Miracle-dz/UniFAR-FTUs huggingface dataset:
Antenna structures and cloud‐to‐ground lightning location: 1995–2015

Spatial analyses of cloud‐to‐ground (CG) lightning occurrence due to a rapid expansion in the number of antenna towers across the United States are explored by gridding 20 years of National Lightning Detection Network data at 500 m spatial resolution. The 99.8% of grid cells with ≥100 CGs were within 1 km of an antenna tower registered with the Federal Communications Commission. Tower height is positively correlated with CG occurrence; towers taller than 400 m above ground level experience a median increase of 150% in CG lightning density compared to a region 2 km to 5 km away. In the northern Great Plains, the cumulative CG lightning density near the tower was around 138% (117%) higher than a region 2 to 5 km away in the September–February (March–August) months. Higher CG frequencies typically also occur in the first full year following new tower construction, creating new lightning hot spots.
Dos ordres d'aràcnids (Arachnida; opiliones i escorpiones)

catalaDos ordres d'aracnids, opilions i escorpins, son citats per primera vegada de l'Arxipelag de Cabrera. Foren recol·lectades tres especies (dos opilions i un escorpi): Dasylobus ferrugineus, Scotolemon krausi i Euscorpius carpathicus. Les dues primeres especies son endemiques de les Balears. EnglishTwo orders of arachnida, opiliones and scorpiones, are recorded for first time in the Archipelago of Cabrera. Three species were collected (two harvestmen and one scorpion): Dasylobus ferrugineus, Scotolemons krausi and Euscorpius carpathicus. The two first species are endemic of the Balearic islands.
Corpora amylacea negatively correlate with hippocampal tau pathology in Alzheimer’s disease

Introduction Severity and distribution of aggregated tau and neurofibrillary tangles (NFT) are strongly correlated with the clinical presentation of Alzheimer’s disease (AD). Clearance of aggregated tau could decrease the rate of NFT formation and delay AD onset. Recent studies implicate corpora amylacea (CA) as a regulator of onset or accumulation of tau pathology. Normally, CA clear brain waste products by amassing cellular debris, which are then extruded into the cerebrospinal fluid to be phagocytosed. The proper functioning of CA may slow progression of AD-associated NFT pathology, and this relationship may be influenced by amount and distribution of phospho-tau (pTau) produced, age, sex, and genetic risk. Objective The goal of this study was to determine if CA size and number are associated with hippocampal location and local pTau severity while accounting for variations in age, sex, and genetic risk. Methods Postmortem brain hippocampal tissue sections from 40 AD and 38 unaffected donors were immunohistochemically stained with AT8 (pTau) and counter stained with periodic acid Schiff (PAS). Stained sections of the CA1 and CA3 regions of the hippocampus were analyzed. The percent area occupied (%AO) of CA, pTau, and NFT was calculated. Pairwise comparisons and regression modeling were used to analyze the influence of age, pTau %AO, and genetic risk on %AO by CA in each region, separately in donors with AD and unaffected donors. Results CA %AO was significantly higher in the CA3 region compared to CA1 in both groups. A significant negative correlation of CA %AO with both pTau %AO and neurofibrillary tangle %AO in the CA3 region of AD brain donors was found. Regression analysis in the CA3 region revealed a significant negative association between CA with both pTau and age. Conclusion We found an increase of CA in the CA3 region, compared to CA1 region, in AD and unaffected donors. This may suggest that the CA3 region is a hub for waste removal. Additionally, the negative correlation between %AO by CA and NFT in the CA3 region of the hippocampus in donors with AD suggests CA could play a role in AD pathologic progression by influencing tau clearance.
Investigating the Role of Mechanosensitive Ion Channels in Urothelial Cell Pressure Mechanotransduction

i Acknowledgments.v List of Figures.x List of Tables.xiii Chapter 1: Introduction and Background 1.1 Clinical Significance.1 1.2 Bladder Anatomy.1 1.3 Bladder Physiology.4 1.4 Cellular Mechanotransduction . 7 1.5 Mechanosensitive Ion Channels.9 1.6 Ion Channel-Mediated Mechanotransduction in Mammalian Cells.17 1.7 Urothelial Mechanotransduction.21 1.8 Role of Ion Channels in Urothelial Mechanotransduction.23
Assessing Collector Bias: A Geometric Morphometric Analysis of a Collection of Isolated Clovis Points from the Midcontinent

ABSTRACT Clovis points are found across the contiguous United States as isolated surface finds and as elements of assemblages in surface and subsurface deposits. Despite being scattered over the continent, Clovis points exhibit a remarkable degree of standardization, yet there is still a demonstrable level of variation in their shapes across regions. Including isolated points in regional comparative analyses would significantly increase sample sizes and spatial coverage of these analyses; however, the effects of collector bias—the tendency to collect the most typical and aesthetically pleasing points—are unknown. Here, we examine the shape of a sample of isolated Clovis points from the midcontinent using geometric morphometric techniques. We show that resharpening had little effect on the shape of points and that our sample of isolated points are similar in shape to points from assemblages in the midcontinent. Our findings suggest that isolated points have research potential when collector bias is limited.
Spatial patterns of ice storm disturbance in the forested landscape of Ouachita Mountains, Arkansas and Oklahoma

Spatial Patterns of Ice Storm Disturbance in the Forested Landscape of Ouachita Mountains, Arkansas and Oklahoma. (August 2007) Rachel E. Isaacs, B.A., University of Hawai’i – Hilo Chair of Advisory Committee: Dr. Charles W. Lafon Large-extent ice storms have received relatively little attention from researchers. This research investigates the effects of abiotic and biotic factors on the spatial patterns of ice storm disturbance on a forested landscape. This investigation provides a landscape-level perspective on the impacts of ice storm disturbance, clarifies the effects on ecosystem dynamics, and will aid future forest management plans. The study was conducted in Ouachita National Forest (ONF) in west-central Arkansas and southeastern Oklahoma and examined approximately 6000 km of forest between 150 and 800 m elevation. Normalized Difference Vegeation Index (NDVI) difference values were calculated using two Landsat 7 ETM+ scenes to identify NDVI changes that potentially were associated with ice storm damage to the forests. Forty-six geolocated field sites were used to determine the relationship of NDVI difference to actual forest damage caused by the ice storm by counting the number of downed tree boles intersecting a 100 m transect. These field sites encompassed a broad range of each of the physical variables (i.e. elevation, slope, and aspect), forest type, and degree of damage. The linear regression model determined the relationship between NDVI difference and ice storm damage. Elevation, slope, and aspect were calculated based on individual pixels from the
Community education as a lifelong learning to support sustainable tourism

A community-based tourism (CBT) approach has become an integral part of rural and sustainable tourism development strategies. However, CBT has some constraints on being sustainable due to some internal weaknesses and external concerns, including poor marketing competencies. As a top-down government initiative, Malaysian CBT homestay is blessed with numerous assistance to ensure its survival which in the other hand, creates over-reliance toward other parties. This lack of marketing expertise challenges homestay sustainability. This study examines the importance of community education in marketing activities as a lifelong learning towards sustainable tourism in Malaysia. Qualitative case study is employed to gain an extensive and in-depth explanation on homestay’s marketing implementation and its issues. A semi-structured interview was taken to 9 homestay operators. This study employs a thematic data analysis to investigate the interview results. Research findings show that in majority, the community enjoys the comfort zone provided by government and travel agents, hence encourage the resistance for change. However, a few homestay demonstrates positive respond to involve lifelong learning through community education to support sustainable tourism. More importantly, partnership between various key stakeholders are the utmost important to ensure policy planning and to improve community education quality.
Impressive predictive model for Breast Cancer based on Machine Learning

INTRODUCTION: Breast cancer is a major health concern for women all over the world. OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade. METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei. RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature. CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.
Intention to Use Paylater Feature in a Traveling Platform in Emerging Market: Traveloka Case

This study aims to determine the relationship between customer’s attitude, customer’s subjective norm, and the customer has perceived behavior control on the intention to use PayLater as a payment method in Traveloka. This study uses an online and offline survey conducted in Jakarta and Bandung, with 400 respondents. Structural equation modeling was processed using SmartPLS 3.2. 9 for this study. The finding shows that customers’ attitudes, subjective norms, and perceived behavior control positively influence the intention to use PayLater in Traveloka. This study can be used for another application or company that wants to develop a similar payment method to encourage higher use of PayLater. This study explores the inner working of customers’ behavior in connection with the financial service of e-payment, in this case, Traveloka PayLater.
Symptom reduction in mal de débarquement syndrome with attenuation of the velocity storage contribution in the central vestibular pathways

Background The velocity storage mechanism of the central vestibular system is closely associated with the vestibulo-ocular reflex (VOR), but also contributes to the sense of orientation in space and the perception of self-motion. We postulate that mal de débarquement syndrome (MdDS) is a consequence of inappropriate sensory adaptation of velocity storage. The premise that a maladapted velocity storage may be corrected by spatial readaptation of the VOR has recently been translated into the development of the first effective treatment for MdDS. However, this treatment's initial impact may be reversed by subsequent re-triggering events. Presently, we hypothesized that MdDS symptoms could alternatively be reduced by attenuating the velocity storage contribution in the central vestibular pathways. Methods Forty-three patients with MdDS (aged 47 ± 14 yo; 36 women) were randomly assigned to two treatment groups and followed for 6 months. The horizontal VOR was tested with chair rotation during laboratory visits, and the strength of velocity storage was quantified with model-based parameters—the time constant (Tc) and the gain of coupling from the vestibular primary afferent signals (g0). To attenuate velocity storage, Group 1 underwent a progressively intensifying series of low-frequency earth-vertical oscillatory rotation coupled to conflicting visual stimuli. Group 2 underwent an established protocol combining head tilts and visual stimulation, designed to correct maladapted spatial orientation but not change the velocity storage strength. The symptom severity was self-rated on an 11-point scale and reported before and up to 6 months after the treatment. Results In Group 1, velocity storage was modified through reduction of g0 (p < 0.001) but not Tc. The symptom rating was at least halved initially in 43% of Group 1 (p = 0.04), the majority of whom retained a similar level of improvement during the 6-month follow-up period. In Group 2, no systematic change was induced in the parameters of velocity storage strength, as expected. The symptom rating was at least halved initially in 80% of Group 2 (p < 0.001), but paralleling previous findings, symptoms often returned subsequently. Conclusion Attenuation of velocity storage shows promise as a lasting remedy for MdDS that can complement the VOR readaptation approach.
A Nomograph to Incorporate Geophysical Heterogeneity in Soil Moisture Downscaling

Hydrological applications require robust and periodic spatially distributed soil moisture data. Radiometer‐based soil moisture (~30–60‐km resolution), after being appropriately downscaled (<5‐km resolution), can be a valuable resource for providing such data globally. However, the accuracy of available downscaling algorithms is severely affected by subgrid variability in geophysical factors and precipitation within a satellite footprint. In this work, we introduce a scaling nomograph that incorporates the scale and site specific dependence of soil moisture on geophysical heterogeneity and antecedent wetness conditions to overcome this limitation. We developed functional scaling relationships to estimate the semivariogram of downscaled soil moisture change without any available fine‐scale soil moisture data. The nomograph enables these relationships to be specific to the geophysical heterogeneity and antecedent wetness within a radiometer‐based satellite footprint through footprint specific heterogeneity and wetness indices. The heterogeneity index quantifies the subgrid scale variability and covariability of soil, vegetation, and topography within the footprint, and the wetness index is a measure of antecedent precipitation. The nomograph was developed for Arizona, Iowa, and Oklahoma and can enable downscaling to scales varying between 0.8 and 6.4 km. The true power of the nomograph is to enable the use of static dominant factors like soil to define dynamic scale specific scaling relationships for soil moisture for different kinds of land use and land cover in a data driven yet scientific approach, thus providing spatial transferability to the downscaling scheme. The spatial transferability of the nomograph was validated by downscaling Soil Moisture Ocean Salinity data in Manitoba, Canada.
Investigating the Influence of Phosphorus Availability on Belowground Processes in Forested Ecosystems

DORKOSKI, RYAN, Ph.D., April 2016, Environmental and Plant Biology Investigating the Influence of Phosphorus Availability on Belowground Processes in Forested Ecosystems Director of Dissertation: Jared L. DeForest As a consequence of a profound change in the chemical environment, acid rain has been altering our forest ecosystem structure and function over the last several decades. Acid rain deposition acidifies the soil and adds nitrogen (N), which may shift historic N limitation in temperate forests to more emphasis being placed on phosphorus (P). While ecosystem N dynamics are fairly well understood, further research is needed on how decreased P availability may be impacting microbial community structure and function, and inputs of soil carbon from fine root turnover. This series of investigations made use of field sites established in 2009 based on soil age by considering glaciated and unglaciated soils in Eastern Ohio. Field sites were manipulated annually to indirectly and directly increase P availability, with the intent of reaching ecosystem P saturation. Overall, yearly P additions decreased soil P-acquiring enzyme activity compared with ambient soils, and increased immobilization rates. Interestingly, soil P-acquiring enzyme activity was increasingly suppressed over the six years since site initiation due to indirect P treatments (P < 0.01) in the glaciated (r = 0.50) and unglaciated soils (r = 0.42). Direct P additions resulted in nearly double the microbial P immobilization rates compared with ambient soils (P < 0.03), signifying that chronic acid deposition may reduce immobilization ability. Decreased soil biota P-acquiring enzyme dependence in
Evaluation of optical sensor technologies to optimize winter wheat (Triticum aestivum L.) management

Sensor technology has become more important in precision agriculture, by real time sensing for site specific management to monitor crops during the season especially nitrogen (N). In Kansas N available in the soils can vary year to year or over a course of a year. The objective of this study was to compare current available passive (PS) and active optical sensor technologies (AOS) performance in regards to sky conditions effects and derive the NDVI (normalized difference vegetation index) relationship to wheat yield, as well as evaluate KSU optical sensor-based N recommendations against KSU soil test N recommendation system and sUAS (small unmanned aircraft systems) based recommendation algorithms with the PS and AOS platforms. Each year (2015-2016 & 2016-2017) five field trails across Kansas were conducted during the winter wheat crop year in cooperation with county ag agents, farmers, and KSU Agronomy Experiment Fields. Treatments consisted of N response curve, 1 and 2 generation KSU N recommendation algorithms, sUAS based recommendation algorithms, and KSU soil test based N recommendations applied in the spring using N rates ranging from 0 to 140 kg ha. Results indicate the Holland Scientific Rapid Scan and MicaSense RedEdge NDVI data was strongly correlated and generated strong relationships with grain yield at 0.60 and 0.57 R respectively. DJI X3 lacks an NIR band producing uncalibrated false NDVI and no relationship to grain yield at 0.03 R. Calibrated NDVI from both sensors are effective for assessing yield potential and could be utilized for developing N recommendation algorithms. However, sensor based treatments preformed equal to higher yields compared the KSU soil test recommendations, as well as reduced the amount of fertilizer applied compared to the soil test recommendation. The intensive management algorithm was the most effective in determining appropriate N recommendations across locations. This allows farmers to take advantage of potential N mineralization that can occur in the spring. Further research is needed considering on setting the NUE (nitrogen use efficiency) in KSU N rec. algorithms for effects of management practice, weather, and grain protein for continued refinement.
New AMS Dates from the Shukubai-Kaso Site (Loc. Sankakuyama), Hokkaido (Japan): Refining the Chronology of Small Flake-Based Assemblages during the Early Upper Paleolithic in the Paleo-Sakhalin-Hokkaido-Kuril Peninsula

ABSTRACT This paper reports accelerator radiocarbon dates obtained from 14 charcoal specimens recovered from the Shukubai-Kaso site (Loc. Sankakuyama), which was excavated in 1973. The small flake-based assemblage from the site has been chronologically attributed to represent the oldest archaeological occupation in Hokkaido. Because the new radiocarbon dates, stratigraphic sequences, and site context do not contradict each other, we conclude that the ages of the archaeological components in Layer III of Shukubai-Kaso (Loc. Sankakuyama) fall within the interval of 29,530 and 28,480 cal yr BP. These ages are close to previously estimated dates for other small flake-based assemblages including the Wakabanomori, Kamiitaira, and Kyu-Shirataki 3 sites. They are also 1500 years older than last glacial maximum (LGM) assemblages on the Paleo-Sakhalin-Hokkaido-Kuril peninsula. These new ages reliably place the small flake-based assemblages in the chronological phase prior to the onset of the LGM.
of deep learning

To answer the question of why have a course on the mathematics of deep learning, it is first instructive to consider why have a course on deep learning at all. To many of you, the answer to this question may seem clear or the question itself rhetorical. Indeed, already at MSU there are several courses on deep learning or that address deep learning as part of their course content, including courses in CSE, ECE, and MTH. Nevertheless, let us consider this question for a moment. Deep learning refers to a class of algorithms in machine learning and more generally artificial intelligence. One of the hallmarks of deep learning algorithms is that they compose a sequence of many simple functions, often alternating between linear or affine functions, point-wise nonlinear functions, and pooling operations. Figure 1 gives an illustration of the VGG16 network [1], which is a powerful and very popular convolutional neural network, that consists of 16 layers of linear/non-linear pairs of operations, as well as pooling operations (where the length and width of the image stacks shrink) every few layers. Note that all of the linear functions are learned from the given training data and the associated task, which for VGG16 was image classification on the the ImageNet data base [2] (more on this later). Thus the the input to the VGG16 network is an RGB image, and the output is a class label. The compositional structure illustrated in the VGG network, and used in all of deep learning (this is where the “deep” comes from), has been incredibly successful in machine learning and artificial intelligence tasks over the last decade. Indeed, deep learning is now used in a multitude of different contexts, from computer vision to natural language processing to playing games to biology to physics and more. One of the most striking examples of the success of deep learning (and in this case, reinforcement learning), is the success of AlphaGo
Haiyang Qiangguo: China as a Maritime Power

Abstract : At its 18th Party Congress in November 2012, the Chinese Communist Party adopted a new goalthat China should enhance our capacity for exploiting marine resources, develop the marine economy, protect the marine ecological environment, resolutely safeguard Chinas maritime rights and interests, and build China into a strong maritime power (emphasis added). Footnote 3 Subsequent commentary by Chinese leaders and national-level documents characterize the goal of becoming a maritime power as essential to Chinas national development strategy, the peoples well-being, the safeguarding of national sovereignty, and the rejuvenation of the Chinese nation. Footnote 4