Search Results (183)

Background: Polymerase chain reaction (PCR) is highly sensitive and specific for the rapid diagnosis of invasive fungal disease (IFD) but is not yet widely implemented due to concerns regarding limited standardisation between assays, the lack of commercial options and the absence of clear guidance on interpreting results. Objectives and Methods: This review provides an update on technical and clinical aspects of PCR for the diagnosis of the most pertinent fungal pathogens, including Aspergillus, Candida, Pneumocystis jirovecii, Mucorales spp., and endemic mycoses. Summary: Recent meta-analyses have demonstrated that quantitative PCR (qPCR) offers high sensitivity for diagnosing IFD, surpassing conventional microscopy, culture and most serological tests. The reported specificity of qPCR is likely underestimated due to comparison with imperfect reference standards with variable sensitivity. Although the very low limit of detection of qPCR can generate false positive results due to procedural contamination or patient colonisation (particularly in pulmonary specimens), the rates are comparable to those observed for biomarker testing. When interpreting qPCR results, it is essential to consider the pre-test probability, determined by the patient population, host factors, clinical presentation and risk factors. For patients with low to moderate pre-test probability, the use of sensitive molecular tests, often in conjunction with serological testing or biomarkers, can effectively exclude IFD when all tests return negative results, reducing the need for empirical antifungal therapy. Conversely, for patients with high pre-test probability and clinical features of IFD, qPCR testing on invasive specimens from the site of infection (such as tissue or bronchoalveolar lavage fluid) can confidently rule in the disease. The development of next-generation sequencing methods to detect fungal infection has the potential to enhance the diagnosis of IFD, but standardisation and optimisation are essential, with improved accessibility underpinning clinical utility. Full article

Objective: Physical joint examination is fundamental in rheumatoid arthritis (RA) assessment. This study evaluated the diagnostic accuracy and agreement between standardized and non-standardized physical joint examinations in RA patients using musculoskeletal ultrasound as the reference standard. Methods: We assessed the joints for tenderness and swelling, calculating sensitivity, specificity, and predictive values. Musculoskeletal ultrasound was used as the reference standard, with adjustment for imperfect reference bias. Agreement between the methods was evaluated using the average kappa coefficient. Results: A total of 1496 joints were evaluated. Without adjustment for imperfect reference bias, standardized examination showed higher sensitivity for detecting pain and swelling than non-standardized examination. Specificity was similar for pain but higher for swelling in standardized examination. After bias adjustment, standardized examination sensitivity improved for pain (93.8% vs. 77.3%; 95% CI: 0.14–0.19) and swelling (91.9% vs. 60.0%; 95% CI: 0.29–0.34). Tenderness specificity remained comparable (standardized examination: 75.4%, non-standardized examination: 76.3%), while the non-standardized examination maintained superior swelling specificity (85.7% vs. 77.1%). Standardized joint examination demonstrated significantly higher concordance than non-standardized assessment in evaluating joint tenderness; standardized assessment yielded significantly greater average kappa coefficients under both false-positive-prioritized (0.44 vs. 0.37; p = 0.01) and false-negative-prioritized scenarios (0.59 vs. 0.45; p < 0.0001). For joint swelling, standardized evaluation showed significantly higher concordance when false negatives were considered more critical (0.59 vs. 0.37; p < 0.0001), whereas differences under false-positive prioritization were not statistically significant. Conclusions: Standardization of the physical joint examination significantly improves diagnostic accuracy and agreement in detecting joint tenderness and swelling in patients with rheumatoid arthritis. Implementing a standardized physical examination protocol may enhance disease activity diagnosis and optimize clinical management of RA. Full article

This study introduces an analytical framework that integrates the state-space method with generalized thermoelasticity theory to obtain exact solutions for the static and dynamic behaviors of laminated plates featuring imperfect interfaces and resting on a Winkler foundation. The model comprehensively accounts for the foundation-structure interaction, interfacial imperfection, and the coupling between the thermal and mechanical fields. A parametric analysis explores the impact of the dimensionless foundation coefficient, interface flexibility coefficient, and thermal conductivity on the static and dynamic behaviors of the laminated plates. The results indicate that a lower foundation stiffness results in higher sensitivity of structural deformation with respect to the foundation parameter. Furthermore, an increase in interfacial flexibility significantly reduces the global stiffness and induces discontinuities in the distribution of stress and temperature. Additionally, thermal conductivity governs the continuity of interfacial heat flux, while thermo-mechanical coupling amplifies the variations in specific field variables. The findings offer valuable insights into the design and reliability evaluation of composite structures operating in thermally coupled environments. Full article

Reducing setup costs and improving product quality are critical objectives in a sustainable production processes. The significance of these goals lies in their direct impact on efficiency. It affects competitiveness and customer satisfaction. Businesses can reduce setup costs to maximize resource usage. It can reduce downtime between production runs and improve overall operational agility. Sustained performance and expansion in contemporary manufacturing environments focus on setup cost reduction and product quality improvement. The present paper discusses a production inventory model for the product, which produces by-products as secondary products from the same manufacturing process. Setup cost is reduced for the setup of production and refining processes. A production process may change from being under control to an uncontrolled one. As a result of this, imperfect products are formed. This paper considers product quality improvement for both produced and processed items. The outcome shows that dealing with by-products helps make the system more profitable. Sensitivity analysis is performed for various costs and parameters. Mathematica 11 software was used for calculation and graphical work. Full article

Near-unstable cavities hold promise for reducing thermal noise in next-generation gravitational wave detectors and for enhancing light–matter interactions in quantum electrodynamics. However, operating close to the edge of geometrical stability presents significant challenges, including increased coupling to higher-order modes and heightened sensitivity to small cavity length changes and mirror imperfections. This study employs Finesse v3 simulations to systematically investigate the modal behavior of a plano-concave cavity as it approaches instability, incorporating measured mirror surface defects and anisotropic curvature to replicate realistic conditions. The simulations highlight the degradation of beam purity and control signals as the cavity approaches instability. By validating the simulations against experimental data, we confirm Finesse’s reliability for modeling cavities while identifying critical limitations in regimes close to the edge of stability. These findings provide essential guidance for optimizing cavity designs in future gravitational wave detectors, balancing performance gains against the challenges of operating at the stability edge. Full article

Over the last three decades, composite structures have become increasingly more common in everyday life, such as in wind turbines as part of the solution to produce clean energy, and their use in the aerospace industry due to their advantages over conventional materials. Most of these advantages are dependent upon the reliability and quality of the manufacturing process to ensure that there are no defects/faults or imperfections during manufacturing. Thus, it is critical to monitor the enclosed environment of moulds during fabrication in real time. This need has caused many researchers—past and present—to create or apply many sensing technologies to achieve real-time monitoring of the manufacturing processes of composite structures to ensure that the structures can meet their requirements. A consequence of these research activities is the myriad of sensing schemes, (for example, optical, electrical, piezo, and nanomaterial schemes and the use of digital twins) available to consider, and the investigations all of them have both strengths and weaknesses for a given application, with no apparent option having a distinct advantage. This review reveals that the best possible sensing solution depends upon a large set of parameters, the geometry of the composite structure, the required specification, and budget limits, to name a few. Furthermore, challenges remain for researchers trying to find solutions, such as a sensing scheme that can directly detect wrinkles/waviness during the laying-up procedure, real-time detection of the resin flow front throughout the mould, and the monitoring of the resin curing spatially, all at a spatial resolution of ~1 cm with the required sensitivity along with the need to obtain the true interpretation of the real-time data. This review offers signposts through the variety of sensing options, with their advantages and failings, to readers from the composite and sensing community to aid in making an informed decision on the possible sensing approaches to help them meet their composite structure’s desired function and tolerances, and the challenges that remain. Full article

The study constructed a supply chain inventory model for sellers and buyers that integrates payment-time-dependent demand, product defects, misclassification risks, and carbon emission tax considerations. The model was designed to optimize payment time, replenishment time, and order quantities to maximize the seller’s profit per unit time. Theoretical analysis showed that profit exhibited joint concavity with respect to both payment time and replenishment time. An algorithm was also formulated to derive optimal solutions. Finally, numerical experiments and sensitivity analyses validated the model and offered practical insights for managing inventories involving imperfect products. Results indicated that higher responsiveness of demand to payment timing, greater demand coefficients, better product prices, and higher scrap values led to increased seller profits, while greater misclassification, credit default risks, and carbon tax rate reduced it. These insights help decision-makers select suitable parameter values for efficient operations. Full article

Currently, the phenomenon of deepfakes is becoming increasingly significant, as they enable the creation of extremely realistic images capable of deceiving anyone thanks to deep learning tools based on generative adversarial networks (GANs). These images are used as profile pictures on social media with the intent to sow discord and perpetrate scams on a global scale. In this study, we demonstrate that these images can be identified through various imperfections present in the synthesized eyes, such as the irregular shape of the pupil and the difference between the corneal reflections of the two eyes. These defects result from the absence of physical and physiological constraints in most GAN models. We develop a two-level architecture capable of detecting these fake images. This approach begins with an automatic segmentation method for the pupils to verify their shape, as real image pupils naturally have a regular shape, typically round. Next, for all images where the pupils are not regular, the entire image is analyzed to verify the reflections. This step involves passing the facial image through an architecture that extracts and compares the specular reflections of the corneas of the two eyes, assuming that the eyes of real people observing a light source should reflect the same thing. Our experiments with a large dataset of real images from the Flickr-FacesHQ and CelebA datasets, as well as fake images from StyleGAN2 and ProGAN, show the effectiveness of our method. Our experimental results on the Flickr-Faces-HQ (FFHQ) dataset and images generated by StyleGAN2 demonstrated that our algorithm achieved a remarkable detection accuracy of 0.968 and a sensitivity of 0.911. Additionally, the method had a specificity of 0.907 and a precision of 0.90 for this same dataset. And our experimental results on the CelebA dataset and images generated by ProGAN also demonstrated that our algorithm achieved a detection accuracy of 0.870 and a sensitivity of 0.901. Moreover, the method had a specificity of 0.807 and a precision of 0.88 for this same dataset. Our approach maintains good stability of physiological properties during deep learning, making it as robust as some single-class deepfake detection methods. The results of the tests on the selected datasets demonstrate higher accuracy compared to other methods. Full article

Uganda is gradually transitioning from communal to private land tenure systems. However, establishing privatized land rights has faced ongoing criticism, particularly concerning their impact on vulnerable groups. Despite the enactment of a national land policy, its benefits have not fully reached rural populations. Issues of land tenure insecurity and unclear ownership continue to generate confusion and have reportedly weakened traditional communal land systems, undermining sustainable agricultural production and long-term investment. This paper examines rural land rights, land markets, and the broader structural transformation of Uganda’s land sector, drawing on the existing literature and published reports. This review reveals that land tenure and administrative challenges persist, largely due to the dominance of customary tenure systems. Although land markets are active, they remain imperfect due to tenure insecurity and legal ambiguities. The findings highlight the need for increased public sensitization regarding land policy, gender-sensitive policies that promote joint ownership, continuous incentives for formalization, the acquisition of land documents, and the harmonization and strengthening of relevant land governance institutions. Full article

This model extends the classical economic production quantity (EPQ) model to address the complexities within a two-echelon supply chain system. The model integrates the cost of raw materials necessary for production and takes into account the presence of imperfect quality items within the acquired raw materials. Upon receipt of the raw material, a thorough screening process is conducted to identify imperfect quality items. Combining imperfect raw material and the concept of shifting production rate, two different inventory models for deteriorating products are formulated under imperfect production with demand dependent on the stock level. In the first model, the imperfect raw materials are sold at a discounted price at the end of the screening period, whereas in the second one, imperfect items are kept in stock until the end of the inventory cycle and then returned to the supplier. Numerical analysis reveals that selling imperfect raw materials yields a favourable financial outcome, with an optimal inventory level $I_1$ = 11,774 units, optimal cycle time $T=2140$ h, and a total profit per hour of USD 183, while keeping the imperfect raw materials to return them to the supplier results in a negative profit of USD $-4.44\times {10}^3$ per hour, indicating an unfavourable financial outcome with the optimal inventory level $I_1$ and optimal cycle time T of 26,349 units and 4702.6 h, respectively. The findings show the importance of selling imperfect raw materials rather than returning them and provide valuable insights for inventory management in systems with deteriorating products and imperfect production processes. Sensitivity analysis further demonstrates the robustness of the model. This study contributes to satisfying the need for inventory models that consider both the procurement of imperfect raw materials, stock-dependent demand, and deteriorating products, along with shifts in production rates in a multi-echelon supply chain. Full article

Predictive uncertainty analysis focuses on defensible variability in model projected values after estimation of the posterior parameter distribution. Inverse-style parameter estimation selects posterior parameters through history matching where parameters are varied and resulting model simulation values are compared to observations, and parameters are selected balancing goodness-of-fit between simulated and observed values and expert knowledge. When inverse-style parameter estimation approaches are used, parameter sensitivity, which is the change in simulated outputs relative to the change in parameter values, is an important consideration. Variation in null space parameters has a limited impact on history matching skill; however, these parameters become important when they impact predictions. A new null space sensitivity analysis for ensemble methods of data assimilation (DA) using observation error models is developed and implemented for an integrated hydrological model. Empirical parameter sensitivity is estimated by comparing the spreads of prior and posterior parameter distributions. Sensitivity analysis is generated by an ensemble of models with insensitive parameters varying across the prior parameter distribution and sensitive parameters fixed to best-fit model values. The result is identification of insensitive aquifer storage parameters that change storage-related model predictions by as much as two times. This null space analysis describes uncertainty from data insufficiency. Ensemble methods using observation error models also describe predictive uncertainty from noisy measurements and imperfect models. Full article

This paper investigates the effect of electrostatic nonlinearity on the rate-sensing performance of imperfect ring-based Coriolis Vibrating Gyroscopes (CVGs) for devices having 8 and 16 evenly distributed electrodes. Mathematical models are developed for CVGs operating in (i) an open loop for a linear electrostatically trimmed device, (ii) a closed loop where a sense force balancing is applied to negate the sense quadrature response, and the effects of electrostatic nonlinearity are investigated for increasing drive amplitudes. The modeling indicates the nonlinear responses for 8- and 16-electrode arrangements are quite different, and this can be attributed to the nonlinear frequency imbalance, which depends on the drive and sense frequency softening as well as the presence of self-induced parametric excitation in the sense response. In open loop the 16-electrode arrangement exhibits much weaker levels of nonlinearity than the 8-electrode arrangement because the nonlinear frequency imbalance is less sensitive to drive amplitude. For devices operating in closed-loop with sense force balancing to ensure the drive and sense responses are in-phase/anti-phase, it is shown that ideal rate-sensing performance is achieved at large drive amplitudes for both 8- and 16-electrode arrangements. Using sense force balancing, rate sensing can be achieved using either the sense response or the required balancing voltage. For the latter, large nonlinear frequency imbalances and low damping levels enhance rate-sensing performance. Full article

Rolling bearings are essential components of a rotating machinery system. Surface imperfections on bearings can alter vibration patterns, and monitoring these changes allows for the precise prediction of the bearing’s remaining useful life (RUL). To address the limitations, such as inadequate sensitivity to features and constrained time–frequency feature extraction capabilities, in conventional methods for predicting the RUL of rolling bearings in the early stages of degradation, this paper introduces a novel predictive framework that combines dynamic weighting mechanisms with hybrid deep learning. This framework incorporates a continuous wavelet transform to generate two-dimensional time–frequency feature maps as degradation indicators, employs CNN for extracting local detailed features, integrates iTransformer modules with dynamic weighting mechanisms to enhance the focus on early subtle features, and leverages the time-dependent modeling capabilities of BiLSTM. The experimental findings using truncated samples from IEEE-PHM2012 datasets show a 71.82% reduction in errors compared with traditional CNN in the early prediction stages, where it effectively mitigated the challenge of early degradation features being overshadowed by noise. Ablation experiments on model components further validated the effectiveness of the model architecture design, where the dynamic weighting mechanism contributed significantly (29.92%) to improving the mean absolute error (MAE). Full article

The aim of almost every production firm is to gain maximum profit along with customer satisfaction. The formation of imperfect products is an obvious process in a production system, which is not a good thing from a business point of view. This paper considers an inventory model for an imperfect production system. All the imperfect products are assumed to be reworkable. An investment occurs for in-process inspection to reduce the rate of formation of imperfect items. A comparison is performed with a production system without in-process inspection to demonstrate the effectiveness of the model. The study shows that the implementation of in-process inspection significantly reduces the total cost of the system as compared to a production system without in-process inspection. The results obtained show that the use of in-process inspection can reduce the total cost by up to 9.3%. Moreover, reducing the formation of defective items saves energy as well as resources. In addition, to reduce carbon emissions, a penalty is implemented on carbon emissions caused by manufacturing, reworking, disposal, and indirect emissions caused by the transportation of disposed items to the treatment facility. As everyone should now be concerned about the environment, green technology is implemented to reduce the amount of carbon emissions to some extent. A classical optimization technique is used to achieve decision variables, i.e., optimal production quantity (Q), fraction of profit invested in in-process inspection ($P_f$), and green technology investment (G), such that the total cost of the system is minimized. A sensitivity analysis is performed to determine the effects of various parameters on the decision variables and total cost. Maple 18 and Mathematica 11 software are used for mathematical work and graphical representation. Full article

Under the low carbon goal, recycling power batteries (PBs) from new energy vehicles (NEVs) is a crucial measure to address resource shortages and reduce carbon emissions. This study examined the insufficient collaboration among the responsible entities and the imperfections in market mechanisms within the PB recycling system. We overcome the limitations of traditional tripartite evolutionary game models by developing a four-party evolutionary game model that incorporates the government, manufacturers, recyclers, and consumers to investigate the strategic interactions within the extended producer responsibility (EPR) framework. Using MATLAB 2023a numerical simulations and Lyapunov stability analysis, we found that the system’s stability and efficiency depend on stakeholder collaboration and effective government policy guidance. The system evolves toward a Pareto optimal state when all parties adopt proactive recycling strategies. Meanwhile, ensuring substantial profits for manufacturers and recyclers is critical for the feasibility and stable operation of compliant recycling channels. While manufacturers and recyclers are more sensitive to subsidies than consumers, consumer decision-making is key to market stability. Long-term excessive subsidies may lead to diminishing marginal benefits. Strategic recommendations are provided for policymakers and stakeholders to enhance the efficiency and sustainability of the PB recycling system. Full article