Towards Extended Bit Tracking for Scalable and Robust RFID Tag Identification Systems

The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradigm shift in the development of viable radio frequency identification technology (RFID)-based solutions for ubiquitous real-Time monitoring and tracking. Bit tracking-based anti-collision algorithms have attracted considerable attention, recently, due to its positive impact on decreasing the identification time. We aim to extend bit tracking to work effectively over erroneous channels and scalable multi RFID readers systems. Towards this objective, we extend the bit tracking technique along two dimensions. First, we introduce and evaluate a type of bit errors that appears only in bit tracking-based anti-collision algorithms called false collided bit error in single reader RFID systems. A false collided bit error occurs when a reader perceives a bit sent by tag as an erroneous bit due to channel imperfection and not because of a physical collision. This phenomenon results in a significant increase in the identification delay. We introduce a novel, zero overhead algorithm called false collided bit error selective recovery tackling the error. There is a repetition gain in bit tracking-based anti-collision algorithms due to their nature, which can be utilized to detect and correct false collided bit errors without adding extra coding bits. Second, we extend bit tracking to 'error-free' scalable mutli-reader systems, while leaving the study of multi-readers tag identification over imperfect channels for future work. We propose the multi-reader RFID tag identification using bit tracking (MRTI-BT) algorithm which allows concurrent tag identification, by neighboring RFID readers, as opposed to time-consuming scheduling. MRTI-BT identifies tags exclusive to different RFIDs, concurrently. The concept of bit tracking and the proposed parallel identification property are leveraged to reduce the identification time compared to the state-of-The-Art. 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 7-684-1-127. The work of A. Fahim and T. ElBatt was supported by the Vodafone Egypt Foundation.Scopu

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

Context: To increase the efficiency of wastewater treatment, modeling and optimization of pollutant removal processes are the best solutions. The relationship between input and output parameters in wastewater treatment processes (WWTP) is a complicated one, and it is difficult for designing models using statistics. Artificial Intelligence (AI) models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearity and missing data. Objective: Studies on WWTP of AI-based are increasing day by day. Therefore, it is crucial to systematically review the AI techniques available which are implemented for WWTP. Such kind of review helps for classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, can sort out the best AI technique to design predictive models for WWTP. Method: With the help of the most relevant digital libraries, the total number of papers collected is 1222 which are based on AI modeling on WWTP. Then the filtration of the papers is mainly based on the inclusion and exclusion criteria. Also, to identify new relevant papers, snowballing is the other technique applied. Results: Finally selected 76 primary papers to reach the result were published between 2004 and 2020. Conclusion: ANN with MLP approach on BP algorithm become a supervised neural network called BPNN is the most used AI modeling for WWTP and around 40% of the experimental research done with BPNN. Then there are some limitations on AI modeling of WWTP using photoreforming which is the current study of WWTP represents a promising path for generating renewable and sustainable energy resources like chemicals and fuels

A survey on artificial intelligence techniques for various wastewater treatment processes

Pollutant removal percentage is a key parameter for every WWTPs, and it is crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can be increased with the help of modeling and its optimization. Statistical models are not practical enough for wastewater treatments due to complicated relationship among input and output parameters. AI models are generally more flexible while modeling complex datasets with missing data and nonlinearities. Many AI techniques are available, and the aim is to sort out the best AI technique to design predictive models for WWTPs. Deep Learning and Ensemble are the main techniques reviewed in this work. The Ensemble Learning models showing the most successful performance among other techniques by generally showed their accuracy and efficiency

A review on predictive models designed from artificial intelligence techniques in the wastewater treatment process

Modeling and optimization of pollutant removal processes are the best solutions to increase the efficiency of wastewater treatment. The relationship between input and output parameters in wastewater treatment processes (WWTP) are complicated. Artificial intelligence (AI) models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearity and missing data. Studies on AI-based WWTP are increasing day by day. Therefore, it is crucial to review the AI techniques available which are implemented for WWTP. Such a review helps classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, it can sort out the best AI technique to design predictive models for WWTPs

A Supervised Neural Network-based predictive model for petrochemical wastewater treatment dataset

It is understood that water is the most valuable natural resource and as like wastewater treatment plants are necessary base to control the environmental balance where they are installed. To ensure good quality effluents, the dynamic and complicated wastewater treatment procedure must be handled efficiently. A global interest has been prompted in conservation, reuse, and alternative water sources due to growing treats over water supply scarcity. Water utilities are searching for more efficient ways to maintain their resources globally. The development of machine learning techniques is starting to offer real opportunities to operate water treatment systems in more efficient manners. This paperwork shows research as well as its development work implemented to predict the performance of petrochemical wastewater treatment. The data were used from a reputed chemical plant and the predictive models were developed by implementation of Backpropagation Neural Network using sample datasets with the parameters of wastewater dataset

Predictive models using supervised neural network for pollutant removal efficiency in petrochemical wastewater treatment

The important process in wastewater treatment is the removal of pollutants, and the dataset having so many features may cause difficulty training the data and predicting key variables. This work aims to propose set parameters through normalization techniques, feature selection techniques, and AI techniques. The datasets have 36 features and a key parameter, and experimental datasets contain 628. Constant factor, Z-score, and Min-max normalization are the normalization techniques used to normalize the petrochemical wastewater dataset. SelectKBest, ExtraTreeClassifier, PCA, and RFE are the feature selection techniques for data mining. Then finally done with AI implementation with the help of a supervised neural network technique called backpropagation neural network (BPNN)

Microencapsulation of fish oil using Hydroxypropyl methylcellulose as a carrier material by spray drying

Spray drying is an important method in the food industry for the production of encapsulated oil to improve the handling and flow properties of the powder. In this study, the effect of mixture of polymers on the encapsulation of fish oil by spray drying was investigated. Fish oil powder were produced using different ratios of mixtures of hydroxypropyl methylcellulose (HPMC) 15 cps and HPMC 5 cps. Scanning electron microscopy and the amount of extracted oil from the surface revealed that the formulation containing high concentration of polymer mixture provided the highest protective and prolonged effect on the covering of fish oil. The particle sizes of less than 60 μm were obtained for all the formulations. The powder density was very suitable, which improves the flowability of the powder. Microencapsulation efficiency (69.16–74.75%) and surface morphology of encapsulated oil showed that the stability was increased and hence increased its acceptability as alternative primary polymers

Equity in healthcare: status, barriers, and challenges

Global health provides a challenge for primary care and general practice which will become increasingly important in the future as the prevalence of multimorbidity increases. There is increasing likelihood of survival from acute illnesses and increase an in the elderly population. This literature review focuses on the health inequities, the role of family medicine and the factors that are essential in overcoming these inequalities. Health disparities refer to gaps in the quality of health and delivery of health care across racial, ethnic, gender and socioeconomic groups. The health disparities vary among different countries and the factors that lead to these disparities differ across the world. Family medicine plays a crucial role in bridging this gap and is an essential backbone of the society in developing nations as well as the wealthier nations in providing equity in health care to all people. There are many factors leading to inequity in health care. Family medicine should be recognized as a specialty across the world, as family medicine with its person centered care can bring about a global change in health care. This issue has to be taken up more seriously by the institutions like the WHO, UN and also individual governments along with the political parties to create uniformity in health care. In the current setting of the global economic and financial crisis, a truly global solution is needed. The WHO has come up with various strategies to solve the issue of financial crises and ensuring equity in health globally. This will ensure equal health care to all people especially the underprivileged in developing countries who do not have access to better healthcare due to lack of resources. This factor is a major contributor to the premature death of individuals at all stages of life from new born to the elderly and includes infant mortality and mortality due to chronic diseases. This is important in creating uniformity in health care across the world but has to be considered at a global level to have an impact

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication