Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression(VAR)Model

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.

New Industrialization: Characteristics, System Development and Implementation Pathway

New industrialization in China, different from its past economic development pattern or patterns in developed nations, is the country’s theoretical innovation based on the positive and negative experiences of industrialization at home and worldwide. New industrialization has various novel characteristics, including new sources of efficiency, new factors of production, new organizational forms, and new constraints. In addition, it has certain particularities arising from modernization with Chinese characteristics. This article summarizes the characteristics of new industrialization from the perspectives of people-centered approach, quality-first concept, independent innovation, green low-carbon economics, digital-real integration, and open circulation. There are four systems for promoting new industrialization: A self-sustained scientific and technological system, a high-end advanced manufacturing system, a green low-carbon circular system, and a division of labor system with domestic and international circulation. The Chinese new industrialization proposes the pathway and policy measures considering the new global situation and the requirements of new goals of strengthening organization and leadership, reducing factor cost, accelerating independent technological innovation, smoothing domestic and international circulation, and optimizing competition environment.

World Industrial Revolutions and the Development of Artificial Intelligence System

The objective-scientific conclusions obtained from the researches conducted in various fields of science prove that era and worldview are in unity and are phenomena that determine one another,and era and worldview are the most important phenomena in the understanding of geniuses,historical events,including personalities who have left a mark on the history of politics,and every individual as a whole.And it is appropriate to briefly consider the problem in the context of human and personality factors.It is known that man has tried to understand natural phenomena since the beginning of time.Contact with the material world naturally affects his consciousness and even his subconscious as he solves problems that are important or useful for human life.During this understanding,the worldview changes and is formed.Thus,depending on the material and moral development of all spheres of life,the content and essence of the progress events,as the civilizations replaced each other in different periods,the event of periodization took place and became a system.If we take Europe,the people of the Ice Age of 300,000 years ago,who engaged in hunting to solve their hunger needs,in other words,the age of dinosaurs,have spread to many parts of the world from Africa,where they lived in order to survive and meet more of their daily needs.The extensive integration of agricultural Ice Age People into the Earth included farming,fishing,animal husbandry,hunting,as well as handicrafts,etc.,and has led to the revolutionary development of the fields.As economic activities led these first inhabitants of the planet from caves to less comfortable shelters,then to good houses,then to palaces,labor activities in various occupations,including crafts,developed rapidly.Thus,the fads of the era who differed from the crowd(later this class will be called personalities,geniuses...-Kh.G.)began to appear.If we approach the issue from the point of view of history,we witness that the world view determines the development in different periods.This idea can be expressed in such a way that each period can be considered to have developed or experienced a crisis according to the level of worldview.In this direction of our thoughts,the question arises:So,what is the phenomenon of worldview of this era-XXI century?Based on the general content of the current events,characterized as the globalization stage of the modern world,we can say that the outlook of the historical stage we live in is based on the achievements of the last stage of the industrial revolution.In this article,by analyzing the history of the artificial intelligence system during the world industrial revolutions,we will study both the concept of progress of the industrial revolutions and the progressive and at the same time regressive development of the artificial intelligence system.

Progress, challenge and significance of building a carbon industry system in the context of carbon neutrality strategy

Carbon dioxide storage and utilization has become an inevitable trend and choice for sustainable development under the background of global climate change and carbon neutrality.Carbon industry which is dominated by CO_(2) capture,utilization and storage/CO_(2) capture and storage(CCUS/CCS)is becoming a new strategic industry under the goal of carbon neutrality.The sustainable development of carbon industry needs to learn from the experiences of global oil and gas industry development.There are three types of“carbon”in the earth system.Black carbon is the CO_(2) that has not been sequestered or used and remains in the atmosphere for a long time;grey carbon is the CO_(2) that has been fixed or permanently sequestered in the geological body,and blue carbon is the CO_(2) that could be converted into products for human use through biological,physical,chemical and other ways.The carbon industry system covers carbon generation,carbon capture,carbon transportation,carbon utilization,carbon sequestration,carbon products,carbon finance,and other businesses.It is a revolutionary industrial field to completely eliminate“black carbon”.The development of carbon industry technical system takes carbon emission reduction,zero carbon,negative carbon and carbon economy as the connotation,and the construction of a low-cost and energy-efficient carbon industry system based on CCUS/CCS are strategic measures to achieve the goal of carbon neutrality and clean energy utilization globally.This will promote the“four 80%s”transformation of China's energy supply,namely,to 2060,the percentage of zero-carbon new energy in the energy consumption will be over 80%and the CO_(2) emission will be decreased by 80%to ensure the carbon emission reduction of total 80×10^(8) t from the percentage of carbon-based fossil energy in the energy consumption of over 80%,and the percentage of CO_(2) emission from energy of over 80%in 2021.The carbon industry in China is facing three challenges,large CO_(2) emissions,high percentage of coal in energy consumption,and poor innovative system.Three strategic measures are proposed accordingly,including:(1)unswervingly develop carbon industrial system and ensure the achievement of carbon neutrality as scheduled by 2060;(2)vigorously develop new energy sources and promote a revolutionary transformation of China’s energy production and consumption structure;(3)accelerate the establishment of scientific and technological innovation system of the whole CO_(2) industry.It is of great significance for continuously optimization of ecological environment and construction of green earth and ecological earth to develop the carbon industry system,utilize clean energy,and achieve the strategic goal of global carbon neutrality.

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Spatiotemporal Evolution and Influencing Factors of Urban-rural Construction Land in Rural Industrialized Areas in China:Case Studies in Changyuan City and Xinxiang County of Henan Province

Since China’s reform and opening up in 1978,the acceleration of industrialization and urbanization in China had led to dramatic changes in the pattern of urban-rural land use.In this paper,we focused on the rural industrialized areas in central China(Xinxiang County and Changyuan City of Henan Province).We used the average nearest neighbor index,spatial statistical analysis,and a structural equation model to analyze the spatiotemporal evolution and influencing factors of urban-rural construction land based on multisource spatial data and survey data.The results showed that:1)from 1975 to 2019,the spatial distribution of urban-rural construction land in rural industrialized areas had evolved from homogeneous distribution to local agglomeration.In terms of comparative analysis of cases,the spatial distribution of urban-rural construction land in Changyuan City had shown a trend from diffusion to agglomeration,and Xinxiang County had overall shown a spatial change from homogenization to agglomeration and then to regional integration development.2)The hot spots with increased urban-rural construction land significantly expanded,and they had a high degree of spatial overlap with industrial development.Among them,Xinxiang County was concentrated in central and marginal areas,and Changyuan was mainly concentrated in central urban areas.3)From the evolution of spatial proximity of urban-rural construction land,rural industrialized areas generally decline,showing the characteristics of internal differentiation in the rate of change.4)Industrial development,social economy,the policy environment,and urban development played a positive role in promoting the expansion of urban-rural construction land in rural industrialized areas.To promote the optimal use of regional land and the integrated development of urban-rural areas,we should combine the advantages of regional endowment,formulate development strategies according to local conditions,and adjust the way that land is used in a timely manner.

A long way to go:impacts of urbanization on migrants’livelihoods and rural ecology in less industrialized regions

Urbanization,often coupled with industrialization,is generally considered as a boost for improving livelihood as well as alleviating ecological pressures of the rural areas.However,this experience needs to be tested in less industrialized areas,particularly where the urbanization is driven by non-economic factors such as urban public services and relevant government policies rather than employment opportunities.Taking two villages on the Qinghai-Tibet Plateau as examples,through the field investigation by using semi-structured questionnaires and in-depth interviews,and statistical data analysis,we explored the impacts of urbanization on migrants’livelihoods and the ecological conditions of their original village rangeland.We found that the disposable income and savings of emigrant households were less than the stay households,which might be correlated to the limited employment due to the lagged industrialization in the local township,and the mismatch between their traditional labor skills and the demands of urban services.Their home-village rangeland overuse was not alleviated since the increasing grazing pressure caused by the stay households,though the emigrant households intended to decrease the number of livestock on their home pasture.We concluded that the urbanization misaligned with local industrialization may fail to improve migrant livelihoods and local ecological conditions in less industrialized areas.Our research supplements the previous urbanization theory by highlighting the non-industrialization driven urbanization,and is particularly pertinent for the transitional countries worldwide.

An Evaluation of the Coupling Coordination of Technological Innovation System in China’s Marine Biopharmaceutical Industry

The marine biopharmaceutical industry(MBI)has been considered as an important part of the blue economy.The high-quality development of this industry depends on the high-level coordinated development of technological innovation system(TIS).In the present study,the coupling mechanism of industrial innovation input subsystem and innovation output subsystem was analyzed for the first time.On this basis,the development level and coupling coordination level of TIS in China’s MBI during 2008-2018 were empirically evaluated with the capacity coupling coordination model.Then,the obstacle factors were diagnosed and recognized with the obstacle model.The results showed that the innovation input index fluctuated at a low level in China’s MBI.The innovation output index has basically maintained a growth trend,whereas the quality of development was not high.Although the coupling coordination level of TIS showed a positive change as mild disordered→primary coordinated→well-coordinated,the development type of innovation system has changed from the lagging output of innovation into the lagging input of innovation.Insufficient input of innovation factors remained the main obstacle to the improvement of coordination level.Based on the above analysis,suggestions were put forward from the perspectives of policy and fund guarantees to improve the coupling coordination level in China’s MBI.

Smart contract token-based privacy-preserving access control system for industrial Internet of Things

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

GRU-based Buzzer Ensemble for Abnormal Detection in Industrial Control Systems

Recently,Industrial Control Systems(ICSs)have been changing from a closed environment to an open environment because of the expansion of digital transformation,smart factories,and Industrial Internet of Things(IIoT).Since security accidents that occur in ICSs can cause national confusion and human casualties,research on detecting abnormalities by using normal operation data learning is being actively conducted.The single technique proposed by existing studies does not detect abnormalities well or provide satisfactory results.In this paper,we propose a GRU-based Buzzer Ensemble for AbnormalDetection(GBE-AD)model for detecting anomalies in industrial control systems to ensure rapid response and process availability.The newly proposed ensemble model of the buzzer method resolves False Negatives(FNs)by complementing the limited range that can be detected in a single model because of the internal models composing GBE-AD.Because the internal models remain suppressed for False Positives(FPs),GBE-AD provides better generalization.In addition,we generated mean prediction error data in GBE-AD and inferred abnormal processes using soft and hard clustering.We confirmed that the detection model’s Time-series Aware Precision(TaP)suppressed FPs at 97.67%.The final performance was 94.04%in an experiment using anHIL-basedAugmented ICS(HAI)Security Dataset(ver.21.03)among public datasets.

An Intelligent Approach for Intrusion Detection in Industrial Control System

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions.In recent years,there has been a lot of focus on the security of industrial control systems.Due to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically increased.Because they are so inextricably tied to human life,any damage to them might have devastating consequences.To provide an efficient solution to such problems,this paper proposes a new approach to intrusion detection.First,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning process.Then,a prior estimation of the class is proposed based on a support vector machine.Simulation results show that the proposed approach has better anomaly detection performance than existing algorithms.

Greenhouse gas reduction of co-benefit-type wastewater treatment system for fish-processing industry: A real-scale case study in Indonesia

This study examined the application of co-benefit-type wastewater treatment technology in the fish-processing industry. Given that there was a dearth of information on fish-processing industrial wastewater in Indonesia, site surveys were conducted. For the entire fish-processing industry throughout the country, the dissemination rate of wastewater treatment facilities was less than 50%. Using a co-benefit approach, a real-scale swim-bed technology (SBT) and a system combining an anaerobic baffled reactor (ABR) with SBT (ABR–SBT) were installed in a fishmeal processing factory in Bali, Indonesia, and the wastewater system process performance was evaluated. In a business-as-usual scenario, the estimated chemical oxygen demand load and greenhouse gas (GHG) emissions from wastewater from the Indonesian fish-processing industry were 33 000 tons per year and 220 000 tons of equivalent CO_(2) per year, respectively. On the other hand, the GHG emissions in the co-benefit scenarios of the SBT system and ABR–SBT system were 98 149 and 26 720 tons per year, respectively. Therefore, introducing co-benefit-type wastewater treatment to Indonesia’s fish-processing industry would significantly reduce pollution loads and GHG emissions.

Information Security Evaluation of Industrial Control Systems Using Probabilistic Linguistic MCDM Method

Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.

Programmable Logic Controller Block Monitoring System for Memory Attack Defense in Industrial Control Systems

Cyberattacks targeting industrial control systems(ICS)are becoming more sophisticated and advanced than in the past.A programmable logic controller(PLC),a core component of ICS,controls and monitors sensors and actuators in the field.However,PLC has memory attack threats such as program injection and manipulation,which has long been a major target for attackers,and it is important to detect these attacks for ICS security.To detect PLC memory attacks,a security system is required to acquire and monitor PLC memory directly.In addition,the performance impact of the security system on the PLC makes it difficult to apply to the ICS.To address these challenges,this paper proposes a system to detect PLC memory attacks by continuously acquiring and monitoring PLC memory.The proposed system detects PLC memory attacks by acquiring the program blocks and block information directly from the same layer as the PLC and then comparing them in bytes with previous data.Experiments with Siemens S7-300 and S7-400 PLC were conducted to evaluate the PLC memory detection performance and performance impact on PLC.The experimental results demonstrate that the proposed system detects all malicious organization block(OB)injection and data block(DB)manipulation,and the increment of PLC cycle time,the impact on PLC performance,was less than 1 ms.The proposed system detects PLC memory attacks with a simpler detection method than earlier studies.Furthermore,the proposed system can be applied to ICS with a small performance impact on PLC.

GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-DDoS2019 dataset,which contains various Distributed Denial-of-Service(DDoS)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels.Then,the CIC-DDoS2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system(CPS)is a concept that integrates every computer-driven system interacting closely with its physical environment.Internet-of-things(IoT)is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds.Since the complexity level of the CPS increases,an adversary attack becomes possible in several ways.Assuring security is a vital aspect of the CPS environment.Due to the massive surge in the data size,the design of anomaly detection techniques becomes a challenging issue,and domain-specific knowledge can be applied to resolve it.This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection(AOPTML-AD)technique in the CPS environment.The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment.The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format.Besides,the improved Aquila optimization algorithm-based feature selection(IAOA-FS)algorithm is designed to choose an optimal feature subset.Along with that,the chimp optimization algorithm(ChOA)with an adaptive neuro-fuzzy inference system(ANFIS)model can be employed to recognise anomalies in the CPS environment.The ChOA is applied for optimal adjusting of the membership function(MF)indulged in the ANFIS method.The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset.The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models,with an accuracy of 99.37%.

Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

Visible light communication(VLC)has a paramount role in industrial implementations,especially for better energy efficiency,high speed-data rates,and low susceptibility to interference.However,since studies on VLC for industrial implementations are in scarcity,areas concerning illumination optimisation and communication performances demand further investigation.As such,this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication performances.The proposed model was evaluated based on various semi-angles at half power(SAAHP)and different height levels for several parameters,including received power,signal to noise ratio(SNR),and bit error rate(BER).The results revealed improvement in terms of received power and SNR with 30 Mbps data rate.Various modulations were studied to improve the link quality,whereby better average BER values of 5.55×10^(−15) and 1.06×10^(−10) had been achieved with 4 PAM and 8 PPM,respectively.The simulation outcomes are indeed viable for the practical warehouse model.

Geometric Calibration Method of Robot Based on Measurement System Including Position and Orientation Parameters

Industrial robot which can acquire high accuracy has been widely used in automatic assembly.Usually,the geometric parameter of industrial robot should be inspected during manufacturing and application.High precision measurement equipment was utilized to acquire the position and orientation of robot’s end⁃effector,when calibrating the geometric parameter of robot.A kind of measurement system based on a draw⁃wire encoder was presented,since the current measurement equipment has some disadvantages,such as the cost and the requirements of working environment are high.According to this kind of measurement system,a sort of geometric calibration method of robot was presented including position and orientation parameters.The uncertain arc length of the cable length between robot end⁃effector and the measurement can be exactly acquired according to the position and orientation parameters.The pose⁃solving model of robot end⁃effector was associated with the kinematic model of robot,and robot’s geometric parameter can be computed by using the least⁃squares methods.Validate instance was conducted,the result showed that the optimal number of the calibration pose was 47 with little improvement in accuracy,even if increasing the number of calibration pose.Robot calibration experiment was performed and the results showed that the absolute accuracy of robot decreased from 4.32 mm to 0.87 mm after calibration,which improved the robot’s absolute accuracy effectively.

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults.In this paper,a deep learning-based observer,which combines the convolutional neural network(CNN)and the long short-term memory network(LSTM),is employed to approximate the nonlinear driving control system.CNN layers are introduced to extract dynamic features of the data,whereas LSTM layers perform time-sequential prediction of the target system.In terms of application,normal samples are fed into the observer to build an offline prediction model for the target system.The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs.Online fault detection can be realized by analyzing the residuals.Finally,an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme.Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

Entrepreneurial Orientation and Innovation Ecosystems in the Industrial Sector, Central Region, Kampala, Uganda: A Review

This review explores entrepreneurial orientation and innovation ecosystems in the industrial sector of the Central Region, Kampala, Uganda, through an analysis of ten scholarly articles. The study contextualizes the research within the regional landscape and establishes a theoretical framework through a focused literature review. Key findings highlight the intersection of entrepreneurial activities and innovation dynamics, emphasizing the region’s unique contributions to the broader field. Discussions on discrepancies and unexplored territories within the articles offer insights into limitations and research gaps. The manuscript concludes by identifying future research avenues, providing a roadmap for ongoing inquiry into the entrepreneurial and innovative dimensions of the Central Region’s industrial sector. This synthesis underscores the importance of cultivating an entrepreneurial mindset and collaborative innovation strategies for sustainable industrial development in the region.