemic inflammation and immune status. However, it is not yet known whether SIRI is a potential predictor of subsequent death in hip fracture patients. Therefore, this study aimed to investigate the association between SIRI and mortality in older patients with hip fracture.MethodsA total of 1,206 older hip fracture patients undergoing surgery between January 2013 and December 2022 were consecutively derived from our longitudinal database. Patients were divided into three groups according to SIRI tertiles, calculated as neutrophil × monocyte / lymphocyte. Survival status was obtained from medical records or telephone interviews, and the study outcome was all-cause mortality after hip fracture at the longest follow-up. Multivariate Cox proportional hazard model and restricted cubic spline (RCS) regression model were used to evaluate the association between SIRI and mortality. Moreover, a series of sensitivity analyses were conducted to further validate the robustness of the association.ResultsDuring a median follow-up of 43.85 months, 337 patients (27.94%) died. After full adjustment, each unit increase in SIRI was significantly associated with a 2.2% increase in overall mortality (95% confidence interval [CI]: 1.001–1.042, p = 0.029). Similarly, compared with the first tertile of SIRI, the second and third tertile showed a 1.335-fold (95% CI: 1.011–1.762, p = 0.042) and 1.447-fold (95% CI, 1.093–1.917, p = 0.010) higher risk of death. Sensitivity analyses confirmed the stability of the association. Moreover, RCS analysis revealed a positive non-linear relationship between SIRI and mortality (P for nonlinearity = 0.021).ConclusionHigh SIRI level at admission was significantly and positively associated with an increased risk of death, suggesting that SIRI may be an independent predictor of mortality in older patients with hip fracture

Central Angiotensin II Stimulation Promotes β Amyloid Production in Sprague Dawley Rats

BACKGROUND: Stress and various stress hormones, including catecholamines and glucocorticoids, have recently been implicated in the pathogenesis of Alzheimer's disease (AD), which represents the greatest unresolved medical challenge in neurology. Angiotensin receptor blockers have shown benefits in AD and prone-to-AD animals. However, the mechanisms responsible for their efficacy remain unknown, and no studies have directly addressed the role of central angiotensin II (Ang II), a fundamental stress hormone, in the pathogenesis of AD. The present study focused on the role of central Ang II in amyloidogenesis, the critical process in AD neuropathology, and aimed to provide direct evidence for the role of this stress hormone in the pathogenesis of AD. METHODOLOGY/PRINCIPAL FINDINGS: Increased central Ang II levels during stress response were modeled by intracerebroventricular (ICV) administration of graded doses of Ang II (6 ng/hr low dose, 60 ng/hr medium dose, and 600 ng/hr high dose, all delivered at a rate of 0.25 µl/hr) to male Sprague Dawley rats (280-310 g) via osmotic pumps. After 1 week of continuous Ang II infusion, the stimulation of Ang II type 1 receptors was accompanied by the modulation of amyloid precursor protein, α-, β-and γ-secretase, and increased β amyloid production. These effects could be completely abolished by concomitant ICV infusion of losartan, indicating that central Ang II played a causative role in these alterations. CONCLUSIONS/SIGNIFICANCE: Central Ang II is essential to the stress response, and the results of this study suggest that increased central Ang II levels play an important role in amyloidogenesis during stress, and that central Ang II-directed stress prevention and treatment might represent a novel anti-AD strategy

Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning

Accurate and efficient parameter extraction of PV models from I-V characteristic curves is significant for modeling, evaluation and fault diagnosis of PV modules/arrays. Recently, a large number of algorithms are proposed for this problem, but there are still some issues like premature convergence, low accurate and instability. In this paper, a new improved shuffled complex evolution algorithm enhanced by the opposition-based learning strategy (ESCE-OBL) is proposed. The proposed algorithm improves the quality of the candidate solution by the opposition-based learning strategy. Moreover, the basic SCE algorithm evolves with the traditional competition complex evolution (CCE) strategy, but it converges slowly and is prone to be trapped in local optima. In order to improve the exploration capability, the complex in the basic SCE is evolved by a new enhanced CCE. The ESCE-OBL algorithm is compared with some state-of-the-art algorithms on the single diode model (SDM) and double diode model (DDM) using benchmark I-V curves data. The comparison results demonstrate that the proposed ESCE-OBL algorithm can achieve faster convergence, stronger robustness and higher efficiency