Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning of Model Preconditions for Model-Deviation Aware Planning in Deformable Object Manipulation

—This paper presents an algorithm for actively selecting trajectories in order to train an estimator that predicts model deviation of an inaccurate dynamics model. The trained estimator is then used during test time to constrain planning to regions of state-action space where the dynamics model is predicted to be accurate with high probability. As collecting state-action space data can be costly, we propose an active learning algorithm to minimize data collection needed to reliably train a model deviation estimator. Preliminary results show improvements in both test performance, and in the qualitative selection of trajectories selected by the active learning approaches. We further show experiments that analyze the effect of using different acquisition functions on trajectories chosen during training, and on the test performance.
Descr ibing distance: from the plane to spectral tr iples

Geometry draws its power from the abstract structures that govern the shapes found in the real world. These abstractions often provide deeper insights into the underlying mathematical objects. In this snap-shot, we give a glimpse into how certain “curved spaces” called manifolds can be better understood by looking at the (complex) differentiable functions they admit.
Feed the Future Innovation Lab for Food Security Policy

Despite economic transformations and urbanization, declining shares of the workforce employed in the agricultural sector, and gradual growth of agricultural mechanization, production costs in the agricultural sector and food prices remain high in Nigeria relative to those in some of the other developing countries. Understanding how the adoption of mechanical technologies is related to agricultural productivity is therefore important for countries like Nigeria. Using farm household data from northern Nigeria as well as various spatial agroclimatic data, this study shows that the adoption of key mechanical technologies in Nigerian agriculture (animal traction, tractors, or both) has been high in areas that are more agroclimatically similar to the locations of agricultural research and development (R&D) stations, and this effect is heterogeneous, being particularly strong among relatively larger farms. Furthermore, such effects are likely to have been driven by the rise in returns on scale in the underlying production function caused by the adoption of these mechanical technologies. Agricultural mechanization, represented here as the switch from manual labor to animal traction and tractors, has been not only raising the average return on scale but also potentially magnifying the effects of productivity-enhancing public-sector R&D on spatial variations in agricultural productivity in countries like Nigeria.
The growth performance and prospects in Europe: A Kaldorian approach

This paper purports to apply the balance of payments constrainedgrowth model to explain the European growth performance in the last forty years and to discuss the likely prospects for the future. After a formal reconsideration of the long-run and short-run arguments supporting the validity of the PostKeynesian approach to economic growth, a simplified and an extended version of the basic model are outlined. The application of the model to the European experience shows that the so-called Thirlwall’s law performs quite well in explaining facts in all the decades under consideration. The fundamental reasons behind the unsatisfactory recent EU growth experience lie therefore in a decreasing (absolutely and relatively to other advanced countries) exports dynamics and in an increasing imports dependence. On the basis of the existing trends, the prospects for the future appear to be gloomy, unless structural reforms of the productive system are promoted in order to improve the position of Europe in the international division of labour.
STATISTICAL ANALYSIS FOR THE FINANCIAL REPORT FROM ENERGY STREAM/REVENUE IN IRANIAN SMALL AND MEDIUM-SIZED INDUSTRIES

, The Environmental Impact Assessment (EIA) plan is an indispensable part of all cycles of industrial projects. By the way, it results to compose the initial matrix of data in the preliminary assessment of the screening step. Iranian industrial projects recognized miscellaneous material streams introduced into industrial ambient and in released shapes. One of the important assessments posed in the EIA plan refers to costs/revenue assessment that assigns Data Envelopment Analysis (DEA) to discover the performance of industrial projects along with statistical tests. The objective of the current study prosecuted to discern the cost/revenue analysis in statistical language. Therefore, the empirical equations and SPSS Software of IBM 20 were employed in the analysis of financial reports. The findings were succeeded to present linear relationships for the dependent and independent variables and in full compliance with diagrams developed by SPSS software. The results of the DEA model ended up with the emergence of an efficiency border for 8 industrial groups (334 industries) around a range of 0 to 0.4 by scatter plot. There were no significant differences between dependent and independent variables in different models assessed via T and F and ANOVA tests. It can be concluded that the findings of the DEA model displayed by the scatter plot are a benchmarking level for industrial projects once before complete construction. of Technology and Education of the Amazon (ITEGAM). This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
A Spatial Analysis of Forest Fire Survival and a Marked Cluster Process for Simulating Fire Load

The duration of a forest fire depends on many factors, such as weather, fuel type and fuel moisture, as well as fire management strategies. Understanding how these impact the duration of a fire can lead to more effective suppression efforts as this information can be incorporated into decision support systems used by fire management agencies to help allocate suppression resources. This thesis presents a thorough survival analysis of lightning and people-caused fires in the Intensive fire management zone of Ontario, Canada from 1989 through 2004. The analysis is then extended to investigate spatial patterns across this region using proportional hazards Gaussian shared frailty models. The resulting posterior estimates suggest spatial patterns across this zone. A fire load model is also developed by coupling a fire occurrence model with a survival model and is explored via simulation. Marked cluster processes were found to nicely capture the overall fire load trend over the fire season.
Understanding Cultural Perceptions of Health in Middle School Females for Obesity Prevention: A Case Study

The purpose of this study was to understand acculturation and race/ethnicity influences in the home and school environment that affect physical activity and nutrition in female adolescents attending middle school. A convenience sample of eight female adolescents (n = 2 Asian American, n = 2 Black, n =2 Latinx/Hispanic, and n = 2 White) was interviewed individually via Zoom. Responses regarding acculturation, physical activity, and nutrition in the home and school environment were analyzed using qualitative case study analysis. Three themes were identified: 1) experiences related to home, health, and culture, 2) the intersection between school meals and personal culture, and 3) the intersection between school physical activity and personal culture. Study findings can inform policies on school nutrition and physical activity and lead to opportunities for students and families to collaborate with schools to improve adolescent health.
Design of a Compact Calibration Platform for Characterization of Tunneling Magnetoresistive Sensors under Varying Temperatures

The temperature dependence of magnetic sensors' performance is a crucial factor that can impact measurement accuracy, necessitating precise characterization to compensate for errors caused by thermal drift. Previous research involved placing magnetic sensors in an oven to control temperature and measuring their responses to magnetic fields induced by current-carrying wires to determine temperature dependence. However, this approach was typically time-consuming and introduced errors due to the non-uniform magnetic field generated by the current-carrying conductor. In this study, we developed a compact calibration platform specifically designed to characterize magnetic sensors at different temperatures. The platform incorporates a thermoelectric cooler (TEC) to adjust the sensor's temperature. A temperature controller, implemented using a proportional-integral-derivative (PID) controller, regulates the temperature of the TEC, with a thermistor providing real-time temperature feedback. This compact platform enables temperature variations ranging from -25°C to 85°C for calibrating magnetic sensors at different temperatures. To validate the proposed design, we conducted sensitivity and offset drift characterizations of a commercial tunneling magnetoresistive sensor using the built platform. The obtained results aligned with the typical values provided in the sensor's datasheet, thus confirming the effectiveness of the proposed design.
Making children read more and better by integrating natural language processing and gamification techniques into mobile applications

It is well known that nowadays children tend to spend lot of time playing games on mobile devices at the expense of reading books. The current research aims to explore ways to design and develop a mobile application, attractive for children, that uses voice recognition technology and gamification techniques to help children read more and better and to track the progress of reading. The application is implemented with Flutter, Dart and Firebase and is addressed to children, the books being made available in encyclopedia format. By integrating technologies such as NLP and gamification the application aims to improve reading fluency among children, but also to give them the opportunity to accumulate new and exciting information in the same time.
Remaining Useful Life Prediction Considering Data and Model Uncertainties

Remaining useful life (RUL) prediction plays a great part in prognostics health management (PHM). Recently, machine learning (ML) methods have been widely used in RUL prediction due to their scalability and generalization. However, most of the research focuses on RUL point estimation and neglects the uncertainties that are caused by data noise and modeling inaccuracy. To get more reliable and practical results, an RUL interval prediction method with data and model uncertainty quantification is proposed. Firstly, a series of neural network models with various parameters and structures are constructed and they are trained with bootstrapped data. After that, the outputs of the models are fused to form new time series labels which contain data and model uncertainties, and a new training set consisting of sequential features and new labels is generated. Finally, a heteroscedastic neural network (HNN) is trained to capture both uncertainties as well as output RUL point estimation. The effectiveness of the proposed method is validated on CMAPSS datasets.
Using Spatial Indexing Systems to Optimize Emergency Response Routes

In an emergency, dedicated vehicles play an important role. The response time is one issue that must be considered. In this article, we propose a framework for simulating emergency situations that takes into account the following parameters for problem solving: vehicles with capacities (ambulance vehicles equipped with specific equipment or with a limited number of seats, fire engines, etc.), types of intervention (fires, natural disasters, road accidents, etc.), buildings/dispatches (which have a limited number of places available for treatment or have the necessary equipment). The ideas of space indexing and route optimization are merged to handle these characteristics.
A Parsing-Based Approach for Constructing Executable EFSM

The Extended Finite State Machine (EFSM) is one of the most popular modeling approaches for model-based testing. However, EFSM-based test case generation is susceptible to the infeasible (inexecutable) path problem, which stems from the conflict of predicates (guards) between transitions in the path. Therefore, in order to derive feasible test cases, a test generation algorithm needs to dynamically acquire information about the model and verify the feasibility of the generated test path through the simulation execution of the model. The traditional method of constructing executable models using hard-coding for different EFSM models under test has limitations such as inflexibility, time-consuming and error-prone. To address this issue, this paper proposes a parsing-based approach, PBACE, for building the executable EFSM for testing EFSM-specified systems, which can support the automatic generation of executable test cases. It decouples the EFSM model description, parsing and simulation execution functions from the test generation algorithm, which can effectively improve the efficiency and quality of test generation. In particular, PBACE first uses a well-designed JSON syntax to describe the specific EFSM under test. Then, based on the model description file, it uses lexical and syntactic parsers to dynamically extract model information to construct various model objects in memory such as state configurations, transitions, etc. Finally, the proposed method provides a series of service interfaces to support model information acquisition, transition feasibility evaluation, and model simulation execution. A case study of test sequence generation for the SCP protocol model demonstrates the capability and promise of our approach to serve executable test cases.
On the Existence of Distinct Inter-Area Electro-Mechanical Modes in North American Electric Grids

This paper examines the extent to which distinct inter-area electromechanical modes exist in large-scale electric grids. Electric grids oscillate, and these oscillations have often been described using the linear systems concept of modes. However, electric grids are nonlinear systems, and are becoming increasingly nonlinear with the growth of inverter-based controls, deadbands, and other devices that often operate at their limits. Hence adequate linearizations may no longer exist, calling into question the extent to which distinct modes exist. For a mode to exist at an operating point its frequency, damping and shape must be independent of the disturbance used to excite it. The paper shows for one synthetic and two actual models of North American electric grids that these criteria are not always met, particularly for the largest electric grids.
Marine Radar Small Target Classification Based on Normalized Time-Frequency Image and Residual Network

In this article, a small target classification method is proposed, which can effectively distinguish different types of sea surface targets under different sea states and different radars. The proposed classification method is mainly based on normalized time-frequency spectrograms and Residual Network (ResNet). The time-frequency images of the radar returns reflect the motion characteristics of the target, the fluctuation of the target Radar Cross Section (RCS) and the relationship between the target and the wave. The effect of different radar parameters on target classification can be eliminated by standardizing range resolution and carrier frequency of radar returns. By exploiting ResNet, the proposed method can effectively extract the deeply hidden features of normalized time-frequency images and solve the problems of gradient disappearance, gradient explosion and degradation in deep network. Datasets consisting of four types of targets, floating ball, floating boat, yacht and low altitude Unmanned Aerial Vehicle (UAV) are constructed to test the performance of the proposed method. The experiment results show that the proposed classifier combines normalized time-frequency images and ResNet attains better classification performance compared with the existing classification methods.
Traffic Flow Prediction Method Based on Big Data Mining

To predict the state of urban short-term traffic flow in real time and accurately, this paper puts forward a feasible scheme to realize traffic flow prediction by big data mining technology. Combined with the characteristics of urban traffic flow, we process and extract the collected data based on wavelet. Then, the relevant knowledge in the field of data mining is used to find the hypothesis rules most similar to current traffic flow characteristics. We also discuss the feasibility of machine learning to mine data with similar change trend from massive data for short-term traffic prediction, and provide the specific solution process. Finally, a more optimized frequent set computing method and implementation scheme are proposed through MapReduce on the cloud computing platform. The test results of the prediction model show that the excavated congestion conduction rules can accurately analyze the traffic congestion and it can be used to provide guidance paths for vehicles in real time within a local range.
First language attrition: A crosslinguistic study of language contact and language attrition

Introduction Most treatments of language contact presuppose one or more of the following conceptual models: (1) language contact as borrowing, or an additive model, such as Bloomfield (1933); (2) contact as opposing forces, or a resistance-to-mixture model, such as Sapir (1921); or (3) contact as selective blending, or the interference model, such as Weinreich (1953). Recent studies, however, provide empirical data which contradict the neat logic of these concepts. While it is clear that borrowing, interference, and autonomous change may and do occur in language contact situations, other linguistic effects are discernible as well. And these effects, in many cases, overshadow both borrowing and interference in shaping the language forms which result from the contact situation. As Einar Haugen (1978: 37) points out: As the learner builds new systems in the language he acquires, he dismantles and reorders the systems of the language he already knows. It is this dismantling and reordering process which is the subject of this article. One of the best places to study this process is in the enclave speech community. Enclaves, for this purpose, are communities where speakers of one language, A, are surrounded and/or dominated by speakers of a different language, B, in a defined political or geographic area.
Vulnerability Assessment of UHV Converter Station Considering Cognitive Uncertainty

UHV converter station is a complex system that includes a variety of electrical equipment and buildings. In order to study the seismic resilience of UHV converter station, a systematic analysis method based on successful flow is proposed. This method comprehensively considers relationship between the electrical equipment, buildings’ location, and electrical power transmission paths, and then establishes reliability evaluation model of the UHV converter station which takes aftershock residual transmission capability as evaluation index. With the seismic vulnerability curve of the electrical equipment, the Monte Carlo simulation method is used to realize the aftershock vulnerability analysis. The model is used to evaluate seismic resilience of one real UHV converter station in Sichuan Province of China. Computation results shows its residual electrical power transmission capability when meets different level earthquakes. The research benefits to evaluate power grids’ seismic resilience and improve power supply reliability.
Innovation Patterns

Innovation in services has become a topic of interest to researchers due to the worldwide shift to "services" economics. This comes from the growth of services economies and the shift to services businesses by manufacturing industries, including IT-related industries. This growth of services encourages us to study basic business system changes from product-based to service economies and to define the fundamental challenges to accelerate service innovations. These interdisciplinary activities are called services sciences, management, and engineering (SSME), which includes computer science, operations research, industrial engineering, business strategy, management sciences, social and cognitive sciences, legal sciences, and innovation management. Innovations, in particular, service innovations are difficult to articulate their structures and mechanisms, due to intangibility coming from services characteristics, and a lack of languages to describe them. In this paper, we investigate 1. How can we capture the characteristics of innovations as patterns? 2. What are the categories of patterns for innovations? We introduce innovation pattern categories and patterns from enterprise viewpoints, and apply them to innovation cases. We identify future study areas, such as innovation ecosystem modeling, and innovation lifecycle studies using innovation patterns.
Assessment and reclamation of contaminated land

The Extent of Contaminated Land Problems and the Scientific Response The New UK Contaminated Land Regime Identifying and Dealing with Contaminated Land Contaminated Land and the Link with Health Human Health Risk Assessment: Guideline Values and Magic Numbers Ecological Risk Assessment under the New Contaminated Land Regime Remediation Methods for Contaminated Sites Legal Liabilities and Insurance Aspects of Contaminated Land Subject Index.
Consideration in WSN Applying for the Health Monitoring of Transport Aircraft

The possibility of employing Wireless Sensor Network (WSN) on future transport aircraft can bring obvious benefits such as weight reduction and flexible location. The application principles of WSN are summarized, and various scenarios with WSN are proposed and analyzed. To achieve the objective of replacing traditional wired sensors with wireless sensors without exerting evident impacts on crucial communication and navigation equipment, a typical WSN architecture is presented. Power supply of WSN is of great importance, and possible power supply methods are discussed. Also, trade-offs are made for the selection of multiple data transmission frequencies to balance the transmission efficiency and possible interference.
Analysis of Power Transformers under Geomagnetically Induced Currents

This paper describes the development of a low-frequency model to analyze impacts of Geomagnetically Induced Currents (GIC) on power transformers. GIC causes power transformer core and tank saturation which can conduce to flow of harmonic currents, voltage-control problems, and heating of the transformer internal components, ultimately leading to possible damages and gas relay alarm/operation. To analyze the transformer, first, an accurate low-frequency transient model is developed to allow the flow of GIC in the power transformer windings. The developed transformer transient model is based on the topological representation of the transformer core, flux air paths, and tank and is accurate enough for considering low and mid-frequency transients including GIC. The calculated currents are fed into a Finite Element Model of the transformer developed for the purpose of GIC study in this research work. Utilizing the FEM model, the pattern of electromagnetic flux distribution and hot spots caused by GIC in the transformer core and tank are specified. The extracted characteristic features of the transformer will provide helpful insights into the optimal design and production of power transformers.
Three reﬂections

We present several problems that can be solved in a very short way using properties of a glide reﬂection. In our conﬁgurations the glide reﬂection will be obtained as a composition of three reﬂections. Idedicatethis paper to the memory of Professor Edmund Puczy(cid:32)lowski.