Transformation Methodology Research Articles

Utilization of CRISPR-Cas genome editing technology in filamentous fungi: function and advancement potentiality.

Filamentous fungi play a crucial role in environmental pollution control, protein secretion, and the production of active secondary metabolites. The evolution of gene editing technology has significantly improved the study of filamentous fungi, which in the past was laborious and time-consuming. But recently, CRISPR-Cas systems, which utilize small guide RNA (sgRNA) to mediate clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas), have demonstrated considerable promise in research and application for filamentous fungi. The principle, function, and classification of CRISPR-Cas, along with its application strategies and research progress in filamentous fungi, will all be covered in the review. Additionally, we will go over general matters to take into account when editing a genome with the CRISPR-Cas system, including the creation of vectors, different transformation methodologies, multiple editing approaches, CRISPR-mediated transcriptional activation (CRISPRa) or interference (CRISPRi), base editors (BEs), and Prime editors (PEs).

A Comprehensive Framework for Face age Synthesis, Voice Transformation, and Cartoon Generation

This paper presents a comprehensive framework addressing age-invariant face recognition (AIFR), face age synthesis (FAS), voice transformation, and avatar generation. Traditional AIFR techniques focus on minimizing age-related variations in face recognition but lack visual interpretability, while FAS methods aim to synthesize faces of different ages but often compromise recognition due to artifacts. To address these limitations, we propose MTLFace, a multi-task learning framework that simultaneously handles AIFR and FAS tasks. MTLFace employs attention-based feature decomposition to separate identity and age-related features spatially, improving interpretability. Additionally, we introduce an identity conditional module for fine-grained face age synthesis, enhancing the naturalness of synthesized faces. Unlike conventional methods that achieve age group-level synthesis, our identity conditional module enables identity-level synthesis, resulting in smoother age transitions and preserving individual facial characteristics. Leveraging high-quality synthesized faces, we enhance AIFR performance via selective fine-tuning, where synthesized faces are used to augment training data, leading to improved robustness against age variations. Furthermore, we contribute a large cross-age face dataset with annotations, facilitating research in age-related tasks. In addition to AIFR and FAS, we explore limitations in existing methodologies for voice transformation and propose advancements to improve speech quality and address current challenges. Lastly, we introduce a novel approach for cartoon face generation, utilizing component-based facial feature extraction and template matching to produce diverse and stylistic cartoon faces. Experimental results across various benchmarks demonstrate the effectiveness of our approaches in achieving superior performance in face recognition, voice transformation, and cartoon face generation, thereby contributing to the advancement of multimodal synthesis technologies with potential applications in entertainment, virtual communication, and assistive technologies

Machine Learning-based Classification of Variable Stars

The classification of variable stars, essential for revealing information on stellar properties and cosmic distances, traditionally relied on statistical methods and limited data. With the emergence of transformative machine learning methodologies and large stellar surveys, we are able to perform more efficient, accurate, and robust handling of expansive databases. We deploy two different machine learning models—random forest and XGBoost—that effectively classify variable stars identified in the most recent phase of the Optical Gravitational Lensing Experiment using four features: (1) time of minimum brightness, (2) I-band amplitude, (3) mean or maximum I-band magnitude, and (4) period. The two models achieve a cross-correlation score of 99.00%, indicating largely shared classifications. The results produced by the models uncover new insights into the precision of supervised learning models in predicting variable stars with the aid of the most up-to-date survey data.

Biocatalytic Hydrogen-Borrowing Cascade in Organic Synthesis.

Biocatalytic hydrogen borrowing represents an environmentally friendly and highly efficient synthetic method. This innovative approach involves converting various substrates into high-value-added products, typically via a one-pot, two/three-step sequence encompassing dehydrogenation (intermediate transformation) and hydrogenation processes employing the hydride shuffling between NAD(P)+ and NAD(P)H. Represented key transformations in hydrogen borrowing include stereoisomer conversion within alcohols, conversion between alcohols and amines, conversion of allylic alcohols to saturated carbonyl counterparts, and α,β-unsaturated aldehydes to saturated carboxylic acids, etc. The direct transformation methodology and environmentally benign characteristics of hydrogen borrowing have contributed to its advancements in fine chemical synthesis or drug developments. Over the past decades, the hydrogen borrowing strategy in biocatalysis has led to the creation of diverse catalytic systems, demonstrating substantial potential for straightforward synthesis as well as asymmetric transformations. This perspective serves as a detailed exposition of the recent advancements in biocatalytic reactions employing the hydrogen borrowing strategy. It provides insights into the potential of this approach for future development, shedding light on its promising prospects in the field of biocatalysis.

A rapid and efficient Agrobacterium-mediated transient transformation system in grape berries.

Transient transformation is extremely useful for rapid in vivo assessment of gene function, especially for fruit-related genes. Grape berry, while an important fruit crop, is recalcitrant to transient transformation, due to the high turgor pressure in its mesocarp cells that limits the ability of Agrobacterium to penetrate into the tissue. It is urgent to establish a simple transient transformation system for rapid analysis of gene function. In this study, different injection methods, grape genotypes, and developmental stages were tested in order to develop a rapid and efficient Agrobacterium-mediated transient transformation methodology for grape berries. Two injection methods, namely punch injection and direct injection, were evaluated using the β-glucuronidase (GUS) gene and by x-gluc tissue staining and 4-methylumbelliferyl-β-D-glucuronide fluorescence analysis. The results indicated that there were no significant differences on transformation effects between the two methods, but the latter was more suitable because of its simplicity and convenience. Six grape cultivars ('Hanxiangmi', 'Moldova', 'Zijixin', 'Jumeigui', 'Shine-Muscat', and 'A17') were tested for transient transformation. 'Hanxiangmi', 'Moldova', and 'Zijixin' grape berries were not suitable for agroinfiltration due to frequently fruit cracking, browning, and formation of scar skin. The fruit integrity rates of 'Jumeigui', 'Shine-Muscat', and 'A17' berries were all above 80%, and GUS activity was detected in the berries of the three cultivars 3-14days after injection with the Agrobacterium culture, while higher GUS activities were observed in the 'Jumeigui' berries. The levels of GUS activity in injected berries at 7-8weeks after full blooming (WAFB) were more than twice at 6 WAFB. In subsequent assays, the over-expression of MYB transcription factor VvMYB44 via transient transformation accelerated the anthocyanin accumulation and fruit coloring through raising the expression levels of VvLAR1, VvUFGT, VvLDOX, VvANS, and VvDFR, which verified the effectiveness of this transformation system. These experiments finally identified the reliable grape cultivars and suitable operational approach for transient transformation and further indicated that this Agrobacterium-mediated transient transformation system was efficient and suitable for the elucidation of gene function in grape berries.

Analysis of Marchantia polymorpha-microorganism interactions: basis for understanding plant-microbe and plant-pathogen interactions.

Marchantia polymorpha is a bryophyte gaining significance as a model plant in evolutionary studies in recent years. This is attributed to its small-sequenced genome, standardized transformation methodology, global distribution, and easy and rapid in vitro culturing. As an evolutionary model, M. polymorpha contributes to our understanding of the evolution of plant defensive responses and the associated hormonal signaling pathways. Through its interaction with microorganisms, M. polymorpha serves as a valuable source of knowledge, yielding insights into new microbial species and bioactive compounds. Bibliographic analysis involved collecting, reading, and categorizing documents obtained from the Scopus and Web of Science databases using different search terms. The review was based on 30 articles published between 1995 and 2023, with Japanese and Spanish authors emerging as the most prolific contributors in this field. These articles have been grouped into four main themes: antimicrobial metabolites produced by M. polymorpha; identification and characterization of epiphytic, endophytic, and pathogenic microorganisms; molecular studies of the direct interaction between M. polymorpha and microorganisms; and plant transformation using bacterial vectors. This review highlights the key findings from these articles and identifies potential future research directions.

Global Food Security and Sustainability Issues: The Road to 2030 from Nutrition and Sustainable Healthy Diets to Food Systems Change.

The accomplishment of food/nutrition security for all across sustainable food systems (SFS) is tied to the Sustainable Development Goals (SDGs). SFS is connected to all SDGs via the traditional framework of social inclusion, economic development, environmental safety, inclusivity, and the development of sustainable food systems. We suggest that, for the world to achieve sustainable development, a shift to SFS is necessary to guarantee food/nutrition security for all, while operating within planetary boundaries to protect ecosystems and adapt to and mitigate climate change. Therefore, there is a requirement for original approaches that implement systemic and more participatory methods to engage with a wider range of food system stakeholders. However, the lack of skills and tools regarding novel methodologies for food system transformation is a key obstacle to the deployment of such approaches in practice. In the first part of this review, a summary of some challenges that occur in the governance of food system transformation is given. Through a case study of plant-based proteins and their biological and chemical modification as diets shift towards alternative proteins, we demonstrate that resource-efficient food systems and food waste, through system transformation, are useful in understanding both (i) how food system transformation has ensued and (ii) how the required transformation is prohibited. Finally, we discuss the implications of food system transformation in terms of nutrition and sustainable healthy diets, which are needed to achieve changes in food safety systems in the future. The linkage of food and the environment is evident, focusing on nutrition and sustainable healthy diets. This cannot be accomplished without system change and research towards new foods and, more specifically, new proteins such as plant-based ones and their biological and chemical modification.

Optimization of in planta methodology for genome editing and transformation in Citrus.

Genetic transformation of many plant species relies on in vitro tissue culture-based approaches. This can be a labor-intensive process, requiring aseptic conditions and regenerating often recalcitrant species from tissue culture. Here, we have optimized an in planta transformation protocol to rapidly transform commercial citrus cultivars, bypassing the need for tissue culture. As a proof of concept, we used in planta transformation to introduce CRISPR/Cas9 constructs into Limoneira 8A Lisbon lemon and Pineapple sweet orange, cultivars that are challenging to transform with conventional techniques. Using our optimized protocol, the regeneration rate was significantly increased from 4.8% to over 95%, resulting in multiple gene-edited lines in lemon. We also successfully recovered gene-edited Pineapple sweet orange lines using this protocol; the transformation efficiency for these cultivars ranged between 0.63% and 4.17%. Remarkably, these lines were obtained within three months, making this in planta protocol a rapid methodology to obtain transformed citrus plants. This approach can rapidly and effectively introduce key genetic changes into a wide variety of citrus cultivars.

Direct synthesis of carbamates, thiocarbamates, and ureas from Boc-protected amines: a sustainable and efficient approach.

In the quest for sustainable and efficient synthetic methodologies within medicinal chemistry, the synthesis of carbamates and their derivatives holds a pivotal role due to their widespread application in bioactive compounds. This investigation unveils a novel methodology for the straightforward transformation of Boc-protected amines into carbamates, thiocarbamates, and ureas, utilizing tert-butoxide lithium as the sole base. This approach effectively obviates the necessity for hazardous reagents and metal catalysts, presenting marked enhancements compared to traditional synthetic pathways. Notably, the method demonstrates facile scalability to gram-level production. This study contributes to the advancement of sustainable synthetic methodologies, offering a more benign and efficient alternative for the synthesis of key chemical intermediates with implications for broad pharmaceutical and agrochemical applications.

Проектирование человека в ФРГ и ГДР в 1970-е годы: основные подходы и практики

The article analyzes theoretical approaches to understanding human nature and the prospects for its devel-opment in West Germany (the Federal Republic of Germany, or FDR) and East Germany (the German Demo-cratic Republic, or GDR) in the 1970s. The term “engineering” in this context refers not only to governmental agendas that dictate human transformation goals and methodologies, but also to scholarly inquiries and discus-sions on the subject. Philosophers, sociologists, and educators’ manifestos and writings offer evidence of these changes and help reconstruct social engineering projects. They also identify the theoretical foundations behind visions of the future. In West Germany, there was a shift away from the religious-philosophical understanding of humanity as inherently predisposed to kindness, which had been prevalent in the 1960s. There was also a lot of debate in the 1970s about whether pacifism could be instilled in inherently aggressive individuals. During this time, education also became more politicized, with teachers aiming to shape a worldview centered on key liberal values such as freedom, democracy, and tolerance. At the same time, two projects of humanity were implement-ed in East Germany. The first one was the state project, which aligned with the idea of socialist building and emphasized that only socialism could fully guarantee peace worldwide. The second project was implemented by the evangelical church and educated young people about peace in a way that reflected a different understanding of humanity similar to Christian humanitarian pedagogy. By the 1970s, such ideas had lost their popularity in West Germany.

A New Approach to Enhance Smart City Applications Through Integrating GIS BIM Technology

The research deals with the study and analysis of smart cities, the requirements for their establishment, and the role of Building Information Modeling (BIM) in building sustainable, energy-efficient smart cities. The primary objective is to put a smart city transformation methodology and apply it on Lattakia city as a case study, using Building Information Modeling (BIM). We proposed new tools in an integrated methodology to transform from ordinary to smart city, and implemented basics of its objects, including GISBIM technologies integration, insuring both sustainable and energy saving vision at general planning designs. Implementing the chosen methodology steps at an organized area in Syria – Lattakia City, starting with building an accurate geodatabase, then, obtaining Sun Shadow Frequency analysis, which shows that December contains the highest shade values, with cumulative coverage rate 79.84%, while June instead contain the lowest shade value, reaching only 33.84%. But in general, most of the area has shade coverage that does not exceed 15% most of the day, therefore no need to make an adjustment to the distribution of building blocks. After that, in order to secure the energy needed for the study Area, analyzing the Land Surface Temperature (LST) done using the ArcGIS Pro® software, and locating a renewable energy station site (solar plant). On the other hand, analyzing a residential building energy through annual PV analysis has been done, by using Revit® software through Insight™ extension, with two cases: first one, building without changing building material, while second one done after changing building material to save solar energy inside and rising roof slope up to 10%, and we achieve valuable energy savings by applying this modification to building material. And finally, introducing the infrastructure underground network, that serving transformation to smart city.

The ethics of knowledge sharing: a feminist examination of intellectual property rights and open-source materials in gender transformative methodologies.

Debates on intellectual property rights and open source frequently stem from the business sector and higher education, where goals are typically oriented toward profit, academic status, credit, and/or reputation. What happens if we reconsider the ethics of intellectual property rights and open source when our driving motivation is advancing women's health and rights? How does this prioritization complicate our assumptions of copyright and open access? How can we embark on a journey that validates the complex realities of multiple stakeholders who have good intent, but do not always consider the unintended impacts and the broader power dynamics at play? This paper explores the tensions and nuances of sharing methodologies that aim to transform harmful gender norms in an ecosystem that does not always consider the complex challenges behind intellectual property and open-source material. As a thought-collective dedicated to using a feminist approach to unpack and promote the principles of ethical, effective, and sustainable scale, we hope to underscore how the current research and debates on intellectual property rights and open-source material have good aims but may also fall short in encompassing the realities of gendered social norms change in and with communities around the world. We conclude with key recommendations for donors, researchers, International Development Corporations, International Non-Governmental Organizations, and those interested in using or adapting dynamic, gender transformative materials created by others.

Real-Time Noise Suppression Method Using Deep Learning Algorithm

In this study, we present an innovative approach to real-time facial feature detection utilizing the MTCNN (MultiTask Cascaded Neural Network) deep learning architecture. Unlike traditional methods, our novel framework combines advanced techniques to achieve unparalleled precision and efficiency in facial feature localization. Through a meticulous exploration of MTCNN's capabilities, we unveil a transformative methodology that significantly enhances the speed and accuracy of real-time facial detection. Our research focuses on pushing the boundaries of existing facial recognition technologies, introducing a fresh perspective on the application of MTCNN. By leveraging its unique architecture, we not only address the challenges associated with real-time detection but also enhance the overall robustness of the system. The proposed approach showcases the untapped potential of MTCNN, establishing it as a key player in the realm of facial feature detection. Through rigorous experimentation and evaluation, we demonstrate the superiority of our approach over conventional methods, highlighting its effectiveness in diverse scenarios. This work contributes to the ongoing evolution of deep learning applications in computer vision, with implications for security, surveillance, and various human-computer interaction domains. Our findings open new avenues for researchers and practitioners seeking cutting-edge solutions in the dynamic field of real-time facial feature detection.

Semi‐Active Vibration Control of the Thickening Mill Equipment Based on the MR Damper

The thickening mill equipment installed on the second floor is damage for the structural and personal health. In this study, the vibration conditions of thickening mill equipment and its vibration isolating based on MR damper technology are investigated experimentally and simulacrum. First, the vibration test of thickening mill equipment installed on rigid foundations is operated, and the results show that while the rigid foundation can suppress equipment vibration, it also deteriorates vibration transmission to the ground, which results in vibration displacement of the ground exceeds the permissible range by 120%. Second, a MR damper utilized to form the elastic foundation is tested to acquire the force–velocity characteristics, which is described by the proposed Bingham model. The comparison results between the model predicted and measurement data delicate that can effectively describe the correlation between the dynamic properties of MR damper and the coil current/excitation conditions. Finally, an elastic foundation consisting of eight coil springs and several MR dampers is proposed to replace the rigid foundation of the thickening mill equipment. Together with the ON–OFF skyhook control algorithm, the properties of the elastic foundation to reduce the force transmission rates are tested based on the simulation model. The simulation results suggest that the designed elastic foundation can effectively reduce the vibration transmission from the thickening mill equipment to the ground, which can decrease the peak force transmission rate by 95.5%. This study provides a guide to technological transformation methodology of the thickening mill equipment to reduce structural vibration.

A Praxis/future-driven Inclusive Science Learning Through STEAM-based Design Thinking Project

Engaging the prospective science teacher in a praxis/future-driven orientation holding transformative STEAM-based design thinking project designing and implementing approach is new in the Nepalese science teacher education program. It could support critically reflecting on- action and in-action for reducing the gap between theory and practice for action. This paper attempted to explore the research question: How could I engage prospective science teachers in a STEAM-based design thinking project for a praxis/future-driven inclusive science learning? So, the transformative action research methodology has been launched in my workplace purposively by selecting eight research participants. I designed and implemented the STEAM-based design thinking project in my science classroom. It motivated my research participants to know this approach's conceptual, theoretical, and practical dimensions by actively participating in critical discourse. I viewed my and my participants' feelings, beliefs, and actions from the theoretical lens of critical-reflexive Bildung, which has the learners' high content and skills development orientation. It helped me to visualize the deep-rooted disempowering forces and the viable alternative ways for action. Moreover, it invited me to change my cultural reproduction-dominated science teaching. This research also envisages a curriculum as a mandala, pedagogy as/for the public good, and assessment as/for learning for praxis/future-driven science classrooms.

Enterprise Transformation Projects

This article illustrates the role and how to design and implement an Enterprise Transformation Projects (ETP) in-house Polymathic Security Learn Process (Concept) that can use the previous ETP’s Holistic Security Concept (ETP-HSC) [1] with a major change, that includes a generic and holistic transcendent Artificial Intelligence (AI) based ETP-PSLP Concept (simply the Concept). The Concept includes interfaces to practically all AI and related technology domains, like Author’s AI modules, Machine Learning (ML), Deep Learning (DL), Big Data (BD), Data Sciences (DS), and others (simply Intelligence). The Concept uses the author’s Polymathic transformation framework and methodology that in this article focuses on ETP’s security by using Polymathic Learning Processes (PLP) [2]. ETPs are complex and have very high level of failure rates (at about 95 percent), and due to this fact, adding difficulties that are related to Security frameworks, Interfaceable AI products, inexistent PLP approaches, can result in a siloed and unmaintainable ETP’s integration. Such a transformation becomes unmanageable. The Concept ensures enterprise’s (simply an Entity) security, business long-term sustainability, and operational excellence, by applying the PLPs to solve encountered problems. ETP’s main problem is in the acceptance of a PLP and Concept [2]. The Concept shows how an ETP integrates PLP based Intelligence to support and resolve Entity’s security breaches. The PLP is supported by the author’s (today usable) Applied Holistic Mathematical Model (AHMM) for PLP based AI (AHMM4AI). The AHMM4AI is the result of many years in research and development in the fields of security, AI, business, financial and organizational engineering, and applied mathematical models. This article is cross-functional research and on an authentic mixed-research method, the Heuristic Decision Tree (HDT) that is ETP’s internal decision-making motor [3,7]. The AHMM4AI based Concept supports: 1) A mixed-method empirical Decision-Making System (DMS); 2) A well-established Action Research (AR) (ideal for PLPs) method for Concept; and 3) A framework for a successful finalization of secured ETPs. The Concept is a new block in the author’s Research and Development Project (RDP) and is a continuation with the aim is to offer an example of an In-House Implemented (IHI) Transformation Framework (IHITF). The Concept includes many of the author’s research works on the applications of ETPs (simply a Project), security concepts, AI, Cloud Services (CS), and Mathematical Models (MM).

Integrated business model of sustainable development for agricultural sector

Purpose. The purpose of the article is to improve the methodology of the integration process of business model to ensure environmental, social and financial results of entrepreneurial activity taking into account the interests and needs of stakeholders and to study the possibilities of implementing sustainable practices in different segments of the business model and the sustainable value created by these transformations. Results. The study highlights a number of approaches to the methodology of creating sustainable business models, which are the Business Model Canvas, the Teece Business Model, the Ecosystem Pie Model, and the Sustainable Value Creation Model. Each of the methodologies has its advantages and disadvantages, but they all have common elements that can be combined and integrated when considering industry specifics. The author used the methodology of creating sustainable value, which is formed through the implementation of new approaches to the implementation of sustainability, in particular, the methods of ecodesign, climate-smart agriculture, organic farming, biodynamic agriculture, sustainable intensification and renewable agriculture. Sustainable value is created through activities in different segments of the business model, at the same time the Business Model Canvas (BMC) is a template for mapping the transformations in each segment. The integrative business modeling approach is able to provide a detailed qualitative overview of a specific business by combining social, environmental and economic components with stakeholder theory. Therefore, taking into account the needs of a wider range of stakeholders than is provided for in the "key partners" field of the Canvas model took place through the implementation of sustainable value in the complex Ecosystem Pie Model. Scientific novelty. The scientific novelty consists in the adaptation of the methodology of transformation of traditional business models into sustainable ones for the agricultural sector, which, unlike the existing ones, allows taking into account sustainable value through new sustainable approaches that cause changes in the fields of Business Model Canvas with the subsequent transfer of their individual elements to the Ecosystem Pie Model for a systematic visual perception of both the current situation and prospects for further development for the enterprise. Practical value. The results of the research can be used by scientists, representatives of business and state institutions dealing with the issue of introducing sustainability principles into existing traditional business models, based on the needs and interests of stakeholders.

INNOVATIONS IN PROJECT MANAGEMENT: TRENDS AND BEST PRACTICES

In an era where technological disruptions and methodological innovations are reshaping project management, this study delves into the evolving dynamics of the field. The paper aims to explore how advancements in technology and the adoption of new methodologies are influencing project management practices, with a particular focus on the integration of digital transformation, artificial intelligence, and agile methodologies. Employing a systematic literature review, the study meticulously analyzes a range of contemporary sources, including case studies and academic research, to provide a comprehensive understanding of the current trends and challenges in project management. The scope of the paper encompasses the impact of cutting-edge technologies like AI, machine learning, and big data analytics on decision-making processes, the effectiveness of virtual and augmented reality in project planning, and the crucial role of emotional intelligence and leadership in ensuring project success. The main findings reveal a significant shift towards more flexible, technology-driven project management practices. The study highlights the necessity for project managers to adapt to these changes by developing skills in digital technologies and innovative practices. It also underscores the importance of strategic alignment of project management practices with organizational goals and stakeholder expectations. Conclusively, the study recommends fostering a culture of innovation and flexibility within organizations to navigate the complexities of modern project environments effectively. It emphasizes the need for continuous learning and skill development to keep pace with technological advancements and advocates for a strategic approach to the adoption and implementation of new project management methodologies.
 Keywords: Project Management, Digital Transformation, Agile Methodologies, Technological Advancements

Enhanced methodology for graphene oxide transformation: Unraveling the effects of ultrasonication on morphological and structural characteristics

Enhanced methodology for graphene oxide transformation: Unraveling the effects of ultrasonication on morphological and structural characteristics

Automatic model generation for material flow simulations of Third-Party Logistics

AbstractThe use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system’s functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems.