Bias Range Research Articles

On the relationship between electrical and electro-optical characteristics in vertical cavity surface emitting lasers for chip scale Rb atomic clocks

We report on a comprehensive study of the electrical and electro-optical properties of 795 nm vertical cavity surface emitting lasers (VCSELs) designed for chip scale Rb atomic clocks. We highlight several key findings including the observation that the current flow at moderate bias levels comprises several parallel paths which are identified by an analysis of the I−V characteristic also confirmed by a numerical simulation. Resistance is a key parameter in any VCSEL. We analyze it in detail at all bias levels and find that above transparency, when the VCSEL enters the high injection regime, the current flow mechanism is modified significantly from an exponential to a power law characteristic. Consequently, resistance attains a nonlinear contribution which is quadratic in the spontaneous emission regime and quasi-linear above the threshold. This nonlinear contribution is not considered in common models. The optoelectronic properties are strongly correlated with the electrical characteristics what allow to explain several peculiarities of the VCSEL performance. We designed and fabricated the VCSELs according to the requirements of miniature Rb atomic clocks, including optimal operation at high temperatures. Their minimum threshold occurs at 363 K where they emit at 794.7 nm. The modal and polarization discrimination in the bias range where these VCSELs operate in practical miniature atomic clocks is well above 30 dB.

Study on dynamic and static magnetic properties of polycrystalline cobalt ferrite under bias magnetic field

Owing to their low resistivity, metal-based magnetostrictive materials are prone to eddy current loss under high-frequency magnetic field excitation, leading to a significant decline in output characteristics. Cobalt ferrite, a new non-metal-based magnetostrictive material, possesses both high magnetostriction strain and high resistivity, making it highly promising for high-frequency driving applications. However, reports on cobalt ferrite as the core driving material for ultrasonic transducers remain limited, and research on its high-frequency vibration characteristics is insufficient. In this study, magnetostrictive samples of cobalt ferrite were prepared using a solid-state reaction method, and their morphologies and magnetic properties were characterized. Under a bias magnetic field, the dynamic magnetic properties of cobalt ferrite and temperature variations with the driving magnetic field amplitude and prestress were analyzed. The results indicated that the density of the sample prepared by solid-state reaction can reach 88 % and the saturation magnetostriction coefficient λs can reach 186 × 10−6, with an optimal bias range of 400–600 Oe for the cobalt ferrite. When the driving magnetic field amplitude is 100 Oe in the optimal bias range, the output amplitude of the cobalt ferrite is 2160 nm, meeting the output requirement of high-frequency magnetostrictive ultrasonic transducers.

B-015 Analytical Verification of MR-proADM Biomarker in Human EDTA Plasma on Immunoassay Analyzer LIAISON® XL of Diasorin

Abstract Background Adrenomedullin (ADM) is a potent vasodilator peptide of 52-aminoacid belonging to the calcitonin family of peptides. Midregional pro-ADM (MR-ProADM) is a precursor for ADM that circulates is in a 1:1 ratio with it, and proportionally represents its levels and activity. Increased ADM circulating concentrations have been described for several disease states, including dysfunction of the cardiovascular system and sepsis. However, reliable ADM quantification has been hampered by the short half-life, the existence of a binding protein, and its physical properties. Hence, quantificacion of MR-proADM represents a more suitable option. The biomarker MR-proADM has been shown to be an accurate prognostic marker for outcome prediction in patients with lower respiratory infections, sepsis, urinary tract infections, and kidney disease. The objective of this study was the verification of the metrological characteristics and the verification of the linearity of quantitative measurement range, as declared by the manufacturer, of the LIAISON® Brahms MR-proADMTM on Liaison® XL (DiaSorin). Methods The verification was performed with two levels of quality controls (QC) from the manufacturer (x̄L=1,19 nmol/L and x̄H=5,48 nmol/L), and residual EDTA plasma samples from patients with an active sepsis code. The evaluation included the assessment of: • Detection capability (including limit of quantification (LoQ) and limit of detection (LoD), measuring three samples at the manufacturer's stated concentration (0.21 nmol/L and 0.09 nmol/L, respectively) in duplicate within 4 runs). • Precision (CV, %) and accuracy (bias, %) within, measuring 10 replicates of each QC level; and between run, measuring 5 replicates for each QC level within 5 runs. • Linearity of the quantitative range of measurement (six intermediate samples prepared by mixing a high concentration sample- XH=9.64 nmol/L- and a low concentration sample, XL=0.59 nmol/L; measured in triplicate). All studies were performed in accordance with the recommendations of the Clinical Laboratory Standards Institute guidelines. Statistical analysis was performed using Microsoft Excel®, 2016. Results All results with diluted samples presented a signal (RLU) higher than that detected in samples without analyte, so the LoD was verified. The precision at the LoQ concentration was 26.7% (acceptance criteria was set as <20%). CV for within-run precision were 4.3% and 2.0% and for between-run precision 6.8% and 7.9% for low (L1) and high (L2) control level, respectively. The average bias was 3.60% for L1 and 4.23% for L2, meeting acceptance criteria (<8%). Linearity was confirmed between 0.59 and 9.64 nmol/L (R2 = 0.995). The biases between the expected and the obtained value were ≤7% through the quantitative range of measurement, except for the lowest concentration sample (close to the quantitation limit). Conclusions Our study showed good compliance with the metrological characteristics declared by the manufacturer for LoD, precision, bias and linearity range. Although the LoQ was not verified, it is outside the clinical decision value in septic patients (< 2.25 nmol/L) and it is not deemed a limitation for clinical practice. Additionally, the Liaison XL® platform permits us to configure an emergency protocol circuit (24/7) with a TAT of 35 min.

Validity of the estimated core temperature algorithm during real-world prolonged walking exercise under warm ambient conditions

BackgroundNon-invasive methods to estimate core body temperature (TC) are increasingly available. We examined the group-level and individual participant-level validity of the Estimated Core Temperature (ECTemp™) algorithm to estimate TC based on sequential heart rate (HR) measurements during real-world prolonged walking exercise in warm ambient conditions. MethodsParticipants walked 30 (n = 3), 40 (n = 13) or 50 (n = 2) km on a self-selected pace during which TC was measured every minute using an ingestible temperature capsule. HR was measured every second and used to compute the estimated core temperature (TC-est) using the ECTemp™ algorithm. Bland-Altman analyses were performed to assess agreement between TC and TC-est. A systematic bias <0.1 °C was considered acceptable. Results18 participants (56 ± 16 years, 11 males) walked for 549 min (range 418–645 min), while ambient temperature increased from 22 °C to 29 °C. Average HR was 108 ± 13 bpm and TC ranged from 36.9 to 39.2 °C, whereas TC-est ranged from 36.8 to 38.9 °C (n = 8572 observations). Group level data revealed a systematic bias of 0.09 °C (p < 0.001) with limits of agreements of ±0.44 °C. A weak correlation was found between TC and TC-est (r = 0.28; p < 0.001). Large inter-individual differences in bias (range −0.45 °C to 0.62 °C) and correlation coefficients (range −0.09 to 0.95) were found, while only 3 participants (17%) had an acceptable systemic bias of <0.1 °C. ConclusionGroup level data showed that the ECTemp™ algorithm had an acceptable systematic bias during prolonged walking exercise in warm ambient conditions, but only 3 out of 18 participants had an acceptable systemic bias. Future studies are needed to improve the accuracy of the algorithm before individual users can rely on their estimated TC during real-world exercise.

External quality assessment-based tumor marker harmonization simulation; insights in achievable harmonization for CA 15-3 and CEA

Abstract Objectives CA 15-3 and CEA are tumor markers used in routine clinical care for breast cancer and colorectal cancer, among others. Current measurement procedures (MP) for these tumor markers are considered to be insufficiently harmonized. This study investigated the achievable harmonization for CA 15-3 and CEA by using an in silico simulation of external quality assessment (EQA) data from multiple EQA programs using patient-pool based samples. Methods CA 15-3 and CEA data from SKML (2021), UK NEQAS (2020–2021) and KEQAS (2020–2021) were used. A harmonization protocol was defined in which MPs that were considered equivalent were used to value assign EQA samples, and recalibration was only required if the MP had a bias of &gt;5 % with value assigned EQA. Harmonization status was assessed by determining the mean level of agreement and residual variation by CV (%). Results Only MPs from Abbott, Beckman, Roche and Siemens were available in all EQA programs. For CA 15-3, recalibration was proposed for Beckman MP only and for CEA, recalibration was proposed for Siemens MP only. When the harmonization procedures were applied, for CA 15-3 the pre-harmonization mean bias range per MP was reduced from −29.28 to 9.86 %, into −0.09–0.12 % after harmonization. For CEA, the mean bias range per MP was reduced from −23.78 to 2.00 % pre-harmonization to −3.13–1.42 % post-harmonization. Conclusions The present study suggests that a significant improvement in the harmonization status of CA 15-3 and CEA may be achieved by recalibration of a limited number of MPs.

A cognitive bias awareness matrix for enhancing ERP Decision-Making in entrepreneurial firms

Purpose The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making. Design/methodology/approach The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency. Findings The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests. Research limitations/implications The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs. Practical implications The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making. Social implications The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem. Originality/value This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.

Magnetic tunnel junction based on bilayer LaI2 as perfect spin filter device

The discovery of van der Waals intrinsic magnets has expanded the possibilities of realizing spintronics devices. We investigate the transmission, tunneling magnetoresistance ratio, and spin injection efficiency of bilayer LaI2 using a combination of first-principles calculations and the non-equilibrium Green’s function method. Multilayer graphene electrodes are employed to build a magnetic tunnel junction with bilayer LaI2 as ferromagnetic barrier. The magnetic tunnel junction turns out to be a perfect spin filter device with an outstanding tunneling magnetoresistance ratio of 653% under a bias of 0.1 V and a still excellent performance in a wide bias range. In combination with the obtained high spin injection efficiency this opens up great potential from the application point of view.

Enhancing tetrafullerene based photosensors and photovoltaic cells by graphene oxide doping

This study explores the postulated potential of tetrafullerene as a charge transport material when combined with dispersed graphene oxide (GO), over concerns about fullerene’s limited performance in similar devices. Incorporating GO is expected to augment carrier concentration, increase surface area, and reduce operating voltages. Devices fabricated with p-Si/tetrafullerene with varying GO concentration exhibit notable enhancements in both dark and illuminated characteristics. The results demonstrate improved electron concentration and transport, leading to enhanced rectification ratios, ideality factors, and interface state densities across a broad bias and illumination range. Notably, devices doped with 0.2GO exhibit the most promising electrical and photoresponse characteristics, showcasing potential for applications in photosensing and photovoltaics.

Ion confinement and separation using asymmetric electrodynamic fields in structures for lossless ion manipulations.

TW-SLIM ion mobility separations have demonstrated exceptional resolution by leveraging long paths with minimal loss. All previously reported experiments have used electrode surfaces which are mirrored to generate symmetrically opposing electric fields for ion confinement. However, work with other planar ion optics indicates this may be unnecessary. This study explores conditions under which separations may be obtained using a SLIM with asymmetric electric fields. The asymmetric field configuration was defined by applying a uniform DC potential to all electrodes of the top PCB of a standard TW-SLIM board pair, with no electrode placement modifications. This configuration was simulated in SIMION to assess transmission through the SLIM. A benchtop TW-SLIM instrument outfitted with a Faraday plate detector was modified likewise, so the top PCB had a uniform DC potential applied to all electrodes, while the bottom board was operated normally. Simulations show full ion transmission for four different m/z ion populations over a range of DC biases applied to the "pusher" board. Likewise, the modified benchtop instrument is capable of transmitting, separating, and cycling ions with minimal losses. The effect of pusher strength on separation quality is explored, and comparisons between the standard and modified SLIM are made with respect to resolving the +2 and +3 charge states of neurotensin ions. A functional IMS instrument using asymmetric confining fields demonstrates additional field modifications may be a means to achieve additional functionality with limited interruption of the analysis. A TW-SLIM PCB specifically designed as a pusher board would benefit from minimized manufacturing cost, simplifying assembly, reducing drive electronics, and improved field consistency.

Behavioral Biases and Decision-Making in Investment among working individuals in the education sector

This study investigates the impact of behavioral biases on investment decisions among working individuals within the education sector. Behavioral finance theory serves as the theoretical foundation, emphasizing the role of psychological factors in shaping investment choices. The study employs a mixed-methods approach, combining surveys and in-depth interviews to gain insights into the investment behavior of educators, administrators, and support staff. Our findings reveal that a range of behavioral biases, including loss aversion, overconfidence, and herding, significantly influence investment decisions. Working professionals in the education sector often exhibit a strong preference for conservative investment strategies, even when market conditions may suggest otherwise. Psychological factors, such as fear of losses and a tendency to follow trends, play a vital role in shaping investment behavior. These biases are further exacerbated by a lack of financial literacy and investment education. The implications of this research suggest a need for targeted financial education programs to mitigate behavioral biases and enhance decision-making among working individuals in the education sector. Addressing these biases can lead to more informed and diversified investment strategies, potentially improving financial security and long-term wealth accumulation for this demographic. This abstract provides a brief overview of the research focus, methodology, key findings, and the potential implications of the study. It gives readers a clear understanding of the research's scope and significance.

(Invited) Analysis of Hysteresis in OTFTs Using a Physically-Based Compact Model

Organic TFTs (OTFs) have the potential to become essential devices for flexible, wearable and printed electronics. However, several issues related to their behavior still needs to be studied in detail and completely understood. One of these issues is hysteresis. In this abstract we analyze the hysteresis behavior in OTFT by applying a previously developed physically-based model to the direct (forward) and reverse (backward) dc sweeps of the transfer characteristics at different drain voltages. The identification of the model parameters which change provides more information about the hysteresis mechanism.The so-called UMEM (unified model and extraction method) model [1,2] developed for OTFT has been demonstrated to accurately reproduce the I-V characteristics for a wide range of geometries, bias and temperature conditions [3], as well as to extract physical values of most parameters. In this work we will show that it can reproduce the post-hysteresis I-V-characteristics of OTFT and that only a few parameters need to be changed.In the above threshold regime, where Variable Range Hopping dominates, the current is proportional to a field-effect mobility μFET= μ0(VGS-VT)γ/VAA γ , being VT the threshold voltage. The γ parameter depends on the DOS characteristic temperature T0 as = 2(T0/T-1).In the subthreshold regime diffusion dominates and the current is exponentially dependent on VGS:IDS=IDS0 exp (2.3(VGS-VT)/S) (1-exp(-VDS/Vth))where S is the subthreshold swing and Vth the thermal voltage.Aunified model of the drain current, valid and continuous from subthreshold to the above threshold regime, is finally obtained by means of a hyperbolic tangent function [1,2].In order to analyze the hysteresis behavior, this model was applied to both direct and reverse dc sweeps of the I-V characteristics of OTFT samples fabricated at CEA-Liten (Grenoble). Figs. 1 and 2 shows very good agreement from subthreshold toabove threshold. The parameter values extracted for the direct and reverse dc sweeps are shown in Table I. It is observed that the threshold voltage is shifted. The increase of γ indicates a higher degree of disorder in the reverse dc sweep characteristics. However, we can see that although IDS0 is higher, the extracted subthreshold swing value, S, does not change. The saturation parameter α is lower in the reverse sweep, but the rest of model parameters are not affected. Only γ and α show a variation with VDS, although rather small.[1] B. Iñiguez, et al., "New Compact Modeling Solutions for Organic and Amorphous Oxide TFTs," IEEE Journal of the Electron Devices Society, vol. 9, pp. 911-932, September 2021[2] C. H. Kim, et al., , “A Compact Model for Organic Field-Effect Transistors With Improved Output Asymptotic Behaviors,”IEEE Trans. on Electron Devices, vol. 60, no. 3, pp. 1136-1141, March 2013.[3] H. Cortés-Ordóñez, et al., “Parameter extraction and compact modeling of OTFT from 150 to 350K,”, Trans. on Electron Devices, vol. 67, no. 12, pp.5695-5692, Dec 2020.Acknowledgements:This work was funded by the Spanish Ministry of Science (PRX21/00726), and the EU EIC-PATHFINDER (BAYFLEX, no 101099555 Figure 1

Atmospheric mesoscale modeling to simulate annual and seasonal wind speeds for wind energy production in Mexico

Numerical models have been used widely to reproduce wind resources around the globe. Mexico’s vast territory has a wide range of geographical characteristics with abundant wind potential. This work explores WRF simulations applied to reproduce the wind speed and capacity factor (CF) of 22 wind masts, organized into seven regions delimited by geographic conditions and consisting of 33 years of data. Biases, correlations, dispersion indexes and terrain gradient are selected to study the model and experimental data annually and seasonally. Results indicate that WRF simulations show a persistent positive bias in all regions, leading to overestimating CF. In a seasonal analysis, 86% of the CF data falls between the -0.1 and 0.1 bias range. Bias is not related to a physical seasonal phenomenon; instead, it appears to be related to geographic conditions. The findings indicate that different combinations of settings should be chosen to better reflect the geographical conditions and physical phenomena that affect the intricate Mexican landscape for wind energy production. This research identify regions with best reproducibility and suggests potential areas for future research on wind energy forecasting.

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

Reconstruction of environmental site conditions by the integration of SCADA and reanalysis data

For the operational optimization of wind farms, AEP estimation and other tasks, high quality data of environmental conditions at the site are necessary. However, such data is often not available or has insufficient quality. This work tries to fill this gap, by integrating two data sources: the (usually available) operational data from the SCADA (Supervisory Control and Data Acquisition) system, and reanalysis data. SCADA data streams contain measurements from each wind turbine in the farm, but they are affected by various sources of uncertainty (including local flow effects, miscalibration, etc.), and might contain gaps. Meteorological reanalysis datasets can be used to fill gaps and complement SCADA data. However, modelled data can contain a wide range of biases and errors, due to limited model fidelity, coarse spatial and temporal resolution, inaccuracies in the input data feeding the model, etc. This study considers various methods to extract and merge wind speed and direction information from these diverse data sources. The analysis is based on field data measured at two experimental test sites, an offshore site equipped with 111 multi-MW turbines and a lidar buoy, and an onshore site equipped with 14 multi-MW wind turbines and a lidar. The methods are evaluated in the spectral and temporal domains by comparing the reconstructed wind characteristics with measurements from the lidars.

High rectification effects of heterojunctions based on C3N4 nanoribbons

Two heterojunctions based on zigzag graphitic carbon nitride nanoribbons (ZCNNRs), namely, BnAn and CnAn, are considered and their electronic transport properties are studied using the nonequilibrium Green's function (NEGF) in combination with the density functional theory (DFT). The BnAn is formed by the joining of bare and partially hydrogenated ZCNNRs. The CnAn is created by the attachment of bare and fully hydrogenated ZCNNRs which leads to a Schottky barrier. The results show that current–voltage characteristics (I–V) can be significantly tuned by the length variation of heterojunctions. Furthermore, it is found that both heterojunctions have a remarkable nonlinear behavior in the whole bias range. The B4A4, B3A4, and B4A3 have forward rectifier performance whereas B2A2, B3A3, C2A2, C3A3, C4A4, C3A4, C4A3, C5A3, and C6A3 show the reverse rectification behavior. In the case of BnAn, the maximum rectification ratio (RR) of B4A4 is boosted up to 3.97 × 107 at 0.4 V. Additionally, for CnAn, the maximum RR of C4A3 can reach 1.37 × 105 at 1.0 V. Our investigation reveals that the BnAn heterojunction has negative differential resistance (NDR) under positive bias.The peculiar transport behaviors of B4A4 and C4A3 are explained due to the energy level shifts of electrodes with applied bias voltage and great alterations in the transmission spectrum. Furthermore, the Molecular projected self-consistent Hamiltonian (MPSH) and projected local density of states (PLDOS) are analyzed for these devices. The findings of this work reveal unique opportunities to design the ZCNNRs nanoelectronic devices.

Estimation of stellar mass and star formation rate based on galaxy images

ABSTRACT It is crucial for a deeper understanding of the formation and evolution of galaxies in the Universe to study stellar mass (M*) and star formation rate (SFR). Traditionally, astronomers infer the properties of galaxies from spectra, which are highly informative, but expensive and hard to be obtained. Fortunately, modern sky surveys obtained a vast amount of high-spatial-resolution photometric images. The photometric images are obtained relatively economically than spectra, and it is very helpful for related studies if M* and SFR can be estimated from photometric images. Therefore, this paper conducted some preliminary researches and explorations on this regard. We constructed a deep learning model named Galaxy Efficient Network (GalEffNet) for estimating integrated M* and specific star formation rate (sSFR) from Dark Energy Spectroscopic Instrument galaxy images. The GalEffNet primarily consists of a general feature extraction module and a parameter feature extractor. The research results indicate that the proposed GalEffNet exhibits good performance in estimating M* and sSFR, with σ reaching 0.218 and 0.410 dex. To further assess the robustness of the network, prediction uncertainty was performed. The results show that our model maintains good consistency within a reasonable bias range. We also compared the performance of various network architectures and further tested the proposed scheme using image sets with various resolutions and wavelength bands. Furthermore, we conducted applicability analysis on galaxies of various sizes, redshifts, and morphological types. The results indicate that our model performs well across galaxies with various characteristics and indicate its potentials of broad applicability.

Tunable Inductive Coupler for High-Fidelity Gates Between Fluxonium Qubits

The fluxonium qubit is a promising candidate for quantum computation due to its long coherence times and large anharmonicity. We present a tunable coupler that realizes strong inductive coupling between two heavy-fluxonium qubits, each with approximately 50-MHz frequencies and approximately 5-GHz anharmonicities. The coupler enables the qubits to have a large tuning range of XX coupling strengths (−35 to 75 MHz). The ZZ coupling strength is &lt;3 kHz across the entire coupler bias range and &lt;100 Hz at the coupler off position. These qualities lead to fast high-fidelity single- and two-qubit gates. By driving at the difference frequency of the two qubits, we realize a iSWAP gate in 258 ns with fidelity 99.72%, and by driving at the sum frequency of the two qubits, we achieve a bSWAP gate in 102 ns with fidelity 99.91%. This latter gate is only five qubit Larmor periods in length. We run cross-entropy benchmarking for over 20 consecutive hours and measure stable gate fidelities, with bSWAP drift (2σ) &lt;0.02% and iSWAP drift &lt;0.08%. Published by the American Physical Society 2024

A natural language model to automate scoring of autobiographical memories

Biases in the retrieval of personal, autobiographical memories are a core feature of multiple mental health disorders, and are associated with poor clinical prognosis. However, current assessments of memory bias are either reliant on human scoring, restricting their administration in clinical settings, or when computerized, are only able to identify one memory type. Here, we developed a natural language model able to classify text-based memories as one of five different autobiographical memory types (specific, categoric, extended, semantic associate, omission), allowing easy assessment of a wider range of memory biases, including reduced memory specificity and impaired memory flexibility. Our model was trained on 17,632 text-based, human-scored memories obtained from individuals with and without experience of memory bias and mental health challenges, which was then tested on a dataset of 5880 memories. We used 20-fold cross-validation setup, and the model was fine-tuned over BERT. Relative to benchmarking and an existing support vector model, our model achieved high accuracy (95.7%) and precision (91.0%). We provide an open-source version of the model which is able to be used without further coding, by those with no coding experience, to facilitate the assessment of autobiographical memory bias in clinical settings, and aid implementation of memory-based interventions within treatment services.

Study of optical float-zone grown gallium oxide Schottky barrier diode

β−Ga2O3 based lateral Schottky barrier diodes are fabricated using Ni/Au as Schottky contact and Ti/Au as Ohmic contact. The Sn-doped β−Ga2O3 sample is grown by the optical float-zone technique. The effect of trenches, which are used for mesa isolation, on breakdown voltage is investigated. The device shows near-ideal characteristics in terms of built-in potential. The parasitic series and shunt resistances are extracted, and their dependency on temperature is established. Modeling of temperature-dependent reverse leakage current is demonstrated using the thermionic emission model, Poole–Frenkel emission model, and Fowler-Nordheim tunneling mechanism. The procedure to extract relevant parameters is explained in terms of bias and temperature dependency. The combined model shows excellent agreement with experimental data over a wide range of bias and temperature. The maximum electric field of 2.3 MV cm−1 is achieved.

Exploring the potential of objective metabolite measures in dietary assessment: randomised cross over feeding study

Reliable dietary assessment is key to our understanding of diet-health interactions(1). Most current dietary assessment methods are self-reported, making them prone to a range of biases(2). Objective markers, such as certain metabolite concentrations in human tissue, may prove a more reliable method of dietary assessment in the future while reducing participant burden(2,3). An example of these metabolite markers are alkylresorcinols, a family of compounds found in the bran layer of common grains. High concentrations of alkylresorcinols reflect high wholegrain intakes, very low levels indicate only refined grain intake, while no alkylresorcinols would indicate a gluten-free diet. We conducted a randomised crossover feeding study of three interventions (one standardisation day followed by a feeding day where known amounts were consumed under observation). Each feeding day differed by which food groups were provided (e.g., chicken, legumes, and fruit) or entirely absent (e.g., grains, fish, and dairy). Participants provided 24-hour urine samples on all six days (standardisation days and feeding days), as well as a 24-recalls and fasted blood samples the morning after each day. Known metabolite markers of dietary intake (approximately 70) were identified in blood and urine with LC-MSqToF, and semi-quantified with a known standard. Twenty-four hour urine sodium content was also measured as the current best known objective marker of dietary intake(4). Twenty-four participants (74% female, age (SD) 24.8 (6.1), BMI 24.1 (4.0)) commenced the study, with 21 (88%) completing all three interventions. Mean energy intake on feeding days (11720 kJ (2943.01)) was higher than self-reported energy intake on standardisation days (9243.57 kJ (3582.92)). Meals were well tolerated, with mean (range) intakes of 9.8 (6.3 – 16.1) serves whole grains, 2.4 (1.6 – 4.8) serves fish, 3.1 (1.9 – 5.5) serves dairy, 5.6 (4.5 – 9.1) serves chicken, 8.2 (7.0 – 14.1) serves legumes, 3.1 (1.3 – 4.6) serves fruit, 3.9 (2.6 – 6.4) serves red meat, 1.7 (1.35 – 2.6) serves nuts and seeds, or 13.4 (9.4 – 19.5) serves vegetables on their respective feeding days. The three feeding days provided clearly identifiable clusters when assessing the overall metabolic profile, both in terms of what was measured on the feeding days, and the difference in metabolite concentrations between standardisation day and feeding day. The relative correlations between self-reported intakes and individual metabolite concentrations reflecting specific foods or food groups with the known dietary intakes from feeding days will be presented first at the conference.