Alternative Methodology Research Articles

Inter- and intra-observer reliability and agreement of O2Pulse inflection during cardiopulmonary exercise testing: A comparison of subjective and novel objective methodology.

Cardiopulmonary exercise testing (CPET) is the 'gold standard' method for evaluating functional capacity, with oxygen pulse (O2Pulse) inflections serving as a potential indicator of myocardial ischaemia. However, the reliability and agreement of identifying these inflections have not been thoroughly investigated. This study aimed to assess the inter- and intra-observer reliability and agreement of a subjective quantification method for identifying O2Pulse inflections during CPET, and to propose a more robust and objective novel algorithm as an alternative methodology. A retrospective analysis was conducted using baseline data from the HIIT or MISS UK trial. The O2Pulse curves were visually inspected by two independent examiners, and compared against an objective algorithm. Fleiss' Kappa was used to determine the reliability of agreement between the three groups of observations. The results showed almost perfect agreement between the algorithm and both examiners, with a Fleiss' Kappa statistic of 0.89. The algorithm also demonstrated excellent inter-rater reliability (ICC) when compared to both examiners (0.92-0.98). However, a significant level (P ≤0.05) of systematic bias was observed in Bland-Altman analysis for comparisons involving the novice examiner. In conclusion, this study provides evidence for the reliability of both subjective and novel objective methods for identifying inflections in O2Pulse during CPET. These findings suggest that further research into the clinical significance of O2Pulse inflections is warranted, and that the adoption of a novel objective means of quantification may be preferable to ensure equality of outcome for patients.

Exploring approaches to contemporary clinical incident analysis methods within acute care settings: a scoping review protocol.

This review will explore the literature on contemporary incident analysis methods used in acute hospital settings, identifying types and characteristics of these methods and how they are used to minimize, prevent, or learn from errors and improve patient safety. Safety is a major focus in health care; however, despite best efforts, errors and incidents still occur, leading to harm or potential harm to patients, families, carers, staff, or the organization. Incident analysis methods aim to reduce risk of harm. Traditional methods have been criticized for failing to consider the complexity of health care and the dynamic nature of acute care settings. Alternative methodologies are being sought to achieve higher levels of patient safety and care quality care in hospitals. Learning from errors and communicating with those involved in incidents are key requirements in contemporary incident analysis. This review will consider empirical research published since 2013, reporting on the use of clinical incident analysis methods within acute care settings. The review will explore ways in which consumers or stakeholders (eg, clinicians or other hospital workers, patients, families, carers, visitors) have been included in these analysis methods and how data have been used to support changes in the service or organization. Following JBI methods and PRISMA-ScR reporting guidance, we will search PubMed, CINAHL (EBSCOhost), Embase, Scopus, the Cochrane Library, Web of Science, and ProQuest Dissertations and Theses. Studies will be reviewed independently, with results presented in tables, figures, and narrative summaries according to the concepts of interest.

Evaluation of the impact of wastewater irrigation on the soil's dielectric parameters

AbstractThis study investigates the impact of wastewater irrigation on soil dielectric parameters, focusing on the often‐overlooked aspect of dielectric properties in contaminated soil. Wastewater, a significant contributor to soil and groundwater degradation, has long‐term effects on soil physical, chemical, and dielectric characteristics. Experimental measurements conducted overtime at 2.45 and 5.8 GHz frequencies reveal a correlation between wastewater irrigation and changes in dielectric properties. Emphasizing time variation as a pivotal independent variable, the study sheds light on its substantial influence on soil dielectric behavior. Notably, the research introduces an alternative methodology for the comprehensive evaluation of wastewater‐induced pollution effects on soil properties. This approach is substantiated by meticulous physicochemical analyses conducted on both pollution sources and the affected soil. The study further presents a robust response surface method‐based model, showcasing impressive accuracy with dielectric properties (ε′ and ε″) and penetration depth with R2 values of 0.9845, 0.9831, and 0.9528, respectively. Beyond unveiling crucial insights into the intricate interplay of wastewater and soil dielectric behavior, this research contributes to the development of effective strategies for pollution monitoring and management in soil ecosystems.

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

A method based on multi-agent reinforcement learning is proposed to tackle the challenges to capture escaping Target by Unmanned Ground Vehicles (UGVs). Initially, this study introduces environment and motion models tailored for cooperative UGV capture, along with clearly defined success criteria for direct capture. An attention mechanism integrated into the Soft Actor-Critic (SAC) is leveraged, directing focus towards pivotal state features pertinent to the task while effectively managing less relevant aspects. This allows capturing agents to concentrate on the whereabouts and activities of the target agent, thereby enhancing coordination and collaboration during pursuit. This focus on the target agent aids in refining the capture process and ensures precise estimation of value functions. The reduction in superfluous activities and unproductive scenarios amplifies efficiency and robustness. Furthermore, the attention weights dynamically adapt to environmental shifts. To address constrained incentives arising in scenarios with multiple vehicles capturing targets, the study introduces a revamped reward system. It divides the reward function into individual and cooperative components, thereby optimizing both global and localized incentives. By facilitating cooperative collaboration among capturing UGVs, this approach curtails the action space of the target UGV, leading to successful capture outcomes. The proposed technique demonstrates enhanced capture success compared to previous SAC algorithms. Simulation trials and comparisons with alternative learning methodologies validate the effectiveness of the algorithm and the design approach of the reward function.

Feasibility of Community Needs Assessment Tools as an Alternative For Health Survey in Describing Health Profile of A Community - A Mixed Method Study In Muddungere, Karnataka, India

Introduction: In India, even with planned health services, it is difficult to reach the health goals without community participation. While Community needs assessment approach (CNAA) tools like Focussed group Discussion (FGD), Village mapping and matrix ranking have been effective in quickly gathering information, surveys have been conventionally preferred. Aim & Objective: The present study compares qualitative and quantitative methods to understand feasibility of using CNAA tools in place of survey as an alternative tool Methodology: Mixed Method research employing Survey and CNAA methods of FGD, Village Mapping, transect walk and Matrix ranking score was carried out as a part of academic activity of National Service Scheme(NSS). Data was represented in form of percentages and frequency tables. Association was measured using Chi Square using Epi Info TM version 7.2.1software. Results: Using quantitative survey, we found that 71% used piped supply and 93.1% of the households used toilets. But 6.9% of them still preferred open fields for defecation. Amongst the participants of FGD, it was found that 90% participants preferred going to government hospitals. Comparable data found for variables like Maternal and child health indicators, Sanitation practices and burden of Non communicable diseases. Partially comparable results were found for burden of communicable diseases. Conclusion: CNAA research tools demonstrated comparability with survey in most areas of health concern and reduced the time required for conducting research without compromising the quality of results

Fuzzy Divergence Measure Based on Technique for Order of Preference by Similarity to Ideal Solution Method for Staff Performance Appraisal

Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Given the inherent uncertainty and ambiguity in multi-criteria decision-making (MCDM) scenarios, the concept of the fuzzy α-cut is leveraged. This allows experts to establish a broader spectrum of rankings, accommodating fluctuations in their confidence levels. To produce consistent criteria weights with the existence of outliers, the fuzzy Method based on the Removal Effects of Criteria (MEREC) is employed. To showcase the viability and effectiveness of the proposed approach, a quantitative illustration is provided through a staff performance review. In this context, the findings are compared with other MCDM methodologies, considering correlation coefficients and CPU time. The results demonstrate that the proposed technique aligns with current distance measure approaches, with all correlation coefficient values exceeding 0.9. Notably, the proposed method also boasts the shortest CPU time when compared to alternative divergence measure methodologies. As a result, it becomes evident that the proposed technique yields more sensible and practical results compared to its counterparts in this category.

Using machine learning to determine factors affecting product and product–service innovation

PurposeMachine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).Design/methodology/approachThe ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.FindingsThe analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.Research limitations/implicationsThe analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.Originality/valueThe novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

Development of alternative methodology for normalization of the small animals PET scanner of LIM/CDTN

Normalization is a step taken to correct variations in count rates between detection channels of small animals PET scanners. In general, in laboratory practice, normalization is performed using a commercial source of germanium 68. Given the half-life of the Ge-68 isotope, the normalization source must be replaced every two years at most, which generates a significant spent for the Laboratory. The objective of this work was to develop and test an alternative rechargeable source to be used on normalization routine. The results indicated that the rechargeable source (18F-FDG) can be used to replace the commercial source.

Imaging of cylindrical inhomogeneites in a parallel plate waveguide with reverse time migration method

Abstract While reverse time migration (RTM) algorithm is commonly used in geophysical explorations, this paper addresses the RTM imaging procedure for reconstructing of lossy dielectric discontinuities in a planar waveguide using electromagnetic waves at a single frequency. The direct problem of the related configuration is solved via method of moments (MoM) to produce the synthetic scattered data to be used in RTM. The achievements of the method are examined and verified by including different numerical examples. It is shown that the RTM approach can be used as an alternative imaging methodology in parallel plate waveguide problems.

Bitcoin forks: What drives the branches?

Despite frequent Blockchain splits stemming from Bitcoin, few studies have examined the determinants of Bitcoin fork returns. In this paper, we investigate the relationships between the returns of Bitcoin forks and a range of common risk factors, including Bitcoin, currency, network and equity-based factors. From a statistical perspective, we find consistent and significant associations between fork returns, their Bitcoin counterparts, and equity markets. Other common factors, such as the equity small-minus-big factor and changes in the Japanese Yen, are found to have occasional links with fork returns. From an economic perspective, Bitcoin returns are the predominant driver of fork returns, accounting for essentially all of the explained variation. These findings are confirmed using orthogonalised common factors and with an alternative methodology, quantile regression. This research broadens our understanding of Bitcoin forks, indicating that a change in blockchain protocol is insufficient to sever links with the Bitcoin parent.

The use of alternative methodologies as a strategy in teaching Geography

Considering the constant changes in the teaching of Geography, with the objective of directly impacting basic education through methodologies that facilitate the teaching-learning process, the interest in this theme arises. In this context, the exploration of methodological alternatives for teaching-learning has become essential. Resources such as music, information and communication technologies (ICTs), literature and cartoons were addressed, with an emphasis on the latter, through a bibliographical review. These methods were presented as tools that revolutionized the teaching of geography, facilitating the transmission of content combined with geographical themes. The idea is to awaken in educators the interest in methodologies that align with their reality and didactic, overcoming paradigms associated with traditional geography. This aims to lead students to a more critical, subjective and contextualized approach within the school geography.

Mapping health services for adults with cerebral palsy in Ireland: a pilot study

Background Cerebral palsy (CP) is a common cause of physical disability in childhood. The majority of children with CP survive to adulthood. Once discharged from children’s services, adults with CP find it challenging to navigate health services. The aim of this study was to pilot and refine a methodology to map services for adults with CP in Ireland. Methods We used a multi-informant mapping methodology consisting of: 1. Defining health services; 2. Identifying informants; 3. Designing a survey; 4. Collecting data; 5. Data checking and analysis. We collected data on services from service users and service providers using an online survey. We verified data against information available online and by asking organisations to provide details about the service. Results Fifteen service users and nine service providers completed the online survey. Data on 265 unique services at 32 organisations were provided. The most commonly provided services were physiotherapy (12%) and occupational therapy (11%). We confirmed the name of 89 services (34%) against online information. We received further details from eight organisations about 27 services. Specifically, we received details about the organisation name for 27 of the 265 services (10%), service name for 25 services (9%), service type for 25 services (9%), a website for 19 services (7%), and data on eligibility criteria and types of supports provided for between 25 or 26 services (9% or 10%). Conclusion This pilot study highlighted the complexity of mapping services for adults with CP in Ireland. We recommend that an alternative methodology should be used to map services for adults with CP in Ireland.

An Alternative Sensitivity Analysis for the Evaluation of MCDA Applications: The Significance of Brand Value in the Comparative Financial Performance Analysis of BIST High-End Companies

Multi-criteria decision analysis (MCDA) applications consist of techniques that enable the decision maker to make clearer decisions in scenarios where there is more than one alternative and criterion. The general approach for sensitivity analysis in MCDA applications implies sensitivity to the weight coefficient. In this study, as an alternative approach, we reinterpret sensitivity by using the statistical relationship between the final ranking produced by an MCDA method and a constant external factor. Thus, we both verify through an anchor and reveal to what extent the change in the weight coefficient changes the external relations of MCDA. The motivation for this study is to propose an alternative sensitivity methodology. On the other hand, brand value is a parameter that contains critical information about the future of the company, which has not integrated into financial performance studies made with MCDAs before. To that end, the financial performance of 31 companies with the highest brand value in Turkey and trading on Borsa Istanbul between 2013 and 2022 was analyzed with seven different MCDA applications via integrating brand value into the criteria for the first time. The study’s findings revealed that the proposed innovative sensitivity tests produced similarly robust results as traditional tests. In addition, brand value has been proved to be an advantageous criterion to be implemented into MCDAs for financial performance problems through the sensitivity analysis made.

Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Exo skeleton pertinence and control techniques: A state-of-the-art review

Exo-Skeleton is a wearable robotic device which was emerged in later 1960s that has a multitude of applications ranging from weightlifting to wearer’s stability improvement. This paper makes a novel approach in reviewing the various exo-skeleton models that are available at the present. The idea of this paper is to study and compare the models in terms of medical applicability like gait rehabilitation, physiotherapy, human strength augmentation, various control strategies of exo-skeleton, ergonomic study, and need for exo-skeleton system for enhancing the life of humans. Since exo-skeletons are wearable devices, it requires precise controlling of the actuators and repeatability, and accuracy plays a vital role. The paper elucidated with a detailed analysis, reviewed and summarized the core essence of 300 research papers and patents in the field of exo-skeleton with the aforesaid aspects of application and drive system with their control methodologies from 2007 to 2022 and shown a trend with year-wise data from which it is clear that the future of industrial work will become a collaborative activity involving the exo-skeleton system with human labours. Human – robot interaction is vital and must for enabling an integration which can be achieved with the amelioration in the control and actuation techniques for precise control. With the furtherance in the technology the exo-skeletons can be purpose built that can be of rigid or flexible structures with active, passive, or quasi-passive controls based on the user needs. Joint handed operation of Exo-skeleton with the alternative treatment methodologies will yield a plethora of benefits in the near future in comparison with the present conventional treatment modes that could result in reduction of surgeries.

A Novel Approach for Determining Cutting Geometry for TBM Using Full-scale Laboratory Linear Rock Cutting and PFC3D-Based Numerical Simulations

Efficient rock excavation requires an optimal s-p ratio, where adjacent cuts interact to completely plane the rock surface. Traditionally, the specific energy (SE) methodology has been used to determine this ratio, but its U-shaped curve is sometimes incomplete or absent, making it difficult to find the optimal s-p ratio. In this research, an alternative methodology is proposed. This methodology relies on the morphology of the excavated rock surface, specifically, the amounts of overbreaks. To evaluate the proposed methodology, full-scale rock cutting experiments were conducted in the laboratory. Numerical simulations were also conducted in 3D particle flow code (PFC3D) and the results were validated by the full-scale laboratory rock cutting experimental data. The study also involved conducting sensitivity analyses to evaluate the effectiveness of downscaling and upscaling the PFC3D-based model to obtain results for full-scale applications. Following the excavation, laser profilometry was employed to characterize the excavated rock surfaces, and the resulting profiles were used to calculate the volume of overbreaks. Plots of these volumes against the s-p ratio were generated to identify the optimal s-p ratio. The findings indicated the presence of a noticeable optimal s-p ratio in both the PFC3D-based simulations and full-scale laboratory experiments, which was not evident when the specific energy methodology was employed. This proposed methodology exhibits considerable potential and can prove beneficial in determining cutting geometry that is optimal for site-specifc and TBM-specific, as well as in the design and production of tunnel boring machines.

Eco‐friendly materials for next‐generation vaccination: From concept to clinical reality

AbstractThe vaccine is a premier healthcare intervention strategy in the battle against infectious infections. However, the development and production of vaccines present challenges in terms of complexity, cost, and time consumption. Alternative methodologies, such as nonthermal plasma and plant‐based technologies, have emerged as potential alternatives for conventional vaccine manufacturing processes. While plasma‐based approaches offer a rapid and efficient pathogen inactivation method devoid of harsh reagents, plant‐based techniques present a more economically viable and scalable avenue for vaccine production. The imperative urges these approaches to address pressing global health challenges posed by emerging and recurring infectious diseases, surpassing the limitations of traditional vaccine fabrication methods. The primary goal of this review is to provide a comprehensive overview of the current research landscape, covering conceptualization, production, and potential advantages of plasma‐based and plant‐based vaccines. Furthermore, exploring the obstacles and opportunities intrinsic to these strategies is undertaken, elucidating their potential impact on vaccination strategies. This systematic presentation specifies a detailed outline of recent vaccine research and developments, emphasizing the possibility of advanced green approaches to produce effective and secure vaccination programs.

Investigation of medical image segmentation techniques and analysis of key applications

This research examines the application of the UNet convolutional neural network model, specifically for semantic segmentation tasks in the field of medical imaging, juxtaposing its efficacy with Fully Convolutional Networks (FCNs). The primary focus of this comparative analysis rests on the performance of the UNet model on the dataset employed for this study. Surpassing our initial expectations, the UNet model demonstrated remarkable performance superiority over the FCN model on the curated dataset, thereby suggesting its potential applicability and utility for analogous tasks within the realm of medical imaging. In a surprising turn of events, our trials revealed that data augmentation techniques did not usher in a notable enhancement in segmentation accuracy. This observation was especially striking given the substantial size of the dataset employed for the experiments, encompassing as many as 1000 images. This outcome suggests that the merits of data augmentation may not always come to the fore when dealing with considerably large datasets. This intriguing discovery prompts further exploration and investigation to uncover the underlying reasons behind this observed phenomenon. Moreover, it brings to light an open-ended research query - the quest for alternative methodologies that could potentially amplify segmentation accuracy when operating on large scale datasets in the sphere of medical imaging. As the field continues to evolve and mature, it is these open questions that will continue to push the boundaries of what is possible in medical image analysis.

Economics and the dreamtime revisited: Creating a truly Australian economic history?

AbstractThe Economics and the Dreamtime was a landmark in Australian Economic History where Noel Butlin elevated awareness of the central importance of Indigenous economic history. It was a sprawling inter‐disciplinary work that used economic tools to understand Indigenous society before first contact and in the early colonial period. This article revisits that book to provide a critical evaluation of the major contributions of Butlin's research on Indigenous Australians. His primary contribution was to make Indigenous people more visible in the Australian economy in the early colonial period. He created a unique backcasting methodology that allowed Indigenous population to be estimated in the first six decades of the colony based on depopulation from disease, resource loss and frontier violence. I argue that the two main shortcomings of Butlin's research is that his method used colonial estimates of the population and that the population estimates are not sufficiently geographically differentiated. The main criticism of Butlin's research in the literature is that it is too speculative. However, his methodological innovation allows considerable transparency in the assumptions used and can create a range of plausible estimates that give us a sense of the unreliability of the existing population estimates. Alternative methodologies based on estimating population densities in 1788 from anthropological evidence are historically point estimates, which do not provide a sense of how uncertain the estimates might be. The way forward for this debate is to combine Butlin's demographic backcast methodology with population density estimates that take into account the selective mortality from disease and frontier violence. Finally, in order to create a truly Australian Economic History, it is necessary to also augment the methodology to incorporate Indigenous perspectives into the analysis and to utilise a local geography that acknowledges the diversity of Indigenous Australia.