Size Range Research Articles

Structural, optical, luminescent and magnetic properties of Gd2O3 nanoparticles: A comparative study on the effect of different green fuels in the sol–gel synthesis tactics

In this study, five samples of Gd2O3 NPs (size range: 4–33 nm) were synthesized by five different green facile and diverse natural products acting as reducing & stabilizing agents such as: fresh juices of sugarcane, lemon, tomato & orange fruits and citric acid via self combustion sol–gel tactics. Powder X-ray diffraction (PXRD), High resolution transmission electron microscopy (HRTEM), Fourier transform infrared (FTIR) and Raman analysis affirmed cubic-C type crystal structure of Gd2O3 NPs prepared using citric acid, lemon juice and sugarcane juice. Mixed cubic and monoclinic phases were observed in Gd2O3 NPs prepared using tomato and orange juices. Notably, NPs of Gd2O3 in quantum dot regime (size: 6 ± 2 nm) were produced in the fresh sugarcane juice mediated synthesis protocol. Fresh lemon juice and sugarcane juice were appeared to be the most efficient green fuel for producing quality samples of Gd2O3 NPs. Optical absorption studies showed that bandgap in these Gd2O3 NPs is strongly dependent on the preparation protocol and type of green fuel. Interestingly, highest and lowest band gap of 4.91 eV and 4.43 eV were obtained for Gd2O3 NPs fabricated with orange and lemon juices, respectively. Photoluminescence (PL) and electron spin resonance (ESR) analysis affirmed formation of Schottky and Frenkel surface defects along with self trapped excitons and oxygen vacancies in these Gd2O3 NPs. Paramagnetic character of these Gd2O3 NPs at 300 K is validated by dc-magnetization and ESR studies. Present study demonstrated that microstructural, optical absorption and photoluminescence properties of Gd2O3 NPs can be easily customized by choice of natural products in the synthesis tactics.

A Pilot Study of a Simulation-Based Mastery Learning Procedural Curriculum for Pediatric Emergency Medicine Fellows.

Pediatric emergency medicine (PEM) fellows are expected to perform high-risk procedures across a wide range of patient age, size, and physiology including procedures that are no longer required during pediatric residency training. Examples include central venous catheter (CVC) placement, endotracheal intubation of children and adolescents, and tube thoracostomy placement. Simulation-based mastery learning has demonstrated decreased patient morbidity. In this study, we describe implementation of a simulation-based mastery learning (SBML) procedural curriculum for PEM fellows. Our PEM fellows underwent an SBML procedural curriculum for lumbar puncture (LP), CVC placement, endotracheal intubation, and tube thoracostomy placement. These procedures are mandatory for fellows to learn, have known association with potential iatrogenic injury, and are widely available commercial task trainers. Fellows underwent baseline assessments, group demonstration, deliberate practice, and then postassessments. For both pre- and postassessments, we used internally developed checklists with minimum passing scores (MPSs) calculated by the Mastery-Angoff technique. Nineteen pediatrics residency-trained PEM fellows underwent this curriculum over a 2-year period. Six fellows (31.58%) achieved the MPS on all four procedures during their first posttest attempt. All fellows achieved the MPS on all four procedures by the second posttest attempt. Most fellows (17/19 or 89.47%) did not achieve the MPS on baseline LP assessments, despite inclusion of this procedure as an Accreditation Council of Graduate Medical Education (ACGME) requirement during their pediatric residency training. All participating PEM fellows demonstrated competency after training on four procedures associated with iatrogenic injuries. Using an SBML framework is a feasible method to teach procedural skills to PEM fellows, allowing them to demonstrate objective measures of competency in the simulation laboratory.

Viscoelastic particle focusing and separation in a microfluidic channel with a cruciform section.

Considerable attention has been given to elasto-inertial microfluidics, which are widely applied for the focusing, sorting, and separation of particles/cells. In this work, we propose a novel yet simple fabrication process for a microchannel with a cruciform section, where elasto-inertial particle focusing is explored in a viscoelastic fluid. SU-8 master molds for polydimethylsiloxane (PDMS) structures were fabricated via standard photolithography, and then plasma bonding, following self-alignment between two PDMS structures, was performed for the formation of a microchannel with a cruciform section. The particle behaviors inside the fabricated microchannel were experimentally investigated for various flow rates and particle sizes and compared with those inside a microchannel with a square cross section. The experimental results revealed that 3D particle focusing was achieved in the center under viscoelastic fluid flow over a wide range of flow rates without any shear thinning. Even for small particles (∼2 μm), single-line particle focusing was observed in the microchannel with a cruciform section but not in a square microchannel with the same hydraulic diameter (Dh = 75 μm). The effects of four reflex angles (270°) on particle focusing were quantitatively evaluated through numerical simulation. The simulation revealed that the migration pattern of particles is governed by the combined effect of the reflex angles and fluid inertia, leading to characteristic particle focusing behavior within the cross section of the cruciform microchannel. These findings agree well with the experimental results, which highlight the superior capability of the cruciform microchannel for inertial particle focusing across a wide range of particle sizes.

Microencapsulation of Hibiscus sabdariffa extract in taro starch: Determination of optimal conditions of bioactive compound retention using response surface methodology

Hibiscus sabdariffa (HS) extract contains a large amount of bioactive compounds with antioxidant, anticarcinogenic, and antihypertensive effects. The extraction of total phenols, flavonoids, and anthocyanins is more efficient at boiling temperature (~91 °C) than that performed at room temperature (~27 °C). In this study, a central composite rotatable design (CCRD) was used to optimize the inlet temperature conditions of a spray dryer and the concentration of taro starch solids to obtain microcapsules with the highest retention of bioactive compounds in hibiscus extracts. Optimized microcapsules (OM) were obtained at an inlet temperature of 118 °C and solid concentration of 26.5 %. Low moisture content (4.54 %) and water activity (0.2) and high content of total phenols (3374.91 mg GAE/100 g), flavonoids (372.81 mg QE/100 g), monomeric anthocyanins (36.74 mg C-3-GE/100 g), and angiotensin-converting enzyme inhibitory activity (80.01 %), were determined in OM using response surface methodology. OM showed a range of spherical agglomerate sizes (10––32 μm). These results indicate that CCRD is a good tool for establishing the optimal conditions for solid concentration and drying temperature to maximize the protection of bioactive compounds using taro starch as the wall material.

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models.

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that Adan finds an ϵ-approximate first-order stationary point within O(ϵ-3.5) stochastic gradient complexity on the non-convex stochastic problems (e.g., deep learning problems), matching the best-known lower bound. Extensive experimental results show that Adan consistently surpasses the corresponding SoTA optimizers on vision, language, and RL tasks and sets new SoTAs for many popular networks and frameworks, e.g., ResNet, ConvNext, ViT, Swin, MAE, DETR, GPT-2, Transformer-XL, and BERT. More surprisingly, Adan can use half of the training cost (epochs) of SoTA optimizers to achieve higher or comparable performance on ViT, GPT-2, MAE, etc, and also shows great tolerance to a large range of minibatch size, e.g., from 1 k to 32 k.

Learning to Holistically Detect Bridges From Large-Size VHR Remote Sensing Imagery.

Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection. Due to the limitation of GPU memory in tackling large-size images, deep learning-based object detection methods commonly adopt the cropping strategy, which inevitably results in label fragmentation and discontinuous prediction. To ameliorate the scarcity of datasets, this paper proposes a large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from diverse geographic locations across the globe. These images encompass a wide range of sizes, varying from 2,048 × 2,048 to 16,384 × 16,384 pixels, and collectively feature 59,737 bridges. These bridges span diverse backgrounds, and each of them has been manually annotated, using both an oriented bounding box (OBB) and a horizontal bounding box (HBB). Furthermore, we present an efficient network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net presents a separate detector-based feature fusion (SDFF) architecture and is optimized via a shape-sensitive sample re-weighting (SSRW) strategy. The SDFF architecture performs inter-layer feature fusion (IFF) to incorporate multi-scale context in the dynamic image pyramid (DIP) of the large-size image, and the SSRW strategy is employed to ensure an equitable balance in the regression weight of bridges with various aspect ratios. Based on the proposed GLH-Bridge dataset, we establish a bridge detection benchmark including the OBB and HBB tasks, and validate the effectiveness of the proposed HBD-Net. Additionally, cross-dataset generalization experiments on two publicly available datasets illustrate the strong generalization capability of the GLH-Bridge dataset.

Preparation, characterization and properties of chitosan-coated Epsilon-poly-lysine nanoliposomes in apple juice

Epsilon-poly-lysine (EPL) is widely used in food preservatives. However, EPL can react with components in food substrates, resulting in the formation of precipitates that reduce its antimicrobial properties. Nanoencapsulation is a promising technique and represents a novel approach to enhance EPL activity. In this study, EPL nanoliposomes (EPLLP) were prepared by the thin film hydration method, and chitosan-coated nanoliposomes (CS-EPLLP) were developed by modifying EPLLP with 0.5 mg/mL chitosan. The obtained CS-EPLLP exhibited a spherical morphology with a particle size range of 85.27–89.77 nm, a polydispersity index (PDI) of 0.293–0.301, and an encapsulation efficiency of 83.01 ± 4.00 %. After 30 days of storage, no significant difference in particle size was observed and the PDI was <0.30. The release rate of EPLLP was up to 40 % in 1 h, while that of CS-EPLLP was <20 %. The physical and thermal stability of EPLLP could be effectively improved by chitosan. Moreover, CS-EPLLP could effectively eradicate A. acidocaldarius and A. acidoterrestris in apple juice at concentrations of 2.5 and 0.625 mg/L, respectively. The cytotoxicity test showed that CS-EPLLP exhibited excellent biocompatibility. Therefore, this study has developed a novel nanoliposome material that provides new insights into the control of bacterial contamination in fruit juices.

Structure patterns of one-step synthesis of CuNi nanopowders in air environment: Experiment and atomistic simulations

A possibility for one-step synthesis of bimetallic CuNi nanopowders in a different ratio of Ni to Cu by solution combustion synthesis technique under normal air atmosphere without any post reduction is reported. The effect of different types of fuels like citric acid and glycine on the combustion process and characteristics of resultant solid products were investigated. XRD results showed the existing of CuNi as a main phase and small amounts of CuO and (Ni,Cu)4N. Determined CuNi particle sizes were in the range of up to 50 nm. Computer simulation was performed using the molecular dynamics method for similar concentration compositions, but in size range of 4.5–5.5 nm, as a result of cooling the system from 1700 K to 300 K. In addition, two types of melting scenario of binary CuNi NPs were studied: 1) heterogeneous melting of monocrystalline Cu and Ni NPs; 2) melting of the crystallization products of binary NPs. Melting temperatures weakly depend on the choice of the above-mentioned melting scenario. However, the nature of subsequent crystallization can be influenced by the initial energy of the system, which is higher for case 1. The characteristic temperatures of phase transitions of melting and crystallization are determined based on the analysis of hysteresis loops of the specific potential part of the internal energy of NPs. The patterns of atomic and structural segregation in binary CuNi NPs were studied.

Insights into the spatial ecology of the world’s most ancient dog: high-altitude movements of New Guinea dingoes

Knowledge of an animal’s activity patterns, home range size and space use is fundamental to understanding their basic ecology, and obtaining spatial data is an important research priority for newly discovered species, data-deficient species of conservation concern, or species of great scientific or public interest. Here we report the first spatial data obtained from wild-living dingoes near Puncak Jaya, New Guinea. Based on information from four dingoes monitored with GPS tracking collars between 2018 and 2022, we report that dingo home range sizes can be up to 128km2, dingoes travel up to 56.8km per day, and they utilise rainforest and alpine habitats up to 4,630m above sea level. Dingoes at the site regularly traversed steep, rocky and barren alpine mountain crevasses to access more fertile areas at lower altitudes on the other side. These results imply that New Guinea dingoes may have physiological and genetic adaptations that enable them to live in high-altitude low-oxygen environments similar to Himalayan wolves, Ethiopian wolves, and other canids found in high-altitude areas. As the first domesticated animal and the world’s most ancient dog, and given their historic and current cultural significance, their illusiveness in the wild, and their trophic position as the largest terrestrial predators on the second-largest island in the world, we believe that further research on the ecology of New Guinea dingoes will continue to reveal important insights valuable for our understanding of human and animal ecology in this global biodiversity hotspot.

Multiscale Crystallization of Inorganic Materials

How to form functional materials has long been the cornerstone in materials physics and chemistry, which stimulates general materials scientists to eagerly explore a variety of physicochemical methods toward fabricating desirable materials. In materials formation, constituent elements undergo related phase transitions from atoms/ions/molecules to colloids, nanocrystals, microcrystals, powders/films, or bulk single crystals. These phase transition processes dramatically limit a series of materials’ characteristics/features/performances, such as structures, sizes, morphologies, and properties/functions. In this paper, the formation of materials is uncovered from the viewpoint of multiscale crystallization, from both theoretical studies and experimental observations. The entire crystallization process includes both nucleation and crystal growth stages, respectively controlling the involved phase transitions among clusters/materials with multiscale features. Taking some typical materials as examples, we illustrated the dominant role of variable materials status with multiple sizes during crystallization processes in their practical applications toward energy storage and sustainability. The compromise/coupling between thermodynamics and kinetics controls variant factors to span multiscale modes during materials formation. Uncovering materials physics and chemistry from the viewpoint of multiscale crystallization may deepen our understanding in materials formation in the micro/mesoscale size range and facilitate function optimizations.

Neurotensin Conjugated Polymeric Porous Microparticles Suppress Inflammation and Improve Angiogenesis Aiding in Diabetic Wound Healing.

Neurotensin (NT), a bioactive tridecapeptide aids in diabetic wound healing by modulating inflammation and angiogenesis. However, its rapid degradation in peptidase-rich wound environment (plasma half-life <2 min) limits its efficacy. To address this, neurotensin-conjugated polymeric porous microparticles (NT-PMP) were developed and loaded in gelatin (hydrogel 15% w/v) for topical application, enabling sustained NT release to enhance therapeutic outcomes. NT-PMP exhibited a size range of 60 - 240 µm (mean: 120.63 ± 40.71 µm) and pore size of 5 - 16 µm (average: 10.68 ± 3.47 µm). In vitro studies demonstrated cytocompatibility of NT-PMP in fibroblasts and reduced TNF-α levels in inflammation-induced macrophages (1256 ± 167.02 pg/ml). Further NT-PMP scaffold depicted excellent cell adhesion and migration properties upon seeding of dermal fibroblasts on surface of PMPs. In vivo studies in diabetic wound rat model demonstrated effective wound management, characterized by notable regenerative and healing attributes in the presence of NT-PMP. This included complete re-epithelialization, reducing pro-inflammatory cytokine (TNF-α), and enhancing VEGF expression, ultimately leading to the development of a well-organized collagen matrix in diabetic wounds upon application of NT-PMP gel.Altogether, NT conjugated PMP loaded in hydrogel demonstrated significant regenerative and healing properties, suggesting its potential as an alternative treatment for diabetic wounds.

Calculated ionization energies, orbital eigenvalues (HOMO), and related QSAR descriptors of organic molecules: a set of 61 experimental values enables elimination of systematic errors and provides realistic error estimates.

Ionization energies (IEs) of organic compounds come in different forms-adiabatic, vertical, as electrode potentials, or as orbital eigenvalues via Koopmans' theorem. They have been linked to the reactivity towards electrophiles and have been used to quantitatively describe electron transfer processes. The de novo prediction of IEs is only meaningful when an estimate of the prediction's uncertainty is included. Pulsed-field ionization (PFI) experiments have quantified adiabatic IEs with unprecedented precision. In this work, a new set of PFI-derived IEs is compiled from the literature as a benchmark for prediction methods. This set includes many common functional groups, a size range from diatomics to two aromatic rings, and IEs between 7 and 14 eV. The first-principles CCSD(T)/CBS protocol presently used reproduces these values within 0.05 eV. For adiabatic IEs and vertical IEs/orbital eigenvalues predicted using approximate density functional theory (DFT), linear regression models are proposed, so that IEs calculated using different methods can be directly compared on a physical scale. This elimination of systematic errors improves the error statistics and allows the performance of predicted IEs to be evaluated if used in quantitative structure-property or -activity relationships, as the latter implicitly correct a descriptor's bias. Owing to the structural scope of the test set, the minimum and maximum deviations from experiment should correspond to those expected for common organic molecules. Deviations from reference values found for orbital eigenvalues but also for IEs calculated explicitly with HF or semi-empirical MO methods were as large as 0.5 eV to 2.0 eV. Such large errors could also propagate into quantitative structure-property models, as shown in illustrative examples of oxidation rate constants in solution.

Features of Lung Cysts in Birt-Hogg-Dubé Syndrome from Patients with Multiple Lung Cysts.

High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosis of Birt-Hogg-Dubé syndrome (BHD). This study aims to analyze the differences of lung cyst between BHD and other cystic lung diseases. From January 2020 to December 2022, we retrospectively screened all patients who underwent chest CT at Gangnam Severance Hospital. We included the patients with multiple lung cysts for the analysis of chest CT images. Over a three-year period, out of 52,823 patients who underwent a chest CT scan, 301 patients (0.6%) with multiple lung cysts were enrolled, of which 24 (8.0%) were diagnosed with BHD. Notably, 95.8% and 83.3% of BHD patients exhibited bilateral cysts and basal predominance, and had larger cysts with a maximal diameter (averaging 32.1mm [interquartile range 26.5mm to 43.5mm]) than lymphangioleiomyomatosis (17.0mm [13.2;19.1], p<0.001) and others' group (11.3mm [7.9;17.0], p<0.001). Additionally, 95.8% of BHD patients has a diverse range in cyst sizes and morphologies. Multivariate logistic regression analysis identified bilateral cysts (OR 12.393, 95% CI: 1.613-274.682, p=0.038), basal predominance (OR 8.511, 95% CI: 2.252-39.392, p=0.002), maximum diameter (OR 1.053, 95% CI: 1.009-1.108, p=0.032), and diversity of morphology (OR 19.513, 95% CI: 2.833-398.119, p=0.010) as factors associated with BHD diagnosis. By stepwise selection, a multivariate prediction model for BHD diagnosis was established, demonstrating a sensitivity of 95.83%, a specificity of 81.22%, and an AUC of 0.951 (95% CI: 0.914-0.987). Distinguishing features of lung cyst from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape. The predictive model can assist in identifying undiagnosed patients with BHD.

Technical note: High-resolution analyses of concentrations and sizes of refractory black carbon particles deposited in northwestern Greenland over the past 350 years – Part 1: Continuous flow analysis of the SIGMA-D ice core using the wide-range Single-Particle Soot Photometer and a high-efficiency nebulizer

Abstract. Ice cores can provide long-term records of refractory black carbon (rBC), an important aerosol species closely linked to the climate and environment. However, previous studies of ice cores only analyzed rBC particles with a diameter of &lt; 500 nm, which could have led to an underestimation of rBC mass concentrations. Information on the size distribution of rBC particles is very limited, and there are no Arctic ice core records of the temporal variation in rBC size distribution. In this study, we applied a recently developed improved technique to analyze the rBC concentration in an ice core drilled at the SIGMA-D site in northwestern Greenland. The improved technique, which uses the modified Single-Particle Soot Photometer (SP2) and a high-efficiency nebulizer, widens the measurable range of rBC particle size. For high-resolution continuous analyses of ice cores, we developed a continuous flow analysis (CFA) system. Coupling of the improved rBC measurement technique with the CFA system allows accurate high-resolution measurements of the size distribution and concentration of rBC particles with a diameter between 70 nm and 4 µm, with minimal particle losses. Using this technique, we reconstructed the size distributions and the number and mass concentrations of rBC particles during the past 350 years. On the basis of the size distributions, we assessed the underestimation of rBC mass concentrations measured using the conventional SP2s. For the period 2003–2013, the underestimation of the average mass concentration would have been 12 %–31 % for the SIGMA-D core.

Some measures to enhance the energy output performances of triboelectric nanogenerators

Abstract Triboelectric nanogenerators (TENGs) have been developed as innovative devices for harvesting various forms of mechanical energy generated by our bodies and surroundings, which provide green and sustainable power for increasingly miniaturized and mobile electronics, especially wearables. In this article, the largest possible output energy per cycle of a TENG in the two basic working modes, namely, the vertical contact-separation (CS) mode and the contact-sliding (LS) mode, is analyzed and the energy collected by a capacitor is tested. It is found that more energy output and collected from a vertical CS mode TENG than that from a LS mode TENG with the same size and triboelectric layer materials when the size and displacement range of the TENG are suitable for a wearable energy harvesting device. In order to improve the energy output of a TENG, three methods have been proposed to increase its surface charge density, such as adding a BaTiO3 film or a polydimethylsiloxane (PDMS)-BaTiO3 composite film between the triboelectric layer and the metal electrode, and using a PDMS-BaTiO3 composite film as a negative triboelectric layer, and corresponding TENGs are fabricated for experimental testing. These measures have effectively enhanced the output of the TENGs.

Highly Permeable, Electrically Switchable Filter for Multidimensional Sorting of Suspended Particles

The creation of highly specific particle systems in the nano- and micrometer size range is a challenging task. The demand for particle systems with narrowly distributed properties is increasing in many applications, especially for use in high-tech products. Conventional separation techniques often reach their limits in the micrometer size range or become (labor-)intensive, which makes them economically or ecologically unsustainable. In addition, sorting based on several properties is rarely feasible in just one separator. Dielectrophoretic processes can be a viable option for complex sorting tasks like this, given their ability to address several particle properties and their high degree of selectivity. In this paper, we summarize the progress of a project in which the capability of dielectrophoretic methods for multidimensional sorting of microparticles was investigated. We were able to develop an operation mode for multidimensional sorting of microparticles using dielectrophoresis as well as a scalable electrically switchable filter. This creates a basis for high-throughput and multi-target sorting of technical microparticles using dielectrophoretic processes.

A 4D-EnKF Method via a Modified Cholesky Decomposition and Line Search Optimization for Non-Linear Data Assimilation

This paper introduces an efficient approach for implementing the Four-Dimensional Variational Ensemble Kalman Filter (4D-EnKF) for non-linear data assimilation, leveraging a modified Cholesky decomposition (4D-EnKF-MC). In this method, control spaces at observation times are represented by full-rank square root approximations of background error covariance matrices, derived using the modified Cholesky decomposition. To ensure global convergence, we integrate line-search optimization into the filter formulation. The performance of the 4D-EnKF-MC is evaluated through experimental tests using the Lorenz 96 model, and its accuracy is compared to that of a 4D-Var extension of the Maximum-Likelihood Ensemble Filter (4D-MLEF). Through Root Mean Square Error (RMSE) analysis, we demonstrate that the proposed method outperforms the 4D-MLEF across a range of ensemble sizes and observational network configurations, providing a robust and scalable solution for non-linear data assimilation in complex systems.

Clinical study design strategies to mitigate confounding effects of time-dependent clearance on dose optimization of therapeutic antibodies.

Time-dependent pharmacokinetics (TDPK) is a frequent confounding factor that misleads exposure-response (ER) analysis of therapeutic antibodies, where a decline in clearance results in increased drug exposure over time in patients who respond to therapy, causing a false-positive ER finding. The object of our simulation study was to explore the influence of clinical trial designs on the frequency of false-positive ER findings. Two previously published population PK models representative of slow- (pembrolizumab) and fast-onset (rituximab) TDPK were used to simulate virtual patient cohorts with time-dependent clearance and the frequency of false-positive ER findings. The impact of varying the number of dose groups, dose range, and sample size was evaluated over time. Study designs with a single tested dose level showed a high probability of showing a false-positive ER finding. When TDPK has a slow onset, use of exposure measures from early timepoints in ER analysis significantly reduces the risk of a false-positive, while with fast onset it did not. Randomization of patients to two dose levels greatly reduced the risk, with a threefold or greater dose range offering the greatest benefit. The likelihood of false-positive increases with a larger sample size, where greater care should be taken to identify confounding factors. Clinical trial simulation supports that appropriate clinical study design and analysis with adequate dose exploration can reduce but cannot entirely eliminate the risk of misleading ER findings.

An In-depth Analysis of Quiet-Sun IRIS Brightenings

Small-scale brightenings are ubiquitous, dynamic, and energetic phenomena found in the chromosphere. An advanced filter-detection algorithm applied to high-resolution observations from the Interface Region Imaging Spectrograph enables the detection of these brightenings close to the noise level. This algorithm also tracks the movement of these brightenings and extracts their characteristics. This work outlines the results of an in-depth analysis of a quiet-Sun data set including a comparison of a brighter domain—associated with a supergranular boundary—to the quiescent internetwork domains. Several characteristics of brightenings from both domains are extracted and analysed, providing a range of sizes, durations, brightness values, travel distances, and speeds. The “active” quiet-Sun events tend to travel shorter distances and at slower speeds across the plane of the sky than their “true” quiet-Sun counterparts. These results are consistent with the magnetic field model of supergranular photospheric structures and the magnetic canopy model of the chromosphere above. Spectroscopic analyses reveal that bright points demonstrate blueshift (as well as some bidirectionality) and that they may rise from the chromosphere into the transition region. We believe that these bright points are magnetic in nature, are likely the result of magnetic reconnection, and follow current sheets between magnetic field gradients, rather than travel along magnetic field lines themselves.

Big data processing using hybrid Gaussian mixture model with salp swarm algorithm

The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.