Data Obsolescence Detection in the Light of Newly Acquired Valid Observations

The information describing the conditions of a system or a person is constantly evolving and may become obsolete and contradict other information. A database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. In this paper, we propose a novel approach for dealing with the information obsolescence problem. Our approach aims to detect, in real-time, contradictions between observations and then identify the obsolete ones, given a representation model. Since we work within an uncertain environment characterized by the lack of information, we choose to use a Bayesian network as our representation model and propose a new approximate concept, $\epsilon$-Contradiction. The new concept is parameterised by a confidence level of having a contradiction in a set of observations. We propose a polynomial-time algorithm for detecting obsolete information. We show that the resulting obsolete information is better represented by an AND-OR tree than a simple set of observations. Finally, we demonstrate the effectiveness of our approach on a real elderly fall-prevention database and showcase how this tree can be used to give reliable recommendations to doctors. Our experiments give systematically and substantially very good results

Synthesis, characterization and coordination chemistry of substituted β-amino dicarbonyls

AbstractAn efficient and facile method for the synthesis of novel structurally diverse β-amino dicarbonyl compounds is described by exploring the aza-Michael addition reaction in an aqueous medium as a key step. Thereby, 2-(aryl-disubstituted-amino-1-yl-methyl)-malonic acid diethyl esters were achieved in a good to excellent yields. These products were easily isolated with enough purity just by using simple recrystallization. The crystals of the compounds (17) and (24) have been obtained and studied by X-ray crystallographic analyses

Minimizing the total annualized cost of “SIDEM” seawater desalination unit

This paper presents a steady state analysis of a multi-effect thermal vapor compression desalination plant (MED-TVC) installed in the Tunisian Chemical Group (GCT) factory. A thermodynamic model includes mass and energy balances of the system are presented. An economic model is developed to estimate the cost of produced water ($/m3). The proposed models to minimize the total annualized cost (TAC) of the desalination unit are based on a combination between the process simulator Aspen HYSYS and Matlab. The effects of the operating parameters variations on the system’s performance were studied. The simulation results show a good agreement with the industrial data of the pilot unit.This work was supported by the Applied Thermodynamics Research Unit (UR 11ES80) and the University of Gabes (Tunisia)

A battery health monitoring method using machine learning: A data-driven approach

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures

Plant mediated synthesis of flower-like Cu2O microbeads from Artimisia campestris L. extract for the catalyzed synthesis of 1,4-disubstituted 1,2,3-triazole derivatives

This study presents a novel method for synthesizing 1,4-disubstituted 1,2,3-triazole derivatives through a one-pot, multi-component addition reaction using flower-like Cu2O microbeads as a catalyst. The flower-like Cu2O microbeads were synthesized using an aqueous extract of Artimisia Campestris L. This extract demonstrated the capability to reduce and stabilize Cu2O particles during their initial formation, resulting in the formation of a porous flower-like morphology. These Cu2O microbeads exhibit distinctive features, including a cubic close-packed (ccp) crystal structure with an average crystallite size of 22.8 nm, bandgap energy of 2.7 eV and a particle size of 6 µm. Their catalytic activity in synthesizing 1,4-disubstituted 1,2,3-triazole derivatives was investigated through systematic exploration of key parameters such as catalyst quantity (1, 5, 10, 15, 20, and 30 mg/mL), solvent type (dimethylformamide/H2O, ethanol/H2O, dichloromethane/H2O, chloroform, acetone, and dimethyl sulfoxide), and catalyst reusability (four cycles). The Cu2O microbeads significantly increased the product yield from 20% to 85.3%. The green synthesis and outstanding catalytic attributes make these flower-like Cu2O microbeads promising, efficient, and recyclable catalysts for sustainable and effective chemical transformations

Prevalence of DDC genotypes in patients with aromatic L-amino acid decarboxylase (AADC) deficiency and in silico prediction of structural protein changes

Aromatic L-amino acid decarboxylase (AADC) deficiency is a rare autosomal recessive genetic disorder affecting the biosynthesis of dopamine, a precursor of both norepinephrine and epinephrine, and serotonin. Diagnosis is based on the analysis of CSF or plasma metabolites, AADC activity in plasma and genetic testing for variants in the DDC gene. The exact prevalence of AADC deficiency, the number of patients, and the variant and genotype prevalence are not known. Here, we present the DDC variant (n = 143) and genotype (n = 151) prevalence of 348 patients with AADC deficiency, 121 of whom were previously not reported. In addition, we report 26 new DDC variants, classify them according to the ACMG/AMP/ACGS recommendations for pathogenicity and score them based on the predicted structural effect. The splice variant c.714+4A>T, with a founder effect in Taiwan and China, was the most common variant (allele frequency = 32.4%), and c.[714+4A>T];[714+4A>T] was the most common genotype (genotype frequency = 21.3%). Approximately 90% of genotypes had variants classified as pathogenic or likely pathogenic, while 7% had one VUS allele and 3% had two VUS alleles. Only one benign variant was reported. Homozygous and compound heterozygous genotypes were interpreted in terms of AADC protein and categorized as: i) devoid of full-length AADC, ii) bearing one type of AADC homodimeric variant or iii) producing an AADC protein population composed of two homodimeric and one heterodimeric variant. Based on structural features, a score was attributed for all homodimers, and a tentative prediction was advanced for the heterodimer. Almost all AADC protein variants were pathogenic or likely pathogenic

Prevalence of DDC genotypes in patients with aromatic L-amino acid decarboxylase (AADC) deficiency and in silico prediction of structural protein changes

Aromatic L-amino acid decarboxylase (AADC) deficiency is a rare autosomal recessive genetic disorder affecting the biosynthesis of dopamine, a precursor of both norepinephrine and epinephrine, and serotonin. Diagnosis is based on the analysis of CSF or plasma metabolites, AADC activity in plasma and genetic testing for variants in the DDC gene. The exact prevalence of AADC deficiency, the number of patients, and the variant and genotype prevalence are not known. Here, we present the DDC variant (n = 143) and genotype (n = 151) prevalence of 348 patients with AADC deficiency, 121 of whom were previously not reported. In addition, we report 26 new DDC variants, classify them according to the ACMG/AMP/ACGS recommendations for pathogenicity and score them based on the predicted structural effect. The splice variant c.714+4A>T, with a founder effect in Taiwan and China, was the most common variant (allele frequency = 32.4%), and c.[714+4A>T];[714+4A>T] was the most common genotype (genotype frequency = 21.3%). Approximately 90% of genotypes had variants classified as pathogenic or likely pathogenic, while 7% had one VUS allele and 3% had two VUS alleles. Only one benign variant was reported. Homozygous and compound heterozygous genotypes were interpreted in terms of AADC protein and categorized as: i) devoid of full-length AADC, ii) bearing one type of AADC homodimeric variant or iii) producing an AADC protein population composed of two homodimeric and one heterodimeric variant. Based on structural features, a score was attributed for all homodimers, and a tentative prediction was advanced for the heterodimer. Almost all AADC protein variants were pathogenic or likely pathogenic

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods: We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings: Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1-4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0-8·4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6-9·2) for males and 6·5% (5·4-7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs 1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-harm and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). Interpretation: Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury