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Little is known about the formation pathway of colloidal semiconductor magic‐size clusters (MSCs). Here, the synthesis of the first single‐ensemble ZnSe MSCs, which exhibit a sharp optical absorption singlet peaking at 299 nm, is reported; their formation is independent of Zn and Se precursors used. It is proposed that the formation of MSCs starts with precursor self‐assembly followed by Zn and Se covalent bond formation to result in immediate precursors (IPs) which can transform into the MSCs. It is demonstrated that the IPs in cyclohexane appear transparent in optical absorption, and become visible as MSCs exhibiting one sharp optical absorption peak when a primary amine is added at room temperature. It is shown that when the preparation of the IP is controlled to be within the induction period, which occurs prior to nucleation and growth of conventional quantum dots (QDs), the resulting MSCs can be produced without the complication of the simultaneous coproduction of conventional QDs. The present study reveals the existence of precursor self‐assembly which leads to the formation of colloidal semiconductor MSCs and provides insights into a multistep nucleation process in cluster science. Precursor self‐assembly, with ZnSe as a model system is proposed as a general pathway for the formation of colloidal semiconductor magic‐size clusters (MSCs). The self‐assembly followed by ZnSe bond formation gives rise to the formation of immediate precursors of MSCs prior to formation of conventional quantum dots. ZnSe MSC‐299 forms from reactions of various Zn and Se precursors.

In mixed‐stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non‐overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad‐scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad‐scale or regional baselines. We demonstrate the utility of our integrated multistage model using a synthesized data set made up of Chinook salmon, Oncorhynchus tshawytscha, from the North Bering Sea of Alaska. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high‐resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost‐effective than updating range‐wide baselines with all regionally important markers.

With the continuous advancement of machine learning and the increasing availability of internet-based information, there is a belief that these approaches and datasets enhance the accuracy of price prediction. However, this study aims to investigate the validity of this claim. The study examines the effectiveness of a large dataset and sophisticated methodologies in forecasting foreign exchange rates (FX) and commodity prices. Specifically, we employ sentiment analysis to construct a robust sentiment index and explore whether combining sentiment analysis with machine learning surpasses the performance of a large dataset when predicting FX and commodity prices. Additionally, we apply machine learning methodologies such as random forest (RF), eXtreme gradient boosting (XGB), and long short-term memory (LSTM), alongside the classical statistical model autoregressive integrated moving average (ARIMA), to forecast these prices and compare the models’ performance. Based on the results, we propose novel methodologies that integrate wavelet transformation with classical ARIMA and machine learning techniques (seasonal-decomposition-ARIMA-LSTM, wavelet-ARIMA-LSTM, wavelet-ARIMA-RF, wavelet-ARIMA-XGB). We apply this analysis procedure to the commodity gold futures prices and the euro foreign exchange rates against the US dollar.

Summary In the traditional extended Kalman filter approach, unknown structural parameters are included in the extended state vector. Then, the sizes of the extended state vector and the corresponding state equation are quite large, and the state equation is highly nonlinear with respect to the extended state vector. This may cause identification divergent for a large number of unknown parameters. Also, such strategy requires large computational effort and storage capacities, which is not appropriate for intelligent structural damage detection implemented by smart sensors with microprocessors. In this paper, an algorithm based on a two‐step Kalman filter approach is proposed to remove the aforementioned drawbacks of the traditional extended Kalman filter. In the first step, recursive estimation of structural state vector is derived by Kalman filter with assumed structural parameters. In the second step, structural parameters and the updated structural state vector are estimated by the Kalman filter and the recursive estimation in the first step. Thus, the number of estimated variables in each step is reduced, which reduces the computational effort and storage requirements. This superiority is important for intelligent structural damage detection implemented by smart sensor in wireless sensor network. The proposed algorithm is first validated by numerical simulations results of structural damage detection of the phase‐I 3‐D ASCE benchmark building for structural health monitoring, a 30‐story shear building with minor damage, and an experimental test of damage detection of a lab multistory frame model. Then, it is applied to structural damage detection of a lab multistory model‐employed smart sensors embedded with the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Context Differential diagnosis of thiazide-associated hyponatremia (TAH) is challenging. Patients can either have volume depletion or a syndrome of inappropriate antidiuresis (SIAD)-like presentation. Objective To evaluate the impact of the simplified apparent strong ion difference in serum (aSID; sodium + potassium − chloride) as well as the urine chloride and potassium score (ChU; chloride − potassium in urine) in the differential diagnosis of TAH, in addition to assessment of fractional uric acid excretion (FUA). Methods Post hoc analysis of prospectively collected data from June 2011 to August 2013 from 98 hospitalized patients with TAH < 125 mmol/L enrolled at University Hospital Basel and University Medical Clinic Aarau, Switzerland. Patients were categorized according to treatment response in volume-depleted TAH requiring volume substitution or SIAD-like TAH requiring fluid restriction. We computed sensitivity analyses with ROC curves for positive predictive value (PPV) and negative predictive value (NPV) of aSID, ChU, and FUA in differential diagnosis of TAH. Results An aSID > 42 mmol/L had a PPV of 79.1% in identifying patients with volume-depleted TAH, whereas a value < 39 mmol/L excluded it with a NPV of 76.5%. In patients for whom aSID was inconclusive, a ChU < 15 mmol/L had a PPV of 100% and a NPV of 83.3%, whereas FUA < 12% had a PPV of 85.7% and a NPV of 64.3% in identifying patients with volume-depleted TAH. Conclusion In patients with TAH, assessment of aSID, potassium, and chloride in urine can help identifying patients with volume-depleted TAH requiring fluid substitution vs patients with SIAD-like TAH requiring fluid restriction.

A two-step approach is proposed to accomplish high-fidelity whole-core resonance self-shielding calculation. Direct slowing-down equation solving based on the pin-cell scale is performed as the first step to simulate different operating conditions of the reactor. Resonance database is fitted using the results from the pin-cell calculation. Several techniques are used in the generation of the resonance database to estimate multiple types of resonance effects. The second step is the calculation of practical whole-core problem using the resonance database obtained from the first step. The transport solver is embedded both at the first step and the second step to establish the equivalence relationship between the fuel rod in the practical problem and the pin-cell at the first step. The numerical results show that the new approach have capability to perform high-fidelity resonance calculations for practical problem.

Allogeneic hematopoietic stem cell transplantation (HSCT) provides the only potentially curative option for multiple hematological conditions. However, allogeneic HSCT outcomes rely on an optimal balance of effective immune recovery, minimal graft-versus-host disease (GVHD), and lasting control of disease. The quest to attain this balance has proven challenging over the past few decades. The two-step approach to HSCT was conceptualized and pioneered at Thomas Jefferson University in 2005 and remains the main platform for allografting at our institution. Following administration of the transplant conditioning regimen, patients receive a fixed dose of donor CD3+ cells (HSCT step one-DLI) as the lymphoid portion of the graft on day -6 with the aim of optimizing and controlling T cell dosing. Cyclophosphamide (CY) is administered after the DLI (days -3 and -2) to induce donor-recipient bidirectional tolerance. On day 0, a CD34-selected stem cell graft is given as the myeloid portion of the graft (step two). In this two-step approach, the stem cell graft is infused after CY tolerization, which avoids exposure of the stem cells to an alkylating agent, allowing rapid count recovery. Here, the two-step platform is described with a focus on key results from studies over the past two decades. Finally, this review details lessons learned and current strategies to optimize the graft-versus-tumor effect and limit transplant-related toxicities.

Background Although some post myocardial infarction (post‐MI) and dilated cardiomyopathy (DCM) patients with mid‐range ejection fraction heart failure (HFmrEF/40%‐49%) face an increased risk for arrhythmic sudden cardiac death (SCD), current guidelines do not recommend an implantable cardiac defibrilator (ICD). We risk stratified hospitalized HFmrEF patients for SCD with a combined non‐invasive risk factors (NIRFs) guiding to programmed ventricular stimulation (PVS) two‐step approach. Methods Forty‐eight patients (male = 83%, age = 64 ± 14 years, LVEF = 45 ± 5%, CAD = 69%, DCM = 31%) underwent a NIRFs screening first‐step with electrocardiogram (ECG), SAECG, Echocardiography and 24‐hour ambulatory ECG (AECG). Thirty‐two patients with presence of one of three NIRFs (SAECG ≥ 2 positive criteria for late potentials, ventricular premature beats ≥ 240/24 hours, and non‐sustained ventricular tachycardia [VT] episode ≥ 1/24 hours) were further investigated with PVS. Patients were classified as either low risk (Group 1, n = 16, NIRFs−), moderate risk (Group 2, n = 18, NIRFs+/PVS−), and high risk (Group 3, n = 14, NIRFs+/PVS+). All in Group 3 received an ICD. Results After 41 ± 18 months, 9 of 48 patients, experienced the major arrhythmic event (MAE) endpoint (clinical VT/fibrillation = 3, appropriate ICD activation = 6). The endpoint occurred more frequently in Group 3 (7/14, 50%) than in Groups 1 and 2 (2/34, 5.8%). Logistic regression model adjusted for PVS, age, and LVEF revealed that PVS was an independent MAE predictor (OR: 21.152, 95% CI: 2.618‐170.887, P = .004). Kaplan‐Meier curves diverged significantly (log rank, P < .001) while PVS negative predictive value was 94%. Conclusions In hospitalized HFmrEF post‐MI and DCM patients, a NIRFs guiding to PVS two‐step approach efficiently detected the subgroup at increased risk for MAE. We applied a combined non‐invasive risk factors (NIRFs) leading to programmed ventricular stimulation (PVS), a two‐step risk stratification algorithm, to detect the HFmrEF/40‐49% patients at risk for major arrhythmic events.This combined two‐step approach efficiently detected the patients at increased arrhythmic risk.

A novel n-MgFe 2 O 4 –n-ZnS heterojunction catalyst was employed via two step approach for photodegradation of organic dyes such as Methylene Blue (MB) and Crystal Violet (CV) dyes under visible light irradiation. The synthesized MgFe 2 O 4 /ZnS NCs were characterized using PXRD, FTIR, UV–Visible spectroscopy, PL and FESEM analysis which reveals the formation of flake like structure with size as to be ~ 50 nm. The photocatalytic experimental result demonstrates that the MgFe 2 O 4 /ZnS nanocomposites (NCs) improve photodegradation performance with 98.0% and 91% decomposition of MB and CV dyes within 120 min illumination during simulated visible light irradiation. From the result, MgFe 2 O 4 /ZnS NCs has higher photocatalytic performance than that of MgFe 2 O 4 , and ZnS due to efficient separation of the photo-induced electron–hole pairs and effective electron–hole generation transfer by the formation of n-MgFe 2 O 4 –n-ZnS heterojunction. Hence, photodegradation performance implies that the synthesized MgFe 2 O 4 /ZnS NCs can be effectively utilized in waste water purification systems.

Increasing research and development (R&D) investment is the key to the sustainable development of the manufacturing industry. With the development of financialization, manufacturing enterprises allocate greater funds to the financial field, which may significantly affect their level of R&D investment. However, few studies have explored the relationship between the two. Using the data of manufacturing listed companies in China from 2007 to 2018, this paper investigates the impact of financialization on manufacturing R&D investment and further analyzes the moderating effect of government subsidies on the relationship between the two, mainly using Heckman’s two-step approach. The results show that, on the whole, financialization has a significant restraining effect on China’s manufacturing R&D investment, and that government subsidies exacerbate this negative effect. However, there are some differences in the statistical significance and in the level of influence of financialization on R&D investment, which are based on enterprise type, industry, region, and financing constraints. Additionally, the moderating effects of government subsidies under heterogeneous samples differ in sign, statistical significance, and impact magnitude. This paper not only conducts a comprehensive study on the impact of financialization on manufacturing R&D investment but also introduces government subsidies as the moderating variable into the analysis, which is conducive to a better understanding of the relationship between corporate financialization and manufacturing R&D investment in China.