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Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Overstaffing production in underground coal mining is not convenient for daily management, and incomplete information of coal miners hinders the rescue process of firefighters during mine accidents. To address this safety sustainability issue, a novel face recognition method based on an improved multiscale neural network is proposed in this paper. A new depthwise separable (DS)-inception block is designed and a joint supervised loss function based on center loss theory is developed to construct a new multiscale model. The miniers can be recognized in the harsh underground environment during the life rescue. Experimental results show that the accuracy, recall and F1-score indexes of the proposed method for the miner face recognition in the underground mining environment are 97.26%, 94.17% and 95.42%, respectively. Transfer model with joint supervised loss can effectively improve the recognition accuracy by about 0.5–1.5%. In addition, the average recognition accuracy of the proposed face recognition method achieves to 91.34% and the miss detection rate is less than 5% in the dugout tunnel of coal mine.

Methodological aspects of assessing harmful impacts on the natural environment are presented, aimed at determining the indicator of ecosystem stability. The use of such an indicator makes it possible to determine environmental changes as a result of anthropogenic activity, as well as to determine the significance of these changes. A system is presented that systematizes the variety of consequences of anthropogenic impact on CLS. A qualitative scale of reducing harmful anthropogenic impact is proposed. It is proposed to conduct assessment of the categories of significance of harmful effects and ecological risk on the basis of a comprehensive evaluation of impacts on individual storeys and subsystems in the compartment from different sources of influence, taking into account their magnitude and intensity. The corresponding scales, a way of complex formation, categories of impact significance have been developed; also, an example of constructing an environmental risk matrix has been presented.

As a challenge in the field of smart medicine, medical picture segmentation gives important decisions and is the basis for future diagnosis by doctors. In the past decade, FCN-based network topologies have made amazing progress in the field. However, the limited perceptual capacity of convolutional kernels in FCN network topologies limits the network’s ability to acquire a global field of view. We propose BSANet, a 3D medical image segmentation network based on self-focus and multi-scale information fusion with a high-performance feature extraction module. BSANet can help the network to extract deeper features by obtaining a larger range of perceptual capabilities by using its self-focus and multi-scale information aggregation pooling modules. Brain tumor segmentation dataset and multi-organ segmentation dataset are used to train and evaluate our model. BSANet produces excellent results with its high-performance feature extraction network with an attention module and multi-scale information fusion module.

The online calibration method for current transformers is an important research direction in the field of smart grids. This article constructs a transformer error prediction model based on spatiotemporal integration. This model draws inspiration from the structure of forgetting gates in gated loop units and combines it with a graph convolutional network (GCN) that is good at capturing the spatial relationships within the graph attention network to construct an adaptive GCN. The spatial module formed by this adaptive GCN is used to model the spatial relationships in the circuit network, and the attention mechanism and gated time convolutional network are combined to form a time module to learn the temporal relationships in the circuit network. The layer that combines the time and space modules is used, which consists of a gating mechanism for spatiotemporal fusion, and a transformer error prediction model based on a spatiotemporal correlation analysis is constructed. Finally, it is verified on a real power grid operation dataset, and compared with the existing prediction methods to analyze its performance.

Oily sludge-derived activated carbon was prepared using the potassium hydroxide (KOH) activation method using oily sludge as a raw material, and one-factor experiments determined the best conditions for preparing activated carbon. The activated carbon’s morphological structure and surface chemical properties were analyzed by scanning different characterization tools, and the adsorption behavior of tetracycline hydrochloride was investigated. The results showed that the optimum conditions for preparing oily sludge-derived activated carbon were an activation temperature of 400 °C, activation time of 30 min, activator concentration of 1 mol/L, and impregnation ratio of 2 mL/g. After activation, the activated carbon had more pores and a more orderly crystal structure arrangement, the specific surface area was 2.07 times higher than that before activation, and the surface was rich in functional groups such as -HO, -C-O, -C=C, and -C-H, which increased the active sites of activated carbon. Physicochemical effects dominated the adsorption process. It belonged to the spontaneous heat absorption process under the quasi-secondary kinetic and Langmuir isothermal models. The maximum monolayer adsorption capacity of KOH-activated carbon was 205.1 mg·g−1.

Increasing the energy efficiency of production processes is closely related to minimizing the impact on the environment and is one of the priorities of the concept of sustainable development. Electric discharge is an effective tool for multilevel grinding of non-metallic materials in various working fluids and obtaining coarse and fine suspensions. We introduce the technique for calculating the electrotechnological parameters necessary for energy-efficient electric discharge dispersion. This technique considers the strength characteristics of the crushed material (dispersed phase) and the electrical conductivity of the working fluid (dispersed medium). It is also essential to consider the energy stored in the capacitor bank, the energy criterion, the critical value of the working fluid’s electrical strength, the radius of the high-voltage electrode point, and the distance from the discharge channel axis to the disintegration object. All this allows obtaining a given granulometric composition of the dispersed phase with minimal energy consumption. Experiments confirmed the validity of the proposed calculation technique. We obtained the water-brown coal suspension with a given dispersion two times faster and consumed four times less energy in comparison with the known methods that did not take into account the electrical conductivity of the working liquid and the mechanical strength of the crushed material.

This study aims to determine the optimum composition of the CeBr1−xIx compound to achieve the maximum light output. It is based on calculations of the band energy structure of crystals, specifically taking into account the characteristics of the mutual location of local and band 5d states of the Ce3+ ions. The band energy structures for CeBr2I and CeBrI2 crystals were calculated using the projector augmented wave method. The valence band was found to be formed by the hybridized states of 4p Br and 5p I. The 4f states of Ce3+ are located in the energy forbidden band gap. The conduction band is formed by the localized 5d1 states, which are created by the interaction between the 5d states of Ce3+ and the 4f0 hole of the cerium ion. The higher-lying delocalized 5d2 states of Ce3+ correspond to the energy levels of the 5d states of Ce3+ in the field of the halide Cl0 (Br0) hole. The relative location of 5d1 and 5d2 bands determines the intensity of 5d–4f luminescence. The bottom of the conduction band is formed by localized 5d1 states in the CeBr2I crystal. The local character of the bottom of the conduction band in the CeBr2I crystal favors the formation of self-trapped Frenkel excitons. Transitions between the 5d1 and 4f states are responsible for 5d–4f exciton luminescence. In the CeBrI2 crystal, the conduction band is formed by mixing the localized 5d1 and delocalized 5d2 states, which leads to quenching the 5d–4f luminescence and a decrease in the light output despite the decrease in the forbidden band gap. CsBr2I is the optimum composition of the system to achieve the maximum light output.

Large areas become unsuitable for full-fledged life after mining activity. To improve the state of environmental safety of post-industrial landscapes and the rational use of disturbed territories, a Decision Support System (DSS) should be created. This system should also contain proposals for restoring the soil cover and plant communities that are proposed in this article. The purpose of this study is to determine the influence of arbuscular mycorrhizal fungi on the process of vegetation formation in the post-industrial landscape of a sulfur quarry. During reclamation works in human-made ecotopes, vegetation has already formed there in a certain way due to natural succession processes. We assessed the level of vegetation self-restoration and, on the basis of the obtained data, the need and direction of phytoreclamation in relation to specific ecotopes. The set of restoration of soil cover and plant communities makes it possible to solve the problem of reusing post-industrial landscapes. The positive effect of the treatment of seedlings with a spore remedy of arbuscular, mycorrhizal fungi on the studied breeds’ height increase was observed. In the process of the revitalization of disturbed landscapes through the mycorrhization of planting material, there is a tendency to restore and increase phytodiversity at the floristic and coenotic levels.

This article discusses the characteristics of the design of thermoelectric generators (TEGs) for cold climates. Since the thermocouples of thermoelectric batteries are produced from different materials, their major properties are studied. Particular attention is given to nanostructured materials regarding the modern class of thermoelectric materials. Two-, three-, and four-component alloys (metallic glasses) of the Fe-Ni(Cu)-P-B system are chosen based on the experience of thermoelectric thermometry. The close chemical composition of two thermoelectrodes enables their compatibility in thermocouple production and satisfactory thermoelectric efficiency of batteries during long-term operation. The improvement of the thermoelectric battery characteristics related to a unit of mass is evaluated. The materials studied are distinguished by the absence of toxic components harmful to the environment at the manufacturing and operating stages.

This study evaluates the effect of aging in artificial saliva and thermal shocks on the microhardness of the bulk-fill composite compared to the nanohybrid composite. Two commercial composites, Filtek Z550 (3M ESPE) (Z550) and Filtek Bulk-Fill (3M ESPE) (B-F), were tested. The samples were exposed to artificial saliva (AS) for one month (control group). Then, 50% of the samples from each composite were subjected to thermal cycling (temperature range: 5–55 °C, cycle time: 30 s, number of cycles: 10,000) and another 50% were put back into the laboratory incubator for another 25 months of aging in artificial saliva. The samples’ microhardness was measured using the Knoop method after each stage of conditioning (after 1 month, after 10,000 thermocycles, after another 25 months of aging). The two composites in the control group differed considerably in hardness (HK = 89 for Z550, HK = 61 for B-F). After thermocycling, the microhardness decrease was for Z550 approximately 22–24% and for B-F approximately 12–15%. Hardness after 26 months of aging decreased for Z550 (approximately 3–5%) and B-F (15–17%). B-F had a significantly lower initial hardness than Z550, but it showed an approximately 10% lower relative reduction in hardness.

The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

The city of Lviv faces a scarcity of parking lots, especially in its central zone. A comparison of the results of the in situ studies carried out in 2016 and 2021 in the city center showed the efficiency of the planning activities, due to which the number of on-street parking spaces increased by 72%, whereas the amount of on-street parking spaces in prohibited areas decreased by 32%. The extremely high demand shows that parking spaces in the city center should always be charged. It is reasonable to develop and implement a smart parking system compatible with the concept of the internet of things. In this case, drivers can get a real-time map of available parking spaces. The implementation of such a system should be preceded by a reasonable organization of the on-street parking zones. The high population density of Lviv and the development of urban subcenters require the expansion of the existing parking zones not only in the historic center but also in the remote areas of the city to balance the high demand for parking spaces and to ensure the sustainable development of the whole transport system.

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.

(1) Background. Nanotechnology offers significant alternative ways to solve scientific, medical, and human health issues. Dental biomaterials were improved by nanotechnology. It manufactures better materials or improves the existing ones and forms the basis of novel methods for disease diagnosis and prevention. Modern nanotechnology makes oral health care services more acceptable for patients. Nanotechnology is now important area of research, covering a broad range of applications in dentistry. (2) Methods. Relevant literature from Scopus published in English was selected using the keywords “nanoparticle” and “dentistry”. To the selected articles we applied the inclusion and exclusion criteria to choose the relevant ones. (3) Results. Based on the relevant articles, a literature review was prepared. This review provides an insight into the applications of nanotechnology in various branches of dentistry. We applied several regression models to fit number of papers versus time and chose the best one. We used it to construct the forecast and its 95%-confidence interval for the number of publications in 2022–2026. (4) Conclusions. It shows that a significant rise in papers is expected. This review familiarizes dentists with properties and benefits of nanomaterials and nanotechnology. Additionally, it can help scientists to consider the direction of their research and to plan prospective research projects.

The results of the calculation of the energy band structure and luminescent research of CeF3 crystals are presented. The existence of two 5d1 and 5d2 subbands of the conduction band genetically derived from 5d states of Ce3+ ions with different effective electron masses of 4.9 me and 0.9 me, respectively, is revealed. The large electron effective mass in the 5d1 subband facilitates the localization of electronic excitations forming the 4f-5d cerium Frenkel self-trapped excitons responsible for the CeF3 luminescence. The structure of the excitation spectra of the exciton luminescence peaked at 290 nm, and the defect luminescence at 340 nm confirms the aforementioned calculated features of the conduction band of CeF3 crystals. The peculiarities of the excitation spectra of the luminescence of CaF2:Ce crystals dependent on the cerium concentration are considered with respect to the phase formation possibility of CeF3.

With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.

The authors offer to solve the problem of providing traceability of measurements by increasing metrological autonomy of in-plant measuring systems. The paper shows the expedience of increasing metrological autonomy by creating a "virtual" reference. There are analysed possible variants of implementation of the "virtual" reference, which will provide high metrological stability of measurements at insignificant additional expenses. The authors point out the necessity of creation of universal technical and programmatic means of mutual comparison for the in-plant measuring systems to increase the reliability of measurements in the conditions of metrological autonomy.

In communication networks, the volume of traffic, the number of connected devices and users continues to grow. As a result, the energy consumption generated by the communication infrastructure has become an important parameter that needs to be carefully considered and optimized both when designing the network and when operating it in real-time. In this paper, the methodology of calculation of complex parameters of energy consumption for transport telecommunication networks is proposed. Unlike the known techniques, the proposed methodology takes into account heterogeneity and multilayer networks. It also takes into account the energy consumption parameter during the downtime of the network equipment in the process of processing the service data blocks, which is quite an important task for improving the accuracy of energy consumption at the stage of implementing the energy-saving network. We also developed simulation software to estimate and manage the energy consumption of the optical transport network using the LabVIEW environment. This software tool allows telecommunication network designers to evaluate energy consumption, which allows them to choose the optimal solution for the desired projects. The use of electro-and acousto-optical devices for optical transport networks is analyzed. We recommended using electro-optical devices for optical modulators and acousto-optical devices for optical switches. The gain from using this combination of optical devices and the parameter of rij electro-optical coefficient and M2 acousto-optical quality parameter found in the paper is about 36.1% relative to the complex criterion of energy consumption.

In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.

The method of periodic verification in operation condition of the main thermocouple of thermocouple based sensor with controlled profile of temperature field (TBS with CPTP) is proposed in this paper. This periodic verification can be done without dismantling out of sensor from operation place as well as without any working standards. The reference thermocouples of either 1st or 3rd level of hierarchy scheme can be used for verification of TBS with CPTP right after produce. Evaluation of self verification error in this case has shown value of error at either 1 ?C or 1.2 ?C respectively.

One of the most effective methods of reducing the impact of error from acquired thermoelectric inhomogeneity of thermocouples' electrodes is discussed in this article. The method consists in stabilizing the profile of the temperature field along the main thermocouple electrodes (which measure the temperature of the object). There is also discussed the design of device for profile stabilization of the temperature field and methods for control this profile.