Transformation Capabilities Research Articles

Revolutionizing Brain Analysis: AI-Powered Insights for Neuroscience

The integration of artificial intelligence (AI) into neuroscience has revolutionized brain analysis, offering unprecedented insights into neural structures, functions, and disorders. This study explores the application of AI techniques such as machine learning and deep learning in brain imaging, cognitive analysis, and neurological diagnostics. By leveraging advanced algorithms, we analyze complex datasets from modalities like MRI, fMRI, and EEG, enabling the identification of intricate patterns and anomalies in brain activity. The findings demonstrate the potential of AI in improving diagnostic accuracy, predicting neurodegenerative conditions, and enhancing our understanding of the human brain. Despite its transformative capabilities, challenges such as data privacy, interpretability, and ethical considerations persist. This research underscores the critical role of AI in shaping the future of neuroscience and paves the way for its wider adoption in clinical and research settings. Keywords - fMRI (Functional Magnetic Resonance), Imaging, EEG (Electroencephalogram), Neurological Diagnostics, Neurodegenerative Conditions, Pattern Recognition.

Transgenic poplar for resistance against pest and pathogen attack in forests: an overview

Forests are potential habitats for immense terrestrial ecosystems and aquatic biodiversity, performing an essential role in ecological preservation and regulation of climate. The anthropogenic pressures on the forests lead to forest loss, fragmentation and degradation. Requirements for sustainable methodologies for forest protection are of utmost priority under the climate change regime. Among forest trees, poplar trees (Populus L.) have attracted attention in global forestry as a promising material for improving the quality and quantity of urban landscapes. These plants provide wood, which can be utilized as raw resources for the paper industry and as a potential source of biofuel. However, several biotic stresses, such as attacks by pests and pathogens, severely affect poplar production and productivity. The improvement of Populus trees through conventional tree breeding methods is restricted due to their long-life cycles and the lack of suitable donors with resistance genes. Populus has been utilized as a model plant for studying gene functions due to its highly efficient genetic transformation capabilities. The present review will provide a comprehensive overview of pest and pathogen attacks on poplar, focusing on their infection mechanisms, transmission routes, and control strategies. Additionally, it will examine the most widely used genetic transformation methods (gene gun-mediated, Agrobacterium tumefaciens-mediated, protoplast transformation, micro-RNA mediated and micro-RNA clustered regularly interspaced short palindromic repeats (CRISPR)-associated (CRISPR-Cas) systems methods and RNA interference) for improving tolerance in poplar trees against pest and pathogens attack. Furthermore, it will delve into prospects, challenges, and recent advances in molecular biology tools and their safe application for genetic transformation to improve insect and pest resistance in poplar trees. Finally, the regeneration of transgenic poplar trees with enhanced resistance, developed through various genetic engineering techniques, is discussed.

Generative Artificial Intelligence (GenAI) in Business: A Systematic Review on the Threshold of Transformation

This systematic review examines the transformative potential of Generative Artificial Intelligence (GenAI) across diverse sectors, including information technology, education, manufacturing, creative industries, healthcare, transportation, management, marketing, finance, energy, law, media, agriculture, and e-commerce. By analyzing its applications, the study highlights how GenAI enhances efficiency, fosters innovation, and addresses sector-specific challenges. Key benefits include the automation of complex processes, optimization of resource use, and acceleration of decision-making. However, delayed adoption risks such as workforce displacement and ethical dilemmas are also discussed. The review identifies critical barriers like data privacy concerns, algorithmic bias, and regulatory challenges. Practical strategies for successful GenAI integration are explored, emphasizing infrastructure readiness, workforce upskilling, and ethical governance. This includes leveraging generative models such as Generative Adversarial Networks (GANs), Transformer-based models, Variational Autoencoders (VAEs), and diffusion models to adapt to industry-specific demands. Furthermore, the study underscores the necessity of balancing technological advancements with responsible AI deployment to minimize risks and maximize societal benefits. By synthesizing existing research, this review provides actionable insights for stakeholders aiming to leverage GenAI's transformative capabilities responsibly. It emphasizes the urgency of adopting GenAI technologies to maintain competitiveness and sustainability in rapidly evolving markets. As the study concludes, it advocates for cross-sectoral collaboration to address the complex challenges posed by this paradigm-shifting technology and calls for adaptive policies to align innovation with ethical principles and societal values.

The Transition towards Artificial Intelligence in Healthcare: A Systematic Review of Cases from Community Pharmacies

This review aims to assess the role of artificial intelligence (AI) in community pharmacy by analyzing the most recent studies and identifying trends, gaps, and future directions for integrating AI technologies in these settings. A systematic literature review was performed on the PubMed, Scopus, and Google Scholar databases, looking at papers from 2019 to 2024. The search was further refined using the terms "AI in pharmacy," "telepharmacy," or "clinical decision support systems." Fourteen studies were included in the review after applying specific inclusion and exclusion criteria for further analysis. AI technologies have potential effects on community pharmacy practice. The most beneficial impact was noted in medication management, where 15% of medication errors were reduced, and patient compliance improved by 10%. In telepharmacy, AI supports encouraging adherence and access to pharmacy services where geographical barriers exist. However, there are concerns such as lack of privacy for system users, implementation costs, and onboarding pharmacists to such systems. AI has significant supportive and transformative capabilities for community pharmacy, but crucial barriers must be overcome first. Addressing the barriers and their ethical aspects is critical for further research.

Enhancing Plasma Torch Efficiency: Wet Steam Cooling

The efficient use of wet steam for plasma torch cooling and water plasma generation is important for reliable plasma generator design. Wet steam, due to phase transformation capability, improves heat removal at lower gas flow rates if compared to liquid water. To evaluate the wet steam cooling potential, a numerical model is proposed, incorporating governing equations (mass, momentum, energy, current, and Ampere's law) are expressed in the cylindrical coordinate system. The model is applied to investigate the wet steam cooling feasibility for the anode of a direct current plasma torch with non-transferred arc. Electric arc modeling examines anode spot location and anode surface temperature distribution for a 120 A arc current and gas flow rates of 42, 90, and 140 l/min under steady flow conditions. Analysis of spatial vapor content distribution in the refrigeration channel highlights unfavorable conditions when wet steam becomes dry.

The Impact of Artificial Intelligence on Digital Marketing: Leveraging Potential in a Competitive Business Landscape

This study aims to thoroughly investigate how Artificial Intelligence (AI) is strategically integrated into digital marketing practices and its consequential effects on Indonesia’s fiercely competitive business environment. Employing a quantitative research approach, this study meticulously examines Indonesian enterprises’ prevailing strategies for AI utilization. The research method employed in this study is quantitative, with the unit of analysis being Indonesian companies. The sample size comprises 100 companies selected through the stratified random sampling technique. Analysis of the data is conducted using the SPSS statistical package. Through detailed analysis of survey data and advanced statistical techniques, the research reveals a significant positive correlation between the integration of AI in digital marketing and improved marketing effectiveness. The study highlights a noticeable increase in customer engagement metrics and noteworthy enhancements in conversion rates among businesses proficient in leveraging AI technologies, further reinforcing this correlation. Additionally, the findings suggest that companies embracing AI demonstrate significantly heightened adaptability to the constantly evolving market dynamics, strengthening their competitive positioning. These insightful discoveries underscore the critical importance of harnessing AI’s transformative capabilities within digital marketing strategies to sustain and bolster a competitive edge in the marketplace. Furthermore, the study discusses its contributions to existing knowledge and provides practical implications for marketers and business policymakers in Indonesia. Doi: 10.28991/ESJ-2024-08-06-012 Full Text: PDF

Exploring supply chain managers' complex perceptions of dynamic capabilities for digital transformation

Exploring supply chain managers' complex perceptions of dynamic capabilities for digital transformation

The strategic advantage of innovative organizational culture: An exploratory analysis in digital transformation and big data analytics capabilities

Abstract This study examines the antecedent role of organizational culture and the mediating role of digital transformation when promoting big data analytics capabilities. Employing the Competing Values Framework, we scrutinize the influence of various cultural typologies, including digital culture on the successful deployment of digital transformation and the enhancement of big data analytics capabilities. Our analysis utilizes Partial Least Squares Structural Equation Modeling on a dataset of 183 firms to evaluate our hypotheses. The findings reveal that adhocratic, digital and hierarchical cultures significantly foster big data analytics capabilities mediated by digital transformation, which is a dynamic process that needs supportive digital and innovative values. In contrast, market and clan cultures exhibit weaker linkages. By providing empirical evidence and practical implications, this study highlights how organizations with a strong adhocratic and digital cultures outperform those with traditional cultures in their digital transformation and big data analytics capabilities efforts.

Transformer based 3D tooth segmentation via point cloud region partition

Automatic and accurate tooth segmentation on 3D dental point clouds plays a pivotal role in computer-aided dentistry. Existing Transformer-based methods focus on aggregating local features, but fail to directly model global contexts due to memory limitations and high computational cost. In this paper, we propose a novel Transformer-based 3D tooth segmentation network, called PointRegion, which can process the entire point cloud at a low cost. Following a novel modeling of semantic segmentation that interprets the point cloud as a tessellation of learnable regions, we first design a RegionPartition module to partition the 3D point cloud into a certain number of regions and project these regions as embeddings in an effective way. Then, an offset-attention based RegionEncoder module is applied on all region embeddings to model global context among regions and predict the class logits for each region. Considering the irregularity and disorder of 3D point cloud data, a novel mechanism is proposed to build the point-to-region association to replace traditional convolutional operations. The mechanism, as a medium between points and regions, automatically learns the probabilities that each point belongs to its neighboring regions from the similarity between point and region features, achieving the goal of point-level segmentation. Since the number of regions is far less than the number of points, our proposed PointRegion model can leverage the capability of the global-based Transformer on large-scale point clouds with low computational cost and memory consumption. Finally, extensive experiments demonstrate the effectiveness and superiority of our method on our dental dataset.

Assessing the impact of digital transformation capability for international market growth: the role of leadership support

PurposeThis study aims to examine the impact of an organization’s digital transformation capability (DTC) on its dynamic capabilities and strategic depth, which, in turn, could impact the organization’s international market growth (IMG). This study also investigates the moderating role of leadership support (LES) in using the DTC.Design/methodology/approachThe study reviews various kinds of literature related to international marketing, dynamic capabilities, digital transformation, strategic marketing and LES. Following this, a dynamic capability view is adopted to develop a research model. This research model is later tested using partial least squares structure equation modeling with data from 385 respondents from different types of organizations.FindingsThe study demonstrates that an organization’s DTC significantly enhances its dynamic capabilities and strategic depth. LES is vital for effectively applying and leveraging DTCs since it influences the association between DTCs, competitive advantage and global marketing intelligence. The study also recognizes that strategic depth and dynamic capabilities contribute to IMG.Research limitations/implicationsThe study uncovers the impacts of DTCs, influencing IMG. The study also provides valuable insights regarding the role of leadership in effectively leveraging the organization’s DTCs. It suggests the organization’s management should invest in improving DTC.Originality/valueThis study contributes to the prevailing literature on DTC, leadership and IMG. The study highlights the importance of DTC and how it impacts more established capabilities and leads to IMG of the organization. Further, this study is also unique as it establishes the importance of LES in leveraging the maximum benefits of the DTC.

Digital transformation capability and employee turnover intentions: the role of knowledge management capability and digital leadership

PurposeThe primary objective of this research is to examine the effects of digital transformation capability (DTC) on employee turnover intention (ETI) in Jordanian universities. Based on the social cognitive theory (SCT). The study also investigates the mediated moderation role of knowledge management capability (KMC) and digital leadership (DL) in the relationship between DTC and ETI.Design/methodology/approachA survey questionnaire was used to obtain data from 384 faculty members at Jordanian universities in Jordan, and the SmartPLS4 and SPSS software were used to test the hypotheses.FindingsThe results reveal that DTC significantly mitigates ETI through providing a favourable environment for knowledge sharing and innovation. Using DL as a moderator, the results show that the relationship between DTC and ETI becomes stronger in the presence of high DL.Originality/valueThis research uniquely integrates SCT to explore the combined impact of DTC, KMC and DL on faculty turnover intentions at Jordanian universities. It offers novel viewpoints on retaining academic staff by implementing smart digital attempts.

Size and Charge Dual‐Switchable Nanoparticles for Achieving Chemosensitization and Immune Infiltration against Pancreatic Ductal Adenocarcinoma

AbstractPancreatic ductal adenocarcinoma (PDAC), one of the most challenging cancers, is uniquely characterized by a biological barrier composed of multiple components in the extracellular matrix, preventing penetration of chemotherapeutic agents and hindering clinical drug treatment. Collagen I arrangement is critical. In this study, gemcitabine is chosen as the chemotherapeutic agent and dasatinib as the collagen‐I‐disrupting agent. When collagen I arrangement is disrupted, not only the chemotherapeutic drug enters the tumor area better, but the infiltration of CD8+ T cells into the tumor also increases. Therefore, a hypoxia‐responsive linker‐crosslinked polymer, DGD/L@GBI, is designed, which can be simultaneously loaded with two therapeutic agents, gemcitabine and dasatinib. The polymer is encapsulated by nucleic acid aptamers that target the PDAC stromal microenvironment and shield the surface. The aptamer is removed upon arrival at the PDAC hypoxic microenvironment, exposing the positively charged core to achieve charge reversal. Particle size transformation is achieved using linkers that respond to a hypoxic microenvironment. Increased intratumoral penetration is achieved using the dual transformation capabilities of the formulation. Pairing these two agents can result in a powerful efficacy in chemotherapy sensitization and immune infiltration.

Emerging Spatiotemporal Dynamics in Multiterminal Neuromorphic Nanowire Networks Through Conductance Matrices and Voltage Maps

AbstractSelf‐organizing memristive nanowire (NW) networks are promising candidates for neuromorphic‐type data processing in a physical reservoir computing framework because of their collective emergent behavior, which enables spatiotemporal signal processing. However, understanding emergent dynamics in multiterminal networks remains challenging. Here experimental spatiotemporal characterization of memristive NW networks dynamics in multiterminal configuration is reported, analyzing the activation and relaxation of network's global and local conductance, as well as the inherent spatial nonlinear transformation capabilities. Emergent effects are analyzed i) during activation, by investigating the spatiotemporal dynamics of the electric field distribution across the network through voltage mapping; ii) during relaxation, by monitoring the evolution of the conductance matrix of the multiterminal system. The multiterminal approach also allowed monitoring the spatial distribution of nonlinear activity, demonstrating the impact of different network areas on the system's information processing capabilities. Nonlinear transformation tasks are experimentally performed by driving the network into different conductive states, demonstrating the importance of selecting proper operating conditions for efficient information processing. This work allows a better understanding of the local nonlinear dynamics in NW networks and their impact on the information processing capabilities, providing new insights for a rational design of self‐organizing neuromorphic systems.

Bending and sensing performances of electro-ionic soft actuators based on carboxylated cellulose nanofibers reinforced with graphene nanoplatelets

Abstract Soft robots not only possess greater degrees of freedom and the capability for continuous transformation, but they also offer exceptionally high safety in human–robot interactions, avoiding harm to the human body. Soft actuators are essential for developing high-performance soft robots, offering significant bending deformation, rapid response times, and prolonged operational capabilities. Herein, we present an ionic electroactive soft actuator based on functional cellulose nanofibers, graphene nanoplatelets, and ionic liquid. The proposed actuator achieved a large displacement about ±8 mm under 2.0 V at 0.1 Hz, with long working stability (98% of initial peak displacement maintained after 1260 cycles of cycling). The human–robot interaction applications of this actuator were explored by simulating human fingers. More importantly, the static and dynamic sensing performances of the actuator were investigated, finding that it generated a sensing voltage of 0.37 V at a vibration displacement of only 1.75 mm. The designed actuator provides a promising approach for developing high-performance soft robots, soft actuators, flexible sensors, and flexible active devices.

Influence of Thermomechanical Treatments and Chemical Composition on the Phase Transformation of Cu-Al-Mn Shape Memory Alloy Thin Sheets

This paper investigates the interrelated effects of thermomechanical treatments and chemical composition on the phase transformation capabilities of thin sheets made from Cu-Al-Mn shape memory alloys. The transformation capacity and transition temperatures were determined using DSC and DMA testing, while composition measurements were performed using SEM/EDX analysis. The results demonstrate that applying hot-rolling treatments to alloys of reduced thickness leads to manganese oxidation and modifications in chemical composition, adversely impacting the phase transformation performance. This effect can be mitigated by the use of cold rolling. Additionally, the presence of phosphorus impurities can create inclusions that bind manganese, preventing it from remaining in the solid solution and further affecting phase transformation capabilities.

Enhancing Lung and Breast Cancer Screening with Advanced AI and Image Processing Techniques

This research investigates the application of CNNs for diagnostics improvements in lung and breast cancers based on AI image classification approaches. Using the datasets with 15,000 images describing lung cancer and 10,000 images describing cases of breast cancer, the models showed high performance: 90% for lung cancer and 99% for breast cancer classification. The descriptive analysis pointed out different features in imaging, such as dense tissue structure and irregular cell patterns; the models successfully identified these. The findings underlined the vital role that AI could play in assisting radiologists by delivering preliminary analysis, triaging high-risk cases, and leading to early cancer detection. Essential challenges were highlighted: ethical considerations concerning patients' privacy and AI algorithms' transparency. The limitation of the dataset diversity resulted in the conclusion that only broader data can ensure good generalization in various clinical settings. They recommended integrating the AI tool with clinical workflow and also called for training radiologists for effectiveness. Future research directions include real-time imaging and patient data integration for comprehensive diagnostic support and multi-modal approaches that combine imaging with genomics for more precise predictions. This leads to a more personalized cancer diagnosis and treatment plan, thus ultimately improving the results of the patients. This research, therefore, underlines the transformative capabilities of AI and image processing in modernizing cancer screening and diagnostics toward more accurate and efficient healthcare practices.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Evaluation for digital transformation capability of construction enterprises: a case study in China

Evaluation for digital transformation capability of construction enterprises: a case study in China

Accountability management system of superior elementary school for digital transformation

The purpose of this research is to find out i) the quality of the accountability management system of superior schools, ii) elementary school digital transformation capabilities, and iii) the relationship between the quality of the accountability management system for superior schools and the digital transformation capabilities of elementary schools in Indonesia. The research approach used is quantitative. The research location is in public elementary schools in Malang City. The population of this research is all 195 public elementary schools. The research sample is 22 public elementary schools. The research subjects were school principals, class teachers, and subject teachers totaling 51 people. Data collection is done by using a questionnaire. Elementary schools with superior school accountability management systems have the ability to drive their digital transformation capabilities. Excellent school accountability management systems and school digital transformation capabilities guarantee equal access to education for all students. Implications for improving teaching and learning are also discussed in this paper. The variables studied can be used to determine the priority scale for planning, implementing and evaluating superior school accountability management systems in digital transformation, in increasing teacher competency.

Decoupled Edge Physics Algorithms for Collaborative XR Simulations

ABSTRACTThis work proposes a novel approach to transform any modern game engine pipeline, for optimized performance and enhanced user experiences in extended reality (XR) environments decoupling the physics engine from the game engine pipeline and using a client‐server architecture creates a scalable solution, efficiently serving multiple graphics clients on head‐mounted displays (HMDs) with a single physics engine on edge‐cloud infrastructure. This approach ensures better synchronization in multiplayer scenarios without introducing overhead in single‐player experiences, maintaining session continuity despite changes in user participation. Relocating the Physics Engine to an edge or cloud node reduces strain on local hardware, dedicating more resources to high‐quality rendering and unlocking the full potential of untethered HMDs. We present four algorithms that decouple the physics engine, increasing frame rates and Quality of Experience (QoE) in VR simulations, supporting advanced interactions, numerous physics objects, and multiuser sessions with over 100 concurrent users. Incorporating a Geometric Algebra interpolator reduces inter‐calls between dissected parts, maintaining QoE and easing network stress. Experimental validation, with more than 100 concurrent users, 10,000 physics objects, and softbody simulations, confirms the technical viability of the proposed architecture, showcasing transformative capabilities for more immersive and collaborative XR applications without compromising performance.