Miracle-dz/UniFAR-FTUs huggingface dataset:
THE METABOLISM AND DETOXIFICATION OF DDT IN MAMMALS

This study was carried out in three phases; (l) the development of a method for DDA analysis having greater sensitivity and specificity than methods presently available, (2) a study of DDT metabolism or biodegradation in mammals and, (3) to work out a method to monitor DDT ex­ posure in the human population by analysis of DDA in urine. A method for the analysis of DDA in urine and tissue samples was worked out using a diazomethane esterification to convert the DDA to its methyl ester (DDA-Me). Detection can be carried out by either electron capture or microcoulometric gas chromatography. It was also found that the DDA can be analyzed as the free acid but is degraded in the detec­ tion step to dibenzophenone (DBP). Several species of mammals were dosed with DDT orally and the excretory products analyzed to determine the major degradation product(s) resulting. It was found that DDA is the major urinary excretory product resulting, while DDT, DDE and DDD are present at very low levels. Oral dosing studies on mice were carried out with p,p'-(DDT, DDD, DDMU, DDE and DDA) to determine their metabolism and excretion pattern. The pro­ duction of DDA in the urine was used to follow the postulated breakdown sequence of DDT as proposed by earlier workers. It was found that. DDE is not an intermediate in the degradation of DDT to DDA, that DDD leads to an increase in the production of DDA in urine over that found from DDT administration and is presumably an intermediate in the breakdown of DDT
Recognition and Retrieval of Mathematical Expressions from Arabic Documents

Recently, researchers have strived to enhance existing search tools to enable the retrieval of a diversity of data types to help users in finding what they are looking for from the Web, however, very limited research is applicable to Arabic content. In this research, we introduce a rule-based approach to search for mathematical expressions written in Arabic and/or English from Arabic stored documents. A set of normalization as well as math expressions’ equivalence rules were built to enhance the capabilities of a math search engine. Furthermore, rules for structural search to enable search for sub-expressions were also built. An indexing mechanism and a mapping between Arabic and English expressions during the search process was also produced. The approach was applied using a set of forty queries; written in Arabic and/or English, was applied on a manually collected dataset of 100 documents which has produced an overall accuracy of 75%.
Effects of Management Strategies and Molecular Breeding Values on Cattle Performance and Carcass Traits

Forty-eight Gelbvieh x Angus steers (265 ± 40 kg) were utilized to determine the relationships among molecular breeding values (MBV), steer performance, and carcass traits. Body weight (BW), hip height (HH), hip width (HW), exit velocity (EV; rate at which steers exited the squeeze chute and traversed 1.8 m) and body ultrasound measurements of steers were recorded at d 0, 93 and 154 of grazing mixed stockpiled endophyte-infected and -free tall fescue. Tissue samples were collected for genomic profiling (Igenity, Merial Limited, Duluth, GA). Steers were transported to the Oklahoma State U fed for 159 d, harvested and carcass parameters recorded. At d 0 and 154 of grazing, BW was correlated (P < 0.05) with MBV for ADG (r = 0.31 and 0.32 for d 0 and 154, respectively). Hip width was correlated (P < 0.05) with MBV for ADG (r = 0.33 and 0.32 for d 0 and 154, respectively) at d 0 and 154. An inverse correlation between EV and MBV for LM area on d 0 (P < 0.01; r = -0.48) and d 154 (P < 0.03; r = -0.03) of grazing was observed; on d 93, EV and MBV for LM area tended to be inversely correlated. Ultrasound measurements for intramuscular fat on d 0 were correlated (P < 0.05) with MBV for docility (r = 0.40). Predictive potential of MBV from the stepwise procedure for steer performance and carcass composition was low (r 2 ≤ 0.22). Molecular breeding values were correlated with several measurable traits that can be obtained on-farm. Incorporation of MBV may aid cattle producers in more accurate selection practices to increase profitability of beef
Fingerprint Minutiae Matching without Global Alignment Using Local Structures

This paper presents a method of minutiae based fingerprint matching that is robust to deformations and does not do fingerprint alignment. It concentrates on comparing rotation and translation invariant local structures defined by minutiae point and its neighboring minutiae points. Then the collection of most probable correspondences of matched minutiae is found. Finally, the local structures of higher order are validated. All three steps are completely rotation and translation invariant, robust to nonlinear deformations and do not use any fingerprint alignment. Experimental results on publicly available as well as internal databases show an improved performance of the proposed method in comparison with the traditional minutiae based algorithms that perform fingerprint registration.
Joint Availability Guarantee and Resource Optimization of Virtual Network Function Placement in Data Center Networks

Network Function Virtualization (NFV) is a promising technology that decouples network functions from the physical device on which they deployed. Network service in NFV is deployed as Service Function Chain (SFC) that consists of an ordered set of Virtual Network Functions (VNFs). In this paper, we focus on the VNF placement problem in data center networks considering availability guarantee and resource optimization. Firstly, we define an availability model that takes both physical device failures and VNF failures into consideration when evaluating the availability of SFC. Secondly, we propose a novel Joint Path-VNF (JPV) backup model that combines path backup and VNF backup in a joint way. In the JPV backup model, resource consumption can be effectively reduced. Finally, we design an Affinity-Based Algorithm (ABA) to reduce physical link consumption when map VNFs. The evaluation results show that ABA and JPV can achieve better availability improvement (99.99%) with less resource (reduce 40% PL consumption).
A Systematic Approach to the Optimal Planning of Energy Mix for Electric Vehicle Policy

Electric vehicle offers a cleaner and sustainable alternative to transportation as it eliminates direct carbon dioxide emission through the conventional internal combustion engine. With the increase in the global population and economic development, the demand for transportation and the adoption of electric vehicles is unprecedented. However, the adoption of electric vehicle on a national-scale requires long-term planning of infrastructure development, and energy generation and distribution. The study focuses on the development of a systematic mathematical programming approach in the optimal planning of the energy mix of the additional power generation capacity arising from the adoption of the electric vehicle in a developing country. The study considers the 2030 horizon which includes, the cost of power generation and distribution per energy mix, and the forecasted commissioning and decommissioning of energy plants. The study proposes a fuzzy mixed-integer non-linear programming model in the optimal planning of the energy mix for the adoption of EV while minimizing carbon footprint, minimizing the total capital cost, and minimizing the electricity cost. A case study in the adoption of electric vehicle in the Philippines will be utilized to demonstrate the capability of the model. In addition, a comparison of the electricity cost of the business as usual (BAU) scenario and this study has been evaluated. The results show that the various renewable energy technologies for power generation are selected initially from 2019 to 2022 and 2029 to 2030, while the fossil-fuel based power plants were utilized from 2023 to 2028. The results revealed the electricity cost from the study is relatively lower than the BAU scenario. The results of the model are intended to aid and guide policymakers in the potential adoption of electric vehicles, especially in the energy planning sector.
VO and GPS integrated navigation algorithm based on plane constraint

In view of the deficiencies of GPS in the urban environment, such as signal occlusion, multipath and Non-LOS, this paper proposes a solution that using a low-cost monocular camera instead of inertial navigation system to combine with GPS. Firstly, considering that the actual road conditions of the vehicle are mostly plane and in order to reduce the computational resources of visual odometer (VO), this paper establishes a VO solving model based on plane constraint; then the VO&GPS loose coupling model is proposed. The position error and yaw angle error of the two sensors are taken as input, and the errors are filtered by the adaptive Kalman filter. Experiments show that the positioning effect of VO&GPS loose coupling system is better than that of single GPS, and it can suppress the drift of VO, which can meet the positioning requirements when satellite signals are interfered in a short time.
The Effects ofWalking orWalking-with-Poles Training on Tissue Oxygenation in Patients with Peripheral Arterial Disease

1Department of Veterans Affairs, Center for Management of Complex Chronic Care, Edward Hines Jr., VA Hospital, Hines, IL 60141, USA 2Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612, USA 3Department of Veterans Affairs, Research & Development, Edward Hines Jr., VA Hospital, Hines, IL 60141, USA 4Cooperative Studies Program, Department of Veterans Affairs, Edward Hines Jr., VA Hospital, Hines, IL 60141, USA
Improving SSD Read Latency via Coding

We study the potential enhancement of the read access speed in high-performance solid-state drives (SSDs) by coding, given speed variations across the multiple flash interfaces and assuming occasional local memory failures. Our analysis is based on a queuing model that incorporates both read request failures and NAND element failures. The NAND element failure in the present context reflects various limitations on the memory element level such as bad blocks, dies or chips that cannot be corrected by error control coding (ECC) typically employed to protect pages read off the NAND cells. Our analysis provides a clear picture of the storage-overhead and read-latency trade-offs given read failures and NAND element failures. We investigate two different ways to mitigate the effect of NAND element failures using the notion of multi-class jobs with different priorities. A strong motivation for this work is to understand the reliability requirement of NAND chip components given an additional layer of failure protection, under the latency/storage-overhead constraints.
Neural differentiation of enbryonic stem cells: clonal properties and sensitivity to cytomegalovirus infection

In this thesis we focused on stem cell differentiation into neural lineage and an application of this differentiation. We have shown that all selected clonal ESC express markers of the early ICM, have the capacity to be differentiated towards more than one cell type and can generate embryoid bodies including cells of the three germ layers. We also studied cell tropism, infectious dynamics of CMV infection on proliferation, regeneration and differentiation of neural cells. We used ESC differentiated into neural lineage to investigate neural permissiveness to CMV infections. We showed that undifferentiated ESC are well protected against CMV infection but their differentiation into NPC makes them permissive to the virus.
Instruction on rhythmic structure during learning enhances the breaststroke kick efficiency

Motor learning can be fostered by visual or auditory instruction conveying information on different features of the skill, like spatial and rhythmic characteristics. For swimming skills like the breaststroke kick, manuals predominantly emphasise spatial features, neglecting rhythmic aspects – even though motor learning considers movement rhythm crucial for distinguishing one skill from another. We aimed to analyse the impact of instruction containing or not the adequate rhythmic pattern information, conveyed by visual or auditory stimulus, on learning the breaststroke kick. Fifty university students, assigned to four experimental groups (auditory, visual, with and without rhythmic information), performed 400 acquisition plus 50 retention and 50 transfer trials during which stroke index, rhythmic and spatial pattern indices and instruction request frequency were mapped. Results showed a marginal difference (p= .075) between higher indices in the retention test of participants receiving information about adequate rhythm, as well as a strong correlation between stroke index and rhythm pattern index, but not with spatial pattern index. No difference between auditory and visual groups was found. This result supports earlier research on the impact of rhythmic information on the learning of complex motor skills, and emphasises the role rhythm plays in skill efficiency and consequently in motor learning.
Defect diagnosis technology of typical components on transmission line based on Fully Convolutional Network

This paper presents an intelligent defect diagnosis algorithm based on fully convolutional neural network for typical transmission line components. Based on the region-based fully convolutional neural network algorithm and combined with deformable convolution, feature context fusion, clustering, the feature expression ability of neural network is improved. Through the improvement, the algorithm adapts to the object deformation and scale difference. In addition, the training effect is improved by improving the sample labeling strategy. In this paper, the defect diagnosis of four kinds of transmission line components(insulator, vibration damper, grading ring, wire clamp) is realized.
Detection and classification of right whale calls using an 'edge' detector operating on a smoothed spectrogram

A detector has been developed which can reliably detect right whale calls and distinguish them from those of other marine mammals and industrial noise. Detection is a two stage process. In the first, the spectrogram is smoothed by convolving it with a Gaussian kernel and the 'outlines' of sounds are extracted using an edge detection algorithm. This allows a number of parameters to be measured for each sound, including duration, bandwidth and details of the frequency contour such as the positions of maximum and minimum frequency. In the second stage, these parameters are used in a classification function in order to determine which sounds are from right whales. The classifier has been tuned by comparing data from a period when large numbers of right whales were known to be in the vicinity of bottom mounted recorders with data collected on days when it was believed, based on ship and aerial surveys, that no right whales were present. Overall, the detection system is capable of picking out a high proportion of right whale calls logged by a human operator, while at the same time working at a false alarm rate of only one or two calls per day, even in the presence of background noise from humpback whales and seismic exploration. Although it is impossible to reduce the false alarm rate for individual calls to zero whilst still maintaining adequate efficiency, by requiring the detection of several calls within a set waiting time, it is possible to reduce false alarm rate to a negligible level.
Linear Modulation Scheme in Multilevel Inverter

Pulse width modulation (PWM) strategies used in a conventional inverter can be modified to use in multilevel converters. The advent of the multilevel converter PWM modulation methodologies can be classified according to switching frequency as been illustrated. The three multilevel PWM methods most discussed in the literature have been multilevel carrier-based PWM, selective harmonic elimination, and multilevel space vector PWM; all are extensions of traditional two-level PWM strategies to several levels. Other multilevel PWM methods have been used to a much lesser extent by researchers; therefore, only the three major techniques will be discussed in this chapter.
Modeling Dynamic Missingness of Implicit Feedback for Sequential Recommendation

Implicit feedback is widely used in collaborative filtering methods for sequential recommendation. It is well known that implicit feedback contains a large number of values that are missing not at random (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn users’ negative preferences. Recent studies modeled exposure, a latent missingness variable which indicates whether an item is exposed to a user, to give each missing entry a confidence of being negative feedback. However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be an essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named “user intent ” to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporates it into matrix factorization (MF) for recommendation. We further extend the proposed framework to capture the dynamic preference of users, which results in a unified framework that is able to model different evolution patterns of user intent and user preference. We also explore two types of constraints to achieve a more compact and interpretable representation of user intents. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.
Mathematical modeling of complex biological systems: from parts lists to understanding systems behavior.

To understand complex biological systems such as cells, tissues, or even the human body, it is not sufficient to identify and characterize the individual molecules in the system. It also is necessary to obtain a thorough understanding of the interaction between molecules and pathways. This is even truer for understanding complex diseases such as cancer, Alzheimer’s disease, or alcoholism. With recent technological advances enabling researchers to monitor complex cellular processes on the molecular level, the focus is shifting toward interpreting the data generated by these so-called “–omics” technologies. Mathematical models allow researchers to investigate how complex regulatory processes are connected and how disruptions of these processes may contribute to the development of disease. In addition, computational models help investigators to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. Numerous mathematical methods have been developed to address different categories of biological processes, such as metabolic processes or signaling and regulatory pathways. Today, modeling approaches are essential for biologists, enabling them to analyze complex physiological processes, as well as for the pharmaceutical industry, as a means for supporting drug discovery and development programs.
Is XML the Next Big Thing

Bank applications are emerging XML is a set of simple rules for converting the meaning of a document written in any software into a globally standardized format that any other software can understand. The current buzz in IT departments about the language is that it will soon rival the current web workhorse, HTML (Hypertext Markup Language) for use in e-commerce, and eventually devour it. Both languages enable people to send messages over the Internet. The essential difference is that HTML lets a sender's browser tell a receiver's web server how to display the format of a message, while XML lets a sender tell a receiver how to handle the content of a message. This seemingly esoteric difference is precisely what's needed at this juncture in the Internet revolution, XML buffs say. The new language will enable banks to create knowledge systems and to exchange financial information with customers and such third-party service providers as stockbrokers, mortgage and insurance companies, and mutual funds. A simplified example can illustrate the differences between XML and HTML. Both use "tags" (stylistic definitions set off in pointed brackets, [less than]thus[greater than]). Imagine you are a firm's web server and you get this HTML message: [less than]H1[greater than] blab, blah, blah [less than]/H1[greater than] You know that H1 means that you should print "bla bla bla" in the size and style of your firm's first-level header. You don't know or care what the words mean. Now imagine you are the same server equipped to handle this XML message: [less than]amount[greater than] 125.75 [less than]/amount[greater than] If this message comes in the context of, say, a billpay transaction, it's likely you are also set up to take $125.75 out of a customer's checking account (similarly defined) and send it somewhere (similarly defined), and so on. You can program your system to do this without calling up a separate routine at each step of the billpay transaction. That saves a lot of processing time. XML isn't the actual language that defines meanings; it's a meta language of rules for creating specialized vocabularies for any number of generic (usually industry-wide) applications, called schemas. Banks are late-adopters of XML, but some interesting applications are beginning to emerge. Wells Fargo leads One of the first banks to use XML in a major application was e-banking pioneer Wells Fargo. Early last year, the bank's Institutional Trust & Brokerage unit decided to upgrade its intranet knowledge base to make it more useful for some 1,800 users in the group. …
Lifetime and Performance Enhancement in WSN by Energy-Buffer Residual Status of Nodes and the Multiple Mobile Sink

Wireless sensor networks (WSNs) are consisting of spatially scattered independent nodes with sensors to sense and keep the ecological states. In WSNs, the nodes are outfitted with constrained sources of energy and buffer, hence, energy efficiency is an important challenge. Therefore, efficient techniques in routing based on energy and buffer are essentially required in WSNs to provide the network lifetime (NL) enhancement with less energy utilization. To enhance the NL and network performance in WSNs energy-efficient direction-finding practice is tremendously needed with multiple sink. Hence, by using clustering and energy balancing process with the help of multiple sink it can be achieved effortlessly in WSN. Furthermore, for those networks having constrained energy and storage capacities, well-organized power consumption is a significant issue to sustain, expand and enhance the network lifetime (NL). For that purpose, we are using data aggregation technique in an efficient energy approach to collect and aggregate data, so that network lifetime will enhanced. These all terms can be dominated with energy-efficiency routing techniques. Since, it is one of the basic sources for organizing the total energy of the wireless network. Hence, cluster-based routing in with energy-balancing is proposed for achieving the network performance, lifetime as well as energy-efficiency in WSN. Furthermore, by making multiple movable sink the network performance and network lifetime can be further enhanced for the outsized WSNs. Hence, we proposed a routing protocol “Energy-Buffer Residual Status (EBRS) with Multiple Mobile Sink” to enhance the lifetime and performance of the network in WSN. The outcomes show that our protocol outperforms to improve the networks performance and prevent the creation of intermediate bottleneck node in comparison with the existing routing protocols.