Analysis Methodology Research Articles

Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology

AbstractThe nonlinear progression of COVID-19 positive cases, their fluctuations, the correlations in amplitudes and phases across different regions, along with seasonality or periodicity, pose challenges to thoroughly examining the data for revealing similarities or detecting anomalous trajectories. To address this, we conducted a nonlinear time series analysis combining wavelet and persistent homology to detect the qualitative properties underlying COVID-19 daily infection numbers at the state level from the pandemic’s onset to June 2024 in Malaysia. The first phase involved investigating the evolution of daily confirmed cases by state in the time-frequency domain using wavelets. Subsequently, a topological feature-based time series clustering is performed by reconstructing a higher-dimensional phase space through a delay embedding method. Our findings reveal a prominent 7-day periodicity in case numbers from mid-2021 to the end of 2022. The state-wise daily cases are moderately correlated in both amplitudes and phases during the Delta and Omicron waves. Biweekly averaged data significantly enhances the detection of topological loops associated with these waves. Selangor demonstrates unique case trajectories, while Pahang shows the highest similarity with other states. This methodological framework provides a more detailed understanding of epidemiological time series data, offering valuable insights for preparing for future public health crises.

Institutional quality configuration for encouraging private capital participation in PPP projects: evidence from 36 belt and road countries

PurposeThis study aims to explore how institutional quality impacts private capital participation in large-scale infrastructure development, particularly in public–private partnership (PPP) projects, aiming to enhance incentives for private sector involvement.Design/methodology/approachBuilding on new institutional theory, a triangular theoretical framework was constructed to analyze the high participation of private capital in PPP projects, focusing on seven key institutional factors. Data from 1,319 PPP projects across 36 Belt and Road Initiative (BRI) countries from 2015 to 2020 were then analyzed using a combination of necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) to evaluate the combined impact and interactions of these factors.FindingsResults indicate that high private capital participation does not hinge on a single institutional quality factor but results from the synergistic influence of multiple factors. The paths leading to high private capital participation can be categorized as regulatory-led, normative-cognitive synergistic, regulatory-normative synergistic and institutional failure-led. Among these, regulatory quality plays a central role in the regulatory-led; the synergy between political stability and voice and accountability is pivotal in the normative-cognitive synergistic, and the rule of law, in combination with voice and accountability, is essential to the regulatory-normative synergistic.Originality/valueThis research systematically examines the multidimensional impact of institutional quality, revealing how different institutional factors interact to influence private capital’s willingness to participate and behavior. It enriches applied research in institutional economics within PPP projects and provides a new theoretical perspective and methodological framework to the scholarly community.

Exploring Design-Based Research as a framework for addressing pedagogical problems faced by higher education

Design-Based Research presents a pragmatic approach to addressing both pedagogical problems and innovations within a rigorous framework that aims to build transferable practice. This panel discussion will explore four examples of implementing DBR in various higher education contexts and draw participants into a discussion of how they might apply DBR to address identified pedagogical problems or innovations in their own contexts. Why DBR? This panel discussion is a focal point for exploring how design-based research (DBR) in higher education in 2024, can address pedagogical problems brought about by the global pandemic and the explosion of generative artificial intelligence use in assessment and learning (For examples). In a period of continuous disruption in higher education, flexible and ethical research strategies that embrace unexpected influences in teaching and learning is paramount (Cochrane et al., 2023), leading to a new generation of educational researchers embracing DBR. Furthermore, practical examples demonstrating students as partners in curriculum design to inform ‘what works’ in practice are essential for researchers to gain confidence in adopting research-teaching-practice methods of inquiry (Bakker, 2018; Cochrane & Munn, 2020). Understanding how the processes and outputs of DBR can meet these needs is crucial to alleviating both business and educational tensions about timing, funding, resources, and workload planning (Cochrane, 2022; McKenney & Reeves, 2019; Reeves & Lin, 2020). At the core of DBR is the opportunity to explore pedagogical problems or innovation through four phases: problem analysis, develop solutions to the identified problem, explore and evaluate the impact of the design in iterative interventions in real learning situations, leading to the development of transferable design principles and theory building (McKenney & Reeves, 2019) Various research methods can be applied to progressively ask participant perspectives, including students, teachers and learning designers, on appropriate approaches or technologies to solve an identified problem (McKenney & Reeves, 2019). The intended outcome of this panel discussion is to collaborate with participants in implementing DBR in higher education in response to specific pedagogical problems identified by the participants. Beginning with a brief background to DBR followed by four examples to stimulate discussion on its application in broader contexts to address identified problems in curriculum design. Relevance to Higher Education The relevance of debating the role of design-based research in higher education in 2024 is to stimulate discussion for how this pragmatic research approach informs ‘what works’ in situated learning contexts to develop cutting edge design principles and theory that can be transferrable (Galvin & Cochrane, 2023). The methodological framework of DBR invites students, teachers, and learning designers to collaborate from the conception of a design artefact or educational approach to solve a learning problem through to iterative stages of delivery. Following a new normal in higher education where change is inevitable, and fast-paced, research design that does not shy away from the progressive and ‘messy’ elements of educational settings to improve real time learning is essential. Finding a balance between naturalistic and interventionist research methods to explore this complexity is a way forward that DBR can provide. It is acknowledged that greater understanding for the value of DBR and how to plan practical stages with multiple participants is needed for teachers, researchers, and leaders in higher education to confidently adopt this approach (Haagen-Schützenhöfer et al., 2024). Opening discussion with peers models the collaborative ethos of DBR and opens potential connections and networking avenues to establish and generate knowledge for DBR in higher education. The Panel Members The panel will be comprised of project leaders from four DBR projects: Clinical reasoning development, interdisciplinary engineering education, biomechanics, performance anxiety. These projects were introduced in a concise paper for the ASCILITE 2023 conference (Cochrane et al., 2023) and the authors are in the process of collaborating on a full journal article analysing these DBR projects (Cochrane et al., 2024) that should be published in time for the ASCILITE 2024 conference.

Vulnerability to Achievement Stressors: More Evidence That Students With Learning Disabilities Require Intensive Intervention

In this article, we introduce the term vulnerability to achievement stressors, which refers to differentially low achievement when shifts in the educational environment “stress” or threaten the capacity of an individual or a group of individuals to make academic progress. We also introduce a methodological framework for assessing vulnerability to achievement stressors. Vulnerability to achievement stressors in students with learning disabilities (LD), relative to students without disabilities, is illustrated with two achievement stressors: (a) the shift in learning standards codified in Common Core State Standards, specifically the increase in complexity of the fourth-grade fractions curriculum, and (b) the COVID-19 pandemic, which decreased instructional structure by disrupting in-person teaching. Because these illustrations were embedded within randomized controlled trials, each with an inclusive instruction condition and an intensive intervention condition, they also provide the basis for concluding that intensive intervention is more effective than inclusive instruction for addressing students with LD’s vulnerability to achievement stressors and for narrowing their persistently severe achievement gaps.

Gender-sensitive and intersectionality-informed health indicators for health reporting: a scoping review protocol

IntroductionGender is a well-established social determinant of health and health (in)equality. Gender-sensitive health indicators for health monitoring and health reporting can support gender mainstreaming in relevant policy areas and inform...

Cut a Tree, Cut a Man: Charcoal Production as a Bane to Green Economy in Nigeria

Symbiotically speaking, cutting a tree is akin to cutting a man or any of its counterparts in the animal kingdom. Besides solar energy, heat energy's role in humans’ daily life calls for its taming, artificial ignition, and fuelling of same for its reuse as and when due. Hence, humans’ ability to ignite and fuel fire marked a significant achievement in human evolution. Meanwhile, wood was the most traditional source of fuel in human history, which is, mainly, of two forms: firewood and charcoal. Dried wood was the common source of fuel for domestic cooking and commercial use both in the rural and urban areas in the primitive era. Although, firewood has relatively little burden on nature, charcoal, which has more adverse effects on the environment, is now taking over. Sadly, the discovery of better alternative sources such as LPG and electricity could not bring about a remarkable shift in many developing nations, including Nigeria, despite their efficiency. Taking expository and critical analysis methodologies, this paper argues that wood-fuelling hinders the achievement of the green economy in Africa, and it persists for many reasons; poor state of electricity, unavailability and/or unaffordability of the LPG, poverty, poor environmental awareness, unsophisticated and poor enforcement of environmental laws and lack of compensation for the conservators among others. In addition to the ever-suggested solutions, this paper recommends a ‘carrot and cane' system. By this, the offenders should not only be accordingly penalised but also the conservers be compensated duly. It also recommends the use of briquettes as viable alternatives.

Antibiotics in honey: a comprehensive review on occurrence and analytical methodologies.

Honey is a food of great nutritional importance and has always been used for human consumption. The production of honey and other beekeeping products depends on the proper functioning of this extremely important sector, as it has a direct impact on other sectors such as agriculture. The decline in bee colony numbers has been linked, among other factors, to bacterial diseases affecting bees, including American and European foulbrood, and Nosema spp. disease. In this matter, prophylactic or therapeutic use of veterinary drugs in apiculture is common but can lead to their accumulation in bees and in honey. Consumption of contaminated honey can have adverse effects such as allergic or hypersensitivity reactions, carcinogenicity, reproductive effects, and teratogenicity. Commission Regulation (EU) N⍛ 37/2010 sets MRLs for antibiotics in various foods, but these limits are not set for api-products. The lack of harmonized rules has led some countries to set recommended concentrations and minimum performance limits. Nonetheless, to achieve this goal, development of accurate and precise analytical methodologies is crucial. In recent years, the analysis of antibiotics in honey has led to the development of methods in an extensive range of families, including aminoglycosides, amphenicols, lincosamides, macrolides, nitroimidazoles, quinolones, sulfonamides, tetracyclines and nitrofurans. This review work entails an in-depth exploration of occurrence studies, extraction methodologies, and analytical techniques for the determination of antibiotics in apiculture products. It was found that the most used extraction methods include solid-phase extraction, dispersed solid or liquid phase extraction and QuEChERS. Due to the complexity of the honey matrix, samples are often diluted or acidified using McIlvaine buffer, H2O, MeOH, acidified ACN and TCA solution. This is usually followed by a purification step using SPE cartridges or PSA. Golden analytical methodologies include high-performance liquid chromatography coupled to a triple quadrupole mass spectrometer (MS/MS) with Orbitrap or Q-ToF detectors.

Human‐AI collaboration: Designing artificial agents to facilitate socially shared regulation among learners

AbstractSocially shared regulation of learning (SSRL) is a crucial process for groups of learners to successfully collaborate. Detecting and supporting SSRL is a challenge, especially in real time, but hybrid intelligence approaches such as Artificial Intelligence (AI) agents may make this possible. Leveraging the concept of trigger events which invite SSRL, we present a design of an AI agent, MAI, which can detect SSRL and prompt students to raise their group‐level metacognitive awareness with the aim of facilitating SSRL. We present the methodology we used to design MAI, drawing on the Echeloned DSR (eDSR) Methodological Framework and making use of the Wizard of Oz prototyping paradigm. We likewise present empirical results evaluating our initial prototype of MAI, using lexical alignment between speakers as a quantitative measure of the effect of MAI's prompts on facilitating SSRL, the Partner Model Questionnaire as a quantitative measure of perceptions of MAI, and interviews as qualitative context for these perceptions. We found that the first prototype of MAI did not facilitate SSRL as hoped, possibly owing to mixed perceptions of MAI's reliability and lack of clarity about MAI's role in the collaborative learning task. From these findings, we offer revised prompts for the next iteration of prototyping this agent and a refined set of design requirements for future development of metacognitive AI agents for supporting SSRL. Practitioner notesWhat is already known about this topic Socially Shared Regulation of Learning (SSRL) is recognized as a critical component for the success of collaborative learning, emphasizing the importance of group‐level regulatory processes in achieving shared goals, enacting strategies and monitoring learning progress. Supporting SSRL in face‐to‐face collaborative learning environments presents challenges, including the complexity of coordinating and synchronizing individual contributions and regulatory actions within a group context. What this paper adds This paper introduces the design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes. Through empirical research, the study offers lessons learned and design considerations for developing artificial agents on facilitating and enhancing SSRL among learners, demonstrating how AI can play a pivotal role in collaborative learning environments. The findings highlight the critical importance of multidisciplinary knowledge in the design of multi‐agent interfaces (MAI) that provide real‐time, adaptive support for group metacognitive processes and decision‐making. Implications for practice and/or policy Educational technologists can utilize the proposed design principles in the development and integration of MAI tools to enhance SSRL. Educators can incorporate the principles of MAI and our relevant findings into their teaching strategies to actively foster and support socially shared regulation of learning among students. Policymakers should consider revising educational frameworks to include the use of AI technologies that support SSRL strategies in collaborative learning.

Experiencing online training and educational inequality in TVET delivery among trainers and trainees in Kenya during the COVID-19 pandemic: A case study

AbstractDuring the COVID-19 pandemic, the switch to online training exposed the digital divide between higher education institutions, with some significantly superior to others in terms of equipment and experience, as well as among students enrolled in the same institution. The purpose of this study was to investigate online training and educational inequality in TVET (Technical and Vocational Education and Training) delivery in Kenya, during the COVID-19. A total of 16 unstructured discussions were conducted in four TVET institutions in the Western Kenya Region. Each college organized four conversations (two males only and two females only groups). The study took into account various reports and literature to provide the background and analyze the data to respond to the research questions. Additionally, the study collected data through desk reviews, unstructured conversations, reports, emails, WhatsApp messages, and phone calls that aid in situation analysis, utilizing a qualitative research methodology of situational analysis. Lack of experience with online training by trainers and trainees, along with inadequate training assistance, resulted in feelings of anxiety and unease. Trainees were frequently incapable of self-regulation and remained motivated to learn at home and they were unsure about the quality of the materials accessible to them. Recommendations on four major issues are given: providing complete support to trainees, trainers, and other institution personnel; improving remote and online training; ensuring policy certainty, implementation, and monitoring; and discovering and implementing effective interventions and innovations.

Financial management of an environmental enterprise

The article examines financial management of environmental enterprises from the standpoint of evaluating the results of financial analysis and specifics of its implementation. Increasing the competitiveness of environmental organisations in the Russian Federation and ensuring their prospects in the face of sanctions and restrictions at this stage of development is becoming increasingly relevant. The authors propose a methodology for financial analysis of industrial and environmental activities of an enterprise based on the calculation of key financial and private environmental indicators of environmental enterprises. The novelty of this study lies in an integrated approach to financial management based on the results of the analysis of financial and private environmental indicators of environmental organisations, covering the financial and environmental aspects of their activities. The integrated approach to financial management is innovative because it is founded on the close relationship between financial and environmental activities of enterprises. The presented article develops analytical support and methodological tools of the analysis for use by environmental enterprises, with consideration to their specifics, in order to improve the efficiency of financial management. The practical significance of this article lies in the analytical provision of effective financial management for the sustainable development of environmental enterprises.

Maintaining Integrity and Confidentiality of Patients’ Records Using an Enhanced Security Technique

Medical record storage on paper is currently being phased out and replaced by more sophisticated Electronic Health Record (EHR) systems. However, the ease of access to data and the digitization of medical records also come with the potential of misuse of personally identifiable information and healthcare data breaches. How technology will protect patient privacy is one of the primary concerns about EHRs. Network connectivity is another major issue, therefore keeping private health information online may expose it to major information leaks to external parties. Around the world, data management and privacy are major concerns. The main concerns that prevent businesses from using cloud computing are security and information authenticity, as they fear that data may be lost to unauthorized parties. The quantity of patient-oriented data in the healthcare system is continuously increasing, and the current medical systems are confronted with security issues such as weak access control, data breaches or unauthorized disclosure, and insufficient authorization and authentication. An Enhanced Security Model was created in this study to secure and protect Electronic Health Records. The records were encrypted for security and privacy utilizing the Advanced Encryption Standard (AES), One Time Password (OTP), and National Identity Number (NIN). The system was designed using Object Oriented Analysis and Design Methodology (OOADM). The New System improved patient record security by utilizing NIN for cloud identity management, OTP for authentication, and AES for privacy and security. While the MySQL server was used to implement the database, HTML, CSS, PHP, and JavaScript were used to program the system. Comparing the new system to the old system, the results of the performance assessment analysis based on security, user-friendliness, dependability, and privacy show that the new system has very good performance efficiency and integrity.

Enhancing DOX efficacy against NSCLC through UDCA-mediated modulation of the TGF-β/MAPK autophagy pathways

Lung carcinoma, predominantly manifested as non-small cell lung cancer (NSCLC), significantly contributes to oncological mortality, underscoring an imperative for novel therapeutic paradigms. Amidst this context, the present investigation delineates the synergistic potentiation of doxorubicin (DOX)—a canonical chemotherapeutic—by Ursodeoxycholic acid (UDCA), a compound with a historical pedigree in hepatobiliary medicine, now repositioned within oncological pharmacotherapy due to its dichotomous cellular modulation—affording cytoprotection to non-malignant epithelia whilst eliciting apoptotic cascades in neoplastic counterparts. This study, through a rigorous methodological framework, elucidates UDCA’s capacity to inhibit NSCLC cellular proliferation and induce apoptosis, thereby significantly amplifying DOX’s chemotherapeutic efficacy. Notably, the co-administration of UDCA and DOX was observed to attenuate DOX-induced autophagy via the modulation of the TGF-β/MAPK signaling axis, a pathway pivotal in mediating cellular survival and autophagic mechanisms. Such findings not only underscore the therapeutic potential of UDCA as a chemosensitizer but also illuminate the molecular underpinnings of its modulatory effects, thereby contributing to the corpus of knowledge necessary to surmount chemoresistance in NSCLC. The implications of this research are twofold: firstly, it offers a compelling evidence base for the clinical reevaluation of UDCA in combinatory chemotherapeutic regimens; secondly, it posits a novel mechanistic insight into the modulation of chemotherapeutic efficacy and resistance. Collectively, these insights advocate for the expedited clinical translation of UDCA-DOX synergy, potentially heralding a paradigm shift in the management of NSCLC, thereby addressing a critical lacuna in contemporary oncological therapy.

Identifying gene-level mechanisms of successful dispersal of Vibrio parahaemolyticus during El Niño events

El Niño events, the warm phase of the El Niño Southern Oscillation, facilitate the movement of warm surface waters eastwards across the Pacific Ocean. Marine organisms transported by these waters can act as biological corridors for water-borne bacteria with attachment abilities. El Niño events have been hypothesized as driving the recent emergence of Vibrio parahaemolyticus (Vp) variants, marine bacterium causing gastroenteritis, in South America, but the lack of a robust methodological framework limited any further exploration. Here, we introduce two new analysis approaches to explore Vp dynamics in South America, which will be central to uncovering Vp dynamics in the future. Distributed non-linear lag models found that strong El Niño events increase the relative probability of Vp detection in Peru, with a 3–4-month lag time. Machine learning found that the presence of a specific gene (vopZ) involved in attachment to plankton in a pandemic Vp clone in South America was temporally associated with strong El Niño events, offering a possible strategy for survival over long-range dispersal, such as that offered by El Niño events. Robust surveillance of marine pathogens and methodological development are necessary to produce resolute conclusions on the effect of El Niño events on water-borne diseases.

Labour productivity, its main indicators, forms of manifestation, and ways to improve

The purposes of the article are to study the content of labour productivity, generalise its main indicators, essence of constant trends manifested in the form of an economic law, disclose its forms of manifestation and ways to increase as well as to attract the attention of subjects of these processes to take regulatory measures. The object of the study is labour in the main link of the economy. The subject are political and economic, social and labour relations between participants of the production process regarding the increase in the labour productivity. Its methodological framework covers provisions explaining the nature of the continuity of labour processes based on the labour productivity. Its main sources are considered using the methods of scientific abstraction, comparison, logical, historical, causal, dialectical methods. Among the scientific results is the conclusion about the essence of the economic law of increasing productivity of social labour, about some properties of the forms of its manifestation, number of directions for accelerating the increase and continuity of the labour process. The scientific and practical significance of the article lies in the possible usefulness for the main link of the economy which corrects its own strategies and development programmes of authorities at various levels during the implementation of economic policy as well as of higher educational institutions of the country where economic theory and labour economics are taught.

Using Visual Basic for Applications to Implement Markov Analysis in Market Share Forecasting

Objective: This paper is an examination of the use of Markov analysis in market share prediction, and the use of Visual Basic for Applications (VBA) to increase the speed and precision of the computation and model. Theoretical Framework: The Markov analysis, used to predict future states from current transitions, sheds light on the consumer behavior and brand loyalty dynamics With VBA, the data processing can be automated, and transition matrices built that are dynamic, so that forecasts can be updated in real time. Method: Markov analysis is based on transition matrices, which define the probabilities of transitioning from one state to another. These matrices are then used to predict long term probabilities by raising the transition matrix to some power that is equal to the number of future steps being studied. Results and Discussion: By utilizing Visual Basic for Applications (VBA), the study facilitates the swift creation of transition matrices and enables real-time modifications in response to new data. This implies that with the use of VBA and Markov analysis, not only is the forecasting process made easier, but also more accurate predictions are made, and for that reason VBA should be one of the tools that any firm looking to maximize its market share strategies should employ. Research Implications: This capability results in a more flexible and responsive forecasting instrument, enhancing the overall adaptability of market share predictions. Originality/Value: The originality of this research is found in its innovative methodological framework that builds upon conventional Markov analysis.

Functions of the particle üldse ‘at all’ in questions in Estonian everyday conversations

Abstract The goal of the paper is to examine the use of the particle üldse ‘at all; ever; generally; absolutely (not)’ in questions in Estonian everyday face-to-face and telephone conversations. The analysis is based on the methodological framework of interactional linguistics. The particle üldse is found to serve three central functions in questions: (a) marking topic shifts and topic changes, (b) intensifying doubt or challenges, (c) emphasizing someone’s norm-violating behavior. Questions containing the particle üldse can be divided into two groups: neutral information-seeking questions and multifunctional questions that perform several social actions simultaneously. The particle üldse is commonly backward looking and serves both interpersonal and textual functions. Its use is often associated with non-preference, disagreement, or contradiction.

Interdisciplinary participation in climate-adapted urban land-use planning – findings of two gaming simulations in the city of Bottrop, Germany

Purpose Sustainable and climate-adapted urban development requires integrative governance approaches and forms of collaboration between different disciplinary actors in urban society. Integrative approaches are a particular challenge for those planning cultures in which they are not yet sufficiently established. This also applies to formal urban land-use planning in Germany, which forms the governance setting of this study. This study aims to examine how interdisciplinary participation in formal urban land-use planning contributes to the consideration of climate adaptation in the planning process. Design/methodology/approach This paper traces the process of two differently designed interdisciplinary participation formats based on the methodological framework of a gaming simulation, each in the context of a formal urban land-use planning procedure in Bottrop, Germany. The gaming simulations are designed as performance simulations in which we involved several representatives from different public authorities. Findings The gaming simulations show that interdisciplinary participation can lead to an increased awareness of climate adaptation requirements in particular and a mutual understanding of different logics of action in the context of comprehensive and sustainable urban planning in general. In addition, this paper provides recommendations as to how and under what conditions the benefits of the simulations can be transferred to municipal practice. Originality/value While integrative and interdisciplinary formats are increasingly being used in the context of informal urban planning, this does not apply to formal urban land-use planning. Participation in formal urban land-use planning procedures is classically linear and multidisciplinary in Germany. Using two simulated interdisciplinary participation formats, this paper tested to what extent the consideration of climate adaptation requirements as a cross-sectoral task can benefit from interdisciplinarity in the context of two formal urban land-use planning procedures.

Causal relationship between leukocyte telomere length and two cardiomyopathies based on a bidirectional Mendelian randomization approach

This study aims to employ the Mendelian randomization (MR) approach to investigate the relationship between leukocyte telomere length (TL) and 2 prevalent forms of cardiomyopathies. Using R software (4.3.1) for MR study, independent genetic variants associated with leukocyte TL were extracted from the Integrative Epidemiology Unit database, while cardiomyopathies data were pooled from FinnGen and European Bioinformatics Institute databases. Analytical methodologies included inverse-variance weighting, MR-Egger regression, and weighted median methods. Further analyses involved MR-Egger intercept and MR-PRESSO for handling horizontal pleiotropy and Cochran Q test for study heterogeneity. Our forward Mendelian randomization study indicates a positive correlation between longer leukocyte TL and the risk of 2 forms of cardiomyopathies: the longer the leukocyte telomere, the higher is the risk of cardiomyopathies. Specifically, for hypertrophic obstructive cardiomyopathy the OR is 2.23 (95% CI: 1.19–4.14, P = .01), for hypertrophic cardiomyopathy the OR is 1.80 (95% CI: 1.14–2.85, P = .01), and for dilated cardiomyopathy the OR is 1.32 (95% CI: 1.01–1.71, P = .04). In contrast, our reverse Mendelian randomization showed that cardiomyopathies were not directly associated with TL, and the inverse-variance-weighted test was not statistically significant for any of the 3 (P > .05). The reliability tests for the forward Mendelian randomization, including both MR-Egger intercept and MR-PRESSO tests, show no evidence of horizontal pleiotropy, and Cochran Q test indicates no heterogeneity. The “leave-one-out” sensitivity analysis revealed no outlier genes. The reliability tests for the reverse Mendelian randomization, including both MR-Egger intercept and MR-PRESSO tests, also indicate no genetic pleiotropy. Despite the heterogeneity shown in our study between hypertrophic cardiomyopathy and leukocyte TL, the sensitivity analysis did not identify any anomalies. Our Mendelian randomization study suggests that longer leukocyte TL is associated with an increased risk of hypertrophic obstructive cardiomyopathy, hypertrophic cardiomyopathy, and dilated cardiomyopathy. However, the onset of these 2 kinds of disease does not directly lead to changes in leukocyte TL.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

Evaluative criteria for least-cost economic analysis of citywide inclusive sanitation: a scoping review

ABSTRACT Compared to health interventions, where evaluative criteria have been established, uncertainty about how comparative economic analysis methodologies should be applied to heterogeneous urban sanitation interventions in cities of low- and middle-income countries persists. Gaps between least-cost principles and current approaches constrain emerging citywide inclusive sanitation (CWIS) planning frameworks. Furthermore, a lack of comparable evidence about the economic efficiency of service provision interventions impacts the delivery of urban sanitation as an inclusive public service. This review scopes how least-cost principles may be applied to methodologies for urban sanitation interventions in two iterations. We devise and discuss evaluative criteria, embodying CWIS principles, that will improve the quality of these methodologies. They include how (i) the inclusion of diverse cost perspectives, particularly populations with marginal access to sanitation, are accommodated within an analysis; (ii) a context-specific without-project case may be used as an appropriate basis for comparison; (iii) tangible and intangible costs and outcomes may influence decisions; and (iv) diverse mixtures of interventions at different scales may be compared with integrity. The resulting evaluative criteria define principles that may inform future systematic reviews on practices that enhance CWIS planning frameworks.