•Natural Edible Films in Food Packaging.•Polysaccharide-Based Edible Films.•Animal, Plant and Marine origin polysaccharides.
Polysaccharides, such as pectin, starch, alginate, carrageenan, and ...xanthan gum, have been used as biopolymer materials to create coatings and edible films to reduce traditional plastic packages. Petrochemical polymers, extensively used for food packaging, are non-renewable and non-biodegradable and need landfills. Thus, there is a requirement to find alternative packaging materials that are easily degradable and renewable. Natural edible polymers are the materials made from natural edible constituents that can be consumed by animals or human beings with no health risk. Since they are directly consumed with food, nothing is left for disposal. Polysaccharides, Protein and Lipid-Based Natural edible polymers are used to make coatings and edible films surrounding the surface of the food. These natural edible polymers are generally categorized into polysaccharides, lipids and proteins. This review article summarizes the importance of various natural polymers used for making coatings and edible films.
•Novel bidirectional LSTM architecture.•Novel asymmetric objective function for making safe predictions.•Novel approach for generating remaining useful life targets for training.•Improved performance ...for short sequences with random starts.•The turbofan engine dataset from NASA's repository is used for comparison.
Unpredictable failures and unscheduled maintenance of physical systems increases production resources, produces more harmful waste for the environment, and increases system life cycle costs. Efficient remaining useful life (RUL) estimation can alleviate such an issue. The RUL is predicted by making use of the data collected from several types of sensors that continuously record different indicators about a working asset, such as vibration intensity or exerted pressure. This type of continuous monitoring data is sequential in time, as it is collected at a certain rate from the sensors during the asset's work. Long Short-Term Memory (LSTM) neural network models have been demonstrated to be efficient throughout the literature when dealing with sequential data because of their ability to retain a lot of information over time about previous states of the system. This paper proposes using a new LSTM architecture for predicting the RUL when given short sequences of monitored observations with random initial wear. By using LSTM, this paper proposes a new objective function that is suitable for the RUL estimation problem, as well as a new target generation approach for training LSTM networks, which requires making lesser assumptions about the actual degradation of the system.
•CMC prepared from mesquite tree was applied for synthesizing CMC/Fe3O4 nanocomposite.•The nanocomposite showed homogenous spherical magnetic nanoparticles with diameter ∼25 nm.•CMC/Fe3O4 was ...investigated as cost effective and sustainable adsorbent for dye removal.•The adsorption capacity of the nanocomposite for MB removal is 1597 mg/g.
In the current article cellulose pulp extracted from mesquite tree was characterized and used as a raw material for preparation of carboxymethyl cellulose (CMC) which further used for preparing nanocomposite. CMC/Fe3O4 nanocomposite was synthesized by co-precipitation of iron (II) and (III) salts by aqueous ammonia in CMC solution. Cellulose pulp, CMC and CMC/Fe3O4 materials were characterized by using FTIR, XRD, TGA, SEM and TEM analysis. The results showed that homogenous spherical magnetic nanoparticles with diameter ∼25 nm were formed. The nanocomposite was further applied to remove methylene blue (MB) from aqueous solutions. The adsorption experiments showed the maximum adsorption capacity at pH 7. The adsorption results were analyzed by different isotherm and kinetic models and the results were fitted well with the Langmuir model and pseudo second-order model respectively. The current article showed that mesquite tree is a new resource for cellulose pulp which could be employed for preparing sustainable and environmentally friendly composite materials.
Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American ...setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.
This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way ...based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data, thereby supporting accurate “end-to-end learning” in industrial fault classification applications. Moreover, the generated polygons can play a significant role in the interpretation of trained deep learning fault classifiers. The performance of the proposed method was demonstrated using a benchmark dataset in the process industry. It was also tested successfully to classify challenging faults in major equipment in a thermomechanical pulp mill located in Canada. The results of the proposed method show better performance than other comparable fault classifiers.
Aortic valve calcification is a significant and serious clinical problem for which there are no effective medical treatments. Individuals born with bicuspid aortic valves, 1-2% of the population, are ...at the highest risk of developing aortic valve calcification. Aortic valve calcification involves increased expression of calcification and inflammatory genes. Bicuspid aortic valve leaflets experience increased biomechanical strain as compared to normal tricuspid aortic valves. The molecular pathogenesis involved in the calcification of BAVs are not well understood, especially the molecular response to mechanical stretch. HOTAIR is a long non-coding RNA (lncRNA) that has been implicated with cancer but has not been studied in cardiac disease. We have found that HOTAIR levels are decreased in BAVs and in human aortic interstitial cells (AVICs) exposed to cyclic stretch. Reducing HOTAIR levels via siRNA in AVICs results in increased expression of calcification genes. Our data suggest that β-catenin is a stretch responsive signaling pathway that represses HOTAIR. This is the first report demonstrating that HOTAIR is mechanoresponsive and repressed by WNT β-catenin signaling. These findings provide novel evidence that HOTAIR is involved in aortic valve calcification.
•Optimal condition-based maintenance strategies with multi-level repairs.•Development of generic Data-driven modeling and solution methodology.•Remaining useful life estimation using ...reliability-based method.•Reward function for reinforcement learning includes remaining useful life.•A real case study is proposed with optimality validation for the obtained results.
Optimizing condition-based maintenance (CBM) strategies based on machine learning (ML) methods as reinforcement learning (RL) have been receiving increasing attention due to their competencies. However, most existing research depends on simplifying assumptions about the deterioration process and aims to obtain only a threshold for preventive maintenance using the maintenance cost as RL's reward function. To tackle these limitations, this paper proposes a data-driven CBM optimization methodology that combines ML prediction model and RL method with a reliability-based method for remaining useful life (RUL) estimation. The prediction model learns the system's actual deterioration from historical deterioration data. The RL method integrates the prediction model with a customized reward function that minimizes the average maintenance cost. This reward function incorporates the maintenance cost and the system's RUL. The RUL estimation method utilizes the Nonparametric reliability method's Kaplan-Meier (KM) product limit. The CBM decision-making problem enables preventive and corrective replacements besides multi-level preventive repairs that restore the system's condition to a specific previous deterioration level. Contrary to the widely proposed threshold CBM strategies, the proposed methodology provides a strategy that maps each system state to the optimal maintenance action. A real case study is proposed to validate the methodology.
In the present work, hematite (α-Fe₂O₃) nanopowders were successfully prepared via a hydrothermal route. The morphology and microstructure of the synthesized nanopowders were analyzed by using ...scanning and transmission electron microscopy (SEM and TEM, respectively) analysis and X-ray diffraction. Gas sensing devices were fabricated by printing α-Fe₂O₃ nanopowders on alumina substrates provided with an interdigitated platinum electrode. To determine the sensor sensitivity toward NO₂, one of the main environmental pollutants, tests with low concentrations of NO₂ in air were carried out. The results of sensing tests performed at the operating temperature of 200 °C have shown that the α-Fe₂O₃ sensor exhibits p-type semiconductor behavior and high sensitivity. Further, the dynamics exhibited by the sensor are also very fast. Lastly, to determine the selectivity of the α-Fe₂O₃ sensor, it was tested toward different gases. The sensor displayed large selectivity to nitrogen dioxide, which can be attributed to larger affinity towards NO₂ in comparison to other pollutant gases present in the environment, such as CO and CO₂.
•Nanocellulose (NC) extracted from agricultural residue and chitosan (Ch) were used to modify paper sheets.•Modified paper sheets showed minor improvement in burst and air permeability ...properties.•Coating of paper sheets with NC/Ch formulations enhanced their mechanical and air permeability properties.•Paper with antibacterial power have been obtained.
Paper sheets made from bagasse pulp have been modified using nanocellulose (NC) obtained from the same raw material. Modification of paper sheets have been carried out either through loading of paper with different concentrations of NC and antibacterial agent, Chitosan (Ch) during making sheets, or by surface coating of the paper. Crystals of NC extracted using concentrated sulfuric acid from bagasse pulp were found to have crystallinity index (CrI) 90%. Morphology of obtained NC has been confirmed by TEM and images revealed formation of NC crystals with large size distribution ranges from 4 to 60nm. Mechanical properties and air permeability of paper sheets loaded with different ratios of NC and Ch have been investigated. The results showed that presence of NC did not negatively affect the obtained modified paper sheets, while air permeability decreased with adding 8% NC to paper matrix. On the other hand, surface coverage of paper sheets with NC greatly reduced air permeability. Antimicrobial investigations carried out by optical density method indicated that presence of Ch in the paper sheets as an additive or in a coating formulation enhanced paper resistance to different microorganisms especially those causing food poisoning. The current study confirms that the modified paper can have potential application in food packaging.
Abstract Background Legg-Calve-Perthes disease (LCPD) is a childhood condition characterized by femoral head osteonecrosis. The osteonecrotic lesion eventually heals, but persistent deformity to the ...proximal femur and acetabulum might induce hip discomfort and dysfunction following recovery. Previous reports have noted that between 30% and 50% of individuals affected by LCPD to have residual hip symptoms that persist and a frequent precursor to hip osteoarthritis in young adulthood. Aim of the Work The aim of the present study is to review the efficacy of intra-articular procedures through surgical hip dislocation approach to residual hip deformity secondary to Legg- Calve-perthes disease. Patients and Methods A Systematic Review and Meta-Analysis, We will include: Randomized control trails (RCTs), controlled clinical trials, retrospective cohort studies and we will exclude: case reports, case series studies, cross sectional studies and non English studies. Results The surgical hip dislocation approach allows the surgeon to dynamically evaluate the hip, identify sources of impingement, and treat intraarticular and extraarticular abnormalities. Conclusion Head and neck osteochondroplasty performed through the surgical dislocation approach, combined with other procedures such as FHRO, RFNL, ITO and acetubular osteotomies relieved pain, improved ROM and restored function in most patients with no major complications.
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