Miracle-dz/UniFAR-FTUs huggingface dataset:
To Tell or Not to Tell: Preference Elicitation with and without Emphasis on Scientific Uncertainty

Decisions about the optimal use of coastal and marine resources must be made with high uncertainty about environmental impacts and may conflict with public perception of the risk associated with current blue growth initiatives. In a discrete choice experiment conducted in valuation workshops in five communities in Arctic Norway, we examine public preferences for various aquaculture expansion paths. Respondents prefer a smaller expansion in terms of the number of aquaculture sites compared with the planned expansion. Emphasizing scientific uncertainty regarding the negative environmental impacts of aquaculture leads to lower resistance against the planned expansion.
Mutiscale Hybrid Attention Transformer for Remote Sensing Image Pansharpening

Pansharpening methods play a crucial role for remote sensing image processing. The existing pansharpening methods, in general, have the problems of spectral distortion and lack of spatial detail information. To mitigate these problems, we propose a multiscale hybrid attention Transformer pansharpening network (MHATP-Net). In the proposed network, the shallow feature (SF) is first acquired through an SF extraction module (SFEM), which contains the convolutional block attention module (CBAM) and dynamic convolution blocks. The CBAM in this module can filter initial information roughly, and the dynamic convolution blocks can enrich the SF information. Then, the multiscale Transformer module is used to obtain multiencoding feature images. We propose a hybrid attention module (HAM) in the multiscale feature recovery module to effectively address the balance between the spectral feature retention and the spatial feature recovery. In the training process, we use deep semantic statistics matching (D2SM) loss to optimize the output model. We have conducted extensive experiments on several known datasets, and the results show that this article has good performance compared with other state of the art (SOTA) methods.
Knowledge-Aware Global Reasoning for Situation Recognition

The task of situation recognition aims to solve the visual reasoning problem with the ability to predict the activity happening (salient action) in an image and the nouns of all associated semantic roles playing in the activity. This poses severe challenges due to long-tailed data distributions and local class ambiguities. Prior works only propagate the local noun-level features on one single image without utilizing global information. We propose a Knowledge-aware Global Reasoning (KGR) framework to endow neural networks with the capability of adaptive global reasoning over nouns by exploiting diverse statistical knowledge. Our KGR is a local-global architecture, which consists of a local encoder to generate noun features using local relations and a global encoder to enhance the noun features via global reasoning supervised by an external global knowledge pool. The global knowledge pool is created by counting the pairwise relationships of nouns in the dataset. In this paper, we design an action-guided pairwise knowledge as the global knowledge pool based on the characteristic of the situation recognition task. Extensive experiments have shown that our KGR not only achieves state-of-the-art results on a large-scale situation recognition benchmark, but also effectively solves the long-tailed problem of noun classification by our global knowledge.
RCS Diagnostic Imaging Using Parameter Extraction Technique of State Space Method

The signature extraction and processing are the key aspects of enhancing recognition ability for radar target characteristics. In this paper, to reveal the intrinsic property of the target and improve the accuracy of radar cross section (RCS), we proposed RCS diagnostic imaging technique, extracting the parameters of scattering center model by state space method (SSM), to analyze the signature of GTD‐based returns. To demonstrate the effectiveness of SSM, the comparison with estimating signal parameter via rotational invariance techniques method by the analysis of goodness‐of‐fit and root mean square error, reconstructing the RCS profile through original signal model, and employing SSM to extract the signature of specular and creeping wave from the PEC sphere. Furthermore, the RCS diagnostic imaging by SSM extracting position and amplitude is discussed, and range‐isolated pole technique specific to the creeping wave extraction is addressed the advantage of SSM.
A SPAD Depth Sensor Robust Against Ambient Light: The Importance of Pixel Scaling and Demonstration of a 2.5μm Pixel with 21.8% PDE at 940nm

We present scaled down Single Photon Avalanche Diode (SPAD) pixels to prevent Photon Detection Efficiency (PDE) degradation under high ambient light. This study is carried out on Back-Illuminated (BI) 3D-stacked SPAD array sensors with 3.3, 3.0, and 2. 5μm pixel pitches fabricated using a CMOS 300mm platform. To achieve pixel scaling, the developed pixels introduce a sub-micron avalanche region to prevent premature edge breakdown while allowing for a low Dark Count Rate (DCR). Moreover, optimized optical lenses enable scaled down pixels to achieve approximately 100% fill factor, thereby boosting the PDE at λ=940nm beyond 20%, even under sunlight conditions. These sensors offer cost-efficient and high accuracy depth-sensing capabilities, even under challenging ambient light conditions.
Modelling Aircraft Priority Assignment by Air Traffic Controllers During Taxiing Conflicts Using Machine Learning

Conflicts between taxiing aircraft are resolved by making the aircraft with lower priority wait, slow down, or change their path. Prevalent priority assignment is based on rules such as First Come First Serve. However, this is not viable as priority assignment done by an air-traffic controller (ATC) based on multiple factors. Thus, a machine learning approach is proposed to mimic an ATC's priority assignment. Firstly, the potential conflict scenarios between two aircraft from historical data, which are resolved, are detected and extracted. Then a Random Forest model is developed to learn ATC's behaviors. The model mimics ATC's behavior with an accuracy of 89% and can thus be an effective approach for priority assignment in path-planning and conflict resolution. Further analysis indicates that features such as unimpeded time difference, distance to destination and start, and speed are major considerations that affect the ATC's decisions.
Design Guidelines of Thermally Stable Hafnia Ferroelectrics for the Fabrication of 3D Memory Devices

We provide a methodology for designing thermally stable hafnia ferroelectric (FE) materials to be taken into account while fabricating 3D memory devices. We reveal the underlying origins for the thermal instability of hafnia FE materials in terms of kinetics and material science. Furthermore, we suggest adopting dopants whose ionic radius is smaller than Hf in the FE matrix as a feasible option to demonstrate a thermally stable hafnia FE material. Using this approach, robust ferroelectricity is achieved even at a subsequent thermal budget (TB) of 750 °C for 30 min. The improved thermal stability stems from the reduced formation of both the m-phase and oxygen vacancy (VO) in hafnia FE materials at high TB. The in-depth electrical and material analysis in this study serves a framework for further research into the thermal stability of FE. This work contributes to the commercialization of FE devices by filling the gap between the functionality of FE materials and their process applicability for 3D devices.
Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19

COVID-19 related crimes like counterfeit Personal Protective Equipment (PPE) involve complex supply chains with partly unobservable behavior and sparse data, making it challenging to construct a reliable simulation model. Model calibration can help with this, as it is the process of tuning and estimating the model parameters with observed data of the system. A subset of model calibration techniques seems to be able to deal with sparse data in other fields: Genetic Algorithms and Bayesian Inference. However, it is unknown how these techniques perform when accurately calibrating simulation models with sparse data. This research analyzes the quality-of-fit of these two model calibration techniques for a counterfeit PPE simulation model given an increasing degree of data sparseness. The results demonstrate that these techniques are suitable for calibrating a linear supply chain model with randomly missing values. Further research should focus on other techniques, larger set of models, and structural uncertainty.
The Least Squares Method Application for the Two-Dimensional Interpolation in Radiation Pattern Measurements

The Global Navigation Satellite System spacecraft signal power received at the Earth’s surface is one of the main signal parameters. The navigation signals power measurements have been carried on using a specialized metrological measuring complex based on the large-aperture dish antennas. It is necessary to measure radiation pattern of the spacecraft antenna to estimate signal power at an arbitrary point on the earth’s surface. A description of spacecraft antenna amplitude radiation pattern shape reconstructing based on the two large-aperture antennas observation results is given. The least squares method application in estimating the parameters of the navigation spacecraft antenna amplitude radiation pattern is considered. The amplitude radiation pattern is estimated from the ground-based measurements results of the satellite signals energy characteristics using a special complex based on the large-aperture dish antenna. The method of radial basis functions is used for pattern interpolation. Theoretical and experimental results are presented, the interpolation accuracy is estimated. Examples of amplitude radiation patterns reconstruction by piecewise linear approximation method and radial-basis functions (two-dimensional interpolation) are shown.
Vertical energy propagation of inertial waves: A vector spectral analysis of velocity profiles

Vertical propagation of near-inertial period waves has been detected in a series of recent velocity profiles by a technique of vector spectral analysis. This method, previously applied to vector series in time, has been used to study the vertical spatial structure of velocity profiles obtained in the Mid-Ocean Dynamics Experiment (MODE). Prior to the use of spectral analysis, however, it is necessary to minimize the influence of vertical variations of the Brunt-Vaisala frequency. The procedure was to normalize the current amplitudes and stretch the vertical coordinate according to a WKB scheme. The vector spectral analysis, applied to the normalized and stretched profiles, yielded wave polarization estimates which are related to the sign of the vertical group velocity of internal waves. The analysis of a set of velocity profiles indicates that the net energy flux of the waves near the inertial frequency is downward.
Simulation-based Mastery Learning Improves Critical Care Skills of Advanced Practice Providers

Background Advanced practice providers (APPs) are essential members of intensive care unit (ICU) interprofessional teams and are expected to be competent in performing procedures. There are no published criteria for establishing when APPs can independently perform procedures. Simulation-based mastery learning (SBML) is an effective strategy for improving critical care skills but has not been applied to practicing ICU APPs. Objective The purpose of this study was to evaluate if an SBML curriculum could improve the critical care skills and procedural self-confidence of ICU APPs. Methods We performed a pretest–posttest study of central venous catheter (CVC) insertion, thoracentesis, and mechanical ventilation (MV) management skills among ICU APPs who participated in an SBML course at an academic hospital. For each skill, APPs underwent baseline skills assessments (pretests) on a simulator using previously published checklists, followed by didactic sessions and deliberate practice with individualized feedback. Within 2 weeks, participants were required to meet or exceed previously established minimum passing standards (MPS) on simulated skills assessments (posttests) using the same checklists. Further deliberate practice was provided for those unable to meet the MPS until they retested and met this standard. We compared pretest to posttest skills checklist scores and procedural confidence. Results All 12 eligible ICU APPs participated in internal jugular CVC, subclavian CVC, and MV training. Five APPs participated in thoracentesis training. At baseline, no APPs met the MPS on all skills. At training completion, all APPs achieved the mastery standard. Internal jugular CVC pretest performance improved from a mean of 67.2% (standard deviation [SD], 28.8%) items correct to 97.1% (SD, 3.8%) at posttest (P = 0.005). Subclavian CVC pretest performance improved from 29.2% (SD, 32.7%) items correct to 93.1% (SD 3.9%) at posttest (P < 0.001). Thoracentesis pretest skill improved from 63.9% (SD, 30.6%) items correct to 99.2% (SD, 1.7%) at posttest (P = 0.054). Pretest MV skills improved from 54.8% (SD, 19.7%) items correct to 92.3% (SD, 5.0%) at posttest (P < 0.001). APP procedural confidence improved for each skill from pre to posttest. Conclusion SBML is effective for training APPs to perform ICU skills. Relying on traditional educational methods does not reliably ensure that APPs are adequately prepared to perform skills such as CVC insertion, thoracentesis, and MV management.
Generalized Hamiltonian dynamics. I. Formulation on T*Q⊕TQ

The Dirac–Bergmann generalized Hamiltonian dynamics for a degenerate‐Lagrangian system is formulated on the Whitney sum T*Q⊕TQ of the phase space T*Q and the velocity space TQ over the configuration space Q. The formulation is related to those on T*Q and TQ. Some ambiguities concerning generalized dynamics that have appeared in the literature are clarified.
Insights of Platinum Drug Interaction with Spinel Magnetic Nanocomposites for Targeted Anti-Cancer Effect

Simple Summary Magnetic ferrite nanocomposite has drawn huge interest in nanomedicine in areas related to thermotherapy, cell labeling-tracking and magnetic resonance imaging. Manganese ferrite spinel is an interesting magnetic nanocomposite due to its superparamagnetic nature, strong T2 MRI contrast, low synthesis cost, and eco-friendliness. The present study investigated the suitability of two different nanocarriers: one with a silica base (MnFe2O4/silica), and another with a carbon base (MnFe2O4/Graphene oxide) for targeted cancer therapy. The phase, textural and morphological variation of the two different nanoformulations was examined using various physico-chemical techniques. Pegylated and as-such nanoformulations were studied in drug delivery and in vitro using cancerous and non-cancerous cell lines. Density functional theory was used to calculate the binding energies between cisplatin on single-silica or multi-layered graphene oxide. Immunofluorescence images were captured using c-caspase 3/7 and TEM analysis. MnFe2O4/silica/cisplatin nanocomposites was found be a better chemotherapeutic drug delivery option than MnFe2O4/GO/cisplatin nanocomposites. Abstract In nanotherapeutics, gaining insight about the drug interaction with the pore architecture and surface functional groups of nanocarriers is crucial to aid in the development of targeted drug delivery. Manganese ferrite impregnated graphene oxide (MnFe2O4/GO) with a two-dimensional sheet and spherical silica with a three-dimensional interconnected porous structure (MnFe2O4/silica) were evaluated for cisplatin release and cytotoxic effects. Characterization studies revealed the presence of Mn2+ species with a variable spinel cubic phase and superparamagnetic effect. We used first principles calculations to study the physisorption of cisplatin on monodispersed silica and on single- and multi-layered GO. The binding energy of cisplatin on silica and single-layer GO was ~1.5 eV, while it was about double that value for the multilayer GO structure. Moreover, we treated MCF-7 (breast cancer cells) and HFF-1 (human foreskin fibroblast) with our nanocomposites and used the cell viability assay MTT. Both nanocomposites significantly reduced the cell viability. Pt4+ species of cisplatin on the spinel ferrite/silica nanocomposite had a better effect on the cytotoxic capability when compared to GO. The EC50 for MnFe2O4/silica/cisplatin and MnFe2O4/GO/cisplatin on MCF-7 was: 48.43 µg/mL and 85.36 µg/mL, respectively. The EC50 for the same conditions on HFF was: 102.92 µg/mL and 102.21 µg/mL, respectively. In addition, immunofluorescence images using c-caspase 3/7, and TEM analysis indicated that treating cells with these nanocomposites resulted in apoptosis as the major mechanism of cell death.
A 5-D Large-Workspace Magnetic Localization and Actuation System Based on an Eye-in-Hand Magnetic Sensor Array and Mobile Coils

Simultaneous magnetic localization and actuation in a human-scale workspace are challenging but essential for medical applications such as whole gastrointestinal (GI) tract monitoring and blood clot clearance in the leg. In this article, we propose a 5-D magnetic localization and actuation system that can track and actuate magnetic robots in a cylindrical workspace with a diameter of 500 mm. Based on the eye-in-hand configuration of the sensor array and the flexibility of the mobile electromagnetic manipulation system (mEMS), we realize a high localization accuracy in a large workspace with only 25 sensors. Moreover, we design an accurate magnetic field modeling method that can attenuate the electromagnets’ hysteresis and provide a precise actuation field estimation. Taking advantage of the mobile sensor array, we developed a signal-quality-based tracking strategy (STS) that could improve the localization performance by adjusting the movement of the sensor array. The effectiveness and advantages of the proposed methods are verified by simulations and experiments. Results show that the STS could decrease 25% position root-mean-square error (RMSE). The resulting position RMSE and orientation RMSE are 2.02 mm and ${\mathrm {8.24}}^{\circ} $ , respectively. We experimentally prove the viability of the proposed strategy by propelling a magnetic helical robot through a 400-mm long 3-D twisted silicone phantom.
Predictive Value of Annenxin A1 for Disease Severity and Prognosis in Patients with Community-Acquired Pneumonia

This prospective, single-center study evaluated the clinical utility of annenxin (Anx)A1 level as a biomarker for determining the severity of illness and predicting the risk of death in hospitalized patients with community-acquired pneumonia (CAP). A total of 105 patients (53 with severe [S]CAP, 52 with non-SCAP) were enrolled from December 2020 to June 2021. Demographic and clinical data were recorded. Serum AnxA1 concentration on days one and six after admission was measured by enzyme-linked immunosorbent assay. AnxA1 level at admission was significantly higher in SCAP patients than in non-SCAP patients (p < 0.001) irrespective of CAP etiology and was positively correlated with Pneumonia Severity Index and Confusion, Uremia, Respiratory Rate, Blood Pressure, and Age ≥ 65 Years score. AnxA1 level was significantly lower on day six after treatment than on day one (p = 0.01). Disease severity was significantly higher in patents with AnxA1 level ≥254.13 ng/mL than in those with a level <254.13 ng/mL (p < 0.001). Kaplan–Meier analysis of 30-day mortality showed that AnxA1 level ≤670.84 ng/mL was associated with a significantly higher survival rate than a level >670.84 ng/mL. These results indicate that AnxA1 is a useful biomarker for early diagnosis and prognostic assessment of CAP.
Dutasteride for Androgenetic Alopecia: Review Article

Background: The disorder known as androgenetic alopecia (AGA), which is androgen-mediated, is characterised by a gradual loss of apparent scalp hair density. Dihydrotestosterone (DHT), which causes hair follicles to gradually shrink and thin out, targets genetically susceptible hair follicles. Hair loss, which typically starts at the hairline on both sides of the forehead, eventually causes the hair on top of the head to fall out. Dutasteride, a dual 5ARI, has been utilised in the clinic to treat AGA, demonstrating a unique mechanism and potent therapeutic impact. For patients with AGA, dutasteride could be an alternate therapy. Objective: This article aimed to highlights the dutasteride for androgenetic alopecia. Methods: PubMed, Google scholar and Science direct were searched using the following keywords: Dutasteride , Hair loss and Androgenetic alopecia. The authors also screened references from the relevant literature, including all the identified studies and reviews, only the most recent or complete studies were included. Conclusion: Dutasteride is now becoming popular treatment option in AGA, due to its good response for treatment Androgenetic alopecia.
CMOS Power Amplifier With Novel Transformer Power Combiner

A novel transformer combiner structure for watt-level CMOS power amplifier design is presented in this brief. The proposed active skewered transformer (AST) utilizes the slab inductor as the secondary winding and overcomes the quality factor degradation issue when input cells are increased. Slab inductor has been previously used in the distributed active transformer (DAT) as the primary winding and achieved superior passive efficiency for the transformer combined CMOS power amplifier. However, we find that the slab inductor could be more beneficial as the secondary winding when we are aiming for higher output power from the CMOS power amplifier. The full-wave EM simulation has been performed and shows the advantage of AST over the conventional series-combining transformers (SCTs). Finally, a 2.4GHz CMOS power amplifier is designed based on the AST structure and fabricated in 0.18um CMOS process. The measured output power and power-added efficiency of the proposed power amplifier satisfying LTE specification with 5/20MHz bandwidth are recorded as 26.0/25.0dBm and 24.0/20.9%, respectively.
Some applications of an approximation theorem for inverse limits

Introduction. In 1954 C. E. Capel proved [1] the following theorems: Let S be the inverse limit of a sequence of arcs (simple closed curves) where the bounding maps are onto and monotone. Then S is an arc (simple closed curve). It may be noted that if f is a monotone map of an arc (simple closed curve) onto itself, then f is the uniform limit of a sequence of onto homeomorphisms.2 We call such a map a nearhomeomorphism. In this paper we prove the following two theorems: (1) If S is the inverse limit of a sequence of copies of a given compact metric space X and the bonding maps are near-homeomorphisms, then S is homeomorphic to X. (2) Let f: XY, g: Y-*X, where f, g are maps and X, Y are compact metric spaces. Suppose fg and gf are nearhomeomorphisms. Then X is homeomorphic to Y. The second theorem follows directly from the first. In order to establish the first theorem we develop an approximation theorem which has interest in its own right. DEFINITIONS AND NOTATION. Let Xi be a sequence of compact metric spaces, and for i ? 2 letf* map Xi into Xi-,. Then the subspace3 S= {zEz -II Xilfij(zj) =zi} of I1 Xi is the limit space of the inverse system (Xi, fi); in notation S = Lim (Xi, ft). Let f map X into Y where X, Y are compact metric spaces. Then for e>O:L(e, f)=Sup13 cn.
Junction Properties and Gap States of ZnO Thin Film Prepared by Sol-Gel Process

N-type ZnO thin films with a crack- and columnar-free structure were successfully prepared on p-type Si substrates by the sol-gel process. The current-voltage (I-V) characteristics of the heterojunction of n-ZnO/p-Si show a rectification, and the capacitance-voltage (C-V) characteristics show an approximately linear C-2-V relationship in the reverse bias condition. In the ZnO thin film, two gap states located at 0.054 eV and 0.12 eV below the conduction band were measured by isothermal capacitance transient spectroscopy (ICTS).