Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

Objective: Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameter estimation performed by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, depending on the selected data-model, the adopted estimation scheme (least-squares error, maximum likelihood, or Bayesian), and the prior assumptions on the model parameters and noise distributions, any estimation algorithm used in this context has an upper performance bound, which is not exceedable (for the same model and assumptions). Method: In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature; namely bases expansions (including polynomials) and sum of Gaussian functions.Results: The developed framework is evaluated over real and synthetic data, for three popular applications: T/R ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context, and compared with the derived theoretical CRLBs.Conclusion and Significance: The proposed framework and the derived CRLBs provide fact-based guidelines for the selection of data-models, sampling frequency (beyond the Nyquist requirements), modeling segment length, the number of beats required for average ECG beat extraction, and other factors that influence the accuracy of ECG-based clinical parameter estimation.
Power and Procedure: Gaining Legitimacy in the Workplace

Abstract:To ensure effectiveness in reaching organizational goals, authorities must gain legitimacy (i.e., the support and approval of their subordinates). This study uniquely brings together an identity-based approach, stressing an authority's use of fair decision-making procedures, a resource-based approach, highlighting an authority's use of power in a benevolent fashion, and the status of the authority (represented by workplace experience) as sources of legitimacy. We argue that assessments of the nature of the authority's behaviors and experience—whether they enhance interactional justice and/or engender trust, thereby signaling positive social relationships—mediate the direct effects of the sources. Adult study participants (404 males, 403 females) read one of eight vignettes in which we factorially combined two levels of each proposed antecedent. Questionnaire items measured perceived interactional justice, trust, and legitimacy. Results from structural equation modeling show that procedural justice, benevolent power use, and the authority's workplace experience exert positive effects on the perceived legitimacy of the manager and on assessments of interactional justice and trust, which ultimately mediate the direct pathways to legitimacy. These findings highlight the nature of actions important to helping members of groups not typically holding positions of authority (e.g., women and minorities) to gain legitimacy and ensure effectiveness.
Penyuluhan Kesehatan Berpengaruh Terhadap Pengetahuan Keluarga Tentang Stunting

Stunting is one of the national health problems caused by multidimensional factors. Stunting children have an increased risk of suffering from repeated infections that can lead to death such as diarrhea, pneumonia, smallpox, and an increased risk of chronic heart disease in childhood. The purpose of the activity was to increase the knowledge or behavior of the community about stunting in Betteng village, Pamboang sub-district. The target of the activity was the community in the working area of ​​the Pamboang Public Health Center. This counseling emphasized more on the method of delivering information about stunting, especially in the causes, prevention, and control which were carried out in the form of explanations for families who have stunting children. In conclusion, health education affects changes in knowledge about stunting. Therefore, it is recommended to carry out health education activities if public knowledge is to be increased. ABSTRAK Kejadian Stunting merupakan salah satu permasalah kesehatan secara nasional yang diakibatkan oleh faktor multidimensi. Anak stunting meningkat resiko untuk menderita infeksi berulang yang dapat mengakibatkan kematian seperti diare, pneumonia dan cacar serta meningkatkan resiko mengalami penyakit jantung kronik di usia anak. Tujuan kegiatan pengabdian masyarakat untuk mengetahui pengaruh penyuluhan kesehatan terhadap stunting. Untuk itu perlu diperlukan pengetahuan yang baik oleh semua pihak. Metode kegiatan pengabdian kepada masyarakat Metode kuantitatif dengan pre-eksperimental dengan menggunakan suatu rancangan One Group Pre test and Post test design dengan memberikan penyuluhan kepada para petugas kesehatan tentang stunting terkait penyebab dan upaya penanggulangannya. Hasil pengukurannya menunjukkan bahwa pada pengukuran sebelum dengan pengukuran setelah intervensi memiliki nilai p = 0,084. Dengan nilai α ( 0,05 ) > nilai p value ( 0,084 ) sehingga ada perbedaan pengetahuan sebelum dan sesudah intervensi atau ada pengaruh intervensi penyuluhan penguatan keluarga dalam pengendalian stunting. Kesimpulannya bahwa penyuluhan kesehatan berpengaruh terhadap perubahan pengetahuan tetang stunting. Sehingga direkomendasikan melakukan kegiatan penyuluhan kesehatan jika ingin meningkatkan pengetahuan masyarakat.
Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. In order to explore the performance of the proposed algorithm in practical systems, the online scenario with the completely random requests is used in the simulation considering C-RAN and NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model is formulated to generate the optimal solution as the benchmark. The simulation results show that DRL-based algorithm converges in a short time, and its performance closes to the optimal benchmark obtained by ILP in terms of latency and bandwidth for the online scenarios. In addition, the performance of the generated policies based on DRL is compared with a classic heuristic algorithm, i.e., first-fit algorithm. The performance of DRL-based algorithm is superior to the first-fit algorithm from above two perspectives. The fast convergence and the near-optimal performance prove that the DRL-based algorithm is a promising approach for the BBF placement and routing of RAN in 5G and Beyond.
Climate on the Blanca Massif, Sangre de Cristo Mountains, Colorado, USA, during the Last Glacial Maximum

Temperature-index modeling is used to determine the magnitude of temperature depression on the Blanca Massif, Colorado, required to maintain steady-state mass balances of nine reconstructed glaciers at their extent during the Last Glacial Maximum (LGM). The mean temperature depression thus determined is ~8.6 +0.7/−0.9 °C where the uncertainties account for those inherent in the glacier reconstructions, in model parameters (e.g., melt factors), and possible modest changes in LGM precipitation. Associated equilibrium-line altitudes (ELAs) exhibit a statistically significant directional dependency being lower toward the north and east. Under the assumption that regional temperature change was uniform, required changes in precipitation vary systematically—also exhibiting a directional dependency coinciding with that in ELAs—and indicate increases (over modern) occurred on the eastern side of the massif while decreases occurred on the western side. This disparity represents a strengthening of a precipitation asymmetry, particularly winter precipitation, which exists today. The modern precipitation asymmetry may be a consequence of snow being blown over to the eastern side of the massif (advective transport) by southwesterly flow. Intensification of this flow during the LGM would have enhanced advection, and augmented snow accumulation on glaciers, thus explaining the lower ELAs and increased precipitation on that side of the massif.
Characterization of Two Hydrogen Peroxide Resistant Peroxidases from Rhodococcus opacus 1CP

The dye-decolorizing peroxidases (DyP) are a family of heme-dependent enzymes present on a broad spectrum of microorganisms. While the natural function of these enzymes is not fully understood, their capacity to degrade highly contaminant pigments such as azo dyes or anthraquinones make them excellent candidates for applications in bioremediation and organic synthesis. In this work, two novel DyP peroxidases from the organism Rhodococcus opacus 1CP (DypA and DypB) were cloned and expressed in Escherichia coli. The enzymes were purified and biochemically characterized. The activities of the two DyPs via 2,2′-azino-bis [3-ethylbenzthiazoline-6-sulphonic acid] (ABTS) assay and against Reactive Blue 5 were assessed and optimized. Results showed varying trends for DypA and DypB. Remarkably, these enzymes presented a particularly high tolerance towards H2O2, retaining its activities at about 10 mM H2O2 for DypA and about 4.9 mM H2O2 for DypB.
Synthesis and characterization of magnetic nanoparticles functionalized with different starch types

Abstract The present study enabled green synthesis and characterization of starch-functionalized magnetic nanoparticles to be released by the chemical co-precipitation of magnetite phase from aqueous solution in a different type of polymeric starch matrix (corn and wheat). To examine the structural changes which would occur when preparing starch-functionalized magnetic nanocomposites, the parameters which affect the synthesis such as starch type, the concentration of the precipitating agent, and aging time were studied. SEM/EDX, TEM, XRD, FTIR, VSM, and zeta potential measurements were employed to characterize and compare the as-synthesized products. The calculations based on the XRD results showed that magnetic starch nanocomposites had well-defined nanocomposite structures and average sizes of approximately 4.55 ± 1.2 nm and 3.78 ± 1.2 nm, respectively. Furthermore, the size varies from 7.95 ± 1.2 nm to 4.55 ± 1.2 nm and 6.91 ± 1.2 nm to 3.78 ± 1.2 nm by changing starch concentration from 0.05% to 2% for wheat and corn starches, respectively. The BET surface area of starch-stabilized magnetite nanobiocomposites for MW_S3 and MC_S3 were found to be 126.20 and 151.29 m2/g, respectively. The TEM and SEM results indicated that the spherical nanoparticles were dispersed into the polymer matrix. VSM results of pure-Fe3O4 (44.63 emu/g) is higher than those of MW_S3 (41.20 emu/g) and MC_S3 (39.88 emu/g) based on starch functionalization.
Cultural Complexity and Situated Mediation: Chinese Visitors at Port Arthur Historic Site

ABSTRACT The tourism industry and research community are cognizant of the importance of mediation in creating desirable tourist experiences. This paper argues that the mediation process should not be general but needs to be situated, that is a tourism mediator must take into account the nature of the tourist product, the specific social and environmental circumstances of consumption, the background of tourists and the inter-personal dynamics during the activity. Focusing on Chinese visitors, this study uses a cultural complexity frame that understands culture as a resource for members to express, control and navigate diverse situations and encounters to understand how experiences are dynamically shaped in diverse circumstances. Based on fieldwork at a British colonial convict heritage site – Port Arthur Historic Site, Tasmania, Australia – this study contributes to the understanding of using culture as a pool of productive resource in the mediation of the Chinese tourist experience.
Multi-Objective Optimization of Different Channel Shapes in Heat Exchangers

The effect of geometric parameters of the zigzag, rectangular, and serpentine channels on convective heat transfer coefficient and pressure drop was investigated using computational fluid dynamics (CFD). In all channels, the same boundary conditions were considered, and the number of steps was equal to 10. The simulations were performed for turbulent flows (liquid water as the operating fluid), and Reynolds number (Re) range between 20000 and 60000 was selected. The zigzag channel showed a best thermal performance and the serpentine channel showed the best hydraulic performance. The thermal-hydraulic performance (THP) factor was employed for comparing the channels. As the complexity of the channels surfaces increased, the two parameters of convective heat transfer coefficient (positive factor) and pressure drop (negative factor) increased simultaneously. Therefore, predictive correlations for friction factor and Nusselt number were presented using genetic algorithm (GA), and the multi-objective optimization was performed to obtain the most appropriate Nusselt number and minimum friction factor as the two basic objective functions. The resulting Pareto set, which includes the optimum geometric dimensions of the heat exchangers, allows a designer to choice the geometries based on higher heat transfer or lower pumping power.
The Str(AI)ght Scoop: Artificial Intelligence Cues Reduce Perceptions of Hostile Media Bias

Abstract In the face of increasing public distrust for journalistic institutions, stories sourced from artificially intelligent (AI) journalists have the potential to lower hostile media bias by activating the machine heuristic—a mental shortcut assuming machines are more unbiased, systematic, and accurate than are humans. An online experiment targeting issue partisans found support for the prediction: a story presented as sourced from an AI journalist activated the machine heuristic that, in turn, mitigated hostile media bias. This mediation effect was moderated: perceived bias was more strongly reduced as partisan-attitude extremity increased.
Brain Natriuretic Peptide as a Marker of Adverse Neurological Outcomes Among Survivors of Cardiac Arrest

Background Neurological prognosis after cardiac arrest remains ill-defined. Plasma brain natriuretic peptide (BNP) may relate to poor neurological prognosis in brain-injury patients, though it has not been well studied in survivors of cardiac arrest. Methods We performed a retrospective review and examined the association of BNP with mortality and neurological outcomes at discharge in a cohort of cardiac arrest survivors enrolled from January 2012 to December 2016 at the Wake Forest Baptist Hospital, in North Carolina. Cerebral performance category (CPC) and modified Rankin scales were calculated from the chart based on neurological evaluation performed at the time of discharge. The cohort was subdivided into quartiles based on their BNP levels after which multivariable adjusted logistic regression models were applied to assess for an association between BNP and poor neurological outcomes as defined by a CPC of 3 to 4 and a modified Rankin scale of 4 to 5. Results Of the 657 patients included in the study, 254 patients survived until discharge. Among these, poor neurological status was observed in 101 (39.8%) patients that had a CPC score of 3 to 4 and 97 patients (38.2%) that had a modified Rankin scale of 4 to 5. Mean BNP levels were higher in patients with poor neurological status compared to those with good neurological status at discharge (P = .03 for CPC 3-4 and P = .02 for modified Rankin score 4-5). BNP levels however, did not vary significantly between patients that survived and those that expired (P = .22). BNP did emerge as a significant discriminator between patients with severe neurological disability at discharge when compared to those without. The area under the curve for BNP predicting a modified Rankin score of 4 to 5 was 0.800 (95% confidence interval [CI] 0.756-0.844, P < .001) and for predicting CPC 3 to 4 was 0.797 (95% CI 0.756-0.838, P < .001). BNP was able to significantly improve the net reclassification index and integrated discriminatory increment (P < .05). BNP was not associated with long-term all-cause mortality (P > .05). Conclusions In survivors of either inpatient or out-of-hospital cardiac arrest, increased BNP levels measured at the time of arrest predicted severe neurological disability at discharge. We did not observe an independent association between BNP levels and long-term all-cause mortality. BNP may be a useful biomarker for predicting adverse neurological outcomes in survivors of cardiac arrest.
Influence of Green Light Added with Red and Blue LEDs on the Growth, Leaf Microstructure and Quality of Spinach (Spinacia oleracea L.)

The aim of this study was to investigate the effects of green light, added with red and blue LEDs, on the growth, leaf microstructure and quality of spinach plants. Plants were transplanted and grown hydroponically for 30 days under different combinations of red:blue with a 4:1 ratio (R4B1), red:blue:green with a 5:2:3 ratio (R5B2G3) and red:blue:green with a 1:1:1 ratio (R1B1G1), at a 190 µmoL m−2·s−1 photosynthetic photon flux density (PPFD). The results showed that green light, added to red and blue LEDs at a reasonable ratio, could reduce the growth, leaf microstructure and quality of spinach plants, but not the organic acid content. The highest values for the growth parameters, photosynthetic pigments, leaf structure characteristics and quality of the spinach plant were observed for the R4B1 treatment, but not for the organic acid content. Therefore, our results suggest that green light added to red and blue LEDs at a reasonable ratio is not suitable for the growth of spinach.
Analysis of Uncertainties and Levels of Foreknowledge in Relation to Major Features of Emerging Technologies—The Context of Foresight Research for the Fourth Industrial Revolution

One of the key roles in the development of Industry 4.0 systems is played by “emerging technologies” as new tools with promising—though with a high level of uncertainty—capabilities. The management of such systems should be based on a comprehensive—future-oriented—research approach. Such activities are enabled by the foresight methodology. The main purpose of this publication is to attempt to answer the following research question: “What levels of foreknowledge and knowledge in the context of the development of emerging technologies—in relation to their features in Industry 4.0—should be taken into account during the analysis of uncertainties in the sense of foresight research based on different anticipated options? ” In detail, the examination covered the relationship of classes of research foresight methods with regard to types of future, scopes of uncertainty, cycles of knowledge and original levels of foreknowledge in the field of the development of emerging technologies in Industry 4.0. Emerging technologies combined with the research on foreknowledge and uncertainties is an interesting research area with many theoretical and practical potential implications. The study uses the results of the analysis and criticism of the literature, mental experiments, and the intuitive method as the main research methods. This provides a basis for performing conceptual modeling.
Phenotypic and Genetic Analyses of Korean Patients with Familial Hypercholesterolemia: Results from the KFH Registry 2020

Aims: Familial hypercholesterolemia (FH) is currently a worldwide health issue. Understanding the characteristics of patients is important for proper diagnosis and treatment. This study aimed to analyze the phenotypic and genetic features, including threshold cholesterol levels, of Korean patients with FH. Methods: A total of 296 patients enrolled in the Korean FH registry were included, according to the following criteria: low-density lipoprotein-cholesterol (LDL-C) ＞190 mg/dL with tendon xanthoma or family history compatible with FH, or LDL-C ＞225 mg/dL. DNA sequences of three FH-associated genes were obtained using whole-exome or target exome sequencing. Threshold cholesterol levels for differentiating patients with FH/pathogenic variant (PV) carriers and predictors of PVs were identified. Results: Of the 296 patients, 104 had PVs and showed more obvious clinical findings, including higher cholesterol levels. PV rates ranged from 30% to 64% when patients were categorized by possible or definite type according to the Simon Broome criteria. Frequent PV types included missense variants and copy number variations (CNVs), while the most frequent location of PVs was p.P685L inLDLR. The threshold LDL-C levels for patient differentiation and PV prediction were 177 and 225 mg/dL, respectively. Younger age, tendon xanthoma, and higher LDL-C levels were identified as independent predictors of PVs, while traditional cardiovascular risk factors were predictors of coronary artery disease. Conclusions: Korean patients with FH had variable PV rates depending on diagnostic criteria and distinctive PV locations. The reported threshold LDL-C levels pave the way for efficient patient care in this population.
Aggregation of Radial Distribution System Bus with Volt-Var Control

The high penetration of the distributed energy resources (DERs) encourages themselves to implement grid-supporting functions, such as volt-var control. The quasi-static time-series (QSTS) simulation is an essential technique to evaluate the impact of active DERs on the grid. Meanwhile, the increase of complexity on the circuit model causes a heavy computational burden of QSTS simulation. Although circuit reduction methods have been proposed, there have been few methods that can appropriately handle the distribution system (DS) with multiple voltage control devices, such as DERs implementing volt-var control. To address the remaining issues, this paper proposes an offline bus aggregation method for DS with volt-var control. The method determines the volt-var curve for the aggregated bus on the basis of historical data to reduce error in the aggregated model, and its offline process solves the computational convergence issue concerned in the online one. The effectiveness of the proposed method is validated in the simulation using a Japanese low-voltage DS model. The simulation results show that the proposed method can reduce the voltage error and computational time. Furthermore, the versatility of the proposed method is verified to show the performance does not heavily depend on how to select historical data for model-building.
Joint Feature Enhancement and Speaker Recognition with Multi-Objective Task-Oriented Network

Recently, increasing attention has been paid to the joint training of upstream and downstream tasks, and to address the challenge of how to synchronize various loss functions in a multi-objective scenario. In this paper, to address the competing gradient directions between the speaker classiﬁcation loss and the feature enhancement loss, we propose an asynchronous subregion optimization approach for the joint training of feature enhancement and speaker embedding neural networks. For the asynchronous subregion optimization, the squeeze and excitation (SE) method is introduced in the enhancement network to adaptively select important channels for speaker embedding. Furthermore, channel-wise feature concatenation is applied be-tween the input feature and the enhanced feature to address the distortion of speaker information that is caused by enhancement loss. By using the proposed joint training network with asynchronous subregion optimization and channel-wise feature concatenation, we obtained relative gains of 11.95 % and 6.43 % in equal error rate on a noisy version of Voxceleb1 and VOiCES corpus, respectively.