Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated clinical pathway standardization using SNOMED CT- based semantic relatedness

The increasing number of patients and heavy workload drive health care institutions to search for efficient and cost-effective methods to deliver optimal care. Clinical pathways are promising care plans that proved to be efficient in reducing costs and optimizing resource usage. However, most clinical pathways are circulated in paper-based formats. Clinical pathway computerization is an emerging research field that aims to integrate clinical pathways with health information systems. A key process in clinical pathway computerization is the standardization of clinical pathway terminology to comply with digital terminology systems. Since clinical pathways include sensitive medical terms, clinical pathway standardization is performed manually and is difficult to automate using machines. The objective of this research is to introduce automation to clinical pathway standardization. The proposed approach utilizes a semantic score-based algorithm that automates the search for SNOMED CT terms. The algorithm was implemented in a software system with a graphical user interface component that physicians can use to standardize clinical pathways by searching for and comparing relevant SNOMED CT retrieved automatically by the algorithm. The system has been tested and validated on SNOMED CT ontology. The experimental results show that the system reached a maximum search space reduction of 98.9% within any single iteration of the algorithm and an overall average of 71.3%. The system enables physicians to locate the proper terms precisely, quickly, and more efficiently. This is demonstrated using case studies, and the results show that human-guided automation is a promising methodology in the field of clinical pathway standardization and computerization.
Oppositional biogeography-based optimisation applied to SMES and TCSC-based load frequency control with generation rate constraints and time delay

This article introduces biogeography-based optimisation (BBO) and oppositional BBO for solving load frequency control (LFC) in power system. Two widely employed test systems, viz. two-area multi-unit thermal-thermal and three-area thermal power plant, are considered for analysis. To make the study realistic, inherent power system nonlinearities like time delay, generation rate constraint and reheater are included. 2nd order Pade approximation is used to find linear model of time delay. To provide additional damping to frequency and tie-line power oscillations caused by sudden load perturbation, optimal thyristor controlled series compensator (TCSC) and superconducting magnetic energy storage (SMES) are design in coordination to LFC for the concerned power systems. An extensive comparative analysis is performed to established superiority of proposed method over other recently published algorithms. Simulation results show that coordinated LFC-TCSC-SMES controller yields greater dynamic performance compared to other. Finally, sensitivity analysis is performed to demonstrate the robustness of the proposed controller.
Understanding Perinatal Mortality Causes in Indramayu Indonesia

Perinatal mortality is a useful indicator for assessing pregnancy and delivery care. Indramayu was the fourth-highest perinatal mortality in West Java province in 2015. The cause of death can be prevented from several factors including health workers, patients, referrals, and the availability of healthcare facilities. This study aimed to analyze the causes of perinatal mortality in Indramayu. The Study found 375 perinatal deaths but only 296 cases have documents according to the inclusion criteria. Statistical analysis showed there was an association between maternal disease and referral delays with preventable perinatal mortality (p≤0.05). Maternal disease in Indramayu cannot early be detected due to the lack of antenatal care services and behavioral factors on choosing a helper contributed to referral delays. It is necessary to improve the quality of antenatal care services by doing minimum standard of antenatal care for early detection of maternal disease and improving health promotion about danger sign of pregnancy and choosing birth attendants to reduce referral delays.
Roles of Tidal Cycling, Hyporheic Exchange and Bioirrigation on Metal Release From Estuary Sediments

A numerical model is developed to investigate metal release from estuarine sediments. The model includes three‐dimensional (3D) large‐eddy simulation of water above the sediment‐water interface, 3D advective and diffusive transport within sediments by both physical and biological processes (bioturbation and bioirrigation), and biogeochemical processes within the sediment including reduction of electron acceptors, acid‐base reactions, and metal sorption and precipitation. The model was applied to explore the influence of overlying water chemistry and sediment chemical, physical and biological processes on metal release from sediments. Overlying water conditions (pH, salinity, oxygen saturation) may vary diurnally due to tidal cycling and control the short‐term release of metals, while over longer times (months), metals are controlled by long‐term averages of sediment convective processes (groundwater upwelling, hyporheic exchange and bioirrigation). Metal release is significantly enhanced when there is bioroughness and bioirrigation due to local oxidation of surficial sediments.
Mitochondrial Respiratory Chain Dysfunction—A Hallmark Pathology of Idiopathic Parkinson’s Disease?

Parkinson’s disease (PD) is the most common age-dependent neurodegenerative synucleinopathy. Loss of dopaminergic neurons of the substantia nigra pars compacta, together with region- and cell-specific aggregations of α -synuclein are considered main pathological hallmarks of PD, but its etiopathogenesis remains largely unknown. Mitochondrial dysfunction, in particular quantitative and/or functional deficiencies of the mitochondrial respiratory chain (MRC), has been associated with the disease. However, after decades of research in this field, the pervasiveness and anatomical extent of MRC dysfunction in PD remain largely unknown. Moreover, it is not known whether the observed MRC defects are pathogenic, compensatory responses, or secondary epiphenomena. In this perspective, we give an overview of current evidence for MRC dysfunction in PD, highlight pertinent knowledge gaps, and propose potential strategies for future research.
Real-Time LiDAR Point Cloud Compression Using Bi-Directional Prediction and Range-Adaptive Floating-Point Coding

Due to the large amount of data involved in the three-dimensional (3D) LiDAR point clouds, point cloud compression (PCC) becomes indispensable to many real-time applications. In autonomous driving of connected vehicles for example, point clouds are constantly acquired along the time and subjected to be compressed. Among the existing PCC methods, very few of them have effectively removed the temporal redundancy inherited in the point clouds. To address this issue, a novel lossy LiDAR PCC system is proposed in this paper, which consists of the inter-frame coding and the intra-frame coding. For the former, a deep-learning approach is proposed to conduct bi-directional frame prediction using an asymmetric residual module and 3D space-time convolutions; the proposed network is called the bi-directional prediction network (BPNet). For the latter, a novel range-adaptive floating-point coding (RAFC) algorithm is proposed for encoding the reference frames and the B-frame prediction residuals in the 32-bit floating-point precision. Since the pixel-value distribution of these two types of data are quite different, various encoding modes are designed for providing adaptive selection. Extensive simulation experiments have been conducted using multiple point cloud datasets, and the results clearly show that our proposed PCC system consistently outperforms the state-of-the-art MPEG G-PCC in terms of data fidelity and localization, while delivering real-time performance.
SIMP for Complex Structures

Designing structures with frequency constraints is an important task in aerospace engineering. Aerodynamic loading, gust loading, and engine vibrations all impart dynamic loads upon an airframe. To avoid structural resonance and excessive vibration, the natural frequencies of the structure must be shifted away from the frequency range of any dynamic loads. Care must also be taken to ensure that the modal frequencies of a structure do not coalesce, which can lead to dramatic structural failure. So far in industry, no aircraft lifting surfaces are designed from the ground up with frequency optimisation as the primary goal. This paper will explore computational methods for achieving this task.This paper will present a topology optimisation algorithm employing the Solid Isotropic Microstructure with Penalisation (SIMP) method for the design of an optimal aircraft wing structure for rejection of frequency excitation.
Microbiome structure and response to watering in rhizosphere of Nitrosalsola vermiculata and surrounding bulk soil

The plant rhizosphere microbiomes were thought to help the plant stands adverse condition. The study aims at deciphering signatures of rhizosphere soil microbiomes of the medicinal plant Nitrosalsola vermiculata and those of the surrounding bulk soil as well as to detect influence of watering in restructuring soil microbes that can improve the plant’s ability to tolerate drought stress. Amplicon sequencing of partial 16S rRNA gene indicated that alpha diversity indices are higher in rhizosphere than in bulk soils, while no distinctive differences were observed due to the watering. Relative abundance of phylum Cyanobacteria and its descendent unidentified genus is the highest among phyla and genera of bulk soil. Relative abundance of phyla Euryarchaeota, Chloroflexi, Actinobacteria, Proteobacteria, Bacteroidetes, Firmicutes, Acidobacteria and Gemmatimonadetes as well as genera Bacillus, Ammoniphilus, Sphingomonas, Microvirga, Pontibacter, Adhaeribacter and Arthrobacter was significantly higher in rhizosphere soil. The latter taxa were reported to act as plant growth-promoting bacteria (PGPB) through symbiotic associations. We speculate that relative abundance and mutual dominance of these taxa in rhizosphere of N. vermiculata were due to the intensity and type of plant root exudates. Other factors include soil pH where microbes favoring high soil pH can show better growth in rhizosphere soil. Also, co-existence of phyla that promote sustainability of cohabiting phyla in the rhizosphere and have high synergism prevalence in biofilm formation can be one extra factor. Quorum sensing (QS) also mediates bacterial population density in a given environment and elicit specific plant responses. The low abundance of Cyanobacteria in rhizosphere soil can be due to the inhibitory effect of highly abundant members of Firmicutes, especially those of genus Bacillus. The latter conclusion was confirmed by the occurrence of high expression rate of comQ gene triggering QS in genus Bacillus. Highly abundant microbes whose abundance was not changed due to watering are phyla Firmicutes, Proteobacteria, Chloroflexi and Cyanobacteria and their descendent genera Bacillus, Ammoniphilus, Sphingomonas, Microvirga and unidentified genus of Cyanobacteria. We speculate that non-responsive taxa to watering were drought tolerant and can help plants stand adverse conditions of water scarce. In conclusion, insights on the factors involved in shaping microbiome signatures and those eliciting differential plant responses to drought stress are raised and warrant further investigations.
Teaching personal and social responsibility practitioners’ perceptions of the application of culturally relevant pedagogies

ABSTRACT After-school hours are critical to youths’ physical, academic and social development. This time serves as an opportunity for youth to engage in academic programs, sports, positive youth development programs, arts, apprenticeships and more. These programs are common, with 56% of youths participating in sport and physical activity (PA) programs each year Child and Adolescent Health Measurement Initiative (2018). However, there is scant research examining the quality and equity of after school programs. Therefore, it is salient to evaluate the quality and equity of afterschool programs. One of the sports-based youth development models used in after-school programming is Hellison’s Teaching Personal and Social Responsibility (TPSR) Model. TPSR provides youth experiences of sport and PA, as well as life skills and transference (Hellison, 2011). An area of research that has not been explored is how TPSR practitioners perceive their programming to be culturally relevant, and how culture impacts their programs. Walsh and Wright (2016) indicated that limited work has been done in implementing TPSR programs in a way that addresses exclusions due to race, ethnicity, gender and social inequalities. The theory of culturally relevant pedagogies (CRP) offers a way to conceptualize the aims of this study. The purpose is to explore to what extent TPSR practitioners perceive that they incorporate CRP into their program. Six TPSR practitioners were interviewed to investigate their implementation of CRP. Cross-case analysis indicated that TPSR practitioners were conscious of their culture and the youths’ cultures, as well as how gender and ethnicity may inform youths’ interests and values. TPSR provided to be a viable model that promotes CRP through inclusive programming, promotion of respect and program format. Areas of concern that did not promote CRP consisted of lack of academic development and lack of promotion of critical consciousness among the youth participants.
Biochar-Based Compost Affects Bacterial Community Structure and Induces a Priming Effect on Soil Organic Carbon Mineralization

Urban forests are key to mitigating the Urban Heat Island Effect, which contributes to temperature increases in urban areas. However, the trees in these forests are usually under stress because urban soil is typically degraded. Biochar/compost amendments help with soil management by improving the physiochemical properties and bacterial communities of soil. Here, we compared the physiochemical properties and bacterial communities before and after (1) biochar-only and (2) biochar-based compost amendments. Our results suggested that biochar-only application did not improve soil properties after 1 year of treatment, whereas in the biochar-based compost treatment, the soil properties and bacterial communities changed after just four months. The increase in potassium and decrease in organic material, calcium, and available phosphorus in the soil of the former treatment indicated that the nutrient uptake of its trees had improved. Although there was no significant variation in the soil’s total nitrogen, the higher abundance of potential nitrogen-fixing bacteria in the biochar-based treatment suggested that the soil contained a supplement to nitrogen. Our results show that biochar-based compost amendment improves soil quality and associated bacterial communities in urban forest management.
A multidimensional approach to understanding the value deficiencies that drive enterprise transformation

ABSTRACT It is crucial for an enterprise in a dynamic market to understand both realized and potential performance. This allows the enterprise to counteract anticipated value deficiencies before they are actually experienced. Our multidimensional model of superior performance identifies the dimensions of realized and potential. Experienced performance in relation to rivals, whether superior or inferior, is measured as the dimension of realized. Anticipated performance, whether superior or inferior (an anticipated value deficiency), is projected as the dimension of Potential. We validate our approach by applying a functional data analysis methodology to our data set of 397 firms. Our findings confirm that performance cannot be fully explained with reference to just one dimension, confirming the importance of accurately estimating potential performance in dynamic markets.
Enrichment of Anaerobic Microbial Communities from Midgut and Hindgut of Sun Beetle Larvae (Pachnoda marginata) on Wheat Straw: Effect of Inoculum Preparation

The Pachnoda marginata larva have complex gut microbiota capable of the effective conversion of lignocellulosic biomass. Biotechnological utilization of these microorganisms in an engineered system can be achieved by establishing enrichment cultures using a lignocellulosic substrate. We established enrichment cultures from contents of the midgut and hindgut of the beetle larva using wheat straw in an alkaline medium at mesophilic conditions. Two different inoculation preparations were used: procedure 1 (P1) was performed in a sterile bench under oxic conditions using 0.4% inoculum and small gauge needles. Procedure 2 (P2) was carried out under anoxic conditions using more inoculum (4%) and bigger gauge needles. Higher methane production was achieved with P2, while the highest acetic acid concentrations were observed with P1. In the enrichment cultures, the most abundant bacterial families were Dysgonomonadaceae, Heliobacteriaceae, Ruminococcaceae, and Marinilabiliaceae. Further, the most abundant methanogenic genera were Methanobrevibacter, Methanoculleus, and Methanosarcina. Our observations suggest that in samples processed with P1, the volatile fatty acids were not completely converted to methane. This is supported by the finding that enrichment cultures obtained with P2 included acetoclastic methanogens, which might have prevented the accumulation of acetic acid. We conclude that differences in the inoculum preparation may have a major influence on the outcome of enrichment cultures from the P. marginata larvae gut.
Sabotage in dynamic tournaments with heterogeneous contestants: Evidence from European football

This paper analyses how sabotage, measured as the number of fouls and yellow and red cards, is affected by the relative performance of and asymmetries between teams, determined based on score margin and the odds gap between one team and its opponent. By applying detailed controls for within-match dynamics and differences in team quality, we first observe that badly losing favourites are more willing than losing underdogs to increase their unsporting behaviour in heterogeneous contests. There are more yellow cards and fouls as the game progresses. We further find that most sabotage (yellow cards) can be observed when the (absolute) goal difference is equal to 1. Teams decrease their level of sabotage (fouls) by increasing the number of goals of lead or trail. In addition, weaker teams have an incentive to engage in more unfair and destructive strategies, such as committing more fouls, including those penalized with yellow cards. However, the incidence of red cards is not influenced by the score margin or strength gap.
Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals

In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading, we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding, which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in words-per-minute (WPM)) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this article.
Coding Qualitative Data at Scale: Guidance for Large Coder Teams Based on 18 Studies

We outline a process for using large coder teams (10 + coders) to code large-scale qualitative data sets. The process reflects experience recruiting and managing large teams of novice and trainee coders for 18 projects in the last decade, each engaging a coding team of 12 (minimum) to 54 (maximum) coders. We identify four unique challenges to large coder teams that are not presently discussed in the methodological literature: (1) recruiting and training coders, (2) providing coder compensation and incentives, (3) maintaining data quality and ensuring coding reliability at scale, and (4) building team cohesion and morale. For each challenge, we provide associated guidance. We conclude with a discussion of advantages and disadvantages of large coder teams for qualitative research and provide notes of caution for anyone considering hiring and/or managing large coder teams for research (whether in academia, government and non-profit sectors, or industry).
Genetic tuning of PI controller for speed control of DC motor drive

The genetic algorithm (GA) is emerging as a powerful optimization tool and has recently been applied in a few fields. This paper illustrates the use of GA for the design of controller parameters of a separately excited DC motor drive. The design criterion is modified as an optimization problem and the general rules of GA are applied. In the proposed GA, a novel selection procedure is adopted by combining roulette wheel selection with elitism, resulting in faster convergence to the optimal solution. Simulation of the DC drive is carried with optimized values of controller parameters and the dynamic response of the drive confirms the validity of the new disposition.
Dual-Task Network Embeddings for Influence Prediction in Social Internet of Things

Social Internet of Things (SIoT) is an emerging application area that supports the spread of multimedia information. Detecting critical nodes based on the results of influence prediction is the main solution to maximizing the information propagation in SIoT. However, while the tasks of predicting an influence probability and a cascade size are both critical in influence prediction, there has not yet a study that thoroughly investigates these two influence parameters. This article presents an end-to-end method that learns dual-task network embeddings to jointly predict influence probabilities and cascade sizes, which is called a multidimensional influence-to-vector method (Multi-Influor). First, multidimensional influence contexts are generated based on random walks, incorporating multiple estimating factors for pairwise node interaction, network structure, and global preference similarity. A new method of learning dual-task network embeddings is then devised to simultaneously capture influence probabilities and cascade sizes. The two tasks are both formulated into a unified framework via enforcing an information-sharing embedding matrix. Finally, stochastic gradient descent (SGD) is used to optimize two loss functions for influence prediction in an alternating manner, and the tasks are jointly accomplished that produces an accurate prediction. Extensive simulation results on real-world data sets show that Multi-Influor outperforms six state-of-the-art methods in accuracy and efficiency, and that a joint training method for the two tasks improves the overall prediction performance. Moreover, Multi-Influor is a practical method for SIoT sustainable computing.