Adaptive Measurement Research Articles

Dynamical systems analysis of a reaction-diffusion SIRS model with optimal control for the COVID-19 spread

We examine an SIRS reaction-diffusion model with local dispersal and spatial heterogeneity to study COVID-19 dynamics. Using the operator semigroup approach, we establish the existence of disease-free equilibrium (DFE) and endemic equilibrium (EE), and derive the basic reproduction number, R 0 . Simulations show that without dispersal, reinfection and limited medical resources problems can cause a plateau in cases. Dispersal and spatial heterogeneity intensify localised outbreaks, while integrated control strategies (vaccination and treatment) effectively reduce infection numbers and epidemic duration. The possibility of reinfection demonstrates the need for adaptable health measures. These insights can guide optimised control strategies for enhanced public health preparedness.

Network Tomography Based on Adaptive Measurements in Probabilistic Environments

Network Tomography Based on Adaptive Measurements in Probabilistic Environments

Hierarchical meta-porous materials as sound absorbers

The absorption of sound has great significance in many scientific and engineering applications, from room acoustics to noise mitigation. In this context, porous materials have emerged as a viable solution towards high absorption performance and lightweight designs. However, their performance is somewhat limited in the low-frequency regime. Inspired by the concept of recursive patterns over multiple length scales, typical of many natural materials, here, we propose a hierarchical organization of multilayered porous media and investigate their performance in terms of sound absorption. Two types of designs are considered: a hierarchical periodic (HP) and a hierarchical gradient (HG). In both cases, it is found that in some frequency ranges the introduction of multiple levels of hierarchy simultaneously allows for: (i) an increase in the level of absorption compared with the corresponding bulk block of porous material (or to the previous hierarchical levels (HLs)), and (ii) a reduction in the quantity of porous material required. Another advantage of using this approach is on the fabrication, since only one porous material is required. The performances are examined for both normal and oblique incidences, as well as for different values of the static air-flow resistivity. The methodological approach is based on the transfer-matrix method, optimization algorithms such as the metaheuristic greedy randomized adaptive search procedure (GRASP), finite-element calculations and measurements performed in an impedance tube.

Developing a Digital Disaster Documents System for Essential Documents: Perspectives of Decision-Makers in Disaster and Emergency Management in Canada

Despite a growing recognition in the literature concerning the intricate relationship between innovation as an adaptive measure to effectively achieve the overarching objectives of disaster risk reduction and resilience, limited studies have examined how social innovation can be tailored to the local context. This study fills this gap by examining decision-makers' perspectives on the Digital Disaster Documents System (D3S), which digitizes vital documents for disaster response and recovery. A web-based survey was completed by 21 decision-makers across Canada, analyzing their responses using thematic analysis and descriptive statistics. Overall, decision-makers exhibit a positive attitude toward the innovation of D3S as a means to enhance disaster preparedness. Moreover, their constructive feedback on various aspects (content, organization, and storage) of the D3S paves the way for necessary adjustments and enhancements tailored to local needs. This research underscores the necessity for social innovations in emergency and disaster preparedness, especially in ways that are inclusive and equitable.

Swift action: scrutinizing emergency measures from the COVID-19 pandemic

This manuscript evaluates India’s emergency measures during the COVID-19 pandemic. Key actions including a nationwide lockdown to curb virus spread and strengthen healthcare infrastructure are thoroughly studied and its effects analysed. Healthcare responses included scaling up testing, medical supplies, and using the “Aarogya Setu” app for contact tracing are briefly looked at to assess effectiveness of the policies implemented by the government. The largest vaccination drive in the world which covered the vast population was no mean feat and worth applauding. Challenges included supply chain disruptions, healthcare disparities, and socio-economic inequalities. Comparisons with global practices highlighted areas for improvement. The evaluation underscores the importance of swift, adaptable, and inclusive policy measures, offering valuable lessons for future pandemic preparedness. The article aims to outline the various measures undertaken by the government and determine the effectiveness of them in controlling the outbreak of COVID-19 in the country.

Seroprevalence of SARS-CoV-2 antibodies among healthy blood donors: a systematic review and meta-analysis

IntroductionThe development of a potent immune response and antibodies against SARS-CoV-2 is important for herd immunity. The serological response may be due to a previous infection or vaccination. Healthy blood donors could represent and provide information on the immune status of the general population. Therefore, we estimated the global and regional prevalence of SARS-CoV-2 antibodies among healthy blood donors.MethodsWe conducted a systematic search of PubMed, Scopus, and ProQuest from December 2019 to January 2023. After critical appraisal and quality assessment, a qualitative synthesis of the identified relevant articles was performed. The random-effects model was used to estimate the pooled prevalence of SARS-CoV-2 antibodies. Funnel plots and Egger’s test were used to assess publication bias. Sensitivity analysis was performed, and heterogeneity was quantified using I2 statistics.ResultsA total of 70 peer-reviewed articles were selected that together included 2,454,192 blood donors. The global estimated pooled prevalence of SARS-CoV-2 antibodies among healthy blood donors was 10.3% (95% CI: 4.6 – 18.0%, n = 70). The highest seroprevalence was observed in Asia (17.7%), followed by Africa (16.1%). The seroprevalence in studies conducted before the introduction of the vaccine was 6.1%, whereas those of studies conducted after vaccines were available was 27.6%. High seroprevalence was observed in studies that measured antibodies against the S protein of the virus (15.2%), while lower (12.5%) in those that measured antibodies against the N protein. A high seroprevalence was observed in studies that only measured IgG antibodies (16.3%), and it was 5.9% in studies that measured total antibodies.ConclusionThe prevalence of SARS-CoV-2 antibodies among healthy blood donors varies, potentially indicating geographical disparities in transmission and vaccination rates. To enhance community resilience, addressing these differences through inclusive health policies and adaptable public health measures is crucial.

Mapping quantum circuits to shallow-depth measurement patterns based on graph states

Abstract The paradigm of measurement-based quantum computing (MBQC) starts from a highly entangled resource state on which unitary operations are executed through adaptive measurements and corrections ensuring determinism. This is set in contrast to the more common quantum circuit model, in which unitary operations are directly implemented through quantum gates prior to final measurements. In this work, we incorporate concepts from MBQC into the circuit model to create a hybrid simulation technique, permitting us to split any quantum circuit into a classically efficiently simulatable Clifford-part and a second part consisting of a stabilizer state and local (adaptive) measurement instructions—a so-called standard form—which is executed on a quantum computer. We further process the stabilizer state with the graph state formalism, thus, enabling a significant decrease in circuit depth for certain applications. We show that groups of mutually-commuting operators can be implemented using fully-parallel, i.e. non-adaptive, measurements within our protocol. In addition, we discuss how groups of mutually commuting observables can be simulatenously measured by adjusting the resource state, rather than performing a costly basis transformation prior to the measurement as it is done in the circuit model. Finally, we demonstrate the utility of our technique on two examples of high practical relevance—the Quantum Approximate Optimization Algorithm and the Variational Quantum Eigensolver (VQE) for the ground-state energy estimation of the water molecule. For the VQE, we find a reduction of the depth by a factor of 4 to 5 using measurement patterns vs. the standard circuit model. At the same time, since we incorporate the simultaneous measurements, our patterns allow us to save shots by a factor of at least 3.5 compared to measuring Pauli strings individually in the circuit model.

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

Measurements of chromatic adaptation and luminous efficiency while wearing colored filters.

The visual system adapts dynamically to stabilize perception over widely varying illuminations. Such adaptation allows the colors of objects to appear constant despite changes in spectral illumination. Similarly, the wearing of colored filters also alters spectral content, but this alteration can be more extreme than typically encountered in nature, presenting a unique challenge to color constancy mechanisms. While it is known that chromatic adaptation is affected by surrounding spatial context, a recent study reported a gradual temporal adaptation effect to colored filters such that colors initially appear strongly shifted but over hours of wear are perceived as closer to an unfiltered appearance. Presently, it is not clear whether the luminance system adapts spatially and temporally like the chromatic system. To address this, spatial and temporal adaptation effects to a colored filter were measured using tasks that assess chromatic and luminance adaptation separately. Prior to and for 1 hour after putting on a pair of colored filters, participants made achromatic and heterochromatic flicker photometry (HFP) settings to measure chromatic and luminance adaptation, respectively. Results showed significant chromatic adaptation with achromatic settings moving closer to baseline settings over 1 hour of wearing the filters and greater adaptation with spatial context. Conversely, there was no significant luminance adaptation and HFP matches fell close to what was predicted photometrically. The results are discussed in the context of prior studies of chromatic and luminance adaptation.

Denoiser Learning for Infrared and Visible Image Fusion.

Infrared image (IR) and visible image (VI) fusion creates fusion images that contain richer information and gain improved visual effects. Existing methods generally use the operators of manual design, such as intensity and gradient operators, to mine the image information. However, it is hard for them to achieve a complete and accurate description of information, which limits the image fusion performance. To this end, a novel information measurement method is proposed to achieve IR and VI fusion. Its core idea is to guide a generator in achieving image fusion by learning the denoisers. Specifically, by using denoisers to restore fusion images with different noise interference to source images, a mutual competition relationship is formed between denoisers, which helps the generator thoroughly explore the data specificity of the source images and guide it to achieve more accurate feature representation. In addition, a semantic adaptive measurement loss function is proposed to constrain the generator, which fuses semantic information adaptively by considering the semantic information density of different source images. The results of quantitative and qualitative experiments have shown that the proposed method can achieve a higher quality information fusion and has a faster fusion speed on three public datasets when compared with advanced methods.

Adapting to Remote Teaching: Experiences and Technological Adjustments of Rural Physical Science Teachers During the COVID-19 Pandemic

The COVID-19 pandemic necessitated an abrupt shift to remote teaching, presenting unique challenges for rural Physical Science teachers due to their limited access to technological resources. This qualitative study employed a phenomenological approach to explore the lived experiences of six Physical Science teachers from rural South Africa during this transition. Data were gathered through semi-structured interviews, classroom observations, and document analysis. The findings reveal significant disruptions in teaching and learning processes, with a pronounced negative impact on student engagement and learning outcomes. The study highlights teachers’ adaptative measures, including using WhatsApp and other digital platforms to mitigate educational interruptions. Despite their efforts, the lack of infrastructure and digital tools severely restricted effective teaching. This research highlights the necessity of enhanced technological support and resources for rural educators to ensure resilient and adaptable educational practices in the face of future disruptions. The experiences detailed herein reflect broader implications for policy and practice, advocating for a strategic focus on closing the digital divide and supporting rural educators comprehensively.

Anomaly inflow, dualities, and quantum simulation of Abelian lattice gauge theories induced by measurements

Previous work [] has demonstrated that quantum simulation of Abelian lattice gauge theories (Wegner models including the toric code in a limit) in general dimensions can be achieved by local adaptive measurements on symmetry-protected topological (SPT) states with higher-form generalized global symmetries. The entanglement structure of the resource SPT state reflects the geometric structure of the gauge theory. In this work we explicitly demonstrate the anomaly inflow mechanism between the deconfining phase of the simulated gauge theory on the boundary and the SPT state in the bulk by showing that the anomalous gauge variation of the boundary state obtained by bulk measurement matches that of the bulk theory. Moreover, we construct the resource state and the measurement pattern for the measurement-based quantum simulation of a lattice gauge theory with a matter field (Fradkin-Shenker model), where a simple scheme to protect gauge invariance of the simulated state against errors is proposed. We further consider taking an overlap between the wave function of the resource state for lattice gauge theories and that of a parameterized product state, and we derive precise dualities between partition functions with insertion of defects corresponding to gauging higher-form global symmetries, as well as measurement-induced phases where states induced by a partial overlap possess different (symmetry-protected) topological orders. Measurement-assisted operators to dualize quantum Hamiltonians of lattice gauge theories and their noninvertibility are also presented. Published by the American Physical Society 2024

The integrated performance of adaptive temperature control and temperature measurement of PTC materials with high thermal cycle repeatability

Abstract As the temperature increases, the resistance of the positive temperature coefficient (PTC) material becomes larger. Using this material can reduce the complexity of the temperature control system. In this paper, a new PTC material is developed by means of melt blending, which still has operable PTC characteristics after 60 high and low temperature cycles. The data points collected in multiple thermal cycles were fitted to obtain the relationship between the sample’s temperature and resistance. In addition, the integrated performance of adaptive temperature control and temperature measurement of PTC materials is proposed for the first time. Based on the prepared samples, the temperature control characteristics of PTC materials on the controlled object under different environmental conditions are studied experimentally, and the temperature value converted from the resistance value is compared with the temperature value measured by the platinum resistance. The results show that the prepared sample has good temperature control performance, and the material has an operable temperature measurement function in the PTC effect range.

Teachers' professional journey: Charting achievement recognition, career adaptability, and task performance

The rapid technological advancement in higher education necessitates understanding the factors influencing university teachers' professional growth and task performance. This study investigates the relationships among Achievement Recognition, Career Adaptability, and Organizational Seniority, focusing on their impact on the task performance of Chinese university teachers. A longitudinal, time-lagged survey design was employed, collecting data from 1444 Chinese university teachers over three waves, each one month apart. The survey included Achievement Recognition, Career Adaptability, and Task Performance measures, with Organizational Seniority as a moderating variable. Achievement Recognition significantly predicted Career Adaptability. The interaction between career adaptability and organizational seniority was significant, indicating that the positive effect of career adaptability on task performance varied with levels of organizational seniority. Conditional Effects were substantial. Career Adaptability positively affected Work Performance for individuals with less than five years of seniority but had a weaker effect for those with 11–15 years and an adverse effect for those with >21 years of seniority. The indirect impact of Achievement Recognition on Work Performance through Career Adaptability was significant for newly employed workers but non-significant for experienced workers. The study underscores the importance of Achievement Recognition in enhancing Career Adaptability and, subsequently, Work Performance. The moderating role of Organizational Seniority suggests that interventions to boost Career Adaptability and Work Performance should be tailored to different career stages. Academic institutions should implement recognition programs, adaptability training, and mentorship schemes that consider faculty members' career stages to foster a high-performing, adaptable workforce.

Salient Region Guided Colour Image Restoration using Deep Learning with Adaptive Compressive Sensing

In this research, a unique colour image reconstruction method based on Compressive Sensing is presented, aimed at enhancing efficiency and quality. The proposed method incorporates deep learning techniques, adaptive measurement rate setting, and salient region detection. The process begins with the collection of an input RGB images, followed by pre-processing to convert them to the Y+ colour space., including optimized Faster R-CNN (OFRCNN), are employed to detect and segment salient regions in the Y-channel. An adaptive measurement rate setting using Deep Q-Network (DQN) dynamically adjusts measurement rates based on input data characteristics. To optimize the Y-channel compression, a Dual attention-based CS Network (CSNet) model is applied. Moreover, adaptive weighted reconstruction is introduced, utilizing salient region masks and a hybrid the Walrus Optimization Algorithm (WaOA) and mountain gazelle optimizer (MGO), called Hybrid Walrus with Mountain Gazelle Optimizer (HWMGO). Finally, the restored Y-channel is combined with the reconstructed U and V channels to reconstruct full-colour in the RGB colour space. Experimental evaluations on a diverse dataset demonstrate the effectiveness of the proposed algorithm in accurately reconstructing high-quality colour images from CS data. This research contributes to the advancement of colour image reconstruction techniques, offering a comprehensive and efficient framework for various applications.

Deep learning of 3D point clouds for detecting geometric defects in gears

Geometric integrity directly impacts the functionality, reliability, and safety of final manufactured products, making the qualification of parts based on measurements of their geometry a fundamental quality control activity in modern manufacturing. Recent advancements in three-dimensional (3D) metrology technologies have enabled fine-scale inspection of geometric integrity characterized by dimensional accuracy, surface quality, and shape conformity. However, the widespread adoption of high-resolution 3D metrology in manufacturing faces some significant challenges posed by the inherent data structures of 3D point clouds such as high dimensionality, unstructured nature, and sparsity in defective regions. To address these challenges, this paper first creates a “MFGNet-gear” dataset, which is a scalable and comprehensive benchmark dataset comprising 12 part designs with four quality classes for each design. Subsequently, we develop a deep learning model adapted from the PointNet++ architecture to enable automated, end-to-end analysis of 3D point clouds. The model can be configured for different decision-making tasks including part design classification and multi-class geometric defect detection. Implementations of the proposed model on the “MFGNet-gear” dataset achieve accuracies up to 100% in classifying gear designs and up to 85% in four-class quality inspection. Additionally, we systematically investigate the impacts of measurement resolution and precision on the classification performance through a series of case studies. The obtained results highlight the potential of using deep learning methods for automated analysis of 3D point clouds for a variety of quality control tasks beyond gear manufacturing. This study also proposes future research directions, including the development of new deep learning architectures specifically designed for manufacturing 3D point clouds and strategies for adaptive measurement planning.

LEGAL JUSTICE FOR LOW-INCOME COMMUNITIES IN BALANCED RESIDENTIAL DEVELOPMENT

This research investigates legal protection for low-income communities in the context of balanced residential development in Indonesia. The study aims to analyze the policies and regulations surrounding balanced housing development while identifying solutions to promote social justice for low-income people (MBR). Grounded in Law No. 1 of 2011 on Housing and Settlement Areas, which mandates the government to address housing needs for MBR, the research reveals various implementation challenges. Utilizing a normative juridical approach, the analysis employs legal protection theory, development law theory, and human rights theory as frameworks. Findings indicate significant obstacles, including limited land availability and rising land prices, which hinder low-income individuals' access to decent housing. The research underscores the necessity for stricter legal protections and adaptable policy measures to achieve the objectives of balanced housing development effectively.

Validation of the German version of the Perceived Cohesion Scale.

Team or group cohesion is a multifaceted construct with a variety of definitions and measurement instruments. However, most of these measures are context and group-specific and/or time-consuming. There is no adaptable and economic measure of group cohesion in Germany. Therefore, the aim of this study was to validate the German version of the 6-item Perceived Cohesion Scale (PCS) in a sample of adult nurses in a German University Hospital. The German version of the PCS was generated according to existing guidelines. Confirmatory factor analysis was conducted to assess factor structure. Reliability was tested via internal consistency. To assess convergent and divergent validity, the authors applied the Copenhagen Psychosocial Questionnaire (COPSOQ), the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Social Support Inventory (ESSI), the Effort-Reward Imbalance (ERI) scale and the Patient Health Questionnaire (PHQ-4). The confirmatory factor analysis confirmed a 2-factor structure. Psychometric properties of the German PCS prove satisfactory. Internal consistency was excellent for the whole scale (α = 0.95), as well as for both subscales: sense of belonging (SOB) (α = 0.94) and morale (MOR) (α = 0.93). Moderate to strong correlations with the subscales of COPSOQ (Kendall's Tau (τ) = 0.239 - 0.471) indicated very good convergent validity. Regarding divergent validity, the correlations of the PCS subscales and the PHQ-4 were low (τ = -0.109-[-0.143]), as were the correlations with ESSI (τ = 0.045-0.136). Correlations with ERI were low to moderate (τ = -0.181-0.283). The German version of the PCS showed good psychometric properties. Due to its economic and universal deployment, group cohesion can be measured in a variety of contexts and settings. Int J Occup Med Environ Health. 2024;37(4):421-32.

Adaptive Phase Estimation with Squeezed Vacuum Approaching the Quantum Limit

Phase estimation plays a central role in communications, sensing, and information processing. Quantum correlated states, such as squeezed states, enable phase estimation beyond the shot-noise limit, and in principle approach the ultimate quantum limit in precision, when paired with optimal quantum measurements. However, physical realizations of optimal quantum measurements for optical phase estimation with quantum-correlated states are still unknown. Here we address this problem by introducing an adaptive Gaussian measurement strategy for optical phase estimation with squeezed vacuum states that, by construction, approaches the quantum limit in precision. This strategy builds from a comprehensive set of locally optimal POVMs through rotations and homodyne measurements and uses the Adaptive Quantum State Estimation framework for optimizing the adaptive measurement process, which, under certain regularity conditions, guarantees asymptotic optimality for this quantum parameter estimation problem. As a result, the adaptive phase estimation strategy based on locally-optimal homodyne measurements achieves the quantum limit within the phase interval of [0,π/2). Furthermore, we generalize this strategy by including heterodyne measurements, enabling phase estimation across the full range of phases from [0,π), where squeezed vacuum allows for unambiguous phase encoding. Remarkably, for this phase interval, which is the maximum range of phases that can be encoded in squeezed vacuum, this estimation strategy maintains an asymptotic quantum-optimal performance, representing a significant advancement in quantum metrology.

Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001–2022)

Increasing trends in allergic respiratory diseases may be linked to climate changes affecting pollen levels. This study aimed to investigate the temporal trends in seasonal indexes of 9 allergenic pollen types in the Veneto Region, Northern Italy. Available daily data from 20 monitoring stations covered 2001–2022 for Corylaceae, Cupressaceae, Poaceae, Oleaceae, and Urticaceae (pollen families), and 2006–2022 for Alnus, Betula, Ambrosia, and Artemisia (pollen genera). The 95-percentage method was used to identify the start/end dates of pollen seasons annually, and the seasonal cumulative pollen concentration (Seasonal Pollen Integral, SPIn) was calculated. The non-parametric Theil-Sen median slope method was used to examine trends in pollen seasons’ start day, duration, and SPIn in the whole region and for climatic areas (alpine, subcontinental eastern, subcontinental western). The results showed a clear upward trend in SPIn for several pollens, with median slopes ranging from 326 (Alnus) to 1089 p/m3 per 10yrs (Corylaceae); exceptions were Artemisia, whose SPIn decreased over time (−26 p/m3 per 10yrs), and Ambrosia, Betula, and Urticaceae, which did not show a clear trend. For families, pollen seasons showed a trend for anticipation (from −4.7, Urticaceae, to −13.5 days/10yrs, Oleaceae) and extended duration (from 8.3, Cupressaceae, to 13.3 days/10yrs, Oleaceae). An evident heterogeneity across the climatic areas was seen for the SPIn, with the subcontinental western area exhibiting the highest loads and most prominent increases over time. In conclusion, increased levels and prolonged seasons for several pollen taxa observed in the Veneto Region, particularly in subcontinental areas, suggest increasing risks for individuals with pollen allergies. Addressing this issue requires measures of mitigation and adaptation to climate-driven changes in pollen exposure.