External Tools Research Articles

Moving from assessments to implementation: promising practices for strengthening multisectoral antimicrobial resistance containment capacity

BackgroundAntimicrobial resistance (AMR) poses a global threat to human, animal, and environmental health. AMR is a technical area in the Global Health Security Agenda initiative which uses the Joint External Evaluation tool to evaluate national AMR containment capacity. This paper describes four promising practices for strengthening national antimicrobial resistance containment capacity based on the experiences of the US Agency for International Development’s Medicines, Technologies, and Pharmaceutical Services Program work with 13 countries to implement their national action plans on AMR in the areas of multisectoral coordination, infection prevention and control, and antimicrobial stewardship.MethodsWe use the World Health Organization (WHO) Benchmarks on International Health Regulations Capacities (2019) to guide national, subnational, and facility actions that advance Joint External Evaluation capacity levels from 1 (no capacity) to 5 (sustainable capacity). Our technical approach is based on scoping visits, baseline Joint External Evaluation scores, benchmarks tool guidance, and country resources and priorities.ResultsWe gleaned four promising practices to achieve AMR containment objectives: (1) implement appropriate actions using the WHO benchmarks tool, which prioritizes actions, making it easier for countries to incrementally increase their Joint External Evaluation capacity from level 1 to 5; (2) integrate AMR into national and global agendas. Ongoing agendas and programs at international, regional, and national levels provide opportunities to mainstream and interlink AMR containment efforts; (3) improve governance through multisectoral coordination on AMR. Strengthening multisectoral bodies’ and their technical working groups’ governance improved functioning, which led to better engagement with animal/agricultural sectors and a more coordinated COVID-19 pandemic response; and (4) mobilize and diversify funding for AMR containment. Long-term funding from diversified funding streams is vital for advancing and sustaining countries’ Joint External Evaluation capacities.ConclusionsThe Global Health Security Agenda work has provided practical support to countries to frame and conduct AMR containment actions in terms of pandemic preparedness and health security. The WHO benchmarks tool that Global Health Security Agenda uses serves as a standardized organizing framework to prioritize capacity-appropriate AMR containment actions and transfer skills to help operationalize national action plans on AMR.

User-Customizable Transpilation of Scripting Languages

A transpiler converts code from one programming language to another. Many practical uses of transpilers require the user to be able to guide or customize the program produced from a given input program. This customizability is important for satisfying many application-specific goals for the produced code such as ensuring performance, readability, ease of exposition or maintainability, compatibility with external environment or analysis tools, and so on. Conventional transpilers are deterministic rule-driven systems often written without offering customizability per user and per program. Recent advances in transpilers based on neural networks offer some customizability to users, e.g. through interactive prompts, but they are still difficult to precisely control the production of a desired output. Both conventional and neural transpilation also suffer from the "last mile" problem: they produce correct code on average, i.e., on most parts of a given program, but not necessarily for all parts of it. We propose a new transpilation approach that offers fine-grained customizability and reusability of transpilation rules created by others, without burdening the user to understand the global semantics of the given source program. Our approach is mostly automatic and incremental, i.e., constructs translation rules needed to transpile the given program as per the user's guidance piece-by-piece. Users can rely on existing transpilation rules to translate most of the program correctly while focusing their effort locally, only on parts that are incorrect or need customization. This improves the correctness of the end result. We implement the transpiler as a tool called DuoGlot, which translates Python to Javascript programs, and evaluate it on the popular GeeksForGeeks benchmarks. DuoGlot achieves 90% translation accuracy and so it outperforms all existing translators (both handcrafted and neural-based), while it produces readable code. We evaluate DuoGlot on two additional benchmarks, containing more challenging and longer programs, and similarly observe improved accuracy compared to the other transpilers.

Abstract 4256: cBioPortal for Cancer Genomics

Abstract cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale clinico-genomic data sets. cBioPortal provides a suite of user-friendly visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. The public site (https://www.cbioportal.org) is accessed by >35,000 unique visitors each month and hosts data from >350 studies spanning individual labs and large consortia. In addition, at least 74 instances of cBioPortal are installed at academic institutions and companies worldwide. To better support all users, we unified our documentation (https://docs.cbioportal.org) and added a user guide and an ongoing series of ‘how-to’ videos to address common questions. In 2022 we added 32 studies (>38,000 samples) to the public site. In addition, we added a nonsynonymous tumor mutation burden (TMB) value for all samples and enhanced the TCGA PanCancer Atlas studies with DNA methylation and treatment data. All data is available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >165,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). The GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment history. The first BPC cohorts are now available: ~2,000 non-small cell lung cancer samples and ~1,500 colorectal cancer samples. Support for multimodal data analysis has been a major focus, including several new integrations with external tools. Single cell data is now available in the CPTAC GBM study and can be visualized throughout cBioPortal, and via integration with cellxgene. On the patient page, H&E and mIF images can be visualized via integration with Minerva, and the genomic overview now integrates IGV. We continue to enhance existing features. In the study view, users can now add charts comparing categorical vs continuous data, and the plots tab includes a heatmap option. We replaced the existing fusion data type with a generalized structural variant data type that supports detailed information including breakpoints and orientation, to enable new visualizations and analyses. Pathway level analysis has been extended with a new integration with NDEx. cBioPortal is fully open source (https://github.com/cBioPortal/). Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Caris Life Sciences, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Ino de Bruijn, Tali Mazor, Adam Abeshouse, Diana Baiceanu, Stephanie Carrero, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Bryan Lai, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Yusuf Ziya Özgül, Oleguer Plantalech, Sander Rodenburg, Baby Anusha Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Jianjiong Gao, Nikolaus Schultz. cBioPortal for Cancer Genomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4256.

A Survey Paper on Hard Disk Failure Prediction Using Machine Learning

Abstract: Failure of Hard Disk is a term most companies and people, fear about. People get concerned regarding data loss. Therefore, predicting the failure of the HDD is an important and to ensure the storage security of the data center. There exist a system named, S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. Our project will be predicting the failure of hard drive whether it will fail or not. This prediction will be based on Machine Learning algorithm. S.M.A.R.T. values of hard disk will be extracted from external tool.

The Mental Models Training App: Enhancing verbal reasoning through a cognitive training mobile application.

Reasoning is a complex form of human cognition whose nature has long been debated. While a number of neurocognitive mechanisms for deductive reasoning have been offered, one of the most prominent accounts is Mental Model Theory (MMT). According to MMT, humans are able to manipulate and represent information for reasoning and problem solving by leveraging the brain's evolved visuospatial resources. Thus, when solving deductive reasoning problems, reasoners build "mental models" of the essential pieces of information conveyed in the premises, with their relations to each other represented spatially-even when the information contained within a reasoning problem is not intrinsically spatial. Crucially, taking a spatially-based approach, such as building mental models, supports higher accuracy on deductive reasoning problems. However, no study has empirically tested whether explicitly training this mental modeling ability leads to improved deductive reasoning performance. Therefore, we designed the Mental Models Training App, a cognitive training mobile application which requires participants to complete increasingly difficult reasoning problems while using an external mental modeling tool. In this preregistered study (https://osf.io/4b7kn), we conducted a between-subjects experiment (N = 301) which compared the Mental Models Training App to 3 distinct control conditions in order to examine which specific components (if any) of the training were causally responsible for improved reasoning performance. Results demonstrate that, when compared to a passive control condition, the Mental Models Training App led to improvements in adults' verbal deductive reasoning performance both during and after the training intervention. However, contrary to our preregistered hypotheses, the training-induced improvements were not significantly larger than the effects of the active control conditions-one which included adaptive practice of the reasoning problems, and one which included adaptive practice as well as a spatial alphabetization control task. Therefore, while the present results demonstrate the ability of the Mental Models Training App to enhance verbal deductive reasoning, they do not support the hypothesis that directly training participants mental modeling ability yields improved performance beyond the effects of adaptive practice of reasoning. Future research should examine the long-term effects of repeated usage of the Mental Models Training App, as well as transfer effects to other forms of reasoning. Finally, we present the Mental Models Training App as a free mobile application available on the Apple App store (https://apps.apple.com/us/app/mental-models-training/id1664939931), in the hope that this translational research may be utilized by the general public to improve their reasoning ability.

The diagnostics of windings of vehicle alternator units

INTRODUCTION: An alternator unit is a necessary unit in a vehicle, as it provides all the consumers with electricity. Therefore, the operation capability of the whole vehicle depends on the alternator units operational state. The issue of the diagnostics of a vehicle alternator unit by means of determination of alternator phases windings active resistance is addressed.
 AIM: Improvement of diagnostics informativity by means of voltage controllers with a self-diagnostic function.
 METHODS: It is proposed to perform the alternator unit diagnostics without dismantling or disassembly by means of plugging to the diagnostic points.
 RESULTS: The existing and known methods of vehicle alternator unit diagnostics with embedded diagnostic tools and with use of external diagnostic tools were analyzed. The method of diagnostics of phase winding active resistance with use of reference resistance was proposed. The algorithm of implementation of the proposed method was suggested. The recommendations for use of the proposed method were given.
 CONCLUSIONS: The proposed diagnostics method can be used in engine control units or by technicians of maintenance stations during the alternator unit maintenance without dismantling or disassembly.

ADPG: Biomedical entity recognition based on Automatic Dependency Parsing Graph

Named entity recognition is a key task in text mining. In the biomedical field, entity recognition focuses on extracting key information from large-scale biomedical texts for the downstream information extraction task. Biomedical literature contains a large amount of long-dependent text, and previous studies use external syntactic parsing tools to capture word dependencies in sentences to achieve nested biomedical entity recognition. However, the addition of external parsing tools often introduces unnecessary noise to the current auxiliary task and cannot improve the performance of entity recognition in an end-to-end way. Therefore, we propose a novel automatic dependency parsing approach, namely the ADPG model, to fuse syntactic structure information in an end-to-end way to recognize biomedical entities. Specifically, the method is based on a multilayer Tree-Transformer structure to automatically extract the semantic representation and syntactic structure in long-dependent sentences, and then combines a multilayer graph attention neural network (GAT) to extract the dependency paths between words in the syntactic structure to improve the performance of biomedical entity recognition. We evaluated our ADPG model on three biomedical domain and one news domain datasets, and the experimental results demonstrate that our model achieves state-of-the-art results on these four datasets with certain generalization performance. Our model is released on GitHub: https://github.com/Yumeng-Y/ADPG.

The JWST Resolved Stellar Populations Early Release Science Program. III. Photometric Star–Galaxy Separations for NIRCam

We present criteria for separately classifying stars and unresolved background galaxies in photometric catalogs generated with the point-spread function fitting photometry software DOLPHOT from images taken of Draco II, WLM, and M92 with the Near Infrared Camera (NIRCam) on JWST. Photometric quality metrics from DOLPHOT in one or two filters can recover a pure sample of stars. Conversely, colors formed between short-wavelength and long-wavelength filters can be used to effectively identify pure samples of galaxies. Our results highlight that the existing DOLPHOT output parameters can be used to reliably classify stars in our NIRCam data without the need to resort to external tools or more complex heuristics.

The Evaluation of Complementary Metal-Oxide Semiconductor (CMOS) in Smartphone to Test X-Ray Tube Radiation Leakage

Radiation detection method has been developed through years. It started from the complex module tools to simple handheld instrument. Recently, the more ease method has been developed with the help of smartphone application. To detect radiation using smartphone usually required the external tools connected to the phone as detector, but there is one application that did not. This application only required complementary metal-oxide semiconductor (CMOS) part of camera. The application is a game changer in radiation protection because nowadays smartphone is in everyone’s pocket. The application needs to be tested to ensure its effectiveness to detect radiation. The application has been tested by the previous research and it is effective to detect radiation. In this research, CMOS will be tested to detect radiation leakage of x-ray tube. The aim of this research is to find the effectiveness of CMOS in smartphone for radiation leakage detection of x-ray tube. The finding will help the radiation worker detect leakage radiation of x-ray tube using smartphone in case of the absences of surveymeter in the facility. The radiation from x-ray machine were detected and measured three times by Iphone 6s, Xs, and 11 using RadioactivityCounter. To ensure there was a leakage, surveymeter is used as a comparative modul. The data obtained from the experiment was analyzed using t-test. The result show that percentage error of Iphone 6s, Xs, and 11 Consecutively were 93.4%, 98.2%, and 98.9%. which mean CMOS in these said phone could detect and measured radiation ineffectively. This due to the low leaked intensity x-ray that came from x-ray machine. From the T-test anaysis found that only Iphone 11 had linear comparison to surveymeter

MOOSE Reactor Module: An Open-Source Capability for Meshing Nuclear Reactor Geometries

The U.S. Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has developed numerous physics solvers utilizing the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) framework for multiphysics reactor analysis. These solvers require input finite element meshes representing the discretized spatial domain. Typically, reactor analysts turn to licensed tools for the creation of reactor geometry meshes. Recently, open-source functionality has been added to the MOOSE framework to mesh common reactor geometries and improve MOOSE-based nuclear reactor application user workflows. The new functionality is primarily contained in the new Reactor module of MOOSE and includes support for hexagonal pins, assemblies, and cores, extended Cartesian geometry support, options for modeling static and rotating control drums within a hexagonal assembly, core periphery triangulation, and automatic tagging of pin, assembly, plane, and depletion regions for easier post processing of physics results. A set of reactor geometry mesh builder objects further streamlines the construction of hexagonal and Cartesian cores and allows mapping of materials to regions during mesh generation. The meshes produced with the MOOSE Reactor module may be used directly within MOOSE-based applications or exported as Exodus II files for use in other finite element solvers. The tools have been demonstrated and verified using a variety of NEAMS physics solvers on a range of reactor applications, including a sodium-cooled fast reactor core analysis using Griffin, a fast reactor assembly thermal deformation analysis using MOOSE Tensor Mechanics, and a heat pipe–cooled microreactor coupled analysis using Griffin, Bison, and Sockeye. MOOSE’s Reactor module provides significant advantages compared to the use of external meshing tools when analyzing Cartesian and hexagonal reactor lattices using MOOSE-based applications: immediate accessibility (open-source) to the end user, low barrier to entry for new users, speed of mesh generation, volume preservation of meshed fuel pins, and simplification of analysis workflow when used in conjunction with MOOSE-based applications.

Golden Power and Anti-Takeover Corporate Mechanisms

Abstract 591 The golden power regime allows the Italian government – as a last resort, if facing a threat to national interests – to oppose the acquisition of control of companies operating in strategic sectors. This article analyses the relationship between golden power and anti-takeover mechanisms provided for by Italian law, with particular focus on anti-takeover defensive techniques and increased voting rights. The study aims to identify methods of interference and overlaps between external and internal defence tools so as to understand if, when, and under which limits internal corporate defence mechanisms can represent a valid (and desirable) alternative to the use of golden power in precluding the acquisition of corporate control. The analysis shows that where the acquisition of control is more contentious, all available defence tools potentially come into play and the difference in purpose between the diverse regimes justifies the usefulness of golden power.592

Performance assessment of primary health care facilities in Brazil: Concordance between web-based questionnaire and in-person interviews with health personnel.

This study is a concordance analysis comparing answers to two external assessment tools for Primary Health Care (PHC) facilities that use two different data collection methodologies: (a) external assessment through structured interviews and direct observation of facilities conducted by the National Program for Improvement of Access and Quality of Primary Care (AE-PMAQ-AB), and (b) a computerized web-based self-administered questionnaire for Assessment of the Quality of Primary Health Care Services (QualiAB). The two surveys were answered by 1,898 facilities located in 437 municipalities in the state of São Paulo, Brazil, between 2017 and 2018. Both surveys aimed to assess the management and organization of PHC facilities. A total of 158 equivalent questions were identified. The answers were grouped by thematic similarity into nine domains: Territory characteristics; Local management and external support; Structure; Health promotion, disease prevention, and therapeutic procedures; Attention to unscheduled patients; Women's health; Children's health; Attention to chronic conditions; and Oral health. The results show a high level of concordance between the answers, with 81% of the 158 compared questions showing concordance higher than 0.700. We showed that the information obtained by the web-based survey QualiAB was comparable to that of the structured interview-based AE-PMAQ-AB, which is considered the gold standard. This is important because web-based surveys are more practical and convenient, and do not require trained interviewers. Online assessment surveys can allow immediate access to answers, reports and guidelines for each evaluated facility, as provided by the QualiAB system. In this way, the answers to this type of survey can be directly employed by users, allowing the assessment to fulfill all phases of an assessment process.

Quantifying microstructures of earth materials using higher-order spatial correlations and deep generative adversarial networks

The key to most subsurface processes is to determine how structural and topological features at small length scales, i.e., the microstructure, control the effective and macroscopic properties of earth materials. Recent progress in imaging technology has enabled us to visualise and characterise microstructures at different length scales and dimensions. However, one limitation of these technologies is the trade-off between resolution and sample size (or representativeness). A promising approach to this problem is image reconstruction which aims to generate statistically equivalent microstructures but at a larger scale and/or additional dimension. In this work, a stochastic method and three generative adversarial networks (GANs), namely deep convolutional GAN (DCGAN), Wasserstein GAN with gradient penalty (WGAN-GP), and StyleGAN2 with adaptive discriminator augmentation (ADA), are used to reconstruct two-dimensional images of two hydrothermally rocks with varying degrees of complexity. For the first time, we evaluate and compare the performance of these methods using multi-point spatial correlation functions—known as statistical microstructural descriptors (SMDs)—ultimately used as external tools to the loss functions. Our findings suggest that a well-trained GAN can reconstruct higher-order, spatially-correlated patterns of complex earth materials, capturing underlying structural and morphological properties. Comparing our results with a stochastic reconstruction method based on a two-point correlation function, we show the importance of coupling training/assessment of GANs with higher-order SMDs, especially in the case of complex microstructures. More importantly, by quantifying original and reconstructed microstructures via different GANs, we highlight the interpretability of these SMDs and show how they can provide valuable insights into the spatial patterns in the synthetic images, allowing us to detect common artefacts and failure cases in training GANs.

Semantic wikis as flexible database interfaces for biomedical applications

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm.

Impairment due to Contextual Factors: External Assessment and Self-Assessment of Rehabilitants in Psychosomatic Rehabilitation

In addition to an existing external assessment tool for the ICF-based assessment of the impairment of rehabilitation patients due to environmental and personal contextual factors, a self-assessment tool was developed and used for rehabilitation patients in a psychosomatic rehabilitation clinic. The aim was to compare the assessment of the impairment by contextual factors rated by the external assessment and by the rehabilitation patients on the other hand. Furthermore, the aim of the study was to obtain initial indications of the applicability of the newly designed self-evaluation instrument. A sample of N=103 rehabilitation patients was examined via both the external and the self-evaluation tools. A comparative evaluation of the assessment of the external assessor and the rehabilitants' assessments was carried out using measures of assessor agreement (Cohen's Kappa, percentual agreement). The results were set in relation to the data of the routine assessment of the rehabilitation facility (Eta-Coefficient). The external assessor and the rehabilitants get to different results in their assessment of the extent of impairment by individual contextual factors. For eight of the 15 context factors, there was only a moderate degree of agreement between the assessors (Cohen's Kappa). The percentage of non-agreement between rating of the external assessor and self-assessment lies between approx. 20% and up to approx. 40% of the cases for the majority of the context factors. Whether or not there is an impairment across all contextual factors (overall measure) was answered non-concordantly in just under a third of the cases. Mostly moderate correlations were found between the context factors classified as particularly relevant in the self-evaluation tool and scales of psychometric survey (e. g. BDI-II). This result provides a first indication of the convergent validity of the self-report tool. More attention should be paid to a supplementary assessment of impairment by contextual factors via the rehabilitation patient's self-assessment in addition to the external assessment. There is a need for further research with regard to the development of the self-assessment tool presented.

Hog (HDL on Git): An easy system to handle HDL on a git-based repository

Coordinating firmware development among many international collaborators is becoming a very widespread problem in high-energy physics. Guaranteeing firmware synthesis reproducibility and assuring traceability of binary files is paramount.We devised Hog - HDL on git (cern.ch/hog), a set of Tcl and Shell scripts that tackles these issues and is deeply integrated with HDL IDEs, such as Xilinx Vivado Design Suite and ISE PlanAhead or Intel Quartus Prime, and all major simulation tools, like Siemens ModelSim or Aldec Riviera Pro.Git is a very powerful tool and has been chosen as standard by several research institutions, including CERN. Hog perfectly integrates with git to assure an absolute control of HDL source files, constraint files, IDE and simulation settings. It guarantees traceability by automatically embedding the git commit SHA and a numeric version into the binary file, also automatically renamed.Hog does not rely on any external tool apart from the HDL IDE and git, so it is extremely compatible and does not require any installation. Developers can get quickly up to speed: clone the repository, run the Hog script, work normally with the IDE.The learning curve to use Hog for the users is minimal. Once the HDL project is created, developers can work on it either using the IDE graphical interface, or with the provided Shell scripts to run the workflow.Hog works on Windows and Linux, supports IPbus, Sigasi and provides pre-made YAML files to set up a working Continuous Integration on GitLab (Hog-CI) with no additional effort, which runs the HDL implementation for the desired projects. Other features of Hog-CI are the automatic creation of tags and GitLab releases with timing and utilisation reports.Currently, Hog is successfully used by several firmware projects within the High-Energy Physics community, e.g. in the ATLAS and CMS Phase-II upgrades.

How to Embed SRL in Online Learning Settings? Design Through Learning Analytics and Personalized Learning Design in Moodle

One of the main generic competencies in Higher Education is “Learning to Learn”. The key component of this competence is the capacity for self-regulated learning (SRL). For this competence to be developed, peer feedback seems useful because it fosters evaluative judgement. Following the principles of peer feedback processes, an online peer feedback proposal was created in the context of complex tasks in several degree courses (n = 448 students) at the University of Barcelona. A specific didactic sequence supported by digital technologies through Moodle was designed and validated after a deep analysis of the theoretical underpinnings of SRL models. A peer review proposal was ratified by the students, who pointed out the benefits of both receiving and, especially, giving feedback. The integration of Moodle tools with external tools such as Personalized Learning Design was highly valued by reviewers (n = 3 expert judges) and the possibilities for students to monitor their own progress through task completion was revealed as a positive aspect. Furthermore, the creation of personalized alerts to support learning processes also received a very high rating. The final model includes the changes derived from the validation process and suggests a reliable technology-supported intervention in an LMS, which is transferable to a larger scale.

Proposal to Adopt a New Joint External Evaluation Tool to Build Vaccine Confidence

Proposal to Adopt a New Joint External Evaluation Tool to Build Vaccine Confidence

Do audits serve as an external risk oversight tool to boost firm performance?

This study explores the relationship between audits, concentrated managerial power, and firm performance in the Chinese manufacturing industry. Analyzing 1,264 publicly listed manufacturing firms over the five-year period (2017–2021), this study provides evidence that heavily concentrated management control hurts firm performance. The finding that heavily concentrated management control hurts firm performance is consistent with existing research on emerging markets (Debnath et al., 2021). Furthermore, consistent with existing research on audits protecting shareholder interest (Beneish, 1999) and improving firm earnings (Baxter & Cotter, 2009), the results of this study demonstrate that audits have the potential to operate as a risk oversight mechanism, reducing the likelihood of concentrated management control and therefore improving firm performance overall. This role of audits in corporate governance may be especially important in China, where the protection of minority shareholder interests may be more crucial (Chen et al., 2013), and in fact, the current study shows that audits mitigate the negative effects of concentrated management control on firm performance. However, the current research also demonstrates that the effects of audits on firm performance depend critically on how audits are identified. While longer-term, more stable auditing relationships decrease the likelihood of concentrated management power and mitigate the negative impact of concentrated power on firm performance, higher auditing fees, on the contrary, are associated with more concentrated management power, exacerbating the damage concentrated power does to firm performance. The empirical results are robust when replicated using propensity score matching (PSM) and entropy balancing techniques. Overall, the results demonstrate the effectiveness of audits as a tool in corporate governance but suggest the existence of conflicts of interest in fee-based auditing, which exacerbate agency costs.

Problems of agency costs in construction corporations

The article reflects the results of research on the identification and delineation of the problem of agency costs in construction corporations of Ukraine from the standpoint of neo-institutional theory and its separate element - agency theory. The evolution of scientists' views on the emergence of the agency problem in corporations is analyzed. The main approaches to solving the agency problem are differentiated. The conditions for reducing agent opportunism and increasing the efficiency of corporate management are outlined. Ways to minimize the negative impact of the agency problem in the corporate governance system are analyzed. Attention is focused on the negative impact of information asymmetry between principals and agents. It was determined that the application of certain methods of minimizing agency costs in integration with other mechanisms is not always able to generate a net positive effect for the corporation, sometimes leading to an increase in the costs of their implementation and causing excessive regulation of the activities of agents. The probability of manipulation of the spectrum of financial reporting indicators with the help of accounting policy tools and creative practices is emphasized, which is a consequence of the generation of an agency problem between users of accounting and analytical information and the subjects of its formation. It has been proven that the combination of internal and external corporate management tools will contribute to mitigating the consequences of the agency problem, without providing a full solution to it, since a certain range of tools, methods and measures are optimal only for a specific corporation in a certain period of time. The procedure for implementing a range of mechanisms and methods for minimizing agency costs and consequences of the agency problem is recommended to be carried out taking into account existing practices, as well as the results of monitoring the economic and informational environment of the corporation, which will ultimately contribute to the formation of their optimal combination. The vectors of minimization of agency costs and the consequences of the agency problem in the management system of construction corporations from the accounting point of view are determined. A matrix classification of approaches to solving the consequences of the agency problem and minimizing agency costs in construction corporations has been formed. Based on the application of the provisions of the neo-institutional theory in order to minimize agency costs and the consequences of the agency problem, the priority directions for strengthening the effectiveness of corporate management in the context of improving the system of its accounting and analytical support are determined.