Opaque Box Research Articles

Self‐Powered Luminescent Solar Concentrators‐Integrated Temperature Detection System with High Thermal Tolerance by Reversible Thermal Photoluminescence Luminophores

AbstractIt is still a challenge to prepare the red‐emissive perovskite‐polymer composite with high photoluminescence quantum yield (PLQY) and reversible thermal photoluminescence (RTPL), which is key for improving the thermal tolerance of luminescent solar concentrators (LSCs). In this work, the red‐emissive (661 nm) CsPb(Br0.25I0.75)3‐PMMA composite with high PLQY of 85.96% and favorable RTPL below 160 °C is reported via a strategy of the defect passivation by halide ligand additives. The composite film possesses excellent long‐term stability in air, water, and high temperature environments. Then, a self‐powered temperature detection system, which consists of LSCs and a temperature detection module based on the RTPL composite film, is fabricated. On one hand, RTPL composite makes LSCs of superior thermal tolerance even after 100 cycles of temperature between room temperature and 160 °C. On the other hand, RTPL composite in the opaque box of the temperature detection module can give rise to the signal light without the interference of ambient light. As a result, the self‐powered temperature detection system achieves the quantitative temperature data display at different environment lights for 24 h in a day.

A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry

Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance.

Performance during object retrieval tasks in young children with and without Down syndrome: A pilot study

AbstractObject retrieval skills requiring means‐end exploration are an area of relative weakness in young children with Down syndrome (DS). The current pilot study examined the object retrieval skills of a small sample of young children with DS for tasks with varying level of complexity. Thirteen children with DS (31.55 ± 4.14 months) and 13 mental age‐matched typically developing (TD) children (17.31 ± 1.64 months) completed three tasks involving retrieving a toy from a transparent box (Task 1), an opaque box (Task 2) and a transparent box hidden inside an opaque box (Task 3). Both DS and TD group performed similarly for the simpler Task 1, but for Tasks 2 and 3, the DS group had a lower success rate, less frequent use of complex asymmetric hand actions and/or longer execution times compared to the TD group. Future studies should examine the developmental trends and specificity of means‐end exploratory behaviours in children with DS.

Seeing and understanding epistemic actions.

Many actions have instrumental aims, in which we move our bodies to achieve a physical outcome in the environment. However, we also perform actions with epistemic aims, in which we move our bodies to acquire information and learn about the world. A large literature on action recognition investigates how observers represent and understand the former class of actions; but what about the latter class? Can one person tell, just by observing another person's movements, what they are trying to learn? Here, five experiments explore epistemic action understanding. We filmed volunteers playing a "physics game" consisting of two rounds: Players shook an opaque box and attempted to determine i) the number of objects hidden inside, or ii) the shape of the objects inside. Then, independent subjects watched these videos and were asked to determine which videos came from which round: Who was shaking for number and who was shaking for shape? Across several variations, observers successfully determined what an actor was trying to learn, based only on their actions (i.e., how they shook the box)-even when the box's contents were identical across rounds. These results demonstrate that humans can infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding.

A Mobile App That Addresses Interpretability Challenges in Machine Learning-Based Diabetes Predictions: Survey-Based User Study.

Machine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust. This study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, designed to empower health care professionals with a clear understanding of AI-generated predictions and recommendations for diabetes care. XAI4Diabetes not only delivers diabetes risk predictions but also furnishes easily interpretable explanations for complex machine learning models and their outcomes. XAI4Diabetes features a versatile multimodule explanation framework that leverages machine learning, knowledge graphs, and ontologies. The platform comprises the following four essential modules: (1) knowledge base, (2) knowledge matching, (3) prediction, and (4) interpretation. By harnessing AI techniques, XAI4Diabetes forecasts diabetes risk and provides valuable insights into the prediction process and outcomes. A structured, survey-based user study assessed the app's usability and influence on participants' comprehension of machine learning predictions in real-world patient scenarios. A prototype mobile app was meticulously developed and subjected to thorough usability studies and satisfaction surveys. The evaluation study findings underscore the substantial improvement in medical professionals' comprehension of key aspects, including the (1) diabetes prediction process, (2) data sets used for model training, (3) data features used, and (4) relative significance of different features in prediction outcomes. Most participants reported heightened understanding of and trust in AI predictions following their use of XAI4Diabetes. The satisfaction survey results further revealed a high level of overall user satisfaction with the tool. This study introduces XAI4Diabetes, a versatile multi-model explainable prediction platform tailored to diabetes care. By enabling transparent diabetes risk predictions and delivering interpretable insights, XAI4Diabetes empowers health care professionals to comprehend the AI-driven decision-making process, thereby fostering transparency and trust. These advancements hold the potential to mitigate biases and facilitate the broader integration of AI in diabetes care.

DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence

Background and ObjectiveArtificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performance, but their use is limited since they are still largely viewed as opaque black boxes. This distrust remains the primary factor for their limited real application, particularly in healthcare. As a result, there is a need for interpretable predictors that provide better predictions and also explain their predictions. MethodsThis study introduces “DeepXplainer”, a new interpretable hybrid deep learning-based technique for detecting lung cancer and providing explanations of the predictions. This technique is based on a convolutional neural network and XGBoost. XGBoost is used for class label prediction after “DeepXplainer” has automatically learned the features of the input using its many convolutional layers. For providing explanations or explainability of the predictions, an explainable artificial intelligence method known as “SHAP” is implemented. ResultsThe open-source “Survey Lung Cancer” dataset was processed using this method. On multiple parameters, including accuracy, sensitivity, F1-score, etc., the proposed method outperformed the existing methods. The proposed method obtained an accuracy of 97.43%, a sensitivity of 98.71%, and an F1-score of 98.08. After the model has made predictions with this high degree of accuracy, each prediction is explained by implementing an explainable artificial intelligence method at both the local and global levels. ConclusionsA deep learning-based classification model for lung cancer is proposed with three primary components: one for feature learning, another for classification, and a third for providing explanations for the predictions made by the proposed hybrid (ConvXGB) model. The proposed “DeepXplainer” has been evaluated using a variety of metrics, and the results demonstrate that it outperforms the current benchmarks. Providing explanations for the predictions, the proposed approach may help doctors in detecting and treating lung cancer patients more effectively.

Do preschoolers trust a competent robot pointer?

How young children learn from different informants has been widely studied. However, most studies investigate how children learn verbally conveyed information. Furthermore, most studies investigate how children learn from humans. This study sought to investigate how 3-year-old children learn from, and come to trust, a competent robot versus an incompetent human when competency is established using a pointing paradigm. During an induction phase, a robot informant pointed at a toy inside a transparent box, whereas a human pointed at an empty box. During the test phase, both agents pointed at opaque boxes. We found that young children asked the robot for help to locate a hidden toy more than the human (ask questions) and correctly identified the robot to be accurate (judgment questions). However, children equally endorsed the locations pointed at by both the robot and the human (endorse questions). This suggests that 3-year-olds are sensitive to the epistemic characteristics of the informant even when its displayed social properties are minimal.

Young children experience both regret and relief in a gain-or-loss context

ABSTRACT Recent research has provided compelling evidence that children experience the negative counterfactual emotion of regret, by manipulating the presence of a counterfactual action that would have led to participants receiving a better outcome. However, it remains unclear if children similarly experience regret’s positive counterpart, relief. The current study examined children’s negative and positive counterfactual emotions in a novel gain-or-loss context. Four- to 9-year-old children (N = 136) were presented with two opaque boxes concealing information that would lead to a gain or loss of stickers, respectively. Half of the children chose between two keys that matched each box, whereas the other half were compelled to select one box because only one of the two keys matched. After seeing inside the alternative, non-chosen box, children were significantly more likely to report a change in emotion when they could have opened that box than when they could not have. The effects were similar for children who lost stickers and won stickers, and neither effect varied with age. These findings suggest that children may become capable of experiencing regret and relief around the same time, although their expression of these counterfactual emotions may vary with actual and counterfactual gains and losses.

Explainability in process outcome prediction: Guidelines to obtain interpretable and faithful models

Process outcome prediction pertains to the classification of ongoing cases of (business) processes into a given set of categorical outcomes. This field of research has seen a strong uptake in recent years due to advances in machine and deep learning. Although a recent shift has been made in the field of process outcome prediction to use models from the explainable artificial intelligence field, the evaluation still occurs mainly through predictive performance-based metrics, thus not accounting for the explainability, actionability, and the implications of the results of the models. This paper addresses explainability through the properties interpretability and faithfulness in the field of process outcome prediction. We introduce metrics to analyse these properties along the main dimensions of process data: the event, case, and control flow attributes. This allows for comparing explanations produced by transparent models with explanations generated by (post-hoc) explainability techniques on top of opaque black box models. We utilise thirteen real-life event logs and seven classifiers, encompassing a variety of transparent and non-transparent machine learning and deep learning models, complemented with (post-hoc) explainability techniques. Next, this paper contributes a set of guidelines named X-MOP for obtaining explainable models for outcome prediction, which helps to select the most suitable model by providing insight into how the varying preprocessing, model complexity, and explainability techniques typical in process outcome prediction influence the explainability of the model.

An object's categorizability impacts whether infants encode surface features into their object representations.

Infants encode the surface features of simple, unfamiliar objects (e.g., red triangle) and the categorical identities of familiar, categorizable objects (e.g., car) into their representations of these objects. We asked whether 16-18-month-olds ignore non-diagnostic surface features (e.g., color) in favor of encoding an object's categorical identity (e.g., car) when objects are from familiar categories. In Experiment 1 (n=18), we hid a categorizable object inside an opaque box. In No Switch trials, infants retrieved the object that was hidden. In Switch trials, infants retrieved a different object: an object from a different category (Between-Category-Switch trials) or a different object from the same category (Within-Category-Switch trials). We measured infants' subsequent searching in the box. Infants' pattern of searching suggested that only infants who completed a Within-Category-Switch trial as their first Switch trial encoded objects' surface features, and an exploratory analysis suggested that infants who completed a Between-Category-Switch trial as their first Switch trial only encoded objects' categories. In Experiment 2 (n=18), we confirmed that these results were due to objects' categorizability. These results suggest infants may tailor the way they encode categorizable objects depending on which object dimensions are perceived to be task relevant.

Revealing Honesty in Children through Game: A Case Study of Elementary School’s Students in Banda Aceh

Exposure to honesty in young children is a need in order to evaluate how the concept is internalized in themselves. It has an impact on the formation of children’s character in the future. This study aimed to reveal honest behavior in children. A total of 36 elementary school students consisting of 12 boys and 24 girls with aged 6 to 9 years old in Banda Aceh, Indonesia were selected as participants using random sampling. A ball-drawing task was used as a method to observe children’s honesty, every child was asked to draw a ball from an opaque box containing equal numbers of red and blue balls, in a closed room with a hidden camera. The results showed that there were only 25% of elementary school students behaved honestly, while the other 75% behaved dishonestly. It means that only a small number of students act honestly. Follow-up is needed to provide education related to the value of honesty in children from an early age.

Connecting ethics and epistemology of AI

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework ‘epistemology-cum-ethics’ to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment.

Ceramic Wafer Scintillation Screen by Utilizing Near‐Unity Blue‐Emitting Lead‐Free Metal Halide (C8H20N)2Cu2Br4

AbstractScintillators with high light yield, low detection limit, large X‐ray attenuation efficiency as well as stable and nontoxic compositions are of great importance for radiation detection applications. Here, 0D (C8H20N)2Cu2Br4 single crystals are obtained and show blue emission peaking at 468 nm with a near‐unity photoluminescence quantum yield of 99.7%, a large Stokes shift of 148 nm (i.e., negligible self‐absorption), and a good environmental stability along with strong X‐ray absorption capability. Moreover, a high light yield of up to ≈ 91 300 photons/MeV and a low detection limit of 52.1 nGyair s−1 are realized, which is more than one hundred times lower than the dose rate of 5.5 µGyair s−1 required for X‐ray medical diagnostics. (C8H20N)2Cu2Br4 ceramic wafer scintillation screen is fabricated by a cold pressing sintering process, and the clear contrast images of opaque metal box and electronic component with a spatial resolution of 9.54 lp mm−1 are realized. This study not only designs a new lead‐free metal halide scintillator, but also develops a universal strategy for the preparation of large‐sized scintillator screen in nondestructive X‐ray imaging.

Barriers and Expectations of Adolescents Regarding the Identification and Management of Their Psychoactive Substance Use by Their General Practitioner.

Aims. General practitioners (GPs), who are the most frequently consulted health professionals by adolescents, play a key role in screening for psychoactive substance (PAS) use. The purpose of our study was to determine the barriers and expectations of adolescents regarding the identification and management of their PAS use by their general practitioner. Methods. Descriptive, cross-sectional study of a population of adolescents aged 12 to 17 years, followed up in general practice in France. Adolescents were recruited from general practice offices by open-access questionnaires. An opaque box was provided to ensure the anonymity of the adolescents. Results. A total of 277 adolescents were included: 155 girls, mean age 14.5 ± 1.7 years, 113 adolescents (41%) had used a PAS at least once in the past 12 months. Alcohol was the most used PAS, followed by tobacco and cannabis. Three groups were identified: the nonusers group (n = 134); the group of moderate users (n = 71); the group of users at risk of substance abuse or misusing (n = 38). Regardless of group, adolescents felt that their GP was attentive, responsive, competent, understanding, and took the time to ask the appropriate questions in their role. The at-risk group was less confident and less comfortable, and they felt more judged and more afraid of the GP telling their parents. Despite this, the at-risk group was the most willing to talk to their GP about their PAS. Almost half of the adolescents surveyed found it useful to use a questionnaire to discuss PAS. Conclusions. Reminding each consultation of the principles of the relationship of trust and confidentiality while maintaining an empathetic attitude would make it easier for GPs to remove adolescents’ inhibitions about communicating about their PAS use.

A type-2 fuzzy system-based approach for image data fusion to create building information models

Building Information Modelling (BIM) is a standard digital process that fuses buildings information from different sources into a 3D model during their lifecycle. For new construction sites using BIM, it is possible to monitor the cost, schedule, and changes throughout the lifecycle; however, existing buildings do not have a BIM model. Manually creating the BIM models for existing buildings is a high-cost task, both in time and money, hence there is a need for extracting information from available paper-based documentation and fuse it into a BIM model. The struggle of facility management and utility companies to fully adopt a BIM process (due to their high volumes of paper-based documentation of existing buildings) has led to the research on creating these 3D BIM models from 2D floor plan images.This paper presents a novel processing pipeline to extract 2D digital information from floorplans, fusing it into a 3D BIM model. The work focuses on fusing the available information to create the structure of the building in BIM format, which is considered the essential step before looking on working with other sources of data. In this process, we introduce a type-2 fuzzy logic based Explainable Artificial Intelligence (XAI) approach for the semantic segmentation step. The approach consists of using the outputs of type-2 fuzzy logic systems to classify a pixel as wall or background, by using information around and from the pixel of interest as the inputs to the system. After the semantic segmentation step, the output of the type-2 fuzzy logic goes through a noise removal process and finally a transformation from 2D to 3D by assigning the corresponding BIM tag to each identified element. The proposed type-2 fuzzy logic semantic segmentation approach produced comparable results (97.3% mean Intersection over Union (IoU) performance metric value) to the opaque box model approach based on Convolutional Neural Network (CNN) (99.3% mean IoU performance metric value). However, the type-2 fuzzy XAI system benefits from being an augmentable and interpretable model, which means that human users can understand the decision process and modify the model using their expert knowledge.

Characterization of a Jumping Stereotypy in Gerbils (Meriones unguiculatus) and Assessment of Opaque Tubing Enrichment on Stereotypies and Breeding.

Mongolian gerbils can develop stereotypic behaviors, including corner digging. At our institution, gerbils also engage in repetitive corner jumping, which we sought to characterize as a potentially novel stereotypy in gerbils. We then attempted to mitigate this behavior by mimicking the natural habitat by adding intracage environmental complexity. Seventeen gerbil breeding pairs were video recorded in their home cages during the light cycle. Repetitive corner jumping and digging were compared between different times of day to assess when the behaviors occurred and whether they were temporally associated. To determine whether we could reduce the incidence of stereotypic behaviors, we tested a straight tube or 1 of 3 angled opaque tubes in different orientations, which were fitted to the gerbils' preexisting opaque nesting box. Behavior was assessed at baseline and at 1, 4, 8, and 12 wk to evaluate opaque tube placement as an intervention. In addition, breeding efficiency, valuated as the number of gerbil pups born and weaned per breeder pair, was compared with pre- and poststudy data. The number of corner jumps was highest at the end of the light cycle and the majority were associated with corner digging. After placement of the enrichment tubes, an initial increase in corner digging behavior was observed and persisted throughout the study period. The opaque tubes were not associated with significant changes in corner jumping. After adjusting for age, the addition of opaque tubing to gerbil breeding cages was not associated with significant changes in breeding efficiency. The addition of opaque tubing did not effectively address concerns about stereotypic behaviors and was associated with a chronic increase in stereotypic corner digging among breeding gerbil pairs.

Plastic bonded plywood using waste polypropylene container

Plastic waste management is one of the most critical issues of the present era due to non-biodegradability of the material along with the enormous quantity of plastic waste generated globally. Repurposing the plastic waste is one of the possible solutions for managing such material. The present study explores the possibilities of using single use plastic food containers as an adhesive for making wood veneer based composite material akin plywood. The concept also addresses the formaldehyde emission based concerns during the manufacturing of conventional plywood using traditionally used adhesive i.e. Urea Formaldehyde, Phenol Formaldehyde, Melamine Urea Formaldehyde etc. The rate of generation of plastic waste is estimated to be 400 Mt year -1. In the present study the study focuses on preparation of plywood using Melia dubia veneers and polypropylene polymer based food containers as the binding agent. The containers were cut open to transform into sheet form and were placed between veneers and the assembly was hot-pressed to get panels. The effect of pressing temperature and type of food container (transparent and opaque) on the properties of the panels. Physical properties such as density, water absorption, thickness swelling and volumetric swelling were evaluated and mechanical properties like modulus of rupture, modulus of elasticity, tensile strength and glue share strength were also evaluated. There was no significant change in physical properties with variation in temperature and type of polypropylene boxes, however with change in pressing temperature there was significant change in mechanical properties. The modulus of rupture of plywood bonded with opaque polypropylene boxes at 165 °C and 185 °C was observed to be 85 ± 6.92 MPa and 73 ± 6 MPa, respectively.

Some Black Boxes are made of Flesh; Others are made of Silicon

Automated decisions, especially those that operate using AI, face accusations of being opaque black boxes. This accusation is to be taken seriously, but it is put into perspective to a certain extent if one contrasts it with the criticism of human-generated reasoning for decisions in the judiciary and administration. This article explains the functions that decision rationales fulfill from the perspective of legal scholarship and examines the qualities and shortcomings of human and machine approaches.

Object individuation and labelling in 6-month-old infants

The ability to determine how many objects are involved in physical events is fundamental for reasoning about the world that surrounds us. Previous studies suggest that infants can fail to individuate objects in ambiguous occlusion events until their first birthday and that learning words for the objects may play a crucial role in the development of this ability. The present eye-tracking study tested whether the classical object individuation experiments underestimate young infants’ ability to individuate objects and the role word learning plays in this process. Three groups of 6-month-old infants (N = 72) saw two opaque boxes side by side on the eye-tracker screen so that the content of the boxes was not visible. During a familiarization phase, two visually identical objects emerged sequentially from one box and two visually different objects from the other box. For one group of infants the familiarization was silent (Visual Only condition). For a second group of infants the objects were accompanied with nonsense words so that objects’ shape and linguistic labels indicated the same number of objects in the two boxes (Visual & Language condition). For the third group of infants, objects’ shape and linguistic labels were in conflict (Visual vs. Language condition). Following the familiarization, it was revealed that both boxes contained the same number of objects (e.g. one or two). In the Visual Only condition, infants looked longer to the box with incorrect number of objects at test, showing that they could individuate objects using visual cues alone. In the Visual & Language condition infants showed the same looking pattern. However, in the Visual vs Language condition infants looked longer to the box with incorrect number of objects according to linguistic labels. The results show that infants can individuate objects in a complex object individuation paradigm considerably earlier than previously thought and that linguistic cues enforce their own preference in object individuation. The results are consistent with the idea that when language and visual information are in conflict, language can exert an influence on how young infants reason about the visual world.

The effects of home safety intervention on guardians’ behaviors of injury prevention in injured children: a double blind randomized controlled study

Purpose The optimal time for home safety intervention for children’s injury prevention is during the injury-related visits to emergency departments. The authors investigated the effect of home safety equipment provision on the guardians’ perception of injury prevention and attitude toward it, and the use of home safety equipment. Methods We conducted a double blind randomized controlled study on guardians of children aged 7 years or younger who visited the emergency department with accidental injuries. After completing the structured, pre-intervention survey on home safety, the guardians were randomly assigned to receive either home safety equipment (the intervention group) or stationery (the control group) in the same opaque boxes enclosing pamphlets about home safety education. After 4 weeks, the guardians were contacted for the post-intervention survey. The questionnaires for the latter survey consisted of the same contents with 2 added questions regarding the use of new home safety equipment after intervention. Logistic regressions were conducted to identify factors associated with the outcome (i.e., behavioral change). Results From April through October 2019, we approached 972 guardians. Of these, 59 guardians answered both pre- and post-intervention surveys. No differences were found in the perception and attitude, and use of home safety equipment between the intervention and control groups at the pre- and post-intervention surveys. No variables were associated with the primary outcome. Conclusion Provision of home safety equipment may be inadequate to improve guardians’ behaviors about prevention of domestic injuries. Key Words: Accidental Injuries; Accident Prevention; Child; Safety; Education