Holistic Methodology Research Articles

Beyond influence: unraveling the complex tapestry of digital influencer dynamics in hyperconnected cultures

Purpose This paper aims to the intricate relationship between digital influencers (DIs) and their followers, aiming to develop a comprehensive framework that explains how influence works in the digital world. It focuses on understanding the cultural aspects that shape these relationships in today’s global and digital marketplace. Design/methodology/approach This study employs a holistic methodology, intertwining historical, cultural and theoretical insights to decode the DI phenomenon. Applying a Grounded Theory approach, this paper coded articles into categories, developed abstract concepts and refined them through cycles of literature collection and analysis that allowed identifing gaps in the Influencer Marketing field. This comprehensive review and inductive analysis of globalization, mediated communication and digital interactions aim to unravel the intricacies of digital and virtual influence. This paper’s theoretical development advances propositions that dissect the facets influencing digital adoption, usage, interest and value perception, leading to a detailed model of digital influence grounded in both theory and real-world examples. Findings This research uncovers the significant impact DIs have, driven by global connections and the way we communicate in the digital age. Historical context situates DIs within the broader narrative of mediated persuasive communication. A preliminary typology of DIs and influence contexts forms the foundation for further exploration. Research limitations/implications This study enhances the discussion around DIs by considering the influence of technology and culture together. It draws from the thoughts of leading thinkers on how technology connects us, providing a strong foundation for future studies. Practical implications As digital influence and the surrounding technology continue to change, it’s important to think critically about these trends. This research offers valuable insights for businesses looking to navigate the digital landscape effectively, helping them make better strategic decisions about their online presence. Originality/value This study breaks new ground by offering a detailed categorization of DIs and proposing a fresh way to understand their role. It links important ideas from the past about persuasion through media to the current state of digital influence, offering insights into how digital trends might affect communication strategies.

Safety 4.0: Harnessing computer vision for advanced industrial protection

In the pursuit of enhanced productivity, reduced costs, and minimized lead times, manufacturers are transitioning from traditional systems to autonomous systems. This shift, driven by the emergence of smart manufacturing and technological advancements such as robotics, collaborative robots (Cobots), automation, and digitalization, necessitates a parallel evolution in safety protocols—termed Safety 4.0—to mitigate the risks associated with such dynamic environments. The integration of smart technologies within manufacturing significantly transforms traditional workflows and intensifies the need for comprehensive safety training and guidelines. Innovations like smart personal protective equipment (PPE) and wearable sensors are pivotal in this transition, yet they often prove financially burdensome for manufacturers due to high costs and the scale of workforce deployment. Moreover, the effective use of these technologies requires continuous monitoring and data analysis, further straining resources. To address these challenges, this paper proposes the adoption of computer vision technology to enhance safety measures within manufacturing facilities, focusing on human and PPE detection. It details a holistic methodology encompassing data collection, preprocessing, training, and execution. The discussion extends to the implementation framework of this technology, emphasizing its role in enabling autonomous decision-making—a crucial step beyond mere detection. Furthermore, the paper explores the utilization of the accumulated data to develop immersive training modules employing Mixed Reality, thereby reinforcing safety protocols and fostering an environment of continuous learning and adaptation. This approach not only contributes to safeguarding personnel but also aligns with the financial and reputational interests of forward-thinking manufacturers.

Holistic Methodology of Hypersoft in Tandem with Uncertainty-based Triangular Neutrosophic for Wave Energy Converter Optimization

Global warming, environmental degradation, and climate change have recently emerged as compelling arguments against the combustion of fossil fuels. As well Eliminating and forgoing the intake of fossil fuels is also encouraged by the Sustainable Development Agenda's goals. The healthy substitute for fossil fuels, renewable energy is one of the main devoid power sources found in our natural surroundings. An effective proposition for harnessing wave energy with an emphasis on increasing energy and efficiency are wave energy converters, or WEnCos. It contributes to producing a clean, limitless energy source that might drastically lessen our need for fossil fuels. Generally speaking, the selecting of the appropriate converter is an important endeavor that this study takes on. The process of selecting the optimal converter has been conducted based on effective benchmarks or criteria and its attributes. Multiple Criteria Decision Making (MCDM) techniques seem to be effective tools when it comes to handling the complex issue of benchmarking WEnCos. These approaches consider a variety of criteria, allowing for a comprehensive and nuanced examination. Each utilized technique plays vital role in our decision problem. Hence, CRiteria Importance Through Inter-criteria Correlation (CRITIC) method—which determines the weights of criteria and its attributes in decision making problems. These weights are utilized in Weighted Sum Product (WISP) method. Totally, these techniques are implemented under uncertainty theory of Neutrosophic which combined with soft sets notion to generate Triangular Neutrosophic HyperSoft (TrNSHSS). Totally, all these techniques are contributed to constructing soft decision-making model for analyzing and recommending optimal WEnCo.

Determination of Environmental Flow Using a Holistic Methodology in Three River Paths in the Tempisque River Basin, Costa Rica

Determination of Environmental Flow Using a Holistic Methodology in Three River Paths in the Tempisque River Basin, Costa Rica

Dachaihu Decoction alleviates chronic pancreatitis by regulating MAPK signaling pathway: Insights from network pharmacology and experimental validation

Ethnopharmacological relevanceChronic pancreatitis (CP), a syndrome characterized by inflammatory fibrosis, can impair both the internal and external secretory functions of the pancreas. The global incidence of this disease is gradually increasing. However, the exact pathogenesis remains unclear, resulting in a lack of targeted clinical therapies. According to the principles of traditional Chinese medicine, CP can be attributed to Shaoyang and Yangming syndromes, which manifest as abdominal pain and hypochondriac distension. Dachaihu Decoction (DCHD) is a classic formula from the “Treatise on Febrile and Miscellaneous Disease.” It is frequently prescribed for conditions associated with combined Shaoyang and Yangming syndromes. However, the specific mechanisms by which DCHD prevents and treats CP remain unclear and require further investigation. Aim of the studyUsing a holistic methodology, including network pharmacology, molecular docking, transcriptomic profiling, and animal experimentation, we explored the potential therapeutic mechanisms of DCHD in CP. Materials and methodsIn a mouse model, caerulein was used to induce CP, and DCHD was administered via gastric lavage to assess its therapeutic effect on pancreatic injury caused by caerulein-induced CP. Subsequently, pancreatic tissues were collected for transcriptomic analysis. Screening of DCHD-active ingredient-target pathways for CP treatment was conducted using network pharmacology and further preliminary validation was performed using molecular docking techniques. Additionally, in vivo and in vitro validation was conducted using animal and cells experiments based on the predicted results. ResultsOur findings suggest that DCHD ameliorates pancreatic acinar cell injury, pancreatic inflammation, and fibrosis in mice with CP. Network pharmacology identified 385 potential targets of DCHD associated with CP. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the therapeutic effect of DCHD on CP may be linked to the mitogen-activated protein kinase (MAPK) signaling pathway. Transcriptomic data supported this finding, as it confirmed that DCHD inhibited the pancreatic MAPK signaling pathway in CP. Molecular docking studies further revealed that the top ten active components of DCHD exhibited strong docking activity with key molecules within the MAPK signaling pathway. Finally, animal experiments confirmed that DCHD effectively reduced the phosphorylation of P38, Jun N-terminal kinase (JNK), and extracellular signal-regulated kinase (ERK) in pancreatic tissues. In addition, the expression of p-P38, p-JNK, and p-ERK was reduced in pancreatic stellate cells and macrophages in the DCHD group. We further treated CP mice, human pancreatic stellate cell line (hPSCs), and macrophage cell line RAW264.7 with the active component baicalin from DCHD, and found that baicalin effectively reduced pancreatic damage in CP. Additionally, the expression of key proteins in the MAPK signaling pathway was significantly decreased in both hPSCs and RAW264.7. ConclusionIn summary, DCHD plays an important role in the treatment of chronic pancreatitis, and it may become a promising drug against the progression of CP. The role of DCHD in alleviating pancreatic inflammatory cell infiltration and fibrosis may be related to the regulation of the MAPK signaling pathway.

Integrating Multi‐Scale Feature Boundary Module and Feature Fusion With CNN for Accurate Skin Cancer Segmentation and Classification

ABSTRACTThe skin, a crucial organ, plays a protective role in the human body, emphasizing the significance of early detection of skin diseases to prevent potential progression to skin cancer. The challenge lies in diagnosing these diseases at their early stages, where visual resemblance complicates differentiation, highlighting the need for an innovative automated method for precisely identifying skin lesions in biomedical images. This paper introduces a holistic methodology that combines DenseNet, multi‐scale feature boundary module (MFBM), and feature fusion and decoding engine (FFDE) to tackle challenges in existing deep‐learning image segmentation methods. Furthermore, a convolutional neural network model is designed for the classification of segmented images. The DenseNet encoder efficiently extracts features at four resolution levels, leveraging dense connectivity to capture intricate hierarchical features. The proposed MFBM plays a crucial role in extracting boundary information, employing parallel dilated convolutions with various dilation rates for effective multi‐scale information capture. To overcome potential disadvantages related to the conversion of features during segmentation, our approach ensures the preservation of context features. The proposed FFDE method adaptively fuses features from different levels, restoring skin lesion location information while preserving local details. The evaluation of the model is conducted on the HAM10000 dataset, which consists of 10 015 dermoscopy images, yielding promising results.

A High-Level Structured Methodology for Development of AI Systems in Africa

AI is a potential game changer for Africa to address the specific challenges she faces in sectors like healthcare, climate change and water-related issues. However, the regulation of AI is still largely underdeveloped in Africa with some existing policies and frameworks still being young. Therefore, as the adoption of AI systems spreads across Africa, so does the need for a structured methodology to guide organizations in either developing new AI systems or onboarding existing ones while maintaining the quality and ethicality of these systems. This paper aims to develop a holistic methodology that provides comprehensive guidance to companies considering to develop new AI systems or onboard existing systems. The goal is to support the development and deployment of AI systems tailored to the specific needs of Africa. The proposed methodology employs a lifecycle approach that integrates both Agile and Waterfall frameworks. By combining the adaptive flexibility of Agile with the structured progression of Waterfall, this methodology ensures adaptability and thoroughness throughout the AI system's development and implementation phases. The integration of these methodologies offers a robust, adaptable framework that can be tailored to the unique demands of AI projects in Africa, from design to implementation, deployment as well as maintenance phases, thereby maximizing the potential impact of AI technologies in the region.

Immunology of the “sensible world” and of the “intelligible world”: the evolutionary decline and the “constructed reality”

The term “immunity” was introduced in medicine due to Ilya Mechnikov. However, the concept of infection resistance existed in European culture long before Mechnikov in the form of s.c. “vital force”. According to holistic methodology, the history of immunology can be divided into three unequal periods: 1) vitalistic – from antiquity to the discoveries of Mechnikov and Ehrlich; 2) “Mechnikov time” (1890s – 1916); and 3) a post-Mechnikov period (1916 – present). At the beginning of 18th century, the existence of “vital force” ideas created the possibility to adapt variolation in Europe, which was based on medieval magical-mystical concepts. Without generalizing of variolation experience, Edward Jenner would hardly have been able to discover vaccination. Scientific ideas of Mechnikov were not a categorical denial of the previous period of immunology. For almost 20 years, he himself had to fend off teleological and vitalistic accusations from the side of those biologists who denied the possibility of phagocytes to act expediently. An analogy helped Mechnikov to confirm his intuition was the phenomenon of intracellular digestion of amoebas or ciliates. Besides, he experimentally proved that phagocytosis was the product of evolution and not a “god gift”. However, in his works, Mechnikov also addressed to the category of “healing forces of the body.” Modern immunology now faces a number of theoretical contradictions and methodological difficulties related to reductionists paradigm: as far as the “split” between the world of “immune objects” (immune cells receptors, antibodies, cytokines, etc.) and “subjectness” of its basic categories “self-nonself”, “immune aggression, “immune defence”, etc., Ilya Mechnikov tried to overcome this contradiction substantiating the phenomenon of phagocytosis as functional system acting in accordance with defense purpose. The construction of “holistic” subject-object models of immunity (such as the symbiosis model of plant immunity) could be a way of overcoming the aforementioned contradictions.

Global electric vehicle charging station site evaluation and placement based on large-scale empirical data from Germany

Electromobility is a key technology to decarbonize transportation and thereby avoid the worst impacts of anthropogenic climate change. To power such vehicles when away from their home or depot, public charging infrastructure is required which can be split into enroute and destination charging. We define the latter as charging events that occur while users are busy with other activities. To fulfill this purpose, chargers need to be placed in locations where people spend time. This paper introduces a novel approach to do so based on a neural network trained on several thousand public charging stations in Germany. Within the training sample, the approach is able to predict how much energy was charged per station and day with an R2 of 0.61 for the training set and a RMSE of 13 kWh/day. Using the network, we predict utilization across urban, suburban and industrial areas in Europe and make those predictions available through an easy-to-use web interface. It is further possible to perform predictions and, thereby, extrapolate the learnings from Germany to any country with sufficient OpenStreetMap data. The introduced holistic methodology with its prediction and visualization phase is a first-of-its-kind by applying large-scale measured charging data to the placement problem while being usable in areas which have not yet rolled out electromobility.

Investigating the efficacy of phenolic Schiff bases as corrosion inhibitors for mild steel in acidic environments: Integrating experimental and theoretical approaches (Part B)

Our research employs a holistic methodology to assess the efficacy of a series of phenolic Schiff bases as corrosion inhibitors for mild steel in a 1 M HCl solution. Through complementary studies, we aim to unravel the corrosion inhibition mechanism by investigating neutral, protonated, and complex forms of inhibitors using advanced computational and statistical methods. This initial investigation specifically involves evaluating the effectiveness of two phenolic Schiff bases through weight loss tests (WL) and electrochemical techniques. The study focuses on the corrosion inhibitory properties of two phenolic Schiff bases, namely 4,4′-((1E,1′E)-(ethane-1,2-diylbis(azaneylylidene))bis(methaneylylidene))diphenol (PSB7) and (1E,1′E)-N,N'-(ethane-1,2-diyl)bis(1-(4-nitrophenyl)methanimine) (PSB8), on mild steel (MS). Results from weight loss and electrochemical methods indicate that inhibition efficiency improves with decreasing temperature and increasing inhibitor concentration, reaching 94 % (for PSB7) and 93 % (for PSB8) at 303 K and 1 × 10−3 M, respectively. Consequently, both PSB7 and PSB8 function effectively as inhibitors in a mixed form. The adherence of both inhibitors to the surface follows the Langmuir adsorption isotherm model. Additionally, SEM and EDS experiments provide evidence of adsorption on the surface. The findings from Density Functional Theory (DFT) and Molecular Dynamic Simulations (MDS) consistently match the actual data, maintaining the order of inhibition efficiency (PSB7 > PSB8).

Virtual Plug-In Hybrid Concept Development and Optimization under Real-World Boundary Conditions

The automotive industry faces development challenges due to emerging technologies, regulatory demands, societal trends, and evolving customer mobility needs. These factors contribute to a wide range of vehicle variants and increasingly complex powertrains. The layout of a vehicle is usually based on standardized driving cycles such as WLTC, gradeability, acceleration test cases, and many more. In real-world driving cycles, however, this can lead to limitations under certain boundary conditions. To ensure that all customer requirements are met, vehicle testing is conducted under extreme environmental conditions, e.g., in Sweden or Spain. One way to reduce the development time while ensuring high product quality and cost-effectiveness is to use model-based methods for the comprehensive design of powertrains. This study presents a layout methodology using a top-down approach. Initially, powertrain-relevant requirements for an exemplary target customer are translated into a specification sheet with specific test cases. An overall vehicle model with detailed thermal sub-models is developed to evaluate the different requirements. A baseline design for a C-segment plug-in hybrid vehicle was developed as part of the FVV research project HyFlex-ICE using standardized test cases, highlighting the influence of customer profiles on the design outcome through varying weighting factors. The target customer’s design is analyzed in four real driving scenarios, considering variations in parameters such as the ambient temperature, traffic, driver type, trailer pulling, and battery state-of-charge, to assess their influence on the target variables. In the next step, the potential of hardware technologies and predictive driving functions is examined in selected driving scenarios based on the identified constraints of the baseline design. As a result, four application-specific technology packages (Cost neutral, Cold country, Hot country, and Premium) for different customer requirements and sales markets are defined, which, finally, demonstrates the applicability of the holistic methodology.

Machine learning enables automated screening for systematic reviews and meta-analysis in urology

PurposeTo investigate and implement semiautomated screening for meta-analyses (MA) in urology under consideration of class imbalance.MethodsMachine learning algorithms were trained on data from three MA with detailed information of the screening process. Different methods to account for class imbalance (Sampling (up- and downsampling, weighting and cost-sensitive learning), thresholding) were implemented in different machine learning (ML) algorithms (Random Forest, Logistic Regression with Elastic Net Regularization, Support Vector Machines). Models were optimized for sensitivity. Besides metrics such as specificity, receiver operating curves, total missed studies, and work saved over sampling were calculated.ResultsDuring training, models trained after downsampling achieved the best results consistently among all algorithms. Computing time ranged between 251 and 5834 s. However, when evaluated on the final test data set, the weighting approach performed best. In addition, thresholding helped to improve results as compared to the standard of 0.5. However, due to heterogeneity of results no clear recommendation can be made for a universal sample size. Misses of relevant studies were 0 for the optimized models except for one review.ConclusionIt will be necessary to design a holistic methodology that implements the presented methods in a practical manner, but also takes into account other algorithms and the most sophisticated methods for text preprocessing. In addition, the different methods of a cost-sensitive learning approach can be the subject of further investigations.

Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework’s effectiveness, and the results demonstrate its proficiency in predicting the EVs’ plugged-in status, with XGBoost’s classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs’ power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape.

A holistic methodology for designing novel flat plate evacuated solar thermal collectors: Modelling and experimental assessment

This paper presents a comprehensive analysis of the design, implementation, experimentation, and optimization of a novel evacuated flat-plate solar thermal collector characterized by a vacuum level between the glass cover and the absorber plate. The paper addresses different research themes, primarily focusing on developing a low-cost evacuated solar thermal collector with a high innovation level. Additionally, it introduces a comprehensive approach for the conceptualization, design, fabrication, testing, and optimization of solar thermal collectors. Specifically, this study aims to highlight the characteristics of a vacuum-enhanced solar thermal collector and demonstrate a step-by-step process involved in its design, fabrication, testing, and optimization. The proposed methodology is based on the adoption of two software tools and an extensive experimental analysis: the commercial software ANSYS is utilized for structural analysis, while MatLab is employed to develop a suitable mathematical tool for assessing the energy performance of the system and optimizing it. The outcomes show the reliability of the methodology and the performance of the low-cost evacuated solar thermal collector under different vacuum levels, providing insights into energy, economic, and environmental aspects. This work innovates by presenting a thorough analysis covering all solar thermal collector production phases, from conceptualization to optimization. The conclusions highlight the production of a reliable approach, and proof-of-concept results demonstrate how increasing the vacuum level enhances the thermal efficiency of a solar collector.

A Framework Analyzing Climate Change, Air Quality and Greenery to Unveil Environmental Stress Risk Hotspots

Rapid urbanization has resulted in increased environmental challenges, compounding worries about deteriorating air quality and rising temperatures. As cities become hubs of human activity, understanding the complex interplay of numerous environmental elements is critical for developing effective mitigation solutions. Recognizing this urgency, a framework to highlight the hotspots with critical environmental issues emerges as a comprehensive approach that incorporates key criteria such as the surface urban heat island intensity (SUHII), heat index (HI) and air quality index (AQI) to assess and address the complex web of environmental stressors that grip urban landscapes. Employing the multicriteria decision analysis approach, the proposed framework, named the environmental risk hotspot mapping framework (ERHMF), innovatively applies the analytic hierarchy process at a sub-criteria level, considering long-term heat island trends with recent fluctuations in the HI and AQI. Climate change impact has been symbolized through rising temperatures, as reflected by surface urban heat island intensity trends over two decades. The robustness and correctness of the weights have been assessed by computing the consistency ratio, which came out as 0.046, 0.065 and 0.044 for the sub-criteria of the SUHII, AQI and HI, respectively. Furthermore, the framework delves into the nexus between environmental stressors and vegetation cover, elucidating the role of green spaces in mitigating urban environmental risks. Augmented by spatial and demographic data, the ERHMF adeptly discerns high-risk areas where environmental stress converges with urban development, vulnerable population concentrations and critical vegetation status, thereby facilitating targeted risk management interventions. The framework’s effectiveness has been demonstrated in a regional case study in Italy, underscoring its ability to pinpoint risk hotspots and inform specific policy interventions. The quantitative study undertaken at the sub-administrative level revealed that approximately 6,000,000 m2 of land in Bologna are classified as being under high to extremely high environmental stress, with over 4,000,000 m2 lying only within the extremely high stress group (90–100). Similarly, 1,000,000 m2 of land in Piacenza and Modena have high levels of environmental stress (80–90). In conclusion, the ERHMF presents a holistic methodology for delineating high-risk urban hotspots, providing essential insights for policymakers, urban planners and stakeholders, with the potential to enhance overall urban resilience and foster sustainable development efforts.

Mechanistic insights into xanthomicrol as the active anti-HCC ingredient of Phytolacca acinosa Roxb.: A network pharmacology analysis and transcriptomics integrated experimental verification

Ethnopharmacological relevancePhytolacca acinosa Roxb. (PAR) is a Traditional Chinese Medicinal (TCM) plant with a broad global distribution encompassing 35 species, four of which are found in the People's Republic of China. It occupies a significant role in both Oriental and American traditional medicine, employed in treating a range of conditions such as edema, inflammation, dermatitis, and rheumatism. PAR is also used as a molluscicide and for addressing tumors and bronchitis. The plant is documented in the Chinese Pharmacopoeia and has a longstanding history in TCM, particularly for its diuretic properties and in treating ailments such as edema, swelling, and ulcers. Notably, PAR has demonstrated potent inhibitory effects against the A549 human lung cancer cell line, underscoring its potential in contributing to the development of novel cancer therapeutics. Aim of the studyThe research aims to elucidate the active components of PAR and their mechanisms in treating hepatocellular carcinoma (HCC). Materials and methodsEmploying network pharmacology, this study predicted the principal active compounds and key targets of PAR. A holistic methodology incorporating biological network analysis, transcriptomics sequencing, molecular docking, and molecular dynamics (MD) simulations was utilized to forecast the effects of PAR on HCC, with empirical evidence supporting these findings. ResultsNetwork pharmacology identified xanthomicrol as the foremost active compound in PAR. The tumor-suppressive functions of PAR, as indicated by KEGG pathway analysis and transcriptomics sequencing, predominantly occur via the PI3K/AKT pathway. Molecular docking and dynamics simulations demonstrated the high affinity of xanthomicrol towards TNF, MMP9, PPARG, KDR, and MMP2. In vivo experiments verified the efficacy of xanthomicrol in curtailing HCC tumor growth, while in vitro assessments revealed its substantial impact on the proliferation and apoptosis of HepG2 and HCCLM3 cells. Moreover, the study indicates that xanthomicrol may modulate the expression of TNF, MMP9, PPARG, KDR, and MMP2 in HCC cells and inhibit the activation of the PI3K/AKT pathway. ConclusionsXanthomicrol, a principal active component of PAR, has been identified to impede the growth of HCC by targeting the PI3K/Akt/MMP9 pathway. This insight could enhance therapeutic approaches for HCC.

The company of the future: Integrating sustainability, growth, and profitability in contemporary business models

This paper examines the evolving nature of corporate organizations, focusing on integrating sustainable practices within traditional business models to achieve economic growth and profitability. Highlighting the importance of embedding sustainability into business strategies, the study explores how modern enterprises can adapt to changing market demands through digital transformation and strategic business planning. The research underscores the rise of hybrid business models that blend conventional commercial approaches with innovative sustainability initiatives, thereby creating value-driven companies. By proposing a holistic methodology, the paper assesses the potential of future organizational models to promote economic sustainability, operational efficiency, and market competitiveness while enhancing corporate responsibility and stakeholder engagement. This study seeks constructive insights into managing the complex dynamics between growth, sustainability, and profitability in contemporary business environments through comparative analyses with established frameworks and real-world case studies.

Holistic characterization of an under-expanded high-enthalpy jet under uncertainty

Elaborate methodologies have been developed to study the thermo-chemical response of materials in high-enthalpy flows. To reach the high magnitudes of heat flux encountered in some hypersonic applications, one can resort to supersonic jets. They involve several physical effects, such as detached shocks ahead of probes. Because of these features, characterizing supersonic flows is a challenging task, especially when one accounts for experimental and modeling uncertainties. Building on the development of stochastic approaches, we propose a holistic methodology to determine the quantities of interest in an optimal manner for an under-expanded high-enthalpy jet, using both experimental measurements and high-fidelity flow simulations. Given the high computational cost of the high-fidelity simulations needed to describe the flow, we built an adaptive/multi-fidelity surrogate model to replace the estimation of the costly computer solver. A Bayesian inference method then allowed for characterizing an experiment carried out in the von Karman Institute's Plasmatron facility, for which no robust methodology currently exists. We show that the reservoir pressure and temperature and the nitrogen catalytic recombination coefficient of the copper probes can be accurately determined from the available measurements. Contrarily, the test conditions do not allow us to estimate the oxygen catalytic recombination coefficient. Finally, the characterized uncertainties are propagated through the numerical solver, yielding an uncertainty-based high-fidelity representation of the hypersonic flow's structure variability.

Adware Detection using LSTM and Comparison with other ML Models

Abstract: In recent years, the internet has become the predominant communication medium, paralleled by a surge in cyber threats where attackers actively exploit vulnerabilities to obtain sensitive information. Among the various malware tactics employed, Adware poses a significant challenge. Addressing this concern, cyber security specialists leverage Machine Learning (ML) techniques, with this paper proposing a novel Long Short-Term Memory (LSTM) algorithm for adware detection. The research employs a holistic methodology involving diverse data pre-processing techniques, feature selection, and ML algorithms to effectively identify adware samples within the dataset. A comparative analysis of ML classifiers, including Random Forest (RF), k - Nearest Neighbors (k-NN), Decision Tree (DT), and Logistic Regression (LR), reveals optimal detection accuracies of 98.66%, 98.10%, and 98.05% for DT and k-NN, respectively. These findings underscore the efficacy of the proposed approach in fortifying cyber security against adware threats.

A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients’ health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.