Miracle-dz/UniFAR-FTUs huggingface dataset:
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction

As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, classical spatio-temporal prediction learning methods including deep learning, require large amounts of training data. In reality, some newly developed cities with insufficient sensors would not hold that assumption, and the data scarcity makes predictive performance worse. In such situation, the learning method on insufficient data is known as few-shot learning (FSL), and the FSL of traffic prediction remains challenges. On the one hand, graph structures’ irregularity and dynamic nature of graphs cannot hold the performance of spatio-temporal learning method. On the other hand, conventional domain adaptation methods cannot work well on insufficient training data, when transferring knowledge from different domains to the intended target domain.To address these challenges, we propose a novel spatio-temporal domain adaptation (STDA) method that learns transferable spatio-temporal meta-knowledge from data-sufficient cities in an adversarial manner. This learned meta-knowledge can improve the prediction performance of data-scarce cities. Specifically, we train the STDA model using a Model-Agnostic Meta-Learning (MAML) based episode learning process, which is a model-agnostic meta-learning framework that enables the model to solve new learning tasks using only a small number of training samples. We conduct numerous experiments on four traffic prediction datasets, and our results show that the prediction performance of our model has improved by 7% compared to baseline models on the two metrics of MAE and RMSE.
Attitudes of Local Communities Towards Tourism Development in Obudu Ranch Resort

: Cross River State has in recent times experienced a significant level of tourism growth which has positively impacted on the social and economic status of the host communities. However recent reports indicate that one of the most significant tourism destination in the state-Obudu Ranch Resort is under a dysfucntional state. A questioniarres survey was carried out on 308 residents in five communities around the resort. The data generated were analyzed using discriptive statistics such as means, percentages, frequencies and chi square test. Majority of the respondents 72.73% submitted that the local communities were hospitable towards tourist while a greater part of respondents also were of the view that the local communities supported the programmes of the resort. A very high percentage of respondents 84.73% agreed that tourism development in the resort has led to a remarkable increase in the prices of goods and services. A higher percentage of repondents were of the view that tourism has encouraged gender empowerment 80.84% and a high level of accessibility 46.42%. However majority 94.29% and 46.42% were of the perception that entrepreneurial trainiung programmes were not put in place by the resort, management for local communities. The impact on tourism on the environment was also appreciated by majority of the respondents.
U.S. Population Projections: 2005-2050

If current trends continue the population of the United States will rise to 438 million in 2050 from 296 million in 2005 and 82% of the increase will be due to immigrants arriving from 2005 to 2050 and their U.S.-born descendants according to new projections developed by the Pew Research Center. Of the 117 million people added to the population during this period due to the effect of new immigration 67 million will be the immigrants themselves and 50 million will be their U.S.-born children or grandchildren. Among the other key population projections: Nearly one in five Americans (19%) will be an immigrant in 2050 compared with one in eight (12%) in 2005. By 2025 the immigrant or foreign-born share of the population will surpass the peak during the last great wave of immigration a century ago. The major role of immigration in national growth builds on the pattern of recent decades during which immigrants and their U.S.-born children and grandchildren accounted for most population increase. Immigrations importance increased as the average number of births to U.S.-born women dropped sharply before leveling off. (excerpt)
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data

Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive personal data. Existing approaches for fingerprint generation are limited in their ability to generate diverse impressions of the same finger, a key property for providing effective data for training recognition models. To address this gap, we present FPGAN-Control, an identity preserving image generation framework which enables control over the fingerprint's image appearance (e.g., fingerprint type, acquisition device, pressure level) of generated fingerprints. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. In our experiments, we used the publicly available NIST SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap. Finally, training recognition models using only synthetic datasets generated by FPGAN-Control lead to recognition accuracies that are on par or even surpass models trained using real data. To the best of our knowledge, this is the first work to demonstrate this.
XLPE Cable Line Thermal Regime in the Power Supply System of a Metallurgical Enterprise

The problem of the required thermal regime of ca-ble lines does not reach the end, although it pays great attention, and is also widely applicable explicitly, but is practically not used to verify a mathematical model with experimental data. In this work, the mathematical thermal model of cable is being verified with experimental data; as a result of this work, they are a novel-ty of a real-life system-forming fiber and its mathematical model of impact. The work focuses on the operating cable voltage lines of 110 kV brand APvVng 1x500/120 of the system-forming power supply network of the substation of blind input 110/10 kV and above the design features of cable lines, a thermal model is pro-posed and thermal regime calculations are implemented. The results of thermal mode simulation calculations must be compared with the results of studies of thermal mode measurements with a thermal imager at the same load. On the basis of experi-mental studies and the results of mathematical modeling, studies are proposed to detect the required thermal area. Studies and processing of experimental data were carried out using a Fluke Tis 10 Thermal Imager, the study of models by the calculation method. The result and practical shooting are the possibility of using a mathematical model of the thermal regime when collecting software tools for controlling the thermal regime.
Backward and Forward Inference in Interacting Independent-Cascade Processes: A Scalable and Convergent Message-Passing Approach

We study the problems of estimating the past and future evolutions of two diffusion processes that spread concurrently on a network. Specifically, given a known network $G=(V, \overrightarrow{E})$ and a (possibly noisy) snapshot $\mathcal{O}_n$ of its state taken at (a possibly unknown) time $W$, we wish to determine the posterior distributions of the initial state of the network and the infection times of its nodes. These distributions are useful in finding source nodes of epidemics and rumors -- $\textit{backward inference}$ -- , and estimating the spread of a fixed set of source nodes -- $\textit{forward inference}$. To model the interaction between the two processes, we study an extension of the independent-cascade (IC) model where, when a node gets infected with either process, its susceptibility to the other one changes. First, we derive the exact joint probability of the initial state of the network and the observation-snapshot $\mathcal{O}_n$. Then, using the machinery of factor-graphs, factor-graph transformations, and the generalized distributive-law, we derive a Belief-Propagation (BP) based algorithm that is scalable to large networks and can converge on graphs of arbitrary topology (at a likely expense in approximation accuracy).
Effects of Ambient O3 on Respiratory Mortality, Especially the Combined Effects of PM2.5 and O3

Background: In China, the increasing concentration of ozone (O3) has emerged as a significant air pollution issue, leading to adverse effects on public health, particularly the respiratory system. Despite the progress made in managing air pollution in China, it is crucial to address the problem of environmental O3 pollution at present. Methods: The connection between O3 exposure and respiratory mortality in Shenyang, China, from 2014 to 2018 was analyzed by a time-series generalized additive regression model (GAM) with quasi-Poisson regression. Additionally, the potential combined effects of fine particulate matter (PM2.5) and O3 were investigated using the synergy index (SI). Results: Our findings indicate that each 10 μg/m3 increase in O3 at lag 2 days was associated with a maximum relative risk (RR) of 1.0150 (95% CI: 1.0098–1.0202) for respiratory mortality in the total population. For individuals aged ≥55 years, unmarried individuals, those engaged in indoor occupations, and those with low educational attainment, each 10 μg/m3 increase in O3 at lag 07 days was linked to RR values of 1.0301 (95% CI: 1.0187–1.0417), 1.0437 (95% CI: 1.0266–1.0610), 1.0317 (95% CI: 1.0186–1.0450), and 1.0346 (95% CI: 1.0222–1.0471), respectively. Importantly, we discovered a synergistic effect of PM2.5 and O3, resulting in an SI of 2.372 on the occurrence of respiratory mortality. Conclusions: This study confirmed a positive association between O3 exposure and respiratory mortality. Furthermore, it highlighted the interaction between O3 and PM2.5 in exacerbating respiratory deaths.
A record minimum arctic sea ice extent and area in 2002

Arctic sea ice extent and area in September 2002 reached their lowest levels recorded since 1978. These conditions likely resulted from (1) anomalous warm southerly winds in spring, advecting ice poleward from the Siberian coast (2) persistent low pressure and high temperatures over the Arctic Ocean in summer, promoting ice divergence and rapid melt.
Circular Polarized Compact Dual Antenna Set for L-Band Space Applications

A new circularly polarized antenna set for L1 and L5-band satellite applications is presented. A compact design is obtained by having one antenna inside the other without obstructing the antenna radiation patterns. The proposed antenna gives good selectable RH and LH circular polarization at L1/L5 band and provides matching without any further steps. The fabricated antenna shows a good agreement between the measured and simulated results.
Uncertainty Aware Learning Model for Thermal Comfort in Smart Residential Buildings

Smart energy management for homes is a potential area of research. Establishing a trade-off between occupant comfort and energy saving is quite a crucial issue to handle. To address this issue, the paper presents a multi-objective optimisation routine-based approach for handling this conflicting trade-off. Air quality, visual, and thermal comforts are important factors when considering occupant comfort. Heating, ventilation, and air conditioning systems employ the predicted values for comfort temperature and air quality comfort inside the room. The lighting system is used to maintain visual comfort inside the room. A data-driven strategy is put out in this research to forecast user thermal comfort for each occupant in residential homes. Six deep-learning approaches are applied to estimate each occupant's interior comfort temperature. These approaches are evaluated with the help of mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalised mean square error (NMSE), and R-squared score (R2 score). Bi-directional gated recurrent units (Bi-GRU) have shown to be the best. Bi-GRU outperforms in the following metrics: MSE, MAPE, RMSE, NMSE, and normalised R2 score values are 0.0104, 8.1768, 0.1022, 7.5121, and 1, respectively; these metrics are found optimal when compared with other techniques. The overall system is optimised with the application of particle swarm optimisation. Summer, winter, and monsoon seasons weather data in India are used to test the proposed model. The influence of temperature uncertainty on total energy consumption, overall comfort, and energy consumption cost is also being examined in all three seasons.
Rank-Based Assessment of Grid-Connected Rooftop Solar Panel Deployments Considering Scenarios for a Postponed Installation

Installing solar photovoltaic panels on building rooftops can help property managers generate renewable energy and reduce electricity costs. However, the existence of multiple efficiency indicators and ambiguity in interpreting these metrics limits the comparison of the performance of individual installation projects. This paper presents a methodology using data envelopment analysis to evaluate suitable candidates for rooftop solar panel installation. This approach integrates rooftop area, solar irradiation, temperature, costs, energy yield, and revenue to evaluate the relative efficiency of each building. To demonstrate the methodology, it was applied to rank 22 residential buildings, revealing the top performers for installation in 2022. The approach was subsequently adapted to assess potential outcomes under deferred implementation up to 2030, encompassing a diverse range of climate and pricing scenarios. Five installations were found to be optimal irrespective of the future scenarios. In addition, a super-efficiency approach was applied to overcome the low level of discrimination among the possible installations and to rank each individual unit uniquely. The analysis is designed to guide property owners in identifying favorable solar photovoltaic investments within their portfolios under changing conditions.
Ultra-Thin RF Wireless Power Transmitter

There are several use cases for low-power sensors in an aircraft such as temperature distribution measurement or structural health monitoring. Conventional cabling increases installation efforts while the use of batteries requires additional maintenance. Wireless Power Transfer (WPT) is an alternative method to provide cable-less power to aircraft sensors. In this work, we present an ultra-thin and flexible RF wireless power transmitter printed on a polyimide-based flexible circuit board to enable its seamless integration into onboard aircraft cabins. At a thickness of less than 0.8 mm, it can be integrated in the cabin side wall panels of an aircraft and thus drastically reduce the cabling effort for sensors. Using the printed transmitter, we demonstrate the remote powering of low-power sensors and their associated electronics including a ZigBee module. Furthermore, we present the correlation between the received power versus the wireless transmission distance to determine the range of potential use cases that can be supported with this WPT system for various environments.
An Integration Scheme for Highway Rest Area Integrating the Distributed Photovoltaic Generation and Energy Storage

With the large-scale expansionary of electric vehicles (EVs), charging facilities on highway have also been developed rapidly as supporting services, providing convenient and fast charging services for electric vehicle users. Due to the limited number and capacity of charging facilities, it is difficult to meet diverse queue waiting time needs of EV users during peak charging periods. Meanwhile, considering the integration of distributed photovoltaic and distributed energy storage system (DPV-DESS) on highway, this paper aims at proposing a strategy for the highway to coordinate multiple resources and meet the diverse charging demand of EVs. In this paper, a highway integration scheme with DPV-DESS is established to maximize the EV charging simultaneity and EV users’ satisfaction while achieving the efficient utilization of DPV-DESS. Firstly, based on the DPV-DESS configuration, the model of photovoltaic (PV), energy storage and EV charging parameters are built. Secondly, considering PV output characteristics and the diverse charging needs, a two-layer optimization model is put forward. In the upper layer, multi-source coordination optimization model is proposed to realize efficient utilization. In the lower layer, an orderly charging strategy is raised for EVs senseless charging. Finally, case studies have been conducted on a self-sustained DPV-DESS consisting of a modified test system with 20 charging piles. Numerical results indicate that the proposed scheme can maximize the EV users’ satisfaction while minimizing the operating cost on the premise of the self-sustained highway transportation energy system operation.
A Monolithically Polyjet 3-D Printed Millimeter-Wave Quasi-Planar Air-Filled Cavity-Backed Patch Antenna With Enhanced Gain

In this paper, a new class of monolithically integrated millimeter-wave quasi-planar cavity-backed patch antenna is proposed. The antenna is shaped in the cross-sectional profile of the backed cavity and an air-filled trapezoidal backed cavity is developed with flexible design in the height and inclination. The shaped air-filled trapezoidal backed cavity allows the antenna gain to be improved significantly and it is tailored for being additively manufactured. A $Ka$-band antenna prototype is devised, manufactured, and tested. The antenna prototype is manufactured monolithically by employing high-precision Polyjet 3-D printing process. The RF-measured result of the antenna shows reasonable directional radiation performance with a reflection coefficient of less than -10 dB from 26.9 to 31.7 GHz (a relative bandwidth of about 16.4%) and an in-band peak realized gain of 7.8 dBi.
A spurious elevation of serum creatinine level in a patient with Crohn’s disease without histologic kidney damage: a case report and review of the literature

ABSTRACT Patients with inflammatory bowel disease (IBD) are prone to develop kidney injury. Renal involvement in IBD patients is usually diagnosed by the measurement of serum creatinine and the estimation of the glomerular filtration rate. We describe a patient with IBD who presented with large fluctuations in his serum creatinine level (~3.0-fold) without significant histologic abnormalities and with a normal cystatin C level. This appears to be related to a high-protein diet and intermittent fasting. Even though the impact of a high-protein diet on mild elevations of the serum creatinine level has been described, large fluctuations in serum creatinine from diet alone, as seen in this case, have never been reported, raising the question about the potential contribution of inflamed bowel on gut absorption or metabolism of creatinine. This case highlights the importance of a detailed history, including the dietary habits, when encountering a patient with increased serum creatinine level, and careful interpretation of serum creatinine in a patient with a creatinine high-protein diet or underlying IBD.
Optimal Static Output Feedback Stabilization of Fractional-Order Systems With Caputo Derivative Order 1 ≤ α < 2

The design of optimal static output feedback controllers for fractional-order linear systems with Caputo derivative order 1 ≤ α < 2 is addressed in this paper. The cost function to be minimized is the Frobenius norm of the feedback matrix. First, based on the barrier method, we demonstrate how to create an auxiliary minimization problem that is easier to solve than the original optimization problem. The auxiliary problem is then solved numerically to obtain an approximate solution to the original problem. Necessary conditions for the optimal solution of the auxiliary minimization problem are derived using the Lagrange multiplier method. A numerical example is provided to validate the proposed method.
Parallel Execution of the Viola-Jones Algorithm on MCUs for Low-Cost Automated Pest Detection

This paper presents an energy-optimized solution for on-device pest detection, preventing image streaming from the traps installed in the monitored orchard surface. We optimize the Viola-Jones object detector to run on a low-cost 8-core MicroController (MCU), which is coupled to a low-power image sensor. Thanks to the adopted memory management scheme and the algorithm parallelization, our solution presents a processing latency of only 0.4 sec per image while scoring >80% detection accuracy on low-quality images. This design reduces the memory requirements and complexity of precedent processing pipelines based on Deep Neural Networks and presents a power envelope > 10x lower than more expensive existing solutions.
Hydrologic impact of grazing on infiltration: A critical review

The hydrologic importance of grazing is receiving increased attention on rangelands in the United States. The literature on this topic is fragmented. This paper explores the available literature for information useful in understanding the hydrologic impacts of grazing intensity as related primarily to infiltration and runoff. Generally, data relative to range condition are not adequate for evaluating hydrologic impacts. Data relating grazing intensity to infiltration rates are available, yet distinct limitations are evident. These limitations are discussed in terms of identifying future research needs. The greatest need appears to be a detailed definition of the long-term effects of grazing (by year and season) on infiltration rates as a function of site, range condition, and grazing intensity. Once obtained, infiltration rates must be coupled with an appropriate method for generating runoff volumes, storm hydrographs, and long-term water yields.
Study on the Spatial Distribution and Utilization of Children's Public Service Facilities in Foshan City Based on Big Data

The construction of children's public service facilities is an important part of child-friendly community planning, and the improvement of the richness of such facilities has also become an important indicator of the development of urban infrastructure, and the study of this index will play an important role in the improvement and optimization of urban infrastructure service facilities in the territorial space planning of the city and county level. Based on the perspective of big data, taking Foshan City as an example, we took children's public service facilities, such as schools, parks and libraries as research objects; obtained relevant basic POI data through big data technology, visually expressed and analyzed data by geographic information system software ArcGIS, discussed the spatial distribution and utilization of public service facilities in Foshan, and put forward relevant optimization suggestions and improvement directions based on the research results.