Physical Units Research Articles

Magnetic Molecules as Building Blocks for Quantum Technologies

AbstractSince the initial observation of quantum effects, scientists have worked diligently to understand and harness their potential. Thanks to many pioneers, a level where quantum effects can be exploited is reached. Numerous cutting‐edge technologies, such as quantum sensing and quantum computing, are proposed. A common trait in all technologies is the need to manipulate and read out their states; therefore, the quantum characteristics of the building blocks must adhere to strict guidelines. Magnetic Molecules (MMs) are promising candidates. They can be obtained indistinguishably, and the control over their structural and electronic properties, makes them appealing to act as quantum bits or “qubits”. MMs can be connected to other units while preserving their coherence properties, enabling the implementation of quantum gates. Furthermore, the low‐lying energy levels can be exploited as qudits, which can exist in more than 2 states simultaneously (d > 2), allowing them to hold more information efficiently. The larger electronic/nuclear space in qudits can decrease the number of physical units and enhance computational efficiency, reducing error and making them promise for complex problem‐solving. In this perspective article, the physical characteristics of MMs and key achievements that position them as promising candidates for quantum technologies, are described.

Early In-Bed Cycle Ergometry With Critically Ill, Mechanically Ventilated Patients: Statistical Analysis Plan for CYCLE (Critical Care Cycling to Improve Lower Extremity Strength), an International, Multicenter, Randomized Clinical Trial.

Survivors of critical illness are at risk of developing physical dysfunction following intensive care unit (ICU) discharge. ICU-based rehabilitation interventions, such as early in-bed cycle ergometry, may improve patients' short-term physical function. Before unblinding and trial database lock, we describe a prespecified statistical analysis plan (SAP) for the CYCLE (Critical Care Cycling to Improve Lower Extremity Strength) randomized controlled trial (RCT). CYCLE is a 360-patient, international, multicenter, open-label, parallel-group RCT (1:1 ratio) with blinded primary outcome assessment at 3 days post-ICU discharge. The principal investigator and statisticians of CYCLE prepared this SAP with approval from the steering committee and coinvestigators. The SAP defines the primary and secondary outcomes (including adverse events) and describes the planned primary, secondary, and subgroup analyses. The primary outcome of the CYCLE trial is the Physical Function Intensive Care Unit Test-scored (PFIT-s) at 3 days post-ICU discharge. The PFIT-s is a reliable and valid performance-based measure. We plan to use a frequentist statistical framework for all analyses. We will conduct a linear regression to evaluate the primary outcome, incorporating randomization as an independent variable and adjusting for age (≥65 years versus <65 years) and center. The regression results will be reported as mean differences in PFIT-s scores with corresponding 95% CIs and P values. We consider a 1-point difference in PFIT-s score to be clinically important. Additionally, we plan to conduct 3 subgroup analyses: age (≥65 years versus <65 years), frailty (Baseline Clinical Frailty Scale ≥5 versus <5), and sex (male versus female). CYCLE was funded in 2017, and enrollment was completed in May 2023. Data analyses are complete, and the first results were submitted for publication in 2024. We developed and present an SAP for the CYCLE RCT and will adhere to it for all analyses. This study will add to the growing body of evidence evaluating the efficacy and safety of ICU-based rehabilitation interventions. ClinicalTrials.gov NCT03471247; https://clinicaltrials.gov/ct2/show/NCT03471247 and NCT02377830; https://clinicaltrials.gov/ct2/show/NCT02377830. RR1-10.2196/54451.

The effectiveness of a proposed unit in Radiation Physics for developing literacy of radioactive pollution risks among students of the physics Department Faculty of Education، Upper Nile University and Juba University - Republic of South Sudan

The effectiveness of a proposed unit in Radiation Physics for developing literacy of radioactive pollution risks among students of the physics Department Faculty of Education، Upper Nile University and Juba University - Republic of South Sudan

The Effect of TENS and Ultrasound Diathermy on Osteoarthritis Patients in Physical Medicine and Rehabilitation Unit of Patut Patuh Patju Hospital, West Nusa Tenggara

Osteoarthritis is generally recognized as a common musculoskeletal condition in Indonesia, characterized by the degeneration of joint cartilage and narrowing of the joint space. The form of therapy that can be given to knee OA patients is a combination of TENS therapy and ultrasound diathermy (USD). WOMAC is an index used to measure osteoarthritis patiens based on pain, stiffness, and physical function. Patut Patuh Patju General Hospital is the only hospital in West Lombok district where physical medicine and medical rehabilitation have recently been introduced. This research aims to determine the effects of administering TENS and ultra sound diathermy on OA patients in the physical medicine and rehabilitation unit of Patut Patuh Patju Hospital, a newly established district hospital in NTB, which has been operational for the past year. This type of research uses a pre-experimental design with a one group pretest-posttest method. Sampling was taken using a purposive sampling technique with a total sample of 30. The research population was knee osteoartritis patients undergoing physiotherapy treatment at the Medical Rehabilitation Unit of Patut Patuh Patju General Hospital. Data was collected using the WOMAC questionnaire before and after 6 until 8 times therapy. The distribution of research data was analyzed using the Shapiro-Wilk test. If the data distribution is normal, it is analyzed using the paired t-test, whereas if it is not normal, it is analyzed using the Wilcoxon test. The combination of TENS therapy and ultra sound diathermy results in improvement of WOMAC scores in knee osteoarthritis patients with p-value = 0.000 (p &lt; 0.05).

Physics simulation capabilities of LLMs

Large Language Models (LLMs) can solve some undergraduate-level to graduate-level physics textbook problems and are proficient at coding. Combining these two capabilities could one day enable AI systems to simulate and predict the physical world. We present an evaluation of state-of-the-art (SOTA) LLMs on PhD-level to research-level computational physics problems. We condition LLM generation on the use of well-documented and widely-used packages to elicit coding capabilities in the physics and astrophysics domains. We contribute ∼50 original and challenging problems in celestial mechanics (with REBOUND), stellar physics (with MESA), 1D fluid dynamics (with Dedalus) and non-linear dynamics (with SciPy). Since our problems do not admit unique solutions, we evaluate LLM performance on several soft metrics: counts of lines that contain different types of errors (coding, physics, necessity and sufficiency) as well as a more educational’ Pass-Fail metric focused on capturing the salient physical ingredients of the problem at hand. As expected, today's SOTA LLM (GPT4) zero-shot fails most of our problems, although about 40% of the solutions could plausibly get a passing grade. About 70%–90% of the code lines produced are necessary, sufficient and correct (coding & physics). Physics and coding errors are the most common, with some unnecessary or insufficient lines. We observe significant variations across problem class and difficulty. We identify several failure modes of GPT4 in the computational physics domain, such as poor physical units handling, poor code versioning, tendency to hallucinate plausible sub-modules, lack of physical justification for global run parameters (e.g., simulation time, or upper-lower bounds for parametric exploration) and inability to define steady-state or stopping conditions reliably. Our reconnaissance work provides a snapshot of current computational capabilities in classical physics and points to obvious improvement targets if AI systems are ever to reach a basic level of autonomy in physics simulation capabilities.

Additional revenues estimation in a market-based redispatch: An opportunity for flexibility

The technical constraints in the transmission networks are exerting significant strain on the functioning of electricity markets as the share of variable renewable energy sources is increasing. To manage this issue, the transition to a nodal pricing market has been proposed in the literature. Given that the current structure of the European Union electricity market corresponds to a zonal pricing, the transition to nodal pricing would imply fundamental changes in its market structure. In order to avoid the latter, this study develops a method to identify locational price signals in a decentralized market with zonal pricing and subsequent market-based redispatch. To this end, first, nodes with structural technical constraints are identified –using energy programmes–. Then, the additional revenues of physical units located at these nodes are estimated –using electricity prices–. Finally, this strategic information will guide the development of flexibility solutions –manageable generation capacity and demand response– in these geographic locations, mimicking the main advantage of nodal pricing. Our methodology has been empirically tested on the Spanish power system, using hourly data from the System Operator and Market Operator for four full years, from 2019 to 2022. The results show a higher and constant economic revenue in certain nodes of this system –upward redispatch price is 74.90% higher than day-ahead market price–. Thus, this method can be used by market participants to evaluate publicly available data from a decentralized market with zonal pricing and subsequent market-based redispatch, and obtain essential information to make the best investment decisions.

Smart analysis of sandwich foldable cylinders as gymnastic accessories

A novel smart analysis of sandwich composite cylindrical shell is presented in this article. It is assumed that the sandwich shell is manufactured from the graphene origami reinforced copper core between two intelligent layers. The proposed structure in this article can be used as an idealized model for cylinder shapes in gymnastic sport and physical therapy units. The virtual work principle in the piezo elasticity framework and the cylindrical coordinate system is extended to derive governing equations. To arrive at more accurate results, a third order shear deformable model is extended for kinematic relations. The electro elastic responses are explored using the analytical method to seek impact of main parameters of the composite structure such as foldability parameter, graphene content percent, and applied electric potential on the electro elastic strain, deformation and stress results.

Measuring quantity in ecosystem markets and ecosystem accounts

AbstractThe quantity of ecosystem services produced from land cannot be readily measured at the site level needed for participation in ecosystem markets, or at a regional level needed to create ecosystem accounts. This paper applies biological scaling principles to develop a quantity metric in which areas of ecosystem (extent) scale allometrically to ecosystem services (a capacity measure) according to a scaling exponent defined by the fractal dimension of ecosystem vegetation. A key conclusion of this paper is that the quantity of ecosystem services arising from ecosystem degradation and conservation activities cannot be estimated unless information about the space‐filling properties of vegetation is observed and included in the quantity measurement methodology. The paper demonstrates how remote sensing techniques can be applied to systematically measure ecosystem extent and fractal dimension. It illustrates the economic efficiency and environmental outcome implications of such a quantity metric through comparison with current quantity estimation methods that assume isometric scaling. The quantity metric proposed has potential applications to ecosystem accounting. It enables information currently reported in land accounts to be combined with information reported in ecosystem condition accounts to create ecosystem stock accounts measured in physical units.

New Physical Quantity and Unit of Type of Substance

New Physical Quantity and Unit of Type of Substance

Equivalence of magnetic flux and energy

Magnetic flux and energy equivalence is the principle that everything that has magnetic flux has an equivalent amount of energy, and vice versa. The main methods used: transformation of natural units, algebra, analogy. The equivalence of magnetic flux and energy, despite its widespread use in describing the principles of physics, has not yet been formulated. This paper presents the formula for this equivalence. This is done based on known measurement systems, parameters and principles of physics. Five examples (Stoney units, Planck units, standard gravitational parameter, Lorentz force, and Ampere force) of the algebraic representation of this principle show its universality. Five examples should justify the universality of the new principle and its use in physics and astrophysics. Using the equivalence of magnetic flux and energy, new physical units of mass and magnetic flux are proposed, each of which is suitable for measuring both mass and magnetic flux. Natural values of electric current and voltage, linear density of electric capacitance and inductance, linear density of magnetic flux, linear density of electric charge, as well as natural units of electric voltage and current, electrical resistance, magnetic flux, electrical capacitance and inductance are also described. The article presents the Einstein field equation (additional magnetic stress–energy–momentum tensor) and a standard astrophysical parameter for refining the orbits of celestial bodies.

Feature selection by replicate reproducibility and non-redundancy.

A fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new synthetic coordinates and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability, and modularity. A common criterion for unsupervized feature selection is variance or dynamic range. However, in practice, it can occur that high-variance features are noisy, that important features have low variance, or that variances are simply not comparable across features because they are measured in unrelated numeric scales or physical units. Moreover, users may want to include measures of signal-to-noise ratio and non-redundancy into feature selection. Here, we introduce the RNR algorithm, which selects features based on (i) the reproducibility of their signal across replicates and (ii) their non-redundancy, measured by linear dependence. It takes as input a typically large set of features measured on a collection of objects with two or more replicates per object. It returns an ordered list of features, i1,i2,…,ik, where feature i1 is the one with the highest reproducibility across replicates, i2 that with the highest reproducibility across replicates after projecting out the dimension spanned by i1, and so on. Applications to microscopy-based imaging of cells and proteomics highlight benefits of the approach. The RNR method is available via Bioconductor (Huber W, Carey VJ, Gentleman R et al. (Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 2015;12:115-21.) in the R package FeatSeekR. Its source code is also available at https://github.com/tcapraz/FeatSeekR under the GPL-3 open source license.

Non-invasive measurement of wall shear stress in microfluidic chip for osteoblast cell culture using improved depth estimation of defocus particle tracking method.

The development of a non-invasive method for measuring the internal fluid behavior and dynamics of microchannels in microfluidics poses critical challenges to biological research, such as understanding the impact of wall shear stress (WSS) in the growth of a bone-forming osteoblast. This study used the General Defocus Particle Tracking (GDPT) technique to develop a non-invasive method for quantifying the fluid velocity profile and calculated the WSS within a microfluidic chip. The GDPT estimates particle motion in a three-dimensional space by analyzing two-dimensional images and video captured using a single camera. However, without a lens to introduce aberration, GDPT is prone to error in estimating the displacement direction for out-of-focus particles, and without knowing the exact refractive indices, the scaling from estimated values to physical units is inaccurate. The proposed approach addresses both challenges by using theoretical knowledge on laminar flow and integrating results obtained from multiple analyses. The proposed approach was validated using computational fluid dynamics (CFD) simulations and experimental video of a microfluidic chip that can generate different WSS levels under steady-state flow conditions. By comparing the CFD and GDPT velocity profiles, it was found that the Mean Pearson Correlation Coefficient is 0.77 (max = 0.90) and the Mean Intraclass Correlation Coefficient is 0.66 (max = 0.82). The densitometry analysis of osteoblast cells cultured on the designed microfluidic chip for four days revealed that the cell proliferation rate correlates positively with the measured WSS values. The proposed analysis can be applied to quantify the laminar flow in microfluidic chip experiments without specialized equipment.

3D Quantitative-Amplified Magnetic Resonance Imaging (3D q-aMRI).

Amplified MRI (aMRI) is a promising new technique that can visualize pulsatile brain tissue motion by amplifying sub-voxel motion in cine MRI data, but it lacks the ability to quantify the sub-voxel motion field in physical units. Here, we introduce a novel post-processing algorithm called 3D quantitative amplified MRI (3D q-aMRI). This algorithm enables the visualization and quantification of pulsatile brain motion. 3D q-aMRI was validated and optimized on a 3D digital phantom and was applied in vivo on healthy volunteers for its ability to accurately measure brain parenchyma and CSF voxel displacement. Simulation results show that 3D q-aMRI can accurately quantify sub-voxel motions in the order of 0.01 of a voxel size. The algorithm hyperparameters were optimized and tested on in vivo data. The repeatability and reproducibility of 3D q-aMRI were shown on six healthy volunteers. The voxel displacement field extracted by 3D q-aMRI is highly correlated with the displacement measurements estimated by phase contrast (PC) MRI. In addition, the voxel displacement profile through the cerebral aqueduct resembled the CSF flow profile reported in previous literature. Differences in brain motion was observed in patients with dementia compared with age-matched healthy controls. In summary, 3D q-aMRI is a promising new technique that can both visualize and quantify pulsatile brain motion. Its ability to accurately quantify sub-voxel motion in physical units holds potential for the assessment of pulsatile brain motion as well as the indirect assessment of CSF homeostasis. While further research is warranted, 3D q-aMRI may provide important diagnostic information for neurological disorders such as Alzheimer's disease.

An Infralimbic Cortex Neuronal Ensemble Encoded During Learning Attenuates Fear Generalization Expression.

Generalization allows for experience to flexibly guide behavior when conditions change. A basic physical unit of memory storage and expression in the brain are sparse, distributed groups of neurons known as ensembles (i.e., the engram). The infralimbic (IL) subregion of the ventral medial prefrontal cortex plays a key role in modulating conditioned defensive responses. How IL neuronal ensembles established during learning contribute to generalized responses is unknown. In this set of experiments, generalization was tested in male and female mice by presenting a novel, ambiguous, tone generalization stimulus following Pavlovian defensive (fear) conditioning. The first experiment was designed to test a role for IL in generalization using chemogenetic manipulations. Results show IL bidirectionally regulates defensive behavior. IL silencing promotes a switch in defensive state from vigilant scanning to generalized freezing, while IL stimulation reduces freezing in favor of scanning. Leveraging activity-dependent tagging technology (ArcCreERT2 x eYFP system), a neuronal ensemble, preferentially located in IL superficial layer 2/3, was associated with the generalization stimulus. Remarkably, in the identical discrete location, fewer reactivated neurons were associated with the generalization stimulus at the remote timepoint (30 days) following learning. When an IL neuronal ensemble established during learning was selectively chemogenetically silenced, generalization increased. Conversely, IL neuronal ensemble stimulation reduced generalization. Overall, these data identify a crucial role for IL in suppressing generalized responses. Further, we uncover an IL neuronal ensemble, formed during learning, functions to later attenuate the expression of generalization in the presence of ambiguous threat stimuli.

COVID-19 disease severity and affectations in electroneuromyographic studies

IntroductionEmerging research has highlighted COVID-19's profound impact on the nervous system, with ongoing debate regarding its severity and the emergence of peripheral neurological disorders. This study delves into the correlation between COVID-19 severity and electromyographic abnormalities in hospitalized patients, aiming to elucidate the nature and extent of neuromuscular dysfunction associated with the disease. MethodsIn a retrospective, cross-sectional, observational study, we analyzed data from 170 patients treated at the North Physical Medicine and Rehabilitation Unit between July 2020 and April 2021. Post-COVID-19 patients underwent comprehensive motor and sensory nerve conduction studies. Spearman's correlation and Kruskal–Wallis tests were used to examine the relationship between disease severity and electromyographic findings. ResultsOf the 170 patients in the study, 71.17% were male and 28.82% female, with an average age of 48 years; the subgroup of patients with severe clinical classification predominated at 68.23%. Spearman's correlation yielded values higher than 0.5 for all parameters analyzed concerning severity classification. A significant difference was found in the average of non-evoked motor and sensory nerves, motor nerves with latency, amplitude, and conduction velocity impairment, and affected muscles regarding critical classification. ConclusionOur study showed a relationship between the severity of COVID-19 and an increase in non-evoked motor and sensory nerves, impaired motor nerve latency, amplitude, and velocity, and more affected muscles, particularly in critical cases.

Multidimensional Representation of Semantic Relations between Physical Theories, Fundamental Constants and Units of Measurement with Formal Concept Analysis

We propose several hierarchical graphs that represent the semantic relations between physical theories, their fundamental constants and units of measurement. We begin with an alternative representation of Zel’manov’s cube of fundamental constants as a concept lattice. We then propose the inclusion of a new fundamental constant, Milgrom’s critical acceleration, and discuss the implications of such analysis. We then look for the same fundamental constants in a graph that relates magnitudes and units of measurement in the International System of Units. This exercise shows the potential of visualizing hierarchical networks as a tool to better comprehend the symmetries, interrelations and dependencies of physical magnitudes, units and theories. New regimes of application may be deduced, as well as an interesting reflection on our ontologies and corresponding theoretical objects.

Assessing the Effects of POGIL-Based Instruction versus Lecture-Based Instruction on Grade 12 Self-Efficacy and Performance in Circular Motion Unit

This study investigates the impact of lecture-based instruction and process-oriented guided inquiry learning (POGIL)-based instruction on the self-efficacy and performance of Grade 12 students. The researchers used a quasi-experimental, pretest-posttest design to compare the effects of POGIL-based instruction to lecture-based instruction, as measured by three cognitive outcomes: knowing, applying, and reasoning (KAR)." self-efficacy as measured by physics learning variables", "understanding of physics", and "willingness to learn". The study included 110 participants (54 in the treatment group and 56 in the control group) and was conducted in two government high schools in Alain, one for boys and one for girls. POGIL-based instruction was used to teach a circular motion unit in physics to the treatment group, while lecture-based instruction was used for the control group. The findings show that POGIL-based instruction had a statistically significant positive impact on science performance and self-efficacy when compared to lecture-based instruction. Furthermore, after the intervention, there was a positive correlation between participants' KAR test performance and their self-efficacy toward scientific inquiry. The study recommends a shift toward POGIL-based instruction to improve students' performance and self-efficacy and suggests that future research should include a broader range of schools, teachers, and advisors.

Monocular Absolute Depth Estimation from Motion for Small Unmanned Aerial Vehicles by Geometry-Based Scale Recovery.

In recent years, there has been extensive research and application of unsupervised monocular depth estimation methods for intelligent vehicles. However, a major limitation of most existing approaches is their inability to predict absolute depth values in physical units, as they generally suffer from the scale problem. Furthermore, most research efforts have focused on ground vehicles, neglecting the potential application of these methods to unmanned aerial vehicles (UAVs). To address these gaps, this paper proposes a novel absolute depth estimation method specifically designed for flight scenes using a monocular vision sensor, in which a geometry-based scale recovery algorithm serves as a post-processing stage of relative depth estimation results with scale consistency. By exploiting the feature correspondence between successive images and using the pose data provided by equipped navigation sensors, the scale factor between relative and absolute scales is calculated according to a multi-view geometry model, and then absolute depth maps are generated by pixel-wise multiplication of relative depth maps with the scale factor. As a result, the unsupervised monocular depth estimation technology is extended from relative depth estimation in semi-structured scenes to absolute depth estimation in unstructured scenes. Experiments on the publicly available Mid-Air dataset and customized data demonstrate the effectiveness of our method in different cases and settings, as well as its robustness to navigation sensor noise. The proposed method only requires UAVs to be equipped with monocular camera and common navigation sensors, and the obtained absolute depth information can be directly used for downstream tasks, which is significant for this kind of vehicle that has rarely been explored in previous depth estimation studies.

StarDICE II: Calibration of an Uncooled Infrared Thermal Camera for Atmospheric Gray Extinction Characterization.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission is a significant source of systematic uncertainty. We propose a solution relying on an uncooled infrared thermal camera to evaluate gray extinction variations. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to temperature-induced effects, compromising their spatial and temporal precision. Moreover, these systems cannot provide scene radiance in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements were conducted inside a climatic chamber, with a FLIR Tau2 camera imaging a thermoregulated blackbody source. The results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel dispersion of 0.09 W m-2 sr-1 and residual spatial noise of 0.03 W m-2 sr-1. We emphasize that the accuracy of scene radiance retrieval can be systematically affected by the camera's close thermal environment, especially when the ambient temperature exceeds that of the scene.

Do we accurately measure what we consume?

Understanding how consumption patterns affect the environment and shape well-being hinges on the rationale that the data collected on what is consumed, who consumes it, and where it is consumed are indeed accurate. To identify these consumption patterns and recommend corresponding policies, researchers and policy makers often rely on national surveys. Studies have explored the accuracy of individual surveys and the level of agreement across surveys of the same type (e.g. household expenditures), but no studies have compared representative national surveys measuring consumption in different ways. This study compares household consumption measured as expenditures and as material consumption (i.e. physical units) to assess how well we currently measure what we consume. We use multiple rigorous, national surveys to estimate meat consumption, household energy use, and private automobile use in the United States, with consumption profiles parsed by affluence, race/ethnicity, and education. Our results indicate that commonly used surveys may not accurately track important aspects of household consumption. For meat consumption, which included 30 consumption profiles detailing the consumption patterns across different demographic characteristics and meat types (e.g. kilograms beef consumed/capita for Caucasians), there is considerable disagreement between data sources for 20 profiles. By contrast, national surveys accurately measure household energy and transport (disagreement for four profiles). Our findings indicate that national surveys more accurately measure consistently tracked, standardized consumables like electricity than irregularly tracked, variable goods such as food. These results cast doubt on studies that use national surveys to draw conclusions about the how the environmental impacts of food, and, potentially, other goods (e.g. manufactured goods) vary across demographic groups. Overcoming this challenge will necessitate new surveys, updating legacy databases, and harnessing breakthroughs in data science.