Miracle-dz/UniFAR-FTUs huggingface dataset:
Appraisement and benchmarking of third-party logistic service provider by exploration of risk-based approach

Abstract In the present era, Reverse Logistics Support has monitored as a momentous realm, where stuffs are transferred from point of consumption to origin. The companies who provide the logistic equipments, i.e. Truck, Joseph Cyril Bomford, and Shipment, etc. to its partner’s firms called Third-Party Logistics (3PL) service provider. Today, the feasible 3PL service provider evaluation-opt problem is yet an amorous dilemma. The appraisement and benchmarking of logistics service providers in extent of index; allied risk-based indices and their interrelated metrics; outlooked as a great tool for each international firm, in order that firm could obtain their core goals. The novelty of manuscript is that here, a hairy-based approach has been integrated and then implemented upon a novel developed multi hierarchical third-party logistics (3PL) service providers appraisement index in purpose to umpire the 3PL provider for their strong and ill’s core indices. Moreover, the overall score (S i ) system has also been carried out for benchmarking the 3PL provider companies, where s1 has been found as the best 3PL service provider. The developed approach enabled the manager of firms to make the verdict towards the best inclusive evaluation process of 3PL performance appraisement and benchmarking. A numerical illustration has also been provided to validate the verdict support system.
Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR

Neural networks have generated valuable Quantitative Structure‐Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so‐called “shallow” neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm‐shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome “activity cliffs” in QSAR data sets.
LINGUISTIC RUIN? LOL! INSTANT MESSAGING AND TEEN LANGUAGE

This article presents an analysis of Instant Messaging (IM), a one-to-one synchronous medium of computer-mediated communication. Innumerable articles in the popular press suggest that increasing use of IM by teens is leading to a break- down in the English language. The analyses presented here are based on a unique corpus involving 72 teenagers and over a million words of natural, unmonitored IM. In addition, a corpus of speech from the same teenagers is examined for comparison. Targeting well-known IM features and four areas of grammar, we show that IM is firmly rooted in the model of the extant language. It reflects the same structured heteroge- neity (variation) and the same dynamic, ongoing processes of linguistic change that are currently under way in contemporary varieties of English. At the same time, IM is a unique new hybrid register, exhibiting a fusion of the full range of variants from the speech community—formal, informal, and highly vernacular. Teenagers in the early twenty-first century are using home com- puters for communication at unprecedented rates in ever-expanding virtual communities. A particularly favorite medium, at least when we conducted this research, was Instant Messaging (IM). IM is "a one-to-one synchronous form of computer-mediated communication" (Baron 2004, 13). It is "direct, immediate, casual online contact" (Schiano et al. 2002). In essence, IM is real-time "interactive written discourse" (Ferrara, Brunner, and Whittemore
Design Issues in Implementing MPPT for Improved Tracking and Dynamic Performance

In solar cells, the output power is maximized only at a specific output voltage. This output power and the corresponding operating voltage is also affected by solar irradiation and ambient temperature leading to continual variation in total power available at the output of the solar cells. Considering high installation costs, it is required to track this maximum power point (MPP) at all times. This is done using a system called MPP trackers (MPPT). MPPTs utilize a power converter in series with the solar panel to extract energy from solar cells. The operating point at which the power is maximized is located using a variety of algorithms essentially based on well-known gradient following method. Algorithmic deficiencies, in this method lead to a tradeoff between dynamic performance and steady state oscillations. With continually changing atmospheric conditions, this tradeoff leads to poor utilization of solar cells - an avoidable situation for obvious reasons. The present work discusses design issues in implementing algorithms to eliminate this tradeoff
An ensemble classifier for link prediction in location based social network

Social Networks (SNs) have gained a lot of popularity on the Internet and become a hot research topic attracting many professionals from diverse areas. Recently, Location-based Social Networks (LBSNs) have attracted millions of users, experiencing a huge popularity increase over a short period of time. In Location-based social network, users can easily set their locations as a new interactive way to share with friends to inform them of their current location. In this paper, we explores the community structure of a location based social network data and propose a new link predictor for its. In the proposed approach, the network is firstly partitioned into a number of groups. Then, a supervised link predictor is learnt for each group. To do this, we have used several learning algorithms and compared their prediction accuracy to achieve the best performance. The results show that the proposed method obtains 97.88 accuracy.
A neural network model for bankruptcy prediction

A neural network model is developed for prediction of bankruptcy, and it is tested using financial data from various companies. The same set of data is analyzed using a more traditional method of bankruptcy prediction, multivariate discriminant analysis. A comparison of the predictive abilities of both the neural network and the discriminant analysis method is presented. The results show that neural networks might be applicable to this problem
Dancer Identification within Detected Shot Boundaries

This project began with an aim to develop tools to categorize identities and attributes of dancers and their surroundings from song-dance sequences of Telugu cinema. We developed a framework, DanceGanceX, in C++ which heavily relies on OpenCV to track such values as the amount of movement, color histograms, and intensities per dancer over time. Shot boundary detection and Paint-Trails(camera stabilization) are the other features of DanceGanceX used to pre-segment the song-dance videos. Dance-GanceX will facilitate better understanding of the culturally influential song-dance sequences that define popular Indian Cinema, providing critics, commentators, and artists a reliable tool to extract meaningful data and hypothesize from.
Neisserial Lipooligosaccharide Is a Target for Complement Component C4b

We identified Neisseria meningitidis lipooligosaccharide (LOS) as an acceptor for complement component C4b (C4b). Phosphoethanolamine (PEA) residues on the second heptose (HepII) residue in the LOS core structure formed amide linkages with C4b. PEA at the 6-position of HepII (6-PEA) was more efficient than 3-PEA in binding C4b. Strains bearing 6-PEA bound more C4b than strains with 3-PEA and were more susceptible to complement-mediated killing in serum bactericidal assays. Deleting 3-PEA from a strain that expressed both 3- and 6-PEA simultaneously on HepII did not decrease C4b binding. Glycose chain extension of the first heptose residue (HepI) influenced the nature of the C4b-LOS linkage. Predominantly ester C4b-LOS bonds were seen when lacto-N-neotetraose formed the terminus of the glycose chain extension of HepI with 3-PEA on HepII in the LOS core. Related LOS species with more truncated chain extensions from HepI bound C4b via amide linkages to 3-PEA on HepII. However, 6-PEA in the LOS core bound C4b even when the glycose chain from HepI bore lacto-N-neotetraose at the terminus. The C4A isoform exclusively formed amide linkages, whereas C4B bound meningococci preferentially via ester linkages. These data may serve to explain the preponderance of 3-PEA-bearing meningococci among clinical isolates, because 6-PEA enhances C4b binding that may facilitate clearance of 6-PEA-bearing strains resulting from enhanced serum killing by the classical pathway of complement.
Wavelet scalogram analysis of phonopulmonographic signals

Mortality rate due to lung-related diseases can be minimised by a precise preliminary diagnosis technique. Human lung sound (respiratory sound) has the diagnostic potential that helps the physician to diagnose the pulmonary disease and some other disorders of their patient. Lung auscultation in phonopulmonographic configuration will become more reliable and accurate diagnostic tool by effective lung sound signal analysis. In this paper a time frequency analysis of normal and abnormal acquired lung sound signals is presented to differentiate between normal and abnormal case. For this purpose, wavelet transform-based scalogram is drawn and analysed. The result of this analysis showed that it has a potential to effectively differentiate between normal and abnormal cases.
Nonresponse to first‐line pharmacotherapy may predict relapse and recurrence of remitted geriatric depression

The authors examined whether nonresponse to first‐line pharmacotherapy was associated with an increased probability of relapse or recurrence following remission of an episode of geriatric depression. The study group consisted of 74 elderly patients whose index episode of nonpsychotic unipolar major depression had responded to antidepressant pharmacotherapy. In 6 of these patients, the depressive episode had not responded to first‐line pharmacotherapy (8 weeks of nortriptyline, including 2 weeks of adjunctive lithium) but it had responded to second‐line treatment (phenelzine with or without adjunctive lithium). The 74 patients were maintained on acute doses of the medications that had led to response and were followed for 2 years or until relapse or recurrence, whichever occurred first. The cumulative probability of relapse or recurrence was 67% for patients who responded to second‐line treatment compared with 18% for patients who responded to first‐line treatment (P=0.0003). As expected, mean time to response was significantly longer for patients who responded to second‐line treatment but this factor did not account for the difference in out‐come between the two groups. These findings suggest that pharmacotherapy resistance may constitute a risk factor for relapse or recurrence of remitted geriatric depression, even when patients are maintained on the medication that they eventually respond to. Depression and Anxiety 13:125–131, 2001. © 2001 Wiley‐Liss, Inc.
An Osteometric Study of Northern Indian Populations for Sexual Dimorphism in Craniofacial Region

Abstract: Sex identification is an initial crucial objective in the revelation of the complete identity of the human skeleton as it also renders significantly clearer guidance towards age and stature estimation. Population specific standards are of great practical relevance in the present era of increasing population intermixing. Size differences and robusticity are the two well‐elaborated pillars holding most of the dimorphic burdens of the skull. This study is designed to explore dimorphic characteristics of the craniofacial region to establish anthropometric standards for contemporary North Indian populations, which have not been available so far. One hundred and twelve adult crania of known age (23–65 years) and sex (M:F; 82:30) were collected in the Department of Forensic Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India. Ten standard metric parameters of craniofacial region were measured and subjected to stepwise and direct discriminant function analysis employing SPSS 16.00. Bizygomatic breadth emerged as the single best parameter in stepwise analysis, providing an average accuracy of 85.5%.
Iteration One More Time

A neologicist set theory based on an abstraction principle (NewerV) codifying the iterative conception of set is investigated, and its strength is compared to Boolos's NewV. The new principle, unlike NewV, fails to imply the axiom of replacement, but does secure powerset. Like NewV, however, it also fails to entail the axiom of infinity. A set theory based on the conjunction of these two principles is then examined. It turns out that this set theory, supplemented by a principle stating that there are infinitely many nonsets, captures all (or enough) of standard second-order ZFC. Issues pertaining to the axiom of foundation are also investigated, and I conclude by arguing that this treatment provides the neologicist with the most viable reconstruction of set theory he is likely to obtain.
Bose-Einstein correlations of pion pairs and kaon pairs from relativistic quantum molecular dynamics.

Bose-Einstein correlations between charged pion and [ital K][sup +] pairs are calculated using the space-time and four-momentum distributions from relativistic quantum molecular dynamics for 200 GeV/nucleon [sup 32][ital S]+Pb and compared to pion correlation measurements from two CERN experiments. Good agreement is found. The predicted apparent radii from the [ital K][sup +] correlation functions are smaller than the corresponding pion radii.
A survey of energy efficient routing protocol in MANET

Despite of much research activity over the past decades on mobile ad hoc wireless network (MANET), there is an important challenging issue related to mobile hosts/nodes battery power. This is owing to the prevalent constraints to extend the energy of nodes so that it could work for longer time. Moreover, battery power is also not transferable from one node to another node. The survey classifies the energy aware routing protocols, their functionality, benefits, limitations and finally the analysis of energy efficient routing protocols. The paper aims to assist those ad hoc networks, researchers and application developers in selecting appropriate energy efficient routing protocols for their work.
A Query Oriented Implementation of a Topologic Data Structure for 3-Dimensional Vector Maps

Abstract The conceptual Formal Data Structure of Molenaar (1990, 1992) for describing 3-dimensional (3-D) representations of terrain was implemented in a relational database model. It is shown how complex topologic relations among spatial objects can be analysed through standard SQL operations. Topics like consistency rules and updating operations will not be handled, they should be dealt with separately. The emphasis is here on topologic queries, so that no attention will be paid to a thematic structure. The geometric data set has been structured so that information on topology, shape and size, and position can be retrieved. A demonstration of the implementation of the 3-D formal data structure (FDS) is given by querying a test database.
Data mining and intelligent queries in a knowledge-based multimedia medical database system

Multimedia medical databases have accumulated large quantities of data and information about patients and their medical conditions. Patterns and relationships within this data could provide new knowledge for making better medical decisions. Unfortunately, few technologies have been developed and applied to discover and use this hidden knowledge. We are currently developing a next generation knowledge-based multimedia medical database, named MedBase, with advanced behaviors for data analysis and data fusion. As part of this R&D effort, a knowledge-rich data model is constructed to incorporate data mining techniques/tools to assist the building of medical knowledge bases, and to facilitate intelligent answering of users' investigative and knowledge queries in the database. Techniques such as data generalization, classification, clustering, semantic structures, and concept hierarchies, are used to acquire and represent both symbolic and spatial knowledge implicit in the database. With the availability of semantic structures, concept hierarchies and generalized knowledge, queries may be posed and answered at multiple levels of abstraction. In this article we provide a general description of the approaches and efforts undertaken so far in the MedBase project.
Managing the 'seamless service': Primary Care Groups in the new NHS

This paper reports on empirical work examining the conseqences for the development of the role of Primary Care Groups of the shift from competition (in the internal market) to relationships based on collaboration and partnership. Previous work (Conway and Willcocks, 1998; Willcocks and Conway, 1999) firstly developed a conceptual framework emphasizing the development of relationships between Primary Care Groups and their key stakeholders (for example, Health Authorities, Local Authorities and Trusts) and, secondly, sought to test this framework empirically through case study analysis of two Primary Care Groups in the north of England. This paper takes the research further and focuses on one of these Primary Care Groups, providing an in-depth analysis of particular issues such as the rationale for the particular configuration of this organization and the chosen working arrangements, the role and function of the Board and individual members, and the problems encountered in the first year of operation.
Chronic pain management in the emergency department: a survey of attitudes and beliefs.

OBJECTIVE The emergency department (ED) can be a particularly challenging environment in which to offer care for chronic pain. This study tried to determine if beliefs held by patients and providers about noncancer-related chronic pain affect evaluation and management of pain in ED. INTERVENTION We surveyed 103 patients presenting to the ED with chronic pain, 34 ED physicians, and 44 ED nurses to assess the influence of 15 possible barriers to managing chronic pain in the ED. RESULTS Patients were significantly more likely than providers to believe that their pain had to have a diagnosed physical component to be treated. Providers were significantly more likely than patients to believe that patients came to the ED because they lacked a primary care physician. All agreed that chronic pain treatment was not a priority in the ED and the potential for addiction, dependence, diversion, and forged prescriptions was low. CONCLUSIONS Patients in chronic pain may need to be reassured that their pain will be treated, even in the absence of objective signs or magnified symptoms. Providers may wrongly believe that lack of a primary care physician brings these patients to the ED. Providers and patients appear to believe that treating chronic pain in the ED has a low priority. Both groups may underestimate the problems inherent with prescribing opioids in this setting.
Energy harvesting system integrated on wearable contact lens

An electrically small antenna with a high quality factor is designed to enable wirelessly powered wearable electronics via resonant inductive coupling using smart-phones. This antenna facilitates the use of passive high frequency transponders for glucose monitoring on system-integrated contact lenses. Full wave numerical simulators are used to simulate the system in its preliminary design stage with realistic environmental factors. Several important design elements such as quality factor, coupling coefficient, and insertion loss are computed from the resulting S-parameters; a system demo using proof-of-concept prototype is presented here.
How accurate are predicted waiting times, determined upon a patient's arrival in the Emergency Department?

Background Information regarding waiting times has been shown to be a key determinant of patient satisfaction. This study aimed to examine the potential accuracy of predicted waiting times determined on the patient's arrival in the Emergency Department (ED). Methods A retrospective study of 50 000 consecutive patients attending a single ED was undertaken. A linear regression model was developed to predict waiting times, assessing a number of different measures derived from the waiting times of patients seen immediately prior to an individual patient's time of arrival. To assess the fit of the model, the mean absolute difference between the patient's actual and predicted waiting times was determined. Results 6726 patients had incomplete data and were excluded from the analysis. The mean waiting time across all streams was 64.6 (SD 43.7) min. The best performing linear regression model used two variables to predict a patient's waiting time, calculated across the entire sample of patients. This model predicted 27% of the variability in waiting time. The mean absolute difference between actual and predicted waiting times across all streams was 29.0 (SD 23.5) min. The mean absolute difference in waiting times was similar across the streams. Conclusions There is a considerable difference between predicted and actual waiting times using this method. Further investigation is required to determine whether such a degree of inaccuracy is acceptable to patients and improves satisfaction more than the provision of no information regarding waiting times.
High sensitivity SnO2 single-nanorod sensors for the detection of H2 gas at low temperature

Uniform SnO2 nanorods were grown by inductively coupled plasma-enhanced chemical vapor deposition without catalysts and additional heating. The SnO2 nanorods were aligned on a pair of Au/Ti electrodes by the dielectrophoresis method. SnO2 single-nanorod gas sensors were fabricated by connecting individual SnO2 nanorods to a pair of Au/Ti electrodes with Pt stripes deposited by a focused ion beam. The sensing properties of the SnO2 single-nanorod sensor were studied. The SnO2 single-nanorod sensor could detect 100 ppm H2 at room temperature with repeated response and showed a large change of resistance, fast response time and good reversibility at an elevated operating temperature of 200 °C. The optimal sensing performance of the sensor is achieved at the operating temperature of around 250 °C.
Using training set selection methods to improve text mining on market prediction via news headlines

Nowadays, with the development of technology and social media, people's opinions and news about a particular product or currency is significantly expanded. In addition to expanding volumes of text data, the unstructured characteristics of them makes the analysis of these types of data with the vital challenges. In this study, the proposed method utilized different Training Set Selection (TSS) approaches like: ModelCS, ENN, MultiEdit, AllKNN, POP and MENN. Moreover, the target's features have been selected to improve the prediction of currency market (dollars, euros) based on news headlines. The evaluation of the proposed method has been done by 3-layered algorithm and the experimental results reveal that the proposed method has achieved more robust accuracies than the others.