Miracle-dz/UniFAR-FTUs huggingface dataset:
Competitive Algorithms for Online Multidimensional Knapsack Problems

In this paper, we study the online multidimensional knapsack problem (called OMdKP) in which there is a knapsack whose capacity is represented in m dimensions, each dimension could have a different capacity. Then, n items with different scalar profit values and m-dimensional weights arrive in an online manner and the goal is to admit or decline items upon their arrival such that the total profit obtained by admitted items is maximized and the capacity of knapsack across all dimensions is respected. This is a natural generalization of the classic single-dimension knapsack problem and finds several relevant applications such as in virtual machine allocation, job scheduling, and all-or-nothing flow maximization over a graph. We develop two algorithms for OMdKP that use linear and exponential reservation functions to make online admission decisions. Our competitive analysis shows that the linear and exponential algorithms achieve the competitive ratios of O(θα ) and O(łogł(θα)), respectively, where α is the ratio between the aggregate knapsack capacity and the minimum capacity over a single dimension and θ is the ratio between the maximum and minimum item unit values. We also characterize a lower bound for the competitive ratio of any online algorithm solving OMdKP and show that the competitive ratio of our algorithm with exponential reservation function matches the lower bound up to a constant factor.
Depression Severity Detection Using Read Speech with a Divide-and-Conquer Approach

We propose a divide-and-conquer approach to detect depression severity using speech. We divide speech features based on their attributes, i.e., acoustic, prosodic, and language features, then fuse them in a modeling stage with fully connected deep neural networks. Experiments with 76 clinically depressed patients (38 severe and 38 moderate in terms of Montgomery-Asberg Depression Rating Scale (MADRS)), we obtain 78% accuracy while patients’ self-reporting scores can classify their own status with 79% accuracy.
Determinants and health status of under five children according to 4 D’s of Rashtriya Bal Swasthya Karyakram in field practice area of rural health training centre

Background: Rashtriya Bal Swasthya Karyakram (RBSK) or ‘Child Health Screening and Early Intervention Services’ Program was launched by the Ministry of Health and Family Welfare in February 2013, an ambitious program that is aimed to cover 27 crore children in India, was launched. 4 D’s identified under RBSK are Defects at birth, Diseases in children, Deficiency conditions and Developmental Delays including Disabilities. Hence the present study was conducted to study the determinants and health status of under 5 children according to 4 D’s of RBSK program in field practice area of rural health training centre. Methods: A cross-sectional community based study was carried out among 400 under 5 children in field practice area of rural health training center of Dept. of Community Medicine of Govt. Medical College, Aurangabad, Maharashtra, India using probability proportionate to size (PPS) sampling technique during the period of Jan. 2017 to Dec. 2018. Data was entered in MS Excel 2007 worksheet and analyzed using open Epi version 3.01 Results: The prevalence of 4 D’s condition in under five year children was 18.25%. The association of age and 4Ds condition among under five year children was statistically not significant (p>0.05). Out of complete immunized children, only 7.25% were having 4 D’s conditions present among them compared to unimmunized children in which the 4 D’s conditions were present in 42.86% and this difference was found statistically significant (p<0.05). Conclusion: Child Health Screening and promotion of Early Intervention Services is more important for improvement in health status of children. It will also be very helpful in reducing the extent of disability, in improving the quality of life and enabling all persons to achieve their full potential. Keyword: Determinants of health, Rashtriya Bal Swasthya Karyakram, under five children
The Relations between Ethical Reasoning and Moral Intuitions among Engineering Students in China

Rockwell F. Clancy is a lecturer at TU Delft. Before joining Delft, he was an Associate Teaching Professor in engineering ethics and philosophy at the University of Michigan-Shanghai Jiao Tong University Joint Institute and Research Fellow in the Institute of Social Cognition and Decision-making, both in Shanghai Jiao Tong University. His research and teaching interests include engineering ethics, moral psychology, philosophy of technology, Chinese philosophy, and political philosophy. Rockwell completed his PhD at Purdue University, West Lafayette, MA at the Katholieke Universiteit, Leuven, Belgium, and BA at Fordham University, New York.
High Frequential Resolution Networks: Considerations on a New Functional Brain Connectivity Framework

Connectivity analyses are widely used to assess the interaction brain networks. This type of analyses is usually conducted considering the well-known classical frequency bands: delta, theta, alpha, beta, and gamma. However, this parcellation of the frequency content can bias the analyses, since it does not consider the between-subject variability or the particular idiosyncrasies of the connectivity patterns that occur within a band. In this study, we addressed these limitations by introducing the High Frequential Resolution Networks (HFRNs). HFRNs were constructed, using a narrow-bandwidth FIR bank filter of 1 Hz bandwidth, for two different connectivity metrics (Amplitude Envelope Correlation, AEC, and Phase Lag index, PLI) and for 3 different databases of MEG and EEG recordings. Results showed a noticeable similarity between the frequential evolution of PLI, AEC, and the Power Spectral Density (PSD) from MEG and EEG signals. Nonetheless, some technical remarks should be considered: (i) results at the gamma band should exclude the frequency range around 50 Hz due to abnormal connectivity patterns, consequence of the previously applied 50 Hz notch-filter; (ii) HFRNs patterns barely vary with the connection distance; and (iii) a low sampling frequency can exert a remarkable influence on HFRNs. To conclude, we proposed a new framework to perform connectivity analyses that allow to further analyze the frequency-based distribution of brain networks.
Green synthesis of silver nanoparticles and assay of their antibacterial activity

Nanoparticles have gained considerable attention in recent era among which, silver nanoparticles (AgNPs) have demonstrated exceptional characteristics in medical sciences, particularly. Use of plants for AgNP synthesis represents an eco-friendly, low cost and energy-efficient approach to combat microbial diseases and growing antibiotic resistance. The present study aimed at synthesizing AgNPs using extracts of Aloe vera peel as well as Ranunculus paludosus and Matthiola incana flowers and assessment of their antibacterial potential against clinically isolated multi-drug resistant (MDR) bacteria. The biocidal potential of crude extracts as well as synergistic action of crude extracts and selected antibiotics was determined by Kirby-Bauer disk diffusion method. Results showed that crude extracts alone or with selected antibiotics showed no antibacterial potential against MDR pathogens including Proteus vulgaris, Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus and Klebsiella pneumoniae. AgNPs were synthesized by addition of silver nitrate solution to crude extracts. UV-Visible spectroscopy analysis was done to confirm the synthesis of AgNPs while Fourier transform infrared (FTIR) spectroscopy analysis was performed to depict the functional groups associated with formation of AgNPs. Antimicrobial activity of synthesized AgNPs alone and their synergistic activity with selected antibiotics was then estimated. Results demonstrated the biocidal effectiveness of synthesized AgNPs against MDR pathogens which was further enhanced when used in combination with antibiotics as depicted by wide and clear zones of inhibition. These observations are suggestive of promising antibacterial activity of Aloe vera peel as well as flowers of Ranunculus paludosus and Matthiola incana which may be further validated by extensive research.
Emerging Design Factors in Game-Based Learning: Incentives, Social Presence, and

For many of the design factors described by the model of gamebased learning (Plass, Homer, & Kinzer, 2015; Plass, Mayer, Homer, & Kinzer, chapter 1 in this volume), sufficient research exists to allow design recommendations for game designers. These factors include instructional support, feedback, and coaching (Lester, Spain, Rowe, & Mott, chapter 8 in this volume), selfregulation and reflection (Taub, Bradbury, Mudrick, & Azevedo, chapter 9 in this volume), adaptivity and personalization (Plass & Pawar, chapter 10 in this volume), narratives (Dickey, chapter 11 in this volume), multimedia design princi ples (Nelson & Kim, chapter 12 in this volume), and social mode of play (Ke, chapter 13 in this volume). A number of other design factors that are described in the model, however, only have a small body of research in the context of gamebased learning. These emerging factors include incentive system design, identity design, and social presence in games. In this chapter, we define these factors and summarize the limited research available on their design and effectiveness in the context of games for learning. Other emerging factors are reviewed by Pawar, Tam, and Plass (chapter 14 in this volume).
Schizophrenia Classification Using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level

Disrupted functional and structural connectivity measures have been used to distinguish schizophrenia patients from healthy controls. Classification methods based on functional connectivity derived from EEG signals are limited by the volume conduction problem. Recorded time series at scalp electrodes capture a mixture of common sources signals, resulting in spurious connections. We have transformed sensor level resting state EEG times series to source level EEG signals utilizing a source reconstruction method. Functional connectivity networks were calculated by computing phase lag values between brain regions at both the sensor and source level. Brain complex network analysis was used to extract features and the best features were selected by a feature selection method. A logistic regression classifier was used to distinguish schizophrenia patients from healthy controls at five different frequency bands. The best classifier performance was based on connectivity measures derived from the source space and the theta band.The transformation of scalp EEG signals to source signals combined with functional connectivity analysis may provide superior features for machine learning applications
SCOPE2: A Platform for Sars-COv-2 Primer covErage Evaluation

Currently, there is an increasing number and speed of SARS-CoV-2 mutation taking place around the world, posing a threat to promising public health and challenge to existing diagnostic tools. RT-PCR technology is recognized as the gold standard diagnosing methodology but has shown inaccuracy under some mutated SARS-CoV-2 circumstances. In this study, we developed a platform named SCOPE2 (Sars-COv-2 Primer covErage Evaluation) based on our previous publication. Testing by commonly-used SARS-COV-2 PCR primers, SCOPE2 is proved to effectively and efficiently assess the quality in terms of detection coverage, which may provide a practical tool for primer selection acceleration and primer design improvement.Clinical Relevance—This assists in single SARS-COV-2 Primer selection and suggestion of different SARS-COV-2 Primer combinations.
Directed Motivational Current Benefits to Second Language Learning: Can they Facilitate Future Learning?1

Directed motivational currents (DMCs) are a unique and intense motivational drive capable of supporting second language learning. Learners throughout the world can experience these unique motivational feelings regardless of their teaching and learning contexts. Therefore, researchers and language teachers have been exploring cases of DMCs since the phase of their conceptualization to facilitate their classroom application. Despite the growing interest in the field, very few studies have focused on DMC benefits. The present study is an attempt to fill the empirical gap by examining DMC benefits and their usage in learning English as a foreign language. Learner diaries were used to collect data from five students who had already experienced a purposefully facilitated DMC. A thematic analysis of the data revealed that linguistic competence; that is, grammar, vocabulary, and spelling were developed in addition to real-life skills, such as presentation skills and positive group work dynamics. Learners not only used their newly acquired knowledge in their English classes, but also took advantage of it in their content-based courses, such as organization and administrative methods, to achieve their learning goals. The findings of the present study can be useful for second language teachers, who want to explicitly teach their learners how to use the benefits generated from their DMC experiences so that they can reach their long-term learning goals when the motivational currents fade.
INFLUENCE OF TEACHERS’ EXAMINATION MANAGEMENT ON LEARNERS ACADEMIC PERFORMANCE IN PUBLIC PRIMARY SCHOOLS IN MITUBIRI EDUCATION ZONE

Master teachers exhibit demonstrable skills in the two administrations to students just as to the calling. The study was anchored on ‘Supervision as Moral Action and theory of educational productivity. Data was collected using a structured questionnaire, interview schedule and document analysis in line with the study objectives. Crosssectional descriptive survey designs as well as phenomenology design were used to guide the study. The target population for this study was 2433. The researcher sampled 388 respondents. The majority of the learners had never had their work randomly checked by the headteachers and in most of the learners, the random check was rare. The study concluded that the teachers are appraised on examination management. The appraisal is done every month on the opener, midterm, and end-term examinations.
NBER WORKING PAPER SERIES TRADABILITY AND THE LABOR-MARKET IMPACT OF IMMIGRATION : THEORY AND EVIDENCE FROM THE

In this paper, we show that labor-market adjustment to immigration differs across tradable and nontradable occupations. Theoretically, we derive a simple condition under which the arrival of foreign-born labor crowds native-born workers out of (or into) immigrant-intensive jobs, thus lowering (or raising) relative wages in these occupations, and explain why this process differs within tradable versus within nontradable activities. Using data for U.S. commuting zones over the period 1980 to 2012, we find that consistent with our theory a local influx of immigrants crowds out employment of native-born workers in more relative to less immigrant-intensive nontradable jobs, but has no such effect within tradable occupations. Further analysis of occupation wage bills is consistent with adjustment to immigration within tradables occurring more through changes in output (versus changes in prices) when compared to adjustment within nontradables, thus confirming the theoretical mechanism behind differential crowding out between the two sets of jobs. We then build on these insights to construct a quantitative framework to evaluate the consequences of counterfactual changes in U.S. immigration. Reducing inflows from Latin America, which tends to send low-skilled immigrants to specific U.S. regions, raises local wages for native-born workers in more relative to less-exposed nontradable occupations by much more than for similarly differentially exposed tradable jobs. By contrast, increasing the inflow of high-skilled immigrants, who are not so concentrated geographically, causes tradables and nontradables to adjust in a more similar fashion. For the nontradable-tradable distinction in labor-market adjustment to be manifest, as we find to be the case in our empirical analysis, regional economies must vary in their exposure to an immigration shock. Ariel Burstein Department of Economics Bunche Hall 8365 Box 951477 UCLA Los Angeles, CA 90095-1477 and NBER arielb@econ.ucla. edu Gordon Hanson IR/PS 0519 University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0519 and NBER gohanson@ucsd.edu Lin Tian Columbia University 420 West 118th Street Columbia Dept. of Economics NY, NY 10027 lt2475@columbia.edu Jonathan Vogel Department of Economics Columbia University 420 West 118th Street New York, NY 10027 and NBER jvogel@columbia.edu
Genetic algorithm based deformation control and clamping force optimisation of workpiece fixture system

In re-engineering mass production industry, design and production of components are frequently changed according to the customer needs within a very short span of time. This leads to rising difficulty in maintaining the accuracy of every finished component. It also induces the long setup time of the cutting tools and machine tools affecting the production rate of the components. To reduce the production time and to improve the accuracy of the finished product, proper fixture element is essential. The accuracy of the finished work-piece strongly depends on the position of the locators and clamps in the work-piece fixture system. The finite element analysis tool is well suited to predict the active and passive forces on the work piece-fixture system. The accuracy of the workpiece not only depends on the clamping force but also depends upon the locator force (reaction force). This paper presents the optimisation of the locations of active (clamp) and passive (locator/support) elements in the workpiece-fixture system using genetic algorithm (GA) with ANSYS parametric design language (APDL) of finite element analysis. Three case studies are given to illustrate the application of the proposed approach. Finally the case study results have been compared with the Li and Melkote (1999a) [Li, B. and Melkote, S.N. , 1999a. An elastic contact model for the prediction of workpiece-fixture contact forces in clamping. ASME Journal of Manufacturing Science and Engineering, 121(3), 485–493] study. This presented prediction method is conceptually simple and computationally efficient.
Exploring EFL Teachers' Perception on Readiness to Use Smartphones for Teaching Oral English Communication Skills at Tertiary Level 1

This study aims to examine the EFL teachers’ perception of their readiness to use different smartphone applications for teaching oral skills at the tertiary level in Bangladesh and compares this to the relevant demographic factors. To reach these objectives, a mixed method research design was implemented. A survey questionnaire was answered by 46 English as a Foreign Language (EFL) teachers from twelve different universities in Bangladesh and a semi-structured interview was conducted with five of these teachers. The results showed that these EFL teachers had a high degree of readiness to use smartphones for teaching oral skills. The teachers agreed that smartphones could help them to deal with the problems like a large class size, a lack of authentic materials, learners' inactiveness, as well as a lack of an oral practice environment inside and outside the classroom. Moreover, there was no statistically significant relationship between the EFL teachers' demographics and their readiness to use a smartphone. The results suggest that smartphone applications have certain potentials/benefits for the university teachers, and that a smartphone-based teaching method for developing EFL learners’ oral skills should be promoted in an EFL context like Bangladesh.
Safety Impacts of Design Exceptions on Nonfreeway Segments

Safety (in terms of expected crash frequency and severity) was compared on road segments where design exceptions had been approved and constructed and on similar road segments where no design exceptions had been approved or constructed. Data were collected for design exceptions in Utah from 2001 to 2006. Multiple data sources were used to identify and define road segments with and without design exceptions. Propensity scores were applied to assess similarities between treatment and comparison sites. Ultimately, 34 total nonfreeway segments with design exceptions and 80 nonfreeway segments without design exceptions were used for modeling. The relationship between the presence of design exceptions and crash frequency was explored with a negative binomial regression modeling approach. The relationship between the presence of design exceptions and crash severity was explored in three ways: (a) computation of severity distributions at locations with and without design exceptions, (b) estimation of separate negative binomial regression models by severity level, and (c) estimation of multinomial logit models to predict the severity outcome of a crash. The presence of design exceptions was represented in the regression models by an indicator variable (where 1 5 one design exception or more and 0 5 no design exceptions). Crash data from 2007 through 2010 were used for model estimation. No significant differences were observed in expected crash frequencies and crash severities between nonfreeway road segments with and without design exceptions. This overall finding was consistent with two previous related efforts in Kentucky and Indiana.
Microwave low phase noise oscillators using T-shaped stepped-impedance-resonator filters

Low phase noise microwave oscillators using T-shaped step-impedance-resonator (TSIR) filters are proposed and experimentally verified. Instead of using a high-quality factor resonator to design an oscillator, the TSIR filter is embedded into the feedback loop as a frequency stabilisation element. As the oscillator is designed at the group-delay-peak frequency of the filter, one can improve the phase-noise performance. Moreover, to miniaturise the oscillator circuit size, the TSIR filter can be further evolved into a compact net-type filter by the folding technique. The developed oscillators using TSIR filters at 1 and 5.2 GHz have the measured phase noises of 2131 and 2132 dBc/Hz at 1 MHz offset frequency, respectively. On the other hand, as compared with oscillators using TSIR filters, using a net-type TSIR filter to develop the filter-based oscillator at 1 GHz can achieve a 38% circuit size reduction and a 4-dB phase noise improvement at 1 MHz offset frequency.
Mapping available Ocean Thermal Energy Conversion resources around the main Hawaiian Islands with state-of-the-art tools

This paper aims to demonstrate how the evaluation of Ocean Thermal Energy Conversion (OTEC) resources can benefit from currently available high-resolution ocean models. The case of waters around the main Hawaiian Islands is presented because of its relevance to the future development of OTEC. OTEC resources are defined here by ocean temperature differences between water depths of 20 and 1000 m, with little loss of generality. Using state-of-the-art tools like the HYCOM+NCODA (1/12°) model affords the possibility to track changes on a daily basis over a wide area (e.g., 17 °N to 24 °N and 153 °W to 162 °W). An examination of numerical data over a time period of 2 years reveals interesting geographical patterns. It is found that average OTEC temperature differences are consistently higher (by about 1 °C) west of the islands, whereas the amplitude of the yearly cycle globally decreases from north to south as expected. Better OTEC resources in the lee of the islands are attributed to the narrow eastward-flowi.