Projective Measurements Research Articles

Qualitative study on sustainability of intervention measures in the Shandong Provincial Department's Joint Salt Reduction Project

Objective: To employ qualitative research methods to evaluate the sustainability of the Shandong-Ministry of Health Action on Salt and Hypertension (SMASH) interventions and their influencing factors. Method: In September 2023, interviewees meeting the inclusion criteria were recruited through purposive sampling in Jinan, Shandong Province. A semi-structured interview guide was designed based on the CFIR and RE-AIM frameworks. Personal in-depth interviews and focus group discussions were conducted to gather insights on the feasibility, effectiveness, and sustainability of the project interventions from various stakeholders, including representatives from the government, food manufacturers, restaurants, academia, and residents. Results: A total of 15 individuals participated in in-depth interviews, involving six representatives from food manufacturers, four from restaurants, three from the government, and two from academia. There were four focus group discussions with 30 residents. The study found that at the individual resident level, health awareness and the availability of sodium reduction tools in the market could affect their salt reduction practices. For food manufacturers and restaurants, consumers' preferences, technical challenges in reformulation, and government support were key factors determining the smooth progress of their salt reduction efforts. At the governmental level, multi-sectional coordination, chronic disease management demonstration areas, and the impact of the pandemic were the main factors influencing the implementation of sodium reduction interventions. Conclusion: Future endeavours should strengthen long-term management and optimise the complex influencing factors associated with intervention measures. This will be essential in sustaining and expanding the positive health outcomes achieved through the Shandong population sodium reduction strategy.

Cost Impact Factors and Control Measures of Road and Bridge Projects Based on Linear Regression Model

Cost Impact Factors and Control Measures of Road and Bridge Projects Based on Linear Regression Model

Speed excess and total acceleration: a kinematical approach to entanglement

Abstract The total variance of a spin state is defined as the average of the variances of spin projection measurements along three orthogonal axes. We show that this quantity also gives the squared rotational speed of the state in projective space, averaged over all rotation axes. We compute the addition law, under system composition, for this quantity and find that, in the case of separable states, it is of simple pythagorean form. In the presence of entanglement, we find that the composite state "rotates faster than its parts", thus unveiling a kinematical origin for the correlation of total variance with entanglement. We analyze a similar definition for the acceleration of a state under rotations, for both pure and mixed states, and probe numerically its relation with a wide array of entanglement related measures.

Entanglement – nonstabilizerness separation in hybrid quantum circuits

Nonstabilizerness describes the distance of a quantum state to its closest stabilizer state. It is—like entanglement—a necessary resource for a quantum advantage over classical computing. We study nonstabilizerness, quantified by stabilizer entropy, in a hybrid quantum circuit with projective measurements and a controlled injection of non-Clifford resources. We discover a phase transition between a power law and constant scaling of nonstabilizerness with system size controlled by the rate of measurements. The same circuit also exhibits a phase transition in entanglement that appears, however, at a different critical measurement rate. This mechanism shows how, from the viewpoint of a quantum advantage, hybrid circuits can host multiple distinct transitions where not only entanglement, but also other nonlinear properties of the density matrix come into play. Published by the American Physical Society 2024

Robust projective measurements through measuring code-inspired observables

Quantum measurements are ubiquitous in quantum information processing tasks, but errors can render their outputs unreliable. Here, we present a scheme that implements a robust projective measurement through measuring code-inspired observables. Namely, given a projective POVM, a classical code, and a constraint on the number of measurement outcomes each observable can have, we construct commuting observables whose measurement is equivalent to the projective measurement in the noiseless setting. Moreover, we can correct t errors on the classical outcomes of the observables’ measurement if the classical code corrects t errors. Since our scheme does not require the encoding of quantum data onto a quantum error correction code, it can help construct robust measurements for near-term quantum algorithms that do not use quantum error correction. Moreover, our scheme works for any projective POVM, and hence can allow robust syndrome extraction procedures in non-stabilizer quantum error correction codes.

Cybersecurity Transformation: Cyber-Resilient IT Project Management Framework

In response to the escalating threats of cybersecurity attacks and breaches, ensuring the development and deployment of secure IT products has become paramount for organizations in their cybersecurity transformation. This work emphasizes the critical need for a comprehensive and secure IT project management life cycle that safeguards products from their initial development stages through decommissioning. The primary objective is to seamlessly integrate security considerations into every facet of IT project management life cycles. This work embraces a cyber-resilient IT project management framework and advocates the inclusion of cybersecurity measures in IT projects and their strategic, organized, continuous, and systematic integration throughout the entire product life cycle. It introduces a pioneering framework that harmonizes the cybersecurity risk management process with the IT project management life cycle. This framework delineates a methodical sequence of steps, each encompassing a distinct set of activities. The effectiveness and practical applicability of the proposed framework were validated through a comprehensive case study focused on the Personal Health Record (PHR) system. The PHR case study served as a real-world scenario to assess the framework’s ability to address cybersecurity challenges in a specific domain. The results of the experiment demonstrated the framework’s efficacy in enhancing the security posture of IT projects, showcasing its adaptability and scalability across diverse applications.

Unbounded Sharing of Nonlocality Using Qubit Projective Measurements.

The prevailing consensus is that the sequential sharing of nonlocality in a Bell experiment requires generalized unsharp measurements, given that a sharp measurement inevitably destroys the entanglement of the shared state. In contrast, a recent work [A. Steffinlongo and A. Tavakoli, Projective measurements are sufficient for recycling nonlocality, Phys. Rev. Lett. 129, 230402 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.230402] demonstrated the sharing of nonlocality up to two sequential observers by employing projective measurement aided with local randomness. Here, we introduce a form of local randomness-assisted qubit projective measurement protocol that enables the sharing of nonlocality by an arbitrary number of sequential observers (Bobs) with a single spatially separated observer (Alice). We achieve this by inspecting the diversity involved in implementing generalized measurements that harness nonlocality by preserving a sufficient amount of entanglement of the shared two-qubit entangled state. Furthermore, we highlight the crucial interplay between the degrees of measurement incompatibility of Alice and Bob in demonstrating the unbounded sharing of nonlocality.

Robustness of a state with Ising topological order against local projective measurements

Robustness of a state with Ising topological order against local projective measurements

Multifractality in monitored single-particle dynamics

We study multifractal properties in time evolution of a single particle subject to repeated measurements. For quantum systems, we consider circuit models consisting of local unitary gates and local projective measurements. For classical systems, we consider models for estimating the trajectory of a particle evolved under local transition processes by partially measuring particle occupations. In both cases, multifractal behaviors appear in the ensemble of wave functions or probability distributions conditioned on measurement outcomes after a sufficiently long time. While the nature of particle transport (diffusive or ballistic) qualitatively affects the multifractal properties, they are even quantitatively robust to the measurement rate or specific protocols. On the other hand, multifractality is generically lost by generalized measurements allowing erroneous outcomes or by postselection of the outcomes with no particle detection. We demonstrate these properties by numerical simulations and also propose several simplified models, which allow us to analytically obtain multifractal properties in the monitored single-particle systems. Published by the American Physical Society 2024

Long-Term Nose Evaluation After Treatment of Bilateral Cleft Lip Using the Spina Technique.

Bilateral cleft lip (BCL) repair is a challenge due to anatomic and functional distortions, worse than unilateral cleft. With regard to the nose, BCL can result in certain characteristics, such as a short columella, a flattened and widened tip, and horizontally oriented nostrils. Lip and nose treatment is advocated by some surgeons to achieve a better outcome. One of the techniques used to treat this condition is the one recommended by Victor Spina, in a complex protocol involving more than one surgical procedure and long-term follow-up. The aim of this study is to evaluate the growth of the nose in patients treated using the Spina technique. This was a retrospective and analytical study of the medical records of patients who were treated using the Spina technique at the Assistance Center for Cleft Lip and Palate (CAIF) at Hospital do Trabalhador in Curitiba, Brazil. Anthropometric measurements of interalar distance and nose tip projection were made from frontal and submental photographs taken at three moments: pre-first surgery, pre-definitive surgery, and late follow-up. Furthermore, other analyzed parameters were: sex, date of birth, age at time of surgery, and late follow-up. With these data, a comparison of nose growth was made between patients with BCL and a population without craniofacial anomalies observed in a study by FARKAS et al (1992). The comparison also took sex and age variables into account. Regarding the interalar distance measurements, both sexes showed a widening of the nose before starting treatment. This widening was maintained throughout follow-up. The growth curve, in turn, remained similar among males, with an average growth rate of only 3.2% higher in patients with BCL. Among females, the difference was one of 16.92%. In terms of nasal tip projection, there was an average increase of 1.12cm (86.15%) in males and 0.69cm (57.02%) in females. It was possible to notice a nasal growth rate in those with BCL similar to the normal pattern and a significant improvement in nasal tip projection in male patients. However, with regard to the data of both sexes, but especially females, the low number of patients jeopardized the significance of the results.

Enhancing Large-Diameter Tunnel Construction Safety with Robust Optimization and Machine Learning Integrated into BIM

Aim This study aims to enhance safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). By acquiring and preprocessing various datasets, implementing feature engineering, and using algorithms like SVM, decision trees, ANN, and random forests, the study demonstrates the effectiveness of ML models in risk prediction and mitigation, ultimately advancing safety performance in civil engineering projects. Background Large diameter tunnel construction presents significant safety challenges. Traditional methods often fall short of effectively predicting and mitigating risks. This study addresses these gaps by integrating robust optimization and machine learning (ML) approaches with Building Information Modeling (BIM) technology. By acquiring and preprocessing diverse datasets, implementing feature engineering, and employing ML algorithms, the study aims to enhance risk prediction and safety measures in tunnel construction projects. Objective The objective of this study is to improve safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). This involves acquiring and preprocessing diverse datasets, using feature engineering to extract key parameters, and applying ML algorithms like SVM, decision trees, ANN, and random forests to predict and mitigate risks, ultimately enhancing safety performance in civil engineering projects. Methods The study's methods include acquiring and preprocessing various datasets (geological, structural, environmental, operational, historical, and simulation). Feature engineering techniques are used to extract key safety parameters for tunnels. Machine learning algorithms, such as decision trees, support vector machines (SVM), artificial neural networks, and random forests, are employed to analyze the data and predict construction risks. The SVM algorithm, with a 98.76% accuracy, is the most reliable predictor. Results The study found that the Support Vector Machine (SVM) algorithm was the most accurate predictor of risks in large diameter tunnel construction, achieving a 98.76% accuracy rate. Other models, such as decision trees, artificial neural networks, and random forests, also performed well, validating the effectiveness of ML-based solutions for risk assessment and mitigation. These predictive models enable stakeholders to monitor construction, allocate resources, and implement preventative measures effectively. Conclusion The study concludes that integrating machine learning (ML) approaches with Building Information Modeling (BIM) significantly improves safety in large diameter tunnel construction. The Support Vector Machine (SVM) algorithm, with 98.76% accuracy, is the most reliable predictor of risks. Other models, like decision trees, artificial neural networks, and random forests, also perform well, validating ML-based solutions for risk assessment. Adopting these ML approaches enhances safety performance and resource management in civil engineering projects.

Noise sensors - Part 1: Usage for the redesign of neighbourhoods

An interdisciplinary research project at the Stuttgart University of Applied Sciences with participation of the departments of urban planning, surveying and geoinformatics, business psychology and acoustics, present alternative urban scenarios. Different aspects for the redesign of the district, especially measures to reduce the individual motorised transport, were investigated. Virtual reality and augmented reality experiments are used in public spaces, to show redesign possibilities. Surveys of people, who had the experience of the future scenarios via the AR application and VR application, were carried out. Additionally, acoustical surveys and measurements in this research project were performed using so called noise sensors. These sensors based on MEMS microphones were developed for a simple and cost-effective measurement of external noise in a citizen sciences project. The measurement results of the noise sensors are saved online and can be accessed on the homepage "Sensor.Community". One of the noise sensors was used in the project area over a longer period. Measurement values were generated in third octave bands at intervals of 5 s and are available for further evaluations, e.g. for the calculation of indoor noise levels. The paper presents the research project and reports about the application of the noise sensor.

The impact of quantum correlations on parameter estimation in a spin reservoir

We study the impact of quantum correlations existing within the system-environment thermal equilibrium state while estimating the parameters of the spin reservoir. By employing various physical situations of interest, we present results for the reservoir temperature and its coupling strength with the central two-level system. The central system (probe) interacts with the bunch of randomly oriented spin systems and attains a thermal equilibrium state. We consider a projective measurement which prepares the probe’s initial state, and then the global system (probe and reservoir) evolves unitarily. The reduced density operator encapsulates the information about the spin reservoir which can be extracted by doing measurements on the probe. The precision of such measurement is quantified by quantum Fisher information. We repeat this process if the probe-reservoir initial state is not correlated (product state). We compare the estimation results for both with and without the outturn of initial correlations. In the temperature estimation case, our results are promising as one can significantly improve the accuracy of the estimates by including the effect of initial correlations. A similar trend prevails in the case of coupling strength estimation especially at low temperatures.

High-fidelity spin readout via the double latching mechanism

Projective measurement of single-electron spins, or spin readout, is among the most fundamental technologies for spin-based quantum information processing. Implementing spin readout with both high-fidelity and scalability is indispensable for developing fault-tolerant quantum computers in large-scale spin-qubit arrays. To achieve high fidelity, a latching mechanism is useful. However, the fidelity can be decreased by spin relaxation and charge state leakage, and the scalability is currently challenging. Here, we propose and demonstrate a double-latching high-fidelity spin readout scheme, which suppresses errors via an additional latching process. We experimentally show that the double-latching mechanism provides significantly higher fidelity than the conventional latching mechanism and estimate a potential spin readout fidelity of 99.94% using highly spin-dependent tunnel rates. Due to isolation from error-inducing processes, the double-latching mechanism combined with scalable charge readout is expected to be useful for large-scale spin-qubit arrays while maintaining high fidelity.

FOBICS: Assessing project security level through a metrics framework that evaluates DevSecOps performance

Context:In today’s software development landscape, the DevSecOps approach has gained traction due to its focus on the software development process and bolstering security measures in projects, a task in light of the ever-evolving cybersecurity threats. Objective:This study aims to address the lack of metrics for quantitatively assessing its efficacy from both security and business logic perspectives. Methods:To tackle this issue, the research introduces the Framework of Business Index Concerning Security (FOBICS), a set of metrics designed to enable transparent evaluations of project security. FOBICS considers various perspectives relevant to DevSecOps practices. It includes factors such as project duration and financial outcomes, making it appealing for implementation in business settings. Results:The effectiveness of FOBICS is validated theoretically and empirically via its application in two real-world projects: the results from these implementations show a correlation between FOBICS metrics and the security strategies employed as the development methodologies adopted by diverse teams throughout the projects. Conclusion:Hence, FOBICS emerges as a tool for assessing and continuously monitoring project security, offering insights into areas of strength and areas that may require enhancement. FOBICS is shown to be effective in assessing the level of DevSecOps implementation. The ease of calculating FOBICS metrics makes them easily interpretable and continuously verifiable. Moreover, FOBICS summarizes most of the other quantitative and qualitative metrics in the literature.

A Denoising Diffusion Probabilistic Model for Metal Artifact Reduction in CT.

The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compared to conditionally trained approaches. The performance of the proposed technique was evaluated and compared to the state-of-the-art normalized MAR (NMAR) approach as well as to CNN-based and GAN-based MAR approaches. The DDPM-based approach provided significantly higher SSIM and PSNR, as compared to NMAR (SSIM: p [Formula: see text]; PSNR: p [Formula: see text]), the CNN (SSIM: p [Formula: see text]; PSNR: p [Formula: see text]) and the GAN (SSIM: p [Formula: see text]; PSNR: p <0.05) methods. The DDPM-MAR technique was further evaluated based on clinically relevant image quality metrics on clinical CT images with virtually introduced metal objects and metal artifacts, demonstrating superior quality relative to the other three models. In general, the AI-based techniques showed improved MAR performance compared to the non-AI-based NMAR approach. The proposed methodology shows promise in enhancing the effectiveness of MAR, and therefore improving the diagnostic accuracy of CT.

Q-Rung Orthopair Fuzzy Approach for Multiple-Attribute Group Decision-Making and Application to Commercial Computer Network Security Evaluation

Nowadays, with the increasing use of computer network technology in various fields, coupled with the openness of the Internet, computer networks are facing various information security issues, which will have a very important impact on their applications. Both enterprises and individuals may cause significant economic losses due to these problems, so certain measures need to be taken to protect the information security of computer networks. The commercial computer network security (CCNS) evaluation is MAGDM. In this research, based on projection measure (PROJM) and bidirectional projection measure (BPROJM), some forms of projection measure with q-rung orthopair fuzzy sets (q-ROFSs) are depicted. Then, two weighed projection approaches are depicted to put forward the MAGDM. Lastly, numerical examples for CCNS evaluation and different comparative analysis is employed to verify the developed approaches.

Memory effects in a sequence of measurements of noncommuting observables

We use continuous, stochastic quantum trajectories within a framework of quantum state diffusion (QSD) to describe alternating measurements of two noncommuting observables. Projective measurement of an observable completely destroys memory of the outcome of a previous measurement of the conjugate observable. In contrast, measurement under QSD is not projective and it is possible to vary the rate at which information about previous measurement outcomes is lost by changing the strength of measurement. We apply our methods to a spin 1/2 system and a spin 1 system undergoing alternating measurements of the Sz and Sx spin observables. Performing strong Sz measurements and weak Sx measurements on the spin 1 system, we demonstrate return to the same eigenstate of Sz to a degree beyond that expected from projective measurements and the Born rule. Such a memory effect appears to be greater for return to the ±1 eigenstates than the 0 eigenstate. Furthermore, the spin 1 system follows a measurement cascade process where an initial superposition of the three eigenstates of the observable evolves into a superposition of just two, before finally collapsing into a single eigenstate, giving rise to a distinctive pattern of evolution of the spin components. Published by the American Physical Society 2024

Improving bidirectional controlled remote preparation of arbitrary number of qudits in noisy environment

By elaborately selecting entangled channel, we put forward a universal scheme to achieve bidirectional remote preparation of qudit states with arbitrary numbers of particles under the control of the supervisor. Each sender needs to perform a positive-operator-value measurement and a projective measurement, while the supervisor requires to execute a projective measurement. Based on their measurement outcomes, the receivers can recover the desired states concurrently by carrying out single-qudit generalized Pauli operations which are given by general formulas. A point to highlight is that classical communication cost is greatly reduced at the supervisor’s broadcast channel by taking advantage of network coding. In addition, we analyze the influence of amplitude-damping and phase-damping noises, and utilize weak measurement and environment-assisted measurement to suppress noise.

Race, Inequality, and Political Trust in Latin America

Abstract During the last decades, political distrust has seemingly become a common trend across Latin American democracies, however, differences in the levels of confidence among groups have also been identified. This article considers the potential effects of ethno-racial structures and their interactions with other forms of socioeconomic inequalities on political trust. Building on data from four waves of the Latinobarometer project and contextual measures from different sources, we analyze these relations and find that both socioeconomic and ethno-racial inequalities affect political trust and impact on the formation of different relations with the political system across Latin America. Furthermore, in particular it is found that at the individual-level interactions between inequalities shape political trust differently depending on the particular ethno-racial identification. These findings contribute to the understanding of ethnicity and race and its associations with other structural inequalities in shaping mass political culture.