Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Characteristics of the Built Neighborhood Environment and Negative Self-Rated Health in Brazilian Community-Dwelling Older Adults

ABSTRACT Introduction Research indicates that self-rated health is associated with worse health outcomes in older adults. Thus, it is important to assess the association between built neighborhoods and self-rated health to establish strategies to improve the quality of life in the older adult population. Objective To evaluate the association between the perceived characteristics of the built neighborhood and the negative self-rated health in Brazilian community-dwelling older adults. Methods A cross-sectional study for finite random samples was performed with older adults who used the municipal public health service. The outcome was negative self-rated health using the question “How do you perceive your health? ” The regular, bad, and very bad responses were grouped as negative self-perception. The exposure variables were the perceived characteristics of the built neighborhood, which was evaluated by the adapted version of the Neighborhood Environment Walkability Scale. Results A total of 308 older adults were analyzed. Living close to food establishments (OR: 0.46; 95% CI: 0.25;0.85), fitness/bodybuilding gyms and/or clubs (OR: 0.51; 95% CI: 0.28;0.93), and safety to walk during the day (OR: 0.37; 95% CI: 0.16;0.87) and night (OR: 0.47; 95% CI: 0.26;0.84) were protective against negative self-rated health. Conclusion Therefore, living in neighborhoods with better characteristics was less associated with negative self-rated health, demonstrating the importance of promoting strategies to improve the neighborhood infrastructure and this population’s perception of health.
A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies

In large-scale disasters, such as earthquakes and tsunamis, quick and sufficient transportation of emergency relief supplies is required. Logistics activities conducted to quickly provide appropriate aid supplies (relief goods) to people affected by disasters are known as humanitarian logistics (HL), and play an important role in terms of saving the lives of those affected. In the previous last-mile distribution of HL, supplies are transported by trucks and helicopters, but these transport methods are sometimes not feasible. Therefore, the use of unmanned aerial vehicles (UAVs) to transport supplies is attracting attention due to their convenience regardless of the disaster conditions. However, existing transportation planning that utilizes UAVs may not meet some of the requirements for post-disaster transport of supplies. Equitable distribution of supplies among affected shelters is particularly important in a crisis situation, but it has not been a major consideration in the logistics of UAVs in the existing study. Therefore, this study proposes transportation planning by introducing three crucial performance metrics: (1) the rapidity of supplies, (2) the urgency of supplies, and (3) the equity of supply amounts. We formulated the routing problem of UAVs as the multi-objective, multi-trip, multi-item, and multi-UAV problem, and optimize the problem with Q-learning (QL), one of the reinforcement learning methods. We performed reinforcement learning for multiple cases with different rewards and quantitatively evaluated the transportation of each countermeasure by comparing them. The results suggest that the model improved the stability of the supply of emergency relief supplies to all evacuation centers when compared to other models.
EMCS: An Energy-Efficient Makespan Cost-Aware Scheduling Algorithm Using Evolutionary Learning Approach for Cloud-Fog-Based IoT Applications

The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful CO2 to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.
ROBUST AND DYNAMICALLY CONSISTENT REDUCED ORDER MODELS

The need for reduced order models (ROMs) has become considerable higher with the increasing technological advances that allows one to model complex dynamical systems. When using ROMs, the following two questions always arise: 1) “What is the lowest dimensional ROM? ” and 2) “How well does the ROM capture the dynamics of the full scale system model? ” This paper considers the newly developed concepts the authors refer to as subspace robustness — the ROM is valid over a range of initial conditions, forcing functions, and system parameters — and dynamical consistency — the ROM embeds the nonlinear manifold — which quanitatively answers each question. An eighteen degree-of-freedom pinned-pinned beam which is supported by two nonlinear springs is forced periodically and stochastically for building ROMs. Smooth and proper orthogonal decompositions (SOD and POD, respectively) based ROMs are dynamically consistent in four or greater dimensions. In the strictest sense POD-based ROMs are not considered coherent whereas, SOD-based ROMs are coherent in roughly five dimesions and greater. Is is shown that in the periodically forced case, the full scale dynamics are captured in a five-dimensional POD and SOD-based ROM. For the randomly forced case, POD and SOD-based ROMs need three dimensions but SOD captures the dynamics better in a lower-dimensional space. When the ROM is developed from a different set of initial conditions and forcing values, SOD outperforms POD in periodic forcing case and are equal in the random forcing case.Copyright © 2013 by ASME
Analytical modelling of the formation temperature stabilization during the borehole shut-in period

SUMMARY The problem of formation temperature stabilization during the shut-in period is solved analytically by the approximate generalized integral-balance method. The model accounts for the thermal history of the borehole exploitation, which may include a finite number of circulation and shut-in periods, and different flow regimes during circulation periods. The latter is determined by the temperatures of the circulating fluid and different Biot numbers that depend on intensity of the heat transfer on the bore-face. Normally the temperature fields in the well and surrounding rocks are calculated numerically by the finite-difference and finite-element methods or analytically, by applying the Laplace-transform method. Formulae, analytically obtained by Laplace transform, are rather bulky and require tedious non-trivial numerical evaluations. Moreover, in previous research the heat interactions of the circulating fluid with formation were treated under the condition of constant bore-face temperatures. In the present study the temperature field in the formation disturbed by the heat flow from the borehole is modelled by the heat conduction equation and thermal interaction of the circulating fluid with formation is approximated by the Newton relationship on the bore-face. The problem for circulation and shut-in periods is solved analytically using the heat balance integral method, where the radius of thermal influence, which defines the thermally disturbed domain, is a function of time, which satisfies the algebraic equation. Within this method, the approximate solution of the heat conduction problem is sought in the form of a finite sum of functions which belong to a complete set of the linearly independent functions defined on the finite interval bounded by the radius of thermal influence and satisfy the homogeneous boundary condition on the bore-face. It can be proved theoretically that the approximate solution found by this method converges to the exact one. Numerical results illustrate quite good agreement between the approximate and exact solutions. As a result of its simplicity and accuracy, the derived solution is convenient for geophysical practitioners and can be readily used, for instance, for predicting equilibrium formation temperatures.
Spectral stability estimates for the eigenfunctions of second order elliptic operators

Stability of the eigenfunctions of nonnegative selfadjoint second‐order linear elliptic operators subject to homogeneous Dirichlet boundary data under domain perturbation is investigated. Let Ω, \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\Omega ^{\prime } \subset \mathbb {R}^n$\end{document} be bounded open sets. The main result gives estimates for the variation of the eigenfunctions under perturbations Ω′ of Ω such that \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\Omega _{\varepsilon } = \lbrace x \in \Omega : {\rm dist}(x,\, \mathbb {R}^n \! \setminus \! \Omega ) > \varepsilon \rbrace \subset \Omega ^{\prime } \subset \overline{\Omega ^{\prime }} \subset \Omega$\end{document} in terms of powers of ε, where the parameter ε > 0 is sufficiently small. The estimates obtained here hold under some regularity assumptions on Ω, Ω′. They are obtained by using the notion of a gap between linear operators, which has been recently extended by the authors to differential operators defined on different open sets.
Redundancy Management for Fast Service (Rates) in Edge Computing Systems

Edge computing operates between the cloud and end-users and strives to provide fast computing services for multiple users. Because of their proximity to users, edge services have a low communication delay and can provide low latency with sufficient computing and storage resources. However, edge computing and storage resources are limited. Thus, directing more resources to some computing jobs will block (and pass to the cloud) the execution of others. We evaluate the edge system performance using two metrics: job computing time and job blocking probability. Edge nodes often operate in highly unpredictable environments and handle jobs needing fast or no service. Thus, jobs not getting into service upon arrival get blocked and passed to the cloud. In unpredictable environments, replicating a job to multiple servers when resources allow shortens its computing time. However, such replication makes the resources unavailable to other users, and their execution is blocked. We show that the job computing time decreases with increasing replication factor, but the job blocking probability does not. Therefore, there is a tradeoff. This paper uses the average system time and service rates as performance metrics to evaluate the tradeoff. We conclude that the optimal number replication factor that minimizes the average system time changes with the distribution parameters and the arrival rate.
Air bubbles in accreted ice

On the assumption that the concentration of air bubbles formed in freezing water is a function of the freezing rate, an expression for the concentration of air bubbles in accreted ice has been deduced. This expression has been confirmed by measurements of the bubble concentrations and size distributions in frozen bulk water and in ice accreted on rotating cylinders. Consequently, in both the wet and dry growth regimes it is the rate of freezing which essentially determines the bubble concentration. The size distribution of bubbles is determined by the amount of air available for bubble growth. This depends on the rate at which air diffuses out of the individual droplets during the freezing process. In the wet growth regime the freezing rate depends on the rate at which heat is dissipated from the accreting object by forced convection and also, depending on the fraction of unfrozen water, on the way in which the spongy deposits are finally frozen. In the dry growth regime the freezing rate of the accreted droplets depends on their size and speed of impact, since these determine the extent to which the droplets spread, and on the temperature of the deposit. The air bubble concentrations and size distributions were determined using ordinary optical microscopy so that only bubbles of radius greater than 1μm were counted and sized. In the dry growth regime the visible bubble concentrations range from about 106 to 108cm−3, depending primarily on the deposit temperature. The mean radii of the bubbles in the deposits range from 1 to 4μm. In the wet growth regime the bubble concentrations generally lie between 105 and 106cm−3 while the mean radii of the bubbles range from 10 to 50μm. Consequently, the two growth regimes may be readily distinguished.
The solubility of potassium-feldspar in calcic plagioclase : experimental study and application to natural occurrences

Unusual feldspar intergrowths were found in two metamorphosed basalt dykes from Departure Rocks, Antarctica. They are composed of host calcic plagioclase (Kf 0 _3 An7 5_9 1 Ab9_25) and potassium-rich alkali feldspar (Kf90 _96 Ano_5 Ab0_8) lamellae. Their bulk composition is estimated to be Kf An 21 65 Ab14. The complex and yet regular lamella textures of these intergrowths suggest that they are exsolution products. This requires the existence of a formerly homogeneous plagioclase, compositionally equivalent to the estimated bulk composition of these intergrowths. Experimental studies in the binary system anorthite K-feldspar and in the albite-poor ternery feldspar system have proven the existence of such homogeneous plagioclase, which has been synthesized at 1150°C and 10 [(bar under dry conditions. The unusual feldspar intergrowths were homogenized under these same conditions, further supporting the hypothesized exsolution origin of these intergrowths. In an experiment of plagioclase crystallization in a synthetic rock system, a K-rich plagioclase (Kf 19.6 An70 Ab10 . 4) was obtained from a K-rich melt at 1140°C and 10 kbar, indicating that the proposed parental plagioclase for the unusual feldspar intergrowths can be formed in a complex (rock) system. Such K-rich melt could be generated through interaction of the dyke magma and its surrounding charnockitic magma. It is concluded that limits of solid solutions in feldspar systems are increased at high pressures due to the increase of feldspar melting temperatures. Pressure may have an effect on the extend of feldspar solid solutions, but it is considered to be very small.
New perspectives on carbon reinforced concrete structures—Why new composites need new design strategies

In civil engineering, carbon is typically regarded as a modern material to serve as reinforcement in concrete structures. Compared to steel reinforcement, it features two substantial benefits: It is not sensitive to corrosion, and has an enormously increased tensile strength. In contrast, carbon reinforcement is sensitive to lateral pressure and lacks the property of strain hardening. As a first step of establishing carbon reinforced concrete as a new building composite material, carbon reinforcement has basically served to replace the state‐of‐the‐art steel reinforcement. This target led to pioneering findings with respect to determining the material properties of the composite and developing advanced individual components. However, barely substituting steel by carbon does not allow to fully utilize the carbon's benefits while its disadvantageous properties reveal the limits of this approach. Instead, novel design principles are required to meet the material's nature aiming at appropriately using its beneficial properties. Currently, new construction principles are being researched for high‐performance building material combinations such as textile and carbon reinforced concrete. This paper provides an overview of baselines in the preliminary stages of this research. The overview includes history, inspiration, concrete matrices, non‐metallic reinforcement, structural elements, modeling, production, tomography, and sustainability. The objective of the study is to provide a baseline for the envisaged development of principles for future construction: radically new concepts for the design, modeling, construction, manufacturing, and use of sustainable, resource‐efficient building elements made of mineral building materials with the aim of entirely benefiting from the materials' potential.
Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction

Fingerprints are widely recognized as one of the most unique and reliable characteristics of human identity. Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process. Various types of fingerprint sensors, such as optical, capacitive, and ultrasonic sensors, employ distinct techniques to gather and analyze fingerprint data. This dependency on specific hardware or sensors creates a barrier or challenge for the broader adoption of fingerprint based biometric systems. This limitation hinders the widespread adoption of fingerprint authentication in various applications and scenarios. Border control, healthcare systems, educational institutions, financial transactions, and airport security face challenges when fingerprint sensors are not universally available. To mitigate the dependence on additional hardware, the use of contactless fingerprints has emerged as an alternative. Developing precise fingerprint segmentation methods, accurate fingerprint extraction tools, and reliable fingerprint matchers are crucial for the successful implementation of a robust contactless fingerprint authentication system. This paper focuses on the development of a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. Our system leverages deep learning techniques to achieve high segmentation accuracy and reliable extraction of fingerprints from contactless fingerprint images. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%. These results demonstrate the effectiveness of our novel contactless fingerprint segmentation and extraction tools.
Retirement Plan and Long-term Savings Pattern among Private College Faculty

In contemporary years, there has been an increasing acknowledgement of the significance of retirement planning and long-term savings patterns among faculty members in academic institutions. As the landscape of higher education continues to evolve, faculty members are increasingly being encouraged to take a proactive approach to securing their financial futures. Traditionally, tenure-track positions offered a sense of job security and retirement benefits through pension plans. However, the shifting dynamics within academia, including the rise of adjunct and part-time positions, have prompted a reevaluation of retirement planning strategies. Faculty members are now faced with a more diverse range of employment arrangements, leading to varying access to retirement benefits and different savings challenges. In response, universities and colleges have begun to offer more comprehensive financial education and planning resources tailored to faculty needs. Seminars, workshops, and one-on-one consultations are being provided to help faculty members navigate the complexities of retirement options, investment strategies and other investment accounts. Moreover, faculty members are increasingly encouraged to diversify their retirement portfolios, considering a mix of traditional and alternative investments to mitigate risks and potentially enhance returns. This shift aligns with the broader trend in personal finance toward a more proactive and adaptable approach to retirement planning. The Coimbatore city is the study area and 200 college faculty members from private college have been selected for the study. The primary data have been collected using Google forms. In conclusion, the retirement plan and long-term savings pattern among faculty are undergoing significant changes in response to the evolving landscape of higher education and the diverse employment arrangements available. With universities taking a more active role in providing tailored financial guidance, faculty members are better equipped to make informed decisions about their retirement futures and secure their financial well-being.
An Improved Neural Network Non-uniformity Correction for IRFPA

The non-uniformity correction (NUC) is a key part that affects the image quality of IR imaging systems. In this paper, the NUC technique for staring IR imaging system is studied based on the research of the response characteristic and the noise components of IRFPA. Especially, the improvement of the traditional neural network correction method is also studied. According to the theory of neural network, we analyzed the reasons that cause the defects of traditional neural network correction, which are the difficulty for choosing the length of study-step, poor performance of the offset non-uniformity correction and the side-effect of "ghosting", then found methods to improve them. So the correction ability and the applicability of the improved NUC method are enhanced. To test the effect of the new NUC method, we implemented the arithmetic on a hardware platform using the Digital Signal Processor TS201, and made experiments in real terrestrial scene and offshore scene. The results prove that the improved neural network correction arithmetic is effective.
Designing for Situation Awareness the World behind the Glass

: In the design of human-machine interfaces and automation, an important question is how to obtain and validate a design that is capable of supporting the operator’s understanding and situation awareness of the process under control. Whereas many research efforts address the question of ‘what is the operator aware of? ’ – the awareness – only a few investigations focus on studying what the operator should be aware of in the ﬁrst place, i.e., ‘what is the situation? ’ In this paper we brieﬂy discuss some of our research activities which aim at answering this second question, following an ‘ecological approach’ to interface design. The clever use of automation tools and novel visualizations will be presented that allows human operators working in aviation (pilots, air trafﬁc controllers) in dealing with complex tasks. The airborne self-separation task will be discussed, as an example of showing how ecological interfaces can support pilots in their decision making.