JPL/Caltech ArtCenter

The role of the chief scientist (CS), a key administrative position in various Israeli ministries, is to fund policy‐oriented research and support evidence‐based decision‐making. Has the CS's role promoted or constrained incorporation of scientific advice regarding climate change adaptation into governmental policy? Have administrative traditions affected the adaptation planning process in Israel? Analysis of documents and 26 in‐depth interviews with key stakeholders sheds light on the ongoing climate change adaptation policy formulation process. Our study reveals that the CS of the Ministry of Environmental Protection functions as a bridge at different interfaces and can be characterized as a boundary worker between institutions. The inherent independence of this position facilitates the CS's ability to initiate, foster, and prioritize complex issues such as adaptation. Our findings further suggest that the perception that Israel has already adapted, or will easily adapt, has negatively affected the adaptation process. 科学建议和行政传统：首席科学家在气候变化适应工作中的作用 首席科学家是以色列各部门的一个关键行政职务, 其具体作用是为政策性研究提供资金, 并支持循证决策。对于有关气候变化适应的科学建议, 首席科学家的作用是促进还是限制了其被纳入到政府政策中？行政传统是否影响了以色列的气候适应规划进程？通过文件分析及与关键利益相关者的26次深入访谈, 笔者揭示了正在进行的适应气候变化政策的制定过程。研究表明, 环保部门的首席科学家在不同的层面上起着桥梁作用, 在不同机构的分界线上工作以彼此连接。该职位的内在独立性能促进首席科学家在发起问题、培养和优先考虑包括适应气候等复杂问题的能力。研究进一步表明, 以色列已经适应了或即将轻易适应某些观点, 而这已经对气候适应过程产生了负面影响。 Asesoria cientifica y tradiciones administrativas: el papel que juegan los cientificos principales en la adaptacion al cambio climatico El papel que juegan los cientificos principales, un puesto administrativo clave en varios ministerios israelies, esta especificamente designado para el financiamiento de investigacion orientada hacia la politica y el apoyo a decisiones basadas en evidencia. ?El papel que juegan los cientificos principales ha promovido o limitado la incorporacion de asesoria cientifica en lo que concierne a la adaptacion al cambio climatico dentro de las politicas del gobierno? ?Las tradiciones administrativas han afectado el proceso de planeacion para la adaptacion en Israel? El analisis de los documentos en 26 entrevistas exhaustivas con partes afectadas clave ilustra el proceso de formulacion de las politicas de adaptacion al cambio climatico actual. Nuestro estudio revela que el Cientifico Principal del Ministerio de Proteccion Ambiental funciona como un puente para diferentes interfaces y puede ser caracterizado como un trabajador limitrofe entre las instituciones. La independencia inherente de esta posicion facilita la habilidad del cientifico principal para iniciar, fomentar y darle prioridad a complejos problemas como la adaptacion. Nuestros hallazgos sugieren que la percepcion de Israel ya se ha adaptado, o se adaptara facilmente, y ha afectado negativamente el proceso de adaptacion.

This study aims to determine the epistemological and didactical characteristics, develop an effective didactical design for teaching Greatest Common Factor (GCF) and Least Common Multiple (LCM), and evaluate how its implementation leads to changes in students’ scientific knowledge. The study involved 24 fifth-grade students’ (10 males and 14 females) aged 10–11 years from a public elementary school in Bantul Regency, Yogyakarta, Indonesia. Data sources were collected using tests on students, interviews with students’ and teacher, observation, and documentation. The data analysis was mainly internal and carried out by confronting a priori and a posteriori analyses. This study identified notable learning obstacles, including students’ difficulties in differentiating GCF and LCM concept, as revealed through test responses and interviews. To address these obstacles, the didactical design was structured systematically based on the results of epistemological analysis, which included students’ prerequisite knowledge, common misconceptions, and contextual classroom conditions. Implementing this design showed indications of reduced learning obstacles and improvements in students’ conceptual understanding of GCF and LCM.

On the technical side, both traditional, shallow feature-based methods (e.g., Support Vector Machines and Random Forest) and deep neural network models (e.g., LSTM and CNN types) have been utilized (Khare et al., 2024;Pepa et al., 2023). A key focus has been the development of multimodal systems that integrate diverse data sources, such as facial expressions, voice tone, dialogue sentiment, and physiological signals (Geetha et al., 2024). Multiple signals enable the aggregation of complementary information, leveraging the strengths of various measurement techniques to enhance robustness and accuracy.Technology is only effective with users; thus, beyond developing methods, fostering adoption among professionals and citizens is essential. Alongside technical advancements, researchers have explored psychological and social factors influencing the acceptance and trust of AI systems, particularly in healthcare and education. Trust has proven pivotal for AI adoption, with studies investigating how to build it and what system attributes ensure trustworthiness (Li et al., 2024). Key trust factors include explainability, transparency in processes and data usage, and the credibility of the institutions behind AI development.This Research Topic explores novel theories, methodologies, and applications of Emotion AI in healthcare and education. The published works address technical aspects, models, and AI acceptance and literacy. Burgess et al. (2023) evaluated automated facial coding software for parent-infant interactions across five studies with fathers and mothers. Automated detection rates were low (~25%) compared to manual coding in naturalistic settings but strongly correlated with manual assessments, particularly for positive expressions, when successful. Key challenges included poor lighting, facial occlusion, and rapid movements, highlighting the need for greater robustness in real-world conditions. Despite these limitations, the study demonstrated the potential of automated systems for analyzing authentic emotional expressions in parent-child interactions. 2023) introduced an innovative semi-supervised learning approach for stress monitoring. Using physiological data from 14 participants across five experiments, they achieved 77% accuracy with label propagation and 76% with deep autoencoders despite utilizing only 17% labeled data. Their method matched the performance of fully supervised approaches while substantially reducing annotation requirements, offering a practical solution for continuous stress monitoring in real-world healthcare applications. Žvanut and Mihelič (2024) identified four distinct attitudes among older adults toward domestic social robots: Cautious Optimists, Skeptical Traditionalists, Positive Optimists, and Technophiles. Through interviews with 24 participants, they highlighted the influence of factors like technology familiarity, privacy concerns, and perceived utility on AI acceptance. Their findings offer valuable insights for designing emotionally intelligent robotic assistants tailored to diverse user needs and concerns. Shen and Cui (2024) investigated the link between psychological needs satisfaction and AI literacy in 445 university students. Their findings revealed that technical and teacher support positively impacted students' autonomy and competence, subsequently improving AI literacy. Notably, satisfying psychological needs proved more critical than direct support in enhancing AI literacy. These insights offer valuable guidance for designing AI-enhanced educational environments that foster better learning and engagement. Gong et al. (2024) examined patients' trust in AI-powered pharmacy intravenous admixture services across five studies. They found that patients generally trusted AI PIVAS less than human services, primarily due to a limited subjective understanding of AI systems. However, informed consent significantly improved trust by enhancing patients' understanding. This study underscores the critical role of transparent communication and psychological factors in adopting AI in healthcare.The articles in this Research Topic advance methods for analyzing real-world datasets and explore how Emotion AI can be effectively integrated into healthcare and education, addressing challenges related to trust, privacy, and user acceptance. While Emotion AI shows promise, its use in these fields raises technical and ethical concerns, including data privacy, surveillance, and potential misuse of personal information. Clinical applications are further constrained by small sample sizes, lack of control groups, limited real-world testing, and methodological variability, which hinder reproducibility and reliability (Pepa et al., 2023). These studies tackle these challenges by developing novel model-training approaches for limited and noisy labeled data and identifying factors influencing AI acceptance and trust.Future research should prioritize enhancing the robustness of emotion recognition in naturalistic settings, developing efficient learning methods to reduce dependence on labeled data, and addressing ethical considerations to support user acceptance of AI. Emphasis on cross-cultural validation and long-term evaluation in real-world applications is crucial. Recent generative AI (GenAI) foundation models, such as GPT-4, offer significant potential for advancing Emotion AI due to their versatility (Cheng et al., 2023). GenAI has already shown promise in recognizing emotional indicators (Elyoseph et al., 2024) and could help alleviate data scarcity challenges that hinder specialized model development. Additionally, there is growing interest in the emotional analysis of groups and crowds, broadening the field's scope (Veltmeijer et al., 2023;Li et al. 2024).

The lack of teacher information regarding the scientific knowledge contained in the process of making tempeh gembus has an impact on students' low analytical thinking skills. This study aims to: (1) analyze the scientific knowledge in the process of making tempeh gembus; (2) analyze the potential of scientific knowledge in the process of making tempeh gembus towards empowering students' analytical thinking skills; and (3) analyze the correlation of scientific knowledge in the process of making tempeh gembuswith the basic competencies in science learning materials. This research is a qualitative descriptive study with an ethnographic approach. The primary data sources for this research are from observations and interviews with tempeh gembus producers and junior high school science teachers in the Karangpandan District. The sampling technique used is snowball sampling. Data collection is carried out through observation, interviews, and documentation. The instrument validity test technique uses expert validation (two experts). Data validity test technique uses triangulation of data collection techniques. Data analysis is conducted using Miles and Huberman's interactive model analysis. The results show that the process of making tempeh gembus contains scientific knowledge (aspects of physics, chemistry, and biology). The scientific knowledge in the process of making tempeh gembus has the potential to empower students' analytical thinking skills. The scientific knowledge in the process of making tempeh gembus presented in booklet form is related to the basic competencies in junior high school science learning materials

Stream data is the data that is generated continuously from the different data sources and ideally defined as the data that has no discrete beginning or end. Processing the stream data is a part of big data analytics that aims at querying the continuously arriving data and extracting meaningful information from the stream. Although earlier processing of such stream was using batch analytics, nowadays there are applications like the stock market, patient monitoring, and traffic analysis which can cause a drastic difference in processing, if the output is generated in levels of hours and minutes. The primary goal of any real-time stream processing system is to process the stream data as soon as it arrives. Correspondingly, analytics of the stream data also needs consideration of surrounding dependent data. For example, stock market analytics results are often useless if we do not consider their associated or dependent parameters which affect the result. In a real-world application, these dependent stream data usually arrive from the distributed environment. Hence, the stream processing system has to be designed, which can deal with the delay in the arrival of such data from distributed sources. We have designed the stream processing model which can deal with all the possible latency and provide an end-to-end low latency system. We have performed the stock market prediction by considering affecting parameters, such as USD, OIL Price, and Gold Price with an equal arrival rate. We have calculated the Normalized Root Mean Square Error (NRMSE) which simplifies the comparison among models with different scales. A comparative analysis of the experiment presented in the report shows a significant improvement in the result when considering the affecting parameters. In this work, we have used the statistical approach to forecast the probability of possible data latency arrives from distributed sources. Moreover, we have performed preprocessing of stream data to ensure at-least-once delivery semantics. In the direction towards providing low latency in processing, we have also implemented exactly-once processing semantics. Extensive experiments have been performed with varying sizes of the window and data arrival rate. We have concluded that system latency can be reduced when the window size is equal to the data arrival rate.

As deep learning technology paves its way, real-world applications that make use of it become popular these days. Edge computing architecture is one of the service architectures to realize the deep learning based service, which makes use of the resources near the data source or client. In Edge computing architecture it becomes important to manage resource usage, and there is research on optimization of deep learning, such as pruning or binarization, which makes deep learning models more lightweight, along with the research for the efficient distribution of workloads on cloud or edge resources. Those are to reduce the workload on edge resources. In this paper, a usage optimization method with batch and model management is proposed. The proposed method is to increase the utilization of GPU resource by modifying the batch size of the input of an inference application. To this end, the inference pipelines are identified to see how the different kinds of resources are used, and then the effect of batch inference on GPU is measured. The proposed method consists of a few modules, including a tool for batch size management which is able to change a batch size with respect to the available resources, and another one for model management which supports on-the-fly update of a model. The proposed methods are implemented on a real-time video analysis application and deployed in the Kubernetes cluster as a Docker container. The result shows that the proposed method can optimize the usage of edge resources for real-time video analysis deep learning applications.

Many real-world applications, which are focused on addressing the needs of a human, require information pertaining to the activities being performed. The UCAmI Cup is an event held within the context of the International Conference on Ubiquitous Computing and Ambient Intelligence, where delegates are given the opportunity to use their tools and techniques to analyse a previously unseen human activity recognition dataset and to compare their results with others working in the same domain. In this paper, the human activity recognition dataset used relates to activities of daily living generated in the UJAmI Smart Lab, University of Jaén. The dataset chosen for the first edition of the UCAmI Cup represents 246 activities performed over a period of ten days carried out by a single inhabitant. The dataset includes four data sources: (i) event streams from 30 binary sensors, (ii) intelligent floor location data, (iii) proximity data between a smart watch worn by the inhabitant and 15 Bluetooth Low Energy beacons and (iv) acceleration of the smart watch. In this first edition of the UCAmI Cup, 26 participants from 10 different countries contacted the organizers to obtain the dataset.‬‬‬‬‬

Introduction: Standard economic evaluation methods may underestimate the value of health equity interventions by focusing exclusively on program costs and direct effects on participants' health. Yet, these interventions have spillover effects – wherein participants transition from being intervention recipients to becoming health advocates for their social networks. Consequently, interventions may improve the health of participants and other community members who are not directly connected with interventions. This study demonstrates the utility of incorporating spillover effects by comparing incremental cost-effectiveness ratios via a patient navigation intervention to promote mammography screening among African American (AA) women. Specifically, we compare the relative costs and cost-effectiveness when including mammography uptake of: (1) AA intervention participants (egos) only versus (2) AA intervention participants (egos) and their screening eligible social network members (alters). Methods: Our study draws from two studies: (1) an individual randomized trial to test the efficacy of patient navigation on mammography uptake (Patient Navigation in Medically Underserved Areas [PNMUA]) and (2) an observational ancillary study to test the effects of PNMUA on breast cancer survivor egos and their alters (Offering AA Survivors Increased Support [OASIS]). Overall, we used a healthcare system perspective. For 2021 cost data, we collected data from study records and expense reports. For effects data, we used: (1) medical record data for egos' mammography uptake, (2) self-report data from egos regarding their alters' mammography uptake, and (3) self-report data from alters about their own mammography uptake. We consequently computed incremental cost-effectiveness ratios (ICERs), using different data sources, to assess the impact of estimating spillover effects on economic evaluation of patient navigation. Results: Total cost of the intervention was $196,601. The greatest expense were breast cancer navigators' salaries and fringe rates ($126,745). In PNMUA, more navigated vs. non-navigated egos obtained biennial mammograms (45% vs. 39%). In terms of spillover effects, more navigation arm alters obtained biennial mammograms compared to alters in the non-navigated arm (ego self-report: n=1296 vs 949; alter self-report: n=1521 vs. 1195). Navigation had lower value when only incorporating participants' mammography uptake ($3,277 per each additional woman screened) versus when incorporating spillover effects ($2,027-$2,114 per each additional woman screened). Conclusion: Our results suggest breast cancer navigation programs may be more valuable when including spillover effects. This case study provides insight with real-world applicability into integrating spillover effects into economic evaluation. Our methods offer a new avenue for improved cost and effect estimates of health equity interventions, which may be useful for assessing future resource allocation in healthcare practice and policy. Citation Format: Nyahne Bergeron, Veronica Fitzpatrick, Carl Asche, Karriem S. Watson, Aditya S. Khanna, Bridgette Hempstead, Elizabeth A. Calhoun, Jean McDougall, Yamilé Molina. The value of estimating spillover effects in health equity interventions: A case study to promote mammogram uptake among African American women and their social networks [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-208.

Harnessing the power of transformers and data fusion in smart irrigation

This paper reports on an aspect of a larger research study conceptualised within a teacher development project in Mthatha, Eastern Cape Province. The project was initiated with the objective to develop appropriate skills to use dynamic geometry software (DGS) effectively and strategically as a teaching and learning tool for mathematics. The study reported in this paper aims specifically to ascertain how selected mathematics teachers integrated co-developed technologically aided visualisation tools in the observed lessons. The case study involved two teachers from different schools. The data sources were the classroom observations followed by stimulated reflective interviews with the teachers. The data were analysed to study the use of DGS visualisation tools in relation to Kilpatrick’s framework of teaching proficiency. The lessons evidenced a displayed alignment with the elements of teaching proficiency in the context of teaching geometry. The dynamic visualisation opportunities offered by DGS proved to supplement the teaching repertoire for the participating teachers. Pedagogical practices influence the use of DGS, as evident from the lessons when the participating teachers incorporated collaboratively developed GeoGebra applets into their classrooms. We argue that the collaborative engagement between teachers appears to be a positive way forward in closing the gap between having access to technology and adapting it for effective use in mathematics classrooms.

Unsupervised Domain Adaptation (UDA) has attracted a surge of interest over the past decade but is difficult to be used in real-world applications. Considering the privacy-preservation issues and security concerns, in this work, we study a practical problem of Source-Free Domain Adaptation (SFDA), which eliminates the reliance on annotated source data. Current SFDA methods focus on extracting domain knowledge from the source-trained model but neglects the intrinsic structure of the target domain. Moreover, they typically utilize pseudo labels for self-training in the target domain, but suffer from the notorious error accumulation problem. To address these issues, we propose a new SFDA framework, called Region-to-Pixel Adaptation Network (RPANet), which learns the region-level and pixel-level discriminative representations through coarse-to-fine self-supervision. The proposed RPANet consists of two modules, Foreground-aware Contrastive Learning (FCL) and Confidence-Calibrated Pseudo-Labeling (CCPL), which explicitly address the key challenges of “how to distinguish” and “how to refine”. To be specific, FCL introduces a supervised contrastive learning paradigm in the region level to contrast different region centroids across different target images, which efficiently involves all pseudo labels while robust to noisy samples. CCPL designs a novel fusion strategy to reduce the overconfidence problem of pseudo labels by fusing two different target predictions without introducing any additional network modules. Extensive experiments on three cross-domain polyp segmentation tasks reveal that RPANet significantly outperforms state-of-the-art SFDA and UDA methods without access to source data, revealing the potential of SFDA in medical applications.

We are undertaking a long-term project to provide comprehensive visual, photographic and videographic supporting evidence for the concepts, reaction outcomes and reaction mechanisms covered in core Organic Chemistry courses. The brilliant colors and chelating ability of porphyrins make them valuable teaching tools for these courses. Four demonstrations that illustrate their pedagogical value are presented. In all cases, the demonstration is ”layered,” meaning that it can be repeated and extended to reinforce later material within the course. In addition, each experiment is designed to meet one or more of the following criteria: A. It demonstrates a process of historical importance. B. It illustrates a process or concept that has real-world applications. C. It should have pedagogical value in other disciplines. Experiment 1 provides visual evidence for the existence of antibonding orbitals by using chlorophyll fluorescence. It can later be used to introduce the concept of photosynthesis. Experiment 2 demonstrates porphyrin chelation as an example of a one-pot Brønsted acid-base and Lewis acid-base reaction. The visual impact of the experiment was maximized by using a divided U-tube. Experiment 3 extends Experiment 2 into a competitive metallation study that illustrates the distinction between kinetic and thermodynamic control. Experiment 4 demonstrates the existence of π-cation radicals. All four experiments can also be carried out as real-time demonstrations. These experiments clearly illustrate the pedagogical value of porphyrins as visual tools in the core Organic Chemistry curriculum.

The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the imputation process using visualization tools, as well as to produce high-quality graphics for publications. This article focuses on the different imputation techniques available in the package. Four different imputation methods are currently implemented in VIM, namely hot-deck imputation, k-nearest neighbor imputation, regression imputation and iterative robust model-based imputation (Templ, Kowarik, and Filzmoser 2011). All of these methods are implemented in a flexible manner with many options for customization. Furthermore in this article practical examples are provided to highlight the use of the implemented methods on real-world applications. In addition, the graphical user interface of VIM has been re-implemented from scratch resulting in the package VIMGUI (Schopfhauser, Templ, Alfons, Kowarik, and Prantner 2016) to enable users without extensive R skills to access these imputation and visualization methods.

This paper takes 153 articles about Perezhivanie research published in international authoritative journals between 2009 and 2024 in Web of Science core database as data source, analyzing the core literature of international Perezhivanie research in the past 15 years with the help of Citespace. Some scientific knowledge graphs of international Perezhivanie research are drawen. Citespace was used to analyze the core literature of international Perezhivanie research in the past 15 years by graphping the scientific knowledge of international Perezhivanie research.The aim of this study is to explore the core areas of Perezhivanie research and the high academic impact literature in this field, analyzing the hot topics of Perezhivanie research through key lineage clustering. It is found that over the past 15 years, the hot topics of international Perezhivanie research mainly include the dialectical unity of cognition and emotion, and the combination with other topics, such as motivation and agency. The research methodology mainly uses qualitative analysis tools.The article concludes with an outlook on the direction of development of Perezhivanie research in the context of the artificial intelligence era, where research in the future can be conducted by using a combination of quantitative and qualitative research methods.

Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.