Thermostat Model Research Articles

Metalens Topology Optimization

Photonic metasurfaces are thin optical elements comprised of structures arranged strategically upon a surface to manipulate electromagnetic waves. To produce metasurfaces at a large scale, it is beneficial to obtain not just an optimal design, but also an understanding of which areas of the metasurface are most influential on the overall performance of the device and require greater manufacturing accuracy. My work consisted of implementing and testing a new physics-based method for identifying the relative importance of different regions of a proposed metasurface design. This method built on an existing topology optimization code, where the designable region of the metasurface was discretized, and the material density of each point in the design was optimized such that incoming light was maximally focussed to a target location. The existing optimization algorithm was then coupled to a molecular dynamics model called a Nosé-Hoover thermostat, by modelling the discrete locations on the design region as particles and their corresponding material densities as their positions. By numerically integrating the equations of motion of the thermostat model, I was able to generate metasurface designs at different thermostat temperatures, and observe how the designs changed with increasing temperature. To determine the most important areas of the metasurface design, I calculated the entropy of each of the "particles" for all temperature samples, and looked for design regions that had low entropy even at high temperatures, indicating strong convergence on an optimal material density value amidst high thermal noise. Once I implemented this design analysis framework, I tested it on both a metalens and a reflector design at multiple wavelengths of incoming light. My results demonstrated that this physics-based method provides easily interpretable information about the relative importance of different elements of a metasurface design.

Economic growth versus the environment: government spending, trust, and citizen support for environmental protection

ABSTRACT Support for sustainability activities among citizens will also increase the probability of more sustainable choices being made by governments. In this study, we investigate the relationship between prioritization of environmental protection over economic growth and government spending on environmental protection in 27 OECD countries and more than 65 000 respondents. Drawing on the perspectives of the thermostatic model, complex belief systems and cognitive dissonance theory, we hypothesize that government spending and attitudes towards environmental protection are related. We further argue that this relationship will be moderated by trust in government. To investigate our hypotheses, we employ OECD statistics on government spending in combination with survey data from WVS and EVS from the years 1995 to 2019. Our findings show that increasing government spending on environmental protection is related to attitudes in favor of economic growth at the expense of environmental concerns, a relationship which is significantly strengthened by having low trust in government.

A Thermostatic Model of Congressional Elections

Are policymakers rewarded in elections when they succeed in moving public policy in their ideological direction? Or do they face a thermostatic backlash, as citizens judge their policy moves as too hot or too cold? Our analysis of Congressional election outcomes since 1948 adds information on Congressional policy actions to traditional election models emphasizing the surge and decline of presidential support and referendums based on presidential approval and the economy. We find that the electorate reacts to the ideological direction of policy, voting against parties that push policy further to the left or the right in both midterm and presidential years. Even after accounting for policy and traditional explanations, however, there remains a large midterm penalty for the president’s party.

Can the Brain's Thermostatic Mechanism Generate Sleep-Wake and NREM-REM Sleep Cycles? A Nested Doll Model of Sleep-Regulating Processes.

Evidence is gradually accumulating in support of the hypothesis that a process of thermostatic brain cooling and warming underlies sleep cycles, i.e., the alternations between non-rapid-eye-movement and rapid-eye-movement sleep throughout the sleep phase of the sleep-wake cycle. A mathematical thermostat model predicts an exponential shape of fluctuations in temperature above and below the desired temperature setpoint. If the thermostatic process underlies sleep cycles, can this model explain the mechanisms governing the sleep cyclicities in humans? The proposed nested doll model incorporates Process s generating sleep cycles into Process S generating sleep-wake cycles of the two-process model of sleep-wake regulation. Process s produces ultradian fluctuations around the setpoint, while Process S turns this setpoint up and down in accord with the durations of the preceding wake phase and the following sleep phase of the sleep-wake cycle, respectively. Predictions of the model were obtained in an in silico study and confirmed by simulations of oscillations of spectral electroencephalographic indexes of sleep regulation obtained from night sleep and multiple napping attempts. Only simple-inverse exponential and exponential-functions from the thermostatic model were used for predictions and simulations of rather complex and varying shapes of sleep cycles during an all-night sleep episode. To further test the proposed model, experiments on mammal species with monophasic sleep are required. If supported, this model can provide a valuable framework for understanding the involvement of sleep-wake regulatory processes in the mechanism of thermostatic brain cooling/warming.

Advancements in Smart Thermostat Technology for Enhanced HVAC Energy Management

This study examines the transformative potential of smart thermostat technology in revolutionizing Heating, Ventilation, and Air Conditioning (HVAC) energy management. Through a combination of literature review and empirical analysis, the research explores the significance of energy-efficient HVAC systems in reducing carbon footprint and energy costs. Traditional thermostat limitations, including inflexible scheduling and lack of user engagement, underscore the need for innovative solutions. The evolution of smart thermostat technology is traced from basic programmable thermostats to advanced systems integrated with learning algorithms, occupancy sensing, and the Internet of Things (IoT). Empirical data from surveys, case studies, and energy consumption analysis showcase the tangible impact of smart thermostat adoption. Energy savings of up to 30% in commercial settings and 25% in residential contexts underscore the technology's potential for efficiency. User satisfaction surveys reveal improved comfort levels and satisfaction with remote control capabilities. Challenges such as installation complexities and data security concerns highlight areas for improvement. Data analytics and machine learning emerge as pivotal in enhancing smart thermostat efficiency, contributing to additional energy savings. Comparisons between smart thermostat models underscore the importance of occupancy sensing and remote control features in optimizing energy efficiency and user satisfaction. The study's findings suggest a promising avenue for transforming HVAC energy management towards a more sustainable and user-centric approach

Existence and uniqueness of blow-up solution to a fully fractional thermostat model

In this article, we deal with a fully fractional thermostat model in the settings of the Riemann–Liouville fractional derivatives. The equivalences between the fractional differential equations and the corresponding Volterra–Fredholm integral equations are rigorously derived. By choosing an appropriate weighted Banach space of continuous functions, we employ two standard fixed-point theorems, Leray–Schauder alternative and Banach contraction principle, to establish the existence of blow-up solutions — the unbounded mathematical solutions in the operational interval. Furthermore, an implicit numerical scheme based on the right product rectangle rule is presented, which provides the numerical approximation of the obtained solution. Some examples are provided to validate our theoretical findings, along with numerical simulations of the solutions.

Approximate solutions for a fractional thermostat model boundary value problem via Bernstein's collocation method with Legendre polynomials

Numerous classical models of thermostats have been analyzed in the literature, and while existence and uniqueness results have been established, the investigation of other properties, such as stability, can present significant challenges. Therefore, there is a need for fractional mathematical models that are nonlocal problems and allow for the straightforward study of the asymptotic behavior of their solutions. However, solving these problems is often exceedingly complex or even impossible, necessitating the use of numerical methods to obtain approximate solutions. The focus of this research is a generalized boundary value problem that is based on a classical thermostat model. Our initial step involves determining the associated integral equation for the problem at hand. The properties of existence and uniqueness are proved by exploiting the classical contraction principle of Banach together with another kind called ‐ ‐contraction. Afterwards, thanks to the Bernstein polynomials and fractional matrix of derivative, the approximate solutions of our nonlinear fractional thermostat model problem are obtained. This approach utilizes the Legendre operational matrix of fractional derivatives in combination with a truncated Legendre series for the numerical integration of fractional differential equations. By doing so, the problem can be reduced to solving a system of algebraic equations, resulting in significant simplification. This method has been successfully used to solve both linear and nonlinear fractional differential equations. Finally, an example on this type of problem have been studied and the approximate solution are plotted.

Stuart N. Soroka and Christopher Wlezien. Information and Democracy: Public Policy in the News. Cambridge University Press. 2022. $99.99 (cloth). $34.99 (paper).

This book is an empirical investigation of the role of media in democracy in the United States since 1980. It uses texts from 17 major newspapers, six broadcast news stations, and a social media outlet to identify content and sentiment regarding policy spending in five domains of defense, welfare, health, education, and environment. Its authors, Stuart Soroka and Christopher Wlezien, spent their careers identifying patterns in public preference formation in response to policies and policymaker representation of public preferences. Few opinion-policy researchers are unfamiliar with their thermostatic model. To date, this rich subfield lacks attention to what happens in-between public preferences (opinion) and policy outcomes. Enter this book. Media is a key theoretical mechanism transmitting policy to public preferences and vice versa, and this book ambitiously seeks to (1) ascertain the quantity and quality of US media content in these domains, and (2) test for a role of media in public preferences and policy, and feedback between. As chapter 1 points out, media research should help us understand democracy, specifically, whether media provides accurate information about government actions. Chapter 2 reviews previous work on the content and framing of policy in media but shows it does little to answer the question of whether there is enough accurate coverage to enable informed citizen decisions and voting to hold policymakers accountable. Their original thermostatic model included relative policy preferences (⁠R⁠) as a function of policy (⁠P⁠) and absolute preferences for policy (proxied by O⁠), where ΔP (change in policy) is the “signal” the public uses to change their preferences. The authors now add media (represented by M⁠) for a more complete picture (Equation 2.8, p. 33): ...

On a Lyapunov-Type Inequality for Control of a ψ-Model Thermostat and the Existence of Its Solutions

In this paper, a new structure of an applied model of thermostat is defined using the generalized ψ-operators with three-point boundary conditions. Some useful properties of the relevant Green’s function are established, and based on these properties, the Lyapunov-type inequality is constructed for the given extended ψ-model thermostat with the help of Jensen’s inequality. By defining mild solutions for such an extended system, the existence and non-existence conditions are discussed.

Immigration as a thermostat? Public opinion and immigration policy across Western Europe (1980–2017)

ABSTRACT An important focus of empirical accounts of representative democracy is the policy-opinion nexus. Drawing from the thermostatic model (Wlezien, 1995), this study examines the dynamic relationship between public opinion and immigration policy, one of the more salient issue domains that have reshaped European democracies since the 1980s. As a counter-factual to social identity accounts of immigration politics, this study argues citizens have policy preferences and when immigration policy changes, the demand responds. The result is a known movement between opinions and policy that we describe as an ‘immigration thermostat’. We rely on dozens of high-quality surveys (more than 500 separate series, corresponding to nearly 2,500 marginals) and a dyadic-ratios algorithm to design comparable immigration opinion measures for 13 countries. We find evidence of both public and policy responsiveness for immigration, although not to the same extent. This suggests an asymmetrical ‘immigration thermostat’ but effective representation in the immigration domain across Western Europe.

A Hessian-Free Method to Prevent Zero-Point Energy Leakage in Classical Trajectories.

The problem associated with the zero-point energy (ZPE) leak in classical trajectory calculations is well known. Since ZPE is a manifestation of the quantum uncertainty principle, there are no restrictions on energy during the classical propagation of nuclei. This phenomenon can lead to unphysical results, such as forming products without the ZPE in the internal vibrational degrees of freedom (DOFs). The ZPE leakage also permits reactions below the quantum threshold for the reaction. We have developed a new Hessian-free method, inspired by the Lowe-Andersen thermostat model, to prevent energy dipping below a threshold in the local-pair (LP) vibrational DOFs. The idea is to pump the leaked energy to the corresponding local vibrational mode taken from the other vibrational DOFs. We have applied the new correction protocol on the ab-initio ground-state molecular dynamics simulation of the water dimer (H2O)2, which dissociates due to unphysical ZPE spilling from high-frequency OH modes. The LP-ZPE method has been able to prevent the ZPE spilling of the OH stretching modes by pumping back the leaked energy into the corresponding modes, while this energy is taken from the other modes of the dimer itself, keeping the system as a microcanonical ensemble.

Transfer Learning Auto-Encoder Neural Networks for Anomaly Detection of DDoS Generating IoT Devices

Machine Learning based anomaly detection ap- proaches have long training and validation cycles. With IoT devices rapidly proliferating, training anomaly models on a per device basis is impractical. This work explores the “transfer- ability” of a pre-trained autoencoder model across devices of similar and different nature. We hypothesized that devices of similar nature would have similar high level feature character- istics represented by the initial layers of the autoencoder, while the more distinct features are captured by the innermost layer of the neural network. In our experiments, the centre-most layers of autoencoder models were re-trained with limited new data belonging to a different device. Datasets of seven Mirai infected and nine Bashlite infected IoT devices were used; each dataset also included benign records representing un-infected behaviour. We observed that the model’s detection accuracy improved by an average of 9.52% for Mirai and 44.59% for Bashlite. The highest performance improvement of 26.68% and 73.00% was observed when the anomaly model of Ecobee thermostat was tested on other devices before and after transfer learning for Mirai and Bashlite respectively. Additionally, transfer learning took 47.31% and 58.27% less time for Mirai and Bashlite respectively. We further trialed the efficacy of the autoencoder based anomaly model on flow based records of network traffic using the CIC- IDS2017 dataset. It was observed that the model performed best when distinct outliers in the dataset were present, whereas the model failed to perform decently in cases where the malicious activity did not cause significant deviation in network traffic’s footprint.

A Caputo discrete fractional-order thermostat model with one and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality

A thermostat model described by a second-order fractional difference equation is proposed in this paper with one sensor and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality. By means of well-known contraction mapping and the Brouwer fixed-point theorem, we provide new results on the existence and uniqueness of solutions. In this work by use of the Caputo fractional difference operator and Hyer–Ulam stability definitions we check the sufficient conditions and solution of the equations to be stable, while most researchers have examined the necessary conditions in different ways. Further, we also establish some results regarding Hyers–Ulam, generalized Hyers–Ulam, Hyers–Ulam–Rassias, and generalized Hyers–Ulam–Rassias stability for our discrete fractional-order thermostat models. To support the theoretical results, we present suitable examples describing the thermostat models that are illustrated by graphical representation.

Acylated homoserine lactones regulate the response of methane metabolism and nitrogen metabolism to florfenicol in anaerobic fermentation

Antimicrobial agents enter the ecological environment through animal excreta and disrupt metabolism in environmental microorganisms. Quorum sensing (QS) can help bacteria adapt to their surroundings. To explore how acyl-homoserine lactone (AHL) can adjust the influence of florfenicol on nitrogen cycling and methane metabolism in anaerobic fermentation, a small indoor thermostatic anaerobic fermentation model was established by adding exogenous acylated homoserine lactone (AHL) signal molecules with florfenicol as the stress factor.Through bacterial function prediction by PICRUST, we found that the addition of AHL further increased the promotion of methanogenesis_by_CO2_reduction_with_H2 and hydrogenotrophic methanogenesis by florfenicol. Before the third sampling, florfenicol significantly inhibited the enrichment of the denitrification pathway microbiota, whereas the addition of AHL significantly promoted the enrichment of the denitrification pathway microbiota. Functional annotation showed that florfenicol exposure stress significantly affected nitrogen and methane metabolism, and the addition of AHLs reduced the response of functional genes to florfenicol. All nitrogen cycling enzymes with significantly different abundances in treatment groups were substantially associated with methane-metabolizing enzymes. Glutamate metabolism is significant in the process of anaerobic fermentation, and is a correlation point between nitrogen and methane metabolism.In our experiment, AHL was the influencing factor at the highest latitude that directly regulates the metabolism of NO3−-N and the degradation process of florfenicol. The addition of AHL curbed the inhibitory effect of florfenicol on some functional microbiota, improved the stability of fermentation microbiota, and weakened the impact of antibiotic residues by improving its degradation efficiency.

On the Fractional Variable Order Thermostat Model: Existence Theory on Cones via Piece-Wise Constant Functions

In the current manuscript, we intend to investigate the existence, uniqueness, and the stability of positive solution in relation to a fractional version of variable order thermostat model equipped with nonlocal boundary values in the Caputo sense. In fact, we will get help from the constant piece-wise functions for transforming our variable order model into an auxiliary standard model of thermostat. By Guo-Krasnoselskii’s fixed point theorem on cones, we derive the required conditions ensuring the existence property for positive solutions. An example is illustrated to examine the validity of the observed results.

Derivation of Liouville-like equations for the n-state probability density of an open system with thermalized particle reservoirs and its link to molecular simulation

A physico-mathematical model of open systems proposed in a previous paper (Delle Site and Klein 2020 J. Math. Phys. 61 083102) can represent a guiding reference in designing an accurate simulation scheme for an open molecular system embedded in a reservoir of energy and particles. The derived equations and the corresponding boundary conditions are obtained without assuming the action of an external source of heat that assures thermodynamic consistency of the open system with respect to a state of reference. However, in numerical schemes the temperature in the reservoir must be controlled by an external heat bath otherwise thermodynamic consistency cannot be achieved. In this perspective, the question to address is whether the explicit addition of an external heat bath in the theoretical model modifies the equations of the open system and its boundary conditions. In this work we consider this aspect and explicitly describe the evolution of the reservoir employing the Bergmann–Lebowitz statistical model of thermostat. It is shown that the resulting equations for the open system itself are not affected by this change and an example of numerical application is reviewed where the current result shows its conceptual relevance. Finally, a list of pending mathematical and modelling problems is discussed the solution of which would strengthen the mathematical rigour of the model and offer new perspectives for the further development of a new multiscale simulation scheme.

On a generalized fractional boundary value problem based on the thermostat model and its numerical solutions via Bernstein polynomials

In this paper, we introduce a new structure of the generalized multi-point thermostat control model motivated by its standard model. By presenting integral solution of this boundary problem, the existence property along with the uniqueness property are investigated by means of a special version of contractions named μ-φ-contractions and the Banach contraction principle. Then, on the given nonlinear generalized BVP of thermostat, the Bernstein polynomials are introduced and numerical solutions obtained by them are presented. At the end, three different structures of nonlinear thermostat models are designed and the results are examined.

Investigation of the Fractional Strongly Singular Thermostat Model via Fixed Point Techniques

Our main purpose in this paper is to prove the existence of solutions for the fractional strongly singular thermostat model under some generalized boundary conditions. In this way, we use some recent nonlinear fixed-point techniques involving α-ψ-contractions and α-admissible maps. Further, we establish the similar results for the hybrid version of the given fractional strongly singular thermostat control model. Some examples are studied to illustrate the consistency of our results.

Thermodynamic modeling of continuous flow liquid helium thermostat

Since the discovery of cryogenic superconductivity, liquid helium-based cryogenic superconductivity has been successfully applied in many disciplines. And continuous flow cry-ostats have great importance for the efficient and stable operation of the entire cryogenic system, which can provide a cryogenic working environment for large scale cryogenic applications. In this paper, a thermodynamic model of the liquid helium thermostat is developed based on the principle of continuous flow helium cryostat and the thermodynamic processes between liquid helium and gas helium, using mass conservation, energy conservation and pressure-volume-temperature relationships. The research work can provide theoretical guidance for the stability analysis of continuous flow cryostats, and also has practical value in large-scale cryogenic applications.

Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

Various methods to control thermal conditions of building spaces have been developed to investigate their performances of energy use and thermal comfort in the system levels. However, the high control precision used in several studies dealing with data-driven methods may cause energy increases and the high energy efficiency may be disadvantageous for maintaining indoor environmental quality. This study proposes a model that optimizes the supply air condition to effectively reach the setting values by two-way controls of the supply air conditions. In such a process, if the results of the thermal comfort level are outside the range of the initial setting values, an adaptive model starts to work to send additional signals to adjust the set-point temperature. In order to assess its efficiency, the conventional thermostat model and fuzzy deterministic model are adopted as comparators. Comparing the results of the proposed network-based model with conventional control models, an improved control performance from 15.5% to 29.3% in thermal comfort indices was identified, as well as an over 30% improvement in energy efficiency. As a consequence, the network-based adaptive control rule supervising thermal comfort indices properly operates to abate increases in its energy use without compromising its thermal comfort. This performance can be significant in places where many spaces are woven at high density, and in situations where better thermal comfort can increase users’ workability and productivity.