Trading Electricity with Blockchain Systems

On the wave of the development of new ICT technologies and renewable energy, the power system will certainly experience great changes to its outdated architecture over the next several decades. One of the key drivers of change in the power system is distributed energy resources. They are completely changing the paradigm of the power system as a system with a centralized hierarchy and one-way power flows from generation to customer and from high voltage to low voltage. Because the goals of net zero greenhouse gas emissions are gathering pace and are being accepted by countries around the world, slowdown in the integration of distributed energy resources cannot be expected. Another reason why we can expect faster integration is the development of technology for energy production which is becoming more available to power consumers. Because of the problems that are currently occurring in the distribution system, it is clear that the system must be modernized in line with the development of these technologies. The technology that will likely have the greatest impact on the modernization of the power system is blockchain technology combined with the smart grid paradigm. Blockchain has the ability to completely change the way the power system is managed and optimized for performance

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber

Casehardening characterization in western hemlock wood

Casehardening (σi) is an internal stress-related phenomenon that develops in kiln-dried wood materials, causing shape distortion. This study focuses on the impact of drying schedule and wood parameters (e.g., density, moisture) on σi. Results indicated that the wood's final moisture content level has the highest impact when it is above 15%. However, the wood moisture gradient is the most influential factor when wood's moisture content is below 15%. Drying schedule aggressiveness and post-drying treatments also affect σi; an aggressive drying schedule followed by conditioning remarkably reduces σi occurrence

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber

Association of GADD45A Gene Polymorphism with Systemic Lupus Erythematosus Disease among Patients in South of Iran

Abstract Background: Systemic lupus erythematosus (SLE) is an autoimmune disease with broad clinical manifestations, but unclear etiology. Extensive tissue damage occurs due to the production of auto-antibody against nuclear and cytoplasmic antigens. Regarding the involvement of GADD45A gene in cell cycle control, T-cell proliferation suppression, and genome epigenetic regulation, this case-control study was done for the first time to evaluate the association of rs581000 polymorphism in 5’ near gene with the risk of SLE among patients in south of Iran. Materials and Methods: This study was performed on 102 patients with SLE in comparison with 118 healthy controls. Genotyping of the GADD45A rs581000 polymorphism was performed using T-ARMS PCR. Results: The T allele was significantly more frequent in the controls (0.13) than in the patients (0.01) with SLE (p<0.001). The frequency of genotypes carrying at least one C allele (CC+CG) was higher in control group (14.4%) compared to patient group (1%), and this allele showed protective effect against the risk of SLE (p<0.001, CI: 0.009-0.5, OR=0.06) Conclusion: It seems that GADD45A rs581000 polymorphism involved in the SLE pathogenesis

Evaluation of Abnormal Mammographic Findings in Initial Screening of 1000 Patients during 2008-2009 in Radiology Department of Imam Reza Hospital

Introduction: The aim of this study was to categorize abnormal mammographic findings in 1000 patients according to Breast Imaging-Reporting and Data System (BI-RADS) and to report most common age of abnormal mammographic findings, average age of incidence of malignancy and determination of suitable age to start the screening process in women. Materials and Methods:1000 patients attending Radiology department for mammographic screening during 2007-2008 were asked to participate in this study. One radiologist evaluated the mammograms and categorized patients based on the BI-RADS. Results: The average age of patients diagnosed with invasive ductal carcinoma and in situ ductal carcinoma was 48±6.51years. The average age of patients in category 5 (that translates into “probably malignant”) was (53±7.87) years. The average age of patients in other categories except for category 0 and category 1 that translates into “negative” was (52.07±7.81) years. There was also a significant relationship between patient’s age and BI-RADS categories according to findings of this study. Conclusion: Based on early incidence of irregular mammographies (52.07 years) and early incidence of malignant findings in such mammographies (48± 6.51 years), it is suggested to implement screening programs in a great scale for patients older than 35 years old. Based on high percentage of patients with increased breast density findings in mammograms (11.6%) and diagnosis of two cases with invasive ductal carcinoma in this group, we recommend sonography of patients with such findings to rule out the presence or absence of malignancy with a higher accuracy for which mammograms lack a diagnostic value

Mobile phone applications to overcome malnutrition among preschoolers: a systematic review

Abstract Background Malnutrition is one of the most important reasons for child mortality in developing countries, especially during the first 5 years of life. We set out to systematically review evaluations of interventions that use mobile phone applications to overcome malnutrition among preschoolers. Methods The review was conducted and reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses: the PRISMA statement. To be eligible, the study had to have evaluated mobile phone interventions to increase nutrition knowledge or enhance behavior related to nutrition in order to cope with malnutrition (under nutrition or overweight) in preschoolers. Articles addressing other research topics, older children or adults, review papers, theoretical and conceptual articles, editorials, and letters were excluded. The PubMed, Web of Science and Scopus databases covering both medical and technical literature were searched for studies addressing preschoolers’ malnutrition using mobile technology. Results Seven articles were identified that fulfilled the review criteria. The studies reported in the main positive signals concerning the acceptance of mobile phone based nutritional interventions addressing preschoolers. Important infrastructural and technical limitations to implement mHealth in low and middle income countries (LMICs) were also communicated, ranging from low network capacity and low access to mobile phones to specific technical barriers. Only one study was identified evaluating primary anthropometric outcomes. Conclusions The review findings indicated a need for more controlled evaluations using anthropometric primary endpoints and put relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for mHealth use against malnutrition in LMICs