New Perspectives for Workflow Analysis in the Health Italian Sector through Discrete Event Simulation: The Case of a Department of Laboratory Medicine

The management systems currently used in the Italian healthcare sector provide fragmented and incomplete information on this system and are generally unlikely to give accurate information on the performances of the healthcare processes. The present paper introduces a combined discrete event simulation (DES)/business process management (BPM) approach as innovative means to study the workflow of the activities within the Department of Laboratory Medicine of the “San Paolo” Hospital in Naples (Italy). After a first “As-Is” analysis to identify the current workflows of the system and to gather information regarding its behaviour, a following DES-based “What-If” analysis is implemented to figure out alternative work hypotheses in order to highlight possible modifications to the system’s response under varying operating conditions and improve its overall performances. The structure of the simulation program is explained and the results of the scenario analysis are discussed. The paper starts with a brief exploration of the use of DES in healthcare and ends with general observations on the subject.

Category Theoretic Properties of the A. Rényi and C. Tsallis Entropies

The problem of embedding the Tsallis, Rényi and generalized Rényi entropies in the framework of category theory and their axiomatic foundation is studied. To this end, we construct a special category MES related to measured spaces. We prove that both of the Rényi and Tsallis entropies can be imbedded in the formalism of category theory by proving that the same basic partition functional that appears in their definitions, as well as in the associated Lebesgue space norms, has good algebraic compatibility properties. We prove that this functional is both additive and multiplicative with respect to the direct product and the disjoint sum (the coproduct) in the category MES, so it is a natural candidate for the measure of information or uncertainty. We prove that the category MES can be extended to monoidal category, both with respect to the direct product as well as to the coproduct. The basic axioms of the original Rényi entropy theory are generalized and reformulated in the framework of category MES and we prove that these axioms foresee the existence of an universal exponent having the same values for all the objects of the category MES. In addition, this universal exponent is the parameter, which appears in the definition of the Tsallis and Rényi entropies. It is proved that in a similar manner, the partition functional that appears in the definition of the Generalized Rényi entropy is a multiplicative functional with respect to direct product and additive with respect to the disjoint sum, but its symmetry group is reduced compared to the case of classical Rényi entropy.

Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?

Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)- averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histological-cytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.

Novel Micromixer with Complex 3D-Shape Inner Units: Design, Simulation and Additive Manufacturing

In this paper,a novel micromixer with complex 3D-shape inner units was put forward and fabricated by metal Additive Manufacturing(AM).The design of the micromixer combined the constraints of selective laser melting technology and the factors to improve mixing efficiency.Villermaux-Dushman reaction system and Compute Fluid Design(CFD)simulation were conducted to investigate the performance and the mechanism of this novel micromixer to improve mixing efficiency.The research found that the best mixing efficiency of this novel micromixer could be gained when the inner units divided fluid into five pieces with a uniform volume.Compared with a conventional micromixer without obstacle in the channel,the micromixer designed in this research achieved higher mixing efficiency and reduce the pressure drop by 10.34%.The mixing behaviour in this novel micromixer was discussed,which mainly contains two types:collisions and swirls.Via collisions,the fluid micro masses would hit each other directly,which broke the boundaries of micro masses and promoted the interchange of species in the whole flow field.In swirls,the fluid micro masses were drawn into thin and long slices,which increased the size of the contact area and enhanced molecule diffusion.Finally,the application scheme of this novel micromixer was briefly discussed.

Portable and Flexible Communication Protocol Stacks for Smart Metering Projects

Wireless M-Bus according to EN-13757-4 is a major contender for local metrological network （LMN） in smart metering and smart grid applications, as it holds the promise of flexible and optimized solutions. It enjoys wide popularity in continental Europe, but increasingly in many other regions of the world. However, Wireless M-Bus is characterized by a wide variety of different operation modes （C-, F-, N-, P-, Q-, R-, S-, and T-modes）, which work in different frequency bands （i.e., 868 MHz, 433 MHz, and 169 MHz）. Its application layer can be enhanced by extensions, being defined from vendor alliances, like the Open Metering System （OMS） Group, or from national bodies.

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G context.However,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network connectivity.To address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction.In light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels.The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood.Besides,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative environment.The proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River inMalaysia.The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction

Post-translational modifications(PTMs)have key roles in extending the functional diversity of proteins and,as a result,regulating diverse cellular processes in prokaryotic and eukaryotic organisms.Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes.Disorders in the phosphorylation process lead to multiple diseases,including neurological disorders and cancers.The purpose of this review is to organize this body of knowledge associated with phosphorylation site(p-site)prediction to facilitate future research in this field.At first,we comprehensively review all related databases and introduce all steps regarding dataset creation,data preprocessing,and method evaluation in p-site prediction.Next,we investigate p-site prediction methods,which are divided into two computational groups:algorithmic and machine learning(ML).Additionally,it is shown that there are basically two main approaches for p-site prediction by ML:conventional and end-to-end deep learning methods,both of which are given an overview.Moreover,this review introduces the most important feature extraction techniques,which have mostly been used in p-site prediction.Finally,we create three test sets from new proteins related to the released version of the database of protein post-translational modifications(dbPTM)in 2022 based on general and human species.Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release,distinct from those in the dbPTM 2019 release,reveals their limitations.In other words,the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research pape.

3D imaging lipidometry in single cell by in-flow holographic tomography

The most recent discoveries in the biochemical field are highlighting the increasingly important role of lipid droplets(LDs)in several regulatory mechanisms in living cells.LDs are dynamic organelles and therefore their complete characterization in terms of number,size,spatial positioning and relative distribution in the cell volume can shed light on the roles played by LDs.Until now,fluorescence microscopy and transmission electron microscopy are assessed as the gold standard methods for identifying LDs due to their high sensitivity and specificity.However,such methods generally only provide 2D assays and partial measurements.Furthermore,both can be destructive and with low productivity,thus limiting analysis of large cell numbers in a sample.Here we demonstrate for the first time the capability of 3D visualization and the full LD characterization in high-throughput with a tomographic phase-contrast flow-cytometer,by using ovarian cancer cells and monocyte cell lines as models.A strategy for retrieving significant parameters on spatial correlations and LD 3D positioning inside each cell volume is reported.The information gathered by this new method could allow more in depth understanding and lead to new discoveries on how LDs are correlated to cellular functions.

Approximate Dynamic Programming for Stochastic Resource Allocation Problems

A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.

A Design Concept and Kinematic Model for a Soft Aquatic Robot with Complex Bio-mimicking Motion

Fish mortality assessments for turbine passages are currently performed by live-animal testing with up to a hundred thousand fish per year in Germany.A propelled sensor device could act as a fish surrogate.In this context,the study presented here investigates the state of the art via a thorough literature review on propulsion systems for aquatic robots.An evaluation of propulsion performance,weight,size and complexity of the motion achievable allows for the selection of an optimal concept for such a fish mimicking device carrying the sensors.In the second step,the design of a bioinspired soft robotic fish driven by an unconventional drive system is described.It is based on piezoceramic actuators,which allow for motion with five degrees of freedom(DOF)and the creation of complex bio-mimicking body motions.A kinematic model for the motion’s characteristics is developed,to achieve accurate position feedback with the use of strain gauges.Optical measurements validate the complex deformation of the body and deliver the basis for the calibration of the kinematic model.Finally,it can be shown,that the calibrated model presented allows the tracking of the deformation of the entire body with an accuracy of 0.1 mm.

Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

Research at the University of Kent and Subsequent Research Activities

在应用光学组，承担的作者研究活动在坎特伯雷的肯特的大学被考察在他从 19881992 和 19941996 的在那里的时间期间，由最近的研究的一篇摘要列在后面。兴趣的区域是高灵巧戒指共鸣器，悦耳的光过滤器，玻璃和聚合物里的新奇光纤维栅栏传感器， femtosecond 激光铭文和 micromachining，监视的环境污染，激活的 Pd 在硅上拍摄的氢和硅晶片上的杂质测量。

H_(∞)-PID distributed control for output leader-tracking and containment of heterogeneous MASs with external disturbances

This paper addresses both the output leader-tracking and output containment control prob-lems for heterogeneous linear high-order Multi-Agent Systems(MASs)sharing information over a directed communication topology and subject to external unknown disturbances.To solve these control problems,we propose a robust fully distributed Proportional-Integral-Derivative(PID)control strategy,equipped with a filter on the derivative action that allows obtaining both a simpler closed-loop formulation,without the need of the descriptor transformation,and good tracking performances in the case of fast reference signal behaviours.The stability analysis is analytically proven via the Lyapunov theory and the H_(∞) approach.The derived robust stability conditions are expressed as a set Linear Matrix Inequalities(LMIs)whose solution provides the proper tuning of the robust PID control gains.Numerical simulations confirm the effectiveness and robustness of the proposed approach in solving both the output leader-tracking and output containment control problems.

Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

Internet of Things(IoT)networks leverage wireless communication protocols,which adversaries can exploit.Impersonation attacks,injection attacks,and flooding are several examples of different attacks existing in Wi-Fi networks.Intrusion Detection System(IDS)became one solution to distinguish those attacks from benign traffic.Deep learning techniques have been intensively utilized to classify the attacks.However,the main issue of utilizing deep learning models is projecting the data,notably tabular data,into an image.This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network(CNN)models,EfficientNet.We define the particular sequence of placing the attribute values in a grid that would be captured as an image.Combining the most important subset of attributes and EfficientNet,we aim for an accurate and lightweight IDS module deployed in IoT networks.We examine the proposed model using the Wi-Fi attacks dataset,called the AWID2 dataset.We achieve the best performance by a 99.91%F1 score and 0.11%false-positive rate.In addition,our proposed model achieved comparable results with other statistical machine learning models,which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy.

SBOOSP for Massive Devices in 5G WSNs Using Conformable Chaotic Maps

The commercialization of the fifth-generation(5G)wireless network has begun.Massive devices are being integrated into 5G-enabled wireless sensor networks(5GWSNs)to deliver a variety of valuable services to network users.However,there are rising fears that 5GWSNs will expose sensitive user data to new security vulnerabilities.For secure end-to-end communication,key agreement and user authentication have been proposed.However,when billions of massive devices are networked to collect and analyze complex user data,more stringent security approaches are required.Data integrity,nonrepudiation,and authentication necessitate special-purpose subtree-based signature mechanisms that are pretty difficult to create in practice.To address this issue,this work provides an efficient,provably secure,lightweight subtreebased online/offline signature procedure(SBOOSP)and its aggregation(Agg-SBOOSP)for massive devices in 5G WSNs using conformable chaotic maps.The SBOOSP enables multi-time offline storage access while reducing processing time.As a result,the signer can utilize the pre-stored offline information in polynomial time.This feature distinguishes our presented SBOOSP from previous online/offline-signing procedures that only allow for one signature.Furthermore,the new procedure supports a secret key during the pre-registration process,but no secret key is necessary during the offline stage.The suggested SBOOSP is secure in the logic of unforgeability on the chosen message attack in the random oracle.Additionally,SBOOSP and Agg-SBOOSP had the lowest computing costs compared to other contending schemes.Overall,the suggested SBOOSP outperforms several preliminary security schemes in terms of performance and computational overhead.

An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy concerns.Two users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key agreement.Most of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and cost-prohibitive.In order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic maps.The new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password table.The projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT architectures.Test results show that our new technique is resistant to password guessing attacks since it does not use a password.Furthermore,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no harm.Finally,our new technique offers better security features than the techniques currently available in the literature.

Fundamental Investigations in the Design of Five-Axis Nanopositioning Machines for Measurement and Fabrication Purposes

The majority of nanopositioning and nanomeasuring machines(NPMMs)are based on three independent linear movements in a Cartesian coordinate system.This in combination with the specific nature of sensors and tools limits the addressable part geometries.An enhancement of an NPMM is introduced by the implementation of rotational movements while keeping the precision in the nanometer range.For this purpose,a parameter-based dynamic evaluation system with quantifiable technological parameters has been set up and employed to identify and assess general solution concepts and adequate substructures.Evaluations taken show high potential for three linear movements of the object in combination with two angular movements of the tool.The influence of the additional rotation systems on the existing structure of NPMMs has been investigated further on.Test series on the repeatability of an NPMM enhanced by a chosen combination of a rotary stage and a goniometer setup are realized.As a result of these test series,the necessity of in situ position determination of the tool became very clear.The tool position is measured in situ in relation to a hemispherical reference mirror by three Fabry-Perot interferometers.FEA optimization has been used to enhance the overall system structure with regard to reproducibility and long-term stability.Results have been experimentally investigated by use of a retroreflector as a tool and the various laser interferometers of the NPMM.The knowledge gained has been formed into general rules for the verification and optimization of design solutions for multiaxial nanopositioning machines.