Innovative Methodology Research Articles

Auto‐Pressurized Multi‐Stage Tesla‐Valve Type Microreactors in Carbon Monoliths Obtained Through 3D Printing: Impact of Design on Fluid Dynamics and Catalytic Activity

AbstractThe present research exploits an innovative methodology for producing auto‐pressurized carbon microreactors with a precise and controlled structure analyzing the influence of their design on the fluid dynamics and their catalytic performance. Carbon monoliths with Tesla‐valve shape channels (Tesla, T, and modified Tesla, Tm) are synthesized through the combination of 3D printing and sol–gel process and further probed as Ni/CeO2 supports on CO2 methanation. The experimental results and mathematical modeling corroborated the improved performance obtained through the complex design compared to a conventional one. In addition to chaotic fluid flow induced by the deviation in flow direction, which improves the reagents‐active phase interaction, local pressure increases due to convergence of flows may enhance the Sabatier reaction according to Le Châtelier's principle. Conversely to straight channels, T and Tm are not affected by flow rate and presented chemical control. Tesla‐valve with curved angle (Tm) improved the mass transfer, achieving higher conversion and ≈30% reaction rate increase regarding right angle (T). Thus, this auto‐pressurized multi‐stage Tesla‐valve monolith opens the gate to design specific and advanced functional materials for multitude chemical reactions where not only the reactant‐active phase contact can be maximized but also the reaction conditions can be controlled to maximize the reaction kinetics.

Enhancing AI interpretation and decision-making: Integrating cognitive computational models with deep learning for advanced uncertain reasoning systems

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach.

Specific Small-Molecule Detection Using Designed Nucleic Acid Nanostructure Carriers and Nanopores.

In the realm of nanopore sensor technology, an enduring challenge lies in achieving the discerning detection of small biomolecules with a sufficiently high signal-to-noise ratio. This study introduces a method for reliably quantifying the concentration of target small molecules, utilizing tetrahedral DNA nanostructures as surrogates for the captured molecules through a magnetic-bead-based competition substitution mechanism. Magnetic Fe3O4-DNA tetrahedron nanoparticles (MNPs) are incorporated into a nanopore electrochemical system for small-molecule sensing. In the presence of the target, the DNA tetrahedron, featuring an aptamer tail acting as a molecular carrier, detaches from the MNPs due to aptamer deformation. Following removal of the MNPs, the DNA tetrahedron bound to the target traversed the nanopore by applying a positive potential. This approach exhibits various advantages, including heightened sensitivity, selectivity, an improved signal-to-noise ratio (SNR), and robust anti-interference capabilities. Our findings demonstrate that this innovative methodology has the potential to significantly enhance the sensing of various small-molecule targets by nanopores, thereby advancing the sensitivity and dynamic range. This progress holds promise for the development of precise clinical diagnostic tools.

A Review on Bridging Molecular Biology and Ecological Dynamics through Integrative Approaches in Zoology

The integration of molecular biology with ecological dynamics has emerged as a transformative approach in zoology, enhancing our understanding of biodiversity, ecosystem health, and the adaptive responses of species to environmental changes. This review synthesizes key developments and methodological innovations at the intersection of molecular biology and ecological dynamics, highlighting the application of DNA barcoding, environmental DNA (eDNA) analyses, molecular phylogenetics, and advanced computational models in elucidating complex biological interactions and evolutionary patterns. Significant advancements include the use of high-throughput sequencing technologies and CRISPR-Cas systems that have expanded our ability to explore genetic diversity and manipulate genetic material for conservation purposes. The review discusses the predictive capabilities of integrative models that combine genetic with ecological data, offering insights into species resilience and ecosystem stability under varying environmental scenarios. Challenges in data integration, such as issues of scale, complexity, and the necessity for interdisciplinary cooperation, are critically examined. Technical limitations related to data management and ethical considerations in the use of genetic information are also explored. Looking forward, the review identifies emerging technologies and their potential impacts on ecological and conservation biology, emphasizing the need for policies that support sustainable management and conservation strategies. This review underscores the profound impact of integrating molecular biology with ecological dynamics, which not only enhances our scientific understanding but also provides practical frameworks for addressing global environmental challenges.

Enforcing Traffic Safety: A Deep Learning Approach for Detecting Motorcyclists’ Helmet Violations Using YOLOv8 and Deep Convolutional Generative Adversarial Network-Generated Images

In this study, we introduce an innovative methodology for the detection of helmet usage violations among motorcyclists, integrating the YOLOv8 object detection algorithm with deep convolutional generative adversarial networks (DCGANs). The objective of this research is to enhance the precision of existing helmet violation detection techniques, which are typically reliant on manual inspection and susceptible to inaccuracies. The proposed methodology involves model training on an extensive dataset comprising both authentic and synthetic images, and demonstrates high accuracy in identifying helmet violations, including scenarios with multiple riders. Data augmentation, in conjunction with synthetic images produced by DCGANs, is utilized to expand the training data volume, particularly focusing on imbalanced classes, thereby facilitating superior model generalization to real-world circumstances. The stand-alone YOLOv8 model exhibited an F1 score of 0.91 for all classes at a confidence level of 0.617, whereas the DCGANs + YOLOv8 model demonstrated an F1 score of 0.96 for all classes at a reduced confidence level of 0.334. These findings highlight the potential of DCGANs in enhancing the accuracy of helmet rule violation detection, thus fostering safer motorcycling practices.

Assessing high‐quality process performance using the quality‐yield index: An innovative methodology

AbstractManufacturers must meet high‐quality standards and exceed customer expectations to stay competitive due to significant technological advancements in recent decades. While implementing the yield measure is useful for achieving process performance by focusing on products that fall within specified limits, it does not accommodate specific customer requirements, particularly when a product's quality characteristic deviates from target value. To address this need, the quality‐yield index (Q‐yield) has been proposed, which combines the process‐yield index and loss‐based capability index, providing a more advanced performance measure. However, the Q‐yield index's confidence interval is challenging to derive due to the complicated sampling distribution involved. Several existing methods have attempted to construct an approximate confidence interval but none have performed well. Therefore, this article proposes an innovative approach, called the generalized confidence intervals (GCIs), that utilizes the idea of generalized pivotal quantities to establish the confidence interval for the Q‐yield index. The proposed approach is evaluated through simulations and compared to existing methods. The results reveal that the proposed approach provides the most accurate results for constructing the lower confidence bound of the Q‐yield index. This approach is recommended to evaluate process performance using the Q‐yield index for high‐quality customer requirements.

Transitioning to blended learning during COVID‐19: Exploring instructors and adult learners' experiences in three Ghanaian universities

AbstractTeaching and learning in higher education have increasingly become digitalized and associated with innovative pedagogical methodologies over the past decades. Following the sudden onset of the pandemic in March 2020, several studies tended to focus on traditional students' experiences with emergency remote education while literature is scarce on non‐traditional students (or adult learners) pedagogical experiences in Distance Education contexts. Using a qualitative case study approach, this study explored how digital technologies mediated instructors' and adult learners' educational experiences during COVID‐19 and their first‐time experiences with blended learning. Between October 2021 and July 2022, semi‐structured interviews were conducted with 40 adult learners and 20 instructors in three selected public higher education institutions (HEIs) in Ghana. Data analysis followed Braun and Clarke's (2012) thematic analysis approach. The results showed that the transition to blended learning was supported by the adoption and integration of varied virtual online technologies. The themes highlight the positive and negative impacts of technology in mediating the educational experiences of instructors and adult learners in blended learning environments. The themes which reflected both instructors' and adult learners' experiences were enhanced course delivery and pedagogy, competency development, technological issues and poor‐quality pedagogy. Blended learning facilitated by technology could be the ‘new normal learning’ post‐pandemic for adult learners pursuing Distance Education in Ghana. The study recommends the implementation of agile strategies and policies by HEIs to ensure sustainable quality education in distance learning. Practitioner notesWhat is already known about this topic The COVID‐19 pandemic necessitated the adoption of innovative pedagogical approaches in higher education contexts. Technological transformations in information and communication technology (ICT) have enhanced remote teaching in higher education institutions globally. The educational experiences of educators and learners differ in blended learning contexts. What this paper adds Instructors and adult learners' positive experiences with navigating a variety of web‐based technologies during the educational process are negatively impacted by technological difficulties during online instruction. Participants' experiences of blended learning are mixed with a preference for face‐to‐face dimension of blended learning instead of the online dimension. The paper identifies four themes that characterize instructors and adult learners' experiences with technology‐enhanced learning including enhanced course delivery and pedagogy, competency development, technological issues and poor‐quality pedagogy. Implications for practice and/or policy The study provides evidence‐based information on the relevance of digitizing distance education for sustainable development and promotion of lifelong learning opportunities for distance education students. The research recommends that higher education institutions (HEIs) implement agile policies to facilitate a seamless shift to distance learning. HEIs may adopt open distance learning frameworks to streamline 21st century pedagogical and learning practices in distance‐blended learning environments for quality course instruction. The study highlights the potential distance learning modalities that HEIs can consider for Distance Education students to sustain effective quality teaching and learning.

Mindfulness and psychological capital: examining the role of intention from the person perspective in a multi-week mindfulness training program

PurposeWhen evaluating the efficacy of mindfulness interventions, most studies take a linear approach to explore how an intervention impacts different outcomes for individuals, and rarely is the role of intention examined. This research takes a configural approach to consider how various elements of a participant’s training expectations and their experience in the training condition combine to predict increases in psychological capital.Design/methodology/approachEmployees from hospital settings were randomized into three conditions (mindfulness training, active control (Pilates), and wait-list control group) and completed surveys at three time points (baseline, post-training and three months post-training). A qualitative comparative analysis was applied to see what combinations of motivational elements were associated with increases in psychological capital.FindingsWe find that all three conditions can boost their psychological capital based on different configurations involving efficacy beliefs, baseline states of well-being (psychological capital and perceived stress) and changes in levels of mindfulness and perceived stress.Research limitations/implicationsIndividual characteristics, like motivation, expectancy and baseline needs, are an important consideration in addition to the training condition itself when determining whether a training is efficacious.Practical implicationsIt is of increasing importance that organizations find ways to support employee well-being. Offering a variety of psychological and physical interventions can improve psychological capital. Applying needs assessments that clarify the desires, needs and expectations employees hold may help with intervention efficacy.Originality/valueThe current study offers an innovative methodology through which realist evaluation approaches can consider multiple factors to predict outcomes.

A review of EDLC and pseudocapacitance with synergistic integration of carbon-based and metal oxide materials for enhanced electrochemical energy storage

This paper provides a comprehensive analysis of the recent advancements in electrochemical double-layer capacitors (EDLCs) and pseudocapacitance, emphasizing the pivotal role of carbon-based materials, metal oxides, conducting polymers, and composites in augmenting the energy storage landscape. Through a systematic collection of electrochemical performance metrics, we elucidate the inherent advantages of these materials, such as superior electrical conductivity, enhanced surface area, and chemical robustness, which are instrumental in the development of next-generation supercapacitors. We discuss the integration of novel materials like reduced graphene oxide/hexagonal boron nitride composites, carbon onions, and various metal oxides that contribute to a significant increase in energy density and cycling stability. This work also highlights innovative fabrication techniques and synthesis methodologies that have resulted in electrodes with remarkable performance enhancements. Furthermore, the exploration of synergistic effects between different materials has led to hybrid structures that exhibit improved capacitive properties and operational efficiency.

Preparation and performance characteristics study of thermoplastic polyurethane elastomer (TPU) for basketball shoe soles

Thermoplastic Polyurethane Elastomer (TPU) materials have found extensive applications in the preparation of basketball shoe soles, owing to their excellent elasticity, lightweight properties, and durability. However, TPU materials still face challenges related to breathability, temperature sensitivity, and weather resistance, necessitating further research and improvement. This paper primarily investigates the advancements in E-TPU materials with high rebound foam particles tailored for basketball shoe sole applications. It emphasizes the factors influencing their performance and presents performance control methods from the perspectives of synthesis, molding, and usage. This research provides innovative approaches and methodologies for basketball shoe sole design.

Assessment of the Efficiency of a Hybrid Photovoltaic and Photovoltaic Heating System (PV–Solar) in the Context of a Warehouse for a Housing Community in Poland

In light of global challenges such as the war in Ukraine and the depletion of fossil fuel resources, it is essential to explore sustainable energy solutions. Hybrid energy systems represent a potential solution, offering energy independence to urban housing estates and reducing CO2 emissions. This article aims to explore the feasibility of integrating photovoltaic systems (utilizing vacuum collectors) and combined utilities (system heat and electricity) in a hybrid setup, leveraging existing technical infrastructure with necessary modifications. A key aspect is to perform calculations on the amount of heat and electricity generated from these systems. The study analyzes the demand for heat and electricity among consumers compared to the estimated production from renewable sources. Calculations also include the potential energy savings and CO2 emission reductions achievable through the proposed solutions. The findings indicate that hybrid photovoltaic systems with heat storage could effectively address energy issues in urban housing estates, given adequate support and community involvement. The innovative methodology employed in this study encompasses both analytical and experimental research approaches. The analysis employs advanced statistical techniques and data integration to enhance understanding of the phenomena studied, while the experimental research provides robust results through controlled variable manipulation and precise measurement tools, thereby verifying the study’s objectives.

Case Study of Skilful Use of LLM-Based Generative AI in Management of a Hypothetical Business

Now that generative AI technology products are already spreading worldwide and are improving their capabilities very fast, it is high time we examine what it can do to decision making, which is the essence of managing businesses. The present paper has used an innovative methodology where one of the currently available Large Language Model based product, namely ChatGPT, is given a series of questions/prompts to use its own capabilities to give its self-assessment of supporting the different dimensions of managing a business. Multiple conversations illustrated the need for well-structured prompts/questions to get the most out of ChatGPT, otherwise it can get confused and provide illogical response. The quality of mutual learning improves by constructive feedback between the human interlocutor and generative AI. Probing it further with multiple scenarios for a hypothetical mid-sized business has verified and confirmed those self-assessments. The insights and conclusions drawn from those conversations are that skillful use of the LLM-based generative AI products can assist human managers in making better quality decisions at all levels of management in a timely manner. Senior managers of businesses need to begin an effort to test-drive an available LLM-based generative AI product and plan to leverage it in the ongoing management to become a learning organization in the interest of the business.

Linguistic response surface methodology approach to measure the quality of nonlinear frame‐pixel and bit place‐based video steganography

AbstractSteganography refers to the practice of hiding sensitive information inside seemingly unrelated data sets. Steganography in the video is one of the best methods available for hiding data without compromising the film's appearance. For improved security and compatibility, the traditional system uses different video steganography techniques with linear or precise positions. Traditional linear video steganography practices face vulnerability, a lack of security, limited embedding options, and inadequate compatibility. Here nonlinear frame(s) and pixel positions based information hiding techniques have been developed to overwhelm the following. Both the nonlinear frame positions and nonlinear pixel positions are selected for the video‐based steganography. In the beginning, the nonlinear frame positions are selected through the key and the key may be with any prescribed range and alphanumeric characters. A single or more frames may be selected through the key and that entirely depends upon the corresponding run‐through. Then the nonlinear pixel and bit positions are also selected through a similar key. The proposed method is also compared with some former techniques and gives a magnificent result. Furthermore, a security analysis of the suggested algorithm has also been conducted using the differential attack method. To validate the suggested method and ensure that it is accurate, the author of this article made use of a very specific and innovative methodology known as the linguistic response surface methodology (LRSM). This model is framed based on achieving a few steganography assessment measures like PSNR, SSIM, and MSE metric values after incorporating hidden text in various nonlinear frames' nonlinear pixel locations of the video. The analysis of the variance using LRSM for PSNR, SSIM, and MSE response reveals very substantial results with confirmation.

Modular Ship Design

In this paper, we introduce a modular approach to ship design, utilizing design modules to streamline the initial design phases. Ships are conceived as combinations of a primary 'ship module' and various 'design modules' tailored for specific spaces. These modules encompass predefined geometries, layouts, and equipment configurations. We introduce an optimization model that integrates decision variables for ship attributes and configuration variables for module selection. Through this framework, we aim to simplify design complexities, accelerate the production of detailed drawings, and foster innovation in ship design methodologies.

A review on application and innovation of wind-solar power generation technologies based on dual carbon targets

With the increasing depletion of traditional energy sources, the greenhouse effect is becoming increasingly serious, mankind is in urgent need of clean and renewable new energy demand is increasing, in order to support the carbon peak&carbon neutral goal, in the future a longer period of time, the improvement of wind power age innovation is the principal procedure for the advancement of numerous nations today, many countries and international organizations to vigorously promote the development of wind power and photovoltaic power generation system will also continue to accelerate the development of new energy generation systems in China. Chinas renewable power age framework, which is basically founded on wind power and will keep on progressing, is being fiercely promoted by many nations and international organizations. This thesis mainly exemplifies the many applications of wind power generation technology in China based on the dual-carbon target through the approaches of literature reviews, and incorporates the inventive innovation model of wind power age innovation and different methodologies. Combined with domestic and international cases, this review summarizes how to solve the dilemmas faced by renewable energy technologies and how to optimize the operation and improve the efficiency of power generation by using innovative technologies, which can provide technical support for the realization of the dual-carbon goal.

Innovative Methodologies of Active Learning to Develop the Competencies of the Future of Work

International organizations promoting economic growth and global development are increasingly requesting higher education systems to prepare students with the fundamental attitude and skills to be competitive in the future workplace. After conducting two separate literature reviews on active learning strategies and on the most demanded competencies in the future of work, the paper proposes a comprehensive framework of 31 competencies pivotal for future professionals to thrive in an innovative work landscape. In the effort to nurture these skills, it further explores the contributions of the most popular active learning methodologies and delves into the role of digital technologies in enhancing the learning experience. Significantly, the paper reveals that active learning not only develops technical and methodological competencies but also enriches social, personal, and particularly digital skills, positioning students to effectively navigate and contribute to the digital economy. The emergent pedagogies underscore the importance of fostering lifelong learning, self-agency, and the adept use of diverse tools and resources, essential for the professionals of today and tomorrow.

Chemical bonding and electronic properties along Group 13 metal oxides

ContextThe present work provides a systematic theoretical analysis of the nature of the chemical bond in Al2O3, Ga2O3, and In2O3 group 13 cubic crystal structure metal oxides. The influence of the functional in the resulting band gap is assessed. The topological analysis of the electron density provides unambiguous information about the degree of ionicity along the group which is linearly correlated with the band gap values and with the cost of forming a single oxygen vacancy. Overall, this study offers a comprehensive insight into the electronic structure of metal oxides and their interrelations. This will help researchers to harness information effectively, boosting the development of novel metal oxide catalysts or innovative methodologies for their preparation.MethodsPeriodic density functional theory was used to predict the atomic structure of the materials of interest. Structure optimization was carried out using the PBE functional, using a plane wave basis set and the PAW representation of the atomic cores, using the VASP code. Next, the electronic properties were computed by carrying out single point calculations employing PBE, PBE + U functionals using VASP and also with PBE and the hybrid HSE06 functionals using the FHI-AIMS software. For the hybrid HSE06, the impact of the screening parameter, ω, and mixing parameter, α, on the calculated band gap has also been assessed.

Teaching Family? Care/Work Policy in Selected Family Courses in Canada’s Research-Intensive Universities

To what degree is explicit care/work policy taught in family courses in Canada’s leading research-intensive universities? We analyze family courses in sociology departments and in political studies and women’s/gender studies programs in Canada’s 15 R1 universities to make a contribution to the scholarship of teaching and learning. This national scan marks a methodological innovation from curriculum studies that generally adopt a single-program or single-site focus. From a Canadian universe of 74 family courses, we identify 15 whose formal course calendar description explicitly addresses care/work family policy (measures to reconcile caring for young children with employment, through early learning and childcare, parental leaves, and child benefits). Sociology predominates among courses where family policy is taught, yet care/work policy content is not common. Given growing concerns about the care crisis and the care deficit in Canada, the low profile of care/work family policy content in family courses is significant. This study sheds light on the value of national postsecondary education curricular reviews and suggests that family curriculum renewal is warranted.

Beyond Neural Connections: Using Strathern to Explore Knowledge-making at the Intersections of the Social and Neurosciences

This article considers how Marilyn Strathern’s work on Western knowledge conventions can usefully contribute to debates at the intersection of the social and neurosciences: first, to understanding the nature of work at this intersection; and second, to providing new avenues for interdisciplinary engagement. The neuroscience explosion in the 1990s and the early twenty-first century held out the promise of understanding increasingly complex phenomena in terms of the brain. As the neurosciences ventured into questions of human sociality, social scientists found it difficult to make meaningful contributions within the confines of conventional models of neuroscientific work. This highlighted the need for conceptual and methodological innovation. In this article, we illustrate how Strathern’s concept of the merographic connection, which conceptualizes the link between overlapping parts belonging to separate wholes, is a useful frame for thinking about the shared objects of the social and neurosciences. We show how her consideration of analogy as an apparatus bringing different domains together assists in understanding work in this area. With social science experiments in working with neuroscientists ongoing, Strathern’s work encourages precision about what is being brought into relationship in interdisciplinary encounters and prompts innovative thinking about what might productively be brought into association.

Identification of Key Genes of Fruit Shape Variation in Jujube with Integrating Elliptic Fourier Descriptors and Transcriptome.

Jujube (Ziziphus jujuba) exhibits a rich diversity in fruit shape, with natural occurrences of gourd-like, flattened, and other special shapes. Despite the ongoing research into fruit shape, studies integrating elliptical Fourier descriptors (EFDs) with both Short Time-series Expression Miner (STEM) and weighted gene co-expression network analysis (WGCNA) for gene discovery remain scarce. In this study, six cultivars of jujube fruits with distinct shapes were selected, and samples were collected from the fruit set period to the white mature stage across five time points for shape analysis and transcriptome studies. By combining EFDs with WGCNA and STEM, the study aimed to identify the critical periods and key genes involved in the formation of jujube fruit shape. The findings indicated that the D25 (25 days after flowering) is crucial for the development of jujube fruit shape. Moreover, ZjAGL80, ZjABI3, and eight other genes have been implicated to regulate the shape development of jujubes at different periods of fruit development, through seed development and fruit development pathway. In this research, EFDs were employed to precisely delineate the shape of jujube fruits. This approach, in conjunction with transcriptome, enhanced the precision of gene identification, and offered an innovative methodology for fruit shape analysis. This integration facilitates the advancement of research into the morphological characteristics of plant fruits, underpinning the development of a refined framework for the genetic underpinnings of fruit shape variation.