Uncontrolled Environment Research Articles

Bioinspired cooperation in a heterogeneous robot swarm using ferrofluid artificial pheromones for uncontrolled environments

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.

The rate and predictors of violence against EMS personnel

BackgroundViolence against Emergency Medical Services (EMS) personnel vary between studies. Current studies are mainly based on self-reporting, thus other designs are needed to provide more perspective. The purpose of this study was to explore the rate and predictors of violent behavior targeted at EMS personnel by exploring the Electronic patient care records (ePCR) documentation by EMS personnel.MethodsThis was a retrospective cohort study of EMS patients in Finland. The data were collected from three regions between 1st June and 30th November 2018. Text mining and manual evaluation were used to identify and explore predictors of violence targeted at EMS personnel from the ePCR narratives. Multivariable logistic regressions were used to determine factors that were independently associated with violent behavior. The results are presented with odds ratios (ORs) with 95% confidence intervals (CIs).ResultsThe EMS personnel reported experiences of violence in a total of 297 identified missions (0.7%) of all EMS missions (n = 40,263). The violence was mostly verbal (62.3%) and the most common violence perpetrator was the patient (98.0%). The police were alarmed to many missions where violence was reported (40.7%). Sometimes violence occurred suddenly although the police were present. The multivariable logistic regression model indicates that violence occurred typically in urban areas (OR 1.699; 95% CI 1.283 to 2.248), at weekend nights (OR 1.357; 95% CI 1.043 to 1.765), by male (OR 1.501; 95% CI 1.160 to 1.942), and patients influenced by alcohol (OR 3.464; 95% CI 2.644 to 4.538). A NEWS2 score of 3 in any parameter (vs. score 0–4, OR 2.386; 95% CI: 1.788 to 3.185) and ALS unit type (vs. BLS, OR 1.373; 95% CI: 1.009 to 1.866) increased the likelihood as well.ConclusionsThe documentation in ePCRs show low rates of violence targeted at EMS personnel. However, violence is a multidimensional phenomenon connected to unfamiliar patients, rushed situations, and an uncontrolled environment. This means that the EMS personnels’ safety cannot be ensured in all situations. Therefore, a balance between safety margins and treating patients needs to be considered.

Comprehensive review of recent research trends in dielectric elastomer transducers: applications, materials, and fabrication

Abstract While rigid transducers have led to innovations in industrial automation and control systems,
their rigidity limits them to the controlled environment of the factory. Recently, soft transducers
have become an attractive area of research because their compliant nature presents potential to
be applied to soft-bodied robots operating in uncontrolled environments. Dielectric elastomer
transducers (DETs) demonstrate large electrically-driven deformations, high energy density, fast
response, long lifespans, and self-healing capabilities which makes them a favorable replacement
for rigid transducers. This review first introduces the working principles of DETs, modeling work,
and different transducer designs. Then the applications of DETs to robotics, generators, and
electronics are reviewed. Lastly, the challenges currently faced by DET technology are discussed
and potential approaches are explored.

Deep learning-based gamma spectroscopic analysis considering multiple variables for in situ applications

Gamma spectroscopy in environments with fluctuating temperatures, magnetic fields, and radiation doses can lead to inaccurate material analysis and increased radiation exposure for workers. In this manuscript, we propose a deep learning model that reduces human intervention and remains robust against variables in uncontrolled environments, thereby enhancing the applicability of gamma spectroscopy in the field. The process of establishing a dataset to train, validate, and test the deep learning model involved simultaneous consideration of multiple variables, including gain shifts, detector energy resolutions, the number of mixed radioisotopes (RIs), mixed RI ratios, and statistical fluctuations of the spectrum. The model was trained using multi-label learning to perform RI identification and quantification simultaneously. Specifically, the RI quantification output of our model structure was designed to predict full-energy peak areas and mixed ratios for each RI. Highlighting the importance of a high-performance training dataset, the training and validation results of the gamma spectroscopy model were analyzed in depth. In the test results, organized by RI type and number of mixed RIs, the model demonstrated that following: even under complex conditions with multiple variables, all outcomes for RI identification and quantification were predicted with high accuracy and precision, exceeding 98%.

Pain assessment from facial expression images utilizing Statistical Frei-Chen Mask (SFCM)-based features and DenseNet

Estimating pain levels is crucial for patients with serious illnesses, those recovering from brain surgery, and those receiving intensive care etc. An automatic pain intensity estimator is proposed in this study that gathers information about pain and intensity from the user’s expressions. The faces in the database are first cropped using a ‘Chehra’ face detector, which performs well even in wildly uncontrolled environments with a wide range of lighting and position fluctuations. The suggested technique extracts the beneficial and distinct patterns from facial expressions using novel Statistical Frei-Chen Mask (SFCM)-based features and DenseNet-based features. As it offers quick as well as accurate pain identification and pain intensity estimation, the Radial Basis Function Based Extreme Learning Machine (RBF-ELM) is employed for pain recognition and pain intensity level estimation using the characteristics. All the data is kept, updated and protected in the cloud because availability and high-performance decision-making are so important for informing physicians and auxiliary IoT nodes (such as wearable sensors). In addition, cloud computing reduces the time complexity of the training phase of Machine Learning algorithms in situations where it is possible to build a complete cloud/edge architecture by allocating additional computational resources and memory in use. The facial expression images from the UNBC-McMaster Shoulder Pain Expression Archive and 2D face dataset are used to test the proposed method. The measurement of pain intensity uses four stages. When compared to the results from the literature, the proposed work attains enhanced performance.

Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments

While individuals fail to assess their mental health subjectively in their day-to-day activities, the recent development of consumer-grade wearable devices has enormous potential to monitor daily workload objectively by acquiring physiological signals. Therefore, this work collected consumer-grade physiological signals from twenty-four participants, following a four-hour cognitive load elicitation paradigm with self-chosen tasks in uncontrolled environments and a four-hour mental workload elicitation paradigm in a controlled environment. The recorded dataset of approximately 315 hours consists of electroencephalography, acceleration, electrodermal activity, and photoplethysmogram data balanced across low and high load levels. Participants performed office-like tasks in the controlled environment (mental arithmetic, Stroop, N-Back, and Sudoku) with two defined difficulty levels and in the uncontrolled environments (mainly researching, programming, and writing emails). Each task label was provided by participants using two 5-point Likert scales of mental workload and stress and the pairwise NASA-TLX questionnaire. This data is suitable for developing real-time mental health assessment methods, conducting research on signal processing techniques for challenging environments, and developing personal cognitive load assistants.

Label distribution learning for compound facial expression recognition in‐the‐wild: A comparative study

AbstractHuman emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. This phenomenon reflects the complexity and richness of human states, where facial features dynamically convey a combination of feelings. This study embarks on a pioneering exploration of Compound Facial Expression Recognition (CFER), with a distinctive emphasis on leveraging the Label Distribution Learning (LDL) paradigm. This strategic application of LDL aims to address the ambiguity and complexity inherent in compound expressions, marking a significant departure from the dominant Single Label Learning (SLL) and Multi‐Label Learning (MLL) paradigms. Within this framework, we rigorously investigate the potential of LDL for a critical challenge in Facial Expression Recognition (FER): recognizing compound facial expressions in uncontrolled environments. We utilize the recently introduced RAF‐CE dataset, meticulously designed for compound expression assessment. By conducting a comprehensive comparative analysis pitting LDL against conventional SLL and MLL approaches on RAF‐CE, we aim to definitively establish LDL's superiority in handling this complex task. Furthermore, we assess the generalizability of LDL models trained on RAF‐CE by evaluating their performance on the EmotioNet and RAF‐DB Compound datasets. This demonstrates their effectiveness without domain adaptation. To solidify these findings, we conduct a comprehensive comparative analysis of 12 cutting‐edge LDL algorithms on RAF‐CE, S‐BU3DFE, and S‐JAFFE datasets, providing valuable insights into the most effective LDL techniques for FER in‐the‐wild.

6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments.

Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis (tz) and the camera's focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, tz is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed tz. In the second stage, we predict the true value of tz while scaling the focal length based on the tz update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method.

Improving the evidential value of low-quality face images with aggregation of deep neural network embeddings

In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal proceedings. On the other hand, in forensic casework, multiple images of the person of interest are usually available. In this paper, we propose to aggregate deep neural network embeddings from various images of the same person to improve the performance in forensic comparison of facial images. We observe significant performance improvements, especially for low-quality images. Further improvements are obtained by aggregating embeddings of more images and by applying quality-weighted aggregation. We demonstrate the benefits of this approach in forensic evaluation settings with the development and validation of common-source likelihood ratio systems and report improvements in Cllr both for CCTV images and for social media images.

Predicted multispecies unintended effects from outdoor genome editing

CRISPR/Cas9, a potent genetic engineering tool widely adopted in agriculture, is capable of introducing new characteristics into plants on a large scale and without conventional breeding methods. Despite its remarkable efficiency, concerns have arisen regarding unintended consequences in uncontrolled environments. Our aim was to assess potential activity in organisms that could be exposed to genome editing in uncontrolled environments. We developed three scenarios, using irrigation, fumigation and fertilization as delivery methods, based on outdoor uses in agriculture, namely pest and disease control. Using publicly available software (Cas-OFFinder, NCBI Genome Data Viewer and STRING), off-target effects were predicted in multiple species commonly found in the agroecosystem, including humans (16 of 38 (42 %) sampled). Metabolic enrichment analysis (gene IDs), by connecting off-target genes into a physiological network, predicted effects on the development of nervous and respiratory systems. Our findings emphasize the importance of exercising caution when considering the use of this genome editing in uncontrolled environments. Unintended genomic alterations may occur in unintended organisms, underscoring the significance of understanding potential hazards and implementing safety measures to protect human health and the environment.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

An Improved Image Enhancement Technique for Low Light Images Using Deep Learning Approach

Image enhancement in facial detection is a critical component of facial recognition systems. Face identification in an uncontrolled environment is affected by a multitude of difficulties such as poor light levels, low-resolution cameras, occlusions from surrounding objects, and tiny faces in distant photographs. Low signal-to-noise ratio, low brightness, and noise in low-light photographs lead to issues such as color distortion and poor visibility, which makes it challenging to identify faces. Many techniques to enhance low-light images have been developed, improving the face detection system’s accuracy. This will improve the picture at the expense of higher running costs and lower model robustness. The proposed technique, DCE-Net, uses performance-intensive deep learning and light-enhanced image properties. A non-referential deep learning technique was employed to acquire and modify the image attributes. A set of loss functions designed to perform without ground-truth images is the foundation of the deep network learning employed. Compared to the current referential methods, straightforward non-referential light estimation curve mapping minimizes the computational demand for low-light image improvement. Several experiments conducted on standard datasets demonstrated the efficacy and reduced computational requirements of the approach. The effectiveness of this method is supported by both the qualitative and quantitative outcomes. The PSNR and SSIM computation for paired images shows promising results using the proposed image enhancement technique.

A systematic approach to the analysis of illicit drugs for DNA with an overview of the problems encountered

Due to the restricted nature of illicit drugs, it is difficult to conduct research surrounding the analysis of this drug material for any potential DNA in sufficient quantities acceptable for high numbers of replicates. Therefore, the current research available in peer reviewed journals thus far regarding analysing illicit drugs for DNA has been performed under varying experimental conditions, often using surrogate chemicals in place of illicit drugs. The data presented within this study originated from the analysis of genuine illicit drugs prepared both in controlled environments and those seized at the Australian border (and therefore from an uncontrolled environment) to determine if DNA can be obtained from this type of material. This study has been separated into three main parts (total n=114 samples): firstly, methamphetamine synthesised within a controlled environment was spiked with both saliva and trace DNA to determine the yield following DNA extraction; secondly, methamphetamine also synthesised in a controlled environment but on a larger scale was tested for the amount of DNA added incidentally throughout the synthesis, including the additional steps of recrystallising, homogenising and “cutting” the drug material to simulate preparation for distribution; and thirdly, the detection of human DNA within samples of cocaine and heroin seized at the Australian border. The DNA Fast Flow Microcon Device was utilised to concentrate all replicates from the same source into one combined extract to improve the DNA profiles for the samples where no DNA spiking occurred. Full STR profiles were successfully obtained from drug samples spiked with both saliva and trace DNA. Methamphetamine was present in the final DNA extracts and caused incompatibilities with the quantification of DNA using Qubit. The yields of DNA from drugs not spiked with DNA sources were much lower, resulting in 36 % of samples yielding alleles where all others did not. These results were not unexpected given these were realistic drug samples where the history of the drug material was unknown. This is the first study to obtain DNA profiles from genuine illicit drug material in both controlled and uncontrolled environments and indicates that the analysis of illicit drugs for DNA is an avenue worth pursuing to provide information which can in turn assist with disrupting the supply of these drugs. Given that DNA profiling is carried out worldwide using essentially the same systems as described within this study, the potential for impact is on a national and international scale.

Translating Human and Animal Model Studies to Dogs' and Cats' Veterinary Care: Beta-Glucans Application for Skin Disease, Osteoarthritis, and Inflammatory Bowel Disease Management.

The inclusion of beta-glucans in dog and cat food is associated with numerous beneficial effects on the health of these animals. In this regard, there is an effort to elucidate the potential of this nutraceutical in chronic patients. Since there is a lack of a review on the topic, this review article aims to compile and discuss the evidence found to date. Atopic dermatitis, inflammatory bowel disease, and osteoarthritis are diseases of significant clinical relevance in dogs and cats. In general, the pathophysiology of these chronic conditions is related to immune-mediated and inflammatory mechanisms. Therefore, the immunomodulation and anti-inflammatory effects of beta-glucans are highlighted throughout this review. The available information seems to indicate that the studies on beta-glucans' impact on allergic processes in dogs indicate a reduction in clinical signs in atopic dermatitis cases. Additionally, while beta-glucans show promise as a safe supplement, particularly for osteoarthritis, further clinical trials are imperative, especially in uncontrolled environments. Beta-glucans emerge as a potential nutraceutical offering immune benefits for inflammatory bowel disease patients, although extensive research is required to define its optimal origin, molecular weight, dosage, and specific applications across animals suffering from this disease.

VAD system under uncontrolled environment: A solution for strengthening the noise robustness using MMSE-SPZC

VAD system under uncontrolled environment: A solution for strengthening the noise robustness using MMSE-SPZC

An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks

Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios.

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

Investigating cognitive-motor effects during slacklining using mobile EEG.

Balancing is a very important skill, supporting many daily life activities. Cognitive-motor interference (CMI) dual-tasking paradigms have been established to identify the cognitive load of complex natural motor tasks, such as running and cycling. Here we used wireless, smartphone-recorded electroencephalography (EEG) and motion sensors while participants were either standing on firm ground or on a slackline, either performing an auditory oddball task (dual-task condition) or no task simultaneously (single-task condition). We expected a reduced amplitude and increased latency of the P3 event-related potential (ERP) component to target sounds for the complex balancing compared to the standing on ground condition, and a further decrease in the dual-task compared to the single-task balancing condition. Further, we expected greater postural sway during slacklining while performing the concurrent auditory attention task. Twenty young, experienced slackliners performed an auditory oddball task, silently counting rare target tones presented in a series of frequently occurring standard tones. Results revealed similar P3 topographies and morphologies during both movement conditions. Contrary to our predictions we observed neither significantly reduced P3 amplitudes, nor significantly increased latencies during slacklining. Unexpectedly, we found greater postural sway during slacklining with no additional task compared to dual-tasking. Further, we found a significant correlation between the participant's skill level and P3 latency, but not between skill level and P3 amplitude or postural sway. This pattern of results indicates an interference effect for less skilled individuals, whereas individuals with a high skill level may have shown a facilitation effect. Our study adds to the growing field of research demonstrating that ERPs obtained in uncontrolled, daily-life situations can provide meaningful results. We argue that the individual CMI effects on the P3 ERP reflects how demanding the balancing task is for untrained individuals, which draws on limited resources that are otherwise available for auditory attention processing. In future work, the analysis of concurrently recorded motion-sensor signals will help to identify the cognitive demands of motor tasks executed in natural, uncontrolled environments.

Index Air Quality Monitoring for Light and Active Mobility.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

Calculating the Carbon Footprint of Interventive and Preventive Conservation at English Heritage, UK

ABSTRACT English Heritage (EH) is a large cultural heritage organisation, which cares for 1million artefacts over 136 sites. Preventive conservators and conservation scientists manage a mixture of controlled and uncontrolled historic site environments housing diverse object types. Two fine art conservators manage treatment programmes for easel and wall paintings, gilded frames and gilded furniture. Other objects are managed by preventive conservators and outsourced to private conservation studios for treatment. Sustainability is becoming an increasingly important part of the conservation team's agenda. Measuring and understanding the carbon impact of both the preventive and interventive conservators’ work was an important starting point for creating more sustainable solutions. To create a baseline for future monitoring and carbon reduction, carbon footprint studies were undertaken to evaluate the carbon impact of interventive painting conservation treatments; preventive conservation framing (adapting frames and making microclimates for displaying paintings in non-ideal environments); and running conservation (humidistatic) heating vs air conditioning systems in display rooms.