Protein–Energy Malnutrition: A Risk Factor for Various Ailments

The wheel of industrialization that spun throughout the last century resulted in urbanization coupled with modifications in lifestyles and dietary habits. However, the communities living in developing economies are facing many problems related to their diet and health. Amongst, the prevalence of nutritional problems especially protein–energy malnutrition (PEM) and micronutrients deficiencies are the rising issues. Moreover, the immunity or susceptibility to infect-parasitic diseases is also directly linked with the nutritional status of the host. Likewise, disease-related malnutrition that includes an inflammatory component is commonly observed in clinical practice thus affecting the quality of life. The PEM is treatable but early detection is a key for its appropriate management. However, controlling the menace of PEM requires an aggressive partnership between the physician and the dietitian. This review mainly attempts to describe the pathophysiology, prevalence and consequences of PEM and aims to highlight the importance of this clinical syndrome and the recent growth in our understanding of the processes behind its development. Some management strategies/remedies to overcome PEM are also the limelight of the article. In the nutshell, early recognition, prompt management, and robust follow up are critical for best outcomes in preventing and treating PEM.     

Ytterbium (III) oxide reinforced novel TeO2–B2O3–V2O5 glass system: Synthesis and optical,structural, physical and thermal properties

Different samples of xTeO₂.(25-y)B₂O₃.zV₂O₅.yYb₂O₃ (or TBVY) new glass material were synthesized by the classical melt-quenching method. Structural, optical, physical, and thermal analyses of the synthesized glasses were performed in addition to Monte Carlo simulation to test radiation shielding properties. The results showed that increasing ratios of Yb₂O₃ (y = 0.0, 0.5, 1.0, and 1.5 mol%) produced monotonic density values of the synthesized glasses ranging from 4.70058 g cm^?3 to 5.01038 g cm^?3. XRD and FTIR analyses were used to confirm the glass structure of all samples. Optical transmittance and absorption parameters varied almost monotonically with increasing ratios of Yb₂O₃ indicating the ability to predict and control these properties using Yb₂O₃ additive. Furthermore, simulated radiation interaction parameters, such as attenuation coefficients and half-value layer, exhibited well-behaved dependence on the concentration ratio of the Yb₂O₃ additive. This approach to glass material synthesis demonstrate the useful synergetic effect of combining structural, optical, and radiation characteristics.     

Properties and curing behavior of reactive blended allyl novolak with bismaleimide using dicumyl peroxide as a novel curing agent

For reducing the cure temperature and improving the thermal stability and mechanical properties, a thermosetting resin system composed of novolak and bismaleimide (BMI) was developed by reactive blending and using dicumyl peroxide (DCP) as a novel curing agent. Novolak was allylated and reacted with BMI to produce bismaleimide allylated novolak (BAN), and the effect of DCP on flexural, impact and heat distortion temperature of cured resin were investigated. On the basis of improved mechanical and thermal properties at 0.5% DCP contents, the curing behavior of DCP/BAN resin system was evaluated by DSC analysis. Ene, Diels‐Alder, homo‐polymerization and alternating copolymerization which occurred in DCP/BAN resin system were further verified using FTIR at sequential cure conditions from 140 to 200°C. Kissinger and Ozawa‐Flynn‐wall methods were used to optimize the process and curing reactions of DCP/BAN resin system. The results showed that the addition of 0.5% DCP in BAN reduced the curing temperature and time of the modified resin. For evaluating process ability of the modified system, composite samples using polyvinyl acetyl fiber were molded and tested for flexural properties. The resulting samples showed better flexural properties when compared with the composite made with neat BAN. The modified 0.5% DCP/BAN resin system with good mechanical properties and manufacturability can be used for making bulk molding compounds and fiber reinforced composites required in various commercial and aerospace applications. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41829.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock

In this paper, a recovery model is developed for managing sudden supply delays that affect retailers’ economic order quantity model. For this, a mathematical model is developed that considers fuzzy demand and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. An efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment with scenario-based analysis is conducted to test our heuristic and to analyse the results. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the generalised reduced gradient method, which is widely applied in constrained mathematical programming. A simulation model is also designed to bring the recovery model closer to real-world processes. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan. With the help of the proposed model, supply chain decision-makers can make accurate and prompt decision regarding recovery plans in case of sudden supply delay.     

Gamma-ray shielding properties of zinc oxide soda lime silica glasses

In the present work, the gamma ray shielding properties of zinc oxide soda lime silica, (ZnO)_x(SLS)_1?x glasses with 0 ≥ x ≥ 50 wt% have been investigated. By using WinXCom computer software, the mass attenuation coefficient (µ/ρ) and half value layer (HVL) for total photon interaction in the energy range of 1 keV–100 GeV were calculated. Furthermore and by Geometric Progression method exposure buildup factor values were calculated for incident photon energy 0.015–15 MeV up to penetration depths of 40 mfp (mean free path). The addition of zinc oxide (ZnO) into soda lime silica (SLS) glass resulted in an increase the mass attenuation coefficient and decreases both the half value layer and exposure buildup factor. The obtained results of the selected glass series have been compared, in terms of mass attenuation coefficient, half value layer and exposure buildup factor with some common shielding materials. The shielding effectiveness of the selected glasses is found comparable to that of common ones; which indicates that the SLS glasses with suitable ZnO content may be developed as gamma ray shielding materials.     

Rotational Effect on the Propagation of Waves in a Magneto-Micropolar Thermoelastic Medium

The present paper aims to explore how the magnetic field, ramp parameter, and rotation affect a generalized micropolar thermoelastic medium that is standardized isotropic within the half-space. By employing normal mode analysis and Lame’s potential theory, the authors could express analytically the components of displacement, stress, couple stress, and temperature field in the physical domain. They calculated such manners of expression numerically and plotted the matching graphs to highlight and make comparisons with theoretical findings. The highlights of the paper cover the impacts of various parameters on the rotating micropolar thermoelastic half-space. Nevertheless, the non-dimensional temperature is not affected by the rotation and the magnetic field. Specific attention is paid to studying the impact of the magnetic field, rotation, and ramp parameter of the distribution of temperature, displacement, stress, and couple stress. The study highlighted the significant impact of the rotation, magnetic field, and ramp parameter on the micropolar thermoelastic medium. In conclusion, graphical presentations were provided to evaluate the impacts of different parameters on the propagation of plane waves in thermoelastic media of different nature. The study may help the designers and engineers develop a structural control system in several applied fields.     

An effective method for classification with missing values

Classification is one of the most important tasks in machine learning with a huge number of real-life applications. In many practical classification problems, the available information for making object classification is partial or incomplete because some attribute values can be missing due to various reasons. These missing values can significantly affect the efficacy of the classification model. So it is crucial to develop effective techniques to impute these missing values. A number of methods have been introduced for solving classification problem with missing values. However they have various problems. So, we introduce an effective method for imputing missing values using the correlation among the attributes. Other methods which consider correlation for imputing missing values works better either for categorical or numeric data, or designed for a particular application only. Moreover they will not work if all the records have at least one missing attribute. Our method, Model based Missing value Imputation using Correlation (MMIC), can effectively impute both categorical and numeric data. It uses an effective model based technique for filling the missing values attribute wise and reusing then effectively using the model. Extensive performance analyzes show that our proposed approach achieves high performance in imputing missing values and thus increases the efficacy of the classifier. The experimental results also show that our method outperforms various existing methods for handling missing data in classification.     

A game theoretic-based distributed detection method for VM-to-hypervisor attacks in cloud environment

Cloud computing is a pool of scalable virtual resources serving a large number of users who pay fees depending on the extent of utilized service. From payment perspective, cloud is like electricity and water as people who use more of this shared pool should pay larger fees. Cloud computing involves a diverse set of technologies including networking, virtualization and transaction scheduling. Thus, it is vulnerable to a wide range of security threats. Some of the most important security issues threatening the cloud computing systems originate from virtualization technology, as it constitutes the main body and basis of these systems. The most important virtualization-based security threats include VM side channel, VM escape and rootkit attacks. The previous works on the subject of virtualization security rely on hardware approaches such as the use of firewalls, which are expensive, the use of schedulers to control the side channels along with noise injection, which impose high overhead, or the use of agents to collect information and send them back to a central intrusion detection system, which itself can become the target of attacker. In the method presented in this paper, a group of mobile agents act as the sensors of invalid actions in the cloud environment. They start a noncooperative game with the suspected attacker and then calculate the Nash equilibrium value and utility so as to differentiate an attack from legitimate requests and determine the severity of attack and its point of origin. The simulation results show that this method can detect the attacks with 86% accuracy. The use of mobile agents and their trainability feature has led to reduced system overhead and accelerated detection process.