Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Inert Trace Constituent Transport between the University of Wisconsin Isentropic–Sigma Model and the NCAR Community Climate Model

Abstract Five- and 10-day inert trace constituent distributions prognostically simulated with the University of Wisconsin (UW) hybrid isentropic–sigma (θ–σ) model, the nominally identical UW sigma (σ) model, and the National Center for Atmospheric Research Community Climate Model 2 (CCM2) are analyzed and compared in this study. The UW θ–σ and σ gridpoint models utilize the flux form of the primitive equations, while CCM2 is based on the spectral representation and uses semi-Lagrangian transport (SLT) for trace constituents. Results are also compared against a version of the CCM that uses spectral transport for the trace constituent. These comparisons 1) contrast the spatial and temporal evolution of the filamentary transport of inert trace constituents simulated with the UW θ–σ and σ models against a “state of the art” GCM under both isentropic and nonisentropic conditions and 2) examine the ability of the models to conserve the initial trace constituent maximum value during 10-day integrations. Results .
Data Symmetries and Learning in Fully Connected Neural Networks

Symmetries in the data and how they constrain the learned weights of modern deep networks is still an open problem. In this work we study the simple case of fully connected shallow non-linear neural networks and consider two types of symmetries: full dataset symmetries where the dataset <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> is mapped into itself by any transformation <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula>, i.e. <inline-formula> <tex-math notation="LaTeX">$gX=X$ </tex-math></inline-formula> or single data point symmetries where <inline-formula> <tex-math notation="LaTeX">$gx=x$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$x\in X$ </tex-math></inline-formula>. We prove and experimentally confirm that symmetries in the data are directly inherited at the level of the network’s learned weights and relate these findings with the common practice of data augmentation in modern machine learning. Finally, we show how symmetry constraints have a profound impact on the spectrum of the learned weights, an aspect of the so-called network implicit bias.
Extended state observer-based synergetic control for n-DOF robot manipulators using joint position measurements only

ABSTRACT This research work focuses on the need to provide a control scheme able to tolerate multiple robotic control challenges related to noisy measurements, unknown disturbances and uncertainties using the joint position measurements only. The proposed control scheme avails the advantages of integrating the Extended State Observer (ESO) with the synergetic control to operate n-DOF robot manipulators. The ESO is known as a powerful tool that can provide the online estimation of the uncertainties, nonlinearities and disturbances to be rejected or compensated by a feedback stabilizer. Correspondingly, implementing ESO in the control scheme makes it work with less knowledge of the system model dynamics and states. On the other hand, the synergetic control is a proper controller that can provide the desired tracking performance, but it has a high dependency on the system model which makes it sensitive to control parameters variation and operating conditions. Hence, combining both techniques in one scheme is a convenient approach to overcoming the shortcoming of each method. The proposed method is tested experimentally on the MICO 4-DOF Kinova robot and its performance is compared to the conventional synergetic control considering three case studies to validate its effectiveness. GRAPHICAL ABSTRACT
On almost optimal priority rules for preemptive scheduling of stochastic jobs on parallel machines

We consider scheduling a batch of jobs with stochastic processing times on single or parallel machines, with the objective of minimizing the expected holding costs. Preemption of jobs is allowed, and the holding costs of preempted jobs may depend on the stage of completion. We provide a new proof of the optimality of a Gittins priority rule for the single machine and use the same proof to show that the Gittins priority rule is nearly optimal for parallel machines.
CdTe focal plane detector for hard x-ray focusing optics

The demand for higher resolution x-ray optics (a few arcseconds or better) in the areas of astrophysics and solar science has, in turn, driven the development of complementary detectors. These detectors should have fine pixels, necessary to appropriately oversample the optics at a given focal length, and an energy response also matched to that of the optics. Rutherford Appleton Laboratory have developed a 3-side buttable, 20 mm x 20 mm CdTe-based detector with 250 μm square pixels (80x80 pixels) which achieves 1 keV FWHM @ 60 keV and gives full spectroscopy between 5 keV and 200 keV. An added advantage of these detectors is that they have a full-frame readout rate of 10 kHz. Working with NASA Goddard Space Flight Center and Marshall Space Flight Center, 4 of these 1mm-thick CdTe detectors are tiled into a 2x2 array for use at the focal plane of a balloon-borne hard-x-ray telescope, and a similar configuration could be suitable for astrophysics and solar space-based missions. This effort encompasses the fabrication and testing of flightsuitable front-end electronics and calibration of the assembled detector arrays. We explain the operation of the pixelated ASIC readout and measurements, front-end electronics development, preliminary X-ray imaging and spectral performance, and plans for full calibration of the detector assemblies. Work done in conjunction with the NASA Centers is funded through the NASA Science Mission Directorate Astrophysics Research and Analysis Program.
A 12-Gbps, 0.24-pJ/b/dB PAM-4 Receiver With Dead-Zone Free SS-MMSE PD for CIS Link

This paper presents a PAM-4 receiver (RX) for CMOS image sensor (CIS) link. A robust baud-rate PAM-4 phase detector (PD) is proposed, which alleviates the dead zone issue encountered in the conventional sign-sign Mueller-Muller (SS-MM) PD when combined with an adaptive decision feedback equalizer (DFE). An optimum and unique locking point is reached by using a sign-sign minimum mean squared error (SS-MMSE) PD with a biased state which exhibits a constant probability for all phase error ranges. The proposed PD with the biased state evades the dead zone and prevents wandering. Furthermore, the phase-detecting transitions of the proposed SS-MMSE PD are 1.5 times higher compared to the conventional SS-MM PD. The proposed solution is verified with a prototype PAM-4 RX chip using 40-nm CMOS technology. It demonstrates a bit error rate (BER) of less than $10^{-9}$ with a 15.8 dB loss channel. The total power consumption is 46.6 mW at 12 Gb/s, achieving a figure of merit (energy efficiency per channel loss at Nyquist frequency) of 0.24 pJ/b/dB.
A Large-Eddy-Simulation Model for the Study of Planetary Boundary-Layer Turbulence

Abstract A large-eddy-simulation (LFS) model explicitly calculates the large-eddy field and parameterizes the small eddies. The large eddies in the atmospheric boundary layer are believed to be much more important and more flow-dependent than the small eddies. The LES model results are therefore believed to be relatively insensitive to the parameterization scheme for the small eddies. Deardorff first applied this type of numerical model to boundary-layer turbulence. In order to continue his important work, and to take advantage of the fast Fourier transformation algorithm, a new LES model code which uses a mixed pseudospectral finite-difference method was developed. This LES model is described here and tested with a simple vortex flow and with the Wangara day-33 data. This model will be used to systematically investigate fundamental problems in the area of boundary-layer turbulence. It is hoped that three-dimensional simulations will give useful statistical information about turbulence structural and impr.
Ocular Manifestations After Acute Methanol Poisoning

ABSTRACT Acute methanol poisoning is first and foremost life-threatening. Otherwise, functional prognosis is mainly based on ocular impairment. In this case series we aimed to describe the ocular manifestations after acute methanol poisoning during an outbreak in Tunisia. The data from 21 patients (41 eyes) were analysed. All patients underwent a complete ophthalmological examination including visual fields, colour vision test and optical coherence tomography with evaluation of the retinal nerve fibre layer. Patients were classified into two groups. Group 1 included patients with visual symptoms and group 2 included patients with no visual symptoms. Ocular abnormalities were seen in 81.8% of patients with ocular symptoms. They included: optic neuropathy in 7 patients (63.6%); central retinal artery occlusion in 1 patient (9.1%); and central serous chorioretinopathy in 1 patient (9.1%). Mean blood methanol levels were significantly higher in patients without ocular symptoms (p = .03).
Quality-by-Design-Assisted Optimization of Carvacrol Oil-Loaded Niosomal Gel for Anti-Inflammatory Efficacy by Topical Route

Niosomes are multilamellar vesicles that effectively transfer active ingredients into the skin’s layers. To improve the active substance’s penetration across the skin, these carriers are frequently utilized as topical drug delivery systems. Essential oils (EOs) have garnered significant interest in the field of research and development owing to their various pharmacological activities, cost-effectiveness, and simple manufacturing techniques. However, these ingredients undergo degradation and oxidation over time, leading to a loss of functionality. Niosome formulations have been developed to deal with these challenges. The main goal of this work was to create a niosomal gel of carvacrol oil (CVC) to improve its penetration into the skin for anti-inflammatory actions and stability. By changing the ratio of drug, cholesterol and surfactant, various formulations of CVC niosomes were formulated using Box Behnken Design (BBD). A thin-film hydration technique using a rotary evaporator was employed for the development of niosomes. Following optimization, the CVC-loaded niosomes had shown: 180.23 nm, 0.265, −31.70 mV, and 90.61% of vesicle size, PDI, zeta potential, and EE%. An in vitro study on drug release discovered the rates of drug release for CVC-Ns and CVC suspension, which were found to be 70.24 ± 1.21 and 32.87 ± 1.03, respectively. The release of CVC from niosomes best fit the Higuchi model, and the Korsmeyer–Peppas model suggests that the release of the drug followed the non-Fickian diffusion. In a dermatokinetic investigation, niosome gel significantly increased CVC transport in the skin layers when compared to CVC–conventional formulation gel (CVC-CFG). Confocal laser scanning microscopy (CLSM) of rat skin exposed to the rhodamine B-loaded niosome formulation showed a deeper penetration of 25.0 µm compared to the hydroalcoholic rhodamine B solution (5.0 µm). Additionally, the CVC-N gel antioxidant activity was higher than that of free CVC. The formulation coded F4 was selected as the optimized formulation and then gelled with carbopol to improve its topical application. Niosomal gel underwent tests for pH determination, spreadability, texture analysis, and CLSM. Our findings imply that the niosomal gel formulations could represent a potential strategy for the topical delivery of CVC in the treatment of inflammatory disease.
A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography

In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.
Bilateral Testicular Torsion: A Systematic Review of Case Reports

Bilateral testicular torsion is a rare but serious condition characterized by twisting both testicles around their respective spermatic cords, leading to reduced blood flow and potential loss of the testicles. Treatment of this condition may involve surgical detorsion of the affected testicles and fixation to prevent recurrence and, in some cases, removal of severely damaged testicles. In April 2023, a systematic review of case reports was performed to examine the presentation, clinical features, diagnostic process, and management of bilateral testicular torsion. Our search encompassed the following databases: PubMed, ScienceDirect, and Google Scholar. Out of 340 studies, only eight cases met our criteria. However, this review discusses bilateral testicular torsion symptoms, investigation, and outcome.
Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.
Americans’ COVID-19 risk perceptions and risk perception predictors changed over time

Abstract Identifying and understanding risk perceptions—“how bad are the harms” to humans or to what they value that people see as potentially or actually arising from entities or events—has been critical for risk analysis, both for its own sake, and for expected associations between risk perceptions and subsequent outcomes, such as risky or protective behavior, or support for hazard management policies. Cross-sectional surveys have been the dominant method for identifying and understanding risk perceptions, yielding valuable data. However, cross-sectional surveys are unable to probe the dynamics of risk perceptions over time, which is critical to do while living in a dynamically hazardous world and to build causal understandings. Building upon earlier longitudinal panel studies of Americans’ Ebola and Zika risk perceptions using multi-level modeling to assess temporal changes in these views and inter-individual factors affecting them, we examined patterns in Americans’ COVID-19 risk perceptions in six waves across 14 months. The findings suggest that, in general, risk perceptions increased from February 2020 to April 2021, but with varying trends across different risk perception measures (personal, collective, affective, affect, severity, and duration). Factors in baseline risk perceptions (Wave 1) and inter-individual differences across waves differed even more: baseline ratings were associated with how immediate the threat is (temporal distance) and how likely the threat would affect people like oneself (social distance), and following the United States news about the pandemic. Inter-individual trend differences were shaped most by temporal distance, whether local coronavirus infections were accelerating their upward trend, and subjective knowledge about viral transmission. Associations of subjective knowledge and risk trend with risk perceptions could change signs (e.g. from positive to negative) over time. These findings hold theoretical implications for risk perception dynamics and taxonomies, and research design implications for studying risk perception dynamics and their comparison across hazards.
Robust Collision Avoidance for Mobile Robots in the Presence of Moving Obstacles

This letter is concerned with the collision avoidance for mobile robots with uncertain dynamics in the presence of obstacles that can considerably change their velocities over time. To address this problem, we propose a robust collision-avoidance method based on the continuous-control-obstacles one. The proposed method uses an arbitrary-order overdamped low-pass filter to generate sufficiently smooth position commands for the robot and a high-order sliding mode differentiator to robustly estimate the obstacles’ maximum accelerations. Based on these estimates, we define a set of possible future positions for the obstacles according to how each one is changing its velocity to calculate a robust position command for the robot. The method has been numerically evaluated using a conventional quadcopter flying among moving obstacles and has been shown to be effective in providing collision avoidance and velocity constraints satisfaction.
An Examination of Occupational Therapy Telehealth Service Delivery Among Novice Users During the COVID-19 Pandemic

The COVID-19 pandemic allowed for widespread implementation of telehealth as a delivery method for occupational therapy (OT) services. The purpose of this study was to investigate the perceptions of novice telehealth OT practitioners regarding telehealth as a delivery method for OT services. Quantitative data was collected through a modified version of the Telehealth Usability Questionnaire and analyzed via descriptive statistics. Qualitative data was collected by open-ended questions and analyzed via thematic analysis. OT practitioners' responses revealed four major themes: logistics of telehealth practice, role of client champions, capacity of the OT practitioner, and styles and approaches. The study revealed that OT sessions delivered via telehealth increased access to clients and continuity of services. Client champion engagement, effective coaching strategies, and practitioner flexibility supported the success of OT telehealth sessions.
REVIEW ARTICLE ROTATION OF PRINCIPAL COMPONENTS

Recent research has pointed to a number of inherent disadvantages of unrotated principal components and empirical orthogonal functions when these techniques are used to depict individual modes of variation of data matrices in exploratory analyses. The various pitfalls are outlined and illustrated with an alternative method introduced to minimize these problems via available linear transformations known as simple structure rotations. The rationale and theory behind simple structure rotation and Procrustes target rotation is examined in the context of meteorological/climatological applications. This includes a discussion of the six unique ways to decompose a rotated data set in order to maximize the physical interpretability of the rotated results. The various analytic simple structure rotations available are compared by a Monte Carlo simulation, which is a modification of a similar technique developed by Tucker (1983), revealing that the DAPPFR and Promax k = 2 rotations are the most accurate in recovering the input structure of the modes of variation over a wide range of conditions. Additionally, these results allow the investigator the opportunity to check the accuracy of the unrotated or rotated solution for specific types of data. This is important because, in the past, the decision of whether or not to apply a specific rotation has been a ‘blind decision’. In response to this, a methodology is presented herein by which the researcher can assess the degree of simple structure embedded within any meteorological data set and then apply known information about the data to the Monte Carlo results to optimize the likelihood of achieving physically meaningful results from a principal component analysis.
Existence of Stable Coalition Structures in Three-player Games with Graph-constrained Solution

The stability of coalition structures is investigated in the sense that no player prefers to individually deviate from the current coalition considering his utility. This principle is close to the concept of the Nash equilibrium. The evaluation of each player's utility is determined with respect to the solution concept- the Shapley value with exogenous directed graph constraint. The existence of a stable coalition structure with respect to such a solution is examined for two-player as well as three-player games.
An unscheduled journey: from cosmic rays into cosmic X-rays

My research career began with cosmic-ray physics. Invited to the Netherlands, I was engaged in the measurement of cosmic-ray electrons. In parallel, we began balloon observations of the cosmic X-ray background. Coming back to Nagoya, we carried out rocket observations of soft X-rays with a thin polypropylene window. Since moving to the Institute of Space and Aeronautical Science (ISAS: later reorganized to the Institute of Space and Astronautical Science), I have been involved in several astronomical satellite programs, in particular X-ray astronomy missions. The main features and major results of those programs are mentioned. Features characteristic of the Japanese space program and the specific roles of ISAS are explained.