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.     

An overview of smart packaging technologies for monitoring safety and quality of meat and meat products

The food packaging sector has experienced much development since its inception. In the past few decades, innovations in packaging sector have led to the development of smart packaging (SP) systems that carve a niche in a highly competitive food industry. SP systems have great potential for improving the shelf‐life, and safety of food products apart from their basic roles of protecting the products against unwanted biological, chemical, and physical damage and keeping them clean. Indicators and sensors, SP components, are used for real‐time monitoring of meat quality and subsequently inform the retailers and consumers about the freshness, microbiological, temperature, and shelf life status of the products. Barcodes and radio‐frequency identification tags are employed in meat packaging for real‐time information about the authenticity, and traceability of the products in the supply chain. Recently, innovations in SP technologies resulted in fast, sensitive, and effective detection, sensing, and record keeping of freshness, microbiological, and shelf life status of meat and meat products. The SP system shows promise for extensive utilization in the meat industry in response to the consumer appreciation for safe, and quality meat products, as well as their waste reduction notions. This paper gives an updated overview of ongoing scientific research, and recent technological advances that offer the perspectives of developing smart meat packaging systems that are capable of monitoring the physical, microbial, and chemical changes of the package contents from producer to the point of sale and even beyond, and remediating potential adverse reactions.     

Miniaturized,Battery‐Free Optofluidic Systems with Potential for Wireless Pharmacology and Optogenetics

Combination of optogenetics and pharmacology represents a unique approach to dissect neural circuitry with high specificity and versatility. However, conventional tools available to perform these experiments, such as optical fibers and metal cannula, are limited due to their tethered operation and lack of biomechanical compatibility. To address these issues, a miniaturized, battery‐free, soft optofluidic system that can provide wireless drug delivery and optical stimulation for spatiotemporal control of the targeted neural circuit in freely behaving animals is reported. The device integrates microscale inorganic light‐emitting diodes and microfluidic drug delivery systems with a tiny stretchable multichannel radiofrequency antenna, which not only eliminates the need for bulky batteries but also offers fully wireless, independent control of light and fluid delivery. This design enables a miniature (125 mm³), lightweight (220 mg), soft, and flexible platform, thus facilitating seamless implantation and operation in the body without causing disturbance of naturalistic behavior. The proof‐of‐principle experiments and analytical studies validate the feasibility and reliability of the fully implantable optofluidic systems for use in freely moving animals, demonstrating its potential for wireless in vivo pharmacology and optogenetics.     

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.     

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.     

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.     

Automating identification of services and their variability for product lines using NSGA-II

Architecture-level business services are identified based on business processes; and likewise, in service-oriented product lines, identifying the domain architecture-level business services and their variability is preferred to be based on business processes and their variability. Identification of business services for a product line satisfying a set of given design metrics (such as cohesion and coupling) is extremely difficult for a domain architect, since there are many product configurations for which the services must be proper at the same time. This means that the identified services must have proper values for n metrics in m different configurations at the same time. The problem becomes more serious when there are high degrees of variability and complexity embedded in the business processes that are the basis for service identification.We contribute to solve the multi-objective optimization problem of identifying business services for a product line by partitioning the graph of a business process variability model utilizing Non-dominated Sorting Genetic Algorithm-II. The service specification is achieved based on the results of the partitioning. The variability of the services is then determined in terms of mandatory and optional services as well as variability relationships, which are all represented in a Service Variability Model. The method was empirically evaluated through experimentation, and showed proper levels of reusability and variability. Furthermore, the resulting models were fully consistent.     

Synthesis and characterization of functionalized MWCNTs/PMMA composites: device fabrication for RH sensing

ABSTRACT

Functionalization of multiwalled carbon nanotubes (MWCNTs) with amine group using 4,4′-diaminodiphenyl sulfone (DDS) is achieved after carboxylation of pure MWCNTs (p-MWCNTs). p-MWCNTs, a-MWCNTs (acid functional MWCNTs) and f-MWCNTs (amine functional MWCNTs) were used to fabricate their nanocomposite with PMMA. A comparative study of the humidity characteristics of p-MWCNTs/PMMA, a-MWCNTs/PMMA and f-MWCNTs/PMMA-based capacitive humidity sensors was carried. f-MWCNTs/PMMA-based capacitive humidity sensor shows low hysteresis loss (5%), good response recovery behavior and highest sensitivity (S ~ 9736%) among all the studied composites. The maximum capacitance drift under different relative humidity was 1%RH.