Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Models for Early Detection of Invasive Species across Spread Pathways

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.
Puncture Intervertebral Disc Degeneration Model: A Standard on Rabbit

At present, a large number of people are suffering from low back pain, which becomes an urgent public health concern, intervertebral disc degeneration (IDD) is one of the most significant reasons of this disease. This study is aimed to select an optimal needle size to establish a rabbit IDD model by intervertebral disc puncture and the validity was verified by magnetic resonance imaging, histology and immunohistochemical staining. We divided 12 rabbits into four groups of 3 randomly with number table method, including 1 control group and 3 experimental groups. Then the lumbar 3/4 (L3/4), lumbar 4/5 (L4/5) and lumbar 5/6 (L5/6) discs were punctured by 14G, 16G and 21G needles respectively. According to the MRI and histology, the 16G-needle-punctured disc showed a progressive degeneration, the 14G-needle-punctured disc degenerated acutely one week after the operation while the 21G-needle-punctured disc did not show much degeneration. To sum up, the 16 G needle is the most suitable for making a model of IDD, which can replicate the occurrence, development and outcome of various histopathology of IDD in clinic. Although the 14G needle can cause the acute intervertebral disc injury, it is not an ideal approach to establish an IDD model. 21G needle causes little disc degeneration inversely, and it could be a promising approach to inject kinds of medicine to treat or prevent IDD.
Perceived Employability Skills of Accounting Graduates: The Insights from Employers

Graduates employability skills become necessary in challenging job market. The main focus of this study is to determine the employers insight on the skills needed by local accounting graduates at the time of entry to the profession. By using a content analysis approach, the findings show that information technology skills, interpersonal skills and personal skills were most sought by employers. Importantly, the most sought of information technology skills are the ability to use appropriate spreadsheets and accounting software. For interpersonal and personal skills, working in a team, good communication skill and optimistic are appeared to be the most valued skills. From the findings, it suggests higher education providers should embed technological contents and generic skills in accounting curricula.
Quantification of polyacetylenes by LC-MS in human plasma after intake of fresh carrot juice (Daucus carota L.)

Citation for pulished version (APA): Hansen-Møller, J., Hansen, S. L. , Christensen, L. P., Jespersen, L. , Brandt, K. , & Haraldsdóttir, J. (2002). Quantification of polyacetylenes by LC-MS in human plasma after intake of fresh carrot juice (Daucus carota L.). In K. Brandt, & B. Åkesson (Eds.), Health promoting compounds in vegetables and fruit: Proceedings of workshop in Karrebæksminde, Denmark, 6-8 Nov. 2002 (Vol. 29, pp. 137-138). Aarhus Universitet, Det Jordbrugsvidenskabelige Fakultet. DIAS report Horticulture, No. 29
Seasonal distribution of increased precipitation in maternal environments influences offspring performance of Potentilla tanacetifolia in a temperate steppe ecosystem

Precipitation is predicted to increase in arid and semiarid regions under climate change, with greater changes in intra- and inter-annual distribution in the future. As a major limiting factor in these regions, changes in precipitation undoubtedly influence plant growth and productivity. However, how the temporal shifts in precipitation will impact plant populations are uncertain. A 3-year field experiment and a greenhouse experiment were conducted in a temperate grassland in northern China to examine the impacts of seasonal (spring and summer) increased precipitation on offspring performance of a common species, Potentilla tanacetifolia. Our results showed that the amounts and timing of increased precipitation both played important roles in regulating offspring performance of P. tanacetifolia in the temperate steppe ecosystem. Increased precipitation in spring at maternal stage stimulated seed production, germination percentage and seedling biomass, whereas increased precipitation in summer at maternal stage stimulated seedling biomass. The timing of increased precipitation influenced seed attributes, whereas the amount of increased precipitation influenced offspring seedling biomass. Our results indicate that population development of P. tanacetifolia may be underestimated under future increased precipitation regime, if the transgenerational effect is not taken into account.
A Haze Feature Extraction and Pollution Level Identification Pre-warning Algorithm

: The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract high-level features. eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features, as well as predict haze. Establish PM2.5 concentration pollution grade classification index, and grade the forecast data. The expert experience knowledge is utilized to assist the optimization of the pre-warning results. The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine (SVM) and Back Propagation (BP) widely used at present, the accuracy has greatly improved compared with SVM and BP.
Application of High Density Linkage Map Derived from Genotyping by Sequencing for Detection of QTL Conferring Resistance to Leaf Rust Races Spread in Egypt

Wheat is a major source of carbohydrates in Egypt, leaf rust disease is known to be the most common rust disease affected wheat genotypes. This study aimed to apply a constructed high density linkage map through Genotyping-by- sequencing (GBS) for detection of QTL resistant to important leaf rust races spread in Egypt. The applied map contained 3,641 markers distributed on 21 chromosomes and spanned 1,959 cM with an average distance of 1.8 cM between markers. A mapping population of 204 RILs (F6:8) obtained from the cross between two parents of winter wheat ‘Harry’ x ‘Wesley’ through single seed decent method was used to identify QTL region associated with leaf rust resistance genes in wheat. Under the Green house condition in Egypt leaf rust pathotypes i.e NTTJT, PTTGS, PTTTT, TTTBT and TTTTT were used. High-density linkage map based on GBS derived SNPs were applied in this study for QTL mapping. The wheat genotype Harry was resistant to all tested pathotype while, the wheat genotype Wesley was susceptible to all tested pathotype. One major stable QTLs, for race NTTJT was identified on chromosome 6A flanked by markers XSNP3958 and XSNP3957 with a LOD 4.22. The identified SNP marker may be used to screen for resistance to specific races of leaf rust found in Egypt in further spring wheat breeding programs as marker assisted selection.
A Sensory Similarities Approach to Load Disaggregation of Charging Stations in Internet of Electric Vehicles

Intelligent transportation systems (ITSs) have become popular in recent years as an essential requirement for safer and more efficient transportation systems. Internet of Electric vehicles (IoEV) as well as their hybrid forms provide an ideal means of supporting sustainability within an ITS. The control of charging/discharging of EV is still a challenge, despite the tremendous research progress to date in the field. In this paper, the use of charging station data and binary vectorization are proposed in order to provide timely insights on the dynamic behavior of charging processes. A Bag-of-Power-States model has been created for similarity measurement of charging stations within given time periods. The results of experimentations using synthetic data have shown that the proposed ${{Bag-of-Power-States}}$ model is computationally feasible and provides useful results for optimizing the scheduling of power supply to charging stations that may be located across a wide range of distances, over the same period of time.
User Acceptance of Online Tracking if 'Forgetting' was an Option

Users are constantly bombarded with targeted ads based on their online behavior. Researchers have found that users often feel uncomfortable when they see targeted ads based on sensitive topics. The hypothesis behind this study is that perhaps the problem isn't the tracking, per se, but the retention of so much information about users in an opaque fashion. Hence this thesis aims to understand if giving users fine-grained control over retention of online tracking data (related to specific targeted ads) would change the acceptability of online tracking. We decided to test this hypothesis by conducting two user studies on views of online tracking and data retention and whether a hypothetical "forget" option that deleted all data related to the displayed ad would change those views. Our results indicate that such control does make online tracking more acceptable to users.
An Efficient Detection Approach of Content Aware Image Resizing

: Content aware image resizing (CAIR) is an excellent technology used widely for image retarget. It can also be used to tamper with images and bring the trust crisis of image content to the public. Once an image is processed by CAIR, the correlation of local neighborhood pixels will be destructive. Although local binary patterns (LBP) can effectively describe the local texture, it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise. Therefore, to deal with the detection of CAIR, a novel forensic method based on improved local ternary patterns (ILTP) feature and gradient energy feature (GEF) is proposed in this paper. Firstly, the adaptive threshold of the original local ternary patterns (LTP) operator is improved, and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR. Secondly, the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection. Then, the ILTP features and the gradient energy features are concatenated into the combined features, and the combined features are used to train classifier. Finally support vector machine (SVM) is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not. The candidate images are extracted from uncompressed color image database (UCID), then the training and testing sets are created. The experimental results with many test images show that the proposed method can detect CAIR tampering effectively, and that its performance is improved compared with other methods. It can achieve a better performance than the state-of-the-art approaches.
Does acoustic trauma occur in pointers due to firearm noise? A prospective study on 50 hunting dogs

This study investigates the possibility of acoustic trauma occurence in hunting dogs exposed to high noise with pure stimulant such as firearms. Ten puppies (Control Group-CG) were used to establish reference for Brainstem Auditory Evoked Response (BAER) and tympanometry records. BAER and tympanograms were collected from the 40 cases in active hunting group (Acoustic Trauma Group-ATG). The severities of trauma of which the cases were exposed to were determined by field study. While tympanometric pressure values of ATG cases were higher than that in CG, they were found to be in normal range (ATG left: 35,63±38,79/right: 34,00±38,25; CG left: -23,90±44,30/ right: -29,20±36,87 daPa). BAER records were saved using both click and tone burst stimuli. Records were taken at the frequencies of 0,5, 1, 2, 4, 6, 8-kHz with tone burst stimulus. Wave I latency values obtained with click stimulus were only found to be significant in right ear at 30 dB intensity. Wave V was observed to be the last disappearing and the most observed wave among the waves obtained with high frequency stimulus. Based on this study, it was concluded that dogs experience acoustic trauma due to firearm noise, as revealed by a substantial decline in amplitude values in BAER records obtained with click stimulus. It can be said that the use of products developed for dogs and raising awareness in hunters may also be beneficial to prevent harmful effects of acoustic trauma.
Isolation and Characterization of 16-0-(3/, 4/, 5/– Trimethoxybenzoyl) -3-0-β-D-Diglucopyranosylcholest -15- Ene from the Leaves of Spondias mombin linn

This research work was carried out to identify the bioactive constituents of the leaves of Spondias mombin linn a medicinally important plant of the Anacardiaceae family. This study revealed the presence of a steroid. Steroids have been found to be important hormone regulators which possess anti-inflammatory, antioxidant, anti-asthmatic, bronchodilator, anti-spasmodic and oxytocic properties. The chemical investigation resulted in the isolation of a new steroidal glucoside, 16-0-(3/,4/, 5/– trimethoxybenzoyl) -3-0-β-D-diglucopyranosyl cholest -15- ene. The structure was elucidated using two dimensional NMR spectroscopy, NMR ( 1H, 13C ) spectroscopy in combination with IR and MS spectra data.
Parallel visual circuitry in a basal chordate

A common CNS architecture is observed in all chordates, from vertebrates to basal chordates like the ascidian Ciona. Currently Ciona stands apart among chordates in having a complete larval CNS connectome. Starting with visuomotor circuits predicted by the Ciona connectome, we used expression maps of neurotransmitter use with behavioral assays and pharmacology to identify two parallel visuomotor circuits that are responsive to different components of visual stimuli. The first circuit is characterized by glutamatergic photoreceptors and responds to the direction of light. These photoreceptors project to cholinergic motor neurons, via two tiers of cholinergic interneurons. The second circuit is responsive to changes in ambient light and mediates an escape response. This circuit starts with novel GABAergic photoreceptors which project to GABAergic interneurons, and then to cholinergic interneurons shared with the first circuit. Our observations on neurotransmitter use and the behavior of larvae lacking photoreceptors indicate the second circuit is disinhibitory.
Decomposition of the Sum of Cubes, the Sum Raised to the Power of Four and Codeviance

The purpose of this paper is to achieve decomposition formulas of sums regarding deviation cubes, the sum of deviation raised to the power of four and codeviance, because they allow to evaluate the contribution of different components of the above three absolute measures regarding asymmetry, disnormality and concordance. We have obtained more significant formulas that are valid only for two groups, in addition to the formulas valid for r groups, and we have prepared an example to emphasize how useful those formulas were.
Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.
DUAL BAND CIRCULAR POLARIZED PRINTED DIPOLE ANTENNA FOR S AND C BAND WIRELESS APPLICATIONS

In this article, a modified circular shape printed dipole structure is studied to achieve wide bandwidth and dual-band circular polarization (CP) behavior along with dual polarizations. The idea behind this structure is that asymmetric geometry can give rise to circular polarization with an optimized position of coaxial probe feed. The circular patches on both sides of the substrate are altered with elliptical slots at an optimized location in association with opening slots. With these alterations the impedance bandwidth for S11 < −10 dB is ranging from 2.36–7.34 GHz (4.98 GHz) which is nearly 102.5% about mid-point frequency 4.85 GHz. The antenna resonates at a lower band (1.55 GHz) and shows linear polarization (LP) operation at that band whereas dual CP bands with dual senses are obtained at higher frequency ranges 4.00–4.60 GHz and 6.07–7.13 GHz respectively with 3-dB axial ratio bandwidth of 13.7% and 16.6%. The simulated and measured experimental results are in close agreement. This antenna is suitable to be used for navigation purposes, radar communication, and wireless communication (especially wireless avionics intra communications) in S and C bands, respectively.
Activated thiol Sepharose-based proteomic approach to globally quantify protein oxidation

Reactive oxygen species (ROS) can act as second messengers for various signaling pathways, and abnormal oxidation contributes to multiple diseases, including cancer. Detecting and quantifying protein oxidation is crucial for a detailed understanding of redox signaling. We developed an Activated Thiol Sepharose (ATS)-based proteomic approach (ATSP) to globally quantify protein oxidation. ATSP can enrich for H2O2-sensitive thiol peptides, which are more likely to contain reactive cysteines involved in redox signaling. We applied our approach to analyze hereditary leiomyomatosis and renal cell carcinoma (HLRCC), a type of kidney cancer that harbors fumarate hydratase (FH)-inactivating mutations and has elevated ROS levels. Multiple proteins were oxidized in FH-deficient cells, including many metabolic proteins, such as the M2 isoform of pyruvate kinase (PKM2). Treatment of HLRCC cells with dimethyl fumarate (DMF) or PKM2 activators alter PKM2 oxidation levels. This newly developed redox proteomics method can globally detect oxidation of reactive cysteines and can be employed to analyze multiple physiological and pathological conditions.
Human Motion Recognition Based on Spatio-temporal Convolutional Neural Network

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.
PROCEDURES FOR ANALYSIS OF SOLIDS AND LIQUORS FROM CELLULOSIC SOURCES

Cellulosic materials are ground or milled to a suitable size. Ash analysis is made by determination of the residue after ignition at 600/sup 0/C. Extractives are defined as the material removed by extraction with the benzene/ethanol azeotrope. The lignin is determined as the acid-insoluble residue after 2-stage hydrolysis with H/sub 2/SO/sub 4/. The carbohydrate content is determined as the monomer sugars generated by the hydrolysis. The liquors from the acid or base treatment or cellulosic materials are analyzed for the monomer and polymer sugar content. (JSR)
THE INFLUENCE OF PLYOMETRIC TRAINING ON THE IMPROVEMENT OF SPECIAL PHYSICAL PREPARATION OF HIGH-QUALIFIED VISUALLY IMPAIRED SPRINTERS DURING PRE-COMPETITION STAGE

Purpose: describes the effectiveness of applying plyometric training on different flooring of highly qualified visually impaired sprinters at the pre-competition stage. Material and methods: the indicators of special physical fitness of the Honored Master of Sports of Ukraine in para-athletics among athletes with visual impairments during the pre-competition stage of the annual macro-cycle were studied. The following methods were used: analysis and generalization of scientific and methodological literature, analysis of training loads and processing of parameters of training activity, testing of the level of development of high-speed and speed-power, pedagogical observation, methods of mathematical statistics. Results: changing the flooring on which plyometric exercises were performed led to positive changes in the performance of the athlete's physical fitness. Conclusions: the use of preparation at the pre-competition stage of a highly qualified visually impaired sprinter in the six-week plyometric program contributed to a significant increase in speed and speed-power indices of the athlete (p <0.05-0.01).