Potential Pitfalls Research Articles

Transferring Causal Mechanism over Meta-representations for Target-Unknown Cross-domain Recommendation

Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user/item correspondence. Existing approaches have predominantly depended on auxiliary information, such as user reviews and item tags, to establish inter-domain connectivity, but these resources may become inaccessible due to privacy and commercial constraints. To address these limitations, our study introduces an in-depth exploration of Target-unknown Cross-domain Recommendation (CDR), which contends with the distinct challenge of lacking target domain information during the training phase in the source domain. We illustrate two critical obstacles inherent to Target-unknown CDR: the lack of an inter-domain bridge due to insufficient user/item correspondence or side information and the potential pitfalls of source-domain training biases when confronting distribution shifts across domains. To surmount these obstacles, we propose the CMCDR framework, a novel approach that leverages causal mechanisms extracted from meta-user/item representations. The CMCDR framework employs a vector-quantized encoder–decoder architecture, enabling the disentanglement of user/item characteristics. We posit that domain-transferable knowledge is more readily discernible from user/item characteristics, i.e., the meta-representations, rather than raw users and items. Capitalizing on these meta-representations, our CMCDR framework adeptly incorporates an attention-driven predictor that approximates the front-door adjustment method grounded in causal theory. This cutting-edge strategy effectively mitigates source-domain training biases and enhances generalization capabilities against distribution shifts. Extensive experiments demonstrate the empirical effectiveness and the rationality of CMCDR for target-unknown cross-domain recommendation.

Best practices recommendations for estimating dissipation rates from shear probes

As a part of the Scientific Committee on Oceanographic Research (SCOR) Working Group #160 “Analyzing ocean turbulence observations to quantify mixing” (ATOMIX), we have developed recommendations on best practices for estimating the rate of dissipation of kinetic energy, ε, from measurements of turbulence shear using shear probes. The recommendations provided here are platform-independent and cover the conceivable range of dissipation rates in the ocean, seas, and other natural waters. They are applicable to commonly deployed platforms that include vertical profilers, fixed and moored instruments, towed profilers, submarines, self-propelled ocean gliders, and other autonomous underwater vehicles. The procedure for preparing the shear data for spectral estimation is discussed in detail, as are the quality control metrics that should accompany each estimate of ε. The methods are illustrated using a high-quality ‘benchmark’ dataset, while potential pitfalls are demonstrated with a second dataset containing common faults.

Intrinsic & extrinsic motivation & its impact on organizational performance at Rajkot city: A review

Motivation is a critical driver of employee engagement and, subsequently, organizational performance. This abstract explores the concepts of intrinsic and extrinsic motivation and their profound influence on the productivity, innovation, and overall success of an organization. Intrinsic motivation stems from internal factors like personal fulfillment and passion, while extrinsic motivation is driven by external rewards such as monetary incentives and recognition. This paper delves into the dynamics of both types of motivation and their interconnectedness within the workplace.Research indicates that a balanced integration of intrinsic and extrinsic motivation strategies can lead to improved job satisfaction, higher productivity, and enhanced organizational performance. Organizations that effectively tap into intrinsic motivation empower employees to find purpose and meaning in their work, fostering long-term commitment and innovation. Additionally, extrinsic motivators, when thoughtfully administered, can provide short-term boosts in productivity and drive employees to achieve specific goals.The paper also discusses the potential pitfalls of overreliance on extrinsic motivation, which may inadvertently stifle intrinsic motivation, creativity, and intrinsic job satisfaction. It emphasizes the importance of understanding individual differences in motivation preferences and tailoring strategies to the unique needs and aspirations of employees.In conclusion, recognizing the intricate interplay between intrinsic and extrinsic motivation and strategically incorporating both can significantly enhance organizational performance. This paper encourages organizations to strike a balance between these motivational forces, empowering employees to contribute their best to achieve the organization's goals while also finding personal fulfillment in their work. Intrinsic motivation, Extrinsic motivation, Organizational performance, Employee engagement, Job satisfaction, Motivation strategies, Employee productivity, Innovation, Leadership, Rewards and recognition, Employee motivation, Workplace culture, Employee empowerment, Individual differences, Work environment, Employee commitment, Performance enhancement, Employee job satisfaction, Motivational factors, Employee motivation balance.

Viewpoint: the evolving landscape of peer review

Purpose The purpose of this viewpoint is to spotlight the role of reviewers within the collaborative triad of academic publishing. It argues that the significance of reviewers is often disregarded, leading to a gap in our understanding of the peer review process. This perspective emphasizes reviewers as unsung heroes in the publishing ecosystem, providing intentional and thought-provoking insights into the less-discussed yet impactful developments in the evolving peer review landscape. Design/methodology/approach Leveraging the author’s distinguished background as a recipient of the Journal of Service Marketing Outstanding Reviewer Award, this paper offers a personal reflection and synthesised viewpoints on the peer review process. Serving as a representative voice for reviewers, it provides insightful perspectives from the vantage point of a peer reviewer, diverging from conventional editorials and commentaries authored by editors. Findings Acknowledging the shrinking reviewer pool, this viewpoint suggests a mandatory “review for review” system alongside incentives like editorial positions, while considering financial rewards for reviewers. The rise of generative artificial intelligence (AI) in review prompts ethical concerns but offers solutions for handling diverse submissions and dealing with “Reviewer 2.” While embracing open review for its transparency, potential pitfalls surrounding article confidence and copyright require attention. Ultimately, this viewpoint advocates for a collaborative approach, valuing reviewers, exploring innovative solutions, navigating ethical dilemmas in the technological age and implementing transparent practices responsibly for the betterment of scholarly discourse. Originality/value This viewpoint highlights the invaluable contributions of reviewers, enriching the scholarly community and promoting intellectual growth.

Challenges, Limitations and Pitfalls of PET and Advanced MRI in Patients with Brain Tumors - A Report of the PET/RANO Group.

Brain tumor diagnostics have significantly evolved with the use of PET and advanced MRI techniques. In addition to anatomical MRI, these modalities may provide valuable information for several clinical applications such as differential diagnosis, delineation of tumor extent, prognostication, differentiation between tumor relapse and treatment-related changes, and the evaluation of response to anticancer therapy. In particular, joint recommendations of the RANO group, the EANO, and major European and American Nuclear Medicine societies highlighted that the additional clinical value of radiolabeled amino acids compared to anatomical MRI alone is outstanding and that its widespread clinical use should be supported. For advanced MRI and its steadily increasing use in clinical practice, the Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition provided more recently an updated acquisition protocol for the widely used dynamic susceptibility contrast perfusion MRI. Besides amino acid PET and perfusion MRI, other PET tracers and advanced MRI techniques (e.g., MR spectroscopy) are of considerable clinical interest and are increasingly integrated into everyday clinical practice. Nevertheless, these modalities have shortcomings which should be considered in clinical routine. This comprehensive review provides an overview of potential challenges, limitations and pitfalls associated with PET imaging and advanced MRI techniques in patients with gliomas or brain metastases. Despite these issues, PET imaging and advanced MRI techniques continue to play an indispensable role in brain tumor management. Acknowledging and mitigating these challenges through interdisciplinary collaboration, standardized protocols, and continuous innovation will further enhance the utility of these modalities in guiding optimal patient care.

Exploring Latent Dirichlet Allocation (LDA) in Topic Modeling: Theory, Applications, and Future Directions

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

Assessing the effect of absorbable steroid sinus implant: a state-of-the-art systematic review.

Endoscopic sinus surgery is not a definitive treatment for chronic rhinosinusitis (CRS). The use of sinus stents after surgery to maintain sinus patency and deliver local steroids has gained popularity. The first steroid-eluting bioabsorbable implant (SEBI) approved for this indication, later Propel, was developed in 2011. This state-of-the-art review aims to summarize the available evidence, as well as to point out potential pitfalls and lack of specific analyses to guide future research on this new therapeutic option. Pubmed (Medline), the Cochrane Library, EMBASE, SciELO. Nine research questions were defined: Are steroid-eluting Sinus implants useful for the control of CRS symptoms after surgery? Do they improve surgical field healing after CRS surgery? Do they decrease polyp regrowth after ESS? Do they decrease the need for ESS? Are they useful in symptom control as in-office procedure? Are they better than other steroid-impregnated resorbable materials? Do they have a positive impact on olfaction? Are they safe? Are they cost-effective? Retrieved articles were reviewed by two authors. Twenty nine studies were included: 3 metanalysis, 1 systematic review, 10 randomized clinical trials, 4 quasi-experimental studies, 1 retrospective cohort study, 4 cost studies, 3 case seriesand 2 expert consensus. The review encompassed a population of 3,012 patients treatedwith SEBI and 2826 controls. This is the first state-of-the-art review assessing steroid eluting bioabsorbable stent evidence. Despite the effort in recent years, still several questions remain unanswered. This review will hopefully guide future research efforts to better define the role of SEBI in the otolaryngology practice.

Evidence-based policy making in Japan’s public expenditure: compatibility of fiscal health and investing for the future

ABSTRACT This article focuses on the introduction of evidence-based policy making (EBPM) in Japan, especially in the context of the Kishida administration’s Grand Design and Action Plan for a New Form of Capitalism. First, the introduction and current situation of EBPM in Japan is summarized. It, consists of three allows: key performance indicators (KPIs), policy evaluation, and administrative project review. These are evaluated and briefly compared to EBPM in the US and UK. Next, investment and expenditure for green transformation proposed in the Grand Design is assessed from the perspective of EBPM, pointing to potential pitfalls and what must be done to ensure wise use of public resources for economic growth and sustainability in the future in Japan. This may serve as a reference for other countries.

Data science essentials in business administration: A multidisciplinary perspective

This study presents an overview of data science, exploring the role of data scientists and the methods they employ to investigate analytical problems within business administration. It discusses the responsibilities of data scientists, emphasizing their multidisciplinary nature and the importance of technical skills, domain knowledge, and effective communication. The paper outlines commonly utilized methods by data scientists, drawing from computer science, statistics, and operations research. It illustrates the practical application of these methods in addressing analytical problems, including descriptive analytics, predictive analytics, and prescriptive analytics. The paper also highlights potential pitfalls and challenges encountered in the analytic process, such as problem formulation errors and results interpretation.

Abstract A034: A new international program to accelerate novel therapeutics for brain tumors: presenting the brain tumor research novel therapeutics accelerator

Abstract Each year, we see many journal manuscripts highlighting successful pre-clinical experiments for potential new brain tumor therapies. Only a few of these therapies move forward into successful clinical trials. Indeed, developing a comprehensive translational program covers many aspects, including but not limited to, medicinal chemistry, blood brain barrier penetration, tumor heterogeneity, delivery, commercialization, and navigating the regulatory environment. Because of this, bringing a successful translational project from bench to bedside requires access to a diverse set of cutting-edge expertise to give promising therapies the best chance of succeeding. Recognizing that this journey is not easy to navigate, the Tessa Jowell Brain Cancer Mission has launched an international Brain Tumor Research Novel Therapeutics Accelerator (BTR-NTA). This program is led by an international group of drug development and brain tumor experts who provide independent guidance to researchers to position their therapeutic along a realistic pathway to clinical trials. The BTR-NTA is open to any international academic and industry researchers developing a medicinal product, delivery technology or device for brain tumors. The BTR-NTA welcomes groups at any stage of pre-clinical or clinical work. Academics and smaller biotech’s could benefit from feedback on their pre-clinical studies and plans for commercialization, while larger pharmaceutical companies could benefit from access to brain tumor specific expertise and patient input into clinical trial design. Researchers can apply to BTR-NTA twice a year, and if accepted will go through the following process: 1. Complete an application form covering work to date and future plans under strict confidentiality agreements; 2. Attend an in-person meeting with a tailored group of experts to further discuss their work; and 3. Receive a comprehensive written report from the BTR-NTA committee with feedback on strengths and weaknesses and guidance on next steps. The expert committee dedicates half a day to reviewing the applicants’ work in-person, and extensive time pre- and post-meeting. Throughout the process, applicants receive up to 150 hours of expert input. The multidisciplinary nature of the review committee spans the entirety of the drug development pathway to support longitudinal collaboration and avoid a siloed approach to research. The BTR-NTA was designed after a similar program in another rare disease field yielded impressive results. Thanks to charitable funding, the BTR-NTA is free to academics and at low cost for industry. The BTR-NTA aims to help address barriers for researchers by highlighting potential pitfalls or risks, providing multi-disciplinary feedback on work to date, giving constructive guidance on next steps and strengthening international collaboration in the brain tumor field. The ultimate goal of the BTR-NTA is to enable researchers with therapy for brain tumors to navigate the drug development journey and make it to the clinic in a low-cost and timely way. Citation Format: Nicky Huskens, Charlotte Aitken, Katie Bushby. A new international program to accelerate novel therapeutics for brain tumors: presenting the brain tumor research novel therapeutics accelerator [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A034.

Data for critical infrastructure network modelling of natural hazard impacts: Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

Negotiating the insider–outsider dilemma in urban research: Experiences of a graduate student returning home for fieldwork

AbstractAfrican doctoral students studying abroad and returning to their home countries for fieldwork face multiple and complex challenges. This paper reflexively addresses the question of positionality from the experiences of conducting research on urban governance and the spatial politics of street traders in Harare, Zimbabwe. The paper discusses dilemmas associated with navigating insider and outsider identities, showcasing how these categories continually shift while conducting research on street traders within a distinct socio‐cultural and political context. Moreover, the author's background as a former street trader, now pursuing a PhD at the University of Western Ontario in Canada, adds a layer of complexity to the situation, offering valuable insights into how these ‘multiple’ positionalities can either facilitate or hinder data collection. The paper underscores the nuanced experiences of the researcher in the field, shedding light on the potential challenges, pitfalls and opportunities inherent in grappling with one's positionality. By foregrounding these complexities, the paper contributes to our understanding of the positionalities of researchers in the social sciences and adds to the growing body of literature on methodologies for conducting urban studies, particularly with vulnerable populations.

A review of microfinancing's role in entrepreneurial growth in African Nations

This comprehensive review explores the pivotal role that microfinancing plays in fostering entrepreneurial growth within African nations. Against a backdrop of economic challenges and limited access to traditional financial services, microfinancing has emerged as a powerful tool for empowering aspiring entrepreneurs and driving economic development across the continent. The study delves into the multifaceted dimensions of microfinancing, examining its impact on entrepreneurship, poverty alleviation, and sustainable development. The paper begins by providing a contextual overview of the economic landscape in African nations, emphasizing the persistent barriers that hinder entrepreneurial endeavors, such as a lack of collateral and credit history. It then highlights the transformative potential of microfinancing as a means to overcome these barriers, enabling even the most economically marginalized individuals to access capital and kickstart their entrepreneurial ventures. The review underscores the symbiotic relationship between microfinancing and entrepreneurial growth, emphasizing how these financial mechanisms act as catalysts for innovation and job creation. By analyzing case studies and empirical evidence from diverse African contexts, the paper elucidates the positive correlation between microfinancing initiatives and the establishment of small and medium-sized enterprises (SMEs). Additionally, it examines the ways in which microfinancing contributes to the empowerment of women entrepreneurs, thereby fostering gender inclusivity and promoting social equity. Furthermore, the study explores the challenges and potential pitfalls associated with microfinancing programs, addressing issues such as sustainability, scalability, and the need for regulatory frameworks. It critically assesses the effectiveness of different microfinancing models and identifies best practices that can be adopted to maximize impact. In conclusion, this review provides valuable insights into the nuanced dynamics of microfinancing in African nations, highlighting its indispensable role in catalyzing entrepreneurial growth, alleviating poverty, and fostering sustainable development. The findings underscore the importance of continued research, strategic policy implementation, and collaborative efforts between governments, financial institutions, and grassroots organizations to optimize the impact of microfinancing on entrepreneurship in the African context.

Mendelian Randomization Applied to Neurology: Promises and Challenges

The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.

Rapid reaction optimization by robust and economical quantitative benchtop 19F NMR spectroscopy.

The instrumental analysis of reaction mixtures is usually the rate-determining step in the optimization of chemical processes. Traditionally, reactions are analyzed by gas chromatography, HPLC or quantitative NMR spectroscopy on high-field spectrometers. However, chromatographic methods require elaborate work-up and calibration protocols, and high-field NMR spectrometers are expensive to purchase and operate. This protocol describes an inexpensive and highly effective analysis method based on low-field benchtop NMR spectroscopy. Its key feature is the use of fluorine-labeled model substrates that, because of the wide chemical shift range and high sensitivity of 19F, enable separate, quantitative detection of product and by-product signals even on low-field, permanent magnet spectrometers. An external lock/shim device obviates the need for deuterated solvents, permitting the direct, noninvasive measurement of crude reaction mixtures with minimal workup. The low field-strength facilitates a homogeneous excitation over a wide chemical shift range, minimizing systematic integration errors. The addition of the optimal amount of the nonshifting relaxation agent tris(acetylacetonato) iron(III) minimizes relaxation delays at full resolution, reducing the analysis time to 32 s per sample. The correct choice of processing parameters is also crucial. A step-by-step guideline is provided, the influence of all parameters, including adjustments needed when using high-field spectrometers, is discussed and potential pitfalls are highlighted. The wide applicability of the analytical protocol for reaction optimization is illustrated by three examples: a Buchwald-Hartwig amination, a Suzuki coupling and a C-H arylation reaction.

Accurate identification of Helicoverpa armigera-Helicoverpa zea hybrids using genome admixture analysis: implications for genomic surveillance.

Helicoverpa armigera, the cotton bollworm moth, is one of the world's most important crop pests, and is spreading throughout the New World from its original range in the Old World. In Brazil, invasive H. armigera has been reported to hybridize with local populations of Helicoverpa zea. The correct identification of H. armigera-H. zea hybrids is important in understanding the origin, spread and future outlook for New World regions that are affected by outbreaks, given that hybridization can potentially facilitate H. zea pesticide resistance and host plant range via introgression of H. armigera genes. Here, we present a genome admixture analysis of high quality genome sequences generated from two H. armigera-H. zea F1 hybrids generated in two different labs. Our admixture pipeline predicts 48.8% and 48.9% H. armigera for the two F1 hybrids, confirming its accuracy. Genome sequences from five H. zea and one H. armigera that were generated as part of the study show no evidence of hybridization. Interestingly, we show that four H. zea genomes generated from a previous study are predicted to possess a proportion of H. armigera genetic material. Using unsupervised clustering to identify non-hybridized H. armigera and H. zea genomes, 8511 ancestry informative markers (AIMs) were identified. Their relative frequencies are consistent with a minor H. armigera component in the four genomes, however its origin remains to be established. We show that the size and quality of genomic reference datasets are critical for accurate hybridization prediction. Consequently, we discuss potential pitfalls in genome admixture analysis of H. armigera-H. zea hybrids, and suggest measures that will improve such analyses.

Re-examining our feedback model: strategies for enhancing student learning and cultivating feedback literacy through formative assessments

Distance education has increased steadily since the onset of Covid-19. As online offerings become a staple in programs of study, rather than the exception, it is important to consider how instructors design their courses to incorporate feedback as well as how they promote active student involvement in feedback processes. In this paper, the authors revisit a feedback model proposed for the online environment, which includes intentional strategies to alleviate potential pitfalls in the feedback cycle, first by conducting a citation analysis of how the model was interpreted and applied by others in their practice and scholarship, and then by describing how they adjusted the model for further inquiry with fully online graduate students. Finally, the authors detail how their model aligns to the four features of the feedback literacy framework. Findings discuss the importance of formative assessments within online coursework to provide space for the application of feedback as well as strategies for creating individualized dialogue with students around their feedback and learning. The findings warrant additional research regarding how the model may provide a structured approach to developing students’ feedback literacy.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Legal guidelines for identifying online illegal fund-raising crimes in China

Purpose This paper aims to elucidate the responsiveness of China’s judicial system in addressing the challenges of identifying online illegal fund-raising crimes that have emerged in recent years. This study systematically evaluates the efficacy and potential pitfalls of legal guidelines contained in judicial interpretations, such as holistic determination, sampling verification and presumption of the nature of funds. In addition, the research endeavors to propose pertinent recommendations for refining the existing judicial rules. Design/methodology/approach This research mainly uses a doctrinal methodology, focusing on the principal judicial interpretations formulated by the Supreme People’s Court and other central judicial entities in China. The scope encompasses the realm of online illegal fund-raising crimes as well as other cybercrimes. The analytical framework involves a comprehensive examination of these authoritative judicial documents, coupled with a theoretical and critical analysis of relevant academic materials. Findings This research underscores that while judicial interpretations serve as an effective legal strategy to confront the challenges posed by online illegal fund-raising crimes, their implementation introduces a nuanced landscape. These legal guidelines, often emanating from diverse judicial departments and tackling specific issues, carry the inherent risk of giving rise to new complexities and fostering inconsistency. Judicial authorities shall exercise prudence in both the formulation and application of these guidelines, ensuring their harmonization with existing legal norms and fundamental legal principles. Originality/value This research constitutes a critical and comprehensive examination of judicial interpretations in China pertaining to online illegal fund-raising crimes. It offers valuable insights into the country’s judicial interpretation system and its legal responses to financial crimes. The paper serves as a valuable resource for academics, law enforcement professionals, policymakers, legislators and researchers.

Urban expansion in Greater Irbid Municipality, Jordan: the spatial patterns and the driving factors

Urban expansion within Greater Irbid Municipality (GIM) witnessed an extraordinary rise, expanding approximately ninefold between 1967 and 2020. Recent trends revealed a shift in urban growth towards southern and eastern regions. These dynamics carry critical implications for urban planners and environmental managers, urging a comprehensive understanding of the driving factors behind this expansion to anticipate future challenges. Employing logistic regression (LR) and geographically weighted logistic regression (GWLR) analyses using remote sensing data and GIS, spatially variant coefficients for driving factors emerged, illuminating the evolving landscape of urban development drivers within GIM. Yarmouk University historically promoted urban expansion, but recent proximity to Yarmouk University and JUST University, coupled with higher existing building percentages, inhibited further urbanization. The analysis also revealed that elevation and slope had negligible impacts on urban expansion. These findings underline the evolving dynamics of urban development drivers within the study region. The local perspective depicted significant spatial disparities in coefficients, highlighting variations in magnitude and direction. GWLR emerged as a more robust methodology, effectively capturing regional variations and enhancing model reliability. These findings hold immense value for informing current and future urban planning practices in Greater Irbid Municipality. Proactively addressing identified challenges and understanding the intricate dynamics of urban expansion can assist Irbid in shaping a sustainable and resilient future, avoiding potential pitfalls in its urban development endeavors.