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1. Raman Spectroscopy and Modeling and Simulation of Quantum Dots and Nanomaterials for Optoelectronic and Sensing Applications

   https://doi.org/10.32604/icces.2024.013296  

   Misra, Prabhakar; Alghamdi, Hawazin; Garcia-Sanchez, Raul; Mitchell, Wyatt; Powell, Allison; Vohra, Nikhil (January 2024, The International Conference on Computational & Experimental Engineering and Sciences)

   Semiconducting quantum dots (Q-dots) with strain-tunable electronic properties are good contenders for quantum computing devices, as they hold promise to exhibit a high level of photon entanglement. The optical and electronic properties of Q-dots vary with their size, shape, and makeup. An assortment of Q-dots has been studied, including ZnO, ZnS, CdSe and perovskites [1]. We have employed both Raman spectroscopy (to precisely determine their vibrational frequencies) and UV-VIS spectroscopy (to determine accurately their band gap energies). The electronic band structure and density of states of the ZnO and ZnS Q-dots have been investigated under strain using Density Functional Theory (DFT). The computer program SIESTA (Spanish Initiative for Electronic Simulations with Thousands of Atoms) was used to perform the DFT calculations via the linear combination of atomic orbitals (LCAO) method. The spin polarization of such systems may itself be used to encode information or influence the electronic properties of semiconducting Q-dots, which deserve special attention, as they have potential applications in lasers, photovoltaic cells, and imaging. In addition, we have investigated pristine and functionalized graphene nanoplatelets and metal oxides for sensing applications. 

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2. Evolvability and Macroevolution: Overview and Synthesis

   https://doi.org/10.1007/s11692-022-09570-4  

   Jablonski, David (September 2022, Evolutionary Biology)

   Abstract Evolvability is best addressed from a multi-level, macroevolutionary perspective through a comparative approach that tests for among-clade differences in phenotypic diversification in response to an opportunity, such as encountered after a mass extinction, entering a new adaptive zone, or entering a new geographic area. Analyzing the dynamics of clades under similar environmental conditions can (partially) factor out shared external drivers to recognize intrinsic differences in evolvability, aiming for a macroevolutionary analog of a common-garden experiment. Analyses will be most powerful when integrating neontological and paleontological data: determining differences among extant populations that can be hypothesized to generate large-scale, long-term contrasts in evolvability among clades; or observing large-scale differences among clade histories that can by hypothesized to reflect contrasts in genetics and development observed directly in extant populations. However, many comparative analyses can be informative on their own, as explored in this overview. Differences in clade-level evolvability can be visualized in diversity-disparity plots, which can quantify positive and negative departures of phenotypic productivity from stochastic expectations scaled to taxonomic diversification. Factors that evidently can promote evolvability include modularity—when selection aligns with modular structure or with morphological integration patterns; pronounced ontogenetic changes in morphology, as in allometry or multiphase life cycles; genome size; and a variety of evolutionary novelties, which can also be evaluated using macroevolutionary lags between the acquisition of a trait and phenotypic diversification, and dead-clade-walking patterns that may signal a loss of evolvability when extrinsic factors can be excluded. High speciation rates may indirectly foster phenotypic evolvability, and vice versa. Mechanisms are controversial, but clade evolvability may be higher in the Cambrian, and possibly early in the history of clades at other times; in the tropics; and, for marine organisms, in shallow-water disturbed habitats. 

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3. AND testing and robust judgement aggregation

   https://doi.org/10.1145/3357713.3384254  

   Filmus, Y; Lifshitz, N; Minzer, D; Mossel, E. (June 2020, STOC 2020: Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing)

   A function f∶{0,1}n→ {0,1} is called an approximate AND-homomorphism if choosing x,y∈n uniformly at random, we have that f(x∧ y) = f(x)∧ f(y) with probability at least 1−ε, where x∧ y = (x1∧ y1,…,xn∧ yn). We prove that if f∶ {0,1}n → {0,1} is an approximate AND-homomorphism, then f is δ-close to either a constant function or an AND function, where δ(ε) → 0 as ε→ 0. This improves on a result of Nehama, who proved a similar statement in which δ depends on n. Our theorem implies a strong result on judgement aggregation in computational social choice. In the language of social choice, our result shows that if f is ε-close to satisfying judgement aggregation, then it is δ(ε)-close to an oligarchy (the name for the AND function in social choice theory). This improves on Nehama’s result, in which δ decays polynomially with n. Our result follows from a more general one, in which we characterize approximate solutions to the eigenvalue equation f = λ g, where is the downwards noise operator f(x) = y[f(x ∧ y)], f is [0,1]-valued, and g is {0,1}-valued. We identify all exact solutions to this equation, and show that any approximate solution in which f and λ g are close is close to an exact solution. 

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4. Blockchains and Databases: Opportunities and Challenges for the Permissioned and the Permissionless

   https://doi.org/10.1007/978-3-030-54832-2_1  

   Agrawal, D.; El Abbadi, A.; Amiri, M.J.; Maiyya, S.; Zakhary, V. (August 2020, Advances in Databases and Information Systems)

   Darmont, J; Novikov, B.; Wrembel, R. (Ed.)

   Bitcoin [12] is a successful and interesting example of a global scale peer-to-peer cryptocurrency that integrates many techniques and protocols from cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In a blockchain, nodes agree on their shared states across a large network of untrusted participants. Although originally devised for cryptocurrencies, recent systems exploit its many unique features such as transparency, provenance, fault tolerance, and authenticity to support a wide range of distributed applications. Bitcoin and other cryptocurrencies use permissionless blockchains. In a permissionless blockchain, the network is public, and anyone can participate without a specific identity. Many other distributed applications, such as supply chain management and healthcare, are deployed on permissioned blockchains consisting of a set of known, identified nodes that still might not fully trust each other. This paper illustrates some of the main challenges and opportunities from a database perspective in the many novel and interesting application domains of blockchains. These opportunities are illustrated using various examples from recent research in both permissionless and permissioned blockchains. Two main themes unite the various examples: (1) the important role of distribution and consensus in managing large scale systems and (2) the need to tolerate malicious failures. The advent of cloud computing and large data centers shifted large scale data management infrastructures from centralized databases to distributed systems. One of the main challenges in designing distributed systems is the need for fault-tolerance. Cloud-based systems typically assume trusted infrastructures, since data centers are owned by the enterprises managing the data, and hence the design typically only assumes and tolerates crash failures. The advent of blockchain and the underlying premise that copies of the blockchain are distributed among untrusted entities has shifted the focus of fault-tolerance from tolerating crash failures to tolerating malicious failures. These interesting and challenging settings pose great opportunities for database researchers. 

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5. Lifespan and Failures of SSDs and HDDs: Similarities, Differences, and Prediction Models

   https://doi.org/10.1109/TDSC.2021.3131571  

   Pinciroli, Riccardo; Yang, Lishan; Alter, Jacob; Smirni, Evgenia (January 2023, IEEE Transactions on Dependable and Secure Computing)

   Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using six-year field data of 100,000 HDDs of different models from the same manufacturer from the Backblaze dataset and six-year field data of 30,000 SSDs of three models from a Google data center, we characterize the workload conditions that lead to failures. We illustrate that their root failure causes differ from common expectations and that they remain difficult to discern. For the case of HDDs we observe that young and old drives do not present many differences in their failures. Instead, failures may be distinguished by discriminating drives based on the time spent for head positioning. For SSDs, we observe high levels of infant mortality and characterize the differences between infant and non-infant failures. We develop several machine learning failure prediction models that are shown to be surprisingly accurate, achieving high recall and low false positive rates. These models are used beyond simple prediction as they aid us to untangle the complex interaction of workload characteristics that lead to failures and identify failure root causes from monitored symptoms. 

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