The fast pace of technological growth has created a heightened need for intelligent, autonomous monitoring systems in a variety of fields, especially in environmental applications. This project shows the design process and implementation of a proper dual node (master-slave) IoT-based monitoring system using STM32F103C8T6 microcontrollers. The structure of the wireless monitoring system studies the environmental conditions in real-time and can measure parameters like temperature, humidity, soil moisture, raindrop detection and obstacle distance. The relay of information occurs between the primary master node (designated as the Green House) to the slave node (the Red House) employing the HC-05 Bluetooth module for information transmission. Each node displays the sensor data on OLED screens and a visual or auditory alert is triggered based on predetermined thresholds. A comparative analysis of STM32 (ARM Cortex-M3) and Arduino (AVR) is presented to justify the STM32 used in this work for greater processing power, less energy use, and better peripherals. Practical challenges in this project arise from power distribution and Bluetooth configuration limits. Future work will explore the transition of a Wi-Fi communication protocol and develop a mobile monitoring robot to enhance scalability of the system. Finally, this research shows that ARM based embedded systems can provide real-time environmental monitoring systems that are reliable and consume low power.
In WSN/IoT, node localization is essential to long-running applications for accurate environment monitoring and event detection, often covering a large area in the field. Due to the lower time resolution of typical WSN/IoT platforms (e.g., 1 microsecond on ESP32 platforms) and the jitters in timestamping, packet-level localization techniques cannot provide meter-level resolution. For high-precision localization as well as world-wide interoperability via 2.4-GHz ISM band, a new variant of LoRa, called LoRa 2.4 GHz, was proposed by semtech, which provides a radio frequency (RF) time of flight (ToF) ranging method for meter-level localization. However, the existing datasets reported in the literature are limited in their coverages and do not take into account varying environmental factors such as temperature and humidity. To address these issues, LoRa 2.4 GHz RF ToF ranging data was collected on a sports field at the XJTLU south campus, where three LoRa nodes logged samples of ranging with a LoRa base station, together with temperature and humidity, at reference points arranged as a 3x3 grid covering 400 square meter over three weeks and uploaded all measurement records to the base station equipped with an ESP32-based transceiver for machine and user communications. The results of a preliminary investigation based on a simple deep neural network (DNN) model demonstrate that the environmental factors, including the temperature and humidity, significantly affect the accuracy of ranging, which calls for advanced methods of compensating for the effects of environmental factors on LoRa RF ToF ranging outdoors.
Unmanned Aerial Vehicles (UAVS) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the UAVS pipeline, thus reducing energy consumption. However, existing techniques tend to adopt static and conservative strategies in dynamic scenarios, leading to inefficient energy reduction. Dynamically adjusting the navigation strategy requires overcoming the challenges including the task pipeline interdependencies, the environmental-strategy correlations, and the selecting parameters. To solve the aforementioned problems, this paper proposes a method to dynamically adjust the navigation strategy of the UAVS by analyzing its dynamic characteristics and the temporal characteristics of the autonomous navigation pipeline, thereby reducing UAVS energy consumption in response to environmental changes. We compare our method with the baseline through hardware-in-the-loop (HIL) simulation and real-world experiments, showing our method 3.2X and 2.6X improvements in mission time, 2.4X and 1.6X improvements in energy, respectively.
Tomoei Takahashi, George Chikenji, Kei Tokita
et al.
How typical elements that shape organisms, such as protein secondary structures, have evolved, or how evolutionarily susceptible/resistant they are to environmental changes, are significant issues in evolutionary biology, structural biology, and biophysics. According to Darwinian evolution, natural selection and genetic mutations are the primary drivers of biological evolution. However, the concept of ``robustness of the phenotype to environmental perturbations across successive generations," which seems crucial from the perspective of natural selection, has not been formalized or analyzed. In this study, through Bayesian learning and statistical mechanics we formalize the stability of the free energy in the space of amino acid sequences that can design particular protein structure against perturbations of the chemical potential of water surrounding a protein as such robustness. This evolutionary stability is defined as a decreasing function of a quantity analogous to the susceptibility in the statistical mechanics of magnetic bodies specific to the amino acid sequence of a protein. Consequently, in a two-dimensional square lattice protein model composed of 36 residues, we found that as we increase the stability of the free energy against perturbations in environmental conditions, the structural space shows a steep step-like reduction. Furthermore, lattice protein structures with higher stability against perturbations in environmental conditions tend to have a higher proportion of $α$-helices and a lower proportion of $β$-sheets. This result is qualitatively confirmed by comparing the histograms of the percentage of secondary structures of evolutionarily robust proteins and randomly selected proteins through an empirical validation using a protein database.
A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additional feature extraction. Initially, an imperfect physical model is derived and identified to capture the fundamental hydrodynamic characteristics of marine vehicles. This model is then integrated with a feedforward network through a residual block. Additionally, feature extraction from trigonometric transformations is employed in the machine learning component to account for the periodic influence of currents and waves. The proposed method is evaluated using real navigational data from the 'JH7500' unmanned surface vehicle. The results demonstrate the robust generalizability and accurate long-term prediction capabilities of the nonlinear dynamic model in specific environmental conditions. This approach has the potential to be extended and applied to develop a comprehensive high-fidelity simulator.
The future space-borne Laser Interferometer Space Antenna (LISA) is expected to detect gravitational waves (GW) from Extreme Mass Ratio Inspiral (EMRI) binaries which may live in nontrivial environments such as accretion disks. In this work, we apply the Fisher matrix Principal Component Analysis (PCA) method to assess how well LISA observations can jointly constrain the source parameters and environmental densities around EMRIs. Specifically, we calculate the Fisher matrix from the post-Newtonian parameters of an EMRI binary embedded in a fluid with a constant density profile. We determine that the most dominant measurable parameter combination is dominated by contributions from environmental effects, namely, gravitational drag, accretion, and gravitational pull (in order of contribution). The proposed reparameterization of the PN parameters can be used to improve the power and efficiency of future detection and parameter estimation methods.
The small mass limit is derived for a McKean-Vlasov equation subject to environmental noise with state-dependent friction. By applying the averaging approach to a non-autonomous stochastic slow-fast system with the microscopic and macroscopic scales, the convergence in distribution is obtained.
Air quality and human exposure to mobile source pollutants have become major concerns in urban transportation. Existing studies mainly focus on mitigating traffic congestion and reducing carbon footprints, with limited understanding of traffic-related health impacts from the environmental justice perspective. To address this gap, we present an innovative integrated simulation platform that models traffic-related air quality and human exposure at the microscopic level. The platform consists of five modules: SUMO for traffic modeling, MOVES for emissions modeling, a 3D grid-based dispersion model, a Matlab-based concentration visualizer, and a human exposure model. Our case study on multi-modal mobility on-demand services demonstrates that a distributed pickup strategy can reduce human cancer risk associated with PM2.5 by 33.4% compared to centralized pickup. Our platform offers quantitative results of traffic-related air quality and health impacts, useful for evaluating environmental issues and improving transportation systems management and operations strategies.
Jean-Michel Guay, Ranjana Rautela, Samantha Scarfe
et al.
Graphene has demonstrated great promise for technological use, yet control over material growth and understanding of how material imperfections affect the performance of devices are challenges that hamper the development of applications. In this work we reveal new insight into the connections between the performance of the graphene devices as environmental sensors and the microscopic details of the interactions at the sensing surface. Specifically, we monitor changes in the resistance of the chemical-vapour deposition grown graphene devices as exposed to different concentrations of ethanol. We perform thermal surface treatments after the devices are fabricated, use scanning probe microscopy to visualize their effects on the graphene sensing surface down to nanometer scale and correlate them with the measured performance of the device as an ethanol sensor. Our observations are compared to theoretical calculations of charge transfers between molecules and the graphene surface. We find that, although often overlooked, the surface cleanliness after device fabrication is responsible for the device performance and reliability. These results further our understanding of the mechanisms of sensing in graphene-based environmental sensors and pave the way to optimizing such devices, especially for their miniaturization, as with decreasing size of the active zone the potential role of contaminants will rise.
About 35 years ago Wheeler introduced the motto `law without law' to highlight the possibility that (at least a part of) Physics may be understood only following {\em regularity principles} and few relevant facts, rather than relying on a treatment in terms of fundamental theories. Such a proposal can be seen as part of a more general attempt (including the maximum entropy approach) summarized by the slogan `it from bit', which privileges the information as the basic ingredient. Apparently it seems that it is possible to obtain, without the use of physical laws, some important results in an easy way, for instance, the probability distribution of the canonical ensemble. In this paper we will present a general discussion on those ideas of Wheeler's that originated the motto `law without law'. In particular we will show how the claimed simplicity is only apparent and it is rather easy to produce wrong results. We will show that it is possible to obtain some of the results treated by Wheeler in the realm of the statistical mechanics, using precise assumptions and nontrivial results of probability theory, mainly concerning ergodicity and limit theorems.
Scanning electron microscopy (SEM) of nanoscale objects in their native conditions and at different temperatures are of critical importance in revealing details of their interactions with ambient environments. Currently available environmental capsules are equipped with thin electron transparent membranes and allow imaging the samples at atmospheric pressure. However these capsules do not provide the temperature control over the sample. Here we developed and tested a thermoelectric cooling / heating setup for available environmental capsules to allow ambient pressure in situ SEM studies over the -15 °C to 100 °C temperature range in gaseous, liquid, and frozen environments. The design of the setup also allows correlation of the SEM with optical microscopy and spectroscopy. As a demonstration of the possibilities of the developed approach, we performed real-time in situ microscopy studies of water condensation on a surface of wing scales of Morpho sulkowskyi butterfly. We have found that the initial water nucleation takes place on the top of the scale ridges. These results confirmed earlier discovery of a polarity gradient of the ridges of Morpho butterflies. Our developed thermoelectric cooling / heating setup for available SEM environmental capsules promises to impact diverse needs for in-situ nano-characterization including materials science and catalysis, micro-instrumentation and device reliability, chemistry and biology.
Adam D. Law, Ludger Harnau, Matthias Troendle
et al.
Within mean-field theory we determine the universal scaling function for the effective force acting on a single colloid located near the interface between two coexisting liquid phases of a binary liquid mixture close to its critical consolute point. This is the first study of critical Casimir forces emerging from the confinement of a fluctuating medium by at least one soft interface, instead by rigid walls only as studied previously. For this specific system, our semi-analytical calculation illustrates that knowledge of the colloid-induced, deformed shape of the interface allows one to accurately describe the effective interaction potential between the colloid and the interface. Moreover, our analysis demonstrates that the critical Casimir force involving a deformable interface is accurately described by a universal scaling function, the shape of which differs from that one for rigid walls.
C. S. Burton, Matt J. Jarvis, D. J. B. Smith
et al.
We compare the environmental and star formation properties of far-infrared detected and non--far-infrared detected galaxies out to $z \sim0.5$. Using optical spectroscopy and photometry from the Galaxy And Mass Assembly (GAMA) and Sloan Digital Sky Survey (SDSS), with far-infrared observations from the {\em Herschel}-ATLAS Science Demonstration Phase (SDP), we apply the technique of Voronoi Tessellations to analyse the environmental densities of individual galaxies. Applying statistical analyses to colour, $r-$band magnitude and redshift-matched samples, we show there to be a significant difference at the 3.5$σ$ level between the normalized environmental densities of these two populations. This is such that infrared emission (a tracer of star formation activity) favours underdense regions compared to those inhabited by exclusively optically observed galaxies selected to be of the same $r-$band magnitude, colour and redshift. Thus more highly star-forming galaxies are found to reside in the most underdense environments, confirming previous studies that have proposed such a correlation. However, the degeneracy between redshift and far-infrared luminosity in our flux-density limited sample means that we are unable to make a stronger statement in this respect. We then apply our method to synthetic light cones generated from semi-analytic models, finding that over the whole redshift distribution the same correlations between star-formation rate and environmental density are found.
Studies of the mechanical properties of single-walled carbon nanotubes are hindered by the availability only of ensembles of tubes with a range of diameters. Tunable Raman excitation spectroscopy picks out identifiable tubes. Under high pressure, the radial breathing mode shows a strong environmental effect shown here to be largely independent of the nature of the environment . For the G-mode, the pressure coefficient varies with diameter consistent with the thick-wall tube model. However, results show an unexpectedly strong environmental effect on the pressure coefficients. Reappraisal of data for graphene and graphite gives the G-mode Grueuneisen parameter gamma = 1.34 and the shear deformation parameter beta = 1.34.
We make use of four galaxy catalogs based on four different semi-analytical models (SAMs) implemented in the Millennium simulation to study the environmental effects and the model dependence of galaxy merger rate. We begin the analyses by finding that galaxy merger rate in the SAMs has mild redshift evolution, consistent with results of previous works. To study the environmental dependence of galaxy merger rate, we adopt two estimators, the local overdensity (1+δn) defined as the surface density from the nth-nearest-neighbor (n = 6 is chosen in this study) and the host halo mass Mh. We find that galaxy merger rate Fmg shows strong dependence on the local overdensity (1+δn) and the dependence is similar at all redshifts. For the overdensity estimator, the merger rate Fmg is found about twenty times larger in the densest regions than in under-dense ones in two of the four models while it is roughly four times higher in the other two. In other words, the discrepancies of the merger rate difference between two extremes can differ by a factor of ~ five depending on the SAMs adopted. On the other hand for the halo mass estimator, Fmg does not monotonically increase with the host halo mass Mh, but peaks in the $M_h$ range between 10^12 and 10^13 h-1 MΘ, which corresponds to group environments. High merger rate in high local density regions corresponds primarily to the high merge rate in group environments......
A possible source of black hole entropy could be the entanglement of quantum fields in and out the horizon. The entanglement entropy of the ground state obeys the area law. However, a correction term proportional to a fractional power of area results when the field is in a superposition of ground and excited states. Inspired by the power-law corrections to entropy and adopting the viewpoint that gravity emerges as an entropic force, we derive modified Newton's law of gravitation as well as the corrections to Friedmann equations. In a different approach, we obtained power-law corrected Friedmann equation by starting from the first law of thermodynamics at apparent horizon of a FRW universe, and assuming that the associated entropy with apparent horizon has a power-law corrected relation. Our study shows a consistency between the obtained results of these two approaches. We also examine the time evolution of the total entropy including the power-law corrected entropy associated with the apparent horizon together with the matter field entropy inside the apparent horizon and show that the generalized second law of thermodynamics is fulfilled in a region enclosed by the apparent horizon.
We investigate how environmental effects by gas stripping alter the growth of a super massive black hole (SMBH) and its host galaxy evolution, by means of 1D hydrodynamical simulations that include both mechanical and radiative AGN feedback effects. By changing the truncation radius of the gas distribution (R_t), beyond which gas stripping is assumed to be effective, we simulate possible environments for satellite and central galaxies in galaxy clusters and groups. The continuous escape of gas outside the truncation radius strongly suppresses star formation, while the growth of the SMBH is less affected by gas stripping because the SMBH accretion is primarily ruled by the density of the central region. As we allow for increasing environmental effects - the truncation radius decreasing from about 410 to 50 kpc - we find that the final SMBH mass declines from about 10^9 to 8 x 10^8 Msol, but the outflowing mass is roughly constant at about 2 x 10^10 Msol. There are larger change in the mass of stars formed, which declines from about 2 x 10^10 to 2 x 10^9 Msol, and the final thermal X-ray gas, which declines from about 10^9 to 5 x 10^8 Msol, with increasing environmental stripping. Most dramatic is the decline in the total time that the objects would be seen as quasars, which declines from 52 Myr (for R_t = 377 kpc) to 7.9 Myr (for R_t = 51 kpc). The typical case might be interpreted as a red and dead galaxy having episodic cooling flows followed by AGN feedback effects resulting in temporary transitions of the overall galaxy color from red to green or to blue, with (cluster) central galaxies spending a much larger fraction of their time in the elevated state than do satellite galaxies.(Abridged)
We searched for X-ray flashes (XRFs) -- which we defined as ~10s duration transient X-ray events observable in the 0.4-15 keV passband -- in fields observed using XMM-Newton with the EPIC/pn detector. While we find two non-Poissonian events, the astrophysical nature of the events is not confirmed in fully simultaneous observations with the EPIC/MOS detectors, and we conclude that the events are anomalous to the EPIC/pn detector. We find a 90% upper limit on the number of flashes per sky per year at two different incoming flash fluxes: 4.0x10^9 events / sky / year for a flux of 7.1x10^-13 erg / cm^2 / s and 6.8x10^7 events / sky / year for 1.4x10^-11 erg / cm^2 / s. These limits are consistent with an extrapolation from the BeppoSAX/WFC XRF rate at much higher fluxes (about a factor of 10^5), assuming an homogenous population, and with a previous, more stringent limit derived from ROSAT pointed observations.