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Purpose: The purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector. Methodology: The study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation. Results: The results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption. Implication: The implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation. Contribution: This study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance. Article Highlights: Efficient data management leads to better decision-making and improved operational effectiveness. Technology plays a key role in leveraging data for innovation in finance. Risk management and compliance are enhanced through effective information management practices. Collaboration and partnerships foster financial innovation and the development of innovative solutions. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and the dimensionality-reduced representation, leading to better interpretability. In this research, we present a tied-weight autoencoder as a dimensionality reduction model with the merit of both linear and nonlinear methods. Although the tied-weight autoencoder is a nonlinear dimensionality reduction model, we approximate it to function as a linear model. This is achieved by removing the hidden layer units that are largely inactivated by the input data, while preserving the model's effectiveness. We evaluate the proposed model by comparing its performance with other linear and nonlinear models using benchmark datasets. Our results show that the proposed model performs comparably to the nonlinear model of a similar autoencoder structure to the proposed model. More importantly, we show that the proposed model outperforms the linear models in various metrics, including the mean square error, data reconstruction, and the classification of low-dimensional projections of the input data. Thus, our study provides general recommendations for best practices in dimensionality reduction. [ABSTRACT FROM AUTHOR]

Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices. Protocol registration: The Stage 1 protocol for this Registered Report was accepted in principle on 25th October 2023. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.24541816.v1. The authors conduct a comprehensive assessment of statistical methods for identifying differentially accessible (DA) regions in scATAC-seq data, using a compendium of scATAC-seq experiments paired with bulk ATAC-seq or scRNA-seq from the same cells to evaluate accuracy, bias, robustness, and scalability. [ABSTRACT FROM AUTHOR]

Over the last 20 years, the South-East Crater (SEC) of Etna has produced high-intensity explosive activity including Strombolian explosions and lava fountains associated with lava effusion. In the 2020–2022 period, SEC volcanic activity was characterized by two paroxysmal sequences from 13 December 2020 to 1 Apr 2021 (SEQ1) and from 19 May to 23 October 2021 (SEQ2); another two paroxysms occurred on 10 and 21 February 2022. The 2020–2022 eruptive activity was monitored by the Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) with instrumental networks, field surveys, and laboratory analyses. A detailed record of the chemical variations of glass was acquired in the framework of the syn-eruptive petrological monitoring; chemical (major and trace elements) and isotopic (Sr and Nd) compositions of bulk rocks completed the study. The findings of the petrological monitoring, coupled with volcanological parameters and literature data, allowed magma evolution to be tracked and the subsurface magmatic processes responsible for the observed compositional variations to be inferred. Changes in magma supply rates and mixing between an evolved magma stored in the shallow reservoir and a more primitive magma ascending from intermediate depths controlled magma evolution during the 2020–2022 paroxysmal activity. We discuss magmatic processes in the plumbing system before and during the eruption and the relationship between rate of magma supply and the eruptive dynamics. Finally, the 2020–2022 paroxysmal activity of SEC tested the INGV-OE procedures of petrological monitoring, in the framework of monitoring best practices. [ABSTRACT FROM AUTHOR]

Method engineering emerged in the 1990s as a discipline aiming to design, construct, and adapt methods, techniques, and tools for the development of information systems. By executing a method step by step, users can follow a well-defined process to achieve the intended results for which the method was created. To create methods in a more guided and systematic manner, a framework of methods can serve as a template. This allows individuals to leverage the expertise of method engineers who have consolidated their best practices within these frameworks. However, the creation and adoption of a method can be challenging in the absence of tools to support these activities. Additionally, method engineers may lack the programming skills required to implement such tools. In this context, we extend an approach inspired by the low-code paradigm for method engineering. By integrating construction rules for guidance (called here protocols), the goal of this approach is to assist method engineers in creating new methods or adapting existing frameworks. It automatically provides tool support, enabling method experts to effectively execute the method. This paper builds upon previous work and presents the approach through a proof-of-concept implementation, LOMET. We present a second version of LOMET, which has been refined based on feedback received during an empirical evaluation conducted through semi-structured interviews. [ABSTRACT FROM AUTHOR]

Background: Though proposed by Lake and Yeoh in 1978 for vulcanized rubber characterization and possessing unique advantages with respect to traditional fracture characterization approaches, the Y-shaped cutting technique has been applied to a limited number of materials. Objective: This limited implementation may be due to researchers' unfamiliarity with the effects of Y-shaped cutting conditions and technique limitations, as well as a lack of standards. This review and best practices guide aims to provide a detailed road-map of the capabilities of Y-shaped cutting, with guidance for designing, executing, and interpreting its results. Method: By performing Y-shaped cutting at a constant blade propagation rate, fracture initiation effects encountered in many 'tearing' tests are bypassed. Meanwhile, unlike other contact-driven fracture conditions (needle insertion or cutting), the 'leg' separation renders the cutting nearly 'frictionless' under a variety of conditions. Results: Y-shaped cutting possesses two unique attributes. Under certain conditions (Zhang and Hutchens in Soft Matter 17(28):6728–6741, 2021), it can yield a fracture energy independent of sample and cutting implement geometry. In contrast to soft solid crack blunting, cutting reduces both the finite stretch and failure process zones to within a field of view readily imaged on a microscope, useful for microstructural studeis. To facilitate access to the above advantages, we summarize experimental variables and their role the fracture response and/or successful cutting and establish a pseudo-standard using silicone. Conclusion: This overview and recommendations empowers researchers to implement this highly-tunable cutting method in order to provide insights into other classes of materials in the future. [ABSTRACT FROM AUTHOR]

The reuse of industrial heritage sites has become increasingly prevalent in the context of hosting mega-events. This paper aims to investigate this trend, specifically in the context of Olympic Games or World Expos, and its impact on urban regeneration strategies. It introduces a methodological tool, namely, the relationship matrix, to identify key factors from 'best practices' criteria for managing industrial sites and planning mega-event legacies. We use this matrix to address two research objectives, namely, to determine the role of industrial heritage reuse in mega-event urban projects and to evaluate whether this creates a favourable environment for place-making. This matrix enables us to compare and analyse the Shanghai 2010 World Expo and the London 2012 Summer Olympic Games as illustrative case studies. While guidelines and strategies for mega-events and industrial heritage have evolved towards a proactive people-centred approach, our findings still reveal a bias towards material renewal in dominant discourses during the planning phase. This bias often overlooks or even excludes the memories and experiences of local communities. [ABSTRACT FROM AUTHOR]

The global priority for sustainable societies drives the transition to green energy, with geothermal power as a promising alternative. Latin-American countries benefit from the active volcanism along the Pacific Rim, which fuels their significant geothermal potential. Geothermal electricity production in the region is steadily growing and currently represents approximately 11 % of global output (16 GW). This paper provides details on the installed capacity of electrical generation in the most geothermally significant Latin-American countries, as well as the estimated potential production from existing prospects in the region. We also discuss the multiple challenges that limit the widespread development and exploitation of this valuable resource in Latin-America. As México stands as the top electricity producer in the region and ranks sixth worldwide, we offer an overview of its geothermal potential, the use of electromagnetic imaging technologies to enhance Mexican geothermal resource exploration, and the challenges and limitations associated with traditional exploration techniques. Additionally, we present recent case studies on the combined use of these technologies in México, highlighting best practices and lessons learned. The paper identifies open questions and outlines future research directions, particularly in México, to unlock the geothermal potential of the entire region. [ABSTRACT FROM AUTHOR]

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases. [ABSTRACT FROM AUTHOR]

Objectives: We provide a brief overview of collider bias and its implications for criminological research. Methods: Owing to the nature of the topics studied, as well as the common data sources used to carry out much of this research, work in the field may often become vulnerable to a specific methodological problem known as collider bias. Collider bias occurs when exposure variables and outcomes independently cause a third variable, and this variable is included in statistical models. Colliders represent somewhat of a paradox in that there is scholarship discussing the issue, yet it has managed to remain a relatively cryptic threat compared to other sources of bias. Results: We argue that, far from being an obscure concern, colliders almost certainly have pervasive impact in criminal justice and criminology. Conclusion: We close by offering a general set of strategies for addressing the challenges posed by collider bias. While there is no panacea, there are better practices, many of which are underutilized in the disciplines that study crime and its attendant topics. [ABSTRACT FROM AUTHOR]

Educational feedback is a crucial factor in the student's learning journey, as through it, students are able to identify their areas of deficiencies and improve self-regulation. However, the literature shows that this is an area of great dissatisfaction, especially in higher education. Providing effective feedback becomes an increasingly challenging task as the number of students increases. Therefore, this article explores the use of automated content analysis to examine instructor feedback based on reputable models from the literature that provide best practices and classify feedback at different levels. For this, this article proposes using the transformer model BERT to classify feedback messages. The proposed method outperforms previous works by up to 35.71% in terms of Cohen's kappa. Finally, this study adopted an explainable artificial intelligence to provide insights into the most predictive features for each classifier analyzed. [ABSTRACT FROM AUTHOR]

Crisis Negotiation Teams (CNT) and Special Weapons and Tactics (SWAT) teams respond together to a critical incident. Although CNT and SWAT have the same goal of resolving a situation, their approaches are very different. Training together has been identified as necessary to balance the two approaches. This study examined practices and perceptions of CNT best practices concerning SWAT. A web-based survey was conducted, and 115 crisis negotiators answered questions about their perceptions about what are commonly perceived as best practices in crisis negotiations. Kendall's tau statistical analyses were conducted to assist in comparing respondents' perceptions of best practices to their implementation/usage within their department. In addition, focus groups were conducted with subject matter experts (SMEs) in crisis negotiation to gain further insight into their perceptions of best practices and experiences of working with SWAT. Given that SMEs had at least 10 years of experience, Kendall's tau statistical analysis was conducted to compare this variable with the perceptions of the best practices and their frequency of use. There were significant relationships between the relative importance and frequency of use in all best practices related to the relationship between CNT and SWAT and training together; however, experience as a negotiator did not have a significant relationship with the best practices except training. The narratives from the focus groups expanded on the importance of training with the SWAT and discussed the roles of each of the entities during critical incidents. [ABSTRACT FROM AUTHOR]

High-quality strategic environmental assessment (SEA) guidelines are essential for ensuring effective SEA implementation. However, empirical studies appraising the quality of existing guidelines are scarce, particularly in Africa. This paper evaluates the quality of national SEA guidelines in Tanzania, Kenya and Uganda, focusing on their potential to support strategic thinking and practice. Data were drawn from guideline documents and analysed using content analysis against best practice, strategic criteria. The analysis reveals that while guidelines share some commonalities, they differ in scope, specificity and strategic orientation. They all incorporate important guiding principles, but only partially address other best practice criteria. Notably, they offer inconsistent guidance on structuring the assessment process and engaging decision windows, which may limit their practical usefulness. The findings highlight opportunities for improving future guideline development, revision and updates. This research and policy recommendations will benefit practitioners, policy-makers and other stakeholders in the case countries and similar jurisdictions. [ABSTRACT FROM AUTHOR]

This systematic review investigated the literature between 1990—2021 to identify trends regarding science, technology, engineering, and math (STEM) scientists conducting K-12 outreach. The review identified 50 publications that reflected the scholarship on scientist-led K-12 outreach. This includes literature on the effective strategies regarding scientists' outreach efforts and how scientist-teacher partnerships could be improved for more meaningful and impactful K-12 outreach. In addition to best practice recommendations, this review revealed patterns in outreach participants and barriers to effective scientist-teacher partnerships. The results of this study suggest that there is a need for more rigorous and published scholarship on scientist-led K-12 outreach so that scientists and K-12 stakeholders can better understand the best practices and barriers related to outreach. Additionally, the review calls for better integration of the perspectives of educators into educational outreach activities from the onset of outreach. These strategies may lead to more valuable scientist-led K-12 outreach programs that more effectively broaden participation in STEM, a major goal of broader impact activities. [ABSTRACT FROM AUTHOR]

To understand the current state of research, a systematic review and meta-analysis were conducted to synthesize the effects of suicide prevention gatekeeper training (GKT) on participant behavioral intention to intervene and participant suicide intervention behaviors. Included studies examined GKT with adult participants and measured either behavioral intention to conduct a suicide intervention or the utilization of suicide intervention skills. Searches yielded a total of 43 studies from 46 reports, comprising 21,720 participants. To quantify change over time, the standardized mean gain effect size metric was utilized. Large effect sizes were found for behavioral intention from pre-training to post-training (1.03, 95% CI [0.80, 1.25]) and short-term follow-up (0.78, 95% CI [0.59, 0.97]). Smaller effect sizes were found for intervention behavior from pre-training to short-term (0.33, 95% CI [0.21, 0.46]) and long-term follow-up (0.22, 95% CI [0.14, 0.30]). Although this meta-analysis reveals a positive effect for GKT on behavioral outcomes, the low methodological quality of the currently available evidence limits the ability to draw conclusions from the synthesis. This work informs policymakers and interventionists on best practices for GKT and highlights that additional, rigorous research is needed. [ABSTRACT FROM AUTHOR]

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy. [ABSTRACT FROM AUTHOR]

Early readers who have learned letter–sound correspondences do not always learn to read words through best practice reading instruction. This suggests a missing stimulus control for textual responses and the literature lacks an intervention to address this problem. Five kindergarten students who did not learn textual responses from reading instruction participated in this study. We used a combined pre- and posttest and multiple probe design to test the effects of a composite-to-component vocal phoneme segmentation (CtCVPS) intervention on the number of correct untaught (a) textual responses, (b) dictated written spelling responses, (c) vocal phoneme blends, (d) abstracted vocal phoneme segmentations, as well as (e) cumulative correct textual responses during reading instruction before and after the intervention. During the CtCVPS intervention participants were taught to listen to a set of composite words and vocalize the component phonemes. Correct responses emerged across all variables for all participants as a function of the intervention and all participants learned new textual responses from reading instruction after the CtCVPS intervention. These findings demonstrated that acquiring a generalized vocal phoneme segmentation repertoire through the CtCVPS intervention established necessary stimulus control for textual responding. Implications on the role of acquiring composite-to-component responses through the vocal phoneme segmentation intervention in learning to read and write are discussed. [ABSTRACT FROM AUTHOR]

There are still important theoretical and empirical gaps in understanding the role of best practices (BPs), such as quality management, lean and new product development, in generating firm's performance advantage and overcoming trade-offs across distinct performance dimensions. We examine these issues through the perspective of performance frontiers, integrating in novel ways the resource-based theory with the emergent practice-based view. Hypotheses on relationships between BPs, performance advantage, and trade-offs are developed and tested with stationary and longitudinal (recall) data from a global survey of manufacturing firms. We use data envelopment analysis, which overcomes limitations of mainstream methods based on central tendency. Our findings support the view that BPs may serve as a source of enduring competitive advantage, based on their ability to lead to a heterogeneous range of dominant and difficult-to-imitate competitive positions. The study provides new insights on contemporary debates about the role of BPs in generating performance advantage and how practitioners can sustain internal support and extract benefits from them. [ABSTRACT FROM AUTHOR]

The growing demand for agricultural products, driven by the Green Revolution, has led to a significant increase in food production. However, the demand is surpassing production, making food security a major concern, especially under climatic variation. The Indian agriculture sector is highly vulnerable to extreme rainfall, drought, pests, and diseases in the present climate change scenario. Nonetheless, the key agriculture sub-sectors such as livestock, rice cultivation, and biomass burning also significantly contribute to greenhouse gas (GHG) emissions, a driver of global climate change. Agriculture activities alone account for 10–12% of global GHG emissions. India is an agrarian economy and a hub for global food production, which is met by intensive agricultural inputs leading to the deterioration of natural resources. It further contributes to 14% of the country's total GHG emissions. Identifying the drivers and best mitigation strategies in the sector is thus crucial for rigorous GHG mitigation. Therefore, this review aims to identify and expound the key drivers of GHG emissions in Indian agriculture and present the best strategies available in the existing literature. This will help the scientific community, policymakers, and stakeholders to evaluate the current agricultural practices and uphold the best approach available. We also discussed the socio-economic, and environmental implications to understand the impacts that may arise from intensive agriculture. Finally, we examined the current national climate policies, areas for further research, and policy amendments to help bridge the knowledge gap among researchers, policymakers, and the public in the national interest toward GHG reduction goals. [ABSTRACT FROM AUTHOR]

This paper examines the Saudi Food and Drug Authority's (SFDA) Guidance on Artificial Intelligence (AI) and Machine Learning (ML) technologies based Medical Devices (the MDS-G010). The SFDA has pioneered binding requirements designed for manufacturers to obtain Medical Device Marketing Authorization. The regulation of AI in health is at an early stage worldwide. Therefore, it is critical to examine the scope and nature of the MDS-G010, its influences, and its future directions. It is argued that the guidance is a patchwork of existing international best practices concerning AI regulation, incorporates adapted forms of non-AI-based guidelines, and builds on existing legal requirements in the SFDA's existing regulatory architecture. There is particular congruence with the approaches of the US Food and Drug Administration (FDA) and the International Medical Device Regulators Forum (IMDRF), but the SFDA goes beyond those approaches to incorporate other best practices into its guidance. Additionally, the binding nature of the MDS-G010 is complex. There are binding 'components' within the guidance, but the incorporation of non-binding international best practices which are subordinate to national law results in a lack of clarity about how penalties for non-compliance will operate. [ABSTRACT FROM AUTHOR]

The massive emergence of digital technologies requires not only enterprises but also educational institutions to transform their strategies and values for maintaining competitiveness. While research on digital transformation in higher education is becoming more available in the literature, structured examinations regarding their concerns are scarce. Through a systematic literature review with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) model, this standalone overview examines principal concerns of research on digital transformation in post-secondary education, nominates better practices and lines upcoming investigations. Results indicate that considerable attention has been paid to the strategies and barriers to digitally transforming universities whilst leadership was marginally explored. The practice of this transformation may be centralised in pedagogical innovations supported by organisational measures and led by transformational leadership. Digital transformation leadership, evidence-based maturity assessment and integrated technological, pedagogical and organisational measures are imperative areas for future research. The present review contributes to the early development of studies on digital transformation in higher education. [ABSTRACT FROM AUTHOR]

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics. The effects of different choices on preprocessing pipelines for functional connectomics remain unclear. Here, the authors systematically evaluate a multitude of pipelines on resting-state fMRI, revealing a number of optimal pipelines for functional brain network analysis. [ABSTRACT FROM AUTHOR]

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate Veterans Treatment Courts' (VTCs) operation during the COVID-19 pandemic. Since March of 2020, there have been efforts to transition to a virtual world to guarantee criminal defendants their rights in court during this public health crisis. While many courts had to adapt to the changing conditions due to COVID-19, little is known about what these changes looked like for VTCs, if they varied from court to court, and whether these changes were successful. Through a nationwide survey of VTC courtroom actors such as judges, program coordinators, and Veterans Justice Outreach Specialists, we examine how VTCs' day-to-day operations changed during the COVID-19 pandemic as well as program retention and participants' success in the treatment program during a public health emergency. We discuss VTCs' lessons learned to inform other VTCs and specialty courts about best practices for continuing operations. [ABSTRACT FROM AUTHOR]

Interoperability stands as a critical hurdle in developing and overseeing distributed and collaborative systems. Thus, it becomes imperative to gain a deep comprehension of the primary obstacles hindering interoperability and the essential criteria that systems must satisfy to achieve it. In light of this objective, in the initial phase of this research, we conducted a survey questionnaire involving stakeholders and practitioners engaged in distributed and collaborative systems. This effort resulted in the identification of eight essential interoperability requirements, along with their corresponding challenges. Then, the second part of our study encompassed a critical review of the literature to assess the effectiveness of prevailing conceptual approaches and associated technologies in addressing the identified requirements. This analysis led to the identification of a set of components that promise to deliver the desired interoperability by addressing the requirements identified earlier. These elements subsequently form the foundation for the third part of our study, a reference architecture for interoperability-fostering frameworks that is proposed in this paper. The results of our research can significantly impact the software engineering of interoperable systems by introducing their fundamental requirements and the best practices to address them, but also by identifying the key elements of a framework facilitating interoperability in Systems of Systems. [ABSTRACT FROM AUTHOR]

China's architecture, engineering, and construction (AEC) industry needs a clear and sustainable development roadmap. Drawing inspiration from speeches delivered at a seminar hosted by the Chinese Academy of Engineering and extensive literature research on Chinese policies, this article presents a summary of the current trends in the AEC industry in four dimensions: industrialization as the foundation, intelligence as the enabler, lean management as the strategy, and green development as the goal. These four dimensions are intricately interconnected and rooted in multiple disciplines. The article provides a detailed review of the current practices, challenges, and future directions associated with each dimension. Additionally, ten grand challenges were proposed to stimulate discussions on the future of the AEC industry. This article offers an overarching understanding of the AEC industry and presents a four-dimension framework for sustainable development, which can be valuable for AEC practitioners. [ABSTRACT FROM AUTHOR]

Although retractions have been occurring in journals for many years, there is little standardization on what should warrant a retraction and how the retraction should be handled. In this manuscript, we lay out six broad classes for why retractions should be considered. We also lay out best practices for journals approaching a manuscript for which concerns have been raised. [ABSTRACT FROM AUTHOR]

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the "wheel is very often reinvented". It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its "as code" representation, that is, reproducibility, auditability, debuggability, reusability, & scalability. [ABSTRACT FROM AUTHOR]

Our experiment assesses the level of coordination on clinical best practice among physicians and investigates whether the release of guidelines helps in supporting coordination. Based on three clinical vignettes using current national guidelines, physicians evaluate the appropriateness of each of the proposed courses of action. Afterwards, physicians are allowed to ask which action corresponds to national guidelines and change their ratings, if desired. On average, slightly more than half of the sample coordinated on appropriateness evaluations. Empirical analysis indicates that several organizational and individual variables influence the level of coordination. Additionally, the release of national guidelines improved both the level of conformity and coordination. Our findings suggest changes in implementation practices to increase the impact of these shared protocols in the health field. [ABSTRACT FROM AUTHOR]

Background: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. Aim of review: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. Key scientific concepts of review: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies. [ABSTRACT FROM AUTHOR]

Background: Quality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities. Aim of review: A present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry. Key scientific concepts of review: Community engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a "living guidance" document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field. [ABSTRACT FROM AUTHOR]

Evidence-based home visiting services (EBHV) are available in states and localities nationwide through the federally-funded Maternal, Infant, and Early Childhood Home Visiting (MIECHV) program. Nevertheless, the anticipated benefits of EBHV, such as improved child developmental outcomes and increased positive parenting practices, may be undermined by the fact that most families withdraw from services earlier than the model developers planned. Prior studies have linked family attrition with staff turnover. The current study used a mixed methods design to investigate the conditions under which families remained active in the home visiting program after their assigned home visitor resigned. Coincidence Analysis revealed that giving families advance notice (at least 1 month) prior to the home visitors' upcoming resignation or developing a strong positive working alliance with the inheriting home visitor appears to independently make a difference for ongoing family engagement at 3 and 6 months following a staff transition. These findings suggest that emphasizing how staff turnover is managed may mitigate the risk of family withdrawal during these transitions. [ABSTRACT FROM AUTHOR]

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography–mass spectrometry (LC–MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice. [ABSTRACT FROM AUTHOR]

The aim of this policy paper is to characterize the current situation of Tunisian municipalities in matters of climate change policies and actions and to understand the barriers and best practices. By surveying municipalities in Tunisia, we were able to provide an overview of the Tunisian municipalities in matters of climate change actions and strategies, as well as finding the main barriers and best practices. First, we found that most Tunisian municipalities are facing different effects of climate change. Second, the majority of municipalities do not have a strategy for mitigation and adaptation of climate change at the local level. Third, the main barrier that municipalities face in investing in climate change actions is the lack of financial resources and funding. Four, we have identified some of the best climate change mitigation and adaptation practices of surveyed cities, which should be taken into consideration and implemented also by the other cities. [ABSTRACT FROM AUTHOR]

The present work investigates the failure of a bearing cap bolt of a commercial vehicle that suffered a premature failure after running for 1810 kms. Detailed failure analysis has been carried out involving visual inspection, magnetic particle inspection, fractography, microstructural analysis and hardness test. The investigation concludes that the bolt fractured in the shank region due to the presence of seam defect. The crack originated from the seam defect leading to failure of the bolt. It is recommended to emphasize upon adopting best rolling practices pertaining to scale formation and drawing controls for minimization of such defects. [ABSTRACT FROM AUTHOR]

Introduction: Estimating the burden of lower respiratory tract infections (LRTIs) increasingly relies on administrative databases using International Classification of Diseases (ICD) codes, but no standard methodology exists. We defined best practices for ICD-based algorithms that estimate LRTI incidence in adults. Methods: We conducted a systematic review of validation studies assessing the use of ICD code-based algorithms to identify hospitalized LRTIs in adults, published in Medline, EMBASE, and LILACS between January 1996 and January 2022, according to PRISMA guidelines. We assessed sensitivity, specificity, and other accuracy measures of different algorithms. Results: We included 26 publications that used a variety of ICD code-based algorithms and gold standard criteria, and 18 reported sensitivity and/or specificity. Sensitivity was below 80% in 72% (38/53) of algorithms and specificity exceeded 90% in 77% (37/48). Algorithms for all-cause LRTI (n = 18) that included only pneumonia codes in primary position (n = 3) had specificity greater than 90% but low sensitivity (55–72%). Sensitivity increased by 5–15%, with minimal loss in specificity, with the addition of primary codes for severe pneumonia (e.g. sepsis) while pneumonia codes were in secondary position, and by 13% with codes from LRTI-related infections (e.g. viral) or other respiratory diseases (e.g. empyema). Sensitivity increased by 8% when pneumonia codes were in any position, but specificity was not reported. In hospital-acquired pneumonia and pneumococcal-specific pneumonia, algorithms containing only nosocomial- or pathogen-specific ICD codes had poor sensitivity, which improved when broader pneumonia codes were added, in particular codes for unspecified organisms. Conclusion: Our systematic review highlights that most ICD code-based algorithms are relatively specific, but miss a substantial number of hospitalized LRTI adult cases. Best practices to estimate LRTI incidence in this population include the use of all pneumonia ICD codes for any LRTI outcome and, to a lesser extent, those for other LRTI-related infections or respiratory diseases. [ABSTRACT FROM AUTHOR]

Tic disorders (TD), including Tourette Syndrome, are characterized by involuntary, repetitive movements and/or vocalizations that can lead to persistent disability and impairment across the lifespan. Existing research demonstrates that video-based behavioral coding (VBBC) methods can be used to reliably quantify tics, enabling a more objective approach to tic measurement above and beyond standardly used TD questionnaires. VBBC is becoming more popular given the ease and ubiquity of obtaining patient videos. However, rigor and reproducibility of this work has been limited by undescribed and unstandardized approaches to using VBBC methods in TD research. The current paper describes "best practices" for VBBC in TD research, which have been tested and refined in our research over the past 15+ years, including considerations for data acquisition, coding implementation, interrater reliability demonstration, and methods reporting. We also address ethical considerations for researchers using this method. [ABSTRACT FROM AUTHOR]

Beyond the challenge of keeping up to date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the {performance} package of the easystats ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency. A preprint of this paper is available at: 10.31234/osf.io/bu6nt. [ABSTRACT FROM AUTHOR]

Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed. [ABSTRACT FROM AUTHOR]

Ports are important and central hubs for logistical activities in transportation chains that contribute to economic growth. Nevertheless, port activities are associated with undesirable impacts such as energy consumption and air pollutant emissions. Various policy instruments and measures have been developed and adopted to reduce energy consumption and emissions from ports. In an effort to manage all undesirable impacts of port operations, the search for best practices has proven to be an effective approach. This study examines one- and two-stage Data Envelopment Analysis (DEA) models for measuring port environmental efficiency. The adequacy of the models was analyzed using two case studies, i.e., the port of Koper and port of Dublin. The results of the study provided the best practices and the main differences between one-stage and two-stage models. Distance metrics were used to identify the necessary improvements of inefficient decision-making units (DMUs) to achieve the best practices. [ABSTRACT FROM AUTHOR]

We sought to understand the individual level predictors of intermediate outcomes in a diversion drug court. Outcomes included acceptance to drug court, sanction(s), incentives(s), drug test results, and graduation. Because much of the prior literature on individual level predictors of these outcomes is mixed, drug courts can benefit from analysis of their own data to understand what factors are important, or not, for their population. We analyzed administrative data and found significant predictors varied by outcome. Results showed that the court adhered to best practices (Florida Adult Drug Court Best Practice Standards, 2017), because there were no race or sex differences between those accepted to drug court or not, those sanctioned when in drug court, and those graduated successfully versus unsuccessfully discharged. The court team can use these findings to better understand their population, their program, and ensure compliance with best practices. Results also contribute to the broader drug court literature. [ABSTRACT FROM AUTHOR]

Nearly 200 governments rely on the Intergovernmental Panel on Climate Change (IPCC) for scientific assessments of climate change. IPCC figures are important for conveying key findings, but can be difficult for policymakers and practitioners to understand. Best practices in graph design, summarized in the IPCC's visual style guide, recommend conducting interviews with members of the target audience before finalizing figures. Therefore, we interviewed 20 policy makers and practitioners from different countries about three figures drafted for the second order draft of the summary for policymakers associated with IPCC's Working Group III Sixth Assessment Report. Half were frequent users and half were occasional users of climate science, but similar comments emerged from both groups. The figures received a median rating of 3, on a scale from 1 (= not easy at all to understand) to 5 (= very easy to understand). Showing the caption did not always improve these ratings. Overall, two types of recommendations emerged. First, participants suggested focusing each figure on one key message for policymakers, and removing irrelevant details. For IPCC authors, this involves making hard choices about what to show in the figure and what to leave for the text. Additionally, participants suggested straightforward fixes such as using clear titles, labels, and captions that support the key message. Based on our findings, we present recommendations for the design of climate change figures, and examples of revised figures. These recommendations should be useful for the next round of IPCC reports, and for other organizations that communicate about climate science with policymakers and practitioners. [ABSTRACT FROM AUTHOR]

Electroanalytical methods have undergone development and change from the first polarographic applications to the sensor strategies in today's sense. However, the most important goals are always to detect a wide variety of analytes in complex environments with high selectivity, sensitivity, user-friendliness, and low cost. In these respects, electrochemistry has always been a prominent method. This review explains the evaluation of electrochemical applications from the past to the future. An overview of the electroanalytical techniques, such as cyclic voltammetry (CV), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), and amperometry, is given with their most recent applications in the determination of various analytes. Today, it is aimed to develop next-generation strategies using electroanalytical techniques. Analyses with easily applicable, fast-response, portable sensor devices have been taken to a different level. Electrochemistry-based glucose sensors, biosensors, nanosensors, and wearable sensors developed for this purpose and their latest applications are discussed in detail. The point electroanalytical applications have reached today is the use and commercialization of highly advantageous sensor devices in biomedical, clinical, and environmental applications. This review aims to shed light on the developments from the past applications to the present, give an idea about current practices, and offer a comprehensive perspective to researchers who want to work in this field. [ABSTRACT FROM AUTHOR]

Virtual assembly has become a popular trend in recent years and is used for various purposes, including selective assembly and adaptive tooling. Monte Carlo approaches based on Finite Element Method (FEM) simulations are commonly used for production applications. However, during the design phase, when testing different configurations and design options, a variational method is more suitable. This paper aims to test different implementations of the Method of System Moments applied to the second-order tolerance analysis method when actual distributions, which are non-centered and non-normal, are used as input for the simulation. The study reveals that the simulation results can significantly vary depending on the simulation settings in some cases. As a result, a series of best practices are highlighted to improve the accuracy and reliability of the simulation outcomes. [ABSTRACT FROM AUTHOR]

One challenge that comes with studying eye movement behavior is deciding how to clean the eye movement data (e.g., fixation durations) before conducting analyses. Reading researchers must decide which data cleaning methods they will use and which thresholds they will set to remove eye movements that are not reflective of lexical processing. The purpose of this project was to determine what data cleaning methods are typically used and if there are any consequences of using different data cleaning methods. In the first study, an analysis of 192 recently published articles indicated that there is inconsistency in the reporting and application of data cleaning methods. In the second study, three different data cleaning methods were applied based on the literature analysis in the first study. Analyses were conducted to determine the impact of different data cleaning methods on three commonly studied effects in reading research (frequency, predictability, and length). Overall, standardized estimates decreased for each effect when more data were removed; however, removing more data also resulted in decreased variance. As a result, effects remained significant with each data cleaning method, and simulated power remained high for both a moderate and small sample size. Effect sizes remained consistent for most effects but decreased for the length effect as more data were removed. Seven suggestions are provided that are based on open science practices with the intention of helping researchers, reviewers, and the field as a whole. [ABSTRACT FROM AUTHOR]

Pavement engineers are constantly trying to develop correlations between functional and structural indicators to develop best practices for pavement evaluation and make more informative maintenance and repair (M&R) decisions. This study aims to thoroughly investigate the interaction between the International Roughness Index (IRI), a standard index used to quantify the functional properties of a pavement, and various deflection-based parameters (DBPs) used to detect changes in the structural integrity of pavement. To achieve this goal, long-term pavement performance (LTPP) data from two different Pavements A and B are analyzed. The focus is to diagnose roughness problems detected on pavement surfaces by analyzing the predictability of DBPs based on IRI and pavement layer thickness. It is observed that two IRI values are at least needed to properly reflect the pavement profile condition; a median level and an upper level corresponding to the 90th percentile of a subsection's IRI level. For Pavement A, the median IRI is considered a good predictor, with an R2 value of 0.71, of the Surface Curvature Index that reflects the condition of the asphalt layers, whereas for Pavement B, the upper IRI is considered a good predictor, with a lower R2 value of 0.65, of the deflection index that reflects the pavement's substructure condition (i.e., the condition at higher depths). From a practical perspective, the interaction between roughness and individual DBPs can help engineers and practitioners better identify the potential origin depth of roughness problems and optimize pavement condition assessment strategy. [ABSTRACT FROM AUTHOR]

Readers often struggle to identify the main ideas in expository texts. Existing research and instruction provide some guidance on how to encourage readers to identify main ideas. However, there is substantial variability in how main ideas are operationalized and how readers are prompted to identify main ideas. This variability hinders identification of best practices for instruction and intervention. The goal of the current series of experiments was to systematically examine the extent to which different tasks (e.g., selecting main ideas vs. deleting details) and different operationalizations of main ideas (e.g., "important ideas" vs. "main ideas") influenced adult readers' identification of sentences containing main ideas as they read 11 expository texts. Across experiments, the results showed that readers were generally unreliable in identifying main idea sentences; however, they were more reliable when they were instructed to select main idea sentences compared to when they were instructed to delete sentences comprised of details, and more skilled readers were more reliable than less skilled readers. The findings from the current experiments challenge existing instructional approaches and call for additional research to better understand readers' main idea selection. [ABSTRACT FROM AUTHOR]

Background: Standard concentration infusions and 'smart-pumps' are recognised as best practice in the paediatric setting. Implementation rates in European hospitals remain low. Children's Health Ireland (CHI) developed a paediatric 'smart-pump' drug library using standardised concentrations. At time of development, other Irish hospitals continued to use traditional pumps and weight-based paediatric infusions. Aim: To expand best paediatric infusion practices by nationalising use of the CHI drug library. Setting: Tertiary paediatric, maternity and general acute hospitals, and associated transport services in Ireland. Development: The CHI drug library was first developed for paediatric intensive care and then adapted over a 10-year period for use in emergency departments, general paediatric wards, neonatal units, adult intensive care and transport services. The original library (42 drug lines, 1 'care-unit') was substantially expanded (223 drug lines, 6 'care-units'). A neonatal sub-library was created. Implementation: Executive support, dedicated resources and governance structures were secured. Implementation and training packages were developed. Implementation has occurred across CHI, in paediatric and neonatal transport services, 58% (n = 11) of neonatal units, and 23% (n = 6) of paediatric sites. Evaluation: A before and after study demonstrated significant reductions in infusion prescribing errors (29.0% versus 8.4%, p < 0.001). Direct observation of infusions (n = 1023) found high compliance rates (98.9%) and low programming errors (1.6%). 100% of nurses (n = 132) surveyed 9 months after general ward implementation considered the drug library had enhanced patient safety. Conclusion: Strategic planning and collaboration can standardise infusion practices. The CHI drug library has been approved as a National Standard of Care, with implementation continuing. [ABSTRACT FROM AUTHOR]

Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. And thus can be a vital tool for biodiversity assessments in ecological studies. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species. [ABSTRACT FROM AUTHOR]

This manuscript explores the importance of an evidence-based education system in forensic education and its implications for improving forensic training and practice. Background The demand for graduates in forensic science, equipped with knowledge aligned with the latest best practices, is increasingly critical not only on a national scale but also in the global context. Incidents and trends underscore the urgency of implementing evidence-based education in forensic science, ensuring that the next generation of professionals is prepared to address complex crime scenes and challenges. Body Collaboration between academia and practicing professionals is essential to keep forensic education relevant and responsive to the dynamic field's needs. An illustrative example of successful collaboration can be seen in the partnership between then India's "National Institute of Criminology and Forensic Science (NICFS)" and the "Central Bureau of Investigation (CBI)." This collaboration has significantly enhanced forensic education in India, with joint initiatives bridging the gap between theoretical knowledge and practical application. In the realm of faculty development and training, effective strategies and programs have significantly enhanced faculty skills and knowledge in forensic science. For example, immersive workshops and mentorship programs provide educators with practical experience and keep them updated on the latest forensic techniques. Online courses and webinars further facilitate ongoing professional development, expanding educators' expertise. Turning to the evidence-based education scenario in India, recent developments highlight substantial progress. Collaborations between Indian forensic institutions and international bodies have enriched curricula and enhanced training methodologies. India is well-positioned to meet the growing demands of the forensic science field with a cadre of highly skilled professionals. Evidence-based education in forensic science acts as a bridge between academia and practice, enhancing training and the practice's quality. Collaborations, faculty development, and the adoption of global best practices ensure graduates are well-prepared to navigate the complexities of real-world forensic scenarios. Conclusion By adopting evidence-based principles, addressing challenges, and implementing strategies, forensic education prepares professionals for successful careers in the field. Beyond its immediate impact, evidence-based education holds the potential to contribute to the broader justice system and enhance public trust. Through a well-prepared workforce equipped with critical thinking and problem-solving skills, evidence-based forensic education not only empowers practitioners but also fosters greater confidence in the reliability and integrity of forensic science within society. [ABSTRACT FROM AUTHOR]