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The article presents the results of estimates of the subsistence minimum (ME) as well as social minimum baskets (MS) in 2022. Both indicators are important for assessing the well-being of households.The subsistence minimum (ME) sets the model for meeting needs at the minimum level below which there is a biological threat to life. The social minimum (MS), in turn, represents the model cost of living for households that are beginning to be threatened by the sphere of deprivation. Between these two extremes, the sphere of deprivation spreads, in different facets.At the regional level, the value of the subsistence minimum in 2022 for a family with two children was highest in the Dolnośląskie Voivodeship (+5.3 p.p. in relation to the national average) and lowest in the Podkarpackie Voivodeship (-5.5 p.p.). With high inflation (14.4% for Poland), the values of the subsistence minimum in 2022 increased from 13.3 p.p. (Zachodniopomorskie Voivodeship) to 18.5 p.p. (Pomeranian Voivodeship). The spreads between the extreme values in the voivodeships oscillate similarly to the previous one (10.8 p.p.). For one-person households, however, the spreads were higher (16.2 p.p. at working age and 17.2 p.p. at retirement age).Across city categories of towns, there was little variation in ME values. In the largest agglomerations with more than 500,000 inhabitants, the ME value for a family with two children was 3.9 p.p. higher, and in cities with 50,000 to 100,000 people, ME values were only 1.3 p.p. lower.In the period under study, the lowest values of the social minimum were observed in the Lubelskie voivodeship (-3.8 p.p.) and the highest - in the Dolnośląskie voivodeship (+4.3 p.p.). The spread between regions with extreme values was 8 p.p. (compared to 7 p.p. in 2021 and 8.2 p.p. in 2020). The scale of the spread could be considered small, especially when the level of variation in real household expenditure (the results of the latest CSO survey concern 2021) was more than five times higher. In the cross-section of city classes, the differences in the value of the social minimum were not large. Its amount in the largest agglomerations was higher than the average by only 2.8 p.p. (in 2021, it was 3.4 p.p.). The lowest value of the social minimum occurred in cities with 50,000 to 100,000 inhabitants: it was lower than the average by only 1.6 p.p.

The fact that every change in the minimum wage requires an act of Congress means that debate over the wisdom of having a minimum is repeatedly returned to the political arena. As inflation continues to erode the value of the minimum wage, each legislative delay means that a larger increase is required. The larger the increase, the more resistance to its passage, so that by the time Congress acts, the political compromise is an increase that is too little and too late to be of much help in lifting workers out of poverty. Automatic adjustment of the wage, with increases keyed to measures of private sector productivity, would eliminate this problem. With the institution of a mechanism that provides regular and incremental increases, Congress will no longer be forced to revisit the issue, employers will not be confronted by sudden and large increases, and the value of the wage will be maintained.

Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data. ; anadian Institutes of Health Research (CIHR) KRS-134297 Fonds de recherche du Quebec -Sante (FRQS) Canadian Institutes of Health Research (CIHR) Canadian Institutes of Health Research (CIHR) FRQS Masters Training Awards Vanier Canada Graduate Scholarship FRQS Postdoctoral Training Fellowship Research Institute of the McGill University Health Centre G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University Cumming School of Medicine, University of Calgary Alberta Health Services through the Calgary Health Trust Hotchkiss Brain Institute Senior Health Scholar award from Alberta Innovates Health Solutions Health Research Council of New Zealand Lundbeck International Tehran University of Medical Sciences M-288 Department of Education, National Institute on Disability and Rehabilitation Research, Spinal Cord Injury Model Systems: University of Washington H133N060033 Baylor College of Medicine H133N060003 University of Michigan System H133N060032 National Health and Medical Research Council of Australia 1002160 Safe Work Australia Australian Research Council FT130101444 European Foundation for Study of Diabetes Chinese Diabetes Society Lilly Foundation Asia Diabetes Foundation Liao Wun Yuk Diabetes Memorial Fund United States National Institute of Mental Health (NIMH) grant 5F30MH096664 United States Department of Health & Human Services National Institutes of Health (NIH) - USA United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH Fogarty International Center (FIC) United States Department of Health & Human Services National Institutes of Health (NIH) - USA National Cancer Center United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Heart Lung & Blood Institute (NHLBI) NIH Office of Research for Women's Health through the Fogarty Global Health Fellows Program Consortium 1R25TW00934001 American Recovery and Reinvestment Act United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) R24MH071604 / R34 MH072925/ K02 MH65919 / P30 DK50456 / R24 MH56858 / RO1 MH073687 /RO1-MH069666 / R34MH084673 /R24 MH071604 United States Department of Health & Human Services Centers for Disease Control & Prevention - USA R49 CE002093 St Anne's Community Services, Leeds, UK US National Center for Medical Rehabilitation Research RO1 HD39415 Federal Ministry of Education & Research (BMBF) 01GY1150 United States Department of Health & Human Services National Institutes of Health (NIH) - USA T37 MD001449 / T32 GM07356 Ohio Board of Regents Research and Development Administration Office, University of Macau MYRG2015-00109-FSS Federal Ministry of Education & Research (BMBF) 01 GD 9802/4 ; 01 GD 0101 Federation of German Pension Insurance Institute Federal Ministry of Education & Research (BMBF) Perpetual Trustees Flora and Frank Leith Charitable Trust Jack Brockhoff Foundation Grosvenor Settlement Sunshine Foundation Danks Trust Canadian Institutes of Health Research (CIHR) FRN 83518 Scleroderma Society of Canada Scleroderma Society of Ontario Scleroderma Society of Saskatchewan Sclerodermie Quebec Cure Scleroderma Foundation Inova Diagnostics Inc Euroimmun FRQS Canadian Arthritis Network Lady Davis Institute of Medical Research of the Jewish General Hospital, Montreal, QC FRQS Senior Investigator Award National Strategic Reference Framework European Union (EU) Greek Ministry of Education, Lifelong Learning and Religious Affairs (ARISTEIA-ABREVIATE) 1259 Ministry of Health, Labour and Welfare, Japan UK National Institute for Health Research under its Programme Grants for Applied Research Programme RP-PG-0606-1142 Canada Research Chair in Neurological Health Services Research AIHS Population Health Investigator Award National Health and Medical Research Council of Australia 1088313 Netherlands Organization for Health Research and Development 945-03-047 National Health Research Institutes - Taiwan NHRI-EX97-9706PI Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand 49086 Reitoria de Pesquisa da Universidade de Sao Paulo 09.1.01689.17.7 Banco Santander 10.1.01232.17.9 Pfizer medical faculty of the University of Heidelberg, Germany 121/2000 Research University Grant Scheme from Universiti Putra Malaysia, Malaysia Postgraduate Research Student Support Accounts of the University of Auckland, New Zealand National Program for Centers of Excellence (PRONEX/FAPERGS/CNPq, Brazil) Pfizer US Pharmaceutical Inc. PQ-CNPq-2 301321/2016-7 Belgian Ministry of Public Health and Social Affairs Pfizer Ministry of Health, Italy UK National Health Service Lothian Neuro-Oncology Endowment Fund Universiti Sains Malaysia United States Department of Health & Human Services United States Health Resources & Service Administration (HRSA) R40MC07840 United States Department of Health & Human Services Agency for Healthcare Research & Quality R36 HS018246 United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Research Resources (NCRR) TL1 RR024135 University of Melbourne Hunter Medical Research Institute Innovatiefonds Zorgverzekeraars Netherlands Organization for Health Research and Development (ZonMw) Mental Health Program 100.003.005 100.002.021 Academic Medical Center/University of Amsterdam Fund for Innovation and Competitiveness of the Chilean Ministry of Economy, Development and Tourism, through the Millennium Scientific Initiative IS130005 US Department of Veteran Affairs US Department of Veteran Affairs United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Heart Lung & Blood Institute (NHLBI) R01 HL079235 American Federation for Ageing Research Robert Wood Johnson Foundation (RWJF) Ischemia Research and Education Foundation

Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and cluster's number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.

The paper analyses the phenomenon of market concentration in the context of
the most popular industrial organization approaches: the structure-
conduct-performance (SCP) approach and the efficiency hypothesis (EH). The
theory of market concentration evaluation is briefly presented, followed by
an analysis of the main concentration indicators and their application in
recent empirical research. The main problem in the practical application of
the Herfindahl-Hirschman index (HHI) - the necessity to incorporate the
market shares of all the enterprises in the market - is further addressed
and the issue of calculating the minimum value of the HHI is analysed from
the theoretical point of view and by modelling hypothetical markets.

We analyze test design and certification standards when an uninformed seller has the option to generate and disclose costly information regarding asset quality. We characterize equilibria by a minimum principle: the test and disclosure policy are chosen to minimize the asset's value conditional on nondisclosure. Thus, when sellers choose the information provided, simple pass/fail certification tests are likely to dominate the market. A social planner could raise informational and allocative efficiency, and lower deadweight testing costs, by raising the certification standard. Monopolist certifiers also satisfy the minimum principle but set a higher standard and reduce testing rates to maximize revenue. (JEL D42, D83, L12, L15)

The official statistics - the National Institute of Statistics (NIS) - has, among other activities, the calculation of the minimum consumption basket, the monthly update of its value with the consumer price index. NIS uses the European methodology to determine the poverty line. The minimum monthly consumption basket was introduced in Romania by "Government Emergency Ordinance no. 217 of November 24, 2000 regarding the approval of the minimum monthly consumption basket", for a household of average size considered to be 2,804 people, including all expenses necessary for living in a month through the purchase of: (1) food products, (2) non-food products and (3) services. In accordance with O.U.G. no. 217/2000 the minimum monthly consumption basket is the basis of the foundation of the minimum basic salary on the economy, the salary policy and other policies in the social field. The increase in the minimum wage was done without any objective criteria on the basis of which the increases should be made. The absence of such criteria creates a high degree of unpredictability for the business environment, companies being put in the position of not being able to estimate what the evolution of salary costs will be, which has a negative impact especially in the sectors of activity characterized by a high weight of minimum wage contracts.