The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

The aim of this study is to investigate transient dynamic flow characteristics of the fluid flow through 5-way electric coolant valve (PCCV : Penta-Control Coolant Valve). To achieve this goal, the present paper attempts the three-dimensional dynamic simulation of the fluid flow through the valve using a commercial CFD solver with moving mesh technique to consider flow inertia and dynamic flow in the opening and closing stages of the ball valve rotating motion. Firstly, dynamic flow characteristics and the thermal mixing inside the PCCV ball valve during the opening and closing stage was analyzed. It is found that the discrepancies between dynamic and steady state simulation is remarkable when fluxes with different levels of enthalpy and momentum flow into the PCCV leads to strong flow interference and flow inertia, while the discrepancies are relatively small at low rotation speed and weak flow interference. Then, the effect of dynamic flow characteristics of the valve on dynamic thermal mixing characteristics at two different ball valve rotational speeds and rotational directions were investigated. It was found that the dynamic flow and thermal mixing characteristics inside the PCCV are greatly affected by the rotation speed, rotation direction, and degree of flow interference between fluxes. It is helpful for the design of the better coolant control strategies and the improvement of the FCEV thermal management system.

This study evaluates audiogenic epilepsy in Krushinsky-Molodkina rats using a wireless EEG system to overcome the limitations of traditional methods that restrict animal movement. Nine male KM rats, prone to audiogenic seizures, were implanted with electrodes in areas critical for seizure activity (inferior colliculus, ventrolateral periaqueductal gray, dorsal area of the secondary auditory cortex, and motor cortex). This setup allowed for the observation of seizures without movement restriction. Results showed that targeted neural intervention via electrode implantation reduced the intensity of convulsive seizures in half of the subjects, suggesting a potential therapeutic approach. The severity of seizures was influenced by the pre-phonostimulation EEG background activity in specific brain regions, identifying them as key in the genesis and generalization of seizures. Observations of dual generalized seizure waves and induced hyperkinesis challenge conventional epilepsy models and suggest new directions for research and therapy development.

The ordinary 3D wave equation for nondissipative, homogeneous, isotropic media admits solutions where the point sources are permitted to move, but it does not admit solutions where the receiver is allowed to move. To overcome this limitation, the new wave equation is derived, permitting both the receiver and the source to move. This new wave equation is a generalization of the standard wave equation, and it reduces to the standard wave equation when the receiver is at rest. To derive this new wave equation, we first mathematically define a diverging spherical wave caused by the stationary point source. From this purely mathematical definition, the wave equation for the stationary source and the moving receiver is derived together with the corresponding free-space Green's function. Utilizing the derived Green's function, it was shown that this new wave equation also permits the solutions where both the receiver and the source are permitted to move. In conclusion, this paper demonstrates that, instead of an ordinary wave equation, the wave equation for the moving source and the moving receiver governs the waves emitted by the moving point sources and received by the moving receivers.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess of energy that would be otherwise lost in pressure reducing valves or head loss chambers. In on-grid applications, there are many research works and implementations of PATs. However, there is still limited research for the optimization of efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs, by optimizing and analysing the energy efficiency and stability for a wide range of operation. A self-excited induction generator (SEIG) coupled to the PAT is provided to cover the conventional type of electric ma-chines typically coupled to the hydraulic pumps. In this context, a methodology is proposed, based on the hydraulic, mechanical and electric subsystems, to define the PAT-SEIG operational area, to maximize energy conversion and the system efficiency. This will guarantee stable electric output quantities when operating in a DC off-grid system. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers are suitable to design and optimize an off-grid PAT-SEIG system and to define the best hydraulic machines, electronic equipment, and all different control elements to maximize energy conversion within the target of operational limits. To develop and verify this new methodology, two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions. The proposed solution proved to be resilient under a variable load regime. The system's maximum efficiency can be adapted using different capacitor values for the excitation of the SEIG, and the operational range is limited to the electric components. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system were between 0.7 and 0.8 p.u.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

This article provides a comprehensive review of the intersection of 5G technology and Beyond, and Smart Grids, focusing on the potential of Vehicle-to-Fog as an industrial alternative for addressing energy efficiency challenges in data processing. This study aims to investigate relevant articles and patents to understand the collaborative potential between 5G and Smart Grids. It explores how to establish new regulatory standards within the architectural framework of 5G to facilitate decentralized data processing and remodel energy consumption patterns. The hypothesis suggests that decentralized 5G architecture and Smart Grids can be most effectively applied through vehicular fogging powered by solar energy. A combined quantitative and qualitative methodology guides the exploration of articles and patents. The study concludes by advocating for new standards that embrace decentralized 5G data processing, emphasizing the potential of Vehicle-to-Fog and the future deployment of 6G technology in Smart Grids. These advancements can significantly enhance energy efficiency in the context of 5G and pave the way for future innovations.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

The work addresses the problem of Power Quality (PQ) metrics (or indexes) suitable for DC grids, encompassing Low and Medium Voltage applications, including electric transports, all-electric ships and aircrafts, electric vehicles, distributed generation and microgrids, modern data centers, etc. The two main pillars on which such PQ indexes are discussed and built are: i) the physical justification, so the electric phenomena affecting DC grids and components (PV panels, fuel cells, capacitors, batteries, etc.), causing e.g. stress of materials, ageing, distortion, grid instability; ii) the existing standardization framework, pointing out desirable coverage and extension, similarity with AC grids standards, but also inconsistencies. The first point is made more clear and usable by a graphical overview of the discussed phenomena. On this basis PQ is interpreted beyond the usual low-frequency range, including thus supraharmonics and common-mode disturbance, and filling the gap with the Electromagnetic Compatibility domain. However, phenomena typical of EMC and electrical safety (such as various types of overvoltages and fast transients) are excluded. Suitable PQ indexes are then reviewed, suggesting integrations and modifications, to cover the relevant phenomena and technological progress, and to better follow the normative exigencies.

With the deterioration of the bridge performance and ever-increasing amount of traffic, the bridge safety is becoming a concern for engineering community. A method that can assess the bridge's condition in real-time is urgently needed. The main factors that hinder the bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA) based method is developed for structural damage detection of bridges in operational environments in this paper. Two main operational environmental factors: the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate to assess the bridge condition in operational environments.

Electricity sector is among the main carbon emissions contributors. This sector has the potential to reduce its carbon emissions through producing electric energy from zero emitting facilities and through the optimization of the consumption in a way to better accommodate low carbon emissions. Hydrogen society per se combined with smart grids, as this is analyzed in this manuscript, can present substantial contribution towards climate change mitigation.

This paper presents an extension for principles of Differential Geometry on Surfaces (re-hashed through the budding field of CMS, the Calculus of Moving Surfaces). It analyzes mostly 2D Hypersurfaces with Riemannian Geometry and proposes the construction of a 3D Static Frame combining the Surface Basis Vectors with the Orthogonal Normal Field as a 3D Orthogonal Vector Frame. The paper introduces conventions for manipulating Tensors defined using this 3D Orthogonal Vector Frame as well as Curvature Connections associated with this Vector Frame. It then finally introduces Symbols and Tensors to describe Inner Products and Variance within the 3D Vector Frame and then extends all the above concepts to a surface which is Dynamic utilizing principles from CMS. This formulation has potential to extend identities and concepts from CMS and from Differential Geometry in a compact Tensorial Framework, which agrees with the new Framework proposed by CMS.

Quasi-interpolation is a powerful tool for approximating functions using radial basis functions (RBFs) such as Gaussian kernels. This avoids solving large systems of equations as in RBF interpolation. However, quasi-interpolation with Gaussian kernels on compact intervals can have significant errors near the boundaries. This paper proposes a quasi-interpolation method with Gaussian kernels using Chebyshev points and boundary corrections to improve the approximation near the boundaries. The boundary corrections use a linear approximation of the function beyond the interval to estimate the truncation error and add correction terms. Numerical studies on test functions show that the proposed method reduces errors significantly near boundaries compared to quasi-interpolation without corrections, for both equally spaced and Chebyshev points. The convergence and accuracy with the boundary corrections are generally better with Chebyshev points compared to equally spaced points. The proposed method provides an efficient way to perform quasi-interpolation on compact intervals while controlling the boundary errors.

Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though a lot and research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from working with district heating and cooling projects, along with relevant research, corresponding research gaps and proposed research directions.

In power systems, flow allocation (FA) methods allow to allocate usage and costs of the transmission grid to each single market participant. Based on predefined assumptions, the power flow is split into isolated generator specific or producer specific sub-flows. Two prominent FA methods, Marginal Participation (MP) and Equivalent Bilateral Exchanges (EBE), build upon the linearized power flow and thus on the Power Transfer Distribution Factors (PTDF). Despite their intuitive and computationally efficient concept, they are restricted to networks with \emph{passive} transmission elements only. As soon as a significant number of \emph{controllable} transmission elements, such as High-voltage direct current (HVDC) lines, operate in the system, they loose their applicability. This work reformulates the two methods in terms of Virtual Injection Patters (VIP) which allows to efficiently introduce a shift parameter $q$, tuning contributions of net sources and net sinks in the network. Major properties and differences of the methods are pointed out. Finally, it is shown how the MA and EBE algorithm can be applied to generic meshed AC-DC electricity grids: Introducing a \emph{pseudo-impedance} which reflects the operational state of controllable elements, allows to extend the PTDF matrix under the assumption of knowing the current system's flow. Basic properties from graph theory are used for solving the pseudo-impedance dependent on the position in the network. This directly enables \emph{e.g.} HVDC lines to be considered in the MP and EBE algorithm. The extended methods are applied to a low-carbon European network model (PyPSA-EUR) with a spatial resolution of N=181 and an 18\% transmission expansion. The allocations of VIP and MP, show that countries with high wind potentials profit most from the transmission grid expansion. Based on the average usage of transmission system expansion a method of distributing operational and capital expenditures is proposed. Further it is shown, how injections from renewable resources strongly drive country-to-country allocations and thus cross-border electricity flows.

Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our compute kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager-worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a 10-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198s using 128 nodes of Piz Daint. This translates to an overall performance of 352TFLOPS. On the node-level, we provide results for two GPUs, Nvidia's Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all compute kernels and devices, demonstrating the performance portability of our approach.

The energy transition towards renewable and more decentral power production triggers the need for grid and storage expansion on all voltage levels. Today's power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. System costs are minimized by applying a linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series lead to substantial overall savings.

Starting from unidentified objects moving inside a two-dimensional Euclidean manifold, we propose a simple method to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, which allows the detection of topological holes development and disappearance, makes it possible to solve the uncertainty due to disconnectedness, lack of information and absence of objects’ sharp boundaries, i.e., the three troubling issues which prevent scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial dynamical phenomena, such as fire propagation, wireless sensor networks, migration flows, neural networks’ and cosmic bodies’ analysis.

In the weak-field approximation of covariant theory of gravitation the problem of 4/3 is formulated for internal and external gravitational fields of a body in the form of a ball. The dependence of the energy and the mass of the moving substance on the energy of field accompanying the substance, as well as the dependence on the characteristic size of the volume occupied by the substance are described. Additives in the energy and the momentum of the body, defined by energy and momentum of the gravitational and electromagnetic fields associated with the body are explicitly calculated. The conclusion is made that the energy and the mass of the body can be described by the energy of ordinary and strong gravitation, and through the energies of electromagnetic fields of particles that compose the body.

We applied evolutionary game theory to extend a resource constrained security game model for confidentiality attacks in an Advanced Metering Infrastructure (AMI), which is a component of IoT-enabled Smart Grids. The AMI is modelled as a tree structure where each node aggregates the information of its children before encrypting it and passing it on to its parent. As a part of the model, we developed a discretization scheme for solving the replicator equations. The aim of this work is to explore the space of possible behaviours of attackers and to develop a framework where the AMI nodes adaptively select the most profitable strategies. Using this model, we simulated the evolution of a population of attackers and defenders on various cases resembling the real life implementation of AMI. We discuss in depth how to enhance security in AMI using evolutionary game theory either by a priori analysis or as a tool to run dynamic and adaptive infrastructure defence.

Due to the high solar irradiance or energy price, certain regions in the U.S. may reach 100% PV penetration and experience degradation of frequency response greater than the interconnection as a whole. Therefore, in this section, the 100% PV penetration region in each interconnection is simulated to study the local high PV penetration effects. The study was performed by quantifying RoCoF, frequency nadir, and settling frequency at different regional PV penetration levels. The impact of high regional PV penetration on the compliance of grid code on frequency response is also studied.

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

The link between ICTs, freight transport, and CO₂ emissions has not received much explicit examination by researchers, despite freight transportation being an egregious contributor to environmental degradation. This paper investigates how ICTs can affect environmental quality when interacting with freight transport activities in 43 countries over the period 2002-2014, using carbon emissions as a proxy for environmental damages. ICTs are measured in terms of internet, telephone, and mobile phone technologies. Using GMM methodology, the results show that ICTs contribute to dampening environmental degradation when interacting with freight transport activities. Specifically, a 10% increase in the interaction between ICTs and freight transportation will decrease carbon emissions by between 1.27% and 3.02%. The results further suggest that fixed and smartphone technologies are the main contributors to reducing emissions when adopted in some specific transport sectors (i.e. road, rail, and inland), while the internet is the most efficient technology when interacting with air transport activity. In addition, the interaction between ICTs and multimodality accelerates environmental quality. The policy implications of these findings are discussed.

Since the integration of electronic devices and intelligent electronic devices into the power grid, power quality (PQ) has consistently remained a significant concern for system operators and experts. Maintaining high standards of power quality is crucial to prevent malfunctions and faults in electric assets and in connected loads. Recently, PQ studies have shifted their focus to a specific frequency range, previously not considered problematic, the supraharmonic 2kHz–150kHz range. This range is not populated by the harmonic components of the 50-60 Hz mains fundamental, but a combination of intentional emissions, switching and non-linearity by-products and various types of resonances. This paper aims to provide a detailed analysis of the impact of SH on power network operation and assets, focusing on the most relevant documented negative effects, namely power loss and heating of grid elements, aging of dielectric materials, failure of medium voltage (MV) cable terminations, and interference to equipment and to power line communication (PLC) technology in particular. Under some shareable assumptions limits are derived and compared to existing ones for harmonic phenomena, providing a clear identification of the primary issues associated with supraharmonics and suggestions for the standardization process. Strictly related is the problem of grid monitoring and assessment of SH distortion, discussing the suitability of normative requirements for instrument transformers (ITs) with specific focus on their accuracy.

In the Neighborhood Area Network (NAN), the Advanced Metering Infrastructure (AMI) enables a bidirectional connection between the Smart Meter (SM) and the Data Concentrator (DC). Sensors, such as smart meter node or Radio Frequency transceiver, play a crucial role in collecting and transmitting data from meters to central unit for advanced monitoring, management, and analysis of energy consumption. Wired and wireless communication technologies can be used to implement the AMI-NAN. This paper delves into a novel approach for optimizing the choice of communication medium, Radio Frequency (RF) or Power-Line Communication (PLC), between the SM and DC in the context of AMI-NAN. The authors methodically select the specific technologies, RF and NB-PLC (Narrow Band Power-Line Communication), and meticulously characterize their attributes. Then, a comparative analysis spanning rural, urban, and industrial settings is conducted to evaluate the proposed method. The overall reliability performance of the AMI-NAN system requires a Packet Error Rate (PER) lower than 10%. To this end, a comprehensive methodology is introduced to assess and enhance the reliability of NB-PLC and RF for AMI-NAN applications. Simulation results demonstrate that wireless communication is the optimal choice for the rural scenario, especially for Signal to Noise Ratio (SNR) lower than 25 dB. However, in urban environments characterized by higher SNR values and moderately dense networks, NB-PLC gains prominence. In denser networks, it outperforms wireless communication, exhibiting a remarkable 10 dB gain for a bit error rate (BER) of 10-3. Moreover, in industrial zones characterized by intricate network topologies and non-linear loads, the powerline channel emerges as the optimal choice for data transmission, affording a gain surpassing 20 dB.

In this paper, we introduce the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the prioritization in the FMECA analysis applied in cyber-power grids. Classical FMECA assesses the risk level through the Risk Priority Number (RPN). The multiplication between three integer numbers computes this, called risk factors, representing the severity, occurrence, and detectability of each failure mode and are defined by a team of experts. The RPN does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language; this is the main FMECA shortcoming criticized in the literature. Our approach considers fuzzy variables defined by FMECA experts to represent the uncertainty associated with the human language and a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. To test our approach, we select a cyber-power grid previously analyzed by the authors using the classical FMECA. The results reveal our proposed fuzzy approach as promissory to represent the uncertainty associated with expert knowledge and to perform an accurate prioritization of failure modes in the context of electrical power systems.

In interceptive treatments in which impacted teeth (incisor or first permanent molar) should be included into the dental arch, the main clinical problem is the provision of an anchorage area and a suitable appliance. The use of laser sintered metal orthodontic grids, designed and printed digitally, has expanded the boundaries of orthodontic treatment, especially in its interceptive phase. Our aim is to describe a digital model for providing anchorage during orthodontic-surgical traction of impacted teeth in the mixed dentition period. We present the treatment of three clinical cases with digitally projected and printed metal grids used for anchorage. In the described clinical cases, individually designed anchorage systems were used. They were created through design software and after that the metal grids were printed. The devices are placed in the oral area and don’t disturb the aesthetics of the patient in any way. The advantages of individual designed metal support grids allow to accommodate the features of relief of the hard palate and alveolar bone. This makes the grids comfortable for the patient and rigid enough to perform orthodontic tractions at the same time. The printed additional grids are applicable in mixed dentition and in complicated clinical cases in permanent dentition. Their individuality allows segmental treatments or treatments in cases requiring additional anchorage further from the active force.

In the wake of COVID-19, there is an urgent need for a diverse public health work force to address problems presented or exacerbated by the global pandemic. Educational programs that create our work force both train and shape the makeup of access through graduate applications. The Graduate Record Exam has a number of standing issues, with additional barriers created by the pandemic. We trace the GRE waiver movement over several years, focusing on the gradual adoption in CEPH accredited programs and the rapid expansion of temporary waivers as a response to testing access. Going forward, we need to consider gaps in waivers during the pandemic and how this data can be used to shape our future use of the GRE.

Advancements in DNA methods and biotechnology have enabled forensic scientists to explore the DNA evidence found as part of a criminal investigation on a much more comprehensive and predictive level. This has led to a rise in research into DNA intelligence tools such as phenotypic prediction (i.e., eye and hair colour) and inference of biogeographical ancestry. Both of which can be applied to gain further insights about a scene or sample in question. Although microorganisms have played a role in forensics for decades, investigations were focused on the pathogenicity aspect, mainly to determine the cause and time of death. Recent progress in studying the human microbiome has implicated the potential use of this data in forensics. Since each individual, place, or item has its own microbial pattern, a new suite of tools are now available to be exploited in criminal investigations. Although there is much interest and potential for these emerging metagenomic and microbial forensic tools, best practices and reference ranges need to be established before they are implemented. Here, we discuss existing DNA intelligence tools applied to forensic science, the application of microbial forensics and metagenomics along with the challenges and concerns that future developments entail.

In this study, we investigate the behavior of randomly moving particles with a defined average energy, focusing on their biased motion around a specific point. These particles' velocity magnitudes follow a Maxwell distribution with a specific scale parameter. Our analysis derives the stochastic rotation rules for these particles and introduces the Ito equation conditions for biased stochastic rotation at a given curl value. We also consider the special relativistic-like effects in this context. The findings are validated through a representative example.

The phenomenon of droplet impact on moving surfaces is widely observed in fields such as transportation, rotating machinery, and inkjet printing. Droplets exhibit non-axisymmetric behavior due to the motion of solid surfaces which significantly determines core parameters such as contact time, maximum spreading radius, and bounding velocity, thereby affecting the efficiency of related applications. In this study, we focus on kinetics and morphology of the non-axisymmetric bouncing behaviors for droplets impacting on a moving superhydrophobic surface (SHPS) within the normal (Wen) and tangential (Wet) Weber numbers. Considering the influences of moving surface on the contact area and contact time, the previous scaling formula for the horizontal velocity of droplets has been improved. Based on the velocity superposition hypothesis, we establish a theoretical model for the ratio of the maximum spreading radius at both ends depending on Wen and Wet. This research provides both experimental and theoretical evidence for understanding and controlling the non-axisymmetric behavior of droplets impacting on moving surfaces.

In the polar region, the gravity vector and Earth's rotation vector tend to be in the same direction, leading to slower convergence speed and longer alignment time of the moving base alignment. When the alignment time is short, the alignment cannot converge, resulting in low azimuth accuracy. To address this issue, we propose a polar moving base alignment method based on backtracking scheme. Notably, this work first derives a polar coarse alignment method with the inertial frame based on the transverse Earth model. On this basis, we design a polar coarse alignment method based on backtracking scheme and optimize the data storage scheme. Then, an inverse navigation algorithm based on the transverse inertial navigation mechanical arrangement scheme was derived, and a polar fine alignment method based on backtracking scheme was designed. Semi-physical simulation experiments showed that the alignment algorithm based on backtracking scheme can converge in a short time with high alignment accuracy.

The wave-particle description of quantum mechanics is briefly reviewed to set an analogy between the quantum mechanics of particles and fields and quantum cosmology. It is shown that quantum cosmology can be seen as a quantum field theory developed in the space of three metrics (the metrics of the spatial sections of the spacetime), where the classical evolution of the universe corresponds to a definite trajectory and the quantum mechanics of matter fields emerges as the first order quantum correction. Like in a quantum field theory developed in an isotropic background, the universes are created in universe-antiuniverse pairs, which might provide us with an explanation for the matter-antimatter asymmetry observed in our universe without going beyond the standard model. Moreover, the states of the matter and the antimatter fields of the pair of universes are entangled and that makes that their initial states are in a non-vacuum state (presumably in a thermal state). That might open the possibility of looking for observable imprints of the creation of universes in pairs is the astronomical data.

In the present article we describe a few simple and efficient finite volume type schemes on moving grids in one spatial dimension for the sake of simplicity. The underlying finite volume scheme is conservative and it is accurate up to the second order in space. The main novelty consists in the motion of the grid. This new dynamic aspect can be used to resolve better the areas with high solution gradients or any other special features. No interpolation procedure is employed, thus an unnecessary solution smearing is avoided. Thus, our method enjoys excellent conservation properties. The resulting grid is completely redistributed according the choice of the so-called monitor function. Several more or less universal choices of the monitor function are provided. Finally, the performance of the proposed algorithm is illustrated on several examples stemming from the simple linear advection to the simulation of complex shallow water waves. We believe that the techniques described in our paper can be beneficially used to model tsunami wave propagation and run-up.

Recent researches proved that the underbridge geometry can be reconstructed by mounting a 3D laser scanner on a motorized cart travelling on a walkway located under the bridge. The walkway is moved by a truck and the accuracy of the bridge model depends on the accuracy of the trajectory of the scanning head with respect to a fixed reference system. In this paper, we describe the metrological characterization of a method that uses non-contact systems to identify the relative motion of the cart with respect to the walkway; the orientation of the walkway with respect to the bridge is determined using inclinometers and optical rails, while the position of the truck with respect to the bridge is measured using a conventional odometer. The measurement uncertainty of the proposed system was initially evaluated by numerical simulations and successively verified by experiments in laboratory conditions. The complete system has then been tested in operative conditions; the validity of the proposed approach has been demonstrated by comparing the geometry of buildings reconstructed with the proposed system with the geometry obtained with a static scan. Results evidenced that the errors are approximately 6 mm.

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically Heat Pumps (HPs) and Electric Vehicles (EVs). Additionally, it addresses Photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to get economic benefits for private households and at the same time facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with Phase Change Material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of active distribution grid with 8 houses using Power Factory software and DigSILENT Simulation Language (DSL). This scalable size of the active distribution network facilitates to carefully study symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

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.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

Offshore grids can have a key role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling. This paper quantifies the socio-economic value of offshore grids and identifies their major drivers, performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050. The increasing wake loss with the size of hub-connected wind farms is integrated in the modelling. We find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. Therefore, it can be strongly stated that offshore grid infrastructure development should not be discussed as a separate political topic, but seen in connection to sector coupling.

Multiple sensor nodes known as detection stations make the sensor networks; each node is light and portable. Every sensor node contains power source, microcomputer, transducer and transceiver. Power source provides power to each node. Micro-computer is used for storing and processing the output coming from the sensors. The transducer is used to generate the signals and the transceiver is used to receive and transmit data to the central computer. Eavesdropping gets facilitated with wireless communication, and it has many useful applications in military, homeland, hostile and uncontrolled environments. So it is prone to the high level of security. The process in which information is gathered to form a summarized type for analysis is known as data aggregation, as it is used to reduce the energy consumption in wireless sensor networks. The security issues have become crucial in data aggregation, especially when gets deployed in hostile and remote environment. In wireless sensor networks many secure aggregations have been proposed. It still faces some resource constrained that’s why new techniques are needed. In our survey we will discuss those approaches and their pros and cons.

In a particle swarm with a fixed kinetic energy, the constituent particles move randomly and their velocity magnitudes follow a Maxwell distribution characterized by specific parameters. Within a certain time frame, particles in a sub-swarm may exhibit a bias in their movement direction. Thus, the particles may exhibit a special characteristic (biased random motion), where the group velocity in a particular direction remains constant u. For this biased particle swarm, composed of randomly moving particles with a fixed average speed c, particles have a higher probability of moving in a specific direction. Conversely, their likelihood of moving in all other directions is equally reduced. This study starts from the perspective of biased random walks and using Mathematica software proves that the diffusion rate of particles (in the reference frame R_u observed from R₀) in all directions is slower than that of an unbiased particle swarm with the same average speed c. Specifically, the degree of reduction is determined by the Lorentz-like factor. Lastly, we present the Ito equation for this biased random motion and provide associated verification examples. This study aims to serve as a reference for comprehending the underlying principles of the special relativity effect.

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

Recently, the triple-network convergence system (TNCS) has emerged from the deep integration of the power grid, transportation network, and information network. Fault recovery research in the TNCS is important since this system's complexity and interactivity can expand the faults scale and increase faults impact. Currently, fault recovery focuses primarily on single power grids and cyber-physical systems, but there are certain shortcomings, such as ignoring uncertainties including generator start-up failures and the occurrence of new faults during recovery, energy supply-demand imbalances leading to system security issues and communication delay caused by network attacks. In this study, we propose a recovery method based on the improved TD3 algorithm, factoring in shortcomings of the existing research. Specifically, we establish a TNCS model to analyze interaction mechanisms and design a state matrix to represent the uncertainty changes in the TNCS, a negative reward to reflect the impact of unit start-up failures, a special reward to reflect the impact of communication delay and an improved Actor network update mechanism. Experimental results show that our method obtains the optimal recovery decisions, maximizes restoration benefit in power grid failure scenarios and demonstrates a strong resilience against communication delay caused by DoS attacks.

Wind loads have become one of the key influence factors for the running safety of vehicles and comfort of passengers. Investigation on the wind speed spectrum characteristics of a moving vehicle under turbulent crosswinds is of great influence. Expressions of the wind speed spectrum of a moving vehicle was obtained from the von Kármán spectrum based on Taylor’s frozen flow hypothesis. The influence factors, including the ratio of the vehicle speed to the wind speed and the wind yaw angle from 15° to 175°, were analyzed. The maximum value of the wind speed spectrum and the corresponding frequency were studied as well. The results show that the maximum values of the wind speed spectrum of the moving vehicle were larger than those of the static vehicle. The maximum value of the wind speed spectrum corresponding to the moving vehicle first increased and then decreased as the wind yaw angle increased. Some of the frequencies corresponding to the longitudinal wind speed spectrum values of the moving vehicles were smaller than those of the static vehicle. For moving vehicles, the frequency values corresponding to the maximum values of the longitudinal wind speed spectrum first increased and then decreased as the ratio of the vehicle speed to the wind speed and the wind yaw angle increased.

In recent years, intensive work has been carried out to achieve maximum energy production in photovoltaic power plants. Today, this challenge has to be solved for arrays of solar panels that move with the object on which they are installed (vehicles, trucks, ships, etc.). The present work tries to find the best solution for both constant voltage systems and for cases of maximum intensity. Once found, these solutions will be applied in real time for the dynamic recharging of battery packs, trying to achieve energetically autonomous vehicles.To solve these situations, a new computational method of voltage and amperage calculation has been developed in this paper, based on Dijkstra’s minimum path finding algorithm on graph theory, adapted to electrical circuits. For this purpose, panel performance analysis sensors, developed at the University of Malaga, have been combined with different electronic solutions (wifi relay devices using esp8266 or similar chips) achieving an accurate and sufficiently fast solution at very low cost. With this, series or parallel configurations can be carried out depending on the type of energy generation sought. The theoretical solutions using Minkowski paths, achieved in the past, have been simulated and subsequently constructed in this article, indicating the diagrams necessary for their realisation.

The principle of the constancy of the velocity of light, which stated that the light velocity is invariant to the motion of the emitter, was well established and directly proven by experiments. Interestingly, the further assumption that the light velocity is independent of the motion of the observer was, arguably, never directly proven by any experiment for a century. This paper proposed the design of two experiments to directly test this assumption, which tried to address some perceived technical difficulty in such experiments. One is to directly measure the light speed as to moving sensors. The experiment setup is designed in such a way that the concerns of time synchronization and dilation can be avoided. Another experiment is to test the isotropy of the light speed to a moving particle in the electromagnetic accelerator by measuring the momentum to acceleration ratio. The experiment result, if positive, will provide direct and solid proof of the assumption. Otherwise, it may imply a need for further investigation. Since the light speed invariance to moving observers is a key assumption of some fundamental physical theory, either way, the experiments will have significant meanings.

We study wave patterns of gravity-capillary waves from moving localized sources within the classic setup of the problem of ship wakes. The focus is made on the co-existence of two wave systems with opposite signatures of group velocity relative to the localized source. It leads to the problem of choice of signs for phase functions of the gravity (“slow”) and capillary (“fast”) branches of the dispersion relation: the question generally ignored when constructing phase patterns of the solutions. We detail characteristic angles of the wake patterns: (i) angle of demarcation of gravity and capillary waves – “the phase Mach” cone, (ii) angle of the minimal group velocity of gravity-capillary waves – “the group Mach” cone, (iii, iv) angles of cusps of isophases that appear after a threshold current speed. The outer cusp cone is naturally associated with the classic cone of Kelvin for pure gravity waves. The inner one results from the effect of capillarity and tends to the “group Mach” pattern at high speeds of current. Amplitudes of the wave patterns are estimated within the recently proposed approach of reference functions for the problem of propagation of packets of linear dispersive waves. The effect of shape is discussed for elliptic reference sources.

Wireless Sensor Network is an emerging technology and the collaboration of wireless sensors becomes one of the active research areas to utilize sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if occurs, any radical change. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing or disappearing. If the abrupt appearance of sensors is a zero-day attack and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The pre-defined sensor requirements or meta-data based sensors verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only, without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes 1) quality preservation of sensor data for mining analytics and 2) authenticity verification of dynamically moving sensors with no external consultation.

In this study, the unsteady Reynolds-averaged Navier-Stokes (URANS) equations are employed in conjunction with the Menter Shear Stress Transport (SST)-Scale-Adaptive Simulation (SAS) turbulence model in compressible flow, unstructured mesh and complex geometry. Whereas, other scale-resolving approaches in space and time, such as Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES), supply more comprehensive information about the turbulent energy spectra of the fluctuating component of the flow; however, previously mentioned avenues imply computationally intensive situations, usually performed over structured meshes and relatively simply geometries. In contrast, the SAS approach is designed according to “physically” prescribed length scales of the flow. More precisely, it operates by locally comparing the length scale of the modelled turbulence to the von Karman length scale (which is prescribed according to local fluid velocity derivatives and depends on the grid point distribution in the computational domain). This length scale ratio allows the flow to dynamically adjust the local eddy viscosity in order to better capture the large scale motions (LSM) in unsteady regions of URANS simulations. While SAS may be constrained to model only low flow frequencies or waivenumbers (i.e., LSM), its versatility and computational low-cost make it attractive to obtain a quick first insight of the flow physics; particularly, in those situations dominated by strong flow unsteadiness. The selected numerical application is the well-known M219 three-dimensional rectangular acoustic cavity from the literature at two different freestream Mach numbers, M∞ (0.85 and 1.35) and a length-to-depth ratio of 5:1. Thus, we consider the “deep configuration" in experiments by Henshaw. The SST-SAS model has demonstrated a satisfactory compromise between simplicity, accuracy and flow physics description.

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces a Perfect Load Forecasting Strategy (PLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed PLFS consists of two sequential phases, which are; Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selec-tion is done using Advanced Leopard Seal Optimization (ALSO) as a new natural inspired opti-mization technique, Citation: To be added by editorial staff during production. Academic Editor: Firstname Last-name Received: date Revised: date Accepted: date Published: date Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). While outlier rejection is accomplished through Interquartile Range (IQR) as a measure of statis-tical dispersion. On the other hand, actual load forecasting takes place in LFP using a new pre-dictor called; Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed PLFS has been tested through excessive experiments. Results have shown that PLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

Surround modulation is a phenomenon whereby costimulation of the extra-classical receptive field and classical receptive field would modulate the visual responses induced individually by classical receptive field. However, there lacks systematic study about surround modulation properties existing in avian optic tectum. In this study, neuronal activities are recorded from pigeon optic tectum, and the responses to moving and flashed squares and bars of different sizes are compared. The statistical results showed that most tectal neurons presented surround suppression as stimuli size grew larger both in moving and flashed paradigms, and the suppression degree induced by larger flashed square was comparable with that by moving one when it crossed near the cell’s RF center, which corresponds to fully surrounding condition. The suppression degree grew weaker when the stimuli move across the RF border, which corresponds to partially surrounding condition. Meanwhile, the fully surround suppression induced by flashed square was also more intense than partially surrounded by flashed bars. The results provide new insight for understanding the spatial arrangement of lateral inhibitions from feedback or feedforward streams, which would help to make clear the generation mechanism of surround modulation found in avian optic tectum.

This article discusses an international scientific expedition to Greenland that researched geography, geodesy, botany, and glaciology of the area. The results here focus on the geodetic and glaciological results obtained with the eBee drone in the eastern part of Greenland at the front of the Knud Rasmussen glacier. From two overflights nearby the glacier front, it was possible to obtain the speed of the glacier flow and the distribution of velocities in the glacier stream. The results correlate with other measurement methods and this technology has been shown as feasible. Of course, there are more accurate and long-term options or devices for monitoring the flow of glaciers. In this case of short-term visits to the site, the possibility of using a drone is interesting and the results show not only the flow speed of the glacier, but also the shape and structure from a height of up to 200m. The second part of the paper focuses on the analysis of modern satellite images of the Knud Rasmussen glacier from Google Earth (Landsat series 1984-2016) and a comparison with historical aerial images from 1932-1933. Experimentally, historical images were processed photogrammetrically into a 3D model.

This study addresses the escalating threat of data theft ransomware, a form of cyberattack that exfiltrates sensitive information from victim networks and demands ransom for its non-disclosure. Unlike traditional crypto-ransomware, data theft ransomware leverages various infiltration techniques to gain unauthorized access and control over critical data assets. The study introduces a novel Moving Target Defense (MTD) framework, specifically designed for the Windows operating system environment, to counter these sophisticated attacks. MTD increases the unpredictability of the system by dynamically altering its configurations, thereby disrupting the adversary's ability to execute successful attacks. Our research includes the development and empirical evaluation of the MTD framework, demonstrating its effectiveness in reducing ransomware operational capabilities through quantitative analysis, qualitative observations, and statistical significance testing. We also explore the challenges and complexities involved in implementing MTD in real-world scenarios and propose strategies to overcome these barriers. The study concludes with recommendations for future research directions and the potential of MTD in enhancing cybersecurity defense mechanisms.

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (Model Predictive Control) framework explicitly including a generic flexibility mechanism which is easy to particularise to specific strategies such as Demand Response, Flexible Production and Energy Efficiency Services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case which aims to further clarify the use of the framework and thus, to ease its adoption by other researchers in their specific flexibility mechanisms applications.

There is a growing need to utilize community interventions to address modifiable behaviors that lead to poor health outcomes like obesity, diabetes, and heart disease. Poor health outcomes can be tied to community-level factors such as food deserts (identified areas with low access to fresh fruit, vegetables, and other healthful whole foods) and individual behaviors like sedentary lifestyles, consuming large portion sizes, and eating high-calorie fast food and processed foods. Through a social ecological approach with family, organization and community, the Faithful Families Cooking and Eating Smart (FFCES) intervention was created to address these concerns in a rural South Carolina community. FFCES used gatekeepers to identify 18 churches and 4 apartment complexes in low-income areas. 176 participants completed both pre- and post- survey measures. Student’s t-test measures found statistically significant change in participant perception of food security (0.39, p-value=0.005), self-efficacy with physical activity and healthy eating (0.26, p-value=000), and cooking confidence (0.17, p-value=.01). There was not significant change in cooking behaviors as assessed through the Cooking Behaviors Scale. FFCES shows that a social ecological approach can be effective at increasing and improving individual healthy behaviors and addressing community-level factors in low-income rural communities.

K-Rb-83Kr based atomic co-magnetometer for measuring moving MEGs is theoretically studied in this paper. Parameters such as the spin exchange rates, the spin dephasing rates and the polarization of the nuclear spins are studied to configure the co-magnetometer. Results show that the nuclear spin could generate around 700 nT magnetic field under which the nuclear spin could compensate wide range of magnetic fields. We also showed the hybrid optical pumping vapor cell fabrication process in this paper. Alkali metals were mixed in a glove box and then was connected to the alkali vapor cell fabrication system. The vapor cell fabrication process is illustrated in this paper.

De novo metastatic hormone-sensitive PC (mHSPC) accounts for 5-10% of all prostate cancer (PC) diagnoses but it is responsible for nearly 50% of PC-related deaths. Since 2015, the prognosis of mHSPC has slightly improved thanks to the introduction of new hormonal agents and chemotherapy combined with androgen deprivation therapy from the first-line setting. This review describes the current therapeutic opportunities in de novo mHSPC, focusing on potential molecular biomarkers identified in the main clinical trials that have changed the standard of care, the genomic features of de novo mHSPC, and the principal ongoing trials that are investigating new therapeutic approaches and the efficacy of a biomarker-guided treatment in this setting. The road towards a personalized treatment for de novo mHSPC is still long considering that the randomized clinical trials, which have furnished the basis of the current therapeutic options, stratified patients according to clinical criteria that not necessarily reflect the biological rationale of the chosen therapy. The role of transcriptomic profiling of mHSPC as predictive biomarker requires further validation, as well as it remains to ascertain how the genomic alterations detected in mHSPC, that are considered predictive in the castration-resistant disease, can be exploited in the mHSPC setting.

Light travels in a moving medium through densities that differ from the density of that medium at rest. This study derives a formula for light velocity through a moving medium for any direction of light propagation in that medium that, applied to the Fizeau experiment, gives a fringe shift of 0.23541. The Fresnel drag coefficient applied to the Fizeau experiment offers a fringe shift of 0.20466. The mean of the fringe shift observations in the Fizeau experiment of 0.23016 confirms both expectations.

The article deals with the current state of energy consumption and CO2 emissions in the urban area of building clusters. There are many requirements, such as those set by the EU (FIT 55, decarbonisation in 2050, full electromobility in 2035, or mandatory annual reductions in energy consumption and CO2 production) or societal requirements, such as stable energy supply and its optimisation while significantly reducing CO2 emissions. This inspired us to design an energy model of a building (EMB) with smart grid implementation in a decentralized sustainable energy system. Simulation model studies were carried out to show the possibility of their application in the process of fully satisfying energy needs in terms of reducing their environmental impact. A decentralized photovoltaic system (microgrid) connected to a distribution grid. The main objective is to propose an original methodology for the design of smart "Nearly Zero Energy Buildings" (NZEB) and a subsequent solution for energy sustainability. This has led us to use HOMER, PV*SOL, Monte Carlo and DesignBuilder software which were chosen from the range of options that were and are available. Subsequently, a synthesis of the selected software solutions was carried out and a new model - the "Energy Model of a Building" (EMB) - was proposed in the process of integration with the energy performance of buildings, as an original optimization basis for designing smart buildings and smart areas, and even cities. The proposed EMB has been verified and evaluated within the experiment.

The evolution and miniaturization of the technologies for processing, storage, and communication have enabled computer systems to process a high volume of information and make decisions without human intervention. Within this context, several systems architectures and models have gained prominences, such as the Internet of Things (IoT) and Smart Grids (SGs). SGs use communication protocols to exchange information, among which the Open Smart Grid Protocol (OSGP) stands out. In contrast, this protocol does not have integration support with IoT systems that use some already consolidated communication protocols, such as the Constrained Application Protocol (CoAP). Thus, this work develops the integration of the protocols OSGP and CoAP to allow the communication between conventional IoT systems and systems dedicated to SGs. Results demonstrate the effectiveness of this integration, with the minimum impact on the flow of commands and data, making possible the use of the developed CoAP-OSGP Interface for Internet of Things (COIIoT).

In this paper a variable’s involved in assessing the quality of a distributed generation system are reviewed, aiming to minimize the electric power losses (unused power consumption) and optimize the voltage profile. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA) and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of longer processing time.

Ballasted railway tracks can be modelled using reduced/simplified models composed of several layers of discrete components. This paper deals with the two-layer model, which is very popular due to its computational efficiency. In order to provide some recommendations for track design, it is necessary to identify which set of parameters leads to some irregular/unexpected behavior. Such irregularity is investigated at three levels, such as: (i) critical velocity of moving constant force; (ii) instability of a single moving mass; (iii) instability of two moving masses. All results are presented in dimensionless form to cover a wide range of real parameters. Irregular cases are identified by sets of parameters leading to them and then general conclusions are drawn. Regarding the method, all results are obtained analytically or semianalytically, where “semi” refers to solving the roots of a given polynomial by predefined numerical procedures in symbolic software. No numerical integration is involved in all results presented. This means that the results are highly accurate and refer to exact values, so any kind of parametric or sensitivity analyses is readily possible.

Consideration of physical processes in the reference frame K' moving relative to the CMB, provided that clocks synchronization in this frame ensures the independence of time from spatial coordinates, leads to the conclusion about the existence in the K' of spatial anisotropy which should manifest itself in the dependence of physical effects on the speed vector V of the frame relative to the CМB. Some of these effects in the fields of electrodynamics and optics are considered. In particular, it is shown that weak magnetic fields proportional to the vector V should exist in this frame around motionless electric charges. Experimental verification, according to our assessment, is apparently possible.

In this study, a matched-mode autoregressive source depth estimation method (MMAR) based on autoregressive (AR) wavenumber estimation is proposed for a moving source in shallow water waveguides. The signal original frequency and the environmental parameters, namely, the sound speed profile and bottom properties are known as a prior knowledge. The mode wavenumbers are estimated by the AR modal wavenumber spectrum. On the basis of the mode wavenumber estimation, the mode amplitudes can be estimated by the wavenumber spectrum that is obtained by generalized Hankel transform. The source depth estimation is determined by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with other methods of moving source depth estimation, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The selection of horizontal line array depth is illustrated by simulation and normal mode theory in details.

Traditional aquaculture systems appear to be confronted with the high total ammonia nitrogen (TAN) produced during production, which can be harmful to aquatic life. As the demand for global fish production continues to increase, farmers should adopt Recirculating Aquaculture Systems (RAS) to improve production. The biofilter plays a vital role in ammonia removal. However, the drawback of the biofilter operation is needed an automatic and controlled system with a water quality monitoring system to guarantee optimal performance. RAS utilize a monitoring and control system to maintain the water quality and control the biofilm growth in the biofilter to reduce the ammonia. Therefore, this study focuses on developing a water quality monitoring and control system to achieve optimum biofilm performance. From 35 days of the experiment, the water quality was controlled at a level save for the aquatic environment, such as dissolved oxygen, pH, temperature, and TDS. Thus, the highest TAN removal efficiency is 50% when biofilm thickness reaches 119.88 µm.

This paper studies an effective finite difference scheme for solving two-dimensional Heston stochastic volatility option pricing model problems. A dynamically balanced up-downwind strategy for approximating the cross-derivative is implemented and analyzed. Semi-discretized and spatially nonuniform platforms are utilized. The numerical method comprised is simple, straightforward with reliable first order overall approximations. The spectral norm is used throughout the investigation and numerical stability is proven. Simulation experiments are given to illustrate our results.

Despite recent advances in optimizing reliever, controller and maintenance therapy in asthma, a large subgroup of asthmatic patients continues to suffer from severe asthma, affecting their psychosociological status, their quality of life and even their life expectancy. Identifying and recognizing the specific phenotypic and endotypic characteristics in asthma has provided the medical community to drive the development of targeted therapies that are proved to benefit patients in terms of symptom control, exacerbation rate, quality of life and lung function. Adopting a more tailored and targeted approach is proving to be the key factor in reforming severe asthma treatment, minimizing the patients’ burden, as well as the socioeconomical impact worldwide. In this review, we are going to describe the different phenotypes and endotypes of asthma, the currently approved targeted therapies in severe asthma as imprinted in Global Initiative for Asthma (GINA) 2023, as well as other potential pharmacological agents and strategies investigated or currently under investigation for the treatment of severe asthma.

The prediction of an extremely severe cyclonic storm (ESCS) is one of the challenging issues due to increasing intensity and its life period. In this study, an ESCS Fani that developed over Bay of Bengal region during 27 April - 4May, 2019 and made landfall over Odisha coast of India is investigated to forecast the storm track, intensity and structure. Two numerical experiments (changing two air-sea flux parameterization schemes; namely FLUX-1 and FLUX-2) are conducted with the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) model by using a moving nest with fine horizontal resolution about 3 km. The high resolution (25 km) NCEP operational Global Forecast System (GFS) analysis and forecast datasets are used to derive the initial and boundary conditions, the ARW model initialized at 00 UTC 29 April 2019 and forecasted for 108 hours. The forecasted track and intensity of Fani is validated with available India Meteorological Department (IMD) best-fit track datasets. Result shows that the track, landfall (position and time) and intensity in terms of minimum sea level pressure (MSLP) and maximum surface wind (MSW) of the storm is well predicted in the moving nested domain of the WRF model using FLUX-1 experiment. The track forecast errors on day-1 to day-4 are ~ 47 km, 123 km, 96 km, and 27 km in FLUX-1 and ~54 km, 142 km, 152 km and 166 km in Flux-2 respectively. The intensity is better predicted in FLUX-1 during the first 60 h followed by FLUX -2 for the remaining period. The structure in terms of relative humidity, water vapor, maximum reflectivity and temperature anomaly of the storm is also discussed and compared with available satellite and Doppler Weather Radar observations.

This review paper addresses the growing challenges and developments in frequency control within power systems influenced by increasing penetration of renewable energy sources. It evaluates the advancements and limitations of renewable-based control technologies and explores the critical role of diverse energy storage technologies in providing fast frequency response ancillary services. Through a comprehensive analysis of global literature, this paper categorizes energy storage solutions according to their efficacy in meeting fast frequency response demands and potential for revenue generation. It reveals significant gaps in current research, which predominantly focuses on battery energy storage systems and microgrid applications, with insufficient attention to grid-scale storage solutions and innovative energy storage technologies. This analysis identifies a lack of detailed technical simulations and hybrid storage models for frequency control, as well as a minimal exploration of the environmental benefits, particularly in terms of carbon dioxide emission reductions, associated with deploying new energy storage technologies in ancillary service markets. The paper concludes by highlighting the necessity for further research incorporating detailed techno-economic evaluations and carbon dioxide reduction potential of modular, scalable energy storage technologies, facilitated by advanced network simulation models and comprehensive market analysis.

The study examines the behavior of the Mechanically Stabilized Earth (MSE) wall using available reinforcement materials in Danang, Vietnam. The MSE was reinforced by the self-fabricated galvanized steel grids using CB300V steel with 3 cm ribs. The backfill soil is a sandy clay soil from the local area with a low cohesion. A full-scale model with full instrumentation was installed to investigate the distribution of tensile forces along the reinforcement layers. The highest load that caused the wall to collapse due to internal instability (reinforcement rupture) was 302 kN/m2, which is 15 times the design load of 20 kN/m2. The failure surface within the reinforced soil had a parabolic sliding shape which is similar to the theoretical studies. At the failure load level, the maximum lateral displacement at the top of the wall facing was small (3.9 mm) which is significantly lower than the allowable displacements for the retaining wall. Furthermore, a numerical model using FLAC software was applied to simulate the performance of the MSE wall. The modeling results are in good agreement with the physical model. Thus, the self-fabricated galvanized steel grids could be confidentially used in combination with the local backfill soil for the MSE walls.

Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing the continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, the operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently, thereby leading to the fast degradation of expensive tap-changer transformers. In the islanding mode, the difficulties also come from the drop of voltage and frequency upon disconnecting from the main grid. To appropriately model the above, the nonlinear AC power flow constraints are necessary. Computationally, the discrete nature of tap-changer operations and the stochasticity caused by renewables add two layers of difficulty on top of a complicated AC-OPF problem. To resolve the above computational difficulties, the main principles of the recently-developed "l1-proximal" Surrogate Lagrangian Relaxation are extended. Testing results based on 9-bus system demonstrate the efficiency of the method to obtain the exact feasible solutions for micro-grid operations thereby avoiding approximations inherent to existing methods, while demonstrating that through the optimization, 1. the number of tap changes is drastically reduced, and 2. the method is capable of handling networks with meshed topologies.

The existence of semantic inaccuracy §2 articles by A. Einstein “On the electrodynamics of moving bodies”, illustrating the principle of the relativity of simultaneity. In a sentence “The observers co-moving with the moving rod would thus find that the two clocks do not run synchronously while the observers in the system at rest would declare them synchronous”, the preposition “not” must be moved and placed before the word “synchronous”, at the end of the sentence. The criterion for synchronism of the clocks on the rod is the time it takes for the beam to travel from clock A to clock B and back, and for observers on the rod it will be the same both when the rod is at rest and when the rod is moving. Otherwise, observers on the rod would have had at their disposal an experiment that would allow them to determine whether the rod is moving or at rest, which contradicts the first postulate of the Special Theory of Relativity. The inaccuracy is found unchanged in modern editions in English and Russian, and can become a source of difficulties when teaching the basics of the Special Theory of Relativity based on primary sources, as well as make it difficult to understand the author’s logic when studying independently. Nobel laureate R. Feynman, having expanded and modified A. Einstein's thought experiment, eliminated the inaccuracy described in the article.

Evaluating mutation testing behavior can help decide whether refactoring successfully maintains the expected initial test results. Moreover, manually performing this analytical work is both time-consuming and prone to errors. This paper extends an approach to assess test code behavior and proposes a tool called MeteoR. This tool comprises an IDE plugin to detect issues that may arise during test code refactoring, reducing the effort required to perform evaluations. A preliminary assessment was conducted to validate the tool and ensure the proposed test code refactoring approach is adequate. By analyzing not only the mutation score but also the generated mutants in the pre- and post-refactoring process, results show that the approach is capable of checking whether the behavior of the mutants remains unchanged throughout the refactoring process. This proposal represents one more step toward the practice of test code refactoring. It can improve overall software quality, allowing developers and testers to safely refactor the test code in a scalable and automated way.

The purpose of the present study was to investigate the effect of the laterally moving tactile stimuli to the sole on the anticipatory postural adjustment (APA) of the gait initiation. Thirteen healthy males participated in this study. A sound cue was provided at the beginning of each trial. The moving tactile stimuli were delivered to the sole after the start cue. The loci of the tactile stimuli moved from the left to right most side of the sole, and then moved from the right to left most side of that in a stimuli cycle. The duration of one stimuli cycle was 960 ms, and this cycle repeated 16 times in a trial. The participants made three steps forward from quiet stance at the participants' preferred time after the start cue. The APA did not onset at the specific direction or phase of the moving tactile stimuli, indicating that they did not use the stimuli as a trigger for initiating the gait. The moving tactile stimuli decreased the amplitude and increased the duration of the APA. The moving tactile stimuli increased the time between the APA onset and toe-off of the initial support leg, indicating that they moved slower when initiating gait during the moving tactile stimuli. Those findings are explained by the view that suppression of the APA induced by the moving tactile stimuli to the sole is caused by the decrease in the velocity of the gait initiation, and the slower velocity of the gait initiation is caused by masking tactile sensation of the sole.

This paper reveals the physical properties of Jeffery nanofluid flow past a moving plate embedded in porous medium under the existence of radiation and thermal diffusion. The analysis is carried out in three cases of moving plate, namely stationary plate λ = 0, forth-moving plate λ = 1, back-moving plate λ = −1. Finite difference method is applied to solve the governing equations of the flow and pointed out the variations in velocity, temperature and concentration with the use of graphical presentations. The impact of several parameters on local skin friction, Nusselt number and Sherwood number is also noticed and discussed. Enhancement of velocity is observed under the impact of Jeffery parameter for the cases of stationary plate and back-moving plate, whereas reverse nature is found in the case of forth-moving plate. The velocity enhances as the values of porosity parameter increases for the case of stationary plate and forth-moving plate but a reverse nature is noticed in the case of back-moving plate.

Count data are used in many fields of practice, especially Poisson distribution as a popular choice for the marginal process distribution. If these counts exhibit serial dependence, a popular approach is to use a Poisson INAR(1) model to describe the autocorrelation structure of process. In this paper, the explicit formulas are proposed to evaluate performance characteristics of Double Moving Average control chart (DMA) for Integer valued autoregressive of serial dependence Poisson process. The characteristics of the control chart are frequently measured as Average Run Length (ARL) which means that the average of observations are taken before a system is signaled to be out-of-control. These proposed explicit formulas of ARL are simple and easy to implement for practitioner. The numerical results show that the DMA chart performs better than others when the magnitudes of shift are moderate and large.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

A method of measuring the absolute pose parameters of a moving rigid body using a monocular camera is proposed, aiming at addressing calibration difficulties and inconsistencies of repeated measurements of the rigid-body pose for a camera having a varying focal length. The proposed method does not require calibration beforehand. Using more than six non-coplanar control points symmetrically arranged in the rigid-body and world coordinate systems, the matrices of rotation and translation between the camera and two coordinate systems are obtained and the absolute pose of the rigid body measured. In this paper, formulas of the absolute pose measurement of a moving rigid body are deduced systematically and the complete implementation is presented. Position and attitude measurement experiments carried out on a three-axis precision turntable show that the average absolute error in the attitude angle of a moving rigid body measured by an uncalibrated camera at different positions changes by no more than 0.2 degrees. Analysis of the three-dimensional coordinate errors of the centroid of a moving rigid body shows little deviation in measurements made at three camera positions, with the maximum deviation of the average absolute error being 0.53 cm and the maximum deviation of the standard deviation being 0.66 cm. The proposed method can measure the absolute pose of a rigid body and is insensitive to the position of the camera in the measurement process. This work thus provides guidance for the repeated measurement of the absolute pose of a moving rigid body using a monocular camera.

Introducing the Moving Least-Squares Aided Finite Element Method (MLS-FEM), we present a robust computational technique that effectively evaluates the development of mixing in polymeric mixing devices. Our approach uses the Moving Least-Squares (MLS) interpolant to enhance the classic finite element shape functions. As a result, it becomes possible to treat fixed or moving components, such as rotors of a mixer, as discontinuities within the flow domain. This allows analysis of the flow field with a background mesh independent of the rotors' shape, orientation, and position. We compared the MLS-FEM results with the classical boundary-fitted mesh FEM, which comprises the mesh regeneration steps by the movement of the rotors. We then showed that the new method provides accurate results for the flow field parameters (i.e., the velocity components and the pressure values) and the mixing evolution without requiring mesh regeneration and without compromising accuracy.

In the energy-momentum density expressions for a relativistic perfect fluid with a bulk motion, one comes across a couple of pressure-dependent terms, which though well known, are to an extent, lacking in their conceptual basis and the ensuing physical interpretation. In the expression for the energy density, the rest mass density along with the kinetic energy density of the fluid constituents due to their random motion, which contributes to the pressure as well, are already included. However, in a fluid with a bulk motion, there are, in addition, a couple of explicit, pressure-dependent terms in the energy-momentum densities, whose presence to an extent, is shrouded in mystery, especially from a physical perspective. We show here that one such pressure-dependent term appearing in the energy density, represents the work done by the fluid pressure against the Lorentz contraction during transition from the rest frame of the fluid to another frame in which the fluid has a bulk motion. This applies equally to the electromagnetic energy density of electrically charged systems in motion and explains in a natural manner an apparently paradoxical result that the field energy of a charged capacitor system decreases with an increase in the system velocity. The momentum density includes another pressure-dependent term, that represents an energy flow across the system, due to the opposite signs of work being done by pressure on two opposite sides of the moving fluid. From Maxwell's stress tensor we demonstrate that in the expression for electromagnetic momentum of an electric charged particle, it is the presence of a similar pressure term, arising from electrical self-repulsion forces in the charged sphere, that yields a natural explanation for the famous, more than a century old, 4/3 factor in the electromagnetic mass.

The complexity of modern power grids, exacerbated by integrating diverse energy sources, espe-cially inverter-based resources (IBRs), presents a significant challenge to grid operation and plan-ning since linear models fail to capture the intricate IBR dynamics. This study employs the Sparse Identification of Nonlinear Dynamics (SINDy) method to bridge the gap between theoretical un-derstanding and practical implementation in power system analysis. It introduces the novel Volterra-based Nonlinearity Index (VNI) to examine system-level nonlinearity comprehensively. The distinction of dynamics into first-order linearizable terms, second-order nonlinear dynamics, and third-order noise elucidates the intricacy of power systems. The findings demonstrate a fundamental shift in system dynamics as power sources transit to IBRs, revealing system-level nonlinearity compared to module-level nonlinearity in conventional syn-chronous generators. The VNI quantifies nonlinear-to-linear relationships, enriching our comprehension of power system behavior and offering a versatile tool for distinguishing between different nonlinearities and visualizing their distinct patterns through the proposed VIN profile.

In this study, MBBR, utilizing kaldnes as a media to attach and grow microorganisms, was used to treat organics in a Compact Septage Treatment Plant (CSTP). Seeding and acclimatization processes were done initially to ensure the microorganisms were able to grow and adapt well within their environments. This research was done to determine the time required for those processes, the optimum conditions, and the growth mechanism of microorganisms during the acclimatization process. Both processes were run in a batch mode. Changes in colour and weight of kaldnes were analyzed for the seeding stage; while COD, MLVSS, pH & temperature were observed for the acclimatization stage. Seeding and acclimatization were run for 6 and 16 days respectively. Acclimatization stage was halted when MLVSS, pH, temperature and COD reduction efficiency were stable. Three phases of microorganism growth were clearly observed in the end of the acclimatization stage, i.e., lag, constant increasing and declining rates. This indicated that microorganisms could adapt well with the MBBR system, and were ready to be utilized further to treat septage in the MBBR of the CSTP. Further research is required to investigate the efficacy of the MBBR with the acclimatized microorganisms in the CSTP.

The research work examined the trend of HIV/AIDS, Tuberculosis, and Hepatitis diseases in Plateau state. Annual data from 2003 to 2018 was collected from the department of biostatistics at Plateau State Specialist Hospital (PSSH), Jos. The methods of analysis used are the Poisson Autoregressive Model (PAR(1)) and the Poisson Exponentially Weighted Moving Average Model (PEWMA). The results revealed a significant annual decrease of 23.9% and 4% in Tuberculosis and HIV/AIDS respectively. Furthermore, the results showed a significant annual increase of 46% in Hepatitis. The PEWMA model used revealed that TB increased by 0.02% when there is an increase in HIV but not significant, while Hepatitis significantly aggravates TB by at least 0.24%. Also, there is a significant rise in HIV by 0.85% when TB increases but Hepatitis has no such effect on HIV. Lastly, PEWMA model indicated a rise of 0.5% in Hepatitis cases when there is an increase in TB, but a surge in HIV has no such effect on Hepatitis cases in Jos. The study recommended that fight against TB should be intensified since TB cases significantly affect both HIV and Hepatitis in Jos, Nigeria.

This paper analyzes low-Earth orbit (LEO) satellite downlinks when an airborne interference source moves parallel to the satellite trajectory by considering the relative angle differences be-tween the satellites and the interference sources. To make the experimental interference situations more like actual environments, the LEO trajectories are obtained from two-line element set (TLE) data. Airborne interference sources with various altitudes move parallel to the LEO trajectories, and the jamming to signal (J/S) ratio is calculated based on the relative angle differences between the ground station, the LEO satellite, and the interference source. In order to calculate the relative angle difference psi, the coordinates of the satellite and the interference source are converted from the World Geodetic System 1984 (WGS84) to the ground station-centered east-north-up (ENU) system. By applying the relative angle difference psi, we can obtain the sidelobe gain of the ground station antenna in the direction from which the interference comes. The results of the study confirm that, from a J/S ratio perspective, the distances between the ground station, LEO satellite, and airborne interference source are important, and in particular, the relative angle difference psi between the interference source and the satellite is more critical factor.

Animal welfare remains a very important issue in the livestock sector but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare based upon physiological measurements instead of audio-visual scoring of behaviour would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) on pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on pig’s body to test whether it allows getting reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely 2^nd, 4^th and 6^th order Derivative of Gaussian (DOG, are tested. We show results of the developed PPG-based algorithm against electrocardiograms (ECG) as a reference measure for heart rate and this for an anesthetized versus a non-anesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between PPG-based heart rate technique and reference sensor goes from 91 to 95 percentage. In this paper we showed the potential of using the PPG-based technology to assess pig’s hear rate.

A number of experimental and numerical studies point out that incorporating a rotating cylinder can superiorly enhance the aerofoil performance, especially for higher velocity ratios. Yet, there have been less or no studies exploring the effects of lower velocity ratio at a higher Reynolds number. In the present study, we investigated the effects of Moving Surface Boundary-layer Control (MSBC) at lower velocity ratios (i.e. cylinder tangential velocity to free stream velocity) and higher Reynolds number on a symmetric aerofoil (e.g. NACA 0021) and an asymmetric aerofoil (e.g. NACA 23018). In particular, the aerodynamic performance with and without rotating cylinder at the leading edge of the NACA 0021 and NACA 23018 aerofoil was studied on the wind tunnel installed at Aerodynamics Laboratory. The aerofoil section was tested in the low subsonic wind tunnel, and the lift coefficient and the drag coefficient were studied for different angles of attack. The experiments were conducted for two Reynolds numbers: 200000 and 250000 corresponding to two free stream velocities: 20 m/s and 25 m/s, respectively, for six different angle of attacks (-5°, 0°, 5°, 10°, 15° and 20°). This study demonstrates that the incorporation of a leading edge rotating cylinder results in an increase of lift coefficient at lower angle of attacks (maximum around 33%) and delay in stall angle (from 10° to 15°) relative to the aerofoil without rotating cylinder.

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

Rapid climate change requires more powerful and precise modeling methods to forecast future climate variability. The GSTARIMA Model is efficient, combining space-time analysis with the Autoregressive Moving Average (ARIMA) Model. The integration of heteroscedasticity error and the Kriging method can strengthen the Model's ability to handle the problem of non-constant error variance in the GSTARIMA Model and forecast at unobserved locations of climate observations. This paper's Systematics Literature Review (SLR) is presented comprehensively with the principal aim of developing a thorough understanding of applying the GSTARIMA Model with heteroskedasticity error and the Kriging Method in climate forecasting following the Data Analytics Lifecycle methodology. The Systematic Literature Review (SLR) process consists of three main stages. We sourced the articles from databases such as Scopus, Dimensions, and EBSCO-Host The subsequent stage involved conducting a comprehensive literature review using the PRISMA method to ensure rigor and depth. Additionally, we performed bibliometric analysis to enhance rigor. Lastly, we conducted a gap analysis session to scrutinize existing research on the GSTARIMA Model and identify new opportunities. This literature review reveals that integrating GSTARIMA Model with heteroscedasticity errors and the Kriging method is suitable for climate forecasting. This research inspires researchers to contribute to the improvement and refinement of the Model, making it a more potent and valuable tool in climate forecasting.

We study the effect of the generation of the mechanical transverse wave (MTW)　travelling in the opposite direction (OD) to a moving medium (MM) on the relativistic energy conservation law (RECL). From the viewpoint of the relativity of simultaneity (RS), the time on the coordinate coinciding with the advance end of the wave (AEW) travelling toward the rear of the MM passes faster than that on the coordinate coinciding with the wave source (WS). Then the AEW in the MM travels forward compared to that in the rest frame of reference (RFR) which is stationary relative to the medium when the time on the coordinate coinciding with the WS is same for each inertial frame of reference (IFR). Hence, the coordinate interval (CI) between the AEW and WS in the MM is observed to be larger than that between them in the RFR. We show that this difference holds true for the CI of any portion having transverse velocities mutually converted by the Lorentz transformation (LT). This difference in the CI leads to that in the rest mass (RM). We demonstrate that the RM included in wave motion (WM) in the MM is larger than one included in WM in the RFR when comparing the portions having transverse velocities mutually converted by the LT. This relation holds true for all portions in WM. Therefore, the total coordinate interval of the portion (CIP) and total RM (TRM) included in WM in the MM (WMMM) are large compared to them included in WM in the RFR. Furthermore, we compare the relativistic kinetic energy (RKE) of the MTW travelling in the OD to the MM (ODMM) with that of the MTW propagating in the direction vertical to the moving direction of the medium. We prove that the CIP and RM included in the former MTW are larger than them included in the latter MTW when comparing each portion with the same transverse velocity (TV). Moreover, the total CIP and TRM included in the former MTW are also large compared to them included in the latter MTW. The reason for these is that the latter CIP and RM are equal to them in the RFR when comparing the portions having transverse velocities mutually converted by the LT. On the other hand, the energy supplied to generate each MTW is the same. From these, we demonstrate that the RKE of the MTW travelling in the ODMM can be larger than the total relativistic energy (TRE) of the MTW propagating in the direction vertical to the moving direction of the medium. Consequently, we propose a violation of the RECL and Einstein’s principle of relativity (EPR) because the TRE is not necessarily conserved in the IFR in which the medium is moving.

Water resource is considered as a significant factor in development of regional environment and society. Water consumption prediction can provide important decision basis for the regional water supply scheduling optimisations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model is proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points are used to verify the effectiveness of the model, and the future water consumption is predicted, in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.

Aim: To establish, through molecular modelling, safe and clinically acceptable putative antagonists of E571K-mutated exportin-1 among the bioactive compounds in various parts of Juglans mandshurica. Methods: The bioactive compounds were subjected to compendium of druglikeness and lead-likeness filter workflows prior to docking of the resultant compounds into E571K exportin-1 active site using PyRx AutoDock vina to establish their binding affinity and interaction profile. The evolutionary algorithm of Osiris property explorer DataWarrior software as well as lead-likeness filter were employed for generation of novel non-promiscuous analogues of the lead compound with better putative selectivity and clinical acceptability as E571K Exportin-1 antagonists.Results: The findings of this study present taxifolin as the putatively effective and lead-like E571K Exportin-1 inhibitor with high potential of qualifying for clinical evaluation but is associated with high promiscuity tendency in high throughput screening. The evolutionary derivation of novel analogues of the compound, however, results in the generation of putatively non-promiscuous, non-toxic, and lead-like E571K Exportin-1 antagonists with high synthetic accessibility and clinical developability for evaluation in the strategy for treatment of drug-resistant KRAS-mutant lung adenocarcinoma condition.

The article analyzes the problems connected with ensuring the coordinated operation of slipway drives that arise during the launch of a ship. The dynamic model of load of the electric drive of the ship's cart is obtained taking into account the peculiarities of the construction of the ship-lifting complex, which allows to analyze the influence of external factors and random influences during the entire process of launching the ship. A linearized mathematical model of the dynamics of a complex vessel movement in the process of descent in the space of states is developed, which allows to identify the mode of operation of the multi-drive system, taking into account its structure. The analysis of efficiency of application of recurrent methods of identification (stochastic approximation and least squares) of the parameters of the linearized model in the space of states is carried out. A decision support system has been developed in the automated system of operational control by the module for estimating the situation and the control synthesis to ensure a coherent motion of a complex ship-carts object in a two-phase environment.

In this paper, a horizontally moving suspended mass pendulum base is designed and controlled using robust control theory. H infinity optimal loop shaping with first and second order desired loop shaping function controllers are used to improve the performance of the system using Matlab/Simulink Toolbox. Comparison of the H infinity optimal loop shaping with first and second order desired loop shaping function controllers for the proposed system have been done to track the desired angular position of the pendulum using step and sine wave input signals and a promising result has been obtained succesfully.

Taking advantage of tourists’ intensive flow, the SARS-CoV-2 virus rapidly spread causing thousands of deaths globally. Trying to contain the already pandemic virus, government travel restrictions were suddenly imposed. Consequently, the tourism industry, which at that moment employed one in ten workers globally, suddenly collapsed. Hundreds of thousands of workers immediately lost their income. Flights were cancelled, and thousands of tourists were stuck abroad with no means to return to their home countries. The gravity of the situation raised the question of whether there was scholarly knowledge that could have helped manage tourism during the current pandemic. To answer this question, a methodical literature review was performed, allowing for up to 900 publications to be analysed. Keywords used were pandemic, tourism, tourist and travel. Based on this process, 63 publications were selected for further analysis. Among these, less than 5% were focused on the tourism side of the problem. As such, this research concludes that, by the time the novel coronavirus emerged, there was, virtually, no scholarly knowledge on how to manage tourism during pandemic times so as to avoid chaos, and that the scholarly community studying related issues is very small. Moving forward, this article recommends that research funding agencies and universities encourage the sound development of this area of knowledge. Aspects that should be investigated include when, how and by whom should tourism be halted, as well as the feasibility of a Tourism World Fund for supporting related costs.

In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision-making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the Power System Operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision-making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future.

This study explores the dynamics of liquid holdup in a lab-scale co-current two-phase upflow moving packed bed reactor, specifically examining how superficial gas velocity influences the line average external liquid holdup at a fixed superficial liquid velocity. Utilizing Gamma-ray Densitometry (GRD) for precise measurements, this research extends to determining line average internal porosity within catalyst particles and the overall external bed porosity. Conducted with an air-water system within a bed packed with 3 mm extrudate porous particles, the study presents a novel methodology to calculate liquid holdup, grounded in Beer–Lambert's law. Findings reveal a decrease in liquid holdup corresponding with increased superficial gas velocity across axial and radial locations, with a notable transition from bubbly to pulse flow regime at a critical velocity of 3.8 cm/sec. Additionally, the lower sections of the packed bed exhibited higher external liquid holdup compared to the middle sections at varied gas velocities. The liquid holdup distribution appeared uniform at lower flow rates, whereas higher flow rates favored the middle sections.

In this study, we mainly focus on the behavior of an electron gas produced by argon plasma that is bounded by a moving rigid flat plate. The effects of an external magnetic field on the electrons collected with each other, with positive ions, and with neutral atoms in the plasma fluid are investigated. We used the BGK model of the Boltzmann kinetic equation, which is fundamental in the study of the gas dynamics from the continuum flow to the free-molecular regimes with Maxwellian velocity distribution functions of the various species. An exact analytical solution of the model equations for the unsteady flow is given using the moment and the traveling wave methods. The behavior of the mean velocity of electrons is illustrated, which compatible with the variation of the shear stress, viscosity coefficient, and the initial and boundary conditions. Besides, the thermodynamic prediction is investigated by applying the principles of irreversible thermodynamics and Gibbs formula. It is found that the thermodynamic variables verify the Boltzmann’s H–theorem, the principle of Le Chatelier, and the Thermodynamics Second Law for the non-equilibrium processes of the system. Qualitative agreements with previous related papers are introducing. 3-Dimensional graphics for the calculating variables are offering, and their behavior is deeply discussed.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.