Recognition and ranking of organizational performance management based on process-driven approach in state organization

Performance evaluation and the presentation of a comprehensive performance model have always been considered as one of the important concerns of managers. The provision of a comprehensive performance model at the level of state organizations is a matter that has been investigated for many years. The main purpose of this study is recognition and ranking of organizational performance management based on process driven approach in state organizations. This model has been proposed in three dimensions: individual, group and organizational. For this purpose, first, the identification of components has been concerned. So, expert interview tool has been used. In the second part, 384 experts from state organizations were used to examine the fitness of the model. The results of examining of the fitness of the model illustrated that the final model has a suitable statistical fitness. Finally, in order to prioritize the components of the research, analytical hierarchy method and expert choice software were used. The results exhibited that the inconsistency rate at this stage was calculated at the level of 0.09, which indicates the suitability of pairwise of comparisons situation. Organizational factors with weight of 0.48, individual factors with weight of 0.3, and group factors with weight of 0.22 have the highest and lowest weight among the main dimensions respectively.

The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm (WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness, and generality in cost forecasting of substation projects.

To share or not to share data: how valid are trials evaluating first-line ovulation induction for polycystic ovary syndrome?

BACKGROUND In our recent individual participant data (IPD) meta-analysis evaluating the effectiveness of first-line ovulation induction for polycystic ovary syndrome (PCOS), IPD were only available from 20 studies of 53 randomized controlled trials (RCTs). We noticed that the summary effect sizes of meta-analyses of RCTs without IPD sharing were different from those of RCTs with IPD sharing. Granting access to IPD for secondary analysis has implications for promoting fair and transparent conduct of RCTs. It is, however, still common for authors to choose to withhold IPD, limiting the impact of and confidence in the results of RCTs and systematic reviews based on aggregate data. OBJECTIVE AND RATIONALE We performed a meta-epidemiologic study to elucidate if RCTs without IPD sharing have lower quality and more methodological issues than those with IPD sharing in an IPD meta-analysis evaluating first-line ovulation induction for PCOS. SEARCH METHODS We included RCTs identified for the IPD meta-analysis. We dichotomized RCTs according to whether they provided IPD (shared group) or not (non-shared group) in the IPD meta-analysis. We restricted RCTs to full-text published trials written in English. We assessed and compared RCTs in the shared and non-shared groups on the following criteria: Risk of Bias (RoB 2.0), GRADE approach, adequacy of trial registration; description of statistical methods and reproducibility of univariable statistical analysis; excessive similarity or difference in baseline characteristics that is not compatible with chance; and other miscellaneous methodological issues. OUTCOMES In total, 45 trials (8697 women) were included in this study. IPD were available from 17 RCTs and 28 trials were categorized as the non-shared IPD group. Pooled risk rates obtained from the shared and non-shared groups were different. Overall low risk of bias was associated with 13/17 (76%) of shared RCTs versus 7/28 (25%) of non-shared RCTs. For RCTs that started recruitment after 1 July 2005, adequate trial registration was found in 3/9 (33%) of shared IPD RCTs versus 0/16 (0%) in non-shared RCTs. In total, 7/17 (41%) of shared RCTs and 19/28 (68%) of non-shared RCTs had issues with the statistical methods described. The median (range) of inconsistency rate per study, between reported and reproduced analyses for baseline variables, was 0% (0–92%) (6 RCTs applicable) in the shared group and 54% (0–100%) (13 RCTs applicable) in the non-shared group. The median (range) of inconsistency rate of univariable statistical results for the outcome(s) per study was 0% (0–63%) (14 RCTs applicable) in the shared group and 44% (0–100%) (24 RCTs applicable) in the non-shared group. The distributions of simulation-generated P-values from comparisons of baseline continuous variables between intervention and control arms suggested that RCTs in the shared group are likely to be consistent with properly conducted randomization (P = 0.163), whereas this was not the case for the RCTs in the non-shared group (P = 4.535 × 10−8). WIDER IMPLICATIONS IPD meta-analysis on evaluating first-line ovulation induction for PCOS preserves validity and generates more accurate estimates of risk than meta-analyses using aggregate data, which enables more transparent assessments of benefits and risks. The availability of IPD and the willingness to share these data may be a good indicator of quality, methodological soundness and integrity of RCTs when they are being considered for inclusion in systematic reviews and meta-analyses.

The Effect of Topographic Correction on Forest Tree Species Classification Accuracy

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA–DIR–DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels.

The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

The backup recovery strategy selection to maintain the business continuity plan

Indonesia faces the risks of volcanic eruptions, earthquakes, floods and tsunamis, which destruct its land areas and also result in damage to client and server computer systems. The demand for the information technology availability and performance becomes high. Disaster recovery plan is designed to ensure the vital business processes continuation in the event a disaster occurs. The problem is how to make the best way in selecting backup recovery strategy based on the benefits to the cost ratio so as to minimize the business losses that will be caused by the failure of an application system. This research aims to make decisions that can help make certain parties take the best decision in choosing the backup recovery strategy for a business continuity plan in the Trilogy University. The research method used is the multi-criteria decision-making and analytical hierarchy process by using the expert choice software. From the research results can be concluded that the first order in backup strategy is hot standby option 59,4%, followed by cold standby option 23,3%, then the choice of warm standby option 17,4%. The data inconsistency rate is 0.02, smaller than 0.1 as the maximum value of inconsistency ratio.

Voter's Intention to Use Electronic Voting Systems

Although the need for comprehensive assessment of mobile health (mHealth) systems is critical, most existing research focuses solely on these systems' technical merits. The purpose of this study is to prioritize different aspects and indicators of assessing the quality of mHealth services and compare four popular Iranian mHealth systems using this framework. Statistical population of this research included experts who have more than three years of active experience in the field. Using Judgmental sampling method, statistical sample size included 28 persons who responded to questionnaires. Content validity was confirmed by an expert panel, and reliability was confirmed by an inconsistency rate of less than 0.1. The study findings show that results quality is the most important component of quality assessment for mHealth systems. It also shows that indicators of confidentiality, responsiveness and customer orientation are likewise important.

Pregnancy scanning can be used as a source of data for genetic evaluation of reproductive traits of ewes

Reproductive traits generated from mothering up lambs to ewes (n = 59 603 records) were compared with data resulting from pregnancy scanning (n = 46 663 records), to examine the consistency between the two data sources for deriving specific reproductive traits and to estimate genetic parameters. The reproductive traits considered were fertility (FERT: 0/1) of ewes joined, total litter size (LSIZE: lambs born), the number of lambs surviving at weaning (LSIZEW: lambs weaned) and the percentage of lambs surviving (LSURV = LSIZEW/LSIZE) for ewes that lambed, along with the composite traits number of lambs born (NLB) and number weaned (NLW) for ewes joined. Corresponding trait values were derived from pregnancy scan data (FERT_S, LSIZE_S and NLB_S) for comparison, and were classified as inconsistent if the trait values did not match from scanning and lambing records. Data were obtained from four flocks, representing different time frames, locations, management and breeds or bloodlines. Each flock recorded scan data separately from lambing outcomes. Genetic parameters were estimated separately within each flock. Average levels of inconsistency between scan- and lambing-data values varied between 4.6% and 14.8% across flocks, tending to be highest (9.1–18.5%) for litter size of ewes scanned with multiple fetuses, and lowest (0.29–7.3%) for assignment of fertility. Inconsistencies did not have a significant impact on estimates of trait heritabilities, suggesting recording errors were independent of genetic merit. In three flocks, the genetic correlations (ra) between comparable traits derived from the different data sources were not different from unity (ra ≥ 0.99) even when phenotypic correlations (rp) were lower (rp ≥ 0.84). In the flock with the highest inconsistency rate between data sources, the range in ra varied between 0.60 (fertility) and 1.0 (litter size). Therefore, pregnancy scan data can be directly substituted for reproductive traits traditionally based on lambing data, but attention should be paid to ensuring accuracy of the data sources used. Scan data also provide no information on lamb-survival outcomes after birth, so does not constitute complete data on reproductive outcomes. Genetic evaluation systems might also benefit from fine tuning for scale-induced effects (due to litter size) on parameters to improve the accuracy of across flock prediction of breeding values for reproductive traits.