Transparent Method Research Articles

Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.

Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.

Rapid reviews methods series: Guidance on assessing the certainty of evidence

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RRs) use modified systematic review methods to accelerate the review process...

Blockchain meets machine learning: a survey

Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved.

Sosialisasi Penerapan Sistem Informasi Desa (SID) Berbasis Teknologi Informasi Dan Komunikasi (TIK) Di Desa Pitra Tabanan

The lack of public awareness and knowledge about technological advances, especially the Village Information System and the use of information and communication technology is the main problem of Pitra Tabanan Village. Most bureaucratic activities in Pitra Village are still carried out conventionally. Villagers continue to use strategic conventions for everything from information dissemination to administration and even data collection. Therefore, we initiated counseling, education, and mentoring programs for Village Information Systems Based on Technology, Information, and Communication. This activity program aims to introduce transparent, effective, and efficient work systems and methods. The people of Pitra Village are increasingly aware of the importance of technological progress as a result of this work program. because it helps and relieves conventional human work. It is hoped that this ICT-based village information system can serve as an example for Pitra Village to continue to develop and develop into a more advanced community and village. keywords : socialization, information and technology systems, village development

A closer look into the ‘black box’ of coaching – linguistic research into the local effectiveness of coaching with the help of conversation analysis

ABSTRACT This paper, located in the paradigm of Linguistic Coaching Process Research, theoretically introduces Conversation Analysis (CA) as a means to explore the ‘black box’ of coaching, i.e., the concrete conversation, and to address pressing research gaps in this field. CA, developed to describe, analyse, and understand practices and patterns of talk-in-interaction, presents a rigorous, systematic, and transparent qualitative method to document and research the sequentially organised conversational structure of coaching as well as the discursive practices of coach and client. The analytical power of CA lies in identifying change-inducing practices and in detailing how these contribute to the local effectiveness of coaching. A case in point are questioning practices, whose transformative power as local agents of change emerges in their sequential set-up as ‘coaches’ question – clients’ response – coaches’ reaction in third position’. In an exemplary analysis using CA, we illustrate the (un-)successful processing of questions in coaching. The two data extracts stem from the ongoing project Questioning Sequences in Coaching. While the paper aims to shed predominantly theoretical light on how to linguistically unpack the ‘black box’ of coaching, it also aims to sensitise practitioners to consider interventions, e.g., questions, as embedded in the locally transpiring, turn-by-turn interaction with their clients.

A Transparent Data-Driven Method for Stability-Constrained Load Restoration Considering Multi-Phase Load Dynamics

Load dynamics significantly complex the system stability behavior during the load restoration process after an outage event. It is highly challenging to model the load dynamics and solve the stability-constrained load restoration problem. In this paper, the load dynamic model considering the inrush phase and enduring phase of the cold load pickup effects are firstly modeled. Then, the load restoration optimization model is formulated with stability constraints, ramping constraints, and system operation models. A transparent data-driven method based on optimal decision trees is developed to address all the short-term stability criteria (including frequency, voltage, and transient stability) considering the inrush phase of load dynamics. A set of accurate, linear, and tractable stability constraints are extracted offline and implemented for fast online decision-making. Numerical studies based on the New-England testing system are conducted to validate the proposed method.

Reactions From the Experts: Implications of Open-Source ActiGraph Counts for Analyzing Accelerometer Data

In 2022, it became possible to produce ActiGraph counts from raw accelerometer data without use of ActiLife software. This supports the availability and use of transparent, open-source methods for producing physical behavior outcomes from accelerometer data. However, questions remain regarding the implications of the availability of open-source ActiGraph counts. This Expert Question and Answer paper solicited and summarized feedback from several noted physical behavior measurement experts on five questions related to open-source counts. The experts agreed that open-source, transparent, and translatable methods help with harmonization of accelerometer methods. However, there were mixed views as to the importance of open-source counts and their place in the field moving forward. This Expert Question and Answer provides initial feedback, but more research both within this special issue and to be conducted moving forward will help to inform whether and how open-source counts will be accepted and adopted for use for device-based physical behavior assessments.

Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors.

MELHOR DO QUE NADA? A IDEIA DE “JUSTIÇA POSSÍVEL” PARA A COMPENSAÇÃO INDIVIDUAL EM CASOS DE DESASTRES

Abstract When affecting a considerable number of victims, post-disaster remedy processes face significant obstacles related to assessing and measuring the exact extent of individual losses. Recent cases have adopted the concept of “rough justice”, a method also applied to the September 11th Victim Compensation Fund and in transitional justice contexts. While achieving full compensation may be difficult, time-consuming, and sometimes impossible, resorting to “rough justice” can serve as a theoretical framework to legitimize insufficient reparations and the perpetuation of rights abuses. The article examines the application of the “rough justice” paradigm and assesses the relevance of the United Nations Guiding Principles on Business and Human Rights (UNGPs) in disaster cases. It focuses on holding companies accountable for compensating damages incurred by individuals. The conclusion emphasizes the necessity for any accepted parametrization to be rigorous, based on solid and transparent methods for assessing the losses of affected individuals and communities. The process should be guided by the needs of those affected and incorporate traditional knowledge, establishing minimum values while allowing for individual adjustments when possible. Furthermore, procedures should be open to review whenever new data arises regarding the impacts of the disaster or the evolving needs of the affected parties, especially considering ongoing and future losses.

Stakeholder engagement in agro-climate service planning

The impacts of weather, climate variability and climate change on agricultural production underline the increasing importance of actionable agro-climatic services. Transitioning from supply-driven provision of climate and agricultural information to demand-driven agro-climate services (ACS) at scale cannot be accomplished in a top-down manner but requires the engagement of diverse stakeholders in all phases of ACS development and implementation. This requires methods and tools to handle the diversity and dynamics of interactions between relevant stakeholders, including during the pre-financing stage of the ACS. We propose a transparent method to identify and engage stakeholders in the ACS planning phase and demonstrate this method as part of the socio-economic development planning process in Dien Bien, Vietnam. We find that considering stakeholder attributes such as availability, experience, gender, expertise, benefits and costs for each stakeholder, interest, influence, relevance, and attitude, combined with insights about the socio-economic development planning processes, is crucial for the engagement of stakeholders. We also find that facilitating collaborative interaction between ACS stakeholders is pivotal in supporting the planning of demand-driven ACS. Our methodology for engaging stakeholders is transferrable to designing and planning other interventions in complex systems.

The landscape of open science in behavioral addiction research: Current practices and future directions.

Open science refers to a set of practices that aim to make scientific research more transparent, accessible, and reproducible, including pre-registration of study protocols, sharing of data and materials, the use of transparent research methods, and open access publishing. In this commentary, we describe and evaluate the current state of open science practices in behavioral addiction research. We highlight the specific value of open science practices for the field; discuss recent field-specific meta-scientific reviews that show the adoption of such practices remains in its infancy; address the challenges to engaging with open science; and make recommendations for how researchers, journals, and scientific institutions can work to overcome these challenges and promote high-quality, transparently reported behavioral addiction research. By collaboratively promoting open science practices, the field can create a more sustainable and productive research environment that benefits both the scientific community and society as a whole.

Schätzung der Wirksamkeit der Grippeimpfung anhand von Sekundärdaten: Eine Kohortenstudie und Propensity-Score-Matching-Analyse von Leistungsdaten aus Baden-Württemberg

Ziel war es die Wirksamkeit der Influenza-Impfung (VE) für die Grippesaison 2014/2015 auf Grundlage von Routinedaten aus Krankenkassendatensatz zu schätzen und zu replizieren. Zusätzlich sollten methodische Aspekte untersucht werden. Es wurden Abrechnungsdaten von 2,64 Millionen Versicherten der AOK Baden-Württemberg mit dortigem Wohnsitz ab 15 Jahren analysiert. Basierend auf Abrechnungsdaten für die Influenza-Impfung 2014, wurden die Teilnehmer als ungeimpft oder geimpft klassifiziert. Kovariablen, die den Zusammenhang zwischen Impfung und Influenzainfektion beeinträchtigen könnten, wurden berücksichtigt. Hierzu gehörten Alter, Geschlecht, Wohnort sowie Kovariablen, die auf den Gesundheitszustand und die Inanspruchnahme von Gesundheitsdienstleistungen hinweisen. Der primäre Endpunkt war ein Krankenhausaufenthalt wegen Influenza während der Grippesaison 2015. Zu den sekundären Endpunkten gehörten unter anderem Krankenhausaufenthalte wegen Lungenentzündung und die Gesamtmortalität. Um eine vergleichbare Gruppe von geimpften und ungeimpften Teilnehmern zu ermitteln, wurde ein Propensity-Score-Matching (PSM) durchgeführt. Es wurde eine Bias-Analyse durchgeführt, bei der die VE vor und nach der Grippesaison geschätzt wurde, also zu Zeitpunkten, in denen angenommen wurde, dass die Influenza nicht in der Bevölkerung zirkulierte und die Impfung nicht wirken konnte. Insgesamt konnten 839.706 Teilnehmer 1:1 gematcht werden. Die geschätzte VE (basierend auf Influenza bedingten Krankenhausaufenthalten) betrug 27% [95%Konfidenzintervall (KI): 17%; 36%], was der Schätzung des RKI für dieselbe Saison (27% [95%KI: -1%; 47%]) entspricht. Die Bias-Analyse zeigte, dass das Ergebnis teilweise durch residuale Konfundierung erklärt werden kann, was zu einer potenziellen Überschätzung des zugrunde liegenden Effekts führt. Die Ergebnisse der sekundären Endpunkte zeigten ähnliche Ergebnisse, obwohl sie wahrscheinlich in höherem Maße durch residuale Konfundierung bedingt sind. Zusammenfassend zeigt sich, dass (1) sekundäre Daten der deutschen Krankenkassen verwendet werden können, um plausible VE-Schätzungen abzuleiten, und dass (2) das PSM eine nützliche und transparente Methode zur Ableitung dieser Schätzungen ist. Darüber hinaus ist (3) residuale Konfundierung ein relevantes Problem in Beobachtungsstudien zu VE und (4) Bias-Analysen vor- und nach der Grippesaison sind eine wesentliche Ergänzung für die Interpretation der Ergebnisse.

Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning.

Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings.

Effect of high-intensity ultrasonic field on fog droplet coagulation efficiency

The relevance. The urgent need to deal with fog for preventing limited visibility and creating improved meteorological conditions. Exposure to gas dispersed systems by mechanical vibrations of the ultrasonic frequency can be considered as the most effective way to deal with fog formation. However, the acoustic method has not found an industrial application for fog destruction mainly due to the lack of effective sources of acoustic exposure. Therefore, it is necessary to create emitters of higher power that provide a significantly greater range of radiation with a sound pressure level of at least 140 dB and study of their functionality. The main aim. Determination of the effectiveness of fog destruction through the use of ultrasonic vibrations generated by the developed ultrasonic emitters; identification of the most promising design of ultrasonic equipment that ensures effective destruction of fogs in open areas. Objects. Combining fog droplets under high-intensity ultrasonic exposure using specially designed ultrasonic emitters for gas media of four types. Methods. Experimental method for studying fog droplets combination under the influence of ultrasonic vibrations and their gravitational deposition. To determine the aerosol (water content and disperse composition) characteristics, during experimental studies, the authors have used the TIPAS-1 meter based on the method of small-angle scattering and the method of spectral transparency. Results. The authors established and shown the effectiveness of ultrasonic effect on fogs, as well as the possibility of its use for fog destruction in an open area. All developed disc ultrasonic emitters are capable of forming a sound pressure level within 140... 145 dB at 22±2.0 kHz. The time of fog natural destruction in the aerosol chamber is reduced by up to 11.5 times. The results obtained demonstrate that it is necessary to use more powerful disc emitters, since they can significantly reduce the time of fog destruction and increase the volumes voiced at the same time. The minimum time for establishing the required metrological range of visibility is provided by means of a directional emitter with a stepped-variable surface, forming a flat wave.

Rapid reviews methods series: Guidance on literature search

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RR) use modified systematic review methods to accelerate the review process...

A process systems engineering view of environmental impact assessment in renewable and sustainable energy production: Status and perspectives

With the increasing concern for climate change, renewable and sustainable energy production has attracted considerable attention from the scientific community, industrial practitioners, and policy and decision-makers. There are many technological alternatives for each sub-category of complex sustainable energy systems. Life cycle assessment (LCA) can be an effective tool to compare the environmental impacts of each pathway and identify the most promising alternatives from an environmental impact perspective. This contribution first reviews the environmental assessment methods and tools developed over the years. Secondly, a comprehensive review of the contribution of the PSE community to the environmental impact analysis of renewable energy systems is performed. It is observed that while LCA is the preferred method, these studies differed widely concerning the choice of impact assessment method used, the level of details shared concerning the underlying LCA calculations, and whether or not sensitivity and uncertainty analyses were carried out, among many others. This makes the comparison of results from different studies difficult and often impossible. It is clear that the PSE community, with its emphasis on systems thinking and holistic approaches, plays a critical role in the design, integration, and operation of complex sustainable energy systems. However, the thorough calculations necessary to ensure a robust and transparent LCA analysis require a shared methodology and a detailed description of the rules. Such explicit, systematic, and transparent methods will set the bar for a minimum requirement for thorough LCA calculations, ensuring fair comparison and discussions of different technical solutions developed in the wider PSE community for sustainable renewables.

Reproducible Research Practices and Barriers to Reproducible Research in Geography: Insights from a Survey

The number of reproduction and replication studies undertaken across the sciences continues to rise, but such studies have not yet become commonplace in geography. Existing attempts to reproduce geographic research suggest that many studies cannot be fully reproduced, or are simply missing components needed to attempt a reproduction. Despite this suggestive evidence, a systematic assessment of geographers’ perceptions of reproducibility and use of reproducible research practices remains absent from the literature, as does an identification of the factors that keep geographers from conducting reproduction studies. We address each of these needs by surveying active geographic researchers selected using probability sampling techniques from a rigorously constructed sampling frame. We identify a clear division in perceptions of reproducibility among geographic subfields. We also find varying levels of familiarity with reproducible research practices and a perceived lack of incentives to attempt and publish reproduction studies. Despite many barriers to reproducibility and divisions between subfields, we also find common foundations for examining and expanding reproducibility in the field. These include interest in publishing transparent and reproducible methods, and in reproducing other researchers’ studies for a variety of motivations including learning, assessing the internal validity of a study, or extending prior work.

A systematic review of the evidence supporting the use of surgical aortic and mitral valves: an update from the CORE MD consortium

Abstract Introduction The European Union (EU) Medical Device Regulation (MDR) 2017/745 challenges the medical community to engage with regulators, notified bodies and industry to develop transparent, rigorous and proportionate methods to evaluate the clinical safety and efficacy of medical devices and to monitor their performance. As part of the EU Horizon 2020 funded CORE-MD project, we performed a systematic review of the published scientific evidence available for surgical heart valve replacements for native aortic and mitral valve pathologies. Purpose The aim of this analysis is to review the scientific evidence available which supports the use of currently available surgical aortic and mitral valves. Methods A systematic literature review was conducted using Ovid, MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL). We included only surgical aortic and mitral valves that received CE Mark approval from the year 2000 onwards. We included any study of prospective design (randomised or non-randomised clinical trials of any design) in humans, published between 2000 and 2021. We excluded retrospective studies, case reports, reviews, systematic reviews, meta-analyses, and expert opinion documents. Title and abstract screening, full-text review, risk of bias evaluation and data collection were performed by two authors independently, with disagreements resolved by discussion with a third author. Results A total of 17 surgical aortic valves and 4 surgical mitral valves were identified based on the CE mark criterion (Figure 1). The initial literature search identified 4901 and 3696 potentially eligible papers in the aortic and mitral valve categories, respectively. Finally, 39 aortic and 3 mitral valve studies were included (Figure 2). The majority of included studies (90%) had a non-randomised, prospective, observational design, with 68% receiving industry funding. The median number of patients included was 295 [interquartile range (IQR): 153, 689] with a median maximum follow up of 24 months [IQR: 12, 60]. Only 50% of the identified studies had a pre-specified registered study protocol and only 5% included a power calculation to estimate the required study size. No study reported patient involvement in the study design and only 15% of studies had a comparator group. Using the ROBINS I AND II assessment tools, 90% of the included studies were classified as having a high or serious risk of bias. Conclusion The evidence available for currently used surgical aortic and mitral replacement heart valves is largely from small, industry funded, non-randomised controlled trials. The median duration of maximum follow up was only 24 months, the majority of studies were classed as having a high/serious risk of bias and only half had a pre-registered study protocol. These results highlight the limitations of the available scientific evidence supporting the surgical aortic and mitral valves used in current clinical practice.Surgical aortic and mitral valvesSummary of literature search results

PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network

Fast and reliable performance monitoring of photovoltaic modules is essential for economic forecasting in large-scale installations. Deep Learning methods, such as convolutional neural networks, have the potential to predict module power directly from electroluminescence images in an automated workflow. However, neural networks must be trained using large numbers of electroluminescence images of defective modules. Due to budgetary or technical limitations, these training images will always be biased, limiting generalization. Here, we demonstrate a transparent method to discriminate the information learned by a convolutional neural network into generally valid physics and bias. Learning of physics is assessed by providing an artificial, unbiased feature list, which is converted into synthetic electroluminescence images. Using these images, we compare the predictions of the neural network trained on the biased dataset to those of a physics-based equivalent. Bias is assessed by a closer look at the deviations between the predictions from the equivalent circuit model and the trained neural network. The assessment of physics knowledge incorporated by a Deep Learning method gives insight into how the method achieves its predictive capacity.

A comprehensive analysis of multi-circulatory disorders in early pressure injury and their diagnostic significance in rat models

Early pressure injury (PI) progression is associated with multi-circulatory disorders and they interplay with each other, resulting in a lack of a satisfactory diagnostic method. We generated early PI and blanchable erythema hairless rat models. Transparent disc method and capillary refilling time test (CRTT) results were recorded with ultraviolet camera to capture the dynamics changes, and the blanching index and refilling index were set for comprehensive analysis. The deteriorated areas of early PI showed non-blanchable erythema (NBE) and an increase in erythema at 0.5 and 6 h with the transparent disc method. CRTT showed a marked refilling delay at 12 h. The comprehensive analysis of blanching index and refilling index showed a significant change in erythema from NBE at 0.5 h and ischemia progressing to hemorrhage at 18 h. There was also a marked difference in the deteriorating and improving areas within the same erythema. Pathological analysis showed inflammatory cell infiltration, with marked edema accompanied by increased hemorrhage and tissue necrosis. Furthermore, small arteries and veins with thrombosis and microthrombi were observed. Consistent ischemia after decompression and subsequent hemorrhage are important indicators, and comprehensive analysis can help increase the positive diagnosis rate over that for other circulatory disorders alone.