Negatively Charged Holey Titania Nanosheets Added Electrolyte to Realize Dendrite-Free Lithium Metal Battery

Electrolyte modulation and electrode structure design are two common strategies to suppress dendrites growth on Li metal anode. In this work, a self-adaptive electrode construction method to suppress Li dendrites growth is reported, which merges the merits of electrolyte modulation and electrode structure design strategies. In detail, negatively charged titania nanosheets with densely packed nanopores on them are prepared. These holey nanosheets in the electrolyte move spontaneously onto the anode under electrical field, building a mesoporous structure on the electrode surface. The as-formed porous electrode has large surface area with good lithiophilicity, which can efficiently transfer lithium ion (Li⁺) inside the electrode, and induce the genuine lithium plating/stripping. Moreover, the negative charges and nanopores on the sheets can also regulate the lithium-ion flux to promote uniform deposition of Li metal. As a result, the symmetric and full cells using the holey titania nanosheets containing electrolyte, show much better performance than the ones using electrolyte without holey nanosheets inside. This work points out a new route for the practical applications of Li-metal batteries.     

New Trends in the Modeling of Diseases Through Computational Techniques

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance. The handling strategies of real-world problems are artificial neural networks (ANN), evolutionary computing (EC), and many more. An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually, with only 25% to 45% reported to the World Health Organization (WHO). It remains one of the top parasitic diseases with outbreak and mortality potential. In 2020, more than ninety percent of new cases reported to World Health Organization (WHO) occurred in ten countries: Brazil, China, Ethiopia, Eritrea, India, Kenya, Somalia, South Sudan, Sudan, and Yemen. The transmission of visceral leishmaniasis is studied dynamically and numerically. The study included positivity, boundedness, equilibria, reproduction number, and local stability of the model in the dynamical analysis. Some detailed methods like Runge Kutta and Euler depend on time steps and violate the physical relevance of the disease. They produce negative and unbounded results, so in disease dynamics, such developments have no biological significance; in other words, these results are meaningless. But the implicit nonstandard finite difference method does not depend on time step, positive, bounded, dynamic and consistent. All the computational techniques and their results were compared using computer simulations.     

Real-time data acquisition and discharge pulse analysis in controlled RC circuit based Micro-EDM

Microsystem Technologies - Owing to the non-isoenergetic discharge pulses in an RC-based micro-electrical discharge machining (µEDM) process, the unit material removal analysis is difficult....     

Energy-efficient selection of relay for UWSNs in the Internet of underwater things

Internet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IOT standards and technologies do not work well in underwater environment, such as infrared, Wi-Fi, and radio frequency (RF) due to high channel errors and limited range up to few meters. Acoustic waves, however, can be used to communicate both in shallow and deep oceans due to their low frequency (kHZ) signal. In context of IoUT, communication based on acoustic links enables different applications such as underwater exploration, environmental monitoring, and disaster prevention even without availability of GPS facility like free space environment. In unpredictable and changing underwater environment, energy efficiency becomes a major challenge during data routing along multiple devices. Batteries of the sensor nodes, autonomous underwater vehicle (AUV), and remotely operated vehicle (ROV) cannot be removed with easiness and difficult to recharge, and the only way out is efficient sensor node selection for relaying to save massive amount of energy. Energy aware channel routing protocol (ECARP) does not consider the depth of the node while selecting the relay nodes to forward the data. Relay node selection in underwater Internet of things (IOUT) is a primary problem addressed in this research based on channel state information (CSI) for establishing best path to relay information among IOUT devices. Our major focus was to develop better technique for the relay node selection using a CSI and select relay node by looking at its depth from ocean surface and residual energy in the proposed ED-CARP. Simulation results validate that proposed ED-CARP can decrease the communication cost and increase the network lifetime.     

Synthesis of Silver Nanoparticles Using Syzygium malaccense Fruit Extract and Evaluation of Their Catalytic Activity and Antibacterial Properties

Journal of Inorganic and Organometallic Polymers and Materials - In this study, green synthesis of silver nanoparticles, an aqueous Syzygium malaccense fruit extract, was employed...     

Investigating the inverted N-shape EKC in the presence of renewable and nuclear energy in a global sample

Clean Technologies and Environmental Policy - A major concern for the modern world is to deal with environmental issues without compromising on economic growth targets. Energy use from renewable...     

Variable thermal conductivity influence on micropolar nanofluid flow triggered by a stretchable disk with Arrhenius activation energy

The analysis of magnetized micro–nanoliquid flows generated by the movable disk is executed in this study. The disk is contained under the porous zone influence. The heat generation, heat sink, and temperature-dependent conductance analysis are reported through the energy equation. The activation energy in terms of a chemical reaction is incorporated through the mass equation. The flow model is normalized through the implementation of similarity transformations. The numerical algorithm Runge–Kutta–Fehlberg is used to solve the reduced system. Results are plotted graphically and in tabular format to investigate the velocity, thermal, and concentration fields. Numeric benchmarks of couple and shear stresses, thermal and concentration rates are also computed. The temperature is augmented against the incremented thermophoretic, variable conductivity, and Brownian movement parameters. The presence of variable conductivity parameter resulted in a weaker rate of heat transportation. The heat transportation rate is boosted with an incremented Prandtl number.     

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Pedestrian detection and tracking are vital elements of today’s surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine learning and tracking them using particle filters. Moreover, a random forest classifier is adopted for image segmentation to identify pedestrians in an image. The result of detection is investigated by particle filter to solve pedestrian tracking. Through the extensive experiment, our system shows 93% segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes. Moreover, the system achieved a detection accuracy of 90% using multiple template matching techniques and 81% accuracy for pedestrian tracking. Furthermore, our system can identify that the detected object is a human. Hence, our system provided the best results compared to the state-of-art systems, which proves the effectiveness of the techniques used for image segmentation, classification, and tracking. The presented method is applicable for human detection/tracking, crowd analysis, and monitoring pedestrians in IR video surveillance.     

Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

The application of fuzzy theory is vital in all scientific disciplines. The construction of mathematical models with fuzziness is little studied in the literature. With this in mind and for a better understanding of the disease, an SEIR model of malaria transmission with fuzziness is examined in this study by extending a classical model of malaria transmission. The parameters and , being function of the malaria virus load, are considered fuzzy numbers. Three steady states and the reproduction number of the model are analyzed in fuzzy senses. A numerical technique is developed in a fuzzy environment to solve the studied model, which retains essential properties such as positivity and dynamic consistency. Moreover, numerical simulations are carried out to illustrate the analytical results of the developed technique. Unlike most of the classical methods in the literature, the proposed approach converges unconditionally and can be considered a reliable tool for studying malaria disease dynamics.