Bounded Model Checking of State-Space Digital Systems: The Impact of Finite Word-Length Effects on the Implementation of Fixed-Point Digital Controllers Based on State-Space Modeling

The extensive use of digital controllers demands a growing effort to prevent design errors that appear due to finite-word length (FWL) effects. However, there is still a gap, regarding verification tools and methodologies to check implementation aspects of control systems. Thus, the present paper describes an approach, which employs bounded model checking (BMC) techniques, to verify fixed-point digital controllers represented by state-space equations. The experimental results demonstrate the sensitivity of such systems to FWL effects and the effectiveness of the proposed approach to detect them. To the best of my knowledge, this is the first contribution tackling formal verification through BMC of fixed-point state-space digital controllers.Comment: International Symposium on the Foundations of Software Engineering 201

Context-aware Collaborative Neuro-Symbolic Inference in Internet of Battlefield Things

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps

Efficient computation of min and max sensor values in multihop networks

Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take sensor readings but individual sensor readings are not very important. It is important however to compute aggregated quantities of these sensor readings. The minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior, for example if the maximum temperature is very high then it may be that a fire has broken out. We propose an algorithm for computing the min or max of sensor readings in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter

Pyrosequencing of Bacterial Symbionts within Axinella corrugata Sponges: Diversity and Seasonal Variability

Background: Marine sponge species are of significant interest to many scientific fields including marine ecology, conservation biology, genetics, host-microbe symbiosis and pharmacology. One of the most intriguing aspects of the sponge ‘‘holobiont’’ system is the unique physiology, interaction with microbes from the marine environment and the development of a complex commensal microbial community. However, intraspecific variability and temporal stability of sponge-associated bacterial symbionts remain relatively unknown. Methodology/Principal Findings: We have characterized the bacterial symbiont community biodiversity of seven different individuals of the Caribbean reef sponge Axinella corrugata, from two different Florida reef locations during variable seasons using multiplex 454 pyrosequencing of 16 S rRNA amplicons. Over 265,512 high-quality 16 S rRNA sequences were generated and analyzed. Utilizing versatile bioinformatics methods and analytical software such as the QIIME and CloVR packages, we have identified 9,444 distinct bacterial operational taxonomic units (OTUs). Approximately 65,550 rRNA sequences (24%) could not be matched to bacteria at the class level, and may therefore represent novel taxa. Differentially abundant classes between seasonal Axinella communities included Gammaproteobacteria, Flavobacteria, Alphaproteobacteria, Cyanobacteria, Acidobacter and Nitrospira. Comparisons with a proximal outgroup sponge species (Amphimedon compressa), and the growing sponge symbiont literature, indicate that this study has identified approximately 330 A. corrugata-specific symbiotic OTUs, many of which are related to the sulfur-oxidizing Ectothiorhodospiraceae. This family appeared exclusively within A. corrugata, comprising \u3e34.5% of all sequenced amplicons. Other A. corrugata symbionts such as Deltaproteobacteria, Bdellovibrio, and Thiocystis among many others are described. Conclusions/Significance: Slight shifts in several bacterial taxa were observed between communities sampled during spring and fall seasons. New 16 S rDNA sequences and concomitant identifications greatly expand the microbial community profile for this model reef sponge, and will likely be useful as a baseline for any future comparisons regarding sponge microbial community dynamics

Genome-wide association study for systemic lupus erythematosus in an egyptian population

Systemic lupus erythematosus (SLE) susceptibility has a strong genetic component. Genome-wide association studies (GWAS) across trans-ancestral populations show both common and distinct genetic variants of susceptibility across European and Asian ancestries, while many other ethnic populations remain underexplored. We conducted the first SLE GWAS on Egyptians–an admixed North African/Middle Eastern population–using 537 patients and 883 controls. To identify novel susceptibility loci and replicate previously known loci, we performed imputation-based association analysis with 6,382,276 SNPs while accounting for individual admixture. We validated the association analysis using adaptive permutation tests (n = 109). We identified a novel genome-wide significant locus near IRS1/miR-5702 (Pcorrected = 1.98 × 10−8) and eight novel suggestive loci (Pcorrected 0.8) with lead SNPs from four suggestive loci (ARMC9, DIAPH3, IFLDT1, and ENTPD3) were associated with differential gene expression (3.5 × 10−95 < p < 1.0 × 10−2) across diverse tissues. These loci are involved in cellular proliferation and invasion—pathways prominent in lupus and nephritis. Our study highlights the utility of GWAS in an admixed Egyptian population for delineating new genetic associations and for understanding SLE pathogenesis

Qur’anic Ethics for Environmental Responsibility: Implications for Business Practice

Despite the growing interest in examining the role of religious beliefs as a guide towards environmental conscious actions, there is still a lack of research informed by an analysis of divine messages. This deficiency includes the extent to which ethics for environmental responsibility are promoted within textual divine messages; types of environmental themes promoted within the text of divine messages; and implications of such religious environmental ethics for business practice. The present study attempts to fill this gap by conducting a thorough content analysis of environmental themes within the divine message of Muslims (the Qur’an) focusing on their related ethical aspects and business implications. The analysis has revealed 675 verses in 84 chapters throughout all 30 parts of the Qur’an, with environmental content relating to the core components of the natural world, i.e. human beings, water, air, land, plants, animals, and other natural resources. This environmental content and its related ethics are grounded on the belief that humans are vicegerents of God on the earth and their behaviours and actions are motivated by earthly and heavenly rewards. Implications of these findings for different sectors/businesses are also highlighted

Fueoogle: A Participatory Sensing Fuel-Efficient Maps Application

This report presents a participatory sensing service, called Fueoogle, that maps vehicular fuel consumption on city streets, allowing drivers to find the most fuel-efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular sensors, available via the OBD-II interface that gives access to most gauges and engine instrumentation. The OBD-II sensors are standardized in all vehicles produced in the US since 1996, constituting some of the largest ``sensor deployments" to date. Using fuel-related measurements contributed by participating vehicles, we develop a route planner that maps normalized fuel-efficiency of city streets, enabling vehicles to compute minimum fuel routes from one point to another. Street congestion, elevation variability, average speed, and average distance between stops (e.g., stop signs) lead to changes in the amount of fuel consumed making fuel-efficient routes potentially different from shortest or fastest routes, and a function of vehicle type. Our experimental study answers two questions related to the viability of the new service. First, how much fuel can it save? Second, can it survive conditions of sparse deployment? The main challenge under such conditions is to generalize from relatively sparse measurements on a subset of streets to estimates of measurements of an entire city. Through extensive experimental data collection and evaluation, conducted over the duration of a month across several different cars and drivers, we show that significant savings can be achieved by choosing the right route. We also provide extensive results pertaining to the accuracy of models that are used for prediction of fuel consumption values.unpublishednot peer reviewe