System Output Research Articles

Channel reconstruction through improvised deep learning architecture for high-speed networks

Efficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy

Photovoltaic Maximum Power Point Tracking Technology Based on Power Prediction Algorithm Combined with Variable Step Length Disturbance Observation Method

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

Fuzzy Logic-Based Smart Control of Wind Energy Conversion System Using Cascaded Doubly Fed Induction Generator

This paper introduces a robust system designed to effectively manage and enhance the electrical output of a Wind Energy Conversion System (WECS) using a Cascaded Doubly Fed Induction Generator (CDFIG) connected to a power grid. The solution that was investigated is the use of a CDFIG that is based on a variable-speed wind power conversion chain. It comprises the electrical and mechanical connection of two DFIGs through their rotors. The originality of this paper lies in the innovative application of a fuzzy logic controller (FLC) in combination with a CDFIG for a WECS. To demonstrate that this novel configuration enhances control precision and performance in WECSs, we conducted a comparison of three different controllers: a proportional–integral (PI) controller, a fractional PID (FPID) controller, and a fuzzy logic controller (FLC). The results highlight the potential of the proposed system in optimizing power generation and improving overall system stability. It turns out that, according to the first results, the FLC performed optimally in terms of tracking and rejecting disturbances. In terms of peak overshoot for power and torque, the findings indicate that the proposed FLC-based technique (3.8639% and 6.9401%) outperforms that of the FOPID (11.2458% and 10.9654%) and PI controllers (11.4219% and 11.0712%), respectively. These results demonstrate the superior performance of the FLC in reducing overshoot, providing better control stability for both power and torque. In terms of rise time, the findings show that all controllers perform similarly for both power and torque. However, the FLC demonstrates superior performance with a rise time of 0.0016 s for both power and torque, compared to the FOPID (1.9999 s and 1.9999 s) and PI (0.0250 s and 0.0247 s) controllers. This highlights the FLC’s enhanced responsiveness in controlling power and torque. In terms of settling time, all three controllers have almost the same performance of 1.9999. An examination of total harmonic distortion (THD) was also employed to validate the superiority of the FLC. In terms of power quality, the findings prove that a WECS based on an FLC (0.93%) has a smaller total harmonic distortion (THD) compared to that of the FOPID (1.21%) and PI (1.51%) controllers. This system solves the problem by removing the requirement for sliding ring–brush contact. Through the utilization of the MATLAB/Simulink environment, the effectiveness of this control and energy management approach was evaluated, thereby demonstrating its capacity to fulfill the objectives that were set.

Enhanced energy harvesting in low-velocity water by downstream interference for piezoelectric energy harvester

The vibration of a single-degree-of-freedom bluff body positioned in water is intensified by the positive excitation, which is affected by the upstream interfering body and also by the downstream obstacles. This paper investigates the effect of downstream interference on galloping piezoelectric energy harvester performance. A mathematical model of the piezoelectric energy harvester under disturbance is established based on the extended Hamilton’s principle, and the theoretical output is further derived. The accuracy of proposed model is verified by the results of circulating water channel experiment. The effect of interference shape and approximate spacing (L/D) on system output is further analyzed, and the results show that the harvester performance is substantially improved when the distance between the bluff body and downstream interference is sufficiently small, and the interference is an elliptical cylinder with an aspect ratio of 0.6, i.e., the output power is 0.624 mW, which is improved by 24.39 % when L/D=1.8 and U=0.5m/s. Precise numerical calculations are further utilized to indicate that the harvester’s output performance is optimal when the downstream interference angle is 30°.

Super Low Profile 5G mmWave Highly Isolated MIMO Antenna with 360∘ Pattern Diversity for Smart City IoT and Vehicular Communication

This study introduces a planar folded super low profile 5G mmWave monopole antenna for small IoT devices with superior efficiency and gain. Low-loss Rogers RT-5880 substrate material has been utilized to achieve higher efficiency, gain, and diversity performance in a relatively compact size. The unit element planar monopole antenna has been designed within 6.15×5.13×0.8mm3 with a partial ground. Four unit element antenna was orthogonally arranged on alternating sides of the substrate plane with narrow inter-component spacing in the multiple input multiple output (MIMO) system. The integration of the double negative (DNG) metamaterial array on the antenna led to a cumulative improvement in the overall performance of the antenna, which includes gain, efficiency, and isolation. The minimum isolation of the antenna increased by 11 dB with substrate-loaded metamaterial. The proposed MIMO antenna model with substrate-loaded metamaterial demonstrates 8 GHz instantaneous bandwidth (33.1 GHz - 41.1 GHz), 0.001 envelope correlation coefficient (ECC), diversity gain (DG) greater than 9.99 dB, channel capacity loss (CCL) less than 0.2bits/s/Hz, and a total active reflection coefficient (TARC) of 25 dB, which suggests that the diversity characteristics of the MIMO antenna system is impressive. Moreover, a densely packed 12-element antenna model has been introduced in this study with 360∘ pattern diversity, capable of receiving signals from any direction. Three 4-element MIMO systems have been arranged in an architecture to develop the 12-element MIMO system, and the size of the 12-element MIMO architecture is 15×15×15mm3. Despite having this compact size and dense packing, the 12-element MIMO antenna model shows similar diversity performance as the 4-element antenna model. Analog equivalent circuit models or resistor-inductor-capacitor (RLC) equivalent systems for the antenna models were created. A concept of a 128-element massive MIMO antenna has been presented to support the end devices, creating an application scenario of an IoT-interconnected smart city.

Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application

Compared to the conventional control algorithms that use mean and variance as indicators, predictive probability density function (PDF) control can effectively handle the output PDF control problem of non-Gaussian stochastic distribution systems. However, the existing predictive PDF control method does not consider the construction error between output PDF and weight, thus the control performance is still unsatisfactory. Therefore, this paper proposes a new Enhanced Predictive PDF control method (En-PDF) to improve the output PDF control performance of stochastic distribution systems. The proposed method mainly consists of two parts: the predictive PDF control part and the neural network compensation control part aiming to reduce the bias of output PDF. First, the Radical Basis Functions (RBFs) are used to approximate the PDF of the stochastic systems output, and then a prediction model representing the relationship between input and weight is established using the subspace identification algorithm to design the predictive PDF control for the stochastic systems. Next, the Kullback-Leibler (KL) divergence is used to measure the similarity between the output PDF and the set PDF, combined with the weight error and compensation to design a new performance index. Based on this, the parameters of the neural network are adjusted using the gradient descent algorithm to obtain the optimal compensation, and the stability and tracking performance of the proposed algorithm are analyzed using inductive reasoning method. Finally, the predictive PDF control input with the compensation work together on the controlled plant to achieve high-performance control of the output PDF of non-Gaussian stochastic distribution systems. Both simulation experiments and physical control experiments validate the effectiveness and superiority of the proposed method.

Sampled‐Data Based Second‐Order Discrete‐Time Sliding Mode Control via Output Feedback

ABSTRACTThis article proposes a second‐order discrete‐time sliding mode controller (DSMC) by only using sampled‐data information of the system output. On one hand, different from the continuous‐time sliding mode control, the finite‐time stability of the closed‐loop system under DSMC is usually not kept due to the limited sampling frequency. On the other hand, in the output feedback framework, the second‐order sliding mode observer cannot independently estimate the internal state of the system if there exist unknown mismatched term and unknown time‐varying coefficient of the control input. To solve the above problems, a joint design strategy of second‐order discrete‐time sliding mode observer and sliding mode controller based on the “non‐separation principle” is proposed. The stability and robustness of the closed‐loop system is analyzed based on the Lyapunov design method. Finally, simulation example verifies the correctness of the theory.

Portable X-ray fluorescence of zinc and selenium with nail clippings-Mother and Infant Nutrition Investigation (MINI).

Zinc and selenium are essential minerals for human nutrition. Reliable biomarkers of zinc status and selenium status in humans are therefore important. This work investigates a novel portable X-ray fluorescence (XRF) method with the ability to rapidly assess zinc and selenium in nail clippings. This approach used a mono-energetic X-ray beam to excite characteristic X-rays from the clippings. Nail clippings were obtained from the Mother and Infant Nutrition Investigation (MINI), a study designed to assess nutrition in a population of women and their breastfed children in New Zealand. Twenty mother-infant pairings were selected to provide nail clippings at two time points (visit 1 at 3 months postpartum; visit 2 at 6 months postpartum). Nail clippings from each mother-infant pairing were divided into three groupings of clippings prior to analysis: those obtained from a big toe of the mother, those from the other toes of the mother, and those from the toes and fingers of the infant. Clippings were prepared and mounted prior to XRF measurement, providing four distinct fragments from each clipping grouping. These fragments were assessed by XRF using a measurement time of either 300 s (visit 1) or 180 s (visit 2). XRF results were determined through both an automated system output and an analysis of the X-ray energy spectrum. Following this assessment of zinc and selenium with the non-destructive XRF method, clippings were measured for zinc and selenium concentration using a "gold standard" technique of inductively coupled plasma mass spectrometry (ICP-MS). Mean ICP-MS concentrations ranged from 122 μg/g to 127 μg/g for zinc, and from 0.646 μg/g to 0.659 μg/g for selenium. Precision, assessed by a relative standard deviation of measurement, was superior for ICP-MS relative to XRF. For both zinc and selenium, XRF results were compared with ICP-MS concentrations. Linear equations of best fit were determined for each comparison between XRF and ICP-MS results. Coefficients of determination (r2) were stronger for zinc (from 0.74 to 0.95) than selenium (from 0.53 to 0.70). A decrease in XRF measurement time from 300 s to 180 s did not appear to adversely affect the correlation between XRF and ICP-MS results. Using the mono-energetic portable XRF method, the correlation of XRF zinc results with ICP-MS zinc concentrations was improved over previous findings, and selenium measurement was reported for the first time. The method may prove useful for future applications to trace element analysis using nail clippings as a biomarker.

Artificial Intelligence for Clinical Decision-Making: Gross Negligence Manslaughter and Corporate Manslaughter.

This paper discusses the risk of gross negligence manslaughter (GNM) and corporate manslaughter charges (CM) when clinicians use an artificially intelligent system's (AIS's) outputs in their practice. I identify the elements of these offenses within the context of the law of England and Wales and explore how they could be applied in a potential scenario where a patient's death has followed AIS use by a clinician. The risk of a conviction due to making an AIS-augmented workplace mistake highlights the non-trivial nature of AIS adoption in healthcare, and that the consequences of its use must be considered by all interested parties prior to AIS adoption.

Energy, environmental, economic, and social assessment of photovoltaic potential on expressway slopes: A case in Fujian Province, China

Expressway slopes within the boundary of expressway construction land have the potential to be transformed from conventional land use to valuable utilization through the installation of photovoltaic (PV) systems. Considering the continuous increase in expressway mileage, the PV potential could be enormous; therefore, a method to quantify such potential is urgently needed. This study proposes a potential assessment method for expressway slope photovoltaics. First, the suitable locations of expressway slopes for installing PV systems are identified on the basis of publicly available digital maps, combined with the principles of PV site selection and geographic information systems. Then, the PVsyst model is used to simulate the power generation efficiency and power output of the PV systems and to evaluate their PV potential, contribution, and economic benefits. The implementation of the proposed method in Fujian Province, China, indicates that the installed capacity of PV systems could reach 16.3 GW, with an annual power output of 17,940 GWh, contributing to the peak shaving of power grids and regional energy supply. The reductions in CO2, SOx, and NOx emissions are 16.65 Mt, 8970 t, and 3947 t per year, respectively. Considering carbon trading, the payback period is shortened to 11.7 years, with the return on investment of 65.38 % and the net present value of 52.6 billion RMB over a 25-year operational period. Additionally, this project could create 19.88 % of jobs in the energy sector in Fujian Province, providing 27,861 jobs during the construction phase and 18,248 jobs during the operation and maintenance phase. Thus, this project is economically viable and generates positive energy, environmental, and social benefits, contributing to the promotion of local energy transition and sustainable development.

Massive MIMO uplink and downlink joint representation based on couple dictionary learning

AbstractThe challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross‐correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high‐precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly.

Adaptive Fuzzy Finite‐Time Command Filtered Control for Stochastic Nonlinear Systems With Unmodeled Dynamics and Dead‐Zone Constraints

ABSTRACTIn this article, the issue of adaptive fuzzy finite‐time command filtered control is discussed for nonlinear stochastic systems subject to unknown dead‐zone constraints and unmodeled dynamics. The packaged unknown nonlinearities are approximated by introducing fuzzy logic systems. An improved technique is introduced to cope with unknown functions with the structure of nonstrict‐feedback in the operation of controller design. Under the criterion of finite‐time stability, a novel fast convergent control scheme is developed. Additionally, the effect of filter errors bought by the command filters is diminished via applying corresponding error compensating signals and a measurable dynamic signal is adopted to handle unmodeled dynamics. The improved designed controller not only guarantees all the closed‐loop signals remain finite‐time bounded, but also makes the system output follows the given desirable trajectory under the bounded error. The usefulness of the designed strategy can be verified through the numerical and practical examples.

Thermal Bending Simulation and Experimental Study of 3D Ultra-Thin Glass Components for Smartwatches.

The heating system is an essential component of the glass molding process. It is responsible for heating the glass to an appropriate temperature, allowing it to soften and be easily molded. However, the energy consumption of the heating system becomes particularly significant in large-scale production. This study utilized G-11 glass for the simulation analysis and developed a finite element model for the thermal conduction of a 3D ultra-thin glass molding system, as well as a thermal bending model for smartwatches. Using finite element software, the heat transfer between the mold and the glass was modeled, and the temperature distribution and thermal stress under various processing conditions were predicted. The findings of the simulation, when subjected to a numerical analysis, showed that heating rate techniques significantly affect energy consumption. This study devised a total of four heating strategies. Upon comparison, optimizing with heating strategy 4, which applies an initial heating rate of 35 mJ/(mm2·s) during the initial phase (0 to 60 s) and subsequently escalates to 45 mJ/(mm2·s) during the second phase (60 to 160 s), resulted in a reduction of 4.396% in the system's thermal output and a notable decrease of 7.875% in the heating duration, respectively. Furthermore, a single-factor research method was employed to study the forming process parameters. By comparing the numerical simulation results, it was found that within the temperature range of 615-625 °C, a molding pressure of 25-35 MPa, a heating rate of 1.5-2.5 °C/s, a cooling rate of 0.5-1 °C/s, and a pulse pressure of 45-55 Hz, the influence on residual stress and shape deviation in the glass was minimal. The relative error range was within the 20% acceptable limit, according to the experimental validation, which offered crucial direction and ideas for process development.

A Power Boosting Method for Wireless Power Transfer Systems Based on a Multilevel Inverter and Dual-Resonant Network

With the deepening of research on wireless power transfer (WPT) technology, there is an urgent need to improve the output power of WPT systems. Therefore, this paper proposes a power boosting method based on a multilevel inverter and dual-resonant network. The system adopts a five-level inverter whose output voltage contains rich fundamental and harmonic components. The transmitting side adopts a dual-resonant compensation network, and, through its parameter configuration, the system can be in a resonant state at both the fundamental and third harmonic frequencies. The receiving side adopts two receiving channels which can simultaneously receive power at both frequencies, thus boosting the output power. Firstly, the overall structure and operating principle of the system are introduced. Then, the relationship between the system output power and efficiency with the inverter modulation parameters is obtained and the strategy for selecting the optimal operating points of the inverter is designed by comprehensively considering the THD of the inverter output voltage and the efficiency of the system. Furthermore, the proposed system is simulated in a Matlab/Simulink platform. Finally, an experimental setup is created to verify the proposed method. The results show that, compared with the single-channel WPT system, the proposed method can enhance the output power from 160 W to 310 W with a boosting coefficient of nearly double and with the highest efficiency exceeding 92%.

Development of a low frequency acoustic energy harvesting system by using piezoelectric transducers mounted on a metallic plate placed inside the centre of a hollow cylinder

Abstract In this work, we are demonstrating an innovative acoustic energy harvesting system that utilizes a hollow polyvinyl dichloride cylinder with a metallic circular plate placed at the centre inside with five piezoelectric transducers mounted on it. A speaker, used as a source of acoustic waves, is fixed at the top of the cylinder by using a clamp. The cylinder, upon exposure to incident acoustic waves, induces resonance within it, generating an amplified stationary wave. The generated vibration drives the metallic plate mounted with piezoelectric transducers, which serves as a diaphragm. As a result, a pressure difference is developed across it and due to piezoelectric effect on the transducers, electricity is produced. Experimental results show that the system output voltage and power depends on various factors, including the elastic properties of the metallic plate, resonance frequency, and the distance of separation between the acoustic source and the plate. At an incident sound pressure level of 75 dB, the experimental results show that, at an acoustic resonance frequency of 310 Hz, the system yielded a maximum peak to peak voltage of 1000 mV and output power of 0.612 µW. Incorporation of a voltage doubler circuit with the harvester increases its power to 57.6 µW. The simplicity in design and cost-effectiveness of the proposed acoustic energy harvesting system renders it to be a promising avenue for energy harvesting implementations.

Enhanced stochastic coding framework for discriminating faults and cyber incidents in hybrid systems

AbstractThis article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window‐based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L2‐L∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low‐pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four‐tank hybrid system. This practical simulation showcases the adaptability of the approach to real‐world scenarios, highlighting its potential in diverse applications within the field of hybrid systems.

Bifocal system of two reflect arrays

A technique has been developed for accurately solving the problem of geometric-optical synthesis of a bifocal system of two flat reflect arrays, based on the sequential determination of the distribution of the eikonal introduced by different sections of the arrays, with its assignment at the initial section of one of the arrays, while the introduced eikonal at the initial section of the other array is found as a result of synthesis of the output flat front for the central position of the feed. The technique ensures the continuity of functions describing the distributions of introduced eikonals, as well as their first and second derivatives (along one coordinate) at the boundaries of sections. The dependence of the value of the mean square eikonal aberration at the system output on the arrays parameters has been studied. As an example of the application of the developed technique, the bifocal systems for visual angles of 40°and 65°were synthesized and optimized. A study of the radiation characteristics of an antenna based on synthesized and optimized bifocal systems was carried out.

Analysis of Fractional Order-Adaptive Systems Represented by Error Model 1 Using a Fractional-Order Gradient Approach

In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient’s performance improves as the order, β, increases with β>1, leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems.

SIMULATION OF FUZZY LOGIC CONTROLLER AS DIRECTION ADJUSTMENT FOR LINE FOLLOWING ROBOT

Line Follower is a robot that follows a line, and in it there is an Integrated Circuit that contains programming to run it. In this experiment, the line follower using fuzzy logic control provides another alternative in the control system. In fuzzy logic control, there is no need for a mathematical model of the system because fuzzy logic control works based on rules. The designed control is a one input, two output system and uses the Mamdani system as an inference mechanism. The output error in the program will increase when the robot turns to follow the trajectory, the error can occur due to the calculation of the membership function input and rules as a determinant of robot action when detecting the trajectory in front of it. Therefore, in this line follower experiment, the rule will be a reference in processing the input value of the distance and speed of the right and left motors, the rule also determines the direction of the robot using the help of a camera sensor.

Paranoid About Androids: A Review of Robotics in Radiology.

In tandem with the ever-increasing global population, the demand for diagnostic radiology service provision is on the rise and at a disproportionate rate compared to the number of radiologists available to practice. The current "revolution in robotics" promises to alleviate personnel shortages in many sectors of industry, including medicine. Despite negative depictions of robots in popular culture, their multiple potential benefits cannot be overlooked, in particular when it comes to health service provision. The type of robots used for interventional procedures are largely robotic-assistance devices, such as the Da Vinci surgical robot. Advances have also been made with regards to robots for image-guided percutaneous needle placement, which have demonstrated superior accuracy compared to manual methods. It is likely that artificial intelligence will come to play a key role in the field of robotics and will result in an increase in the levels of robotic autonomy attainable. However, this concept is not without ethical and legal considerations, most notably who is responsible should an error occur; the physician, the robot manufacturer, software engineers, or the robot itself? Efforts have been made to legislate in order to protect against the potentially harmful effects of unexplainable "black-box" decision outputs of artificial intelligence systems. In order to be accepted by patients, studies have shown that the perceived level of trustworthiness and predictability of robots is crucial. Ultimately, effective, widespread implementation of medical robotic systems will be contingent on developers remaining cognizant of factors that increase human acceptance, as well as ensuring compliance with regulations.