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An inconsistent database is a database that violates one or more integrity constraints, such as key constraints and functional dependencies. Consistent Query Answering (CQA) is a rigorous and principled approach to the semantics of queries posed against inconsistent databases. The consistent answers to a query on an inconsistent database are the intersection of the answers to the query on every repair, i.e., on every consistent database that differs from the given inconsistent one in a minimal way. Computing the consistent answers of a fixed conjunctive query on a given inconsistent database can be a coNP-hard problem, even though every fixed conjunctive query is efficiently computable on a given consistent database.The notion of consistent answers is extended to the notion of range consistent answers for queries with aggregation operators with or without the grouping constructs. The range consistent answers to an aggregation query is the interval [glb, lub] of the greatest lower bound and the least upper bound such that the answer to the query on every repair falls within the range. Computing the range consistent answers to a fixed aggregation query can be an NP-hard problem. In fact, we show that computing the range consistent answers to an aggregation query can be NP-hard even if the consistent answers to its underlying conjunctive query (i.e., the query without aggregation operators) are first-order rewritable, thus, computable in polynomial-time.Despite several attempts towards building practical systems for consistent query answering, no comprehensive and scalable system for consistent query answering exists at present; this state of affairs has impeded the broader adoption of the framework of repairs and consistent answers as a principled alternative to data cleaning.We designed, implemented, and evaluated CAvSAT, the first SAT-based system for consistent query answering. CAvSAT leverages a set of natural reductions from the problem of computing the consistent answers to variants of the Boolean Satisfiability problem. The system is capable of handling unions of conjunctive queries and arbitrary denial constraints, which include functional dependencies as a special case. Moreover, it is also the first system capable of computing the range consistent answers of general aggregation queries with the COUNT(A), COUNT(*), and SUM(A) operators, and with or without grouping constructs. We report results from experiments evaluating CAvSAT on both synthetic and real-world databases. We carry out an extensive set of experiments on both synthetic and real-world databases that demonstrate the usefulness and the scalability of CAvSAT.

Phenibut is a misused substance which has shown an increase in use over the past decade. Marketed as a "dietary supplement," it is not approved in the United States for use and is not regulated by the Food and Drug Administration. The substance, however, is readily available for purchase through online markets. It has a similar drug profile as alcohol, gabapentin and benzodiazepines. Clinical effects of this drug include physiologic dependence, euphoria, anxiolysis, antispasticity, sedation, and possible nootropic properties. While there are emerging new cases of managing phenibut withdrawal, no cases currently feature phenibut addiction and withdrawal management in the geriatric population. Here we discuss such a case of phenibut addiction and withdrawal in a 68-year-old male who initially began misusing phenibut to alleviate anxiety and insomnia precipitated by worsening affective disorder, sedative, hypnotic, or anxiolytic use disorder, and alcohol use disorder.

Background: Most Americans perceive lysergic acid diethylamide (LSD) to be a high-risk drug, despite infrequent serious adverse events associated with its use. Though LSD use is rising in the United States, little has been written about whether perceived risk of LSD (prLSD) is changing or factors influencing prLSD. Methods: Using National Survey on Drug Use and Health data from 2015-2019, we investigated correlates of and temporal trends in prLSD. Results: On multivariable modeling for respondents 18 years of age and older, lower prLSD was associated in a statistically significant manner with later survey year, personal LSD use, younger age, higher education level, male gender, identifying as a sexual minority, having less self-influential religious beliefs, past year psychological distress, and other factors. Higher prLSD was associated in a statistically significant manner with identifying as Black or Hispanic, past year suicide attempt, and having children in the home. From 2015-2019, there was a statistically significant linear decreasing trend in proportion of respondents who perceived “great” risk in trying LSD [from 70.5% to 64.8%; (pConclusions: We observed important differences in prLSD among subgroups, suggesting factors such as gender, race, ethnicity, and personal history of LSD use could significantly influence prLSD. Our findings also indicate that Americans as a whole appear to be assessing LSD’s risk profile more favorably in recent years.

Most Americans perceive lysergic acid diethylamide (LSD) to be a high-risk drug, despite infrequent serious adverse events associated with its use. Though LSD use is rising in the United States, little is known about whether perceived risk of LSD (prLSD) is changing or factors influencing prLSD. Using National Survey on Drug Use and Health data from 2015–2019, we investigated correlates of and temporal trends in prLSD. On multivariable modeling for respondents 18 years of age and older, lower prLSD was associated with, among other factors, later survey year, personal LSD use, younger age, higher education level, male gender, identifying as a sexual minority, having less self-influential religious beliefs, and past year psychological distress. Higher prLSD was associated with identifying as Black or Hispanic, past year suicide attempt, and having children in the home. From 2015–2019, there was a statistically significant linear decreasing trend in proportion of respondents who perceived “great” risk in trying LSD [from 70.5–64.8%; (p

Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can be incomplete, skewed, contradictory, and redundant impacting the reasoning. Basing decisions on these data can lead to inconsistent, irrational, and unreasonable cyber-physical systems’ movements, adversely impacting the system’s reliability and integrity. This paper presents the assessment of robust reasoning for autonomous cyber-physical systems in dynamic environments. In this work, robust reasoning is considered as 1) the capability of drawing conclusions with available data by applying classical and non-classical reasoning strategies and algorithms and 2) act and react robustly and safely in dynamic environments by employing robustness analysis to provide options on possible actions and evaluate alternative decisions. The result of the research shows that different common existing strategies, algorithms and analyses can be provided together with a comparison of their applicabilities, benefits, and drawbacks in the context of cyber-physical systems operating in dynamically changing environments. The conclusion is that robust reasoning in cyber-physical systems can handle dynamic environments. Moreover, combining these strategies and algorithms with robustness analysis can support achieving robust behavior in autonomous cyber-physical systems while operating in dynamically changing environments.

Prescription drug misuse is a public health concern. Governments are attempting to restrict the incidence of medication diversion by establishing laws to curb drug access from human healthcare providers. Veterinarians, who also prescribe controlled substances and see hundreds of millions of patients annually in the USA, are often overlooked as a source of prescription drug misuse. Evidence suggests that drug misuse occurs in the veterinary setting, and veterinarians acknowledge this as a common problem. Although analgesics are alleged to be the most common drug class diverted, a recent cross-sectional study suggests veterinarian opioid prescribing rate continues to increase. Prescription misuse in veterinarian settings is likely underestimated and needs to be better investigated. Human healthcare providers must familiarize themselves with this potential channel to prescription drugs and specifically question patients on veterinary drug misuse.

A gait model capable of generating human-like walking behavior at both the kinematic and the muscular level can be a very useful framework for developing control schemes for humanoids and wearable robots such as exoskeletons and prostheses. In this work we demonstrated the feasibility of using deep reinforcement learning based approach for neuromuscular gait modelling. A lower limb gait model consists of seven segments, fourteen degrees of freedom, and twenty two Hill-type muscles was built to capture human leg dynamics and the characteristics of muscle properties. We implemented the proximal policy optimization algorithm to learn the sensory-motor mappings (control policy) and generate human-like walking behavior for the model. Human motion capture data, muscle activation patterns and metabolic cost estimation were included in the reward function for training. The results show that the model can closely reproduce the human kinematics and ground reaction forces during walking. It is capable of generating human walking behavior in a speed range from 0.6 m/s to 1.2 m/s. It is also able to withstand unexpected hip torque perturbations during walking. We further explored the advantages of using the neuromuscular based model over the ideal joint torque based model. We observed that the neuromuscular model is more sample efficient compared to the torque model.

Rhizobial inoculation plays an important role in yielding enhancement of soybean, but it is frequently disturbed by competition with bacterial population present in the soil. Identification of potential indigenous rhizobia as competitive inoculants for efficient nodulation and N(2)-fixation of soybean was assessed under laboratory and field conditions. Two indigenous bradyrhizobial isolates (MPSR033 and MPSR220) and its derived different antibiotic (streptomycin and gentamicin) and phage (RT5 and RT6)-resistant mutant strains were used for competition study. Nodulation occupancy between parent and mutant strains was compared on soybean cultivar JS335 under exotic condition. Strain MPSR033 Sm(r) V(r) was found highly competitive for nodule occupancy in all treatment combinations. On the basis of laboratory experiments four indigenous strains (MPSR033, MPSR033 Sm(r), MPSR033 Sm(r) V(r), MPSR220) were selected for their symbiotic performance along with two exotic strains (USDA123 and USDA94) on two soybean cultivars under field conditions. A significant symbiotic interaction between Bradyrhizobium strains and soybean cultivar was observed. Strain MPSR033 Sm(r) V(r) was found superior among the rhizobial treatments in seed yield production with both cultivars. The 16S rRNA region sequence analysis of the indigenous strains showed close relationship with Bradyrhizobium yuanmingense strain. These findings widen out the usefulness of antibiotic-resistance marked phage-resistant bradyrhizobial strains in interactive mode for studying their symbiotic effectiveness with host plant, and open the way to study the mechanism of contact-dependent growth inhibition in rhizobia.

Tejas Karia, Akhil Anand and Bahaar Dhawan consider the change in the law in India and how the Indian courts have developed case law regarding reliance on electronic evidence. Index words: India; Supreme Court; admissibility; electronic evidence

Analysis of genetic diversity among indigenous rhizobia and its symbiotic effectiveness with soybean cultivar is important for development of knowledge about rhizobial ecology. In India, little is known about the genetic resources and diversity of rhizobia nodulating soybean. Indigenous bradyrhizobia isolated from root nodules of soybean plants, collected from traditional cultivating regions of two states (Madhya Pradesh and Uttar Pradesh) of India, were screened for bacteriophage sensitivity to identify successful broad host range symbiotic effectivity. Of 172 rhizobial isolates, 91 showed sensitivities to eight lytic phages and form ten groups on the basis of sensitivity patterns. The genetic diversity of 23 isolates belonging to different phage groups was assessed along with that of strains USDA123 and USDA94 by the restriction fragment length polymorphism (RFLP) analysis of 16S rDNA, intergenic spacer (IGS) (16S–23S rDNA), and DnaK regions. RFLP analysis of 16S rDNA formed 5 groups, whereas 19 and 9 groups were revealed by IGS and the DnaK genes, respectively. The IGS regions showed many amplified polymorphic bands. Nine isolates which revealed high RFLP polymorphism in the abovementioned regions (16S rRNA, IGS, DnaK) were used for 16S rRNA sequence analyses. The results indicate that taxonomically, all isolates were related to Rhizobium etli, Bradyrhizobium spp., and Bradyrhizobium yuanmingense. The doubling time of isolates varied from 9 h (MPSR155) to 16.2 h (MPSR068) in YM broth. Five isolates which did not show cross infectivity with isolated phage strains were studied for symbiotic efficiency. All isolates showed broad host range symbiotic effectiveness forming effective nodules on Vigna mungo, Vigna radiata, Vigna unguiculata, and Cajanus cajan. The present study provides information on genetic diversity and host range symbiosis of indigenous soybean rhizobia typed by different phages.