Bias Control Research Articles

A 0.053 mm2 10-bit 10-ks/s 40-nW SAR ADC with pseudo single ended switching procedure for bio-related applications

This paper presents a power and area efficient pseudo single ended switching ADC for portable biomedical signal monitoring applications. The switching method features an 8-bit binary-weighted DAC array with a novel layout method called limited bottom-plate routing (LBPR) maintaining both feasibility and symmetry, a dynamic biased comparator and dynamic control logic. As a result, the proposed ADC saves 96.7% power consumption and 87.5% area occupation compared with conventional SAR ADC. Also, a novel DAC array layout method is promoted maintaining both feasibility and symmetry. A prototyped chip including a proposed ADC is fabricated in a 0.13 μm CMOS process. On condition of 0.6 V supply voltage and 10 kS/s sampling rate, the measured signal-to-noise-distortion (SNDR) and spurious free dynamic range (SFDR) are 57.8 dB and 64.3 dB, respectively. Besides, the ADC consumes only 40 nW power and 0.053 mm2 area achieving a figure-of-merit (FoM) of 6.26 fJ/conversion-step. Low power and low area occupation make it a good candidate for biomedical signal monitoring applications.

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.

Clinical characteristics and outcomes of infection with human T-lymphotropic virus in a non-endemic area: a single institution study.

Understanding of human T-lymphotropic virus (HTLV) remains largely based on epidemiologic and clinical data from endemic areas. Globalization has resulted in migration of persons living with HTLV (PLHTLV) from endemic to non-endemic areas, and a rise of HTLV infection in the United States. Yet, due to the historical rarity of this disease, affected patients are often under- and mis-diagnosed. Thus, we sought to characterize the epidemiology, clinical features, comorbidities, and survival of HTLV-1- or HTLV-2-positive individuals identified in a non-endemic area. Our study was a single institution, retrospective case-control analysis of HTLV-1 or HTLV-2 patients between 1998 and 2020. We utilized two HTLV-negative controls, matched for age, sex, and ethnicity, for each HTLV-positive case. We evaluated associations between HTLV infection and several hematologic, neurologic, infectious, and rheumatologic covariates. Finally, clinical factors predictive of overall survival (OS) were assessed. We identified 38 cases of HTLV infection, of whom 23 were HTLV-1 and 15 were HTLV-2 positive. The majority (~54%) of patients in our control group received HTLV testing for transplant evaluation, compared to ~24% of HTLV-seropositive patients. Co-morbidities associated with HTLV, hepatitis C seropositivity were higher in HTLV-seropositive patients compared to controls (OR 10.7, 95% CI = 3.2-59.0, p < 0.001). Hepatitis C and HTLV co-infection resulted in decreased OS, compared to no infection, hepatitis C infection alone, or HTLV infection alone. Patients with any cancer diagnosis and HTLV infection had worse OS compared to patients with cancer or HTLV alone. HTLV-1 positive patients had lower median OS compared to HTLV-2 patients (47.7 months vs. 77.4 months). In univariate analysis, the hazard for 1-year all-cause mortality was increased among patients with HTLV-seropositivity, adult T-cell leukemia, acute myelogenous leukemia, and hepatitis C infection. When corrected, multivariate analysis showed that HTLV seropositivity was no longer associated with 1 year all-cause mortality; however association with AML and hepatitis C infection remained significant. HTLV-seropositivity was not associated with increased 1 year mortality in multivariate analysis. However, our study is limited by our small patient sample size, as well as the biased patient control population due to selection factors for HTLV testing.

SPATIAL CONCENTRATION AND FIRM-LEVEL PRODUCTIVITY IN KAZAKHSTAN

Purpose of the research. This paper studies the effect of spatial agglomeration on firms’ total factor productivity in Kazakhstan using panel data from 2009 to 2017.Methodology. We employ a two-stage estimation strategy and control for endogeneity biases by making use of the GMM approach. The firm-level data is obtained from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan.Originality / value of the research. This study contributes to an empirical study of spatial concentration and firm-level productivity in developing countries and provides valuable insights for policymakers to consider before implementing government programs.Findings. The results suggest that productivity increases with clustering: a 10 % increase in the number of employees of the neighboring firms inside the same industry increases firm-level productivity by 1.36 %, while a 10 % increase in employment in other industries enhances firm performance by 1.95 %. The productivity gains are higher at the 2-digit regional level rather than at the 9-digit sub-regional level of geographical aggregation, implying that the denser geography increases firms’ performance.

Numerical evidence against advantage with quantum fidelity kernels on classical data

Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential ``flattening'' of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter-tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless novel techniques are developed to control the inductive bias of quantum kernels, they are unlikely to provide a quantum advantage on classical data that lacks special structure.

A 52-to-57 GHz CMOS Phase-Tunable Quadrature VCO Based on a Body Bias Control Technique

This paper presents a 52-to-57 GHz CMOS quadrature voltage-controlled oscillator (QVCO) with a novel I/Q phase tuning technique based on a body bias control method. The QVCO employs an in-phase injection-coupling (IPIC) network comprising four diode-connected FETs for the quadrature phase generation. The I/Q phase error is calibrated by controlling the body bias voltage offset of the QVCO’s four core FETs. This technique effectively covers a wide range of I/Q phase error between −13.4° and +10.7°. It also minimally induces the unwanted variations in the phase noise, current dissipation, and oscillation frequency, which were found to be only 0.4 dB, 0.07%, and 36 MHz, respectively. After the IPIC-QVCO, a phase-tunable two-stage LO buffer employing a 3-bit switched-capacitor bank was added for additional phase tuning, leading to the extension of the phase tuning range up to −22.7–+20.0°. The proposed QVCO is implemented in a 40 nm RF CMOS process. The measured results show that the QVCO covers a frequency band from 52.4 to 57.6 GHz while consuming 26.2 mW. The phase noise and the figure-of-merit of the QVCO are −91.8 dBc/Hz at 1 MHz offset and −172.4 dBc/Hz, respectively. We also realized a fully integrated 55 GHz quadrature RF transmitter employing the phase-tunable QVCO and LO generator. The effectiveness of the proposed phase-tunable LO generator was confirmed by verifying the image rejection ratio (IRR) calibration at the RF output.

Multiple Adaptive Attention-Bias-Modification Programs to Alter Normative Increase in the Error-Related Negativity in Adolescents.

In the current article, we examined the impact of two home-delivered attentional-bias-modification (ABM) programs on a biomarker of anxiety (i.e., the error-related negativity [ERN]). The ERN is sensitivity to ABM-related changes; however, it is unclear whether ABM exerts its influence on the ERN and anxiety by increasing general attentional control or by disengaging spatial allocation of attention. In this study, we measured the ERN, anxiety, attention bias, and attention control before and after two versions of ABM training and a waitlist control group in 546 adolescents. An ABM designed to increase attention control modulated the ERN but had no impact on anxiety. An ABM designed to reduce attentional bias changed bias and self-reported anxiety in youths but had no impact on the ERN or parent-reported anxiety. These results suggest that the ERN and normative anxiety may be modified using attention training.

Implementation of Optimized Planar Hall Effect Sensor as Tunable Resistors With DC Bias Control

The emerging requirements of tunable resistors or pseudoresistors for electrical and electronic circuits demand the rigorous attention of engineers in recent times. The frequency-dependent behavior of conventional pseudoresistors operating with gate-controlled metal oxide field effect transistors (FET) creates a subtle challenge to operate in high-frequency applications. Therefore, this letter investigates a tunable resistor with a planar Hall plate to eliminate the frequency-dependent limitations of FETs. The proposed design of a tunable resistor with an optimized Hall effect sensor is able to achieve resistance tuning in the range of 1.8 Ω–247 KΩ. Apart from that, this letter investigates different geometries of planar Hall plate devices and their impact on biasing and magnetic fields that produce a compelling choice for tunable resistors. The proposed scheme exhibits a THD value of 1.4e-8% for a 10-MHz input signal, which is superior to its counterparts based on gate-controlled field effect devices.

Gözlemsel sağlık araştırmaları için tamamlayıcı bir yan tutma riski değerlendirme aracı (BiRDA kontrol listesi): Metodolojik bir çalışma

Assessment of bias in health studies is important and not easy to measure objectively. The aim of this study was to develop an easy-to-use, comprehensive, cost effective and time efficient bias control tool for observational studies. This was a methodological study conducted between June 2018 – June 2020. Literature review to extract items, expert opinions, Delphi panels, construction of the framework and the content of the tool, statistical analysis and reporting of the study were the main steps. Literature review was conducted with prespecified keywords by researchers. “Expert Assessment Form” was used to evaluate expert opinions. Although Content Validity Ratio (CVR) was used to check the content validity, it was mainly based on consensus of experts. Three Delphi panels were carried out. The name of the developed tool was decided to be Bias Risk Assessment Tool (BiRAT). It was considered to use the abbreviations BiRAT-CS for cross-sectional studies, BiRAT-CC for case-control studies and BiRAT-Co for cohort studies. Descriptive statistics A total of 71 expert assessment forms were sent to 67 experts and 44 of them were received. As a result of the assessments made after the Delphi panels; 67-item BiRAT-CS, 69-item BiRAT-CC and 70-item BiRAT-Co were developed. BiRAT tools may be used in training, preparing for a study, or publication process. However, bias assessment tools should be used with mobile / online applications or artificial intelligence technologies for easier use and further development since the use of them were generally impractical.

A review of the best practice in traffic calming evaluation

Traffic calming (TC) has been applied widely for several decades, although approaches to evaluating its effects on speeds have been inconsistent. This resulted in limited comparable and robust evidence to support practitioner guidance for TC design. To fill this gap and suggest best practices for the evaluation of TC effects on speeds, we performed a systematic review of 158 publications. We distilled information related to five research questions: Which measurement sensor was used? How was speed measured? Was free-flow speed considered? What was the sampling density? How were sample considerations reported? In addition to coding the studies based on these research questions, we rated them based on scientific robustness. The review confirmed the inconsistent state of evaluation practice. Most common evaluation approaches employed static detectors, low levels of control for bias (simple before-after), and unknown survey periods. The review found that the most robust evaluation practices involved multivariate before-after or cross-sectional study designs, used dynamic measurement of speeds (e.g., probe vehicles, simulations), large samples of vehicles, and more precise evaluation of speed changes using speed-distance graphs. These findings could guide more consistent and robust evaluation practice, and thus help to improve evidence-based TC guidance for creating safer and more sustainable neighborhoods.

DC and RF performance of HR Si(111)-based AlGaN/GaN MIS-HEMT with a symmetrical multi-finger grid array structure for 5G N28 700MHz low-bias-control applications

HR Si(111)-based AlGaN/GaN metal insulator semiconductor high electron mobility transistor (MIS-HEMT) for 5G mobile communication at N28 700 MHz band is proposed in this paper. The device is ingeniously designed with a symmetrical dual-column multi-finger grid array structure. In addition, the device is designed with an MIS gate with a 6 nm SiN dielectric film to suppress the leakage current and a source field plate to suppress the current collapse effect. The fabrication process of the device is studied in detail, including the epitaxial surface treatment, F ion implantation isolation, ohmic contact, electron beam lithography gate process, surface passivation, and source field plate. A device with a gate length of 0.25 μm, gate-source space of 1 μm, a gate width of 1200 μm (the number of gate fingers is 12 with a single-finger gate width of 100 μm), and source field plate length of 0.2 μm is fabricated successfully. Results of electrical tests show that the HEMT device has a threshold voltage of about −7.5 V, a low knee voltage of about 4 V, a high saturation drain current density of 750 mA/mm, and a gate leakage current as low as less than 160 pA at the gate bias of 0 V. At the frequency band of 703–803 MHz, with low bias control of 10 V, the S21 of HEMT is about 5.923–7.261 dB, the gain flatness is 1.338 dB, the current gain is about 27.3 dB, and the stability factor is more than 1. This device would be a good candidate for future 5G low-frequency and low-voltage-control applications.

Real-time stealth optical transmission via dither-remodulation in a bias controller of a Mach-Zehnder modulator.

The physical layer transmission security is a promising technology against security threats. As an effective supplement to the encryption strategy, steganography has received widespread attention. We report a real-time 2 kbps stealth transmission in the 10 Gbps dual polarization QPSK public optical communication. The stealth data is embedded in dither signals via precise and stable bias control technique for a Mach-Zehnder modulator. In the receiver, the stealth data can be recovered from the normal transmission signals by low SNR signal processing and digital down conversion. The stealth transmission has been verified to pose almost no impact on the public channel over a 117 km distance. The proposed scheme is compatible with existing optical transmission systems, so that no new hardware needs to be employed. It can be accomplished and is exceeded economically by adding simple algorithms, which utilizes only a small amount of FPGA resources. The proposed method can cooperate with encryption strategies or cryptographic protocols at different network layers to reduce the communication overhead and improve the overall security of the system.

Piezostrain-controlled magnetization compensation temperature in ferrimagnetic GdFeCo alloy films

The electrical control of magnetization compensation temperature (${T}_{\mathrm{com}}$) is investigated in ferrimagnetic GdFeCo alloy deposited on ferroelectric $\mathrm{Pb}({\mathrm{Mg}}_{1/3}{\mathrm{Nb}}_{2/3}){\mathrm{O}}_{3}\text{\ensuremath{-}}30%\mathrm{PbTi}{\mathrm{O}}_{3}$ (PMN-PT). It is found that the applied electric field can cause a 19 K shift in ${T}_{\mathrm{com}}$. ${T}_{\mathrm{com}}$ under different applied electric fields across the PMN-PT substrate suggests that such electrical control is induced by piezostrain at the ferroelectric-ferrimagnetic interface. Spin-polarized first-principles calculations show that the change of exchange coupling strength in GdFeCo are caused by the lattice strains. Nonvolatile switching of resistance and direct bipolar control of exchange bias are achieved when an electric field is applied to appropriately designed multilayers. Our findings pave the way for electric field control of ${T}_{\mathrm{com}}$ in ferrimagnetic GdFeCo alloy and have implications for the development of energy efficient and nonvolatile ferrimagnetic magnetic random access memory.

Nanometer-Sized Te Pistons in Carbon Cylinders as Nano-Motor Prototypes: Implications for Nano-Electromechanical Device Fabrication

Manipulation of nano-objects is fascinating as it not only facilitates the understanding of the essential properties of nanomaterials but also enlightens the design of future nano-devices beyond current micro-electromechanical systems. In this paper, a piston-cylinder-configured nano-motor prototype is designed and fabricated, in which nanometer-sized Te segments are electrically controlled to reciprocate inside the carbon cylinder serving as nano-pistons. The nano-motor prototype has a length of ∼550 nm, an outer diameter of ∼150 nm, and a piston diameter of ∼30 nm. Electron-wind and thermal expansion are two reciprocal forces that create a dynamic equilibrium on the Te pistons and enable the circulating motions. With bias control, a circulating rate of 0–4.3 counts per second has been achieved. This prototype is the smallest piston-cylinder-type nano-motor that has ever been reported and can provide new insights into investment casting-assisted nano-electromechanical device fabrication.

How does the quality of care for type 2 diabetic patients benefit from GPs-nurses' teamwork? A staggered difference-in-differences design based on a French pilot program.

In many countries, policies have explicitly encouraged primary care teams and inter-professional cooperation and skill mix, as a way to improve both productive efficiency gains and quality improvement. France faces barriers to developing team working as well as new and more advanced roles for health care professionals including nurses. We aim to estimate the impact of a national pilot experiment of teamwork between general practitioners (GPs) and advance practitioners nurses (APN)-who substitute and complement GPs-on yearly quality of care process indicators for type two diabetes patients (T2DP). Implemented by a not-for-profit meso-tier organisation and supported by the Ministry of Health, the pilot relied on the voluntary enrolment of newly GPs from 2012 to 2015; the staffing and training of APNs; skill mixing and new remuneration schemes. We use latent-response formulation models, control for endogeneity and selection bias by using controlled before-after and quasi-experimental design combining coarsened exact matching-prior to the treatment, at both GPs (435 treated vs 973 control) and T2DP levels -, with intention to treat (ITT; 18,310 in each group) and per protocol (PP, 2943 in each group) perspectives, as well as difference-in-differences estimates on balanced panel claims data from the National Health Insurance Fund linked to clinical data over the period 2010-2017. We show evidence of a positive and significant positive impact for T2DP followed-up by newly enrolled GPs in the pilot compared to the pretreatment period and the control group. The effect magnitudes were larger for PP than for ITT subsamples.

Multi-energy CT material decomposition using Bayesian deep convolutional neural network with explicit penalty of uncertainty and bias.

Convolutional neural network (CNN)-based material decomposition has the potential to improve image quality (visual appearance) and quantitative accuracy of material maps. Most methods use deterministic CNNs with mean-square-error loss to provide point-estimates of mass densities. Point estimates can be over-confident as the reliability of CNNs is frequently compromised by bias and two major uncertainties - data and model uncertainties originating from noise in inputs and train-test data dissimilarity, respectively. Also, mean-square-error lacks explicit control of uncertainty and bias. To tackle these problems, a Bayesian dual-task CNN (BDT-CNN) with explicit penalization of uncertainty and bias was developed. It is a probabilistic CNN that concurrently conducts material classification and quantification and allows for pixel-wise modeling of bias, data uncertainty, and model uncertainty. CNN was trained with images of physical and simulated tissue-mimicking inserts at varying mass densities. Hydroxyapatite (nominal density 400mg/cc) and blood (nominal density 1095mg/cc) inserts were placed in different-sized body phantoms (30 - 45cm) and used to evaluate mean-absolute-bias (MAB) in predicted mass densities across different images at routine- and half-routine-dose. Patient CT exams were collected to assess generalizability of BDT-CNN in the presence of anatomical background. Noise insertion was used to simulate patient exams at half- and quarter-routine-dose. The deterministic dual-task CNN was used as baseline. In phantoms, BDT-CNN improved consistency of insert delineation, especially edges, and reduced overall bias (average MAB for hydroxyapatite: BDT-CNN 5.4mgHA/cc, baseline 11.0mgHA/cc and blood: BDT-CNN 8.9mgBlood/cc, baseline 14.0mgBlood/cc). In patient images, BDT-CNN improved detail preservation, lesion conspicuity, and structural consistency across different dose levels.

DECLARE: A Comprehensive, Multifaceted Cognitive Forcing Strategy to Confront Complex Cases.

Diagnostic excellence is an important goal in medicine. The enhancement of clinical reasoning skills of physicians, which is at the core of this concept, is a significant challenge. To achieve this improvement, it is necessary to enhance the ability to collect patient history information and to integrate the information. Additionally, the complexity of diagnosis is confounded by biases, noise, uncertainty, and contextual factors, and the impact of these factors is particularly prominent in complex cases. In such cases, the dual process theory, which is a classical reasoning measure, alone is insufficient to cope with these challenges, and a multifaceted and comprehensive approach is required to supplement its limitations. Therefore, the author presents six concrete steps, represented by the acronym DECLARE (Decomposition, Extraction, Causation Link, Assessing Accountability, Recomposition, Explanation and Exploration), that implement the concept of cognitive forcing strategy that has been shown to be effective in bias control, and include reflection, meta-cognition, and the recently popularized decision hygiene procedure. DECLARE is a strategy that should be deployed when faced with more complex diagnostic scenarios. By examining each of the six steps that comprise DECLARE individually, cognitive load can be reduced. Furthermore, by verifying causation and accountability when constructing diagnostic hypotheses, biases can be mitigated, which can also help to address noise and uncertainty, leading to an improvement in the quality of diagnosis and effectiveness in medical education.

An Endless Optical Phase Delay for Harmonic-Free Phase/Frequency Shifting in Coherent-Lite DCIs and Microwave Photonics

An optical IQ modulator (IQM), when driven by quadrature-phase sinusoidal electrical signals, adds a phase/frequency offset to the optical input. However, harmonics of the applied electrical signals are also generated in the optical domain. In this work, we analytically derive the waveform shapes that achieve a harmonic-free phase/frequency shift with high spectral purity using the IQM. Using these waveforms, an endless optical phase delay (EOPD), wherein the phase delay can be increased or decreased continuously and arbitrarily in a reset-free manner, has been presented. The EOPD can be used in coherent-lite or analog coherent receiver based DCIs, phased array LiDAR, and many other applications. In order to adjust for the bias voltages and control signal gains in the EOPD, some of which may be time varying, a multivariate gradient descent algorithm is presented. Use of the EOPD for phase synchronization in a self-homodyne coherent-lite optical link has been demonstrated, making it a promising solution for low-power high-capacity coherent data center interconnects (DCIs).

Association Between Daily Alcohol Intake and Risk of All-Cause Mortality

A previous meta-analysis of the association between alcohol use and all-cause mortality found no statistically significant reductions in mortality risk at low levels of consumption compared with lifetime nondrinkers. However, the risk estimates may have been affected by the number and quality of studies then available, especially those for women and younger cohorts. To investigate the association between alcohol use and all-cause mortality, and how sources of bias may change results. A systematic search of PubMed and Web of Science was performed to identify studies published between January 1980 and July 2021. Cohort studies were identified by systematic review to facilitate comparisons of studies with and without some degree of controls for biases affecting distinctions between abstainers and drinkers. The review identified 107 studies of alcohol use and all-cause mortality published from 1980 to July 2021. Mixed linear regression models were used to model relative risks, first pooled for all studies and then stratified by cohort median age (<56 vs ≥56 years) and sex (male vs female). Data were analyzed from September 2021 to August 2022. Relative risk estimates for the association between mean daily alcohol intake and all-cause mortality. There were 724 risk estimates of all-cause mortality due to alcohol intake from the 107 cohort studies (4 838 825 participants and 425 564 deaths available) for the analysis. In models adjusting for potential confounding effects of sampling variation, former drinker bias, and other prespecified study-level quality criteria, the meta-analysis of all 107 included studies found no significantly reduced risk of all-cause mortality among occasional (>0 to <1.3 g of ethanol per day; relative risk [RR], 0.96; 95% CI, 0.86-1.06; P = .41) or low-volume drinkers (1.3-24.0 g per day; RR, 0.93; P = .07) compared with lifetime nondrinkers. In the fully adjusted model, there was a nonsignificantly increased risk of all-cause mortality among drinkers who drank 25 to 44 g per day (RR, 1.05; P = .28) and significantly increased risk for drinkers who drank 45 to 64 and 65 or more grams per day (RR, 1.19 and 1.35; P < .001). There were significantly larger risks of mortality among female drinkers compared with female lifetime nondrinkers (RR, 1.22; P = .03). In this updated systematic review and meta-analysis, daily low or moderate alcohol intake was not significantly associated with all-cause mortality risk, while increased risk was evident at higher consumption levels, starting at lower levels for women than men.

Dynamic Beam Steering and Focusing Graphene Metasurface Mirror Based on Fermi Energy Control

Beam steering technology is crucial for radio frequency and infrared telecommunication signal processing. Microelectromechanical systems (MEMS) are typically used for beam steering in infrared optics-based fields but have slow operational speeds. An alternative solution is to use tunable metasurfaces. Since graphene has gate-tunable optical properties, it is widely used in electrically tunable optical devices due to ultrathin physical thickness. We propose a tunable metasurface structure using graphene in a metal gap structure that can exhibit a fast-operating speed through bias control. The proposed structure can change beam steering and can focus immediately by controlling the Fermi energy distribution on the metasurface, thus overcoming the limitations of MEMS. The operation is numerically demonstrated through finite element method simulations.