Multiphase Approach Research Articles

AI-Driven Diagnosis of Chronic Diseases Through Pattern Recognition in Medical Data

Fighting against global chronic disease increases the demand for innovative ideas to enable precise and timely diagnosis. A quantum-changing proposal will come about for an artificial intelligence-empowered framework for chronic disease diagnosis that will apply sophisticated automated pattern recognition on a series of medical datasets. Using machine learning algorithms considered appropriate, electronic health records, medical imaging data, and data collected using wearable sensors are analyzed for significant patterns and correlations completely overlooked in the conventional diagnostic approaches. Data preprocessing is followed by a multi-phase approach to directly determining the right quality and relevance of identified information. Due to the intricate nature of the datasets, CNNs, and RNNs are employed to extract features. This is further enhanced through unsupervised learning algorithms that reveal latent patterns inside patient datasets that can suggest the onset or progression of chronic ailments together. We deploy an expansive database comprising diabetic, cardiovascular, and respiratory patients to evaluate the performance of, having seen AI-based models previously outperforming conventional diagnostic approaches, thus showing an enormous potential for real-time applications, emerging in clinical scenarios. We concentrated on studying what challenges AI diagnostics pose to healthcare systems: data privacy, model interpretability, and how healthcare professionals should adjust from working in isolation to cross-disciplinary collaboration with data scientists and policymakers. That is how, in the programming of this study, the crux of AI emerges in establishing a diagnostic approach to treat chronic diseases, allowing yet another paradigm shift in the future.

A multi-phase approach for developing a conceptual model and preliminary content for patient-reported outcome measurement in TKA patients: from a Chinese perspective.

Patient-reported outcome measures (PROMs) are being used more frequently in total knee arthroplasty (TKA). By utilizing high-quality scales, surgeons can achieve a more comprehensive and accurate evaluation of the effectiveness of TKA surgery. Currently, there is no widely accepted conceptual model for TKA PROMs. The objective of this study is to fill this gap by developing a conceptual model and preliminary content for a PROM that is specifically designed for TKA patients in mainland China. The study design consisted of three stages: (1) a targeted literature review followed by the formation of a conceptual model pool; (2) qualitative data collection involving experts and patients, leading to the development of the preliminary Chinese TKA PROM (CTP); and (3) review of the CTP by experts using the Delphi method, along with cognitive debriefing interviews with patients. 64 patients and 28 experts took part in this study. The conceptual model focused on six key concepts: pain, symptom, function, quality of life, expectation, and satisfaction. To match the model, the authors developed a total of 35 items. A conceptual model and preliminary content for CTP was developed with substantial participation from patients and a multidisciplinary group of experts. The integration of patient and clinical perspectives ensured a comprehensive representation of all relevant disease experiences and the focus of clinical practice. With further refinement through psychometric testing, the CTP is positioned to provide a standardized, comprehensive measure for research specific to Chinese TKA patients.

Situational Awareness Errors in Forklift Logistics Operations: A Multiphase Eye-Tracking and Think-Aloud Approach

Background: This study explores forklift operators’ situational awareness (SA) and human errors in logistic operations using a multiphase approach as an innovative methodology. Methods: Ethnography, eye tracking, error taxonomy, and retrospective think-aloud (RTA) were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. After analyzing 566 events across 18 tasks, this research highlighted eye tracking’s potential by offering real-time insights into operator behavior and RTA’s potential as a method for cross-checking the causal factors underlying errors. Results: Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high-cognitive demand tasks, such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable to errors and are the most affected by operator risk generators (ORGs), representing 42% of incident risk events. This study identifies driving, hoisting, and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADASs). Conclusions: This study provides valuable insights into how we may improve safety in logistics operations by proposing a multiphase methodology to uncover the patterns of attention, perception, and cognitive errors and their impact on decision-making.

Enhancing digital intelligence among Thai seniors: developing and validating the digital intelligence quotient scale

PurposeThe purpose of this study is to develop and validate a digital intelligence quotient (DIQ) scale specifically designed to assess cognitive and socio-emotional competencies among the elderly population in Thailand. The study aims to enhance the understanding of digital intelligence within this demographic and provide a practical tool for researchers and practitioners in the fields of gerontology and digital literacy.Design/methodology/approachThe study employed a multi-phase approach, beginning with the formulation of 34 questions based on established DIQ concepts and theories. These questions underwent exploratory factor analysis (EFA) for initial validation, followed by confirmatory factor analysis (CFA) and assessments of convergent validity. The final DIQ scale was tested with a sample of 400 elderly participants in Thailand, ensuring both content and construct validity.FindingsThe EFA identified eight dimensions of DIQ: digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital communication, digital literacy and digital rights. The CFA confirmed the consistency of these dimensions with empirical data, resulting in a reliable and valid instrument for assessing digital intelligence among the elderly. The study highlights the importance of these competencies in navigating the digital landscape and addressing the unique challenges faced by older adults.Originality/valueThis study provides a novel and comprehensive instrument for assessing digital intelligence among the elderly, contributing to the theoretical understanding of digital competencies in this demographic. The DIQ scale offers valuable insights for designing tailored interventions and digital literacy programs, promoting digital resilience and knowledge among older adults. This research addresses a critical gap in the literature and has practical implications for enhancing digital inclusion and safety for the elderly.

Cultural Heritage Environments: Monitoring Strategy for Preventive Conservation of Cultural Assets and Human Health Protection

Objects of historic artistic value, conserved in indoor deposits or exhibited inside museum halls, are strongly influenced by the environmental parameters, as temperature, relative humidity and light quality. Environmental parameters directly impact the structural integrity of constitutive materials and promote microbial colonization on artwork surfaces, leading to biodeterioration. In cultural heritage dedicated environments (CHE), the microbial load may exist both on art works surface and in the environmental aerosol (bioaerosol), maintaining a unique balance. In this study, through a multi-phasic approach the presence of bacteria and fungal colonies in the aerosol and artifacts surface, of an exposure hall, have been investigated. This study defined specific, non-invasive procedures to sample microbial colonies, spread both on artworks surface and in the aerosol of dedicated indoor environments. Results from morphological analysis (microscopy, in vitro culture) and molecular investigation (microbial genomic DNA), provided useful information on the composition of the microbial consortia, allowing a complete understanding. Microorganisms, in addition to inducing artifacts biodeterioration are able to produce and release, in the aerosol (bioaerosol) of surrounding environment, biological particles and molecules (spores, cellular debris, toxins and allergens), potentially dangerous for the health of operators and visitors. The complete understanding of the consortia is peculiar to counteract the microbial colonization, also performing green strategies.

Redesigning quick commerce fresh and short food supply chains: circular economy strategies for sustainable last-mile operations

PurposeFresh and short food supply chains in grocery quick commerce (q-grocery FSFSCs), while valued for swift delivery, face challenges in sustainability, resource efficiency and scalability. This study aims to redesign the last mile of q-grocery FSFSCs by identifying and validating a framework of applicable circular economy (CE) practices. Furthermore, the study assesses the impact of these practices across various sustainability dimensions to understand their level of contribution to sustainable livelihoods and supply chain resilience.Design/methodology/approachThe study employs a multi-phase approach. First, an integrative literature review identifies CE practices and strategies. Second, a survey gathers data on the relationship between these practices and strategies and their impact on sustainability. Third, the framework is validated using the partial least squares structural equation modeling (PLS-SEM) method in WarpPLS 8.0. Finally, k-means cluster analysis in Tableau 2019.4 groups CE practices based on their impact across five sustainability dimensions: environmental, economic, social, resource efficiency and scalability.FindingsThe study validates a framework and identifies high-impact CE practices, such as waste reduction, sustainable packaging, sustainable sourcing, route optimization, sustainable processes and customer convenience. Medium-impact practices include energy efficiency, sustainable fleet, resale and redistribution and information flow. Low-impact practices cover composting, reverse logistics and appropriate storage.Research limitations/implicationsThe findings assist q-grocery companies to adopt CE practices and strategies that support sustainable livelihoods and strengthen supply chain resilience while offering policymakers guidance to promote circularity.Originality/valueThis study is unique in the exploration of the intersection of quick commerce, FSFSCs and CE principles, providing actionable insights for sustainability in the q-grocery industry.

Optimized robust learning framework based on big data for forecasting cardiovascular crises

Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework’s processing time is 436 s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework’s potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy.

Paramedicine educators’ identity needs and impediments to professional emergence: A multiphase mixed-methods participatory approach

This research delves into how identity-needs and philosophies of paramedicine educators influence theoretical advancement and praxis in higher education. It examines the perceptions of paramedicine academics, their roles and the transitional challenges when moving from clinical practice to academia. It explores the potential of transformative pedagogy in fostering social consciousness, justice, and innovation. Challenges and benefits of a constructivist approach to paramedicine education and future-orientation is assessed. Employing a multiphase mixed-methods participatory approach, researchers reached consensus on the guided themes (Phase 1) for staff collaborative engagement (Phase 2) during the inaugural ‘Paramedicine Educators Forum’, which featured participation of academic leaders and staff from four universities and one jurisdictional ambulance service. The study unveils the intricate tapestry of paramedicine educators' identities and philosophies and their impact on theoretical advancements and practical applications in higher education. Conversations revolved around defining the role of paramedics in academia, the potential of transformative pedagogy, and the balance between producing competent paramedics whilst nurturing criticality. Discussions also addressed the shortcomings of current undergraduate degrees in preparing students for technological advancements and the potential for extended degree programs. The findings underscore the need to adapt paramedicine education to meet the evolving demands of the profession, with paramedicine educators playing a leading role in this transformation. These insights may be instructive in providing guidance for educational policies and practices that shape the future of paramedicine education and may have transferability for other allied health professions.

A unified HTC multiphase model of continuum mechanics

In this paper, we present a unified nonequilibrium model of continuum mechanics for compressible multiphase flows. The model, which is formulated within the framework of Hyperbolic Thermodynamically Compatible (HTC) equations, can describe an arbitrary number of phases that can be heat conducting inviscid and viscous fluids, as well as elastoplastic solids. The phases are allowed to have different velocities, pressures, temperatures, and shear stresses, while the material interfaces are treated as diffuse interfaces with the volume fraction playing the role of the interface field. To relate our model to other multiphase approaches, we reformulate the novel HTC governing equations in terms of the phase state parameters and put them in the form of Baer-Nunziato-type models. It is the Baer-Nunziato form of the HTC equations which is then solved numerically using a robust second-order path-conservative MUSCL-Hancock finite volume method on Cartesian meshes. Due to the fact that the obtained governing equations are very challenging we restrict our numerical examples to a simplified version of the model for three-phase mixtures. To address the stiffness properties of the relaxation source terms present in the model, the implemented scheme incorporates a semi-analytical time integration method specifically designed for the non-linear stiff source terms governing the strain relaxation. The validation process involves a wide range of benchmarks and several applications to compressible multiphase problems. Notably, results are presented for multiphase flows in several relaxation limit cases of the model, including inviscid and viscous Newtonian fluids, as well as non-linear hyperelastic and elastoplastic solids. In all cases, the numerical results demonstrate good agreement with established models, including the Euler or Navier-Stokes equations for fluids and the classical hypo-elastic model with plasticity for solids. Importantly, however, this approach achieves these results within a unified multiphase framework of continuum mechanics.

A multi-phase mission success evaluation approach for maritime autonomous surface ships considering equipment performance degradation and system composition changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Continuous adaptation of conversation aids for uterine fibroids treatment options in a four-year multi-center implementation project

BackgroundFibroids are non-cancerous uterine growths that can cause symptoms impacting quality of life. The breadth of treatment options allows for patient-centered preference. While conversation aids are known to facilitate shared decision making, the implementation of these aids for uterine fibroids treatments is limited. We aimed to develop two end-user-acceptable uterine fibroids conversation aids for an implementation project. Our second aim was to outline the adaptations that were made to the conversation aids as implementation occurred.MethodsWe used a multi-phase user-centered participatory approach to develop a text-based and picture-enhanced conversation aid for uterine fibroids. We conducted a focus group with project stakeholders and user-testing interviews with eligible individuals with symptomatic uterine fibroids. We analyzed the results of the user-testing interviews using Morville’s Honeycomb framework. Spanish translations of the conversation aids occurred in parallel with the English iterations. We documented the continuous adaptations of the conversation aids that occurred during the project using an expanded framework for reporting adaptations and modifications to evidence-based interventions (FRAME).ResultsThe first iteration of the conversation aids was developed in December 2018. Focus group participants (n = 6) appreciated the brevity of the tools and suggested changes to the bar graphs and illustrations used in the picture-enhanced version. User-testing with interview participants (n = 9) found that both conversation aids were satisfactory, with minor changes suggested. However, during implementation, significant changes were suggested by patients, other stakeholders, and participating clinicians when they reviewed the content. The most significant changes required the addition or deletion of information about treatment options as newer research was published or as novel interventions were introduced into clinical practice.ConclusionsThis multi-year project revealed the necessity of continuously adapting the uterine fibroids conversation aids so they remain acceptable in an implementation and sustainability context. Therefore, it is important to seek regular user feedback and plan for the need to undertake updates and revisions to conversation aids if they are going to be acceptable for clinical use.

PERANCANGAN UI/UX PADA APLIKASI WISATA LOMBOK MENGGUNAKAN METODE USER CENTERED DESIGN

This study investigates the application of User-Centered Design (UCD) principles in the design UI/UX for the mobile tourism application, 'Wisata Lombok.' The research aims to demonstrate how UCD can significantly improve user interface (UI) and user experience (UX) in context-specific applications. The methodology employed a multi-phase approach, including user research, persona development, iterative design. Data was collected through interviews with local and tourists, online surveys with Lombok residents, and on-site observations. This information guided the creation of user personas and informed the application's feature set and design decisions. Key findings reveal that incorporating local cultural elements into the UI design enhanced user engagement. The implementation of features like maps and cultural event calendars. Usability testing was carried out using an online questionnaire using the UEQ method. The UEQ contains 26 questions which structured in the form of a semantic differential, where each word consists of two terms expressing opposite meanings. The form was distributed to 35 respondents. All of the 35 respondents, 16 identified as male and 19 as female. The age distribution indicates that 29 respondents are within the 18-35 years old range, while 6 respondents are within the 35-56 years old. The UEQ result shows that the perspicuity and efficiency scale has the highest values, 2.29 and 1.96, falling within the “Excellent” range, indicating that the respondents didn't find any difficulties in operating the user interface. The attractiveness, dependability, and novelty scale fall within the “above average” value, indicating that the user interface was based on respondent needs. The stimulation scale has the least value 0.44 which fall within “bad” value , which means that the respondents was bored cause the user interface only has a few features.

DEVELOPING INTERACTIVE MULTIMEDIA-BASED ESP TO IMPROVE ENGLISH PROFICIENCY AND CAREER GUIDANCE AT UPMI MEDAN

This research explored the development of an interactive multimedia-based English for Specific Purposes (ESP) program designed to enhance English proficiency and career guidance at UPMI Medan. It addressed a notable discrepancy between theoretical frameworks and practical needs in ESP, as students increasingly demanded courses that integrated both oral and written communication skills with job-related tasks. Applying the Kemp instructional design model, the research used a comprehensive multi-phase approach: assessing student proficiency levels, identifying instructional needs, designing tailored ESP content for Business Administration, Tourism, and Public Speaking, and incorporating multimedia platforms such as Udemy and Gaminik. Key findings revealed that 40% of students prioritized mastering business correspondence, 58% sought improved communication skills for meetings, and 48% were interested in tourism-specific English. The research highlighted the effectiveness of flipped learning and interactive multimedia in overcoming vocabulary challenges and boosting engagement. Ultimately, it emphasized the need to integrate career-focused modules into ESP courses to better align language skills with career aspirations, thereby enhancing employability and professional readiness.

Mean-field homogenization of liquid metal elastomer composites: comparative study and exact dilute solution of core–shell inclusion

Liquid metal-elastomer composites (LMECs) have gathered significant attention for their potential applications in various functional stretchable devices, with inclusion sizes ranging from micrometers to nanometers. These composites exhibit exceptional properties, such as high electric permittivity and thermal conductivity, surpassing those of the elastomer matrix, thus enabling a broader range of applications without compromising the material's stretchability. To investigate the diverse effective elastic and functional properties of LMECs, micromechanics-based homogenization method based on Eshelby's inclusion solution are invaluable. However, the extreme contrast in elastic constants among the phases in LMECs, particularly for nanosized inclusions where a considerable amount of stiff metal oxide forms around the inclusions, can lead to critical failure in predicting effective properties if inadequate homogenization approach is employed. In this study, we present multiple mean-field homogenization approaches applicable to LMECs with core-shell morphology, namely: (i) multi-phase, (ii) sequential, (iii) pseudo-grain, and (iv) direct approaches. We compare the accuracy of the models concerning effective elastic, thermal, and dielectric properties, evaluated against numerical homogenization results and compared with reported experimental data. Specifically, we highlight homogenization scheme utilizing exact field solutions of dilute core-shell inclusion, emphasizing the importance of accurately capturing the field in the micromechanics of LMECs. Furthermore, we demonstrate that widely utilized interphase model could not properly resolve the core-shell morphology and thus should be avoided. This comprehensive assessment provides critical insights into the proper homogenization strategies for designing advanced LMECs with precise prediction of effective properties.

Obgyn resident research program practices and outcomes after implementation in an academically affiliated residency program

ProblemUnlike other aspects of Obstetrics and Gynecology (OBGYN) residency training, the research education curriculum and requirements are not standardized. It is unclear what constitutes core curricula in educational research programs, which components are most useful and efficacious, and how to use these findings to adapt existing programs.ApproachA multi-phase approach to (1) garner cross-sectional data from United States OBGYN Residency Program Directors and review previously published literature, (2) use Phase I and input from clinical, research and education faculty to develop a comprehensive program, and (3) evaluate the program for resident research productivity, knowledge and satisfaction.OutcomesPhase I: Sixty-one (31%) Program Directors completed the survey. Most reported ≤ 4 research didactics per year and no dedicated research rotation. Programs with post graduate year (PGY) level specific didactics, a research rotation, or allocated time for the Research Director role noted higher odds of resident research resulting in peer-reviewed publication. Phase II: A Resident Research Program was adapted to include a clinical and research faculty co-directorship and seven additional didactics (all of which were PGY-specific). A departmentally funded resident research grant and a repository to monitor completion of milestones and program requirements have been developed. Surveys were used to track residents’ research competence and satisfaction with the research program. While resident involvement in research activities and satisfaction with opportunities for scholarly activity have improved, resident self-evaluations and mentor evaluations have not. In addition, we have not seen an increase in publications or presentations.ConclusionImplementation of components of a research program associated with increased productivity did not result in increased productivity in our program, however, resident satisfaction with scholarly activity did improve.

A convection-diffusion-reaction system with discontinuous flux modelling biofilm growth in slow sand filters

A Slow Sand Filter (SSF) consists of biofilm attached to saturated packed sand with supernatant water on top, through which water flows by gravity. SSFs are used in the production of drinking water to improve water quality and remove pathogens, and, as they use biological filtration via the microbes present in the biofilm, they are simple and cheap to construct. A drawback of SSFs is that the difficult sampling and the complex dynamics associated with biofilm growth of microbes pose an obstacle for analysis and predictions. In order to overcome this issue, a mathematical model consisting of a non-linear convection-diffusion-reaction system with discontinuous flux is developed from first principles as a unified framework for the dynamics in time and depth of the biofilm and microbial community, where a variety of ecological models can be included in the reaction terms, which also contain attachment, detachment and transfer processes. The multi-phase approach considers three main physical volumes: a biofilm matrix, the water volume enclosed by it and the flowing suspension through the SSF surrounding the biofilm. The only input data needed are the inflow concentrations and rate, light irradiation and temperature. The model includes a Cahn–Hilliard-type equation for the biofilm velocity in the supernatant water. A numerical scheme that preserves physical properties of the solution is also presented. This is achieved with a time-splitting scheme using an upwind scheme with an adaptive time-step size that ensures positivity of the solution under a CFL condition and a reasonable assumption on the separate numerical solution of the Cahn–Hilliard-type equation. The latter is solved numerically with a semi-implicit finite-difference scheme using a mixed form of the equation. The flexible model framework with its numerical scheme allows for including a variety of ecological models, making the investigation and development of increasingly complex simulations possible. Simulations with different model parameters are shown to be qualitatively consistent with the literature.

3D printing for teaching and exploration in astronomy for individuals with blindness/visual impairment: textured representations of imagery

ABSTRACT Astronomy, a captivating field that draws upon science, mathematics, and engineering, has traditionally relied on visual representations to convey the wonders of the cosmos. While this approach effectively engages the sighted population, the use of imagery can exclude individuals with blindness or visual impairment (B/VI). Astronomical research is incorporated into press releases, media, outreach efforts, and educational systems aimed at enhancing public interest and often skill in science, but visual materials can hamper a population with B/VI. This paper explores the potential of 3D printing as an assistive technology providing an alternative to imagery. We produced textured 3D prints of astronomical research data from the Hubble Space Telescope (HST). Useability assessment of materials is an important phase of production before integration into structured programs, and we used a multi-phased approach in our prior research to create and test appropriate textures for 3D astronomical prints. This paper describes the last step of reviewing our 3D prints through informal useability sessions with diverse individuals. The assessment indicated our 3D prints provide reliable, informative representations of astronomical data appropriate for public use especially for public information, outreach programs, and science education for individuals with B/VI.

Optimisation and validation of cost-effective water-based solutions of acid fuchsin for detecting fingermarks in blood

Fingermark impressions in blood are commonly encountered at violent crime scenes and represent a critical trace that can link an individual to the scene. A range of techniques are available for detecting and enhancing bloody impressions; however, many chemical methods involve using hazardous solvents or require alternative light sources to visualise fluorescence. This is particularly challenging for bloody impressions on dark substrates. An alternative treatment is the protein dye known as acid fuchsin (commonly known as ‘Hungarian Red’), which can be visualised under both white light and fluorescence lighting. However, there is limited research available on this method, especially concerning its use in detecting bloody fingermarks on dark surfaces and its fluorescence qualities. To address these knowledge gaps, this study broadly aimed to evaluate the effectiveness of acid fuchsin for enhancing bloody fingermarks on a range of common substrates, along with comparing the performance of a formulation made from base components against a commercially-available Hungarian Red reagent. Through a multi-phased experimental approach, results supported an all-in-one treatment that contained 2 % SSA, 0.2 % acid fuchsin, and deionised water as the most effective. This formulation performed as well or better than commercial Hungarian Red, amido black and acid yellow in the validation trial. Enhanced impressions could be visualised under white light on light and dark surfaces, whilst 530 nm excitation provided improved detection via both fluorescence and absorption modes depending on substrate background interference. Moreover, the reagent was applied by spraying directly onto substrates placed at near-vertical angles, with no evidence of any fingermarks being affected by running or inadequate fixing. The ability to enhance and visualise bloody impressions on light and dark surfaces, under white light or excitation, using a single, water-based treatment is highly advantageous to operational crime scene examiners and forensic scientists.

Comparative study of two-phase flow approaches for modeling conjugate heat transfer in a three-dimensional wavy microchannel-heat sink with CuO-water nanofluid

This study aims to numerically investigate the conjugate heat transfer (CHT) and the changes in the hydraulic and thermal terms of laminar flow (300 ≤ Re ≤ 800) in a 3-D wavy microchannel heat sink (MCHS). The obtained results from different nanofluid flow simulated models will be compared. The numerical simulation approaches used include single-phase, mixture, Eulerian, DPM, and DDPM. The effect of adding 1%, 3%, and 6% concentration of CuO nanoparticles (100 nm) in deionized water as the base fluid is examined on various parameters such as local and axial temperature, heat transfer convective coefficient, friction factor, Nusselt number (Nu), velocity distribution, and thermal and hydraulic boundary layer parameters. The substrate surface, due to its larger area surface and the amplitude of the sinusoidal wave, has a greater impact on the nanofluid flow as a channel bottom conjugate surface compared to others. The results obtained from the different models show close agreement with each other for various cases. For example, at Re = 304, the average Nu and friction factor for pure water are approximately 10.43 and 0.0260, respectively. For water with 0.01 concentration of CuO nanoparticles, Nu is found to be in the range of 10.43–10.78, and the friction factor ranges from 0.260 to 0.290, depending on the approach used for simulation. This shows a 2% increase in Nusselt number compared to a 0.05% increase in the friction factor. Moreover, these values increase by approximately 5.7% and 11.3% on average with an increase in CuO nanoparticle concentration to 0.06. Additionally, the DPM and DDPM models show a 15% difference in the scattering of CuO nanoparticles compared to the Eulerian-Eulerian models. The single-phase model presented in this study yields similar results to the multiphase approaches. Furthermore, the PEC parameter increases by about 1.9% with the addition of CuO nanoparticles at a concentration of 0.06.

Analyzing Threat Level of the Backdoor Attack Method for an Organization’s Operation

Backdoor attacks played a critical part in the catastrophe, as well as the overall impact of cyberattacks. Backdoor assaults are additionally influencing the landscape of malware and threats, forcing companies to concentrate more on detecting and establishing vulnerability tactics in order to avoid hostile backdoor threats. Despite advances in cybersecurity systems, backdoor assaults remain a source of concern because of their propensity to remain undetected long after the attack vector has been started. This research is aimed to examine the threats of backdoor attack methods in an organization's operational network, provide a full-scale review, and serve as direction for training and defensive measures. The fundamental inspiration was drawn from the alarming and involving threat in cybersecurity, which necessitates a better awareness of the level of risk and the concurrent requirement for increased security measures. Most traditional security solutions usually fail to detect harmful backdoors due to the stealthy nature of backdoor code within the system, necessitating a unique approach to full-scale threat analysis. A multi-phase approach that begins with considerable reading and examination of existing literature to get insight into typical backdoor attack methodologies and application methods. Following analysis, testing was carried out in a virtual lab in a controlled environment because thorough malware analysis testing must adhere to ethical and legal cyber testing laws to avoid any penalties or foolish breaches. This methodology also included testing on numerous attack channels combined with backdoor attacks, such as detecting software vulnerabilities, phishing emails, and direct payload injection, to determine the complexity of the different attack vectors. Each of the collected data is utilized to create a threat model that predicts the amount of risk associated with the backdoor attack approach. The finding contributes to the development of more resilient defence mechanisms, while also strengthening the overall organization's security architecture and protocols.