A Counterfactual P -Value Approach for Benefit-Risk Assessment in Clinical Trials

Clinical trials generally allow various efficacy and safety outcomes to be collected for health interventions. Benefit-risk assessment is an important issue when evaluating a new drug. Currently, there is a lack of standardized and validated benefit-risk assessment approaches in drug development due to various challenges. To quantify benefits and risks, we propose a counterfactual p-value (CP) approach. Our approach considers a spectrum of weights for weighting benefit-risk values and computes the extreme probabilities of observing the weighted benefit-risk value in one treatment group as if patients were treated in the other treatment group. The proposed approach is applicable to single benefit and single risk outcome as well as multiple benefit and risk outcomes assessment. In addition, the prior information in the weight schemes relevant to the importance of outcomes can be incorporated in the approach. The proposed counterfactual p-values plot is intuitive with a visualized weight pattern. The average area under CP (AUCP) and preferred probability over time are used for overall treatment comparison and a bootstrap approach is applied for statistical inference. We assess the proposed approach using simulated data with multiple efficacy and safety endpoints and compare its performance with a stochastic multi-criteria acceptability analysis (SMAA) approach

CCL3L1 Copy Number Variation and Susceptibility to HIV-1 Infection: A Meta-Analysis

Background: Although several studies have investigated whether CCL3L1 copy number variation (CNV) influences the risk of HIV-1 infection, there are still no clear conclusions. Therefore, we performed a meta-analysis using two models to generate a more robust estimate of the association between CCL3L1 CNV and susceptibility to HIV-1 infection. Methods: We divided the cases and controls into two parts as individuals with CCL3L1 gene copy number (GCN) above the population specific median copy number (PMN) and individuals with CCL3L1 GCN below PMN, respectively. Odds ratios (ORs) with 95 % confidence intervals (95 % CIs) were given for the main analysis. We also conducted stratified analyses by ethnicity, age group and sample size. Relevant literatures were searched through PubMed and ISI Web of Knowledge up t

Study on Electricity Utilization Rules of Light Sockets in Governmental Office Buildings in Chongqing

This paper is based on the energy consumption monitoring platform of the office buildings and large-sized public buildings of the state organs in Chongqing. The writer selected one office building with complete set of data and normal operation as the case building to analyze electricity for lighting sockets in the building and find out the rule and features of electricity utilization. In addition, we selected 5 buildings to demonstrate the rule and compared the results with the standard plants. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.445

Sparse Channel Pruning and Assistant Distillation for Faster Aerial Object Detection

In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA)

Sparse Channel Pruning and Assistant Distillation for Faster Aerial Object Detection

In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA)

An Investigation of the Work Hardening Behavior in Interrupted Cutting Inconel 718 under Cryogenic Conditions

The severe work hardening phenomenon generated in the machining of Inconel 718 is harmful to continue cutting processes, while being good for the component’s service performance. This paper investigates the performance of cryogenic assisted machining used in the cutting processes, which can reduce the waste of fluids. The influence of dry and cryogenic machining conditions with different cutting speeds on the work hardening layer is investigated based on the interrupted cutting of Inconel 718. Cutting temperature distribution obtained from simulations under different conditions is used to discuss the potential mechanism of work hardening. Then, the depth of work hardening and degree of work hardening (DWH) are investigated to analyze the surface deformation behavior, which strengthens the machined surface during metal cutting processes. From the cutting experiments, the depth of the work hardening layer can reach more than 60 μm under the given cutting conditions. In addition, a deeper zone can be obtained by the cooling of liquid nitrogen, which may potentially enhance the wear performance of the component. The results obtained from this work can be utilized to effectively control the work hardening layer beneath the surface, which can be applied to improve the service performance

Quantifying Global Potential Marginal Land Resources for Switchgrass

Switchgrass (Panicum virgatum L.) with its advantages of low maintenance and massive distribution in temperate zones, has long been regarded as a suitable biofuel feedstock with a promising prospect. Currently, there is no validated assessment of marginal land for switchgrass growth on a global scale. Although, on both regional and national scale there have been several studies evaluating the potential marginal lands for growing switchgrass. To obtain the first global map that presents the distribution of switchgrass growing in potential marginal land, we employed a boosted regression tree (BRT) modeling procedure integrated with released switchgrass records along with a series of high-spatial-resolution environmental variables. The result shows that the available marginal land resources satisfying switchgrass growing demands are mainly distributed in the southern and western parts of North America, coastal areas in the southern and eastern parts of South America, central and southern Africa, and northern Oceania, approximately 2229.80 million hectares. Validation reveals that the ensembled BRT models have a considerably high performance (area under the curve: 0.960). According to our analysis, annual cumulative precipitation accounts for 45.84% of the full impact on selecting marginal land resources for switchgrass, followed by land cover (14.97%), maximum annual temperature (12.51%), and mean solar radiation (10.25%). Our findings bring a new perspective on the development of biofuel feedstock

Research on the Performance of Manufactured Sand Concrete with Different Stone Powder Content

The working performance and durability of medium and low strength machine-made sand concrete with different stone powder contents were studied, and the optimal range of stone powder content in machine-made sand concrete was explored. The results show that an appropriate amount of stone powder has a significant impact on the performance of machine-made sand concrete, which can increase the volume of the slurry and increase the density of the concrete, thereby improving the crack resistance of the concrete and reducing the permeability of chloride ions. Comprehensive analysis shows that the optimal range of stone powder content (mass fraction) is between 10% and 15%. In this range, the chloride ion permeability of machine-made sand concrete decreases with the increase of the stone powder content, and the dynamic elastic modulus decreases slightly with the increase of the stone powder content, but the amplitude is small (only 2%), so it is mixed A proper amount of stone powder can improve the aggregate gradation, increase the density of concrete, and improve the advantages of poor concrete workability