Complex Physical Environments Research Articles

Spatial Augmented Reality for Expanding the Reach of Individuals with Tremor beyond Their Physical Limits.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative spatial augmented reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It uses a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: (1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects, (2) evaluating the system's fluidity and stability in performing complex interactive tasks, and (3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrated that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

(Invited) Nanoarray Configured Monoliths for Self-Monitored Multi-Phase Reactive Processes

The incorporation of multiple functions synergistically into one device or platform can enable additions of application merits such as light weight, ease for system integration, reduced system complexity, cost-effectiveness, and programmability. This is especially true for usually complex energy, environmental, and biological systems with the integration need of many active or passive devices and components of various functionalities. In this work, we demonstrated a compact nanoarray configured monolith design with the ability to function as both a catalyst and a sensor to enable the self-monitoring of the complex multi-phase catalytic reaction processes during operation. A bimodular approach has been realized using both electrical and electrochemical interrogation modes for reactive gas and solid phase monitoring directly using the built-in catalysts. This new type of compact and built-in monolithic sensor configuration can lower the catalytic energetics and detect gas/solid composition semi-quantitatively at room temperature up to elevated temperature. Robust calibration curves of built-in sensor responses can be achieved with facile data processing and analyzing. The built-in monolith sensor shows good sensitivity and good selectivity sufficient for the self-monitoring of the reaction process. Besides, the nanoarray based monolith device has shown good environmental tolerance toward practical applications. Such compact monolithic device can serve as a self-monitored reactor, filter, and other function component that can potentially allow in-situ and real time monitoring and functional process control in complex and reactive physical and chemical environments. With further refining of the design and functional integration, this new type of smart monolithic device can provide necessary data and knowledge for future computation-driven situational awareness prediction and intelligent control of various complex dynamic processes and systems.

7.E. Skills building seminar: Novel analytical approaches to exploring the urban environmental exposome and cognitive health

Abstract The number of people worldwide living with dementia and cognitive impairment is increasing, mainly due to people living longer, so we want to figure out how where we live affects dementia and brain health as we get older. Some research suggests that where we live might influence our brain health. For example, poor air quality in towns and cities, can lead to a decline in brain health. As more of us now live in towns and cities, it is important that the environment where we live is scientifically designed and improved to maximise our brain health. The complex social and physical environments where we live make some people more vulnerable than others to developing cognitive impairment. In other words, the factors that account for who is most likely to develop cognitive ill-health due to the environment has less to do with ‘how’ we live and more to do with ‘where’ we live. We do not know how these factors interact to make urban environments a problem for brain health, nor which are the best policies and interventions for promoting healthy ageing and brain health for our poorest communities. The SPACE (Supportive Environments for Physical and Social Activity, Healthy Ageing and Cognitive Health) project (https://www.qub.ac.uk/sites/space/) uses various analytical approaches to investigate the impacts, and possible mechanistic pathways, of urban environments on healthy ageing and the cognitive health, through the novel integration of multi-omics, behaviours, environmental exposures and urban environment. SPACE uses data from NICOLA (Northern Ireland Cohort of Longitudinal Ageing) which is part of the US-HRS (Health and Retirement Study) international family of studies. The data and approaches showcased in this workshop are therefore applicable to those working in other longitudinal ageing cohorts such as TILDA, SHARE and ELSA. Workshop format (60 mins): Five panel members each present their perspective on the use and experience of different analytical approaches on the topic (8 mins each). The 5 analytical approaches include: 1. Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs) 2. Mediation analysis 3. Compositional analysis 4. Application of the Syndemic Theory and analysis of synergy 5. Mendelian randomisation Each panellist will provide a short presentation introducing the analytical approach, rationale for its use, a worked example related to urban environment and cognitive health, and the pros and cons of the approach. This is then followed by 20 minutes of discussion with the audience on the different approaches. At the end of the workshop, participants will have: - Introductory knowledge regarding a range of novel analytical approaches relevant to exploring the urban environmental exposome and cognitive health - An understanding of factors to consider when choosing analytical approaches - New insights into how these analytical approaches can address novel research questions related to urban environmental exposome and cognitive health Key messages • Preventing dementia and cognitive decline is a global health priority. Effective prevention strategies are overdue. • Different analytical approaches can provide novel perspectives and insights on the urban environmental exposome and cognitive health.

Bamboo-inspired composite metastructure for broadband microwave absorption and load bearing

Microwave absorbing structures that can simultaneously cope with the complex electromagnetic and physical environments have become indispensable in contemporary stealth technology. Herein, a bamboo-inspired composite metastructure with integrated functions of broadband electromagnetic microwave absorption and mechanical load bearing is designed and investigated by 3D printing technology with lossy magnetic composite. By integrating the benefits of gradient structure design in both microwave absorption and mechanical load bearing areas, structural optimization and experimental demonstration are conducted to simultaneously achieve a range of -10 dB microwave absorption from 3.2 GHz to 40 GHz, a -15 dB absorption bandwidth in 4.2–5.23 GHz and 6.9–40 GHz, and an average compressive yield stress of 13.27 MPa. Furthermore, this metastructure also exhibits broadband microwave absorption at wide incident angles for different polarizations of transverse electric and transverse magnetic modes. These results indicate that the integrated functional design facilitates practical engineering applications in severe service environments for stealth technology.

Soil moisture and pH differentially drive arbuscular mycorrhizal fungal composition in the riparian zone along an alpine river of Nam Co watershed.

The riparian zone is an important ecological corridor connecting the upstream and downstream rivers. Its highly complex biological and physical environments significantly affect the biogeographical pattern of species and various ecosystem functions. However, in alpine riparian ecosystems, the distribution patterns and drivers of arbuscular mycorrhizal (AM) fungi, a group of functionally important root-associated microorganisms, remain poorly understood. In this study, we investigated the AM fungal diversity and community composition in near-bank (wetland) and far-bank (alpine meadows) soils along the Niaqu River in the Nam Co watershed, and assessed the relative importance of abiotic and biotic filtering in shaping these distributions. Overall, 184 OTUs were identified in the riparian ecosystem, predominantly belonging to the genus Glomus, especially in the downstream soils, and Claroideoglomus in near-bank soils. AM fungal colonization, spore density, and α diversity showed an overall increasing trend along the river, while the extraradical hyphae declined dramatically from the middle of the river. AM fungal communities significantly varied between the wetland and alpine meadows in the riparian zone, mainly driven by the geographic distance, soil water content, soil pH, and plant communities. Specifically, soil pH was the principal predictor of AM fungal community in near-bank wetland soils, while soil water content had a most substantial direct effect in alpine meadows. These findings indicate that abiotic factors are the most important divers in shaping AM fungal communities at the watershed scale, which could be helpful in alpine riparian biodiversity conservation and management.

Effective Force Generation During Mammalian Cell Migration Under Different Molecular and Physical Mechanisms.

We have developed much understanding of actin-driven cell migration and the forces that propel cell motility. However, fewer studies focused on estimating the effective forces generated by migrating cells. Since cells in vivo are exposed to complex physical environments with various barriers, understanding the forces generated by cells will provide insights into how cells manage to navigate challenging environments. In this work, we use theoretical models to discuss actin-driven and water-driven cell migration and the effect of cell shapes on force generation. The results show that the effective force generated by actin-driven cell migration is proportional to the rate of actin polymerization and the strength of focal adhesion; the energy source comes from the actin polymerization against the actin network pressure. The effective force generated by water-driven cell migration is proportional to the rate of active solute flux and the coefficient of external hydraulic resistance; the energy sources come from active solute pumping against the solute concentration gradient. The model further predicts that the actin network distribution is mechanosensitive and the presence of globular actin helps to establish a biphasic cell velocity in the strength of focal adhesion. The cell velocity and effective force generation also depend on the cell shape through the intracellular actin flow field.

A Node Deployment Optimization Algorithm of WSNs Based on Improved Moth Flame Search

Wireless sensor networks, one of the basic technologies of remote environmental monitoring, can provide efficient sensing and communication services under limited energy supply. Coverage control is an important method to ensure efficient communication and reliable data transmission. Given the complex physical environments, which impede the energy supplement and recovery of sensor nodes, the motivation of our research is to repair the coverage holes and reduce the energy consumption during the deployment of sensor nodes. Firstly, the variable spiral position update and the adaptive inertia weight strategy are adopted to improve local development and global search ability of the moth flame algorithm. Secondly, we analyze the virtual force of nodes, including the attractive force of uncovered grid points, the virtual force between adjacent sensor nodes and the repulsive force of boundary. The node resultant force is used as the disturbance factor of moth position updating to optimize the path, which effectively avoids the “premature” problem of the algorithm and accelerates global convergence. Finally, moth search is used to guide nodes to move to the area with coverage holes to achieve coverage optimization. In addition, we limit the random walking range of moths to reduce the moving distance. The simulation results show that compared with VFPSO, VFA and MFO algorithms, the coverage rate of VF-IMFO algorithm is increased by 7.16%, 3.85% and 22.2%, and the average moving distance of nodes is reduced by 9.01 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {m}$ </tex-math></inline-formula> , 0.51 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {m}$ </tex-math></inline-formula> and 32.46 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf {m}$ </tex-math></inline-formula> respectively. Moreover, under different deployment environments, the VF-IMFO algorithm still maintains remarkable performance advantages.

Bioinspired omnidirectional piezoelectric energy harvester with autonomous direction regulation by hovering vibrational stabilization

Multidirectional approaches are essential for harnessing mechanical energy of random varying directions. Traditional multidirectional technologies are difficult to effectively harvesting vibration energy in complex physical environments due to their complex and auxiliary designs. Inspired by the vibrational stabilization of hummingbirds to maintain stable in hovering flight for any desired direction, we propose an omnidirectional piezoelectric energy harvester with the compact coaxial design and autonomous direction regulation capability. Our design scheme is to endow all-around directional sensitivity in a single oscillator rather than multiple installation directions of the oscillators. The dynamics of this omnidirectional vibration energy harvesting device are described for explaining the autonomous direction regulation mechanism realized by vibrational stabilization. Compared to its unidirectional counterpart, this omnidirectional energy harvester with the autonomous direction-regulating ability shows a 696% increase in maximum open-circuit voltage and 49.0 times higher maximum power density when the external excitation direction is perpendicular to the initial vibration direction. Practical applications of powering an electronic clock demonstrate the feasibility of the proposed omnidirectional energy harvester for the engineering community. Field experiments of harvesting energy from bicycle riding or human movement to charge a capacitor also validate the significant improvement of the direction-regulating design strategy in endowing an all-around uniform directional sensitivity. The vibrational stabilization mechanism provides a new design philosophy for omnidirectional energy harvesters. With its high flexibility and desirable enhancement, this direction-regulating design strategy has the potential to be applied in vibration energy harvesters with different mechanisms (electrostatic, electromagnetic or triboelectric ones) to extend their multidirectional capabilities.

A robust underwater superoleophobic aminated polyacrylonitrile membrane embedded with CNTs-COOH for durable oil/water and dyes/oil emulsions separation

Considering the emulsified oil and water-soluble dyes in wastewater, the exploitation of easy-manufacturing, energy-saving and high-efficiency separation materials is urgently required. In this work, integrating the positively charged polyethyleneimine (PEI) with negatively charged CNTs-COOH constructed the superhydrophilic Cassie–Baxter structure onto the electrospun polyacrylonitrile (PAN) membrane surface by ultrasonic, electrostatic interaction and thermal treatment. Based on it, the PEN@CNTs membrane achieved efficient separation for surfactant-free, tween 80-stabilized, SDS-stabilized, and CTAB-stabilized emulsions (the fluxes reached 508–3158 L m−2 h−1, the separation efficiency reached 99.42%) by the splendid water-penetration and oil-repellency, electrostatic interaction, and “aperture sieve”. Moreover, because of the porosity and strong charged surface of PEN@CNTs membrane, the anionic dyes can be quickly removed by one-step filtrate method (∼403 L m−2 h−1). Meanwhile, the PEN@CNTs membrane also achieved synchronous and efficient remediation for oil/dye mixture emulsions after many cycles. More importantly, facing the complex physical and chemical environments, the combination of the stabilized PEN membrane, inactive CNTs-COOH layer, and the bond of embedding method between CNTs-COOH and PEN nanofibers made the PEN@CNTs membrane demonstrated robust stability and durable separation capability.

A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis

In recent years, many researchers have attempted to achieve cross-domain diagnosis of faults through domain adaptation (DA) methods. However, owing to the complex physical environments, applications of DA-based approach are not guaranteed to unknown operating environments. Some existing domain generalization (DG) methods require enough fully labeled source domains to train, which are often unavailable in practical settings. In this study, an adversarial domain generalization network (ADGN) based on class boundary feature detection is proposed. The ADGN can diagnose faults in unknown operating environments, and only one fully labeled domain is used in training. Although ADGN has to access fully unlabeled auxiliary domains, a large number of unlabeled samples exist under actual working conditions. In our method, fuzzy features at a classification boundary are detected by maximizing the classifier differences. Better feature mapping functions and domain-invariant features are obtained by adversarial training. As the training proceeds, the differences in the distribution of features among the source, auxiliary, and unknown domains become smaller so domain-invariant features can be used for fault diagnosis in unknown operating environments. Comprehensive experiments showed that ADGN can achieve higher fault diagnosis accuracies than other methods when only one fully labeled domain is used in an unknown operating environment. The ADGN can even cope comfortably with complex transfer tasks with different speeds and loads.

Knowledge-infused Learning for Entity Prediction in Driving Scenes.

Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines.

Microfabrication and its use in investigating fungal biology.

Advances in microfabrication technology, and its increasing accessibility, allow us to explore fungal biology as never before. By coupling molecular genetics with fluorescence live-cell imaging in custom-designed chambers, we can now probe single yeast cell responses to changing conditions over a lifetime, characterise population heterogeneity and investigate its underlying causes. By growing filamentous fungi in complex physical environments, we can identify cross-species commonalities, reveal species-specific growth responses and examine physiological differences relevant to diverse fungal lifestyles. As affordability and expertise broadens, microfluidic platforms will become a standard technique for examining the role of fungi in cross-kingdom interactions, ranging from rhizosphere to microbiome to interconnected human organ systems. This review brings together the perspectives already gained from studying fungal biology in microfabricated systems and outlines their potential in understanding the role of fungi in the environment, health and disease.

Merging computational fluid dynamics and machine learning to reveal animal migration strategies

Abstract Understanding how migratory animals interact with dynamic physical environments remains a major challenge in migration biology. Interactions between migrants and wind and water currents are often poorly resolved in migration models due to both the lack of high‐resolution environmental data, and a lack of understanding of how migrants respond to fine‐scale structure in the physical environment. Here we develop a generalizable, data‐driven methodology to study the migration of animals through complex physical environments. Our approach combines validated computational fluid dynamic (CFD) modelling with animal tracking data to decompose migratory movements into two components, namely movement caused by physical forcing and movement due to active locomotion. We then use a flexible recurrent neural network model to relate local environmental conditions to locomotion behaviour of the migrating animal, allowing us to predict a migrant's force production, velocity and trajectory over time. We apply this framework to a large dataset containing measured trajectories of migrating Chinook salmon through a section of river in California's Sacramento‐San Joaquin Delta. We show that the model is capable of describing fish migratory movements as a function of local flow variables, and that it is possible to accurately forecast migratory movements on which the model was not trained. After validating our model, we show how our framework can be used to understand how migrants respond to local‐flow conditions, how migratory behaviour changes as overall conditions in the system change and how the energetic cost of migratory movements depends on environmental conditions in space and time. Our framework is flexible and can readily be applied to other species and systems.

VLA

Adversarial example attacks have become a growing menace to neural network-based face recognition systems. Generated by composing facial images with pixel-level perturbations, adversarial examples change key features of inputs and thereby lead to misclassification of neural networks. However, the perturbation loss caused by complex physical environments sometimes prevents existing attack methods from taking effect. In this paper, we focus on designing new attacks that are effective and inconspicuous in the physical world. Motivated by the differences in image-forming principles between cameras and human eyes, we propose VLA, a novel attack against black-box face recognition systems using visible light. In VLA, visible light-based adversarial perturbations are crafted and projected on human faces, which allows an adversary to conduct targeted or un-targeted attacks. VLA decomposes adversarial perturbations into a perturbation frame and a concealing frame, where the former adds modifications on human facial images while the latter makes these modifications inconspicuous to human eyes. We conduct extensive experiments to demonstrate the effectiveness, inconspicuousness, and robustness of the adversarial examples crafted by VLA in physical scenarios.

Virtual-Taobao: Virtualizing Real-World Online Retail Environment for Reinforcement Learning

Applying reinforcement learning in physical-world tasks is extremely challenging. It is commonly infeasible to sample a large number of trials, as required by current reinforcement learning methods, in a physical environment. This paper reports our project on using reinforcement learning for better commodity search in Taobao, one of the largest online retail platforms and meanwhile a physical environment with a high sampling cost. Instead of training reinforcement learning in Taobao directly, we present our environment-building approach: we build Virtual-Taobao, a simulator learned from historical customer behavior data, and then we train policies in Virtual-Taobao with no physical sampling costs. To improve the simulation precision, we propose GAN-SD (GAN for Simulating Distributions) for customer feature generation with better matched distribution; we propose MAIL (Multiagent Adversarial Imitation Learning) for generating better generalizable customer actions. To further avoid overfitting the imperfection of the simulator, we propose ANC (Action Norm Constraint) strategy to regularize the policy model. In experiments, Virtual-Taobao is trained from hundreds of millions of real Taobao customers’ records. Compared with the real Taobao, Virtual-Taobao faithfully recovers important properties of the real environment. We further show that the policies trained purely in Virtual-Taobao, which has zero physical sampling cost, can have significantly superior real-world performance to the traditional supervised approaches, through online A/B tests. We hope this work may shed some light on applying reinforcement learning in complex physical environments.

Understanding natural behavior to improve dairy goat (Capra hircus) management systems.

Public interest is stimulating farming industries to improve animal welfare in production systems. Indoor housing of ruminants has received scrutiny because of perceived intensiveness and lack of naturalness. Animal welfare has traditionally focused on health benefits (e.g., bedding management and reducing disease) and reducing negative experiences (e.g., painful husbandry practices). Recent attention to animals having “a life worth living” extends expectations to provide increased care and opportunities for positive experiences and natural behaviors. Although not all natural behaviors necessarily contribute to improved welfare, we present evidence for why many are important, and for how they can be promoted in commercial systems. Worldwide, commercial dairy goats (Capra hircus) are frequently housed in large open barns with space to move and soft bedding for lying; however, this is not sufficient to promote the range of natural behaviors of goats, which in turn suggests that commercial housing could be improved. The basis for this thinking is from the range of behaviors expressed by the Capra genus. Collectively, these species have evolved cognitive and behavioral strategies to cope with harsh and changing environments, as well as variable and limited vegetation. The rocky and often steep terrain that goats inhabit allows for predator avoidance and access to shelter, so it is not surprising that domesticated goats also seek out elevation and hiding spaces; indeed, their hoof structure is designed for the movement and grip in such rugged environments. The browsing techniques and flexibility in diet selection of wild, feral and extensively managed goats, appears to be equally important to housed goats, highlighting the need for more complexity in how and what goats are fed. Goats naturally live in small, dynamic groups, governed by complex social structures in which horns play a strong role. Commercial housing systems should consider the benefits of more natural-sized social groups and revisit the rationale behind horn removal. We suggest that cognitive stimulation is a potential welfare improvement for goats in commercial settings. Goat cognitive abilities, which enabled success in complex and variable social and physical environments, are unchallenged in uniform environments, potentially leading to negative affective states. We make suggestions for housing improvements that could be readily adopted into current systems without compromising production efficiency.

Challenges and Lessons Learned from Multi-Level Multi-Component Interventions to Prevent and Reduce Childhood Obesity.

Multi-level multi-component (MLMC) strategies have been recommended to prevent and reduce childhood obesity, but results of such trials have been mixed. The present work discusses lessons learned from three recently completed MLMC interventions to inform future research and policy addressing childhood obesity. B’more Healthy Communities for Kids (BHCK), Children’s Healthy Living (CHL), and Health and Local Community (SoL) trials had distinct cultural contexts, global regions, and study designs, but intervened at multiple levels of the socioecological model with strategies that address multiple components of complex food and physical activity environments to prevent childhood obesity. We discuss four common themes: (i) How to engage with community partners and involve them in development of intervention and study design; (ii) build and maintain intervention intensity by creating mutual promotion and reinforcement of the intervention activities across the multiple levels and components; (iii) conduct process evaluation for monitoring, midcourse corrections, and to engage stakeholder groups; and (iv) sustaining MLMC interventions and its effect by developing enduring and systems focused collaborations. The paper expands on each of these themes with specific lessons learned and presents future directions for MLMC trials.

Oracles for Testing Software Timeliness with Uncertainty

Uncertainty in timing properties (e.g., detection time of external events) is a common occurrence in embedded software systems, since these systems interact with complex physical environments. Such time uncertainty leads to non-determinism. For example, time-triggered operations may either generate different valid outputs across different executions or experience failures (e.g., results not being generated in the expected time window) that occur only occasionally over many executions. For these reasons, time uncertainty makes the generation of effective test oracles for timing requirements a challenging task. To address the above challenge, we propose Stochastic Testing with Unique Input Output Sequences, an approach for the automated generation of stochastic oracles that verify the capability of a software system to fulfill timing constraints in the presence of time uncertainty. Such stochastic oracles entail the statistical analysis of repeated test case executions based on test output probabilities predicted by means of statistical model checking. Results from two industrial case studies in the automotive domain demonstrate that this approach improves the fault detection effectiveness of tests suites derived from timed automata compared to traditional approaches.

Barrier Analysis as a Tool to Inform Extension Activity Planning: Insights from Guatemala

Over the past decade, renewed emphasis has been placed on extension services in developing countries to reduce rural poverty and improve food security. Despite this emphasis, complex physical, political, and socioeconomic environments in developing countries pose significant difficulties to extension agents’ success rates of adoption of new practices and/or behavior change among rural populations. In addition, agents have meager resources at their disposal. Development programs in the health sector have had success with employing behavior change theories for program design, driven by the Barrier Analysis as a method for gathering data about target populations. Theory and research suggest this method provides key information about why a target population might adopt new practices. If extension agents in developing countries such as Guatemala had access to such information, they might intentionally design interventions that lead to adoption. This paper provides an examination of examples from the field in Guatemala that illuminate ways in which extension agents can gain formative data that when analyzed, may shape how they encourage adoption of new practices. The implications of this paper suggest that using formative data gathering for planning interventions can lead to the behavior change extension agents and their governments seek.

Causal learning is collaborative: Examining explanation and exploration in social contexts.

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children's explanation-seeking behaviors in the form of "why" questions. Second, we examine parents' elaboration of meaning about causal relations. Finally, we consider parents' interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children's causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.