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Abstract Generative AI has ignited a mixture of excitement and fear within the foresight community. It is therefore important to explore the implications of widespread AI adoption for the future of our field. AI serves as an exceptional research assistant to expedite the horizon scanning process, as it can sift through vast volumes of data much faster than a team possibly can. As part of a horizon scanning process a systematic inventory of key drivers forms the basis of many Trend Scenarios in foresight studies. This study investigated whether the application of a more systematic approach for trend identification using AI could be used to collect more reproducible results than standard methods. Moreover, we specifically examined whether AI could help to better distinguish between types of drivers, long-term trends and (early) signals that are still too weak for a trend, but could have an impact. Using CHATRIVM key publications were scanned with specific prompts and results were compared to an overview of trends using commonly used methods for trend identification, such as a (grey) literature search and stakeholder consultation. Given the objectivity of trends and megatrends, we've found that AI can project their future trajectories with considerable accuracy. Yet, AI's analysis of trends and key drivers is typically standard and may not yield novel insights for those well-versed in the subject. It's useful as a starting point, but results must always be validated and refined by human analysts.

Telecommuting has become an increasingly popular work mode that has generated significant interest from scholars and practitioners alike. With recent advances in technology that enable mobile connections at ever-affordable rates, working away from the office as a telecommuter has become increasingly available to many workers around the world. Since the term telecommuting was first coined in the 1970s, scholars and practitioners have debated the merits of working away from the office, as it represents a fundamental shift in how organizations have historically done business. Complicating efforts to truly understand the implications of telecommuting have been the widely varying definitions and conceptualizations of telecommuting and the diverse fields in which research has taken place. Our objective in this article is to review existing research on telecommuting in an effort to better understand what we as a scientific community know about telecommuting and its implications. In so doing, we aim to bring to the surface some of the intricacies associated with telecommuting research so that we may shed insights into the debate regarding telecommuting's benefits and drawbacks. We attempt to sift through the divergent and at times conflicting literature to develop an overall sense of the status of our scientific findings, in an effort to identify not only what we know and what we think we know about telecommuting, but also what we must yet learn to fully understand this increasingly important work mode. After a brief review of the history of telecommuting and its prevalence, we begin by discussing the definitional challenges inherent within existing literature and offer a comprehensive definition of telecommuting rooted in existing research. Our review starts by highlighting the need to interpret existing findings with an understanding of how the extent of telecommuting practiced by participants in a study is likely to alter conclusions that may be drawn. We then review telecommuting's implications for employees' work-family issues, attitudes, and work outcomes, including job satisfaction, organizational commitment and identification, stress, performance, wages, withdrawal behaviors, and firm-level metrics. Our article continues by discussing research findings concerning salient contextual issues that might influence or alter the impact of telecommuting, including the nature of the work performed while telecommuting, interpersonal processes such as knowledge sharing and innovation, and additional considerations that include motives for telecommuting such as family responsibilities. We also cover organizational culture and support that may shape the telecommuting experience, after which we discuss the community and societal effects of telecommuting, including its effects on traffic and emissions, business continuity, and work opportunities, as well as the potential impact on societal ties. Selected examples of telecommuting legislation and policies are also provided in an effort to inform readers regarding the status of the national debate and its legislative implications. Our synthesis concludes by offering recommendations for telecommuting research and practice that aim to improve the quality of data on telecommuting as well as identify areas of research in need of development.

While the recent emergence of a large number of online course resources has made life more convenient for many people, it has also caused information overload. According to a user’s situation and behavior, course recommendation systems can recommend courses of interest to the user, so that the user can quickly sift through a massive amount of information to find courses that meet his or her needs. This paper provide a systematic review of deep learning-based recommendation systems in e-learning environments. Firstly, the concept of recommendation systems is introduced in e-learning environments, and present a comprehensive survey and classification of deep learning techniques for course recommendation. And then, a detailed analysis of existing recommendation system is conducted based on the collected literature, and an overall course recommendation system framework is presented. Subsequently, this artical main focus is on multilayer perceptual machines, recurrent neural networks, convolutional neural networks, neural attention mechanisms, and deep reinforcement learning-based recommendation, and summarize the existing research on the use of the five techniques mentioned above in e-learning environments. The last section discusses seven flaws in the current recommendation systems used in e-learning environments and identify opportunities for future research.

Background Alpha‐secretase, a member of the ADAM protein family, cleaves amyloid precursor protein (APP) within the amyloid‐beta (Aβ) domain, preventing Aβ formation, a hallmark of Alzheimer's disease (AD). Alpha‐secretase activity, primarily mediated by ADAM10 and ADAM17, has neuroprotective effects. ADAM10 and ADAM17 influence several proteins implicated in AD pathogenesis. For instance, ADAM10 facilitates the shedding of TREM2, which is critical for microglial activation, Aβ clearance, and inflammation control. ADAM17 modulates the release of tumor necrosis factor‐alpha (TNF‐α), a pro‐inflammatory cytokine linked to neuroinflammation and neuronal damage in AD. Dysregulation in these pathways due to genetic variations or altered enzymatic activity of ADAM10 and ADAM17 may exacerbate AD progression. Investigating genetic variations and their effects on enzyme activity is crucial for understanding the broader impact of ADAM10 and ADAM17 in AD pathogenesis and identifying potential therapeutic targets. Therefore, in this study, we aimed to identify non‐synonymous single nucleotide polymorphisms (nsSNPs) in ADAM10 and ADAM17 and evaluate their potential pathogenicity in silico. Method SNPs and protein sequence data for ADAM10 and ADAM17 were retrieved from the dbSNP and Ensembl databases. Protein models were obtained from AlphaFold and modeled using Phyre2. The pathogenic potential of identified SNPs was assessed through SIFT, PolyPhen‐2, PhD‐SNP, PredictSNP1/2, Meta SNP, FATHMM, SNPs&GO, and CADD. Structural stability and disease relevance were evaluated with DynaMut2, MutPred, MUpro, mCSM, mCSM‐Membrane, INPS‐MD, and Missense3D, while protein region conservation was analyzed using PANTHER and Consurf. Result Initial findings suggest that rs1596019164, rs200737587 and rs1896150983 in ADAM10, as well as rs1339437801, and rs951262662 in ADAM17, may disrupt alpha‐secretase activity, potentially altering Aβ production and contributing to AD progression. These variants could impact protein stability and enzymatic efficiency, further influencing disease mechanisms. Notably, some variants are located in conserved and functionally significant regions, suggesting a stronger pathogenic potential. Conclusion The analysis highlights the presence of variants with high pathogenicity scores, suggesting their significant role in disease mechanisms. This study identifies potentially pathogenic nsSNPs in ADAM10 and ADAM17 that may affect their alpha‐secretase activity and contribute to AD pathogenesis. Further experimental validation is essential to confirm these results and clarify their roles in disease mechanisms.

Background The hippocampal networks link the hippocampus and several cortical areas, supporting memory, attention, and learning. Key pathways include the “where” pathway (medial hippocampus to dorsal visual cortex), “what” pathway (lateral hippocampus to ventral visual cortex), “prefrontal” pathway (hippocampus to the prefrontal cortex), and “papez” pathway (hippocampus and limbic regions). Although age‐related hippocampal volume decline is well‐documented, changes in its connectivity with aging remain unclear. This study investigates age‐related hippocampal connectivity changes using advanced diffusion MRI in a large‐scale dataset. Method Tractography data were collected from 480 participants aged 36–90 years from HCP‐Aging dataset. Whole‐brain tractography was generated from diffusion MRI data, preprocessed with the DESIGNER pipeline. Tractography was reconstructed, with terminations constrained to the gray matter‐white matter interfaces. This process utilized fiber orientation distributions (FOD) from the MSMT‐CSD model, anatomically constrained tractography with iFOD2, and the SIFT methods. Whole‐brain connectome was generated based on brain parcellations from co‐registered structural MRI using MRcloud. Main hippocampal pathways were then extracted from the whole‐brain connectome using predefined inclusion and exclusion regions (Figure 1). Hippocampal pathways were characterized using fiber density (FD), fractional anisotropy (FA) and mean diffusivity (MD). Spearman correlation analysis was performed between hippocampal pathways characteristics and age, with Bonferroni correction. Result Fiber density decreased in the left “what” pathway and bilateral “where” pathways and increases in the “prefrontal” pathway with age(p <0.001). FA values across all four pathways exhibited significant negative correlations with age (R from ‐0.14 to ‐0.65, p <0.05), whereas MD values showed significant positive correlations with age (R from 0.29 to 0.59, p <0.001) (Figures 2 and 3). Conclusion This study revealed age‐related degeneration in key hippocampal pathways, evidenced by reduced FA, supporting previous volumetric and perfusion findings of hippocampus. Notably, the increased fiber density pathways connecting the hippocampus to the prefrontal cortex may suggest a compensatory mechanism, but this requires further validation. These findings may offer insights into early age‐related axonal and microstructural alterations underlying macroscale hippocampal changes in later life. Figure 1. Workflow of key hippocampal pathways tractography. Figure 2. Group‐level representation of hippocampal pathways in young and old groups. Figure 3. Correlation analysis between hippocampal pathway measurements and age.

Despite broad investigation in content-based image retrieval (CBIR), issue to lessen the semantic gap between high-level semantics and local attributes of the image is still an important issue. The local attributes of an image such as shape, color, and texture are not sufficient for effective CBIR. Visual similarity is a principal step in CBIR and in the baseline approach. In this article, we introduce a novel approach, which relies on the fusion of visual words of scale-invariant feature transform (SIFT) and binary robust invariant scalable keypoints (BRISK) descriptors based on the visual-bag-of-words approach. The two local feature descriptors are chosen as their fusion adds complementary improvement to CBIR. The SIFT descriptor is capable of detecting objects robustly under cluttering due to its invariance to scale, rotation, noise, and illumination variance.However, SIFT descriptor does not perform well at low illumination or poorly localized keypoints within an image. Due to this reason, the discriminative power of the SIFT descriptor is lost during the quantization process, which also reduces the performance of CBIR. However, the BRISK descriptor provides scale and rotation-invariant scale-space, high quality and adaptive performance in classification based applications. It also performs better at poorly localized keypoints along the edges of an object within an image as compared to the SIFT descriptor. The suggested approach based on the fusion of visual words achieves effective results on the Corel-1K, Corel-1.5K, Corel-5K, and Caltech-256 image repositories as equated to the feature fusion of both descriptors and latest CBIR approaches with the surplus assistances of scalability and fast indexing.

Teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend their communities. Human history is largely a story of people working together in groups to explore, achieve, and conquer. Yet, the modern concept of work in large organizations that developed in the late 19th and early 20th centuries is largely a tale of work as a collection of individual jobs. A variety of global forces unfolding over the last two decades, however, has pushed organizations worldwide to restructure work around teams, to enable more rapid, flexible, and adaptive responses to the unexpected. This shift in the structure of work has made team effectiveness a salient organizational concern. Teams touch our lives everyday and their effectiveness is important to well-being across a wide range of societal functions. There is over 50 years of psychological research--literally thousands of studies--focused on understanding and influencing the processes that underlie team effectiveness. Our goal in this monograph is to sift through this voluminous literature to identify what we know, what we think we know, and what we need to know to improve the effectiveness of work groups and teams. We begin by defining team effectiveness and establishing the conceptual underpinnings of our approach to understanding it. We then turn to our review, which concentrates primarily on topics that have well-developed theoretical and empirical foundations, to ensure that our conclusions and recommendations are on firm footing. Our review begins by focusing on cognitive, motivational/affective, and behavioral team processes--processes that enable team members to combine their resources to resolve task demands and, in so doing, be effective. We then turn our attention to identifying interventions, or \"levers,\" that can shape or align team processes and thereby provide tools and applications that can improve team effectiveness. Topic-specific conclusions and recommendations are given throughout the review. There is a solid foundation for concluding that there is an emerging science of team effectiveness and that findings from this research foundation provide several means to improve team effectiveness. In the concluding section, we summarize our primary findings to highlight specific research, application, and policy recommendations for enhancing the effectiveness of work groups and teams.

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

In the complex interplay between sensory and cognitive processes, the brain must sift through a flood of sensory data to pinpoint relevant signals. This selective mechanism is crucial for the effective control of behaviour, by allowing organisms to focus on important tasks and blocking out distractions. The Inhibition of a Recent Distractor (IRD) Task examines this selection process by exploring how inhibiting distractors influences subsequent eye movements towards an object in the visual environment. In a series of experiments, research by Crawford et al. (2005a) demonstrated a delayed response to a target appearing at the location that was previously occupied by a distractor, demonstrating a legacy inhibition exerted by the distractor on the spatial location of the upcoming target. This study aimed to replicate this effect and to investigate any potential constraints when multiple distractors are presented. Exploring whether the effect is observed in more ecologically relevant scenarios with multiple distractors is crucial for assessing the extent to which it can be applied to a broader range of environments. Experiment 1 successfully replicated the effect, showing a significant IRD effect only with a single distractor. Experiments 2–5 explored a number of possible explanations for this phenomenon.

Social media is full of accusations and counter-accusations of a wrong called ‘cultural appropriation’. Our goal in this article is to sift through these deliberations and identify what cultural appropriation is, what it is not, and what, if anything might be wrong with it. We begin by explaining why public discourse about cultural appropriation should matter to political theorists of multiculturalism, especially in the anti-immigrant mood that has engulfed many immigrant-receiving countries. We then place cultural appropriation under the umbrella of cultural engagement, before identifying two forms of problematic cultural engagement – cultural offence and cultural misrepresentation – that are often conflated with cultural appropriation. In the next section, we define cultural appropriation as the appropriation of something of cultural value, usually a symbol or a practice, to others. We go on to explain that two additional conditions must be present to define an act of cultural appropriation: the presence of significant contestation around the act of appropriation, and the presence of knowledge (or negligent culpability) in the act of appropriation. Although this account of cultural appropriation is normative, cultural appropriation is often wrong only in a trivial sense. One of the ways it can become more serious is through the presence of what we term ‘amplifiers’. The contextual conditions that can render acts of cultural appropriation more egregious include: the existence of a power imbalance between the cultural appropriator and those from whom the practice or symbol is appropriated; the absence of consent; and the presence of profit that accrues to the appropriator. Ultimately, we find that there are very few instances of seriously wrongful cultural appropriation, and that many of the actions decried as cultural appropriation may be wrongful, but not because they appropriate.