Firesetting Research Articles

Group-Motivated and Periodic Fire Setting for Terrorism in Urban Landscapes: An Analysis in Time and Space

One common form of arson is fire raising as an act of terrorism or for extremist cause. The purpose of this study is to contribute to the literature on this ubiquitous problem around the world by analyzing PKK′s systematic use of arsons. Drawing on time and locations of terrorist arson incidents, the research describes temporal patterns and then uses the Risk Terrain Modeling framework to analyze the behavior setting of the outcome event. Results suggest that PKK′s arsons were largely committed around February, over the weekends, and in the dark. In addition, the co-existence of four out of 20 spatial correlates within two street blocks increased the spatial risk of terrorist arson across the study area. The implications of the findings are also discussed for making policies toward these risky environments of terrorist fires settings around symbolic times to control the problem.

Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model

The Ionosphere, a crucial region of Earth’s atmosphere extending from approximately 50 to 1000 km above the Earth’s surface, plays a pivotal role in global communication, navigation, and satellite-based technologies. Among its various parameters, Total Electron Content (TEC) stands out as a key metric, representing the integrated electron density along a specific path through the ionosphere. This research focuses on analyzing TEC variations during Indonesian earthquakes between 2004 and 2024 using a Recurrent Neural Network (RNN) model. The study investigates the ionospheric response to seismic activity, particularly in Indonesia, a region prone to earthquakes due to its location within the Pacific Ring of Fire and complex tectonic setting involving multiple lithospheric plates. The RNN model, designed to predict TEC variations during earthquakes, utilizes data on solar and geomagnetic activity, including the Kp index, solar wind, and geomagnetic storm activity, obtained from the OMNIWEB data centre, along with TEC data from the IONOLAB data servers for the BAKO station in Cibinong, Indonesia. The earthquakes considered for observation include significant events such as the 2004 Indian Ocean earthquake and tsunami, the 2012 Wharton Basin earthquake, and subsequent seismic events up to 2024. Statistical metrics such as Mean Bias Deviation (MBD), Relative Error (REL_E), Absolute Error (ABS_E), and Root Mean Square Error (RMSE) are used to evaluate the predictive capability of the RNN model and compare it with the International Reference Ionosphere (IRI) 2020 model, a widely accepted empirical model for describing ionospheric parameters. The comparison reveals the performance of the RNN model in capturing TEC variations during seismic events, providing valuable insights for earthquake monitoring and early warning systems.

Incendiary: Fire setting behaviors in youth

A 12‐year‐old boy presented for psychiatric assessment with a chief complaint of “fire setting.” The patient was reluctant to speak, however, his caregiver stated that he had set a fire in the basement of his home while the rest of the family were asleep. They were alerted due to the smoke alarm going off and were able to hurriedly contain the situation. The patient had a history of physical and emotional abuse from parents and was residing with his legal guardian, his maternal aunt.

Fire resistance evaluation through synthetic fire tests and generative adversarial networks

This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

Mitigating land fire disasters: Implementation of agricultural land fire risk management information system in Aunupe Village, South Konawe Regency

Climate change, which causes increased temperatures, prolonged droughts, and dry conditions, heightens the risk of land fires. A similar scenario unfolded in Aunupe Village, where several fires occurred on agricultural land over the past three years, resulting in crop failure and degraded soil quality, leading to financial losses for land-owning farmers. To mitigate the risk of land fire disasters, an agricultural land fire disaster risk management information system is necessary in Aunupe Village, South Konawe Regency. The objective of this community-based empowerment program is to mitigate the risk of land fire disasters. To achieve the desired outcomes, initial coordination with farmer group partners is conducted, followed by the design and installation of fire sensor equipment sets in collaboration with farmer groups. Furthermore, focus group discussions (FGD) are conducted with farmer group partners to ensure their understanding and cooperation. The activities result in the development of an agricultural land fire risk management information system, facilitating the prompt detection, monitoring, and response to fire incidents in the agricultural land of the Lembah Harapan Farmers Group. Additionally, farmer group partners acquire knowledge regarding the utilization of fire mitigation systems.

Development of an Education and Training System Based on Augmented Reality to Improve the Firefighting Facility Maintenance Ability of Fire Safety Managers

This study grafts fire safety manager education to augmented reality technology to increase the familiarity of fire safety managers with facility inspection and operation based on the automatic fire alarm system that plays the most critical role in early fire situations. To design the education and training content, the Korea Fire Safety Institute, which educates and trains fire safety managers, arranges the training content in the textbooks provided to actual fire safety managers and sets the sequence of operating principles and inspection methods. The purpose is to provide fire safety managers with realistic inspection methods and practice opportunities by implementing them in augmented reality. The education and training program allows learning about automatic fire alarm systems in the following order: structure and operating principles → inspection and practice → assessment. In structure and operating principles, fire safety managers can acquaint themselves with the role of automatic fire alarm systems and facility components and circuit tests according to operation principles. During inspection and practice, at least three inspection methods are learned for each facility. Finally, the assessment refers to the actual evaluation and achievement standards of the Korea Fire Safety Institute, checking a facility in a given mission without guidance or explanation based on the aspects learned in inspection and practice. In addition, it reviews the aspects learned in theory by including a quiz during inspection to improve proficiency by setting it up to be applied in practice.

Klinefelter syndrome and fire setting behaviors: a case report and scoping review

ABSTRACT This scoping literature review explores the correlation of a behavioral alteration commonly found in people with Klinefelter syndrome (KS), specifically fire setting. KS is the most common sex chromosome aneuploidy, presenting with a heterogeneous pattern of neuropsychiatric disorders. Several studies have specifically described fire-setting behavior in KS. A scoping literature review on KS and fire-setting behavior was conducted, consulting electronic databases. The research extended from 1988 to December 2023 and was limited to the English language. Finally, described is the experience of a young man who was diagnosed with KS and who presented with incendiary behavior despite having no psychiatric history. This article aims to investigate potential gaps in knowledge on this topic, considering its implications in the forensic field. Summarizing the review of literary sources,13 relevant case reports are included. No other reviews on this topic in forensic psychiatry were found. This search helped in assessing criminal liability in a similar case discussed in 2022. Given the further progress in forensic psychiatry, there is an incentive to better understand whether plausible triggers or a preliminary psychopathological predisposition for antisocial traits, for committing arsons, or even to develop a proper psychiatric issue, such as pyromania, might be found in KS.

Field-based generic empirical flame length–fireline intensity relationships for wildland surface fires

Background Fireline intensity (If) quantifies the power of the fireline and is used for various purposes. If and flame length (Lf) are relatable to each other using an empirical power function, which has been considered fuel-specific. Aims The aim of this study was to develop generic Lf − If relationships based on a robust set of field head fires from the literature (n = 797) conducted worldwide in forest, shrubland and grassland. Methods Lf was determined from the base of the fuel bed for comparability across fires in different fuel heights, and the effect of vegetation type was examined. Key results Although If could be approximately described using the same function in forest and shrubland, fires in grassland required different fitted coefficients; we speculate that fuel particles’ surface area-to-mass ratio is the main fuel metric influencing flame structure. Conclusions Fuel-generic relationships for If are reasonably accurate and encompass the high end of surface fire If. Previous studies suggested their unviability, most likely because of limitations in the number of observations and data ranges, difficulty in objectively measuring Lf and variation in Lf definition. Implications The generic relationships presented in this work will be of interest for research and management purposes when specific models for If are non-existent.

The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes

This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the backpropagation (BP) method to train the antecedent parameters. This methodology dynamically changes the parameters of only the level alpha 0, minimizing some criterion functions as new information becomes available to each level alpha k. The precursor sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the proposed EWH IT3 NSFLS-1 (OLS–BP) system. The proposed enhanced fuzzy system and the proposed hybrid learning algorithm were applied in a hot strip mill facility to predict the transfer bar surface temperature at the finishing mill entry zone using, as inputs, (1) the surface temperature measured by the pyrometer located at the roughing mill exit and (2) the time taken to translate the transfer bar from the exit of the roughing mill to the entry of the descale breaker of the finishing mill. Several fuzzy tools were used to make the benchmarking compositions: type-1 singleton fuzzy logic systems (T1 SFLS), type-1 adaptive network fuzzy inference systems (T1 ANFIS), type-1 radial basis function neural networks (T1 RBFNN), interval singleton type-2 fuzzy logic systems (IT2 SFLS), interval type-1 non-singleton type-2 fuzzy logic systems (IT2 NSFLS-1), type-2 ANFIS (IT2 ANFIS), IT2 RBFNN, general singleton type-2 fuzzy logic systems (GT2 SFLS), general type-1 non-singleton type-2 fuzzy logic systems (GT2 NSFLS-1), interval singleton type-3 fuzzy logic systems (IT3 SFLS), and interval type-1 non-singleton type-3 fuzzy systems (IT3 NSFLS-1). The experiments show that the proposed EWH IT3 NSFLS-1 (OLS–BP) system presented superior capability to learn the knowledge and to predict the surface temperature with the lower prediction error.

Research and Implementation of Forest Fire Detection Algorithm Improvement

Abstract To overcome low efficiency and accuracy of existing forest fire detection algorithms, this paper proposes a network model to enhance the real-time and robustness of detection. This structure is based on the YOLOv5 target detection algorithm and combines the backbone network with The feature extraction module combines the attention module dsCBAM improved by depth-separable convolution, and replaces the loss function CIoU of the original model with a VariFocal loss function that is more suitable for the imbalanced characteristics of positive and negative samples in the forest fire data set. Experiments were conducted on a self-made and public forest fire data set. The accuracy and recall rate of the model can reach 87.1% and 81.6%, which are 7.40% and 3.20% higher than the original model, and the number of images processed per second reaches 64 frames, a growth rate of 8.47%. At the same time, this model was compared horizontally with other improved methods. The accuracy, recall rate and processing speed were all improved in the range of 3% to 10%. The effectiveness of the improved method in this article was verified, and the external perception level of the forest fire scene was deeper.

Harm Of Discharging Fireworks and Harm Reduction Measures

In recent years, there has been greater awareness and concern about the impact of pollution from fireworks on the environment. The main research objective of this paper is the impact of fireworks on the earth environment. This paper will talk about the advantages and disadvantages of setting of the fireworks during holidays. Besides, this study will analysis that do people still encourage people to emit the pollution by fireworks under this bad condition of earth. The result shows in two different way. One the one way, fireworks burn produce a lot of harmful substances, including chemicals, particulate matter and heavy metals, which can negatively affect air and soil quality. And sulfide in fireworks can easily lead to acid rain. Secondly, particulate matter emitted by fireworks directly effects atmospheric quality and body health. When particles enter the respiratory tract, they can cause respiratory problems such as asthma, bronchitis, and other respiratory diseases. In addition, fireworks emissions also cause noise pollution. On the one side, human have to admit that setting of the firework give us a vibe that everybody immersed themselves in that merry and people get power from it. In this essay, a lot of research were studied to solve the problem of general effect of fire setting of fireworks to the plant, to human life, to the air, to the physical and mental health of human body, and to the waste of the natural resources. Everyone look forward to find a better way to solve this big issue ,everyone should have a deep thinking to create a feasible solution together in order to avoid environmental crisis that might happen in the future.

Experimental Study of the Effect of Different Insulation Schemes on Fire Performance of FRP Strengthened Concrete: FIRECOAT and REALROCK

The past two decades have witnessed rapid advances in the use of fiber-reinforced polymer (FRP) composites in different engineering fields. Advantages such as high strength-to-weight ratio, corrosion resistance, and tailority have led to immense interest in the use of FRPs in wide spectrum repair and strengthening of structures. Despite their many advantages, FRPs are highly sensitive to high temperatures, which pose a major concern for fire potential structures such as buildings. Applying proper thermal insulation can enhance the fire performance of FRP and reduce the possible fire damage to the FRP strengthened element. This study set out to experimentally investigate the effectiveness of two insulation systems, “FIRECOAT” and “REALROCK” on fire performance of CFRP and GFRP strengthened concrete specimens. Various configurations and exposure durations were considered to evaluate the effectiveness of insulating materials. To perform the experiments, cylindrical concrete specimens were fabricated and strengthened using CFRP or GFRP. After insulating the specimens, they were exposed to a standard fire curve for two different durations of 30 and 60 min. The results indicate that less than 30 min of fire, both insulation systems can provide the required protection. During long exposure duration of 60 min, only REALROCK can provide the required thermal resistance for FRP-strengthened concrete. Within the tested materials, Fire Set 60 outperformed other insulating materials. It was observed that implementing Fire Set 60 in the innermost layer of thermal insulations has crucial importance in preventing the fire induced reductions in strength of FRP-strengthened concrete elements.

PKM-Perancangan Automatic Temperatur Control Menggunakan Pd Pada Mesin Pengering Gabah Tipe Rotary

During the Community Service (PKM) activities carried out by students and lecturers in Opo-opo Village, Krejengan District, Probolinggo Regency, to assist farmers in the grain drying process. Where the simple drying process generally takes 3 days. To overcome the problem of manual drying, the PKM will design an automatic temperature control using pd on a rotary type grain drying machine. The test results were carried out 3 times, including the first test with 8 kg of grain with a fire setting, a temperature of 57 ℃ was obtained, and the required drying time was 50 minutes, so that the final weight was obtained lower than the initial weight due to a decrease in water content with the result the final grain weight was 7.23 kg and there was a decrease in grain weight of 1.37 kg. For the two grain tests of 7 kg with the fire setting, a temperature of 57 ℃ was obtained, and the drying time required was 30 minutes, so that the final weight was obtained which was lower than the initial weight due to a decrease in water content with the final grain weight being 6.73 kg and there was a decrease in grain weight of 1.87 kg. And for the three grains of 6.15 kg with the fire setting, a temperature of 69 ℃ was obtained, and the required drying time was 20 minutes, so that the final weight was obtained which was lower than the initial weight due to a decrease in water content with the final grain weight being 6. 40 Kg and there was a decrease in grain weight of 1.75 Kg.

Social Triage and Exclusions in Community Services for the Criminalized

AbstractThis article examines perspectives and practices related to social triage and the exclusion of criminalized and marginalized individuals in community services such as shelters, mental health, substance use, and court supports. Based on two years of fieldwork and interviews with 105 practitioners, I analyze narratives and practices related to working with people described as having (or being) complex, high-needs, or high-risk. I show that individual factors, such as risk, need, or responsivity, are but one type of factor considered when practitioners make decisions about triage or service eligibility. Building from theory about the governance of “risk” and “risky people,” I examine how organizational and systemic factors shape individualized understandings of and responses to risk. I argue that given current practices in under-resourced community supports, triage and resulting exclusions exacerbate social problems and contribute to punitive exclusions, especially for those who seek services, supports, or housing but have records of sexual offense, fire setting, drug use, violence, self-harm or so-called non-compliance. Examining these dynamics bolsters claims that we should shift the responsibilizing gaze upwards to pressure institutional and state bodies who could transform the landscape for practitioners and their clients.

Prescribed Fire Simulation with Dynamic Ignitions Using Data from UAS-based Sensing

ABSTRACT Prescribed fire is an important tool for wildfire management and land management. Simulation of prescribed fires holds great potential in supporting planning of prescribed burn events. This paper presents a simulation-based study of a prescribed fire using data from Unmanned Aircraft System (UAS)-based sensing. A systematic approach for modelling and simulating prescribed fires with dynamic ignitions is developed. The developed approach is applied to a real prescribed fire where a UAS was used to monitor and collect data about the fire. The dynamic ignition process from multiple fire setting teams is specified, and simulation results are compared to real measurement data from UAS-based sensing. The results demonstrate the effectiveness of the developed modelling approach as well as the utility of using UAS-based fire measurements for prescribed fire simulations.

An urgent call-to-action to protect the nonhuman primates and Indigenous Peoples of the Brazilian Amazon.

Primates are facing an impending extinction crisis. Here, we examine the set of conservation challenges faced by the 100 primate species that inhabit the Brazilian Amazon, the largest remaining area of primary tropical rainforest in the world. The vast majority (86%) of Brazil's Amazonian primate species have declining populations. Primate population decline in Amazonia has been driven principally by deforestation related to the production of forest-risk commodities including soy and cattle ranching, the illegal logging and setting of fires, dam building, road and rail construction, hunting, mining, and the confiscation and conversion of Indigenous Peoples' traditional lands. In a spatial analysis of the Brazilian Amazon, we found that 75% of Indigenous Peoples' lands (IPLs) remained forested compared with 64% of Conservation Units (CUs) and 56% of other lands (OLs). In addition, primate species richness was significantly higher on IPLs than on CUs and OLs. Thus, safeguarding Indigenous Peoples' land rights, systems of knowledge, and human rights is one of the most effective ways to protect Amazonian primates and the conservation value of the ecosystems they inhabit. Intense public and political pressure is required and a global call-to-action is needed to encourage all Amazonian countries, especially Brazil, as well as citizens of consumer nations, to actively commit to changing business as usual, living more sustainably, and doing all they can to protect the Amazon. We end with a set of actions one can take to promote primate conservation in the Brazilian Amazon.

On composite length-biased exponential-Pareto distribution: Properties, simulation, and application in actuarial science

The composite length-biased exponential-Pareto (CLBEP) distribution is a new composite distribution that is introduced in this article. This model's probability density function, moments, and quantiles, among other statistical characteristics, are determined mathematically. The parameters' maximum-likelihood estimation and stochastic ordering are discussed. A comparison study with other new composite and conventional distributions is also included. Specifically, using two actual fire insurance data sets, the goodness of fit of this new model is contrasted with the composite exponential-Pareto, composite lognormal-Pareto, and composite Rayleigh-Pareto distributions (Algerian and Danish fire insurance losses).2010 AMS subject classifications62E10; 60E05.

Characterization of biophysical contexts leading to severe wildfires in Portugal and their environmental controls

Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015–2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.

Bump Attractors and Waves in Networks of Leaky Integrate-and-Fire Neurons

Bump attractors are wandering localised patterns observed in in vivo experiments of spatially-extended neurobiological networks. They are important for the brain's navigational system and specific memory tasks. A bump attractor is characterised by a core in which neurons fire frequently, while those away from the core do not fire. We uncover a relationship between bump attractors and travelling waves in a classical network of excitable, leaky integrate-and-fire neurons. This relationship bears strong similarities to the one between complex spatiotemporal patterns and waves at the onset of pipe turbulence. Waves in the spiking network are determined by a firing set, that is, the collection of times at which neurons reach a threshold and fire as the wave propagates. We define and study analytical properties of the voltage mapping, an operator transforming a solution's firing set into its spatiotemporal profile. This operator allows us to construct localised travelling waves with an arbitrary number of spikes at the core, and to study their linear stability. A homogeneous "laminar" state exists in the network, and it is linearly stable for all values of the principal control parameter. Sufficiently wide disturbances to the homogeneous state elicit the bump attractor. We show that one can construct waves with a seemingly arbitrary number of spikes at the core; the higher the number of spikes, the slower the wave, and the more its profile resembles a stationary bump. As in the fluid-dynamical analogy, such waves coexist with the homogeneous state, are unstable, and the solution branches to which they belong are disconnected from the laminar state; we provide evidence that the dynamics of the bump attractor displays echoes of the unstable waves, which form its building blocks.

Research Spotlights

What surprising patterns do fluid dynamics and neurobiological networks have in common? In this issue's first Research Spotlights article, “Bump Attractors and Waves in Networks of Leaky Integrate-and-Fire Neurons,” a connection is made between the waves at the onset of pipe turbulence and the localized traveling waves in spiking neural networks. Authors Daniele Avitabile, Joshua L. Davis, and Kyle Wedgwood consider a so-called “discrete integrate-and-fire model” to describe the dynamics of a connected neuron firing via a system of piecewise linear ordinary differential equations. Constructing a solution to this system effectively amounts to determining firing times of the neurons. One can then witness “bump” patterns, in this case characterized by a core of neurons that fire frequently, while neurons away from the core do not fire. Such a model's deterministic chaotic behavior can then be studied around a firing set and used to construct waves and analyze their stability. Simulations and analysis demonstrate oscillatory and nested branching of these waves, and such behavior can accumulate in a bump attractor and wandering wave. The authors show that such complex spatiotemporal patterns, and their conditions for stability, are not unlike those witnessed in the transition to turbulence in a pipe. The paper concludes with a challenge to future researchers: can further dynamical systems characterizations of such event-driven models uncover links between localized waves and bumps in complex spatial networks? The second Research Spotlights article addresses the problem of identifying a system from noisy observations of dynamical data. In “Learning Dynamical Systems with Side Information” authors Amir Ali Ahmadi and Bachir El Khadir present a way to incorporate system knowledge to improve the learning process beyond what is possible using data alone. Such knowledge, or “side information,” arises when one has contextual information about the unknown dynamics without knowing the dynamics precisely. Examples of side information covered by the authors' framework include known trajectories (e.g., when one has knowledge of some equilibrium points), group symmetry, and coordinatewise properties (e.g., nonnegativity or monotonicity). Restricting attention to a particular class of functions to be learned and particular side information allows the learning process to be posed as a semidefinite optimization formulation amenable to efficient solution. This approach is demonstrated numerically on systems modeling the diffusion of a contagion, behavior of a simple pendulum, and time evolution of a cancerous tumor, as well as on the classical Lorenz system. In each case, imposing side information via semidefinite programming allows the authors to learn the unknown dynamics from far fewer observations than otherwise possible and provides a potential road map for addressing even broader classes of systems and side information.