Verification Strategy Research Articles

A review and benchmark of feature importance methods for neural networks

Feature attribution methods (AMs) are a simple means to provide explanations for the predictions of black-box models such as neural networks. Due to their conceptual differences, the numerous different methods, however, yield ambiguous explanations. While this allows for obtaining different insights into the model, it also complicates the decision regarding which method to adopt. This article summarizes the current state of the art regarding AMs, which includes the requirements and desiderata of the methods themselves as well as the properties of their explanations. Based on a survey of existing methods, a representative subset consisting of the δ-sensitivity index, permutation feature importance, variance-based feature importance in artificial neural networks and DeepSHAP, is described in greater detail and, for the first time, benchmarked in a regression context. Specifically for this purpose, a new verification strategy for model-specific AMs is proposed. As expected, the explanations’ agreement with the intuition and among each other clearly depends on the AMs’ properties. This has two implications. First, careful reasoning about the selection of an AM is required. Second, it is recommended to apply multiple AMs and combine their insights in order to reduce the model’s opacity even further.

The professional practice of designing tasks: how do pre-service early childhood teachers promote mathematical processes in early algebra?

AbstractSpanish educational curriculum adopts a mathematical process-based approach, which encompasses problem solving, reasoning and proof, communication, connections and representation. A fundamental role in the integration of these processes in mathematics teaching is played by teachers’ professional practice of designing tasks. According to this, our aim is to analyze the ways in which pre-service early childhood teachers understand the mathematical processes in the professional practice of designing early algebra tasks and to identify how they intend to promote these processes through the tasks. Content analysis techniques were used to examine the designed tasks. To illustrate the data analysis and results, six tasks are presented. As a result, pre-service early childhood teachers associate problem solving with challenges and questions. They understand problems as unfamiliar situations but ignore the relationships between students and tasks. Moreover, they do not encourage exploration of phases of problem solving and tend to use strategies more suitable for routine tasks. Communication is identified in all the tasks designed, encouraging interaction and discussion. However, only one task explicitly promotes mathematical language. For reasoning and proof, pre-service teachers begin to use questions to elicit explanations and justifications, but do not encourage verification strategies and various modes of reasoning. The process of connections is only present in one task, reflecting the fragmented nature of mathematics teaching. We conclude that the professional practice of designing mathematical tasks is a powerful in teacher education. However, training programs should place greater emphasis on the meaningful use of mathematical processes.

Optimal banking with delegated monitoring

We propose a model of financial intermediation based on delegated monitoring, where firms' returns are private information that lenders can ascertain through costly state verification. Our model has two key features: lenders cannot commit to their verification strategies and there are aggregate shocks. Simple debt contracts are Pareto optimal with or without intermediation. We show that the benefits of intermediation can be limited by financial instability in the presence of aggregate shocks. However, a well-designed resolution mechanism ensures the Pareto optimality of financial intermediation, and a bail-out policy can restore financial stability.

Pengaruh Motivasi dan Disiplin Kerja Terhadap Kinerja Karyawan CV. Santoso Teknik Karawang

The main objective of this research is to assess the individual and combined impact of work discipline and work motivation on employee performance at CV Santoso Teknik Karawang. Descriptive and verification strategies were used in the quantitative analysis of this research. Fifty people from CV Santoso Teknik Karawang became the population or research subjects. Sampling using saturated sampling means conducting a survey of all workers at CV. Santoso Teknik Karawang has fifty employees. Data were collected via a Google Forms survey in which various items were rated using a Likert scale. Various data validity and reliability tests, as well as classic assumption tests are available in the SPSS version 22 application. Multiple linear regression, t-test, f-test, and coefficient of determination are part of this series of tests. Motivation and work discipline partially influence employee performance, based on the results of the t test. Apart from that, the f test confirms that there is a significant simultaneous influence between motivation and work discipline on the performance of CV Santoso Teknik Karawang..

Exploring the presence of tilapia species in a central western Mexican reservoir using mitochondrial DNA control region sequencing

Oreochromis niloticus is the most important aquacultural species worldwide. Several countries are interested in repopulating natural or artificial reservoirs with the fry of tilapia (Oreochromis sp.) to develop new fisheries or to enhance the existing ones. However, as tilapia is considered an invasive species, it is essential to check the existence of the species in the place before releasing any fry. Characterization of tilapia species with meristic is unreliable because of existing morphological similarities among species. In this work, the composition of the tilapia species of Laguna El Mastranzo (LEM), Nayarit, Mexico, was explored for the first time through a molecular method using the mitochondrial DNA control region. Three tilapia species (Oreochromis niloticus, O. aureus, and O. urolepis) were found in LEM, and it was confirmed that the two commercial strains (Aquamol and Spring) intended for repopulation were O. niloticus. Seven haplotypes of mitochondrial DNA control region sequences were obtained: three in LEM, three in Aquamol, and one in Spring strains. Before repopulating any reservoir, a molecular verification strategy is recommended to avoid spreading the species. The results of this work represent a glimpse into the genetic material of LEM, Nayarit, Mexico.

A WSN and vision based smart, energy efficient, scalable, and reliable parking surveillance system with optical verification at edge for resource constrained IoT devices

As urbanization accelerates, the demand for efficient parking surveillance solutions has increased. However, existing solutions often face challenges related to energy consumption, scalability, and reliability. This paper introduces a smart hybrid parking surveillance system integrating wireless sensor networks (WSNs) with vision based solution at the edge for resource constrained IoT devices to address these challenges. The solution leverages WSNs for periodic readings of parking space occupancy and introduces a low power sleep mode in the network for energy efficiency, along with optical verification strategies using computer vision models like R-CNN and Faster R-CNN FPN on ResNet50 and MobileNetv2 backbones for distinguishing between true and false positives in the WSN data for a greater accuracy in parking space occupancy. The system utilizes edge for computing on edge servers resulting in increased responsiveness of the system, reduced data transmission and real time processing of data. The proposed solution is formulated in such a way that it automatically switches between WSN and vision based sensing resulting in less energy consumption and longer lifespan of the system without compromising on accuracy. Through experimental results it is observed that models trained on the MobileNetv2 backbone demonstrated at least twice faster for both processing the images and training compared to those models trained on the ResNet backbone. On the other hand, both Faster R-CNN FPN (input resolution: 1440) and R-CNN (input resolution: 128) models trained on the MobileNetv2 backbone have slightly lower accuracies than the same models trained on the ResNet50 backbone.

Pengaruh Current Ratio, Return on Equity dan Debt to Equity Ratio Terhadap Harga Saham pada Perusahaan Manufaktur Sub Sektor Property dan Real Estate yang Terdaftar di Bursa Efek Indonesia pada Tahun 2014-2023

With a focus on manufacturing companies in the property and real estate subsector listed on the Indonesia Stock Exchange, the purpose of this study is to ascertain the impact of current ratio, return on equity, and debt to equity ratio on stock prices for the years 2014–2023. This study employs a quantitative methodology that combines a verification and descriptive strategy. The traditional assumption test, multiple linear regression, Pearson correlation coefficient, determination coefficient, and hypothesis testing with partial (t-test) and simultaneous (f-test) tests are among the statistical testing techniques used. Sixty financial reports from businesses in the Manufacturing Companies in the Property and Rail Estate Sub-Sector provided the sample data. Purposive sampling combined with nonprobability sampling is being used. IBM SPSS version 26.0 was the analysis tool used in this investigation. According to the study's findings, stock prices are somewhat influenced by the current ratio, return on equity, and debt to equity ratio. Stock prices are significantly impacted by the study's concurrently obtained current ratio, return on equity, and debt to equity ratio.

Pengaruh Current Ratio, Sales Growth, Net Profit Margin dan Firm Size. Terhadap Harga Saham: pada Perusahaan Pertambangan yang terdaftar di Bursa Efek Indonesia (BEI) periode 2016-2022

The purpose of this research is to ascertain how Return on Assets in Mining Companies Listed on the Indonesia Stock Exchange is affected by Current Ratio, Sales Growth, Net Profit Margin, and Firm Size between 2015 and 2022. This study employs a quantitative methodology that combines a verification and descriptive strategy. Various statistical tests are employed in hypothesis testing, including partial and simultaneous tests (t and f tests), multiple linear regression tests, Pearson product moment correlation coefficient tests, determination coefficient tests, and classical assumption tests. 56 mining companies' financial reports provided the sample data. Purposive sampling combined with nonprobability sampling is being used. IBM SPSS version 26.0 was the analysis tool used in this investigation. Current Ratio has a considerable impact on stock price, according to study data. A major factor influencing stock price is sales growth. The stock price is not influenced in part by firm size or net profit margin. According to the study's findings, the stock price is significantly impacted by the concurrently obtained current ratio, sales growth, net profit margin, and company size.

Pengaruh Debt To Equity Ratio, Net Profit Margin, Current Ratio dan Total Asset Turn Over Terhadap Pertumbuhan Laba: pada Perusahaan Manufaktur Sub Sektor Farmasi yang Terdaftar di Bursa Efek Indonesia Tahun 2014-2023

The purpose of this study is to ascertain the impact of the following factors on earning growth in pharmaceutical manufacturing companies listed on the Indonesia Stock Exchange between 2014 and 2023: debt to equity ratio, net profit margin, current ratio, and total asset turnover. This study employs a quantitative methodology that combines a verification and descriptive strategy. Traditional assumption testing, multiple linear regression testing, Pearson correlation coefficient testing, determination coefficient testing, and hypothesis testing with partial and simultaneous tests (t and f tests) are some of the statistical testing methods used for verification. Sixty financial reports from businesses in the Pharmaceutical Manufacturing Companies provide the sample data. employing nonprobability purposive sampling for sampling. Version 26.0 of SPSS is utilized as a data processing tool. The results of the partial study obtained Debt To Equity Ratio has no effect on Earning growth, Net Profit Margin has an effect on Earning growth, Current Ratio has an effect on Earning growth and total asset turnover has no effect on Earning growth. The results of the simultaneous study showed that Debt to Equity Ratio, Net Profit Margin, Current Ratio and Total Asset Turnover had a significant influence on Earning growth.

Pengaruh Perputaran Arus Kas, Current Ratio, Debt to Asset Ratio dan Debt to Equity Ratio Terhadap Return on Asset pada Perusahaan Manufaktur Sub Sektor Makanan dan Minuman yang terdaftar di Bursa Efek Indonesia Tahun 2015-2022

The purpose of this study is to ascertain how return on assets in food and beverage manufacturing companies listed on the Indonesia Stock Exchange is impacted by cash flow turnover, current ratios, debt to equity ratios, and debt to asset ratios for the years 2015–2022. This study employs a quantitative methodology that combines a verification and descriptive strategy. While verification statistical analysis uses traditional assumption tests, multiple linear regression tests, Pearson product moment correlation coefficient tests, determination coefficient tests, and hypothesis testing with partial and simultaneous tests (t and f tests), descriptive statistical data analysis techniques are used to find mean, maximum, minimum, and standard deviation data. 56 financial reports from businesses in the Food and Beverage Manufacturing Companies provide the sample data. sampling utilizing nonprobability sampling methods combined with purposive sampling. IBM SPSS version 26.0 was the analysis tool used in this investigation. The study's findings indicate that return on assets is not affected partially or simultaneously by cash flow turnover, current ratio, debt to equity, or debt to asset ratio

Refinement Composition Logic

One successful approach to verifying programs is refinement, where one establishes that the implementation (e.g., in C) behaves as specified in its mathematical specification. In this approach, the end result (a whole implementation refines a whole specification) is often established via composing multiple “small” refinements. In this paper, we focus on the task of composing refinements. Our key observation is a novel correspondence between the task of composing refinements and the task of proving entailments in modern separation logic. This correspondence is useful. First, it unlocks tools and abstract constructs developed for separation logic, greatly streamlining the composition proof. Second, it uncovers a fundamentally new verification strategy. We address the key challenge in establishing the correspondence with a novel use of angelic non-determinism. Guided by the correspondence, we develop RCL (Refinement Composition Logic), a logic dedicated to composing refinements. All our results are formalized in Coq.

The effects of debt-to-asset ratio, size of the company, institutional ownership and earnings management on accounting conservatism in manufacturing companies in the food and beverage sub-sector listed on the Indonesia Stock Exchange between 2015 and 2023

This study attempts to ascertain how accounting conservatism is affected in manufacturing businesses in the food and beverage subsector listed on the Indonesia Stock Exchange between 2015 and 2023 by factors such as debt to asset ratio, company size, institutional ownership, and earnings management. This study employs a quantitative methodology that combines a verification and descriptive strategy. Traditional assumption testing, multiple linear regression, Pearson product moment correlation coefficient, determination coefficient, and hypothesis testing with partial and simultaneous tests (f and t tests) are all used in the statistical analysis approach of verification. Sample data comes from 63 financial reports of businesses in the Food and Beverage Manufacturing Subsector. This study employed a purposive sample strategy in conjunction with nonprobability sampling. Version 26.0 of SPSS software is used for data processing. According to preliminary study findings, there is no discernible relationship between accounting conservatism and the debt to asset ratio. Company size has a big impact on accounting conservatism. The impact of institutional ownership on accounting conservatism is negligible at best. Accounting conservatism is neither impacted by or significantly affected by earnings management. The study's findings concurrently demonstrate how the debt to asset ratio, business size, institutional ownership, and earnings management all have an impact on accounting conservatism

Transfer learning may explain pigeons’ ability to detect cancer in histopathology

Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levensonet al2015PLoS One10e0141357). Could it be that knowledge transferred from their bird's-eye views of the earth's surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we recapitulate this biological phenomenon with a machine-learning analog. We model pigeons' visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet; our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in Levensonet al's study 2015PLoS One10e0141357), we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin (H&E) histopathology and radiology images and tasks that the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird's-eye view data results in close agreement with pigeons' performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image datasets representing diverse cancer types.

Garlic ameliorates atherosclerosis by regulating ferroptosis pathway: an integrated strategy of network pharmacology, bioinformatic and experimental verification.

Atherosclerosis (AS) is a chronic arterial pathology and a leading cause of vascular disease-related mortality. Fatty streaks in the arterial wall develop into atherosclerosis and characteristic plaques. Clinical interventions typically involve lipid-lowering medications and drugs for stabilizing vulnerable plaques, but no direct therapeutic agent specifically targets atherosclerosis. Garlic, also locally known as DASUAN, is recognized as a widely sold herbal dietary supplement esteemed for its cardiovascular benefits. However, the specific mechanisms of garlic's anti-atherosclerotic effects remain unclear. This study aims to elucidate the pharmacological mechanisms through which garlic ameliorates atherosclerosis. The study identified the major active components and targets of garlic by screening the TCMSP, TCM-ID, and, ETCM databases. Atherosclerosis-associated targets were obtained from the DisGeNET, GeneCards, and DiGSeE databases, and garlic intervention targets were determined through intersection. Utilizing the intersected genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using R software. A garlic component-disease target network was constructed using Cytoscape. RNA-seq datasets from the GEO database were utilized to identify differentially expressed genes (DEGs) associated with atherosclerosis. The target genes were intersected with DEGs and the FerrDb (ferroptosis database). Molecular docking predicted the binding interactions between active components and the core targets. In vitro and in vivo experiments validated the identified core targets. The integration of garlic drug targets with atherosclerotic disease targets identified 230 target genes. Intersection with RNA-seq DEGs revealed 15 upregulated genes, including 8 target genes related to ferroptosis. Molecular docking indicated favorable affinities between garlic active components [Sobrol A, (+)-L-Alliin, Benzaldoxime, Allicin] and target genes (DPP4, ALOX5, GPX4). Experimental validation showed that GARLIC reduces the expression of ferroptosis-related genes in AS, suggesting its therapeutic potential through the regulation of ferroptosis. Garlic ameliorates atherosclerosis by targeting intra-plaque ferroptosis and reducing lipid peroxidation. These findings provide novel insights into the pharmacological mechanisms underlying the efficacy of garlic in treating AS.

Exploring the anti-inflammatory and immunomodulatory potential of licochalcone B against psoralidin-induced liver injury

Ethnopharmacological relevanceHerb-induced liver injury (HILI) represents an exacerbated inflammatory response, with Psoraleae fructus (PF) and its preparations recently associated with hepatotoxicity. Licorice, historically recognized as a detoxifying herbal remedy, is considered to possess hepatoprotective properties. Our previous research identified bavachin, bakuchiol, and psoralidin (PSO) as potential toxic constituents in PF, while licochalcone B (LCB) and echinatin were identified as bioactive components in licorice. However, evidence regarding the interactions of active compounds in herbs and their underlying mechanisms remains limited. Aim of the studyThe objective of this study is to assess the potential mechanisms through which LCB modulates immunological and anti-inflammatory responses to treat PSO-induced liver injury by using human hepatocyte cells (L02) and LPS-primed mice. MethodsThe ameliorative effects of LCB and echinatin on bavachin, bakuchiol, and PSO-induced liver injury were demonstrated in L02 cells. Subsequently, the efficacy of LCB on PSO-induced idiosyncratic liver injury was further validated in C57BL/6 mice under moderate inflammatory stress induced by LPS priming. The mechanisms were preliminarily explored with an integrated strategy of molecular docking, RT-PCR verification, and untargeted metabolomics. ResultsThe study shows that LCB significantly reduced cell injury induced by the three chemicals in PF and provided substantial protection against PSO-induced hepatic damage, as indicated by the levels of ALT, AST, and LDH. LCB normalized liver function and remarkedly alleviated hepatic lesions and inflammation caused by PSO in mice under moderate inflammatory stress. The mRNA profiles of both L02 cells and mice liver tissue revealed that LCB mitigated PSO-induced hepatotoxicity by regulating the gene expression of pro-inflammatory cytokines IL1B and TNF, as well as immunoinflammatory genes PIK3CA, AKT1, NFKB1, and NLRP3. Furthermore, untargeted metabolomics of liver tissue indicated that LCB could reverse the abnormal expression of 11 discriminatory metabolites, with the interrelationship between differential metabolites and target genes primarily clustering in glycerophospholipid metabolism, arachidonic acid metabolism, and phosphatidylinositol signaling system. ConclusionLCB demonstrated a superior anti-inflammatory and immunomodulatory effect on PSO-induced hepatotoxicity by modulating the inflammatory response and metabolic signaling system. Key interactive targets included phosphatidylcholine, phosphatidic acid, and subunit isoforms of PI3K.

The investigation of potential mechanism of Fuzhengkangfu Decoction against Diabetic myocardial injury based on a combined strategy of network pharmacology, transcriptomics, and experimental verification

Background and ObjectivesDiabetic cardiomyopathy (DCM) is a cardiac condition resulting from myocardial damage caused by diabetes mellitus (DM), currently lacking specific therapeutic interventions. Fuzhengkangfu decoction (FZK) plays an important role in the prevention and treatment of various cardiovascular diseases. However, the efficacy and potential mechanisms of FZK are not fully understood. This study aims to investigate the protective effect and mechanisms of FZK against DCM. MethodologiesRats were given a high-calorie diet along with a low dosage of streptozotocin (STZ) to establish a rat model of DCM. The diabetic rats received FZK or normal saline subcutaneously for 12 weeks. Echocardiography was conducted to evaluate their heart function characteristics. Rat heart morphologies were assessed using Sirius Red staining and H&E staining. Transcriptome sequencing analysis and network pharmacology were used to reveal possible targets and mechanisms. Molecular docking was conducted to validate the association between the primary components of FZK and the essential target molecules. Finally, both in vitro and in vivo studies were conducted on the cardioprotective properties and mechanism of FZK. ResultsAccording to the results of network pharmacology, FZK may prevent DCM by reducing oxidative stress and preventing apoptosis. Transcriptomics confirmed that FZK protected against DCM-induced myocardial fibrosis and remodelling, as predicted by network pharmacology, and suggested that FZK regulated the expression of oxidative stress and apoptosis-related proteins. Integrating network pharmacology and transcriptome analysis results revealed that the AGE-RAGE signalling pathway-associated MMP2, SLC2A1, NOX4, CCND1, and CYP1A1 might be key targets. Molecular docking showed that Poricoic acid A and 5-O-Methylvisammioside had the highest docking activities with these targets. We further conducted in vivo experiments, and the results showed that FZK significantly attenuated left ventricular remodelling, reduced myocardial fibrosis, and improved cardiac contractile function. And, our study demonstrated that FZK effectively reduced oxidative stress and apoptosis of cardiomyocytes. The data showed that Erk, NF-κB, and Caspase 3 phosphorylation was significantly inhibited, and Bcl-2/Bax was significantly increased after FZK treatment. In vitro, FZK significantly reduced AGEs-induced ROS increase and apoptosis in cardiomyocytes. Furthermore, FZK significantly inhibited the phosphorylation of Erk and NF-κB proteins and decreased the expression of MMP2. All the results confirmed that FZK inhibited the activation of the Erk/NF-κB pathway in AGE-RAGE signalling and alleviated oxidative stress and apoptosis of cardiomyocytes. In summary, we verified that FZK protects against DCM by inhibiting myocardial apoptotic remodelling through the suppression of the AGE-RAGE signalling pathway. ConclusionIn conclusion, our research indicates that FZK demonstrates anti-cardiac dysfunction properties by reducing oxidative stress and cardiomyocyte apoptosis through the AGE-RAGE pathway in DCM, showing potential for therapeutic use.

Predictive response to the behaviour of old structures using ambient vibrations

The response of ancient structures such as the Saint-Jean-Baptiste church or the current El-Badr mosque in Mostaganem (Algeria) to earthquakes depends on these physical characteristics and consequently on modal properties such as frequencies, damping and modal deformations. In this paper, ambient vibration tests were carried out on the three parts of the mosque building, and the response spectra and damping obtained will be correlated with the mechanical tests carried out in the second part of the work. Determination of the dynamic characteristics made it possible to distinguish differences in stiffness and to clearly highlight a disparity in frequencies in each structure, which may correspond to the age of each part of the building, the design and construction and differences in levels, etc. The results of the destructive tests give indications that confirm the hypotheses of ageing of the materials. The low young modulus of the various components partly confirms the frequency and damping values obtained. The experimental study of the mosque using ambient vibrations will give an order of magnitude of the vibratory mode periods, which are the first steps in any modelling, reinforcement and/or verification strategy and constitute a health record for this type of heritage.

Emergent knowledge patterns in verification artifacts

AbstractKnowledge graphs have recently been introduced to the verification strategy field successfully representing the complexity of verification in real‐life applications. This format provides a scale‐free analysis of verification strategies compared to the more traditional verification artifacts such as requirement traceability matrices and verification matrices. Complexities can be observed visually and numerically both in terms of the problem scope and the entity interdependencies. In this paper, we retrieve verification strategy information patterns representing different aspects of verification. This is achieved by tapping into the network properties of knowledge graphs. They are dissected to detect knowledge patterns emerging from different parts of the verification artifacts. Similarities and differences between the two verification strategies are explained numerically and semantically. Seemingly unrelated requirements and verification activities are connected through indirect connections, and orthogonalities between independent requirements are analyzed. These findings validate the scalability of verification planning and assessment based on knowledge graphs.

Progressive Test and Evaluation Strategy for Verification of KF-X AESA Radar Development

This paper describes a progressive test and evaluation strategy for verification of Korean Fighter eXperimental (KF-X) AESA(Active Electronically Scanned Array) radar development. Three progressive stages of development test and evaluation were officially performed from simulated test conditions to actual operating conditions according to standards: radar function/performance and avionics integration. KF-X AESA radar development is repeatedly verified by progressive stages consisting of five tests: Roof-lab ground test, System Integration Laboratory(SIL) ground test, Flying Test Bed(FTB) test, KF-X ground test, and KF-X flight test. As a result, the risk factor decreases as stages and tests progress. Therefore, development test and evaluation of KF-X AESA radar are successfully performed at low development risk.

Toolbox of individual-level interventions against online misinformation.

The spread of misinformation through media and social networks threatens many aspects of society, including public health and the state of democracies. One approach to mitigating the effect of misinformation focuses on individual-level interventions, equipping policymakers and the public with essential tools to curb the spread and influence of falsehoods. Here we introduce a toolbox of individual-level interventions for reducing harm from online misinformation. Comprising an up-to-date account of interventions featured in 81 scientific papers from across the globe, the toolbox provides both a conceptual overview of nine main types of interventions, including their target, scope and examples, and a summary of the empirical evidence supporting the interventions, including the methods and experimental paradigms used to test them. The nine types of interventions covered are accuracy prompts, debunking and rebuttals, friction, inoculation, lateral reading and verification strategies, media-literacy tips, social norms, source-credibility labels, and warning and fact-checking labels.