Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION OF CITIZENS’ VOICE & ACCOUNTABILITY

the aim of strengthening “poor people’s role in the exercise of power and foster their influence in society” (Sida 2001: 2). Accountability, participation, legitimacy, transparency, representivity and similarity before the law are presented as fundamental conditions (Sida and MFA 2001). The promotion of human rights and democracy is therefore central to Sweden’s mandate alongside its commitment to poverty reduction. In 2005, USAID commissioned a study to assess the effect of its programmes supporting democracy building around the world. The authors conducted a worldwide cross-national quantitative study based on the entire democracy portfolio of USAID between 1990 and 2003. In order to deal with the problem of attribution, the study tries to determine what a country’s normal “growth” in or “decline” of democracy would have been in the period studied, in order to isolate the effects of USAID interventions. This poses significant challenges as these are complex dynamics that are likely to differ from one context to another. Their findings suggest that, although it is possible to conclude that foreign assistance has a positive impact on democratisation, it is difficult to determine “how large” such impact is for two reasons. First, there is a lagged effect of such interventions, which tend to take several years to mature. Second, although the impact of USAID assistance on democratisation is potentially significant, 42 this has to be set against the reality of the actual outlay for USAID democracy assistance, which in the time period amounted to US$2.07 million per year. Finally, one specific finding of the study is that, although such statistical analysis is complemented by other methods, its explanatory power remains rather limited. The results on the impact of USAID assistance on human rights show a strong negative effect, i.e. that USAID interventions apparently had a detrimental effect on human rights conditions. The authors suggest that this could be the result of an increase in reporting of human rights abuses in the countries affected by USAID interventions, although the evidence available does not support this hypothesis nor does it explain the reason why this could have occurred.
Belief Overreaction and Stock Market Puzzles

We construct an index of long term expected earnings growth for S&P500 firms and show that it has remarkable power to jointly predict errors in these expectations and stock returns, in both the aggregate market and the cross section. The evidence supports a mechanism whereby good news cause investors to become too optimistic about earnings growth, for the market as a whole but especially for specific firms. This leads to inflated stock prices and, as beliefs are systematically disappointed, to subsequent low returns in the aggregate market and for specific firms in the cross section. Overreaction of measured long-term expectations helps resolve major asset pricing puzzles without time series or cross-sectional variation in required returns. in long run risk, or in disaster risk are hard to measure. Here we pursue an orthogonal approach: we keep required returns constant and relax rational expectations of fundamentals. We discipline departures from rationality using data on analyst expectations of future earnings growth of listed firms. We show that expectations of long
Permeable landscapes for climate change adaptation in and around and northern Jefferson Counties

Incompatible land uses, compounded by recent and likely future climate change have impacted our natural habitat types and reduced the permeability of open space and park lands in Boulder and northern Jefferson Counties by roughly two-thirds. A primary climate-smart strategy to conserve biodiversity is to allow species to adapt to habitat change by ensuring a connected landscape. As such, this project was designed to inform decision making about opportunities to maintain, protect, restore, and manage for wildlife connectivity. Potential opportunities to facilitate movement within and surrounding the open space and parks, habitats, and landscapes were identified by mapping “hot spots” across four major habitat types using spatial modeling of landscape permeability. This can help inform management by identifying: Abstract This research was designed to inform decision making about the opportunities to maintain, protect, restore, and manage open space and park lands to ensure wildlife connectivity across Boulder and northern Jefferson Counties, Colorado. A primary strategy to address the impacts of climate change on natural systems and biodiversity is to allow ecological systems to adapt to climate change by ensuring a connected landscape. One approach to understanding landscape connectivity is to model climate-induced habitat shifts for specific wildlife species, yet this is challenging because limited data are available on species-specific life history characteristics, sensitivity to new climate conditions, and capacity to adapt. Moreover, there is high uncertainty in future climate predictions at management relevant scales, especially in a landscape that contains numerous habitat types and diffuse ecotones. As a result, this project measured connectivity using an indicator called landscape permeability, which characterizes the ability of wildlife to move through a landscape while avoiding developed areas with high human activity and ecological processes to function naturally. Overall, permeability has declined by two-thirds from “natural” (no humans) conditions. Not surprisingly, upper and lower montane habitats are much more permeable than lower elevation grassland/shrublands, while permeability in riparian/valley bottoms is variable. The resulting maps were analyzed to identify potential opportunities (“hot spots”) to facilitate movement through: (a) restoration or management activities; (b) protection of additional adjacent or nearby lands to complement existing protected lands; and (c) partnering with adjacent land managers. Also, the datasets can be analyzed in a variety of ways to evaluate additional conservation strategies and for specific situations.
LONG RANGE UNMANNED AERIAL VEHICLE FOR FORESTRY MONITORING USING LIDAR

: Research in forestry is one of the areas where drones have recently found application, thanks to the high-resolution data that can be obtained in a short period of time and at a low cost. While manned air-craft and satellite remote sensing have their place, drones can fill in many of the gaps left by these methods, particularly when it comes to forest management and land use. The goal of this research is to provide a brief overview of the many UAV (Unmanned Aerial Vehicle) applications in forestry, such as forest mapping, forest management planning, canopy height model generation, and mapping forest gaps. This study. They have a wide range of potential applications in the near future and should be implemented quickly in a variety of settings for forest management sustainability . fundamental unmanned aerial vehicles (UAVs) in 2d images and 3d images mapping, various degrees of effectiveness estimation of the height, lai, chlorophyll content, and the size and location of the crown of each tree, well as the estimation This cluster can be linked to a subset of items related to the estimation of derived dendrometric parameters. Plant growth, density, and other forestry duties are all monitored. Deter-mine marketable biomass; species; and inspect forestry operations are all necessary steps in the process. Post-harvest data and infection detection and control. change and changes in woodland biodiversity are driving the use of un-manned aerial vehicles (UAVs) for conservation and restoration efforts in the second cluster. Using unmanned aerial vehicles (UAVs) in particular, ecologists can map and control weed vegetation? Deforestation assessment and monitoring, as well as the presence of invasive alien species In order to aid in the prevention of fires and post-fire monitoring, it is necessary to use gap identification, forest wildfire detection and management, in particular to identify and create risk maps and to monitor after a fire. UAVs and their sensors can also be used to measure quantities. It's a good idea to monitor changes in aboveground biomass (AGB) over time to see how climate change affects it. Changes in land use affect forest ecosystems' global carbon cycle. RGB sensors can be used to estimate fractional object in a specific area, recognize items when using UAV sensors in forestry. The term "invasive species" refers there. When used near-infrared
Study on Methods of the Solution to Robot Kinematics

The solution to some kinematical problems of the robot was introduced.The three-dimensional sculpt design and kinematical simulation analysis of space multi-joint robot were made by using CAD/CAE integration software.The results show that compared to the traditional analytical method,the new analytical method is simple,straight and definite,which has high speed and accuracy.The method can obtain the data of robot kinematics and dynamics through virtual assembly and motion simulation,it can fulfill the functions of data collection,classification,storage automatically.
Spectral PINNs: Fast Uncertainty Propagation with Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) promise to signiﬁcantly speed up partial differential equation (PDE) solvers. However, most PINNs can only solve deterministic PDEs. Here, we consider stochastic PDEs that contain partially unknown parameters. We aim to quickly quantify the impact of uncertain parameters onto the solution of a PDE - that is - we want to perform fast uncertainty propagation. Classical uncertainty propagation methods such as Monte Carlo sampling, stochastic Galerkin, collocation, or discrete projection methods become computationally too expensive with an increasing number of stochastic parameters. For example, the well-known spectral or polynomial chaos expansions achieve to separate the spatiotemporal and probabilistic domains and offer theoretical guarantees and fast computation of stochastic summaries (e.g., mean), but can be computationally expensive to form. Our Spectral PINNs approximate the underlying spectral coefﬁcients with a neural network and reduce the computational cost of the spectral expansion while maintaining guarantees. We derive the method for partial differential equations, discuss runtime, demonstrate initial results on the convection-diffusion equation, and provide steps towards convergence guarantees.
A merged continental planetary boundary layer height 1 dataset based on high-resolution radiosonde measurements, 2 ERA5 reanalysis, and GLDAS 3

The planetary boundary layer (PBL) is the lowermost part of the troposphere that 33 governs the exchange of momentum, mass and heat between surface and atmosphere. To date the radiosonde measurements have been extensively used to estimate PBLH; 35 suffering from low spatial coverage and temporal resolution, the radiosonde data is 36 incapable of providing the diurnal description of PBLH across the globe. To fill this 37 data gap, this paper aims to produce a temporally continuous PBLH dataset during the 38 course of a day over the global land by applying the machine learning algorithms to 39 integrate high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS 40 product. This dataset covers the period from 2011 to 2021 with a temporal resolution 41 of 3-hour and a horizontal resolution of 0.25°×0.25°. The radiosonde dataset contained 42 around 180 million profiles over 370 stations across the globe. The machine learning 43 model was established by taking 18 parameters derived from ERA5 reanalysis and 44 GLDAS as input variables while the PBLH biases between radiosonde observations 45 and ERA5 reanalysis were used as the learning targets. The input variables were 46 presumably representative regarding the land properties, near-surface meteorological 47 conditions, terrain elevations, lower tropospheric stabilities, and solar cycles. Once a 48 state-of-the-art model had been trained, the model was then used to predict the PBLH 49 bias at other grids across the globe with parameters acquired or derived from ERA5 and 50 GLDAS. Eventually, the merged PBLH can be taken as the sum of the predicted PBLH 51 bias and the PBLH retrieved from ERA5 reanalysis. Overall, this merged high- 52 resolution PBLH dataset was globally consistent with the PBLH retrieved from 53 radiosonde observations both in magnitude and spatiotemporal variation, with a mean 54 bias of as low as –0.9 m. The dataset and related codes are publicly available at (Guo et al., 2022), which are of significance 56 for a multitude of scientific research and applications, including LHF, evapotranspiration, transpiration, total precipitation rate
HIGHLIGHTS OF SPECTROSCOPIC TECHINIQUE AND THEIR APPLICATION

: Spectroscopic method is broadly employed for the evaluation of chemical structure of a compound (analyte) accurately. In all methods molecule identified because of interaction with electromagnetic radiation. The electric and magnetic property of radiation is reacted with similar properties of chemical compound, therefore the analyte is identified and characterized for the presence of atom, bond, functional group, basic nucleus, molecular formula and molecular weight. This article gives information of importance of analytical technique and their application. The investigation implies content of basics for interpretation of spectrum, laws, sampling techniques, sample cell, mechanism involved in solvent utilization, appearance of spectra and application of technique. This review also includes significant aspect of various spectroscopic method and represent quantities and qualitative parameters of compound, entire spectroscopic principal, instrumentation and application.
LONGITUDINAL RESEARCH: INFLUENCE BETWEEN MARKET ORIENTATION, INNOVATIVENESS AND BUSINESS PERFORMANCE

The aim of this study is to conduct a longitudinal reasearch into the influence between market orientation, innovativeness and business performance. The study endevours to examine possible changes in the model of the influence of market orientation on innovativeness and business performance in the 2016-2019 period in Croatian companies. To analyse data, we used regression and covariance analysis. The results show that the influence of market orientation, competitor orientation and interfunctional coordination on innovativeness and business performance did not exhibit statistically significant differences in the 2016-2019 period in Croatian companies. At the same time the influence of customer orientation on innovativeness, product innovation, business system innovativeness and on business performance showed a signifcant statistical change. Empirical results provide consequential theoretical implications and implications for the management in implementing market orientation, which would allow for the increase in company competitiveness on the market.
Integrating Force-based Manipulation Primitives with Deep Learning-based Visual Servoing for Robotic Assembly

—This paper explores the idea of combining Deep Learning-based Visual Servoing and dynamic sequences of force-based Manipulation Primitives for robotic assembly tasks. Most current peg-in-hole algorithms assume the initial peg pose is already aligned within a minute deviation range before a tight-clearance insertion is attempted. With the integration of tactile and visual information, highly-accurate peg alignment before insertion can be achieved autonomously. In the alignment phase, the peg mounted on the end-effector can be aligned automatically from an initial pose with large displacement errors to an estimated insertion pose with errors lower than 1.5 mm in translation and 1.5° in rotation, all in one-shot Deep Learning-Based Visual Servoing estimation. A dynamic sequence of Manipulation Primitives will then be automatically generated via Reinforcement Learning to finish the last stage of insertion.
Application of Diagnostic and Educational Tools to Control Childhood Parasitic Infections in the School Environment

- Objective: To evaluate the frequency of the main intestinal parasitic diseases that affected school-age children in the city of Recife, Pernambuco, between 2008 and 2018. And intervene in the school environment through playful educational practices to raise awareness among students, parents, and employees about preventive measures that could prevent contamination by parasites. Methods: This work is a cross-sectional coproparasitological study, addressing quantitative and qualitative aspects of children aged 12 years or less, who were enrolled in public schools or daycare centers selected for this work and presented the Informed Consent Form (ICF) signed by parents or guardians. The collected samples were submitted to the techniques of Hofmann and modified Kinyoun. Abstract- Objective: To evaluate the frequency of the main intestinal parasitic diseases that affected school-age children in the city of Recife, Pernambuco, between 2008 and 2018. And intervene in the school environment through playful educational practices to raise awareness among students, parents, and employees about preventive measures that could prevent contamination by parasites. Methods: This work is a cross-sectional coproparasitological study, addressing quantitative and qualitative aspects of children aged 12 years or less, who were enrolled in public schools or daycare centers selected for this work and presented the Informed Consent Form (ICF) signed by parents or guardians. The collected samples were submitted to the techniques of Hofmann and modified Kinyoun. Also, lectures were realized for parents, guardians, and employees of Schools or Nurseries and recreational actions for children. Results: A total of 705 samples were collected, of which 340 were positive for 12 different parasite species. Giardia lamblia (24.9%), Cryptosporidium spp (23.6%), and Endolimax nana (21.4%) were the most recurrent among schoolchildren between 2010 and 2018. About applied educational playful practices, they proved to be effective in building knowledge and raising awareness about parasitic endemics, evidencing the need for more practices like these in schools and communities in general. Conclusion: This work presented an expanded view of the parasites' permanence in the school environment and the importance of educational practices aimed at transforming life habits that are harmful to health.
C ONTRASTIVE LEARNING OF IMAGE - AND STRUCTURE - BASED REPRESENTATIONS IN DRUG DISCOVERY

Contrastive learning for self-supervised representation learning has brought a strong improvement to many application areas, such as computer vision and natural language processing. With the availability of large collections of unlabeled data in vision and language, contrastive learning of language and image representations has brought impressive results. The contrastive learning methods CLIP and CLOOB have demonstrated that the learned representations are highly transferable to a large set of diverse tasks when trained on multi-modal data from two different domains. In drug discovery, similar large, multi-modal datasets comprising both cell-based microscopy images and chemical structures of molecules are available. However, contrastive learning has not been used for this type of multi-modal data in drug discovery, although transferable representations could be a remedy for the time-consuming and cost-expensive label acquisition in this domain. In this work, we present a contrastive learning method for image-based and structure-based representations of small molecules for drug discovery. Our method, Contrastive Leave One Out boost for Molecule Encoders (CLOOME), is based on CLOOB and comprises an encoder for microscopy data, an encoder for chemical structures and a contrastive learning objective. On the benchmark dataset ºCell Paintingº, we demonstrate the ability of our method to learn transferable representations by performing linear probing for activity prediction tasks. Additionally, we show that the representations could also be useful for bioisosteric replacement tasks.
Productivity Models for Cable Yarding in Alpine Forests

The authors tested the same modern mid-size tower yarder for uphill and downhill extraction. The yarding machine is a popular commercial model offering state-of-the-art yarding technology, including telescoping tower and full hydrostatic transmission. The machine was studied while harvesting selective patch cuts (gap cutting) in similar even-aged Norway Spruce stands, extracting logs between 3 and 6 metres long. The operation was carried out by the same experienced 3-man crew. Productivity ranged between 8.5 and 10 m3/h, including all delays, but excluding set-up and dismantle time. A set of regression equations for estimating machine productivity were calculated as a function of the main work conditions, such as yarding distance, lateral extraction distance, mean log size and number of logs per turn. Machine utilization was about 60%, which is consistent with previous studies. In general, cable logging is more complex and expensive than ground-based logging, which places steep terrain forestry at a general disadvantage in terms of pure harvesting cost. However, modern cable yarding technology can reduce this gap, and productivity models can assist users in refining their work technique, so as to maximize the productive potential of their machines
The development of movement effectiveness in children : a qualitative analysis

Qualitative biomechanical analysis of movement effectiveness can be proposed as an innovative approach to oversee motor skill development in boys and girls of school age, since it permits an appreciation of how effective the movement is in attaining the performance criterion. Analysis of movement effectiveness involves establishing, first, the technical level of the child and, second, the mechanical effectiveness of the movement performed by the child. The assessment of technical level involves basic analysis of the main form of the movement and provides an initiation to biomechanical analysis; whereas, analysis of mechanical effectiveness consists of a follow-up comprehensive analysis. The present research aimed to qualitatively determine, and study gender differences in the development of, movement effectiveness in children. The research included both cross-sectional (N = 187 children) and longitudinal (N = 55 children) studies. A group of adults (N = 31) were used as controls. The subjects were asked to perform two consecutive trials of the soccer kick, the overarm throw and the standing broad jump, all for maximum distance, which were recorded on videotape. In Study 1, hierarchical models were developed to qualitatively establish the technical level of the subjects and to study the development of technical level in children. In Study 2, a model for the qualitative analysis of mechanical effectiveness was constructed. The model allows the integration of phase analysis and mechanical analysis using movement principles in order to select important variables for the analysis. Rating scales for each variable of the three motor skills were outlined and, then, fine-tuned using tests of rater accuracy and intra-rater reliability, in order to create scoring references. Inter-rater reliability in the use of the scoring references was acceptable. In Study 3, the development of mechanical effectiveness in children was examined, and the relative contribution of specific variables to performance was assessed. Generally, in the cross-sectional studies male children showed a higher technical level and higher mechanical effectiveness, and also a faster rate of development, in the soccer kick and the overarm throw. However, male and female children were better matched in the standing broad jump. The longitudinal studies were inconclusive. Not all adult subjects, particularly females, had reached maturity of movement patterns. The knowledge gained in the research may be used to guide effective progression in coaching and curriculum development in education.