In this work, we conducted a numerical study on the cavitation flow around a circular cylinder through the implementation of a porous coating. The primary objective addressed the effectiveness of utilizing a porous surface to control cavitation. We analyzed the cavitation dynamics around the cylinder and the hydrodynamic performance at different permeability levels of the porous surfaces. The flow was governed by the density based homogeneous mixture model and the volume penalization method was used to deal with the porous layer. A high-order accurate numerical method was adopted for the simulation of the cavitating flow through solving the preconditioned multiphase equations. The results revealed that the application of porous layer led to effective suppression of the cavitation vortex shedding. In addition, a reduction of the shedding frequency was obtained which accompanied by thinner and elongated vortices in the wake region of the cylinder. With the proper porous layer, the inception of the cavitation on the cylinder was suppressed and the amplitude of pressure pulsations due to the cavitation shedding mechanism was mitigated.

Fully mechanized mining face production involves complex geological conditions; mining pressure; gas, water, fire and other mining environments. The working face needs a shearer, hydraulic support, a scraper conveyor and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for coal mining robot technology systems, including single robots, robotized equipment, robot clusters and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve customized research and development and standardized production of various types of robots. A specific analysis was conducted on the research progress of common key technologies such as explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mine robot research and development is proposed. Moreover, robotic mining systems should be promoted to enhance the overall intelligence level and mining efficiency of mining equipment. To develop human‒machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots. The digital twinning of the entire mine robot system should be accelerated, the normalized operation level of coal mine robots should be improved, research on coal mining robots, support robots, and transportation robots should be carried out, intelligence should be achieved in fully mechanized mining faces, and equipment support for fewer and unmanned fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization has been summarized, and the technical and engineering feasibility of the coal mining machine population has been verified.

One of the main challenges in geodetic deformation analysis is to infer whether the geometry of some engineering structures or zones of natural hazards has changed from their initial state or not. The pillar that supports such an analysis is tightly based on the fundamentals of statistical hypothesis testing. The null hypothesis model indicates that no displacement has occurred. It is tested against a class of alternative models, which stipulate different displacement patterns. In this contribution, we present an innovative geodetic displacement detection which integrates combinatorial analysis and likelihood ratio tests into a sequential procedure for the case where the differences between observations of two epochs are in play. This framework is applied and investigated to two test scenarios: a synthetic and a real simulated trilateration network. From a statistical point of view, our approach is rigorous, because the alternative model can identify simultaneously more than one unstable point. In addition, the relationship between the unknown parameters and the observations is always linear, even if the problem manifests itself as non-linear. Consequently, we avoid a potential loss of statistical test power due to the model linearization. In addition, the proposed method controls the false positive rate efficiently. One of the problems that arise here is also related to the selection of the maximum number of points to be considered in the test procedure ( ). Here, we provide an innovative methodology based on rank computation of the design matrices to define , which can even be extended to the problem of outlier detection. Determining avoids the problem of having non-separable models in identifying unstable points. The algorithms and data are available in the repository.

Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila Swarm Algorithm based on cross-mutation. The friction tracking error can be deduced by the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.

Predatory mites are a type of biological control agent used in many countries against various vegetable pests, particularly spider mites. Despite the significant presence of predatory mites in the Tibetan plateau, there is limited research on their fitness and prey capacity in the area. Herein, four predatory mite species, including one species Neoseiulus californicus and three species from genus Amblyseius (Amblyseius swirskii, Amblyseius barkeri, and Amblyseius cucumeris) were investigated. Specifically, their fitness and prey capacity on two-spotted mites (Tetranychus urticae) at highland conditions were evaluated. Compared to the other three predatory species, A. swirskii had the highest fecundity (Mean ± SEM=11.60 ± 0.34) and the highest pre-adult survival rate (Mean ± SEM = 83.33 ± 3.33%). Since their juvenile Survival Rate (SR) was extremely low (Mean ± SEM =13.33% ± 5.77%), most A. barkeri nymphs died before emergence. Compared to the other three predatory mites, A. swirskii had the highest predation capacity on T. urticae adult mites at 15 d post-release (Mean ± SEM =14.28 ± 2.24). Collectively, these findings imply notable advantages in employing A. swirskii for controlling two-spotted mites in the Tibetan Plateau. Furthermore, based on the results, A. swirskii was the most effective, and A. barkeri was the least effective in controlling two-spotted mites in the Tibetan Plateau. These findings could inform the development of a feasible biological control method based on suitable predatory mite species to manage T. urticae in the Tibetan Plateau, and would be helpful in delaying the rise or emergence of chemical pesticide resistance in two-spotted mites.

The pneumatic muscle is an actuator well known for its inherited compliance. In the last years, there have been a lot of applications of these actuators, mainly oriented toward medical devices. However, there are few papers that use muscle as a model associated with mechanical structures. These actuators are difficult to control because their stiffness depends on the applied pressure, determining a non-linear behavior. This article presents a model, created in Matlab, considering the muscle as a kinematic structure. The model is simulated as an individual structure and a PID controller is applied to test the response. Then, these results are validated by experimental tests done in the laboratory on different types of muscles. Comparison results validate the accuracy of the model.

Abstract: Theoretical and non-dimensional investigations have been performed to study the vibration control potential of approaches that can be based on viscoelastic but also on endochronic elements. The latter are known from the endochronic theory of plasticity and provide the possibility to establish rate-independent schemes for vibration control. The main question that has to be answered is: Can rate-independent damping be efficiently used to reduce mechanical vibrations? To answer this question non-dimensional models for dynamical systems are derived and analyzed numerically in time-domain as well as in frequency-domain. The results are used to compare the performance of an optimally tuned endochronic absorber to the performance of an optimally tuned dynamic absorber with viscoelastic damping. Based on a novel closed form representation for non-linear systems with endochronic elements, it has been possible to prove that rate-independent control of vibration results in an overall control profit that is close to the control profit obtained by the application of well-established approaches. It has also been found that the new concept is advantageous if anti-resonances have to be considered in broadband vibration control. Based on these novel findings, a practical realization in the context of active vibration control is proposed in which the rate-independent control law is implemented on an appropriate signal processing hardware.

The flow around thick airfoils at low Reynolds numbers and the possibility of using energy methods to control flowfield, increase lift and reduce drag are considered. For calculations, a series of propulsion airfoils is used, obtained by a combination of solving the inverse problem of aerodynamics and stochastic methods of global optimization. The calculations take into account the transition from the laminar flow regime to the turbulent one, which has a significant effect on the lift and airfoil drag. The applicability of various turbulence models for airfoil modelling at low Reynolds numbers is discussed. The results of numerical simulation make it possible to determine the dependence of the lift coefficient on the angle of attack and the optimal flow rate of air taken from the airfoil surface for each angle of attack. The results of numerical simulation are compared with the data of a physical experiment, and conclusions are drawn about the accuracy of various turbulence models.

Commonly, professors, students and researchers from universities around the world use software distributed under a license agreement for computer simulation purposes, which requires a computer with considerable hardware capabilities. Consequently, this implies a high cost to conduct simulations that require implementing numerical methods in all areas of engineering, particularly in the field of robotics and nonlinear control. This paper presents the design and results analysis of a low-cost and open-source frugal computer simulation tool with applications to robotic nonlinear control, for instance, for numerical simulations of manipulator robot control based on dynamic models. Using a single board minicomputer with with reduced computing power, Raspberry Pi, together with free software, GNU-Octave, trajectory tracking control simulations in the joint space of a Selective Conformal Assembly Robot Arm (SCARA) are achieved by solving a system of nonlinear differential equations, represented in matrix form, which includes the control law and the system model. The results of the proposed alternative are compared by running the same simulation code on a laptop computer using MATLAB and GNU Octave, which show minimal deviations and reasonable time complexity. Moreover, considering the frugality curve calculated for this approach, in addition to the low acquisition cost of the simulation software tool, it would allow the creation of a simulation laboratory in some universities with budgetary constraints for educational and research purposes.

The development of artificial intelligence systems assumes that a machine can independently generate an algorithm of actions or a control system to solve the tasks. To do this, the machine must have a formal description of the problem and possess computational methods for solving it. The article deals with the problem of optimal control, which is the main task in the development of control systems, insofar as all systems being developed must be optimal from the point of view of a certain criterion. However, there are certain difficulties in implementing the resulting optimal control modes. The paper considers an extended formulation of the optimal control problem, which implies the creation of such systems that would have the necessary properties for its practical implementation. To solve it, an adaptive synthesized optimal control approach based on the use of numerical methods of machine learning is proposed. The method moves the control object, optimally changing the position of the stable equilibrium point in the presence of some initial position uncertainty. As a result, from all possible synthesized controls, he chooses one that is less sensitive to changes in the initial states. As an example, the optimal control problem of quadcopter with complex phase constraints is considered. To solve this problem? according to the proposed approach, the control synthesis problem is firstly solved to obtain a stable equilibrium point in the state space by a machine learning method of symbolic regression. After that optimal positions of the stable equilibrium point are searched according to source functional from the optimal control problem by particle swarm optimization algorithm. It is shown that such approach allows generating the control system automatically by computer basing on the formal statement of the problem and then directly implementing it onboard as far as they have already had a stabilization system inserted.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid bat-tery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is of particular importance because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system perfor-mance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, and cost-effective energy storage op-tion. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. In this work, battery-supercapacitor storage system (HBSS) is established by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that ef-fectively controls the different power sources. The algorithm's purpose is to regulate the charge and discharge cycles of the HBSS, ensuring that the state of charge (SOC) of both batteries and supercapacitors remains within the desired range. The proposed management algorithm is de-signed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. By maintaining the SOC of these energy storage components within the specified limits, the proposed method ensures their longevity and max-imizes their performance. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

Wearable exoskeleton solutions for upper limb rehabilitation or assistance, particularly for the hand area, have become increasingly attractive to researchers, proving to be effective over time in treating hand movement impairments following various neurological diseases. In the present work, the development of a wearable exoskeleton-type device for active hand rehabilitation has been addressed, based on fluid actuators made of elastomeric material and on myoelectric control by capturing myoelectric signals from the forearm area. The flexion movement of the fingers (phalanges) is actively assisted by pneumatic actuation of the built-in actuators and the extension movement is assisted by a depressurization control of the actuator. The device has been designed to be as easy to use as possible, light in weight, and to provide a high degree of comfort when used by the patient for rehabilitation training or daily life activities (ADL), which can be performed in the comfort of one's own home.

Battery Management System helps to improve the transient performance and life of electro-chemical rechargeable batteries like a Li-ion, lead–acid, etc. The investigation here was focused on the crucial part of the Battery Management System that is, Cell balancing, State of charge of the battery, etc. By analyzing the different control strategies a new optimal control strategy is developed to improve the battery performance according to application. The Control strategy is developed using an active and passive cell balancing model with the help of AVL Cruise and MATLAB/SIMULINK software. For the active cell balancing technique, a switched capacitor technique is chosen. To have longer battery life, the time required for the cell balancing must be as low as possible for which many control logics have been defined. The effect of these cell balancing techniques on the performance of battery packs is investigated and it is observed to produce a promising result.

Purpose: To clarify the mechanism of state anxiety on intertemporal decision-making, two studies were conducted to investigate the role of trait anxiety and inhibitory control. Study 1 examined the moderating effect of trait anxiety. Study 2 continued to investigate the mediating effect of inhibition control on the basis of study 1. Methods: A total of 266 Chinese college students participated. Participants’ state anxiety, trait anxiety and intertemporal decision-making were measured by questionnaires (scales) respectively. And inhibition control was evaluated by two-choice oddball paradigm. Results: The results of Study 1 indicated that state anxiety could predict intertemporal decision-making and that the moderating effect of trait anxiety on this relationship was significant. Study 2 found that inhibitory control could completely moderate the relationship between state anxiety and intertemporal decision-making and that this indirect effect was affected by trait anxiety. Conclusion: 1)Individuals with higher trait anxiety are more inclined to make more impulsive and intertemporal decisions when they have a high level of state anxiety; 2)state anxiety allows inhibitory control ability to have a further effect on intertemporal decision-making; and 3)the higher trait anxiety in individuals, the stronger mediating effect of inhibitory control on the relationship between state anxiety and intertemporal decision-making.

This paper presents a novel DQ-based multicarrier Pulse Width Modulation PWM for a single-phase, three-level PV-powered grid-connected F-type inverter. The main control objective in the proposed inverter is to regulate the grid current with low total harmonic distortion and load power components compensation. Despite the F-type inverter’s advanced advantages, there are only a few works addressing the control issue in the literature yet. The proposed control and switching methods aim to achieve both DC side voltage balance and lowest switching losses. The proposed scheme has been designed based on a modified multi-carrier PWM switching algorithm. Consequently, the proposed control method able to satisfy the requirements of DC side voltage balance and achieve lower switching losses. A further advantage of the proposed control and switching methods is that they retain the main advantage of the F-Type inverter, which is that only 25% of the power switches are exposed to full DC voltage. This is an important advantage since it reduces the overall cost of the inverter and improves its reliability. Overall, the proposed modified multi-carrier PWM switching algorithm appears to be a promising approach for controlling the F-Type inverter, offering improved performance and efficiency compared to other control methods. The theoretical model was verified through simulation using MATLAB/Simulink. According to the simulation results, the grid current and dc capacitor voltages are successfully managed in all operational situations.

This paper presents a comprehensive study and the analysis of a topology of a no isolated DC-DC intituled Double Quadratic Boost converter with high static gain. This converter has the main advantage of high static gain and low voltage stress on its switches. The article will first present the theoretical analysis of the converter operating in an open loop. The objective of the work is the mathematical modeling and the control strategy of the converter, as well as the validation through closed-loop experimental results. Besides, we presented the practice test results to demonstrate the operation of the converter, such as the static gain experimental curve, the practice efficiency of the converter, and the control of the output voltage, as well as the capacitor voltage balance control. The authors designed a prototype for 1 kW, with a switching frequency of fs=50kHz, with FPGA-based command and modulation.

The fixed stroke fatigue test can not simulate the real load condition of the landing gear, and the variable stroke fatigue test has increasingly become the mainstream. A modular test device for fatigue test for aircraft landing gears was designed, the automatic control of the buffer stroke was realized with ectopic displacement control technology effectively, the horizontal loading cylinder could follow the wheel axis actively with the displacement active control technology, follow-up loading along the vertical direction was realized through constructing a polar coordinate system and making the load line automatic alignment, a split-type dummy wheel was designed to improve the loading accuracy, a booster cylinder was designed to realize the control loading of high pressure combined with the hydraulic control system. The test shows that the technical scheme meets the design requirements, and the fatigue test was completed successfully. The device for landing gear variable stroke fatigue testing can be applied to other landing gear static / fatigue tests.

A pandemic can have a huge impact on normal human life and the economy, taking COVID-19 as an example. While the population flow between countries and regions is the main factor affecting the change of a pandemic, exactly as the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, restricted to the differences in prevention and control policies in different areas and privacy issues, the patients’ personal data of the medical center cannot be effectively combined with the passengers’ personal data. This prevents more precise airport control decisions from being made. To the end, this paper designs a novel data sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment shows that the relationship between the epidemic and aircraft transport can be effectively explored by PPChain, without sharing raw data. This approach does not require centralized trust and improves the security of the shareing process.The scheme can help formulate more scientific and rational prevention and control policies on airports’ control. And it can use aerial data to predict pandemics more accurately.

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower-quality datasets be made available for analysis and comparison alongside those of higher-quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies, community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA’s GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.

The objective of this study is to design an optimal model predictive control (MPC) strategy using manipulated variables to control the production of a sufficient amount of hydrogen through low temperature autothermal reforming of dimethyl ether (DME), a reforming reaction performed using PdO/ZnO/γ-Al2O3 catalysts coated on honeycomb cordierite ceramics. Experiments and simulation have verified that the optimal activity temperature of the catalyst is 400 °C, and the hydrogen volume fraction in syngas is over 43%. In the implementation of the hydrogen production system, the MPC controller can precisely determine the feed rates of DME, high-purity air, and water based on the space state equation of the reformer, to achieve the anti-disturbance of the reformer temperature. Thus, the reduction of hydrogen yield and sintering of the catalyst as a result of overheating are prevented. As the static and dynamic performance of hydrogen production exhibits excellent tracking of the setpoints, an autonomous, automated, and reliable continuous system was designed to meet the desired hydrogen demand situation. This study shows that an autonomous, automated, and reliable continuous hydrogen production reforming system can be designed to actively respond to the on-board hydrogen usage situation.

Greenhouse gas (GHG) emissions from agriculture are significant contributors to global change. We experimentally manipulated biogeochemical control points of irrigation and nitrogen (N) to examine management strategies that could impact GHG flux, i.e., carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and soil physiochemical changes over a growing season in an arid New Mexico sorghum (Sorghum bicolor (L.) Moench) cropping system. Sorghum is water and N efficient and amenable to environmental stress. Interrogating how crop systems perform in intense heat, aridity and ultraviolet stress of the southwestern US climate can inform future management in areas that produce more food currently, but that will undergo these stresses in the near future. Water was applied at regionally typical rates, or at ~30% below those rates. Timing N to plant needs may reduce N loss and N2O emissions, and we tested this hypothesis by adding equal amounts of fertilizer to all plots, with half receiving all fertilizer at planting versus plots fertilized at 50:50 planting and 30 days post-planting. Gas flux from soil was analyzed via FTIR. More biomass was harvested from the fully irrigated plots; N timing did not significantly affect biomass. Soil pH fluctuated throughout the season in response to both treatments. Carbon dioxide emissions significantly increased in fully irrigated plots through time. Methane uptake was depressed by full irrigation. Nitrous oxide flux was lower in split N plots, but N2O emissions were not impacted by reduced irrigation. These results suggest that arid adapted crops can maintain economically feasible yield, and biogeochemical monitoring within a growing season can help manage for GHG flux.

During the smelting process of AOD furnace, the unbalanced reaction of material will lead to the occurrence of splashing. It will not only damage the smelting equipment, but also seriously injure the personnel. In this study, first, the information of liquid level, audio information, and vibration information are detected by multiple sensors respectively. Then, the fused information is used to forecast the splashing. Finally, the multitasking fuzzy controller is used to suppress splashing. The results show that the method of forecasting and suppressing splashing can accurately forecast and achieve rapid suppression. Thus, the efficiency of smelting can be improved.

In surveying engineering tasks, close-range photogrammetry belongs to leading technology considering different aspects like the achievable accuracy, availability of hardware and software, accessibility to measured objects, or the economy. Hence, constant studies on photogrammetric data processing are desirable. Especially in industrial applications, the control points for close-range photogrammetry are usually measured using total stations. In the case of small objects, more precise positions of control points can be obtained by deploying and adjusting a three-dimensional linear network set up on the object. The article analyzes the accuracy of the proposed method, based on the measurement of the linear network using a tape with a precision of ±1 mm. The experiment shows that the adjusted positions of the network control points can be determined with high, one-millimeter accuracy. The photogrammetric 3D model derived referring to such control points and stereo-images captured with a non-metric camera is also characterized by the highest possible precision, which qualifies the presented method to accurate measurements used in surveying engineering. The authors prove that the distance between two randomly optional points derived from the 3D model of a dimensioned object is equal to the actual distance measured directly on it with one-millimeter accuracy.

Various designs of spherical robots using barycenter offset mechanisms were previously reported. This kind of robots offers advantages such as turning in placeor moving in any direction at any time. Yet, the approaches resulting in a balanced robot, which could roll with reduced energy costs once accelerated, are scarce. We introduce anovel robot of this type. The feasibility of the approach is verified on a cylindrical robot. This paper presents an open-source, remotely controlled, robotic cylinder. The robot uses a specifically designed mechanism to displace a mass in its center, which causes the cylinder to roll forwards or backwards. The movement characteristics of the cylinder, as well as the mathematical and physical limitations of the mechanism were studied. An intuitive user interface allows the robot to be controlled through WiFi. This robot was manufactured only with readily available and inexpensive materials, which would make it a suitable platform for education, typically in the fields of physics and engineering.

Ammonia known as harmful gas that could impact on health and environment. Typically, ammonia gas emitted by fertilizer industry, rubber factory, etc. There are many options for advance industry to control ammonia gas pollution, absorption tower using water scrubber and using activated carbon as adsorption reaction could be an option. But for middle- and lower-class industry, it is common in Indonesia, as seen on many rubber factories, ammonia gas pollution is serious problem to control, actually they have used wet scrubber but eficiency is lower about 47%. This study measure performance for ammonia elimination as gas pollutant using absorption tower and adsorption tower in laboratory scale. Using electrochemical gas sensor, we measure both ammonia concentration for inlet and outlet simultaneously to settle saturation point of these two types of ammonia emission control. In conclusion, Ammonia absorbed in water proportional to saturation time and absorbent volume. Highest absorbed ammonia concentration 1.538 mg/L on 4000 mL absorbent. For Adsorption system, saturation time proportional and correlated to adsorbent weight and Adsorption capacity reverse correlated to adsorbent weight. Optimum adsorption point can be achieved from intersection curve between saturation time and adsorption capacity which is 1200 grams adsorbent

FPGA-based data acquisition and processing systems play an important role in modern high-speed, multichannel measurement systems, especially in High-Energy and Plasma Physics. Such FPGA-based systems require an extended control and diagnostics part corresponding to the complexity of the controlled system. Managing the complex structure of registers while keeping the tight coupling between hardware and software is a tedious and potentially error-prone process. Various existing solutions aimed at helping that task do not perfectly match all specific requirements of that application area. The paper presents a new solution based on the XML system description, facilitating the automated generation of the control system’s HDL code and software components and enabling easy integration with the control software. The emphasis is put on reusability, ease of maintenance in case of system modification, easy detection of mistakes, and the possibility of use in modern FPGAs. The presented system has been successfully used in data acquisition and preprocessing projects in High-Energy Physics experiments. It enables easy creation and modification of the control system definition and convenient access to the control and diagnostic blocks. The presented system is an open-source solution and may be adopted by the user for particular needs.

Aedes albopictus is an invasive mosquito species responsible for local transmission of chikungunya and dengue viruses in Europe. In the absence of available treatments, insecticides-based control is one of the most important viable strategies to prevent emerging problems. Diflubenzuron (DFB) and Bacillus thuringiensis var. israelensis (Bti) are among the most commonly used larvicides for Ae. albopictus control which pose major concerns for potential resistance development. Studies on the resistance emergence of Ae. albopictus to both DFB and Bti and its persistence in the wild are essential for the efficient and sustainable planning of the control programs. In this concept, larvae from a recently laboratory established population were subjected to increasing selective pressure for nine successive generations against both DFB and Bti. At the end of this process, the resistance levels and the overwintering success of the selected populations relative to control (colonies that received no selection) were determined. Results revealed a substantial and a moderate increase on the resistance levels following selection with DFB and Bti respectively. Interestingly, the selection process to both larvicides had no apparent impacts on the overwintering capability relative to control suggesting the successful persistence of the resistant individuals in the wild from year to year.

Temperature is the abiotic factors that strongly influence the biology and behavior of insects. Thus, we assessed the development of the egg parasitoids, Ooencyrtus submetallicus and Telenomus podisi, parasitizing eggs of the brown stinkbug, Euschistus heros, at different temperatures, in addition to estimating the average number of generations using temperature records for seven representative soybean producing regions in Brazil. The comparative biology study conducted evidenced that O. submetallicus and T. podisi had similar percentages of parasitism and emergence, life cycle duration (egg-adult), and longevity. The sex ratio and the number of adult parasitoid emerged per parasitized egg was superior for O. submetallicus. The study of thermal requirements evaluated temperatures of 16, 19, 22, 25, 28, 31, and 33 °C. Ooencyrtus submetallicus and T. podisi developed at temperatures between 16 and 31°C. The lower temperature (Tb) threshold for O. submetallicus and T. podisi was 9.3° and 6.7°C, while the thermal constant (K) was 336.9 and 272.7 degree days, respectively. The estimated average number of generations per year for both parasitoids was greater than the pest E. heros. Ooencyrtus submetallicus and T. podisi exhibited the same biological capacity to parasitize and to develop parasitizing E. heros eggs under laboratory studied conditions. The temperature range between 16° and 33 °C was favourable for the development of both parasitoids parasitizing E. heros eggs.

Additive manufacturing (AM) has been the core area of sustainable manufacturing commonly recognized for its high efficiency in enabling cost-effective production towards sustainability. There are three models this research constituted: In Collection-Recycling-Manufacturing (CRM) model, technologies and processes are benchmarked followed by Business model that evaluates industrial key criteria. However, these are insufficient for AM to effectively play dominant role as the realization requires human factors such as multi-entities authorities, policy making and AM society to initiate and execute the plan. Strategy control model focuses on human-centric approaches such as demography, population control, policy, regulations, and management. It investigates each nation’s demography, and enables strategy, plan and control to relocate overcrowding population to rural areas. It also produces robust workforce to support AM and materials recycling through the appropriate applications. Through the construction of AM and materials recycling, strategy control model creates job opportunities for those unemployed people. It further builds infrastructure for the livelihood of new residents and supports AM home-based business (HBB).

With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

The onset of the Coronavirus 2019 (COVID-19) pandemic has challenged the worldwide healthcare sector, including dentistry. The highly infectious nature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and risk of transmission through aerosol generating procedures has profoundly impacted the delivery of dental care services globally for months causing prioritization of critical dental needs and suspension of routine dental procedures. As dental practices with strengthened infection control strategies and preventive measures are re-opening in the “new normal” period, it is the responsibility of healthcare professionals to constantly analyze new data and limit the spread of COVID-19 in dental care settings. With the new variants of SARS-CoV-2 rapidly emerging in different geographic locations, a definite need to comply more than ever with the rigorous public health measures to mitigate COVID -19 transmission exists. The aim of this article is to provide dental clinicians with essential information regarding both the spread of SARS-CoV-2 virus and protective measures against COVID-19 transmission in dental facilities. By compiling guidance and standard protocols recommended by credible national and international organizations, this article serves as an aid to navigating through this unprecedented time with ease. Much to learn about COVID-19 exists and because of constant ongoing research, we are not aware of all of the newly found information. However, in this paper, we reviewed the available literature recommended for the best current practices that must be taken for a dental office to function safely and successfully.

Around 14% of the global population does not have access to electricity. About 95% of those are living in rural Sub-Saharan Africa. Often in these regions, diesel generators are the only source of electricity. The operating cost of these diesel generators is high. However, solar and wind energy are available in most of African countries. This study presents the analysis of designing an off-grid hybrid system with a wind turbine, PV, diesel generator, and battery to power a hospital, school, and 200 household village in four locations across Somalia. The research investigated the availability of wind-solar resources in selected locations. Designing of the system and economic-technical calculations were performed using HOMER. The selection of the optimum design was based on the Cost of Electricity and Net Present Cost. The results show that for Kabaal and Ceel Buur, a WT-PV-DG-Battery is the optimal system as the wind resource in these regions is high. For Saakov and Baki, a PV-DG-Battery system proves to be optimum as the wind resource is limited here. The study also evaluated the control strategy and proved that combined dispatch was the most cost-effective for these locations. The study concluded that hybrid systems are more economical than diesel systems.

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, like sensor networks.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

Emerging and reemerging pathogens is a global challenge for public health. Recently, a novel coronavirus disease emerged in Wuhan, Hubei province of China, in December 2019. It is named COVID-19 by World Health Organization (WHO). It is known to be caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that affects the lower respiratory tract and manifests as pneumonia in humans. Coronaviruses (CoVs) are structurally more complicated as compared to other RNA viruses. This viral epidemic has led to the deaths of many, including the elderly or those with chronic disease or compromised immunity. Viruses cause infection and diseases in humans of varying degrees, upper respiratory tract infections (URTIs) cause common cold while lower respiratory tract infections induce pneumonia, bronchitis, and even severe acute respiratory syndrome (SARS). The costs of COVID-19 are not limited. It equally affects all the medical, sociological, psychological, and economic aspects globally. This is regarded as the third deadly outbreak in the last two decades after Severe Acute Respiratory Syndrome SARS (2002–2003) and Middle East Respiratory Syndrome MERS (2012). Based on the sequence homology of SARS-CoV-2, different animal sources including bats, snakes, and pangolins have been reported as potential carriers of this viral strain. Real-time RT-PCR represents the primary method for the diagnosis of new emerging viral strain SARS-CoV-2. The transmission dynamics suggest that SARS-CoV-2 is transmitted from person-to-person through direct contact or coughing, sneezing, and by respiratory droplets. Several anti-viral treatments including lopinavir/ritonavir, remdesivir, chloroquine phosphate, and abidor are also suggested with different recommendations and prescriptions. Protective and preventive strategies as suggested by various health organization i.e. WHO and US Center for Disease Control and Prevention (CDC) must be adopted by everyone. This review covers the important aspects of novel COVID-19 including characteristics, virology, symptoms, diagnostics, clinical aspects, transmission dynamics, and protective measures of COVID-19.

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitors banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, we propose a general formulation of the multi-objective Volt-VAr optimization problem. We consider the applicability of various multi-optimization techniques and discuss the operational interpretation of these solutions. We demonstrate the methods using simulation on a test feeder.

The process model is very essential for the model based control design. The model of the process can be identified using system identification algorithm. The system identification is done through the open loop and closed loop approaches. In this work, the lab scale conical tank setup configured as a non square MIMO system. The conical tank system is identified through the both appraoches, the effectiveness and need of the both approaces are discussed. Based on the open loop identified model the controller designed and the controller implemented in the real time the to record the process data. From this data the closed loop identification are conducted uisng N4SID algorithm. The controller seetings are obtained using the smith predcitor based IMC based PI controller for the obtained model. The proposed identification algorithm and controller tuning show the better reults over the conventional method. Moreover, this method is applicable for all the non square MIMO system.

Objectives: The aim of the present study was to evaluate the association between psoriasis and osteoporosis using two different studies. Methods: Data from the Korean National Health Insurance Service-Health Screening Cohort of participants who were ≥ 40 years old were collected from 2002 to 2013. Psoriasis and osteoporosis were included using ICD-10 codes. In study I (a follow-up study), a total of 25,306 psoriasis participants were matched to 101,224 controls with respect to age group, sex, income group, and region of residence, and the occurrence of osteoporosis was analyzed. Crude (simple) and adjusted hazard ratios (HRs) were analyzed using a stratified Cox proportional hazard model. In study II (a nested case–control study), a total of 79,212 osteoporosis patients were matched to 79,212 controls, and a previous history of psoriasis was analyzed. Crude and adjusted odds ratios (ORs) were analyzed using a conditional logistic regression analysis. Subgroup analyses were conducted according to age group and sex. Results: The adjusted HR of osteoporosis was 1.11 (95% confidence interval [CI] = 1.07-1.15, P < 0.001) in study I. In the subgroup analysis according to age and sex, the results were consistent except for the ≥ 60-year-old women. The adjusted OR of psoriasis was 1.22 (95% CI = 1.16-1.28, P < 0.001) in study II. All subgroups demonstrated high adjusted ORs of osteoporosis for psoriasis. Conclusions: Psoriasis increased the risk of osteoporosis in the population of participants aged ≥ 40 years in Korea.

Dental services are significantly impacted by the COVID-19 pandemic. Almost all dental procedures carry a high infection risk for providers and patients due to spread of aerosols. As a consequence, national public health agencies, and dental professional associations have issued guidelines for enhanced infection control, personal protection equipment and limiting care to urgent or emergency services. However, there is no dental service concept for disaster preparedness or response that might be applied. Similarly, pathways to care provision in a post-pandemic future are missing. We propose Safe Aerosol-free, Emergent Dentistry (SAFE Dentistry) as one approach to dental services during and emerging from pandemics. The concept’s starting point is the identification of the most common patient needs. The next step is to replace common treatments relating to the most common needs with alternative interventions with less infection risk because they do not generate aerosols. SAFE Dentistry is innovative, safe, and responds to the requirements of a pandemic and post-pandemic emergence where the risk of disease transmission remains high. SAFE Dentistry thereby ensures continuity of dental services while protecting providers and patients from infectious pathogens. Moreover, SAFE Dentistry allows dental service providers to remain operational and generate income even under pandemic conditions. Implementation and policy options for SAFE Dentistry include universal availability without co-payments by patients and a uniform bundled payment scheme for providers to simplify budgeting, reimbursement and to provide easier administration during a pandemic. Adaptations and adjustments of the concept are possible and encouraged, as long as the principle of avoiding aerosol-generation is maintained.

There were warnings before; nevertheless the current CoVID-19 pandemic took the world by surprise: within just four month, it conquered the globe and claimed over 200'000 lives. Unprecedented governmental actions put about half of the population under curfew or lock-down. The resulting economic meltdown is expected to eliminate globally 9’000’000’000’000 (9 trillion) USD in 2020 and 2021 alone, a value roughly the size of the yearly GDP of the world’s 150 smallest economies. The resulting crises might cause mass-unemployment and a hunger pandemic later this year. This Essay analyses current statistical data of the CoVID-19 pandemic to develop a guideline for a path through the crisis, minimizing both loss of lives and economic costs. Part 1 details the current situation; part 2 develops a small set of measures, allowing a near normal life until a future vaccination campaign has reached sufficient numbers of people; and part 3 provides some important lessons for the future beyond SARS-CoV-2. The Essay leads to the following key-messages: 1) The CoVID-19 pandemic will stay for at least two more years. This is the minimum time required for a vaccination campaign to reach sufficient numbers of people. 2) The crucial element to control the pandemic is keeping case numbers under the threshold required for a functional tracing, testing & isolation (TTI) strategy. That threshold differs from country to country and strongly depends on culture and the applied tracing technology as well as available testing capacities. 3) The economic burden of a TTI strategy is moderate while fatalities are also reduced. Hence, such an approach is strongly recommended. Its implementation requires a set of simple and cost-effective measures (see figure below), which in combination seem to be sufficient to keep CoVID-2’s reproductive rate at or below 1. 4) Implementing international coordination of actions will be necessary for effective infection-chain tracing5) If case numbers are above the TTI threshold, shutdown measures remain the only option until tracing of infection chains becomes feasible again.6) In the future, neglected pandemic-related research requires a funding boost. Just 1% of the bill of the current crisis could support the research of 45’000 scientist for 20 years.

Background: The world, now in an emergency of preventing the drastic spread of COVID-19. After the infection was first reported in December 2019, almost every country did not pay attention to this highly contaminated disease and failed to react swiftly. Now the whole universe is in an vulnerable state, loosing a great loss of lives and facing difficulties in all socio-economic aspects. That is why we have the urge to develop an efficient mathematical model (quarantine) based on social consciousness to control the epidemic. Methods: This is a quarantine mathematical model. The outcome of the system is dependent on social consciousness. We have calculated the awareness level by considering various socio-economic factor of each country. In our model, the parameters are Education Index, GDP per capita, population density, high literacy and stable economy. To maximize the efficiency of the model, it has to be implemented in initial stage. However, strict application of the method in vigorous stage of epidemic will also bring a satisfactory outcome. Results: Higher social consciousness will decrease the number of infected population dramatically while minimal or lower awareness will do a outburst. Conclusion: Outbreak will be in control of health care system, lower the death rate and will ensure social and economic stability.

The prevention, mitigation and treatment of movement impairments, ideally, requires early diagnosis or identification. As the human movement system has physiological and functional redundancy, movement limitations do not promptly arise at the onset of physical decline. A such, prediction of movement limitations is complex: it is unclear how much decline can be tolerated before movement limitations start. Currently, the term ‘homeostatic reserve’ or ‘physiological reserve’ is used to refer to the redundancy of the human biological system, but these terms do not describe the redundancy in the muscle architecture of the human body. The result of functional redundancy is compensation. Although compensation is an early predictor of movement limitations, clear definitions are lacking and the topic is underexposed in literature. The aim of this article is to provide a definition of compensation and emphasize its importance. Compensation is defined as an alteration in the movement trajectory and/or altering muscle recruitment to complete a movement task. Compensation for capacity is the result of a lack in neuromusculoskeletal reserve, where reserve is defined as the difference between the capacity (physiological abilities of the neuromusculoskeletal system) and the task demand. Compensation for movement objectives is a result of a shift in weighting of movement objectives, reflecting changing priorities. Studying compensation in biomechanics requires altered protocols in experimental set-ups, musculoskeletal models that are not reliant on prescribed movement, and inclusion of alternative movement objectives in optimal control theory.

Merchant marine fleet is under inspections by several parties to ensure maritime regulation compliance. One of the major effects on implementation of regulations by International Maritime Organization (IMO) is indeed Port State Control. This article aims to analyze Paris Memoranda of Understanding (MOU) all detention remarks from 2013 to 2019 for EU15 countries (except Luxemburg and Austria) through an approach based on Analytical Hierarchy Process and demonstrate the results on Geographic Information System (GIS) to guide marine industry on detainable Port State Control remarks and country risk profile. While Analytical Hierarchy Process Approach has been used to indicate the ranking of basic maritime regulations from the perspective of the port state control , GIS help us to demonstrate the regional dispersion amongst EU15. The data of the detained vessel taken from the public website of Paris MOU and each report considered as a professional judgement that causes detention. It has been shown that almost all countries top priorities for regulation are Safety of Life at Sea (SOLAS) and Fire Safety Systems (FSS). Consequently, the results of the study can assist Port State Officers, ship crew, ship owners and managers presenting the facts of their inspection and able to improve themselves. The spatial analysis also expected to guide ship owners and managers to focus their vessel’s deficiencies to prevent sub-standardization.

Geodetic networks are essential for most geodetic, geodynamics and civil projects, such as monitoring the position and deformation of man-made structures, monitoring the crustal deformation of the Earth, establishing and maintaining a geospatial reference frame, mapping, civil engineering projects and so on. Before the installation of geodetic marks and gathering of survey data, geodetic networks need to be designed according to the pre-established quality criteria. In this study, we present a method for designing geodetic networks based on the concept of reliability. We highlight that the method discards the use of the observation vector of Gauss-Markov model. In fact, the only needs are the geometrical network configuration and the uncertainties of the observations. The aim of the proposed method is to find the optimum configuration of the geodetic control points so that the maximum influence of an outlier on the coordinates of the network is minimum. Here, the concept of Minimal Detectable Bias defines the size of the outlier and its propagation on the parameters is used to describe the external reliability. The proposed method is demonstrated by practical application of one simulated levelling network. We highlight that the method can be applied not only for geodetic network problems, but also in any branch of modern science.

Significance: Electrophile signaling is coming into focus as a bona fide cell signaling mechanism. The electrophilic regulation occurs typically through a sensing event (i.e., labeling of a protein) and a signaling event (the labeling event having an effect of the proteins activity, association, etc.). Recent advances: Herein, we focus on the first step of this process, electrophile sensing. Electrophile sensing is typically a deceptively simple reaction between the thiol of a protein-cysteine, of which there are around 200,000 in the human proteome, and a Michael acceptor, of which there are numerous flavors, including enals and enones. Recent data overall paint a picture that despite being a simple chemical reaction, electrophile sensing is a discerning process, showing labeling preferences that are often not in line with reactivity of the electrophile. Critical issues: With a view to trying to decide what brings about highly electrophile-reactive protein-cysteines, and how reactive these sensors may be, we discuss aspects of the thermodynamics and kinetics of covalent/non-covalent binding. Data made available by several laboratories indicate that it is likely that specific proteins exhibit highly stereo- and chemoselective electrophile sensing, which we take as good evidence for recognition between the electrophile and the protein prior to forming a covalent bond. Future directions: We propose experiments that could help us gain a better and more quantitative understanding of the mechanisms through which sensing comes about. We further extoll the importance of performing more detailed experiments on labeling and trying to standardize the way we assess protein-specific electrophile sensing.We propose experiments that could help us gain a better and more quantitative understanding of the mechanisms through which sensing comes about. We further extoll the importance of performing more detailed experiments on labeling and trying to standardize the way we assess protein-specific electrophile sensing.

In addition to provide an extremely clean environment for primary loop of high-temperature gas-cooled reactor (HTR), the primary helium circulator (PHC) using electromagnetic levitation technology also provides an effective means for vibration control. Besides synchronous vibration produced by mass imbalance and sensor runout, double-frequency vibration produced by unbalanced magnetic pull (UMP) is serious in PHC engineering prototype (PHC-EP). In this paper, we firstly analyzed the mechanism of UMP and the multi-frequency vibration characteristics in combination with the PHC-EP. Then we put forward a distributed iterative learning control (ILC) algorithm and a parallel control scheme to suppress the periodic vibrations. Finally, we verified the methods by carrying out experimental researches on the active magnetic bearing (AMB) bench of PHC-EP. The results show that the methods put forward in this paper have significant control effect on the double-frequency vibration generated by UMP of the PHC-EP, and provide theoretical and practical references for the PHC safe operation in HTR.

Cigarette butts, also known as tobacco product waste (TPW), are the single most collected item in environmental trash cleanups worldwide. This study used an online survey tool (Qualtrics) to assess knowledge, attitudes, and perceptions about this issue among individuals representing the Framework Convention Alliance (FCA). The FCA has about 683 members on its listserv, including non-governmental tobacco control advocacy groups that support implementation of the World Health Organization’s (WHO) Framework Convention on Tobacco Control (FCTC). Respondents (n = 65) represented countries from all six WHO regions. The majority (82%) had heard the term TPW, and all considered TPW as an environmental harm at some level. Additionally, 29% of respondents failed to identify that “cigarette filters make smoking easier.” Most (73%) correctly identified TPW components; however, fewer (60%) correctly identified the composition of cigarette butts. The majority (57%) were unfamiliar with Extended Producer Responsibility (EPR) and Product Stewardship (PS) as possible environmental intervention strategies. Respondents expressing opinions concurred that adding a litter fee to fund TPW programs will aid in reducing tobacco use and reduce the environmental impacts of TPW (100%); that prevention, reduction, and mitigation of TPW could be an important part of international tobacco control programs (98%); and that banning smoking in outdoor venues could reduce TPW (95%). Only 16% reported effective prevention or clean-up efforts in their countries. Weighted rankings revealed that respondents’ saw the national government, the tobacco industry, and state governments as most important in addressing TPW. The results of this research will inform continuing international discussions by the FCTC Conference of the Parties (COP) regarding environmental policies that may be addressed within FCTC obligations.

This paper presents a configuration of dual output single phase current source inverter with 6 switches for microgrid applications. The inverter is capable of delivering power to two independent set of loads of equal voltages or different voltages at the load end. The control strategy is based on Integral Sliding Mode Control (ISMC). The remote monitoring of the inverter is performed with cyber infrastructure. The cyber physical test bench is developed based on Reconfigurable I/O processor (NI MyRIO-1900) for control and monitoring of the inverter. The inverter prototype is tested in cyber physical test bench in laboratory conditions. The performance of the inverter is analyzed and monitored through the remote system. Also, the inverter is analyzed with different voltage conditions.

In wireless sensor networks, there is no a central controller to enforce cooperation between nodes. Therefore, nodes may generate selfish behavior to conserve their energy resources. In this paper, we address the problems of transmission power minimization and energy balance in wireless sensor networks using a topology control algorithm. We considered the energy efficiency and energy balance of the nodes, and an improved optimization-integrated utility function is designed by introducing the Theil index. Based on this, a topological control game model of energy balance is established, and it is proved that the topological game model is an ordinal potential game with Pareto optimality. Additionally, an energy-balanced topology control game algorithm (EBTG) is proposed to construct topologies. The simulation and comparison show that, compared with other topological control algorithms based on game theory, the EBTG algorithm can improve energy balance and energy efficiency while reducing the transmitting power of nodes, thus prolonging the network lifetime.

This paper proposes a position control method for low cost EGR valve system in automotive application. Generally, position control system using in automotive application has many restrictions such as cost and space, the mechanical structure of actuator implies high friction and large difference between static friction and coulomb friction. This large friction difference occurs the vibrated position control result when the controller uses conventional linear controller such as P, PI. In this paper, low cost position control method which can apply under the condition of high difference friction mechanical system. Proposed method is verified by comparing conventional control result of experiments.

Graphene oxide (GO), nanographene oxide (nGO) and partially reduced graphene oxide (rGO) have been studied as possible foam stabilizing agents for CO₂ based enhanced oil recovery (EOR). GO was able to stabilize CO₂/synthetic sea water foams. rGO was not able to stabilize foams likely due to the high reduction degree of the material. Particle size had a strong influence on foamability and stability. GO hydrophilicity increased as the particle size decreased and no foams were created when particle size was below 1 µm (nGO). GO brine dispersions showed immediate gel formation, which improved foam stability. Particle growth due to layer stacking was also observed. This mechanism was detrimental for foam formation and stabilization. nGO dispersed in synthetic sea water rapidly formed hydrogels and was not filterable. This work indicates that the particles studied are not suitable for CO₂ EOR purposes.

In order to ensure the sustainability of the shipping industry and marine ecosystem of Montenegro, it is necessary that Montenegro becomes a full member of the Paris Memorandum of Understanding (Paris MoU) on Port State Control. The reasons for doing so are numerous: the full adoption of standards stipulated by the Memorandum in relation to ship control; continuous keeping the pace with and development of new standards in compliance with turbulent changes in the maritime industry and operation (including the increasing scope of maritime transport); decrease in the number of detained ships which meet the requirements stipulated in international conventions and elimination of substandard ships in perspective; prevention of environmental pollution, sea and port incidents. This justified endeavour is supported by the fact that Montenegro is one out of two countries in Europe that are not the full members of the Paris MoU. Additionally, in this context it is necessary to emphasise the fact that the marine ecosystem of Montenegro makes an integral part of the world ocean. Accordingly, the improvement of the quality of national legislation which is compliant with international requirements is an imperative which has positive implications on the regional and global sustainability.

Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, during the charging and the discharging process, there are some parameters that are not controlled by the user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Different control methods have been developed with the goal of protecting the battery and extending its life expectancy, being the most used the constant current-constant voltage. However, several studies show that charging time can be reduced by using Fuzzy Logic Control or Model Predictive Control. Other benefits are; temperature control and an extension of life expectancy. For all these reasons, FLC and MPC have proven to be more efficient than traditional charge control methods.

Pancreatic cancer is one of the most fatal common cancers affecting both men and women, representing about 3 percent of all new cancer cases in the United States. In this study, we aimed to investigate the association of pancreatic cancer risk with alcohol consumption as well as folate intake. We performed a case-control study of 384 patients diagnosed with pancreatic cancer from May 2004 to December 2009 and 983 primary care healthy controls. Our findings showed no significant association between risk of pancreatic cancer and either overall alcohol consumption or type of alcohol consumed (drinks/day). Our study showed dietary folate intake was modestly but significantly inversely associated with pancreatic cancer (OR=0.99, P <.0001). The current study supports the hypothesis that pancreatic cancer risk is reduced with higher food-based folate intake.

This paper focuses on distributed generation (DG) controller of a PV based Micro Grid. An independent DG controller (IDGC) is designed for PV applications to improve maximum power point tracking (MPPT) in Micro Grid (MG). Extreme learning machine (ELM) based MPPT method exactly estimate the reference input of converter control. They are voltage and current at MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here the converter controller, IDGC is improved by measuring directly converter outputs like duty cycle. PWM index in a single DG PV-based MG. It introduces a fast learning Extreme Learning Machine (ELM) using Moore-Penrose pseudo-inverse and an online-sequential ridge re method for robust CR estimation. The approach ensures the stability of the microgrid during PV uncertainties. Internal DG control approach (IDGC) improves the strength of the Micro Grid during, three-phase faults at load bus, partial shading, irradiance change, islanding operation and load change. The model is designed and simulated in MATLAB/SIMULINK platform and some of the results are validated in hardware in simulation (HIL) platform.

In this paper, possibilities for the bandwidth and wavelength utilization in future next-generation passive optical networks are presented and reasons for realizing and utilizing of extended dynamic wavelength and bandwidth algorithms for NG-PON2 networks are analyzed. Next, principles of the effective dynamic bandwidth allocation are introduced in details, focused on the importance of the decision criterion optimization. For achieving better bandwidth utilization of dedicated wavelengths, this paper is focused on the novel effective dynamic bandwidth allocation algorithm with adaptive allocation of wavelengths to ONU units and on the optimization of the decision criterion. For analyzing the novel extended DWBA algorithm utilizing for various wavelength allocation cases possible in NG-PON2 networks, a simulation program with the enhancement of the EDBA algorithm is realized. Finally, an optimization of the decision criterion defining a minimum bandwidth utilization of the actual wavelength is executed for NG-PON2 networks based on the hybrid TWDM technique.

The log interpretation and evaluation of Chang 8 tight sandstone reservoir in Longdong West area, Ordos Basin, is facing great challenges due to the existence of low resistivity contrast oil pays. To better guide the exploration of oil resources in this area, the characteristics and control mechanism of low resistivity contrast oil pays were studied in this research. Firstly, according to the relative value of apparent resistivity increase rate of the target formation, the reservoir was divided into two types: low resistivity contrast oil pay (LRCP) and high resistivity oil pay (HRP). Then, the reservoir characteristics were studied by comparing and analyzing the experimental data, formation water data and logging data collected from the two reservoir types. On this basis, the control mechanism of LRCP was studied and summarized into reservoir micro factors and regional macro factors, respectively. It is found that the reservoir rock composition between LRCP and HRP is basically the same. Compared with HRP reservoir, the average porosity and permeability of LRCP reservoir are relatively better, but the pore structure is relatively poorer because of the high content of micro pores. When the filling pressure of crude oil is sufficient enough, the high oil saturation can be formed in LRCP reservoir. The high irreducible water saturation and high formation water salinity are the main micro factors reduced the reservoir resistivity. Besides, the difference of hydrocarbon expulsion capacity of source rock and the regional difference of formation water salinity are the macro factors influenced the distribution of LRCP and HRP from vertical and horizontal of the region. The formation of LRCP is controlled by the comprehensive influence of reservoir micro factors and regional macro factors. And the comprehensive consideration of the influence of micro factors and macro factors on LRCP is suggested during the log interpretation and evaluation.

In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations.

Full-order state-space models represent the starting point for the development of advanced control methods for wind turbine systems (WTSs). Regarding existing control-oriented WTS models, two research gaps must be noted: (i) There exists no full-order WTS model in form of one overall ordinary differential equation that considers all dynamical effects which significantly influence the electrical power output; (ii) all existing reduced-order WTS models are subject to rather arbitrary simplifications and are not validated against a full-order model. Therefore, in this paper, two full-order nonlinear state-space models (of 11th and 9th-order in the (a, b, c)- and (d, q)-reference frame, resp.) for variable-speed variable-pitch permanent magnet synchronous generator WTSs are derived. The full-order models cover all relevant dynamical effects with significant impact on the system’s power output, including the switching behavior of the power electronic devices. Based on the full-order models, by a step-by-step model reduction procedure, two reduced-order WTS models are deduced: A non-switching (averaging) 7th-order WTS model and a non-switching 3rd-order WTS model. Comparative simulation results reveal that all models capture the dominant system dynamics properly. The full-order models allow for a detailed analysis covering the high frequency oscillations in the instantaneous power output due to the switching in the power converters. The reduced-order models provide a time-averaged instantaneous power output (which still correctly reflects the energy produced by the WTS) and come with a drastically reduced complexity making those models appropriate for large-scale power grid controller design.

Lower limb exoskeletons have the potential to reduce the prevalence of work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls can adapt an exoskeleton’s assistance in real-time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton to minimize the users’ physical effort, a parameter innovatively evaluated by their interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user’s metabolic cost within the HITL control through a machine-learning model. The regression model was able to estimate the metabolic cost, in real-time, with a root-mean-squared error of 0.66 W/kg and a mean absolute percentage error of 26%, making faster (10s) and less noisy estimations than a respirometer (K5, Cosmed) device. The HITL reduced the user’s metabolic cost and interaction torque by 7.3% and 32.3%, respectively, when compared to a zero-torque control. The developed HITL control surpassed both a non-exoskeleton and a zero-torque condition regarding the user’s physical effort, even for a simple task such as slow walking. Furthermore, the user-specific control enabled a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential for HITL controls in assisted working.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

Music-domain visual programming languages (VPLs) have shown to be Turing complete. However, the common lack of built-in flow control structures can obstruct using VPLs implementing general-purpose algorithms, which harms the direct use of algorithms and algorithm theory in art creation processes using VPLs. In this article, we show how to systematically implement general-purpose algorithms in music-domain visual languages by using the Finite State Machines with Datapath computation model. The results expose a finite state machine and a set of internal state variables that walk paths whose speed can be controlled using a metronome ticks and whose path depends on the initial conditions of the algorithm. These elements can be further mapped to music elements according to the musician's intentions. We demonstrate this technique by implementing Euclid's Greatest Common Divider algorithm and using it to control high-level music elements in an implementation of Terry Riley's In C, and to control audio synthesis parameters in a FM synthesizer.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

The use of natural raw substances for food preservation could provide a great contribution to food waste reducing, circular economy enhancing, and green processes application widening. Recent studies indicated that natural preservatives release kinetics significantly influenced the food preservation process. The use of porous materials as absorber for natural essential oils provided nanohybrids with excellent antioxidant and antimicrobial properties. In this study the well-known SBA-15 porous material was used to control the release of the natural preservative thyme oil on food surface and to extend the food shelf-life. Results indicated that the thyme oil loading was higher than other porous materials reported recently and the addition of SBA-15 to the LDPE increases the water/oxygen barrier. The film with the higher thyme-oil@SBA-15 nanohybrid content exhibited the slower release kinetic. The antioxidant activity of the final films ignited after 48 hours, was in the range of 60-70%, and was almost constant for 7 days. Finally, all tests indicated a sufficient improvement by the addition of thyme-oil@SBA-15 nanohybrids in the pure LDPE matrix and the concentration of wt. 10% of such nanocarriers provided the optimum final LDPE/10TEO@SBE-15 active packaging film. This material could be a potential future product for active packaging applications.

Nowadays, in low voltage (LV) electric distribution networks (EDNs), the Distribution Network Operators (DNOs) are encountering a high number of connected small-scale distributed generation units, mainly photovoltaic (PV) prosumers. The irregular behavior of the prosumers, together with the uncertainty degree of the requested and injected powers associated with all end-users from the LV EDN, can cause voltage variations with violations of the allowable limits. In this context, the paper developed an efficient and resilient expert system integrating the rule-based reasoning applied to the On-load Tap Changer (OLTC)-fitted transformer to improve the efficiency of the voltage control in the PV-rich LV EDNs. An in-depth analysis based on 75 scenarios, resulting from the combinations of three indicators: the penetration degree of the PV prosumers, consumption evolution associated with the consumers, and energy production of the PV systems, has been performed to demonstrate the efficiency of the proposed expert system in an LV EDN from a rural area belonging to a Romanian DNO. The success rate of the expert system has been 86.7% (65 out of 75 scenarios didn't have voltage issues, all values being between the allowable limits in 100% of the time slots associated with the analyzed period). For the other scenarios (representing 13.3%), the values have been inside the range [-10%, +10%] at least 95% of the time slots, according to the European Power Quality Standard, and for the rest of the time slots up to 100% being very close to the upper and lower limits of the range, with highly mitigating of the voltage variations.

: Chemical accidents can occur anywhere. The need for chemical management in Korea was realized following the 2012 Gumi hydrofluoric acid accident in 2012. The Chemicals Control Act was enacted in 2015. This system evaluates the risks (high, medium, low) and consequent safety management at all plants that handle hazardous chemical substances. However, the system was criticized as excessive when most plants were designated high-risk without considering their size. Thus, laboratories and hospitals handling very small quantities were subject to regulation. Accordingly, in 2021 Korea revised the system to include off-site consequence analyses and a Korean-style risk analysis. Plants handling very small quantities, such as laboratories and hospitals, were exempt from regulation. In this study, plating and paint manufacturing companies, which were classified as high-risk in the previous system, even though they were medium-size business plants, were re-evaluated as low-risk plants. In the Korean-style risk analysis, it is possible to see at a glance what is lacking in the plants, such as cooperation between local residents and local governments and the construction of safety facilities according to the type of accident scenario. The revised system is a reasonable regulation for medium business plants.

The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal control of the operation of microgrids in islanded mode. The main objective is to minimise production costs while ensuring a reliable energy supply. Algorithm P prioritises the use of photovoltaic (PV) and battery storage and operates the diesel generator at minimum capacity to reduce fuel consumption and maximise the use of renewable energy sources. Algorithm M70, on the other hand, uses a heuristic approach to adaptively manage energy resources in real time. In this study, the performance of both algorithms is evaluated through simulation in different operating scenarios. The results show that both algorithms significantly improve the efficiency of the microgrid, with the M70 algorithm showing better adaptability and cost efficiency in dynamic environments. This research contributes to ongoing efforts to develop robust and scalable energy management systems for future smart grids.

We studied yeast hand carriage of 260 students from medicine (78), nursing (89), and medical laboratory scientist (93). We isolated yeasts in 27 students (10.4%), with no differences between careers or gender. A significant lower prevalence of carriage was shown in the clinical cycle (2.7%) compared to the basic cycle (13.5%) (p=0.022) and the preclinical cycle (13.5%) (p=0.014).Increased handwashing frequency and alcohol gel use decreased yeast carriage. Students who applied moisturizing hand cream two or more times a day had a lower frequency of yeast carriage (3.4%) compared to those who did not use it or used it only once a day (16.5%), showing a significant difference (p=0.016). The most prevalent species was C. parapsilosis sensu stricto (81.5%), followed by Meyerozyma guilliermondii (C. guillermondii) (7.4%), Trichosporon mucoides (7.4%), and R. mucilagenosa (3.7%). And one case showed mixed carriage of C. parapsilosis and C. albicans. All strains were sensitive to voriconazole, caspofungin, and anidulafungin. This study shows the yeast carriage on the hands of health students, especially of C. parapsilosis, and its reduction with the frequent use of infection control measures and moisturizing hand cream.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

Malaria is one of the leading causes of illnesses and deaths in Africa at large and Nigeria in particular, especially amongst pregnant women and children under the age of five years. Our research revealed that though the government has deployed so many intervention systems to contend with this death-causing vector—the mosquitoes, malaria related deaths (MRDs) have continued to increase. This is because people have not sufficiently adopted those intervention systems to protect themselves. Further enquiries into the ineffective compliance of the people to the intervention systems revealed that the interventions are passive in nature. Based on these, we set up three measurable research outcomes to enable us to determine the appropriateness of persuasive technology in solving the malaria problem. We technically avoided a one-size-fits-all design approach and adopted Participatory System Design (PSD) and User-Centered Design (UCD) approaches in our system design methodologies. Well-structured questionnaires were used to extract information from the participants. The data obtained from the research survey was used in modeling the intervention system. The research was conducted in three phases: baseline, development and deployment of an intervention system—the Malaria Prevention and Control Support System (MPCSS), and an evaluation study to determine the performance of the intervention system. The research led to the following achievements: (1) encouraged an increase in the number of people who participated in malaria prevention and control activities by lowering the rate of malaria cases from 96.9% to 68.5% and increasing ownership of mosquito nets from 54% to 85.5%; (2) demonstrated that persuasive technology could be used to increase public awareness and knowledge of a given subject as noted in our evaluation result; and (3) demonstrated that persuasive technology is a veritable active intervention to combat malaria.

Mitochondrial dysfunction is relevant mechanism in cardiac aging. Here, we investigated the effects of late-life enalapril administration at non-antihypertensive dose on mitochondrial genomic stability, oxidative damage, and mitochondrial quality control (MQC) signaling in the heart of aged rats. The protein expression of selected mediators (i.e., mitochondrial antioxidant enzymes, energy metabolism, mitochondrial biogenesis, dynamics, and autophagy) was measured in old rats randomly assigned to receive enalapril (n=8) or placebo (n=8) from 24 to 27 months of age. We also assessed mitochondrial DNA (mtDNA) content, citrate synthase activity, oxidative lesions to protein and mtDNA (i.e., carbonyls and abundance of mtDNA4834 deletion), and mitochondrial transcription factor A (TFAM) binding to specific mtDNA regions. Enalapril attenuated cardiac hypertrophy and oxidative stress-derived damage (mtDNA oxidation, mtDNA4834 deletion, and protein carbonylation), while increasing mitochondrial antioxidant defenses. TFAM binding to mtDNA regions involved in replication and deletion generation was increased following enalapril administration. Increased mitochondrial mass as well as mitochondriogenesis and autophagy signaling was found in enalapril-treated rats. Late-life enalapril administration mitigates age-dependent cardiac hypertrophy and oxidative damage, while increasing mitochondrial mass and modulating MQC signaling. Further analyses are needed to conclusively establish whether enalapril may offer cardioprotection during aging.

Current trends in industrial development strive for maximum digitalization. The digital industrial revolution has been going on for more than a dozen years, Industry 4.0 and the idea of digital twins (DT) is becoming the focus of virtually all industrial sectors. Some sectors are more predisposed to digitalization, for others the process is much more difficult. This depends mainly on the specific characteristics and susceptibility of a given industry, including the current degree of digitalization of enterprises, as well as the knowledge and mental readiness of employees. The individual characteristics of an industry are important. Shipyards belong to the industry with a traditional approach, where the degree of digitalization remains low. As a result, the efficiency of shipbuilding processes and the quality of ships built are not sufficiently controlled. The article addresses this problem, reviews work in the field of digitalization of shipbuilding processes and points out the needs and challenges in this area. The article proposes the concept of a DT system for the entire ship design and production process. Key areas of digitalization of actual processes were defined, and a division was made into planning, monitoring and process analysis activities. Special attention was paid to the area of dimensional quality control and the Dimensional Quality Management Metasystem (DQMM) was featured in the comprehensive DT system. The requirements were defined and the limitations of the proposed solution were identified, taking into account a number of external factors including the degree of readiness of the manufacturer – the shipyard. The developed DT system concept was tested on the example of the construction process of a simplified ship. Practical aspects of the implementation of the proposed solution, in particular DQMM, were indicated.

The Active Distribution Networks (ADNs) are Multi-Input Multi-Output (MIMO) systems with coupled dynamics which cause interactions among the control loops of the Distributed Energy Resources (DERs). This undesired effect lead to performance degradation in the voltage control. To mitigate the effects of these unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In details, each DER is equipped with a double loop PI to control the active and reactive powers output by the Voltage Source Converter (VSC) which connects the DER to the network's node. Furthermore, to guarantee Ancillary Services (ASs), the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a two sequential steps procedure based on the (Internal Model Control) IMC technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in presence of model parameter uncertainty in the MIMO model.

This paper discusses domestic problem of waiting hot water for the shower use till it reaches satisfactory temperature, which result a lot of wastage in fresh water. The outcome from research survey shows that there is no satisfactory solution till now as all solutions were either expensive or with no effective results. Local small inline electric heater equipped with fuzzy logic controller is proposed in this paper to be installed just before the showerhead to measure the water temperature and flow before the showerhead, as control input-variables, and decide the operating voltage of the heater, as control output-variable (MISO). MATLAB Simulink is used to model the proposed system. Different test cases are simulated to prove the performance and the safe operation of the system. Techno-economic study is carried out to determine the “Direct Benefits” and “Indirect Benefits” that can be achieved if such system is implemented in wide range. Azerbaijan data is taken as an example to calculate the economic benefits. The results show important benefits not only for economy but also for climate and the reduction of CO₂ gas emission. Different economic indices are provided to be an easy reference for decision makers and project managers.

Tadpole shrimp (Triops longicaudatus) has become a major pest for California rice farmers. Currently, management relies solely on the insecticide lambda-cyhalothrin. However, resistance to this pyrethroid was confirmed in 2016, thus identifying an effective and practical biological control method for TPS is a priority. Field trials were conducted from 2017 to 2018 to: (1) evaluate the efficacy of the predatory fish Gambusia affinis and the predatory beetles, Laccophilus maculosus (Say) and Tropisternus lateralis (Fabricius), in controlling TPS, (2) test the efficacy of several inoculation rates of Gambusia affinis at controlling TPS and (3) to explore early indicators of TPS activity and damage as monitoring tools. Both Gambusia affinis and the predatory beetle treatments were not significantly different from the commercial standard (lambda-cyhalothrin). Both four and five Gambusia per 1 m2 controlled TPS as well as lambda-cyhalothrin, and we observed that Gambusia affinis was able to reproduce in the field. Water turbidity was significantly correlated with TPS counts (R = 0.85, N = 20, P &lt; 0.0001 (2017); R = 0.58, N = 30, P = 0.0007 (2018)). The number of dislodged seedlings was less reliably correlated with TPS count; in 2017 correlations were significant (R = 0.84, N = 20, P &lt; 0.0001), however, in 2018 correlations were not significant (R = 0.18, N = 30, P = 0.35). With further refinement water turbidity could play a valuable role in monitoring TPS populations.

Identification of Dengue drivers is crucial for pro-active prevention strategies. In semi-arid regions, the relationship between meteorological variables, particularly rainfall, and the dengue vector is complex. This study analyzes associations between the number of dengue cases and rainfall categories in six semi-arid locations in India. Focusing on official website data, we find significant correlations only with heavy rainfall (R>10 mm/day). The findings underscore the importance of heavy rainfall in Dengue incidence and offer insights for pro-active prevention. The study methodology involves rainfall and dengue occurrence over Indian region. This research contributes to the growing body of knowledge on Dengue prevention, emphasizing the need for targeted interventions during periods of heavy rainfall.

High circulating concentrations of the gut microbiota-derived metabolite trimethylamine N-oxide (TMAO) are significantly associated with the risk of obesity and type 2 diabetes (T2D). We aimed at evaluating the impact of glycemic control and bariatric surgery on circulating concentrations of TMAO and its microbiota-dependent intermediate, γ-butyrobetaine (γBB), in newly diagnosed T2D patients and morbidly obese subjects. An additional experiment was carried out in eight male C57BL/6J mice fed a Western-type diet for twelve weeks. The strategy for glycemic control in the T2D patients significantly improved the HbA1c concentrations without producing changes in the estimated glomerular filtration rate. However, the plasma TMAO and γBB concentrations were significantly increased following the glycemic control in T2D patients. Bariatric surgery was very effective in reducing weight in obese subjects. Nevertheless, the surgery reduced plasma γBB concentrations without affecting TMAO concentrations and the estimated glomerular filtration rate. Neither diet-induced obesity nor insulin resistance were associated with circulating TMAO and γBB concentrations in mice. Our findings do not support that glycemic control or bariatric surgery improve the circulating concentrations of TMAO in newly diagnosed T2D and morbidly obese patients. The contribution of antidiabetic drugs to gut microbiota composition could explain TMAO elevations in treated T2D subjects.

Malaria is endemic in Nigeria and remains a major public health problem, taking its greatest toll on children under age 5 and pregnant women, although it is preventable, treatable, and curable. This study investigates the Impact of socio-economic factors and indoor mosquito control on malaria prevalent among pregnant women in Nigeria using logistic regression. To achieve this, secondary data obtained from 2015 Nigeria Malaria Indicator survey, executed by the National Malaria Elimination Programme (NMEP) and the National Population Commission (NPopC), with a nationally representative sample of more than 8,000 consisting of 7,745 households. The results from the logistic regression with odds ratio revealed that pregnant women are more like to be affected by malaria fever (though not significant) compared to women that are not pregnant. The income levels of the household does not significant reduce the incidence of malaria fever among pregnant women in Niger. Concerning the malaria presenting measure, only dwelling sprayed by private company significantly reduce the incidence of malaria fever among pregnant women (P-value=0.020<0.05) compared to dwelling sprayed by government and NGOs and also to Insecticide Treated Net. Also pregnant women in the urban centers are less likely to have malaria fever compared to pregnant women in rural communities in Nigeria. Also, pregnant women with atleast a secondary school level of education are less likely to be affected by malaria fever compared to pregnant women with no formal education. The fitted logistic model passed the goodness-of-test fit; the classification test for the logistic model was correctly classified at about 67.02%. Therefore, this study recommends that government and NGOs should intensify their efforts in the area of dwelling spraying, awareness campaign of the danger of malaria fever among pregnant women and infants, engaged in effective distribution of insecticide treated net in order to reduce the incidence of malaria fever among pregnant women living in rural communities in Nigeria.

The growing integration of renewable energy sources (RES) into the power system causes congestion to occur more frequently. In order to reduce congestion in the short term and to make the utilization of the power system more efficient in the long term, power flow control (PFC) in the transmission system has been proposed. However, exemplary studies show that congestion will increase also in the distribution system if the transmission system is expanded. For this reason, the potential of PFC to reduce congestion in a model of a real 110 kV distribution system is investigated. Several Unified Power Flow Controller (UPFC) devices are optimized in terms of their number and placement in the power system, their size, control parameters, and costs, by using a Parallel Tempering approach as well as a greedy algorithm. Two optimization variants are considered, one reducing the number of degrees of freedom by integrating system knowledge while the other is not. We find that near a critical grid state and disregarding costs, PFC can reduce congestion significantly (99.13 %). When costs of the UPFCs are taken into account, PFC can reduce congestion by 73.2 %. A basic economic analysis of the costs reveals that the usage of UPFCs is profitable. Furthermore, it is found that the reduction of the solution space of the optimization problem leads to better results faster and that, contrary to expectations, the optimization problem is simple to solve. The developed methods allow not only for the determination of the optimal use of UPFCs to minimize congestion, but also to estimate their profitability.

Red mud (RM) is a residue obtained from the production of alumina. It contains a high concentration of metal oxides and waste concentrated sodium hydroxide solution (pH = 13). To increase the value of RM, an environmentally friendly process of transesterification using waste cooking oil (WCO), MeOH and concentrated sodium hydroxide solution (CSHS) from aluminum production was proposed. Triglycerides of WCO reacted with MeOH at 60 oC to yield mixtures of fatty acid methyl esters (FAMEs) in the presence of 2.03% (w/w) CSHS/WCO using the CSHS (0.204 mol L-1, predetermined by potentiometric titration) from aluminum production by the Bayer process or with the addition of 0.68% (w/w) fabric softener (3% w/w cetyltrimethylammonium chloride in solution) as a phase transfer (PTA) agent. The addition of PTA to the catalyst resulted in a better yield of the products (greater than 98% yield). A simplified mechanism is presented to account for the experimental results.

Control of autonomous robot motion in radial mass density field is presented. In that sense the robot motion is described as the function of the radial mass density parameters. The radial mass density field is between the maximal radial mass density and the minimal radial mass density. Between these two limited values one can use n points (n = 1, 2, . . . nmax) and calculate the related radial mass density for each point. The radial mass density is maximal at the minimal gravitational radius and minimal at the maximal gravitational radius. This conclusion is valid for Planck scale, but also for the scales that are less or higher of that one. Using the ratio of the Planck mass and Planck radius it is generated energy conservation constant with value κ = 0.99993392118. Further, in this theory it is possible to connect Planck’s and gravitational parameters as functions of the maximal (or minimal) radial mass density. In that sense the autonomous robot motion in radial mass density field is important for the control of the robot motion at micro and nano scales.

To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over the conventional utility grid systems, however, it faces severe instability issues due to continually increasing constant power loads. To improve the stability of the entire system, load side compensation technique is chosen because of its robustness and cost effectiveness. In this particular occasion, a sliding mode controller is developed for microgrid system in the presence of CPL to assure certain control objective of keeping the output voltage constant at 480V. After that, the robustness analysis of the sliding mode controller against parametric uncertainties is presented. The sliding mode controller robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are illustrated in this paper. Later, the performance of the PID and sliding Mode controller is compared in case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of Sliding Mode controller over PID controller. All the necessary calculations are reckoned mathematically and results are verified in the virtual platform such as MATLAB/Simulink with the appreciable outcome.

The two-thirds power law is a link between angular speed $\omega$ and curvature $\kappa$ observed in voluntary human movements: $\omega$ is proportional to $\kappa^{2/3}$. Squared jerk is known to be a Lagrangian leading to the latter law. However, it leads to unbounded movements and is therefore incompatible with a quasi-periodic dynamics, such as the movement of the tip of a pen drawing ellipses. To solve this drawback, we give a class of higher-derivative Lagrangians that allow for both quasi-periodic and unbounded movements, and at the same time leading to the two-thirds power law. We then perform the Hamiltonian analysis leading to action-angle variables through Ostrogradski's procedure. In this framework, squared jerk is recovered as an action variable, and its minimization may be related to power expenditure minimization during motion. The identified higher-derivative Lagrangians are therefore natural candidates for cost functions, i.e. movement functions that are targeted to be minimal when an individual performs a voluntary movement.

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results demonstrate that these control strategies can effectively reduce vibrations and impacts, significantly enhancing ride comfort, which is crucial for reducing driver fatigue and improving driving safety.

Mesoscopic modeling of complex system involve thermodynamic nonequilibrium of discrete scaling. Furthers from quantum correlation on a chip retrieved quantum nonlinear optics with single photons enabled by strongly interacting atoms. Accompanied by mesofractals as development of meso & micro size fractal structures is required to mimic various biological systems for various functions. Showed through fluorapatite in gelatin-based nanocomposite, fractal in DNA knots driven by balance of fission & fusion in mtDNA/mitochondrial DNA mechanism, for optical engines for light energy detection described the proportional integral derivative [PI(D)]-controller set in microbial cells to HCCI/Homogeneous Charge Compression Ignition.

This study investigates cloud cavitation suppression around a model scale NACA66 hydrofoil using active water injection and explores the effect of multiple injection parameters. Numerical simulations and a mixed-level orthogonal test method are employed to systematically analyze the impact of jet angle αjet, jet location Ljet, and jet velocity Ujet on cavitation suppression efficiency and hydrofoil energy performance. The study reveals that jet location has the greatest influence on cavitation suppression, while jet angle has the greatest influence on hydrofoil energy performance. The optimal parameter combination (Ljet = 0.30C, αjet = +60 degrees, Ujet = 3.25 m/s) effectively balances energy performance and cavitation suppression, reducing cavitation volume by 49.34% and improving lift-drag ratio by 8.55%. The study found that the jet's introduction not only enhances vapor condensation and reduces the intensity of the vapor-liquid exchange process but also disrupts the internal structure of cavitation clouds and elevates pressure on the hydrofoil suction surface, thereby effectively suppressing cavitation. Further analysis shows that positive-going horizontal jet components enhance the lift-drag ratio, while negative-going components have a detrimental effect. Jet arrangements near the trailing edge negatively impact both cavitation suppression and energy performance. These findings provide valuable reference for selecting optimal injection parameters to achieve a balance between cavitation suppression and energy performance in hydrodynamic systems.

Flexible manipulators have been widely used in industrial production. However, due to the poor rigidity of the flexible manipulator, it is easy to generate vibration. It will reduce the working accuracy and service life of the flexible manipulator. It is necessary to suppress the vibration during the operation of the flexible manipulator. Based on the energy method and Hamilton principle, the partial differential equations of the flexible manipulator are established. Secondly, an improved radial basis function (RBF) neural network is combined with the fuzzy backstepping method to identify and suppress the random vibration during the operation of the flexible manipulator, and the Lyapunov function and control law are designed. Finally, Simulink was used to build a simulation platform, three different external disturbances were set up, and the effect of vibration suppression was observed through the change curves of the final velocity error and displacement error. Compared with RBF neural network boundary control method and RBF neural network inversion method, the simulation results show that the effect of RBF neural network fuzzy inversion method is better than the previous two control methods, the system convergence is faster, and the equilibrium position error is smaller.

Nonlinear loads, a crucial component of power systems grids, pose a challenge due to harmonics injection. Our research tackles this issue with a novel approach - a modified green plug / switched filter compensation scheme using fuzzy logic controllers. This innovative scheme, presented in the paper, utilizes dual action pulse width modulation to ensure switching functions from harmonic reduction and capacitive compensation for inrush nonlinear type AC loads.  The scheme's multi-loop regulations and online switching effectively handle dynamic type slow acting inrush, motorized and other rectifier type-nonlinear loads, enhancing power factor, power quality at source and load buses, and reducing total harmonic distortion at key source and sensitive nonlinear load buses. A simulation model in the MATLAB/SIMULINK software environment demonstrates the efficiency of the proposed FACTS technique. The results show that voltage, current, active power, reactive power, power factor regulation, and effective energy utilization are achievable with the designed FACTS-MFCC-SFC. The switched modulated AC green plug filter significantly improves power quality and enhances power factor in the case of inrush and nonlinear loads.

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

MicroRNAs are small non-coding RNAs, which contribute to the regulation of many physiolog-ical and pathological processes. Conventionally, miRNAs perform their activity in the cyto-plasm, where they regulate gene expression by interacting in a sequence-specific manner with mature messenger RNAs. Recent studies point to the presence of mature miRNAs in the nucleus. This review summarizes current findings regarding the molecular activities of nuclear miRNAs. These molecules can regulate gene expression at the transcriptional level by directly binding DNA on the promoter or the enhancer of regulated genes. MiRNAs recruit to these regions dif-ferent protein complexes, resulting both in activation or repression of transcription, through a number of molecular mechanisms. Haematopoiesis is presented as a paradigmatic biological process whereby nuclear miRNAs possess a relevant regulatory role. Nuclear miRNAs may af-fect gene expression also acting on nuclear mRNA processing and on the biogenesis of miRNA themselves by regulating pri-miRNA maturation. Overall, nuclear miRNAs are biologically ac-tive molecules that can be critical for the fine tuning of gene expression and deserve further studies in a number of physiological and pathological conditions

This study investigates the impact of rotor spacing on the aerodynamic performance of a coaxialcopter. It has been observed that a small distance between two rotors can result in significant interaction between the propellers, consequently affecting the rotor's aerodynamic efficiency. Exploiting this characteristic, the present research introduces a coaxialcopter with variable rotor spacing. Employing finite element numerical simulations, we assess the aerodynamic behavior of this novel configuration. Through comprehensive measurements and analysis of its aerodynamic performance across varying rotor spacings from 0.1R to 1R, we validate the effectiveness of a rotor spacing control strategy for enhancing takeoff maneuvers. Our numerical simulation results reveal that the performance characteristics of both upper and lower rotor converges toward that of a single rotor as pitch ratio increases, along with the reduce of their thrust fluctuations and aerodynamic performance periodicity. Considering stable power consumption patterns and endurance performance, we analysis the interrelations binding of pitch distance of the rotors, rotational speed, and pitch angle, vis-à-vis the thrust coefficient and power coefficient. Through parameter optimization method, we demonstrate that adjusting rotor spacing offers a practical means to enhance payload capacity without increasing power input, thereby improving the efficiency, which validates the practicality and efficacy of the parameter optimization approach. Furthermore, optimizing rotor spacing for specific operational scenarios enhances overall aerodynamic performance, suggesting a viable flight control strategy for takeoff and landing conditions.

Diesel is consistently a very popular fuel for internal combustion engines. Its consumption in the European Union has been rising over recent years, as has the number of engines increasingly sensitive to fuel contamination. One of the most important parameters affecting diesel quality is water content. Although its exceedances of the standard’s permissible amount are not common, they carry the risk of serious damage to expensive, modern fuel injection systems. Current methods of measuring water content require both specialised personnel and equipment, as well as transporting the sample to a suitable laboratory. The research presented in this paper was aimed at verifying whether it would be possible to test the moisture content of diesel using impedance spectroscopy. Once refined, such measurement method could be used in situ. Analysing the properties of diesel is challenging, mainly because many types of the fuel are sold, the chemical composition of which is always a well-guarded manufacturer’s secret. The experimental results obtained on many types of commercially available diesel show that it is possible to use impedance spectroscopy to estimate the water content of different types of diesel fuel. The measurement accuracy achieved makes it possible to detect approaching or exceeding the permissible water content.

Aiming at the problem of UAV cluster configuration reconstruction, in order to obtain the cluster reconstruction method, the theory and simulation of cluster reconstruction control strategy is studied by simplifying the model，deducing the theoretical formula and analyzing the example. Taking 10 UAVs cluster as an example, the distribution of UAVs in different configurations was analyzed. According to the change of cluster configuration, the principle of formation reconstruction and the execution ability of configuration, the cluster capability was analyzed. The group behavior is modeled by group behavior rules, reconfiguration topology analysis and reconfiguration cost measurement. Based on the cluster parameters and configuration characteristics, simulation experiments were carried out to obtain the change curve of the reconstruction cost and time and the trajectory movement diagram of the UAV in different configurations.

Background: Diabetes mellitus (DM) and periodontitis are two chronic diseases that are interconnected. The coexistence of these conditions leads to increased severity of periodontitis and challenges in controlling blood glucose levels in diabetic patients. Objectives: The aim of this study was to evaluate both the influence of periodontal disease and its treatment in patients with DM in terms of a reduction in glycosylated hemoglobin levels at 3 and 6 months after treatment. Methods: A sequential, nonprobabilistic, controlled, prospective and longitudinal case−control study was carried out at the University Dental Clinic of the Morales Meseguer Hospital in Murcia, Spain. Thirty diabetic patients were divided into two groups: patients with periodontitis (test group) and patients without periodontitis (control group). The periodontal and endocrine-metabolic variables were measured at baseline and at 3 and 6 months after periodontal treatment for both groups. Results: Statistically significant differences were found at 3 and 6 months after periodontal treatment with respect to HbA1c levels in the group of periodontal patients. However, these differences were not observed in the control group. Conclusion: The presence of periodontal disease increased HbA1c levels in patients with DM. Nonsurgical periodontal treatment significantly decreased the degree of periodontal inflammation and the HbA1c level at 3 and 6 months after treatment.

This paper presents the design and implementation of a remote supervision and control system for a gunpowder drum filling plant. The use of technologies such as SCADA (Ignition), IoT (Node-RED), ERP (Odoo) and digital twins (Factory I/O) to achieve remote monitoring and operation of the plant is described. The solution allows full control of the production process from any location through customized graphical interfaces. The challenges encountered in integrating multiple systems and platforms are discussed, along with the security measures implemented within the Azure cloud environment. The results demonstrate the effectiveness of 4.0 technologies in automating and digitizing critical industrial processes. This research will lay the groundwork for the adoption of these approaches in companies seeking greater efficiency, traceability and safety in their operations. The experience gained allows us to successfully face the digital transformation in modern manufacturing.

The paper proposes a new implementation of the PID algorithm in digital hardware. The proposed circuit implements an advanced PID formula, containing a non ideal derivative component, and weighting coefficients, which enable reducing influence of setpoint changes in the proportional and derivative components. The implementation operates on standard single precision (32 bit) floating-point numbers. The proposed circuit structure is optimized for cost. It uses just one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequential manner. The circuit was implemented in a Cyclone V FPGA device from Intel, using the Quartus Prime software. Proper operation of the circuit was verified by simulation. The proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on standard single precision floating-point numbers, while being significantly cheaper. However, it outperforms by several orders of magnitude the speed of any software-based implementation, including solutions using PLCs, and CPU/MCUs. The proposed circuit structure, together with the overall regulator device concept, suit well the SoC (System on Chip), or SoPC (System on Programmable Chip) idea, i. e. a device, that contains a CPU core immersed in “FPGA fabric” - logic resources characteristic for FPGA devices.

Benzimidazole fungicides are a class of highly effective, low-toxicity, systemic broad-spectrum fungicides developed in the 1960s and 1970s, based on the fungicidal activity of the benzimidazole ring structure. They exhibit biological activities including anticancer, antibacterial, and antiparasitic effects. Due to their particularly outstanding antibacterial properties, they are widely used in agriculture to prevent and control various plant diseases caused by fungi. The main products of benzimidazole fungicides include benomyl, carbendazim, thiabendazole, albendazole, thiophanate, thiophanate-methyl, fuberidazole, carbamic acid, and carbendazim salicylate. This article mainly reviews the physicochemical properties, toxicological properties, and disease control efficacy, and pesticide residue and detection technologies of the aforementioned nine benzimidazole fungicides and their main metabolite (2-aminobenzimidazole). On this basis, a brief outlook on the future research directions of benzimidazole fungicides is presented.

To improve the adaptability of aerial reflective opto-mechanical structures to low-temperature environments, an optimized thermal control design is proposed. Firstly, the relationship be-tween conventional heating methods and axial/radial temperature differences of mirrors with different shapes is analyzed. Based on heat transfer analyses, it is pointed out that optimized thermal control design is necessary to ensure the temperature uniformity of the fused silica mirror and take into account the temperature level when working in low temperature environ-ments. A thermal control scheme based on temperature negative feedback variable power zone heating is proposed for the characteristics of aerial electro-optics systems. Using the thermal control scheme at low temperatures ensures that the average temperature of the mirror fluctu-ates slowly and slightly around 20℃. At the same time, the temperature differences within a mirror and between the primary mirror and the secondary mirror can be controlled within 5℃. Thereby, satisfactory image quality is obtained.