Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

To secure logic circuits against the illegal copying the insertion of the so-called key bit inputs using XOR/XNOR gates has been proposed in . The inserted gates modify (lock) the behavior of the original circuit and only the correct choice of the secret key bits guarantees the equivalent behaviour of the locked circuit to the original circuit. To break this kind of security, i.e. to find the secret key bits, there was a SAT solver based attack proposed in . The aim of this work is to experimentally verify this attack on several locked circuits with various number of primary inputs and outputs and with various number of gates. Except for the random placement of the key gates we evaluate also the positioning with chaining elimination. The obtained and presented results show that we were able to unlock 10 of 11 circuits within few minutes on a common PC.

The lattice reduction aided(LRA) algorithm has attracted widely attention of researchers in MIMO systems because of its low complexity to achieve full diversity. However, most lattice reduction auxiliary algorithms do not directly improve system performance. In this paper, a modified lattice reduction successive interference cancellation (SIC) algorithm is proposed for MIMO OFDM system mild changing the complexity of LRA algorithm. At present, the MMSE SQRD algorithm has the problem of regarding too much useful signals as the residual interference which cannot be cancelled by successive interference cancellation. Since the diagonal elements of useful signals are non-zero, the components of the symbols to be detected still present in the interference term, treating all the signals as interference will be resulted in performance loss. To solve this problem, the modified matrix is proposed.by moving this symbol component into the signal term, the proposed algorithm can provide the better performance than MMSE SQRD algorithm. The analysis shows that the LRA-MMSE-SIC algorithm of proposed in this paper significantly improves the system performance with mild increasing the complexity. The simulation results show that the performance of the proposed algorithm is improved by 1dB and 8dB compared with the comparison algorithm when the bit error rate is 10−3.

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

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

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

As argued by ‘Declaration of Concrete Environment (2010)’ of Korea and ‘Declaration of Asian Concrete Environment (2011)’ of six Asian countries, concrete as a single material has lately shown extremely large impact on environmental issues such as climate change. Assessment of environmental impact from concrete material and production has considerable importance. Concrete is a major material used in the construction industry that emits a large amount of substances with environmental impacts during its life cycle. Accordingly, technologies for the reduction in and assessment of the environmental impact of concrete from the perspective of Life Cycle Assessment must be developed. At present, the studies in relation to greenhouse gas emission from concrete are being carried out globally as a countermeasure against climate change. In this study, a sustainable concrete mix design algorithm was designed using correlation analyses, and its carbon emission and cost reduction performances were assessed. Using correlation analyses, the concrete strength, w/b and s/a ratios, and CO₂ emissions were identified as major variables of concrete mix design that influenced other variables. Also, this study aims to evaluate the CO₂ emission reduction performance of the algorithm-deduced sustainable concrete mix design, and therefore, the CO₂ emissions of the sustainable concrete mix design are compared with those of the actual concrete mix design applied to the construction of the office building A in South Korea.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Green synthesis of silver nanoparticles (AgNPs) using raw Whey has never been reported. Silver nanoparticle formation involves a reduction reaction of silver salt solution with Whey extract. Extract and metal salt concentrations, and temperature can control this reaction. In this work, incubation period, extract, AgNO3, and NaOH concentrations were used as the independent factors using central composite design under response surface methodology. The aim was to elucidate the influence these factors have on the measured UV-Vis absorption spectra fitted with a Voigt function, and to optimize the conditions for nanoparticle formation. The fitting parameters, peak wavelength (λ0), peak area (A), and Full Width at Half Maximum (FWHM), were the responses. Silver nanoparticle formation was only possible in an alkaline environment (pH 10). The peak wavelength and the FWHM are influenced mainly by extract and AgNO3 concentrations. In contrast, the peak area is influenced by a total of 33.54 % from the interaction terms of AgNO3 and extract concentrations with NaOH. Metallic AgNPs were formed for the following parameter ranges: a) Whey (0.4 – 1.6 % v/v), b) AgNO3 (0.15 – 0.6 mM), c) NaOH (10 – 50 mM), and d) Time (20 – 60 min). Silver oxide nanoparticles were also observed.

Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop's Boolean subtraction method, this study combined walled TPMS gyroid structures with normal TPMS gyroid lattice. This made a composite TPMS-gyroid lattice with relative densities ranging from 14% to 54%. Using the ideaMaker software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer aided design (CAD) models and fabricate 36 lattices samples precisely layer by layer technique. Shimadzu 100kN testing equipment was utilized for the mechanical compression experiments. And the validation using finite element analysis (FEA). Further, CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson-Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice's relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS-gyroid lattices in high-speed crash box scenarios, potentially enhancing vehicle safety and performance.

Arylmagnesium and -calcium reagents are easily accessible, however, ether degradation processes limit the storability especially of the calcium-based heavy Grignard reagents. Ortho-bound substituents with phosphanyl donor sites usually block available coordination sites and stabilize such complexes. The reaction of bromo-2,6-bis(diphenylphosphanylmethyl)benzene (1a) with magnesium in tetrahydrofuran yields [Mg{C₆H₃-2,6-(CH₂PPh₂)₂}₂] (2) after recrystallization from 1,2-dimethoxyethane. However, the similarly performed reduction of bromo- (1a) and iodo-2,6-bis(diphenylphosphanylmethyl)benzene (1b) with calcium leads to ether cleavage and subsequent degradation products. α-Deprotonation of THF yields 1,3-bis(diphenylphosphanylmethyl)benzene. Furthermore, the insoluble THF adducts of dimeric calcium diphenylphosphinate halides, [(thf)₃Ca(X)(µ-O₂PPh₂)]₂ [X = Br (3a), I (3b)], precipitate verifying ether decomposition and cleavage of P-C bonds. Ether adducts of calcium halides [such as [(dme)₂(thf)CaBr₂] (4)] form supporting the initial Grignard reaction and a subsequent Schlenk-type dismutation reaction.

This report covers the analysis of Intrusion Detection Systems nowadays. Therefore, it focuses on the IDS classification, which includes not only a well-known explanation of Network/Host-Based IDS type but also cloud-based solutions. The paper explores ways of intrusion detection, providing examples of the best detecting tools (OSSEC, Snort or Bro (ZEEK)). Another aspect covered in this report is the juxtaposition of three systems: IDS, IPS and IRS and their response. Finally, there will be presented Intrusion Detection Systems' evasion techniques and challenges, followed by critical conclusions.

Materials based on platinum metals with very good catalytic activity were tested in the reduction of p–nitrophenol (pNP) to p–aminophenol (pAP). The majority of presented catalytic materials are based on the adsorption of p–nitrophenol on the surface of the catalyst simultaneously with molecular hydrogen, coming from sodium borohydride in an aqueous medium, which reduces it. In the present paper, a catalytic material based on osmium dispersed in n–decanol or n–dodecanol is presented, which also works as an emulsion membrane, for the absorption of p–nitrophenol and molecular hydrogen at the n–alcohol aqueous solution interface. The hydrogenation of p–nitrophenol is carried out in a reaction and separation column in which the acid receiving phase emulsion is dispersed in the osmium nanodispersion, in n–alcohols. This emulsion circulates from the base of the column to its upper part in co-current or counter-current with the source phase consisting of p–nitrophenol and sodium borohydride. Experiments show that the circulation of counter-current phases is superior to that in co-current, and the emulsion based on n–decanol is more efficient than the one based on n–dodecanol. The increase in the pH difference between the source and receiving phases leads to the increase in the conversion of p–nitrophenol to p–aminophenol. The efficiency of separation of the reaction product in the receiving phase is lower than the conversion at the same operating time. The apparent catalytic rate constant (kapp) of the new catalytic material based on the emulsion membrane with nanodispersion of osmium nanoparticles (0.1×10−3 for n–dodecanol and 0.9×10−3 for n-decanol) is lower by an order of magnitude compared to those based on adsorption on catalysts from platinum metal group. The advantage of the tested membrane catalytic material is that it extracts p–aminophenol in the acid receiving phase. The paper proposes a mechanism of the studied reduction system.

In recent years, wireless networks have undoubtedly become a convenient way to connect to the Internet and provide connection to everyone in any corner of the world. In fact, in this era, people are connected to the internet almost everyday and wireless networks give us this privilege in a seamless manner. A wireless network normally consists of access points and nodes where the access points are responsible to amplify the wireless signals, while the nodes are the gadgets that are receiving these signals. However, with such great convenience provided, many challenges are also faced by the users and stakeholders. With no physical connection to devices, wireless networks are evidently more vulnerable to invisible cyber attacks. In this research paper, it the security issues that cause issues in the wireless networks are discussed. Furthermore, an analytical review of privacy challenges found in these networks is performed; these challenges are segregated into security issues and privacy issues. The paper will then present the methods used in conducting a survey and gathering the research results along with further discussion on the results obtained through this study. Finally, a suitable solution is proposed to prevent and overcome the intrusions faced in terms of security and privacy in wireless network scenarios through detection and response mechanism for mitigation of the problems.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

Recently, with the rapid advancement of technology, Internet of Things (IoT‏‏‏‏) security has become more efficient and more complex, especially for resource-limited devices such as embedded devices, wireless sensors and radio frequency identification tags (RFID). Lightweight block ciphers (LBCs) provide security for these technologies to protect them against adversaries, but the need for low power consumption in LBCs is one of the most important challenges for IoT‏ technologies. Furthermore, these LBCs are subject to multiple attacks and side channel attacks (SCAs) are among the mentioned threats to these cryptosystems. A type of SCA is correlation power analysis (CPA) in which the attacker tries to reach the key using the relationship between the power consumption of the chip during the algorithm running, data processing, and operations. In this article, a CPA attack is designed to discover master key of the Midori-64 block cipher. According to the proposed method, an attack is done to the first round S-boxes to get half of the key bits. Then, the second round S-boxes were attacked to other half of key bits just use 300 plain text samples. Finally, the most important physical attacks performed on the Midori, are compared to our proposed CPA attack.

The uncertainty in the secondary path of an Active Noise Control (ANC) headphone affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones, and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized by the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensures more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

We develop an 1/n expansion method for the systematic approxi- mation of the solutions of classical spin systems (tri-dimensional vec- tors) which can describe general spin-spin interactions and phases with complex magnetization pattern. This method is applied to study the phase transitions observed in pyrochlore crystals (e.g. Gd₂ Ti₂ O₇ ) and the results have qualitative agreement with the experimental ones.

Susceptible, infective, recovered, and hospitalized/isolated individuals are placed on the cells of a nxn square lattice, where each cell is occupied by a single individual or is vacant. At discrete time units (typically one day each) all susceptibles and infectives execute a random movement and when a coincidence of the two types occurs the susceptible is converted to infective status according to some probability. Infectives are labelled by the number of days since originally infected. At each time increment the age label of the infectives is increased by one unit. When the label reaches a number like 15 or 20 days the susceptibles recover with some probability or become isolated/hospitalized. Upon reaching some age the latter types either recover or die. Probabilities for the movements and conversions from one status to another are implemented by random numbergeneration. Simulations were carried out to investigate the effect of several probability and age parameters, the size of population (proportional to nxn) and density (related to fraction of occupied cells), and the size of the movements. Mid-term gradual conversion of susceptibles to isolated was explored as intervention policy.

Quantum nonlocality represents correlation properties between subsystems of a composite quantum system, usually including the four types: Bell nonlocality, steerability, entanglement, and quantum correlation (quantum discord). Given a basis $e_{AB}=\{|e_{ij}\>\}_{i\in[d_A],j\in[d_B]}$ for the Hilbert space $\H_A\otimes \H_B$ of a bipartite system $AB$, a density operator $\rho^{AB}$ (quantum state) of $AB$ can be represented as a $d_Ad_B\times d_Ad_B$ matrix $\hat{\rho}_{e_{AB}}=[\]$, called the density matrix of a density operator $\rho^{AB}$. A natural question is what is the relationship between the quantum nonlocality of a density operator $\rho^{AB}$ and its corresponding density matrix $\hat{\rho}_{e_{AB}}$? In this work, we discuss the relationships between quantum locality and basis, and prove that one type of quantum locality of density operators and that of their density matrices under a basis are the same if and only if the chosen basis is the tensor product of the bases of subsystems. Consequently, different bases define different quantum nonlocality density operators.

Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

The aim of this work is to broaden the theory of computation by making use of fuzzy sets and multisets, which are generalization of ordinary sets. We study fuzzy multiset finite automata (FMFA), which are characterized by the incorporation of vagueness when it comes to the choice of the next state and multiple occurrences of the same input symbol. The resulting automaton is a genaralization of both classical automata and fuzzy automata. We discuss the algebraic characteristics of FMFA sub-automata—separatedness, strongly connectedness, cyclic, directable, and triangular FMFA in terms of equivalence classes induced by an equivalence relation defined on state set of FMFA. Intrestingly, for a given FMFA, a new FMFA can be constructed that is a homomorphic image of the original FMFA. We define different posets structures that can be associated with a given FMFA and show that some of them are upper semilattices and discuss the inter-relationship of a given poset, a finite upper semilattice (FUSL), a FMFA and a posets/FUSL associated with a given FMFA. Finally, we introduce the concept of decomposition of a FMFA in two different ways and characterize the strongly connected, triangular and directable FMFA.

A new set of basis functions is presented. The foundation in mathematics is established. To reconstruct a signal, it is compared to the Fourier basis functions. The M-basis functions' potential applications are also presented.

Mobile Ad hoc Networks (MANETs) play an important role in the technological era by enabling wireless communication in dynamic and infrastructure-free contexts. Because of Manet’s unique qualities and decentralized nature, MANETs are subject to many attacks such as the Blackhole attack, grey hole attack, Byzantine attack, Wormhole attack, and Sybil attack. Sybil attacks pose a substantial risk to MANETs. A Sybil attack occurs when a malicious node impersonates many identities inside the network, resulting in the creation of multiple suspicious nodes that appear as independent entities. This study presents a thorough analysis of the literature on the detection of Sybil attacks on MANETs. To find gaps in the literature, current surveys are also looked at. Also taken into consideration are several contemporary systems built on distinct techniques. In terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end latency, some recent methods-based approaches are also evaluated severely. To address the stated state-of-the-art issues in Sybil attack detection, this paper suggests ML-based strategies as the most suitable answer. Machine learning has huge potential for effective wireless network management. According to the researcher, this is the first comprehensive assessment of ML-based techniques for Sybil attack detection in wireless networks. Finally, we considered the unsolved issues in Sybil assault detection in wireless networks.

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

The danger is still very large from cross site scripting attacks. When designing web applications We must follow as much as we can prevention rules and don’t leave any loophole to our programs. Experience is a great factor for programmers to know these attacks and for a trainee programmer is a difficult task to spot all the weak points. In this article we present a tutorial on launching an XSS attack and also we propose simple solutions.

IoTs and WSNs utilize their connectivity to enable solutions supporting a spectrum of industries in different and volatile environments. To effectively enhance the security and quality of service of networks, empirical research should take into account a variety of factors and be structured in a replicable manner. This will not only ensure scalability but also enable verification of conclusions, leading to more constructive outcomes. Cooja offers limited performance analysis capabilities of simulations, which are often extracted and calculated manually. In this paper, we introduce a framework and a toolkit that enable researchers to conduct structured and conclusive experiments considering different factors and metrics, experiment design to results analysis. Besides, this tool summarizes multiple network metrics and allows multiple scenarios. As proof of concept, this paper studies the flooding attacks on IoT and illustrates their impact on the network, leveraging the proposed framework and tools.

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

Biomimicry in additive manufacturing often refers to topology optimization and the use of lattice structures, due to the organic shape of the topology-optimized designs, and the lattices often looking similar to many light-weight structures found in nature such as trabecular bone, wood, sponges, coral, to name a few. Real biomimetic design however involves the use of design principles taken in some way from natural systems. In this work we use a methodology whereby high resolution 3D analysis of a natural material with desirable properties is “reverse-engineered” and the design tested for the purpose. This allows more accurate replication of the desired properties, and adaption of the design parameters to the material used for production (which usually differs from the biological material). One such example is the impact-protective natural design of the glyptodont body armour. In this paper we report on the production of body armour models in metal (Ti4Al4V) and analyze the resulting mechanical properties, assessing their potential for impact protective applications. This is the first biomimetic study using metal additive manufacturing to date.

Master equations define the dynamics that govern the time evolution of various physical processes on lattices. In the continuum limit, master equations lead to Fokker-Planck partial differential equations that represent the dynamics of physical systems in continuous spaces. Over the last few decades, nonlinear Fokker-Planck equations have become very popular in condensed matter physics and in statistical physics. Numerical solutions of these equations require the use of discretization schemes. However, the discrete evolution equation obtained by the discretization of a Fokker-Planck partial differential equation depends on the specific discretization scheme. In general, the discretized form is different from the master equation that has generated the respective Fokker-Planck equation in the continuum limit. Therefore, the knowledge of the master equation associated with a given Fokker-Planck equation is extremely important for the correct numerical integration of the latter, since it provides a unique, physically motivated discretization scheme. This paper shows that the Kinetic Interaction Principle (KIP) that governs the particle kinetics of many body systems, introduced in [G. Kaniadakis, Physica A 296, 405 (2001)], univocally defines a very simple master equation that in the continuum limit yields the nonlinear Fokker-Planck equation in its most general form.

New q-series in the spirit of Jacobi have been found in a publication rst published in 1884 written in Russian and translated into English in 1928. This work was found by chance and appears to be almost totally unknown. From these entirely new q-series, fresh lattice sums have been discovered and are presented here.

A modified Nambu{Jona-Lasinio Model with lattice structure is very instructive. It shows several similar problems and their solutions as the Lattice QCD. We study the limits of the large box size, small cell size and realistic pion mass. In particular, we study the relation of the discrete (bound state) solutions to the physical scattering states, for example the pion-pion scattering.

We report the strong coupling between plasmonic surface lattice resonances (SLRs) and photonic Fabry-Pérot (F-P) resonances in a microcavity embedded with two-dimensional periodic array of metal-insulator-metal nanopillars. For such a plasmonic-photonic system, we show that the SLR can be strongly coupled to the F-P resonances of both the odd- and even orders, and that the splitting energy reaches as high as 138 meV in the visible regime. We expect that this work will provide a new scheme for strong coupling between plasmonic and photonic modes.

Radial basis function (RBF) networks are often viewed as instable when used in multi-layered architectures and therefore are mostly used in a single-layered manner. Universal approximation theorems for single-layered RBF networks further render deeper architectures useless. However, deep neural networks have proven their effectiveness on many different tasks. We show that deeper RBF architectures with multiple radial basis function layers are achievable and able to learn. We introduce an initialization scheme for deep RBF networks based on k-means clustering and covariance estimation. We further show how to make use of convolutions to speed up the calculation of a Mahalanobis distance in a partially-connected fashion, similar to convolutional neural networks (CNNs). Finally, we evaluate our approach on image classification as well as speech emotion recognition tasks. Our results show that deep RBF networks perform similar to simple CNNs.

Diffuse optical tomography (DOT) is an optical medical imaging method that can assess the structural properties of tissues and their functional characteristics, such as hemoglobin concentration, water content, as well as lipid concentration, via three-dimensional image reconstruction. This paper presents the theoretical basis and working principle of diffuse optical tomography. The paper explains how the optical properties of tissue can be imaged by photon migration techniques based on diffusion theory. The author presents the reasoning for near-infrared (NIR) imaging as the most effective technique in terms of quantitative recovery of spectroscopic optical parameters. This work also listed various advantages, applications, and challenges of DOT. The paper also briefly discusses current progress in near-infrared medical imaging and its future direction.

The presence of wireless communication grows undeniably more prevalent each year. Since the introduction of the IEEE 802.11 standard for Wireless Local Area Networks (WLAN) in 1997, technologies have progressed to provide wireless accessibility to industries and consumers with growing ease and convenience. As the usage of personal devices, such as phones and watches, that connect to the Internet through Wi-Fi increases, wireless attacks on users are becoming more critical. This paper provides a novel attack model to offer an organized and comprehensive view of the possible attacks on WiFi latest security standards. All exiting attacks will be investigated, with emphasis on more recent attacks, such as the KRACK and PMKID Dictionary attacks. This paper will then analyze the technology offered in the new Wi-Fi Protected Access III (WPA3) security scheme and provide a comprehensive security analysis and discussion to determine whether it has addressed the vulnerabilities of its predecessor. An interesting finding of this paper is that WPA3 still lacks to address all the issues existed in WPA2 and explore other mitigations for future research.

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Investigating the network response to node removal and the efficacy of the node removal strategies are related and fundamental questions in network science. Research studies proposed many node centralities based on the network structure ranking node to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks, and an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node removal strategies to network dismantle with effective node removal strategies from the literature, such as betweenness and closeness node removal over synthetic and real-world networks. We evaluate the network dismantle along node removal using the size of the largest connected component (LCC). We find that, hence the betweenness nodes attack is the best strategy overall, the new node removal strategies based on RW show the highest efficacy in peculiar network topology. Specifically, RW strategies based on covering times emerge as the most effective strategy on a synthetic lattice network and two real-world road networks. Our results may be useful in selecting the best node attack strategies in a specific class of networks and in building more robust network structures.

The present study evaluates the response to betahistine in patients who presented vestibular drops attacks in the context of Ménière's disease (MD) and the factors that can predict an unfavorable response to it. A total of 43 patients were analyzed, out of which 33 were diagnosed with MD. It is a descriptive, cross-sectional study with retrospective data collection. Data as regards age, accompanying symptoms, etiological diagnosis and response to MD treatment were collected. A statistical analysis was carried out, and we found that the disease evolution time and specific alterations in the vestibulospinal and oculomotor physical examination present an unfavorable response to betahistine, as opposed to the use of intratympanic gentamicin, with which symptomatic control is achieved in 100% of the cases.

In addition to identifying and prosecuting cyber attackers, attack attribution activities can provide valuable information guiding the defenders’ security procedures and giving them greater confidence in incident response and remediation. However, technical analysis involved in cyberattack attribution requires high skills, experience, access to up-to-date Cyber Threat Intelligence, and significant investigators’ effort. Attribution results are not always reliable, and skilful attackers often work hard to cover their traces and mislead or confuse investigators. In this article, we present a tool designed to support technical attack attribution and implemented as a machine learning model extending the OpenCTI platform. We also discuss the tool’s performance in the investigation of a recent cyberattack.

The development of wireless sensor networks (WSNs) technology comes with inherent limitations and vulnerabilities that make it exploitable by intruders. The fundamental goal of this article is to solve security problems related to black hole attacks, which interfere with the proper performance of the network and can lead to data leakage and loss. The proposed solution in this article is to apply IDS to a WSN for the first time. We also used the NS2.35 simulator to compare the three routing protocols, AODV, AODV under Hacker Node (HNAODV), and the proposed solution (IDSHNAODV) in the WSN model to reduce the effects of black hole attacks.

Shark unprovoked attacks consist of fatal and non-fatal cases. Numerous cases have been reported involving shark species from Carcharhinus melanopterus with length of 145.5 cm to half-ton Carcharodon carcharias. Currently there are more (P < 0.05) unprovoked non-fatal cases with the average is 28.46 cases/shark species (95%CI: 3.86-53.1) than unprovoked fatal cases, which the average is 5.12 cases /shark species (95%CI: -0.075-10.3). Hence this paper seeks to select the best shark size model that correlates with the unprovoked fatal and non-fatal cases. The studied sharks consist of 24 shark species with the average length is 268.18 cm (95%CI: 230-306 cm) and the average weight is 225.42 kg (95%CI: 128-323 kg). Based on the model and as described by low values of AIC and the highest values of R² and adjusted R² , shark weight followed by combinations of shark weight and length produced unprovoked fatal and non-fatal cases best models. The model for explaining unprovoked fatal cases is the shark weight with high numbers of cases observed in large size shark (weight⁓fatal cases, AIC = 165.359, R² = 0.72, Adj. R = 0.71). While for non-fatal cases, the best model is also the shark weight (weight⁓non fatal cases, AIC = 246.93, R² = 0.63, Adj. R = 0.59).

In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure: adding a cubic truss in the center of the BCC structure, which is denoted by TLC (Truss-lattice-cube). The different dimensions of the central cube can affect the mechanical properties of the lattice structure notably. With a fixed length (15mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of Polylactic Acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure is analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure are significantly improved. The initial flow stress of the TLC has been increased by 122% at a strain of 0.1; the specific strength has been enhanced by 293% at a strain of 0.5; and the specific energy absorption has been improved by 312% at the strain of 0.6. Printing defects in the lattice structure could deteriorate the mechanical property remarkably. In this work, incorporation of microcracks into the finite element model makes the simulation capture the influence of printing defects and improve the predictive accuracy of simulation significantly.

A typical vibrational spectrum in the ice phase has four separate bands: translation, libration, bending and stretching. Ice X, the final ice phase under high pressure, shows an exotic vibrational spectrum. Theoretically, an ideal crystal of ice X only has one peak at 998 cm-1 for Raman scattering and two peaks at 450 cm-1 and 1507 cm-1 for infrared absorption in this work. These three characteristic peaks are indicators of the phase transition between ice VII/VIII and ice X. Despite much experimental and theoretical work on ice X, only this study has clearly indicated these characteristic peaks in the region of the IR band. The phonon density of states shows quite different features than ice VIII, which could be verified by inelastic neutron scattering in the future. The dynamic processes of 15 vibrational normal modes are discussed and the typical hydrogen bonds are missing.

Over the past two decades, covalent organic frameworks (COFs) have become the most widely studied porous crystalline materials. Its specific physical and chemical properties are determined by the arrangement of atoms (crystal structure). Therefore, the determination of its structure is arguably the most important characterization step for COFs. Although single crystal X-ray diffraction (SCXRD) is the most widely used method for structure determination, the size of COF crystals is too small, or their crystal quality is so poor that confirmation of their structure is limited to lattice fringes by transmission electron microscopy (TEM). At present, many 2D-COFs have clear PXRD patterns, but not all 2D-COFs can obtain specific lattice fringes. This severely hinders the development of the COF field. Here, we discover the lattice shrinkage behavior of COFs under electron beam irradiation by comparing the lattice fringes of 2D-COFs under different conditions. By comparing the lattice fringes of TAPT-TFPT COF at room temperature and under liquid nitrogen freezing conditions, we found that the lattice fringes are in good agreement with the Powder X-ray diffraction (PXRD) and theoretical values of COF (2.213 nm) under freezing conditions, however, the lattice fringe spacing is only 1.656 nm at room temperature. The discovery not only provides new insights into the TEM characterization of COFs, but also further expands the range of crystalline COF materials.

Stabilization of planar premixed flame front on a lattice (porous) burner is considered. It is rigorously mathematically proved that there exist two different stabilization regimes: one with flame front located nearby the surface of the burner, and another with flame front located inside the lattice. These two regimes result in qualitatively different gas temperature profiles along the flow, that is monotonic and non-monotonic, respectively. Boundary between the two regimes is described in terms of dependence of the lattice solid material temperature on flow Peclet number. Likewise temperature profiles, such dependencies may be both monotonic and non-monotonic. Transition between the two types of dependencies is controlled by Arrhenius number. Conclusions of the study are supported by numerical analysis. They also compare favourably with available experimental data. Novelty of the present approach is fundamentally rigorous analytical analysis of the problem.

Real-time evolution of the electron densities under excitations in La₂CuO₄ was calculated by the time-dependent density functional theory (TDDFT). The author found, for the first time, under excitations, the electron cloud of Cu²⁺ changes obviously and the characteristic frequencies are 83 meV and 36 meV, respectively, for two different modes. The results are unexpected and close to that of lattice vibrations. The results show that the electron cloud of Cu²⁺ (just like the lattice) can be the electron-pairing medium in high temperature copper oxide superconductors.

By combing continuum topology optimization (TO) method and lattice structure technique, a sandwich aircraft spoiler with a high stiffness-to-weight is designed. TO method is served to produce the shell of the aircraft spoiler and the lattice structure, used as cores, is employed to support the shell. TO problem is established as maximizing the stiffness of the structure with limited material volume. Density-based method is utilized to achieve a 0/1 solution. We then empirically replace the core of the aircraft spoiler by using 3D kagome lattice structure. Two different materials, i.e., aluminum alloy and titanium alloy, are synthetically applied to further reduce the weight and simultaneously improve the strength of the aircraft spoiler. Numerical simulations are conducted to show that the designed aircraft spoiler can meet the service environment with a reduction of its weight by approximately 80% when compared with that of the initial design model. Finally, we have fabricated the designed model with photosensitive resin by using 3D printing technique.

Attention-Deficit/Hyperactivity Disorder (ADHD) exhibits a notable prevalence across diverse populations, with estimates ranging from 4% to 6%. This wide range prompts an investigation into the evolutionary and genetic underpinnings of ADHD, especially considering the markedly different environments faced by ancestral human populations compared to today's structured societies. This study examines the potential adaptive advantages of ADHD-associated traits in hunter-gatherer contexts, where hyperactivity, impulsivity, and novelty-seeking behaviors may have conferred survival benefits. We employ the principles of population genetics, including Hardy-Weinberg equilibrium, selection coefficients, and genetic drift, to model the dynamics of ADHD allele frequencies over time. Additionally, we discuss balancing selection as a mechanism for maintaining genetic diversity and how gene-environment interactions (GxE) contribute to the expression of ADHD in contemporary settings. Our analysis suggests that traits associated with ADHD, while potentially maladaptive in modern structured environments, could have been preserved through evolutionary processes due to their adaptive value in ancestral contexts. Understanding these dynamics provides a comprehensive view of ADHD's prevalence and highlights the significance of genetic and environmental interplay in shaping human behavior.

Keywords: Obesity; hypothalamus; appetite; glucose homeostasis; weight-loss drugs; AGRP; POMC; NTS; incretins.

DNA methylation, one of the major epigenetic mechanisms, plays critical roles in regulating gene expression, genomic stability and cell lineage commitment. Establishment and maintenance of DNA methylation in mammals is achieved by two groups of DNA methyltransferases: DNMT3A and DNMT3B, which are responsible for installing DNA methylation patterns during gametogenesis and early embryogenesis, and DNMT1, which is essential for propagating DNA methylation patterns during replication. Both groups of DNMTs are multi-modular proteins, containing a large N-terminal regulatory region in addition to the C-terminal methyltransferase domain. Recent structure-function investigations of the individual domains or large fragments of DNMT1 and DNMT3A have revealed the molecular basis for their substrate recognition and specificity, intramolecular domain-domain interactions, as well as their crosstalk with other epigenetic mechanisms. These studies highlight a multifaceted regulation for both DNMT1 and DNMT3A/3B, which is essential for the precise establishment and maintenance of lineage-specific DNA methylation patterns in cells. This review summarizes current understanding of the structure and mechanism of DNMT1- and DNMT3A-mediated DNA methylation, with emphasis on the functional cooperation between the methyltransferase and regulatory domains.

The outer membrane of Gram-negative bacteria contains a variety of pore-forming structures collectively referred to as porins. Some of these are voltage-dependent, but weakly so, closing at high voltages. Triplin, a novel bacterial pore-former, is a three-pore structure, highly voltage dependent, with a complex gating process. The three pores close sequentially: pore 1 at positive potentials, 2 at negative and 3 at positive. A positive domain containing 14 positive charges (the voltage sensor) translocates through the membrane during the closing process and the translocation is proposed to take place by the domain entering the pore and thus blocking it resulting in the closed conformation. This mechanism of pore closure is supported by kinetic measurements that show that in the closing process the voltage sensor travels through most of the transmembrane voltage before reaching the energy barrier. Voltage-dependent blockage of the pores by polyarginine but not by a 500 fold higher concentrations of polylysine, is consistent with the model of pore closure, with the sensor consisting mainly of arginine residues, and with the presence, in each pore, of a complementary surface that serves as a binding site for the sensor.

The Universe has a gravitational horizon with a radius R_h = c/H coincident with that of the Hubble sphere. This surface separates null geodesics approaching us from those receding, and as free-falling observers within the Friedmann-Lemaitre-Robertson-Walker spacetime, we see it retreating at proper speed c, giving rise to the eponymously named cosmological model R_h = ct. As of today, this cosmology has passed over 20 observational tests, often better than LCDM. The gravitational radius R_h therefore appears to be highly relevant to cosmological theory, and in this paper we begin to explore its impact on fundamental physics. We calculate the binding energy of a mass m within the horizon and demonstrate that it is equal to mc². This energy is stored when the particle is at rest near the observer, transitioning to a purely kinetic form equal to the particle's escape energy when it approaches R_h. In other words, a particle's gravitational coupling to that portion of the Universe with which it is causally connected appears to be the origin of rest-mass energy.

We reelaborate on the basic properties of PT symmetry from a geometrical Perspective. The transfer matrix associated with these systems induces a Möbius transformation in the complex plane. The trace of this matrix classifies the actions into three types that represent rotations, translations, and parallel displacements. We find that a PT invariant system can be pictured as a complex conjugation followed by an inversion in a circle. We elucidate the physical meaning of these geometrical operations and link them with measurable properties of the system.

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

Analyzing network robustness against random failures or malicious attacks is a critical research issue in network science as it helps to enhance the robustness of beneficial networks or efficiently disintegrate harmful networks. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, an attack may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on 6 representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines.

The rapid development of quantum computing devices promises powerful machines with capabilities that solve a wide range of problems that traditional computers cannot. Therefore, quantum computers generate new threats at unprecedented speed and scale and specifically pose an enormous threat to encryption. Lattice-based cryptography is considered to be the rival to a quantum computer attack and the future of post-quantum cryptography. So, cryptographic protocols based on lattices have a variety of benefits, like security, efficiency, lower energy consumption, and speed. In this work, we study the most well-known lattice-based cryptosystems while a systematic evaluation and comparison is presented also, and focuses on their strengths and weaknesses.

The performance of lattice-Boltzmann solver implementations usually depends mainly on memory access patterns. Achieving high performance requires then complex code which handles careful data placement and ordering of memory transactions. In this work, we analyse the performance of an implementation based on a new approach called the data-oriented language, which allows the combining of complex memory access patterns with simple source code. As a use case, we present and provide the source code of a solver for D2Q9 lattice and show its performance on GTX Titan Xp GPU for dense and sparse geometries up to 4096 2 nodes. The obtained results are promising, around 1000 lines of code allowed us to achieve performance in the range of 0.6 to 0.7 of maximum theoretical memory bandwidth (over 2.5 and 5.0 GLUPS for double and single precision, respectively) for meshes of size above 1024 2 nodes, which is close to the current state-of-the-art. However, we also observed relatively high and sometimes difficult to predict overheads, especially for sparse data structures. The additional issue was also a rather long compilation, which extended the time of short simulations, and a lack of access to low-level optimisation mechanisms.

Flavonoids are a biochemically diverse group of specialized metabolites in plants that are derived from phenylalanine. While the biosynthesis of the flavonoid aglycone is highly conserved across species and well characterized, numerous species-specific decoration steps and their relevance remained largely unexplored. The flavonoid biosynthesis takes place at the cytosolic site of the endoplasmatic reticulum (ER), but accumulation of various flavonoids was observed in the central vacuole. A universal explanation for the subcellular transport of flavonoids has eluded researchers for decades. Current knowledge suggests that a glutathione S-transferase-like protein (ligandin) protects anthocyanins and potentially proanthocyanidin precursors during the transport to the central vacuole. ABCC transporters and to a lower extend MATE transporters sequester anthocyanins into the vacuole. Glycosides of specific proanthocyanidin precursors are sequestered through MATE transporters. A P-ATPase in the tonoplast and potentially other proteins generate the proton gradient that is required for the MATE-mediated antiport. Vesicle-mediated transport of flavonoids from the ER to the vacuole is considered as an alternative or additional route.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between these two diseases is still unknown. In this paper, we analyzed how these diseases relate to each other from a genomics viewpoint. Using an extensive literature search, we accumulated the list of genes from the major genome-wide association (GWAS) studies. However, we found only one gene (HLA-DRB5) reported in these GWAS studies that are common between AD and PD. We also listed all the miRNAs that have been previously reported for AD and PD. Here we found 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene coexpression network for both AD and PD. Network analysis on these networks show six clusters of genes related to AD and four clusters of genes related to PD.

The Avian retina is far less known than that of mammals such as mouse and macaque, and detailed study is overdue. The chicken (Gallus gallus) has potential as a model, in part because research can build on developmental studies of the eye and nervous system. One can expect differences between bird and mammal retinas simply because whereas most mammals have three types of visual photoreceptor birds normally have six. Spectral pathways and colour vision are of particular interest, because filtering by oil droplets narrows cone spectral sensitivities and birds are probably tetrachromatic. The number of receptor inputs is reflected in the retinal circuitry. The chicken probably has four types of horizontal cell, there are at least 11 types of bipolar cell, often with bi- or tri-stratified axon terminals, and there is a high density of ganglion cells, which make complex connections in the inner plexiform layer. In addition, there is likely to be retinal specialisation, for example chicken photoreceptors and ganglion cells have separate peaks of cell density in the central and dorsal retina, which probably serve different types of behaviour.

Vertebrate color vision is evolutionarily ancient. Jawless fish evolved four main spectral types of cone photoreceptor, almost certainly complemented by retinal circuits to process chromatic opponent signals. Subsequent evolution of photoreceptors and visual pigments are now documented for many vertebrate lineages and species, giving insight into evolutionary variation and ecological adaptation of color vision. We look at organization of the photoreceptor mosaic and the functions different types of cone in teleost fish, primates, and birds and reptiles. By comparison less is known about the underlying neural processing. Here we outline the diversity of vertebrate color vision and summarize our understanding of how spectral information picked up by animal photoreceptor arrays is adapted to natural signals. We then turn to the question of how spectral information is processed in the retina. Here, the quite well known and comparatively ‘simple’ system of mammals such as mice and primates reveals some evolutionarily conserved features such as the mammalian Blue^ON system which compares short and long wavelength receptors signals. We then survey our current understanding of the more complex circuits of fish, amphibians, birds and reptiles. Together, these clades make up more than 90% of vertebrate species, yet we know disturbingly little about their neural circuits for colour vision beyond the photoreceptors. Here, long-standing work on goldfish, freshwater turtles and other species is being complemented by new insights gained from the experimentally amendable retina of zebrafish. From this body of work, one thing is clear: The retinal basis of colour vision in non-mammalian vertebrates is substantially richer compared to mammals: Diverse and complex spectral tunings are established at the level of the cone output via horizontal cell feedforward circuits. From here, zebrafish use cone-selective wiring in bipolar cells to set-up color opponent synaptic layers in the inner retina, which in turn lead a large diversity of color-opponent channels for transmission to the brain. However, while we are starting to build an understanding of the richness of spectral properties in some of these species’ retinal neurons, little is known about inner retinal connectivity and cell-type identify. To gain an understanding of their actual circuits, and thus to build a more generalised understanding of the vertebrate retinal basis of color vision, it will be paramount to expand ongoing efforts in deciphering the retinal circuits of non-mammalian models.

Cloud robot is becoming popular and security of cloud robot is important. However, the researches of cloud robot safety are a few. This work develops a security policy to defense DDoS attack of cloud robot. In this policy, complex, but accurate calculation models are deployed on the cloud, simple but efficient calculation models are deployed on the robot. In the cloud, there are master server and standby server. The master server transfers parameters of complex but accurate models to the standby server periodically and the master executes the start-stop backup policy. Specifically, this work proposes and proves an algorithm to dynamically adjust the interval of parameter transfer. According to a PDRA feature of Netflow, when DDoS attacks, the master server sends warning signals to the robot and standby server. The robot runs local models to avoid stopping work until it is connected to the standby server. Then, standby server provides service to the robot until the master server recover. Finally, this work implemented a gesture recognition cloud robot based on convolutional neural network, hidden Markov model and PDRA feature of Netflow to verify the policy. Experiment shows that the security policy to defense DDoS attack for cloud robot is effective.

3D bioprinting holds great promise in the field of regenerative medicine as it can create complex structures in a layer-by-layer manner using cell-laden bioinks, making it possible to imitate native tissues. Current bioinks lack both the high printability and the biocompatibility required in this respect. Hence, the development of bioinks that are capable of both properties is needed. In our previous study, a furfuryl-gelatin based bioink, crosslinkable by visible light, was used for creating mouse mesenchymal stem cell-laden structures with high fidelity. In this study, lattice mesh geometries were printed in a comparative study to test against the properties of a traditional rectangular-sheet. After 3D printing and crosslinking, both structures were analysed for swelling and rheological properties, and their porosity estimated using scanning electron microscopy. Results showed that the lattice structure was relatively more porous but sturdy and exhibited a lower degradation rate compared to the rectangular-sheet. Further, the lattice allowed encapsulation of a greater number of cells, allowing them to proliferate to a greater extent compared to the rectangular-sheet that retained a lesser number of cells initially. All of these results collectively affirmed that the lattice poses as a superior scaffold design for tissue engineering applications.

Non-uniform grain boundary sliding can induce strain and rotation incompatibilities at perfectly planar interfaces. Explicit analytic expressions of stress and lattice rotation jumps are thus derived at a planar interface in the general framework of heterogeneous anisotropic thermo-elasticity with plasticity and grain boundary sliding. Both elastic fields are directly dependent on in-plane gradients of grain boundary sliding. It is also shown that grain boundary sliding is a mechanism that may relax incompatibility stresses of elastic, plastic and thermal origin although the latter are not resolved on the grain boundary plane. This relaxation may be a driving force for grain boundary sliding in addition to the traditionally considered local shears on the grain boundary plane. Moreover, the obtained analytic expressions are checked by different kinds of bicrystal shearing finite element simulations allowing grain boundary sliding and where a pinned line in the interface plane aims at representing the effect of a triple junction. A very good agreement is found between the analytic solutions and the finite element results. The performed simulations particularly emphasize the role of grain boundary sliding as a possible strong stress generator around the grain boundary close to the triple line because of the presence of pronounced gradients of sliding.

Seeds play a vital role in the perpetuation of plant species, both in natural environments and agriculture. However, they often face challenges from biotic stresses, such as seedborne pathogenic fungi. The transgenerational transmission of these seedborne fungi, along with their dissemination during seed commercialization, can contribute to the emergence of global epidemic diseases, resulting in substantial economic losses. One such pathogenic fungus is Alternaria brassicicola, responsible for causing black spot disease in Brassicaceae. This necrotrophic seedborne fungus negatively impacts plant development, consequently affecting crop production and seed quality. To gain a deeper understanding of the interaction between seeds and necrotrophic fungi, the Arabidopsis thaliana/Alternaria brassicicola model pathosystem has been extensively studied. Recent transcriptomic and genetic studies revealed a distinctive immune response of seeds against this pathogen, divergent from the immune response described in the plant vegetative phase. In germinating seeds and seedlings, the necrotrophic fungus seems to induce susceptible response [1]. It was observed at the RNA level that to the detriment of jasmonic acid pathway, Alternaria induced the salicylic acid and glucosinolate (GSL) pathways. Genetic evidence identified that seedling necrosis, potentially beneficial to the growth of the necrotrophic agent, is hypersensitive response mediated by GSL pathway. Transmission of necrotrophic fungal from seed to seedling unveils novel regulations of the immune response, that would illustrate a trade-off between seedling establishment and fungal infection. This knowledge is promising to sustainably ensure the sanitary quality of crops.

Due to the advancement and availability of internet in present age, device tend to become wireless as well. Mobile ad-hoc network is a wireless environment that consist of advanced agility of nodes and does not contain on any central administrative authority. As it does not contain any central administrative authority so being wireless environment, the network is vulnerable to security threats and suffers in performance as well. Security of MANET is the core issue in this era, meanwhile the data is sent over untrustworthy network. Several techniques were proposed to secure data over Mobile Adhoc Network though cryptography that is one of the most reliable techniques to protect data. In our proposed methodology a Secure Cryptographic technique for Mobile Adhoc Networks (SCTMA) we proposed A Collusion attack prevention (CAP) scheme with ElGamal key generator along with DNA encryption Algorithm is used to make it more secure. The proposed scheme protects the network against collusion attacks and improves the performance as well. Performance analysis will be measured on the basis of key generation time, response time, encryption time and decryption time. The proposed scheme also protects the network against collusion attacks.

The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to train deep learning models effectively. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative Adversarial Networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyber attack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications.

Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attack and normal behaviors makes it challenging to accurately identify disguised attacks. This paper proposes ConLBS, an attack investigation approach that combines the contrastive learning framework and multi-layer Transformer network to realize the classification of behavior sequences. Specifically, ConLBS constructs behavior sequences describing behavior patterns from audit logs, and a novel lemmatization strategy is proposed to map the semantics to the attack pattern layer. Four different augmentation strategies are explored to enhance the differentiation between attack and normal behavior sequences. Moreover, ConLBS can perform unsupervised representation learning on unlabeled sequences, and can be trained either supervised or unsupervised depending on the availability of labeled data. The performance of ConLBS is evaluated in two public datasets. The results show that ConLBS can effectively identify at-tack behavior sequences in the cases of unlabeled data or less labeled data to realize attack investigation, and achieve the superior effectiveness compared to existing methods and models.

Polarons, quasiparticles resulting from the interaction between an impurity and the collective excitations of a medium, play a fundamental role in physics, mainly because they represent an essential building block for understanding more complex many-body phenomena. In this manuscript, we study the spectral properties of a single impurity mixed with identical bosons in a one-dimensional lattice with power-law hopping. In particular, based on the so-called T-matrix approximation, we show the existence of well-defined quasiparticle branches for several tunneling ranges and for both, repulsive and attractive impurity-boson interactions. Furthermore, we demonstrate the persistence of the attractive polaron branch when the impurity-boson bound state is absorbed into the two-body continuum and that the attractive polaron becomes more robust as the range of the hopping increases. The results discussed here are of relevance for the understanding of the equilibrium properties of quantum systems with power-law interactions.

The presented research article shows a functionally graded lattice as an infill structure in designing a 3D printed mechanical element—a bicycle crank arm. The authors want to answer the fundamental question: whether the functionally graded lattice structures can efficiently improve structural performance and how they can be implemented in real complex components. Intuitively, everyone senses that using such elements must bring profit. A good example might be nature, which commonly reaches for such a solution in the skeletons of animals and humans. Two aspects determine their realisations: the lack of adequate construction and analysis methods and the limitations of existing manufacturing methods. Thus, the authors used relatively simple crank arm design and design exploration methods for structural analysis. The design exploration method enabled us to find the optimum solution efficiently. A developed prototype was created utilising additive manufacturing. One of the 3D printing methods - fused filament fabrication technology for metals, allowed the prototype of a crank arm with the optimised infill to be made. As a result, the authors developed a lightweight and manufacturable crank arm showing the new construction and analysis method implementable in similar 3D printed elements.

Laser powder bed fusion (L-PBF) is a type of additive manufacturing technology that processes metal powders into a component. Support structures are an essential part of the L- PBF process as they transfer the laser-induced heat during and shortly after the process to the substrate, sustaining positional accuracy of downward facing surfaces of the component. Since the use of support structures is inevitable, optimized designs for them are crucial in realizing more sustainable production process. In a serial production setup, reducing the lead time and cost of a non-value-added process step like support structure removal is of significance when improving the overall business case and competitiveness.The goal of this study was to verify the applicability of lattice-based support structures for L-PBF. To achieve this, different lattice types as support structures were designed. They were tested, compared and verified for a Siemens gas turbine component. The results showed that the generated lattice-based support structures could be suitable for L-PBF. The supports had to be designed appropriately such that they could preserve the geometry of the part. Furthermore, they had to have a short fabrication time and to be removable easily, preferably without machining or sawing.

We present a unified approach to the thermodynamics of hadron-quark-gluon matter at finite temperatures on the basis of a quark cluster expansion in the form of a generalized Beth-Uhlenbeck approach with a generic ansatz for the hadronic phase shifts that fulfills the Levinson theorem. The change in the composition of the system from a hadron resonance gas to a quark-gluon plasma takes place in the narrow temperature interval of 150−185 MeV where the Mott dissociation of hadrons is triggered by the dropping quark mass as a result of the restoration of chiral symmetry. The deconfinement of quark and gluon degrees of freedom is regulated by the Polyakov loop variable that signals the breaking of the Z(3) center symmetry of the color SU(3) group of QCD. We suggest a Polyakov-loop quark-gluon plasma model with O(αs) virial correction and solve the stationarity condition of the thermodynamic potential (gap equation) for the Polyakov loop. The resulting pressure is in excellent agreement with lattice QCD simulations up to high temperatures.

Efficient member revocation and strong security against attacks are prominent requirements in group signature schemes. Among the revocation approaches Verifier-local revocation is the most flexible and efficient method since it requires to inform only the verifiers regarding the revoked members. The verifier-local revocation technique uses a token system to manage members’ status. However, the existing group signature schemes with verifier-local revocability rely on weaker security. On the other hand, existing static group signature schemes rely on a stronger security notion called, full-anonymity. Achieving the full-anonymity for group signature schemes with verifier-local revocation is a quite challenging task. This paper aims to obtain stronger security for the lattice-based group signature schemes with verifier-local revocability, which is closer to the full-anonymity. Moreover, this paper delivers a new key-generation method which outputs revocation tokens without deriving from the users’ signing keys. By applying the tracing algorithm given in group signature schemes for static groups, this paper also outputs an efficient tracing mechanism. Thus, we deliver a new group signature scheme with verifier-local revocation that satisfies a stronger security from lattices.

The meniscus is a meniscus-shaped fibrocartilage tissue located between the femur and tibia，it is mainly composed of meniscus cells and related extracellular matrix.The synovial limbus area of the knee joint capsule near the meniscus is divided into red areas with rich blood vessels and white areas with less blood supply according to the distribution of blood vessels,there is a transition zone called the red and white zone between the two;Red zone has better self-repair ability,The injury in this area can be treated by conservative treatment or surgical suture;Once the white area of the meniscus is torn and involves the free edge area,It is often necessary to partially remove the damaged meniscus.When most of the entire meniscus is severely torn and involved,not only the course of the disease is very long, but it cannot be repaired by sutures,Often a subtotal or total meniscus resection is required,whether it is a partial meniscus resection, a subtotal meniscus resection or a complete resection.In the later period, it may cause quadriceps atrophy and osteoarthritis (OA）.OA is a refractory multi-system disease,involve the patient’s peripheral joints,it has high disability and teratogenicity, and is very harmful to human health.Chondrocyte pyrolysis, degradation, and inflammation play a vital role in the destruction of OA articular cartilage and chondrocyte apoptosis.Meniscus stem cells have strong proliferation and differentiation ability,has become one of the hot spots in the field of meniscus repair,this article studies the role of meniscal stem cells in the development of OA.

The risk factors associated with COVID-19 related severity, morbidity, and mortality, i.e., obesity (often associated with NAFLD), hyperglycemia, hypertension and dyslipidemia all cluster together as metabolic syndrome (MetS). Instead of studying association of these risk factors with COVID-19, it makes sense studying the association between MetS on one hand and COVID-19 on the other. This study explores a molecular basis underpinning the above association. Severity of COVID-19 patients with MetS could be due to functional alterations of host proteins due to their interactions with viral proteins. We collected data from Enrichr (https://amp.pharm.mssm.edu/Enrichr/), DisGeNET (https://www.disgenet.org/) and others and carried out enrichment analysis using Enrichr. Various biological processes and pathways associated with viral protein interacting partners are known to involve in metabolic diseases. The molecular pathways underlying insulin resistance, insulin signaling and insulin secretion are not only involved in diabetes but also in CVD and obesity (associated with non-alcoholic fatty liver disease; NAFLD). Lipid metabolism/lipogenesis, fatty acid oxidation and inflammation are associated with MetS. Viral interacting host proteins are associated and enriched with terms like hyperglycemia, coronary artery disease, hypertensive disease related to CVD and liver diseases in DisGeNET. Association of viral interacting proteins with disease-relevant biological processes, pathways and disease-related terms suggests that altered host protein function following interaction with viral proteins might contribute to frequent occurrence and/or severity of COVID-19 in subjects with MetS. Such analysis not only provides a molecular basis of comorbidity but also incriminates host proteins in viral replication, growth and identifies possible drug targets for intervention.

The nucleon-pair shell model (NPSM) with broken symmetry basis is studied. The results demonstrate the validity of NPSM with symmetry broken basis, which leads to a significant reduction in the dimensionality of the multi-pair configuration space. We have observed that the axial-deformed basis provides a satisfactory description of the low-lying spectrum, even without the need for the projection procedure. In the case of the triaxial-deformed basis, we have investigated the variation after angular momentum projection, indicating the potential for NPSM to find exact solutions in the half-closed system.

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through deep learning models such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.

Smart grid capabilities have grown significantly in recent years. The smart grid provides advanced real-time handling of faults, advanced automatic control for efficient electricity transmission, monitoring and collection of the electrical system's capacity, and communication for information sharing. Unfortunately, its exposure to public networks makes it increasingly vulnerable to privacy breaches, vulnerabilities, and cyber-attacks. Cyber security threats and vulnerabilities in smart grid networks have become a primary concern that needs to be addressed before deploying a smart grid. Furthermore, the wide range of protocols increases the attack surface of a smart grid. This study focuses on the vulnerability of Modbus, which is regarded as one of the most prevalent protocols in smart grid communication networks. This paper presents preliminary findings of analyzing cyber-attacks against the Modbus protocol using a virtual testbed to investigate its effects on the smart grid network protocol. The concept incorporates an emulated Modbus/TCP network environment built from open-source software components that imitate fundamental industrial control features of the smart grid. Finally, we analyze the cycle of a cyber-attack leading through Reconnaissance to a DoS attack on the Modbus/TCP protocol and propose improvements to the test bed for protocol attack detection and mitigation.

Replacing carbon by hydrogen is a huge step towards reducing CO₂ emissions in the iron- and steel-making industry. The reduction of iron oxides using hydrogen plasma smelting reduction as an alternative to conventional steel-making routes has been studied at Montanuniversitaet Leoben, Austria. The aim of this work was to study the slag formation during the reduction process and the reduction behaviour of iron oxides. Furthermore, the reduction behaviour of iron ore during continuous feeding was assessed. Mixtures of iron ore and calcined lime with a basicity of 0, 0.8, 1.6, 2.3, and 2.9 were melted and reduced by hydrogen. The off-gas composition was measured during the operations to calculate the process parameters. The reduction parameters, namely the degree of reduction, degree of hydrogen utilisation, produced iron, and slag, are presented. The results of the batch-charged experiments showed that at the beginning of the reduction process, the degree of hydrogen utilisation was high, and then, it decreased over the operation time. In contrast, during the continuous-feeding experiment, the degree of hydrogen utilisation could be kept approximately constant. The highest degrees of reduction and hydrogen utilisation were obtained upon the application of a slag with a basicity of 2.3. The experiment showed that upon the continuous feeding of iron ore, the best conditions for the reduction process using hydrogen could be applied.

A triangular lattice model for pattern formation by core-shell particles at fluid interfaces is introduced and studied for the particle to core diameter ratio equal to 3. Repulsion for overlapping shells and attraction at larger distances due to capillary forces are assumed. Ground states and thermodynamic properties are determined analytically and by Monte Carlo simulations for soft outer- and stiffer inner shells, with different decay rates of the interparticle repulsion. We find that thermodynamic properties are qualitatively the same for slow and for fast decay of the repulsive potential, but the ordered phases are stable for temperature range depending strongly on the shape of the repulsive potential. More importantly, there are two types of patterns formed for fixed chemical potential - one for a slow and another one for a fast decay of the repulsion at small distances. In the first case two different patterns - for example clusters or stripes - occur with the same probability for some range of the chemical potential. For fixed concentration, an interface is formed between two ordered phases with the closest concentration, and the surface tension takes the same value for all stable interfaces. In the case of degeneracy, a stable interface cannot be formed for one out of four combinations of the coexisting phases, because of a larger surface tension. Our results show that by tuning the architecture of a thick polymeric shell, many different patterns can be obtained for sufficiently low temperature.

The effects of the ferrocenyl moiety to enhance the nucleophilicity of the carbonyl group, situated at its adjacent position, have been explored in a series of α-ferrocenyl ketocarboxylic acids. In the presence of trifluoroacetic anhydride, 3-ferrocenoylpropionic acid and 4-ferrocenoylbutyric acid gave 5-ferrocenyl-4-trifluoroacetyl-2(3H)-furanone and 6-ferrocenyl-5-trifluoroacetyl-3,4-dihydropyran-2-one, respectively. Under similar reaction conditions, 2-ferrocenylcarbonylbenzoic acid, a keto carboxylic acid without a β-hydrogen, gave a dimerized lactone, 3,3’-diferrocenyl-3,3’-diphthalide possibly due to radical coupling. The nucleophilic attack of carbonyl oxygen, activated by the ferrocenyl moiety, to the carboxylic carbon, is assumed to be the crucial mechanistic step in forming these lactones. When the carbonyl group was reduced to an alcohol to break its conjugation with the ferrocenyl moiety, saturated lactones were isolated after the acidic workup. These results indicate that the α-ferrocenyl carbinols readily undergo solvolysis under acidic conditions, giving ferrocenylcarbenium ions, which are attacked by the carboxy oxygen to give lactones.

Injuries to humans caused by wild animals, particularly bears, are rarely mentioned in the literature. The frequency of such injuries is high in Siberia, which is a territory surrounded by dense forests and is a habitat of brown bears. Only for the last 4 months (September - December) 4 episodes of bear attacks on humans were registered in Khakassia, Russia. We present a clinical case of rehabilitating a patient after a bear attack for multiple fragmentary fracture of the facial skeleton with displacement of bone fragments, subcutaneous emphysema of soft tissues of the face, parietal and right occipital regions, and paranasal sinus hemorrhage on the left side. The nature of the injury was enhanced by the trauma to the upper extremity, with which the patient defended himself against the animal. In addition to the damage to the face, the bear tried to open the cranium, which was evidenced by 4 furrows from the canines, 2 on the frontal and 2 on the occipital bones of the skull. The complex treatment of the patient included both maxillofacial and reconstructive surgery, and at the stage of outpatient treatment the formation of normotrophic scars with the help of neodymium laser and injections of heterogeneous composition consisting of micro-particles of "cross-linked" collagen of animal origin, placed in a gel identical to the natural extracellular matrix.

Although deep learning has received extensive attention and achieved excellent performances in various of scenarios, it suffers from adversarial examples to some extent. Especially, physical attack poses more threats than digital attack. However, existing researches pay less attention to physical attack of object detection in remote sensing images (RSIs). In this work, we systematically analyze the universal adversarial patch attack for multi-scale objects in the remote sensing field. There are two challenges for adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for adversarial patch to show adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide range of height of photography platform causes that the size of objects diverse a lot, which brings challenges for generating universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method on object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Besides, we raise a scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Corona Virus Infectious Disease (COVID-19) is newly emerging infectious disease. This disease is known to the globe in early 2019. Poor status of mental health is often caused by unemployment, ongoing socio-economic condition. Poor mental health may even accelerate the process of panic attack. It has been happening rapidly during COVID-19. It has a great effect on human health. This paper utilizes multiple related factors those have impact on causing panic attack. Recurrent Neural Network (RNN) based framework is utilized in this paper that assembles multiple RNN layers along with other parameters into a single platform. This method is implemented by capturing interfering factors and predicts panic attack tendency of people during COVID-19. Early prediction of panic attacks may assist in saving life from unwanted circumstances.

A new attack against the Kirchhoff-Law-Johnson-Noise (KLJN) key distribution system is introduced. The attack is based on 1) Utilizing the dc-voltage-source - which we put at Alice’s end in our mathematical modeling of the attack-that could exist due to errors, imbalances, Electromagnetic interference and etc.2) On studying the number of samples per bit in the security key that the measured Alice/Bob voltages exceeds or falls below a threshold voltage, respectively. The threshold voltage is the average between dc voltages across low and high resistors- generated by a dc-voltage source. We count the number of samples the voltage at Bob’s end (containing both the noise and dc components) exceeds the threshold voltage and how many times it falls below the threshold to judge whether the resistor can be guessed as low or high for every cycle. Also for a pre-specified key-length we count the number of high resistance estimations per bit –at Bob’s end-according to the previous criterion and the non-successful estimations per bit to judge the final guessed resistor value at Alice’s end and Bob’s end, where if we have more bits with most of its measured samples are above the threshold voltage then we will predict Bob’s resistance as high resistance, otherwise we predict Bob’s resistance to be low resistance. The Simulation was conducted and the attack proved that it is successful unless the temperature increased dramatically to ranges more than a threshold temperature ~ Kelvin that increases when the number of samples per bit increases.

Anyon is collective excitation of two dimensional electron gas subjected to strong magnetic field, carrying fractional charges and exotic statistical character beyond fermion and boson. So far, anyons with serial fractional charges only exist in fractional quantum Hall effect. It is still a challenge to find new serial of fractional charges in other physical system and develop an unified mathematical physics theory based on the same root. Here a topological path fusion theory of propagating electrons in magnetic flux lattice is proposed to explore the physical origin of fractional charges based on a generalization of Feynman's path integral theory and Thurston's train track theory. This mathematical physics theory generated the existed serial of fractional charges in fractional quantum Hall effect and predicted new serial of fractional charges. A serial of irrational charges are predicted in one dimensional lattice of magnetic fluxes. Fractionally charged anyons are also generated in two dimensional and three dimensional lattice of train tracks of electric currents, revealing an exact correspondence between knot lattice model and train track model. A new explanation for the modular symmetry of complex Hall conductance and composite fermion in fractional quantum Hall effect is also derived from this topological path fusion theory. Experimental observation of anyon in three dimensions can be realized by constructing three dimensional interlocking magnetic fluxes or mapping magnetic fluxes into forbidden zones in multi-connected space filled by solid state material. A photonic crystal with porous nano-structures is a promising system for detecting fractional charges and paves a new way for topological quantum computation.

The Landau damping effect was observed in collisionless plasma, as a microscopic resonant mechanism between electromagnetic radiation and the collective modes. In this paper we demonstrate the occurrence of the Landau damping at macroscopic scale in the interaction between water waves and anharmonic lattice of magnetic buoys. By coupling the Navier-Stokes equations for incompressible fluid with the nonlinear dynamics of an anharmonic magnetic lattice we obtain a resonant transfer of momentum and energy between the two systems. The velocity of the flow is obtained in the Stokes approximation with Basset type of drag force. The dynamics of the buoys is calculated in the surfactant approximation for a specific frequency, then we use Fourier analysis to obtain the general time variable interaction. After involving an integral Dirichlet transform we obtain the time dependent expression of the drag force, the interaction waves-lattice with a new term in the form of a Caputo fractional derivative. We compare the results of the model with experiments performed in a wave tank with free floating magnetic buoys under the action of small amplitude gravitational waves. This configuration can be applied in studies for the attenuation with resonant damping of rogue waves, storms or tsunamis.

Diversity as well as temporal and spatial changes of the proportional abundances of different antibiotics (cycling, mixing or combinations thereof) have been hypothesised to be an effective administrative control strategy in hospitals to reduce the prevalence of antibiotic-resistant pathogens in nosocomial or community-acquired infections. However, a rigorous assessment of the efficacy of these control strategies is lacking. The main purpose here is to present a mathematical framework for the assessment of control stategies from a processual stance. To this end, we adopt diverse measures of heterogeneity and diversity of proportional abundances based on the concept of entropy from other fields and adapt them to the needs in assessing the impact of variations in antibiotic consumption on antibiotic resistance. Thereby, we derive a family of diversity measures whose members exhibit different degrees of complexity. Most important, we extent these measures such that they account for the assessment of temporal changes in heterogeneity including otherwise undetected diversity-invariant permutations of antibiotics consumption and prevalence of resistant pathogens. We apply a correlation analysis for the assessment of associations between changes of heterogeneities on the antibiotics and on the pathogen side. As a showcase, which serves as a proof-of-principle, we apply the derived methods to records of antibiotic consumption and prevalence of antibiotic-resistant germs from University Hospital Dresden. Besides the quantification of heterogeneities of antibiotics consumption and antibiotic resistance, we show that a reduction of prevalence of antibiotic-resistant germs correlates with a temporal change of similarity with respect to the first observation of antibiotics consumption, although heterogeneity remains approximately constant. Although an interventional study is pending, our mathematical framework turns out to be a viable concept for the assessment and optimisation of control strategies intended to reduce antibiotic resistance.

“White matter disease” identifies series of different conditions and pathological mechanisms: autoimmune, infectious, toxic-metabolic and vascular. Each of these leads to a global impairment of the neural myelination process through the secondary destruction of previously myelinated structures. To date, the imaging spectrum represents an irreplaceable tool to detect these lesions, describe their distribution patterns and stage them over time. This study aims presents a pictorial review of white matter disease, from pathology to imaging spectrum, reporting current main staging systems with greater emphasis to relationship between cerebrovascular disease and white matter hyper-intensity appearance, and the newest advances in this field.

The osseointegration process in and around additively manufactured (AM) lattice structures of a new titanium alloy, Ti–19Nb–14Zr, was evaluated. Three different implants, including lattices with increasing high sidewalls gradually closing them, were designed, manufactured and implanted in the tibia and metatarsal bone of two sheep for twelve weeks. After removal, they were characterized with X-ray computed tomography (XCT). The 3D XCT images were segmented using machine learning. The bone-interface implant (BII) and bone-implant contact (BIC) were studied. The results show that, since AM naturally leads to high roughness surface finish, the wettability of the implant is increased. The new alloy possesses an increased affinity to the bone enhancing the quality of osseointegration. The lattice provides crevices, in which the biological tissue can jump in and cling. The combination of these factors is pushing ossification beyond its natural limits. Therefore, the quality and speed of the ossification and osseointegration in and around these Ti–19Nb–14Zr AM laterally closed lattice implants open the possibility of bone spline key of prostheses. This enables the stabilization of the implant into the bone while keeping the possibility of punctual hooks allowing the implant to be removed more easily if required.

The changes in the properties of three biological events, that occur with cerebral aging, are discussed. These adverse changes already begin to develop early in mid-life and gradually become more pronounced with senescence. Essentially, they are reflections of the progressive decline in effectiveness of key processes, resulting in the deviation of essential biochemical trajectories to ineffective and ultimately harmful variants of these programs. The emphasis of this review is the major role played by mitochondria in such transition of these three important processes toward more deleterious variants as brain aging proceeds. 1. The immune system. The shift away from an efficient immune response to a more unfocused, continuing inflammatory condition. Such a state is both ineffective and harmful. 2. Reactive oxygen species are important intracellular signaling systems. Also, microglial phagocytic activity utilizing short lived reactive oxygen species contribute to the removal of aberrant or dead cells and bacteria. These processes are transformed into an excessive, untargeted and persistent generation of pro-oxidant free radicals (oxidative stress). 3. The normal efficient neural transmission is modified to a state of undirected, chronic low-level excitatory activity. Each of these changes is characterized by the occurrence of continuous activity that is inefficient and diffuse. The signal/noise ratio of several critical biological events is thus reduced as beneficial responses are gradually replaced by their impaired and deleterious variants.

Abstract Although we know something about single cell neuromuscular junction, It is still mysterious how multiple skeletal muscle cells coordinate to complete the intricate spatial curve movement. Here I propose a hypothesis that skeletal muscle cell populations with action potentials are alligned according to a curved manifolds on space(a curved shape on space) and the skeletal muscle also moves according to this corresponding shape(manifolds) when an specific motor nerve impulses are transmitted. the action potential of motor nerve fibers has the characteristics of time curve manifold and this time manifold curve of motor nerve fibers come from visual cortex in which a spatial geometric manifolds are formed within the synaptic connection of neurons. This spatial geometric manifolds of the synaptic connection of neurons orginate from spatial geometric manifolds in outside nature that are transmitted to brain through the cone cells and ganglion cells of the retina.Further,the essence of life is that life is an object that can move autonomously and the essence of life's autonomous movement is the movement of proteins. theoretically, due to the infinite diversity of geometric manifold shapes in nature, the arrangement and combination of 20 amino acids should have infinite diversity, and the geometric manifold formed by protein three-dimensional spatial structure should also have infinite diversity.

Telomeres are crucial structures that preserve genome stability. Their progressive erosion over rounds of DNA duplication determines senescence of cells and organisms. In a classic view, telomere biology impinges on intracellular signaling pathways regulating DNA damage repair and cell cycle arrest, but new roles of telomeric proteins and transcripts emerge from recent literature. Telomere biology diseases are human disorders associated to telomere attrition. This review wants to overview the recent findings in the field of telomere’s metabolism and to deepen molecular mechanisms of inherited and acquired telomeropathies, explaining new critical connections between telomeric factors and disease pathogenesis

Background: Kidney damage is considered to be one of the risk factors for severity and mortality among COVID-19 patients. However, molecular nature of such observations remains unknown. Hypothesis: Altered gene expressions due to infection and in chronic kidney disease could explain severity in COVID-19 with kidney defects. Methods: We collected gene expression data from publicly available resources Gene Expression Omnibus CKD, Enrichr for deregulated genes in SARS-CoV infected cells in vitro, DisGeNET and others and carried out enrichment analysis using Enrichr. Result: Number of common genes altered in chronic kidney disease (CKD) and SARS-CoV infected cells was 2834. Enrichment analysis revealed that biological processes related viral life cycle and growth, cytokines, immunity, interferon, inflammation, apoptosis, autophagy, oxidative stress and others were significantly enriched with common deregulated genes. Similarly, significantly enriched pathways related to viral and bacterial infections, immunity and inflammation, cell cycle, ubiquitin mediated proteolysis, signaling pathways like Relaxin signaling pathway, mTOR signaling pathway, IL-17 signaling pathway, NF-kappa B signaling pathway were enriched with the common deregulated genes. These processes and pathways are known to be related to kidney damage. DisGeNET terms enriched include and related to Dengue fever, chronic Hepatitis, measles, retroviridae infections, respiratory syncytial virus Infections and many others. Kidney dysfunction related terms ischemia of kidney, renal fibrosis and diabetic nephropathy. Conclusion: Common deregulated genes in SARS-CoV infected cells and chronic kidney disease, as well as their enrichment with molecular processes and pathways relevant for viral pathogenesis and renal dysfunctions, could explain the severity of COVID-19 with kidney disease. This observation not only provides molecular relation of severity in COVID-19 with renal dysfunctions but might also help in the management and treatment targets for these cases.

The rapid evolution of technology has given rise to a connected world, where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While IoT offers incredible convenience and efficiency, it presents a significant challenge in cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques are employed within Intrusion Detection Systems (IDS) to enhance their capabilities in identifying and responding to security threats. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. Experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 seconds.