Implementation Of Methodology Research Articles

An easy methodology for frost tolerance assessment in olive cultivars.

Olive cultivation, like other evergreen fruit crops worldwide, is limited by the occurrence of frost episodes in different times of the year, mainly in winter or early spring. Some contradictory results are reported about cultivars' response to frost, which depends on the physiological stage of the tissues (acclimated or not acclimated) when the cold or frost episode occurs. This work aimed to implement a user-friendly and reliable lab method for discerning frost tolerance. Our methodology considered both detached leaves and potted plantlets. The optimal temperature at which damage differentiated between cultivars was evaluated, as well as the time of exposure to cold and the recovery time for the correct evaluation of the symptoms. Furthermore, a comparative analysis of damage on both young and mature leaves was conducted. To validate the efficacy of the methodology, assessments were conducted on the cultivars 'Arbequina' (tolerant), 'Picual' (moderately tolerant), and 'Frantoio' (susceptible) under acclimated and non-acclimated conditions. The results indicated that, when detached leaves were used for frost evaluation, a temperature of -10°C ± 1°C for 30 min and a recovery time at 26°C for 24-48 h after exposure to cold are enough to induce damages on the leaves and discriminate between cultivar susceptibility. Under these conditions, a precise assessment of symptoms can be made, facilitating the categorization of frost tolerance level in various olive cultivars. Notably, no significant differences were observed between young and mature leaves during the evaluation process. On the other hand, the critical temperature to assess damages on potted plantlets was determined to be -7°C ± 1°C. In addition, it was observed that acclimated plants exhibited fewer symptoms compared to non-acclimated ones, with 'Frantoio' being the most affected alongside 'Picual' and 'Arbequina'. The implemented methodology will allow the assessment of frost tolerance in several olive cultivars within a short timeframe, and it is proven to be user-friendly and reliable.

Subsonic Jet Noise Prediction in Near and Far Field with Optimized Wave-Packet Approach

In this study, the parameters of a wave-packet model for subsonic jet noise prediction are systematically optimized by leveraging near- and far-field data obtained from the large-eddy simulation (LES) of a free jet at a Mach number of 0.9 across various radial distances. The utilization of near-field information is justified by the observation that the scattering surfaces are typically situated within a few nozzle diameters from the jet axis in the radial direction, both in the current and in innovative aircraft configurations. The far-field information is used to guarantee the correct subdivision between the wave-packet radiating noise and the hydrodynamic components. The results show a notable agreement between the LES data and the wave-packet solutions, consistent with findings documented in the existing literature. This agreement underscores the validity and applicability of the implemented methodology, offering an effective method for obtaining an equivalent jet noise acoustic source, easily implementable in acoustic scattering codes, and accounting for the directional behavior of jet noise.

Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading

Innovative Ultrasonic Guided Wave Approach for Structural Health Monitoring of Type IV Composite Overwrapped Pressure Vessels Seyedreza Hashemi, Jan Heimann, Amir Charmi, Eric Duffner, Jens Prager Abstract: The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs). The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV. Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings. Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility . The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.

GREEN MANAGEMENT SYSTEM (GMS): AN OVERVIEW OF INTEGRATED ORGANIZATIONAL SUSTAINABLE DEVELOPMENT

As worldwide concerns about environmental sustainability grow, firms are increasingly turning to Sustainable Green Management Systems (SGMS) to reduce their environmental impact while enhancing operational efficiency, which includes emissions, effluent discharge, waste disposal, and energy efficiency, as a result of growing global concern over climate change and widespread awareness of environmental sustainability and cleaner production. This article provides a all-inclusive view of how GMS serves as a pivotal strategy for achieving Organizational Sustainable Development (OSD), encompassing environmental stewardship, social responsibility, and economic resilience. It investigates integrated approaches within SGMS, focusing on their conceptual frameworks, implementation methodologies, and potential implications for organizational sustainability. The research dives into the theoretical foundations of integrated approaches within SGMS, revealing the synergistic integration of Socio-economic elements. Organizations adopting a holistic view attempt to match their business objectives with sustainable practices, supporting long-term viability and resilience in a rapidly changing global landscape. This study investigates into the potential Merits and Demerits of using an integrated SGMS, While increased environmental performance, company reputation, and regulatory compliance are significant benefits, firms face challenges such as high initial investment costs, complex stakeholder dynamics, and restricted technology capabilities. This article provides helpful ideas for firms looking to implement integrated techniques in their SGMS. These include developing a sustainable culture, harnessing technical advancements, forming strategic collaborations, and adopting a long-term approach to sustainability integration.

The emergence of digitalization to the manufacturing sector in the sustainability context: A multi-stakeholder perspective analysis

Sustainable digitalization in industry requires extensive preplanning to integrate business strategies with the concept of digitalized sustainability. However, stakeholders often have different perspectives, making alignment more challenging. Stakeholders in the digitalized sustainable manufacturing industry must be identified and prioritized for effective implementation. Therefore, the present study combines a Systematic Literature Review and Hybrid Multi-Criteria Decision Techniques, including the Full Consistency Method and Fuzzy-Evaluation based on the Distance from Average Solution method, to prioritize stakeholders' roles in digitalized sustainable manufacturing and associated strategies. This study explores various implementation methodologies and their relations to different stakeholders. It categorizes stakeholders, such as manufacturers, research and development teams, managers, mergers and acquisitions, education programs, the 6R strategy, and eco-efficiency, to illustrate corresponding strategies. Overall, the study highlights the urgent need for meticulous planning to synchronize business strategies with digitalized sustainability while recognizing the difficulty in harmonizing different stakeholders' assessments. The research findings offer valuable insights for industry professionals aiming to navigate the complexities of sustainable digitalization successfully.

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.

Photoacoustic processing of decellularized extracellular matrix for biofabricating living constructs

The diverse biomolecular landscape of tissue-specific decellularized extracellular matrix (dECM) biomaterials provides a multiplicity of bioinstructive cues to target cells, rendering them highly valuable for various biomedical applications. However, the isolation of dECM biomaterials entails cumbersome xenogeneic enzymatic digestions and also additional inactivation procedures. Such, increases processing time, increments costs and introduces residues of non-naturally present proteins in dECM formulations that remain present even after inactivation. To overcome these limitations, herein we report an innovative conjugation of light and ultrasound-mediated dECM biomaterial processing for fabricating dECM biomaterials. Such approach gathers on ultrasound waves to facilitate dECM-in-liquid processing and visible light photocrosslinking of tyrosine residues naturally present in dECM biomaterials. This dual step methodology unlocked the in-air production of cell laden dECM hydrogels or programmable dECM hydrogel spherical-like beads by using superhydrophobic surfaces. These in-air produced units do not require any additional solvents and successfully supported both fibroblasts and breast cancer cells viability upon encapsulation or surface seeding. In addition, the optimized photoacoustic methodology also enabled a rapid formulation of dECM biomaterial inks with suitable features for biofabricating volumetrically defined living constructs through embedded 3D bioprinting. The biofabricated dECM hydrogel constructs supported cell adhesion, spreading and viability for 7 days. Overall, the implemented photoacoustic processing methodology of dECM biomaterials offers a rapid and universal strategy for upgrading their processing from virtually any tissue. Statement of significanceLeveraging decellularized extracellular matrix (dECM) as cell instructive biomaterials has potential to open new avenues for tissue engineering and in vitro disease modelling. The processing of dECM remains however, lengthy, costly and introduces non-naturally present proteins in the final biomaterials formulations. In this regard, here we report an innovative light and ultrasound two-step methodology that enables rapid dECM-in-liquid processing and downstream photocrosslinking of dECM hydrogel beads and 3D bioprinted constructs. Such photoacoustic based processing constitutes a universally applicable method for processing any type of tissue-derived dECM biomaterials.

Continuous Integration of Risk Management in a Business Process Reengineering: Towards Optimization through Machine Learning

To improve their market position and competitiveness, organizations aim to shorten production times, cut costs, and boost the quality and variety of their products. While Business Process Reengineering (BPR) is a good method to achieve these goals, implementing it can be risky: any change to a task in the initial process directly affects the final process's performance in terms of time, cost, and quality. This article, drawing on design science, a literature review, and a field study with responses from executives and managers of two companies in the aerospace and automotive sectors in Morocco to measure their satisfaction and identify their risk management needs, provides an overview of the BPR method, its implementation frameworks and methodologies, and explains the importance of risk management in such projects. It suggests an improved continuous risk management process for BPR projects that enhances the gathering and use of risk management data through machine learning. Doi: 10.28991/ESJ-2024-08-03-019 Full Text: PDF

INNOVATIVE STRATEGIES TO IMPROVE THE QUALITY OF HUMAN RESOURCES THROUGH SKILL AND COMPETENCY DEVELOPMENT

Abstrack High-quality human resources (HR) represent invaluable assets for any organization, particularly in an era characterized by dynamic and competitive business environments. It is essential for companies not only to retain their human capital but also to continually enhance their quality. This enhancement encompasses not only technical skills but also encompasses comprehensive skill development and the nurturing of soft skills.This research adopts a literature review approach to delve into prior findings pertaining to the development of HR skills and competencies. Descriptive analysis is utilized to meticulously dissect library resources and scholarly materials. The primary objective of this research is to provide comprehensive insights into critical questions concerning objectives, implementation methodologies, and innovative strategies for fostering HR skills and competencies.By doing so, this research aims to equip organizations with valuable insights that can be instrumental in effectively managing HR, confronting rapidly evolving business challenges, and ultimately achieving corporate objectives. Through a thorough exploration of best practices and emerging trends in HR development, this research seeks to contribute to the body of knowledge that informs strategic HR management decisions in today's competitive landscape.

IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control.

Methodologies for the design of positive energy districts: A scoping literature review and a proposal for a new approach (PlanPED)

Cities can substantially contribute to European Green Deal targets by accelerating their transition towards smarter, more efficient, and renewable-based energy models. In that context, European cities need to increase their self-sufficiency and the resilience of their energy system especially through the promotion of district and neighbourhood scale energy projects such as positive energy districts (PEDs). Despite the increasing interest for PEDs experienced in the last years, their deployment is currently hindered by the absence of an energy planning culture and the lack of adequately skilled personnel in cities. Indeed, there is a strong need for practical tools and guidelines that support practitioners in the design and implementation of PEDs. In that context, the present paper introduces PlanPED, a framework for the conception of methodologies for PED planning, design, and implementation, to support municipalities in their energy transition. PlanPED aims at providing a multiple perspective approach based on the most important elements from existing methodologies. In that sense, the paper begins with an overview of the state of the art, leading to the identification of trends, gaps, and good practices of current research. Then, based on this analysis, the novel framework PlanPED is introduced. It consists of three interconnected workflows that, if followed, enable to know which steps and resources need to be put into practice to initiate the transition towards PED, while recognizing the variability of contexts and necessities. By doing so, PlanPED seeks to facilitate the elaboration of tailored and practical roadmaps for the deployment of PEDs in cities.

Smartbands and Behavioural Interventions in the Classroom: Multimodal Learning Analytics Stress-Level Visualisations for Primary Education Teachers

ABSTRACT Students’ stress levels may affect their well-being, attentiveness and learning outcomes in primary education classrooms. Positive behavioural interventions and support actions conducted by teachers may alleviate students’ stress levels, especially when addressing special educational needs. In this multimodal learning analytics study, students in a classroom were all given a smartband for their wrist during regular curriculum activities. Data comprised the semester of a single subject as a part of a research project conducted in Sweden. Biobehavioural stress-related arousal of students’ autonomic nervous system was visualised and analysed through distinguished behavioural modes. Additional data include naturalistic observational notes and two short teacher interviews. Research methodology and strategies for innovative implementation were presented and discussed alongside contextual details. For example, stress-level visualisations can aid actionable adjustments of behavioural intervention intensity and provide students’ attentiveness overview for teachers that sequence curricular activities during planning. Findings show an interdisciplinary basis for cost-effective real-time dynamic solutions that involve visual dashboards with advantages to understanding student learning, both at a school-wide system level and for the classroom, if viewed optimistically. However, research on the topic is still in its infancy, notably with ethical risks as a growing pain.

A novel robust black-box fingerprinting scheme for deep classification neural networks

With the growing value and high training cost of deep neural network (DNN) models, there has been increasing research interest in deep neural network model watermarking. However, the existing literature lacks robust model watermarking methods with retrospective functionality, which hinders multi-user distribution and fine-grained copyright verification of deep neural network models. To achieve the model watermarking with retrospective functionality that is generally denoted the model fingerprinting, this study proposes a novel robust black-box fingerprinting scheme for deep classification neural networks, in which a fingerprint that is unique for each user is inserted in the given DNN. Specifically, a DWT-DCT-SVD-based embedding method is introduced to construct a high-quality poisoned image by spreading the fingerprint as a poisoned trigger to the entire image, which both leads to the traceability of the to-be-protected DNN and significantly enhances the stealthiness and anti-forgery of the embedded poisoned trigger. As the amplitude of the embedded fingerprint is rather small, it poses a challenge that makes the model hard to capture features of the embedded fingerprint during the training process. To tackle this challenge, this study then proposes a poisoned feature enhancement module, which groups classification categories of clean images without the fingerprint as one type while taking the poisoned images with the correct fingerprint and the poisoned adversarial images containing the other incorrect fingerprints as another two different types. This module, which can be removed during fingerprint verification, improves the reliability and fidelity of the model fingerprinting. Accordingly, an adversarial training strategy is designed, aiming at enhancing the effective training of classification neural network models that contain the fingerprint with small embedding strength. Combining these strategies then gives rise to the proposed scheme, which is denoted the UfNet for notational convenience. Extensive experimental simulation results demonstrate the excellent invisibility of the fingerprint in the poisoned image and the desirable verification of the fingerprint in case of even a one-bit difference. Additionally, it also exhibits robustness against the related attacks such as STRIP, Neural Cleanse, Fine-Pruning, and GradCAM, which outperforms the state-of-the-arts (SOTAs). Furthermore, the proposed UfNet is robust to collusion attacks and frame the innocent. These results show the feasibility and effectiveness of the introduced model fingerprinting framework and the proposed implementation methodology in this paper.

Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

Interactive Batik & HR Incubation for Art-Technopreneurship: Mantraman Village Pilot Project

The implementation of this activity presents an innovative approach to promote the sustainability of the Javanese batik industry by developing interactive batik designs and fostering human resources in the arts and technopreneurship fields. The research project undertaken in Mantraman Village, Pagelaran, aims to brand Mantraman Village as a unique destination embodying Javanese Cultural Village characteristics. The cutting-edge approach combines age-old batik techniques with the latest in digital augmented reality technology. By doing so, it aims to boost visitor engagement and offer a hands-on experience in the batik design process. This implementation also focuses on fostering human resources in the arts and technopreneurship fields. These educational activities are designed to impart both theoretical knowledge and practical skills, empowering individuals to venture into or further develop their own art-technopreneurial pursuits. Training programs and workshops are conducted to develop participants’ skills and knowledge in batik design, business management, marketing, and related technological aspects. This implementation methodology involves conducting participatory surveys, observations, and interviews with relevant stakeholders, as well as testing and evaluating prototypes of interactive batik design systems. The data collected will be analyzed qualitatively and quantitatively to measure the project’s impact on increasing public interest, improving the quality of batik products, and developing human resources in the arts and technopreneurship fields. The results of the implementation of this activity made a positive contribution through an inventory of interactive batik motif designs with augmented reality technology, contributing to the development of the batik industry in Mantraman Village, Pagelaran Village as an iconic tourism village branding. Through these efforts, the project contributes positively to the sustainable growth of the batik industry and cultural branding of the village. Keywords: interactive, pattern, batik, mantraman village, pagelaran

The unfinished promise of infrastructure in post‐apartheid South Africa

AbstractThis article explores aspects of the “unfinished” using notions of human‐centered design in African public infrastructure and the importance of involving the “users” and “beneficiaries” in infrastructure development and delivery. Infrastructure, both conceptually as an idea and in its constructed material reality, has a huge impact on society, socially and economically, and has been promised as one of the most effective drivers of economic growth in South Africa. Increasingly in South Africa, facilities are falling into disrepair. Infrastructure is being adapted and used in unintended ways that often do not provide the socio‐economic benefits intended. In considering medical infrastructure across three sites in post‐apartheid South Africa, my argument asks how factors such as statecraft, governance and funding models, design considerations, project implementation methodologies, operational and maintenance policies affect the promise of infrastructural change in contemporary South Africa?

Optimizing crop yields while minimizing environmental impact through deep placement of nitrogen fertilizer

Nitrogen (N) serves as an essential nutrient for yield formation across diverse crop types. However, agricultural production encounters numerous challenges, notably high N fertilizer rates coupled with low N use efficiency and serious environmental pollution. Deep placement of nitrogen fertilizer (DPNF) is an agronomic measure that shows promise in addressing these issues. This review aims to offer a comprehensive understanding of DPNF, beginning with a succinct overview of its development and methodologies for implementation. Subsequently, the optimal fertilization depth and influencing factors for different crops are analyzed and discussed. Additionally, it investigates the regulation and mechanism underlying the DPNF on crop development, yield, N use efficiency and greenhouse gas emissions. Finally, the review delineates the limitations and challenges of this technology and provides suggestions for its improvement and application. This review provides valuable insight and reference for the promotion and adoption of DPNF in agricultural practice.

Unlocking the potential: a study on the role of operational excellence in Oman’s energy sector

PurposeOperational excellence (OpEx) is a proven philosophy focusing on continuous improvement in processes and systems for superior performance and efficiency. It plays a crucial role in the energy sector, acting as a catalyst for safety, customer satisfaction, sustainability and competitiveness. This research aims to assess OpEx methodologies in Oman’s energy sector, examining methods, approaches, motivations and sustainability.Design/methodology/approachThis study applies qualitative analysis methodology, involving interviews with 18 industry experts, from the energy sector in a sizeable energy country.FindingsThe analysis revealed a growing demand, particularly, in the oil and gas industry, driven by emerging business needs. Qualitative data analysis has identified 10 themes such as implemented methodologies, motivation drivers, deployment approaches, sustainability factors, benefits and challenges. Additionally, new themes emerged, including influencers to start OpEx, resource requirements, enablers for successful OpEx and systems.Research limitations/implicationsThis research was limited to Oman and the findings drawn from Omani energy companies may have limited applicability to energy companies in other regions. Therefore, if these findings were to be used, the validation of the findings in relation to other countries should be conducted, to ensure the validity of the context and outcome.Practical implicationsThese findings contribute to understanding OpEx dynamics in the Omani energy sector, offering valuable insights for effective utilisation and organisational goal achievement. Furthermore, the study offers valuable insights on how to effectively employ OpEx initiatives in the energy sector to achieve their goals and create value. It addresses the lack of knowledge, offers a framework for successful OpEx implementation, bridges the theory-practice gap and provides insights for optimal utilisation.Originality/valueTo the best of the authors’ knowledge, this is the first empirical study on assessing OpEx methodologies in the energy sector, and therefore it serves as a foundation for many future studies. The study provides a theoretical foundation for the OpEx methodologies in terms of organisational readiness for successful OpEx implementation.

Analysis of the influence of local electrodeposition conditions on the accuracy of electrochemical 3D-printing

Problem. Additive manufacturing of metal parts by local electrodeposition is a new promising area with several unique features. The use of electrolysis makes it possible to manufacture metal parts at room temperature with high accuracy and low energy consumption. The accuracy and speed of electrochemical 3D printing are inversely related, and therefore it is important to find optimal electrodeposition conditions at maximum speed without degrading accuracy. The aim of the study. The purpose of this work was to analyse the influence of electrodeposition parameters (electrode distance, electrical conductivity and electrolyte polarization) on the accuracy of metal deposit formation in a computer model and to conduct experimental verification of optimal conditions for electroforming a real object from copper sulphate electrolyte. Methodology of implementation. To achieve the goals of the study, computer simulation of the electrochemical deposition process in the COMSOL Multiphysics software and electrochemical measurements of the characteristics of copper sulphate electrolytes were used. Research results. Computer simulation established optimal conditions: the distance between the edge of the capillary and the surface on which deposition occurs is not more than 0.5 mm, the electrolyte in which the inverse slope of the cathode polarization curve is not lower than 2000 mA/(V×cm2) and the electrical conductivity is not higher than 0.02 S/сm. The influence of the electrolyte composition on the inverse polarization of the cathode process and its electrical conductivity was investigated. The optimal composition of the electrolyte was selected, containing 200 g/L CuSO4, 60 g/L H2SO4, 0.2 g/L KCl and RUBIN T-200 additive. In the selected electrolyte, the value of the inverse slope of the cathode polarization curve is 2120 mA/(V×cm2). Verification of the process of local electrodeposition in the selected electrolyte during the electroformation of a cylindrical object with a diameter of 4 mm and a height of 100 μm was carried out. It was determined that no more than 5 % of metal was deposited outside the capillary of the working electrode. Conclusions. The results of the work can be used to create an electrochemical 3D printing system. Further research should be aimed at approbation of the obtained parameters of local electrodeposition in an installation that simulates the operation of a 3D printer and establish the accuracy and speed of printing of a three-dimensional object. Key words: additive manufacturing, electrical conductivity, electrolyte composition, polarization, local electrodeposition.