Chain Algorithm Research Articles

Designing Smart Supply Chains Based on Digital AI Instruments and Mobile Technologies

Academic and industrial research on digital technology has been quite thorough. Nevertheless, scant information exists regarding the supply chain implementation of digital technology in industrial companies. To better understand supply chains, this article will look at why and how manufacturing companies are implementing digital technology. The goal of this paper’s literature analysis is to pinpoint current studies and perspectives on how these technologies could help digital supply chains (DSCs) operate better, faster, and more efficiently. The investigation looks at the technologies’ directions and challenges in improving the efficiency of digital help confinement, in addition to underlining their significance for supply chains and logistics. It investigates the managerial and practical issues in developing a new integrated paradigm for digital supply chains. Also, possible obstacles to DSCs and procurement will be examined, along with solutions. Consequently, this study pinpoints multiple opportunities for adding value when implementing Internet of Things (IoT) in the supply chain using mobile technology. Additionally, it suggests a method for creating business models for supply chain algorithms.

Monitoring forest cover and land use change in the Congo Basin under IPCC climate change scenarios

The Congo Basin tropical forests are home to many endemic and endangered species, and a global hotspot for forest fragmentation and loss. Yet, little has been done to document the region’s rapid deforestation, assess its effects and consequences, or project future forest cover loss to aid in effective planning. Here we applied the Random Forest (RF) supervised classification algorithm in Google Earth Engine (GEE) to map and quantify decadal changes in forest cover and land use (LCLU) in the Congo Basin between 1990 and 2020. We cross-validated our LCLU maps with existing global land cover products, and projected our validated results to 2050 under three climate change scenarios, using the Multiperceptron Artificial Neural Network and Markov chain algorithms of the Idrissi Land Change modeller from TerrSet. We found that, over 5.2% (215,938 km2), 1.2% (50,046 km2), and a 2.1% (86,658 km2) of dense forest cover were lost in the Congo Basin between 1990–2000, 2000–2010, and 2010–2020, totaling approximately 8.5% (352,642 km2) loss estimated between 1990–2020. For the period 2020–2050, we estimated a projected 3.7–4.0% (174,860–204,161 km2) loss in dense forest cover under all three climate change scenarios (i.e., 174,860 km2 loss projected for SSP1-2.6, 199,608 km2 for SSP2-4.5, and 204,161 km2 for SSP5-8.5), suggesting that approximately 12.3–12.6% (527,502 km2–556,803 km2) of dense forest cover could be lost over a 60-year period (1990–2050). Our study represents a novel application of spatial modeling tools and Machine Learning algorithms for assessing long-term deforestation and forest degradation within the Congo Basin, under human population growth and IPCC climate change scenarios. We provide spatial and quantitative results required for supporting long-term deforestation and forest degradation monitoring within Congo Basin countries, especially under the United Nations Framework Convention on Climate Change (UNFCCC) REDD+ (Reduce Emissions from Deforestation and Forest Degradation) program.

Parameter recovery of skew multidimensional multiple-group item response models (MGM-MIRT): a comparison of MCMC algorithms and assessment of effects of interest

Multidimensional item response theory (MIRT) models are powerful tools to analyse item response data. Most of them rely on the assumption that the latent traits are normally distributed. However, when such assumption does not hold, the item and the latent traits estimates can be inaccurate. This paper deals, in a very detailed way, through simulation studies, with the utility of the class of the MIRT models developed by Padilla et al. [Multidimensional multiple-group IRT models with skew normal latent trait distributions. J Multivariate Anal 2018;167:250–268], where the multivariate skew-normal distribution under the centred parameterization is employed for modelling the latent traits. The impact of important factors of interest on the parameter recovery is investigated. Such factors are the number of subjects, number of items, number of groups and asymmetry level. In a general way, the results show that our approach outperforms some traditional models when the latent traits follow asymmetric distributions. In addition, different Monte Carlo Markov Chain (MCMC) algorithms for parameter estimation are compared, by considering two schemes of data augmentation and a proposal to speed up their convergence, based on approaches found in the literature, for binary regression models. Guidelines are provided for the correct using of Bayesian inferential methods for the MIRT models.

The Nexus between spatiotemporal land use/land cover dynamics and ecosystem service values in the wabe river catchment, Omo Gibe River Basin, Ethiopia

Changes in land use/land cover (LULC) have emerged as a critical concern, impeding sustainable development and affecting natural ecosystems and their services. To address these challenges, a study investigated the linkage between the spatiotemporal dynamics of LULC changes and ecosystem service values (ESVs) from 1986 to 2022 and projected scenarios for 2041 and 2058 in the Wabe River catchment. Historical Landsat imagery was classified under supervised techniques employing maximum likelihood algorithms in ERDAS Imagine; future changes were forecasted using multilayer perceptron neural network and cellular automata-Markov chain algorithms, and the value transfer valuation technique in conjunction with ArcGIS 10.8 was utilized to quantify the nexus between LULC changes and resulting ESVs. The findings revealed notable increases in built-up areas (576.49%) and agriculture (114.62%) alongside declines in grassland (36.46%), forest (22.45%), and agroforestry (22.48%) from 1986 to 2022. Future projections suggest further expansion of built-up areas (55.80%) and agricultural land (15.72%), with corresponding declines in woodland, forest, and agroforestry. ESVs experienced a substantial decline of US$3227.20 million between 1986 and 2022, and are projected to witness a total decrease of US$1410.52 million from 2022 to 2058, driven by changing LULC dynamics, notably affecting high-value ecosystem providers like agroforestry and forests. The trend of ESVs in different agroecological zones varied, with the lowlands showing an increase, while the midlands and highlands experienced consistent decreases. The study underscores the urgent need for collaborative environmental stewardship, advocating sustainable practices and conservation efforts to reverse the declining trend of ESVs and ensure long-term ecosystem resilience.

Developing high-precision battery electric forklift driving cycle with variable cargo weight

Driving cycles are essential for assessing vehicle energy demand, estimating driving range, and evaluating environmental impacts. Numerous driving cycles have been developed for passenger cars and buses. However, tailored driving cycles for logistics vehicles, especially forklifts, remains limited. Therefore, we introduce high-precision driving cycles for battery electric forklifts, which include profiles of velocity and cargo mass. The construction of driving cycles involves route selection, data acquisition, micro-trip segmentation, characteristic parameters selection, driving pattern categorization, transition probability matrix development, and driving cycle construction and evaluation. The methods proposed for constructing driving cycles are based on Markov Chain, Micro-trips combinations, and genetic algorithms. The constructed driving cycles are evaluated using relative error analysis and a simulation model. The results confirm that these cycles accurately reflect actual forklift operations and can be utilized to estimate their energy consumption.

Modeling spatiotemporal land use/land cover dynamics by coupling multilayer perceptron neural network and cellular automata markov chain algorithms in the Wabe river catchment, Omo Gibe River Basin, Ethiopia

Abstract Land Use/Land Cover (LULC) change has been a substantial environmental concern, hindering sustainable development over the past few decades. To that end, comprehending the past and future patterns of LULC change is vital for conserving and sustainably managing land resources. This study aimed to analyze the spatiotemporal patterns of landscape dynamics from 1986 to 2022 and predict situations for 2041 and 2058, considering a business-as-usual (BAU) scenario in the Wabe River Catchment. The historical land use image classification employed a supervised technique using maximum likelihood algorithms in ERDAS Imagine, and identified six major land cover classes. For future projections of LULC changes in 2041 and 2058, multilayer perceptron neural network and cellular automata-Markov chain algorithms were utilized, incorporating various driving factors and independent spatial datasets. The findings revealed significant and ongoing LULC dynamics in the catchment, with persistent trends expected. Notably, woodland, built-up areas, and agriculture experienced substantial net increases by 0.24%, 1.96%, and 17.22% respectively, while grassland, forest, and agroforestry land faced notable decreases of 4.65%, 3.58%, and 11.20% respectively from 1986 to 2022. If the current rate of change continues, built-up and agricultural lands will expand by 1.28% and 5.07%, while forest and agroforestry land will decline by 2.69% and 3.63% respectively by 2058. However, woodland and grassland cover will exhibit divergent patterns, with a projected decrease of 0.57% in woodland and an anticipated increase of 0.54% in grassland cover. Overall, the observed changes indicated a shift towards intensive agriculture, built-up area expansion, and potentially adverse environmental consequences such as soil degradation, biodiversity loss, and ecosystem decline. To mitigate these consequences and promote sustainable development, immediate action is necessary, including environmentally friendly conservation approaches, sustainable land management practices, habitat protection, and reforestation efforts, ensuring the long-term resilience and viability of the catchment’s ecosystems.

Design and Implementation of Gesture Music Composition System based on RealSense and Recurrent Neural Network

Aiming at the problems of slow human-computer interaction and low accuracy of hand gesture recognition (HGR) in music composition systems such as human-computer interaction, gesture recognition, and algorithmic composition, this paper designs an HGR music composition system based on real-sense technology and recurrent neural network. The system uses an Intel RealSense 3D camera to shoot the user's hand and extract the information on the hand joint points with Smart Wearable Biosensors (SWBSs). The joint points are used to construct a three-dimensional model of the hand bone, and the joint points' equivalent distance and joint deflection are calculated by the joint point information to match the corresponding Curwen HGR. The matching music data is input into the recurrent neural network. GRU is combined with the Markov chain algorithm to complete the composition and avoid the phenomenon of gradient disappearance or gradient explosion. The HGR recognition function and composition algorithm are simulated and tested to verify the method's feasibility. Cohort verification investigations and efficacy evaluations of wearable biosensors are essential to support their clinical acceptability. The research results show that the HGR music composition system based on real-sense technology and recurrent neural networks can effectively identify the tester's HGRs and assist the tester in completing the composition using intelligent biosensors. The composition mode is close to the professional composition process.

Advancing Animal Health: A Web-Based Expert System Utilizing Forward Chaining for Disease Diagnosis

The increasing prevalence of animal diseases, along with the increasing need for animal products, highlights the urgent need for efficient diagnostic tools in veterinary medicine. The aim of this research is to create an expert system that uses a forward chaining algorithm to diagnose animal diseases. The forward chaining algorithm is a deductive reasoning approach that starts with existing facts and uses expert tree rules for hypotheses. This process continues until the desired goal is achieved or no additional conclusions can be drawn. Even though there are developments in expert systems, there are still shortcomings in implementing the forward chain for rapid and precise diagnosis of livestock diseases. This work aims to fill this gap by developing an expert system that improves the accuracy and efficiency of disease diagnosis in the livestock industry. A database of animal diseases and symptoms was created by observing and interacting directly with farmers. The system architecture is specifically intended to optimize data processing and user engagement, enabling rapid diagnosis and treatment recommendations. This test shows a level of accuracy and precision, thereby reducing the possibility of misdiagnosis. The capacity of expert systems to provide fast and reliable diagnoses has the potential to improve livestock health management, thereby helping farmers maintain animal welfare and productivity. The results of this work advance the field of veterinary diagnostics and propose other uses of expert systems in animal health management.

Securing industrial control systems: Developing a SCADA/IoT test bench and evaluating lightweight cipher performance on hardware simulator

This paper addresses the critical need for enhancing security in Supervisory Control and Data Acquisition (SCADA) networks within Industrial Control Systems (ICSs) to protect the industrial processes from cyber-attacks. The purpose of our work is to propose and evaluate lightweight security measures to safeguard critical infrastructure resources. The scope of our effort involves simulating a secure SCADA/IoT-based hardware test bench for ICSs, utilizing Modbus and MQTT communication protocols. Through case studies in remote servo motor control, water distribution systems, and power system voltage level indicators, vulnerabilities such as Denial of Service (DoS) and Man-in-The-Middle (MiTM) attacks are identified, and security recommendations are provided. To execute our work, we deploy lightweight ciphers such as Prime Counter & Hash Chaining (PCHC) and Ascon algorithm with Compression Rate (ACR) for secure information exchange between the plant floor and the control center. Evaluation of these ciphers on Raspberry Pi focuses on execution speed and memory utilization. Additionally, a comparison with the AGA-12 protocol standard for SCADA networks is conducted to underscore the efficacy of the proposed security measures. Our findings include the identification of SCADA network vulnerabilities and the proposal of lightweight security measures to mitigate risks. Performance evaluation of the proposed ciphers on Raspberry Pi demonstrates their effectiveness, emphasizing the importance of deploying such measures to ensure resilience against cyber threats in SCADA environments.

IMPLEMENTASI METODE FORWARD CHAINING PADA DIAGNOSA PENYAKIT MALARIA

Malaria is a serious disease caused by the Plasmodium parasite, transmitted through the bite of Anopheles mosquitoes. This disease can cause various symptoms, ranging from fever, headache, and chills to more severe complications such as severe anemia, organ damage, and even death if not treated properly. Therefore, rapid and accurate diagnosis of malaria is crucial for effective treatment and can save lives. This article discusses the implementation of the forward chaining method in a Python-based program for the diagnosis of malaria. Forward chaining is an inference technique in expert systems used to draw conclusions from a series of available facts or data. In this context, the forward chaining method enables the identification of symptoms associated with malaria based on input data provided by the user. By using the forward chaining algorithm, the system automatically processes this data to provide initial diagnosis recommendations and appropriate treatment. This implementation is expected to assist medical personnel in making faster and more accurate decisions, reducing workload, and improving efficiency in handling malaria cases, especially in endemic areas. This article also evaluates the performance of the developed system and discusses challenges and further development prospects in expert system-based diagnostic applications for malaria. The evaluation shows that this system has great potential in supporting medical personnel and speeding up the diagnosis process, which is crucial in managing malaria

Groundwater LNAPL Contamination Source Identification Based on Stacking Ensemble Surrogate Model

Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R2 of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%.

RawHash2: mapping raw nanopore signals using hash-based seeding and adaptive quantization.

Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including more sensitive quantization and chaining algorithms, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and POD5 and SLOW5 file formats. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0× and up to 9.9×) than RawHash. RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

The role of Markov chain in Monte Carlo simulation

The Markov Chain Monte Carlo method has been applied in several fields and has thrived in academic research over the past few decades. The method is utilized to estimate the interest parameters posterior distribution through random sampling in the probability space. Monte Carlo method alone can accomplish random simulations for some complicated continuous integrals or discrete summations. However, the method requires the sample set corresponding to the distribution, which demands the application of Markov chain. Nevertheless, problems occur when the probability function of the samples remain unknown, under which circumstance the Markov Chain Monte Carlo method is no longer available. Therefore, the article introduced Metropolis-Hastings Algorithm as a resolution. The paper is intended to provide specific insights about Metropolis-Hastings Algorithm for Markov Chain Monte Carlo and the corresponding interdisciplinary applications. Specifically, the paper will discuss about using the theory mentioned above to check the relationships among species in a common ecosystem. The paper will cover several prepositive theories, including the Law of Large Numbers for Markov Chains, binary sequences, and Metropolis-Hastings Algorithm. Overall, specific cases and the way to draw correct conclusions will be most concerned.

Haplotype-aware sequence alignment to pangenome graphs.

Modern pangenome graphs are built using haplotype-resolved genome assemblies. When mapping reads to a pangenome graph, prioritizing alignments that are consistent with the known haplotypes improves genotyping accuracy. However, the existing rigorous formulations for colinear chaining and alignment problems do not consider the haplotype paths in a pangenome graph. This often leads to spurious read alignments to those paths that are unlikely recombinations of the known haplotypes. In this paper, we develop novel formulations and algorithms for sequence-to-graph alignment and chaining problems. Inspired by the genotype imputation models, we assume that a query sequence is an imperfect mosaic of reference haplotypes. Accordingly, we introduce a recombination penalty in the scoring functions for each haplotype switch. First, we solve haplotype-aware sequence-to-graph alignment in [Formula: see text] time, where Q is the query sequence, E is the set of edges, and H is the set of haplotypes represented in the graph. To complement our solution, we prove that an algorithm significantly faster than [Formula: see text] is impossible under the strong exponential time hypothesis (SETH). Second, we propose a haplotype-aware chaining algorithm that runs in [Formula: see text] time after graph preprocessing, where N is the count of input anchors. We then establish that a chaining algorithm significantly faster than [Formula: see text] is impossible under SETH. As a proof-of-concept, we implemented our chaining algorithm in the Minichain aligner. By aligning sequences sampled from the human major histocompatibility complex (MHC) to a pangenome graph of 60 MHC haplotypes, we demonstrate that our algorithm achieves better consistency with ground-truth recombinations compared with a haplotype-agnostic algorithm.

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Predicting bridge condition index using an improved back-propagation neural network

The predicted Bridge Condition Index (BCI) is a guiding indicator for formulating preventive maintenance strategies for bridges and is hardly available even if using the most advanced neural network prediction model, owing to the few measured BCIs and neural network model defects. In this paper, an improved Back-Propagation Neural Network (BPNN) model named CPSO-BP-MC is proposed to predict BCI, which has a BPNN as its core, combined with the Coordinated Particle Swarm Optimization (CPSO) algorithm aims to faster convergence and avoid local optimal solution even with less training sample, and the Markov Chain (MC) algorithm aims to further revise the fluctuation of CPSO-BP prediction output due to maintenance and reinforcement. A case study is presented to demonstrate the efficiency of CPSO-BP-MC predicting BCI with fewer training samples. Comparing the predicted BCI of CPSO-BP-MC with the measured BCI and other models, the results show that CPSO-BP-MC converges faster than other models, and can predict the BCI more stably and accurately, and the error between prediction and measured BCI is less than 3 %.

Adaptive Trotterization for Time-Dependent Hamiltonian Quantum Dynamics Using Piecewise Conservation Laws.

Digital quantum simulation relies on Trotterization to discretize time evolution into elementary quantum gates. On current quantum processors with notable gate imperfections, there is a critical trade-off between improved accuracy for finer time steps, and increased error rate on account of the larger circuit depth. We present an adaptive Trotterization algorithm to cope with time dependent Hamiltonians, where we propose a concept of piecewise "conserved" quantities to estimate errors in the time evolution between two (nearby) points in time; these allow us to bound the errors accumulated over the full simulation period. They reduce to standard conservation laws in the case of time independent Hamiltonians, for which we first developed an adaptive Trotterization scheme [H. Zhao et al., Making Trotterization adaptive and energy-self-correcting for NISQ devices and beyond, PRX Quantum 4, 030319 (2023).2691-339910.1103/PRXQuantum.4.030319]. We validate the algorithm for a time dependent quantum spin chain, demonstrating that it can outperform the conventional Trotter algorithm with a fixed step size at a controlled error.

Sistem Pakar Diagnosis Penyakit ISPA Pada Anak

Acute Respiratory Tract Infections (ARTIs) in children are often challenging to diagnose due to their similar and varied symptoms. Hence, the development of an expert system for diagnosing ARTIs in children is crucial. The aim of this research is to develop an expert system that can assist in the rapid and accurate diagnosis of ARTIs in children. The method employed is the forward chaining approach, where observed symptoms are linked to specific ARTIs through inference rules. Initially, the symptoms of ARTIs and related diseases are identified, forming the knowledge base. Subsequently, the forward chaining algorithm is implemented in the system to facilitate the dissemination of information from observed symptoms to the correct diagnosis. The user interface is designed to facilitate input from users, whether doctors or parents, regarding their child's symptoms. Internal testing is conducted to validate the system's accuracy, involving medical professionals to evaluate and provide feedback on the generated diagnoses. The results indicate that bronchitis is the most likely disease in children based on the total scores of observed symptoms. Treatment recommendations focus on symptomatic care and complication prevention. Thus, the use of an expert system with a forward chaining approach positively contributes to supporting the diagnosis of ARTIs in children by providing faster and more accurate diagnoses and appropriate treatment recommendations.

Forward Chaining Method in Expert System for Diagnosing Pests and Plant Diseases: A Systematic Literature Review

Pest and disease attacks on plants are very detrimental factors for farmers. Lack of knowledge leads to a difficult diagnosis of attacks and slow control. The technology that can help farmers in diagnosing plant pests and diseases is the expert system. One of the most widely used algorithms is forward chaining. Although there have been many used further research is needed to determine the effectiveness of this method. This research wants to find out more advantages, types of platforms used, and benefits of expert systems with forward chaining algorithms. This study uses a systematic method literature review (SLR) to collect, assess, and analyze data systematically from various scientific articles. The results of the study show that the use of forward chaining has advantages and benefits for developers and farmers.

Sideband Vibro-Acoustics Suppression and Numerical Prediction of Permanent Magnet Synchronous Motor Based on Markov Chain Random Carrier Frequency Modulation

This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current harmonics and radial electromagnetic forces introduced by DPWM are characterized and identified. Then, the principle and implementation method of three-state Markov chain random number generation are proposed, and particle swarm optimization (PSO) algorithm is chosen to quickly find the key parameters of transition probability and random gain. A Simulink and JMAG multi-physics field co-simulation model is built to simulate and predict the suppression effect of the MRCFM method on the sideband vibro-acoustic response. Finally, a 12-slot-10-pole PMSM test platform is built for experimental testing. The results show that the sideband current harmonics and vibro-acoustic response are effectively suppressed after the optimization of Markov chain algorithm. The constructed multi-physics field co-simulation model can accurately predict the amplitude characteristics of the sideband current harmonics and vibro-acoustic response.