Reliability analysis of the internet of things using Space Fault Network

The Internet of Things (IoT) is a network topology structure based on the interconnection of many nodes. It realizes the basic functions of IoT through the transmission of information, data, and energy between the nodes. To study the reliability of Internet of Things Network Topology (IoTNT) structure, we must abstract IoT as network topology and study the reliability of the network itself from the topology structure. This paper attempts to apply the Space Fault Network (SFN) to the study the reliability of IoTNT. To achieve this goal, the nodes and edges of IoTNT are equivalent to events and connections of SFN respectively. A structure analysis method based on SFN is proposed and used to study the reliability of IoTNT. At the same time, the influence of possible logical relationship between nodes on the reliability of IoTNT is studied. According to the SFN structure representation methods (SFNSRMs), considering different network structures and induced modes, the analysis methods and calculation methods of the evolution process of target event are given. An example is given to illustrate the analysis and calculation process. The research provides the new methods for the reliability study of IoT and the development of SFN

Coinductive subtyping for abstract compilation of object-oriented languages into Horn formulas

In recent work we have shown how it is possible to define very precise type systems for object-oriented languages by abstractly compiling a program into a Horn formula f. Then type inference amounts to resolving a certain goal w.r.t. the coinductive (that is, the greatest) Herbrand model of f. Type systems defined in this way are idealized, since in the most interesting instantiations both the terms of the coinductive Herbrand universe and goal derivations cannot be finitely represented. However, sound and quite expressive approximations can be implemented by considering only regular terms and derivations. In doing so, it is essential to introduce a proper subtyping relation formalizing the notion of approximation between types. In this paper we study a subtyping relation on coinductive terms built on union and object type constructors. We define an interpretation of types as set of values induced by a quite intuitive relation of membership of values to types, and prove that the definition of subtyping is sound w.r.t. subset inclusion between type interpretations. The proof of soundness has allowed us to simplify the notion of contractive derivation and to discover that the previously given definition of subtyping did not cover all possible representations of the empty type

Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content

The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth’s terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE=5.45 wt %, R2=0.68), soil bulk density (RMSE=0.173 g cm-3, R2=0.203), and soil organic carbon (RMSE=1.47 wt %, R2=0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE~0.035cm3 cm-3 at a SWC=0.40 cm3 cm-3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSEm-2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments

Dialectic tensions in the financial markets: a longitudinal study of pre- and post-crisis regulatory technology

This article presents the findings from a longitudinal research study on regulatory technology in the UK financial services industry. The financial crisis with serious corporate and mutual fund scandals raised the profile of compliance as governmental bodies, institutional and private investors introduced a ‘tsunami’ of financial regulations. Adopting a multi-level analysis, this study examines how regulatory technology was used by financial firms to meet their compliance obligations, pre- and post-crisis. Empirical data collected over 12 years examine the deployment of an investment management system in eight financial firms. Interviews with public regulatory bodies, financial institutions and technology providers reveal a culture of compliance with increased transparency, surveillance and accountability. Findings show that dialectic tensions arise as the pursuit of transparency, surveillance and accountability in compliance mandates is simultaneously rationalized, facilitated and obscured by regulatory technology. Responding to these challenges, regulatory bodies continue to impose revised compliance mandates on financial firms to force them to adapt their financial technologies in an ever-changing multi-jurisdictional regulatory landscape

Effects of intra-annual precipitation patterns on grassland productivity moderated by the dominant species phenology

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at www.sensorium-competition.net

Genotyping of two Neisseria gonorrhoeae fluroquinolone-resistant strains in the Brazilian Amazon Region

We report two ciprofloxacin and ofloxacin-resistant Neisseria gonorrhoeae strains that were isolated from the urethral discharge of male patients at the sexually transmitted diseases outpatient clinic of the Alfredo da Matta Foundation (Manaus, state of Amazonas, Brazil). The gonococci displayed minimal inhibitory concentrations (> 32.00 µg/mL) and three mutations in the quinolone resistance-determining region (S91F and D95G in GyrA and S87R in ParC). Both isolates were genotyped using N. gonorrhoeae multi-antigen sequence typing and the analysis showed that the ST225 which represented an emerging widespread multi-resistant clone that has also been associated with reduced susceptibility to ceftriaxone. We recommend continued surveillance of this pathogen to assess the efficacy of anti-gonococcal antibiotics in Brazil