Investigating the mechanical and physical properties of wood plastic composites (WPC)

Wood and plastic wastes have been a major environmental concern not only in Egypt but also worldwide. Plastic wastes are classified as recyclable plastic such as bottles and non-recyclable plastic such as plastic bags especially contaminated bags (rejected plastic). Plastic waste is a non biodegradable material calling for an appropriate method of disposal; however, the current approach adopted in Egypt relies mainly on throwing away in dumpsites. Therefore, it is a costless raw material which needs to be invested. In this thesis, the wood waste and the rejected plastic were recycled to produce new useful product; Wood Plastic Composite (WPC), having characteristics similar or close to commercial wood. An innovative, clean, cheap, and effective yet simple technology with different procedures was introduced in this thesis to demonstrate the suitability of wood plastic composites\u27 techniques for developing countries. Testing was done for some important mechanical properties; flexural strength and modulus, and physical properties; water absorption and thickness swelling, which has proven an acceptable final product and promising results; especially regarding the physical test. The design and analysis of experimental work was built on using design of experiments. Special type of experimental designs; design with mixtures, was adopted because it deals with dependent factors; mixture ingredients. Talc was added to the mixture as a mineral additive. The impact of factors (wood waste, plastic waste, and talc) on the physical and mechanical properties of the WPC (flexural strength and modulus, water absorption and thickness swelling) was investigated based on full analysis of variance (ANOVA). It showed that the plastic waste was the most negative affecting factor; this was contributed to the variability in batches produced in addition to the impurities content. Talc resulted in increasing the flexural strength and modulus. Wood with size of up to 0.5mm has proven to affect the flexural modulus response negatively; when increased. A mathematical model and a response surface representing the factors and their responses; that could be used for future forecasting of the properties without performing physical experiments, were obtained for flexural strength and modulus after conducting several trials till reaching the final experimental design within the navigation space. All these trials were based on an algorithm that was introduced to reach the best feasible model and response surface. A completed residual analysis of the model was done in every trial of the algorithm; where every point within the design was analyzed, till reaching the final model. The best possible mix that enhances the flexural strength to the maximum possible was obtained when the talc was close to 30%, plastic waste 50% and wood waste (of particle size up to 1.18mm) and wood waste (particle up to 0.5mm) of average percentages of 10%. For the flexural modulus, best mix values were obtained when talc is close to 35%, plastic waste 40%, and wood waste (particle up to 1.18mm) about 15% and wood waste (particle up to 0.5mm) 10%. A comparison study; using hypothesis testing, between 7 types of commercial wood (plywood, pinewood, beech wood, maple wood, Fiberboard, Medium Density Fiber wood (MDF), and compressed wood) and WPC was conducted to validate the application of the WPC. It showed that the WPC had the lowest water absorption and thickness swelling percentages compared to others (maximum of 1.7%, average of 0.4% and standard deviation of 0.28%); in addition, it showed that WPC flexural strength performs like compressed wood. However, flexural strength and modulus were less regarding other types of wood

A low complexity distributed differential scheme based on orthogonal space time block coding for decode-and-forward wireless relay networks

This work proposes a new differential cooperative diversity scheme with high data rate and low decoding complexity using the decode-and-forward protocol. The proposed model does not require either differential encoding or channel state information at the source node, relay nodes, or destination node where the data sequence is directly transmitted and the differential detection method is applied at the relay nodes and the destination node. The proposed technique enjoys a low encoding and decoding complexity at the source node, the relay nodes, and the destination node. Furthermore, the performance of the proposed strategy is analyzed by computer simulations in quasi-static Rayleigh fading channel and using the decode-and-forward protocol. The simulation results show that the proposed differential technique outperforms the corresponding reference strategies

Nano Hydroxyapatite & Mineral Trioxide Aggregate Efficiently Promote Odontogenic Differentiation of Dental Pulp Stem Cells

BACKGROUND: There has been an urge to shift from conventional therapies to the more promising regenerative strategy since conventional treatment relies on synthetic materials to fill defects and replace missing tissues, lacking the ability to restore the tissues’ physiological architecture and function. AIM: The present study focused on the assessment of the role of two commonly used biomaterials namely; mineral trioxide aggregate (MTA) and nano hydroxy-apatite as promoters of odontogenic differentiation of dental pulp stem cells (DPSCs). METHODS: DPSCs were isolated, cultured in odontogenic media and divided into three groups; control group, MTA group and nanohydroxyapatite group. Odontogenic differentiation was assessed by tracing genes characteristic of different stages of odontoblasts via qRT-PCR. Calcific nodules formation was evaluated by Alizarin red staining. RESULTS: Results demonstrated that both MTA and nanohydroxyapatite were capable of enhancing odontogenic differentiation of DPSCs. CONCLUSION: Nano hydroxyapatite was found to have a higher promoting effect. However, in the absence of an odontogenic medium, MTA and nanohydroxyapatite could not enhance the odontogenic differentiation of DPSCs

International longitudinal registry of patients with atrial fibrillation and treated with rivaroxaban: RIVaroxaban Evaluation in Real life setting (RIVER)

Background Real-world data on non-vitamin K oral anticoagulants (NOACs) are essential in determining whether evidence from randomised controlled clinical trials translate into meaningful clinical benefits for patients in everyday practice. RIVER (RIVaroxaban Evaluation in Real life setting) is an ongoing international, prospective registry of patients with newly diagnosed non-valvular atrial fibrillation (NVAF) and at least one investigator-determined risk factor for stroke who received rivaroxaban as an initial treatment for the prevention of thromboembolic stroke. The aim of this paper is to describe the design of the RIVER registry and baseline characteristics of patients with newly diagnosed NVAF who received rivaroxaban as an initial treatment. Methods and results Between January 2014 and June 2017, RIVER investigators recruited 5072 patients at 309 centres in 17 countries. The aim was to enroll consecutive patients at sites where rivaroxaban was already routinely prescribed for stroke prevention. Each patient is being followed up prospectively for a minimum of 2-years. The registry will capture data on the rate and nature of all thromboembolic events (stroke / systemic embolism), bleeding complications, all-cause mortality and other major cardiovascular events as they occur. Data quality is assured through a combination of remote electronic monitoring and onsite monitoring (including source data verification in 10% of cases). Patients were mostly enrolled by cardiologists (n = 3776, 74.6%), by internal medicine specialists 14.2% (n = 718) and by primary care/general practice physicians 8.2% (n = 417). The mean (SD) age of the population was 69.5 (11.0) years, 44.3% were women. Mean (SD) CHADS2 score was 1.9 (1.2) and CHA2DS2-VASc scores was 3.2 (1.6). Almost all patients (98.5%) were prescribed with once daily dose of rivaroxaban, most commonly 20 mg (76.5%) and 15 mg (20.0%) as their initial treatment; 17.9% of patients received concomitant antiplatelet therapy. Most patients enrolled in RIVER met the recommended threshold for AC therapy (86.6% for 2012 ESC Guidelines, and 79.8% of patients according to 2016 ESC Guidelines). Conclusions The RIVER prospective registry will expand our knowledge of how rivaroxaban is prescribed in everyday practice and whether evidence from clinical trials can be translated to the broader cross-section of patients in the real world

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990-2021: findings from the Global Burden of Disease Study 2021

Background Anaemia is a major health problem worldwide. Global estimates of anaemia burden are crucial for developing appropriate interventions to meet current international targets for disease mitigation. We describe the prevalence, years lived with disability, and trends of anaemia and its underlying causes in 204 countries and territories. Methods We estimated population-level distributions of haemoglobin concentration by age and sex for each location from 1990 to 2021. We then calculated anaemia burden by severity and associated years lived with disability (YLDs). With data on prevalence of the causes of anaemia and associated cause-specific shifts in haemoglobin concentrations, we modelled the proportion of anaemia attributed to 37 underlying causes for all locations, years, and demographics in the Global Burden of Disease Study 2021. Findings In 2021, the global prevalence of anaemia across all ages was 24·3% (95% uncertainty interval [UI] 23·9–24·7), corresponding to 1·92 billion (1·89–1·95) prevalent cases, compared with a prevalence of 28·2% (27·8–28·5) and 1·50 billion (1·48–1·52) prevalent cases in 1990. Large variations were observed in anaemia burden by age, sex, and geography, with children younger than 5 years, women, and countries in sub-Saharan Africa and south Asia being particularly affected. Anaemia caused 52·0 million (35·1–75·1) YLDs in 2021, and the YLD rate due to anaemia declined with increasing Socio-demographic Index. The most common causes of anaemia YLDs in 2021 were dietary iron deficiency (cause-specific anaemia YLD rate per 100 000 population: 422·4 [95% UI 286·1–612·9]), haemoglobinopathies and haemolytic anaemias (89·0 [58·2–123·7]), and other neglected tropical diseases (36·3 [24·4–52·8]), collectively accounting for 84·7% (84·1–85·2) of anaemia YLDs. Interpretation Anaemia remains a substantial global health challenge, with persistent disparities according to age, sex, and geography. Estimates of cause-specific anaemia burden can be used to design locally relevant health interventions aimed at improving anaemia management and prevention. Funding Bill & Melinda Gates Foundation

Active Control of Vehicle Suspension

Various types of actively controlled suspension systems for automotive or agricultural applications are theoretically studied on the basis of the well known quarter car model subjected to realistic inputs chosen to represent road surface/forward vehicle speed combinations for a range of different conditions. The vehicle response is evaluated through the performance criteria (the ride comfort, dynamic tyre load and suspension working space) and power requirements (the power input to actuator, power dissipated in damper or actuator and power fluctuation in spring and tyre). The range of suspension systems includes fully active, semi-active, slow-active, single state feedback active, two state switchable damper, continuously variable damper and conventional passive. Computer programmes relating to the general dynamic modelling of ground vehicle suspension systems (generation of the artificial road, random process analysis and human response criteria) are designed. Computer programmes relating specifically to vehicle ride (linear or non linear), derivation of responses and power calculations for linear or non linear models, as well as performance criteria and optimal control of vehicle suspension are also designed. The switchable damper system which involves continuously switching between two discrete settings is of considerable interest because such dampers are currently available. It is shown to offer worthwhile improvements over passive systems in terms of ride performance if a simple control law is followed. Linear optimal control theory is used to obtain the optimal feedback gains of the fully active and slow-active suspension systems. The behaviour of the fully active linear control systems and the possibility of improving their performance by using a non-linear control law is investigated. The performances of the four and two state feedback slow-active systems, using an actuator with limited frequency response up to 3 Hz, are similar. In terms of the power demand, there is little difference between the fully active and slow-active systems, configured with a conventional passive spring in parallel with the actuator, and their ride performances are also similar. The behaviour of the semi-active systems are evaluated with a control law based exactly on the optimal control of the fully active system, except that no power input is available. A method of comparing performance and power requirements is based on the practical viewpoint that the suspension designer is essentially allocated a given amount of working space and must optimise the suspension within this constraint. Hence, all the competing systems are compared on the basis of equal workspace contours. Conclusions and suggestions for further work are discussed with particular reference to the relationship between the predictive models and their practical usefulness in assisting the designer of advanced suspension systems for on and off - road vehicles

AgISM: A Novel Automated Tool for Monitoring Trends of Agricultural Waste Storage and Handling-Related Injuries and Fatalities Data in Real-Time

Availability of summarized occupational injury data is essential for establishing complete incident surveillance systems, targeting incident preventative efforts, assessing the efficacy of prevention programs, and enhancing workplace safety. There are currently limited automated injury monitoring systems for summarizing occupational injuries obtained from electronic news and other sources, or for visualizing real-time data through an output platform. A “near” real-time surveillance tool could enable researchers to visualize data as it is being collected and provide a more rapid monitoring method to identify patterns in injury data. An automated data pipeline method could provide more current, consistent, and reliable information for injury surveillance systems and injury prevention purposes. Such a system could help public policy makers, epidemiologists, and injury prevention professionals spend less time and effort on classifying cases, increase confidence in the data, and respond quicker to “patterns” of specific types of incidents. Currently, injury surveillance approaches generally rely on manual coding of injury data, resulting in inconsistencies in classification of incident, and contributing factors and considerable delays in publishing results. This study focused on developing and testing a more automated coding methodology for use with incident narratives for further data mining, analysis, and interpretation. The concept was tested on 491 documented fatalities or serious injuries involving agricultural waste storage, handling, and transport operations. The approach provided current and real-time summarization of incident data along with data analysis and visualization by using a standard questionnaire for record-keeping, Python data frames, and the MySQL database. Findings in this study provided evidence for the reliability of classifying injury news clipping narratives into external real-time incident categories. Results showed a very encouraging performance for the chosen model to monitor injury and fatality incidents with efficiency, simplicity, data quality, timeliness, and a consistent coding process

AgISM: A Novel Automated Tool for Monitoring Trends of Agricultural Waste Storage and Handling-Related Injuries and Fatalities Data in Real-Time

Availability of summarized occupational injury data is essential for establishing complete incident surveillance systems, targeting incident preventative efforts, assessing the efficacy of prevention programs, and enhancing workplace safety. There are currently limited automated injury monitoring systems for summarizing occupational injuries obtained from electronic news and other sources, or for visualizing real-time data through an output platform. A “near” real-time surveillance tool could enable researchers to visualize data as it is being collected and provide a more rapid monitoring method to identify patterns in injury data. An automated data pipeline method could provide more current, consistent, and reliable information for injury surveillance systems and injury prevention purposes. Such a system could help public policy makers, epidemiologists, and injury prevention professionals spend less time and effort on classifying cases, increase confidence in the data, and respond quicker to “patterns” of specific types of incidents. Currently, injury surveillance approaches generally rely on manual coding of injury data, resulting in inconsistencies in classification of incident, and contributing factors and considerable delays in publishing results. This study focused on developing and testing a more automated coding methodology for use with incident narratives for further data mining, analysis, and interpretation. The concept was tested on 491 documented fatalities or serious injuries involving agricultural waste storage, handling, and transport operations. The approach provided current and real-time summarization of incident data along with data analysis and visualization by using a standard questionnaire for record-keeping, Python data frames, and the MySQL database. Findings in this study provided evidence for the reliability of classifying injury news clipping narratives into external real-time incident categories. Results showed a very encouraging performance for the chosen model to monitor injury and fatality incidents with efficiency, simplicity, data quality, timeliness, and a consistent coding process