Industrial Environment Research Articles

Biofilm formation by Listeria monocytogenes from the meat processing industry environment and the use of different combinations of detergents, sanitizers, and UV-A radiation to control this microorganism in planktonic and sessile forms.

This study aimed to evaluate the ability of biofilm formation by L. monocytogenes from the meat processing industry environment, as well as the use of different combinations of detergents, sanitizers, and UV-A radiation in the control of this microorganism in the planktonic and sessile forms. Four L. monocytogenes isolates were evaluated and showed moderate ability to form biofilm, as well as carried genes related to biofilm production (agrB, agrD, prfA, actA, cheA, cheY, flaA, sigB), and genes related to tolerance to sanitizers (lde and qacH). The biofilm-forming isolates of L. monocytogenes were susceptible to quaternary ammonium compound (QAC) and peracetic acid (PA) in planktonic form, with minimum inhibitory concentrations of 125 and 75 ppm, respectively, for contact times of 10 and 5min. These concentrations are lower than those recommended by the manufacturers, which are at least 200 and 300 ppm for QAC and PA, respectively. Biofilms of L. monocytogenes formed from a pool of isolates on stainless steel and polyurethane coupons were subjected to 14 treatments involving acid and enzymatic detergents, QAC and PA sanitizers, and UV-A radiation at varying concentrations and contact times. All treatments reduced L. monocytogenes counts in the biofilm, indicating that the tested detergents, sanitizers, and UV-A radiation exhibited antimicrobial activity against biofilms on both surface types. Notably, the biofilm formed on polyurethane showed greater tolerance to the evaluated compounds than the biofilm on stainless steel, likely due to the material's surface facilitating faster microbial colonization and the development of a more complex structure, as observed by scanning electron microscopy. Listeria monocytogenes isolates from the meat processing industry carry genes associated with biofilm production and can form biofilms on both stainless steel and polyurethane surfaces, which may contribute to their persistence within meat processing lines. Despite carrying sanitizer tolerance genes, QAC and PA effectively controlled these microorganisms in their planktonic form. However, combinations of detergent (AC and ENZ) with sanitizers (QAC and PA) at minimum concentrations of 125 ppm and 300 ppm, respectively, were the most effective.

The role of European chemical manufacturing companies in promoting effective communication of conditions of safe use by workers.

In 2006, the revised chemicals management legislation mandatedthat manufacturers of hazardous chemical substances conduct risk assessments for the entire substance life cycle. Additionally, they mustcommunicate use-specific safe handling advice (exposure scenarios) to their customer, asannex to the Safety Data Sheet (SDS). Despite significant efforts to develop workable solutions for chemical mixtures, this goal has not yet been fully achieved. Therefore, a Cefic research project (LRI B23) was commissioned on how to ensure meaningful health risk communication for workers across supply chains. The research project determined that risk-based safe use advice generated by manufacturers, often does not reach the intended end-user and was seen as not tailored to specific user needs. Recipients of the advice are also not prepared to act based on information developed by suppliers. From an industry perspective, the complexity of supply chains and substance life cycles are considered major barriers for effective safe use communication. Exposure scenarios for substance use in industrial work environments are often perceived as adding little value compared to existing safe use arrangements required by other health, safety, and environmental legislation applicable to employers and duty-holders. To attain meaningful use-specific safe handling advice for workers, including those at non-industrial premises who may benefit most from such advice, knowledge transfer and close collaboration between manufacturers and formulators remain key elements, supported by enhanced regulatory appreciation.

An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0

Airborne pollutants pose a significant threat in the occupational workplace resulting in adverse health effects. Within the Industry 4.0 environment, new systems and technologies have been investigated for risk management and as health and safety smart tools. The use of predictive algorithms via artificial intelligence (AI) and machine learning (ML) tools, real-time data exchange via the Internet of Things (IoT), cloud computing, and digital twin (DT) simulation provide innovative solutions for accident prevention and risk mitigation. Additionally, the use of smart sensors, wearable devices and virtual (VR) and augmented reality (AR) platforms can support the training of employees in safety practices and signal the alarming concentrations of airborne hazards, providing support in designing safety strategies and hazard control options. Current reviews outline the drawbacks and challenges of these technologies, including the elevated stress levels of employees, cyber-security, data handling, and privacy concerns, while highlighting limitations. Future research should focus on the ethics, policies, and regulatory aspects of these technologies. This perspective puts together the advances and challenges of Industry 4.0 innovations in terms of occupational safety and exposure assessment, aiding in understanding the full potential of these technologies and supporting their application in industrial manufacturing environments.

Thermal characteristics of resistive heating composite panels for the interior parts of railway vehicles

The pandemic of highly infectious diseases like COVID-19 has highlighted the vulnerabilities of public transportation. In particular, railways are more vulnerable due to their dense environment, and the heating system used in winter is a major factor in increasing the transmission and risk of the virus. To improve the existing heating system, a study was conducted to develop a resistive heating composite capable of radiant heating. In this study, the applicability of heating elements, heating performance, and internal thermal deformation factors of laminated panels and sandwich panels used as rail vehicle components were analyzed in detail to consider the actual application of this technology in the railway industry. The experimental results confirmed that both types of panels effectively heated above the target temperature (120℃) in the heating element layer, demonstrating uniform heating characteristics. Additionally, sandwich panel is more affected by thermal deformation compared to the laminate panel. These experimental results are expected to serve as essential foundational data for the effective utilization, safety, and reliability considerations of resistive heating composite materials in real industrial environments.

Improved deep-learning rotor fault diagnosis based on multi vibration sensors and recurrence plots

Deep learning techniques are increasingly applied to time series data, offering promising results in various fields. Deep learning techniques can handle data from multiple sensors to detect anomalies in an industrial environment. This paper proposes a new method of anomaly detection based on a multilayer image representation of different vibration sensors’ recurrence plots. Each sensor’s recurrence plot forms a layer. The performance and reliability of our method were assessed using an experimental database collected under different load conditions and with different types of rotor anomalies. Experiment results demonstrate the effectiveness of GoogLeNet using individual and multi-layered recurrence plots to find rotor faults in an induction motors.

Warm air bending of AZ31B sheets based on an online local contact heating method

Abstract Warm forming holds significant potential in shaping difficult-to-form materials due to the comprehensive advantages compared to the cold and hot forming. Effective heating is essential for specific warm forming processes. This paper investigated a novel warm air bending technique using an online local contact heating method. Preheating stage heat transfer and air bending of AZ31B sheets were examined at temperatures ranging from room temperature to 300 °C by experiments and finite element analysis considering the thermomechanical coupling effect. A noteworthy augmentation in the bending center angle, progressing from 86° to 133°, was obtained as the heating temperature ascends from room temperature to 250 °C. Fractures were successfully eliminated when the heating temperatures of the sheets exceed 150 °C. As the heating temperature increases, a concurrent decrease in the rebound angle is observed, diminishing from 10.6° to 2.3°. The results confirm the feasibility of warm air bending using the online local contact heating in the application in industrial environment, in terms of forming quality and production efficiency. Keywords: warm forming, air bending; contact heating, thermomechanical coupling analysis, AZ31B

Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network

The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.

How Does Low-Carbon Financial Policy Affect Corporate Green Innovation?—Re-Examination of Institutional Characteristics, Influence Mechanisms, and Local Government Behavior

This paper employs a propensity score matching approach to construct a control group and estimate the impact of the CETS pilot policy, a low-carbon financial policy, on corporate green innovation and its impact mechanism in a difference-in-difference manner. The results show that the CETS pilot policy has a significantly positive effect on corporate green innovation. The higher the penalty degree and the carbon price, the more obvious the promotion of the green innovation of pilot enterprises. The mechanism test shows that the improvement of corporate green innovation is mainly due to the incentive effect rather than the anti-driving effect of the CETS pilot policy, that is, the policy promotes corporate green innovation by providing innovation resources and enhancing the willingness to innovate. Further analysis shows that only in regions where local governments have less competitive pressure can the CETS pilot policy effectively promote enterprise innovation resources and that a close and clean government–business relationship can help strengthen the promotion effect of the CETS pilot policy on the willingness of enterprises to innovate. Furthermore, this paper introduces its theoretical framework as a strategic tripod to explore the friction in the process of the CETS pilot policy affecting corporate green innovation from the perspective of the industry environment and corporate resources. This research shows that a lack of industry green technology and corporate human capital may hinder the positive impact of the CETS pilot policy on corporate green innovation. Finally, this study found that the CETS pilot policy has no significant impact on the quality of corporate green innovation, and the lack of industry green technology and corporate human capital may hinder the CETS pilot policy from improving the quality of corporate green innovation.

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

Mindfulness and Emotional Intelligence: A Study on Millennials on Hospitality

This study investigates the relationship between mindfulness, emotional intelligence, and well-being among millennials in the hospitality industry. As this generation increasingly dominates the workforce, understanding their psychological dynamics becomes paramount. Mindfulness, the practice of being present and aware, and emotional intelligence, the ability to recognize, understand, and manage emotions, are two critical constructs influencing workplace behaviour and outcomes. In the context of hospitality, where employees often face high stress and emotional demands, exploring these factors becomes particularly relevant. This research employs a mixed-methods approach, utilizing surveys and interviews to gather data from millennials employed in various roles within the hospitality sector. The study aims to assess the levels of mindfulness and emotional intelligence among participants and examine their impact on job performance, job satisfaction, and overall well-being. By analysing the interplay between these variables, the study seeks to uncover potential pathways through which mindfulness and emotional intelligence contribute to the well-being of millennial employees in the hospitality industry. The findings of this study have implications for both practitioners and policymakers in the hospitality sector. Understanding the role of mindfulness and emotional intelligence in shaping workplace experiences can inform the development of interventions and policies aimed at promoting employee well-being and enhancing organizational effectiveness. Moreover, by shedding light on the psychological needs of millennials in the workplace, this research contributes to a deeper understanding of generational dynamics and offers insights into fostering a more supportive and engaging work environment in the hospitality industry. Keywords: Mindfulness, Emotional Intelligence, Millennials, Hospitality Industry, Job Performance, Job Satisfaction, Well-being, Workplace Dynamics, Employee Engagement, Mixed-Methods Study.

Bridging the Divide: Exploring Nonprofits’ Perceptions of Cannabis Philanthropy in the Changing Legal Landscape of the United States

Cannabis dispensaries in the U.S. aim to contribute to communities through philanthropy, but some nonprofits hesitate to accept donations. This exploratory case study in Colorado investigates nonprofits’ perceptions of cannabis philanthropy by surveying 317 organizations. Despite cannabis being illegal federally but legal at the state level, most respondents viewed cannabis philanthropy positively. Of the respondents who had negative views, many believe their negative views would shift if cannabis were federally legalized. Furthermore, the majority do not consider funds derived from cannabis sales as “tainted” or coming from a morally questionable source. The findings shed light on the complexity of how cannabis philanthropy is perceived, informing how nonprofits can adapt proactively to evolving cannabis philanthropy dynamics, particularly considering potential shifts in federal law. Additionally, the study reveals a favorable environment for the cannabis industry to engage in philanthropy and partnerships with nonprofits, and the potential positive impacts of such activities.

Can CEO's professional connections drive green innovation of SMEs?

AbstractDrawing on social capital theory, we analysis the influence of CEO's professional connections on the green innovation of SMEs. Using data from Chinese manufacturing SMEs, we find that CEO's professional connections can promote green innovation in SMEs. This finding still holds through several endogeneity and robustness tests. We find that this positive relationship is strengthened when the firm faces a more intense industry environment and when the CEO is chairman of the board. By testing the mechanism, we further find that alleviating financing constraints, improving information transparency, and promoting long‐term orientation play a key role in this positive relationship. These findings advance the research process on social capital theory and corporate green innovation and provide important insights into the green innovation practices in SMEs.

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

Genetically dissecting the electron transport chain of a soil bacterium reveals a generalizable mechanism for biological phenazine-1-carboxylic acid oxidation.

The capacity for bacterial extracellular electron transfer via secreted metabolites is widespread in natural, clinical, and industrial environments. Recently, we discovered biological oxidation of phenazine-1-carboxylic acid (PCA), the first example of biological regeneration of a naturally produced extracellular electron shuttle. However, it remained unclear how PCA oxidation was catalyzed. Here, we report the mechanism, which we uncovered by genetically perturbing the branched electron transport chain (ETC) of the soil isolate Citrobacter portucalensis MBL. Biological PCA oxidation is coupled to anaerobic respiration with nitrate, fumarate, dimethyl sulfoxide, or trimethylamine-N-oxide as terminal electron acceptors. Genetically inactivating the catalytic subunits for all redundant complexes for a given terminal electron acceptor abolishes PCA oxidation. In the absence of quinones, PCA can still donate electrons to certain terminal reductases, albeit much less efficiently. In C. portucalensis MBL, PCA oxidation is largely driven by flux through the ETC, which suggests a generalizable mechanism that may be employed by any anaerobically respiring bacterium with an accessible cytoplasmic membrane. This model is supported by analogous genetic experiments during nitrate respiration by Pseudomonas aeruginosa.

Research on the Influencing Factors of the Value of Listed Companies under the New Economic Environment

This paper explores the influencing factors of the value of listed companies in the new economic environment, aiming to deepen the understanding of the formation mechanism of the value of listed companies in the ever-changing economic environment. Firstly, the article analyzes the characteristics of listed companies under the new economic environment and the impact of the new economy on enterprises, including technological innovation, changes in market competition patterns, and so on. Secondly, the paper elaborates on the theoretical framework of the value of listed companies, including the concept of value, influencing factors, and evaluation methods. Finally, the article delves into the influencing factors of the value of listed companies in the new economic environment, including technological factors, innovation capability, industrial environment, and corporate governance. Through the analysis of these factors, this paper provides important theoretical references and practical guidance for understanding and evaluating the value of listed companies in the new economic era.

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

Abstract Autonomous navigation has been a long-standing research topic, and researchers have worked on many challenging problems in indoor and outdoor environments. One application area of navigation solutions is material handling in industrial environments. With Industry 4.0, the simple problem in traditional factories has evolved into the use of autonomous mobile robots within flexible production islands in a self-decision-making structure. Two main stages of such a navigation system are safe transportation of the vehicle from one point to another and reaching destinations at industrial standards. The main concern in the former is roughly determining the vehicle’s pose to follow the route, while the latter aims to reach the target with high accuracy and precision. Often, it may not be possible or require extra effort to satisfy requirements with a single localization method. Therefore, a multi-stage localization approach is proposed in this study. Particle filter-based large-scale localization approaches are utilized during the vehicle’s movement from one point to another, while scan-matching-based methods are used in the docking stage. The localization system enables the appropriate approach based on the vehicle’s status and task through a decision-making mechanism. The decision-making mechanism uses a similarity metric obtained through the correntropy criterion to decide when and how to switch from large-scale localization to precise localization. The feasibility and performance of the developed method are corroborated through field tests. These evaluations demonstrate that the proposed method accomplishes tasks with sub-centimeter and sub-degree accuracy and precision without affecting the operation of the navigation algorithms in real time.

A hybrid lattice Boltzmann/immersed boundary method/finite-difference model for thermal fluid-solid interactions

Particulate flows are frequently observed in both industrial and natural environments, and their simulation becomes particularly challenging when heat transfer is involved. This study introduces a hybrid numerical approach, combining the lattice Boltzmann Method (LBM), Immersed Boundary Method (IBM), and Finite Difference (FD) energy solver to alleviate the limitations observed in double distributed lattice Boltzmann methods and classical Computational Fluid Dynamics (CFD). The model incorporates an enhanced Hermite polynomial-based central moments collision operator. This enhancement boosts stability and overcomes the Galilean-invariant issues observed in classical LBM. For heat transfer, a Finite-Difference technique is employed for resolving the energy equation, discretizing the advection term through a Weighted Essentially Non-Oscillatory (WENO) approach. The Boussinesq approximation handles buoyancy forces, while the direct-forcing IBM manages fluid-particle interaction. Validation across various test cases confirms the model's accuracy, showcasing its ability to account for variations in thermal properties like specific heat capacity Cp over a wide range. The model is further utilized to investigate a reactor containing 300 catalyst particles. In summary, this hybrid methodology is found to be a promising tool for comprehensively studying and designing particle-laden systems with thermal effects.

Adjustments in VAT rates and business innovation: Empirical insights from China

China aims to alleviate corporate financial pressures and encourage high-quality economic development through initiatives such as value-added tax (VAT) rate reform. This study employs the difference-in-difference (DID) approach to investigate the effect of the 2017 VAT rate reform on corporate innovation, taking the reform as a quasi-natural experiment. We find that the VAT rate reform has significantly promoted firm innovation and that increasing profitability serves as the primary channel. The innovation-promoting effects of the VAT rate reform are most prominently seen in a number of different types of businesses: those with lower intermediate input ratios, those operating in less monopolistic industry environments, state-owned enterprises, non-exporting businesses, entities with limited financial resources, and companies located in non-eastern China. This study makes a case for tax reforms in other nations as well as a reference for China's market-oriented reform and further VAT reform.

Visual Servoing Architecture of Mobile Manipulators for Precise Industrial Operations on Moving Objects

Although the use of articulated robots and AGVs is common in many industrial sectors such as automotive or aeronautics, the use of mobile manipulators is not widespread nowadays. Even so, the majority of applications separate the navigation and manipulation tasks, avoiding simultaneous movements of the platform and arm. The capability to use mobile manipulators to perform operations on moving objects would open the door to new applications such as the riveting or screwing of parts transported by conveyor belts or AGVs. This paper presents a novel position-based visual servoing (PBVS) architecture for mobile manipulators for precise industrial operations on moving parts. The proposed architecture includes a state machine to guide the process through the different phases of the task to ensure its correct execution. The approach has been validated in an industrial environment for screw-fastening operations, obtaining promising results and metrics.

Comparative life cycle assessment and multi-criteria decision analysis of coffee capsules made with conventional and innovative materials

The following study arises from the need to replace aluminum and polypropylene, from fossil and non-renewable sources, with innovative materials to produce coffee capsules. The analysis was carried out using Life Cycle Assessment (LCA) and Multi Criteria Decision Analysis (MCDA). In the comparison were considered different materials: aluminum capsules, polypropylene, biocompostable plastic in an industrial environment (industrial compost) and biocompostable plastic in a domestic environment (home compost). The results indicate that, for current disposal, biocompostable plastic capsules, particularly those suitable for domestic compost, have lower environmental impacts. These results are also confirmed for the 10-year disposal scenario. Polypropylene capsules demonstrate greater environmental sustainability when considering a recovery rate of 100 % of the raw material. The MCDA analysis confirms that, at the moment, bioplastics represent the solution with the least environmental impact. However, this advantage is reduced if the capsules are made of aluminum and polypropylene using exclusively fully recycled raw material. Furthermore, it appears that the recycling of fossil materials is a viable alternative to bioplastics. It should be noted, however, that polypropylene can be recycled up to three times, unlike aluminum which can be recycled an indefinite number of times.