The advanced industrial systems are characterized by complex event-driven processes. The specification and computer analysis are illustrated by the common problem of a servo drive with the PWM controller. A mathematical model from the class of Cauchy problems with non-liner control logic is presented to resolve the task discussed. The designed model is performed in two stages: the mathematical model specification (modeling) and implementation (simulation). Two approaches to make a model specification are considered, the first one is the traditional structural technique, and the advanced method is based on the finite state machine paradigm. Structural and finite state software models are built in progressive simulation tools. Computer implementation of the software models of hybrid systems is performed using platform built-in libraries with numerical methods and algorithms for correct event detection. The results of computational experiments for transient processes of phase variables are qualitatively identical, that proves the adequacy of the developed models. The algorithm for asymptotic event detection is considered in detail. This technique demonstrated the best results by the one-sidedness criterion, which is explained by using the author’s algorithm for the integration step determining and the event function dynamics monitoring in the vicinity of the event limits.

This article analyses entropy changes triggered by specific events in deterministic and indeterministic systems. Article considers a simple model consisting of water in a cuvette, an ice cube in the device above the cuvette and a random number generator (RNG) that controls the probability of dropping the ice into water. Article introduces the entropic potential Z(T, A) of an event A occurred in a system R at the moment Т₀, which describes the influence of the event A to the entropy of the system R in the future (for the moments T>Т₀). The entropic potential of an event Z(T,A) can be calculated as the difference between the mathematical expectations of entropy of the system R for the moment T (T>Т₀) made immediately before and immediately after the event A as Z(T, A) = Ŝ_T(Т₀+dT) - Ŝ_T(Т₀-dT). Article also presents examples of calculations of the entropic potentials of events in indeterministic systems with different probabilities of events. Since real-life systems are mostly indeterministic, the entropic potentials of events in real-life usually have non-zero values. The entropic potentials of the events "useful" for the system are negative, and entropic potentials of the events "harmful" for the system are positive.

To assure the opacity, an optimal problem is to retain the occurring events sequences as many as possible. Contrary to such problem, the other optimal problem is to preserve the minimal occurring events sequences. In the paper, based on the choosing cost, an optimal opacity-enforcing model is presented to minimize the sum of discount choosing cost, which is not only to preserve the opacity, but also to retain the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. And then, the solutions produced by the algorithms are proved correctly by theoretical proof. Finally, some illustrations for the algorithms are given.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion as it not only offers extra information on the event described but also enhances trust on the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for IoT-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. Moreover, we have successfully assessed their performance using network simulations with realistic urban traces.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

The pharmaceutical industry is undergoing a shift in processing methods for small-scale production (250-1000 kg/a), leading to an increased focus on continuous production research. Various concepts were developed to overcome challenges in small-scale crystallization for the production of high-quality products. However, further optimization is needed to enhance the industrial applicability of these concepts. This study aims to demonstrate the practical applicability of the Slug Flow Crystallizer (SFC) in the pharmaceutical industry by extending its use to a wider range of substance systems for small-scale API production. Employing an experimental strategy, the research seeks to rapidly assess the SFC's potential for achieving uniform, high-purity product yields, emphasizing the importance of factors such as slug flow stability, residence time distribution, and substance-specific crystallization criteria. This, coupled with our previously published model-based strategy, forms a comprehensive methodology to explore crystallization behavior and modeling for a variety of substance systems within the SFC. The ultimate goal of this study is to showcase the practicality of the SFC in the pharmaceutical industry, enabling the way for its reliable use in long-term, small-scale continuous crystallization and the production of high-quality pharmaceutical products.

An event-based dynamic output feedback control (DOFC) strategy for unmanned marine vehicle (UMV) systems is concerned. The whole UMV systems are composed of an UMV closed-loop system, a land-based control unit and the communication network. To increase the effectiveness of data transmission in the network channel and better enable the control unit against attack, an adaptive event-triggered mechanism (AETM) is applied. Moreover, a quantizer is installed between the sampler and the control unit. The quantizer further reduces the communication burden. The occurrence of aperiodic denial-of-service (DoS) attack is considered in the channel from the control unit to the UMV system. Sufficient criterion for ensuring global exponential stability of system with an expected H∞ disturbance attenuation index is obtained. Co-design of the dynamic output feedback controller and the AETM is derived. The effectiveness of the proposed approach is verified in the given illustrative simulation. The simulation results indicate that the reduction percentages of yaw angle amplitudes and yaw velocity accumulative error of the UMV system with the control strategy proposed in this paper are 43.2%and 45.9%, respectively, which are 0.3% and 5.8% improvement in both metrics compared to the previously published work.

The purpose of this research is to examine the benefits and limitations of implementation of novel digital academic advising systems using automated collection and reporting processes for ABET student outcomes data based on principles of authentic OBE for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools® - Advising Module based on assessment methodology incorporating the Faculty Course Assessment Report, Performance Indicators and hybrid rubrics classified according to the affective, cognitive and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of a) detailed students’ outcomes and Performance Indicators information; and b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the Faculty Course Assessment Report using Performance Indicators and hybrid rubrics can provide comprehensive and realistic outcomes data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better performing outcomes to reinforce natural affinity for learning specialized competencies and pursue related and successful career paths.

New possibilities of Gramian computation by using canonical transformations into diagonal, controllable and observable canonical forms are shown. With the help of such a technique the Gramian matrices can be represented in the form of products of Hadamard matrices of multipliers and matrices of the transformed right-hand side of Lyapunov equations. It is shown that the multiplier matrices are invariant under various canonical transformations of linear continuous systems. The modal Lyapunov equations for continuous SISO LTI systems in diagonal form are obtained and their new solutions based on Hadamard decomposition are proposed. New algorithms for element-by-element computation of Gramian matrices for stable continuous MIMO LTI systems are developed. For continuous SISO LTI systems given by equations of state in controllable and observable canonical forms, new algorithms for the computation of controllability Gramians and their traces in the form of Hadamard products in the form of Xiao matrices are developed. The application of transformations to the canonical forms of controllability and observability allowed to simplify the formulas of spectral decompositions in the form of Xiao matrices. In the paper new spectral decompositions in the form of Hadamard’s products for solutions of Sylvester algebraic and differential equations of MIMO LTI systems, including spectral decompositions of finite and infinite cross-Gramians of continuous MIMO LTI systems. Recommendations on the use of the obtained results are given.

The objective of this study was to determine how application with dolomitic limestone and grazing type (continuous or deferred) affect sheep grazing location and feed preference when the pasture growth rate is maximum. A 4ha field was divided into 4 plots: P1 and P2- without application of dolomitic limestone, continuous (CG) and deferred grazing (DG), respectively; P3 and P4- with application of dolomitic limestone, DG (1AUE), and CG (2.4 AUE), respectively. In DG, animals were placed and removed from the plots depending on the height of the pasture. In each plot, 12 georeferenced sampling points were identified. Throughout the pasture's vegetative cycle (autumn, winter, spring, and early summer) several measurements of pasture height and cut were carried out. From the beginning of March to the beginning of June, animal behavior was observed, by trained observers, through binoculars on six dates. Animals' activity grazing and location was recorded. The results show that: (i) the application of dolomitic limestone combined with CG provided higher values of pasture height; (ii) there were no significant differences in pasture quality between treatments; (iii) Deferred grazing led to 50% more sheep grazing days than continuous grazing; (iv) there were no significant differences in soil compaction between CG and DG; (v) the type of grazing and the treatment with dolomitic limestone did not seem to change the grazing pattern between treatments. This work contributes to understanding the relationships between different types of grazing in dryland pastures, with and without application of dolomitic limestone, and preferred grazing locations for sheep. This work could help agricultural managers make more informed decisions with the aim of promoting the sustainable intensification of livestock production in the Montado ecosystem.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

The livestock sector can be a major contributor to the mitigation of greenhouse (GHG) emissions. Within the sector, beef production produces the largest proportion of the livestock sector’s direct emissions. The objective of this study was to assess the on-farm GHG emissions in semi-arid rangelands in Argentina and to identify the relationships between emissions and current farm management practices. A survey recorded detailed information on farm management and characteristics. Assessments of GHG emissions were based on the IPCC Tier 2 protocols [1]. The relationships between farm management and GHG emissions were identified using General Linear Models. Cluster analysis was used to identify groups of farms that differed from others in emissions and farm characteristics. Emissions per product sold were low on farms that had improved livestock care management, rotational grazing, received technical advice, and had high animal and land productivities. Emissions per hectare of farmland were low on farms that had low stocking rates, low number of grazing paddocks, little or no land dedicated to improved pastures and forage crops, and low land productivity. Our results suggest that the implementation of realistic, relatively easy-to-adopt farming management practices has considerable potential for mitigating GHG emissions in semi-arid rangelands of central Argentina.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

In the evolving landscape of pharmaceutical manufacturing, a comprehensive continuous production process is being crafted for small-scale active pharmaceutical ingredient production. This study focuses on continuous crystallization with separate nucleation and crystal growth units, as well as continuous downstream processing, encompassing filtration, washing, and drying until the formation of free-flowing particles. We introduce a novel continuous nucleator designed based on solubility data and produced via 3D printing, enabling a fast and precise small-scale manufacturing of a nucleator meeting the requirements for nucleation and for the further growth processes. The nucleator was evaluated with regard to its suitability for continuous long-term operation across various coupled crystallizers. As a practical application example it is connected to a slug flow crystallizer to enable high-quality continuous crystallization. Additionally, the full integration of downstream processes using the continuous vacuum screw filter to achieve free-flowing product particles is realized. Even under non-optimized process conditions, with the help of in-situ generation of nuclei free-flowing product particles were successfully obtained. This is particularly useful during drug development when no material is available for seed addition and quickly obtain product for further characterization.

The Fourier Continuous Derivative ($D_C$) offers a unique perspective on fractional differentiation grounded in the theory of Fourier series. This approach has the potential to address problems across various disciplines, including physics, engineering, and mathematics. The primary insight underpinning this approach is that a convex function defined on $\mathbb{Z}$ retains its convexity on $\mathbb{R}$. This paper delves into the Fourier Continuous Derivative, compares it with traditional fractional derivatives, and outlines its possible real-world applications, such as modeling viscoelastic materials, solving wave equations, and financial data analysis.

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attack and packet loss. Firstly, the stochastic characteristics of DoS attack and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of systems. Then, by constructing a general common Lyapunov functional and combining with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through a numerical example.

The immunization of healthcare workers in the early stages of the rollout of COVID-19 vaccines was prioritized in order to ensure uninterrupted medical care provision. At the same time the increasing number of available COVID-19 vaccines may trigger hesitancy towards the decision to get vaccinated. Thus, accumulating reliable information on the adverse events following immunization may educate and urge the general population to receive a COVID-19 vaccine. The present study aimed to evaluate the adverse events (AEs) following immunization with any of the available COVID-19 vaccine among Bulgarian healthcare workers (HCWs). A cross-sectional study among HCWs in Plovdiv, Bulgaria was conducted in the period March – September 2021. Through a semi-structured online questionnaire, the participants reported the adverse events following the administration of the first and second dose of a COVID-19 vaccine. A total of 253 respondents, vaccinated with one of the available vaccines against COVID-19 took part in the study. Of them 71.9% were females, and 75.9% received mRNA-based vaccines, while 24.1% received a viral-vector based vaccine. Overall 91.6% and 82.6% of all participants reported at least one local AE after the first and second dose of a COVID-19 vaccine. The share of respondents reporting at least one systemic AE after the first and second dose of a COVID-19 vaccine was 59.7% and 62.4% respectively. The most common local AE was pain at the injection spot (84.0%), while the most common systemic AEs were fatigue (54.9%), chills (43.2%), and headache (41.7%). The mRNA-based vaccines versions seem to cause higher prevalence of local AEs, while the vector-based vaccines were linked with increased prevalence of systemic AEs. Female HCWs and the younger age group were associated with an increased risk of adverse events generally. Our results added more evidence that mRNA-based and viral-vector based vaccines are generally safe. The reported adverse events were mild, although they occurred in a high share of the respondents. No serious AEs attributable to the vaccines were reported.

In the design of multi-loop Networked Control Systems (NCSs) wherein each control system is characterized by heterogeneous dynamics and associated with certain set of timing specifications and constraints, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop. Majority of the design approaches for sampling, scheduling and control policies include either time-based or event-based metrics to perform pertinent actions in response to the changes of the parameters of interest. We specifically focus in this article on Age-of-Information (AoI) as a recently-developed time-based metric and threshold-based triggering function as a generic event-based metric. As the NCS model, we consider multiple heterogeneous stochastic linear control systems that close their feedback loops over a shared-resource communication network. We investigate the co-design across the NCS, and discuss the pros and cons with AoI and ET approaches in terms of asymptotic control performance measured by linear-quadratic Gaussian (LQG) cost functions. In particular, sampling and scheduling policies combining AoI and stochastic event-triggered metrics are proposed. It is argued that pure AoI functions that generate decision variables solely upon minimizing the average age irrespective of control systems dynamics may not be able to improve the overall NCS performance even compared with pure randomized policies. Our theoretical analyses are successfully validated through several simulation scenarios.

In this paper, we introduce a new class of open sets that is called is*-open set . Also, we present the notion of is*-continuous, is*-open, is*-irresolute, is*-totally continuous, and is-contra-continuous mappings, and we investigate some properties of these mappings. Furthermore, we introduce some is*-separation axioms, and is*-mappings are related with is*-separation axioms. . .

Several factors influence traffic congestion and overall traffic dynamics. Simulation modeling has been utilized to understand the traffic performance parameters during traffic congestions. This paper focuses on driver behavior of route selection by differentiating three distinguishable decisions, which are shortest distance routing, shortest time routing and less crowded road routing. This research generated 864 different scenarios to capture various traffic dynamics under collective driving behavior of route selection. Factors such as vehicle arrival rate, behaviors at system boundary and traffic light phasing were considered. The simulation results revealed that shortest time routing scenario offered the best solution considering all forms of interactions among the factors. Overall, this routing behavior reduces traffic wait time and total time (by 69.5% and 65.72%) compared to shortest distance routing.

A black hole in a Schwarzschild spacetime is considered. A transformation is proposed that describes the relationship between the coordinate systems exterior and interior to an event horizon. Application of this transformation permits considerations of the (a)symmetry of a range of phenomena taking place on both sides of the event horizon. The paper investigates two distinct problems of a uniformly accelerated particle. In one of these, although the equations of motion are the same in the regions on both sides, the solutions turn out to be very different. This manifests the differences of the properties of these two ranges.

Let $(Omega, mu)$ be a measure space and $\{\tau_\alpha\}_{\alpha\in \Omega}$ be a normalized continuous Bessel family for a real Hilbert space $\mathcal{H}$. If the diagonal $\Delta \{(\alpha, \alpha):\alpha \in \Omega\}$ is measurable in the measure space $\Omega\times \Omega$, then we show that \begin{align}\label{CRBA} \sup _{\alpha, \beta \in \Omega, \alpha\neq \beta}\langle \tau_\alpha, \tau_\beta\rangle \geq \frac{-(\mu\times\mu)(\Delta)}{(\mu\times\mu)((\Omega\times\Omega)\setminus\Delta)}. \end{align} We call Inequality (1) as continuous Rankin bound. It improves 76 years old result of Rankin [Ann. of Math., 1947]. It also answers one of the questions asked by K. M. Krishna in the paper [Continuous Welch bounds with applications, Commun. Korean Math. Soc., 2023]. We also derive Banach space version of Inequality (1).

The digital breakthrough of the nineties has profoundly changed the way events are recognized. Starting from EST (Event Segmentation Theory) elaborated by J.M. Zacks and his collaborators, this contribution shows how the way we segment the experiential flow has been changed by the introduction of digital: while throughout modernity the perception of events was based on immediate segmentation processes, founded on five elements (agent, space, time, intention, purpose) and determined by an evident con-clusion, we currently see in the perceptive models of individuals and consequently also in aesthetic texts (films, television series, novels, graphic novels etc.) a weakening of the end. This determines not only a greater difficulty in perceiving an event, but also the need to introduce micro-caesuras between one event and another: it is space that con-stitutes the most salient category in differentiating one event from another, as is evident in the transmedia storytelling that characterizes contemporary aesthetics.

Repeated single-point measurements of thoracic bioimpedance at single (low)-frequency are strongly related to fluid changes during hemodialysis. Extension to semi-continuous measurements may provide longitudinal details in the time pattern of the bioimpedance signal, and multifrequency measurements may add in-depth information on the distribution between intra- and extracellular fluid. This study aims to investigate the feasibility of semi-continuous multifrequency thoracic bioimpedance measurements by a wearable device in hemodialysis patients. Therefore, thoracic bioimpedance was recorded semi-continuously (i.e. every ten minutes) at nine frequencies (8 – 160 kHz) in 68 patients during two consecutive hemodialysis sessions, complemented by a single-point measurement at home in-between both sessions. On average, the resistance signals increased during both hemodialysis sessions and decreased during the interdialytic interval. The increase during dialysis was larger at 8 kHz (∆ 32.6 Ω during session 1 and ∆ 10 Ω during session 2) compared to 160 kHz (∆ 29.5 Ω during session 1 and ∆ 5.1 Ω during session 2). Whereas the resistance at 8 kHz showed a linear time pattern, the evolution of the resistance at 160 kHz was significantly different [p < 0.0001]. Measuring bioimpedance semi-continuously and with a multifrequency current is a major step forward in the understanding of fluid dynamics in hemodialysis patients. This study paves the road towards remote fluid monitoring.

In order to better plan new or update sewer pipe condition assessment protocols, this paper presents systematic comparisons of four most widely-used sewer condition assessment protocols, including the fourth edition of Sewer Rehabilitation Manual (SRM-4) in UK, Pipeline Assessment and Certification Program (PACP) in America, Sewer Physical Condition Grading Protocols (SPCCM) in Canada, and Technical Specification for Inspection and Evaluation of Urban Sewer (TSIEUR) in China. In qualitative comparison, the defects, deduct values and assessment methods of the four protocols were analyzed; in quantitative comparison, protocols were used to evaluate the same 182 sewer pipe segments based on field data and the assessment results were compared. It was found that SRM-4 are the most optimistic with 59% pipes being Grade 1 and Grade 2, while SPCCM gives the most pessimistic results with 62% pipes being Grade 3 and Grade 4. Assessment results by PACP and TSIEUR are in the middle. The main reasons for the different evaluation results were due to the different weight of defect and evaluation methods used.

Stock assessment determines the status of fishery stock to support manage- ment decision making. Considering the iterations between exploratory cal- culations and the need to compare outputs to elicit better model settings, we should focus on not only the accuracy of abundance estimation and the toler- ance of uncertainty but also the efficiency of the project workflow. Although in Japan, a stock assessment model was introduced written in the R language in 2012, the workflow did not sufficiently adjust, creating problems because the current workflow is contrary to the principles of effective value creation. To make our project sustainable, we propose adopting the agile methodol- ogy, an iterative development method used by software developers, for stock assessment. Therefore, we wrote an example report as a package document in the R language. Developed under a continuous integration environment, the report remains up to date, with every modification on component files. This method enabled our work to be efficient and transparent by allowing and documenting scenario branching, error corrections, and annual updates. We show that the iterative development cycle benefit us by allowing us to focus on the essential business problem of the assessment project.

Flood Management remains a major problem in many urban environments. Commonly, catchment models are used to generate the data needed for estimation of flood risk; event-based and continuous-based models have been used for this purpose. Use of catchment models requires calibration and validation with a calibration metric used to assess the predicted catchment response against the recorded catchment response. In this study, a continuous model based on SWMM using the Powells Creek catchment as a case study is investigated. Calibration of the model was obtained using 25 selected events from the monitored data for the catchment. Assessment of the calibration used a normalised peak flow error. Using alternative sets of parameter values to obtain estimates of the peak flow for each of the selected events and different accuracy criteria, the best datasets for each of the accuracy criteria were identified. These datasets were used with SWMM in a continuous simulation mode to predict flow sequences for extraction of Annual Maxima Series for an At-Site Flood Frequency Analysis. From analysis of these At-Site Flood Frequency Analyses, it was concluded that the normalised peak flow error needed to be less than 10% if reliable design flood quantile estimates were to be obtained.

Recently we started the development of Holographic Dendrogramic Theory (DH-theory). It is based on the novel mathematical representation of the relational event universe (in the spirit of (Smolin, Barbour, Rovelli). Elementary events are represented by branches of dendrograms, finite trees, which are generated from data with clustering algorithms. In this note we study the dynamics of the event-universe generated by the appearance of a new event. Generally, each new event can generate the complete reconstruction of the whole dendrogramic universe. However, we found (via numerical simulation) unexpected stability of this universe. Its events are coupled via the hierarchic relational structure which is relatively stable with respect even random generation of new events. We also observe the regularity patterns in location of new events on dendrograms. In the curse of evolution, the dendrogram’s complexity increases and determine the arrow of time the event universe. We use the complexity measure from particle shape dynamics which was shown to be increase in both direction away from a Janus point and thus determine the arrow of time in symmetrical manner away from a Janus point. The particle shape dynamics theory is a relational theory with close ideological resemblance to DH-theory as both relays on Mach’s principle and Leibniz’s relationalism and his principles. By using the complexity measure on dendrograms and its p-adic string representation, we demonstrate the emergence of time arrow from the p-adic zero-dimensional field, where space and time are absent.

Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms, and greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework, and tested in both business-like event logs as recorded in a higher educational ERP system, and a real case scenario involving a set of Dutch municipalities.

The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.

Electronic devices are sensitive to electromagnetic (EM) emissions requiring electromagnetic shielding protection to assure good operation, preventing noise, malfunctioning, or even burning. To assure protection, it is important to develop suitable material and design solutions for electronic enclosures. Most common enclosures are made with metal alloys by traditional manufacturing methods. However, resourcing to thermoplastic composites combined with additive manufac-turing (AM) technologies emerges as an alternative that enables to fabricate complex parts that are lightweight, consolidated and oxidation and corrosion resistant. In this research, an AM technique based in material extrusion was used to print 2 mm thick specimens with a multi-material made of micro-carbon fiber (CF) filled polyamide, reinforced, at specific layers, by continuous carbon fibers stacked with a 90° rotation to each other. Specimens electromagnetic shielding effectiveness (EMSE) was evaluated in the frequency band of 0.03 – 3 GHz by coaxial transmission line method. De-pending on the number of CF layers, the EM shielding obtained can up to 70 dB, with a specific shielding up to 60 dB.cm3/g, predominantly by absorption mechanism, being 22 times higher that without the CF layers. These findings fundament this innovative approach for lightweight cus-tomizable solutions for EM shielding applications.

This is a single center retrospective study to assess safety and tolerability of continuous inhaled iloprost use as rescue therapy for refractory pulmonary hypertension (PH) in critically ill neonates and infants. Retrospective chart review was performed on 58 infants and data was collected at baseline, 1, 6, 12, 24, 48, and 72 hours of iloprost initiation. Primary outcomes were change in heart rate (HR), fraction of inspired oxygen (FiO2), mean airway pressures (MAP), blood pressure (BP) and oxygenation index (OI). Secondary outcomes were need for extracorporeal membrane oxygenation (ECMO) and death. 51 patients treated for > 6 hours were analyzed in 2 age groups, neonate (≤28 days: n=32) and infant (29-365 days: n=19). FiO2 (p

In an effectively managed grazing system, goats as grazers can benefit the land's physical, chemical, biological, and economic aspects. However, little research has evaluated rotational and continuous goat grazing in Kentucky. This observational study investigated the impact of rotational and continuous grazing on forage production and goat performance. One group of 10 does was placed on a rotational pasture, and another group of 10 does were placed on a continuous pasture, each pasture measuring two acres. The pastures were side-by-side with similar forage types and compositions. Goat performance was measured by body weight and eye color score (FAMCHA). Forage production was measured weekly by collecting forage heights at three random spots in each pasture. The rotational grazing field displayed a more evenly grazing pattern, while the continuous field experienced overgrazing in some areas and overgrown of the less desired forage. Goats on the continuous field had a decrease in body weight, while the weights were more consistent in the rotational grazing field. The FAMACHA score was temporarily reduced on the rotational grazing pasture, indicating that properly managed rotational grazing may prevent anemia, possibly by reducing the shedding of gastrointestinal parasites. However, due to drought and lack of forage, the FAMACHA score of goats in the rotational grazing system reverted to its original status by the end, indicating that more extensive management and timely adjustments are necessary to ensure optimal goat performance. Our results suggest that rotational grazing may support more consistent forage yields and body weights of goats and has the potential to reduce the incidence of anemia and parasitic infestations in goats in Kentucky.

In this work, we propose a self-supervised multi-agent system that meets the online learning of clustering tasks for video behavior recognition spatio-temporal tasks. Encoding visual behavioral actions as discrete temporal sequence(DTS). Real-time clustering recognition task in a multi-agent system for continuous model building, training, and correction. Finally, we implemented a fully decentralized multi-agent system and completed its feasibility verification in a surveillance video application scenario on vehicle path clustering.

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style libre 1 (FSL1) to Dexcom G4 (DG4) over 1 year. Patients (n=21, 43±15 years, BMI 25±5, HbA1c 8.1±1.0%) had severe hypoglycemia and/or HbA1c≥8%. AGP metrics (time-in-range (TIR) 70-180 mg/dL, time-below-range (TBR)<70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50±17 vs. 45±16, P=0.036), and lower TBR<70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], P=0.0007), TBR<54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], P=0.007) and %CV (39±5 vs. 45±8, P=0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR<70mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs “non-responders” warrant to be investigated.

The present study describes a comprehensive analysis of the Adverse Drug Reactions (ADRs) documented in the Database of the Spanish Pharmacovigilance System (FEDRA) concerning the use of biological medications used for severe refractory asthma, specifically omalizumab, mepolizumab, reslizumab, benralizumab, dupilumab, and tezepelumab. Emphasis was placed on ADRs not documented in the Technical Sheet (Summary of Product Characteristics, SmPC) of the respective specialties (Xolair®, Nucala®, Cinqaero®, Fasenra®, Dupixent®, and Tezspire®), which could potentially trigger a warning signal in Pharmacovigilance, indicating previously unidentified risks. The analysis encompassed data from the inception of each drug ́s marketing until January 22, 2024, including cases originating from various sources: direct submissions to the Spanish Pharmacovigilance System, communications from the Pharmaceutical Industry, and findings from literature review. Notifications were analyzed independently of their origin, so a complete review of all ADRs associated with these medications was ensured. In essence, this investigation highlights the critical role of post-marketing safety information, emphasizing the relevance of comprehensive reporting by healthcare professionals to facilitate a robust Pharmacovigilance system, ultimately advancing patient care and safety.

A 68-year-old man with malignant metastatic melanoma in complete remission after Pembrolizumab treatment, developed 10 months after Pembrolizumab cessation due a grade 3 diarrhea, confusion, altered mental status, progressive memory loss, gait disturbance. He was admitted to the neurologic department and comprehensive neurological workup, brain magnetic resonance imaging, cerebral fluid analysis, EEG, blood test allowed the diagnosis of autoimmune encephalitis. The patient was treated with plasmapheresis, high dose of intravenous steroids and intravenous immunoglobulins. The patient improved and he is now well with a performance status 1. This case is interesting since the AE developed approximately 10 months after the cessation of immunotherapy, the underlying cancer was in complete remission and the AE showed a good response to the treatment done.

The concept of a black hole is examined through a historical lens, emphasizing the distinctions between its mathematical definition and the observed phenomena in the cosmos. An essential point of contention is highlighted, the challenge of reconciling the ultimate state of gravitational collapse, conceptualized as a black hole with its associated future event horizon, with the vast age of the universe as measured in cosmic time. The work delves into the evolution of black hole theories, tracing their development from the early mathematical solutions, such as the Schwarzschild solution, to the contemporary understanding shaped by contributions from Hawking and Penrose. The complex interplay between mathematical descriptions and observational realities is explored, shedding light on the difficulties inherent in merging theoretical concepts with astrophysical observations. A particular focus is placed on the discrepancy between the theoretical formation of a black hole and the seemingly infinite cosmic time required for the collapse process according to the seminal work of Oppenheimer and Snyder. This apparent incongruity raises questions about the nature of objects labeled as black holes in astrophysics and prompts a critical examination of how observational data align with theoretical predictions. In essence, this exploration underscores the need for a nuanced and critical analysis of the concept of black holes, considering the intricate interplay between theoretical constructs and empirical observations within the framework of General Relativity.

In this article, we review about the nature of Kerr Blackhole, Kerr metric and express kerr metric in Boyer-Lindquist co-ordinates.

Digital filtering is a fundamental technique in digital signal processing, which operates on a digital sequence without any information on how the sequence was generated. This paper proposes a methodology for designing the equivalent of digital filtering for neuromorphic samples, which are a low power alternative to conventional digital samples. In the literature, filtering using neuromorphic samples is done by filtering the reconstructed analog signal, which is required to belong to a predefined input space. We show that this requirement is not necessary, and introduce a new method for computing the neuromorphic samples of the filter output directly from the input samples, backed by theoretical guarantees. We show numerically we can achieve a similar accuracy compared to the conventional method. However, given that we bypass the analog signal reconstruction step, our results show significantly reduced computation time for the proposed method and good performance even when signal recovery is not possible.

Delineating the epidemic of vaccine injury from the coterminous condition long covid is a challenging prospect, but one with many implications not just for treatment, but also has important legal considerations for settlements of vaccine injury. The shared etiological factor of the spike protein in both vaccine injury and long covid make differentiation difficult, and while treatment is largely similar between vaccine injury and long covid, there are important distinctions. Furthermore, diagnostics are important for monitoring treatment progress and assessing the extent of subclinical vaccine injury in population having received a covid-19 vaccine. The development of rigorous diagnostics is an important step towards the recognition of both long covid and vaccine injury, as those suffering these conditions have faced immense challenges in having their conditions recognized, treated, and compensated by insurance companies or national health services.

Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies on resolving traffic problems have proposed the use of crowdsourcing, in which drivers provide real-time traffic information using mobile devices, to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time cost in collecting relevant data. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data and uses these data to detect events that influence traffic. The proposed technique uses various machine-learning methods to more accurately identify events and find accurate location information. Therefore, it is able to resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Its validity and effectiveness were also demonstrated through various performance evaluations.

Background: Epileptic patients frequently encounter cognitive impairment. Functions that are mostly affected involve memory, attention and executive function; however this is mainly dependant on the location of the epileptic activity. The aim of the study is to assess cognitive functions in MRI-negative epilepsy patients by means of neurophysiological and neuropsychological measures. Methods: The patients were enrolled from the outpatient Epilepsy/Clinical Neurophysiology clinic over a time period of 6 months. The study sample comprised 20 MRI negative epilepsy patients [mean age ± standard deviation (SD), 30.3 ± 12.56 years; age range 16-60 years; average disease duration, 13.95 years) and 10 age-matched controls (mean age ± SD, 24.22 ± 15.39 years), that were also education-matched (p>0.05) Patients with epileptogenic lesions were excluded from the study. Informed consent was obtained from all subjects involved in the study. Auditory ERPs and the cognitive screening tool EpiTrack, were administered to all subjects. Results: Latencies of P300 and slow waves were prolonged in patients compared to controls (p<0.05). ASM load and the patients’ performance in the EpiTrack maze subtest were the most significant predictors of P300 latency. A decline in memory, attention, and speed of information processing has been observed in patients with cryptogenic epilepsy compared to age-matched controls, as reflected by P300 latency and EpiTrack scores.

Previous research on video game player aggression has focused on violent content, while recent studies have examined competitive factors. Few research has examined the solely impacts of competitive factors in video games without violent content on aggression, and it is still unknown what neurological processes of these effects. The present study is the first to examine the the electrophysiological characteristics of short-term competitive video game exposure and aggression. Thirty-five participants played a video game in either competitive or solo mode for 15 minutes, followed by an ERP experiment based on the Oddball paradigm and the hot sauce paradigm to measure aggressive behavior. Results showed that playing competitive game mode was associated with faster judgment to aggressive words, larger P300 amplitudes, and selection of more chili powder. The P300 amplitude partially mediated the relationship between competitive game exposure and aggressive behavior. These findings support the general aggression model.

This paper investigates the leader–follower formation tracking control of underactuated surface vessels (USVs) with input saturation. Each vessel is subject to the uncertainties induced by model uncertainties and environmental disturbances. At first, an event-trigged extended state observer (ETESO) is used to recover the velocity, yaw rate and the uncertainties. Then, an estimator is used to estimate velocity of the leader. An event-trigged controller (ETC) is constructed based on the estimator, the observer and extra variables. Specifically, extra variables are used to solve the underactuated problem and input saturation. Stability analysis of control system is given to prove all signals are bounded. Simulations demonstrate that the ETESO can estimate accurately the uncertainties, the velocity and yaw rate; the ETC can largely reduce the action times of actuator.

Vaccination against SARS-CoV-2 has significantly contributed to the recent pandemic control. COVID-19 vaccines are available with different platforms and the primary clinical trials results presented acceptable safety profile of the approved vaccines. Nevertheless, the long-term assessment of the adverse events or rare conditions need to be investigated. The present systematic review, aimed at classification of Iranian case reports following COVID-19 immunization. To achieve this goal, the related published case reports were explored via PubMed, Web of Science and Google scholar according to PRISMA guideline and available up to 14th Dec, 2022. Out of 437 explored studies, the relevant data were fully investigated which totally led to 40 studies including 64 case reports with a new onset of a problem. The cases were then classified according to the various items such as the type of adverse event manifestations and COVID-19 vaccine. The reported COVID-19 vaccines in the studied cases included Sinopharm, AstraZeneca, and COVAXIN. The results showed that the adverse events presented in 8 different categories from which cutaneous problems accounted as the most prevalent manifestations (43.7%) in which rare diseases were also screened such as Steven-Johnson syndrome, Morphea and Toxic Epidermal Necrolysis. Notably, almost 60% of the cases had no comorbidities. Moreover, the obtained data revealed nearly half of the incidences occurred after the first dose of injection and the mean duration of improvement after the symptom onset was 18.72±24.69 days. 73% of all the cases were either significantly improved or fully recovered. Although the advantages of COVID-19 vaccination is undoubtedly significant, the high risk individuals including those with a history of serious disease or comorbidities immunodeficiency conditions should be vaccinated with the utmost caution.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the heath care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization.Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

Endoscopic retrograde cholangiopancreatography (ERCP) is a routinely used therapeutic procedure for the biliary and pancreatic diseases. Population aging may increase the typical indications of ERCP and come with more complexity and difficulties, especially in cannulation. This study aimed to evaluate the incidence, causes, and management of difficult biliary cannulation during ERCP in super-aged patients and the role of difficult cannulation as a risk factor for adverse events. A total of 614 patients, underwent ERCP, were prospectively studied as a cohort and divided into two groups based on their age. There were 146 patients aged 80 years or older in group A and 468 patients aged less than 80 years in group B. The primary outcome measures were the difficulty grade of papilla cannulation, clinical outcomes, and ERCP-related complications in the two groups. The adverse events were analyzed using logistic regression for patient age, co-morbidities, indications, and cannulation difficulty grade variables. There was no difference in the incidence of difficult cannulation between the two groups (32.9% vs. 34.4%, p=0.765) though, as expected, super-aged Group A had a higher prevalence of periampullary diverticulum (29.5% vs. 16.7%, p=0.001). The technical cannulation success rate was (96.6% vs. 96.8%, p= 0.54). All used cannulation techniques in the elderly group were efficient and safe. Logistic regression showed that age ≥80 was not associated with increased adverse events; however, difficult cannulation (adjusted odds ratio [AOR]=3.478; 95% confidence interval [CI]=1.877, 6.442; p<0.001) and CCI ≥2 (AOR=1.824; 95% CI=0.993, 3.349; p=0.045) were more likely to have adverse events. Age ≤65 (AOR=3.460; 95% CI=1.511, 7.922; p=0.003), female gender (AOR=2.362; 95% CI=1.089, 5.124; p=0.030), difficult cannulation (AOR=4.527; 95% CI=2.078, 9.860; p<0.001), and patients with cholangitis (AOR=3.261; 95% CI=1.204, 8.832; p=0.020) were strongly associated with increasing Post-ERCP Pancreatitis (PEP). Advanced age has not proved to be a risk factor of difficult cannulation, and secondary cannulation techniques can be safely and efficaciously utilized for this group. CCI ≥2 and difficult cannulation are associated with increased overall adverse events rate while age ≥80 factor is not.

Natural events such as floods, fires, tsunamis, earthquakes and others have nowadays caused serious damage to human beings and nature. The precise detection of these natural events and especially the earthquake has nowadays become the focus of many computer and geoscientific researchers. Computer science and machine learning algorithms have revolutionized early detection and prediction of these events. Hence, a fuzzy method has been initially used in this article to enhance the authenticity of data based on application of effective variables and then combination of neural network algorithms of the MLP perceptron and radial network of RBF in form of a collective learning system in order to more accurately identify seismic events on a small scale. It was observed after simulating the proposed method that the proposed method has significantly improved based on actual error and root-mean-square error (RMSE) criteria compared to basic methods.

This observational pilot study aimed to investigate continuous glucose monitoring (CGM) metrics in older Japanese patients with type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring system (FreeStyle Libre Pro) for CGM during the first tirzepatide administration and compared the glycemic control measures before and after the treatment. The four patients re-ported a mean age of 79.5 years (standard deviation [SD]: 5.8), mean body mass index of 24.63 kg/m2 (SD: 4.7), and mean glycated hemoglobin level of 9.1% (SD: 2.1). After tirzepatide treatment, the mean of time in range, time above range, and time below range changed from 53.2% to 78.9% (p = 0.041), 45.8% to 19.7% (p = 0.038), and 1.0% to 1.5% (p = 0.206), respectively. Improved hy-perglycemia reduced the oral glucose medication in two cases and decreased the frequency of insulin injections in two cases. All cases demonstrated the CGM index improvement during short-term observation despite some differences. Future studies need to investigate the long-term impact on functional prognosis, safety, and tolerability, especially in nonobese older Asian pa-tients, to elucidate the potential benefits of tirzepatide. However, tirzepatide-associated robust glycemic improvement may simplify and reduce diabetes treatment-associated complication in older patients with T2DM.

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it’s an integration of the Development and Operation team. It is followed to integrate the various stages of the development cycle. DevOps is an extended version of the existing Agile method. DevOps aims at continuous integration, Continuous Delivery, Continuous Improvement, faster feedback and security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps

Cannabis is a flowering plant that has long been used for medicinal, therapeutic, and recreational purposes. Cannabis contains more than 500 different compounds, including a unique class of terpeno-phenolic compounds known as cannabinoids; Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are the most prevalent cannabinoids and have been associated with the therapeutic and medicinal properties of the cannabis plant. In this paper, continuous flow microwave assisted extraction (MAE) is presented and compared with other methods for commercial cannabis extraction. The practical issues of each extraction method are discussed. The main advantages of MAE are: continuous-flow method which allows for higher volumes of biomass to be processed in less time than existing extraction methods, improved extraction efficiency leading to increased final product yields, improved extract consistency and quality because the process does not require stopping and restarting material flows, and ease of scale-up to industrial scale without the use of pressurised batch vessels. Moreover, due to the flexibility of changing the operation conditions, MAE eliminates additional steps required in most extraction methods, such as biomass decarboxylation, winterisation, which typically adds at least a half day to the extraction process. Another factor that sets MAE apart is the ability to achieve high extraction efficiency even at the industrial scale. Whereas the typical recovery of active compounds using supercritical CO¬2 remains around 70-80%, via MAE up to 95% of the active compounds from cannabis biomass can be recovered at the industrial scale.

Bio-separation of natural molecules as well as clinical compounds has been constantly developed in last decades. Several techniques are available but the majority of them presents drawbacks such us impossibility to be applied for industrial purposes. The main limitations for the scaling up are high costs and the fact that the devices work with microfluid dynamics. Nevertheless, magnetic bio-separation is considered the most prone to be used for large scale applications. Herein, we propose a simple magnetic separation method that is not based on microfluid dynamics, can work in a continuous- and high-flow rate and can be easily automated in order to be used for standard separation purposes. It is based on the use of an anisotropic flexible ferric magnetic strip, Teflon hoses and a pumping device. We show the modelling of the separation process along with an experimental test on iron oxide magnetic particles. The results showed that it is possible to remove, and separately collect, more than 92% of magnetic particles from a liquid solution of 100 ml in roughly 15 minutes.

Increasing global concern over COVID-19 has recently brought greater attention to studies due to the ease of person-to-person transmission and the current lack of effective antiviral therapy. Here, we proposed the application of the adverse outcome pathway (AOP) framework to support re-search on the pathogenesis of viral disease. We first constructed adverse outcome pathways (AOPs) applicable to COVID-19 management to understand whether the infection causes severe acute respiratory distress. Based on the AOP framework where mechanistic elucidation of the pathway from the interaction of chemicals (or viruses) to apical endpoints is represented, our COVID-19 AOP indicated that the molecular initiating event (MIE) was angiotensin-converting enzyme 2 (ACE2) interaction, and the key events (KEs) were the increased pro-inflammatory cytokines in immune cells, with increased mortality as an apical adverse outcome (AO). However, there is still limited information on the toxicity mechanisms of AOPs in COVID-19; therefore, detailed KEs and AOs on toxicity mechanisms will be required to fill these gaps in the data. This study demonstrated that the COVID-19 AOP framework is a suitable tool to design new drugs and to integrate crowded-sourced information for the battle against the COVID-19 pandemic.

The benefits of event-driven architecture (EDA) derive from how systems and components are loosely coupled, which can facilitate independent development and deployment of systems, improved scaling and fault tolerance, and integration with external systems, especially in comparison to monolithic architectures. With the advent of new technologies such as containers, and microservices, a new generation of distributed event streaming platforms are commonly used in event-driven architecture for efficient event-driven communication. However, the asynchronous and distributed nature of EDA poses several problems that include handling failures, the dependence of an end-to-end transaction on individual component stability, etc. In this paper, we describe a new approach to designing self-regulating distributed applications with autopoietic and cognitive workflow management. This approach is based on the new science of information processing structures derived from the General Theory of Information. Just as a genome enables self-organizing and self-regulating biological structures, a digital genome enables a specific software application with several components, the ability to use distributed resources and self-regulate the evolution of the system based on functional and non-functional requirements, and best-practice policies that maintain the stability, safety, and survival under non-deterministic fluctuations in the demand for resources. In addition, cognitive workflow management assures end-to-end transaction delivery.

Structured extraction of emergency event information can effectively enhance the ability to re-spond to emergency events. This article focuses on the extraction of Chinese document-level emergency events, which mainly faces two key issues in this field: first, related datasets; Secondly, existing DEE (document-level event extraction) studies mostly use sequence annotation to extract candidate entities in the subtask of candidate entity extraction without considering the problem of role overlapping between candidate entities. On the one hand, this article constructs a Chinese document-level emergency extraction dataset, CDEEE, which first annotates the issues of argu-ment scattering, multiple events, and role overlapping. On the other hand, this article proposes a model RODEE for the problem of role overlapping in DEE tasks. This model first uses two in-dependent modules to represent the head and tail positions of candidate entities, then uses a multiplication attention mechanism to interact with the two to obtain a scoring matrix. Finally, role-overlapping candidate entities are predicted to assist in completing DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results showed that RODEE can effectively solve the problem of role overlapping in candidate entities and improve the performance of the DEE model.

Music and language are hypothesized to share neural resources, particularly at the level of syntax processing. Recent reports suggest that attention modulates this sharing of neural resources, but the time-course of the effects of attention, and the degree to which attention operates similarly on music and language, are yet unclear. In this EEG study we manipulate the syntactic structure of simultaneously presented musical chord progressions and garden-path sentences in a modified rapid serial visual presentation paradigm, while varying top-down attentional demands to the two modalities. The Early Right Anterior Negativity (ERAN) was observed in response to both attended and unattended musical syntax violations. In contrast, an N400 was only observed in response to attended linguistic syntax violations, and a P3 only in response to attended musical syntax violations. Results show that top-down allocation of attention indeed affects the processing of syntax in both music and language, with different neural resources acting upon the two modalities particularly at later stages of cognitive processing. However, the processing of musical syntax at an earlier stage of the perceptual-cognitive pathway, as indexed by the ERAN, is partially automatic, and is strongly indicative of separate neural resources for music and language.

The aim of this paper is to develop an intelligent event-driven Electrocardiogram (ECG) processing module in order to achieve an efficient solution for diagnosis of the cardiac diseases. The suggested method acquires the signal with an event-driven A/D converter (EDADC). The output of EDADC is passed through the activity selection and interpolation blocks. It allows focusing only on the important signal parts and resampling it uniformly. Later on, the signal is de-noised. The autoregressive (AR) method is used to extract the classifiable features of the de-noised signal. Afterwards, the output is classified by employing different robust classification techniques such as support vector machines (SVMs), K- Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The event-driven feature enables to adapt the system processing load according to the signal temporal variations. This interesting feature of the devised system aptitudes a drastic reduction in its processing activity and therefore in the power consumption as compared to the traditional ones. A comparison of the performance of different classifiers is also made in terms of accuracy. Results show that the proposed system is a potential candidate for an automatic diagnosis of the cardiac diseases.

Exploring the growth and physiological response mechanisms of rice under continuous drought stress circumstances can provide a significant scientific foundation and technological assistance for meeting drought difficulties, improving drought resistance and rice (Oryza sativa L.) output, and ensuring food security. In this study, a rice field experiment was conducted under a rain shelter with five different treatments set up: P1 (drought stress from tillering stage), P2 (drought stress from jointing-booting stage), P3 (drought stress from heading-flowering stage), P4 (drought stress from grain filling stage), and CK (adequate water management throughout the growth stage). Continuous drought stress from different growth stages with four levels (mild, medium, serious, and extreme). The results showed that the effects of different drought stress treatments on rice growth varied significantly. Compared with the CK treatment, plant height was reduced by 12.10%, 8.14%, 3.83%, and 1.06% in the P1, P2, P3, and P4 treatments, respectively; and the number of tillers was reduced by 23.83%, 18.91%, 13.47%, and 8.68%, respectively. With the increase in drought stress levels, SPAD values and Rubisco activity of rice leaf continued to decrease; SOD activity showed a decreasing trend, but the decreasing trend of POD and CAT activities was not significant, while MDA content showed an increasing trend. For yield components, continuous drought stress significantly reduced spike length, effective number of spikes, thousand grain weight, grain number per spike, and fruiting rate of rice compared to CK treatments during the growth period. In general, continuous drought stress during the early growth period affected the effective spike number and the grain number per spike. Continuous drought stress after the grain filling stage had the least effect on yield and water use efficiency was much higher than other treatments. The results of these studies reveal the response of rice growth and physiology to continuous drought stress, which is important for agricultural practices.

With the growing trend of urbanization and the growing number of people migrating to cities, the demand for the development and construction of new buildings as well as the infrastructure has risen, meaning that the construction industry must adapt to the trends. The growing demands with shorter deadlines for an industry already known for high costs and not delivering on time means that productivity must be increased without increasing the cost. The solution for this might lie in the application of the Lean philosophy to the construction industry. This paper analyses the application of the Lean philosophy in order to increase the productivity in the airport construction site. The paper highlights the potential for enhancing productivity in construction workplaces by concurrently fostering continuous improvement and sustainability through the implementation of the A3 methodology and Lean principles, resulting in waste reduction and increased value.

Continuous cropping of Platycodon produces problems that significantly impact its yield and quality making it a pressing issue that requires immediate attention. In this study, the sandwich method is employed to evaluate the allelopathic activity of different plant parts (leaves, stems, and roots) of Platycodon. The inhibitory effects of the various Platycodon plant parts were quantified based on their effects on lettuce seedlings growth by Sandwich method, with the following average inhibition percentages at 1 mg/ml (agar) of : leaves (79.4%, 61.8%), stems (58.0%, 45.7%), and roots (53.4%, 49.3%) for lettuce radicals and hypocotyls, respectively. At a concentration of 5 mg/ml, the inhibitory effects were as follow: leaves (91.9%, 72.2%), stems (79.5%, 60.3%), and roots (71.4%, 65.2%). Also, by employing the Plant Box method, it was determined that Platycodon root exudation. The growth inhibition rates of lettuce seedling roots and hypocotyls were 45.5% and 18.9%, respectively. The effect of activated carbon on the adsorption of allelopathic substances was investigated, and the results of the sandwich method with a concentration of 1 mg/ml showed the following growth inhibitory effects on lettuce seedlings and hypocotyls: roots (27.8%, 25.7%), leaves (13.3%, 25.7%), and stem (9.07%, 13.6%). The Plant Box method revealed average rates of inhibition of -16.7% and -4.70% on the growth of lettuce seedlings and hypocotyls, respectively. The results of this study demonstrated that activated carbon has a mitigating effect on the allelopathic inhibition associated with the different plant parts and root exudation of Platycodon, thus may provide a potential solution for overcoming obstacles associated with continuous cropping of Platycodon.

Type 2 diabetes mellitus (T2DM) poses a significant public health challenge. Prioritising its prevention is essential. With the growing interest in wearable health technologies continuous glucose monitoring systems (CGMs) have expanded their scope, transitioning from exclusive use in diabetes management to lifestyle enhancement for individuals without diabetes. While CGMs primarily target glycaemic stabilization, their potential role in mitigating noncommunicable conditions, including T2DM, warrants exploration. This review examines the regulation of CGMs and critically assesses the purported benefits of CGMs for individuals without diabetes, as presented in the 'health and wellness' sector: (1) early dysglycaemia detection through glucose variability observation; (2) refining glycaemic control by tailoring nutrition according to postprandial glucose response; and (3) promoting and fine-tuning physical activity based on instantaneous data feedback. We find a lack of compelling evidence for the utility of CGMs in PNLD, including accuracy, data analysis and interpretation metrics, and user acceptability markedly under-researched. A paucity of research exists on the unanticipated negative consequences of CGM usage, such as obsessive symptom tracking and potential onset of disordered eating, like orthorexia. Given the increasing adoption of CGMs by individuals without diabetes, this review underscores the need of addressing these research gaps to ensure safety and reliability of CGMs in novel contexts. We emphasise the importance of refining CGM regulation in the UK, particularly when considering its ‘off-label’ use. Marrying robust scientific evidence with effective regulation is pivotal in fortifying the coherence, equity, and effectiveness of diabetes prevention initiatives, while protecting the wellbeing of CGM users.

This study aims to investigate the effects of self-paced high-intensity interval training (Sp-HIIT) vs self-paced moderate-intensity continuous training (Sp-MICT) on the aerobic fitness level, psy-chophysiological responses and antioxidant status, to assess the relationship between aerobic fit-ness level and antioxidant markers. Well-trained males were randomised into Sp-HIIT and Sp-MICT. The intervention consisted of 3 weekly sessions during an 8-wk period. Sp-HIIT per-formed two sets of 12–24 x 30-s high-intensity runs ≥85 %HRmax followed by 30-s rest periods, while Sp-MICT performed 24-48 min of continuous running at 60-75 %HRmax. Pre and post-intervention testing included maximal oxygen uptake (VO2max) assessment during a 30-15 intermittent fitness test (30-15 IFT), as well as resting blood samples analysed for oxidative stress markers (Malondialdehyde (MDA)) and intracellular antioxidant enzymes activity (Catalase (CAT), Superoxide Dismutase (SOD) and Reduced (GSH) and Oxidized Glutathione (GSSG)). The Sp-HIIT showed meaningfully greater improvement in velocity of 30-15 IFT, VO2max and MDA responses. Furthermore, the Sp-HIIT demonstrated higher psychophysiological responses than the Sp-MICT, except for anger responses. In conclusion, these results suggest that Sp-HIIT has higher level of exercise-induced beneficial effects in physiological responses with greater perceived exer-tion in young well-trained adults.

Estimating peak flow for a catchment is commonly undertaken using the design event method, however this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of stream flow at the outlet of the catchment. With this data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a one in 100 year annual exceedance probability peak flow, the peaks over threshold method (333m3/s ±50m3/s) was significantly less uncertain than the annual maximum method (427m3/s ±100m3/s). For the one in 100 year annual exceedance probability, the median peak flow from the peaks over threshold method (333m3/s) produced an outcome comparable to the design event method peak flow (328m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, the paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments.

Response of tomato and eggplant to continuous lighting (CL) and abnormal light/dark (L/D) cycles was studied to elucidate the role of the absence of the dark period in the diurnal cycle in the development of leaf injuries under CL. Four light treatments were set: 16/8 h (control), 24/0 h (CL), 6/6 h, and 24/24 h (abnormal L/D cycles). These light treatments provided average daily light integrals of 17.3, 25.9, 13.0 and 13.0 mol m-2 day-1, respectively. Results of this work have shown that in both tomato and eggplant abnormal L/D cycles caused photoinhibition and leaf injuries similar to those in CL-grown plants. Induced defense mechanisms were not strong enough to contend against oxidative stress caused by light treatments that provided plants with even smaller DLI than 16/8 h photoperiod did. Half time during abnormal L/D cycles were dark periods. Despite these facts, abnormal L/D cycles were injurious. It is concluded that photooxidative stress induced by CL can not be attributed to excessive DLI or the continuity of light itself (the absence of dark periods). Therefore, we believe that the hypothesis suggesting circadian asynchrony to be the main triggering factor of CL-induced leaf injuries is the most plausible among many others proposed.

This article discusses the potential advantages of a data processing technique for continuous gravitational wave signals searches in the data measured by ground-based gravitational wave interferometers. Its main advantage over other techniques is that it does not need to search over the signal’s direction of propagation. Although it is a “ coherent method” (i.e. it coherently processes year-long data), it is applied to a data set obtained by multiplying the original time-series with a (half-year) time-shifted copy of it. As a result, the phase modulation due to the interferometer motion around the Sun is automatically canceled in the signal of the synthesized time-series. Although the resulting signal-to-noise ratio is not as high as that of a coherent search, it equals that of current hierarchical methods. In addition, since the signal search is performed over a parameters space of smaller dimensionality, the associated false-alarm probability should be smaller than those characterizing hierarchical methods and result in an improved likelihood of detection.

Steel is one of the essential materials in the world's civilization. It is essential to produce many products such as pipelines, mechanical elements in machines, vehicles, profiles, and beam sections for buildings in many industries. Until the '50s of the 20th century, steel products required a complex process known as ingot casting; for years, steelmakers focused on developing and simplifying this process. The result was the con-tinuous casting process (CCP); it is the most productive method to produce steel. The CCP allows producing significant volumes of steel sections without interruption and is more productive than the formal ingot casting process. The CCP begins by transferring the liquid steel from the steel-ladle to a tundish. This tundish or vessel distributes the liquid steel, by flowing through its volume, to one or more strands having wa-ter-cooled copper molds. The mold is the primary cooling system, PCS, solidifying a steel shell to withstand a liquid core and its friction forces with the mold wall. Further down the mold, the rolls drive the steel section in the SCS. Here the steel section is cooled, solidifying the remaining liquid core, by sprays placed in every cooling segment all around the billet and along the curved section of the machine. Finally, the steel strand goes towards a horizontal-straight free-spray zone, losing heat by radiation mechanism, where the billet cools down further to total solidification. A moving torch cutting-scissor splits the billet to the desired length at the end of this heat-radiant zone.

Additive manufacturing (AM) processes, including stereolithography (SL), can fabricate complex ceramic parts layer by layer using computer-aided design (CAD) models. A ceramic slurry with high solid loading is usually used in SL to fabricate the desired shape, which is further sintered to produce the final part. The traditional SL system utilizes a tank filled with printable material, known as a vat, which for ceramic slurry contributes several limitations and operational difficulties, and further renders it non-recyclable mainly due to its high viscosity and the fragility of the green state. In this study, we utilized a continuous film supply (CFS) printer integrated with a tape casting system using in-house-designed ceramic slurry to print standard prototype specimens. Various printing parameters, including viscosity, layer thickness control, and slurry recycling efficiency, were studied. In addition, post-processing optimizations of the prototype, characterizations, and the microhardness of sintered samples were studied to determine their properties and compare them with traditional methods. The effectiveness of slurry reusability was demonstrated by printing with original and recycled slurry to produce consistent densification of final parts. Post-processing was optimized to achieve a relative sinter density of 99.02% and microhardness of 12.59 GPa. This method provides new opportunities to realize dense complex ceramic features with final properties comparable to those produced by subtractive machining and traditional SL. Furthermore, slurry recycling helps to reduce the overall cost and material consumption.

In this study, a novel task of printing speed optimization for continuous fiber composites is investigated. Using continuous fibers is an innovative approach to reinforce products made by fused filament fabrication (FFF) additive manufacturing (AM) technology. In the printing process of composites with continuous fibers, the printing speed is critical because of its significant effect on the geometric shape of the samples, especially their corners. During optimization in this research, continuous glass fiber (CGF) and polylactic acid (PLA) filaments were utilized as reinforcing phase and matrix, respectively, and were simultaneously fed into the extrusion-based polymer 3D printer to form PLA/CGF composites. The optimization was carried out by calculating the temperature changes of the deposited rasters in the presence and absence of fibers as a first step and then determining the special relationship between the printing speeds and rasters temperature changes. Finally, the optimal and the maximum printing speed was computed based on a hypothesis, which is proved by the results of high-quality printed composites with different geometric shapes.

In this work, we proposed a novel design of microfluidic mixer utilizing the amplified Marangoni chaotic advection induced by AC continuous electrowetting of a metal droplet situated in electrolyte solution, due to the linear and quadratic voltage-dependence of flow velocity at small or large voltages, respectively. Unlike previous researchers exploiting the unidirectional surface stress with DC bias at droplet/medium interface for pumping of electrolyte where the resulting flow rate is linearly proportional to the field intensity, dominance of another kind of dipolar flow pattern caused by local Marangoni stress at the drop surface in sufficiently intense AC electric field is demonstrated by both theoretical analysis and experimental observation, which exhibits a quadratic growth trend as a function of the applied voltage. The dipolar shear stress merely appears at larger voltages and greatly enhances the mixing performance by inducing chaotic advection between the neighboring laminar flow. The mixer design developed herein, on the basis of amplified Marangoni chaotic advection around a liquid metal droplet at larger AC voltages, has great potential for chemical reaction and MEMS actuator applications, because of generating high-throughput and excellent mixing performance at the same time.

Real-time data processing and decision-making are increasingly crucial in various applications, driven by the continuous influx of data streams. Event-driven AI workflows within serverless computing environments offer a promising approach to handle these real-time demands efficiently. This paper presents a framework for simulating and analyzing the performance characteristics of such workflows. Our proposed approach utilizes simulated data with varying event rates and durations to investigate the impact on key performance metrics like latency, throughput, and resource utilization. This enables a comprehensive evaluation of the inherent trade-offs within event-driven AI systems. The key findings reveal a trade-off between latency and throughput. As the event rate increases, average processing latency generally increases while average throughput increases. Resource utilization remains relatively stable across different event rates in the simulated scenarios (e.g., 75.55\% at 2 events/second, 74.51\% at 10 events/second). This framework provides a valuable tool for understanding the performance characteristics of event-driven AI workflows and optimizing resource allocation strategies.

Space exploration introduces astronauts to many challenges such as space radiation and microgravity. Researchers have investigated vitamin C as a potential radiation mitigator and antioxidants for sustaining astronaut health. Building on our own studies, which indicate vitamin C's life-saving radioprotective effects and its potential as a radiation mitigator, our research highlights its promise, even when administered 24 h post-exposure. This is particularly relevant in scenarios where astronauts may be exposed to sudden large solar particle events, potentially resulting in lethal doses of space radiation. The success of vegetable cultivation on the International Space Station using NASA's Veggie system offers fresh, vitamin C-rich food. While approved supplements address somatic function, further research is needed to optimize vitamin C's efficacy in humans and develop antioxidant cocktails for space missions. The variable vitamin C content in vegetables underscores the necessity for utilization of artificial intelligence (AI) to assist astronauts in selecting and cultivating vitamin C-rich vegetables that are best suited to combat high levels of space radiation and microgravity. Particularly, AI algorithms can be utilized in analyzing various factors such as nutritional content, growth patterns, and cultivation methods. In conclusion, vitamin C shows significant potential for mitigating space radiation, and ongoing research aims to enhance astronaut health through optimal dietary strategies.

In recent years, graphene has received so much attention because of its superlative properties and its potential to revolutionize electronics especially in VLSI. This study analyzes the effect of single event upset (SEU) in SRAM cell which employs metal-oxide semiconductor type graphene nano-ribbon field effect transistor (MOS-GNRFET) and compares the results respectively with another SRAM cell designed in PTM 10nm FinFET node. Our simulation show there is a change in data stored in the SRAM after heavy ion strike. However, it recovers from radiation effects after 0.46 ns for GNRFET and 0.51 ns for FinFET. Since the degradation observed in Q and Qb of GNRFET SRAM are 2.7X and 2.16X as compared to PTM nano-MOSFET, we can conclude that GNRFET is less robust to single effect upset. In addition, the stability of SRAM can be improved by increasing the supply voltage VDD.

The aim of this study was to adapt a treadmill-developed method for determination of inner-cycle parameters in cross-country roller ski skating for a field application. The method is based on detecting initial and final ground-contact of poles and skis during cyclic movements. Eleven athletes skied four laps of 2.5 km at low and high endurance-intensity, using two types of skis with different rolling coefficients. Participants were equipped with inertial measurement units (IMUs) attached to their wrists and skis, while insoles with pressure sensors and poles with force measurements were used as reference systems. The method based on IMUs was able to detect more than 97% of the temporal events compared to the reference system. The inner-cycle temporal parameters had a precision ranging from 49 to 59 ms, corresponding to 3.9% to 13.7% of the corresponding inner-cycle duration. Overall, this study showed good reliability of using IMUs on athlete’s wrists and skis to determine temporal events, inner-cycle parameters and the performed sub-techniques in cross-country roller ski skating in field-conditions.

The link between language processing and motor systems has been the focus of increasing interest to Cognitive Neuroscience. Some classical papers studying Event Related Potentials (ERPs) induced by linguistic stimuli have found differences in electrophysiological activity when comparing action and non-action words; more specifically, a bigger p200 for action words. On the other hand, a series of studies have validated the use of a grip force sensor (GFS) to measure language-induced motor activity during both isolated words and sentence listening, finding that action words induce an augmentation in the grip force around 250-300 ms after the onset of the stimulus. The purpose of the present study is to combine both techniques to assess if the p200 is related to the augmentation of the grip force measured by a GFS. We measured ERP and GFS changes elicited by listening to action and non-action words while maintaining an active grasping task in 10 healthy subjects. Our results show that the amplitude of the p200 in central electrodes is correlated to the augmentation in the GFS around 300 ms induced by linguistic stimuli. To our knowledge, this is the first study where the electrophysiological activity and the changes in the grip force induced by auditory language processing are put together, opening new venues of interpretation for the sensorimotor interaction in language processing.

This paper uses event study analysis to estimate the impact of the Fed’s Quantitative Easing (QE) announcements on the mortgage market during zero lower bound period. A total of 35 QE announcements are identified and their effects are evaluated. The best-fitting IGARCH model with skewed t distribution is used to measure the QE announcement effects on daily changes of the 30-year mortgage rate, the 30-year Treasury rate and the spread between them. Announcements suggesting the start of a new round of QE reduced the mortgage rate tremendously, while the effects of further news diminished. Announcements of an increase in mortgage-backed security purchases decreased the mortgage rate more than the Treasury rate and reduced the credit risk of holding mortgage securities over Treasury securities. The long run effects of QE announcements on the mortgage rate were less than short run effects but persistent. We also find that the previous literature overestimate QE effects on interest rates in general.

An automated discharge imaging system (ADIS), a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV) technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship) and cross-section area. The ADIS was deployed at the Yu-Feng gauging station in Shimen Reservoir upper catchment, northern Taiwan. For a rigorous validation, surface velocity measurements were conducted using ADIS/LSPIV and other instruments. In terms of the averaged surface velocity, all measured results were in good agreement with small differences, i.e., 0.004 to 0.39 m/s and 0.023 to 0.345 m/s when compared to those from acoustic Doppler current profiler (ADCP) and surface velocity radar (SVR), respectively. The ADIS/LSPIV was further applied to measure surface velocities and discharges during typhoon events (i.e., Chan-Hom, Soudelor, Goni, and Dujuan) in 2015. The measured water level and surface velocity both showed rapid increases due to flash floods. The estimated discharges from ADIS/LSPIV and ADCP were compared, presenting good consistency with correlation coefficient R = 0.996 and normalized root mean square error NRMSE = 7.96%. The results of sensitivity analysis indicate that components till (τ) and roll (θ) of the camera are most sensitive parameter to affect the surface velocity using ADIS/LSPIV. Overall, the ADIS based upon LSPIV technique effectively measures surface velocities for reliable estimations of river discharges during typhoon events.

Under suitable continuity and uniqueness conditions, solutions of an α order Caputo fractional boundary value problem are differentiated with respect to boundary values and boundary points. This extends well-known results for nth order boundary value problems. The approach used is a standard technique and makes heavy use of recent results for differentiation of solutions of Caputo fractional intial value problems with respect to initial conditions and continuous dependence for Caputo fractional boundary value problems.

The fine chemical and pharmaceutical sectors are starting to advocate the use of flow chemistry due to reasons such as EHS, environmental, efficiency, cost saving and regulatory compliance. The use of trickle bed or fixed bed system could replace a batch autoclave typically used for hydrogenation reactions. However, there are few studies which detail the process from laboratory proof of concept through design to commercial realization. This study, using the production of 1,3-cyclohexanedione from catalytic hydrogenation of resorcinol as a case study demonstrates how the laboratory-scale recycle trickle bed can be used for catalyst screening and selection. Further, design data are generated by operation over a range of design superficial velocities and operating pressure that are used to derive a design correlation that is then used to specify a single stream plant at a level of definition consistent with a Preliminary Design for capital cost estimation. Finally, the further actions required in terms of data generation to increase the level of definition and confidence to a Sanction Grade or Final Design are discussed.

Background: Oscillation and pulmonary expansion (OPE) therapy can decrease postoperative pulmonary complications in a general surgical population, but its effect after cardiac surgery has not been reported, to our knowledge. We hypothesized that using an OPE device after cardiac surgery before extubation would decrease pulmonary complications. Methods: This retrospective cohort study included adults undergoing elective open cardiac surgery at our institution from January 2018 through January 2019, who had an American Society of Anesthesiologists score of 3 or greater. For mechanically ventilated patients after cardiac surgery, a new OPE protocol was adopted July 1, 2019, comprising an initial 10-minute OPE treatment administered in-line with the ventilator circuit, then continued treatments for 48 hours after extubation. Severe postoperative respiratory complications (primary outcome measure) included need for antibiotics, increased supplemental oxygen use, and prolonged hospital length of stay (LOS). Demographic, clinical, and outcomes data were compared for patients before (usual care with postextubation hyperinflation) and after protocol implementation (OPE). Results: Of 104 patients, 54 patients received usual care, and 50 received OPE. Usual-care recipients had more men (74% vs 62%; P=.19) and were older (median, 70 vs 67 years; P=.009) than OPE recipients. The OPE group had a significantly shorter hospital LOS than the usual-care group (mean, 6.2 vs 7.4 days; P=.04). Other measures improved with OPE but did not reach significance: shorter ventilator duration (mean, 0.6 vs 1.1 days with usual care; P=.06) and shorter LOS in the intensive care unit (mean, 2.7 vs 3.4 days; P=.06). On multivariate analysis, intensive care unit LOS was significantly shorter for the OPE group (mean difference, 0.85 days; 95% CI, 1.65 to 0.06; P=.04). The OPE group had a lower percentage of postoperative complications (10% vs 20%). Conclusion: OPE therapy after cardiac surgery is associated with decreased ICU and hospital LOS.

Autonomic dysregulation is associated with cardiovascular consequences in obstructive sleep apnea (OSA). This study aimed to investigate the effect of acute continuous positive airway pressure (CPAP) treatment on autonomic activity and to identify factors contributing to the heart rate variability (HRV) changes in OSA. Frequency domain HRV parameters were calculated and compared between the baseline polysomnography and during the CPAP titration in 402 patients with moderate to severe OSA. There were significant reductions in total power, very low-frequency band power, low-frequency band power, and high-frequency band power during the CPAP titration as compared to the baseline polysomnography. This tendency was more pronounced in men than in women, and in patients with severe OSA than those with moderate OSA. Multivariate analysis found that changes in apnea-hypopnea index and oxygen saturation were significantly associated with changes in sympathetic and parasympathetic activity, respectively. This study demonstrated that HRV parameters significantly changed during the CPAP titration, indicating a beneficial effect of CPAP in restoration of sympathetic and parasympathetic hyperactivity in OSA. Prospective longitudinal studies should determine whether long-term CPAP treatment aids in maintaining the long-lasting improvement of the autonomic functions, thereby contributing to the prevention of cardiovascular and cerebrovascular diseases in patients with OSA.

Abstract: — management of the Continuous Stirred Tank Reactor (CSTR) operation has proven to be a difficult undertaking due to the complex and ever-changing nature of its behavior, which is both nonlinear and dynamic, which is further compounded by its instability. Attempts have been made to use Proportional-Integral-Derivative (PID) controllers for such processes, but the performance is often poor due to the inherent limitations of the controllers. In contrast, heuristic algorithms have been employed to tune the controllers, but these methods can only provide optimal performance for a restricted range of parameter variations. This paper presents a novel approach that involves using a conventional gain scheduling PID controller in combination with a fuzzy gain scheduling (FGS) controller method to calibrate set-point weighted PID controllers for the CSTR process. The paper outlines the design of the controllers and evaluates their performance by subjecting them to varying input signals within the given bandwidth.

Soil washing is a rapid and efficient method for heavy metals removal. In this study, in order to prove the feasibility of leaching remediation technology for remediation of lead-contaminated soil around a smelter, intermittent oscillating washing tests and extensive continuous tests were conducted to remediate Pb contaminated soils. Plant growth tests with wheat (Bainong Aikang 58) were also conducted to investigate the effects of leaching on plant growth and the Pb content of stems and leaves. The results revealed that in soils, Pb mainly exists in the forms of PbCO3, Pb5(PO4)3Cl2, PbSO4, and in small amounts of PbS. The intermittent oscillating washing tests showed that a lower liquidsolid(L/S) ratio and a higher leaching temperature and time were related to increased Pb removal efficiency. The Pb removal efficiency rates in the oscillating washing tests and the extensive continuous tests were 72.50% and 69.94%, respectively. In addition, the chemical and physical characteristics of the soil were basically unchanged after the leaching regimes. The results of the plant growth tests showed that the leaching regimes did not have a negative effect on the survival rate and growth of the wheat, and the heavy metal contents of the stem and leaf were effectively reduced. This study provides a reference for the large-scale remediation of contaminated soils.

Background: To examine the effects of continuous positive airway pressure (CPAP) therapy on central corneal thickness (CCT) in patients with obstructive sleep apnea (OSA) followed for 12 months. Methods: Participants were patients with OSA and an indication for CPAP who were enrolled after diagnosis and graded according to the apnea hypopnea index (AHI) determined in an overnight polysomnography. Each subject underwent a full ophthalmologic examination including ultrasound pachymetry (USP) with an SP3000 USP (Tomey Inc) at the time points just before CPAP onset, and after 3 and 12 months of CPAP treatment. Results: The study sample comprised 40 eyes of 20 patients. Adherence to CPAP was adequate (5.23 ± 1.77 h/night). After receiving 12 months of CPAP, positive correlation was observed between hours of CPAP use and CCT (r = 0.382, p = 0.015). Intermittent hypoxemia pre-CPAP or hypoxia severity pre-CPAP were also positively correlated with the CCT recorded after 12 months of CPAP (r = 0.394, p = 0.012; r = 0.324, p = 0.041). Significant correlations were, however, not observed after only 3 months of CPAP treatment. In addition, compared to measurements at 3 months, intraocular pressure was higher after 12 months of treatment. Conclusions: Our findings indicate that 12 months of correct adherence to CPAP are needed to detect significant CCT thickening, and that the greater the pre-CPAP hypoxemia, the greater the capacity for an increase in CCT to be produced in response to 12 months of this therapy.

In this work we present a study of impact of textile materials used in health care facilities on microbial colonization of textile surfaces. Polypropylene yarns doped with silver phosphate glass and zinc pyrithione were prepared and used for production of bed sheets and clothing for healthcare personnel. Subsequently measurements of airborne particles and viable microorganisms on given textiles were conducted in a private surgery clinic during 3 weeks comparing the counts of viable microorganisms before and after replacing of staff clothing and bedding on examination and surgical bed with said polypropylene cloth. In this study a significant reduction of viable airborne fungi and viable microorganisms on monitored textiles was observed by multiple methods.

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process but, on the other hand, the electrical system has to face the problem of unbalances. Renewable Energies Sources (RES) are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced

In this paper, Hybrid Automata, which is a formal model for hybrid systems, has been introduced. A summary of its theory is presented and some of its special and important classes are listed and some properties that can be studied and checked for it are mentioned. Finally, the purposes of use, the most widely used areas, and the tools that provide H.A. Support are addressed.

The technique of superposition of motions in the space of Lagrange variables is described, which allows us to obtain the equations of combined motion by replacing the Lagrange variables of superimposed (external) motion with Euler variables of nested (internal) motion. The components of velocity and acceleration in the combined motion obtained as a result of differentiating the equations of motion in time coincide with the results of vector addition of the velocities and accelerations of the particles involved in the superimposed motions at each moment of time. Examples of motion and superposition descriptions for absolutely solid and deformable bodies with equations for the main kinematic characteristics of motion, including for robot manipulators with three independent drives, pressing with torsion, bending with tension, and cross– helical rolling, are given. For example, given the fragment of calculation of forces in the kinematic pairs shown the advantages of the description of motion in Lagrangian form for the dynamic analysis of lever mechanisms, allows to determine the required external exposure when performing the energy conservation law at any time in any part of the mechanism.

In this paper, a high-performance antenna array system model is presented to analyze moving-object-skin-returns and track them in the presence of stationary objects using frequency modulated continuous wave (FMCW). The main features of the paper are bonding the aspects of antenna array and electromagnetic (EM) wave multi-skin-return modeling and simulation (M&S) with the aspects of algorithm and measurement/tracking system architecture. The M&S aspect models both phase and amplitude of the signal waveform from a transmitter to the signal processing in a receiver. In the algorithm aspect, a novel scheme for FMCW signal processing is introduced by combining time- and frequency-domain methods, including a vector moving target indication filter and a vector direct current canceller in time-domain, and a constant false alarm rate detector and a mono-pulse digital beamforming angle tracker in frequency-domain. In addition, unlike previous designs of using M×N fast Fourier transform (FFT) for an M×N array, only four FFTs are used, which tremendously saves time and space in hardware. With the presented model, the detection of the moving-target-skin-return in stationary objects under a noisy environment is feasible. Therefore, to track long range and high-speed objects, the proposed technique is promising. Using a scenario having 1) a target with 17 dBm2 radar cross section (RCS) at about 40 km range with 5.93 Mach speed and 11.6 dB post processing signal to noise ratio, and 2) a strong stationary clutter with 37 dBm2 RCS located at the proximity of the target, it demonstrates that the root-mean-square errors of range, angle and Doppler measurements are about 26 meters, 0.68 degree and 1100 Hz, respectively.

To cope the high glycemic variability (GV) is crucial in the management of multiple daily insulin (MDI) in diabetes. We compared the effect of low dose metformin 750mg/d adding vildagliptin 100mg/d (DPP4+LMET) or the high dose metformin 1500mg/d (HMET), in type 2 diabetes (T2D) with MDI, evaluating GV by continuous glucose monitoring (CGM). Single center, open-label, 12 weeks - 2 period crossover design. Twenty T2D with inadequately controlled (7.0% <HbA1c ≦9.0%) with MDI + LMET were enrolled. Primary endpoints were GV and hypoglycemia derived from CGM performed after each 12 weeks treatment periods. There was no significant difference in HbA1c, body weight changes, total daily dose of insulin. DPP4+LMET compared to the HMET, significantly reduced the calculated GV value, mean (7.15±1.3 vs 7.82±1.6, p<0.05), standard deviation (1.78±0.55 vs 2.27±1.11, p=0.03), continuous overlapping net glycemic action (6.44±1.28 vs 7.12±1.69, p<0.05), J-Index (26.7±11.0 vs 34.9±19.8, p<0.05), high blood glucose index (3.01±1.96 vs. 6.73±4.85, p=0.02), and mean amplitude of glycemic excursions (4.53±1.35 vs 5.50±2.34, p=0.03). The GV metrics with hypoglycemia and nocturnal hypoglycemia were not significantly different. DPP4+LMET decreased GV associated with hyperglycemia. Adding DPP4 inhibitor to the lower dose of metformin is an alternative approach to the stable GV in MDI.

In this paper, we discuss voltage control method for buck converter operating in continuous conduction mode (CCM) using analog feedback system. The aim of this work is to control the output voltage of a buck converter during the variation in load current. This is obtained using analog feedback made with operational amplifier (Opamp). However, the same technique can be applied to other DC-DC converters (e.g boost, buck-boost, cuk converter, etc) in CCM mode, but for the purpose of analysis buck converter is chosen as an example.

Using recent developments in econometrics and computational statistics we consider the estimation of the instantaneous rate of asset return process when the underlying Data Generating Mechanism (DGM) is an Ornstein-Uhlenbeck process, driven by fractional noise, and sampled at fixed intervals of length h. To address the problem we adopt throughout the paper an exact discretization approach. This enable us to exploit the fact that a flow sampling scheme arises naturally when observing the DGM. For, while the instantaneous rate of return process is unobservable at points in time, its time integral over successive observations is observable since it equals the increment of log-prices. Exact discretization delivers an ARIMA(1,1,1) model for log-prices with a fractional driving noise. Building on the resulting exact discretization formulae and covariance function, a new Markov Chain Monte Carlo (MCMC) scheme is proposed and we examine the properties of both the time and frequency domain likelihoods / posteriors through Monte Carlo. For the exact discrete model we adopt a general sampling interval of length h. This allow us to determine the optimal choice of h independent of the sample size. An empirical application using high frequency stock price data is presented showing the relevance of aggregation over time issues in modelling asset prices.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

Electroencephalography (EEG) enabled earbuds represent a promising frontier in evaluating brain activity beyond traditional laboratory settings. However, these devices need more comprehensive characterization before widespread health related usage. A toolbox was developed to facilitate and expand the assessment of ear-EEG devices. The first component of this toolbox is a desktop application (“EaR-P Lab”) that integrates EEG validation paradigms. This application is designed to be used with EEG amplifiers compatible with the Lab Streaming Layer (LSL) protocol. The second element of the toolbox introduces an adaptation of the phantom testing concept, to the domain of ear-EEG by using 3D ear scans of the tested individuals, allowing controlled assessment of ear-EEG devices. The EEG paradigms were validated for scalp acquisitions and applied on ear-EEG measurements, under wet ear electrode conditions. The ear-EEG phantom was successful in measuring useful metrics in EEG system characterization, highlighting performance differences within the electrodes on the tested earpieces. This knowledge was factored in to select an optimal electrode reference which resulted in increased response power on the auditory steady-state response (ASSR). Through this work, an ear-EEG evaluation toolkit is made available with the intention to enable future and systematic assessment of novel ear-EEG devices from the hardware to the neural signals.

The study investigates the effect of corporate governance characteristics on the financial performance of 124 Indian-listed companies that have undergone mergers and acquisitions during 2014–2020. It employs several performance measures, such as short-term capital market performance, long-term capital market performance, accounting-based and market-based measures, and firm-level control factors. The study found board size to be a positive and significant factor affecting short-term market performance. Further, it also documents weak linkages with other corporate governance variables like board independence, CEO duality, etc. Regarding control variables, leverage, the company's age, price-to-book ratio, and research and development expenses significantly impact the acquiring companies' financial returns. The study's findings add to our understanding of corporate governance's impact on performance in cases such as mergers and acquisitions.

Cannabidiol, is the first cannabis-derived drug approved for the treatment of Lennox-Gastaut syndrome (LGS), Dravet syndrome (DS), and Tuberous Sclerosis Complex (TSC). The current study performed a descriptive analysis followed by a disproportionality analysis of potential adverse events caused by CBD extracted from the database VigiBase®. Further, biological plausibility of the association between CBD and the serotonin 5-HT1A receptor, as possible cause of adverse events was analyzed and discussed. Data were extracted from VigiBase® database using VigiLyze® signal detection and signal management tool. Adverse events in VigiBase® reports were coded using MedDRA, version 19 of Preferred Terms (PTs). Data were uploaded into SPSS software and analyzed via disproportionality analysis. Statistically significant disproportionality signals for CBD were found for “weight decreased” 5.19 (95% CI: 4.54 - 5.70), “hypophagia” 3.68 (95% CI: 3.22 - 5.27), and “insomnia” 1.6 (95% CI: 1.40 - 1.83). Positive IC025 values were found for “weight decreased” (2.2), “hypophagia” (1.3), and “insomnia” (0.5), indicating a surplus of reported cases. CBD’s interactions with 5-HT1A serotonin receptors may offer a potential biological explanation for the occurrence of insomnia in patients. It is noteworthy that the risk profiles mentioned in the prescribing information for CBD as antiepileptic agent by regulatory agencies showed disparities specifically related to the adverse event “insomnia”.

As a follow-up to previously published research, this paper examines the responses of a group (n=185) of professional and volunteer firefighters directly involved in fighting extreme wildfire events of the 2017 summer season in Portugal. The data used in this research stem from two open-ended questions in response to a web-based survey: i) lessons learned and ii) proposals for improving the Civil Protection service. The replies were qualitatively examined. Participants exhibit incomplete awareness of extreme wildfires, and their only answers are i) the improvement of both the current organization and the technical and human resources of the suppression model (question 1), and ii) the increase in human and material resources, the single command, the in-dependence from Civil Protection and the valorization of experience, as the main aspects for the improvement of Civil Protection (question 2). Little attention is given to prevention and to the concept of control capacity, respectively. The answers to both questions showcase scarce knowledge of the paradigm shift in reference to the prevention model. The ratios of missing re-plies to both questions were 32.43% and 47.57%, respectively. The work stands out for its novelty and originality, as to our knowledge there is a total lack of similar works in all European Union literature.

Home care centers face both an increase in demand and many variations during the execution of routes, compromising the routes initially planned: robust solutions are not effective enough, it is necessary to move on to resilient approaches. We create a close to reality use case supported by interviews of staff at home health care centers, where caregivers are faced with unexpected events that compromise their initial route. We model, analyze and compare three resilient approaches to deal with these disruptions: a baseline approach without any collaboration, a distributed collaborative approach, and a centralized collaborative approach, where we propose a centralization and sharing of information to improve local decision-making. The latter reduces the number of late arrivals by 11% and the total time of late arrival by 21%, and also halves the number of routes exceeding the end of work time (contrary to the distributed collaborative approach, due to the time wasted reaching colleagues). The use of a device, such as a smartphone application, to centralize and share information thus allows better mutual assistance between caregivers. Moreover, we highlight several possible openings like the coupling of simulation and optimization to propose a more resilient approach.

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.