Process Malfunctions Research Articles

Масштабування бізнесу: міжнародні стратегії експансії

In a globalized world, international expansion has become much more accessible. But despite this, entering new markets is still a difficult marathon that requires clear planning and careful preparation. In most cases, system expansion can take years or even longer, depending on the specifics of the product and the chosen method of market penetration. Ensuring sustainable growth and scaling is one of the most prominent business trends of recent years. The following reasons are obvious: the COVID-19 pandemic, military conflicts and economic upheavals can quickly change the conditions and rules of the market. In such an unstable environment, the priority is not the depth of potential, but the resistance of the enterprise to changes and flexibility, which creates the possibility of effective response to challenges, without the loss of employees and resources. The article formulates the essence of the concept of “business scaling”, defines the differences from the concept of “business growth”. The terms “growth” and “scaling” are often used interchangeably. Although they are related, they have significant differences. Growth means increasing revenue at the same rate as the business adds resources (new team members, technology, capital, etc.). On the other hand, scaling occurs when an organization identifies ways to grow more efficiently, resulting in revenue growth at a significantly higher rate than the increase in resources and costs, which is also characteristic of a startup. Speaking about the relevance of the research topic, it was determined the most common mistakes that international companies often make during the scaling process. They consist of: too rapid expansion; prioritizing short-term goals over long-term goals; loss of attention (focus); predominance of quantity over quality; malfunctions of processes and systems to increase efficiency; lack of flexibility. Avoiding mistakes and achieving efficiency in business scaling processes involves building and implementing a strategy that can be adjusted and adapted to changes in macro- and microenvironmental factors as needed. Companies that scale successfully take into account how the scaling process affects all areas of the organization’s functioning. Key aspects of the business scaling process in the context of international expansion typically include: goals of the company, number of team members and internal business processes. In this article the main possibilities of building a system business that works effectively and generates profit in the conditions of the global market are substantiated. Basic approaches to scaling and strategies for international expansion are outlined. Based on the results of the study, the main risks caused by business scaling were identified, and measures to mitigate them were proposed.

Medical errors and adverse events in israel during the COVID-19 pandemic

The pandemic has suddenly challenged many healthcare systems. These systems needed more time to adapt and adjust their patient safety, risk management, and quality improvement programs to the new situation. This study aimed to characterize the pandemic's impact on reporting medical adverse events compared to periods before the pandemic outbreak in Israel. This retrospective cohort study is based on an adverse event reporting database reported to the Israeli Ministry of Health. We analyzed the number and type of adverse events reported to the Ministry of Health in the years before the COVID-19 pandemic (pre-pandemic period 2018–2019) in comparison to the same type of events reported during the pandemic (pandemic period 2020–2021). We found significant differences in reporting of adverse medical events compared to the pre-pandemic period. The following types of events were reported significantly more: medical and surgical procedures, unexpected deaths in general hospitals, complications or deaths of the mother in-hospital and home birth, more severe complications, and stillbirth of born children, and higher escape rate from psychiatric hospitals. A significant decrease was observed in errors in the operation process and device malfunctions. The overall rate of reporting adverse events was higher during the pandemic than pre-pandemic. Reporting of specific types of adverse events increased compared to the pre-pandemic period, though some were reported less. It may be beneficial to try to understand what specific variables played a significant role in affecting those changes. This understanding may help the healthcare system prepare to cope better with future medical crises.

How neurons maintain their axons long-term: an integrated view of axon biology and pathology.

Axons are processes of neurons, up to a metre long, that form the essential biological cables wiring nervous systems. They must survive, often far away from their cell bodies and up to a century in humans. This requires self-sufficient cell biology including structural proteins, organelles, and membrane trafficking, metabolic, signalling, translational, chaperone, and degradation machinery-all maintaining the homeostasis of energy, lipids, proteins, and signalling networks including reactive oxygen species and calcium. Axon maintenance also involves specialised cytoskeleton including the cortical actin-spectrin corset, and bundles of microtubules that provide the highways for motor-driven transport of components and organelles for virtually all the above-mentioned processes. Here, we aim to provide a conceptual overview of key aspects of axon biology and physiology, and the homeostatic networks they form. This homeostasis can be derailed, causing axonopathies through processes of ageing, trauma, poisoning, inflammation or genetic mutations. To illustrate which malfunctions of organelles or cell biological processes can lead to axonopathies, we focus on axonopathy-linked subcellular defects caused by genetic mutations. Based on these descriptions and backed up by our comprehensive data mining of genes linked to neural disorders, we describe the 'dependency cycle of local axon homeostasis' as an integrative model to explain why very different causes can trigger very similar axonopathies, providing new ideas that can drive the quest for strategies able to battle these devastating diseases.

Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems.

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT.

This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

Increasing the Excitatory Drive Rescues Excitatory/Inhibitory Imbalance and Mismatch Negativity Deficit Caused by Parvalbumin Specific GluA1 Deletion

Disturbance in synaptic excitatory and inhibitory (E/I) transmission in the prefrontal cortex is considered a critical factor for cognitive dysfunction, a core symptom in schizophrenia. However, the cortical network pathophysiology induced by E/I imbalance is not well characterized, and an effective therapeutic strategy is lacking. In this study, we simulated imbalanced cortical network by using mice with parvalbumin neuron (PV) specific knockout of GluA1 (AMPA receptor subunit 1) (Gria1-PV KO) as an experimental model. Applying high-content confocal imaging and electrophysiological recordings in the medial prefrontal cortex (mPFC), we found structural and functional alterations in the local network of Gria1-PV KO mice. Additionally, we applied electroencephalography (EEG) to assess potential deficits in mismatch negativity (MMN), the standard readout in the clinic for measuring deviance detection and sensory information processing. Gria1-PV KO animals exhibited abnormal theta oscillation and MMN, which is consistent with clinical findings in cognitively impaired patients. Remarkably, we demonstrated that the glycine transporter 1 (GlyT1) inhibitor, Bitopertin, ameliorates E/I imbalance, hyperexcitability, and sensory processing malfunction in Gria1-PV KO mice. Our results suggest that PV-specific deletion of GluA1 might be an experimental approach for back translating the E/I imbalance observed in schizophrenic patients. Our work offers a systematic workflow to understand the effect of GlyT1 inhibition in restoring cortical network activity from single cells to local brain circuitry. This study highlights that selectively boosting NMDA receptor-mediated excitatory drive to enhance the network inhibitory transmission from interneurons to pyramidal neurons (PYs) is a potential therapeutic strategy for restoring E/I imbalance-associated cognitive-related abnormality.

Patient and clinician perceptions of the trauma and acute care surgery hospitalization discharge transition of care: a qualitative study

ObjectivesTrauma and acute care surgery (TACS) patients face complex barriers associated with hospitalization discharge that hinder successful recovery. We sought to better understand the challenges in the discharge transition of...

Real-Time Fault Detection and Diagnosis of CaCO3 Reactive Crystallization Process by Electrical Resistance Tomography Measurements.

In the present research work, an electrical resistance tomography (ERT) system is utilized as a means for real-time fault detection and diagnosis (FDD) during a reactive crystallization process. The calcium carbonate crystallization is part of the carbon capture and utilization scheme where process monitoring and malfunction diagnostics strategies are presented. The graphical logic representation of the fault tree analysis methodology is used to develop the system failure states. The measurement consistency due to the use of a single electrode from a set of ERT electrodes for malfunction identification is experimentally and quantitatively investigated based on the sensor sensitivity and standard deviation criteria. Electrical current measurements are employed to develop a LabVIEW-based process automation program by using the process-specific knowledge and historical process data. Averaged electrical current is correlated to the mechanical failure of the stirrer through standard deviation evaluation, and slopes of the measured data are used to monitor the pump and concentrations status. The performance of the implemented methodology for detecting the induced faults and abnormalities is tested at different operating conditions, and a basic signal-based alarming technique is developed.

The practical application of tools and process mining methods

The article discusses the implementation of Process Mining methods in the activities of educational institutions. Process Mining should be used in those moments when it concerns the activities of participants in information systems. The sequence of stages is checked very carefully, and the productivity of each participant, division of the organization is monitored. There are doubts that it will not be possible to change the mechanics of the process, but it will be possible to save money on debugging the work of each link, which together will give a tangible effect. These are difficult, long-term, peculiar processes that involve many departments. In this case, Process Mining is no longer looking for a way to accelerate, but a way to simplify the process, rationalize the number of steps, avoid process malfunctions, unnecessary cycles, involving unnecessary participants, etc. Ideal and real models of the course were drawn up and compared.

Monoclonal Gammapathy of Renal Significance: Morphological Variants of Lesion

The article discusses the concept of monoclonal renal gammopathy, which combines various renal diseases caused by the deposition of monoclonal immunoglobulin and / or their components in the glomeruli and tubulointerstitium. This nosological group was identified within the group of monoclonal gammopathies of undetermined significance (in 2012). The data on the study of morphological kidney damage associated with monoclonal renal gammopathy are presented. The spectrum of renal diseases in monoclonal renal gammopathy is diverse, and its classification is based on the localization of renal lesions in the glomeruli, tubules, vascular interstitium and stroma, as well as the peculiarity of the deposition of immunoglobulins (organized and unorganized). Kidney biopsy is required in most cases to locate the lesion, assess its severity, and predict patient survival. Diagnostics requires the integration of morphological changes using light microscopy, immunofluorescence, electron microscopy, and in some cases, staining of monoclonal protein for Ig isotypes is used (staining with hematoxylin / eosin, Schiff stain (PAS reaction), Jones stain, Congo Red stain, Masson’s trichromal stain). Early diagnosis and timely prescription of clone-oriented therapy by a hematologist and / or a hematooncologist can stop the progression of the malignant process and kidney malfunction. A nephrologist should monitor the patient, interacting with the hematologist.

Risk identification of a hospital laboratory pre-analytics through failure mode and effect analysis

Background: Implementing an active system to identify, monitor and manage risk from laboratory errors can enhance patient safety and quality of care.
 Aims and Objectives: Failure Mode and Effect Analysis (FMEA) technique allows evaluating and measuring the hazards of a process malfunction, to decide where to execute improvement actions, and to measure the outcome of those actions. The aim of this study was to assess pre analytical phase of laboratory testing, mitigate risk and thereby increase patient safety.
 Materials and Methods: Steps followed in the study were: planning the study, selecting team members, analysis of the processes, risk analysis, and developing a risk reduction protocol by incorporating corrective actions. A Fault Tree Analysis diagram was used to plot the cascade of faults leading to the pre analytical errors. Risk Priority Number (RPN) was assigned. A minimum cut- off 40 RPN was considered for interventions and highest RPN errors were prioritized with corrective actions. Post intervention RPN score was calculated.
 Results: Eight failure modes had the highest RPN. Corrective actions were prioritized against these errors. RPN scores of test ordering error, sample collection error, transport errors, error in patient identification, site selection, urine samples not received, sample accessioning and sample processing errors decreased, post intervention.
 Conclusion: With thorough planning, we can use FMEA as a common standard to analyze risk in pre analytical phase of laboratory testing.

Ultrasonic Time-of-Flight Computed Tomography for Investigation of Batch Crystallisation Processes.

Crystallisation is a crucial step in many industrial processes. Many sensors are being investigated for monitoring such processes to enhance the efficiency of them. Ultrasound techniques have been used for particle sizing characterization of liquid suspensions, in crystallisation process. An ultrasound tomography system with an array of ultrasound sensors can provide spatial information inside the process when compared to single-measurement systems. In this study, the batch crystallisation experiments have been conducted in a lab-scale reactor in calcium carbonate crystallisation. Real-time ultrasound tomographic imaging is done via a contactless ultrasound tomography sensor array. The effect of the injection rate and the stirring speed was considered as two control parameters in these crystallisation functions. Transmission mode ultrasound tomography comprises 32 piezoelectric transducers with central frequency of 40 kHz has been used. The process-based experimental investigation shows the capability of the proposed ultrasound tomography system for crystallisation process monitoring. Information on process dynamics, as well as process malfunction, can be obtained via the ultrasound tomography system.

Physics-based vibration feature for detecting eccentric workpiece/runout faults during continuous generating gear grinding processes

Continuous generating gear grinding is a well-established and widely used process in the industry for large-scale production gears. It offers an economic/efficient process for finishing gears, which shapes the micro-geometry of the gear tooth flank and improves its surface quality. The resulting quality of ground gears depends on several factors, namely the tool performance, the machine stability as well as the correct clamping/positioning of the workpiece. The grinding step is very crucial since it has a direct impact on the operating quality of gears and in particular on the running noise behaviour of the end product. The potential of online vibration-based gear grinding monitoring has been explored and demonstrated in the previous work (Gryllias et al., 2017) as a means of quality control that could lead to the overall reduction of production losses and to the prevention of sending defective parts to customers. Several features which could be used to monitor the grinding processes and to identify a specific type of defects have been proposed & experimentally validated to some extent. The types of faults include (i) high feed rate, (ii) high infeed, (iii) non-flat workpiece, and (iv) eccentric workpiece. However, a further investigation on a new test campaign revealed that none of the features developed in (Gryllias et al., 2017) was robust enough to detect eccentric workpieces during the grinding process. It is worth mentioning here that an eccentric workpiece fault is unlikely to happen, but it is analogous to a runout on the incoming workpiece quality. In this paper, a qualitative model to predict the vibration signature due to eccentric workpieces/runouts is developed and discussed. Based on the qualitative understanding, a novel feature to detect eccentric workpieces/runouts during gear grinding processes based on vibration signals has been developed. The newly developed feature has been validated on real vibration signals captured during the emulation of process malfunctions on an industrial gear grinding machine. In this study, it is shown that the novel feature seems sensitive and robust for detecting workpiece eccentricities/runout errors of about more than 60 µm. It is also shown in this study that the feature is insensitive to other types of gear grinding faults, which is important for diagnostics/root-cause analysis purposes.

Modeling and Optimization of the Drinking Water Supply Network—A System Case Study from the Czech Republic

In this study, we investigated the modelling and optimization of drinking water supply system reliability in the village of Zaben, Czech Republic. An in depth overview of the water supply network in the municipality, passport processing and accident and malfunction recording is provided based on data provided by the owner and operator of the water mains as well as the data collected by our own field survey. Using the data processed from accident and failure reports in addition to water main documentation, the water supply network in Zaben was evaluated according to the failure modes and effects analysis methods. Subsequently, individual water supply lines were classified based on their structural condition. In addition, a proposed plan for financing the reconstruction of the water supply mains in Zaben was created. As such, this study provides an overall assessment of the water supply network in Zaben alongside a proposed plan for the structural restoration of the water supply system, which accounts for the theoretical service life of the system and the financial resources of the owner.

The Core-Clock Gene NR1D1 Impacts Cell Motility In Vitro and Invasiveness in A Zebrafish Xenograft Colon Cancer Model.

Malfunctions of circadian clock trigger abnormal cellular processes and influence tumorigenesis. Using an in vitro and in vivo xenograft model, we show that circadian clock disruption via the downregulation of the core-clock genes BMAL1, PER2, and NR1D1 impacts the circadian phenotype of MYC, WEE1, and TP53, and affects proliferation, apoptosis, and cell migration. In particular, both our in vitro and in vivo results suggest an impairment of cell motility and a reduction in micrometastasis formation upon knockdown of NR1D1, accompanied by altered expression levels of SNAI1 and CD44. Interestingly we show that differential proliferation and reduced tumour growth in vivo may be due to the additional influence of the host-clock and/or to the 3D tumour architecture. Our results raise new questions concerning host–tumour interaction and show that core-clock genes are involved in key cancer properties, including the regulation of cell migration and invasion by NR1D1 in zebrafish xenografts.

The C-terminal acidic motif of Phafin2 inhibits PH domain binding to phosphatidylinositol 3-phosphate

Changes in membrane curvature are required to control the function of subcellular compartments; malfunctions of such processes are associated with a wide range of human diseases. Membrane remodeling often depends upon the presence of phosphoinositides, which recruit protein effectors for a variety of cellular functions. Phafin2 is a phosphatidylinositol 3-phosphate (PtdIns3P)-binding effector involved in endosomal and lysosomal membrane-associated signaling. Both the Phafin2 PH and the FYVE domains bind PtdIns3P, although their redundant function in the protein is unclear. Through a combination of lipid-binding assays, we found that, unlike the FYVE domain, recognition of the PH domain to PtdIns3P requires a lipid bilayer. Using site-directed mutagenesis and truncation constructs, we discovered that the Phafin2 FYVE domain is constitutive for PtdIns3P binding, whereas PH domain binding to PtdIns3P is autoinhibited by a conserved C-terminal acidic motif. These findings suggest that binding of the Phafin2 PH domain to PtdIns3P in membrane compartments occurs through a highly regulated mechanism. Potential mechanisms are discussed throughout this report.

Structural Fuzziness of the RNA-Organizing Protein SERF Determines a Toxic Gain-of-interaction

The mechanisms by which protein complexes convert from functional to pathogenic are the subject of intensive research. Here, we report how functionally unfavorable protein interactions can be induced by structural fuzziness, i.e., by persisting conformational disorder in protein complexes. We show that extreme disorder in the bound state transforms the intrinsically disordered protein SERF1a from an RNA-organizing factor into a pathogenic enhancer of alpha-synuclein (aSyn) amyloid toxicity. We demonstrate that SERF1a promotes the incorporation of RNA into nucleoli and liquid-like artificial RNA-organelles by retaining an unusually high degree of conformational disorder in the RNA-bound state. However, this type of structural fuzziness also determines an undifferentiated interaction with aSyn. RNA and aSyn both bind to one identical, positively charged site of SERF1a by an analogous electrostatic binding mode, with similar binding affinities, and without any observable disorder-to-order transition. The absence of primary or secondary structure discriminants results in SERF1a being unable to select between nucleic acid and amyloidogenic protein, leading the pro-amyloid aSyn:SERF1a interaction to prevail in the cytosol under conditions of cellular stress. We suggest that fuzzy disorder in SERF1a complexes accounts for an adverse gain-of-interaction which favors toxic binding to aSyn at the expense of nontoxic RNA binding, thereby leading to a functionally distorted and pathogenic process. Thus, structural fuzziness constitutes a direct link between extreme conformational flexibility, amyloid aggregation, and the malfunctioning of RNA-associated cellular processes, three signatures of neurodegenerative proteinopathies.

Niemann–Pick type C disease: cellular pathology and pharmacotherapy

Niemann-Pick type C disease (NPCD) was first described in 1914 and affects approximately 1 in 150000 live births. It is characterized clinically by diverse symptoms affecting liver, spleen, motor control, and brain; premature death invariably results. Its molecular origins were traced, as late as 1997, to a protein of late endosomes and lysosomes which was named NPC1. Mutation or absence of this protein leads to accumulation of cholesterol in these organelles. In this review, we focus on the intracellular events that drive the pathology of this disease. We first introduce endocytosis, a much-studied area of dysfunction in NPCD cells, and survey the various ways in which this process malfunctions. We briefly consider autophagy before attempting to map the more complex pathways by which lysosomal cholesterol storage leads to protein misregulation, mitochondrial dysfunction, and cell death. We then briefly introduce the metabolic pathways of sphingolipids (as these emerge as key species for treatment) and critically examine the various treatment approaches that have been attempted to date.

Overcoming Stability Problems in Microwave-Assisted Heterogeneous Catalytic Processes Affected by Catalyst Coking

Microwave-assisted heterogeneous catalysis (MHC) is gaining attention due to its exciting prospects related to selective catalyst heating, enhanced energy-efficiency, and partial inhibition of detrimental side gas-phase reactions. The induced temperature difference between the catalyst and the comparatively colder surrounding reactive atmosphere is pointed as the main factor of the process selectivity enhancement towards the products of interest in a number of hydrocarbon conversion processes. However, MHC is traditionally restricted to catalytic reactions in the absence of catalyst coking. As excellent MW-susceptors, carbon deposits represent an enormous drawback of the MHC technology, being main responsible of long-term process malfunctions. This work addresses the potentials and limitations of MHC for such processes affected by coking (MHCC). It also intends to evaluate the use of different catalyst and reactor configurations to overcome heating stability problems derived from the undesired coke deposits. The concept of long-term MHCC operation has been experimentally tested/applied to for the methane non-oxidative coupling reaction at 700 °C on Mo/ZSM-5@SiC structured catalysts. Preliminary process scalability tests suggest that a 6-fold power input increases the processing of methane flow by 150 times under the same controlled temperature and spatial velocity conditions. This finding paves the way for the implementation of high-capacity MHCC processes at up-scaled facilities.