Four-step Approach Research Articles

Energy-Efficient UAV Trajectory Design and Velocity Control for Visual Coverage of Terrestrial Regions

In this work, we develop a novel approach for designing the trajectory and controlling the velocity for an unmanned aerial vehicle (UAV) to achieve energy-efficient visual coverage of multiple terrestrial regions. Unlike previous works, our proposed approach allows the UAV to flexibly change both its velocity and its flight altitude during its task tour. To minimize the UAV’s total flight energy consumption during its task tour, we propose a novel four-step approach. The first step devises a simulated annealing (SA)-based searching algorithm to optimize the UAV’s photographing altitude for each region, considering various image resolution requirements and safety requirements across regions. Based on the identified photographing altitudes of all regions, the second step formulates a traveling salesman problem (TSP) and uses an efficient approximate method to determine the visiting order of each region. The third step generates all candidate intra-region trajectories used for visual coverage of each region, of which the optimal one will be decided together with the inter-region trajectory used for transitioning between neighboring regions during the fourth step. Finally, the fourth step employs dynamic programming (DP) and geometry to jointly determine the UAV’s velocity control and complete trajectory during its task tour. Extensive experiments validate the effectiveness and superiority of the proposed approach, compared with several existing methods.

Development, Implementation, and Evaluation of a Health Information System for a Rural Clinic in Pakistan: A Pilot Model for Low-Resource Settings

Background: Health information systems (HIS) play a pivotal role in modern healthcare by improving patient outcomes, enhancing data management, and supporting public health initiatives. Despite these benefits, HIS adoption remains limited in rural areas of low- and middle-income countries (LMICs), where healthcare challenges are more pronounced. This study describes the development, implementation, and evaluation of a clinician led HIS model in a rural clinic in Sadwal Kalaan, Punjab, Pakistan. Methods and Materials: A structured four-step approach was used in developing, implementing and evaluating the HIS: 1) assessing the need for a HIS through interviews and focus group discussions with the clinic manager, physicians, and auxiliary healthcare staff; 2) designing a system tailored to the clinic’s context; 3) implementing a patient intake form designed using a survey questionnaire; and 4) evaluating adoption guided by iterative feedback from key stakeholders and impact on healthcare delivery. Results: The HIS was successfully integrated into the clinic’s workflow, facilitating patient follow-up by enabling retrieval of previous medical visits. Data was collected from 3,900 patient encounters on demographics, medical presentation, management, and overall patient satisfaction. Nearly all (99.8%) of respondents provided sufficient information regarding their condition and treatment. The system enhanced clinic operations by facilitating data-driven decision-making, optimizing resource allocation, and informing medication stock management. Despite initial resistance from staff regarding additional documentation workload, structured training and workflow adaptations ensured successful adoption. Conclusion: Overall, the findings demonstrate that implementing a clinician-led HIS in rural Pakistan is feasible and beneficial, offering scalability for similar settings in other LMICs.

Geopolitical risks and inflation: insights across time horizons

ABSTRACT How does geopolitical risk influence inflation? Using monthly data from 1996 to 2023, this study examines the impact of geopolitical risk on inflation across nine advanced economies, the USA, Canada, Belgium, Germany, Spain, France, the UK, Italy, and the Netherlands. A four-step empirical approach is employed: (1) a fixed-effects panel regression model estimates the baseline relationship, (2) a Two-Stage Least Squares (2SLS) approach addresses potential endogeneity, (3) a Panel Vector Autoregressive (Panel-VAR) model explores dynamic interactions, and (4) impulse response functions (IRFs) assess the persistence of geopolitical shocks. The analysis reveals distinct inflationary patterns, showing that while geopolitical risks generally moderate inflation during non-conflict periods, the Russia-Ukraine conflict significantly heightens inflationary pressures over the short, medium, and long term, reflecting prolonged economic disruptions, supply chain issues, and market volatility. These findings offer valuable insights for policymakers, emphasizing the need to integrate geopolitical risks into inflation forecasting and monetary policy to improve accuracy and resilience in the face of global uncertainties.

Mastering the Art of Caregiving: Instructional Approaches to Teaching Healthcare-Related Procedural Skills to Informal Caregivers-An Integrative Review.

This review aims to explore instructional approaches employed in teaching complex procedural skills among caregivers. Integrative Review. Electronic searches were conducted across seven databases: CINAHL, PubMed, OVID, ScienceDirect, Web of Science, ProQuest Central and Google Scholar. Manual searches of references within relevant studies were also performed. Original, peer-reviewed studies published in English between 2014 and 2025 were reviewed. The Whittemore and Knafl method of integrative review was utilised to comprehensively examine literature encompassing various methodological designs. 17 articles that examined the learning of procedural skills among caregivers were included. The optimal approach to enhancing caregivers' knowledge, competence and confidence involves a learner-centric training model involving multiple phases. Effective caregiving begins with a thorough preparation of the caregiver, which is key to ensuring the success of the training programme. Next, knowledge can be imparted through diverse learning approaches and paradigms to cater to individual learning styles. Subsequently, the translation of knowledge gained to hands-on practice, deepens understanding and enhances caregivers' practical skills competency. Finally, confidence is built through providing opportunities and platforms for repeated practice, leading to mastery and increased confidence over time. While specific learning pedagogies were not highlighted in the literature, the instructional approaches summarised in the existing literature closely resembled an existing teaching pedagogy: Peyton's Four-Step Approach. This approach is a stepwise teaching framework that has been widely used in healthcare teaching. The approaches used in the studies align with this approach and future interventions should consider designing their training accordingly to enhance its efficacy. A well-designed training programme fosters caregiver resilience and preparedness, enabling them to navigate challenges effectively and sustainably. Future research could focus on creating an all-encompassing caregiver training that integrates the various approaches. Its feasibility and effectiveness in improving the caregiver preparation process could then be assessed. No patient or public contribution.

Uncovering future directions in virtual reference service: a bibliometric and topic modeling analysis

PurposeThis study aims to examine the evolution of virtual reference service (VRS) research in the 21st century, aiming to identify emerging trends, research hotspots, prolific scholars and knowledge structures. It seeks to provide insights into global research patterns and collaborations within the field.Design/methodology/approachA four-step approach was used to analyze VRS research. Data were collected from the Scopus database, and tools such as Biblioshiny, Word Cloud, LDAShiny, BERTopic (for topic modeling) and VOSviewer were applied. The study focused on social network analysis and conceptual structure evaluations of 1,633 documents, collected via a specific search string. The analysis covers the period from 2000 to 2023, highlighting trends and collaboration patterns in VRS research.FindingsThe bibliometric analysis of 1,632 publications by 3,108 scholars reveals significant growth in VRS research, with a notable surge during COVID-19 (2020–2022). Technological advancements and global research expansion have driven this rise. A small group of top-tier journals accounts for 33.3% of publications. Collaboration networks among authors and institutions remain limited, while keyword analysis identifies seven key research clusters that highlight various VRS aspects and trends.Practical implicationsThe findings suggest that VRS research is experiencing a technological transformation, driven by innovations such as chatbots, AI assistance and augmented reality, which are expected to shape the future of the field.Originality/valueThis study uniquely integrates VRS practices with modern technological advancements, offering valuable insights for future research and potential breakthroughs in VRS.

Tree height-diameter allometry and implications for biomass estimates in Northeastern Amazonian forests

The relationship between tree height and diameter varies across forest types, introducing uncertainties in height that can affect aboveground biomass estimates in tropical forests. Here, we used a four-step approach to assess whether incorporating height estimates from local height-diameter models, compared to two published equations, improves biomass estimates across spatial scales. First, we measured the diameter and height of 1,962 trees in two representative forest types in the Northeastern Amazon: non-flooded terra-firme and seasonally-flooded várzea forests. Second, we selected the best height-diameter models from a set of 10 candidates to establish local allometric equations. Third, we applied these best local models and two previously published height models (the regional Guyana shield, and the pantropical model) to estimate tree height, and compared these estimates to measured height. Finally, we computed tree biomass using equations that both included and excluded height, and compared these biomass estimates to those calculated using directly measured height. Asymptotic height-diameter models provided the best fit at local and regional scales. The Quadratic model was the best choice for terra-firme and várzea forests separately, while the Weibull and Michaelis-Menten models performed best for both forests. Local models closely matched measured heights, with deviations of only 0.1%, outperforming the regional and pantropical models within each forest type. The regional model underestimated height in terra-firme by 3% and overestimated it in várzea by 29%, while the pantropical model underestimated height in terra-firme by 19% and overestimated it in várzea by 6%. Using local asymptotic models to estimate height improved the accuracy of biomass estimates, with differences of around 1% between biomass computed using measured heights and estimated heights for terra-firme and várzea forests. In contrast, the biomass calculated using estimated heights from both the regional and pantropical models overestimated the biomass in várzea by 41% and 17%, respectively, while the pantropical model underestimated biomass in terra-firme by 17%. The estimated height and biomass of large trees using regional and pantropical models showed the highest deviations from the observed values. Our findings underscore the necessity for height-diameter modeling for different forest types, and highlight the need to increase sampling of large trees to improve biomass estimation accuracy in Northeastern Amazonia.

Parental involvement in education: a comparative study of the United States, Japan, and Iran

ABSTRACT This study uses a qualitative comparative approach to examine parental involvement in education in the U.S. Japan, and Iran, selected for their diverse cultural and educational contexts. Data is sourced from documents, reports, and official publications. The analysis employs the documentary method, Bereday’s four-step approach, and Epstein’s six-type model. Findings show a correlation between higher education levels and increased parental involvement in the U.S. reliance on teacher support among lower socioeconomic mothers in Japan, and the importance of community collaboration in Iran. The study highlights the significance of parental participation, communication, and community cooperation, recommending strategies to address challenges.

Literature Review on Lightweight Authentication Algorithms in Remote Data Transfer

With Smart Devices emerging as a potentially game-changing technology in the future, it is anticipated that it will generate a massive volume of data that may be vulnerable to security vulnerabilities. Therefore, lightweight cryptography has been identified as one of the acceptable mechanisms for protecting Smart Devices applications from hackers. This is since lightweight cryptography is designed to fulfill security demands in hardware and software that are resource constrained. When it comes to building lightweight cryptography for Smart Devices applications, specifically for integrity and authentication algorithms, the purpose of this study is to conduct an analysis of the existing security requirements. There was a four-step approach that was utilized to adopt the Kitchenham systematic review method. In this investigation, the articles that were chosen for research were retrieved from four credible databases, and after meeting the selection criteria, 57 of them were declared appropriate for subsequent analysis. A significant portion of the research was concentrated on assaults directed against Smart Devices applications, system security needs, and techniques for lightweight authentication. For the purpose of achieving the desired security requirements for sustainable security management in Smart Devices, this study may provide researchers with a reference for building user authentication algorithms that are lightweight.

Making the impact of publications within a field comparable by improving the field-weighted citation impact (FWCI): the case of sport management

The Field-Weighted Citation Impact (FWCI) is a metric at the publication level which is standardized across fields. It has obvious advantages over other frequently used metrics, but might also suffer from some potential limitations, raising issues about its reliability. This paper investigates one limitation—the use of different All Science Journal Classification (ASJC) Codes in Scopus for journals from the same field, meaning they may not be assessed against the same benchmark (average citations per article)—and suggests a potential improvement, relative to sport management, which was selected as an exemplar field. A four-step approach was applied: first, identifying sport management journals based on Scimago similarities (percentage of cited publications shared between journals, i.e., shared interests); second, mapping these journals against their different ASJC Codes; third, comparing the different ASJC Codes versus Scimago similarities; fourth, analyzing the consequence of the different ASJC Codes on FWCI. Based on Scimago similarities, 15 sport management journals were identified. They represent 15 different ASJC Codes. Overall, the ASJC Codes are not representative of Scimago similarities. The consequence is that, despite shared interests, sport management journals are not assessed against the same benchmark in the calculation of FWCI. The discussion considers why and how big the issue is. A potential improvement to the FWCI is then suggested: to use Scimago similarities rather than ASJC Codes to determine a field. The paper concludes that the FWCI is superior to other widespread metrics, while acknowledging some remaining issues not addressed by the indicator and its potential improvement.

Understanding and Analysing Causal Relations through Modelling using Causal Machine Learning

The study of causal inference has gained significant attention in artificial intelligence (AI) and machine learning (ML), particularly in areas such as explainability, automated diagnostics, reinforcement learning, and transfer learning.. This research applies causal inference techniques to analyze student placement data, aiming to establish cause-and-effect relationships rather than mere correlations. Using the DoWhy Python library, the study follows a structured four-step approach—Modeling, Identification, Estimation, and Refutation—and introduces a novel 3D framework (Data Correlation, Causal Discovery, and Domain Knowledge) to enhance causal modeling reliability. Causal discovery algorithms, including Peter Clark (PC), Greedy Equivalence Search (GES), and Linear Non-Gaussian Acyclic Model (LiNGAM), are applied to construct and validate a robust causal model. Results indicate that internships (0.155) and academic branch selection (0.148) are the most influential factors in student placements, while CGPA (0.042), projects (0.035), and employability skills (0.016) have moderate effects, and extracurricular activities (0.004) and MOOCs courses (0.012) exhibit minimal impact. This research underscores the significance of causal reasoning in higher education analytics and highlights the effectiveness of causal ML techniques in real-world decision-making. Future work may explore larger datasets, integrate additional educational variables, and extend this approach to other academic disciplines for broader applicability.

The Diagnostic Assessment of Inherited Platelet Function Defects - Part 1: An Overview of the Diagnostic Approach and Laboratory Methods.

In this article, our goal is to offer an introduction and overview of the diagnostic approach to inherited platelet function defects (iPFDs) for clinicians and laboratory personnel who are beginning to engage in the field. We describe the most commonly used laboratory methods and propose a diagnostic four-step approach, wherein each stage requires a higher level of expertise and more specialized methods. It should be noted that our proposed approach differs from the ISTH Guidance on this topic in some points. The first step in the diagnostic approach of iPFD should be a thorough medical history and clinical examination. We strongly advocate for the use of a validated bleeding score like the ISTH-BAT (International Society on Thrombosis and Haemostasis Bleeding Assessment Tool). External factors like diet and medication have to be considered. The second step should rule out plasmatic bleeding disorders and von Willebrand disease. Once this has been accomplished, the third step consists of a thorough platelet investigation of platelet phenotype and function. Established methods consist of blood smear analysis by light microscopy, light transmission aggregometry, and flow cytometry. Additional techniques such as lumiaggregometry, immune fluorescence microscopy, and platelet-dependent thrombin generation help confirm and specify the diagnosis of iPFD. In the fourth and last step, genetic testing can confirm a diagnosis, reveal novel mutations, and allow to compare unclear genetics with lab results. If diagnosis cannot be established through this process, experimental methods such as electron microscopy can give insight into the underlying disease.

Eliciting the Initial Programme Theory in a Realist Evaluation of Facility-Based Maternal Death Reviews in Benin: Methodological Process, Challenges and Lessons Learned

The initial program theory (IPT) is a core element of realist evaluation. An IPT presents a hypothesis about how, for whom, why, and in what contexts an intervention is effective. Despite their importance within realist studies, methodological guidance on how to develop an IPT is relatively scarce. We describe the methodological process of eliciting an IPT of facility-based maternal death reviews (FbMDRs), a multicomponent intervention to improve the quality of maternal care. We used a four-step approach to elicit the IPT through a combination of literature review, document review, and in-depth interviews with key stakeholders while drawing on the experience of the research team. The literature and document reviews enabled us to identify facilitators and barriers influencing the functioning of FbMDRs. We used the causal pathway approach to deconstruct the maternal death review procedure and we elicited a causal explanation for each component on the basis of the results of the document review, the literature review and key respondent interviews. We organised stakeholder consultations that provided insights of key actors and validated its real-world relevance. We discuss the challenges encountered during its development and the lessons learned. The challenges include the fact that maternal death reviews are multicomponent interventions, for which a ‘simple’ programme theory cannot be defined, the positionality of the research team, and the visual representation of a programme theory with multiple components.

Active Learning in UX Instruction: A Four-Step Approach for Teaching Budding UX-ers

Active Learning in UX Instruction: A Four-Step Approach for Teaching Budding UX-ers

Identification of a suitable matching procedure in health services research: Insights into a study for stroke patients

Background STROKE OWL is a quasi-experimental study using claims data from statutory health insurances in Germany to calculate the effect of case managers for stroke. Since there is no recruited control group, a suitable procedure needed to be identified to make the intervention effect measurable. Hence, the objective of this paper is to present an approach for comparing matching procedures before final data analyses take place. Methods We followed a four-step approach to identify an appropriate procedure on a partial dataset. First, we conducted a systematic review for identifying potential confounders of the study’s outcome. Afterwards we checked whether a matching procedure was able to balance the dataset with respect to the outcome, under the assumption that all relevant covariates including the intervention variable were balanced. Within the two last steps we checked covariate balances and remaining group sizes. Three matching procedures – coarsened exact matching, optimal full matching and propensity score matching – were tested. Results The coarsened exact matching was able to balance variables perfectly but on average lost >50% of the observations. Although optimal full and propensity score matching revealed some weaknesses concerning the variable balance, considerably more observations remained in the dataset. Based on the described approach and the external framework conditions of STROKE OWL the optimal full matching was chosen as matching procedure most suitable. Conclusion In summary, it was challenging to identify a suitable matching procedure for this study, since the detailed results of the different balance and group size checks varied.

Integrating Scientific and Stakeholder-Based Knowledge to Simulate Future Urban Growth Scenarios: Findings from Kurunegala and Galle, Sri Lanka

The promotion of sustainability and resilience within urban environments is widely recognized as an essential approach to educating urban communities through innovative strategies and tools. This paper presents a process for integrating stakeholders into urban growth simulation, thereby enhancing sustainable decision-making. Currently, most urban growth models fail to incorporate the perspectives of diverse stakeholders, leading to reduced equitable participation in the decision-making process. To achieve long-term sustainability, it is imperative to include the input and viewpoints of stakeholders. This study follows a four-step approach: identifying relevant stakeholders, developing the framework, evaluating its effectiveness, and documenting lessons learned. The framework involves key steps, including initial participatory modeling, analysis of development pressures and suitability with stakeholders, and technical urban growth modeling. A unique combination of modeling tools and an innovative approach was employed, incorporating the default FUTURES (GRASS-GIS) model alongside the CA-Markov Chain, Agent-Based Modeling (ABM) (NetLogo), the Cellular-Automata-based Python model, and MOLUSCE-QGIS. This integrated approach facilitates the inclusion of stakeholder-based knowledge into conventional urban growth modeling, providing novel local lessons in science, technology, and innovation initiatives. Validation was conducted through both technical and stakeholder mechanisms, confirming the effectiveness of the proposed framework.

ARIE: A Health Equity Framework for Public Health Interventions Informed by Critical Race Theory and Critical Gerontology

Older racialized minorities were particularly vulnerable during the last pandemic due to the interlocking influences of structural racism and ageism, which are often disregarded in public health planning. This oversight not only compromises the social justice and health equity goals of public health efforts but it also calls for a more inclusive approach that systematically addresses these deficiencies at every stage of a public health response. To achieve this, we propose Age- and Race-conscious Interventions done Equitably (ARIE), a novel analytical framework grounded in critical race theory and critical gerontology. ARIE is based on a four-step approach, which aligns with different stages of public health interventions. It will help ensure that structural discrimination influencing access to healthcare resources during a biological event is not ignored, and that public health authorities work actively toward identifying and addressing ageist and racist biases in their response plans and interventions.

Flipped classroom-based application of Peyton's four-step approach in standardized training of ultrasound residents for thyroid and cervical lymph node zoning.

To investigate whether combining the flipped classroom approach with Peyton's four-step method can enhance teaching effectiveness in ultrasound (US) zoning of the thyroid and cervical lymph nodes for standardized residency training. A total of 66 resident training students were randomly divided into a control group and an observation group. The control group received traditional teaching methods, including "see one, do one" learning, lecture-based learning (LBL), and case-based learning (CBL). The observation group was taught using Peyton's four-step teaching method, the flipped classroom approach, and CBL. Assessments were conducted through skill operation and clinical case analysis. A questionnaire survey was used to evaluate student satisfaction. Assessment scores and questionnaire ratings between the two teaching methods were compared. 1) Assessment results demonstrated higher scores in skill operation and clinical case analysis for the observation group compared to the control group [(87.64 ± 3.72) vs. (80.48 ± 5.92) points, (87.94 ± 4.46) vs. (82.85 ± 4.24) points]. 2) The questionnaire survey indicated that resident trainees taught using Peyton's four-step method showed greater improvement in learning interest [(4.61 ± 0.57) vs. (3.70 ± 0.85) points] and experienced reduced exam pressure compared to the control group [(4.52 ± 0.62) vs. (3.21 ± 0.70) points]. These differences were statistically significant (P < 0.05). Peyton's four-step approach combined with the flipped classroom method improved resident training students' scores in skill operation and clinical case analysis for ultrasound zoning of the thyroid and cervical lymph nodes. It also stimulated learning interest and alleviated exam pressure, making it an effective teaching method for enhancing standardized US training outcomes in resident education.

New legal risks for banks related to climate change and possible mitigation strategies

Anthropogenic global warming and climate change-related risks are facts. While physical risk (which can cause damage and losses) and transition risks (due to disorderly policies and transition pathways) are generally understood, new types of risk for financial institutions are emerging in banking supervision and in so-called climate change litigation. An example of the former is the new ‘risk from misalignment’ of banks with European Union (EU) climate objectives, as recently discussed by the European Central Bank (ECB). This risk is caused by the requirement to align with EU climate change goals, which may not be compatible with the path a financial institution in a market economy might choose to adopt during the transition to cleaner energy. The second development is driven by climate activists suing banks in ‘strategic litigations’ based on the concept of ‘duty of care’, which may not require causality as a legal precondition. While these developments can be debated, they are likely to be the ‘new normal’ for financial institutions. It is proposed that these new types of climate change-related legal risks can be managed through a four-step mitigation approach as an iterative process of awareness, monitoring, active mitigation and observing emerging trends.

Exploring Novel Benzopyran Derivatives as Antitubercular Agents

Exploring Novel Benzopyran Derivatives as Antitubercular Agents

A hybrid deep learning framework for short-term load forecasting with improved data cleansing and preprocessing techniques

The dynamic variations in connected loads and the intermittent nature of renewable energy sources significantly impact smart grid reliability. Accurate load and generation forecasting stand pivotal for enhancing grid reliability and efficient operations. In this study, a comprehensive four-step approach is introduced for short-term load forecasting (STLF) aimed at precisely estimating power demand and generation. The process unfolds with data collection, followed by rigorous standardization, preprocessing, and cleansing of demand and generation data. Subsequently, a hybrid deep learning model, comprising bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and a fully connected layer is trained using the clean data. This model harnesses the temporal dependencies within the data for accurate predictions. The model's performance is then evaluated using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), providing forecasted values for both generation and demand on minute, hourly, daily, and weekly intervals. Notably, the proposed approach achieves a remarkable MSE of 0.0058 for load forecasting and 0.0033 for generation forecasting. Comparative analysis with state-of-the-art (SOTA) techniques in terms of accuracy and computational cost underscores the superior accuracy of the proposed framework in forecasting both generation and demand. Importantly, the proposed approach bridges the gap in reliability enhancement for smart grids operating, a facet lacking in many existing methodologies. This signifies the potential of the proposed approach to bolster smart grid reliability, ensuring more reliable operations.