Data Acquisition and Signal Processing

Recommender systems can effectively provide personalized recommendations for products, services, or content by analyzing user behavior, preferences, and historical data. Leveraging machine learning and artificial intelligence technologies, these systems continuously learn and adapt to user preferences over time. This research proposed a novel model designed to enhance the accuracy and efficiency of recommender systems. The proposed model integrates the useritem interaction network, the user social network, and the item collaborative similarity network, while also employing an adaptive self-attention mechanism in the prediction layer. This mechanism enables the learning of adaptive semantic weights and focuses on key user-item interactions, ultimately leading to significant improvements in predicting user preferences. Experimental results on real-world datasets demonstrate that the proposed model outperforms the Improved Graph Convolutional Network for Collaborative Filtering model. The Normalized Discounted Cumulative Gain and Recall metrics show improvements of 3.7% and 4.9%, respectively, over the baseline. These results clearly indicate that combining users' social information, item similarity relationships, and a selfattention mechanism can significantly enhance recommender system performance.

With the increasing traffic in wireless networks, the need for new techniques to improve connectivity, bandwidth, and spectrum access is essential. The combination of massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) technologies has been proposed as a promising solution to meet the increasing data rate demand, using the millimeter wave (mmWave) band and improving system performance. In this research, a mmWave transmission method based on NOMA technology and massive MIMO with a cooperative structure and distributed antenna is presented. The problem of optimizing the total rate in the proposed system is considered under the constraints of the same power and total power of the base station. In order to reduce the complexity of the proposed method, the maximum ratio combining method based on the repetitive strategy (MRC-RS) was used. The main problem in achieving multiuser gain in massive MIMO systems is the dependence on channel state information (CSI). To solve this problem, insertion division multiple access (IDMA) and data-aided channel estimation (DACE) were proposed. Simulation results show that the proposed method performs better than other previous methods in terms of energy efficiency, and spectrum efficiency. Also, the proposed method has achieved significant performance against the pilot contamination problem and reduced complexity in the receiver.

A wide range of nucleon and nuclear structure experiments in Jefferson Lab's Hall A require precise, continuous measurements of the polarization of the electron beam. In our Compton polarimeter, electrons are scattered off photons in a Fabry-Perot cavity; by measuring an asymmetry in the integrated signal of the scattered photons detected in a GSO crystal, we can make non-invasive, continuous measurements of the beam polarization. Our goal is to achieve 1% statistical error within two hours of running. We discuss the design and commissioning of an upgrade to this apparatus, and report preliminary results for experiments conducted at beam energies from 3.5 to 5.9 GeV and photon rates from 5 to 100 kHz.

In May 2012, the Q p Weak collaboration completed a two year measurement program to determine the weak charge of the proton Q p W = 1 -4 sin 2 θ W at the Thomas Jefferson National Accelerator Facility (TJNAF). The experiment was designed to produce a 4.0 % measurement of the weak charge, via a 2.5 % measurement of the parity violating asymmetry in the number of elastically scattered 1.165 GeV electrons from protons, at forward angles. At the proposed precision, the experiment would produce a 0.3 % measurement of the weak mixing angle at a momentum transfer of Q 2 = 0.026 GeV 2 , making it the most precise stand alone measurement of the weak mixing angle at low momentum transfer. In combination with other parity measurements, Q p Weak will also provide a high precision determination of the weak charges of the up and down quarks. At the proposed precision, a significant deviation from the Standard Model prediction could be a signal of new physics at mass scales up to 6 TeV, whereas agreement would place new and significant constraints on possible Standard Model extensions at mass scales up to 2 TeV. This paper provides an overview of the physics and the experiment, as well as a brief look at some preliminary diagnostic and analysis data.

The Q p weak experiment will measure the parity-violating elastic e-p scattering asymmetry to extract the weak charge of the proton. The experiment employs a toroidal magnet to focus electrons scattered at 8 • ± 2 • , corresponding to Q 2 ∼ 0.03 (GeV/c) 2 , on eight Čerenkov detectors located in the focal plane of the spectrometer. Since the asymmetry is proportional to Q 2 , it is crucial to obtain an accurate measure of the acceptance-averaged value of Q 2 . A tracking system will be used in a low-rate counting mode, allowing individual events to be observed. This will enable a determination of the average Q 2 by measuring the scattering angle and interaction vertex, for mapping the response across the surface of the Čerenkov detectors, and for the dilution of the Čerenkov detector signal by background.

The Q[subscript weak] experiment has measured the parity-violating asymmetry in e⃗ p elastic scattering at Q[superscript 2] = 0.025 (GeV/c)[superscript 2], employing 145  μA of 89% longitudinally polarized electrons on a 34.4 cm long liquid hydrogen target at Jefferson Lab. The results of the experiment’s commissioning run, constituting approximately 4% of the data collected in the experiment, are reported here. From these initial results, the measured asymmetry is A[subscript ep]= −279±35 (stat) ± 31 (syst) ppb, which is the smallest and most precise asymmetry ever measured in e⃗ p scattering. The small Q[superscript 2] of this experiment has made possible the first determination of the weak charge of the proton Q[p over W] by incorporating earlier parity-violating electron scattering (PVES) data at higher Q[superscript 2] to constrain hadronic corrections. The value of Q[p over W] obtained in this way is Q[p over W](PVES) = 0.064 ± 0.012, which is in good agreement with the standa...

In May 2012, the QpWeak collaboration completed a two year measurement program to determine the weak charge of the proton QpWeak = (1 - 4 sin2θW at the Thomas Jefferson National Accelerator Facility (TJNAF). The experiment was designed to produce a 4. 0 % measurement of the weak charge, via a 2. 5 % measurement of the parity violating asymmetry in the number of elastically scattered 1. 165 GeV electrons from protons, at forward angles. At the proposed precision, the experiment would produce a 0. 3 % measurement of the weak mixing angle at a momentum transfer of Q2 = 0. 026 GeV2, making it the most precise stand alone measurement of the weak mixing angle at low momentum transfer. In combination with other parity measurements, QpWeak will also provide a high precision determination of the weak charges of the up and down quarks. At the proposed precision, a significant deviation from the Standard Model prediction could be a signal of new physics at mass scales up to ≃ 6 TeV, whereas ag...

We report the measurement of the parity-violating asymmetry for the inelastic scattering of electrons from the proton, at Q2 = 0.082 GeV2 and W = 2.23 GeV, above the resonance region. The result A_Inel = −13.5 ± 2.0(stat) ± 3.9(syst) ppm agrees with theoretical calculations, and helps to validate the modeling of the γ Z interference structure functions Fγ Z 1 and Fγ Z 2 used in those calculations, which are also used for determination of the two-boson exchange γ -Z box diagram (γ Z ) contribution to parity-violating elastic scattering measurements. A positive parity-violating asymmetry for inclusive π− production was observed, as well as positive beam-normal single-spin asymmetry for scattered electrons and a negative beam-normal single-spin asymmetry for inclusive π− production

The Qweak experiment, which took data at Jefferson Lab in the period 2010 - 2012, will precisely determine the weak charge of the proton by measuring the parity-violating asymmetry in elastic e-p scattering at 1.1 GeV using a longitudinally polarized electron beam and a liquid hydrogen target at a low momentum transfer of Q2 = 0.025 (GeV/c)2. The weak charge of the proton is predicted by the Standard Model and any significant deviation would indicate physics beyond the Standard Model. The technical challenges and experimental apparatus for measuring the weak charge of the proton will be discussed, as well as the method of extracting the weak charge of the proton. The results from a small subset of the data, that has been published, will also be presented. Furthermore an update will be given of the current status of the data analysis

The Jefferson Lab Q weak experiment determined the weak charge of the proton by measuring the parityviolating elastic scattering asymmetry of longitudinally polarized electrons from an unpolarized liquid hydrogen target at small momentum transfer. A custom apparatus was designed for this experiment to meet the technical challenges presented by the smallest and most precise ep asymmetry ever measured. Technical milestones were achieved at Jefferson Lab in target power, beam current, beam helicity reversal rate, polarimetry, detected rates, and control of helicity-correlated beam properties. The experiment employed 180 µA of 89% longitudinally polarized electrons whose helicity was reversed 960 times per second. The electrons were accelerated to 1.16 GeV and directed to a beamline with extensive instrumentation to measure helicitycorrelated beam properties that can induce false asymmetries. Møller and Compton polarimetry were used to measure the electron beam polarization to better than 1%. The electron beam was incident on a 34.4 cm liquid hydrogen target. After passing through a triple collimator system, scattered electrons between 5.8 • and 11.6 • were bent in the toroidal magnetic field of a resistive copper-coil magnet. The electrons inside this acceptance were focused onto eight fused silica Čerenkov detectors arrayed symmetrically around the beam axis. A total scattered electron rate of about 7 GHz was incident on the detector array. The detectors were read out in integrating mode by custom-built low-noise pre-amplifiers and 18-bit sampling ADC modules. The momentum transfer Q 2 = 0.025 GeV 2 was determined using dedicated low-current (∼100 pA) measurements with a set of drift chambers before (and a set of drift chambers and trigger scintillation counters after) the toroidal magnet.

The Jefferson Lab Q weak experiment determined the weak charge of the proton by measuring the parityviolating elastic scattering asymmetry of longitudinally polarized electrons from an unpolarized liquid hydrogen target at small momentum transfer. A custom apparatus was designed for this experiment to meet the technical challenges presented by the smallest and most precise ep asymmetry ever measured. Technical milestones were achieved at Jefferson Lab in target power, beam current, beam helicity reversal rate, polarimetry, detected rates, and control of helicity-correlated beam properties. The experiment employed 180 µA of 89% longitudinally polarized electrons whose helicity was reversed 960 times per second. The electrons were accelerated to 1.16 GeV and directed to a beamline with extensive instrumentation to measure helicitycorrelated beam properties that can induce false asymmetries. Møller and Compton polarimetry were used to measure the electron beam polarization to better than 1%. The electron beam was incident on a 34.4 cm liquid hydrogen target. After passing through a triple collimator system, scattered electrons between 5.8 • and 11.6 • were bent in the toroidal magnetic field of a resistive copper-coil magnet. The electrons inside this acceptance were focused onto eight fused silica Čerenkov detectors arrayed symmetrically around the beam axis. A total scattered electron rate of about 7 GHz was incident on the detector array. The detectors were read out in integrating mode by custom-built low-noise pre-amplifiers and 18-bit sampling ADC modules. The momentum transfer Q 2 = 0.025 GeV 2 was determined using dedicated low-current (∼100 pA) measurements with a set of drift chambers before (and a set of drift chambers and trigger scintillation counters after) the toroidal magnet.

A water Cherenkov luminosity monitor system with associated electronics has been developed for the A4 parity violation experiment at MAMI. The detector system measures the luminosity of the hydrogen target hit by the MAMI electron beam and monitors the stability of the liquid hydrogen target. Both is required for the precise study of the count rate asymmetries in the scattering of longitudinally polarized electrons on unpolarized protons. Any helicity correlated fluctuation of the target density leads to false asymmetries. The performance of the luminosity monitor, investigated in about 2000 hours with electron beam, and the results of its application in the A4 experiment are presented.

The Q p weak experiment will measure the parity-violating elastic e-p scattering asymmetry to extract the weak charge of the proton. The experiment employs a toroidal magnet to focus electrons scattered at 8 • ± 2 • , corresponding to Q 2 ∼ 0.03 (GeV/c) 2 , on eight Čerenkov detectors located in the focal plane of the spectrometer. Since the asymmetry is proportional to Q 2 , it is crucial to obtain an accurate measure of the acceptance-averaged value of Q 2 . A tracking system will be used in a low-rate counting mode, allowing individual events to be observed. This will enable a determination of the average Q 2 by measuring the scattering angle and interaction vertex, for mapping the response across the surface of the Čerenkov detectors, and for the dilution of the Čerenkov detector signal by background.

La electromiografía es una técnica ampliamente utilizada en rehabilitación, control de prótesis e interfaces humano-máquina. No obstante, la digitalización de estas señales mediante microcontroladores presenta retos importantes, debido a su baja amplitud en el orden de milivoltios, su naturaleza bipolar y la presencia de ruido y artefactos de movimiento. Estas condiciones pueden provocar saturación o pérdida de información en convertidores analógico-digitales unipolares, típicos en plataformas de bajo costo. Con el fin de garantizar una adquisición confiable, en este trabajo se propone un sistema de acondicionamiento de señal con desplazamiento de nivel offset, estructurado en tres etapas principales: (i) amplificación instrumental de la señal Electromiográfica, (ii) filtrado analógico en el rango de 20-500 Hz para reducir interferencias y artefactos, y (iii) adición de un offset que desplaza la señal hacia un rango compatible con el convertidor analógico-digital del microcontrolador ATMega328P. Los resultados experimentales evidencian que la señal acondicionada mantiene su morfología original, evitando la saturación y permitiendo su digitalización de manera estable en el rango de 0-3.3 V. Esta adaptación posibilita el análisis digital posterior, facilitando la implementación de algoritmos de procesamiento en tiempo real y el desarrollo de aplicaciones biomédicas basadas en Electromiografía. En conclusión, el diseño propuesto ofrece una solución práctica y de bajo costo para la integración segura de señales Electromiográficas en sistemas embebidos, contribuyendo al avance de dispositivos de rehabilitación, control protésico e interfaces humano-máquina.

In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Forecasting new edges in online social networks can give us a better understanding of the growth of these networks. There have been many studies of link prediction in the field of engineering and humanities. Scientists attribute the existence of a new relationship between two individuals for two reasons: 1) Proximity to the graph (structure) 2) Similar properties of the two individuals (Homophile law). However, studying the impact of the two approaches working together to create new edges remains an open problem. Similarity metrics can also be divided into two categories; Neighborhood-based and path-based. So far, the above two theoretical approaches (proximity and homophile) have not been found together in the neighborhood-based metrics. In this paper, we first attempt to provide a solution...

This paper reports an exploratory experimental cold flow test of the first mode in a multi-mode propulsion concept. A small model of a wallmounted pulse detonation rocket (PDR) ejecting into a duct was fabricated and integrated into the supersonic wind tunnel at the University of Texas at Arlington (UTA). Although supersonic testing proved to be inconclusive, the static tests showed that pulsed flows entrain more mass than steady state flows. It also indicated that thrust augmentation is more favorable at low frequencies for this model. Background Pulse detonation engines (PDE) have been researched extensively as an alternate form for high-speed propulsion. A concept has been proposed for the adaptation of a PDE into a type of scramjet in which the PDE ejects into a subsonic or supersonic secondary flow to provide thrust. A model of such a configuration was developed and tested in the supersonic blow-down tunnel at the Aerodynamics Research Center (ARC) at the University of Texas at...

The Wilberforce pendulum is a didactical device often used in class demonstrations to show the amazing phenomenon of coupled oscillations (rotational and longitudinal) producing beats in a special mass-spring set up. The phenomenon is particularly surprising for a distant observer, who easily detects the longitudinal motion, but may skip the presence of rotation. To him (her) the vertical oscillation appears to damp out completely and then it rises again without external action (as if an invisible force would come in). In this paper we propose a revised version of the classical experiment, suggesting a deeper understanding of phenomena through real time data acquisition of both the rotation and the vertical oscillation, using a MEMS accelerometer/gyroscope and the free software Phyphox to show the device motion on a smartphone screen. A detailed analysis of the motion and of the energetical aspects are then performed using the free modeling tool InsightMaker.

In SPR imaging (SPRi), convolutional neural networks (CNNs) excel at extracting diagnostic features from complex images, achieving disease detection accuracies exceeding 90% . Dimensionality reduction techniques like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) further support the visualization and clustering of high-dimensional data, facilitating interpretation and downstream analysis. ML techniques have revolutionized the design of SPR sensors by enabling predictive modeling and inverse design. Neural networks, including multilayer perceptrons (MLPs) and CNNs, have been trained to predict optical responses (e.g., resonance wavelength shifts) from nanostructure geometry and material parameters. These models have reached prediction accuracies of over 99% when combined with statistical techniques like Bayesian Ridge Regression. Advanced generative approaches, such as Generative Adversarial Networks (GANs) and reinforcement learning, allow for the inverse design of nanostructures. These models generate configurations that maximize field enhancement and sensitivity. Metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs), have also been combined with ML models to accelerate multi-parameter optimization, leading to a 75% reduction in design time and sensitivity enhancements up to 5,200 nm/RIU. ML-powered SPR biosensors have demonstrated remarkable promise in clinical diagnostics. Wearable SPR devices, integrated with lightweight ML models, are capable of real-time monitoring of biomarkers such as cardiac enzymes and inflammatory mediators. In infectious disease diagnostics, DL-enhanced SPR systems have achieved femtomolar-level detection of SARS-CoV-2 in under 30 minutes-surpassing the performance of standard RT-PCR tests. CNN-based systems have also been used for cancer detection, enabling identification of exosomes and multiple protein biomarkers (e.g., eight breast cancer indicators) at picogramper-milliliter sensitivity. ML integration has substantially improved the sensitivity, speed, and multiplexing capacity of SPR biosensors. Optimized sensor designs using hybrid materials (e.g., Au-TiO₂-CdS) have improved sensitivity by 30-45%, and CNNs have significantly reduced noise in complex biological media such as serum. However, several challenges remain. The development of high-performing ML models requires large, annotated datasets, which are difficult to generate XLI / Extended Abstracts in biomedical settings. Computational resource demands, particularly for training deep networks, limit the portability of these systems. Moreover, the "black-box" nature of many DL models hampers their interpretability, raising concerns in clinical settings. Lastly, SPR's inherent limitations in detecting low-molecular-weight analytes (<500 Da) and its susceptibility to interference in biological matrices persist as obstacles. Several promising strategies are emerging to address these limitations. Lightweight DL models suitable for edge computing are being developed for use in point-of-care devices. Transfer learning techniques reduce the dependence on large training datasets by repurposing pre-trained models for SPR-specific tasks . Hybrid physics-informed ML models combine domain-specific physical principles with data-driven learning, enhancing both performance and interpretability. Additionally, federated learning offers a path toward collaborative model training across institutions while preserving data privacy, a critical consideration in clinical applications. Machine learning has significantly advanced the capabilities of SPR biosensors. By improving signal interpretation, guiding sensor design, and enabling rapid multiplexed diagnostics, ML is pushing the limits of what SPR can achieve. Although challenges related to data availability, model transparency, and sensor physics remain, ongoing developments in computational modeling and materials science are steadily bridging these gaps. As the convergence between artificial intelligence and nanophotonics continues to evolve, SPR biosensors are poised to play a transformative role in personalized medicine, real-time diagnostics, and global health monitoring.

In this study, the nuclear properties of odd-even dysprosium isotopes (157Dy, 159Dy, 161Dy, 163Dy) were investigated using the Projected Shell Model (PSM). The Yrast spectra, the staggering of energy differences between consecutive spin states (ΔE = E(I) - E(I-1)), and the reduced electromagnetic transition probabilities (B(M1)/ B(E2)) were calculated for these isotopes and compared with experimental data. The results indicate a reduction in the amplitude of the staggering behavior with increasing neutron number, attributed to the neutron orbitals moving further from the Fermi level. The PSM calculations show good agreement with experimental data, confirming the accuracy of this model in analyzing the structure of deformed nuclei. This study provides deeper insights into the rotational dynamics and nucleonic interactions in odd-even nuclei.

The processing of point clouds is one of the growing areas in machine vision. With the advent of inexpensive depth sensors, there has been a great interest in point clouds to detect three-dimensional objects. In general, 3D object recognition methods are alienated into two classes: local and global feature-based methods. In global feature-based methods, the entire shape of the model is described,

Biomedical sensor data acquisition with LabVIEW provides a platform for
biomedical engineering students to get started with Arduino and LabVIEW
programming. Arduino based projects would be helpful to improve the
standards of patient care and monitoring in hospitals and cities as well as
the standard of living by implementing a variety of innovative ideas more
directly. The primary purpose of writing this book is to explain and
illustrate the programming and interfacing of Arduino with biomedical
sensors, communication modules, and LabVIEW GUI

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic modelbased approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.

K-nearest neighbors (KNN) based recommender systems (KRS) are among the most successful recent available recommender systems. These methods involve in predicting the rating of an item based on the mean of ratings given to similar items, with the similarity defined by considering the mean rating given to each item as its feature. This paper presents a KRS developed by combining the following approaches: (a) Using the mean and variance of item ratings as item features to find similar items in an item-wise KRS (IKRS); (b) Using the mean and variance of user ratings as user features to find similar users with a user-wise KRS (UKRS); (c) Using the weighted mean to integrate the ratings of neighboring users/items; (d) Using ensemble learning. Three proposed methods EVMBR, EWVMBR and EWVMBR-G are presented in this paper. All three methods are user-based, in which VM distance is used as a measure of the difference between users / items, to find neighboring users / items, and then the weighted average is weighted, respectively. Also, weights based on the Gaussian combined covariance model are used to predict unknown user ratings. Our empirical evaluations show that the proposed method EVMBR, EWVMBR and EWVMBR-G, which utilizes ensemble learning, are the most accurate among the methods evaluated. Depending on the dataset, the proposed method EWVMBR-G managed to achieve 20 to 30 percent lower mean absolute error than the original MBR. In terms of runtime, the proposed methods are comparable to the MBR and much faster than the slope-one method and the cosine-or Pearson-based KNN recommenders.

The BLAST experiment was operated at the MIT-Bates Linear Accelerator Center from 2003 until 2005. The detector and experimental program were designed to study, in a systematic manner, the spin-dependent electromagnetic interaction in few-nucleon systems. As such the data will provide improved measurements for neutron, proton, and deuteron form factors. The data will also allow details of the reaction mechanism, such as the role of final state interactions, pion production, and resonances to be studied.

This work would not have been possible without the help of a great many people. Special thanks goes to my thesis advisor, Dr. Brad Filippone, who spent hundreds of hours helping me through the experiment, data analysis, and writing of this thesis. His patience, dedication, and wisdom have shown me the best qualities a physicist can achieve. I would also like to thank the scientists and staff of the Kellogg Radiation Laboratory and SLAC Experiment E140 who made this experiment possible. In particular, the spokespersons for E140, Dr. Arie Bodek and Dr. Steve Rock, deserve credit for their many hours of help. Thanks also goes to Dr. Sridhara Dasu for his many contributions to the data analysis presented here. Dr. Dave Potterveld provided much of the software that assisted in writing this thesis, along with many helpful answers to my numerous questions over the years. Thanks to you all. I would also like to thank my many friends, office mates, and room mates that I have had while at Caltech. Special thanks goes to Mike Hoenk, Tom Fric, and Doug Collins, who have provided many of the fond memories I take with me from Caltech. My love and gratitude also goes to Genesis Starr, who has greatly enriched my life here. My stay at Caltech could not have been as fulfilling without her friendship. My appreciation also goes to my "Pasadena parents," Shirley and John Fields, who gave me tremendous support and helped keep me sane. Finally, I would like to thank my parents and family; for without their love and support none of this would have been possible.

N owadays, protecting sensitive resources is one of the most important issues by government politics. So, it is essential that government, in order to achieve this target, utilizes beneficial strategies. In this paper, investment optimization for the protection of sensitive systems has been investigated. Defender minimizes the expected damage with respect to budget and area restrictions of defense equipment. However, the attacker maximizes the expected damage of sensitive targets with respect to cost and weight restrictions of attack equipment. Besides, defender deploys false elements to reduce the probability of system and real target vulnerability. False and real elements cannot be distinguished by the attacker. But the attacker has some probability of successfully detecting false targets. The aim of this study is to determine the optimal strategies for defense of sensitive targets considering probability of a successful attack, attacker capability in detecting false targets, reliability block diagram and game theory approach. Finally, the presented model is illustrated for the case study and final findings are analyzed.

Power augmented ram (PAR) engine is a popular equipment to reduce the requirement of power for takeoff and improve aerodynamic performance. To provide detailed insight into the aerodynamic characteristics of wing-in-ground effect (WIG) craft with PAR engine, numerical simulations are carried out on WIG craft models in cruise. Simplified engine models are applied to the simulations. Two cruise modes for PAR engine are considered. The aerodynamic characteristics of the WIG craft and other features are studied. Comparisons with WIG craft model without PAR show that shutoff of PAR engine results in an increase in drag and less change in lift. Accordingly for the work of PAR engine, the air flow blown from the engineaccelerates the flow around the upper surface and a high-speed attached flow near the trailing edge is recorded. With the schemed PAR flow, more suction force is realized and the flow features over the wing vary noticeably. It is also shown that the Coanda effect, provided wi...

Abstract DDoS (Distributed Denial of Service) attacks have emerged as one of the major threats in the field of cybersecurity, posing significant challenges for organizations that rely on online services. This type of attack sends a large volume of requests to a target, overwhelming its capacity to respond. One of the various types of DDoS attacks is the HTTP Flood attack. This paper examines the use of the CIC-IDS2017 data set, which contains instances of HTTP Flood attacks for research purposes. The results show that 96.76% accuracy can be achieved in recognizing DDoS attacks using a Convolutional Neural Network (CNN) and the World of Anomaly (WOA) design. The findings indicate that models trained on data from 2017 yield a higher level of identification compared to other research conducted by Fauzia Talpur and another study by Sapna Sadhwani. Further analysis of the attacks shows that there are moments of high volatility, with the potential for significant risks if the available defenses are ineffective against new DDoS attack strategies. This highlights the importance of ongoing research to enhance detection and response mechanisms for DDoS attacks.