Explore the vast range of titles available.

City Protector is an Arcade Tower Defense Game with endless element. This game implements touch gesture recognizer feature. This game is developed with Unity game engine and playable in Android-based smartphone. To kill zombies, players must draw the symbols contained in the explosives that brought by zombies. Testing is done by blackbox testing, alpha testing, and beta testing through a survey of 32 respondents. The results indicate that City Protector has interesting features and gameplay concepts. It is provides new experiences because the Touch Gesture Recognizer feature is rarely found in general.

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.

To investigate the effect of RDX on the explosion reaction mechanism of FOX-7 based aluminized explosives in vacuum environment, the explosion field temperature of FOX-7 based aluminized explosives and RDX/FOX-7 based aluminized explosives were measured in an isolated explosion chamber. The results show that adding RDX would increase the equilibrium temperature of explosion field of FOX-7-based aluminized explosives. The equilibrium temperature of FOX-7-based aluminized explosives and RDX/FOX-7-based aluminized explosives increases first and then decreases with the increasing of Al content, which shows the highest equilibrium temperature as the Al content is 30%. When the Al content is less than 25%, the explosion peak temperature of FOX-7-based aluminized explosives would increased by adding RDX, and when the aluminium content is more than 30%, the explosion peak temperature of FOX-7-based aluminized explosives can be reduced by adding RDX.

The Schmidt reaction has been an efficient and widely used synthetic approach to amides and nitriles since its discovery in 1923. However, its application often entails the use of volatile, potentially explosive, and highly toxic azide reagents. Here, we report a sequence whereby triflic anhydride and formic and acetic acids activate the bulk chemical nitromethane to serve as a nitrogen donor in place of azides in Schmidt-like reactions. This protocol further expands the substrate scope to alkynes and simple alkyl benzenes for the preparation of amides and nitriles.

Distrust in scientific expertise 1 – 14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks 2 – 4 , as happened for measles in 2019 5 , 6 . Homemade remedies 7 , 8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice 9 – 11 . There is a lack of understanding about how this distrust evolves at the system level 13 , 14 . Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change 11 , and highlight the key role of network cluster dynamics in multi-species ecologies 15 . Insights into the interactions between pro- and anti-vaccination clusters on Facebook can enable policies and approaches that attempt to interrupt the shift to anti-vaccination views and persuade undecided individuals to adopt a pro-vaccination stance.

A set of explosive devices for generating of the one-side non-stationary loads of different physical nature is described. Two new explosive devices for the formation of low-impulse loads of microsecond duration are proposed. Methods of measuring of the response parameters of thin-walled composite constructions to dynamic and impulse loads are considered. A new method of experimental definition of non-stationary displacements of constructions at the shell stage of deformation is proposed. It is obtained that when investigating the shell stage of deformation of fiberglass thin-walled constructions, the use of wire and foil sensors to measure the deformations provides to close results.

On Dec. 22, 2018, at approximately 20:55–57 local time, Anak Krakatau volcano, located in the Sunda Straits of Indonesia, experienced a major lateral collapse during a period of eruptive activity that began in June. The collapse discharged volcaniclastic material into the 250 m deep caldera southwest of the volcano, which generated a tsunami with runups of up to 13 m on the adjacent coasts of Sumatra and Java. The tsunami caused at least 437 fatalities, the greatest number from a volcanically-induced tsunami since the catastrophic explosive eruption of Krakatau in 1883 and the sector collapse of Ritter Island in 1888. For the first time in over 100 years, the 2018 Anak Krakatau event provides an opportunity to study a major volcanically-generated tsunami that caused widespread loss of life and significant damage. Here, we present numerical simulations of the tsunami, with state-of the-art numerical models, based on a combined landslide-source and bathymetric dataset. We constrain the geometry and magnitude of the landslide source through analyses of pre- and post-event satellite images and aerial photography, which demonstrate that the primary landslide scar bisected the Anak Krakatau volcano, cutting behind the central vent and removing 50% of its subaerial extent. Estimated submarine collapse geometries result in a primary landslide volume range of 0.22–0.30 km 3 , which is used to initialize a tsunami generation and propagation model with two different landslide rheologies (granular and fluid). Observations of a single tsunami, with no subsequent waves, are consistent with our interpretation of landslide failure in a rapid, single phase of movement rather than a more piecemeal process, generating a tsunami which reached nearby coastlines within ~30 minutes. Both modelled rheologies successfully reproduce observed tsunami characteristics from post-event field survey results, tide gauge records, and eyewitness reports, suggesting our estimated landslide volume range is appropriate. This event highlights the significant hazard posed by relatively small-scale lateral volcanic collapses, which can occur en-masse , without any precursory signals, and are an efficient and unpredictable tsunami source. Our successful simulations demonstrate that current numerical models can accurately forecast tsunami hazards from these events. In cases such as Anak Krakatau’s, the absence of precursory warning signals together with the short travel time following tsunami initiation present a major challenge for mitigating tsunami coastal impact.

Molecular imprinting is the process of template-induced formation of specific recognition sites in a polymer. Synthetic receptors prepared using molecular imprinting possess a unique combination of properties such as robustness, high affinity, specificity, and low-cost production, which makes them attractive alternatives to natural receptors. Improvements in polymer science and nanotechnology have contributed to enhanced performance of molecularly imprinted polymer (MIP) sensors. Encouragingly, recent years have seen an increase in high-quality publications describing MIP sensors for the determination of biomolecules, drugs of abuse, and explosives, driving toward applications of this technology in medical and forensic diagnostics. This review aims to provide a focused overview of the latest achievements made in MIP-based sensor technology, with emphasis on research toward real-life applications. Electrochemical and optical sensing based on molecularly imprinted polymers (MIPs) has particular relevance in real-life applications and point-of-care testing in real human samples. MIPs are a leading technology for sensing molecules where there is no available bioreceptor. MIP nanoparticles can be used for direct and indirect detection (labeled or label free). The sensitivity of MIP-based sensors can be enhanced by coupling with nanomaterials such as graphene oxide, carbon nanotubes, or nanoparticles. The present challenges and perspectives of MIP-based electrochemical and optical sensors include exploring the market niches for MIP sensors and identifying the necessary steps toward commercialization.

Speciation rates vary considerably among lineages, and our understanding of what drives the rapid succession of speciation events within young adaptive radiations remains incomplete 1 – 11 . The cichlid fish family provides a notable example of such variation, with many slowly speciating lineages as well as several exceptionally large and rapid radiations 12 . Here, by reconstructing a large phylogeny of all currently described cichlid species, we show that explosive speciation is solely concentrated in species flocks of several large young lakes. Increases in the speciation rate are associated with the absence of top predators; however, this does not sufficiently explain explosive speciation. Across lake radiations, we observe a positive relationship between the speciation rate and enrichment of large insertion or deletion polymorphisms. Assembly of 100 cichlid genomes within the most rapidly speciating cichlid radiation, which is found in Lake Victoria, reveals exceptional ‘genomic potential’—hundreds of ancient haplotypes bear insertion or deletion polymorphisms, many of which are associated with specific ecologies and shared with ecologically similar species from other older radiations elsewhere in Africa. Network analysis reveals fundamentally non-treelike evolution through recombining old haplotypes, and the origins of ecological guilds are concentrated early in the radiation. Our results suggest that the combination of ecological opportunity, sexual selection and exceptional genomic potential is the key to understanding explosive adaptive radiation. Analyses of the genomes of cichlid species reveal that the combination of ecological opportunity, sexual selection and exceptional genomic potential is the key to understanding explosive adaptive radiation in cichlids.

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.