Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey and systematic categorization of parallel K-means and fuzzy-c-means algorithms

Parallel processing hasturned into oneof the emerging ﬁelds ofmachine learning dueto providing consistent workby performing several taskssimultaneously, enhancing reliability (the presence of more than one device ensures the workﬂow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which K-means and Fuzzy-c-means clustering algorithms are integrated with metaheuristic and other traditional algorithms.
The Impact of Screening, Medical Treatment and Invasive Interventions on Patients' Medical Decision-Making Styles: A Cross-Sectional Study with Inferences to the United States Population

Background: Although positive benefits are associated with shared decision making, no previous studies have evaluated the impact of condition on how shared decision making is implemented. Objective: To compare decision-making preferences across three conditions associated with screening, medical treatment, and invasive interventions: Screening tests for colorectal cancer, initiation of prescription medication for hypertension, and surgical treatment for hip or knee osteoarthritis. Methods: We made use of the publicly available National Survey of Medical Decisions (the DECISIONS study) data and our sample comprised of all subjects who completed the following three specific modules of the decisions study: Colorectal cancerscreening tests, high blood-pressure medication, and knee or hip replacement surgery. Our primary outcomes of interest were (1) Who made the final decision? (2) Extent of patients’ involvement in the decision, and (3) How confident they were about their decision. Results: When comparing baseline characteristics across the three conditions, the group undergoing screening was youngest with a mean age of 58.7 years compared to the medication group (61.27 years), while the group undergoing surgery was oldest (63.14 years). Females constituted over half of all three groups (greater than 50%). In the invasive interventions, decisions were made mainly by the patients, unlike the other groups where shared decision making was predominant. Most patients in all three groups preferred high levels of participation in decision making. Patients undergoing surgery were significantly more likely to have greater confidence in their decisions, followed by patients with medication for hypertension. Conclusion: Shared decision making is less predominant among invasive interventions. Further research should describe the reasons for the limited use of shared decisions among conditions with invasive interventions, along with its consequences for healthcare quality.
A control system in a micro cone-beam CT machine

Images data from the micro cone-beam computed tomography (CBCT) are acquired from a rotation of an object located between an X-ray generator and a flat panel detector; therefore, the rotational position of the motor is very important for image quality of 3D cross-section images. This study focuses on designing the position control of the stepping motor using the motion module, which enhances the control 4-axis motor’s efficiency to optimize and increase the accuracy of motor movement. We designed the stepping motor position control system to control the movement of the micro CBCT system to perform ten circular rotations in a single scanning process. A phantom was used to verify the rotational image accuracy by considering the image at the same position each round. Comparison of the motor movement with and without the motion module showed slight differences of projection images causing artifacts in cross-section images. Thus, the design of the rotation position control using the motion module circuit yielded good performance in terms of precision and rotational accuracy on the CBCT.
Matching in the Dark? Inequalities in Student to Degree Match

This paper examines inequalities in the match between student and degree quality using linked administrative data from schools, universities, and tax authorities. We analyze two measures of match at the university subject level: undergraduate enrollment qualifications and graduate earnings. We find for both that disadvantaged students match to lower-quality degrees across the entire distribution of achievement in a setting with uniform fees and a generous financial aid system. While there are negligible gender gaps in academic match, high-attaining women systematically undermatch in terms of expected earnings, driven by subject choice. These inequalities in match are largest among the most undermatched.
Microneedling: a case series associated with drug delivery

Six patients underwent two sessions of microneedling associated to drug delivery on the face, hands or stretch marks, at three-week interval between sessions. Patients were evaluated using clinical analysis and objective measures. Compared to baseline, objective face analysis showed decrease in acne lesions and improvement in the skin texture. The evaluation of stretch marks showed great improvement, and face and hands presented excellent results compared to baseline. The association of microneedling to drug delivery for rejuvenation of the face and hands, and for the treatment of stretch marks presented promising results in our study.
Cost Avoidance and Clinical Benefits Derived from a Pharmacy-Managed Anemia Program

A pharmacy-managed anemia program included distribution and clinical components, with the goal of making epoetin alpha therapy for hemodialysis patients more cost-effective. The Pharmacy Department prepared epoetin alpha doses for patients in unit-dose syringes, utilizing and documenting vial overfill. Pharmacists dosed epoetin alpha and iron (oral and intravenous) per protocol for new and established patients. Baseline data were obtained in 1994, one year prior to implementation of the program, and were re-evaluated in 1995 and 1998. Cost avoidance from utilization of epoetin alpha vial overfill in 1995 and 1998 was $83,560 and $91,148 respectively. In 1995 and 1998, cost avoidance from pharmacy management of anemia was $191,159 and $203,985 respectively. The total cost avoidance from 1995 through 1998 was estimated at $1,018,638. The number of patients with hematocrits under 31% decreased from 32% in 1994 to 21% and 14% in 1995 and 1998 respectively. We conclude that a pharmacy-managed anemia program for hemodialysis patients results in significant cost savings and better achievement of target hematocrits.
Effective Learning and Online Modulation for Robotic Variable Impedance Skills

Learning robot manipulation skills from human demonstration is one of the key technologies for the cooperative robot. However, the traditional learning methods are difficult to be rapidly applied to complex force interaction tasks such as button-pressing, assembly, polishing, etc, which mainly focus on acquiring the movement skill by encoding the kinematics features and lacking consideration of the interactive force, stiffness, and other compliant profiles. Meanwhile, due to a large amount of high-dimensional demonstrations and task constraints, the existing methods are faced with the problems of low learning efficiency and difficult online modulation. To address those problems, a novel framework of robot skill learning is presented in this paper, which not only can simultaneously consider the movement and compliant profiles but also the implementation of effective learning and online modulation. Specifically, a Gaussian Mixture Model-Gaussian Mixture Regression (GMM-GMR) is first proposed for learning underlying dynamics from demonstrated trajectories, such that the skill can be formulated with two parameters at each time step, i.e. mean and variance. And then, a GMMGMR-based vector-valued Gaussian Process is proposed to autonomously modulate the trajectory according to the unexpected environmental changes and perturbations, such as unseen start-, via- or end-points. Those points are introduced by an external-force aware variable impedance controller by human intervention during skill reproduction. Consequently, a nonlinear mapping relationship between the variance and robot stiffness is investigated for encapsulating the compliant profiles. Experimental verification is performed on a button-pressing task with a Franka-Emika panda robot, results indicate that the presented framework can learn humanlike manipulation skills and reduce the complexity of robot application in complex force interaction tasks.
The monitoring role of the financial press around corporate announcements

This study finds that the financial press serves an important monitoring role by interpreting the tone of corporate announcements, moderating its impact to market participants in the process. Using textual analysis, we report that the press attenuates both the positive and negative tone of firm-initiated disclosures. However, the effect is asymmetric with the media mostly downplaying the tone of highly positive corporate press releases, consistent with the premise that management disclosures containing highly positive tone are less convincing. In addition, we find that the tone of the information produced by the financial media has an effect on market reactions above and beyond the impact of the linguistic content of corporate disclosures. Importantly, the impact of the linguistic content of corporate disclosures to market returns is moderated by the tone of new information included in media articles. Overall, this study adds new evidence to a growing body of literature suggesting that the tone of press-originated articles contains incremental information content.
Experimental Comparison of Video Streaming Platforms and Devices with Objective Quality of Experience Metrics Towards Reliable Multimedia Applications in Education

This paper presents the result of experimental comparison of the video quality of streamed videos using streaming devices and streaming platforms. The objective metrics considered for the comparison of these media are the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Visual Information Fidelity (VIF), Information Fidelity Criterion (IFC), and Visual Signal-to-Noise Ratio (VSNR). To determine the better platform for effective multimedia applications especially for educational purposes, videos were obtained and streamed via Data video, YouTube and Facebook. In the first two scenarios, Data video at two different settings were engaged at the encoding end and PotPlayer was used for decoding and recording of the transmitted videos. In the third and fourth scenarios, Videos were streamed via YouTube and Facebook. The aforementioned objective metrics were implemented in MATLAB R2017b for the experimental analyses and our results showed that YouTube produced streamed videos of the best quality with PNSR of 37.98, SIMM of 0.98 and VSNR of 33.12.
Glutenin-directed gold nanoclusters employed for assaying vitamin B1

Various nanoclusters have been conceived to sense different targets by virtue of their promising fluorescence signals. Herein, glutenin-directed gold nanoclusters (AuNCs@Glu) with red fluorescence have been synthesized. Meaningfully, the nanoclusters exhibited multiple advantages, including favorable photostability, low toxicity and stability. The –COOH, –NH2, –OH, and –CO groups on the surfaces of AuNCs@Glu facilitate possible applications of the nanoclusters towards sensing. Thus, the abundant carboxyl and hydroxyl groups of AuNCs@Glu enable vitamin B1 to be adsorbed on the surface, leading to obvious fluorescence quenching through emerging aggregation. On the basis of this phenomenon, AuNCs@Glu were applied for detecting Vitamin B1; they achieved a linear range of 8 to 8000 μm with a detection limit of 115 nM, which broadens the avenues of assaying vitamin B1.
Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.
Are Processes in Acceptance & Commitment Therapy (ACT) Related to Chronic Pain Outcomes Within Individuals Over Time? An Exploratory Study Using n-of-1 Designs

Introduction Acceptance & Commitment Therapy (ACT) explicitly postulates experiential avoidance (EA) and values-based living (VBL) as essential treatment processes. As outcomes from between-subject studies cannot readily be generalized to within-subject processes in individuals, we explored the unfolding of, and relationship between, EA and VBL and levels of pain interference in daily life and emotional well-being within individuals experiencing chronic pain. Methods Using n-of-1 designs, three participants following a multidisciplinary treatment program filled out a 12-item daily questionnaire (87–110 days). After multiple imputation of missing data, McKnight Time-series analysis procedures were performed for each participant separately. The interrelationships of EA, VBL and pain intensity, and the relationship of EA and VBL beyond pain intensity with both chronic pain outcomes were assessed both concurrently (same day) and prospectively (consecutive days). Results Both EA and VBL were associated with at least one of five outcome variables (four domains of pain interference and emotional well-being) beyond pain intensity in two participants, but not in the third participant. These associations primarily existed for concurrent, but not consecutive, days. In contrast to VBL, EA was not associated with emotional well-being for any of the three participants. Conclusions Although the finding that ACT-processes were associated with pain outcomes on concurrent days is consistent with ACT theory, the absence of such associations on consecutive days means that alternative explanations cannot be rule out. One possibility is that pain interference fluctuates within days at a higher variability rate than was currently assessed. Future research should consider using a higher measurement frequency to be able to grasp time-lagged effects.
Engineering Feasibility of Building Blocks Produced from Recycled Rice Husks

Rice husks abundance in Nigeria requires the consideration of their alternate economic uses to prevent environmental pollution from the waste heaps, litter and combustion. This study focused on the determination of the feasibility of blocks made from recycled rice husks for building construction. Twenty-four absolute cubes were moulded from a mixture of fine aggregate (sand), binder (cement) and water. These were used for control experiments. Also, 144 cubes of partially replaced sand with rice husks in the steps of 10, 20, 30, 40, 50 and 60% were produced and cured for 7, 12, 21 and 28 days like the absolute cubes. They were weighed and experimented for some engineering properties including compressive strength in triplicate. The average values of triplicate readings were recorded and documented. Laboratory strengths result at the 28th day were compared with the reference strength of sandcrete block provided in the Federal Building Code to ascertain the performances of the partial sandcrete cubes. The low maximum compressive strength of 0.54N/mm2 obtained at 30% replacement and 28th day curing showed that rice husks were not feasible for replacing fine aggregate in sandcrete blocks at the percentages tested. This strength value is far less than the minimum allowable compressive strength of 1.75N/mm2 of individual blocks provided in Federal Building Code.
Integrating occupants’ voluntary thermal preference responses into personalized thermal control in office buildings

A Bayesian modeling approach which allows incorporating voluntary feedback data (comfort-related responses), collected via participatory interfaces, along with requested feedback data, into a thermal preference learning framework. This is achieved by explicitly considering occupant participation, a type of behavior, in the model. Experiments with human subjects were conducted to collect thermal preference datasets, with both participatory and requested setups, which were used to train personalized thermal preference models. The proposed approach allows using the participatory setup without distorting the thermal preference predictive probabilities. In addition, we propose a concept of smart occupant feedback request algorithm, that determines whether and when to request feedback based on the quantified value of the request. This work will lead to smarter, user-interactive comfort delivery systems that will be continuously updated through interactions with their occupants, and will provide customized indoor environments tailored to individual preferences.
Financial Event Prediction using Machine Learning

This thesis focuses on the use of machine learning in financial event prediction. In the past, finance academics had to be content with mostly linear models that could only ingest a small number of variables of a particular type. Now we can use non-linear models with a larger number of variables and more versatile data types. In this thesis, I show how machine learning can lead to significant improvements in financial event prediction, more specifically, in earnings surprise, bankruptcy and facility closure predictions, all of which have significant financial implications for businesses and stakeholders alike.
Estimating Bedload From Suspended Load and Water Discharge in Sand Bed Rivers

Estimates of fluvial sediment discharge from in situ instruments are an important component of large‐scale sediment budgets that track long‐term geomorphic change. Suspended sediment load can be reliably estimated using acoustic or physical sampling techniques; however, bedload is difficult to measure directly and can consequently be one of the largest sources of uncertainty in estimates of total load. We propose a physically informed predictive empirical model for bedload sand flux as a function of variables that are measured using existing acoustic or physical sampling techniques. This model depends on the assumption that concentration and grain size in suspension are in equilibrium with reach‐averaged boundary conditions. Bayesian inference is used to fit model parameters to data from eight sand‐bed rivers and to simulate bedload flux over the available gage record at one site on the Colorado River in Grand Canyon National Park. We find that the cumulative bedload flux during the 9 year period from 2008 to 2016 was 5% of the cumulative suspended sand load; however, instantaneous bedload flux ranged from as little as 1% of instantaneous suspended sand load to as much as 75% of instantaneous suspended sand load due to fluctuations in flow strength and sediment supply. Changes in bedload flux at a constant discharge are indicative of short‐term sediment supply enrichment and depletion. Long‐term average bedload flux cannot be expected to remain constant in the future as the river adjusts to changes in sediment runoff and the dam‐regulated discharge regime.
Phylogeographic sampling guided by species distribution modeling reveals the Quaternary history of the Mediterranean–Canarian Cistus monspeliensis (Cistaceae)

Accurate inference in phylogeography requires appropriate sampling strategies. Complex questions demand a large sample size at both the population and genetic levels to obtain precise reconstructions. This is the case of the phylogeographic history of Cistus monspeliensis, a plant that displays low plastid (cpDNA) diversity in the Mediterranean Basin but high diversity in the Canary Islands. Here, we aimed to identify Mediterranean refugial areas and to accurately quantify inter‐island colonization events in the Canaries. Using a previous study as starting point, we increased sample size in two ways: (i) additional sampling of plastid genetic markers (from 1041 to 1899 bp); and (ii) additional sampling of populations (from 47 to 69) in long‐term persistence areas suggested by species distribution modeling (SDM). The synergy between SDM and extended population sampling helped find higher genetic diversity. Our deeper phylogeographic sampling of C. monspeliensis revealed the following: (i) potential refugia in long‐term persistence areas with high cpDNA diversity in western Europe and the Canary Islands; and (ii) a significant increase (from 7 to 12) in the number of inferred inter‐island colonization events across the archipelago. Our results stress the usefulness of SDM to identify the genetic signature associated with potential refugial areas. We herein propose a field sampling approach based on SDM that, in combination with a larger cpDNA sampling, can help answer a wide array of phylogeographic questions, such as the location of Quaternary refugia and number of colonizations across archipelagos.