Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology mapping and optimization for reversible and quantum circuits

Quantum information processing is of interest as it offers the potential for a new generation of very powerful computers supporting novel computational paradigms. Over the last couple of decades, different aspects of quantum computers ranging from quantum algorithms to quantum physical design have received growing attention. One of the most important research areas is the synthesis and post-synthesis optimization of reversible and quantum circuits. Many synthesis and optimization approaches can be found in the literature, yet, due to the complexity of the problem, finding approaches leading to optimal, or near optimal, results is still an open problem. The synthesized circuits are usually evaluated based on quantum cost models. Therefore, they are often technology mapped to circuits of more primitive gates. To this end, various technology mapping approaches have also been proposed in the past few years. Related work shows an existing gap in optimized technology mapping for reversible and quantum circuits. In this dissertation, an optimized technology mapping design flow is introduced for mapping reversible circuits to quantum circuits. The contributions of this dissertation are classified as follows: - New reversible circuit optimization methods. - Optimized reversible to quantum mapping approaches. - New quantum gate libraries and new cost models for reversible gates based on the new libraries. - Quantum circuit optimization approaches. The steps above, form an optimized flow for mapping reversible circuits to quantum circuits. At each step of the design flow optimized and consistent approaches are suggested with the goal of reducing the quantum cost of the synthesized reversible circuits. The evaluations show that the proposed mapping methodology leads to significant improvement in the quantum cost of the existing benchmark circuits.
OPTICAL PHENOMENA ASSOCIATED WITH THE LAUNCH OF A BALLISTIC MISSILE" BULAVA" DECEMBER 9, 2009

There are certain types of optical phenomena observed in the upper atmosphere which associated exclusively with the launches of solid propellant rockets. In this article we consider some types of such phenomena observed during “Bulava” launch. First the development of large-scale dynamic structures. The second class of phenomena is the observation in the region of rocket flight rather compact structures with intense turquoise (blue-green) glow. The development of spiral cloud determined by specific mode of engine operation and rocket rotation. Turquoise glow arises from excitation of molecules AlO, formed by the interaction of metallic aluminum contained in combustion products with atmosphere components.
Most stringent bounds on the probability of the union and intersection of m events for systems partially specified by s 1, s 2, …sk , 2 ≦ k < m

For dependent probability systems of m events partially specified by the quantities Sv, the sum of the probabilities of each of the combinations of v events, v = 1, 2, …, 2 ≦ k < m; this paper develops the most stringent upper and lower bounds on P 1, the probability of the union of the m events; and on P [m], the probability of the simultaneous occurrence of the m events.
When Climate Change and Overexploitation Meet in Volcanic Lakes: The Lesson from Lake Bracciano, Rome’s Strategic Reservoir

Lakes worldwide have been strongly affected by several types of human-caused alteration, including changes in water level. This also affects deep lakes, including volcanic ones. Volcanic lakes in the Mediterranean area are of great importance for the local economy, but local human activities can threaten their rich biodiversity. As a European biodiversity hotspot and habitat of endemic species, the volcanic Lake Bracciano (Central Italy) is an ecosystem of primary conservation interest threatened by sharply falling water levels, particularly since 2017. It also plays a key role in human wellbeing by providing important ecosystem services including drinking water, fisheries and various recreational opportunities. Although the lake has historically been considered to enjoy good ecological status, various environmental problems, often amplified by water level changes, have arisen during the last two decades. Given this recent rapid evolution, the lake can be considered an example of a valuable ecosystem at risk as a result of increasing anthropogenic pressures. The aim of this review is to examine the changes that have affected the lake in the last 20 years, considerably reducing its capacity to provide ecosystem services, and to review existing and potential threats in order to better inform the management of such resources.
Uncertainty Treatment for Level-2 Probabilistic Safety Analysis

Abstract In a level-2 probabilistic safety analysis (PSA), two types of uncertainty have to be taken into account: the uncertainty related to random variation (variability) and the uncertainty related to limited knowledge (ignorance). We present a consistent treatment of these two types of uncertainty within a Bayesian framework. This framework allows us to translate both types of uncertainty in the basic parameters into branch probability distributions of the PSA accident progression event tree (APET). This, in turn, results in probability distributions for the different release categories. A generic Monte Carlo algorithm for drawing random samples from branch probability distributions is presented, offering the possibility to directly include information in terms of empirical data. To provide an illustrative example, the developed methods are applied to a specific APET question, related to the temperature-induced rupture of the reactor coolant system in case of a high pressure accident scenario. Although this paper addresses level-2 PSA, the proposed framework is presented in a general form to be applicable to other PSA problems.
Plant Disease Diagnosis and Detection using Type-2 Fuzzy Logic System

The advancement in technological equipment used in farming practices has stimulated the spread of various diseases in farm management. Most of the time, these infections are capable of crossing international borders and spreading quickly. It has become clear that preventing and identifying these diseases is a big problem that requires urgent attention. Farmers have been plagued by plant disease for many years; a solution must be provided to stop the harm. Hence, there is a need to advance plant disease detection methods since accurate plant disease detection will significantly raise the standard of harvested food crops. and lessen the danger that plant diseases bring to the security of food. Numerous domains, including intelligent control, pattern recognition, and disease classification for both humans and plants, have made extensive use of type-2 fuzzy logic. Therefore, this paper proposes plant disease diagnosis and detection using type-2 fuzzy logic (T2FL). The knowledge for detecting plant diseases is represented by a collection of T2FL rules. A plant disease dataset downloaded from the Kaggle website is used to simulate the T2FL model that is being presented. The modeling of the proposed system was implemented with a separate set of rules. A 97% accuracy rate for the system has been attained, and comparison outcomes demonstrate the credibility of the study.
Intelligent hydroponic greenhouse based on Internet of Things

The Internet of Things is the product of the information technology revolution and has a broad application prospect in all walks of life. The intelligent hydroponic greenhouse is not only the product of scientific and technological development, but also the development trend of future agriculture. The intelligent hydroponic greenhouse using Internet of Things technology can not only realize automatic monitoring, intelligent control, remote control and other functions, but also make the development of agriculture more intelligent and modern. Two monitoring networks of nutrient solution and greenhouse environment are respectively built through the Internet of Things to realize real-time monitoring and control of nutrient solution and greenhouse internal environment, truly liberate productivity, greatly reduce the labor intensity of users, not only make agricultural development more accurate and intelligent, but also make agriculture more integrated and industrialized.
Clinical outcomes of intravitreal treatment for ocular toxoplasmosis: systematic review and meta-analysis

ABSTRACT Background: Ocular toxoplasmosis is the leading cause of infectious posterior uveitis worldwide, accounting for 30-50% of all cases in immunocompetent patients. Conventional treatment is associated with adverse effects and does not prevent recurrence. Intravitreal drug administration can improve disease outcomes and reduce side effects. Herein, we conducted a systematic review and meta-analysis on the efficacy of intravitreal injections for treating ocular toxoplasmosis. Methods: The systematic search was conducted using PubMed, SciELO, and Google Scholar with the descriptors “ocular toxoplasmosis” AND “intravitreal”. We analyzed studies that met the inclusion criteria, i.e., experimental cases in patients treated intravitreally for ocular toxoplasmosis. Considering the systematic review, we focused on the number of intravitreal injections, the therapeutic drug class, and the presence of preexisting conditions. To assess the efficacy of intravitreal injections, a meta-analysis was performed using visual acuity, side effects, disease recurrence, and inflammatory responses as variables. Results: Intravitreal injection-induced side effects were rarely observed (0.49% [0.00, 1.51%] ). The use of antiparasitic and anti-inflammatory drugs afforded improved visual acuity (99.81% [98.60, 100.00%]) and marked effectiveness in treating ocular toxoplasmosis. Conclusions: Intravitreal injections may facilitate the successful treatment of ocular toxoplasmosis. However, clinicians should carefully evaluate the presence of preexisting conditions for ocular toxoplasmosis or previous diseases, as these can impact the decision to administer intravitreal injections.
Pre-Clinical Assessment of Roflumilast Therapy in a Thoracic Model of Spinal Cord Injury

The failure of axons to regenerate after a spinal cord injury (SCI) remains one of the greatest challenges in neuroscience. The initial mechanical trauma is followed by a secondary injury cascade, creating a hostile microenvironment, which not only is not permissive to regeneration but also leads to further damage. One of the most promising approaches for promoting axonal regeneration is to maintain the levels of cyclic adenosine monophosphate (cAMP), specifically by a phosphodiesterase-4 (PDE4) inhibitor expressed in neural tissues. Therefore, in our study, we evaluated the therapeutic effect of an FDA-approved PDE4 inhibitor, Roflumilast (Rof), in a thoracic contusion rat model. Results indicate that the treatment was effective in promoting functional recovery. Rof-treated animals showed improvements in both gross and fine motor function. Eight weeks post-injury, the animals significantly recovered by achieving occasional weight-supported plantar steps. Histological assessment revealed a significant decrease in cavity size, less reactive microglia, as well as higher axonal regeneration in treated animals. Molecular analysis revealed that IL-10 and IL-13 levels, as well as VEGF, were increased in the serum of Rof-treated animals. Overall, Roflumilast promotes functional recovery and supports neuroregeneration in a severe thoracic contusion injury model and may be important in SCI treatment.
Individualized or Uniform De-Escalation Strategies for Antiplatelet Therapy in Acute Coronary Syndrome: A Review of Clinical Trials with Platelet Function Testing and Genetic Testing-Based Protocols

This comprehensive literature review assessed the effectiveness of precision medicine approaches in individualizing P2Y12 de-escalation strategies, such as platelet function testing guidance, genetic testing guidance, and uniform de-escalation, for acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI). Analyzing six trials with a total of 13,729 patients, the cumulative analyses demonstrated a significant reduction in major adverse cardiac events (MACE), net adverse clinical events (NACE), and major and minor bleeding events with P2Y12 de-escalation. Specifically, the analysis found a 24% reduction of MACE and a 22% reduction of adverse event risk (relative risk (RR) 0.76, 95% confidence interval (CI): 0.71–0.82, and RR: 0.78, 95% CI 0.67–0.92, respectively). Reductions in bleeding events were highest with uniform unguided de-escalation, followed by guided de-escalations, while ischemic event rates were similarly lower across all three strategies. Although the review highlights the potential of individualized P2Y12 de-escalation strategies to offer a safer alternative to the long-term potent P2Y12 inhibitor-based dual antiplatelet therapy, it also indicates that laboratory-guided precision medicine approaches may not yet offer the expected benefits, necessitating further research to optimize individualized strategies and evaluate the potential of precision medicine approaches in this context.
Multiconductor Transmission-Line Theory That Includes an Antenna Process with a Lumped-Parameter Circuit

Electromagnetic noise was studied theoretically by developing a multiconductor transmission-line (MTL) theory derived from Maxwell’s equations without any approximations. The MTL theory naturally contains the antenna process, and for the case of three lines it is written in terms of normal, common, and antenna modes. Here we develop a theoretical method to solve the MTL equations by introducing boundary conditions consisting of a lumped-parameter circuit in order to describe the performance of any electric circuit for good performance without noise. The finite-difference time-domain (FDTD) method is a powerful algorithm to solve time-dependent coupled differential equations for a combined distributed- and lumped-parameter circuit.
Fault Detection and Identification in an Acid Gas Removal Unit Using Deep Autoencoders

An acid gas removal unit (AGRU) in a natural gas processing plant is designed specifically to remove acidic components, such as carbon dioxide (CO2) and hydrogen sulfide (H2S), from the natural gas. The occurrence of faults, such as foaming, and to a lesser extent, damaged trays and fouling, in AGRUs is a commonly encountered problem; however, they are the least studied in the open literature. Hence, in this paper, shallow and deep sparse autoencoders with SoftMax layers are investigated to facilitate early detection of these three faults before any significant financial loss. The dynamic behavior of process variables in AGRUs in the presence of fault conditions was simulated using Aspen HYSYS Dynamics. The simulated data were used to compare five closely related fault diagnostic models, i.e., a model based on principal component analysis, a shallow sparse autoencoder without fine-tuning, a shallow sparse autoencoder with fine-tuning, a deep sparse autoencoder without fine-tuning, and a deep sparse autoencoder with fine-tuning. All models could distinguish reasonably well between the different fault conditions. The deep sparse autoencoder with fine-tuning was best able to do so with very high accuracy. Visualization of the autoencoder features yielded further insight into the performance of the models, as well as the dynamic behavior of the AGRU. Foaming was relatively difficult to distinguish from normal operating conditions. The features obtained from the fine-tuned deep autoencoder in particular can be used to construct bivariate scatter plots as a basis for automatic monitoring of the process.
Simulation and Dynamic Properties Analysis of the Anaerobic–Anoxic–Oxic Process in a Wastewater Treatment PLANT Based on Koopman Operator and Deep Learning

The accurate simulation of the dynamics of the anaerobic–anoxic–oxic (A2O) process in the biochemical reactions in wastewater treatment plants (WWTPs) is important for system prediction and optimization. Previous studies have used real-time monitoring data of WWTPs to develop data-driven predictive models, but these models cannot be used to provide mathematical analysis of A2O dynamic properties. In this study, we developed a new simulation and analysis method for determining A2O dynamics in biochemical reactions using deep learning and the Koopman operator to address the above problems. This method was validated through data from a real-world WWTP in east China and compared it with the traditional deep learning model. According to the results, the new method achieved high-accuracy prediction. Meanwhile, with the help of the Koopman operator, the new method was able to analyze the asymptotical stability and convergence behavior of the A2O process, which provides a brand-new perspective for the in-depth study of biochemical reactor dynamics.
Neural Network-based Fault Classification in Multi-Level Inverters

Multi-level inverters are widely used in power systems, power electronics and renewable energy applications. High power output, low value of total harmonic distortion THD are the major advantages of Multi-level inverters, which makes them compatible with variable speed applications. As the levels increase, there are more number of faults occurring in the switches. Early detection of faults are necessary to protect the equipment and prevent damage to the power electronic system. This paper focuses on neural network-based fault analysis of multilevel inverters. Comparative analysis of the voltage output under different fault conditions is simulated and results are elaborately presented.
A new generation of Chinese FY-3C microwave sounding measurements and the initial assessments of its observations

ABSTRACT A new Chinese satellite, FY-3C, carrying second-generation microwave sounding instruments, was introduced and the observations were evaluated. On board FY-3C are the Microwave Temperature Sounder −2 (MWTS-2) and Microwave Humidity Sounder−2 (MWHS-2), and these devices are more advanced than those on board the previous satellites, FY-3A/B; the spectral and spatial resolutions of the devices are also higher than the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS). The 50–60 GHz and 183 GHz sounding abilities are similar to Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-orbiting Partnership (SNPP). FY-3C and SNPP were launched in different orbits but are complementary in observation area. In addition, MWHS-2 is the first operational polar-orbiting, satellite-based sensor which has 118 GHz channels. Assessment of MWTS-2 and MWHS-2 observations indicate that striping noises are apparent from the observations. A technique combining principal component analysis (PCA) with Ensemble Empirical Mode Decomposition (EEMD) is used to extract the noise. The striping noise in the first PCA component is extracted from the data. It was determined that the bias and standard deviation (SD) of MWTS-2 noise was larger than that of the ATMS corresponding channels. The biases and SDs of the MWHS-2 noise were comparable in magnitude with ATMS moisture channels. After removing the noise, the biases and SDs of the difference between observation and simulation (O–B) for MWTS-2 were slightly larger than the ATMS. Biases of MWHS-2 were close to the ATMS. The O–B biases of MWHS-2 118 GHz channels were within the range from −0.6 to 0.2 K, and the SDs were approximately 0.5–1.2 K.
Automatic Balance for The Digital AC Bridge

Impedance is an important parameter used to characterize electronic circuits, components, and the materials used to make components. Impedance (Z) is generally defined as the total opposition a device or circuit offers to the flow of an alternating current (AC) at a given frequency. A fast and wide - range balanced theory of the method is designed. In a implementation , an impedance component is based on a GIC ( General immittance converter ) system. Which provides a variable impedance. The principle of this method is comprehensive and its implementation is simple. As a digital measurement technique, the method takes full advantage of the powerful arithmetic and processing ability of a microprocessor.
Nanoparticle-Embedded Polymers and Their Applications: A Review

There has been increasing interest in the study and development of nanoparticle-embedded polymeric materials and their applications to special membranes. Nanoparticle-embedded polymeric materials have been observed to have a desirable compatibility with commonly used membrane matrices, a wide range of functionalities, and tunable physicochemical properties. The development of nanoparticle-embedded polymeric materials has shown great potential to overcome the longstanding challenges faced by the membrane separation industry. One major challenge that has been a bottleneck to the progress and use of membranes is the balance between the selectivity and the permeability of the membranes. Recent developments in the fabrication of nanoparticle-embedded polymeric materials have focused on how to further tune the properties of the nanoparticles and membranes to improve the performance of the membranes even further. Techniques for improving the performance of nanoparticle-embedded membranes by exploiting their surface characteristics and internal pore and channel structures to a significant degree have been incorporated into the fabrication processes. Several fabrication techniques are discussed in this paper and used to produce both mixed-matrix membranes and homogenous nanoparticle-embedded polymeric materials. The discussed fabrication techniques include interfacial polymerization, self-assembly, surface coating, and phase inversion. With the current interest shown in the field of nanoparticle-embedded polymeric materials, it is expected that better-performing membranes will be developed soon.