Transformation Framework Research Articles

A framework for curriculum transformation in quantum information science and technology education

The field of Quantum Information Science and Technology (QIST) is booming. Due to this, many new educational courses and university programs are needed in order to prepare a workforce for the developing industry. Owing to its specialist nature, teaching approaches in this field can easily become disconnected from the substantial degree of science education research which aims to support the best approaches to teaching in Science, Technology, Engineering and Mathematics fields. In order to connect these two communities with a pragmatic and repeatable methodology, we have synthesised this educational research into a decision-tree based theoretical model for the transformation of QIST curricula, intended to provide a didactical perspective for practitioners. The Quantum Curriculum Transformation Framework consists of four steps: (1) choose a topic, (2) choose one or more targeted skills, (3) choose a learning goal and (4) choose a teaching approach that achieves this goal. We show how this can be done using an example curriculum and more specifically quantum teleportation as a basic concept of quantum communication within this curriculum. By approaching curriculum creation and transformation in this way, educational goals and outcomes are more clearly defined which is in the interest of the individual and the industry alike. The framework is intended to structure the narrative of QIST teaching, and with future testing and refinement it will form a basis for further research in the didactics of QIST.

A local-level agricultural transformation in the making? The case of the cassava industry in the Ashanti and Volta regions of Ghana

ABSTRACT In sub-Saharan Africa, Ghana is recognized as one of the countries undergoing rapid economic transformation. Drawing on an agricultural transformation framework, this study sheds light on the current farm and nonfarm developments in the cassava subsector in five communities across four districts in Ghana. Using a qualitative exploratory research design based on field-work surveys, the study identifies several factors that together showcase manifestations of a transformation process in the studied areas. These include a combination of growing farm sizes, use of new technologies, higher productivity, expanding market opportunities, increased profitability, improved lives and livelihoods, and changing labour structures. These micro-level findings allow the study to offer a richer understanding of dynamics of change underway that could complement macro-oriented analyses of agricultural transformation in Africa.

Towards a transformation approach from SecureUML to ‎Alloy

In critical information systems, Role-Based Access Control (RBAC) has been frequently used. The specification of access control policies is an essential step in securing information systems. This article presents a transformative approach for addressing design challenges within these specifications using Model-Driven Architecture (MDA). A method is introduced for specifying and verifying model-based access control (RBAC) rules by combining SecureUML and Alloy, referred to as SecureUML2Alloy. This approach leverages transformation techniques based on metamodels to express the SecureUML source model in the Alloy target model. The SecureUML2Alloy framework allows for the generation of an initial Alloy code that can be extended with facts, functions, and predicates depending on the case. The transformative framework offers a reliable, automated mechanism for verifying security specifications in critical systems, promoting both theoretical and practical advancements in model transformation and formal verification. This method provides a pathway for future automation tools in secure system design and analysis, contributing to the reliability and security of software systems across multiple fields.

Bridging Experiential Learning and Spiritual Wisdom: Integrating Kolb’s Cycle with the Four Noble Truths of Buddhism

Purpose. This study aims to explore the potential integration of Kolb's Experiential Learning Cycle with the Four Noble Truths of Buddhism to offer a comprehensive framework for personal growth and transformation. Methodology. A qualitative analysis was conducted, drawing on existing literature on Kolb's learning theory and Buddhist philosophy. The study focused on identifying parallels and synergies between Kolb's four-stage learning cycle and the Four Noble Truths, elucidating how their integration could enhance understanding and application of spiritual principles in daily life. Results. The analysis revealed significant alignment between Kolb's cycle and the Four Noble Truths. Reflective observation corresponds to the acknowledgment of suffering (First Noble Truth), the identification of its causes (Second Noble Truth). Abstract conceptualization mirrors the understanding of the cessation of suffering (Third Noble Truth) through wisdom and insight into the nature of existence. Active experimentation resonates with the Noble Eightfold Path (Fourth Noble Truth), emphasizing the practical application of ethical conduct, mental cultivation, and wisdom development to alleviate suffering. The integration of Kolb's Experiential Learning Cycle with the Four Noble Truths offers a holistic approach to personal growth and transformation. By combining experiential learning with spiritual wisdom, individuals gain a deeper understanding of the causes of suffering and the path to liberation. This integrated framework provides a structured methodology for cultivating resilience, wisdom, and compassion in daily life, fostering holistic development and contributing to greater well-being and fulfillment. Conclusion. Further research and practical application of this framework holds promise for enhancing individual and collective flourishing in diverse cultural and educational contexts.

Customer-Centric digital transformation framework: Enhancing service delivery in SMES for underserved populations

This paper introduces a customer-centric digital transformation framework designed to enhance service delivery in Small and Medium Enterprises (SMEs) catering to underserved populations. As digital technology becomes increasingly essential in today’s economy, SMEs face the challenge of integrating these tools effectively to serve marginalized communities. This review explores how a customer-centric approach can enable SMEs to create inclusive digital solutions that meet the specific needs of underserved populations, ultimately driving economic empowerment and social equity. The proposed framework encompasses three core components: accessibility, personalization, and engagement. Accessibility ensures that digital platforms are designed with user-friendly interfaces that accommodate individuals with varying levels of digital literacy. By prioritizing mobile optimization and providing resources in local languages, SMEs can eliminate barriers to entry and facilitate greater participation among underserved customers. Personalization is a key element that allows SMEs to tailor their offerings based on customer insights and preferences. Utilizing data analytics and feedback loops, businesses can gain a deeper understanding of their customers' needs, enabling them to deliver customized products and services that resonate with underserved populations. This tailored approach not only enhances customer satisfaction but also fosters loyalty and trust within these communities. Engagement emphasizes the importance of fostering meaningful relationships between SMEs and underserved populations. By leveraging digital channels such as social media, mobile applications, and community forums, SMEs can create platforms for two-way communication and collaboration. This engagement empowers communities, allowing them to voice their needs and influence the development of products and services. In conclusion, the customer-centric digital transformation framework presented in this paper serves as a strategic guide for SMEs seeking to enhance service delivery for underserved populations. By focusing on accessibility, personalization, and engagement, SMEs can drive economic inclusion, promote social equity, and contribute to sustainable community development. This approach not only benefits the businesses involved but also creates lasting positive impacts within underserved communities.. Keywords: Customer-Centric, Digital Transformation, Smes, Underserved Populations, Accessibility, Personalization, Engagement, Economic Empowerment, Social Equity.

EEG-based patient-specific seizure prediction based on Spatial–Temporal Hypergraph Attention Transformer

Background and Objectives:Patient-specific seizure prediction based on Electroencephalogram (EEG) is a challenging and significant neuromedicine study, which could potentially safeguard drug-resistant patients from unforeseen risks in their daily lives. However, prevalent seizure prediction frameworks based on convolutional neural networks (CNNs) prioritize local information while overlooking long-range global dependencies and high-order spatial correlations prevalent among brain leads. Methods:To address the limitations above, we propose a novel Spatial–Temporal Hypergraph Attention Transformer (STHAT) framework for extracting the key features in EEG signals. Aimed at the characteristics of epilepsy EEG data, our framework comprises two primary branches: (1) Long-range Temporal Feature Dependencies: This branch aims to capture temporal dependencies within EEG signals by leveraging Swin Transformer and 2D-CNN. Utilizing shifting windows and convolution operations, we encode local and global content dependencies within segmented EEG samples. (2) High-Order Spatial Information Correlations: We construct a raw graph structure using the Pearson correlation coefficient, enabling the extraction of initial node embeddings through graph convolution operations (GCO). Subsequently, a dynamic hypergraph is formed using K-Nearest Neighbors (KNN) and K-means clustering, allowing us to employ a Hypergraph Attention Network (HAN) to capture structural context relationships among brain regions. By integrating information from both branches, we utilize a linear layer to derive the final seizure prediction outcome. Experiment and Results:Extensive evaluations are conducted on the widely used CHB-MIT dataset. Our comprehensive experimental results demonstrate the effectiveness of the proposed framework in achieving superior performance in automatic seizure data prediction, outperforming state-of-the-art algorithms. The STHAT framework effectively exploits both temporal and spatial information within EEG signals, enhancing seizure prediction accuracy.

Digital Transformation Strategy and Management

This literature review explores the evolving landscape of digital transformation strategy and management, emphasizing the critical role of strategic alignment and leadership in facilitating successful transformation initiatives. As organizations navigate the complexities of digital change, understanding the key drivers, barriers, and best practices becomes essential for achieving long-term competitive advantage. This review synthesizes existing research on digital transformation frameworks, management approaches, and the importance of fostering a culture of innovation and collaboration. It highlights the need for a comprehensive strategy that integrates technology investments with organizational goals, ultimately guiding firms toward effective adaptation in the digital economy. Keywords: Digital Transformation, Strategy, Management, Leadership, Innovation, Competitive Advantage.

Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

Building a culture of excellence by transforming toxic corporate environments

The article explores the profound impact of corporate culture on organizational performance and employee well-being. It delves into the detrimental effects of toxic corporate culture, characterized by poor leadership, ineffective communication, high-stress levels, and unethical behavior, which collectively undermine productivity and morale. The study also examines the significant economic impact of toxic corporate culture, including decreased productivity, high employee turnover, increased absenteeism, reputational damage, legal and ethical costs, and reduced innovation. The article proposes a comprehensive framework for cultural transformation, emphasizing the importance of leadership development, emotional intelligence training, open communication, recognition and rewards, work-life balance initiatives, diversity and inclusion programs, conflict resolution training, and continuous learning and development. By implementing these evidence-based strategies, organizations can transition from toxic environments to thriving, healthy workplaces that foster excellence and sustainable success. This approach not only enhances employee satisfaction and engagement but also drives innovation and economic growth within the organization.

Exploring worker subjectivity: shaping industrial and organizational psychology in post-apartheid South Africa

This article critically evaluates the role of industrial–organizational psychology in post-apartheid South Africa, advocating for a transformative framework that addresses the socio-political and historical inequalities in the workplace. The article highlights how industrial–organizational psychology has traditionally focused on organizational efficiency, often aligned with capitalist interests, while overlooking the socio-historical forces shaping worker experiences, particularly related to race, gender, and class. By introducing the concept of worker subjectivity, the article calls for a reimagined industrial–organizational psychology that integrates critical perspectives from psychology and management studies to better understand how workers navigate their identities and agency within broader socio-political structures. The author emphasizes the need to address the emotional and psychological dimensions of worker experiences, especially in the South African context marked by the legacies of colonialism and apartheid. By focusing on worker subjectivity, industrial–organizational psychology can move beyond traditional quantitative methods and engage with the complexities of workplace inequality. This approach aligns industrial–organizational psychology with the broader mission of the Psychological Society of South Africa to promote socially just and inclusive practices. The article also calls for interdisciplinary collaboration and the reformation of industrial–organizational psychology education to better equip practitioners with the tools needed to address the realities of post-apartheid workplaces.

ADA-UDA: A transferable transformer framework for rumor detection using Adversarial Domain Alignment within Unsupervised Domain Adaptation

The rapid spread of misinformation on social media has posed significant challenges, particularly in the early detection of rumors, which is critical to mitigating their negative impact. However, the limited data available during the initial stages of trending topics poses significant challenges, including analysis constraints and model overfitting. Therefore, the adoption of specific methods are needed to identify patterns from historical sample data and enable effective knowledge transfer, facilitating inductive reasoning with limited data on new topics. To address these challenges, we propose an Adversarial Domain Alignment Unsupervised Domain Adaptation (ADA-UDA) method based on the Transformer architecture. Our approach leverages labeled historical data from the source domain alongside a limited quantity of unlabeled data from new trending topics in the target domain. At the heart of our method is the Parameter Transferable Module (PTM), which guides the Transformer to focus on transferable and distinguishable features, thereby enhancing the model’s ability to perform effective inductive reasoning with limited data. We conducted extensive experiments to evaluate our method, benchmarking it against current mainstream rumor detection techniques. The findings indicate that our ADA-UDA method outperforms existing approaches, underscoring its potential for early and accurate rumor detection in emerging topics.

A Novel Detection Transformer Framework for Ship Detection in Synthetic Aperture Radar Imagery Using Advanced Feature Fusion and Polarimetric Techniques

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security.

Democratising the Music Curriculum: Integrating Ubuntugogy for the Africanisation of South African Higher Education

In recent years, there has been an ongoing debate about the need for redress in South African higher education which is largely informed by Western perspectives, and the music curriculum is no exception. Even though there is growing progress, music education continues to be dominated by the Western classical canon. Therefore, the purpose of this article was to provide a comprehensive investigation of how ubuntugogy can be successfully and effectively merged into the South African higher education music curriculum. The researcher used ubuntugogy as a theoretical framework to advocate for the integration of ubuntugogy—a teaching philosophy rooted in African values and communal learning—to democratise and Africanise the music curriculum in South African higher education. This article used a qualitative content analysis grounded in examining existing literature on a similar subject under the guidance of the research questions developed earlier. The findings of this study show that ubuntugogy provides an approach that can Africanise the curriculum and pedagogy by incorporating indigenous musical practices, oral traditions, and cultural values into the teaching and learning process. The results of this article revealed that applying ubuntugogy as a method of teaching and learning can help in preparing music education students to engage with the world from a culturally informed and empowered perspective. This article concludes by affirming that ubuntugogy as a method of teaching and learning provides a transformative framework for Africanising music education in South African higher education.

SOME LEGAL ASPECTS OF THE IMPLEMENTATION OF THE EUROPEAN GREEN DEAL IN THE CONTEXT OF THE TRANSITION TO THE USE OF RENEWABLE ENERGY SOURCES IN UKRAINE

The article is devoted to the study of the legal aspects of the European Green Deal implementation in Ukraine, in particular in the context of the energy sector transformation the by increasing the share of renewable energy sources. There have been examined the key trends in the legal development of renewable energy and determines the environmental challenges caused by climate change and other negative factors affecting the environment, as well as analyzes the potential possibilities of adapting national environmental legislation to European legal standards in the field of renewable energy use. The author emphasizes the economic, social and environmental benefits of achieving the goals of the European Green Deal on the way to ensuring the sustainable development of Ukraine, calling for the creation of a clear and coherent system of legislation dedicated to the legal regulation of renewable energy in Ukraine. The results of the study points the urgency of creating an effective legal framework for the economy green transformation and the adaptation of energy and environmental policies to European requirements and standards. In the context of modern global environmental problems related to climate change, excessive pollution of atmospheric air, water and forest ecosystems, soil degradation as a result of both increasing anthropogenic load on natural resources and due to the development of objective laws of nature, one of the key priorities European environmental policy outlined the transition to the principles of sustainable development, which, in turn, generated the need to search for optimal mechanisms and concepts for ensuring such a transition. The European Green Course, adopted by the EU in 2019, is an ambitious and progressive strategy for the transformation of the European continent into a climate-neutral one by 2050. As a comprehensive conceptual package of legislative initiatives, to achieve this goal, the European Green Course includes not only the reduction of greenhouse gas emissions into the atmosphere, but also a significant increase in the share of renewable energy sources, an increase in the energy efficiency of European countries, and the integration of the energy markets of individual countries with the EU energy market. For Ukraine, the issue of implementing a policy of sustainable development by transitioning to the use of renewable energy sources is not only part of its European integration obligations, but also a time-tested necessity, taking into account the state of the country’s energy sector both during the war period and in the context of the post-war reconstruction of our country.

Quantitative assessment of cracks in concrete structures using active-learning-integrated transformer and unmanned robotic platform

Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack segmentation transformer (ACS-Former) framework is proposed to maximize segmentation performance with limited annotation resources. The two-branch ACS-Former includes (1) a feature pyramid transformer (FPT) for multi-scale crack segmentation and (2) boundary difficulty-aware active learning (BDAL) to select informative images for labeling and incorporation into FPT training. Additionally, an adhesive climbing robot is proposed for image collection of hard-to-access components of large bridges. The on-site operational feasibility and practicability of the proposed ACS-Former and climbing robot are demonstrated by field experiments performed on in-service bridges, including the quantification of cracks narrower than 0.2 mm, as required by engineering codes.

Transformative Learning Among Organ Procurement Organization Donor Family Volunteers.

Introduction: Some family members who previously made the decision to donate a family member's organs support the organ procurement organization (OPO) through volunteer work. Volunteers were asked to describe their family's organ donation story to educate the general and medical communities about organ donation. This study explored the transformative learning experienced by volunteers during and following the organ donation consent process and throughout their volunteer work. Methods/Approach: A narrative inquiry research methodology and transformative learning theoretical framework was used and 6 volunteers were recruited to participate. Two semistructured virtual interviews were conducted. Interviews were transcribed, radically reduced, and subjected to holistic-content and holistic-form narrative analysis. Findings: Participants experienced strong emotional responses during and after the loss of their family member, characterized as a period of disorientation, which lasted for an extended period preventing transformative learning. After the OPO contacted participants regarding the opportunity to volunteer, participants experienced a period of self-examination when they reengaged with the topic of organ donation. Critical reflection occurred through the act of storytelling about their family's organ donation story during their volunteer work. Storytelling to the community facilitated making meaning of their loss and transformations. Conclusion: Volunteering for an OPO played an important role by initiating self-examination and critical reflection of their prior assumptions. Through storytelling, participants reflected on their roles and made meaning of their organ donation experiences. The act of inviting donor families to reengage with the topic of organ donation through volunteer work fostered positive permanent meaningful transformations.

DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction

BackgroundDrug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges.ResultsHere, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results.ConclusionsDrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT.

An Integrated Smart Manufacturing System for Customer Design Experience

This study focuses on the development of an integrated smart manufacturing system (ISMS) that centers on custom design experiences, utilizing virtual reality (VR) and cloud integration to enhance operational value. Through a Design for Excellence (DFX) approach, this system aims to seamlessly incorporate customer-specific designs into the manufacturing process, thus aligning closely with consumer needs. The research contributes significantly by (1) implementing a customer-focused design system that enhances sales benefits, (2) enabling a smooth transition from design drawings to automated production, thereby increasing manufacturing efficiency, and (3) establishing a digital transformation framework that integrates design, production, and marketing to boost overall business value.

«Зеленая» экономика в контексте международных инвестиционных соглашений

The matters of green economics within the framework of the environmental transformation of the energy sphere (the energy transition), reduction of the anthropogenic impact on the environment in the course of socioeconomic development, give investment opportunities for international business but at the same time pose specific systemic and structural challenges to investors, states that receive investments, researches and individuals that witness the consequences of the dynamic change of the environment in the contemporary conditions. The presented article studies the evolution of approaches to international law regulation of investment activities from the standpoint of solution of environmental protection and conservation tasks in order to analyze opportunities with regards to the named challenges. Such analysis is used as a basis to conclude that the historical and legal method of studying the problems in question gives an opportunity for a systemic and structural evaluation of the original prerequisites for the logic of development of the international law architecture of regulation of investment activities in respect of goals and tasks of publicly significant environmental protection and conservation interests including the sphere of green economics and minimization of negative consequences of the anthropogenic impact as a result of investment activities, and evaluate the logic and limits of application of international investment agreements to achieve the environmental protection and conservation goals.

Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development

The emergence of neuromorphic computing, inspired by the structure and function of the human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates the implications of applying neuromorphic computing to simulate and comprehend complex neural systems affected by conditions like Alzheimer’s, Parkinson’s, and epilepsy, drawing from extensive literature. It explores the intersection of neuromorphic computing with neurology and pharmaceutical development, emphasizing the significance of understanding neural processes and integrating deep learning techniques. Technical considerations, such as integrating neural circuits into CMOS technology and employing memristive devices for synaptic emulation, are discussed. The review evaluates how neuromorphic computing optimizes drug discovery and improves clinical trials by precisely simulating biological systems. It also examines the role of neuromorphic models in comprehending and simulating neurological disorders, facilitating targeted treatment development. Recent progress in neuromorphic drug discovery is highlighted, indicating the potential for transformative therapeutic interventions. As technology advances, the synergy between neuromorphic computing and neuroscience holds promise for revolutionizing the study of the human brain’s complexities and addressing neurological challenges.