Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combined Gated Recurrent Unit and Multi-Layer Perception Neural Network Model for Predicting Shale Gas Production

Shale gas plays an important role in supplementing energy demand and reducing carbon footprint. A precise and effective prediction of shale gas production is important for optimizing completion parameters. This paper established a gated recurrent unit and multilayer perceptron combined neural network (GRU-MLP model) to forecast multistage fractured horizontal shale gas well production. A nondominated sorting genetic algorithm II (NSGA II) was introduced into the model to enable its automatic architectural optimization. In addition, embedded discrete fracture models (EDFM) and a reservoir simulator were used to generate training datasets. Meanwhile, a sensitivity analysis was carried out to find the variable’s importance and support the history matching. The results illustrated that the GRU-MLP model can precisely and efficiently predict the productivity of multistage fractured horizontal shale gas in a rapid and effective manner. Additionally, the model fits better at peak values of shale gas production. The GRU-MLP hybrid model has a higher accuracy within an acceptable computational time range compared to recurrent neural networks (RNN), long short-term memory (LSTM), and GRU models. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) for shale gas production generated by GRU-MLP model were 3.90% and 3.93%, respectively, values 84.87% and 84.88% smaller than those of the GRU model. Consequently, compared with a purely data-driven method, the physics-constrained data-driven method behaved better. The main results of the study will hopefully contribute to the intelligent development of shale gas production prediction.
Low-Power Redundant-Transition-Free TSPC Dual-Edge-Triggering Flip-Flop Using Single-Transistor-Clocked Buffer

In the modern graphics processing unit (GPU)/artificial intelligence (AI) era, flip-flop (FF) has become one of the most power-hungry blocks in processors. To address this issue, a novel single-phase-clock dual-edge-triggering (DET) FF using a single-transistor-clocked (STC) buffer (STCB) is proposed. The STCB uses a single-clocked transistor in the data sampling path, which completely removes clock redundant transitions (RTs) and internal RTs that exist in other DET designs. Verified by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS, when operating at 10% switching activity, the proposed STC-DET outperforms prior state-of-the-art low-power DET in power consumption by 14% and 9.5%, at 0.4 and 0.8 V, respectively. It also achieves the lowest power-delay-product (PDP) among the DETs.
Application and progress of palliative therapy in advanced gastric carcinomas

Gastric carcinomas have high morbidity and mortality. It produces no noticeable symptoms in the early stage while causing complex complications in its advanced stage, making treatment difficult. Palliative therapy aims to relieve the symptoms of cancer patients and focuses on improving their quality of life. At present, five palliative therapies for advanced gastric carcinomas are offered: resection, gastrojejunostomy, stenting, chemotherapy, and radiotherapy. In recent years, palliative therapy has been used in the clinical treatment of advanced gastric carcinomas and related complications because of its efficacy in gastric outlet obstruction and gastric bleeding. In the future, multimodal and interdisciplinary palliative therapies can be applied to control general symptoms to improve patients’ condition, prolong their lifespan and improve their quality of life.
Standardizing fishery-dependent catch-rate information across gears and data collection programs for Alaska sablefish (Anoplopoma fimbria)

Indices of abundance used to inform stock assessment models are commonly derived from fishery-dependent data sources. However, fishery catch-per-unit-effort (CPUE) are often confounded by a myriad of factors for which corrections must be made using model-based standardization methods. The Alaska sablefish (Anoplopoma fimbria) fishery provides a fitting case study of such issues, wherein a regulatory change in 2017 disrupted historic fishery dynamics, promoting a rapid transition in use of pot gear over demersal hook-and-line gear in the Gulf of Alaska. To address this, we combined across both observer and logbook programs (data sources) and gear types to develop an intercalibrated abundance index. We first regressed observer records against vessel logbooks to understand potential biases that may arise from combining data sources during the CPUE standardization process. Here, we found that both data sources exhibited strong agreement in reported CPUEs when compared on a set-by-set basis. Therefore, we intercalibrated both CPUE data sources and developed an index of abundance that incorporated catch records from both demersal hook-and-line and pot gear fisheries for sablefish in Alaska, to account for the recent rapid change in gear use. This standardized index of abundance compared favourably with an index generated from a fishery-independent hook-and-line survey currently used in management, suggesting it is representative of sablefish population trends. Our findings not only represent a valuable contribution to the management of sablefish in Alaska, but also provide a widely applicable framework for standardizing fishery-dependent CPUE data to support the management of multi-gear fisheries.
Predictive Business Process Monitoring Approach Based on Hierarchical Transformer

Predictive business process monitoring is predicting the next stage of the business process based on the sequence of events that have occurred in the business process instance, which is positive for promoting the rational allocation of resources and the improvement of execution efficiency. There are drawbacks in modeling business process instances, such as conceptual drift phenomenon and long event sequences. Therefore, we propose a hierarchical Transformer-based business process prediction model to improve the performance of the Transformer-based predictive business process monitoring model. We encode the event features using two different encoding methods to obtain the relationship between activities and attributes. A drift detection algorithm is proposed to segment the business process and calculate the correlation between activities and segments by using cross-attention. Furthermore, learnable position encoding is designed to capture the relative position information of subsequences. Finally, the information of different granularity, such as event attributes, event subsequences, and complete instances, is fused by different weights. Experiments were run on seven real event logs for the next activity prediction and remaining time prediction, and the next activity prediction accuracy improved by 6.32% on average, and the mean absolute error of remaining time prediction reduces by 21% on average.
Monte Carlo model incorporating many-body effects for determining the gain spectra of quantum dot lasers

We present a Monte Carlo model that simulates the gain spectra of a QD laser material that empirically includes free-carrier effects. We compare simulation results of both Fermi–Dirac and random carrier populations, and compare them with experimental data. The free-carrier effects are highlighted as being more important than the choice of carrier statistics, and routes to improve this simple model are discussed.
Relative energy deficiency in sport: Diagnosis and management in primary care

Relative energy deficiency in sport (RED-S) describes a syndrome that affects multiple bodily systems and functions in both recreational and elite athletes. It is, fundamentally, an imbalance between energy intake and exercise-related energy expenditure, leading to globally impaired physiological function as a result of low energy availability. RED-S developed from the ‘female athlete triad’, describing a relationship between low energy availability (with or without eating disorder), amenorrhea (menstrual dysfunction) and low bone mineral density; it is now recognised to occur across many other bodily systems and also to occur in males. Given its often subtle presentation and consequences early-on, but potentially devastating consequences further down the line, timely recognition and facilitation of management in primary care is crucial to allow safe return to physical activity and to prevent long-term sequelae.
Analysis of Performance of Particle Swarm Optimization with Varied Inertia Weight Values for solving Travelling Salesman Problem

Particle Swarm Optimization is a popular heuristic search technique developed by Eberhart and Kennedy in 1995 which takes its inspiration from the social and cognitive learning of birds or fishes. This algorithm comprises the involvement of swarm intelligence technique for optimization. The most widely accepted variation of the basic PSO technique is PSO with Inertia weight which substantially controls the convergence behaviour and exploration exploitation trade-off in the basic PSO technique. From its initialization onwards a huge range of modifications of Inertia Weight strategy have been recommended. This paper involves the use of PSO with varying values of inertia weight for solving the Travelling Salesman Problem. An analysis of how different inertia weight values effect the solution in terms of time complexity, space complexity and convergence in carried out in order to know the value best suited for setting up the inertia weight.
A mouse model for X-linked Alport syndrome induced by Del-ATGG in the Col4a5 gene

Alport syndrome (AS) is an inherited glomerular basement membrane (GBM) disease leading to end-stage renal disease (ESRD). X-linked AS (XLAS) is caused by pathogenic variants in the COL4A5 gene. Many pathogenic variants causing AS have been detected, but the genetic modifications and pathological alterations leading to ESRD have not been fully characterized. In this study, a novel frameshift variant c. 980_983del ATGG in the exon 17 of the COL4A5 gene detected in a patient with XLAS was introduced into a mouse model in by CRISPR/Cas9 system. Through biochemical urinalysis, histopathology, immunofluorescence, and transmission electron microscopy (TEM) detection, the clinical manifestations and pathological alterations of Del-ATGG mice were characterized. From 16 weeks of age, obvious proteinuria was observed and TEM showed typical alterations of XLAS. The pathological changes included glomerular atrophy, increased monocytes in renal interstitial, and the absence of type IV collagen α5. The expression of Col4a5 was significantly decreased in Del-ATGG mouse model. Transcriptomic analysis showed that differentially expressed genes (DEGs) accounted for 17.45% (4,188/24003) of all genes. GO terms indicated that the functions of identified DEGs were associated with cell adhesion, migration, and proliferation, while KEGG terms found enhanced the degradation of ECM, amino acid metabolism, helper T-cell differentiation, various receptor interactions, and several important pathways such as chemokine signaling pathway, NF-kappa B signaling pathway, JAK–STAT signaling pathway. In conclusion, a mouse model with a frameshift variant in the Col4a5 gene has been generated to demonstrate the biochemical, histological, and pathogenic alterations related to AS. Further gene expression profiling and transcriptomic analysis revealed DEGs and enriched pathways potentially related to the disease progression of AS. This Del-ATGG mouse model could be used to further define the genetic modifiers and potential therapeutic targets for XLAS treatment.
Enhanced design efficiency through least upper bounds

Lower and upper spectral bounds are known for positive-definite matrices in under Loewner (Uber monotone Matrixfunktionen. Math Z. 1934;38:177–216) ordering. Lower and upper singular bounds for matrices of order in derive under an induced ordering. These orderings are combined here to the following effects. Given two first-order experimental designs in their upper singular bound enhances both and in that its Fisher Information matrix dominates those for both and thus ordering essentials in Gauss–Markov estimation. Moreover, if and are dispersion matrices for linear estimators under and respectively, then is the spectral lower bound for in . In essence this algorithm identifies elements in complementary to those of and combines these into . Case studies illustrate gains to be made thereby in first and second-order designs. Specifically, two examples demonstrate that designs optimal under separate criteria may be combined into a single design dominating both. In addition, selected examples demonstrate that classical second-order designs may be improved inter se.
Advances in the Phytochemical Characterisation and Bioactivities of Salvia aurea L. Essential Oil

The Salvia L. genus (Lamiaceae) is largely used in the pharmaceutical and food industry. Several species of biological relevance are extensively employed in traditional medicine, including Salvia aurea L. (syn. S. africana-lutea L.), which is used as a traditional skin disinfectant and in wounds as a healing remedy; nevertheless, these properties have not been validated yet. The aim of the present study is to characterise S. aurea essential oil (EO), unveiling its chemical composition and validating its biological properties. The EO was obtained by hydrodistillation and subsequently analysed by GC-FID and GC-MS. Different biological activities were assessed: the antifungal effect on dermatophytes and yeasts and the anti-inflammatory potential by evaluating nitric oxide (NO) production and COX-2 and iNOS protein levels. Wound-healing properties were assessed using the scratch-healing test, and the anti-aging capacity was estimated through the senescence-associated beta-galactosidase activity. S. aurea EO is mainly characterised by 1,8-cineole (16.7%), β-pinene (11.9%), cis-thujone (10.5%), camphor (9.5%), and (E)-caryophyllene (9.3%). The results showed an effective inhibition of the growth of dermatophytes. Furthermore, it significantly reduced protein levels of iNOS/COX-2 and simultaneously NO release. Additionally, the EO exhibited anti-senescence potential and enhanced wound healing. Overall, this study highlights the remarkable pharmacological properties of Salvia aurea EO, which should be further explored in order to develop innovative, sustainable, and environmentally friendly skin products.
Effects of vitamin D supplementation on the regulation of blood lipid levels in prediabetic subjects: A meta-analysis

This meta-analysis aimed to systematically investigate whether vitamin D supplementation reduces blood lipid—total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), and triglyceride (TG)—levels in prediabetic individuals. Pubmed, Web of Science, Cochrane Library, Embase, CNKI, and WANFANG databases were searched for studies published before 13 February 2022 (including 13 February 2022). Five articles were included. The results showed that vitamin D intervention led to a significant reduction in TG compared with control or placebo treatment (−0.42 [−0.59, −0.25], P < 0.001). Subgroup analyses showed that this effect was particularly significant among the studies that included obese subjects (−0.46 [−0.65, −0.28], P < 0.001), the studies that also included men (not only women) (−0.56 [−0.78, −0.34], P < 0.001), and the studies with intervention durations longer than 1 year (−0.46 [−0.65, −0.28], P < 0.001). Both relatively low doses of 2,857 IU/day (−0.65 [−0.92, −0.38], P < 0.001) and relatively high doses of 8,571 IU/day (−0.28 [−0.54, −0.02] P = 0.04) of vitamin D supplementation reduced TG levels, and the effect was observed both in Northern Europe (−0.65 [−0.92, −0.38], P < 0.001) and Asian (−0.25 [−0.48, −0.03], P = 0.03) country subgroups. No significant effects on TC, HDL-C, and LDL-C were shown. In conclusion, vitamin D supplementation might beneficially affect TG levels in individuals with prediabetes. Particularly longer durations of treatment, more than 1 year, with doses that correct vitamin deficiency/insufficiency, can have a beneficial effect. This meta-analysis was registered at www.crd. york.ac. uk/prospero (CRD42020160780).
Composition of Powdered Freeze-Dried Orange Juice Co-Product as Related to Glucose Absorption In Vitro

The reuse of food by-products is crucial for the well-being of the planet. Considering the high content of nutrients and other bioactive compounds in many of them, investigating their suitability for use as human food ingredients is an interesting challenge. In this study, in addition to the proximate composition, phenol content and antioxidant activity (AOA = 3.2 mmol Trolox equivalent (TE)/100 g, db) of orange juice powder by-product (CoP), different in vitro properties related to carbohydrate metabolism have been characterised. Specifically, the glycaemic index (GI), the glycaemic load (GL), the glucose dialysis retardation index (GDRI = 13.6%), the glucose adsorption capacity (GAC = 22.5 mM) and the inhibition capacity of α-amylase (α-A = 46.9%) and α-glucosidase (α-G = 93.3%) of powdered orange juice waste have been determined and related to fibre and phenolics composition. Taking advantage of the high fibre content of the by-product (36.67%), its GL was calculated for a CoP dose that allows labelling the food to which it is added as a source of fibre. The low GI value (24.4%) and the low GL (0.918 g available carbohydrates per serving) allowed us to conclude that the product studied could be an interesting opportunity for the food industry to offer it as a healthy food ingredient to be included in the diet, especially for those suffering from type 2 diabetes mellitus. Of the total phenolic compounds (TP = 509 mg equivalent of gallic acid (GAE)/100 g, db), 68% were found in free fraction (FP), and their contribution to the total AOA was 40.6%, while this was 54.9% for the 32% of phenols bound to plant tissues (BP).
On the use of deep learning for three-dimensional computational imaging

Deep learning has proven to be an efficient and robust method for many computational imaging systems. The advantages of machine learning, as a rule, are that it is fast—at least in its supervised form after training is complete—and seems exceedingly effective in capturing regularizing priors. Here, we focus the discussion on non-invasive three-dimensional (3D) object reconstruction. One then faces the additional dilemma of choosing the appropriate model of light-matter interaction inside the specimen, i.e. the forward operator. We describe the three stages of approximation that are applicable: weak scattering with weak diffraction (also known as the Radon transform), weak scattering with strong diffraction, and strong scattering. We then overview machine learning approaches for the various models, and glance at the consequences of oversimplifying the forward operator choice.
Electrical characteristics of fully superconducting synchronous generator in persistent excitation mode

A brushless-excited superconducting generator with a superconducting dynamo, i.e., a magnetic flux pump, has been fabricated. Concurrently, a fully superconducting generator in which both armature and field coils are made of superconductors has been investigated. The authors describe the experimental machine model and the test results on electrical characteristics. >
Bayesian Spatial Modelling of HIV Prevalence in Jimma Zone, Ethiopia

Background: Although the human immunodeficiency virus (HIV) is spatially heterogeneous in Ethiopia, current regional estimates of HIV prevalence hide the epidemic’s heterogeneity. A thorough examination of the prevalence of HIV infection using district-level data could assist to develop HIV prevention strategies. The aims of this study were to examine the spatial clustering of HIV prevalence in Jimma Zone at district level and assess the effects of patient characteristics on the prevalence of HIV infection. Methods: The 8440 files of patients who underwent HIV testing in the 22 Districts of Jimma Zone between September 2018 and August 2019 were the source of data for this study. The global Moran’s index, Getis–Ord Gi* local statistic, and Bayesian hierarchical spatial modelling approach were applied to address the research objectives. Results: Positive spatial autocorrelation was observed in the districts and the local indicators of spatial analysis using the Getis–Ord statistic also identified three districts, namely Agaro, Gomma and Nono Benja, as hotspots, and two districts, namely Mancho and Omo Beyam, as coldspots with 95% and 90% confidence levels, respectively, for HIV prevalence. The results also showed eight patient-related characteristics that were considered in the study were associated with HIV prevalence in the study area. Furthermore, after accounting for these characteristics in the fitted model, there was no spatial clustering of HIV prevalence suggesting the patient characteristics had explained most of the heterogeneity in HIV prevalence in Jimma Zone for the study data. Conclusions: The identification of hotspot districts and the spatial dynamic of HIV infection in Jimma Zone at district level may allow health policymakers in the zone or Oromiya region or at national level to develop geographically specific strategies to prevent HIV transmission. Because clinic register data were used in the study, it is important to use caution when interpreting the results. The results are restricted to Jimma Zone districts and may not be generalizable to Ethiopia or the Oromiya region.
Time-domain analysis of planar structures with an arbitrary distribution of permittivity

An algorithm for the time-domain electrodynamic analysis of planar structures with an arbitrary distribution of permittivity e is suggested; in the domain considered, e may take the following values: e > e 0 , e < e 0 , e = 0, and e < 0. A two-parameter algorithm and an expression for the Rτ elements of the equivalent circuit of a space domain are presented. Expressions are obtained that allow one to construct a thin (consisting of a single grid layer) absorbing layer for plane waves and the H m0 eigenmodes of a rectangular waveguide. The accuracy of the method is estimated. An example is given that demonstrates the application of the method to the analysis of radio-pulse scattering by an inhomogeneous dielectric cylinder.
Resurfacing Hip Arthroplasty Is a Safe and Effective Alternative to Total Hip Arthroplasty in Young Patients: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis of randomized controlled trials (RCTs) aimed at comparing resurfacing hip arthroplasty (RHA) and total hip arthroplasty (THA) in terms of rate of complications, revisions, functional outcomes, blood loss, operative time and metal ions levels. The search was conducted on three databases (PubMed, Cochrane and Web of Science) updated until 13 October 2022. The inclusion criteria were RCTs) written in the English language, with no time limitation, comparing RHA and THA. Among the retrieved 4748 articles, 18 RCTs were eligible for a total of 776 patients (mean age 53.1 ± 5.0). A meta-analysis was performed. RHA reported significantly lower blood loss compared to THA (p < 0.001) but with longer operative time (p < 0.001). No statistically significant difference was found between RHA and THA in terms of complications (12.08% and 16.24%, respectively) and revisions (6.32% and 6.14%, respectively). Both RHA and THA provide excellent clinical results in a population of young and active patients. Functional outcomes were not significantly different between the groups. Moreover, no significant difference in metal ion levels was found. These findings provide evidence concerning the safety and clinical effectiveness of RHA. Because of its bone-preserving properties, the lack of drawbacks and good outcomes, RHA appears to be a valid alternative to THA in young and active patients.