Practical Scheme Research Articles

Evaluation of the ENIGMA mentoring pilot; an approach to tackle leadership inequalities for ethnically diverse female pharmacists in general practice

Abstract Introduction Diversity in leadership positively impacts teams and productivity yet remains limited in pharmacy leadership. The NHS Pharmacy Workforce Race Equality Standard (PWRES) report1 highlights significant underrepresentation of ethnically diverse pharmacists in senior NHS Trust roles (Agenda for Change band 8a and above) compared to their representation in the general pharmacist workforce. This disparity is particularly pronounced for female pharmacists from ethnically diverse backgrounds, who make up 31.9% of all pharmacists but only 24.2% of those in band 8a and above, with a steep decline from 27.6% at band 8a to just 5.9% at band 91,2. To begin addressing this, we piloted the ENhancing leadershIp amonGst ethnically diverse feMale phArmacists (ENIGMA) mentoring scheme in general practice within Leeds. Aim The aim was to determine whether ENIGMA mentoring relationships supported leadership skill development, fostered networking, and developed authentic allies. We assessed the success of the pilot by exploring the perspectives of mentors and mentees and identifying their satisfaction with the scheme. Methods We recruited 18 experienced female senior leaders as mentors for ENIGMA. Mentee recruitment was conducted through dissemination of a flyer via social media, Microsoft Teams, email, and citywide meetings, offering places to the first 18 pharmacists who expressed interest, with no selection process. Mentors and mentees were paired based on mentee goals and mentor expertise. Mentees met their mentor 3 times in a 6-month period. Evaluation was undertaken through Microsoft Forms questionnaires completed anonymously by both mentors and mentees exploring how they experienced ENIGMA, benefits of the scheme, if they felt the scheme had enabled leadership skill development and how it had (or anticipated it would) impact(ed) on career development for the mentee. Ethical approval was deemed unnecessary as this was a service evaluation. Neither author-affiliated organisation is associated with the project. Results Of 18 mentoring pairs, 13 successfully completed the 6-month program. For five pairs, mentees did not establish contact. 62% of mentors responded to the questionnaire. 88% felt a positive impact on their mentoring skills and described a noticeable increase in mentee confidence and career focus. 100% of mentors would recommend the scheme. For mentees, 62% responded. 88% were ‘most satisfied’ with the experience and 88% felt their leadership skills improved because of participating. 75% met specific goals and 88% commented particularly on positive impact of the scheme. 100% of mentees would recommend ENIGMA to others. Discussion/conclusion The pilot was a great success, impacting positively on mentors and mentees. All responding participants would recommend it to others. Mentors and mentees expressed some challenges: finding a mutual time for meetings, and support from employers to use work time for meetings. Questionnaires were not returned by 38% of participants, which is a potential limitation. An identified improvement is to offer mentors an alternative mentee if their original mentee does not make contact. Offer of an annual ENIGMA cohort will continue in Leeds. Additionally, a second pilot is planned in another Integrated Care Board (ICB) to establish how ENIGMA could work in a different area, and if successful, we intend to share this model nationally.

Adaptive Free‐Will Arbitrary Time Tracking of a Class of Uncertain SISO Nonlinear Systems

ABSTRACTThis paper investigates the free‐will arbitrary time (FWAT) tracking problem for a class of single‐input single‐output (SISO) strict‐feedback nonlinear systems without/with parametric uncertainties. First, we design an FWAT backstepping tracking controller for strict‐feedback nonlinear systems without uncertainties in a recursive manner. It is shown that the proposed virtual controllers and their derivatives are continuously differentiable for the backstepping design, and the tracking error converges to zero within the pre‐specified settling time independent of design parameters and initial conditions and remains at zero after the pre‐specified settling time. Second, we address the adaptive FWAT tracking problem of SISO strict‐feedback nonlinear systems with parametric uncertainties in the practical stability sense. The continuously differentiable and nonsingular time‐varying adjustment functions for practical FWAT tracking are developed to transform error variables for the recursive backstepping design. Then, the adaptive FWAT backstepping tracking design and stability analysis strategy are established to ensure practical FWAT convergence within the pre‐specified settling time independent of design parameters and initial conditions. The designed adaptive virtual and actual controllers are continuous for all . Furthermore, a practical adaptive prescribed‐time tracking scheme is provided using the proposed practical adaptive FWAT tracking scheme. Finally, the effectiveness of the theoretical approaches is confirmed through a simulation example and an experimental result.

Supervised and Revocable Decentralized Identity Privacy Protection Scheme

Decentralized identity represents an innovative approach based on blockchain to achieve effective identity management. This method utilizes decentralized identifiers and verifiable credential to enable trusted authentication, free circulation of identity information, and self-sovereign control over identity data functionalities. The current decentralized identity systems rely on entirely anonymous identifiers, lacking robust identity regulation. Furthermore, they face challenges such as identity attribute leakage during verifiable credential presentation and the issuers' struggle to reliably revoke credentials. To address these issues, efficient and practical schemes have been designed based on BBS signature, zero-knowledge proof, dynamic accumulator and blockchain technology: one for decentralized identifiers management and the other for verifiable credential privacy protection, both of which are supervised and revocable. The former ensures the privacy of subject identity while achieving regulatability and revocability of identity data by regulator. The latter facilitates selective disclosure of anonymous credentials and reliable revocation. A security analysis shows that the proposed scheme meets anonymity, non-forgeability, regulatory reliability, and revocability reliability, and offers comprehensive and effective privacy protection measures. The experimental results demonstrate that the algorithms designed operate at a millisecond level, which satisfies the demands of blockchain identity management scenarios.

Constructing the Fulde-Ferrell-Larkin-Ovchinnikov State in a CrOCl/NbSe2 van der Waals Heterostructure.

Time reversal symmetry breaking in superconductors, resulting from external magnetic fields or spontaneous magnetization, often leads to unconventional superconducting properties. In this way, an intrinsic phenomenon called the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state may be realized by the Zeeman effect. Here, we construct the FFLO state in an artificial CrOCl/NbSe2 van der Waals (vdW) heterostructure by utilizing the superconducting proximity effect of NbSe2 flakes. The proximity-induced superconductivity demonstrates a considerably weak gap of about 0.12 meV, and the in-plane upper critical field reveals the behavior of the FFLO state. First-principles calculations uncover the origin of the proximitized superconductivity, which indicates the importance of Cr vacancies or line defects in CrOCl. Moreover, the FFLO state could be induced by the inherent large spin splitting in CrOCl. Our findings not only provide a practical scheme for constructing the FFLO state but also inspire the discovery of an exotic FFLO state in other two-dimensional vdW heterostructures.

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Understanding Leakage in Searchable Encryption: a Quantitative Approach

Searchable encryption, or more generally, structured encryption, permits search over encrypted data. It is an important cryptographic tool for securing cloud storage. The standard security notion for structured encryption mandates that a protocol leaks nothing about the data or queries, except for some allowed leakage, defined by the leakage function. This is due to the fact that some leakage is unavoidable for efficient schemes.\\ Unfortunately, it was shown by numerous works that even innocuous-looking leakage can often be exploited by attackers to undermine users' privacy and recover their queries and/or data, despite the structured encryption schemes being provably secure. Nevertheless, the standard security remains the go-to notion used to show the 'security' of structured encryption schemes. While it is not likely that researchers will design practical structured encryption schemes with no leakage, it is not satisfactory that very few works study ways to assess leakage.This work proposes a novel framework to quantify leakage. Our methodology is inspired by the quantitative information flow, and we call our method q-leakage analysis. We show how $q$-leakage analysis is related to the standard security. We also demonstrate the usefulness of q-leakage analysis by analyzing the security of two existing schemes with complex leakage functions.

A practical convertible quantum signature scheme with public verifiability into universal quantum designated verifier signature using self-certified public keys

A practical convertible quantum signature scheme with public verifiability into universal quantum designated verifier signature using self-certified public keys

Optimizing kitchen ventilation with an integrated stove air supply-exhaust system for reducing PM2.5 intake fraction and enhancing energy efficiency

Ensuring good ventilation is crucial for reducing the pollution caused by cooking activities in the indoor environment. Among them, fume exhaust devices as a vital component of kitchen ventilation, and their performance is particularly critical. Merely increasing the air volume of exhaust devices to remove fumes not only leads to the higher energy consumption of exhaust fans, but also has limited practical effect on reducing pollution. For improving the ventilation condition and reducing the energy consumption of supply and exhaust fan, a new ventilation system for kitchen with integrated stove air supply-exhaust (ISASE) was developed in this study. Firstly, the orthogonal experiment was utilized for arranging different combination schemes of five influencing factors, including the emission rate, exhaust flow, upper air supply angle, upper and lower air supply velocities. Then, the effect of ISASE in reducing the intake fraction of PM2.5 under each scheme was studied by using the computational fluid dynamics method. The energy consumption of this system under different ventilation schemes was investigated with on-site testing. Finally, performance-gaining rate was proposed by introducing intake fraction reduction rate and energy consumption growth rate to quantitatively evaluate the performance advantage of ISASE. Polynomial fitting was also used to explore the energy-saving effect of ISASE at high emission rate. The results showed that at low, medium, and high emission rates, the intake fraction of PM2.5 was reduced by 65.0%–85.2%. However, the energy consumption of ISASE merely increased by 7.4%–16.8% compared with traditional integrated stove without air supply conditions. Its maximum performance-gaining rate reached 0.49–0.66. The energy-saving rate of ISASE was 46.7% compared with traditional integrated stove without air supply when the same intake fraction was achieved at high emission rate. The practical schemes of ISASE at different emission rates were given in this study, which optimized the working performance of integrated stove and provided a useful reference for its innovative design.

Robust optical singularity detection enabled by spin-synchronized shearing interference

Singular beams carrying phase and polarization singularities have been extensively studied for applications in optical communication, quantum information processing, optical imaging, and other fields. However, developing a robust method to detect optical singularity that is insensitive to wavelength, polarization, and illumination position remains a significant challenge. In this study, we propose and demonstrate a robust optical singularity detection scheme based on spin-synchronized shearing interference (SSSI) using spin-dependent gradient phase modulation of a quarter-wave liquid–crystal polarization grating. Since the spin-dependent gradient phase modulation of the polarization grating is wavelength-independent, and the two circular bases of the incident field can be measured simultaneously, the proposed method is robust to wavelength and polarization. Additionally, we have verified that SSSI performs well in detecting multi-singularity cylindrical vector beam. This work provides a practical and straightforward scheme for robust optical singularity detection.

A practical scheme for enhancing consistency and independence in wireless key generation for Wi-Fi networks

Wi-Fi networks benefit from physical layer wireless key generation, a theoretically secure method for improving information transmission security. This paper introduces the Consistent Independent Key Generation (CIKG) scheme based on the Wi-Fi signal transmission model. It addresses the dual challenges of channel noise and virtual carrier low-pass effects. The scheme begins with extracting the channel frequency response (CFR) from the entire signal preamble, including both long and short symbols, followed by applying cubic spline interpolation to the short symbol-derived CFR to achieve fine-grained frequency fading corresponding to the long symbols. This refined CFR is averaged to bolster noise resistance, improving key consistency. Subsequently, the inverse Fourier transform is used to obtain the channel impulse response (CIR), and a deconvolution strategy reduces the impact of virtual carrier low-pass filtering on the multipath information of CIR, thus improving key independence. Implemented within Wi-Fi networks, the effectiveness of the CIKG scheme is rigorously tested across diverse scenarios. Quantitative evaluations indicate that the scheme substantially improves key consistency, achieving a 5 dB enhancement in signal-to-noise ratio over traditional CFR-based schemes and elevates information entropy by 20%, significantly boosting key independence. These advances affirm the potential of the CIKG scheme as a formidable solution for developing robust and secure wireless communication networks.

Investigation on two-dimensional ultrasonic vibration plane turning method based on elliptical plane swinging strategy along short axis

In the ultrasonic elliptical vibration cutting (UEVC) process, the surface morphology suffers from the large fluctuation of ridge height and residual height, which has restricted the development of UEVC technology. Therefore, on the basis of the elliptical plane swinging strategy along a short axis, a surface topography improvement method (i.e., swing ultrasonic elliptical vibration turning (SUEVT)) for two-dimensional ultrasonic vibration plane turning is proposed in this study. The core idea of the method is to swing the ultrasonic elliptical vibration plane along the short axis to form a machining inclination angle, which reduces the ridge height of the machined morphology to a certain extent, thereby optimizing the surface microstructure and reducing the surface roughness. First, this study establishes a mathematical model of the tool tip trajectory of ultrasonic elliptical vibration under different swing angles, theoretically analyzes the influence of swing angles on the tool tip trajectory and surface topography, and provides a theoretical basis for revealing the surface topography formation mechanism of SUEVT. Second, an engineering practice scheme of SUEVT is proposed, and a processing experiment platform is built. Finally, cutting experiments of SUEVT for TC4 titanium alloy were conducted, studying the influence of swing angles on surface topography and roughness and assessing the feasibility of the method. Results showed that compared with ultrasonic elliptical vibration turning (UEVT), SUEVT has certain advantages in improving surface morphology. When the swing angle of SUEVT is 15°, the surface roughness decreases the most, reaching 19.00 %. Under the condition of SUEVT θ = 15°, the surface roughness decreases first and then increases with the increase in spindle speed, while it is proportional to the feed speed and ultrasonic power. The method proposed in this study can provide new methods and new ideas for ultrasonic elliptical vibration high surface quality machining.

A guide to protection schemes of synchronous generator-based electric grids

Ensuring the reliability of power systems remains a paramount concern for electrical utilities worldwide. The design and implementation of robust protection is crucial for mitigating faults and ensuring uninterrupted power supply. This paper encompasses the protection deployed in electrical utilities. A comprehensive analysis is conducted on the application protection scheme in practice for the protection of major power components. The reliability of protection schemes is enhanced by adopting the of dual & fast protection schemes on the critical power components for swift & reliable clearing of faults. Additionally, this article also investigates the protection strategies employed for slightly lower priority components, including high-speed main and low-speed backup protection schemes. A thorough review for the protection practices for synchronous power generation, transmission, and distribution has been conducted. In this article, the selection of specific protection schemes and the calculation settings for 600 MW synchronous generator-connected lines and transformers are discussed. Additionally, the limitations of traditional protection schemes in safeguarding inverter-based power systems are explored. The paper also highlights areas for future research in the protection of electrical equipment within evolving grid infrastructures. This study will provide valuable insights for researchers and practicing engineers in planning and implementing protection systems for electrical grids, ensuring high levels of reliability and resilience

Non-Gaussian two mode squeezed thermal states in continuous variable quantum teleportation

We consider a practical scheme for the implementation of non-Gaussian operation, viz., photon subtraction, photon addition, and photon catalysis, on two-mode squeezed thermal state. The generated states are employed as resources in continuous-variable quantum teleportation. The results show that the three non-Gaussian operations can enhance the teleportation fidelity. Considering the success probability of the non-Gaussian operations, we identify single-photon catalysis and single photon subtraction to be optimal for teleporting input coherent states and squeezed vacuum states, at low and intermediate squeezing levels, respectively.

165 MHz highly stable femtosecond Er:ZBLAN fiber laser operating at 2.8 µm.

Three-micrometer mid-infrared (MIR) femtosecond pulse sources with a high repetition rate (HRR) have potential applications in a number of fields such as biological imaging, optical frequency combs, and gas detection. In this paper, by optimizing the fiber length and the cavity structure, we demonstrated a highly stable, self-starting mode-locked fluoride fiber laser (MLFFL) with a fundamental repetition rate of ∼165 MHz and a signal-to-noise ratio (SNR) of 90 dB. As far as we know, this stands as the highest fundamental repetition rate ever acquired directly from an ultrafast MLFFL in the >2.5 µm MIR region. Stable 352-fs pulses at 2795 nm with an average output power of 392 mW and a low integrated relative intensity noise (RIN) of 0.018% [10 Hz, 10 MHz] were generated. The root mean square (RMS) power fluctuation is 0.17% over 2 h, which indicates excellent oscillator stability. This high-performance laser offers a practicable scheme both for scaling the repetition frequency in MIR MLFFLs and high-precision ultrafast applications at longer wavelengths.

Retinal Vessel Segmentation by a Transformer-U-Net Hybrid Model With Dual-Path Decoder.

This paper introduces an effective and efficient framework for retinal vessel segmentation. First, we design a Transformer-CNN hybrid model in which a Transformer module is inserted inside the U-Net to capture long-range interactions. Second, we design a dual-path decoder in the U-Net framework, which contains two decoding paths for multi-task outputs. Specifically, we train the extra decoder to predict vessel skeletons as an auxiliary task which helps the model learn balanced features. The proposed framework, named as TSNet, not only achieves good performances in a fully supervised learning manner but also enables a rough skeleton annotation process. The annotators only need to roughly delineate vessel skeletons instead of giving precise pixel-wise vessel annotations. To learn with rough skeleton annotations plus a few precise vessel annotations, we propose a skeleton semi-supervised learning scheme. We adopt a mean teacher model to produce pseudo vessel annotations and conduct annotation correction for roughly labeled skeletons annotations. This learning scheme can achieve promising performance with fewer annotation efforts. We have evaluated TSNet through extensive experiments on five benchmarking datasets. Experimental results show that TSNet yields state-of-the-art performances on retinal vessel segmentation and provides an efficient training scheme in practice.

Jump Out of Resonance: A Practical NFC Tag Fingerprinting Scheme

Jump Out of Resonance: A Practical NFC Tag Fingerprinting Scheme

Steady-state and dynamic simulation of gas phase polyethylene process

Gas-phase polyethylene (PE) processes are among the most important methods for PE production. A deeper understanding of the process characteristics and dynamic behavior, such as properties of PE and reactor stability, holds substantial interest for both academic researchers and industries. In this study, both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform, which can be used to study a variety of operation tasks for commercial solution polyethylene processes, such as new product development, process control and real-time optimization. The copolymerization kinetic parameters are fitted by industrial data. Additionally, a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed reactor. The accuracy in predicting melt index and density of the polymer, and the dynamic behavior of the developed models are verified by real plant data. Moreover, the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments, since temperature control is important in industrial production. The results reveal that all four schemes effectively track the setpoint temperature. However, only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

Parameter optimisation of the ventilation system for an underground power space using a hybrid model

The ventilation system is one of the essential safety systems in underground power spaces. Over the years, active ventilation has been widely employed for heat dissipation in underground power spaces. In operation, high-power equipment generates significant heat, necessitating sufficient heat dissipation for smooth and efficient functioning. The effectiveness of the ventilation system is influenced by airflow, making aerodynamics a crucial aspect of studying underground power spaces. This study establishes a comprehensive hybrid model (a combination of physical and data-driven models) representing underground power spaces. Ansys Fluent and MATLAB are used to simulate and calculate temperature fields for various structures. The physical model employs model order reduction to achieve efficient computation without compromising accuracy. For the data-driven model, a genetic neural network is developed for multifactor nonlinear optimisation to evaluate and analyse thermal behaviour within the space. The integrated hybrid model enables efficient and high-precision calculations for the underground power space’s ventilation system. The research outcomes provide a theoretical foundation for practical construction and design schemes of underground power spaces, contributing significantly to ensuring their safety and optimal functionality in real-world applications.

GraphiQ: Quantum circuit design for photonic graph states

GraphiQ is a versatile open-source framework for designing photonic graph state generation schemes, with a particular emphasis on photon-emitter hybrid circuits. Built in Python, GraphiQ consists of a suite of design tools, including multiple simulation backends and optimization methods. The library supports scheme optimization in the presence of circuit imperfections, as well as user-defined optimization goals. Our framework thus represents a valuable tool for the development of practical schemes adhering to experimentally-relevant constraints. As graph states are a key resource for measurement-based quantum computing, all-photonic quantum repeaters, and robust quantum metrology, among others, we envision GraphiQ's broad impact for advancing quantum technologies.

Many-Body Entropies and Entanglement from Polynomially Many Local Measurements

Estimating global properties of many-body quantum systems such as entropy or bipartite entanglement is a notoriously difficult task, typically requiring a number of measurements or classical postprocessing resources growing exponentially in the system size. In this work, we address the problem of estimating global entropies and mixed-state entanglement via partial-transposed (PT) moments and show that efficient estimation strategies exist under the assumption that all the spatial correlation lengths are finite. Focusing on one-dimensional systems, we identify a set of approximate factorization conditions (AFCs) on the system density matrix, which allow us to reconstruct entropies and PT moments from information on local subsystems. This identification yields a simple and efficient strategy for entropy and entanglement estimation. Our method could be implemented in different ways, depending on how information on local subsystems is extracted. Focusing on randomized measurements providing a practical and common measurement scheme, we prove that our protocol requires only polynomially many measurements and postprocessing operations, assuming that the state to be measured satisfies the AFCs. We prove that the AFCs hold for finite-depth quantum-circuit states and translation-invariant matrix-product density operators and provide numerical evidence that they are satisfied in more general, physically interesting cases, including thermal states of local Hamiltonians. We argue that our method could be practically useful to detect bipartite mixed-state entanglement for large numbers of qubits available in today’s quantum platforms. Published by the American Physical Society 2024