Miracle-dz/UniFAR-FTUs huggingface dataset:
Competitive priorities of high and low performers in global manufacturing: results from GMRG 2008 data

Based on a large scale manufacturing study pursued by members of Global Manufacturing Research Group we aim to find answers on several questions. (1) do trade-offs between core competences still exist, (2) which best practices use best performers. We divided our sample according to profit margin into three groups. We show that order of importance of competitive priorities have changed since last researches. Through correlation analysis we showed that for high performers correlation between competitive priorities are extremely high (over 0, 5) unlike the middle performers and unlike low performers who experience trade-offs between competitive priorities. Finally we looked at used best practices and find the biggest differences in usage of Six sigma methodology, ISO 14000, cellular manufacturing, JIT and finally factory automation. Our findings are in accordance with the integrative model described in Boyer and Lewis (2002, pp. 11).
Peculiar Features in the Cosmic Microwave Background Radiation

In this thesis, I develop statistics capable of detecting peculiar features in current observations of the cosmic microwave background (CMB) radiation. Such tools scrutinise the very foundations of standard cosmological models. Evidence of peculiar features in the CMB may require a reassessment of these building blocks. More likely, any features may be artefacts of some non-cosmological signal. Nevertheless, whether the origin of these strange attributes is primordial or local, their discovery would be instructive. Existing statistical tools focus on the amplitude of the spherical harmonic coefficients, I look instead at their phases. The method I form checks for the uniformity of the distribution of phase angles using a non-parametric descriptor, which is known as Kuiper's statistic. The method is applied to the COBE-DMR and WMAP sky maps, and departures from uniformity are found in both. The results probably reflect Galactic contamination or the known variation of signal-to-noise across the sky rather than primordial non-Gaussianity. Next, the statistic is adjusted to probe the topology of the universe. The new method exploits the existence of correlations in the phases of the CMB temperature pattern associated with matched pairs of circles seen in the CMB in universes with non-trivial topologies. After this, I turn our attention to the issue of Galactic foreground signals. A diagnostic of foreground contamination is developed based around the Faraday rotation measures (RM) of extragalactic sources. Statistically significant correlations of RM with the preliminary WMAP individual frequency maps are found. These correlations remain significant in CMB-only maps. Later, I use catalogues of rotation measures to construct a template of the Galactic sky. The RM maps may be used as templates for CMB foreground analysis. This idea is illustrated with a cross-correlation analysis between the WMAP data and our maps. I find a significant cross-correlation, again indicating the presence of significant residual contamination. Problems and future developments are discussed at the end.
Challenges and Status on Design and Computation for Emerging Additive Manufacturing Technologies

The revolution of additive manufacturing (AM) has led to many opportunities in fabricating complex and novel products. The increase of printable materials and the emergence of novel fabrication processes continuously expand the possibility of engineering systems in which product components are no longer limited to be single material, single scale, or single function. In fact, a paradigm shift is taking place in industry from geometry-centered usage to supporting functional demands. Consequently, engineers are expected to resolve a wide range of complex and difficult problems related to functional design. Although a higher degree of design freedom beyond geometry has been enabled by AM, there are only very few computational design approaches in this new AM-enabled domain to design objects with tailored properties and functions. The objectives of this review paper are to provide an overview of recent additive manufacturing developments and current computer-aided design methodologies that can be applied to multimaterial, multiscale, multiform, and multifunctional AM technologies. The difficulties encountered in the computational design approaches are summarized and the future development needs are emphasized. In the paper, some present applications and future trends related to additive manufacturing technologies are also discussed.
Optimal Power Flow Analysis by using Hybrid Cuckoo Search Algorithm

This paper proposes a novel algorithm for continuous non linear optimal power flow problem. The objective of the proposed method is to find the steady state operating point which minimizes the fuel cost with proper system performance in terms of limits on generator power voltage and line flow. The proposed approach employs hybrid cuckoo search algorithm for optimal setting of OPF control variables. This optimization algorithm is inspired by the life style cuckoo bird. Similar to the other evolutionary algorithms it starts with an initial population to solve the optimization problem. The proposed technique is tested on the standard IEEE 30 bus system various objectives and is compared with a conventional method. The simulation results verify the effectiveness of the proposed method. Keywords—Optimal power flow, HCSA, Fuel cost, Transmission power loss, L-index,
On the Geometric Side of the Arthur Trace
 Formula for the Symplectic Group of Rank 2

We study the non-semisimple terms in the geometric side of the Arthur trace formula for the split symplectic similitude group or the split symplectic group of rank 2 over any algebraic number field. In particular, we show that the coefficients of unipotent orbital integrals are expressed by the Dedekind zeta function, Hecke L-functions, and the Shintani zeta function for the space of binary quadratic forms.
Comparative Study of IoT Platforms and Support for Edge Computing

The Internet of Things has power to make one’s life comfortable and overcome challenges that people face even in their daily life. Today facilities like smart healthcare, smart homes, smart grid, remote monitoring of devices have become an indispensable part of our lives, the reason being Internet of things. To create smart devices, applications are rapidly moving to the cloud, because amount of data generated is increasing tremendously. But there are some applications that require real time processing, low latency, support for mobility, very large number of nodes, working in remote areas. These applications demand edge computing where data is stored and processed near the end device. IoT connects physical devices and facilitates them to communicate with each other and control them remotely. Now we are entering an era where the applications require data to be stored and processed at the periphery of the network. In this paper our focus is on study of Iot applications, core IoT services providing platforms based on cloud computing, their comparison, challenges and support for edge computing.
ALGORITHMS CONSTRUCTION FOR NONEXPANSIVE MAPPINGS AND INVERSE-STRONGLY MONOTONE MAPPINGS

In this paper, we construct two algorithms for finding a common element of the set of fixed points of a nonexpansive mapping and the set of solutions of the variational inequality for an α-inverse-strongly monotone mapping in a Hilbert space. We show that the sequence converges strongly to a common element of two sets under the some mild conditions on parameters. As special cases of the above two algorithms, we obtain two schemes which both converge strongly to the minimum norm element of the set of fixed points of a nonexpansive mapping and the set of solutions of the variational inequality for an α-inverse-strongly monotone mapping.
Localization Antigen Presentation Without Altering Disrupts a Novel Glycosylation of HLA-DR

The HLA-DR hemizygous B lymphoblastoid cell line, 10.24. 6, has a DRA mutation (Pro 96 3 Ser) that creates a novel glycosylation site at Asn 94 . The mutant DR molecules are primarily associated with nested fragments of invariant chain (class II-associated invariant chain peptides), and their interaction with HLA-DM is impaired. Here we further analyzed the defect in 10.24. 6 cells. Expressing Ser 96 mutant DRA cDNA in DRA-null cells recapitulated the 10.24. 6 phenotype, indicating that the mutation causes the Ag presentation defect. A mutation to Ala 96 a , which does not introduce an extra glycan, generated a normal phenotype; the critical role of the glycan was further supported by experiments in which N -glycosylation was blocked by tunicamycin. We also evaluated whether the 10.24. 6 mutation affected DR3 maturation or trafﬁcking. Metabolic labeling and subcellular fractionation showed that assembly, endosomal transport, and invariant chain proteolysis of mutant DR3 molecules were similar to wild-type. A slight delay in export from the endoplasmic reticulum to the Golgi apparatus in 10.24. 6 cells probably did not contribute signiﬁcantly to the Ag presentation defect, because the abundance of DM and mutant DR in peptide-loading compartments was normal at steady state. Our results indicate that proper localization of these molecules does not depend on their interaction. Journal of Immunology, 1998, 160: 4289–4297.
Journal Pre-proofs On calcium-to-alkalinity anomalies in the North Pacific, Red Sea, Indian Ocean and Southern Ocean

An important factor for predicting the effect of increased CO 2 on future acidification of the ocean is a proper understanding of the interactions controlling production and dissolution of calcium carbonate minerals (CaCO 3 ). The production and dissolution of CaCO 3 in the ocean can be assessed over large spatial scales by measuring seawater calcium concentrations and total alkalinity (A T ), yet past studies suggest that there could be large discrepancies between calcium and A T -based balances of the CaCO 3 cycle in the North Pacific and Indian Oceans. Here, we analyse water column samples collected along transects in the North Pacific, Southern Ocean, tropical Indian Ocean and Red Sea for their concentrations of calcium, nutrients, and A T . We find that there is an excess calcium over A T anomaly in the top 1000 m of the tropical Indian Ocean water-column. The source of this anomaly is the dissolution of subsurface gypsum deposits in the Red Sea. We find no evidence for calcium-over-A T anomalies in the North Pacific, in contrast to previous studies. Our results show that, in most cases, calcium and A T data agree well and can be used to reconstruct the marine CaCO 3 cycle.
ABNORMALLY RAISED CA125 IN A PATIENT OF LUMP IN ABDOMEN, DILEMMA IN DIAGNOSIS

A 42 years old woman presented with a lump in abdomen that caused abdominal protuberance also complained of menorrhagia and stress urinary incontinence. Ultrasonography revealed a 15x14x13 cm mass developed from her uterus. Her cancer antigen 125 (CA125) serum level at presentation was 4,321 U/mL and lactate dehydrogenase (LDH) level was 487 U/L. The suspicion was of a malignant neoplasm, so we undertook an exploratory laparotomy. Intraoperatively multiple uterine fibroids were noted, ovaries found to be normal. Frozen section sent, suggestive of leiomyoma. The pathologic diagnosis was a leiomyoma of the uterus. Both the Cancer Antigen125 and Lactate Dehydrogenase serum levels dropped unusually rapidly to normal after the operation. Usually with such high levels of Cancer Antigen 125 in association of abdominal mass suspicion of ovarian pathology is made but very rarely as in this case, uterine pathology may present with such abnormally raised levels of Cancer Antigen 125.
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

MSC, Purdue University, May 2019. Photoplythesmogram (PPG) Signal Reliability Analysis in a Wearable Sensor-Kit. Major Professor: Mireille Boutin. In recent years, there has been an increase in the popularity of wearable sensors such as electroencephalography (EEG) sensors, electromyography (EMG) sensors, gyroscopes, accelerometers, and photoplethysmography (PPG) sensors. This work is focused on PPG sensors, which are used to measure heart rate in real time. They are currently used in many commercial products such as Fitbit Watch and Muse Headband . Due to their low cost and relative implementation simplicity, they are easy to add to custom-built wearable devices. We built an Arduino-based wearable wrist sensor-kit that consists of a PPG sensor in addition to other low cost commercial biosensors to measure biosignals such as pulse rate, skin temperature, skin conductivity, and hand motion. The purpose of the sensor-kit is to analyze the effects of stress on students in a classroom based on changes in their biometric signals. We noticed some failures in the measured PPG signal, which could negatively affect the accuracy of our analysis. We conjectured that one of the causes of failure is movement. Therefore, in this thesis, we build automatic failure detection methods and use these methods to study the effect of movement on the signal. Using the sensor-kit, PPG signals were collected in two settings. In the first setting, the participants were in a still sitting position. These measured signals were manually labeled and used in signal analysis and method development. In the second setting, the signals were acquired in three different scenarios with increasing levels of activity. These measured signals were used to investigate the effect of movement on the reliability of the PPG sensor.
Ecological Momentary Assessment of Exercise and Neuropathic Pain Experienced by Men with SCI: Participant Perceptions

A couple of laboratory-based studies have examined the effects of exercise on neuropathic pain (NP) in adults with spinal cord injury (SCI). However, pain levels observed within a laboratory may not be representative of what is experienced in the real-world. Ecological momentary assessment (EMA) can address this limitation by assessing pain in real time, and within an individual’s natural environment. Despite these advantages, EMA protocols have had limited use and development in SCI research. This study evaluated participants’ perceptions of: a) their daily NP fluctuations, and b) the utility of EMA to assess their NP patterns. Six physically active men with chronic SCI participated in a 6-day Smartphone based EMA protocol assessing their NP from pre- to post-exercise and four other times throughout the day. A semi-structured exit interview followed this protocol. Qualitative and quantitative data were analyzed to provide comprehensive insight on participants’ perceptions of their NP and their perceived value of using EMA to map their NP patterns. All 6 participants reported that exercise reduced their NP sensations. For some participants, the momentary measurement aspect of EMA increased their awareness of their NP in a negative manner. Conversely, increased awareness was beneficial for others as it made them critically think about the specific NP sensations being felt. Participants stated that the EMA protocol was not onerous and did not impede on daily life, which was supported by compliance rates to the EMA prompts (80.56%-113.89%; M=94.91). Using EMA to gain an understanding of the temporal aspects of NP, and how NP changes following exercise, were perceived to be useful. Tracking NP patterns may have value for those who experience high levels of NP, or when engaging in new activities. Overall, EMA shows promise as a viable methodology to evaluate temporal aspects of NP in the SCI population .
Management of Medical Data Access Using Blockchain Technology

While there is still a considerable amount of debate around the superiority of electronic medical records over paper records, the research literature paints a more realistic picture of the benefits and downsides. The aim of this research work is to develop a blockchain model for enhancing trust and reliability in the medical care sector. The work used the immutable, transparent, secured and distributed properties of the blockchain to create a medical data access environment that allows patients and health administrators to have proper and secure access to their data, which is void of any form of tampering; hence leading to reduction in death rates among patients and quick response to users of the system. (cid:1837) -anonymity and K means algorithm were applied to the medical data obtained to cluster the medical data records based on the patient details to help provide management and monitoring options for both the patients and the health care institution. The result of this work showed a tamper-proof record creation and data access demonstrated by storing the patients’ records into a blockchain and then the proposed k means algorithm was able to efficiently cluster the entire users into four cluster groups based on their medical records. The work was able to build a resilient system that prevents data loss as it is stored in a reliable system that aids maintenance of data integrity, assures individual of their records privacy, forms trusted partnerships and is immutable .
RRTM_A_244912 27..36

Tahir Eyayu 1 Ayalew Jejaw Zeleke Masresha Seyoum Ligabaw Worku 1Department of Medical Laboratory Sciences, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia; 2Department of Medical Parasitology, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia; 3University of Gondar Referral Hospital Laboratory, University of Gondar, Gondar, Amhara Regional State, Ethiopia Purpose: To assess basic coagulation profiles and platelet count among Schistosoma mansoni-infected and non-infected adults. Patients and Methods: A comparative cross-sectional study was conducted from February to April 2019 at Sanja Primary Hospital, northwest Ethiopia. A total of 200 adults (100 cases and 100 controls) were enrolled using convenient sampling technique. Both wet mount and Kato-Katz techniques were performed using a stool sample. The venous blood sample was collected to perform platelet count, basic coagulation and serological tests. The data were coded and entered into EpiData Manager (v4.4.2.1) and analyzed using SPSS version 20. Nonparametric tests were used during data analysis. P-value less than 0.05 was considered as statistically significant. Results: Prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalization ratio (INR) were significantly higher while the platelet count was significantly lower in S. mansoni-infected than healthy adults (P <0.001). There were statistically significant differences in the median [IQR] value of PT, APTT, INR and platelet count between light, moderate and heavy infected groups (P <0.05). Infection intensity had a positive correlation with basic coagulation profiles and a negative correlation with platelet count (P <0.05) of S. mansoni-infected adults. Conclusion: The prevalence of coagulation abnormality was higher in S. mansoni-infected adults than healthy controls. Coagulation test and platelet count should be used to monitor and manage schistosomiasis-related complications.
STAT3 Inhibits Autocrine IFN Signaling in Type I Conventional Dendritic Cells

Type I conventional dendritic cells (cDC1s) are an essential Ag-presenting population required for generating adaptive immunity against intracellular pathogens and tumors. While the transcriptional control of cDC1 development is well understood, the mechanisms by which extracellular stimuli regulate cDC1 function remain unclear. We previously demonstrated that the cytokine-responsive transcriptional regulator STAT3 inhibits polyinosinic:polycytidylic acid [poly(I:C)]-induced cDC1 maturation and cDC1-mediated antitumor immunity in murine breast cancer, indicating an intrinsic, suppressive role for STAT3 in cDC1s. To probe transcriptional mechanisms regulating cDC1 function, we generated novel RNA sequencing datasets representing poly(I:C)-, IL-10–, and STAT3-mediated gene expression responses in murine cDC1s. Bioinformatics analyses indicated that poly(I:C) stimulates multiple inflammatory pathways independent of STAT3, while IL-10–activated STAT3 uniquely inhibits the poly(I:C)-induced type I IFN (IFN-I) transcriptional response. We validated this mechanism using purified cDC1s deficient for STAT3 or IFN signaling. Our data reveal IL-10–activated STAT3 suppresses production of IFN-β and IFN-γ, accrual of tyrosine phosphorylated STAT1, and IFN-stimulated gene expression in cDC1s after poly(I:C) exposure. Moreover, we found that maturation of cDC1s in response to poly(I:C) is dependent on the IFN-I receptor, but not the type II IFN receptor, or IFN-λ. Taken together, we elucidate an essential role for STAT3 in restraining autocrine IFN-I signaling in cDC1s elicited by poly(I:C) stimulation, and we provide novel RNA sequencing datasets that will aid in further delineating inflammatory and anti-inflammatory mechanisms in cDC1s. Key Points RNA-seq profiling delineates cDC1 responses to extracellular stimuli. Poly(I:C) activates autocrine IFN-I signaling in cDC1s. IL-10 inhibits autocrine IFN-I responses in cDC1s via STAT3.
Comparing measures of centrality in bipartite patient-prescriber networks: A study of drug seeking for opioid analgesics

Visiting multiple prescribers is a common method for obtaining prescription opioids for nonmedical use and has played an important role in fueling the United States opioid epidemic, leading to increased drug use disorder and overdose. Recent studies show that centrality of the bipartite network formed by prescription ties between patients and prescribers of opioids is a promising indicator for drug seeking. However, node prominence in bipartite networks is typically estimated with methods that do not fully account for the two-mode topology of the underlying network. Although several algorithms have been proposed recently to address this challenge, it is unclear how these algorithms perform on real-world networks. Here, we compare their performance in the context of identifying opioid drug seeking behaviors by applying them to massive bipartite networks of patients and providers extracted from insurance claims data. We find that two variants of bipartite centrality are significantly better predictors of subsequent opioid overdose than traditional centrality estimates. Moreover, we show that incorporating non-network attributes such as the potency of the opioid prescriptions into the measures can further improve their performance. These findings can be reproduced on different datasets. Our results demonstrate the potential of bipartiteness-aware indices for identifying patterns of high-risk behavior.
Learning to Infer Inner-Body Under Clothing From Monocular Video

Accurately estimating the human inner-body under clothing is very important for body measurement, virtual try-on and VR/AR applications. In this article, we propose the first method to allow everyone to easily reconstruct their own 3D inner-body under daily clothing from a self-captured video with the mean reconstruction error of 0.73cm within 15s. This avoids privacy concerns arising from nudity or minimal clothing. Specifically, we propose a novel two-stage framework with a Semantic-guided Undressing Network (SUNet) and an Intra-Inter Transformer Network (IITNet). SUNet learns semantically related body features to alleviate the complexity and uncertainty of directly estimating 3D inner-bodies under clothing. IITNet reconstructs the 3D inner-body model by making full use of intra-frame and inter-frame information, which addresses the misalignment of inconsistent poses in different frames. Experimental results on both public datasets and our collected dataset demonstrate the effectiveness of the proposed method. The code and dataset is available for research purposes at http://cic.tju. edu.cn/faculty/likun/projects/Inner-Body