Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Social Characteristics of Adaptive Capacity for Wildfire: Insights from Flathead County, Montana

Abstract The increasing threat caused by wildflre in the UnitedStates has spurred the development of a number of policiesand programs that encourage communities to reduce theirrisk by becoming "fire adapted. " Yet despite these goals,there is little understanding of the characteristics that arecrucial in the development of fire adapted communities. Thisresearch seeks to uncover specific aspects of local social con-text that lead to adaptive capacity for wildfire among com-munities in Flathead County, MT, by consulting local fireprofessionals, community representatives and emergencymanagers using focus groups and interviews. Results suggestthat adaptive capacity is highly variable across the countyand influenced by aspects such as community identity, strongcommunication networks and the local wood products indus-try. We contend that a better understanding of adaptive ca-pacity to wildflre requires systematic documentation of howspeciflc characteristics of local people interact to influencetheir ability to deal with change.Keywords: adaptive capacity; wildlandfire; hazards
Resources and development of new energy in China

Many countries pay more attention to the development of new energy.The resources of coal-bed methane,oil sand,shale oil,geothermal energy,fuel ethanol and bio-diesel have been developed in some countries.With the fast development of the economy in China,the conventional petroleum cannot meet the need of economic development.The new energy resources of China are abundant and also greatly developed in recent years.The commercial development of coal-bed methane has been successful in Qinshui Basin.The bitumen in oil sand has been experimentally prospected in Junggar Basin.Oil yield from oil shale increases rapidly and forms a certain scale.Gas hydrate,deep basin gas and shale gas are very promising.The geothermal energy and solar energy are very abundant and can be utilized directly.Fuel ethanol has been also commercially developed.With the development of the technology and solution of some difficulties,the mass production of new energy can meet the need of the economic development in China to a certain extent.
The Role of Audit Committee and Risk Monitoring Committee on Firm’s Hedging Practice

This study aims to analyze the role of audit committee and risk monitoring committee (RMC) on firm risk management measured by hedging. Firms with both committees will have better governance in monitoring risk. The population of this study is companies listed in IDX. This study uses samples of 104 non-financial companies. Using panel data regression, we find that the composition of audit committee members who have financial and/or accounting background have a significantly positive effect on hedging, meaning that an audit committee with such a higher composition will encourage firms to hedge more to reduce risk exposure. Meanwhile meetings and size of audit committee are insignificant. The presence of RMC has a negatively significant effect on firm hedging, meaning that the firm is more capable in identifying risk to determine the most appropriate way to mitigate risk, where not all risk can be mitigated by hedging. Keywords: Audit Committee; Risk Monitoring Committee; Hedging
CLASSIFICATION OF TREES IN HYPERSPECTRAL CANOPY DATA USING MACHINE LEARNING: COMPARATIVE ANALYSIS OF FOREST STRUCTURE COMPLEXITY

Abstract. The classification of tree species by remote sensing is an important task with a broad range of applications, including forest management, environmental monitoring, and climate change studies. Hyperspectral imaging has proven to be a valuable tool for this classification. Additionally, deep learning techniques have obtained outstanding results in hyperspectral classification. In this study, we apply a neural network to the classification of aerial hyperspectral images of trees. The study was conducted at a research station in southern Chile with 32 tree species. Our database has 3080 tree canopies that have been manually segmented and classified. The goal of the work was to study the correlation between forest structure complexity and classification performance across three different forest conditions: native forest, plantation of native species, and plantation of exotic species. The results show that classification performance is higher when forest structure and composition are simpler. We used a ResNet neural network as classifier and compared its performance with support vector machine and random forest. The best performance was obtained using ResNet in exotic plantations, the forest condition with the simplest structure, achieving an F1-score of 85.23%.
The Male:Female Ratio atBirth isDepressed by MalteseParliamentaryElections and Increased by OtherNon-electoralEvents

Aim:The male to female ratio at birth declines with stress. In Malta, parliamentary elections are highly divisive and stressful. This paper looked at the association of M/F with parliamentary elections and ot her important political events in Malta. Methodology :A retrospective analysis of monthly male and female births was carried out. These were obtained from official Malta Government publications back to 1966. Index months were months in which parliamentaryelections were held. Eleven elections were included which comprised all those wherein elections were essentially c ontested by two parties between1966-2013. The monthly sums for live births for each gender were summated for the months prior and following e ach election and for four other political but non-electoral events. Analysis was carried out with chi tests and chi tests for trend. Original ResearchArticle
An Attention-Based Odometry Framework for Multisensory Unmanned Ground Vehicles (UGVs)

Recently, deep learning methods and multisensory fusion have been applied to address odometry challenges in unmanned ground vehicles (UGVs). In this paper, we propose an end-to-end visual-lidar-inertial odometry framework to enhance the accuracy of pose estimation. Grayscale images, 3D point clouds, and inertial data are used as inputs to overcome the limitations of a single sensor. Convolutional neural network (CNN) and recurrent neural network (RNN) are employed as encoders for different sensor modalities. In contrast to previous multisensory odometry methods, our framework introduces a novel attention-based fusion module that remaps feature vectors to adapt to various scenes. Evaluations on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) odometry benchmark demonstrate the effectiveness of our framework.
Review Classification Based on Machine Learning: Classifying Game User Reviews

With the development of the game industry, the maturity of online game sales platforms, and the increasing complexity of game software itself, game companies need to analyze massive amounts of user reviews to understand the hidden defects of the game and the direction of future iterations. Manually reading game reviews is a labor-intensive and time-consuming task, as the number of reviews can go up to several thousand per day. Automatically classifying these game reviews will help alleviate this issue, but traditional classifiers will need a large number of labeled instances for training. In this paper, we propose and implement an approach that combines transfer learning in natural language processing (BERT), unsupervised learning, and active learning to classify game reviews using only a small amount of labeled instances. We found that our approach obtains 88.8% classification accuracy with only 100 labeled training instances. Our implementation can be extended to handle different types of new games by using a small amount of extra labeled instances and manual work.
Altered microheterogeneity at several N‐glycosylation sites in OPSCC in constant protein expression conditions

Protein glycosylation responds sensitively to disease states. It is implicated in every hallmark of cancer and has recently started to be considered as a hallmark itself. Changes in N‐glycosylation microheterogeneity are more dramatic than those of protein expression due to the non‐template nature of protein glycosylation. This enables their potential use in serum‐based diagnostics. Here, we perform glycopeptidomics on serum from patients with oropharyngeal squamous cell carcinoma (OPSCC), compared to controls and comparing between cancers based on etiology (human papilloma virus‐ positive or negative). Using MS2, we then targeted glycoforms, significantly different between the groups, to identify their glycopeptide compositions. Simultaneously we investigate the same serum proteins, comparing whether N‐glycosylation changes reflect protein‐level changes. Significant glycoforms were identified from proteins such as alpha‐1‐antitrypsin (SERPINA1), haptoglobin, and different immunoglobulins. SERPINA1 had glycovariance at 2 N‐glycosylation sites, that were up to 35 times more abundant in even early‐stage OPSCCs, despite minimal differences between SERPINA1 protein levels between groups. Some identified glycoforms' fold changes (FCs) were in line with serum protein level FCs, others were less abundant in early‐stage cancers but with great variance in higher‐stage cancers, such as on immunoglobulin heavy constant gamma 2, despite no change in protein levels. Such findings indicate that glycovariant analysis might be more beneficial than proteomic analysis, which is yet to be fruitful in the search for biomarkers. Highly sensitive glycopeptide changes could potentially be used in the future for cancer screening. Additionally, characterizing the glycopeptide changes in OPSCC is valuable in the search for potential therapeutic targets.
Airborne LiDAR Strip Error Correction and Deformation Monitoring

Airborne light detection and ranging (LiDAR) is a fast ground observation technology that can obtain point cloud data with centimeter precision in height and centimeter resolution in spatial domain, which has been widely applied to construct digital element model (DEM). However, there are still limitations in deformation monitoring, especially in large-scale and high-precision surface deformation monitoring. Namely, when the deformation magnitude is close to the LiDAR monitoring accuracy, the difference results contain obvious strip errors. To solve this problem, a strip error correction method based on the differential results is proposed for airborne LiDAR deformation monitoring including three main steps: quasi-stable adjustment on elevation correction, fixed-axis iterative closest point (ICP), and affine ICP algorithm to correct the strip errors. Experiments show that the proposed method can correct the strip error, and the RMSE of the two-phase point clouds for one flight track was decreased from the original 6.2 and 5.3 cm to 3.5 and 3.3 cm. Deformation patterns and rates are consistent as verified by external results with the small baseline subset InSAR (SBAS-InSAR) of Sentinel-1 SAR data. An internal comparison with the classical ICP algorithm shows this method is more accurate. The effectiveness of the proposed method in monitoring surface deformation is verified.
California GAMA Special Study: Analysis of Carbamazepine, Oxcarbazepine and Metabolites as Wastewater Tracers in Water Resource Studies

Pharmaceuticals in the environment have attracted significant attention in recent years. The method here details the analysis of carbamazepine (CBZ), oxcarbazepine (OXC) and metabolites carbamazepine-epoxide (CBZ-epoxide), 10,11-dihydro-10-hydroxycarbamazepine (CBZ-10OH), and 10,11-dihydro-10,11-hydroxycarbamazepine (CBZ-DiOH), from sampling sites within a wastewater treatment plant (WWTP). Samples (500 mL) were prepared using Oasis HLB solid phase extraction cartridges and analyzed by liquid chromatography tandem mass spectrometry and quantified using an internal standard (CBZ-D10). The limit of quantitation ranged from 0.01 to 0.1 ng on-column. Composite samples were fortified with a mix of parent pharmaceuticals and metabolites for method validation. Reasonable recoveries were assessed for CBZ, CBZ-epoxide, and CBZ-10-OH, and CBZ-DiOH (> 70%) and low recoveries (44%) for OXC were observed. Concentrations of metabolites in effluent are detailed here for the first time for U.S.-based
Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader–follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.
DENSITY MANAGEMENT IN YOUNG WESTERN LARCH STANDS: TREE GROWTH, STAND YIELD, AND CARBON STORAGE 54 YEARS AFTER THINNING

Precommercial thinning (PCT) increases individual tree size and shortens harvest rotation time by affecting the timing and intensity of competitive interactions between trees. Short-term results from PCT and spacing trials often show that the trade-off for rapid individual tree growth at lower densities is a period of time where trees do not fully occupy the site, and stand yield lags behind yields obtained from high density stands. While it is well established that individual tree size and growth increase at lower stand densities there is still some uncertainty over how long-term yield, defined as the total cubic stem volume per unit ground area, responds to early PCT. We re-sampled a 54-year old western larch PCT study in northwest Montana with two objectives: (1) to test how different target densities and thinning schedules affect stand yield, and (2) to analyze and report treeand stand-level mensurational characteristics of a long-term PCT experiment at nominal full rotation age (62 years from stand initiation). The study has three target densities (494 trees ha, 890 trees ha, and 1680 trees ha) and three numbers of entries to achieve those target densities (1, 2, and 4 entries), creating a gradient of competition pathways by which a stand achieves a final density. Analysis of variance and linear contrasts were used to test the affects of density and number of entries on treeand standlevel attributes 54-years after treatment began. Results suggest that if thinning is done early (<10 years) we will see long-term constant yield across a range of densities. We
Flipped classroom with collaborative learning approach in enhancing writing skills of Indonesian university students

The intention of this study was to examine how a flipped classroom with a collaborative learning approach improved the writing skills of Indonesian higher education students. To achieve the study’s aim of 48 Indonesian higher education participants, the Oxford Quick Placement Test (OQPT) was employed. They were divided into two groups: experiment and control. Following that, as a pre-test, both groups were given a written exam. The experimental class (EC) was then positioned in a flipped classroom. Students were permitted to bring smartphones to class and utilize them while learning, and the flipped classroom was equipped with an internet connection, a laptop, and a projector. The control class (CC) got standard classroom instruction. This strategy was employed until the very last session. The experimental group outperformed the control group on the post-test, according to the results of the independent samples t-test and one-way analysis of covariance (ANCOVA). Furthermore, the results revealed a significant difference in post-test performance between the EC and CC.
Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system

Feature selection (FS) plays an important role in the construction of efficient ensemble classifiers; particularly for intrusion detection system (IDS). An IDS is a utilized in a network architecture to protect the availability of sensitive information. However, existing IDSs suffer from redundancy, high dimensionality, and high false alarm rate (FAR). Also, lots of models are constructed for outdated datasets, which makes them less flexible to deal with new assaults. Therefore, this paper proposes a new IDS relies on hybrid FS and ensemble classifiers. A hybrid FS approach consists of two techniques, hard-voting and mean. In contrast to recent papers, we use three different FS approaches: extra tree classifier importance as an embedded FS, recursive feature elimination (RFE) as a wrapper FS, and mutual information (MI) as a filter FS. Then, a hard-voting technique has been used to fuse output of these approaches and obtain a reduced subset of features. Since each feature has three weights, a mean technique has been utilized to assign one weight to each feature and obtain an optimal subset of features. The experimental outcomes, utilizing the modern InSDN dataset, confirm that the proposed hybrid FS with ensemble soft voting classifier achieves better results than other ensemble and individual classifiers due to several measures.
Exploring Book-Tax Differences, Analyst Coverage, and Forecast Optimism: An Empirical Study

This research explores the relationship between book-tax differences (BTD) and the coverage of financial analysts, as well as how these two factors relate to optimistic forecasts. By evaluating how BTD affects analyst coverage, this research expands on earlier studies and finds a negative link. Additionally, it demonstrates that greater BTD indicates a higher probability of earnings manipulation, leading analysts to have a more pessimistic view of their predictions for such organizations. This research contributes to a better understanding of financial markets by shedding light on analyst behavior, financial reporting, and the complex dynamics underlying BTD in determining analyst estimates.
Interaction of endophytic fungi of winter wheat seeds

Seed endophytes are potential bioagents for plant protection and growth promoters. The question of the specifics of their isolation in cultural environments is not clear. The purpose of this study was to establish the nature of the interaction of endophytic fungi of wheat seeds with different levels of aggressiveness and presence in the mycobiota. Dual cultivation was carried out at potato-glucose agar (PGA), comparing with single fungal cultivation. The mutual influence of fungi during joint cultivation was established. Alternaria arborescens , which dominated in the mycobiota of wheat seeds from northeastern Ukraine, suppressed the development of only Penicillium. Nigrospora oryzae, Bipolaris sorokiniana, and Phoma developed faster than A. arborescens . Fusarium poae, and F. sporotrichioides competed for agar medium with N. oryzae . Known bioagents from wheat seeds showed unexpectedly low results. Trichothecium roseum formed a rejection zone during co-cultivation with F. graminearum. Trichoderma sp. Max18 (resistant to fludioxonil) on the 7th day inhibited the development of Penicillium, F. graminearum, and A. arborescens by 55, 48 and 26%, respectively. N. oryzae developed faster than the mycoparasitic fungus, but the latter began to parasitize it only from the 13th day.
Does basic medical insurance promote public health? Evidence from China family panel study

Background To promote common prosperity, China government has devoted much financial resources to the basic medical insurance system, it is of great significance to improve the health level of the insured groups to prevent them from returning to poverty due to illness. Whether or not the basic medical insurance can improve health status is an important policy issue after China has win the absolute poverty alleviation movement. Methods Based on the data of China Family Panel Studies this paper constructs a strong balanced panel data with two levels information, which including variables from family level and personal level. This paper uses the panel data fixed effect model and propensity score matching model to analysis. Results This paper finds that after controlling the family and personal confounding variables, the basic medical insurance has positive effect toward health status. With propensity score matching model, this paper finds that there is causality between basic medical insurance and public health. Conclusion Basic medical insurance has a significant health effect, that is, basic medical insurance has a significant positive impact on individual self-rated health. Participating in basic medical insurance can significantly improve the ability of families to face risk shocks, promote the accumulation of health capital in families, promote the diversification of livelihood strategies, and effectively prevent the occurrence of returning to poverty due to illness.
Nonparametric Analysis of Complex Nonlinear Systems

In this paper we propose a nonparametric methodology designed to facilitate the statistical analysis of complex systems. The proposed approach exploits an ensemble of nonparametric techniques including conditional density function estimation, conditional distribution function estimation, conditional mean estimation (regression) and conditional quantile estimation (quantile regression). By exploiting recent developments in nonparametric methodology and also in open source interactive platforms for data visualization and statistical analysis, we are able to provide an approach that facilitates enhanced understanding of complex empirical phenomenon. We illustrate this approach by exploring the inherent complexity of the Southern Ocean system as a carbon sink, measured in terms of fugacity of carbon dioxide at sea surface temperature (f CO2), in relation to a number of oceanic state variables, all measured in situ during the annual South African National Antarctic Expedition (SANAE) austral summer trips from Cape Town to the Antarctic, and back, between 2010 and 2015.