Miracle-dz/UniFAR-FTUs huggingface dataset:
Firm valuation effects of the decision to adopt relationally governed business process outsourcing arrangements

Business process outsourcing (BPO) has recently grown in incidence and importance. However, academic research on this phenomenon is sparse. Further, studies on outsourcing have primarily used a transaction cost economics (TCE) lens, largely neglecting other key theoretical explanations of the outsourcing decision and performance. While TCE provides a useful framework, it underemphasises hybrid governance structures that reflect relationships that fall between markets and hierarchies. We examine the decision to adopt relationally governed BPO arrangements and the impact on firm value. We recognise ‘discriminatory alignment’ aspects of governance and argue that the nature of the process itself will influence the value that can be garnered through relational governance. Using secondary data on 298 BPO announcements, we test the proposed model and confirm that a higher level of relational governance adoption strongly enhances firm valuation. This positive valuation impact of relational governance adoption reaches an even higher level in situations of primary processes and processes that have had a presence in the outsourcing organisation. The results emphasise the importance of considering all processes for outsourcing, the critical consideration of relational governance and the importance of planning governance structures that are aligned with the nature and experience with the process being outsourced.
Design Specification Determination with Collaborative Approach for Indonesian Short Wheelbase Recumbent Bicycle

Determination of specification for product development is useful, as it includes an overview of product-related information. However, it is not an easy thing, especially for new and innovative product. In the view of designer as the subject who has never made the product, he needs to work closely with the manufacturer so that the concept can be designed to become a feasible product. In addition, in order to reduce product design life cycle and minimize design errors, the designer needs to communicate and collaborate with the manufacturer. This research proposes a design specification based on the paradigm of Concurrent Engineering (CE) from Collaborative Product Development (CPD) strategy in the development of a new ergonomically designed Short Wheel Base (SWB) Recumbent Bicycle. This collaborative process involved an expert from bicycle practitioner which already had experience in building custom bicycle. The result of this study is a frame set design of the bicycle which considers human anthropometry and the detail list of its components. Beside of that, the process of collaboration with practitioners bicycle produces 37% change in the components used and 3 changes in shape of design SWB Recumbent Bicycle.
Variety in Alcohol Use Disorder Relapse Definitions: Should the Term "Relapse" Be Abandoned?

OBJECTIVE The definition of relapse has been the subject of debate for decades, and a semantic ambiguity highlighted in a 2016 article in the Journal of Studies on Alcohol and Drugs remains. The current article replicates and extends the 2016 findings on alcohol use disorder (AUD) relapse definitions. METHOD We conducted a systematic review of 321 articles that examined relapse in patients with AUD, published from 2000 to 2019. Relapse definitions were extracted and a narrative review of definitions was conducted. RESULTS One hundred and one different definitions of relapse were used in 251 (78%) of the reviewed articles. In 70 (22%) of articles, no definition of AUD relapse was provided. Fifty-three articles used diagnostic criteria (i.e., alcohol use after remission of AUD), whereas 99 articles defined relapse as "any alcohol use" or "any use of alcohol/drugs. " Additional articles defined relapse by alcohol outcomes (e.g., percent drinking days), alcohol-related problems, or hospitalizations (n = 97). Only 12 articles described the time window of abstinence preceding a relapse. We observed relatively no meaningful intercontinental or time-related differences in relapse definitions, although the outcome "percent heavy drinking days" was used more frequently in recent studies. CONCLUSIONS A wide variety of relapse definitions were identified. Despite decades of research and discussion, there is still no widely accepted consensus definition of AUD relapse. We propose to shift the focus toward clinical continuous outcomes, course specifiers based on the number of AUD symptoms present, and quality-of-life-related criteria instead of using current dichotomous AUD relapse terminology.
Rifapentine is an entry and replication inhibitor against yellow fever virus both in vitro and in vivo

ABSTRACT Yellow fever virus (YFV) infection is a major public concern that threatens a large population in South America and Africa. No specific anti-YFV drugs are available till now. Here, we report that rifapentine is a potent YFV inhibitor in various cell lines by high-throughput drugs screening, acting at both cell entry and replication steps. Kinetic test and binding assay suggest that rifapentine interferes the viral attachment to the target cells. The application of YFV replicon and surface plasmon resonance assay indicates that rifapentine suppresses viral replication by binding to the RNA-dependent RNA polymerase (RdRp) domain of viral nonstructural protein NS5. Further molecular docking suggests that it might interact with the active centre of RdRp. Rifapentine significantly improves the survival rate, alleviates clinical signs, and reduces virus load and injury in targeted organs both in YFV-infected type I interferon receptor knockout A129−/− and wild-type C57 mice. The antiviral effect in vivo is robust during both prophylactic intervention and therapeutic treatment, and the activity is superior to sofosbuvir, a previously reported YFV inhibitor in mice. Our data show that rifapentine may serve as an effective anti-YFV agent, providing promising prospects in the development of YFV pharmacotherapy.
Serum cytokine levels of systemic lupus erythematosus patients in the presence of concomitant cardiovascular diseases.

BACKGROUND Systemic lupus erythematosus (SLE) is known to be associated with an increased risk of cardiovascular diseases (CVD). SLE patients suffer from CVD 3.5 times more often than healthy people. Cytokine-mediated inflammation is actively involved in the development of cardiovascular pathology. OBJECTIVE Here, we analyzed serum levels of nine cytokines of steroids treated SLE patients depending on the presence of concomitant CVD. METHODS The levels of interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-10, IL-21, tumor necrosis factor α (TNFα), B-cell activating factor (BAFF), and a proliferation-inducing ligand (APRIL) were analyzed using multiplex immunoassay. RESULTS In the total group of SLE patients (n=29), the concentrations of IL-6 and IL-10 were higher, and the APRIL level decreased compared with healthy donors (n=39, p<0.05). The same changes were observed in the group of patients without CVD (n=15): the levels of IL-6 and IL-10 increased, and the level of APRIL was lower than in healthy individuals (p<0.05). In the group of SLE patients with CVD (n=14), the concentrations of IL-4, IL-6, IL-10, and TNFα increased (p<0.05). Interestingly, the levels of TNFα and BAFF in SLE patients with CVD were higher than in patients without cardiovascular pathology. Thus, TNFα and BAFF levels were significantly altered in SLE with concomitant CVD compared to SLE without CVD. CONCLUSION These findings suggest that cytokine profiles in SLE with concomitant CVD and SLE without CVD are different, which should be considered in further research with large samples.
GEOTECHNICAL PROPERTIES OF A COPPER SLAG

The geotechnical properties of a copper slag are presented. The slag, produced at the American Smelting and Refining Company (ASARCO) plant in El Paso, Texas, is a by-product of refining copper ore in oil-fired reverberatory furnaces. The geotechnical properties include sieve analysis, specific gravity, maximum and minimum dry unit weights, angle of shearing resistance, one-dimensional compressibility, and permeability. The results of laboratory tests on this slag have been compared with the representative values obtained for sands. The comparison shows that the values of the slag are similar to those obtained for medium sands, and it can be used as a construction material in place of sand, such as backfill of retaining walls and landfill for construction of shallow foundations. Toxicity tests on the slag for possible groundwater pollution are also included. Based on the current U.S. Environmental Protection Agency (EPA) regulations governing solid waste characteristics, the slag can be classified as nonhazardous.
Effects of an action-guided intervention on optimistic bias and protective behaviors regarding endocrine disruptors in young women

ABSTRACT This study examined the effects of an action-guided intervention on reducing optimistic bias about reproductive health problems related to endocrine disruptors and improving protective behaviors against endocrine disruptors. A quasi-experimental design with a nonequivalent comparison group was used with a pretest and double posttest. With a convenience sample of 140 women, 60-minute online education sessions and a 30-minute booster session were provided to seven small groups, while the comparison group received a leaflet. Data were collected at baseline and 4 and 12 weeks post-intervention. Descriptive statistics, repeated-measures ANOVA, and the McNemar test were conducted. A significant difference was found in protective behaviors between the groups (F = 81.21, p < .001). The effects remained consistent over time (F = 59.96, p < .001) and there was an interaction effect of group and time (F = 41.26, p < .001). The proportion of women with optimistic bias about reproductive health problems related to endocrine disruptors was significantly lower in the experimental group than in the comparison group only at 12 weeks post-intervention (χ2 = 33.56, p = .031). Accurate knowledge and information should be provided to women to build realistic awareness of the lifestyle-related health problems caused by endocrine disruptors.
Longitudinal manganese‐enhanced magnetic resonance imaging of neural projections and activity

Manganese‐enhanced magnetic resonance imaging (MEMRI) holds exceptional promise for preclinical studies of brain‐wide physiology in awake‐behaving animals. The objectives of this review are to update the current information regarding MEMRI and to inform new investigators as to its potential. Mn(II) is a powerful contrast agent for two main reasons: (1) high signal intensity at low doses; and (2) biological interactions, such as projection tracing and neural activity mapping via entry into electrically active neurons in the living brain. High‐spin Mn(II) reduces the relaxation time of water protons: at Mn(II) concentrations typically encountered in MEMRI, robust hyperintensity is obtained without adverse effects. By selectively entering neurons through voltage‐gated calcium channels, Mn(II) highlights active neurons. Safe doses may be repeated over weeks to allow for longitudinal imaging of brain‐wide dynamics in the same individual across time. When delivered by stereotactic intracerebral injection, Mn(II) enters active neurons at the injection site and then travels inside axons for long distances, tracing neuronal projection anatomy. Rates of axonal transport within the brain were measured for the first time in “time‐lapse” MEMRI. When delivered systemically, Mn(II) enters active neurons throughout the brain via voltage‐sensitive calcium channels and clears slowly. Thus behavior can be monitored during Mn(II) uptake and hyperintense signals due to Mn(II) uptake captured retrospectively, allowing pairing of behavior with neural activity maps for the first time. Here we review critical information gained from MEMRI projection mapping about human neuropsychological disorders. We then discuss results from neural activity mapping from systemic Mn(II) imaged longitudinally that have illuminated development of the tonotopic map in the inferior colliculus as well as brain‐wide responses to acute threat and how it evolves over time. MEMRI posed specific challenges for image data analysis that have recently been transcended. We predict a bright future for longitudinal MEMRI in pursuit of solutions to the brain‐behavior mystery.
A multiauxotrophic CHO cell line for the rapid isolation of producers of diverse or high levels of recombinant proteins

Chinese Hamster Ovary (CHO) cells are widely used for the high‐level production of recombinant proteins. We created a multiauxotrophic mutant of CHO‐K1 cells, CHO8A, that is deficient in eight enzymatic steps in the purine/pyrimidine biosynthetic pathways. Prototrophy was restored by transfections with complementary DNA‐based genes for the eight missing activities. CHO8A cells permit: (1) selection of transfectant clones that have incorporated genes for eight or more different polypeptides, suitable for engineering complex proteins, or pathways; and (2) the single‐step selection of high producers of a particular protein. The latter is achieved by simultaneous use of eight vectors, each bearing one of the eight rescue genes and a cargo protein gene. Screening as few as 10 surviving colonies yielded high producers secreting mAbs at 84 picograms per cell per day or more. CHO8A was isolated by CRISPR‐Cas9 knockout of 10 genes in the pathways to pyrimidines (Dhodh, Umps, Ctps1, Ctps2, and Tyms) and purines (Paics, Atic, Impdh1, Impdh2, and Gmps).
Per- and Polyfluoroalkyl Substances: Toxic Chemicals of Concern in North Carolina

Per- and polyfluoroalkyl substances (PFASs), a family of human-made chemicals often described as “forever chemicals,” are observed in a wide range of products utilized by North Carolinians and can cause liver damage, cancer, and infertility. Owing to their widespread use and persistence in the environment, humans and animals are exposed to individual and combinations of PFASs.
Parametric analysis of multilayered unimorph piezoelectric vibration energy harvesters

The use of a multilayer piezoelectric cantilever beam for vibration-based energy harvesting applications has been investigated as an effective technique to increase the harvested electrical power. It has been shown that the multilayered energy harvester performance is very sensitive to the number of layers and their electrical connection due to impedance variations. The objective of this work is to suggest a comprehensive mathematical model of multilayered unimorph piezoelectric energy harvester allowing analytical solution for the harvested voltage and electrical power. The model is used to deeply investigate the influence of different parameters on the harvested power. A distributed-parameter model of the harvester using the Euler–Bernoulli beam theory and Hamilton's principle is derived. Gauss's law is used to derive the electrical equations for parallel and series connections. A closed-form solution is proposed based on the Galerkin procedure and the obtained results are validated with a finite element 3D model. A parametric study is performed to ascertain the influence of the load resistance, the thickness ratio, the number of piezoelectric layers on the tip displacement and the electrical harvested power. It is shown that this model can be easily used to adjust the geometrical and electrical parameters of the energy harvester in order to improve the system's performances. In addition, it is proven that if one of the system's parameter is not correctly tuned, the harvested power can decrease by several orders of magnitude.
Automated prediction of low ferritin concentrations using a machine learning algorithm

Abstract Objectives Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP). Methods Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool. Results Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day. Conclusions Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.
Extremal GloVe : Theoretically Accurate Distributed Word Embedding by Tail Inference

Distributed word embeddings such as Word2Vec and GloVe have been widely adopted in industrial context settings. Major technical applications of GloVe include recommender systems and natural language processing. The fundamental theory behind GloVe relies on the selection of a weighting function in the weighted least squres formulation that computes the powered ratio of word occurrence count and the maximum word count in the corpus. However, the initial formulation of GloVe is not theoretically sound in two aspects, namely the selection of the weighting function and its power exponent is ad-hoc. In this paper, we utilize the theory of extreme value analysis and propose a theoretically accurate version of GloVe. By reformulating the weighted least squares loss function as the expected loss function and accurately choosing the power exponent, we create a theoretically accurate version of GloVe. We demonstrate the competitiveness of our algorithm and show that the initial formulation of GloVe with the suggested optimal parameter can be viewed as a special case of our paradigm.
Segmentation-based Retinal Image Fusion for Hypertension Prediction

Retinal image vessel segmentation or artery/vein segmentation tasks have been investigated for a long time, and hypertension prediction based on statistical data or body indicators has been learned well. However, the gap between retinal image segmentation and retinal image hypertension prediction still exists. In this paper, we bridge the gap by introducing a model-agnostic cross attention module in segmentation part and a semantic image fusion module in hypertension prediction part thus to form a novel segmentation-based pipeline for hypertension prediction. Specifically, the cross attention module adopts cross multiplication to attend encoder and decoder features, which enhances the artery/vein segmentation ability in uncertain region and border region in retinal image. Then we design a semantic image fusion module to fuse segmented artery/vein vessel image and original image as the classifier input to predict hypertension. The experimental results demonstrate that our model can efficiently predict hypertension, and we achieve 94.87% accuracy, 94.74% specificity, 95.00% sensitivity respectively on 393 retinal images from Kaggle ODiR5k dataset at : https://www.kaggle. com/ocular-disease-recognition-odir5k.
Massive MIMO Beamforming Analysis for 5g Systems

Multiple input multiple outputs, MIMO Technology is based on the usage of multiple receivers and transmitters or antennas at the sending and receiving end of a signal, to increase the throughput and capacity of the network. The number of antennas in a MIMO station can reach up to eight. The Massive MIMO is like the simple MIMO, with the only difference is the number of antennas can reach hundreds. Massive MIMO operates in time division duplex mode, which means the channel has reciprocal properties of radio propagation. The base station calculates an estimate the channel capacity from the uplink pilots transmitted by the end user. This gives us the technology advantage of scalability with the respect of the base station's antenna numbers. In this paper, the MIMO and massive MIMO systems are presented and compared in terms of BER and Beamforming capabilities.
MOAFL: Potential Seed Selection with Multi-Objective Particle Swarm Optimization

Fuzzing has become one of the most widely used technology for discovering software vulnerabilities thanks to its effectiveness. However, even the state-of-the-art fuzzers are not very efficient at identifying promising seeds. Coverage-guided fuzzers like American Fuzzy Lop (AFL) usually employ single criterion to evaluate the quality of seeds that may pass up potential seeds. To overcome this problem, we design a potential seed selection scheme, called MOAFL. The key idea is to measure seed potential utilizing multiple objectives and prioritize promising seeds that are more likely to generate interesting seeds via mutation. More specifically, MOAFL leverages lightweight swarm intelligence techniques like Multi-Objective Particle Swarm Optimization (MOPSO) to handle multi-criteria seed selection, which allows MOAFL to choose promising seeds effectively. We implement this scheme based on AFL and our evaluations on LAVA-M dataset and 7 popular real-world programs demonstrate that MOAFL significantly increases the code coverage over AFL.
RankMat: Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement

Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years, distributed word embeddings have inspired innovation in the area of recommender systems. Word2vec and GloVe have been especially emphasized in many industrial application scenario such as Xiaomi's recommender system. In this paper, we propose a new matrix factorization inspired by the theory of power law and GloVe. Instead of the exponential nature of GloVe model, we take advantage of Pareto Distribution to model our loss function. Our method is explainable in theory and easy-to-implement in practice. In the experiment section, we prove our approach is superior to vanilla matrix factorization technique and comparable with GloVe-based model in both accuracy and fairness metrics.