Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of Thermosyphon Solar Water Heaters Using TRNSYS . Part 1 : Improved Model development and Validation

TRNSYS is a standard simulation environment that can be successfully used to evaluate thermal performance of the thermosyphon solar water heaters through the use of component Type 45. However, it is possible for errors to be incurred in the case of performing optimization of some of the design parameters of a thermosyphon system. This is thought to be due to the fixing of the values of collector performance characteristics and tank overall heat loss coefficient throughout the optimization process in the case of changing collector and tank parameters. The suggestion in this study is to add two new components in order to account theoretically for this situation as well as to change the characteristic performance of the collector, pipe and tank loss coefficients from being parameters to instead being inputs to the current version of Type 45. The study has shown that linking these two components with the modified thermosyphon-collector component Type 245 (referred to as ‘Modified TRNSYS Model’) gives results that agree to within an error of less than 7% with the traditional way of evaluating system performance (solar fraction) by using Type 45 (referred to as ’Original TRNSYS Model’). Further a more, the modified TRNSYS model eliminates restrictions on design parameter optimization and gives a wider choice for conducting parametric studies.
Adaptive Priors for Burstiness Analysis in Topic Modeling with Generalized Dirichlet Distributions

Topic modeling is a powerful tool with wide-ranging applications, and many models have been developed and used over the years. However, most of these models have a common weakness: they do not account for the burstiness of word usage. This phenomenon, where a word is more likely to be used again once it appears in a document, is a key feature of natural language. In this paper, we present a new topic modeling approach that integrated Generalized Dirichlet and Dirichlet Compound Multinomial (DCM) distributions to explicitly model word burstiness while accommodating diverse and adaptable patterns of topic proportions. Our experimental results on various text datasets demonstrate the efficiency of our proposed model compared to existing baseline models, achieving better perplexity and coherence scores.
Definition of a continuous human cell line derived from neuroblastoma.

Summary A continuous hyperdiploid human cell line, IMR-32, was derived from neuroblastoma tissue. At least two morphologically distinct cell types comprise the population of IMR-32. These two cell types have the same characteristic chromosomal constitution and hyperdiploid stem line number, indicating a common clonal origin. One of the cell types, generally rarely observed because it occurs in minute numbers, appears to differentiate from the major or predominating cell. Marked ultrastructural differences were noted between the 2 cell types. The cell line proved a relatively poor host for virus replication, particularly for representative viruses of two of the human enterovirus groups. The electrophoretically more mobile lactic dehydrogenase isozymes of IMR-32 were retained after extended subcultivation.
Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.

OBJECTIVE The goal of this study is to devise a machine learning framework to assist care coordination programs in prognostic stratification to design and deliver personalized care plans and to allocate financial and medical resources effectively. MATERIALS AND METHODS This study is based on a de-identified cohort of 2,521 hypertension patients from a chronic care coordination program at the Vanderbilt University Medical Center. Patients were modeled as vectors of features derived from electronic health records (EHRs) over a six-year period. We applied a stepwise regression to identify risk factors associated with a decrease in mean arterial pressure of at least 2 mmHg after program enrollment. The resulting features were subsequently validated via a logistic regression classifier. Finally, risk factors were applied to group the patients through model-based clustering. RESULTS We identified a set of predictive features that consisted of a mix of demographic, medication, and diagnostic concepts. Logistic regression over these features yielded an area under the ROC curve (AUC) of 0.71 (95% CI: [0.67, 0.76]). Based on these features, four clinically meaningful groups are identified through clustering - two of which represented patients with more severe disease profiles, while the remaining represented patients with mild disease profiles. DISCUSSION Patients with hypertension can exhibit significant variation in their blood pressure control status and responsiveness to therapy. Yet this work shows that a clustering analysis can generate more homogeneous patient groups, which may aid clinicians in designing and implementing customized care programs. CONCLUSION The study shows that predictive modeling and clustering using EHR data can be beneficial for providing a systematic, generalized approach for care providers to tailor their management approach based upon patient-level factors.
Advocating for Disability Voting Rights in Framework Ius Constituendum

Protecting and guaranteeing the rights of every citizen is the obligation of the state, but in fact there are still acts of discrimination in the rights of citizens experienced by persons with disabilities. One form of discrimination carried out is related to political rights by setting physical health requirements for prospective members of the DPR, provincial DPRD, and district/city DPRD. Although there is no definite limit to measure the level of physical health. In addition, the electoral law contradicts the law on persons with disabilities which states that persons with disabilities have political rights. The electoral law should provide an open space as a means for people with disabilities to become part of public office. However, this will not be realized without a special quota for persons with disabilities to run as candidates for legislative members, as is the case with the quota of 30% female representation as prospective candidates for the DPR / DPRD and the determination of persons with disabilities as workers in government agencies at least 2% and 1% for private companies. Therefore, regulations should be added related to special quotas for persons with disabilities to become candidates for representation from political parties as election participants
High-throughput prediction of protein antigenicity using protein microarray data

MOTIVATION Discovery of novel protective antigens is fundamental to the development of vaccines for existing and emerging pathogens. Most computational methods for predicting protein antigenicity rely directly on homology with previously characterized protective antigens; however, homology-based methods will fail to discover truly novel protective antigens. Thus, there is a significant need for homology-free methods capable of screening entire proteomes for the antigens most likely to generate a protective humoral immune response. RESULTS Here we begin by curating two types of positive data: (i) antigens that elicit a strong antibody response in protected individuals but not in unprotected individuals, using human immunoglobulin reactivity data obtained from protein microarray analyses; and (ii) known protective antigens from the literature. The resulting datasets are used to train a sequence-based prediction model, ANTIGENpro, to predict the likelihood that a protein is a protective antigen. ANTIGENpro correctly classifies 82% of the known protective antigens when trained using only the protein microarray datasets. The accuracy on the combined dataset is estimated at 76% by cross-validation experiments. Finally, ANTIGENpro performs well when evaluated on an external pathogen proteome for which protein microarray data were obtained after the initial development of ANTIGENpro. AVAILABILITY ANTIGENpro is integrated in the SCRATCH suite of predictors available at http://scratch.proteomics. ics.uci. edu. CONTACT pfbaldi@ics.uci. edu
The Forecasting and Case Study Modeling of COVID-19 in Chicago: A Data-driven Approach

Probabilistic graphical models and machine learning are powerful data-driven tools for extracting useful knowledge from historical data; this knowledge can facilitate improved decision-making. With the prevailing efforts to combat the coronavirus disease 2019 (COVID-19) pandemic, there are still uncertainties that are yet to be discovered about its spread, future impact, and resurgence. In this paper, a data-driven approach has been adopted in distilling the hidden information about COVID-19 and its symptoms. This paper proposes: a Bayesian network which encodes the causal relationships among COVID-19 symptoms, an unsupervised machine learning algorithm that learns symptoms pattern in COVID-19 dataset, a deep neural network which predicts the symptoms class of patients based on clustering experience with a 99.47% testing accuracy, and a time-series forecasting model that predicts the trend of COVID-19. The results from the experiments show the capability of data-driven methods in addressing the concerns of the society and government in understanding the uncertainties about the virus, providing insights on developing policies, and reducing the spread of the virus.
Accumulation of Carbonates Contributes to Coastal Vegetated Ecosystems Keeping Pace With Sea Level Rise in an Arid Region (Arabian Peninsula)

Anthropogenic sea level rise (SLR) presents one of the greatest risks to human lives and infrastructures. Coastal vegetated ecosystems, that is, tidal marshes, seagrass meadows, and mangrove forests, elevate the seabed through soil accretion, providing a natural coastline protection against SLR. The soil accretion of these ecosystems has never been assessed in hot desert climate regions, where water runoff is negligible. However, tropical marine ecosystems are areas of intense calcification that may constitute an important source of sediment supporting seabed elevation, compensating for the lack of terrestrial inputs. We estimated the long‐term (14C‐centennial) and short‐term (210Pb‐20th century) soil accretion rates (SARs) and inorganic carbon (Cinorg) burial in coastal vegetated ecosystems of the Saudi coasts of the central Red Sea and the Arabian Gulf. Short‐term SARs (±SE) in mangroves of the Red Sea (0.27 ± 0.22 cm/year) were twofold the SLR for that region since 1925 (0.13 cm/year). In the Arabian Gulf, only mangrove forest SAR is equivalent to local SLR estimates for the period 1979–2007 (0.21 ± 0.09 compared to 0.22 ± 0.05 cm/year, respectively). Long‐term SARs are comparable or higher than the global estimates of SLR for the late Holocene (0.01 cm/year). In all habitats of the Red Sea and Arabian Gulf, SARs are supported by high carbonate accretion rates, comprising 40% to 60% of the soil volume. Further studies on the role of carbonates in coastal vegetated ecosystems are required to understand their role in adaptation to SLR.
The Requirements for COMPASS and Paf1 in Transcriptional Silencing and Methylation of Histone H3 in Saccharomyces cerevisiae

The Set1-containing complex, COMPASS, methylates histone H3 on lysine 4 (K4) in Saccharomyces cerevisiae. Despite the preferential association of K4-trimethylated H3 with regions of the genome that are transcribed by RNA polymerase II, transcriptional silencing is one of the few cases in S. cerevisiae where histone-methylation defects have a clear effect on gene expression. To better understand the role of COMPASS in transcriptional silencing, we have determined which members of COMPASS are required for silencing at the ribosomal DNA locus (rDNA), a telomere, and the silent mating loci (HM) using Northern analyses. Our findings indicate that most members of COMPASS are required for silencing at the rDNA and telomere, while none are required for silencing of endogenous genes at the HM loci. To complement gene-expression analysis, quantitative Western blot experiments were performed to determine the members of COMPASS that are required for methylation of histone H3. While most are required for trimethylation, cells lacking certain COMPASS proteins maintain reduced levels of K4 mono- and dimethylated H3, suggesting that some COMPASS members have redundant function. Finally, we show Paf1 is required for silencing and K4-methylated H3 at the rDNA, suggesting a possible direct role for K4-methylated H3 in gene silencing.
Using Distributed Analytics to Enable Real-Time Exploration of Discrete Event Simulations

Discrete event simulations (DES) provide a powerful means for modeling complex systems and analyzing their behavior. DES capture all possible interactions between the entities they manage, which makes them highly expressive but also compute-intensive. These computational requirements often impose limitations on the breadth and/or depth of research that can be conducted with a discrete event simulation. This work describes our approach for leveraging the vast quantity of computing and storage resources available in both private organizations and public clouds to enable real-time exploration of a discrete event simulation. Rather than considering the execution speed of a single simulation run, we autonomously generate novel scenario variants to explore an entire subset of the simulation parameter space. These workloads are orchestrated in a distributed fashion across a wide range of commodity hardware. The resulting outputs are analyzed to produce models that accurately forecast simulation outcomes in real time, providing interactive feedback and bolstering research possibilities.
EXAMINING THERMAL CONDUCTIVITIES OF SHALLOW SUBSURFACE MATERIALS FOR GROUND SOURCE HEAT PUMP UTILIZATION IN THE PANNONIAN PART OF CROATIA

Thermogeological and hydrogeological properties of the shallow subsurface in the Croatian part of the Pannonian Basin System (PBS) were investigated in the context of its utilization for ground source heat pump (GSHP) systems installation. Detailed borehole core determinations, sampling and thermal conductivity measurements were conducted for seven boreholes at four locations in diverse geological settings. Results of 418 measurements conducted on 82 sediment and rock samples were compiled and analysed in the context of existing collections of thermal conductivities from Germany and Switzerland which are frequently utilized for borehole heat exchanger (BHE) dimensioning in other countries. Discrepancies have been detected between thermal conductivity data from different geographical regions and geological settings. These results imply that local data sets should be further developed to enable accurate BHE sizing. In four of the boreholes thermal response tests (TRTs) were also conducted so the results were compared to direct measurements on samples. The comparison demonstrated that in the environments with significant groundwater flow (coarser-grained sediments) the laboratory measurements systematically underestimated natural-scale thermal conductivities, while the discrepancies were low in finer-grained sedimentary environments. That is why in the presence of significant groundwater flow the TRTs are recommended for all installations, while at locations with low flow velocities the analogy to values cited in data collections can be used for small-scale installations. It is important to have the collection of measurements for local materials in cases when thermal response testing would not be economically feasible. The values reported in this paper can be a guideline for proper GSHP system dimensioning in order to achieve better performance, especially of small-scale systems in Croatia and other geologically comparable localities in the PBS area.
A quick self-assessment tool to identify individuals at high risk of type 2 diabetes in the Chinese general population

Background Currently available tools for identifying individuals at high risk of type 2 diabetes can be invasive, costly and time consuming. This study aims to develop and validate a self-assessment tool for identifying individuals at high risk of type 2 diabetes in the Chinese general population. Methods A cross-sectional survey was conducted from 2000 to 2001 in a nationally representative sample of 15 540 Chinese adults aged 35–74 years. The diabetes risk level (DRL) was assessed by classification and regression tree (CART) analysis using four predictors: age, body mass index, waist–hip ratio (WHR) and waist circumference (WC). Results The significant predictors for type 2 diabetes were WHR and age for women and WC and age for men. The categories generated by CART analysis stratified women into eight DRL and men into five DRL. The prevalence of type 2 diabetes increased with the increase in DRL in both women and men. A DRL of 6 or greater predicted type 2 diabetes status with a sensitivity of 0.61 (95% CI 0.55 to 0.67), a specificity of 0.71 (95% CI 0.70 to 0.73) in women, and a DRL of 3 or greater predicted type 2 diabetes status with a sensitivity of 0.59 (95% CI 0.52 to 0.65) and a specificity of 0.63 (95% CI 0.62 to 0.65) in men. Conclusions This study demonstrates that application of the DRL has identified a substantial proportion of individuals with type 2 diabetes in the Chinese general population. It suggests that there is a great potential for applying the self-assessment tool in healthcare-limited settings.
Optimization of nutrient media for sweetpotato ( Ipomoea batatas L.) vine multiplication in sandponics: Unlocking the adoption and utilization of improved varieties

Background: Sweetpotato, being a vegetatively propagated crop is prone to seed degeneration, and a continuous source for high quality sweetpotato seed is critical for an efficient seed system. In most Sub-Saharan African countries, the National Agricultural Research Systems use tissue culture to produce limited quantity of pre-basic sweetpotato seed which is then used as starting material to maintain and produce basic seed in mini-screen houses, net tunnels or open field multiplication in low-virus pressure areas by either the private seed companies or vine multipliers. Soil is the predominant media for pre-basic seed multiplication. Multiplying pre-basic sweetpotato seed in sand with fertigation, also known as ‘sandponics’ is a possible opportunity towards sustainable production of pre-basic sweetpotato seed. It would be beneficial to examine the feasibility and the potential to replace soil system with ‘sandponics’ for growing pre-basic sweetpotato seed. Methods: Pot experiments were conducted to study how sweetpotato vine propagation is affected by sequentially omitting nitrogen, phosphorus, calcium, sulfur and boron from fertilizer applications on cv. Kabode. The experiment was laid in a randomized complete block design with five levels of the factor fertilizer, replicated four times with two blocks. The effect of fertilization of nitrogen at (0, 100, 150, 200 & 250), phosphorus at (0, 30, 60, 90 & 120), calcium at (0, 100, 200, 300 & 400), sulfur at (0, 30, 60, 90 & 120) and boron at (0, 0.1, 0.2, 0.3 & 0.4) ppm on sweetpotato vegetative growth parameters was measured 45 days after planting. Results: The obtained results showed that application of 200, 60, 200, 120 and 0.3 ppm of N, P, Ca, S and B respectively recorded the highest values in sweetpotato vegetative growth parameters. Conclusions: These results imply that pre-basic sweetpotato vine yields in sandponics could be increased by using this optimized media.
Limited Rescue of Osteoclast‐Poor Osteopetrosis After Successful Engraftment by Cord Blood From an Unrelated Donor

We report on a case of osteoclast‐poor osteopetrosis who received a hematopoietic stem cell graft and, despite hematological engraftment, showed little signs of response in the skeletal defect. Clinical and laboratory studies supported the concept that the bone microenvironment remained abnormal, thus reducing the clinical response to transplantation.
Biosimilars in Inflammatory Bowel Disease - Accumulating Clinical Evidence

ABSTRACT Introduction: Biologic antagonists to tumor necrosis factor alpha (TNF- α) are effective medications and have become well established in the treatment of both Crohn’s disease and ulcerative colitis. Biosimilar medications, which are medications deemed to be equivalent to reference biologic products in terms of clinical effectiveness, safety, pharmacokinetic analysis, and immunogenicity, have now been approved in inflammatory bowel diseases (IBD) based on indication exploration from clinical data in alternate disease states. Clinicians use these products with caution secondary to lack of clinical experience. Areas Covered: The authors performed a literature search using the following keywords: CT-P13, biosimilar, adalimumab, infliximab, ABP 501, and inflammatory bowel disease. Bibliographies were also reviewed for pertinent articles. Articles pertaining to the clinical efficacy of biosimilars in IBD were included. Expert commentary: The phase 3 trials, which provided the clinical justification to bring TNF- α biosimilars to market, were in rheumatoid arthritis and ankylosing spondylitis; however, new clinical data suggests that biosimilar products have equivalent safety and efficacy to reference products in IBD. This has led to an increased acceptance amongst practicing gastroenterologists and a potential reduction in healthcare costs.
QVal: A Novel Routing Algorithm for Dragonfly Networks

Dragonfly is becoming one of the networks of choice for high-performance computer systems as it offers a sweet spot in terms of cost, simplicity, performance, fault-tolerance and power. In a Dragonfly topology, compute nodes are connected to routers forming fully-connected groups, which are then connected to every other group. This provides very high connectivity with very low diameter which are desired characteristics of high performance interconnection networks. In this work, we propose Quick Valiant, a new routing algorithms for dragonfly networks which leverages Valiant routing with novel proxy selection criteria which reduce the utilization of network resources and eases congestion under pathological loads. These criteria restrict the possible proxies to these that are adjacent to either the local or the remote router, codenamed QVL and QVR, respectively. We compare our proposals with canonical routing functions and observe reductions of average path length of up to 27% and throughput improvements of up to 46% when compared with Valiant routing. Out of these criteria, restricting to local proxies only, QVL, obtains the best results in all scenarios.
NOAH: Newton Method of Optimization with Approximated Hessian

Newton method of optimization is very much useful in machine learning and deep learning optimizations due its order two convergence. The major problems of Newton method of optimization are the high cost of computing and storing the Hessian and its inverse for large-scale settings, which motivate us to use approximated Hessian instead of the true Hessian. However, existing works on Hessian approximation-based Newton methods are either computationally expensive or require more memory compared to first-order methods. To overcome these issues, this paper proposes NOAH, which approximates the Hessian with the help of first row of the true Hessian and directly finds the update step by using Sherman-Morrison formula of matrix inversion, which leads to faster convergence with comparatively less time and space complexities. Like Adam, NOAH uses exponential moving averages of the gradient and the Hessian curvature information to control the fluctuation of the update direction in heterogeneous settings. Experimental results show that, NOAH outperforms stochastic gradient descent (SGD) with momentum and state-of-the-arts Newton method of optimizations, L-BFGS and Nystrom-SGD in term of faster convergence with less time and space complexities.