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Semantic image segmentation is a vast area of interest for computer vision which has gained exceptional attention from the research community. It is the process of classifying each pixel in respective category. In this paper, we exploit the problem of scene understanding and perform the segmentation by combining different classification models as a feature encoder and segmentation models as a feature decoder. All of the experiments were performed on Camvid dataset. It covers a wide range of real-world applications such as autonomous driving, virtual/augmented reality, indoor navigation, etc.

State-of-the-art deep learning models for food recognition do not allow data incremental learning and often suffer from catastrophic interference problems during the class incremental learning. This is an important issue in food recognition since real-world food datasets are open-ended and dynamic, involving a continuous increase in food samples and food classes. Model retraining is often carried out to cope with the dynamic nature of the data, but this demands high-end computational resources and significant time. This paper proposes a new open-ended continual learning framework by employing transfer learning on deep models for feature extraction, Relief F for feature selection, and a novel adaptive reduced class incremental kernel extreme learning machine (ARCIKELM) for classification. Transfer learning is beneficial due to the high generalization ability of deep learning features. Relief F reduces computational complexity by ranking and selecting the extracted features. The novel ARCIKELM classifier dynamically adjusts network architecture to reduce catastrophic forgetting. It addresses domain adaptation problems when new samples of the existing class arrive. To conduct comprehensive experiments, we evaluated the model against four standard food benchmarks and a recently collected Pakistani food dataset. Experimental results show that the proposed framework learns new classes incrementally with less catastrophic inference and adapts domain changes while having competitive classification performance.

Birth weight is considered a major factor when monitoring any signs of abnormalities in the growth of the fetus and taking timely decisions related to labor management. Existing methods involve specialized equipment and training, which makes them less feasible for underdeveloped areas. Therefore, this study proposed a system for prediction of childbirth weight through kernel extreme reservoir machines and optimized the model parameters by the use of particle swarm optimization. Experimental results showed a significant improvement in the recommended method over existing models. The proposed approach is more economical than the traditional ultrasound making it extremely suited to underprivileged communities.

Background: Malnutrition remains prevalent and existing health problem globally. Particularly Undernutrition is a major public health issue in developing countries.Globally the causes of severe acute undernutrition varies across context. The aim of this study was to identify the determinants of severe acute malnutrition to uncover contextual factors based on UNICEF conceptual framework, as there was no study done in a similar context in Ethiopia.Methods: Health facility based (health post) un-matched case control study with Key informant interview was conducted to identify determinant factors of severe acute malnutrition(SAM) among children between 6 to 36 months. 246 children (82 cases and 164) with their mothers or care takers participated the study which was conducted between December 20, 2019 to January 20, 2020 in Kalafo district in Shebele River. Odds ratio with 95% confidence interval was calculated to identify the determinants of SAM among children aged 6-36 months using multivariate logistic regression.Results: The Odds of Severe Acute Malnutrition was 2.28(1.22,4.26); 4.68(2.29,9.58); 2.85(1.26,6.45); 2.39(1.16,4.96) and 3.262(1.46,7.31) and 3.237(1.45,7.23); respectively for mothers with three or more under five children, Children with inadequate dietary diversity, experienced diarrhea in past two weeks, their mothers had not nutrition counselling during pregnancy, Children’s age between 6-11 and 12-17 months as compared to controls.Conclusion: The finding of this study reveals that the main determinants of severe acute malnutrition in riverine context are multi-level. On top of poor childcare and polygamy in qualitative finding, Diarrhea and inadequate dietary intake under the category of immediate cause of malnutrition. Having more than (≥3) under-five children, young age (6-11 and 12-17months) and not having nutrition counseling during pregnancy were also among basic determinants of severe acute malnutrition in riverine context. Decisive and multi-sectoral approach is required to addressing SAM in the riverine area.

Catastrophic forgetting is a major problem that affects neural networks during progressive learning. In it, the previously learned representation vanishes as the network learns new information. The extreme learning machine is one of the variants of the neural network. It is used in many domains due to fast training and good generalization ability. However, like other neural networks, it suffers from catastrophic forgetting and negative forward and backward transfer during the progression of neurons in incremental learning. The study hypothesizes that it is due to overlapping in hidden neurons and output weights. The global representation by an activation function further supports this hypothesis. To address this, the study proposes a neuron clustering approach to mitigate it in an adaptive class incremental extreme learning machine. The neuron clustering method activates k nearest neurons during learning and testing. It helps to partition the network to select overlapping subnetwork. Experimental results on four food datasets show that the proposed approach reduces negative forward and backward transfer when neurons are added incrementally during progressive learning.

Hypertension is an epidemic restricted not only to the developing but also to the developed nations. It is triggered by various lifestyle choices that depend on each individual based on their personal physiology and lifestyle. Early diagnosis is possible, but it requires continuous blood pressure monitoring. Various machine learning methods have been proposed for early diagnosis of hypertension by predicting blood pressure and detecting high spikes in the values. However, these solutions are built upon the generic guidelines which may not be applicable for every patient. Most of these solutions incorporate batch learning and require all data to be present before prediction and do not support any online learning mechanism. This leads to potentially outdated models. Furthermore, there is also a lack of an intelligent approach to handling incomplete time series while training the model. This paper presents a personalized approach to estimate blood pressure that eliminates the need for continuous monitoring based on the Online recurrent extreme learning machine (OR-ELM). The missing values are imputed using Gaussian mixture models. The prediction model learns from the historical data and learns online as more data becomes available. The proposed scheme is developed and deployed on a mobile application for secured prediction results. The method is used to predict blood pressure in Malaysian population and compared with existing batch-learning and online learning methods. The results show that OR-ELM based model outperforms the existing online techniques such as the Online sequential extreme learning machine and batch learning technique such as Extreme learning machine.

In this work, variable active control pitching is applied to overcome the incapability of self-starting in NACA0018 three-bladed Vertical Axis Wind Turbine (VAWT). The study is conducted at low tip speed ratio of zero, and one, using CFD SST turbulent Model in COMSOL Multyphysics (FEM Solver). First, single blade rotor of NACA0018 is investigated at tip speed zero and one respectively to specify the optimal pitching at each azimuthal angle from 0o-360o. Then the calculated data regarding the optimal pitching for single blade is applied to NACA0018 three bladed rotor at the same tip speeds. The resultant computed average torque per revolution from the three optimally pitched blades rotor is 8 times the torque of the same rotor without applying the active optimal pitching in the case of zero tip speed ratio. The torque is improved to 18 times in the case when the tip speed ratio is equal to one.

Typically, it is desired to operate the wind turbine at the maximum power point. However, in small wind turbines which have a storage system integrated with them, harvesting as much energy as possible is more crucial. This may be achieved by reducing the cut-in speed while maximizing the mechanical power. These two goals may be achieved by optimizing the turbine blades. In this article, the turbine blades are optimized using improved blade element momentum theory including Viterna-Corrigan stall model with the objective to yield low cut-in speed and high power level. Using the blade element momentum analysis, the power coefficient curves as functions of tip-speed ratio at various range of wind speeds are obtained for the optimized turbine. Using MATLAB/Simulink tool, a wind energy system, which consists of a wind turbine, permanent magnet synchronous generator and a resistive load, is simulated. The curves obtained from the blade element momentum analysis are used to emulate the wind turbine. The results obtained from the simulation are compared to experimental results. It is noticed that the wind turbine may be optimized to harvest more energy.

Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges such as inter and intraclass variations alongside the practical applications of smart glasses, wearable cameras, and mobile devices require resource-efficient food recognition models with high classification performance. Furthermore, explainable AI is also crucial in health-related domains as it characterizes model performance, enhancing its transparency and objectivity. Our proposed architecture attempts to address these challenges by drawing on the strengths of the transfer learning technique upon initializing MobiletNetV3 with weights from a pre-trained model of ImageNet. The MobileNetV3 achieves superior performance using the squeeze and excitation strategy, providing unequal weight to different input channels and contrasting equal weights in other variants. Despite being fast and efficient, there is a high possibility for it to be stuck in the local optima like other deep neural networks, reducing the desired classification performance of the model. Thus, we overcome this issue by applying the snapshot ensemble approach as it enables the M model in a single training process without any increase in the required training time. As a result, each snapshot in the ensemble visits different local minima before converging to the final solution which enhances recognition performance. On overcoming the challenge of explainability, we argue that explanations cannot be monolithic, since each stakeholder perceive the results’, explanations based on different objectives and aims. Thus, we proposed a user-centered explainable artificial intelligence (AI) framework to increase the trust of the involved parties by inferencing and rationalizing the results according to needs and user profile. Our framework is comprehensive in terms of a dietary assessment app as it detects Food/Non-Food, food categories, and ingredients. Experimental results on the standard food benchmarks and newly contributed Malaysian food dataset for ingredient detection demonstrated superior performance on an integrated set of measures over other methodologies.

Recently, food recognition has received more research attention for mHealth applications that use automated visual-based methods to assess dietary intake. The goal is to improve the food diaries by addressing the challenges faced by existing methodologies. In addition to the classical challenge of the absence of rigid food structure and intra-class variations, food diaries employing deep networks trained with pristine images are susceptible to quality variations in real-world conditions of image acquisition and transmission. Similarly, existing progressive classifiers that use visual features via a convolutional neural network (CNN) classify food categories and cannot detect food ingredients. We aim to provide a system that selects the optimal subset of features from quality resilient CNNs and subsequently incorporates the parallel type of classification to tackle such challenges. The first progressive classifier recognizes food categories, and its multilabel extension detects food ingredients. Following this idea, after extracting features from the quality resilient category and ingredient CNN models by fine-tuning it on synthetic images generated using the novel online data augmentation method random iterative mixup. Our feature selection strategy uses the Shapley additive explanation (SHAP) values from the gradient explainer to select the best features. Then, novel progressive kernel extreme learning machine (PKELM) is exploited to cater to domain variations due to quality distortions, intra-class variations, and so forth, by remodeling the network structure based on activity value with the nodes. PKELM extension for multilabel classification detects ingredients by employing a bipolar step function to process test output and then selecting the column labels of the resulting matrix with a value of one. Moreover, during online learning, the PKELM novelty detection mechanism can label unlabeled instances and detect noisy samples. Experimental results showed superior performance on an integrated set of measures for seven publicly available food datasets.

Due to the fluctuation of the wind speed, wind energy systems are typically connected to the utility grid through back-to-back converters and a DC coupling. Although this topology provides a stable voltage on the grid side, it makes the control system relatively complicated. In this paper, a model for the machine-side converter and the controller is derived in the rotor reference frame. Using αβ0 transformation to detect the voltage phase angle, a decoupled control of the active and the reactive power is achieved. As a result, a simple PI controller is used to control the active and reactive power flow of the machine side converter. The performance of the controller is tested using the MatLab/Simulink simulation. The results show that the controller has provided the desired performance.

This paper discusses the integration of wind energy system in Derna, Libya to the main grid of General Electricity company of Libya (GECOL) through a back-to-back converter. The machine side converter is used to regulate the AC and DC voltages while the grid side converter is used to control the active and reactive power supplied by the wind energy system. To achieve a relatively simple and decoupled control, the controllers are implemented in the QD reference frame. The proposed system is simulated using MatLab/Simulink environment. The simulation results show the rapid response of the control system in following the commanded active and reactive power commands and the robustness of the proposed system to the changes in operating conditions.

A reconfigurable patch antenna design for portable applications is presented in this paper. The antenna consists of patch lines connected in Tree shape on front side and the back side contains a T and L-shaped strips which are shorted with patch on front side for increasing electrical length. Frequency reconfigurablility is achieved by inserting three RF PIN diodes at suitable locations. This antenna is reconfigurable among different frequencies including GSM 900 and LTE bands such as 1.4 GHz, 1.6 GHz, 1.7 GHz, 2.5 GHz, and 2.6 GHz. Antenna is simulated in CST and a prototype is fabricated to compare measured results with simulated results.

The uncertain and devastating outcomes of natural disasters require pre-planning and timely coordination to reduce human and economic loss. While predictive capabilities remain limited - especially in the event of earthquakes, significant research efforts have been made towards increasing the efficiency and preparedness of rescue and relief operations. In this respect, different applications of agent-based modeling exhibits substantial promise for enabling humanitarian committees in terms of better planning and responsiveness in case of a disaster. While the usefulness of more conventional disaster management systems that focus primarily on communication and coordination efforts have a significant role, the ability of agent - based systems to aid decision making under unforeseen circumstances adds a new dimension to the overall effectiveness of such systems. Hence, the following study surveys conventional and agent-based disaster management systems, and aims to determine the performance of both systems

In order to meet the continuous growth in the global energy demand and to reduce the negative impact which is associated with the global warming, adopting renewable energy sources by policy makers need to be considered. Although Libya is very rich in solar energy resource, the country has been recently struggling to satisfy its energy demand. Consequently, centralized PV power plants may be considered as renewable and clean source of energy. This paper, presents the technical and economic feasibility of a 14MW photovoltaic (PV) power plant near the Houn city which is located in the central-south of Libya in the Jufra Region. In addition, In order to prove the design validity of the proposed system, models and simulations in NEPLAN software is carried out for a practical distribution grid. The result show that PV power plant is economically feasible and sustainable.

Background In Ethiopia, the trend of neonatal mortality showed slow pace of reduction from 2000–2016 compared to the reduction in infant and under-five mortality over the same period. This study aimed at unpacking the trends of admission, specific causes and rate of neonatal mortality as well as predictors of neonatal mortality at a general hospital in Somali region of Ethiopia. Methods A hospital based retrospective cohort study was conducted from 25th of May 2017to 10th of June, 2017. All the new-borns admitted in the neonatal Intensive Care Unit of the hospital were reviewed over three years period (Aug2014-May2017). Data were cleaned and exported to SPSS version 20 and both descriptive and analytical analysis were executed. The level of significance was set at P

The escalating energy demand worldwide coupled with the continues development in power electronics over the past few decades has led to an increasing interest in sustainable energy. Wind energy is considered as one of the most promising sources of renewable energy. In this paper, a self-excited induction generator is used to convert kinetic energy derived from the prime mover (wind turbine) into electrical energy. In order to supply the reactive power required by the machine, a three-phase capacitor bank is connected to the generator terminals. A three phase rectifier is used to utilize the conversion of the AC voltage and current at the generator terminals to DC voltage and current. Analytical model of the generator/rectifier system is derived and then simulated using MatLab/Simulink software. The results of the simulation are compared to practical implementation of the system which show a reasonable agreement.

News articles provide a large data set which can be used to classify information such as author's point of view, affiliation and biasedness. Classification on the basis of positive and negative content can help readers to understand the author's sentiments. mydNews application is a proposed a solution to achieve this objective of presenting author's opinion in an article. We performed sentiment analysis using CoreNLP API presented by Stanford and through experimentation we have reached to a conclusion that using 60% sentiment value of text and 40% of headline produces results nearest to human evaluation. We have proposed a new algorithm Recursive Cosine for similarity matching of news. Our algorithm uses classical cosine in a different way. We compared latest news articles with the one's published in same day by different authors and we have achieved 5% better results than classical cosine measure by using the newly constructed algorithm.

In the system level design such as the design of distributed energy resources, it is required to perform system-level optimization. To achieve that without computational burden, the concept of scaling laws is considered. In this paper, an MEC based scaling technique is derived in which transformer size/mass is predicted based upon rated power, specified current density, and frequency. Curve-fitting techniques are used to derive a meta-model for scaled mass and power loss. The meta-model is compared to designs obtained using detailed design code. A strong agreement between the results from the detailed design code and that predicted by the meta-model is achieved.

Due to their robustness, low cost, and low maintenance, induction generators are good candidates for wind energy systems. In addition, when the wind turbine is isolated from the grid, the induction generator can operate within a wide speed range which utilize maximum power extraction. To ensure that voltage buildup will take place between the machine terminals, the reactive power required by the machine must be supplied. To achieve that, a three phase capacitor bank is connected to the generator terminals. Evaluating the adequate value of the capacitance is very crucial since it affects the value of the generated voltage. In this paper, the desired operating voltage and the corresponding value of the magnetizing inductance are defined from the magnetizing curve. By using genetic algorithms (GAs), the corresponding values of the excitation capacitance and the rotor slip are evaluated. Matlab/Simulink model is then used for validation. It is noticed that there is a good agreement between the values predicted by the GAs and the ones obtained from the simulation.

Investigations on key characteristics of ions emitted from Laser Induced plasmas are reported. A Q-switched Nd: YAG pulsed laser at 1064 nm, 9–14 ns and 1.1 MW, is focused on 4 N pure Gold (Au) and Platinum (Pt) targets to produce plasma plume. The ions thus emitted are detected by CR-39 Solid State Nuclear Track Detectors (SSNTD’s), placed at angles 0°, 30°, 60°, and 90° with respect to the target normal. The distances between the targets and detectors are 9 cm. The experiments are performed under a vacuum ∼10−3 torr and the ion tracks are recorded on SSNTD’s for 300 laser shots. The etched detectors are analyzed with the help of Motic Images Optical Microscope (Motic Advance 3.1). The flux of ions exhibits anisotropic behavior where as maximum energies of these ions are not only anisotropic but also forward peaked.

This study concentrates to investigate the relationship between laser fabrication parameters and reflectivity of fiber gratings. The fiber Bragg grating system, comprising Excimer Laser, Mask Aligner, Optical Spectrum Analyzer and Tunable Laser Source was utilized to fabricate the fiber gratings. A series of experiments were carried out for the fabrication of fiber gratings and to observe the effect of laser fabrication parameters on the reflectivity of fiber gratings. These samples were fabricated with different reflectivity's ranging from 27 to 93% at constant pulse rate i.e. 1Hz. The amount of reflectivity is the extent to which the index modulation in the fiber core changes. The higher the reflectivity of a fiber grating, the more microstructural changes occur within the lattice of the photosensitive core and the more the residual axial stress builds up within the cross section of the fiber. Here, we discussed only two laser fabrications parameters i.e. effect of pulse energy on reflectivity of fiber grating and effect of exposure time on reflectivity of fiber grating/Bragg wavelength. Based on the results of this study, it is inferred that reflectivity is exponentially increased, as the pulse energy increase at constant UV exposure time and attained saturation at pulse energy i.e. 165mJ. The low pulse energy produces a low reflectivity fiber Bragg grating even after a longer UV exposure time. It is also concluded that both reflectivity and Bragg wavelength are increased rapidly in the beginning with the increase of UV exposure time and then become saturated at UV exposure time i.e. 110min, even though pulse energy is constant. It means that the induced index of modulation was increased with the increasing of UV exposure time.

In this study, a fiber Bragg grating sensor for temperature measurement is proposed and experimentally demonstrated. In particular, we point out that the method is well-suited for monitoring temperature because they are able to withstand a high temperature environment, where standard thermocouple methods fail. The interrogation technologies of the sensor systems are all simple, low cost and effective as well. In the sensor system, fiber grating was dipped into a water beaker that was placed on a hotplate to control the temperature of water. The temperature was raised in equal increments. The sensing principle is based on tracking of Bragg wavelength shifts caused by the temperature change. So the temperature is measured based on the wavelength-shifts of the FBG induced by the heating water. The fiber grating is high temperature stable excimer-laser-induced grating and has a linear function of wavelength-temperature in the range of 0–285°C. A dynamic range of 0–285°C and a sensitivity of 0.0131 nm/°C almost equal to that of general FBG have been obtained by this sensor system. Furthermore, the correlation of theoretical analysis and experimental results show the capability and feasibility of the purposed technique.

The length of the optical fiber which contains the sensor must be able to withstand the tensile loads that will be placed on it during the duration of its service lifetime. It is important to assess the effect of the UV radiation and removal of coating on the strength of the fiber within the region where the grating has been written. In this study, we have identified the various mechanical and laser fabrication parameters which constrain the strength of fiber gratings. These factors include the laser pulse rate, laser energy per pulse, UV exposure time, methods of coating removal and environmental degradation (moisture). The variation in strength with respect to these factors is discussed in detail. However, it is observed in all the investigations that the average strength of all the fiber grating samples decreased. Finally, the enhancement of mechanical strength of fiber gratings can be achieved by adjusting the laser settings and improving the coating removal methods.

Electromagnetic modeling, design, development, and analysis of a zero-bias rectifier antenna (rectenna) are studied for RF energy harvesting purposes. A specific Schottky diode is selected for its desired parameters and its equivalent nonlinear circuit model with spice and scattering parameters are used to design the zero bias rectifier. The rectifier model is generated using nonlinear microwave and electromagnetic simulation tools and RF and DC responses are obtained, then input impedance is matched to 50ohms by using reactive elements to reduce the reflected RF power at the rectifiers' input. On the other hand, a miniaturized planar inverted F-shaped antenna (PIFA) with an input impedance of 50ohms is designed on a microstrip substrate and its response is simulated using a 3-D simulation tool. Once the circuits are manufactured, (i) different RF signals levels are applied at input of the rectifier and converted DC output voltage measurements are tabulated, (ii) the antenna's gain and radiation pattern are given and s-parameters are measured using a network analyzer, (iii) rectenna performance is measured and the RF energy harvesting potential is presented.

Technology such as Fiber Bragg Grating (FBG) sensors are widely accepted in almost all industries. FBG are being investigated for their applicability in other markets such as smart structures. Fiber optic sensors can also be used in many different applications. Fiber optic sensors are available in several types; among them, the Bragg grating sensor is being studied in this research. For this research work, the main focus was the use of fiber Bragg grating sensors for measuring strain. The key objective of this research; to determine the functionality of fiber Bragg grating sensors compared to that of conventional foil gauges. Fiber Bragg grating sensors were chosen for this research because they have a high potential for various uses in the monitoring of smart structures. The major incentives for this type of research are the current deterioration of civil structures in west Malaysia. The laboratory tests are being reported in this research work including tests of steel straps and an aluminum test specimen. In all the tests, strain was measured using the fiber Bragg grating sensors and compared to values from a conventional foil gauge. The results are being discussed in details. It was inferred that the use of fiber optic technology for the monitoring of civil structures is very promising and the future is sure to bring further advancements and improvements.

A 13-year-old boy with tetralogy of Fallot and pulmonary atresia after a Waterston and Blalock-Taussig shunt underwent successful repair using a cryopreserved pulmonary homograft as a valved extracardiac conduit. Aortic transection gave a good operative field for pulmonary angioplasty and closure of the Waterston anastomosis. The cryopreserved pulmonary homograft provided excellent handling characteristics. The postoperative course was good, and postoperative echocardiography showed good function of the valved conduit.

Multi-agent systems have been applied to integrate heterogeneous information, to work on the behalf of users and to provide decision support in challenging environments. As this technology continues to develop, the numbers of agents that comprise a system and their usage of computing resources are both likely to grow. Distributed agent platforms such as JADE (Java Agent Development framework) provide a partial solution to such scalability issues. The emergence of infrastructure as a service (IAAS) cloud computing environments which allow the dynamic scaling of resources provides us with new ways of harnessing the distributed nature of multi-agent systems and their development environments. In this paper we introduce Elastic-JADE, the aim of which is to allow a local JADE platform to automatically scale up and down using Amazon EC2 resources when the local platform is heavily loaded. This paper describes the system architecture, a prototype and comments on future directions that this work may take.

In this paper we perform a simulation of fiber Bragg grating sensor with different grating lengths. It is shown that the grating length represents as one of the critical parameters in contributing to a high performance fiber Bragg grating sensor. The simulated fiber gratings with different lengths were analyzed and designed by calculating reflection and transmission spectra, and the bandwidth. Such simulations are based on solving coupled mode equations that describe the interaction of guided modes. The coupled mode equations are solved by the Transfer Matrix Method (a fundamental matrix method).

and regained normal immunity to ensure success. In agreement with other authors, ~ we never found that cervicothoracic access was not needed for simple repair of a TEF. Thus the risks of postoperative assisted ventilation (which constitute the main cause of relapse) are greatly reduced, resulting in an acceptable rate of mortality (about 10% according to Marzelle and associates1). A left lateral cervical incision gives very satisfactory access for simple closure of a TEF, with better visibility and adequate operative space compared with the median approach, and has the advantage of being able to be transformed to a cervical incision at the neck if necessary. Placement of a muscular flap between the two viscera is another indispensable element for success. The partial relapse in our second patient was due, in our opinion, to the insertion of a muscular flap that was too thin (omohyoidal muscle). In cases of associated tracheal stenosis, which occurs in about 22% of patients with a TEF, 1 the possible anastomotic resection of even a small segment (a rare event) greatly increases the surgical risk. For this reason, in two of our patients with associated tracheal lesions, we opted for t reatment with a dilating endoprosthesis.

The aim of this study is to investigate the effects of gold nanoparticles on breast cancer tumor. A theoretical approach has been adopted to probe the nanophotolysis technique using short pulse laser. The influences of various parameters including number of ions, shock front, energy distribution function, laser fluence are discussed in detail. Computational results suggest that spherical gold nanoparticles provides a promising platform for selective killing of abnormal cells in breast cancer tumor.

This study deals with Nd:YAG laser cutting nonmetallic materials, which is one of the most important and popular industrial applications of laser. The main theme is to evaluate the effects of Nd:YAG laser beam power besides work piece scanning speed. For approximate cutting depth, a theoretical study is conducted in terms of material property and cutting speed. Results show a nonlinear relation between the cutting depth and input energy. There is no significant effect of speed on cutting depth with the speed being larger than 30 mm/s. An extra energy is utilized in the deep cutting. It is inferred that as the laser power increases, cutting depth increases. The experimental outcomes are in good agreement with theoretical results. This analysis will provide a guideline for laser-based industry to select a suitable laser for cutting, scribing, trimming, engraving, and marking nonmetallic materials.