Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainability of lean manufacturing principles in a production system

The success of manufacturing organizations at large is assessed through the ability to perform day to day business employing efficient methods and techniques of working. Lean manufacturing comprises of several essential principles for executing production in an organization. Several organizations have explored the need to implement lean principles in pursuit of improving productivity thus achieving a competitive advantage in the volatile global business space. This paper investigates the factors which affect the sustainability of lean manufacturing principles in a production system. To obtain an insight on the factors affecting the sustainability of lean manufacturing, a literature study on lean holistically and the principle is explored. The purpose of the literature is to learn proposals made in literature in relation to sustaining the practice of lean principles. The remediation suggested in literature is used to explore possible best practices that can be embedded in the daily business of an organization to promote lean principles sustainability. The literature in this study brings into attention significant themes essential to arrive to significant outcomes. The themes of study range from organizational leadership and culture followed by the organization to promote the best application of the principles. To gather research data, observations are used as the main data collection method. A checklist is used to provide a structure on the observations under the different themes. Without overlooking the other themes of the research, the research reports that of all the observed themes with different observable items the main themes which need attention are shop-floor meetings, training and problem solving. The analysis of the entire research reveals that if the manner with which training is conducted improves could alleviate many other problems as training proves to be the main contributor towards other factors.
The strong-connectivity of word-representable digraphs

A word-graph Gw is a digraph represented by a word w such that the vertex-set V(Gw) is the alphabet of w and the edge-set E(Gw) is determined by non-identical adjacent letter pairs in w. In this paper we study the strong-connectivity of word-graphs. Our main result is that the number of strongly connected word-graphs represented by l-words of over an n-alphabet can be expressed via a recurrence relation T(l,n) on the Stirling numbers of the second kind using a link between word partitions and digraph connectivity.
A Dual Augmented Two-tower Model for Online Large-scale Recommendation

Many modern recommender systems have a very large corpus, and a common industrial recipe for handling large-scale retrieval is to learn query and item representations from their content features with the two-tower model. However, the model suffers from lack of information interaction between the two towers. Besides, imbalanced category data also hinders the model performance. In this paper, we propose a novel model named Dual Augmented Two-tower Model (DAT), which integrates a novel Adaptive-Mimic Mechanism (AMM) and a Category Alignment Loss (CAL). Our AMM customizes an augmented vector for each query and item to mitigate the lack of information interaction. Moreover, our CAL can further improve performance by aligning item representation of uneven categories. Offline experiments on large-scale datasets are conducted to show the superior performance of DAT. More-over, online A/B testings confirm that DAT can lead to improved recommendation quality for industrial applications.
Dirac-Operatoren in der Riemannschen Geometrie : mit einem ausblick auf die Seiberg-Witten-Theorie

Clifford-Algebren - Spin-Strukturen - Dirac-Operatoren - Analytische Eigenschaften der Dirac-Operatoren - Abschatzungen der Eigenwerte des Dirac-Operators und Losungen der Twistorgleichung - Anhang 1: Seiberg-Witten Invarianten - Anhang 2: Hauptfaserbundel und Zusammenhange - Literatur
Integration of a multispectral camera and machine learning for apple sorting

This paper presents a system capable of performing a binary classification (ripe and rotten) of red apples, which is achieved through the use of the Normalized Difference Vegetation Index (NDVI) and an Xception Neural Network. In order to carry out this process a multispectral camera is used to observe details outside the scope of the human eye, reducing the presence of errors during their classification. The selection of NDVI is the result of its comparison with the GNDVI, GVI, NDRE, NDVIR, NG, NGRDI
Bilingual children with Specific Language Impairment: Additionally disadvantaged?

This thesis reports on research on language proficiency of bilingual children (aged 6 to 11 years) with Specific Language Impairment (SLI) in the Netherlands, speaking Dutch as a second language (L2). The L2 proficiency of bilingual children with SLI is compared to the proficiency in Dutch of monolingual children with typical language development, monolingual children with SLI, and bilingual children with typical language development. Several linguistic domains are examined on the differences and similarities between bilingual children with SLI and the three other groups. Comparisons with typically developing monolingual children show that bilingual children with SLI are in an additionally disadvantaged position as far as proficiency in Dutch is concerned. Comparisons with monolingual children with SLI show similarities in the identified subtypes of SLI and differences in the level of proficiency in Dutch. Comparisons with typically developing bilingual children show similarities in the types of errors in spontaneous speech and differences in the level of language proficiency in the first as well as in the second language. These results indicate that bilingual children with SLI should be considered as a special group
Chromosome classification for karyotype composing applying shape representation on wavelet packet transform

Karyotype is an image containing seven groups of chromosomes disposed in pairs. Since a chromosome photo presents the chromosome in random disposition the human interaction become necessary. As the digital image processing techniques have been improving, many techniques have been proposed to make the karyotyping an automatic process. The adopted method depending on the characteristics of the original chromosome image as color or distribution of the chromosome bands. This paper proposes a method for chromosome classification based on the chromosome shape. The chromosome shape is converted in a signature that is decomposed by the wavelet packet transform and its best basis. Then, the wavelet packet coefficients correspondent to the signature of chromosomes are compared in order to classify the chromosome in one of the six groups, A, B, C, D, E, F and G, of the karyotype. The results obtained show that the proposed method provides the classification of the chromosomes.
SLOW DIFFUSION OF COSMIC RAYS AROUND A SUPERNOVA REMNANT

We study the escape of cosmic-ray protons accelerated at a supernova remnant (SNR). We are interested in their propagation in the interstellar medium (ISM) after they leave the shock neighborhood where they are accelerated, but when they are still near the SNR with their energy density higher than that in the average ISM. Using Monte Carlo simulations, we found that the cosmic rays with energies of ≲ TeV excite Alfvén waves around the SNR on a scale of the SNR itself if the ISM is highly ionized. Thus, even if the cosmic rays can leave the shock, scattering by the waves prevents them from moving further away from the SNR. The cosmic rays form a slowly expanding cosmic-ray bubble, and they spend a long time around the SNR. This means that the cosmic rays cannot actually escape from the SNR until a fairly late stage of the SNR evolution. This is consistent with some results of Fermi and H.E. S.S. observations.
Human languages trade off complexity against e�ciency

: One of the fundamental questions about human language is whether all languages are equally complex. To answer this long-standing question, we conduct a large scale quantitative cross-linguistic analysis of written language by training a language model on more than 6,500 different documents as represented in 41 multilingual text collections consisting of ~3.5 billion words or ~9.0 billion characters and covering 2,069 different languages that are spoken as a native language by more than 90% of the world population or ~46% of all languages that have a standardized written representation. Statistically inferring the entropy of each language-model as an index of (un)predictability/complexity allows us to refute the equi-complexity hypothesis, but also unveils a previously undocumented complexity-efficiency trade-off: high entropy languages are information-theoretically more efficient because they tend to need fewer symbols to encode messages. Our findings additionally contribute to debates about language evolution/diversity by showing that this trade-off is partly shaped by the social environment in which languages are being used.
Unconventional localization transition in high dimensions

We study non-interacting systems with a power-law quasiparticle dispersion $\xi_{\bf k}\propto k^\alpha$ and a random short-range-correlated potential. We show that, unlike the case of lower dimensions, for $d>2\alpha$ there exists a critical disorder strength (set by the band width), at which the system exhibits a disorder-driven quantum phase transition at the bottom of the band, that lies in a universality class distinct from the Anderson transition. In contrast to the conventional wisdom, it manifests itself in, e.g., the disorder-averaged density of states. For systems in symmetry classes that permit localisation, the striking signature of this transition is a non-analytic behaviour of the mobility edge, that is pinned to the bottom of the band for subcritical disorder and grows for disorder exceeding a critical strength. Focussing on the density of states, we calculate the critical behaviour (exponents and scaling functions) at this novel transition, using a renormalisation group, controlled by an $\varepsilon=2\alpha-d$ expansion. We also apply our analysis to Dirac materials, e.g., Weyl semimetal, where this transition takes place in physically interesting three dimensions.
The ﬁnal size of an epidemic in a heterogeneous society

This note describes the outcome of an epidemic in a heterogeneous population with a very simple structure. The population is split into two sections in which the epidemic runs a diﬀerent course. The reproduction numbers of the two epidemics are unobserved, only the overall reproduction number is known. For such a population the outcome of the epidemic can be as well far worse as far better than expected on the basis of the overall reproduction number. By considering a very simple model of this population some calculations are feasible under general assumptions on the epidemic itself. These calculations show in which direction models based on the overall reproduction number can misrepre-sent the real-world situation.
Concurrent chemoradiotherapy and salvage chemotherapy for advanced cervical cancer with a pedunculated cervical leiomyoma: a case report

Although concurrent chemoradiotherapy (CCRT) is an effective treatment for advanced cervical cancer, its use in advanced cervical cancer with a pedunculated cervical leiomyoma remains challenging. The prognosis of recurrent cervical cancer is poor, with a low possibility of complete response (CR). In this present study, after completion of external beam radiotherapy (EBRT) and chemotherapy (weekly cisplatin), we performed the resection of a pedunculated cervical leiomyoma. No malignant cells were identified in the pathological specimen. After the myoma resection, no cervical tumor was observed on follow-up magnetic resonance imaging (MRI). High-dose-rate intracavitary brachytherapy (HDR-ICBT) was also performed. Local control of the cervical tumor was achieved after 30 months of treatment. After CCRT, rectal hemorrhage was observed but was effectively controlled via local intervention. Twenty-four months after CCRT, the patient was given salvage chemotherapy (paclitaxel plus carboplatin) due to lymph node metastasis observed at the outside range of EBRT. Thirty months after CCRT, computed tomography showed that the metastatic lymph nodes had disappeared, and the patient achieved CR. Thus, for advanced cervical cancer with a pedunculated cervical leiomyoma, CCRT could be completed following myoma resection. In addition, salvage chemotherapy for lymph node metastasis might result in CR. In this present case, a gastrointestinal adverse event was observed after radiotherapy and salvage chemotherapy with paclitaxel plus carboplatin achieved CR.
Doubly heavy baryon spectra guided by lattice QCD

This paper provides results for the ground state and excited spectra of three-flavored doubly heavy baryons, $bcn$ and $bcs$. We take advantage of the spin-independent interaction recently obtained to reconcile the lattice SU(3) QCD static potential and the results of nonperturbative lattice QCD for the triply heavy baryon spectra. We show that the spin-dependent potential might be constrained on the basis of nonperturbative lattice QCD results for the spin splittings of three-flavored doubly heavy baryons. Our results may also represent a challenge for future lattice QCD work, because a smaller lattice error could help in distinguishing between different prescriptions for the spin-dependent part of the interaction. Thus, by comparing with the reported baryon spectra obtained with parameters estimated from lattice QCD, one can challenge the precision of lattice calculations. The present work supports a coherent description of singly, doubly and triply heavy baryons with the same Cornell-like interacting potential. The possible experimental measurement of these states at LHCb is an incentive for this study.
Consumer adoption of mobile payment services during COVID-19: extending meta-UTAUT with perceived severity and self-efficacy

PurposeThe purpose of this paper is to identify and examine the important factors that could affect consumers' behavioural intention and use behaviour towards mobile payment services during COVID-19.Design/methodology/approachThe proposed model extends meta-Unified Theory of Acceptance and Use of Technology (meta-UTAUT) model with perceived severity and self-efficacy factors affecting consumers' behavioural intention and use behaviour towards mobile payment services. A convenient sampling technique has been utilized to gather data from a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation.FindingsThe findings revealed that performance expectancy, effort expectancy and perceived severity have a significant positive impact on consumers' attitude; facilitating conditions has a significant positive impact on effort expectancy; self-efficacy has a significant positive impact on effort expectancy; attitude has a significant positive impact on behavioural intention; and behavioural intention has a significant positive impact on use behaviour. Social influence did not confirm any significant relationship.Research limitations/implicationsThe current research study has utilized a non-probability convenient sampling technique to gather data through a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation. The respondents were adopters of mobile payment services. The scope of the study is the COVID-19 context or related chronic diseases context where major preventive mechanisms such as social distancing and avoidance of physical contacts are vital.Originality/valueThis study has extended the meta-UTAUT model with the COVID-19 context-specific constructs and relationships. The undertaken work has strengthened the explanability of the model. The inclusion of context relevant variables such as perceived severity and self-efficacy and their association with the existing meta-UTAUT framework have enriched the context of the study. The current study offers a holistic understanding of significant factors influencing Indian consumers’ adoption of mobile payment services in the COVID-19 context.
The Eikonal equation: some results applicable to computer vision

In this paper we investigate certain first order partial differential equations which formulate the relationship between the light reflected from a surface and its shape. Particular emphasis is given to eikonal equations. Two results are presented. First, we prove that a special type of eikonal equation has only one convex and positive C2 solution in some neighborhood of a singular point. Using this result, we show that a restricted form of this equation has exactly two solutions. These results have application in scanning electron microscopy.
Human Herpesvirus 8 Disease

HHV-8 DNA by PCR in specimens drawn at birth from HHV-8-seropositive mothers and their infants. 117 Data indicate increased mortality through age 24 months among infants with HIV born to HHV-8-seropositive mothers compared with HHV-8-seronegative mothers, 114-116,118-123 but these studies could not completely account for other confounding factors affecting infants with HIV. The majority of studies document a substantially higher rate of HHV-8 seropositivity among children born to HHV-8 antibody-positive compared with HHV-8 antibody-negative women. 118-123
Wind Profile Prediction using Online Sequential Extreme Learning Machine Algorithm

There is a drastic increase in the number of wind turbines implanted every year due to the environmental impact of wind energy. The wind power generated by wind turbines changes rapidly due continuous fluctuation of wind speed and direction. This paper applies the recently developed fast and accurate online sequential learning algorithm referred to as online sequential extreme learning machine algorithm (OS-ELM) for single hidden layer feed forward networks (SLFNs) for predicting the speed and direction of wind. OS-ELM can learn the arriving data either one-by-one or chunk-by-chunk. The activation function used in OS-ELM can be either sigmoidal or radial basis function (RBF). The hidden node parameters like input weights and biases are chosen randomly and output weights are determined analytically based on sequentially arriving data. In OS-ELM apart from the number of hidden nodes none of the control parameter is manually selected. Performance results indicates that OS-ELM applied for wind profile prediction produces better prediction accuracy when compared to a batch extreme learning machine algorithm.
CRYPTOGRAPHY IN ELECTRONIC VOTING SYSTEMS

Electronic democracy is a necessity in this era of computers and information technology. Electronic voting (eVoting) is one of the pillars of the e-democracy, which refers to the use of computers or computerised voting equipments to cast and to tabulate ballots in an election in a trustable manner. Due to the nature of electronic systems the security and reliability of the system should be handled properly in order to make the eVoting system as an applicable alternative to the paper based voting system for the governmental elections. The use of cryptography helps to achieve the trustworthiness of the system. Cryptography can be used at certain points in the voting process in order to persuade the citizens that a fully secure system has been developed and the whole election process is fulfilled properly and securely. Based on this fact, eVoting systems have shown an increasing trend towards the use of cryptography to increase public confidence in the eVoting process. This paper discusses the use of cryptography in the eVoting systems. It firstly describes the eVoting process with the associated actors. It also gives an eVoting classification. Then, it explains the challenging eVoting security requirements and shows how cryptography, i.e. key cryptographic primitives, cryptographic building blocks, can be used to overcome these challenges.
On the origin of randomness in quantum mechanics

Quantum statistics originate from the physics of state preparation. It is therefore wrong to think of quantum states as fundamental. In fact, quantum states are merely summaries of dynamical processes that randomize the properties of the system by drawing on the inexhaustible reservoir of quantum fluctuations provided by the physical tools used to control the quantum system. The mathematical form of the "state vector" is actually an expression of the laws of causality which describe the relations between physical properties in terms of the action of transformations. These laws of causality directly associate the macroscopic effects of a physical property in an interaction with the environment with dynamical changes to the system caused by the microscopic properties of that environment.
A NEW APPROACH TO IDENTIFYING THE MOST POWERFUL GRAVITATIONAL LENSING TELESCOPES

The best gravitational lenses for detecting distant galaxies are those with the largest mass concentrations and the most advantageous configurations of that mass along the line of sight. Our new method for finding such gravitational telescopes uses optical data to identify projected concentrations of luminous red galaxies (LRGs). LRGs are biased tracers of the underlying mass distribution, so lines of sight with the highest total luminosity in LRGs are likely to contain the largest total mass. We apply this selection technique to the Sloan Digital Sky Survey and identify the 200 fields with the highest total LRG luminosities projected within a 3. ′5 radius over the redshift range 0.1 ⩽ z ⩽ 0.7. The redshift and angular distributions of LRGs in these fields trace the concentrations of non-LRG galaxies. These fields are diverse; 22.5% contain one known galaxy cluster and 56.0% contain multiple known clusters previously identified in the literature. Thus, our results confirm that these LRGs trace massive structures and that our selection technique identifies fields with large total masses. These fields contain two to three times higher total LRG luminosities than most known strong-lensing clusters and will be among the best gravitational lensing fields for the purpose of detecting the highest redshift galaxies.
MicroRNAs 181a and 125a are highly expressed in early diagnosed RRMS: a pilot case-control study

Background: Recently microRNAs (miRs) have been proposed as possible disease biomarkers in Multiple Sclerosis (MS) �eld. Among miRs, those secreted through microvesicles (MVs) may contribute to different disease phases. Methods: This is a case-control study performed at the Department of Medical and Surgical Sciences, University of Foggia, Italy. Patients have not been yet exposed to disease modifying therapies (DMTs) and had received a con�rmed diagnosis of relapsing remitting MS (RRMS). We analyzed the serum level of 6 MVs miRs by using Quantitative Real-Time PCR, comparing RRMS to HCs, with a 2:1 ratio. Subsequently, the differentially expressed miRNAs, were further tested with receiving operator curves (ROC). We aimed to explore possible role of MVs miRs as disease biomarker at the time of diagnosis. Results: A total of 12 patients were enroled. MiR-181a and miR-125a had higher levels in RRMS patients when compared to HCs (p<.01 and p<.05 respectively). The ROC curve indicated that both miR-181a and 125a could be considered as biomarkers with an area under the curve of 0.896 (p=.014; 95% CI: 0.72–1.00) and 0.785 (p=.046; 95% CI:0.59–1.00) respectively. Conclusions: Our study suggested MVs miR-181a and 125a as possible disease biomarkers in early diagnosed RRMS patients, not yet exposed to DMTs.