Average Ratio Research Articles

Multilevel meta-analysis of single-case experimental designs using robust variance estimation.

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs are prevalent, there is a need for methods to synthesize results across multiple studies. One approach to synthesis uses a multilevel meta-analysis (MLMA) model to describe the distribution of effect sizes across studies and across cases within studies. However, MLMA relies on having accurate sampling variances of effect size estimates for each case, which may not be possible due to auto-correlation in the raw data series. One possible solution is to combine MLMA with robust variance estimation (RVE), which provides valid assessments of uncertainty even if the sampling variances of effect size estimates are inaccurate. Another possible solution is to forgo MLMA and use simpler, ordinary least squares (OLS) methods with RVE. This study evaluates the performance of effect size estimators and methods of synthesizing SCEDs in the presence of auto-correlation, for several different effect size metrics, via a Monte Carlo simulation designed to emulate the features of real data series. Results demonstrate that the MLMA model with RVE performs properly in terms of bias, accuracy, and confidence interval coverage for estimating overall average log response ratios. The OLS estimator corrected with RVE performs the best in estimating overall average Tau effect sizes. None of the available methods perform adequately for meta-analysis of within-case standardized mean differences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Average-Case Analysis of Greedy Matching for Large-Scale D2D Resource Sharing

Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log n)$</tex-math></inline-formula> parallel complexity (in user number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> ) for large-scale D2D sharing. Practical cases indicate that the greedy matching's average performance is far better than the worst-case approximation ratio 50% as compared to the optimum. However, there is no rigorous average-case analysis in the literature to back up such encouraging findings and this paper is the first to present such analysis for multiple representative classes of graphs. For 1D linear networks, we prove that our greedy algorithm performs better than 86.5% of the optimum. For 2D grids, though dynamic programming cannot be directly applied, we still prove this average performance ratio to be above 76%. For the more challenging Erdos-Rényi random graphs, we equivalently reduce to the asymptotic analysis of random trees and successfully prove a ratio up to 79%. Finally, we conduct experiments using real data to simulate realistic D2D networks, and show that our analytical performance measure approximates well practical cases.

Optimization of CO2/N2 injection ratios in goaf by saturation adsorption capacity

The inerting technology is a common means to prevent fires in goaf of coal mine, in which adsorption capacity is the key index to measure the inerting effect. Therefore, this paper obtained the coal spontaneous combustion (CSC) stage of Qianyingzi coal mine through programmed heating experiments, and determined the physical adsorption stage temperature (below 323.15 K) matching the actual environment. The oxidation characteristics of coal samples with different adsorption degrees were further studied by adsorption experiment and TG-DSC test. The results indicate that the combustion activation energy of coal samples follows the sequence of second-stage adsorption coal > dried coal > raw coal > first-stage adsorption coal. This implies that the CO2 saturated adsorption can exert a specific inhibitory impact on coal combustion. Therefore, the index of the saturation adsorption capacity (SAC) was proposed to evaluate the inerting effect. We further studied the adsorption capacity of coal at different ratios of CO2/N2 by molecular simulation under different pressures (0–5 MPa). The results show that CO2 dominates in both single-component adsorption and competitive saturated adsorption. The average of ratios (AOR) and average of differences (AOD) threshold are determined. The priority of SAC is obtained in the order of single-component (CS), CO2/N2 mixture ratio at 4:6 (C4:6), CO2/N2 mixture ratio at 2:8 (C2:8) from largest to smallest. Furthermore, a fuzzy optimization method for injection ratio of CO2/N2 under different pressures was established. During dynamic changes in environmental pressure, it is appropriate to inject CO2 separately at 0.2–2.5 MPa. At 3 MPa, a mixture of CO2/N2 at a ratio of 2:8 is the best for prevent fires in goaf. Similarly, an inert gas with a CO2/N2 ratio of 4:6 is injected at 3–5 MPa.

Remote field-programmable gate array laboratory for signal acquisition and design verification

A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embedded-system design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.

Energy-Aware Positioning Service Provisioning for Cloud-Edge-Vehicle Collaborative Network Based on DRL and Service Function Chain

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for causal inference in multiple experiments on semi-synthetic derivations of our dataset with various types and intensities of confounding. In order to further test the reliability of our approach, we test our model on situations of limited data. We find that our model provides more accurate estimates of relative risk [least sum absolute error (SAE) from ground truth] compared with benchmark estimations. Finally, our model provides an estimate of class-wise antihypertensive effect on cancer risk that is consistent with results derived from RCTs.

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy.

Objective.In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Approach.Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed.Main results.For the full test set, with respect to the MC reference, the predicted prostateD90metric had mean differences of -0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [-1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cchad the largest uncertainty metric of 0.0042.Significance.The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.

Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process.

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions.

Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.

A Proposed Model for Variation Order Management in Construction Projects

This study aims to identify the causes of excessive change orders and their impact on public construction projects in Saudi Arabia. This should support the organizations working in the construction industry to improve variation order management (VOM) as a preventive action by dealing proactively with variation order causes. Therefore, a new methodology was proposed to minimize change orders and their impact on the successful completion of projects as well as cost during the project lifecycle. The methodology involved ten selected turnkey building projects at King Faisal University (KFU) campus, Saudi Arabia. Statistical analyses were conducted to predict the cost overrun in project size and contract value. The findings showed the most significant causes leading to variation order in public construction projects. These include the combined effect of the designer and owner technical committee, designer document, and owner stakeholder committee. Hence, a new model for VOM was developed as a best practice approach, including three stages. The first stage is the initiation process, which includes seven procedures, seven tools, and key responsibilities. The second stage deals with the course of change orders based on a certain number of procedures and weight for each parameter assigned to this phase. It supports decision processes based on a certain average ratio of weights calculation. The third stage is the decision to support decision makers in proceeding or not proceeding with the variation order. Although the present study was conducted in Saudi Arabia’s public building construction project, it is envisaged that these research results are widely applicable to other developing countries. The paper presents a direction for further research to enhance the impact of cost overrun in public sector construction projects in developing countries, i.e., Saudi Arabia.

Efficient Sampling of Two-Stage Multi-Person Pose Estimation and Tracking from Spatiotemporal

Tracking the articulated poses of multiple individuals in complex videos is a highly challenging task due to a variety of factors that compromise the accuracy of estimation and tracking. Existing frameworks often rely on intricate propagation strategies and extensive exchange of flow data between video frames. In this context, we propose a spatiotemporal sampling framework that addresses the degradation of frames at the feature level, offering a simple yet effective network block. Our spatiotemporal sampling mechanism empowers the framework to extract meaningful features from neighboring video frames, thereby optimizing the accuracy of pose detection in the current frame. This approach results in significant improvements in running latency. When evaluated on the COCO dataset and the mixed dataset, our approach outperforms other methods in terms of average precision (AP), recall rate (AR), and acceleration ratio. Specifically, we achieve a 3.7% increase in AP, a 1.77% increase in AR, and a speedup of 1.51 times compared to mainstream state-of-the-art (SOTA) methods. Furthermore, when evaluated on the PoseTrack2018 dataset, our approach demonstrates superior accuracy in multi-object tracking, as measured by the multi-object tracking accuracy (MOTA) metric. Our method achieves an impressive 11.7% increase in MOTA compared to the prevailing SOTA methods.

The Interactive Impacts of Corn Particle Size and Conditioning Temperature on Performance, Carcass Traits, and Intestinal Morphology of Broiler Chickens.

This study aimed to investigate the interactions between corn particle size (PS) and conditioning temperature (CT) on the performance, carcass traits, intestinal morphology, and immune responses in broilers fed a corn-soybean meal-based diet. A total of 360 one-day-old male broiler chicks (Ross 308) were randomly allocated into six dietary treatments in a 2 × 3 factorial arrangement, consisting of two corn PS (finely ground with geometric mean diameter (GMD) of 357 µm (PSF) vs. coarsely ground corn with GMD of 737 µm (PSC), and three CT [unconditioned (CTU), conditioned at 75 °C (CT75) and 90 °C (CT90)]. Birds were accommodated in 30 pens with five replicates and 12 chicks per each pen. There was no interaction between corn PS and CT on the growth performance and immune response of broilers at any growth phases. However, during the starter (0-10 days) period, the average daily weight gain (ADWG) and feed conversion ratio (FCR) of PSF-fed birds were significantly improved compared to those fed PSC (p < 0.05). During the starter (0-10 days) and grower (11-24 days) periods, increasing the conditioning temperature of corn increased the ADWG, while in the starter phase only the CT75 caused a lower FCR (p < 0.05). Broilers fed PSF corn showed the lowest FCR during the finisher (25-42 days) period compared to those fed PSC (p < 0.05). Conditioning corn at 75 °C reduced FCR during the finisher (25-42 days) period compared to the birds fed CTU and CT90 corn (p < 0.05). In whole experimental periods (1-42 days), PSF and CT75 treatment increased the ADWG compared to the PSC and CTU (p < 0.05). The CT75 treatment improved primary total anti-sheep red blood cell (SRBCs) titer (IgT) and IgM and secondary IgT and IgG responses compared to the other experimental groups (CTU and CT90) (p < 0.05). No significant PS × CT interaction was found on the Newcastle disease (ND) antibody titer of broiler chickens (p > 0.05). Feeding CT75 corn reduced duodenum and jejunum relative lengths compared to the birds fed diets containing CTU corn. Significant PS × CT interactions (p < 0.05) were observed for villus height, villus height to crypt depth, crypt depth, muscle thickness, and absorption surface area of the jejunum. The highest carcass yield was observed in the PSF-CT75 group (p < 0.05). In conclusion, the use of finely ground corn (PSF) conditioned at 75 °C (CT75) was beneficial to growth performance, development of the digestive tract, jejunum histomorphometry and the immune responses of broilers.

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings.

Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings' polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = -5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%.

Synthesis and Mechanism of 1D ZnO Based on Alkaline Dissolution-Conversion Strategy of High Stable and Soluble ɛ-Zn(OH)2.

A high soluble and stable ɛ-Zn(OH)2 precursor is synthesized at below room temperature to efficiently prepare ZnO whiskers. The experimental results indicate that the formation of ZnO whiskers is carried out mainly via two steps: the formation of ZnO seeds from ɛ-Zn(OH)2 via the in situ solid conversion, and the following growth of whiskers via dissolution-precipitation route. The decrease of temperature from 25 to 5 °C promotes the formation of ɛ-Zn(OH)2 with higher solubility and stability, which balances the conversion and dissolution rates of precursor. The Rietveld refinement, DFT calculations and MD simulations reveal that the primary reason for these characteristics is the expansion of ɛ-Zn(OH)2 lattice due to temperature, causing difficulties in the dehydration of adjacent ─OH. Simultaneously, the larger specific surface area favors the dissolution of ɛ-Zn(OH)2. Based on this precursor, well-dispersed ZnO whiskers with 9.82µm in length, 242.38nm in diameter, and an average aspect ratio of 41 are successfully synthesized through a SDSN-assisted hydrothermal process at 80 °C. The process has an extremely high solid content of 2.5% (mass ratio of ZnO to solution) and an overall yield of 92%, which offers a new approach for the scaled synthesis of high aspect ratio ZnO whiskers by liquid-phase method.

Differences in the activities of six soil enzymes in response to cadmium contamination of paddy soils in high geological background areas

East Yunnan province in southwest China is a region with elevated natural abundance (high geological background levels) of Cd due to high metal (loid) contents in the soils. Enzyme activities are useful indicators of metal (loid) toxicity in contaminated soils and whether Cd inhibits enzyme activities in paddy soils in high geological background areas is of considerable public concern. A pot experiment combined with field investigation was conducted to assess the effects of Cd on six soil enzymes that are essential to the cycling of C, N, and P in soils. Inhibitory effects of Cd fractions on enzyme activities were assessed using ecological dose-response models. The impact of soil properties on the inhibition of sensitive soil enzymes by Cd were assessed using linear and structural equation models. Cadmium was enriched in the paddy soils with 72.2 % of soil samples from high geological background areas exceeding the Chinese threshold values (GB 15618–2018) of Cd. Enzyme responses to Cd contamination varied markedly with a negative response by catalase but a positive response by invertase. Urease, β-glucosidase, and alkaline phosphatase activities were stimulated at low Cd concentrations and inhibited at high concentrations. The average inhibition ratios of β-glucosidase, urease, and catalase in high Cd levels were 19.9, 38.9, and 51.9%, respectively. Ecological dose-response models indicate that catalase and urease were the most Cd-sensitive of the enzymes studied and were suitable indicators of soil quality in high geological background areas. Structural equation modeling (SEM) indicates that soil properties influenced sensitive enzymes through various pathways, indicating that soil properties were factors determining Cd inhibition of enzyme activities. This suggests that Cd concentrations and soil physicochemical properties under a range of environmental conditions should be considered in addressing soil Cd pollution.

Assessment of electro-discharge machining for nimonic C-263 utilizing aluminum powder-infused green dielectric

ABSTRACT Aluminum powder-mixed EDM is operated on Nimonic C-263 with varied process parameters using eco-friendly deionized water as a dielectric. Performance evaluation includes material removal rate (MRR), tool wear rate (TWR), average surface roughness (Ra), EDM ratio (ER), and SEM analysis. MRR has been found to increase with higher peak current (Ipc), pulse-on-time (To), and pulse-off-time (Tf), achieving a peak of 2.183777 mm3/min at 12 A Ipc and 12 g/lit powder concentration (C). TWR is minimum (0.002455 mm3/min) at 12 g/lit C and 7 µs Tf. The least Ra (1.182 µm) is observed at 9 g/lit C and 7 µs Tf; however, it increases with higher duty cycle. ER rises with Ipc and Tf while it declines with To. SEM images reveal larger craters and debris globules at higher Ipc. Overall, optimized parameters enhance MRR while minimizing TWR and Ra, albeit with observed debris formation at higher Ipc.

Cost-Effectiveness Analysis of Denosumab in the Prevention of Skeletal-Related Events Among Patients With Breast Cancer With Bone Metastasis in India.

Denosumab is clinically superior to zoledronic acid (ZA) for preventing and delaying time to first and subsequent skeletal-related events (SREs) among patients with breast cancer (BC) with bone metastases. We evaluated the cost and health benefits of denosumab and ZA (once every 4 weeks and once every 12 weeks) among four different molecular subtypes of BC with bone metastases in India. A Markov model was developed in Microsoft Excel to estimate lifetime health consequences and resulting costs among cohort of 1,000 patients with BC with bone metastasis, for three intervention scenarios, namely denosumab (once every 4 weeks), ZA (once every 4 weeks), and ZA (once every 12 weeks). The health outcomes were measured in terms of SREs averted and quality-adjusted life-years (QALYs) gained. The cost of each intervention scenario was measured using both the health system and the patient's perspectives. Indirect costs because of lost productivity were not included. The future costs and outcomes were discounted at the standard rate of 3%. Over a lifetime, the incremental number of SREs averted with use of denosumab once every 4 weeks (compared with ZA once every 4 weeks and once every 12 weeks) among patients with luminal A, luminal B, human epidermal growth factor receptor 2-enriched, and triple negative breast cancer were estimated as 0.39, 0.26, 0.25, and 0.19, respectively. The number of QALYs lived were slightly higher in the denosumab arm (1.45-2.80) compared with ZA once every 4 weeks and once every 12 weeks arms (1.44-2.78). However, denosumab once every 4 weeks was not found to be a cost-effective alternative for either of the four molecular subtypes of breast cancer. ZA once every 12 weeks was found to be a cost-effective option with an average cost-effectiveness ratio ranging between ₹68,254 and ₹73,636. ZA once every 12 weeks is the cost-effective treatment option for BC with bone metastases in India. The present study findings hold significance for standard treatment guidelines under India's government-funded health insurance program.

A New Approximate Sum of Absolute Differences Unit for Bioimages Processing

An inexact 6-transistor full adder (FA) cell is presented. It is low-power and gives full-swing outputs because of the applied gate diffusion input (GDI) along with the dynamic threshold (DT) techniques. The FA which has three errors is used in a 10-transistor inexact 4:2 compressor based on the stacking circuit idea. The implementation by a 16 nm carbon nanotube field-effect transistor (CNTFET) technology shows a small area of 0.027 lm2 and 0.045 lm2 for the FA and compressor, respectively. A new approximate sum of absolute differences (SAD) block is presented by the FA and compressor. The average power, power-delay-product (PDP), and peak signalto-noise ratio (PSNR) of the SAD are 6.80 mW, 33.86 nJ, and 42.98 dB, respectively, which confirm its efficiency for bioimages difference detection.

Biodrying of municipal solid waste-correlations between moisture content, organic content, and end of the biodrying process time.

Biodrying refers to the decomposition of organic municipal solid waste (MSW) under aerobic conditions. This process usually lasts 14days and requires large amounts of air to be injected into the waste matrix. The efficiency of the biodrying process depends on several geotechnical parameters, including initial moisture content, initial organic content, bulk density, dry density, solid particle density, and porosity. To examine the potential influence of these parameters on the biodrying process, we analyzed 13 biodried MSW samples. The results revealed a strong positive linear relationship between the initial moisture content and the mass loss percentage. In the first three days of the biodrying process, the waste mass rapidly decreased; afterwards, the daily mass loss occurred at a less rapid, more constant rate. The established average mass removal ratio between the volatile solids and water was 1:6.38 with a standard deviation of 1.06. Dry and solid particle densities were preserved in all 13 experiments; thus, the corresponding void ratio remained unchanged. This finding suggests that the settlement and degradation of MSW that occur during the biodrying process did not significantly influence the airflow rate.