Narrow Results

The sterile insect technique (SIT) is a biological control technique that can be used either to eliminate or decay a wild mosquito population under a given threshold to reduce the nuisance or the epidemiological risk. In this work, we propose a model using a differential system that takes into account the variations of rainfall and temperature over time and study their impacts on sterile males’ releases strategies. Our model is as simple as possible to avoid complexity while being able to capture the temporal variations of an Aedes albopictus mosquito population in a domain treated by SIT, located in Réunion island. The main objective is to determine what period of the year is the most suitable to start a SIT control to minimize the duration of massive releases and the number of sterile males to release, either to reduce the mosquito nuisance, or to reduce the epidemiological risk. Since sterilization is not $100\%$ efficient, we also study the impact of different levels of residual fertility within the released sterile males population. Our study shows that rainfall plays a major role in the dynamics of the mosquito and the SIT control, that the best period to start a massive SIT treatment lasts from July to December, that residual fertility has to be as small as possible, at least for nuisance reduction. Indeed, when the main objective is to reduce the epidemiological risk, we show that residual fertility is not necessarily an issue. Increasing the size of the releases is not always interesting. We also highlight the importance of combining SIT with mechanical control, i.e., the removal of breeding sites, in particular when the initial mosquito population is large. Last but not least our study shows the usefulness of the modeling approach to derive various simulations to anticipate issues and demand in terms of sterile insects’ production.

Contrary to findings from prior empirical studies, which show that temperature affects stock returns linearly, we find that the relation of temperature with stock returns is nonlinear. The results show that investors got higher returns under both extremely hot and cold temperatures than under comfortable temperatures. More specifically, we find that hot temperatures led to higher returns only for investors from warm-climate countries with a tropical or subtropical climate. In contrast, cold temperatures led to higher returns only for investors from cool-climate countries with a temperate or polar climate. With further investigation, we found that such hot-temperature effects on returns in warm-climate countries are enhanced when the investor is optimistic about the stock market due to having recently invested in winner stocks. Conversely, the cold temperature effect on returns in cool-climate countries is strengthened when the investor is pessimistic due to having recently invested in loser stocks.

This paper presents an approach of empirical modeling of the machining process physical quantity with measurement uncertainty parameter included in the resulting power mathematical model coefficients. The proposed approach is presented through an example of modeling of the average temperature in turning like a methodology that is under the influence of various sources of measurement errors. The uncertainty budget accounts for the sources of errors of the experimental measuring system and the cutting process itself. This approach enables estimation of the reliability of the gained mathematical models and gives the possibility of identification and lowering of the main sources of errors.

Microclimate modelling in a greenhouse is complicated due to the model’s irregularity and uncertainty of variable parameters. Evaluating the greenhouse’s changing climate is challenging since the conditions are always changing. As a result, it is necessary to determine the best way to manage the microclimate for the healthy development of growing plants. In order to maximise the growth of blooming plants, a modified leader optimisation algorithm (MLA) is created in this study to control the inside environment of a greenhouse. The implementation is done using greenhouses with a double-span structure located in Punjab and Mohali in India. The recommended approach analyses a number of characteristics, including carbon dioxide (CO₂) concentration, temperature, and humidity, to keep track of the greenhouse’s environment. The humidity, temperature and CO₂ content of flowering plants are studied using the proposed method implemented using MATLAB tool. The evaluated parameters are compared to conventional techniques like Battle Royale Optimisation (BRO), Particle Swarm Optimisation Algorithm (PSO), and BAT algorithm (BAT). Cost and energy consumption are also calculated for both proposed and existing models. Additionally, for the microclimatic parameters, error metrics, including Mean Absolute Error (MAE), Maximum Absolute Error (MaxAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Standard Deviation (STD) are analysed and compared with the conventional approaches. The comparative outcomes highlight the minimal error metrics of a suggested MLA for temperature, humidity, and CO₂ levels in blooming plants. The result analysis proves that the proposed MLA model is better than the previous models for predicting the proper range of CO₂ concentration, suitable temperature, and perfect humidity for flowering plants. This demonstrates the effectiveness of the proposed MLA approach compared to the established methods for developing blooming plants.

Optical microscopy of biological tissues at the 1700nm window has enabled deeper penetration, due to the combined advantage of relatively small water absorption and tissue scattering at this wavelength. Compared with excitation at other wavelengths, such as the commonly used 800nm window for two-photon microscopy, water absorption at the 1700nm window is more than one order of magnitude higher. As a result, more temperature rise can be expected and can be potentially detrimental to biological tissues. Here, we present theoretical estimation of temperature rise at the focus of objective lens at the 1700nm window, purely due to water absorption. Our calculated result shows that under realistic experimental conditions, temperature rise due to water absorption is still below 1K and may not cause tissue damage during imaging.

Interstitial laser immunotherapy (ILIT) is designed to use photothermal and immunological interactions for treatment of metastatic cancers. The photothermal effect is crucial in inducing anti-tumor immune responses in the host. Tissue temperature and tissue optical properties are important factors in this process. In this study, a device combining interstitial photoacoustic (PA) technique and interstitial laser photothermal interaction is proposed. Together with computational simulation, this device was designed to determine temperature distributions and tissue optical properties during laser treatment. Experiments were performed using ex-vivo porcine liver tissue. Our results demonstrated that interstitial PA signal amplitude was linearly dependent on tissue temperature in the temperature ranges of 20–60${}^{\circ }$C, as well as 65–80${}^{\circ }$C, with a different slope, due to the change of tissue optical properties. Using the directly measured temperature in the tissue around the interstitial optical fiber diffusion tip for calibration, the theoretical temperature distribution predicted by the bioheat equation was used to extract optical properties of tissue. Finally, the three-dimensional temperature distribution was simulated to guide tumor destruction and immunological stimulation. Thus, this novel device and method could be used for monitoring and controlling ILIT for cancer treatment.

The text and associated Supplemental Materials contribute internally consistent and therefore entirely comparable regional, temporal, and sectoral risk profiles to a growing literature on regional economic vulnerability to climate change. A large collection of maps populated with graphs of Monte-Carlo simulation results support a communication device in this regard — a convenient visual that we hope will make comparative results tractable and credible and resource allocation decisions more transparent. Since responding to climate change is a risk-management problem, it is important to note that these results address both sides of the risk calculation. They characterize likelihood distributions along four alternative emissions futures (thereby reflecting the mitigation side context); and they characterize consequences along these transient trajectories (which can thereby inform planning for the iterative adaptation side). Looking across the abundance of sectors that are potentially vulnerable to some of the manifestations of climate change, the maps therefore hold the potential of providing comparative information about the magnitude, timing, and regional location of relative risks. This is exactly the information that planners who work to protect property and public welfare by allocating scarce resources across competing venues need to have at their disposal — information about relative vulnerabilities across time and space and contingent on future emissions and future mitigation. It is also the type of information that integrated assessment researchers need to calibrate and update their modeling efforts — scholars who are exemplified by Professor Nordhaus who created and exercised the Dynamic Integrated Climate-Economy and Regional Integrated Climate-Economy models.

Correlation between the elastic and the vibronic behavior of TiO₂ and their responses to the variation of crystal size, applied pressure, and measuring temperature has been investigated based on the bond order–length-strength correlation mechanism. Theoretical reproduction of the measurements clarified that: (i) the elastic modulus (B) and the Raman shifts (Δω) are strongly correlated and we can know either one of the B or the Δω from the other; (ii) the under-coordination induced cohesive energy loss and the energy density gain in the surface up to skin depth determines the size effect; (iii) bond expansion and bond weakening due to thermal vibration originates the thermally softened elastic modulus and the Raman shifts; and (iv) bond compression and bond strengthening results in the mechanically stiffened elastic modulus and the Raman shifts. With the developed premise, one can predict the changing trends of the concerned properties with derivatives of quantitative information of the atomic cohesive energy, binding energy density, Debye temperature, and nonlinear compressibility of the specimen.

Multiferroic ${\mathrm{BiFeO}}_3$ (BFO) and Y, Zr codoped BFO $\left({\mathrm{Bi}}_{1-x}{\mathrm{Y}}_x{\mathrm{Fe}}_{0.95}{\mathrm{Zr}}_{0.05}{\mathrm{O}}_3\right)$ ceramics were prepared and the influence of codoping on the crystal structure and magnetic properties were investigated in this work. Confirmed by the evolution of X-ray diffraction and Raman modes, the codoping has changed the crystal structure from rhombohedral to tetragonal in bulk BFO ceramics. The enhancement of magnetic behaviors is demonstrated by the damage of space-modulated spiral spin structure, and it can be attributed to the crystal structure change and size effects. Meanwhile, Raman spectra from 300 to 800 K demonstrates that lower frequency phonon modes show rapid softening near the Neel temperature.

Climate change poses mounting risks to agricultural development and rural livelihoods in Nigeria. This study investigates the impacts of climate change on agricultural sector employment in Nigeria. Agriculture provides income and sustenance for much of Nigeria’s rural population. However, smallholder rain-fed farming predominates, with minimal resilience to climate shifts. Historical data reveal rising temperatures and declining, erratic rainfall across Nigeria’s agro-ecological zones since the 1970s. Crop modeling predicts further climate changes will reduce yields of key staple crops. This threatens the viability of smallholder agriculture and risks widespread job losses. The study adopts a nonlinear autoregressive distributed lag (NARDL) modeling approach to evaluate climate change effects on agricultural sector employment in Nigeria from 1990 to 2020. Findings reveal reduced rainfall initially raises employment, as farming requires more labor in dry conditions. However, protracted droughts significantly reduce agricultural jobs. Increased temperatures consistently lower farm employment through reduced yields and incomes. Based on these findings, the study recommends that adaptive strategies are urgently needed to build resilience, promote climate-smart agriculture, and safeguard rural livelihoods.

Current human-induced climate change arises primarily from the heating of the planet mainly from changes in atmospheric composition, and temperature change is one manifestation. The increasing greenhouse gases, notably carbon dioxide from burning fossil fuels, lead to Earth’s Energy Imbalance (EEI), altering the flow of energy through the climate system, and the dissemination of excess energy is partly what determines how climate change is manifested. Some of the extremes being experienced, especially those involving drought, convection, storms, flooding, and the water cycle, are mostly driven by aspects of heating and, while temperature contributes through the water-holding capacity of the atmosphere, it is more a consequence than a cause. Afterall, water is the air conditioner of the planet. The United Nations, and especially the Intergovernmental Panel on Climate Change (IPCC) in their Summary for Policy Makers, focus on global temperature targets rather than broader facets of climate change including EEI, and do not always adequately discriminate between temperature and heating. This also has consequences for future climate if or when heating is brought under control by cutting emissions. Improvements are needed in expressing how the climate is changing by properly accounting for the flow of energy through the climate system.

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800^∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

This paper presents an empirical analysis devised to understand the complex relationship between extreme temperatures and mortality in 16 Asian countries where more than 50% of the world's population resides. Using a country-year panel on mortality rates and various measures of high temperatures for 1960–2015, the analysis produces two primary findings. First, high temperatures significantly increase annual mortality rates in Asia. Second, this increase is larger in countries with cooler climates where high temperatures are infrequent. These empirical estimates can help inform climate change impact projections on human health for Asia, which is considered to be highly vulnerable to climate change. The results indicate that unabated warming until the end of the century could increase annual mortality rates by more than 40%, highlighting the need for concrete and rapid actions to help individuals and communities adapt to climate change.

This paper uses historical fluctuations of weather variables within counties in the People's Republic of China to identify their effects on economic growth from 1996 to 2012. We find three primary results. First, higher temperatures significantly reduce the growth rate of county-level gross domestic product per capita: an increase in the annual average temperature of 1°C lowers the growth rate by 1.05%–1.25%. The effect of higher temperatures is nonlinear. Second, fluctuations in temperature and precipitation not only have a level effect, they also have a substantial cumulative effect. Third, weather fluctuations have wide-ranging effects. Beyond their substantial effects on the growth rate of agricultural output, they also affect nonagriculture sectors, labor productivity, and investment. Our findings provide new evidence for the impact of weather changes on economic development and have major implications for adaptation policies.

The present book deals with more than thirty years of research performed in Chemical Physics with uniform supersonic flows and CRESU-like apparatuses. As such, it is linked to processes occurring at temperature close to absolute zero, studied for their theoretical interest or for their key role in ultracold media. The goal of this first chapter is to introduce a few concepts and equations that are ubiquitous within this context. The leading thread is the question of Local Thermodynamic Equilibrium (LTE) in dilute gases, complete or partial, and how it is implied in kinetic processes (Section 1.2), cold astrophysical media (Section 1.3), laboratory methods (Section 1.4), and the derivation of hydrodynamic equations (Section 1.5). It has been chosen to use the Boltzmann equations to derive the link between microscopic quantities (as state-to-state cross sections) and macroscopic quantities (as rate coefficients). How they are used in the derivation of hydrodynamic equations is also highlighted. The key role of distribution functions for translational or internal energies either in astrophysical media (Section 1.3) or laboratory methods (Section 1.4) is discussed. Note that the concept of temperature is itself subtle and is discussed at length in Section 1.2.

In this paper we study a quantum field theoretical approach, where a quantum probe is used to investigate the properties of generic non-flat FRLW space time. The fluctuations related to a massless conformal coupled scalar field defined on a space-time with horizon is identified with a probe and the procedure to measure the local temperature is presented.