Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Custom Contracting and the Infomediary Role of Healthcare GPOs

Manufacturers and distributors of expensive implants and other medical supplies often require buyers to sign non‐disclosure agreements treating all information concerning negotiated prices as trade secrets. Such agreements make it difficult for hospitals to obtain accurate pricing benchmarks. To save on procurement costs and to obtain pricing information, most hospitals in the United States join group purchasing organizations (GPOs). GPOs are believed to lower procurement costs by aggregating hospitals’ demand. Whether GPOs indeed add value to the healthcare supply chain and produce actual savings for hospitals are debated policy issues, as evidenced by the ongoing discussions on the topic in the US Congress. Some hospitals procure using GPO contracts, and some try to improve on prices available via GPO contracts, negotiating custom contracts directly with the GPO vendors. Using a game‐theoretic model, we prove that GPOs that operate independently and allow for custom contracting limit the benefit of demand aggregation to smaller hospitals only. The larger hospitals gain primarily from using the GPO as an infomediary to obtain critical pricing information benchmarks. Our results further explain why the introduction of custom contracting lowers the value of access to this pricing information for the hospitals, and how the savings through custom contracting can be misleading. We reveal how GPO vendors can exploit information asymmetry about their prices and earn even higher profits, and why, contrary to the industry's belief, the resulting savings are never higher for any hospital, not even for the larger ones when the GPOs allow custom contracting.
Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) are key players of the tumor microenvironment (TME) involved in cancer initiation, progression, and resistance to therapy. These cells exhibit aggressive phenotypes affecting, among others, extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining an asynchronous cell- and disease-specific regulatory Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, considering our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis. All analyses are compiled and thoroughly annotated in Jupyter notebooks and R scripts available on a GitLab repository (https://gitlab.com/genhotel/breast-cafs-reverse-warburg-effect) and a Zenodo permanent archive [1].
Critical Current Degradation in HTS Tapes for Superconducting Fault Current Limiter under Repeated Overcurrent

Superconducting fault current limiters (SFCL) can be an alternative to conventional devices limiting short-circuit currents in power systems. SFCL use high-temperature superconducting tapes of the second generation (HTS 2G) in SFCL, which, after reaching the characteristic critical current of the tape, go into the resistive state (quenching), limiting the short-circuit current. The critical current determines the moment of activation of the SFCL. Therefore, its value should not change during the operation of the device due to repeated limitation of short-circuit currents. The constancy of the critical current is a prerequisite for proper cooperation with the power system protection devices. Multiple quenching can cause microdamage in the superconducting layers responsible for lowering of the value of the critical current of the HTS tapes. The article presents the research results on the degradation processes of 2G HTS tapes intended for the construction of SFCL due to the action of prospective short-circuit currents with values exceeding the critical current of the tested tapes. The decrease in the value of the critical current of the HTS tape as a result of multiple transitions to the resistive state was investigated. The amount of energy emitted during the test current pulse of 0.2 s duration was determined. The limitation values of the voltage drop on the tape, which does not cause accelerated degradation processes, were defined. The microstructural tests of cross-sections of new HTS tapes subjected to prospective short-circuit currents were performed.
Design of Automated Solar Floor Cleaner using IOT

Technology makes cleaning more intelligent and accessible. Future energy sources will become saturated and run out. Instead of using nonrenewable energies, consider solar power. Today, practically every field uses solar energy. Cleaning is one household task that never becomes obsolete and welcomes new technology. Floors are cleaned with broomsticks, vacuum cleaners, and advanced robot cleaners like Roomba. Middle-age vacuum cleaners and even advanced robotic cleaners are too expensive for low and middle-class consumers. Traditional vacuum cleaners reduce the amount of human energy needed to clean floors, but the user must remain behind the machine to direct the suction pipe to dusty areas. These vacuum cleaners are likewise plugins, meaning they can only be used while plugged in. Solar energy is used to charge the battery, which powers the driving circuit. This cleaner uses Arduino-Uno and Motor driver L293D. This designed solar floor cleaner is driven autonomously with sensor communication by recognizing obstructions and avoiding them. Another Bluetooth module lets the user steer the cleaner to any desired area. This module accepts commands and drives the model. This household and outdoor cleaner provide easy and rapid cleaning. It avoids regular vacuum cleaners ‘plugin and use’ method by self-moving and cleaning concurrently. Thus, automated solar floor cleaners have efficient cleaning benefits and uses.
A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference

Genome-wide DNA methylation levels measured from a target tissue across a population have become ubiquitous over the last few years, as methylation status is suggested to hold great potential for better understanding the role of epigenetics. Different cell types are known to have different methylation profiles. Therefore, in the common scenario where methylation levels are collected from heterogeneous sources such as blood, convoluted signals are formed according to the cell type composition of the samples. Knowledge of the cell type proportions is important for statistical analysis, and it may provide novel biological insights and contribute to our understanding of disease biology. Since high resolution cell counting is costly and often logistically impractical to obtain in large studies, targeted methods that are inexpensive and practical for estimating cell proportions are needed. Although a supervised approach has been shown to provide reasonable estimates of cell proportions, this approach leverages scarce reference methylation data from sorted cells which are not available for most tissues and are not appropriate for any target population. Here, we introduce BayesCCE, a Bayesian semi-supervised method that leverages prior knowledge on the cell type composition distribution in the studied tissue. As we demonstrate, such prior information is substantially easier to obtain compared to appropriate reference methylation levels from sorted cells. Using real and simulated data, we show that our proposed method is able to construct a set of components, each corresponding to a single cell type, and together providing up to 50% improvement in correlation when compared with existing reference-free methods. We further make a design suggestion for future data collection efforts by showing that results can be further improved using cell count measurements for a small subset of individuals in the study sample or by incorporating external data of individuals with measured cell counts. Our approach provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.
Adaptive scheduling server for power-aware real-time tasks

In this paper, we propose a novel scheduling framework for a dynamic real-time environment with energy constraints. This framework dynamically adjusts the CPU voltage/frequency so that no task in the system misses its deadline and the total energy savings of the system are maximized. In this paper, we consider only realistic, discrete-level speeds.Each task in the system consumes a certain amount of energy, which depends on a speed chosen for execution. The process of selecting speeds for execution while maximizing the energy savings of the system requires the exploration of a large number of combinations, which is too time consuming to be computed online. Thus, we propose an integrated heuristic methodology, which executes an optimization procedure in a low computation time. This scheme allows the scheduler to handle power-aware real-time tasks with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the system. Simulation results show that our heuristic methodology is able to generate power-aware scheduling solutions with near-optimal performance.
BEATING A DEAD HORSE: DOPAMINE AND PARKINSON DISEASE

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Genotypic Diversity of Clinical Actinomyces Species: Phenotype, Source, and Disease Correlation among Genospecies

ABSTRACT We determined the frequency distribution of Actinomyces spp. recovered in a routine clinical laboratory and investigated the clinical significance of accurate identification to the species level. We identified 92 clinical strains of Actinomyces, including 13 strains in the related Arcanobacterium-Actinobaculum taxon, by 16S rRNA gene sequence analysis and recorded their biotypes, sources, and disease associations. The clinical isolates clustered into 21 genogroups. Twelve genogroups (74 strains) correlated with a known species, and nine genogroups (17 strains) did not. The individual species had source and disease correlates. Actinomyces turicensis was the most frequently isolated species and was associated with genitourinary tract specimens, often with other organisms and rarely with inflammatory cells. Actinomyces radingae was most often associated with serious, chronic soft tissue abscesses of the breast, chest, and back. Actinomyces europaeus was associated with skin abscesses of the neck and genital areas. Actinomyces lingnae, Actinomyces gravenitzii, Actinomyces odontolyticus, and Actinomyces meyeri were isolated from respiratory specimens, while A. odontolyticus-like strains were isolated from diverse sources. Several of the species were commonly coisolated with a particular bacterium: Actinomyces israelii was the only Actinomyces spp. coisolated with Actinobacillus (Haemophilus) actinomycetemcomitans; Actinomyces meyeri was coisolated with Peptostreptococcus micros and was the only species other than A. israelii associated with sulfur granules in histological specimens. Most genogroups had consistent biotypes (as determined with the RapID ANA II system); however, strains were misidentified, and many codes were not in the database. One biotype was common to several genogroups, with all of these isolates being identified as A. meyeri. Despite the recent description of new Actinomyces spp. , 19% of the isolates recovered in our routine laboratory belonged to novel genospecies. One novel group with three strains, Actinomyces houstonensis sp. nov., was phenotypically similar to A. meyeri and A. turicensis but was genotypically closest to Actinomyces neuii. A. houstonensis sp. nov. was associated with abscesses. Our data documented consistent site and disease associations for 21 genogroups of Actinomyces spp. that provide greater insights into appropriate treatments. However, we also demonstrated a complexity within the Actinomyces genus that compromises the biochemical identification of Actinomyces that can be performed in most clinical laboratories. It is our hope that this large group of well-defined strains will be used to find a simple and accurate biochemical test for differentiation of the species in routine laboratories.
On the semantics of complex events in active database management systems

Active database management systems have been developed for applications needing an automatic reaction in response to certain events. Events can be simple in nature or complex. Complex events rely on simpler ones and are usually specified with the help of operators of an event algebra. There are quite a few papers dealing with extensions of existing event algebras. However, a systematic and comprehensive analysis of the semantics of complex events is still lacking. As a consequence most proposals suffer from different kinds of peculiarities. Independent aspects are not treated independently, leading to shady mixtures of aspects in operators. Moreover, aspects are not always treated uniformly. Operators may have other semantics than expected. The paper addresses these problems by an extensive and in-depth analysis of the foundations of complex events. As a result of this analysis, a (formal) meta-model for event algebras is introduced that subdivides the semantics of complex events into elementary, independent dimensions. Each of these dimensions are discussed in detail. The resulting language specification fulfils the criteria for a good language design (like orthogonality, symmetry, homogeneity, lean set of language constructs) to a large extent.
Torque ripple reduction strategy of model based predictive torque control for doubly salient permanent magnet synchronous machines

The doubly salient permanent-magnet synchronous machine (DSPMSM) is an emerging type of brushless machine. It has been paid more attention by both academia and industry for some advantages of high power/torque density, simple /robust structure, good thermal dissipation ability, strong redundancy capability, and wide range for high speed cruising. Due to the nature of inherited salient poles in both stator and rotor, the DSPMSM suffers from severe torque and flux ripple for its various electromagnetic equivalent air gap lengths at different rotor positions. During the past couple of years, the conventional switching-table-based direct torque control (DTC) has demonstrated great potentiality due to its merits of quick dynamic response, strong robustness and simple control structure. However, during such DTC algorithm, there still exists large ripples of both torque and air gap flux linkage resulted from the Bang-Bang modification. This paper presents one improved strategy to reduce the torque ripple of DSPMSM drive system by help of model based predictive torque control (MPTC), which is a renewed algorithm for conventional DTC strategy. Different with that the traditional MPTC strategy with complete decoupling requirement, the new MPTC method in this work just needs a semi-decoupling control scheme. By adjusting the voltage vector online to best satisfy the demands of torque and flux linkage, the new MPTC algorithm can successfully reduce both torque and flux ripple obviously in DSPMSM drive system. Comprehensive simulation results are finally presented to validate the theoretical analysis. It gives good guidance or suggestion to increase stable and dynamic performance for PMSM, especially for those with double salient structures, which could extend their applications with requirement of high precision in the future.
Extracting Arbitrary-shaped Stay Regions from Geospatial Trajectories with Outliers and Missing Points

We tackle the problem of extracting stay regions from a geospatial trajectory where a user has stayed longer than a certain time threshold. There are four major difficulties with this problem: (1) stay regions are not only point-type ones such as at a bus-stop but large and arbitrary-shaped ones such as at a shopping mall; (2) trajectories contain spatial outliers; (3) there are missing points in trajectories; and (4) trajectories should be analyzed in an online mode. Previous algorithms cannot overcome these difficulties simultaneously. Density-based batch algorithms have advantages over the previous algorithms in discovering of arbitrary-shaped clusters from spatial data containing outliers; however, they do not consider temporal durations and thus have not been used for extracting stay regions. We extended a density-based algorithm so that it would work in a duration-based manner online and have robustness to missing points in stay regions while keeping its advantages. Experiments on real trajectories of 13 users conducting their daily activities for three weeks demonstrated that our algorithm statistically significantly outperformed five state-of-the-art algorithms in terms of F1 score and works well without trajectory preprocessing consisting of filtering, interpolating, and smoothing.
The role of oxidative stress and inflammatory response in the pathogenesis of mastitis in dairy cows

Mastitis is one of the most frequent diseases of dairy cows throughout the world, therefore it causes the greatest economic losses in dairy cattle industry. These losses are reflected through: reduced milk production, increased costs of medication and the other animal health services, reduced fertility, early culling of animals and the value of discarded milk. Mastitis is also important from the aspects of public health, milk processing and animal welfare. In the pathogenesis of mastitis the key role plays the innate immune response which is the first line of defence against the pathogen invasion of the udder. The innate immune response generates an inflammatory reaction which is the elementary response of an organism to the tissue trauma induced by any physical, chemical or biological causative agent, but primarily it is the protective mechanism of a vital significance which includes increased phagocytic activity, secretion of antimicrobial substances, fibrosis as well as the alterations in tissue structure of affected organ or body cavity. The release of a number of inflammatory mediators as well as reactive oxygen species (ROS) is an important part of inflammatory response. In dairy cows, the metabolic challenge that occurred during the transition from dry period to early lactation may additionally increase the release of ROS which may contribute to development of oxidative stress and inflammatory response. Oxidative stress is defined as a shift in the balance from cellular oxidation-reduction reactions towards oxidation, i.e. to the state of excessive release of oxidants when their removal by antioxidants is impaired and even insufficient. During peripartum period antioxidantive status of dairy cows is seriously impaired and consequently both the oxidative stress and inflammatory response may present the predisposing factors to their higher susceptibility to intramammary infections (IMI) and mastitis. This association between oxidative stress and inflammation during IMI and mastitis indicates their role in the pathogenesis of mastitis. Thus, a better understanding of such a synergism could contribute to development of new approaches to prevention and therapy of IMI and mastitis.
Computing projections via Householder transformations and Gram–Schmidt orthogonalizations

Let x* denote the solution of a linear least‐squares problem of the form $$ {\rm minimize} \quad \|A{\bf x}-{\bf b}\|^{2}_{2}$$ where A is a full rank m × n matrix, m > n. Let r*=b ‐ Ax* denote the corresponding residual vector. In most problems one is satisfied with accurate computation of x*. Yet in some applications, such as affine scaling methods, one is also interested in accurate computation of the unit residual vector r*/∥r*∥2. The difficulties arise when ∥r*∥2 is much smaller than ∥b∥2. Let x̂ and r̂ denote the computed values of x* and r*, respectively. Let εdenote the machine precision in our computations, and assume that r̂ is computed from the equality r̂ =b‐Ax̂. Then, no matter how accurate x̂ is, the unit residual vector û =r̂/∥r̂∥2 contains an error vector whose size is likely to exceed ε∥b∥2/∥r*∥2. That is, the smaller ∥r*∥2 the larger the error. Thus although the computed unit residual should satisfy ATû=0, in practice the size of ∥ATû∥2 is about ε∥A∥2∥b∥2/∥r*∥2. The methods discussed in this paper compute a residual vector, r̂, for which ∥ATr̂∥2 is not much larger than ε∥A∥2∥r̂∥2. Numerical experiments illustrate the difficulties in computing small residuals and the usefulness of the proposed safeguards. Copyright © 2004 John Wiley & Sons, Ltd.
Performance Comparison Between LeNet And MobileNet In Convolutional Neural Network for Lampung Batik Image Identification

Purpose: The rich cultural heritage of Indonesia includes the intricate art of batik, which varies across regions with unique patterns and motifs. This study focuses on Lampung batik, a distinctive type of batik, representing Lampung Province, Indonesia. Leveraging Convolutional Neural Network (CNN) architectures, namely LeNet-5 and MobileNet, the research compares their effectiveness in recognizing and classifying Lampung batik motifs. Data augmentation techniques, including rotation, brightness, and zoom, were employed to enhance the dataset and improve model performance.Methods: The study collected 500 Lampung batik images categorized into 10 classes which were then augmented and divided into training, validation, and testing sets. The model was created using a Deep Learning approach, LeNet And MobileNet. Both models were trained using identical hyperparameters and evaluated based on their accuracy in classifying Lampung batik motifs.Results: The results demonstrate an accuracy of 99.33% for LeNet-5 and 98.00% for MobileNet, outperforming previous studies. LeNet-5, particularly with augmentation, exhibited superior precision and recall in classifying Lampung batik motifs. This research underscores the efficacy of CNN architectures, coupled with data augmentation techniques, in accurately identifying intricate cultural artifacts like Lampung batik.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.