In recent years, generative artificial intelligence (AI) in the form of large language models (LLM) have sparked the interest of society at large. The perceived capabilities of such systems have reignited discussions ...
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Influenza-Like Illness(ILI)constitutes a significant global health concern characterized by its high infec-tion rates and widespread distribution *** influenza viruses,primarily types A and B,are primary contributors ...
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Influenza-Like Illness(ILI)constitutes a significant global health concern characterized by its high infec-tion rates and widespread distribution *** influenza viruses,primarily types A and B,are primary contributors to ILI cases,other respiratory viruses also play a role in its ***,Indonesia's largest and densely populated city,has consistently reported a notable weekly number of ILI cases from 2016 to ***,this pattern of cases is irregular and does not exhibit a direct association with seasonal climate *** response to this complex scenario,we have developed a SLIR mathematical model featuring a cumulative generating operator in the form of a multiple-terms sigmoid function,obtained from weekly cumulative data to derive model solutions.A total of 12 terms within the sigmoid function yielded a decent fit to the actual data spanning 339 *** correlation analysis unveiled distinct temporal relationships within the model,revealing an 8-week time lag between the dynamics of the infection rate and the latent compartment,along with a 2-week lag marking the incubation period between the latent and infected ***,the effective reproduction number displayed recurrent fluctuations around a threshold of 1,indicating the endemic characteristics where infection persists within the *** in-depth comprehension of ILI trans-mission dynamics and effective reproduction numbers plays a significant role in devising control mea-sures and informed policy-making decisions.
In recent years, generative artificial intelligence (AI) in the form of large language models (LLM) have sparked the interest of society at large. The perceived capabilities of such systems have reignited discussions ...
In recent years, generative artificial intelligence (AI) in the form of large language models (LLM) have sparked the interest of society at large. The perceived capabilities of such systems have reignited discussions concerning the actual or potential threats posed by AI. According to Daniel Dennett, these systems make possible the creation of counterfeit people, who can pass as real in digital environments like social media. Dennett claims that by undermining trust in relationships, counterfeit people pose a threat to democracy and human freedom. While the idea of counterfeit people is worrisome in the context of digital manipulation, we claim that human digital twins have the potential to facilitate human rights violations that may pose even greater challenges. High-fidelity human digital twins necessitate encroaching into features that constitute a human’s personhood, such as physical aspects and mental contents. In view of that, their creation raises pressing issues of consent and violations of privacy rights. As a result, because rights to privacy are rights of persons, such violations will simultaneously be human rights violations. Even with consent to use an individual’s data, human digital twins may still cause issues of personhood. The rapid adoption of technologies that facilitate counterfeit people and human digital twins demands that ethical issues not be treated as aside concerns, but at the forefront of technology development.
We apply a game theory framework to cyber war in the areas of Cyber Influence Operations (CIOs), Advanced Persistent Threats (APTs), and Traditional Cyber Attacks (TCAs). For greater generalizability, we rely on a set...
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This research utilizes statistical path modeling to examine the relationship between the number of Venezuelans migrating to Colombia due to economic collapse and the media's coverage of specific topics. We focus o...
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modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need t...
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modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal−organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal− organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate−adsorbate interactions were modeled with Lennard−Jones (LJ) potentials, the adsorbent−adsorbent interactions were described by the DP, and the adsorbent−adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent−adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP−classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for ads
The COVID-19 pandemic caused significant disruptions in the healthcare system,affecting vaccinations and the management of diphtheria *** a consequence of these disruptions,numerous countries have experienced a resurg...
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The COVID-19 pandemic caused significant disruptions in the healthcare system,affecting vaccinations and the management of diphtheria *** a consequence of these disruptions,numerous countries have experienced a resurgence or an increase in diphtheria *** Java province in Indonesia is identified as one of the high-risk areas for diphtheria,experiencing an upward trend in cases from 2021 to *** analyze the situation,we developed an SIR model,which integrated DPT and booster vaccinations to determine the basic reproduction number,an essential parameter for infectious *** spatial analysis of geo-referenced data,we identified hotspots and explained diffusion in diphtheria case *** calculation of R0 resulted in an R0=1.17,indicating the potential for a diphtheria outbreak in West *** control the increasing cases,one possible approach is to raise the booster vaccination coverage from the current 64.84%to 75.15%,as suggested by simulation ***,the spatial analysis revealed that hot spot clusters were present in the western,central,and southern regions,posing a high risk not only in densely populated areas but also in rural *** diffusion pattern of diphtheria clusters displayed an expansion-contagious *** the rising trend of diphtheria cases and their geographic distribution can offer crucial insights for government and health authorities to manage the number of diphtheria cases and make informed decisions regarding the best prevention and intervention strategies.
Standard density functional theory (DFT) molecular dynamic (MD) simulations are prohibitively expensive in terms ofcompute time and memory requirements when the size of the physical system is larger than several hundr...
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With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design cond...
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Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational spa...
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Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires extensive size of the training dataset, making it a less feasible approach. Here, we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. We employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. In this paper, we show that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically important polycry
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