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Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed. [ABSTRACT FROM AUTHOR]

Any wireless communication system's performance depends on channel parameters' accuracy. The classical Rayleigh and Nakagami-m research subjects remain vital even in the most modern millimeter-wave (mm-wave) applications. This research aims to create a generalized cumulative distribution function for describing random changes in wireless channels. It is vital to have a suitable channel representation model to represent varied fifth-generation applications to ease network implementation. This study provides mm-wave measurement data at 28 GHz carrier frequencies in line of sight and non-line of sight propagation. Lognormal, Nakagami, Gaussian, Weibull, and Rayleigh distributions outperform the proposed model's universal exponential density function. The experimental data verified the introduced method. [ABSTRACT FROM AUTHOR]

Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed.

The present investigation deals with development of AA2219 composites with different weight fractions of Cerium oxide (CeO2) reinforcement and characterizing the microstructure and mechanical properties thereof. XRD and SEM micrograph confirm the presence of CeO2 particles and Al2Cu precipitate. The grain size of 0, 0.5, 1.0, 1.5 and 2 wt% CeO2 composites are 483, 453, 411, 362 and 328 μm, respectively. Addition of CeO2 significantly reduces the dendritic morphology and refines the α-Al grain structure. 2 wt% CeO2 addition has yielded the maximum hardness of 124 HV and yield strength of 87 MPa in all the composites developed. Additionally, the developed composites were evaluated by examining the wear characteristics with the co-efficient of friction (COF) and the specific wear rate. In this study, Taguchi's L25 orthogonal array was used to optimize the parameters like reinforcement fraction, load and sliding distance. Signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) are used to obtain the parameters highly influencing the wear behaviour. Adhesive wear is exhibited by ploughing at lower loads and delamination is observed with increasing load. Deep shallow ploughs with embarked abrasion are observed at higher loads.

The Girl-Friendliness Index (GFI) is a statistical tool developed to comprehensively assess the performance of African governments in realising the rights and wellbeing of girls. The framework is composed of three central pillars of the Convention on the Rights of the Child (CRC) and the African Charter on the Rights and Welfare of the Child (ACRWC) which deal with girls' rights to: Protection, Provision and Participation. The construction of the index follows standard procedures. Detailed statistical analysis was also carried out to test and evaluate the strength and reliability of the tool. The analysis confirmed that the GFI is a strong statistical tool to measure and rank governments' friendliness towards girls in Africa. The GFI was calculated for 52 African countries using the most recent comparable data available and the results were cross-analysed with other variables such as national income and state of good governance. According to the GFI results, Mauritius, Tunisia, South Africa, Seychelles, and Algeria were the five top ranking countries, relative to the others, that scored a minimum of 74% each. On the other hand, the five countries at the bottom of the ranking were: South Sudan, Chad, Eritrea, Democratic Republic of Congo, and Niger. These countries scored low (15.9%, 17%, 25%, 27.2%, and 27.7% percent, respectively). Key conclusion drawn from the paper is that top performing countries were countries that have put in place comprehensive laws and policies to protect girls from abuse and exploitation and have also allocated higher shares of their income to improve girls' wellbeing. Poorly performing countries, conversely, were those which have failed to put in place gender-sensitive laws and policies, fell short of allocating adequate budgets, and essentially are ineffective in enforcing laws and policies in place. Another interesting aspect of the analysis is the assessment of the degree of correlation between GFI and other well-known and related indices such as the Human Development Index (HDI) and the Ibrahim Index of African Governance (IIAG). The results have shown that the GFI has a strong positive correlation with both indices. These positive correlations show the linkages of performance in realizing girls' rights with human development and good governance such that the more the government fulfilled the rights of girls the more it contributes to good governance and development. [ABSTRACT FROM AUTHOR]

Statistical tools have shown to be very useful in the optimization of processes such as welding. Optimization is understood as the determination of the welding combinations that will lead to the maximization of a desired property, such as strength. This work proposes a statistical methodology to determine the optimum combination of welding parameters of FSSW in 6060-T5 aluminium alloy. Two Design of Experiment (DOE) statistical tools, Taguchi and Full Factorial Design (FFD) were used to determine the optimum combination of three welding parameters: rotational speed, plunge rate and dwell time. Four samples were produced for each welding combination and then subjected to shear test to evaluate joint strength. Quadratic regression was used to obtain an equation correlating joint strength and welding parameters. With the methodology presented, it was obtained an equation to correlate welding parameters and joint strength with acceptable accuracy. The results have shown that a proper combination of DoE tools like Taguchi and FFD is key to determining the optimum set of welding parameters in the FSSW process.

Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease)., (©Gustavo Gómez, Heather Hufstedler, Carlos Montenegro Morales, Yannik Roell, Anyela Lozano-Parra, Adriana Tami, Tereza Magalhaes, Ernesto T A Marques, Angel Balmaseda, Guilherme Calvet, Eva Harris, Patricia Brasil, Victor Herrera, Luis Villar, Lauren Maxwell, Thomas Jaenisch, ReCoDID Arbovirus harmonization study group. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 23.07.2024.)

There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.

The survival and competitiveness of the business are virtually dependent on the governance system, reputation and laws of the country. Irrespective of the efforts for building a flexible and responsive governance system in India, the rigid rules and improper implementation of regulations had resulted in the drastic failure of many business giants. Also, the incremental increase in the literature on various aspects in this dimension realized an emergent need to synthesize the literature. The purpose of this study is to explore from the extant literature, the progress of corporate governance by performing a meta-analysis on 115 studies by segregating them into seven categories during the time horizon of 2008–2018. The findings indicate that the board, CEO, and family firm characteristics are found to be directly correlated with firm performance. However, financial expertise and super director have a positive influence but are the rarely studied variables, merits the attention of researchers. Whereas board size, CEO turnover, foreign director, political connections are impacting the firm's performance negatively. The findings reveal that more perspectives need to be included to derive innovative solutions for developing a flexible system of governance. [ABSTRACT FROM AUTHOR]

Background: Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate., Objective: This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data., Methods: In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation., Results: The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation., Conclusions: The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model's effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges., (©Ian E Fellows, Carl Corcoran, Anne F McIntyre. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 19.03.2024.)

The problem of underestimating or overestimating the aerosols loading over Kano is readily becoming a global challenge. Recent health outcomes from an extensive effect of aerosols pollution has started manifesting in Kano. The aim of the research is to estimate the aerosols loading and retention over Kano. Thirteen years aerosol optical depth (AOD) data was obtained from the Multi-angle imaging spectroradiometer (MISR). Statistical tools, as well as analytically derived model for aerosols loading were used to obtain the aerosols retention and loading over the area. It was discovered that the average aerosols retention over Kano is 4.9%. The atmospheric constants over Kano were documented. Due to the volume of aerosols over Kano, it is necessary to change the ITU model which relates to signal budgeting.

The problem of underestimating or overestimating the aerosols loading over Mubi is inevitable because of the absence of ground stations over the region. Aerosols pollution is a global challenge to life forms as it affects human health, agricultural produce, thermal comfort and weather. The modulation between high and low thermal comforts over Mubi is quite disturbing. The aim of the research is to seek a more reliable approach to estimate the aerosols loading and retention over Mubi. Thirteen years aerosol optical depth (AOD) data was obtained from the Multi-angle imaging spectroradiometer (MISR). Mubi is located on latitude 10.27oN and longitude 13.27oE. Statistical tools, as well as analytically derived model for aerosols loading were used to obtain the aerosols retention and loading over the area. It was discovered that the highest aerosols retention over Mubi is 12.7%. The atmospheric constants and tuning constants over Mubi were documented as 0.67 and 0.71 respectively. Due to the volume of aerosols over Mubi, it is necessary to change the International Telecommunication Union (ITU) model which relates to signal budgeting.

There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets. [ABSTRACT FROM AUTHOR]

In the last decades, research has focused on the capabilities of microbes to secrete exopolysaccharides (EPS), because these polymers differ from the commercial ones derived essentially from plants or algae in their numerous valuable qualities. These biopolymers have emerged as new polymeric materials with novel and unique physical characteristics that have found extensive applications. In marine microorganisms the produced EPS provide an instrument to survive in adverse conditions: They are found to envelope the cells by allowing the entrapment of nutrients or the adhesion to solid substrates. Even if the processes of synthesis and release of exopolysaccharides request high-energy investments for the bacterium, these biopolymers permit resistance under extreme environmental conditions. Marine bacteria like Bacillus, Halomonas, Planococcus, Enterobacter, Alteromonas, Pseudoalteromonas, Vibrio, Rhodococcus, Zoogloea but also Archaea as Haloferax and Thermococcus are here described as EPS producers underlining biopolymer hyperproduction, related fermentation strategies including the effects of the chemical composition of the media, the physical parameters of the growth conditions and the genetic and predicted experimental design tools.

In the last decades, research has focused on the capabilities of microbes to secrete exopolysaccharides (EPS), because these polymers differ from the commercial ones derived essentially from plants or algae in their numerous valuable qualities. These biopolymers have emerged as new polymeric materials with novel and unique physical characteristics that have found extensive applications. In marine microorganisms the produced EPS provide an instrument to survive in adverse conditions: They are found to envelope the cells by allowing the entrapment of nutrients or the adhesion to solid substrates. Even if the processes of synthesis and release of exopolysaccharides request high-energy investments for the bacterium, these biopolymers permit resistance under extreme environmental conditions. Marine bacteria like Bacillus, Halomonas, Planococcus, Enterobacter, Alteromonas, Pseudoalteromonas, Vibrio, Rhodococcus, Zoogloea but also Archaea as Haloferax and Thermococcus are here described as EPS producers underlining biopolymer hyperproduction, related fermentation strategies including the effects of the chemical composition of the media, the physical parameters of the growth conditions and the genetic and predicted experimental design tools. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present a review on the statistical tools used to deal with multiple comparisons. [•] We discuss the fundamental theoretical concepts and ideas. [•] We present application to metabolomics and proteomics. [ABSTRACT FROM AUTHOR]

In this study, the authors introduce a new weighted Student's t‐mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t‐distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t‐mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well‐known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model‐based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach. [ABSTRACT FROM AUTHOR]

Copyright of Transition: Journal of Economic & Politics of Transition / Tranzicija: Časopis za Ekonomiju i Politiku Tranzicije is the property of Ekonomski Institut Tuzla and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Determination of the geographical origin of food and beverages has been a growing issue over the past decade for all countries around the world, mostly because of the concern of consumers about the authenticity of the food that they eat. An increasing number of research articles in the past five years have investigated the elemental composition and the isotope ratios as indicators to determine the origin of food and beverages. The indicators commonly used are stable-isotope ratios of hydrogen, oxygen, nitrogen, carbon and sulfur, combined with isotope ratios of strontium and lead, and elemental concentrations. This article reviews the application of multi-element and multi-isotope methods and the statistical tools used to determine the geographical origin of food and beverages in the European Union from 2008 to early 2012. We review 48 papers, divided between different food commodities. [Copyright &y& Elsevier]

Relatively few studies have been performed to characterize municipal solid waste (MSW) at household level. This is due in part to the difficulties involved with collecting the data and selecting an appropriate statistical sample size. The previous studies identified in this paper have used statistical tools appropriate for analysing data collected at a material recovery facility or landfill site. This study demonstrates a statistically sound and efficient approach for characterizing MSW at the household level. Moreover, a household approach also allowed for consideration of the socio-economic conditions, level of waste generation, geography, and demography. The study utilized two-stage cluster sampling within strata in Kathmandu Metropolitan City (KMC) to measure MSW for 2 weeks. In KMC, the average household solid waste generation was 161.2 g capita-1 day-1 with an average generation rate between 137.7 and 184.6 g capita-1 day-1 for a 95% confidence interval and 14.5% relative margin of error. The results show a positive relation between income and waste production rate. Organic waste was the biggest portion of MSW, and hazardous waste was the smallest of the total. Sample size considerations suggest that 273 households are required in KMC to attain a 10% relative margin of error with a 95% confidence interval. [ABSTRACT FROM AUTHOR]

DSCTool is a statistical tool for comparing performance of stochastic optimization algorithms on a single benchmark function (i.e. single-problem analysis) or a set of benchmark functions (i.e., multiple-problem analysis). DSCTool implements a recently proposed approach, called Deep Statistical Comparison (DSC), and its variants. DSC ranks optimization algorithms by comparing distributions of obtained solutions for a problem instead of using a simple descriptive statistic such as the mean or the median. The rankings obtained for an individual problem give the relations between the performance of the applied algorithms. To compare optimization algorithms in the multiple-problem scenario, an appropriate statistical test must be applied to the rankings obtained for a set of problems. The main advantage of DSCTool are its REST web services, which means all its functionalities can be accessed from any programming language. In this paper, we present the DSCTool in detail with examples for its usage. • DSCTool - a statistical tool for comparing stochastic optimization algorithms. • REST web services implementation allows access from any programming language. • Identification of statistical and practical significance. • Understanding of exploitation and exploration powers of single-objective algorithms. • Ranking of multi-objective algorithms using an ensemble of quality indicators. [ABSTRACT FROM AUTHOR]