Imperfect Conditions Research Articles

Transverse squeeze flow of thermoplastic composite tape during in-situ consolidation via automated fiber placement

Transverse squeezing of thermoplastic composite tapes during automated fiber placement is a challenge in controlling gaps/overlaps of adjacent bands. A theoretical model may provide insights on direct effect of process parameters on deformation of tape. The developed models in this work evaluate non-Newtonian squeeze flow of molten tape using power-law viscosity under three different no slip, perfect slip, and imperfect slip boundary conditions at interface during in-situ consolidation, aiming to predict the final width of tape with minimal computational costs. The results predicted by models are verified using finite element analysis with close agreement. Subsequently, no slip and perfect slip assumptions underestimated and overestimated the experimental measurements of consolidated widths, respectively. However, the squeeze model with imperfect slip condition may effectively capture the trends in the experimental data. This model includes the effect of intimate contact development on the friction parameter during squeezing, utilizing a new non-Newtonian trapezoidal asperity model.

A Co-Location Study of 87 Low-Cost Environmental Monitors: Assessing Outliers, Variability, and Uncertainty

While field research in indoor air quality (IAQ) increasingly uses large numbers of low-cost sensors, detailed validation of each sensor is becoming infeasible, ignored, or undeclared. Questions remain about whether these sensors meet manufacturer or third-party specifications and whether their utility improves with some validation, even under imperfect conditions. This study details the outcomes of a co-location experiment with 87 low-cost IAQ monitors, conducted with the objective of verifying their performance in measuring total volatile organic compounds (tVOCs), particulate matter 2.5 (PM2.5), carbon dioxide (CO2), temperature, and relative humidity. The monitors were installed next to professional-grade reference IAQ instruments in an office with controlled ventilation. A mixed validation methodology was used, involving outlier detection, uncertainty quantification, and performance metric calculations. Results showed that CO2, temperature, and humidity sensors reliably met manufacturer specifications, while tVOC sensors had significant accuracy issues, deviating by up to 79% from reference measurements—substantially more than the stated uncertainty of ±20%. PM2.5 sensors were more consistent but had an error margin of ±27%, compared to the stated ±15%. A total of 5 of the 87 sensors (5.7%) were outliers for at least one IAQ indicator. Despite the need for further long-term validation, this study highlights the importance of performing an experimental evaluation of low-cost IAQ monitors before field deployment.

Quantum-enhanced mean value estimation via adaptive measurement

Quantum-enhanced (i.e., higher performance by quantum effects than any classical methods) mean value estimation of observables is a fundamental task in various quantum technologies; in particular, it is an essential subroutine in quantum computing algorithms. Notably, the quantum estimation theory identifies the ultimate precision of such an estimator, which is referred to as the quantum Cramér-Rao (QCR) lower bound or equivalently the inverse of the quantum Fisher information. Because the estimation precision directly determines the performance of those quantum technological systems, it is highly demanded to develop a generic and practically implementable estimation method that achieves the QCR bound. Under imperfect conditions, however, such an ultimate and implementable estimator for quantum mean values has not been developed. In this paper, we propose a quantum-enhanced mean value estimation method in a depolarizing noisy environment that asymptotically achieves the QCR bound in the limit of a large number of qubits. To approach the QCR bound in a practical setting, the method adaptively optimizes the amplitude amplification and a specific measurement that can be implemented without any knowledge of state preparation. We provide a rigorous analysis for the statistical properties of the proposed adaptive estimator such as consistency and asymptotic normality. Furthermore, several numerical simulations are provided to demonstrate the effectiveness of the method, particularly showing that the estimator needs only a modest number of measurements to almost saturate the QCR bound.

A Multi-Strategy Hybrid Sparse Reconstruction Method Based on Spatial-Temporal Sparse Wave Number Analysis for Enhancing Pipe Ultrasonic-Guided Wave Anomaly Imaging.

Ultrasonic-guided waves (UGWs) in defective pipes are subject to severe coherent noise caused by imperfect detection conditions, mode conversion, and intrinsic characteristics (dispersion and multiple modes), inducing the limited performance of anomaly imaging. To achieve the high resolution and accuracy of anomaly imaging, a multi-strategy hybrid sparse reconstruction (MHSR) method based on spatial-temporal sparse wavenumber analysis (ST-SWA) is proposed. MHSR leverages the capability of ST-SWA to extract the wavenumber dispersion curves, thereby providing a more refined and precise search space for MHSR. Furthermore, it mitigates the impact of coherent noise by conducting dispersion compensation on the reconstructed signal. The sparse compensated signals through MHSR are employed for sparse reconstruction imaging. To validate the efficacy of the proposed method, UGW testing is performed on the defective steel pipe, and the results demonstrate the significant enhancement of anomaly imaging in defect resolution and positioning accuracy. The lowest estimated errors for axial and circumferential defect positions are 10 mm and 4 mm, respectively.

"What choice do we have?" Reactive and proactive decision-making for aging in place with dementia.

Promoting options for aging in place (AIP) has broad appeal to policymakers and professionals providing services to persons living with dementia (PWD). However, the benefits or burdens of AIP likely vary among individuals and families. We sought to describe factors influencing decision-making to age in place versus seek a higher level of residential care for PWD. A qualitative study was undertaken as part of a larger mixed-methods study utilizing semi-structured interviews with PWD, family care partners, and dementia clinicians. Interview transcripts were analyzed using qualitative content analysis with constant comparison. Sample size was determined by thematic saturation within subgroups. We conducted 74 interviews among 14 PWD, 36 care partners, and 24 clinicians. Preferences for AIP were driven by (1) desire to preserve independence, (2) a sense that the "best care" is delivered by loved ones and in a familiar environment, (3) distrust and fear of care facilities, and (4) caregiver guilt. PWD and care partners frequently considered moving from home as a "last resort" and wanted to avoid planning for future care needs. Many decisions to move were reactive and triggered by patient safety events, physical dependency, or the loss of caregiver. Proactive decision-making was facilitated by (1) prior experience witnessing the challenges of caring for a person with advanced dementia in the home; and (2) having substantial financial resources such that participants could seek major home adaptations or avoid "lower quality" institutions. Decisions regarding care setting for PWD frequently do not feel like a choice and are made under imperfect conditions. Programs using AIP as an outcome measure should recognize the various patient-centered and non-patient-centered factors that influence such choices, and interventions should be designed to promote more informed and equitable decision-making for care setting in dementia.

Enhanced Energy Transfer Efficiency for IoT-Enabled Cyber-Physical Systems in 6G Edge Networks with WPT-MIMO-NOMA

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

Maiolica seen by Vis–NIR hyperspectral imaging spectroscopy: the application of an ultraportable camera at the Museo Nazionale del Bargello

An ultraportable hyperspectral camera operating in the Vis–NIR range (400–1000 nm) was used in this study for the non-invasive analysis of a selection of Italian maiolica wares and sherds from the Museo Nazionale del Bargello in Florence, Italy. The studied objects included authentic archaeological sherds, nineteenth-century forgeries, and ceramics of uncertain origin. The primary aim of this study was to evaluate the efficacy of hyperspectral imaging (HSI) spectroscopy combined with multivariate analysis for examining highly refractive and three-dimensional glazed ceramic artefacts within the constraints of a museum environment, including imperfect lighting conditions. Two data processing pipelines were tested: one based on principal component analysis (PCA) with score plots and the other on spectral angle mapper (SAM) classification obtained through the ENVI Spectral Hourglass Wizard (ENVI-SHW). The analyses enabled the discrimination between original parts and/or restored parts and additions. Among others, a complex assemblage of original sherds, restored parts, and non-ceramic parts was observed in a Renaissance pastiche vase. Furthermore, the method enabled the identification of the various chromophores providing clues on the artistic colour palette. In particular, the identification of chromium in the blue and green glazes of two objects of uncertain chronology suggested that they were not made before the nineteenth century. The acquisition of a larger and more detailed database of HSI data on maiolica glazes is strongly encouraged to improve the applicability of this method in authenticity, art-historical and archaeological studies.

Toward occupant-centric system: Multicriteria optimization of hybrid displacement–personalized ventilation system using computational fluid dynamics with computer-simulated person

Engineers have integrated general ventilation systems with personalized ventilation (PV) to achieve energy efficiency while providing good indoor air quality (IAQ) and thermal comfort. Previous studies conducted parametric analyses of PV by varying its flow rate and temperature, analyzing energy, IAQ, and thermal comfort while keeping main ventilation settings constant, even though the latter significantly affect the general flow field. Therefore, computational fluid dynamics simulation is performed to simultaneously optimize the above-mentioned three outputs in an office with displacement ventilation (DV) and PV by varying the DV supply flow rate and temperature, the fraction of outlet air being resupplied to inlet or DV recirculation, and PV supply flow rate and temperature. We utilize the Taguchi design to minimize the number of simulations as well as use the “technique for order preference by similarity to ideal solution” (TOPSIS) coupled with Taguchi's signal-to-noise ratio analysis for optimization. Four optimization cases are investigated by varying the thermal comfort and IAQ parameters from volume-averaged to occupant-centric values, such as the skin temperature and inhaled concentration. Results show that occupant-centric optimization presents low energy requirements with acceptable IAQ and thermal comfort compared with volume-averaged optimization, which can be attributed to imperfect mixing conditions. Furthermore, the DV settings and thermal plumes significantly affect the direction of jet released by the PV, which does not improve the IAQ. This underscores the importance of considering the aforementioned effects in maximizing the PV potential. Nevertheless, the PV system functions as intended under a flow rate of 6 L/s.

The Effect of Optimism and Gratitude on Subjective Well-Being of Parents of Children with Special Needs

The children who are born with imperfect conditions are often referred as children with special needs. Parents of children with special needs shown to have low levels of optimism and gratitude, which can affect the level of subjective well-being. This research aims to prove the influence of optimism and gratitude on the subjective well-being of parents with special needs children. The present study used a quantitative approach with a survey research type. The research location was in Pidie Regency, Aceh, with a sample size of 132 parents aged 20-55. The sampling technique used simple random sampling. The instruments used in this research include The Satisfaction of Life Scale (SWLS) and Scale of Positive and Negative Experience (SPANE) with a reliability value of 0.710, the Revised Life Orientation Test (LOT-R) with a reliability value of 0.793, and the Gratitude Questionnaire (Q-6) with a reliability value of 0.761. The result shows that optimism and gratitude can simultaneously influence the subjective well-being of parents who have children with special needs (ß=0.087, p=0.000). The practical contribution made by optimism and gratitude to subjective well-being is 97.5%. The study revealed that optimism and gratitude are very influential in increasing subjective well-being in parents. In conclusion, parents with high subjective well-being tend to show confidence, appreciation, and satisfaction in life, thus positively contributing to parental involvement with children.

Performance Analysis of Distributed Reconfigurable-Intelligent-Surface-Assisted Air–Ground Fusion Networks with Non-Ideal Environments

This paper investigates the impact of non-ideal environmental factors, including hardware impairments, random user distributions, and imperfect channel conditions, on the performance of distributed reconfigurable intelligent surface (RIS)-assisted air–ground fusion networks. Using an unmanned aerial vehicle (UAV) as an aerial base station, performance metrics such as the outage probability, ergodic rate, and energy efficiency are analyzed with Nakagami-m fading channels. To highlight the superiority of RIS-assisted air–ground networks, comparisons are made with point-to-point links, amplify-and-forward (AF) relay scenarios, conventional centralized RIS deployment, and fusion networks without hardware impairments. Monte Carlo simulations are employed to validate theoretical analyses, demonstrating that in non-ideal environmental conditions, distributed RIS-assisted air–ground fusion networks outperform benchmark scenarios. This model offers some insights into the improvement of wireless communication networks in emerging smart cities.

Extracting building outlines based on convolutional neural networks using the property of linear connectivity

Abstract. For years, researchers have been developing an automated method that can replace humans by drawing the outlines of individual buildings in a vector format, which plays an important role in GIS creation, environmental monitoring, urban planning, population density estimation, and energy supply. There is no doubt that this is an extremely difficult task, not only because of the labor required to develop such a highly intelligent algorithm, but also because of the challenges posed by imperfect imaging conditions, different building structures, and the complexity of the background. One of the current challenges in extracting building outlines is to accurately recreate the polygonal boundaries of the building while extracting vectorized building masks as output for direct use in various applications. This work provides a comprehensive workflow for building extraction and improves the predicted area of buildings through boundary regularization. First, a convolutional neural network is used to train instance segmentation model, then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on the concepts of linear connectivity and convexity of a set of points. This approach can effectively identify and remove points that do not belong to the detected building but were incorrectly segmented by the algorithm. Based on the results of experiments, the algorithm showed a high level of efficiency, comparable to leading methods for extracting building boundaries as PolyWorld.

Optimizing secure power allocation in massive MIMO systems with an eavesdropper under imperfect CSI conditions

Optimizing secure power allocation in massive MIMO systems with an eavesdropper under imperfect CSI conditions

Multi-robot social-aware cooperative planning in pedestrian environments using attention-based actor-critic

Safe and efficient cooperative planning of multiple robots in pedestrian participation environments is promising for applications. In this paper, a novel multi-robot social-aware efficient cooperative planner on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt a temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relations between each robot and the pedestrians in its field of view (FOV). Also, we introduce a K-step lookahead reward setting in the multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and unnatural motion decisions generated by robots. Moreover, we improve the traditional centralized critic network with a multi-head global attention module to better aggregate local observation information among different robots to guide the process of the individual policy update. Finally, multi-group experimental results verify the effectiveness of the proposed cooperative motion planner.

Strategic planning as an element of management of the development of the pension system of the Russian Federation

The object of the study is the pension system of the Russian Federation, the subject is the long–term strategic planning for the development of the national pension system. The purpose of the research is to develop proposals for improving the strategic planning of the development of the pension system of the Russian Federation. Special attention is paid to the structure and key provisions of the Strategy for the Long-term Development of the Pension System of the Russian Federation until 2030 (the "Strategy"), the possibility of assessing its implementation, the comparability of the parameters of the functioning of the Russian pension system and data from the long-term budget forecast and forecasts of socio-economic development of the Russian Federation, the interconnection of the goals and objectives of the Strategy and national development goals of the Russian Federation, which makes it possible to develop and implement a comprehensive and coordinated state policy in the field of pension provision for the population. The author considers the expediency of maintaining the current high-level goals of the Strategy in the context of modern socio-economic parameters and trends in the functioning of the Russian economy. The author proceeds to systematization and comparison of the provisions of normative legal acts that are strategic planning documents, modeling of the volume of pension rights, coefficient, graphical, comparative, vertical and horizontal analysis of pension provision indicators. In the course of the study, it was found that the strategy is being implemented in conditions of imperfection of strategic planning documents in terms of pension provision, characterized by low reliability of forecasts, outdated terminology and weak alignment of medium- and long-term planning documents in terms of goal setting, forecasting and programming, weak alignment with the national development goals of the Russian Federation until 2030 and for the future until 2036 the year, the lack of program-oriented cost management in the compulsory pension insurance system. The strategy does not contain unambiguously interpreted indicators of its implementation, does not take into account a significant change in the macroeconomic situation, the transition to a point pension formula, does not contain an assessment of the risks of its implementation, and indicators of the implementation of many of its tasks are not disclosed in other types of strategic documents. We have proposed a number of principles for developing a long-term plan for the development of the pension system, including risk assessment, internal stabilizers and a phased breakdown of the values of indicators of its implementation, as well as defined guidelines for its long-term development, including limiting the growth of the share of the budget mechanism in the national pension system.

Membentuk Keluarga Sakinah, Mawaddah, Warahmah bagi Pasangan Suami Isteri Tuna Wicara Perspektif Hukum Islam

Background. Forming a Sakinah, Mawaddah, Warahmah Family for a Spouse of a Speech Impaired Husband and Wife in the Perspective of Islamic Law (Study in Rebang Tangkas District, Way Kanan Regency) based on the results of pre-research to form a sakinah, mawaddah, warahmah family for a married couple with a speech impairment experiences several problems and challenges that are faced differently from other families in general, it does not even rule out the possibility in an effort to form a sakinah, mawaddah, warahmah family in a family of people with speech impairment is very complicated considering the imperfect physical condition that requires great effort and effort, because it has deficiencies. Aim. Based on the above background, the author formulates the main problem, namely, How does a married couple with a speech impairment form a sakinah, mawaddah, warahmah family from the perspective of Islamic law. Methods. This type of research is field research which is a case study located in Rebang Tangkas District. This field research was conducted on a married couple with speech impairment. Data obtained through several methods, namely, observation interviews and documentation, data analysis techniques are carried out through the stages of data collection, data reduction, and data presentation, then analysing using descriptive qualitative analysis methods. Results. The results of research on 4 speech impaired couples in Rebang Tangkas Subdistrict about forming a sakinah, mawaddah, warahmah family are all respondents try to carry out religious obligations as well as possible, participate in community activities and recitation, be patient, try to understand each other and understand the condition of the spouse, always be grateful, listen to parental advice, strengthen each other trying to set an example by acting to be a role model for children, especially in religious guidance. By paying attention to the efforts made by a married couple with a speech impairment, they can be grouped into a Sakinah family.

On the physical layer security performance of full‐duplex cooperative NOMA system with multiple eavesdroppers, imperfect SIC and hardware imperfections

AbstractIn this letter, we propose a control jammer‐assisted framework for improving the physical layer security (PLS) of full‐duplex (FD) ‐ cooperative non‐orthogonal multiple access (FD‐CNOMA) network. We derive analytical expressions for the secrecy outage probabilities (SOPs) of the users for the jammer‐assisted and the no‐jammer scenarios, considering multiple non‐colluding eavesdroppers, residual hardware impairments and imperfect successive interference cancellation conditions. It is demonstrated that the proposed jammer‐assisted framework provides significant reduction of the SOPs experienced by the downlink users in FD‐CNOMA network.

Hugoniot measurements of the 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) formulation T2 up to 70 GPa

We present shock Hugoniot measurements of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) T2 formulation up to 70 GPa. Despite the use of very thin samples, strong reactivity is evidenced above 30 GPa, indicating that our high pressure points are rather overdriven reaction product measurements than inert Hugoniot measurements. However, the comparison of these new high pressure Hugoniot data obtained on a TATB formulation with those previously obtained in a similar pressure range on single-crystal (SC) TATB samples allows us to decouple kinetics effects from the crystalline nature. Additional classical molecular dynamics simulations are performed to help understand the influence of imperfect hydrostatic conditions on SC TATB behavior at moderate and high pressures. The present data can be used to further improve existing models at very high pressures and to perform more conclusive high pressure Hugoniot experiments on both SC and formulated TATB samples.

Robust noise-aware algorithm for randomized neural network and its convergence properties

The concept of randomized neural networks (RNNs), such as the random vector functional link network (RVFL) and extreme learning machine (ELM), is a widely accepted and efficient network method for constructing single-hidden layer feedforward networks (SLFNs). Due to its exceptional approximation capabilities, RNN is being extensively used in various fields. While the RNN concept has shown great promise, its performance can be unpredictable in imperfect conditions, such as weight noises and outliers. Thus, there is a need to develop more reliable and robust RNN algorithms. To address this issue, this paper proposes a new objective function that addresses the combined effect of weight noise and training data outliers for RVFL networks. Based on the half-quadratic optimization method, we then propose a novel algorithm, named noise-aware RNN (NARNN), to optimize the proposed objective function. The convergence of the NARNN is also theoretically validated. We also discuss the way to use the NARNN for ensemble deep RVFL (edRVFL) networks. Finally, we present an extension of the NARNN to concurrently address weight noise, stuck-at-fault, and outliers. The experimental results demonstrate that the proposed algorithm outperforms a number of state-of-the-art robust RNN algorithms.

From lines to Polygons: Polygonal building contour extraction from High-Resolution remote sensing imagery

Automated extraction of polygonal building contours from high-resolution remote sensing images is important for various applications. However, it remains a difficult task to achieve automated extraction of polygonal buildings at the level of human delineation due to diverse building structures and imperfect image conditions. In this paper, we propose Line2Poly, an end-to-end approach that uses feature lines as geometric primitives to achieve polygonal building extraction by recovering topological relationships among these lines within an individual building. To extract building feature lines with precision, we adopt a two-stage strategy that combines Convolutional Neural Network (CNN) and transformer architectures. A CNN-based module extracts preliminary feature lines, which serve as positional priors for initializing positional queries in the subsequent transformer-based module. For polygonal building contour reconstruction, we devise a learnable polygon topology reconstruction module that predicts adjacency relationships among discrete lines, and integrates lines into building polygons. The resultant building polygons, based on feature lines, exhibit inherent regularity that aligns with manual labeling standards. Extensive experiments on the Vectorizing World Buildings dataset, the WHU aerial building dataset and the WHU-Mix (vector) dataset validate Line2Poly’s impressive performance in building feature line extraction and instance-level building detection. Moreover, Line2Poly’s predictions exhibit the highest level of concurrence with manual delineations, with over 83% agreement on the WHU aerial building test set and 68.7/59.7% on the WHU-Mix (vector) test set I and II, respectively.

Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.

This paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll an iterative model-based qMRI reconstruction into a deep learning framework, enabling accelerated MR parameter maps that are highly accurate and robust. Unlike conventional deep learning methods which require large amounts of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using quantitative mapping as an example, the proposed method was applied to the brain, knee and phantom data. The proposed method generates high-quality MR parameter maps that correct for image artifacts, removes noise, and recovers image features in regions of imperfect image conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, that is readily adaptable to the clinical translation of qMRI.