Miracle-dz/UniFAR-FTUs huggingface dataset:
American Accounting Association ' s Financial Accounting Standards Committee Response to FASB Exposure Draft , " Share-Based Payment : An Amendment of FASB Statements No . 123 and 95 "

The Financial Accounting Standards Committee of the American Accounting Association {"the Committee") is charged with responding to requests for comment from standard setters on issues related to financial reporting. The Committee is pleased to respond to the FASB Exposure Draft on Share-Based Payment (hereafter, the ED). The comments in this letter reflect the views of the individuals on the Committee and not those of the American Accounting Association.
Deficit irrigation in nectarine: Fruit quality, return bloom and incidence of double fruits

Summary Deficit irrigation (DI) at phenological stages less sensitive to water stress (pit hardening and post-harvest stage) influenced fruit quality, plant growth, return bloom and incidence of double fruits in nectarines. Four irrigation levels: control irrigation (CI), DI 75 (75 % of CI), DI 58 (58 % of CI) and DI 33 (33 % of DI) were applied in ‘Spring Bright’ and ‘Summer Bright’ nectarines during pit hardening and post-harvest stages. DI 58 and DI 33 treatments resulted in development of intense red fruit colour and early maturity in ‘Spring Bright’. DI 33 improved fruit colour in ‘Summer Bright’, soluble solids concentrations (SSC) and SSC/acid ratio in both cultivars. DI 33 treatment decreased glucose and fructose at harvest in ‘Spring Bright’ and increased glucose in ‘Summer Bright’. DI 58 and DI 33 treatments improved the levels of total phenolics, antioxidant capacity, ascorbic acid and carotenoids in the fruit of both cultivars with acceptable fruit weight in comparison to CI. DI 33 treatment significantly increased the total anthocyanin content in fruit peel and reduced water sprout length in both cultivars. In subsequent season, the DI 58 and DI 33 treatments increased flower density in ‘Spring Bright’ and incidence of double fruit in either cultivar. Conclusively, DI improved fruit colour, SSC/acid ratio and nutraceutical levels; and reduced water sprout length. Further, it increased flower density and double fruits in the subsequent season.
Correlation induced by missing spatial covariates: a connection between variance components models and kriging

Residual spatial correlation in linear models of environmental data is often attributed to spatial patterns in related covariates omitted from the fitted model. We connect the nonunique decomposition of error in geostatistical models into trend and covariance components to the similarly non-unique decomposition of mixed models into fixed and random effects. We specify spatial correlation induced by missing spatial covariates as a function of the strength of association and (spatial) covariation of the missing covariates. The connection with variance components models provides insight into estimation procedures. We showed how missing covariates in spatial linear models actually induces spatial autocorrelation in the covariates. This finding was confirmed through the use of simulated data and the Binary Steve dataset.
Controlling Air Traffic Congestion By Predicting Flight Departure Delays And Duration: Integrating Machine Learning Sampling Techniques And Deep Learning Approaches

Flight delays that may propagate through the entire aviation sector are becoming a cause of flight cancellations and resource reallocation which can result in huge economic losses to airlines. In this study, a novel hierarchical integrated machine learning model is proposed to accurately predict flight delays and durations in series rather than in parallel in the future forecast horizon, above a certain threshold, to avoid ambiguity in decision making. The historical data was provided by an international airline operating in Hong Kong and our findings provide insights for airlines in controlling future flight delays
Radiative MRI Coil Design Using Parasitic Scatterers

Conventionally, radiofrequency (RF) coils used for magnetic resonance imaging (MRI) are electrically small and designed for nearfield operation. Therefore, existing antenna design techniques are mostly irrelevant for RF coils. However, the use of higher frequencies in ultrahigh field (UHF) MRI allows for antenna design techniques to be adapted to RF coil designs. This study proposes the use of parasitic scatterers to improve the performance of an existing 7T MRI coil called the single-sided adapted dipole (SSAD) antenna. The results reveal that scatterers arranged in a Yagi fashion can be applied to reduce local specific absorption rate (SAR) maxima of a reference SSAD by 40% with only a 6% decrease in the propagated B1 field at the tissue depth of 15 cm. The higher directivity of the proposed design also decreasing the coupling with additional elements, making this antenna suitable for use in high density arrays. These findings show the potential of parasitic scatterers as an effective method to improve the performance of existing radiative MRI coils.
Eukaryotic Microbes in the Deep Sea: Abundance, Diversity, and the Effect of Pressure

EUKARYOTIC MICROBES IN THE DEEP SEA: ABUNDANCE, DIVERSITY, AND THE EFFECT OF PRESSURE Danielle Morgan-Smith Old Dominion University, 2012 Director: Dr. Alexander B. Bochdansky The dark ocean is vast, high in pressure, cold, and scarce in resources, but has been shown to support a diverse and active microbial community wherever it is studied. Such studies, however, are scarce due to the difficulty of sampling at such depths, and are difficult to interpret due to compounding effects of pressure and temperature on physiology. Protists, functionally defined as the microbial portion of the domain Eukarya, are particularly neglected in studies of deep-sea microbiology. Here, I present three studies on microbial eukaryotes in the deep sea: first, a study of the abundance of microbial eukaryotes in the deep sea, second, a quantitative approach to study broad-scale diversity in the deep sea; and last, a series of experiments to explore the effect of deepsea conditions on surface-isolated flagellates. In the deep sea, I found that eukaryote abundances decrease much more sharply than prokaryote abundances with depth, though most of this decrease occurs in the upper 1000 m, below which eukaryote abundance is relatively constant. In water masses below 1000 m, 50-70% of total eukaryotes detected by CARD-FISII can be attributed to one of the seven groups (six taxonomic using CARD-FISH and one by morphology when stained with DAPI). In the epipelagic 100 m samples, only 20% of total eukaryotes fall into one of these groups. This difference is driven largely by the morphotype I call the "split nucleus", which does not decrease in absolute abundance with depth, instead increasing in its proportion of the eukaryotic population in deeper waters. Lastly, I found that eukaryotic microbes, typified by two heterotrophic flagellate species which appear to be ubiquitous in the world's oceans, can survive and even grow despite long-term exposure to the cold, high-pressure conditions of the deep sea, indicating that protists transported to the deep sea by advection or on particles can seed populations there.
A Selection Method for Molecular Descriptors and QSPR Equations

The proposed method for the selection of the descriptors and QSPRs is a version of the heuristic Forward Stepwise procedure, using specific criteria to define the "significant" descriptors, the quality of QSPRs, the maximum number of predictors in QSPR, the "near constant" descriptors, the minimum value of the correlation descriptor/Property, the maximum value of the intercorrelation of the descriptors and including a specific criterion to stop the calculation. The method allows the selection of less than 1000 suitable descriptors included in a group of up to one hundred thousand significant descriptors. The method highlights the importance of the selection of the significant descriptors having a low correlation with the dependent property. If the number of selected significant descriptors is smaller than 1000, the heuristic Forward Stepwise procedure is a suitable method for the selection of the QSPR/QSAR equations. Two different QSARs obtained with the same database should be compared through the proposed function of leverages, calculated for the prediction set molecules. The presence or absence of "overfitting" depends on the number and type of descriptors in the initial set. The proposed method was applied with good results in QSAR studies regarding the anesthetic potency of 134 inhalation anesthetics (without validation/prediction set) and the toxicity of 50 phenol derivatives (including MATCH Communications in Mathematical and in Computer Chemistry MATCH Commun. Math. Comput. Chem. 77 (2017) 245-272 ISSN 0340 6253 validation/prediction set). The paper suggests testing some linear hydroxy-ketones, containing 6-12 carbon atoms and various distances between the hydroxyl and carbonyl groups, as new inhalation anesthetics. In analysis of λmax for 66 derivatives of 9,10-anthraquinone the quality of PRECLAV prediction for the same calibration and validation sets is better than the quality of prediction made by ACO + MLR method.
Significance of Methylthioadenosine Metabolism to Plant Growth and Development

Arabidopsis thaliana contains two genes annotated as methylthioadenosine nucleosidases (MTN): MTN1, At4g38800 and MTN2, At4g34840. This enzyme activity hydrolyzes the methylthioadenosine (MTA) produced by nicotianamine (NA), polyamine (PA), and ethylene biosynthesis to methylthioribose (MTR) within the Yang cycle. Comprehensive analysis of the mtn1-1mtn2-1 mutant line with 14 % residual MTN activity revealed a complex phenotype that includes male and female infertility and abnormal vascular development. Based on metabolite profiling, mtn1-1mtn2-1 has a reduced NA content, altered PA profiles with higher putrescine (Put) and lower spermidine (Spd) and spermine (Spm) levels, disrupted metal ion profiles, and abnormal auxin distribution. The modeling of Arabidopsis PA synthases developed by comparison with the crystal structures of human Spd and spermine synthases complexed with MTA suggests that Arabidopsis PA synthases are product inhibited by MTA. Thus, these pleiotropic mutant phenotypes possibly are the result of one metabolite directly inhibiting numerous pathways. By creating and analyzing a series of mutants and transgenic lines with moderate levels of MTN activity the complex phenotype of mtn1-1mtn2-1 was dissected in order to determine the fundamental trait associated with MTN deficiency. Two double mutants were identified by crossing single T-DNA mutants, and an artificial micro RNA (amiRNA) line was generated by transforming mtn1-1 with amiRNA specific to MTN2. The T-DNA double mutants, mtn1 4mtn2-1, and mtn1-1mtn2-5 had 98 % and 28 % MTN activity, respectively, whereas the amiRNA line has 16 % MTN activity. The growth, development, and metabolite analysis of these mutants revealed that their delayed bolting, correlated with an increased number of leaves, was the common trait observed across all lines. Xylem proliferation defects and increased number of vascular bundles per unit area were shared in all lines except mtn1 4mtn2-1. Based on these results, auxin distribution is proposed as the key target of the accumulated MTA that results from MTN deficiency. The infertility related to MTN-deficiency was restored by supplying 100 µM of Spd to the mtn1-1mtn2-1 seedlings over 14 days. The data presented in this thesis reveals two potential links that work synergistically to recover fertility in this mtn1-1mtn2-1 line. Based on a detailed analysis of the female gynoecia morphology, transcript, hormone and metabolite profiles, it is proposed that the Spd partially reverses the mutant phenotypes through the recovery of auxin distribution and /or vascular development. Interestingly, the Spd effect seems to be transgenerational: they give rise to plants that are genotypically mtn1-1mtn2-1 but phenotypically WT over generations. Taken together, all of the results suggest that MTN-deficient mutants provide the potential for unraveling the molecular mechanism associated with nicotianamine, polyamines, auxin, and vascular development with respect to enhancing the efficiency of nutrient use and yields in plants.
AN APPROACH TO REDUCE ELEPHANT DEATH FROM TRAIN ACCIDENT

Train Elephant Conflict (TEC) is one of the major issues across the world which affects both human and elephant. A recent research indicates many elephants and other animals die due to train accidents mostly at night time. Despite railway authorities instructing the drivers to reduce the train speed in forest areas, there has not been much reduction in elephant death from trains. The surveillance and tracking of elephants are difficult due to their size. The proposed system automatically detects the elephants with their sound that are diverse in the field. The input to the proposed system is the acoustic wave generated by the elephants. The acoustic wave receptors are placed on the path of the elephant. Raven pro 1.5 software is used to analyze the acoustic data and to improve signal enhancement, robustness to detect in noisy situations. Using MATLAB software the recorded acoustic waves are filtered to reduce the environmental noise recorded with actual sound of the elephant then the filtered sounds are tested for recorded spectrogram patterns stored previously in the system. On successful match of the sound of the elephants, SMS will be sent to the forest officials, station master and train operator. This paper helps to prevent the elephant death from train accidents.
Rethinking data‐driven decision support in flood risk management for a big data age

Decision‐making in flood risk management is increasingly dependent on access to data, with the availability of data increasing dramatically in recent years. We are therefore moving towards an era of big data, with the added challenges that, in this area, data sources are highly heterogeneous, at a variety of scales, and include a mix of structured and unstructured data. The key requirement is therefore one of integration and subsequent analyses of this complex web of data. This paper examines the potential of a data‐driven approach to support decision‐making in flood risk management, with the goal of investigating a suitable software architecture and associated set of techniques to support a more data‐centric approach. The key contribution of the paper is a cloud‐based data hypercube that achieves the desired level of integration of highly complex data. This hypercube builds on innovations in cloud services for data storage, semantic enrichment and querying, and also features the use of notebook technologies to support open and collaborative scenario analyses in support of decision making. The paper also highlights the success of our agile methodology in weaving together cross‐disciplinary perspectives and in engaging a wide range of stakeholders in exploring possible technological futures for flood risk management.
Cephalometric Assessment of Anterioposterior Discrepancy: A Review of Various Analyses in Chronological Order

Review Article The anteroposterior discrepancy is typically of utmost concern to patients and parents and therefore has received a valuable attention in dentistry. A number of analyses are proposed over the years with varied degrees of accuracy and success in assessing sagittal jaw relationships. It is completely essential that a practitioner should be able to use one or more of the various analyses available in a particular situation. This review provide a valuable idea to use various cephalometric analysis or methods used for analysis of the anteroposterior jaw relationship in chronological order and their clinical implications in modern dental practice.
Sustainable Value as a Tool for Corporate Performance Management within New Public Management Framework

This paper deals with implementation of Sustainable Value approach into Corporate Performance Management within New Public Management framework and its use in public administration environment. Nowadays, public organizations are looking for ways to become more efficient and, in some respect, to gain a competitive advantage but also put the emphasis on sustainable development. The concept of use selected management methods of private sector and their use in public administration is called New Public Management. It has not clearly defined boundaries and it is still open to new possibilities and tools. Due to increasing pressure on organizations, no matter if private or public, on compliance with the principles of sustainable development, authors define crucial economic, environmental and social indicators which should be included in overall performance evaluation of public administration environment. In general implementing sustainable development principles into organizational performance management should bring wide range of effects, for example contribution to cost savings, better efficiency of processes, or strengthen relationship within the community and customer loyalty. In practical terms, the aim of this paper is to introduce Sustainable Value as a proper tool for Corporate Performance Management, which seems to be suitable, within New Public Management framework, for use by regional governments to measure and evaluate performance. Key-Words: Corporate Performance Management, New Public Management, indicators, public sector, private sector, Sustainable Value
Domain-independent interprocedural program analysis using block-abstraction memoization

Whenever a new software-verification technique is developed, additional effort is necessary to extend the new program analysis to an interprocedural one, such that it supports recursive procedures. We would like to reduce that additional effort. Our contribution is an approach to extend an existing analysis in a modular and domain-independent way to an interprocedural analysis without large changes: We present interprocedural block-abstraction memoization (BAM), which is a technique for procedure summarization to analyze (recursive) procedures. For recursive programs, a fix-point algorithm terminates the recursion if every procedure is sufficiently unrolled and summarized to cover the abstract state space. BAM Interprocedural works for data-flow analysis and for model checking, and is independent from the underlying abstract domain. To witness that our interprocedural analysis is generic and configurable, we defined and evaluated the approach for three completely different abstract domains: predicate abstraction, explicit values, and intervals. The interprocedural BAM-based analysis is implemented in the open-source verification framework CPAchecker. The evaluation shows that the overhead for modularity and domain-independence is not prohibitively large and the analysis is still competitive with other state-of-the-art software-verification tools.
ROLE OF SOCIAL CAPITAL IN GOOD GOVERNANCE THE CASE OF PRDP IN BANGLADESH

URPOSE THE study was undertaken with the objective of identifying the institutional mechanisms for promoting transparency, accountability, and villagers’ participation in Gram Committee (GC) activities under Participatory Rural Development Project (PRDP) link model that emanated from creation of social capital in rural society; and to test a research question i.e., does formation of social capital contribute to better implementation of GC activities under PRDP link model?
SENSORLESS BROKEN BAR DETECTION IN INDUCTION MACHINES

This study describes broken bar detection in induction motors before actual breakdown occurs. It is based on fluctuations of stator current Zero-Crossing Times (ZCT). Instead of sampling motor current with an high resolution A/D converter, zero crossing instants are recorded as waveforms cross zero. Fluctuations in the intervals between successive zero-crossings of the three phase current waveforms are analysed using Fast Fourier Transforms (FFT). Rotor bar faults are identifiable from amplitude changes of s sf 2 spectral component where s is the motor
Acute Lymphoblastic Leukemia Detection Using Depthwise Separable Convolutional Neural Networks

In this paper, we present a neural network with depthwise separable convolutions (Xception) for the identiﬁcation of leukemic B-lymphoblast cells, commonly known as Acute Lymphocytic Leukemia (ALL). Earliest possible detection of these cancerous cells is required to minimize the physical toll on the patient and the treatment challenges presented by the disease. Through a transfer learning approach, we tested various convolutional neural network algorithms on our augmented microscopic blood smear image dataset to assess the best performing architecture for classifying leukemic cells, resulting in the Xception architecture. We obtained 99% and 91% accuracy on the training and testing sets, respectively. Furthermore, we achieved a recall rate as high as 98% showing good discrimination power against false negatives. classiﬁed into two cat-egories on a broader sense: (1) Acute leukemia (progresses quickly); and (2) chronic leukemia (progresses slowly). In this paper, we study the presence of Acute Lymphoblastic Leukemia (ALL) only.
A Stochastic Lookahead Approach for Disaster Relief Logistics Operations

Motivation. Many societies in the U.S. are vulnerable to disasters such as earthquakes, hurricanes, floods, wildfires, and terrorist attacks. Hurricanes, in particular, are studied extensively because they affect large areas and populations, and they are relatively easier to predict compared to other disasters. For instance, in 2005, Hurricane Katrina destroyed more than 200,000 houses and cost over $100 billion (Deryugina, Kawano, and Levitt 2018). In recent years, researchers have shown an increased interest in improving the efficiency of disaster relief logistics. In the aftermath of a hurricane landfall, there are usually shortages in water, food, and medicine critical resources for survival. Under this situation, disaster relief logistics informs operational decision making under uncertainty, which involves mobilizing various resources to help the affected population by a disaster in a timely fashion. The importance of the relief logistics operations planning is indisputable. Problem Statement. The most challenging problem in humanitarian logistics is the coordination of logistical arrangements (Kovács and Spens 2009). To analyze the disaster relief logistics, Apte (2009) separates the operations during the disaster life cycle into four phases: mitigation, preparedness, response, and recovery. In this paper, we focus on disaster relief operations planning to deliver relief items to the affected area during the response phase. Vanajakumari, Kumar, and Gupta (2016) stated that most emergency administrators prefer a flexible and realistic optimization model for this problem. In order to have a realistic model, we consider the transportation cost as well as social cost (in terms of the deprivation cost) in the objective of our model. PérezRodŕıguez and Holgúın-Veras (2015) pointed out the importance of not ignoring the social cost incurred by the affected population which could be significantly reduced by using an appropriate relief item distribution strategy. Also, by considering the uncertainty parameters caused by the evolving information during the response phase, our model meets the requirements for flexibility and reality. Specifically, assume that we have a timeline for the response phase starting from day 0 (one day before the landfall), and ending on day T (the last day of the response phase). On day 0, the demand, supply, and road network conditions for remaining days are unclear and we only have access to a stochastic forecast of the track and intensity of the hurricane. After landfall, for