Waiting Nets: State Classes and Taxonomy

In time Petri nets (TPNs), time and control are tightly connected: time measurement for a transition starts only when all resources needed to fire it are available. Further, upper bounds on duration of enabledness can force transitions to fire (this is called urgency). For many systems, one wants to decouple control and time, i.e. start measuring time as soon as a part of the preset of a transition is filled, and fire it after some delay \underline{and} when all needed resources are available. This paper considers an extension of TPN called waiting nets that dissociates time measurement and control. Their semantics allows time measurement to start with incomplete presets, and can ignore urgency when upper bounds of intervals are reached but all resources needed to fire are not yet available. Firing of a transition is then allowed as soon as missing resources are available. It is known that extending bounded TPNs with stopwatches leads to undecidability. Our extension is weaker, and we show how to compute a finite state class graph for bounded waiting nets, yielding decidability of reachability and coverability. We then compare expressiveness of waiting nets with that of other models w.r.t. timed language equivalence, and show that they are strictly more expressive than TPNs

Performance of the Parallelized Monte-Carlo Tree Search Approach for Dots and Boxes

The Monte-Carlo tree search (MCTS) is a method designed to solve difficult learning problems. MCTS performs random simulations from the current situation and stores the results in order to distinguish decisions based on their past success. MCTS will then select the best decision and finally repeat the process. Parallelizing the MCTS means to divide the learning process among independent learners. Then, after a fixed number of simulations, the data is shared and combined. Past research has shown that this approach is faster than non-parallelized approaches. Therefore, we anticipated that the time reduced from dividing the learning outweighs the potential costs from redundant learning. Since it is often difficult to determine the effectiveness of algorithms in complex environments, it is sometimes more advantageous to develop strategies in simple environments such as games that can then be translated for use in broader real-life fields. In this project, we explored how controlling various resources affected the win-ratio performance of the game Dots and Boxes learned through a parallelized Monte Carlo Tree Search approach. The factors that we manipulated included the number of simulations, the number of independent learners, the amount of information shared from these independent learners, and how frequently the independent learners share. The win-ratio performance was determined by taking the number of wins over the number of total games. An algorithm is presented with our findings, along with details and results of our modified Monte-Carlo tree search implementation

Waiting Nets (Extended Version)

In Time Petri nets (TPNs), time and control are tightly connected: time measurement for a transition starts only when all resources needed to fire it are available. For many systems, one wants to start measuring time as soon as a part of the preset of a transition is filled, and fire it after some delay and when all needed resources are available. This paper considers an extension of TPN called waiting nets decoupling time measurement and control. Their semantics ignores clocks when upper bounds of intervals are reached but all resources needed to fire are not yet available. Firing of a transition is then allowed as soon as missing resources are available. It is known that extending bounded TPNs with stopwatches leads to undecidability. Our extension is weaker, and we show how to compute a finite state class graph for bounded waiting nets, yielding decidability of reachability and coverability. We then compare expressiveness of waiting nets with that of other models and show that they are strictly more expressive than TPNs

Non-Covid causes of acute undifferentiated febrile illness during the Covid pandemic: an etiological analysis from Uttar Pradesh, India

Background and Aims: febrile illnesses are one of the leading causes of morbidity and mortality in India, which are very common in the monsoon and post-monsoon season in tropical countries. Acute Undifferentiated Febrile Illness (AUFI) is a term usually used to refer to such conditions until diagnosed. This study was conducted to understand the prevalence of mixed infections, and the etiology and seasonal distribution of AUFI cases during the Corona Virus Disease (COVID) pandemic. Materials and Methods: this study was a hospital-based crosssectional study of six months (August 2021 to January 2022). Samples were collected by random sampling method from SN Medical College, Agra, and Mathura District. The diagnosis was made by Rapid Diagnostic Test for Malaria, and ELISA for Dengue, Chikungunya, Leptospira, and Scrub typhus. Results: a total of 9016 non-repetitive serum samples were collected, from males (4657) and females (4359), with a mean age of 42 years. The most common infections were: dengue (26.5%), malaria (0.85%), leptospira (0.54%), scrub typhus (0.32%), and Chikungunya (0.14%). The commonest co-infection was dengue with scrub typhus. Triple infections were also observed. Conclusions: the diversity of clinical presentations and etiological agents with limited diagnostic facilities demonstrates the complexity of AUFI. The knowledge of the local and seasonal distribution of acute febrile illnesses is thus very useful to formulate clinical, diagnostic, and management algorithms for positive outcomes, reducing hospital costs, and burden on healthcare facilities. Further upliftment of health services at the root level is still a long way to go

Benzothiazole analogues and their biological aspects: A Review

Heterocyclic compounds analogues have attracted strong interest in medicinal chemistry due to their pharmacological properties. Benzothiazole belongs to the heterocyclic class of bicyclic compounds. It is a combination of two rings six membered and five membered and both the rings are responsible for the therapeutic activity. Different methods are used to synthesize benzothiazole compounds and have been found to have numerous biological activities like – anticancer, antimicrobial, anti-inflammatory, anti-leishmanial, antidiabetic activity. This review is mainly an attempt to present the work reported in literature on pharmacological activity of benzothiazole compounds

Benzothiazole analogues and their biological aspects: A Review

1659-1669Heterocyclic compounds analogues have attracted strong interest in medicinal chemistry due to their pharmacological properties. Benzothiazole belongs to the heterocyclic class of bicyclic compounds. It is a combination of two rings six membered and five membered and both the rings are responsible for the therapeutic activity. Different methods are used to synthesize benzothiazole compounds and have been found to have numerous biological activities like – anticancer, antimicrobial, anti-inflammatory, anti-leishmanial, antidiabetic activity. This review is mainly an attempt to present the work reported in literature on pharmacological activity of benzothiazole compounds

A Personalized American Sign Language Game to Improve Short-Term Memory for Deaf Children

95% of deaf children are born to hearing parents and lack continuous exposure to language, which often inhibits learning. We are developing Adaptive CopyCat, an educational game where Deaf children communicate with the computer via American Sign Language (ASL) in order to improve their language skills and working memory. While previous versions of CopyCat relied on custom hardware such as colored gloves with accelerometers for sign verification, our current version of the game utilizes off-the-shelf 4K RGB depth cameras and pose estimators. Before re-creating the game for Deaf children, we evaluate the efficacy of our current CopyCat ASL recognition system with 12 adults. Average user-independent sentence and word accuracies were 85.1% and 95.4%, respectively. To improve the accuracy when new users are introduced, we developed a progressive training model that can adapt to a new user's signing as they play the game. This approach produced a 5% absolute increase in sentence accuracy. To test for generality, a 13th user was recruited six months after the initial experiment and achieved similarly high accuracies. These promising results suggest that our recognizer will be sufficiently accurate for verifying children's signing while playing Adaptive CopyCat.Undergraduat