Reduced Encoding Complexity for LDPC Codes Using Partially Random and Involutory Matrix Concept in the Generation of Parity Check Matrix

There are situations in the real estate market in which a large number of properties have to be valued at the same time. In such cases it is advisable to use mass valuation methods. These methods involve estimating the value of a property on the basis of the values of the attributes defining it. The aim of the paper is to calibrate the influence of attributes on unit values of properties in mass appraisal in order to minimise the valuation error. The research was conducted for 318 residential properties located in Szczecin. The Szczecin Algorithm of Real Estate Mass Appraisal was used along with the econometric, statistical and expert approaches. The econometric approach is based on the ridge regression model, the statistical approach on the partial Kendall T correlation coefficients, and the expert approach on the AHP method. The quadratic programming was co-employed with the statistical and expert approaches in order to minimise the mean square error (MSE) of the valuations. The econometric and statistical approaches with the minimisation of the MSE generated best results. The least accurate results were obtained by means of the statistical and expert approaches without the minimisation of the MSE. However, even though the optimisation of the MSE improves the quality of valuations, it also narrows down their volatility, which might make the valuation of properties from the outside of a given database more problematic.

Pre-specification of statistical analysis approaches in published clinical trial protocols was inadequate

BackgroundPolycystic ovary syndrome (PCOS) is the most common endocrinopathy of reproductive-aged women. Clomiphene is regarded as the first-line medical treatment for ovulation induction in PCOS patients and acupuncture is often used as an alternative and complementary treatment for fertility issues such as those associated with PCOS. The efficacy of acupuncture alone or combined with clomiphene still lacks strong supporting evidence. Factorial 2 × 2 designs can be used for the evaluations of two treatments within a single study, to test the main effects of acupuncture and clomiphene and their interactions.MethodsPCOSAct was designed to test the effect of clomiphene and acupuncture by three two-group comparisons in the original protocol. However, the trial was designed as a standard factorial trial and the factorial analysis approach for analyzing the data that were actually obtained during the trial was found to be more appropriate and more powerful than the three two-group comparisons described in the original protocol, so the statistical analysis approach and different datasets of PCOSAct in the primary publication were accordingly changed.DiscussionAlthough the statistical analysis approach used in the primary publication deviated from the statistical analysis planned in the study protocol, focusing on the main effects of the two interventions and their interactions was a more standard approach to a factorial trial and proved to be more suitable and consistent with the characteristics of the trial data. Statistically, the revision is more powerful and precise and should be more useful to the journal and the readers.Trial registrationChinese clinical trial registry, ChiCTR-TRC-12002081. Registered on 20 March 2012. Clinicaltrials.gov, NCT01573858. Registered on 4 April 2012.

Objective: To describe the proper statistical approach in ophthalmic research for analyzing correlated data for contralateral eyes of the same subjects. Methods: For data from a paired design and a design for two eyes that are commonly used in ophthalmic research, we described the appropriate statistical approaches for analyzing the correlated data for both eyes of the same subjects. As an example, we compared the intraocular pressure between patients < 60 vs. ≥60 years of age, using the inappropriate and appropriate statistical analysis approaches. Results: The inappropriate analysis of data for one eye leaded only to a biased or inefficient estimate of the difference between two groups. The analysis of data for both eyes of the same subjects without accounting for inter-eye correlation had a smaller p-value (P=0.02) than the analysis that accounted for inter-eye correlation (P=0.098). Conclusions: Appropriate statistical analytical approaches should be used to account for the inter-eye correlation. The statistical analysis using data for only one eye or ignoring the inter-eye correlation can lead to an incorrect conclusion. Key words: inter-eye correlation; statistical analysis; ophthalmic data; paired data.

: A new methodology is proposed for Great Lakes flood hazard mapping. The methodology includes a process for sampling and screening storm events and computing water level probabilities based on high-fidelity modeling of significant storm events. A technical analysis framework is provided to construct accurate extremal distributions of total water levels and to accurately estimate base flood elevations. High-resolution, high-fidelity modeling of all historical storm events is simply not feasible due to time, computational and funding constraints. Therefore, the recommended approach is to screen and sample historical events to select the minimum number of events required to accurately model the total water level extremal distributions. This study focused on evaluating the validity of the recommended statistical analysis and storm sampling approach, and determining the adequate storm sample size. For this purpose, several tasks were performed, including: computation of storm-surrogate waves and water levels; definition of full sample and composite storm sets; and evaluation of the statistical analysis approach for a record length of 50 years. It was determined that the ideal number of events that should be sampled to accurately define water level extremal distributions is roughly 150 storms. Also, the prioritization of waves and water levels in storm sampling was extensively evaluated. It was found that approximately 25 percent to 30 percent of all selected storms were both highly ranked surge events and highly ranked wave events, thus minimizing the effects of different event prioritization ratios. Extreme water levels, corresponding to one percent and 0.2 percent annual chance of exceedance, had negligible variation regardless of the event prioritization ratio.

Several important questions have been raised about decision of stopping a trial early and on what basis to reach such a decision. It seemed therefore of interest to investigate the forms of monitoring used in cancer clinical trials and to gather information on the role of interim analyses in the data monitoring process of a clinical trial. The project addressed the following issues: - what is the performance of different interim analysis approaches; - how often interim analyses are used in cancer clinical trials; - which types of statistical analyses are more frequently adopted; - how the data monitoring is organised and which is the weight of statistical analyses in the decisional process. Analysis of performance of different statistical analysis approaches has been conducted by comparing the probability of stopping and the estimation bias on clinical scenarios based on real data of trials performed in ovarian and colorectal cancers. The project also focused on the prevalence of different types of interim analyses and data monitoring for both safety and efficacy in cancer clinical trials. Sources of investigation were the literature data and the protocols of cancer clinical trials included in the in the Italian registry of clinical trials.Results of our research indicate that the more widely used statistical approaches reduce the risk of “incorrect “ early stopping, compared with the adoption of no stopping rule, with similar performance. Analysis of protocols and early reports suggests that the implementation of these procedures in a monitoring strategy is not satisfactory. Use of interim analyses is still limited to the frequentist approach of the alpha-spending function, while the Bayesian is not considered. Interim analysis plans are still scarcely described, even in more recent protocols, denoting a not yet sufficient attention to this issue not only by the researchers, but also by regulatory bodies.

With aggressive scaling down of feature sizes in VLSI fabrication, process variation has become a critical issue in designs. We show that two necessary conditions for the ldquomaxrdquo operation are actually not satisfied in the moment matching based statistical timing analysis approaches. We propose two correlation-aware block-based statistical timing analysis approaches that keep these necessary conditions, and show that our approaches always achieve the lower bound and the upper bound on the timing yield. Our approach combining with moment-matching based statistical static timing analysis (SSTA) approaches can efficiently estimate the maximal possible errors of moment-matching-based SSTA approaches.

Purpose: This research has several objectives. First, determine lexical density and compare the lexical density. Second, to determine the key lexical density and compare the key lexical density. Third, to test the independence of the relationship between lexical variations and the text of President Joko Widodo's and President Susilo Bambang Yudhoyono's speeches. Theoretical Reference: The theoretical basis used in this research is the lexical analysis approach in linguistics. The application of lexical perspective analysis is expected to be able to review the communication used by each individual. The theoretical lexical discussion will also use a statistical independence analysis approach. The application of a statistical independence analysis approach is used to review a person's individual language abilities. Method: This research uses a qualitative and quantitative corpus linguistics approach. The corpus linguistic application used in this research is the KORTARA application (Korpus Nusantara). The research data is a corpus of 9 texts of President Joko Widodo's speeches and a corpus of 9 texts of President Susilo Bambang Yudhoyono which are official speeches every 16 August before the DPR of the Republic of Indonesia. Results and Conclusion: The results of this research reveal that the text corpus of President Joko Widodo's speech is richer and more varied than the text corpus of President Susilo Bambang Yudhoyono's speech in lexical use. This research also revealed that there is a relationship between lexical variation and the type of text of the President of the Republic of Indonesia's speech with a confidence level of 95%. The difference in lexical variation and frequency between the text corpus of President Joko Widodo's speech and the text corpus of Susilo Bambang Yudhoyono's speech is statistically significant at p &lt; 0.05. Implication of Research: The implication of this research is the realization of the KORTARA corpus linguistic approach (Korpus Nusantara) which can facilitate research for small and large scale data. This research also reveals that the application of a statistical approach provides maximum results in the analysis of large-scale linguistic phenomena. Originality/value: The current study makes a valuable empirical contribution by combining statistical analysis using corpus and qualititative analysis to give comprehensive conclusion. This study is the answer toward the question about the reliability and validity of linguistic studies.

Objectives To evaluate how often pre-specified statistical analysis approaches were publicly available for randomised trials, and the frequency of unexplained discrepancies between planned and conducted analyses. Method We reviewed randomised trials published in six general medical journals (BMJ, JAMA, Lancet, NEJM, PLOS Medicine, and Annals of Internal Medicine) from January-April 2018. Main outcome measures were (i) the number of trials with a publicly available pre-specified analysis approach for the primary outcome (in a protocol or statistical analysis plan [SAP]); (ii) the number of trials with no unexplained discrepancies between the trial publication and the pre-specified approach; and (iii) the types of unexplained discrepancies. Discrepancies were classified as unexplained unless the change was specified in a subsequent version of the protocol or SAP or the discrepancy was discussed in the trial publication. Data extraction was performed independently by two reviewers. Results Overall, 89 of 101 eligible trials (88%) had a publicly available pre-specified analysis approach (83 in a protocol, 6 in a SAP); this document was dated after recruitment began for 27/89 trials (30%), and for 21 trials (24%) no date was available. Only 22/89 trials (25%) did not have any unexplained discrepancies (n=5 no discrepancies, n=17 explained discrepancies only). Fifty-four trials (61%) had one or more unexplained discrepancies, and in 13 trials (15%) it was impossible to ascertain whether any unexplained discrepancies occurred due to incomplete reporting of the statistical methods in the trial publication. Unexplained discrepancies were most common for the analysis model (n=30, 34%) and analysis population (n=28, 31%), followed by the use of covariates (n=23, 26%) and handling of missing data (n=16, 18%). Most trials did not report the blinding status of the statistician in relation to database access or final sign-off of the SAP. Conclusions Discrepancies in the statistical analysis approach were common. We identified several barriers preventing an evaluation of whether changes may have introduced bias: (i) many protocols and analysis plans were from after recruitment began, preventing a comparison with the pre-trial analysis approach; (ii) discrepancies were rarely explained or justified in the trial publication; (iii) the blinding status of statisticians in relation to modifications of analysis methods was rarely reported; and (iv) some descriptions of the analysis methods used in the final publication were inadequate, preventing a comparison with the pre-specified approach. Resolution of these barriers is likely to require a multi-faceted approach targeting investigators, journals, and trial registry websites.

The Mechanical Specific Energy (MSE) and Statistical Analysis Approach (SAA) have been widely implemented in oil and/or gas well drilling industry to enhance the Rate of Penetration (ROP) and reduce the operation cost. This work focuses on predicting and optimizing the drilling efficiency and performance in the production section of Mishrif reservoir in southern Iraq fields. The drilling data from twenty-five wells has analyzed and examined to improve the drilling productivity relied upon MSE and statistical approaches. By using MSE technique, the minimum required energy to drill unit volume of each formation has determined to improve the drilling speed and avoid unnecessary energy consumption that may come out in the form of bit wearing / balling or vibration. The optimum energy is achieved when the MSE value comes close to the unconfined compressive strength (UCS) value that obtained from the empirical formula for limestone and shale rocks. The flounder and threshold points have recognized to optimize drilling data in the offset wells to enhance ROP in the future wells. In the statistical approach, the regression coefficients have obtained from the screened and filtered fields drilling data then the empirical equations to estimate ROP have constructed by using linear regression analysis through a commercial software. The optimization techniques lead to an impressive increase in the rate of penetration in the production section of the Mishrif reservoir. The MSE surveillance provides a reliable tool to maximize the ROP and reduce some drilling problems by using sufficient energy to drill each formation below the flounder point. An excessive energy consumption throughout drilling can be observed in the majority of wells been investigated. Thus, the non-productive time has mitigated considerably by utilizing drilling variables that have induced MSE equal to the unconfined compressive strength of the rocks. On another hand, the statistical analysis of real-time data for twenty-seven wells revealed a remarkable improvement in drilling performance by suggesting an empirical equation that predicts ROP through changing some key parameters such as Flow Rate (FL), Weight On Bit (WOB), Torque (TQ), Revolution Per Minute (RPM), Mud Weight (MWT) and Total Flow Area (TFA). The recommended drilling parameters resulted from this work can be used to reduce the drilling cost and prevent/mitigate the time-dependent failure in the production section.

Differential hydrogen exchange-mass spectrometry (HX-MS) measurements are valuable for identification of differences in the higher order structures of proteins. Typically, the data sets are large with many differential HX values corresponding to many peptides monitored at several labeling times. To eliminate subjectivity and reliably identify significant differences in HX-MS measurements, a statistical analysis approach is needed. In this work, we performed null HX-MS measurements (i.e., no meaningful differences) on maltose binding protein and infliximab, a monoclonal antibody, to evaluate the reliability of different statistical analysis approaches. Null measurements are useful for directly evaluating the risk (i.e., falsely classifying a difference as significant) and power (i.e., failing to classify a true difference as significant) associated with different statistical analysis approaches. With null measurements, we identified weaknesses in the approaches commonly used. Individual tests of significance were prone to false positives due to the problem of multiple comparisons. Incorporation of Bonferroni correction led to unacceptably large limits of detection, severely decreasing the power. Analysis methods using a globally estimated significance limit also led to an overestimation of the limit of detection, leading to a loss of power. Here, we demonstrate a hybrid statistical analysis, based on volcano plots, that combines individual significance testing with an estimated global significance limit, that simultaneously decreased the risk of false positives and retained superior power. Furthermore, we highlight the utility of null HX-MS measurements to explicitly evaluate the criteria used to classify a difference in HX as significant.

Rural headwater catchments are important to describe the connectivity of pollution sources to water bodies. Strategies to optimize water quality monitoring networks, as parameter definition, sampling, and statistical approach, have been widely discussed. The objectives of this study were to describe the spatial and temporal dynamics (intra- and inter-events) of water quality and to establish its implications for environmental monitoring programs. The monitoring was carried out in a rural headwater catchment (1.2 km2) with shallow soils, high slopes, and intense agricultural activity in Southern Brazil. To better describe the impact of agriculture on water resources, the monitoring strategy was based on definition of the best set of parameters and different sampling frequency to incorporate intra- and inter-event variability and statistical analysis approach. We also analyzed parameters in different sub-basins with physiographic traits. Three hydrological compartments were analyzed: surface flow, groundwater, and base flow. Physico-chemical parameters, the concentration of elements associated with agricultural activity, and biological parameters were evaluated. Total phosphorus and turbidity were the parameters most affected by agricultural activity. They reflected on the inter- and intra-events, the impacts of soil and water degradation by agricultural activity, and the precarious rural sanitation conditions. Spatiotemporal variability of the parameters characterizes the different mechanisms for transferring pollutants from diffuse sources to water bodies. Spatial and temporal patterns in water quality changes were used to discuss environmental monitoring strategies, such as parameter and sampling frequency definition, to improve soil and water conservation programs at the catchment scale.

The purpose of this work was to analyze the deformability properties of different timolol maleate (TM)-loaded transfersomes by extrusion. This was performed because elastic liposomes may contribute to the elevation of amount and rate of drug permeation through the corneal membrane. This paper describes the optimization of a transfersome formulation by use of Taguchi orthogonal experimental design and two different statistical analysis approaches were utilized. The amount of cholesterol (F1), the amount of edge-activator (F2), the distribution of the drug into the vesicle (F3), the addition of stearylamine (F4) and the type of edge-activator (F5) were selected as causal factors. The deformability index, the phosphorous recovery, the vesicle size, the polydispersity index, the zeta potential and percentage of drug entrapped were fixed as the dependent variables and these responses were evaluated for each formulation. Two different statistical analysis approaches were applied. The better statistical approach was determined by comparing their prediction errors, where regression analysis provided better optimized responses than marginal means. From the study, an optimized formulation of TM-loaded transfersomes was prepared and obtained for the proposed ophthalmic delivery for the treatment of open angle glaucoma. It was found that the lipid to surfactant ratio and type of surfactant are the main key factors for determining the flexibility of the bilayer of transfersomes. From in vitro permeation studies, we can conclude that TM-loaded transfersomes may enhance the corneal transmittance and improve the bioavailability of conventional TM delivery.

Within the agricultural sector of the economy, investments are a key driver of economic growth, which not only ensures the country’s food security but also stimulates the socio-economic development of rural areas and increases the volume of agricultural exports. However, to effectively attract investment in the agri-food industry, it is necessary to understand their nature, species classification, and mechanisms of influence on the region’s investment potential and economic growth. This creates prerequisites for analyzing the theoretical foundations and methodological aspects of investment development of the agricultural sector of a region/country, etc. The purpose of the study is to scientifically substantiate the theoretical and methodological basis for the investment development of the agricultural sector of Ukraine and to identify ways to improve it in the face of modern challenges. The classification of investments by sources of financing, forms, and directions is determined, demonstrating their versatility and importance in optimizing capital management. Theoretical models of investment development (the classical Cobb-Douglas model and Keynesian concepts) are established, emphasizing the relationship between investment and economic growth. The importance of the state investment policy, which forms the conditions for attracting capital to the agricultural sector and determines the main directions of investment, is substantiated. The methodological aspects of the investment development of the agricultural sector of Ukraine are investigated through the formation of scientifically sound approaches to assessing investment attractiveness and analyzing the factors influencing investment activity; quantitative and qualitative methods for assessing investment processes (statistical analysis, modeling, and econometric approaches, SWOT analysis, PEST analysis, risk analysis, etc.) The author proves the need for an integrated approach (which combines quantitative and qualitative methods, analysis tools, and consideration of macroeconomic factors), which allows for a more accurate assessment of the investment potential of the agricultural sector to attract investment and develop an effective strategy for its development. At the same time, introducing an integral assessment that combines various financial and non-financial criteria allows one to obtain a comprehensive picture of the attractive investment of the agricultural sector. The main problems and barriers to attracting investment in the agricultural sector of Ukraine are analyzed. A set of systemic measures aimed at stabilizing the political situation, improving legislation, protecting investors’ rights, developing state support, increasing the transparency of information processes, etc., is identified to improve the investment climate. Current trends and forecasts of investment development in the agricultural sector of Ukraine indicate a gradual recovery after the crisis in 2022. The main growth drives remain the modernization of technologies, development of environmental projects, support for small farms and emphasis on high-value-added products, effective government support programs, expansion of sales markets, etc. Under these conditions, the agricultural sector will realize its investment potential and become the basis for sustainable economic growth in Ukraine. Keywords: investment, investment development, investment potential, agrarian sector, state investment policy, investment climate, assessment methods.

This paper presents a statistical approach for identifying the significant impact of cracks on the output power performance of photovoltaic (PV) modules. Since there are a few statistical analysis of data for investigating the impact of cracks in PV modules in real-time long-term data measurements. Therefore, this paper will demonstrate a statistical approach which uses two statistical techniques: T-test and F-test. Electroluminescence (EL) method is used to scan possible cracks in the examined PV modules. Moreover, virtual instrumentation (VI) LabVIEW software is used to predict the theoretical output power performance of the examined PV modules based on the analysis of I-V and P-V curves. The statistical analysis approach has been validated using 45 polycrystalline PV modules at the University of Huddersfield, UK.