Kalasalingam Academy of Research and Education

Polymer-based materials are increasingly produced through fused deposition modelling (FDM) – an additive manufacturing process, due to its intrinsic advantages in manufacturing complex shapes and structures at low overhead costs. The versatility of this technology has attracted several industries to print complex geometrical structures. This underlines the importance of studying the mechanical strength of FDM printed polymeric materials, especially their fatigue behaviour in cyclic loading conditions. Conventionally manufactured polymeric materials (e.g. injection moulding) have superior fatigue performance than FDM printed materials. Unlike conventionally manufactured polymers, FDM-made polymers have layer by layer adhesion and the influence of printing parameters make fatigue analysis complex and critical. The influences of printing parameters and printing material characteristics have a significant impact on the fatigue behaviour of these materials. The underlying mechanism behind the fatigue of FDM printed polymers is crucial for the assessment of these materials in structural applications. However, the fatigue behaviour of FDM printed polymeric materials has not been reviewed in detail. Therefore, this article aims to evaluate 3D printed polymeric materials’ fatigue properties. The importance of fatigue in the FDM printed biomedical materials is also reviewed, and more importantly, the novel FDM printed architected cellular material fatigue properties are also introduced.

Nanomedicine concerns the use of precision-engineered nanomaterials to develop novel therapeutic and diagnostic modalities for human use. The present study demonstrates the efficacy of biologically synthesized silver nanoparticles (AgNPs) as an antitumor agent using Dalton's lymphoma ascites (DLA) cell lines in vitro and in vivo. The AgNPs showed dose- dependent cytotoxicity against DLA cells through activation of the caspase 3 enzyme, leading to induction of apoptosis which was further confirmed through resulting nuclear fragmentation. Acute toxicity, ie, convulsions, hyperactivity and chronic toxicity such as increased body weight and abnormal hematologic parameters did not occur. AgNPs significantly increased the survival time in the tumor mouse model by about 50% in comparison with tumor controls. AgNPs also decreased the volume of ascitic fluid in tumor-bearing mice by 65%, thereby returning body weight to normal. Elevated white blood cell and platelet counts in ascitic fluid from the tumor-bearing mice were brought to near-normal range. Histopathologic analysis of ascitic fluid showed a reduction in DLA cell count in tumor-bearing mice treated with AgNPs. These findings confirm the antitumor properties of AgNPs, and suggest that they may be a cost-effective alternative in the treatment of cancer and angiogenesis-related disorders.

BACKGROUND: Oxidative stress is imperative for its morbidity towards diabetic complications, where abnormal metabolic milieu as a result of hyperglycemia, leads to the onset of several complications. A biological antioxidant capable of inhibiting oxidative stress mediated diabetic progressions; during hyperglycemia is still the need of the era. The current study was performed to study the effect of biologically synthesized gold nanoparticles (AuNPs) to control the hyperglycemic conditions in streptozotocin induced diabetic mice. RESULTS: The profound control of AuNPs over the anti oxidant enzymes such as GSH, SOD, Catalase and GPx in diabetic mice to normal, by inhibition of lipid peroxidation and ROS generation during hyperglycemia evidence their anti-oxidant effect during hyperglycemia. The AuNPs exhibited an insistent control over the blood glucose level, lipids and serum biochemical profiles in diabetic mice near to the control mice provokes their effective role in controlling and increasing the organ functions for better utilization of blood glucose. Histopathological and hematological studies revealed the non-toxic and protective effect of the gold nanoparticles over the vital organs when administered at dosage of 2.5 mg/kilogram.body.weight/day. ICP-MS analysis revealed the biodistribution of gold nanoparticles in the vital organs showing accumulation of AuNPs in the spleen comparatively greater than other organs. CONCLUSION: The results obtained disclose the effectual role of AuNPs as an anti-oxidative agent, by inhibiting the formation of ROS, scavenging free radicals; thus increasing the anti-oxidant defense enzymes and creating a sustained control over hyperglycemic conditions which consequently evoke the potential of AuNPs as an economic therapeutic remedy in diabetic treatments and its complications.

The natural fiber-reinforced polymer composite materials offered extensive range of properties which are suitable for large number of engineering application. The natural fibers have been abundantly available in the world. It has unique properties compared to synthetic fiber and reduces the plastic usage. This article reports the extraction process of natural fibers, characterization of natural fibers, and preparation of natural fiber-reinforced composites. The mechanical properties such as tensile, flexural, impact, and dynamic properties as well as thermal and machinability properties of the composites with and without chemically treated fibers were reported. The water absorption capability of the composites and its effect on mechanical properties were also reported.

Alkali treatment coupled with high pressure defibrillation and acid treatment have been tried on banana fibers obtained from the pseudo stem of the banana plant Musa sapientum. The structure and morphology of the fibers have been found to be affected on the basis of the concentration of the alkali and acid and also on the pressure applied. Steam explosion in alkaline medium followed by acidic medium is found to be effective in the depolymerization and defibrillation of the fiber to produce banana nanowhiskers. The chemical constituents of raw and steam exploded fibers were analyzed according to the ASTM standards. Structural analysis of steam exploded fibers was carried out by FTIR and XRD. The fiber diameter and percentage crystallinity of the modified fibers were investigated using X-ray diffraction studies. Characterization of the fibers by SFM and TEM supports the evidence for the development of nanofibrils of banana fibers.

The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.

The development of fibre composites in recent years has been remarkably strong, owing to their high performance and durability. Various advancements in fibre composites are emerging because of their increased use in a myriad of applications. One of the popular processing methods is additive manufacturing (AM), however, polymer-fibre composites manufactured through AM have a significantly lower strength compared to the conventional manufacturing processes, for instance, injection moulding. This article is a comprehensive review of the mechanical testing and performance analysis of polymer-fibre composites fabricated through AM, in particular fused deposition modelling (FDM). The review highlights the effect of the various processing parameters, involved in the FDM of polymer-fibre composites, on the observed mechanical properties. In addition, the thermal properties of FDM based fibre composites are also briefly reviewed. Overall, the review article has been structured to provide an impetus for researchers in the concerned engineering domain to gain an insight into the mechanical properties of fibre-reinforced polymeric composites manufactured through AM.

Diabetes is one of the common and growing diseases in several countries and all of them are working to prevent this disease at early stage by predicting the symptoms of diabetes using several methods. The main aim of this study is to compare the performance of algorithms those are used to predict diabetes using data mining techniques. In this paper we compare machine learning classifiers (J48 Decision Tree, K-Nearest Neighbors, and Random Forest, Support Vector Machines) to classify patients with diabetes mellitus. These approaches have been tested with data samples downloaded from UCI machine learning data repository. The performances of the algorithms have been measured in both the cases i.e dataset with noisy data (before pre-processing) and dataset set without noisy data (after pre-processing) and compared in terms of Accuracy, Sensitivity, and Specificity.

Industry 4.0 is a current trend of automation and digitalization of industries. The impacts and importance of Industry 4.0 are reflected in all aspects of our lives. The purpose of this article is to analyze the literatures based on a scoping review method. A lack of digital culture, training, knowledge, and language are also challenges faced by Industry 4.0 while implementing its operations. Digital English and Education 4.0 are also employee competencies of Industry 4.0. The authors have reviewed the literature related to Digital English, Education 4.0, and Industry 4.0 from various resources. Astonishingly, the results show that the studies conducted in these areas are so specific focusing only one of the above-mentioned areas; no research article was identified that detailed the interconnections among these areas. From the scoping review, the study has identified the gaps in the literature. Thus, the study concludes that filling up the gaps and conducting research in these areas are useful to sort out a few of the challenges of Industry 4.0 and it recommends that in future, researchers conduct studies based on the interconnections of Digital English and Education 4.0 for Industry 4.0.

Abstract This investigation is carried out to understand the effects of water absorption on the mechanical properties of hybrid phenol formaldehyde (PF) composite fabricated with Areca Fine Fibres (AFFs) and Calotropis Gigantea Fibre (CGF). Hybrid CGF/AFF/PF composites were manufactured using the hand layup technique at varying weight percentages of fibre reinforcement (25, 35 and 45%). Hybrid composite having 35 wt.% showed better mechanical properties (tensile strength ca. 59 MPa, flexural strength ca. 73 MPa and impact strength 1.43 kJ/m 2 ) under wet and dry conditions as compared to the other hybrid composites. In general, the inclusion of the fibres enhanced the mechanical properties of neat PF. Increase in the fibre content increased the water absorption, however, after 120 h of immersion, all the composites attained an equilibrium state.

This work presents both application and comparison of the metaheuristic techniques to generation expansion planning (GEP) problem. The Metaheuristic techniques such as the genetic algorithm, differential evolution, evolutionary programming, evolutionary strategy, ant colony optimization, particle swarm optimization, tabu search, simulated annealing, and hybrid approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods virtual mapping procedure (VMP) and penalty factor approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, intelligent initial population generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional dynamic programming and the effectiveness of each proposed methods has also been illustrated in detail.

In the recent development of, Internet of Things (IoT) makes all objects interconnected and it has been recognized as the next technical revolution. Some of the applications of Internet of Things are smart parking, smart home, smart city, smart environment, industrial places, agriculture fields and health monitoring process. One such application is in healthcare to monitor the patient health status Internet of Things makes medical equipments more efficient by allowing real time monitoring of patient health, in which sensor acquire data of patient's and reduces the human error. In Internet of Things patient's parameters get transmitted through medical devices via a gateway, where it is stored and analyzed. The significant challenges in the implementation of Internet of Things for healthcare applications is monitoring all patient's from various places. Thus Internet o Things in the medical field brings out the solution for effective patient monitoring at reduced cost and also reduces the trade-off between patient outcome and disease management. In this paper discuss about, monitoring patient's body temperature, respiration rate, heart beat and body movement using Raspberry Pi board.

DNA microarray technology can monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Knowledge gained through microarray data analysis is increasingly important as they are useful for phenotype classification of diseases. This paper presents an effective method for gene classification using Support Vector Machine (SVM). SVM is a supervised learning algorithm capable of solving complex classification problems. Mutual information (MI) between the genes and the class label is used for identifying the informative genes. The selected genes are utilized for training the SVM classifier and the testing ability is evaluated using Leave-one-Out Cross Validation (LOOCV) method. The performance of the proposed approach is evaluated using two cancer microarray datasets. From the simulation study it is observed that the proposed approach reduces the dimension of the input features by identifying the most informative gene subset and improve classification accuracy when compared to other approaches.

At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.

The aim of the present study was to develop antibacterial cellulose (cotton) nanocomposite fabrics (CNCFs) with in situ generated silver nanoparticles using medicinal plant Vitex leaf extract. The developed CNCFs were characterized by scanning electron microscope (SEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) and antibacterial tests. Further, these CNCFs possessed good antibacterial activities. These CNCFs prepared using simple and environmentally friendly method can be considered for medical applications in, such as, surgical aprons, wound cleaning, wound dressing, and hospital bed materials.

A crude oil spill is a common issue during offshore oil drilling, transport and transfer to onshore. Second, the production of petroleum refinery effluent is known to cause pollution due to its toxic effluent discharge. Sea habitats and onshore soil biota are affected by total petroleum hydrocarbons (TPH) as a pollutant in their natural environment. Crude oil pollution in seawater, estuaries and beaches requires an efficient process of cleaning. To remove crude oil pollutants from seawater, various physicochemical and biological treatment methods have been applied worldwide. A biological treatment method using bacteria, fungi and algae has recently gained a lot of attention due to its efficiency and lower cost. This review introduces various studies related to the bioremediation of crude oil, TPH and related petroleum products by bioaugmentation and biostimulation or both together. Bioremediation studies mentioned in this paper can be used for treatment such as emulsified residual spilled oil in seawater with floating oil spill containment booms as an enclosed basin such as a bioreactor, for petroleum hydrocarbons as a pollutant that will help environmental researchers solve these problems and completely clean-up oil spills in seawater.

This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost; the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost; the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

Abstract Background Probiotics are live microbial supplements known for its health benefits. Consumption of probiotics reported to improve several health benefits including intestinal flora composition, resistance against pathogens. In the recent years, there is an increasing trend of probiotic-based food products in the market. Main body Probiotics cells are targeted to reach the large intestine, and the probiotics must survive through the acidic conditions of the gastric environment. It is recommended to formulate the probiotic bacteria in the range of 10 8 –10 9 cfu/g for consumption and maintain the therapeutic efficacy of 10 6 –10 7 cfu/g in the large intestine. During the gastrointestinal transit, the probiotics will drastically lose its viability in the gastric environment (pH 2). Maintaining cell viability until it reaches the large intestine remains challenging task. Encapsulating the probiotics cells with suitable wall material helps to sustain the survival of probiotics during industrial processing and in gastrointestinal transit. In the encapsulation process, cells are completely enclosed in the wall material, through different techniques including spray drying, freeze drying, extrusion, spray freeze drying, emulsification, etc. However, spray-drying and freeze-drying techniques are successfully used for the commercial formulation; thus, we limited to review those encapsulation techniques. Short conclusions The survival rate of spray-dried probiotics during simulated digestion mainly depends on the inlet air temperature, wall material and exposure in the GI condition. And fermentation, pH and freeze-drying time are the important process parameters for maintaining the viability of bacterial cells in the gastric condition. Improving the viability of probiotic cells during industrial processing and extending the cell viability during storage and digestion will be the main concern for successful commercialization. Graphical abstract

This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor based on the combination of two algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MR image and its exact position and the shape also determined. The stage of the tumor is displayed based on the amount of area calculated from the cluster.

Degenerative diseases are results of deterioration of cells and tissues with aging either by unhealthy lifestyle or normal senescence. The degenerative disease likely affects central nervous system and cardiovascular system to a great extent. Certain medications and therapies have emerged for the treatment of degenerative diseases, but in most cases bearing with poor solubility, lower bioavailability, drug resistance, and incapability to cross the blood–brain barrier (BBB). Hence, it has to be overcome with conventional treatment system; in this connection, nanotechnology has gained a great deal of interest in recent years. Moreover, nanotechnology and nanocarrier-based approach drug delivery system could revolutionize the treatment of degenerative diseases by faster absorption of drug, targeted interaction at specific site, and its release in a controlled manner into human body with minimal side effects. The core objective of this review is to customize and formulate therapeutically active molecules with specific site of action and without affecting other organs and tissues to obtain effective result in the improvement of quality of health. In addition, the review provides a concise insight into the recent developments and applications of nanotech and nanocarrier-based drug delivery for the treatment of various degenerative diseases.