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The effect of three levels of welding arc energy input on the metallurgical, mechanical, and corrosion behavior of AISI 304L stainless steel was studied with the aim of investigating and recommending the most useful range of process parameters for improved performance of engineering structures made from this industrially important alloy. The gas tungsten arc welding process was employed, and all the joints were butt-welded using a single-V groove design. With an increase in heat input, the δ-ferrite phase changed from lathy to vermicular, besides the heat-affected zones of all the welds exhibiting grain coarsening effects. Postweld thermal aging led to the preferential occurrence of carbides in the interdendritic regions, which degraded their corrosion performance. Thermal aging did not affect the tensile strength of these welds, but their ductility was significantly impacted. The extent of segregation of Cr and C, especially, was observed to be the least in the case of low heat input welds. This study establishes that while designing welding procedures for AISI 304L structures, low welding arc energy should be used as it promotes better mechanical as well as corrosion properties. [ABSTRACT FROM AUTHOR]

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem. [ABSTRACT FROM AUTHOR]

This study is aimed to investigate the effects of introducing dysprosium (Dy) into Zn-based hydroxyapatite (HAp) at various concentrations (0.45, 0.90, 1.35 and 1.80%). The structural and optical properties of pure and doped HAp were thoroughly examined through theoretical and empirical analyses, including X-ray diffraction, Raman spectroscopy and Fourier transform infrared (FTIR) studies. The results confirmed the successful incorporation of Dy into the HAp lattice without significantly affecting its thermal stability or stoichiometry. The introduction of Dy into Zn-based HAp led to notable alterations in several material parameters. The lattice parameters, crystallinity, lattice stress, strain and anisotropic energy density varied with different Dy concentrations. In comparison with undoped HAp, all Dy-doped Zn-based HAps exhibited reduced crystallite size values, indicating a change in the microstructure. Furthermore, the material density increased from 3.159 to 3.228 g cm−3 with Dy doping. The band gap (BG), an important parameter for optical applications, is consistently decreased from 4.6 to 3.9 eV as the Dy concentration increased. This decrease in BG suggests the potential for improved photocatalytic or optoelectronic properties in Dy-doped Zn-based HAp. Additionally, the linear absorption coefficient (LAC) increased, indicating enhanced light absorption. Overall, this study provides a comprehensive understanding of structural and optical modifications induced by Dy doping in Zn-based HAp. These findings contribute to the potential application of Dy-doped Zn-based HAp in various fields, including biomedicine, photocatalysis and optoelectronics. [ABSTRACT FROM AUTHOR]

The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves. In this study, we investigate the effectiveness of three machine learning (ML) algorithms: extreme gradient boosting (XGB), multilayer perceptron (MLP) and random forest (RF), for the detection of IoT-targeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing state-of-the-art, and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks. The CICIoT2023 dataset was derived from the latest real-world IoT traffic, incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering, the data was fed into our models for both training and validation, where findings suggest that while all three models exhibit commendable accuracy in detecting DoS/DDoS attacks, the use of particle swarm optimization (PSO) for feature selection has made great improvements in the performance (accuracy, precsion recall and F1-score of 99.93% for XGB) of the ML models and their execution time (491.023 sceonds for XGB) compared to recursive feature elimination (RFE) and random forest feature importance (RFI) methods. The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time. This involves employing the best-performing ML algorithm for detection and the integration of warning mechanisms. We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments. [ABSTRACT FROM AUTHOR]

Nowadays, smartphones are used for various activities, including checking emails, paying bills, and playing games, which have become essential parts of daily life. Also, IoT devices can be managed and controlled using applications. While applications can provide numerous benefits, they have also led to several security risks, such as theft of data, eavesdropping, compromised data, and denial-of-service attacks. This study examines security breaches, attacks targeting Android system applications, and vulnerabilities present at every layer of the Android architecture. Additionally, the study aims to compare and evaluate various treatment methods to identify their advantages and disadvantages. Furthermore, the study aims to examine Android's architecture for weaknesses that might lead to app vulnerabilities and potential attacks. To achieve the objectives of this study, a comprehensive analysis of security breaches and attacks targeting Android system applications will be conducted. Various treatment methods will be compared and evaluated through rigorous examination. Additionally, Android's architecture will be thoroughly examined to identify potential weaknesses and vulnerabilities. The analysis will focus on identifying the security risks associated with the use of applications on smartphones and IoT devices. The vulnerabilities present at every layer of the Android architecture will also be analyzed. Furthermore, the advantages and disadvantages of various treatment methods will be assessed. The findings of this study will reveal the various security risks, vulnerabilities, and potential weaknesses present in Android system applications and the Android architecture. The advantages and disadvantages of different treatment methods will also be highlighted. This study contributes to the development of more precise and robust security measures for Android, aiming to mitigate security breaches, attacks, and vulnerabilities. By identifying weaknesses and vulnerabilities, this study provides valuable insights for improving the overall security of Android system applications. Doi: 10.28991/HIJ-2024-05-03-020 Full Text: PDF

The quest for nonlinear optical (NLO) crystals, which are essential for future optical understanding, detailed evaluations, and present laser gadgets, is a prominent concern in the materials investigation. In this systematic analytical evaluation, the optical, electrical, and nonlinear optical (NLO) parameters of 26Adamanzne which was doped endohedrally with alkaline earth and exohedrally with alkali metals, resulted with nine electron-rich complexes Mº(26ADZ)M (where (Mº = Be, Mg, Ca) and (M = Li, K, Na)), were designed. Geometrical, thermodynamic, and electronic computations executed through density functional theory (DFT) and 6-31G (d,p) basis set. All metals doped 26ADZ are within the range of effective NLO materials such as a lowered band gap in the range of 6.22–3.86 eV which was very higher in case of pure surface. At the same level of theory, transition density matrix (TDM), non-covalent interaction analysis (NCI), and electron density distribution map (EDDM) were carried out. The NBO analysis anticipated the charge transfer between donor and acceptor moieties. Computational investigations like binding energy (Eb), interaction energy, and vertical ionization potential confirm the high stability of the proposed complexes. The UV-visible assessment reveals that all complexes are transparent in the ultraviolet range and have maximum absorptivity up to 1009 nm (visible and NIR range). Doping techniques in all complexes profoundly impacted the oscillator strength and dipole moment, leading to an escalating value of hyperpolarizability βtot up to 421.042 × 10−30 esu. The optical efficiency of proposed complexes is approximated by using the value of isotropic linear polarizability (αiso) which is boosted from 2.538 × 10−30 to 8.665 × 10−23 esu. The strong optical response of Mº(26ADZ)M suggests its potential as a promising candidate for nonlinear optics, paving the way for synthesizing additional materials with practical applications in optoelectronics. [ABSTRACT FROM AUTHOR]

Email bombing, a growingly prevalent and destructive cyber attack, involves inundating a target's email inbox with subscription confirmation messages from legitimate services. This type of attack, particularly challenging for conventional spam filters, is not easily detected as the emails often appear benign. In our research, we introduce an innovative real-time technique to identify and mitigate email bombing. Our method leverages a unique time gap threshold for attack detection, proving effective across various bombing types, including mass mailing and subscription bombing. Upon detecting an attack, we utilize a novel mechanism of nine features extracted from email headers to build a machine learning model. This model distinguishes between genuine and bombing emails with approximately 97% accuracy. Our study not only explores email bombing attacks but also offers a comprehensive solution, combining attack detection and a machine learning-based approach to accurately classify emails, thereby effectively mitigating such cyber threats. [ABSTRACT FROM AUTHOR]

The titanium dioxide (TiO2) semiconductor's wide band gap property restricts its application in a variety of fields. To ensure cost-effectiveness and time efficiency, researchers emphasized material modeling and theoretical analysis. This study employs a Density Functional Theory approach to investigate how the presence of transition metal elements (TME) like rhodium (Rh) and rhenium (Re) affects the optoelectronic properties of TiO2. The study was carried out using the VASP software package and the plane-wave pseudopotential technique. The formation energy, electronic band structure, and optical properties of TiO2 were affected by the addition of both Rh and Re as doping materials. The outcomes of the band structure and total density of states (TDOS), point out notable alterations in the energy gap (Eg) of TiO2. The plot of the band structure illustrates that the introduction of Re leads to a more substantial reduction in the TiO2 band gap compared to Rh. The introduction of numerous sub-states into the band gap causes the valence band to approach the conduction band at the Gamma point. The band gap reduction caused by the addition of Rh and Re TME is confirmed by the relocation of the TDOS to lower energies in the doped TiO2 structure. Furthermore, significant changes in the partial density of states of TiO2 upon Re doping have been observed at the bottom of the conduction band, highlighting the effectiveness of Re doping in modifying the electronic band structure of TiO2. The outcomes of band structure and TDOS proved that TME with high atomic mass is certain to decrease TiO2's band gap to the range required for usage as a photocatalytic material. Rh and Re doping in TiO2 alter their optical properties, making them suitable for optoelectronic devices. [ABSTRACT FROM AUTHOR]

During the research process, it was found that compounds with the "565" ring structure exhibited excellent detonation performance, which led to further investigation. TIOP (4a,5,7a, 8-tetrahydro-4H-imidazolo [4,5-b][1,2,5] oxadiazolo [3,4-e] pyrazine-6 (7H) -ketone) energetic compound has been designed. This article uses TIOP as the precursor and designs 54 "TIOP-based energetic derivatives" by introducing energetic groups such as –ONO2, –NHNH2, and –NH2. Among these energetic compounds, their maximum density, maximum detonation velocity, and maximum detonation pressure can reach 2.09 g/cm (D6), 10.07 km/s (D4), and 46.58 GPa (D7). Comprehensive analysis shows that energetic compounds A7, D4, D7, and F7 can reach a good equilibrium among high energy and low sensitivity, and can be further studied as potential High Energy Density Compounds. Using Gaussian16 software and the Multiwfn 3.8 software package, the B3LYP method in density functional theory was used to optimize the structure of 54 derivatives, calculate their heat of formation, and further analyze the intermolecular interactions. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing DoS attack detection in IoT systems through a Machine Learning approach that combines class balancing, feature selection, and optimized classifiers. Utilizing the Edge IIoT dataset, we applied SMOTE and Random Un-dersampling for class balance and employed DNN, Random Forest, and PCA for feature selection. We evaluated six technique combinations across four classifiers (DNN, SVM, XGBoost, and Random Forest), finding that certain combinations notably improve detection efficiency and accuracy. This research contributes to IoT security by offering an effective methodology for DoS attack detection, setting a foundation for further advances in IoT system protection against security threats. [ABSTRACT FROM AUTHOR]

The structural and electronic properties of pure graphene, graphene with a vacancy, graphene with two vacancies and molybdenum‐doped graphene were investigated. In addition, the adsorption of Mo atoms on graphene (G), reduced graphene (rG) and reduced graphene oxide (rGO) was examined. The possible energies of different active adsorption sites of nanostructured Mo‐decorated G, rG and rGO have been calculated using density functional theory (DFT). Mo atoms are predicted to create bonds with six C atoms in G, three C atoms in rG, and both C and O atoms in rGO sheets after geometry optimizations. The study focused on changing the electronic structure of G, including opening the zero band gap and controlling the band structure, which was done by creating defects and adding impurities. The present study revealed a significant correlation between the adsorption of the Mo atom and the characteristics exhibited by frontier orbitals. The results indicated that the adsorption characteristics of Mo atoms in pure G, rG and rGO are different, despite chemisorption being the common mechanism. Specifically, Mo‐decorated rG exhibited higher adsorption energy, while Mo‐decorated G demonstrated a lower adsorption energy. According to these findings, it is reasonable to anticipate that Mo‐decorated rG could be applied as a novel adsorbent for the removal of pollutants. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) algorithms play a crucial role in fortifying the security of Internet of Things (IoT) environments. In this study, we focus on several key ML algorithms, namely Random Forest, AdaBoost, Decision Trees, Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). These algorithms are evaluated within the unique context of IoT security, employing an original dataset meticulously crafted for this study. The dataset OGU-IoT23 is designed to capture the intricacies of cyber threats in an IoT network, featuring attacks such as DDoS, HTTP Flood, SYN Flood, Port Scan, and UDP Flood. This original dataset OGU-IoT23 serves as a foundation for the comprehensive evaluation of ML algorithms, allowing us to assess their effectiveness in identifying and mitigating diverse attack patterns targeting IoT devices. The algorithms are examined based on their performance metrics such as accuracy, F1-score, precision, recall, and test accuracy, emphasizing their suitability for real-world IoT security applications. The results show that Random Forest and AdaBoost are the top performers in terms of performance metrics. The study aims to provide valuable insights into the strengths and limitations of these ML algorithms, aiding researchers and practitioners in developing resilient security measures designed for IoT settings. [ABSTRACT FROM AUTHOR]

Cybersecurity is becoming increasingly important with the rise in Internet usage. The two most frequent cyberattacks that can seriously harm a website or a server and render them inaccessible to other customers are denial of service (DoS) and distributed denial of service (DDoS) attacks. These attacks are so common and take many different forms, it is difficult to identify and respond to them with previous methods. Furthermore, computational complexity, inconsistency, and irrelevant data are problems for traditional intrusion detection methods. As a result, a powerful deep learning-based technique is applied in this study for the identification and categorisation of DoS and DDoS attacks. Refined Attention Pyramid Network (RAPNet)-based feature extraction is used in this proposed framework to extract features from the input data. Then, Binary Pigeon Optimisation Algorithm (BPOA) is used to determine the best features. After choosing optimal characteristics, Densenet201-based deep learning is deployed to categorise the assaults in Bot-IoT, CICIDS2017, and CICIDS2019 datasets. Furthermore, the Conditional Generative Adversarial Network (CGAN) is used to provide extra data samples for minority classes to address the issue of imbalanced data. The findings show that the proposed model can precisely identify and categorise DoS and DDoS assaults in comparison to the existing intrusion detection approaches with 99.43%, 99.26%, and 99.38% accuracy for CICIDS2019, CICIDS2017, and BoT-IoT correspondingly. [ABSTRACT FROM AUTHOR]

Using density functional theory (DFT), we investigate the structural, stability, and electronic properties of boron nitride (BN) dimers cooped graphene quantum dots (GQDs). A molecule with seven benzene rings and a hydrogen atom at the end of each edge carbon atom (C24H12) as a model for GQDs is used. We assumed that the geometrical arrangement of boron nitride (BN) dimers in GQDs can improve the electronic properties of GQDs. Different positions of B and N atoms are employed to create the three types of BN arrangement, ortho, meta, and para dimers. It has been discovered that the HOMO-LUMO energy gaps of the GQDs structure are significantly affected by the presence of BN dimers. It is noted the energy gap of GQDs changes from − 16.7% to -32.4% by adding BN dimers in different positions, which leads to significant applications. As a result of the value of the electronic gap being around 2.7 eV, these BN dimers can generate an electronic transition for the GQDs system, transforming it from an insulator to a semiconductor. Also, the arrangement of the BN dimers within the GQDs impacts the structural, stabilities, and electronic properties of the nanostructure. These results promote the development of nanodevices with GQDs energetic electronic abilities and improve our knowledge of how chemical doping affects their performance. [ABSTRACT FROM AUTHOR]

While Unmanned Aerial Vehicles (UAVs) have found applications across numerous industries, they still remain vulnerable to various cybersecurity challenges. Different types of cyberattacks target UAVs. Early detection of these cyberattacks is considered the most important step in ensuring the cybersecurity of UAVs. In this article, an artificial intelligence method based on machine learning was developed for detecting different types of Denial of Service (DoS) attacks targeting the UAV network. Initially in this work, feature selection methods are implemented to select the most important features. Then, machine learning methods are used to classify attacks. According to the conducted experiments, the proposed method outperformed others with an accuracy of 99.51 % and a prediction time of 0.1 s. Additionally, a novel dataset is used in this work, which offers several advantages. The dataset was created within a real-world environment rather than a simulated one. Furthermore, the data were collected within a 5G network.

Background: It remains unclear whether addition of docetaxel to the combination of a platinum and fluoropyrimidine could provide more clinical benefits than doublet chemotherapies in the perioperative treatment for locally advanced gastric/gastro-esophageal junction (LAG/GEJ) cancer in Asia. In this randomized, phase 2 study, we assessed the efficacy and safety of perioperative docetaxel plus oxaliplatin and S-1 (DOS) versus oxaliplatin plus S-1 (SOX) in LAG/GEJ adenocarcinoma patients. Methods: Patients with cT3–4 Nany M0 G/GEJ adenocarcinoma were randomized (1:1) to receive 4 cycles of preoperative DOS or SOX followed by D2 gastrectomy and another 4 cycles of postoperative chemotherapy. The primary endpoint was major pathological response (MPR). Results: From Aug, 2015 to Dec, 2019,154 patients were enrolled and 147 patients included in final analysis, with a median age of 60 (26–73) years. DOS resulted in significantly higher MPR (25.4 vs. 11.8%, P = 0.04). R0 resection rate, the 3-year PFS and 3-year OS rates were 78.9 vs. 61.8% (P = 0.02), 52.3 vs. 35% (HR 0.667, 95% CI: 0.432–1.029, Log rank P = 0.07) and 57.5 vs. 49.2% (HR 0.685, 95% CI: 0.429–1.095, Log rank P = 0.11) in the DOS and SOX groups, respectively. Patients who acquired MPR experienced significantly better survival. DOS had similar tolerance to SOX. Conclusions: Perioperative DOS improved MPR significantly and tended to produce longer PFS compared to SOX in LAG/GEJ cancer in Asia, and might be considered as a preferred option for perioperative chemotherapy and worth further investigation. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have shown high optical absorption and consequently provide high conversion efficiency with stable performance. In our work, CH3NH3PbI3 (MAPbI3) as an absorber layer is analyzed for different crystalline structures. Cubic, tetragonal, and orthorhombic phases of perovskite material are investigated to check the impact of the crystalline structure on the solar cell performance. Both density of states and band structure are studied using Quantum-ESPRESSO package depending on density functional theory. Then, all relevant parameters were employed in SCAPS software and comprehensive study was done for examining the effect of the crystalline structure of perovskite layer on the solar cell performance. In-depth, analyses were conducted to evaluate key parameters, including open circuit voltage (Voc), short circuit current (Isc), fill factor (FF), and power conversion efficiency (PCE) considering the variations of perovskite layer thickness and bulk defect densities. The obtained results indicate that cells with cubic MAPbI3, which shows a notably higher bandgap of 1.7 eV and an enhanced optical absorption coefficient, especially in the higher wavelength range (around 105 cm−1), show better performance for almost all three scenarios. Cubic MAPbI3 cells achieve relatively higher peak efficiency of 26% when the absorber layer thickness is almost 900 nm. The investigation into absorber bulk defect densities reveals the critical role of defect levels in PSC performance. Adjusting defect levels from 1014 cm−3 to 1018 cm−3 results in deteriorating trends in Voc, Jsc, FF, and PCE. Jsc remains stable until a defect level of 1017 cm−3, highlighting a threshold where defects begin to impact charge carrier generation and separation. Doping effect has been studied, PCE remains stable until a critical doping level of 1016 cm−3 after which it drops significantly which indicates that doping is cautioned against due to its adverse effects on material and carrier transport. This finding holds significant promise for experimental solar cell fabrication, as it suggests that cubic MAPbI3's superior bandgap and enhanced optical absorption could lead to more efficient and robust photovoltaic devices in real-world applications. [ABSTRACT FROM AUTHOR]

The atomic structures, growth patterns, new spintronic, magneto-optic, and electrical properties of 2D armchair nanoribbons of group-IV transition metal nitrides (TiN, ZrN, and HfN) have been investigated. The atomic unit cells of TiN, ZrN, HfN, TiZrN, TiHfN, and ZrHfN have been transformed into the matching armchair nanoribbons. By comparing the proposed nanoribbon to the bulk and monolayer structures of these metal nitrides, the structural stability, density, and projected density of states, band structure, magnetic behavior, and polarization were evaluated. The outcomes demonstrate that, in comparison to the crystalline structure, the suggested systems exhibit ferrimagnetic behavior and a remarkable magnetic moment. Also, a higher magnetic moment of about 24 μB in the supercells of the suggested nanoribbons was discovered due to the increased atom count. The proposed structures demonstrated indirect band gap semiconductor capabilities in the range between 1.66 eV and 2.16 eV which is again an unknown phenomenon in the bulk structure of transition metal nitrides. Owing to these characteristics, the proposed structures can be employed in many optoelectronic applications, such as solar cell devices, LEDs, laser diodes, and optical fibers where the band gap is the key requirement. Apart from this, they can also be useful in coating biomedical implanted chips, transformers, switches, and batteries. Memory storage devices will also benefit from the unique characteristics of the proposed nanoribbon structures. [ABSTRACT FROM AUTHOR]

IPv6 was created to resolve the problem of adopting IPv4 addresses by providing many address spaces. Currently, security is becoming an increasingly important concern in exploiting networks and reaping the benefits of IPv6. ICMPv6 is a key protocol in IPv6 implementation that is utilized for neighbor and router discovery. However, attackers can use this protocol to deny network services using ICMPv6 DDoS flooding attacks, which reduce network performance. DDoS is a difficult challenge on the internet since it is one of the most common attacks impacting a network, causing enormous economic harm to people as well as companies. This paper provides an intelligent ICMPv6-based DDoS flooding-attack detection system based on an artificial neural network to address this issue. This study additionally investigates and examines the suggested framework's detection accuracy. Using real datasets, we illustrate the efficiency of our methodology. To validate our system, we chose different machine learning algorithms and compared their outcomes. The findings show that the proposed framework can identify ICMPv6 DDoS flood assaults with detection accuracy rates of 99.98% for the first dataset and 85.91% for the second dataset. [ABSTRACT FROM AUTHOR]

The density functional theory at the B3LYP/6-311G(d,p) level was applied to investigate the structural, electronic, and physical parameters such as bond length, angles, dipole moments, total energy, chemical potential, electrophilicity index, softness, Mülliken atomic charges, density of states, natural bond orbitals, and quantum theory of atoms in molecules, of pristine and Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, N, As, Sb, and Bi-doped phosphorene. The obtained results revealed that the largest structural deformation was observed in the Bi-doped structure. The Cu-doped phosphorene has the least bandgap and the highest conductivity. Doping of transition metals changes the dipole moments of the pristine phosphorene more than group V-doped models. The NBO calculations proved the largest E(2) belongs to the delocalization from σ* of P30-V94 to σ* of P62-V94. The positive value of implies the closed shell interactions. The chemical hardness of the structures is reduced with the doping of the elements. [ABSTRACT FROM AUTHOR]

In the present study, two-dimensional (2-D) pristine and distorted hexagonal Zig-Zag Boron Nitride nano-ribbon (h-BNNR) has been used as a gas sensing material to detect Nitrogen dioxide (NO2). Distorted structures such as Al-doped, P-doped, and Stone–Wales defected structures are considered here as an electro-sensing material. The First principle density functional theory (DFT) computations-based ab initio approach has been used to investigate the electrochemical properties of pristine and distorted h-ZBNNR in presence of NO2. Upon NO2 adsorption, the conduction band/valance band and energy gap of the ribbon are decreased significantly. An increase in the transmission of electrons has been observed in increasing the bias voltage. Extensive simulations using Quantum wise have been done to validate the efficacy of the proposed gas sensor. [ABSTRACT FROM AUTHOR]

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches. Most proposed works in this research field aim to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns by utilizing the Short-Term Fourier transform (STFT). The resulting high-quality image dataset allowed us to devise an anomaly detection system for IDS using a simple lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSLKDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have demonstrated the efficacy of our raw dataset to image dataset conversion methodology, which is evident as an excellent generalization of the proposed lightweight CNN model was observed, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed versions. [ABSTRACT FROM AUTHOR]

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This study analyzed the aspirin molecule (C9H8O4) using Density Functional Theory (DFT) on Gaussian 09W software. First, the structure of aspirin was optimized using the DFT method with the B3LYP functional and the 6-311+G (d,p) basis set. A global reactivity study was employed to understand the reactivity of aspirin in gas and solvent water for both anion and neutral states. To understand the involvement of orbitals in chemical stability and electron conductivity, we calculated the HOMO-LUMO. The thermodynamic function of a molecule was understood using thermochemistry. Molecular Electrostatic Potential (MEP) was employed to understand the physiochemical properties of aspirin. We observed the Mulliken atomic charge to calculate the atomic charge of aspirin. Finally, the title molecule's UV–Vis, FTIR, and Raman spectra are analyzed and compared with the experimental data.

The data presented in this paper characterize stable kaolinite edge surfaces. Their equilibrium structure is obtained by DFT methods using the first-principles package CASTEP. The calculations are performed using a basis set of plane waves in the PBE exchange-correlation potential. The dispersion forces are taken into account using the semi-empirical Grimme-D2 correction. For selected kaolinite surfaces: (001),(001¯),(010),(110) the paper presents their optimized crystal structure, values of terminal hydroxyl groups charges, density of electronic states, and work function. The presented data allow characterizing the stability of the surfaces and giving their comparative evaluation. This set of surfaces can be used for a comparative study of adsorption of atoms, ions and other molecular systems.

This paper describes an Intrusion Detection System (IDS) which uses several existing known IDS algorithms and Triple Modular Redundancy (TMR) algorithm to make decision about eventual existing attack by majority voting in one constructed ensemble model which solves practically the problem of binary classification. Proposed novel model belongs to so called stacking ensemble methods of machine learning algorithms which uses exactly four algorithms from the group of best binary classification algorithms: Decision trees, Naive Bayes, Support Vector Machine, k-nearest neighbors, logistic regression and AdaBoost and is applicable for any similar problem. Using proposed method, we get a more precisely determined threshold than it is case using whatever of in method individual applied algorithm as well as those algorithms that are the state of the art in the field of binary classification. Besides that, one of the main disadvantages of classic TMR used for classification network traffic, which is the problem of bad over-voting was successfully avoided by improving classic TMR with a new algorithm proposed by the authors. Today a denial of service attacks (DoS) and distributed denial of service (DDoS) are one of most present type on Internet and that is why the authors in this paper paid special attention to them and because of that the authors of proposed method chose to use known KAGGLE dataset which contains data of these type of attacks for the examination of quality of proposed IDS method implemented in suitable software. The dataset itself consists of a wide range of simulated intrusions in a military network environment in United States. Obtained results showed that IDS software with implemented proposed method worked precise and timely, which means alarms was trigger properly and efficiently with better results of quality of classification in most important measures than the individual included algorithms who are the state of the art in binary classification.

This paper outlines a method for identifying and counteracting distributed denial of service (DDoS) and low-rate denial of service (DoS) attacks. These impair significant threats to network security and can disrupt the accessibility and efficacy of systems under attack. The proposed method combines Time-Frequency Analysis (TFA) using Short-Time Fourier Transform (STFT) and a Deep Learning model (DLM), namely Recurrent Neural Network (RNN), to enhance network security. By leveraging the strengths of STFT and RNN, the approach achieves improved detection capabilities and enables timely response and effective mitigation. The CICDDoS2019 dataset has been employed to conduct the evaluation, which provides a diverse set of realistic attack traffic scenarios. The results show that the proposed approach is effective, with an impressive accuracy rate of 99.1%. Compared to traditional methods, the integrated achieves higher accuracy and lower false positive rates. This research highlights the potential of Multimodal Fusion method, for addressing the growing need for advanced defense mechanisms in today's evolving threat landscape.

The MQTTset dataset has been extensively investigated for enhancing anomaly detection in IoT-based systems, with a focus on identifying Denial of Service (DoS) attacks. The research addresses a critical gap in MQTT traffic anomaly detection by proposing the incorporation of the ‘source’ attribute from PCAP files and utilizing hand-crafted feature engineering techniques. Various filtering methods, including data conversion, attribute filtering, handling missing values, and scaling, are employed. Anomalies are categorized and prioritized based on frequency of occurrence, with a specific emphasis on DoS attacks. The study compares the performance of the decision tree and its eight variant models (ID3, C4.5, Random Forest, CatBoost, LightGBM, XGBoost, CART, and Gradient Boosting) for anomaly detection in IoT-based systems. Evaluation metrics such as prediction accuracy, F1 score, and computational times (training and testing) are utilized. Hyperparameter fine-tuning techniques like grid search and random search are applied to enhance model performance, accuracy, and reduce computational costs. Results indicate that the benchmark Decision Tree model achieved 92.57% accuracy and a 92.38% F1 score with training and testing times of 2.95 seconds and 0.86 seconds, respectively. The Feature Engineering (Modified) dataset demonstrated a substantial improvement, reaching 98.56% accuracy and a 98.50% F1 score, with comparable training and testing times of 0.70 seconds and 0.02 seconds. Furthermore, the Modified Decision Tree Algorithm significantly improved accuracy to 99.27%, F1 score to 99.26%, and reduced training time to 0.73 seconds and testing time to 0.14 seconds. The research contributes valuable insights into feature engineering and guides the selection of effective approaches for anomaly detection in IoT-based systems, providing early threat warnings and enhancing overall system security and reliability.

Forthcoming 6G/NextG networks highlight the need for advanced Artificial Intelligence (AI)-based security mechanisms to identify malicious activities and adapt to emerging threats. In this context, the integration of computer vision techniques into the cybersecurity field is promising due to their potential for sophisticated pattern recognition. In this paper we introduce a computationally efficient classification scheme acting directly on the raw packets collected at base stations and enforcing real-time conversion of packets into images. The innovative points of the proposed solution are the lightweight implementation, aligning well with the demands of future 6G networks, and the operation at network edge, enabling early threat identification as close as possible to the packet origin. We investigate the performance of this approach both in terms of F1-score and prediction time using state-of-the-art computer vision architectures and a customized Convolutional Neural Network (CNN) in an intrusion detection problem using a 5G dataset. Experimental results show the superiority of the CNN architecture over complex models. Across multiple packet window sizes $N$ (i.e., 10, 50, 100 packets), the CNN consistently outperforms the other state-of-the-art computer vision models, achieving very high F1-scores (namely, 0.99593, 0.99860, 0.99895). A scalability analysis highlights a trade-off between CNN scalability and performance, where larger $N$ values lead to increased prediction time. On the other hand, the other computer vision models exhibit better scalability, enabling an optimal model selection without trade-offs.

The calculation and visualization of the dose field in the decommissioning of nuclear facilities is one of the important functions of the decommissioning virtual simulation system. The dose field simulation tools can provide radiation field distribution and play an important role in determining the decommissioning plan and protecting personnel during the engineering implementation process. This article investigates the development of dose field calculation and visualization in the reactor decommissioning virtual simulation systems. A preliminary technology plan suitable for the development of the decommissioning dose field calculation and visualization display programs of the first Heavy Water Research Reactor (HWRR) in China is proposed. The applicability of the selected scheme is analyzed. The functional requirement and development direction of the HWRR reactor decommissioning dose field tool are preliminarily determined. Furthermore, the reactor vessel of HWRR reactor is modeled, the dose field distribution is calculated and visualized based on the preliminary decommissioning code. This research can provide technical support for the development of the decommissioning simulation system for the first HWRR reactor in China.

In this paper, D(HE)at, a practical denial-of-service (DoS) attack targeting the finite field Diffie-Hellman (DH) key exchange protocol, is presented, allowing remote users to send non-public keys to the victim, triggering expensive server-side DH modular-exponentiation calculations. The attack was disclosed in November 2021 with an assigned CVE-2002-20001 number. Additionally, the “long exponent” issue, an implementation flaw in cryptographic libraries where unreasonably large private keys are used, deviating from the recommended NIST guidelines, and making D(HE)at more effective, is presented. This issue was disclosed in November 2022 with an assigned CVE-2022-40735 number. A thorough analysis of the D(HE)at attack, along with proof of concept code that has the potential to compromise all existing protocols employing DH key exchange, such as TLS or SSH, is presented in this paper, highlighting the necessity of additional security measures for effective safeguarding. The potential of reaching full 100% CPU utilization by the D(HE)at attack is demonstrated, even on the most up-to-date operating systems, without any significant computation on the client side. With minimal bandwidth and a low request rate per second (rps), the D(HE)at attack can be carried out against target machines from a single laptop. In this study, the consequences of these issues are explored, and a comparative security and performance analysis is conducted among the most commonly used general-purpose cryptographic libraries, including OpenSSL, BoringSSL, LibreSSL, GnuTLS, NSS, Mbed TLS, OpenJDK, Oracle JDK, and WolfSSL. Based on Shodan measurements, it has been found that 87% of servers worldwide support DH key exchange in the SSH protocol, and according to our scan, 55% of the top 1 million websites support DH in TLS. As a result of this study, it is recommended that developers and administrators consider exclusively enabling Elliptic Curve Diffie-Hellman (ECDH), a significantly more efficient protocol, in their server configurations.

Industry 4.0, which was proposed ten years ago to address both the industry's strengths and faults, has finally been replaced by Industry 5.0. It seeks to put human welfare at the core of manufacturing systems, achieving societal goals beyond employment and growth to firmly provide wealth for the long-term advancement of all of humanity. The purpose of this research is to examine the risks involved in the adoption of Industry 5.0's architecture. The paper discusses the significance of Industry 5.0 and the advanced technology needed for this industrial revolution, followed by a detailed discussion of Industry 5.0's human-centric strategy. The comprehensive literature review has resulted in the identification of risks and their mitigation strategies in Industry 5.0 architecture. A taxonomy with respect to different categories of risks has also been proposed. This study classifies Industry 5.0 system assets, identifies platform-independent risks, and develops countermeasures to protect against potential threats, irrespective of the business or domain. [ABSTRACT FROM AUTHOR]

MXenes for supercapacitor (SC) electrodes is a very promising area of research. Supercapacitors are still behind batteries when to be used as primary energy source due to their low energy densities. Hence, additional research is vital to explore materials like MXenes for SC electrodes. The electronic properties of molybdenum nitride Mo2N are studied and its quantum capacitance (QC) is calculated to explore its potential for SC electrodes. The MXene is found to be metallic in nature. The QC values for Mo2N MXene are extremely high around Fermi level. Maximum value of 746.69 µF cm−2 is achieved which is way higher than that of graphene. The value remains high for the negative bias voltage as well. [ABSTRACT FROM AUTHOR]

A novel copper iodide hybrid compound, (DABCO-H)CuKI3, featuring a perovskite structure is here reported. Characterization techniques, including single crystal X-ray diffraction, X-ray photoelectron spectroscopy (XPS), and photoluminescence studies, elucidate the structural and luminescent properties. The compound exhibits blue phosphorescence, attributed to mixed metal-to-ligand and halide-to-ligand charge transfer transitions, as supported by density functional theory (DFT) calculations and XPS valence band analysis. Furthermore, (DABCO-H)CuKI3 displays a remarkable adsorption capacity towards methylene blue dye. Kinetic modelling reveals that both film and intra-particle diffusion processes contribute to the adsorption rate. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) offers an innovative model over the separation of the data plane, control plane and management plane. This separation would result in more effective network management, including cost reductions for hardware and manpower, and the ability to deliver on-demand solutions using programmable SDN approaches. As network policy and on-demand services become more impartial, SDN is becoming more popular. However, there are safety risks associated with the SDN network due to malicious floods such as Distributed Denial of Service (DDoS) attacks and Denial of Service (DoS) attacks directed at the SDN Controller, OpenFlow Virtual Switch (OVS), and end nodes, which must be addressed. Because of these assaults, network throughput is reduced, resulting in a lapse in the availability of network services and a reduction in business operations. The main emphasis of this study is on the detection and mitigation of DDoS and DoS assaults in the SDN network, which is accomplished by the use of both unsupervised and supervised learning approaches. The use of the Dynamic Access Control List (DACL) allows for the performance of mitigation operations in the SDN network, which has been implemented using the mininet. The outcome of the experiment demonstrates that malicious (DDoS and DoS) flood is reduced as a consequence of the mitigation technique. [ABSTRACT FROM AUTHOR]

Organometallic perovskites have become one of the most common multifunctional materials in optoelectronic research fields. This research studies density functional theory calculation on orthorhombic hydrazinium lead iodide (N2H5PbI3) perovskite by replacing A‐site cation with a borane ammonium (BH2NH3+) ion. The perovskite showed a significant structural deformation and an orthorhombic to triclinic phase transition due to A‐site ion replacement. The N2H5PbI3 perovskite has a band gap of 1.64 eV, suitable for the solar cell absorber layer. The band gap has increased to 2.12 eV after complete A‐site ion replacement. All structures showed a high absorption coefficient over 104 cm−1 in the low wavelength region and an increase in refractive index from 2.5 to 2.75 due to ion replacement. All the structures showed high optical conductivity of 1015 s−1 order in the blue wavelength region. These new perovskite structures hold the potential to provide a revolution in optoelectronic research. [ABSTRACT FROM AUTHOR]

The focus of this study is the structural, electronic, and magnetic characteristics of the ZnSeTe solid solution with chromium impurity. The structure of this solid solution was determined in two steps. At the first stage the internal coordinates of atoms are optimized, and at the second one the lattice parameter relaxation, including re-optimization of internal coordinates, were done. Further, after the introduction of Cr substitution impurity, electronic energy spectra and densities of electronic states were calculated. Significant changes in parameters of the electronic structure and magnetic properties caused by cationic vacancy were revealed. It was found that the doped solid solution of Cr:ZnSeTe is a semiconductor for both electron spin polarizations. The study of the supercell with a cationic vacancy revealed significant differences in the parameters of electronic energy bands as well as magnetic properties, compared with those in crystal with no defects. [ABSTRACT FROM AUTHOR]

In this theoretical study, the electronic, structural, and optical properties of copper-doped zinc oxide (CZO) were investigated using the full-potential linearized enhanced plane wave method (FP-LAPW) based on the density functional theory (DFT). The Tran–Blaha modified Becke–Johnson exchange potential approximation (TB-mBJ) was employed to enhance the accuracy of the electronic structure description. The introduction of copper atoms as donors in the ZnO resulted in a reduction in the material's band gap from 2.82 eV to 2.72 eV, indicating enhanced conductivity. This reduction was attributed to the Co-3d intra-band transitions, primarily in the spin-down configuration, leading to increased optical absorption in the visible range. The Fermi level of the pure ZnO shifted towards the conduction band, indicating metal-like characteristics in the CZO. Additionally, the CZO nanowires displayed a significant blue shift in their optical properties, suggesting a change in the energy band structure. These findings not only contribute to a deeper understanding of the CZO's fundamental properties but also open avenues for its potential applications in optoelectronic and photonic devices, where tailored electronic and optical characteristics are crucial. This study underscores the significance of computational techniques in predicting and understanding the behavior of doped semiconductors, offering valuable insights for the design and development of novel materials for advanced electronic applications. [ABSTRACT FROM AUTHOR]

The physisorption procedure and charge transfer phenomena are very important features in surface physics, it contribute to many of important applications in solid state physics. Density functional theory (DFT) with the package of Gaussian 09 at the basis set LanL2Dz, B3LYP level has been utilized to attain the geometrical nanostructure, density contour maps, infrared spectra, electrostatic potential, LUMO energy, HOMO energy, energy band gap, electron affinity, ionization potential, density of states, dipole moment and the polarizability for the geometrical nanostructures (Cu9),(Cu10), (Cu9-OTS),(Cu9-Pyrimidine). Calculations of the geometrical nanostructure (Cu10-OTS) has been accomplished at the basis set 6-31G, but the calculations of the geometrical nanostructure (Cu10- Pyrimidine) has been accomplished at basis set LanL2MB. The interactions between the tosylate (OTS) and the nanocopper surface leads to charge transfer phenomena. The interaction between pyrimidine and the nanocopper surface leads to physisortion phenomena. Charge transfer phenomena and physisorption precedures demonstrate apparently in the pictures of contour density maps. Calculations of the energy band gap of the pure geometrical nanostructure (Cu9) and (Cu10) show that the value of energy gap of those nanostructures is pioneering in the manufacture of the electronic devices. Calculations of the ionization energy and electron affinity disclosure the best geometrical nanostructure to endow the electrons in an interaction, so the calculations disclosure the best geometrical nanostructure to acquire the electrons in an interaction. Calculations of dipole moment and polarizability disclosure the largest dipole moment and the highest activity geometrical nanostructure in the research under the treatise. Physisorption procedure and charge transfer phenomena make all hybrid geometrical nanostructures have the symmetry C1 . Physisorption phenomena makes some geometrical nanostructures have anti-ferromagnetic features, but it makes other geometrical nanostructures loses the anti-ferromagnetic feature. [ABSTRACT FROM AUTHOR]

Network security is important to maintain security on a network, the data and information contained in it. Based on the protection of data/information security in a network, generally all data-based security theories are created and applied to secure a particular network. Network security methods that have emerged such as using IDS (intrusion detection system), Intrusion Detection System (IDS) or intruder detection system are computer systems (can be a combination of software and hardware) that attempt to detect intrusions. At this time DOS attacks or software that occur to the general public in the form of (browsers, files, videos or images) that often occur with the aim that the server or network is unable to accommodate the traffic, causing the website/service down and cannot operate. Therefore, to assist in these attacks, it is necessary to apply blocking IP and PORT by using blocking so as to be able to add research on network protection in the internet world.