Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly efficient quantum-dot light emitting diodes with sol-gel ZnO electron contact

We demonstrated an efficient inverted CdSe/CdS/ZnS quantum dot light emitting diode (QLED) using sol-gel ZnO (s-ZnO) as the electron-injection layer (EIL). The device performance is comparable to that of a device based on the common used nanoparticle ZnO (n-ZnO) EIL. The peak efficiency (12.5 cd/A) and luminance (13000 cd/m2) for the s-ZnO based device was found to be similar to the n-ZnO based device (11.2 cd/A and 15000 cd/m2). The morphology properties of these two types of ZnO films were investigated by scanning electron microscope (SEM) and atomic force microscope (AFM) measurements. A very smooth surface was achieved for the s-ZnO film. Moreover, the quantum dot (QD) layer on the s-ZnO also possesses high quality with a close packed structure.
Modern Risk Stratification of Acute Myeloid Leukemia in 2023: Integrating Established and Emerging Prognostic Factors

Simple Summary Several factors, both patient- and disease-related, are essential to accurately estimate acute myeloid leukemia (AML) prognosis. The rapidly evolving field, from both the genetic and therapeutic standpoints, and the availability of measurable residual disease (MRD) data have made traditional prognostic factors less reliable. Consequently, updated recommendations are trying to recapitulate the current scenario, but several questions remain to be answered, including the impact of complex co-mutational patterns and the role of clonal architecture. In the present review, we summarize established and new AML risk factors, and we discuss the emerging comprehensive approaches to effectively integrate all relevant prognostic data to better inform patient care. Abstract An accurate estimation of AML prognosis is complex since it depends on patient-related factors, AML manifestations at diagnosis, and disease genetics. Furthermore, the depth of response, evaluated using the level of MRD, has been established as a strong prognostic factor in several AML subgroups. In recent years, this rapidly evolving field has made the prognostic evaluation of AML more challenging. Traditional prognostic factors, established in cohorts of patients treated with standard intensive chemotherapy, are becoming less accurate as new effective therapies are emerging. The widespread availability of next-generation sequencing platforms has improved our knowledge of AML biology and, consequently, the recent ELN 2022 recommendations significantly expanded the role of new gene mutations. However, the impact of rare co-mutational patterns remains to be fully disclosed, and large international consortia such as the HARMONY project will hopefully be instrumental to this aim. Moreover, accumulating evidence suggests that clonal architecture plays a significant prognostic role. The integration of clinical, cytogenetic, and molecular factors is essential, but hierarchical methods are reaching their limit. Thus, innovative approaches are being extensively explored, including those based on “knowledge banks”. Indeed, more robust prognostic estimations can be obtained by matching each patient’s genomic and clinical data with the ones derived from very large cohorts, but further improvements are needed.
Using In Silico Approach for Metabolomic and Toxicity Prediction of Alternariol

Alternariol is a metabolite produced by Alternaria fungus that can contaminate a variety of food and feed materials. The objective of the present paper was to provide a prediction of Phase I and II metabolites of alternariol and a detailed ADME/Tox profile for alternariol and its metabolites using an in silico working model based on the MetaTox, SwissADME, pKCMS, and PASS online computational programs. A number of 12 metabolites were identified as corresponding to the metabolomic profile of alternariol. ADME profile for AOH and predicted metabolites indicated a moderate or high intestinal absorption probability but a low probability to penetrate the blood–brain barrier. In addition to cytotoxic, mutagenic, carcinogenic, and endocrine disruptor effects, the computational model has predicted other toxicological endpoints for the analyzed compounds, such as vascular toxicity, haemato-toxicity, diarrhea, and nephrotoxicity. AOH and its metabolites have been predicted to act as a substrate for different isoforms of phase I and II drug-metabolizing enzymes and to interact with the response to oxidative stress. In conclusion, in silico methods can represent a viable alternative to in vitro and in vivo tests for the prediction of mycotoxins metabolism and toxicity.
Possible Batesian mimicry to sexually different models in the tussock moth Numenes albofascia with a great sexual color dimorphism

Mimicry with warning colors includes Batesian and Müllerian mimicries. If we divide mimicry by sex, there are theoretically four types of mimicry: unimodal, female‐limited, male‐limited and dual mimicry. The latter three cases cause sexual dimorphism in body color and marking pattern but are rarely reported. In this study, we show that the tussock moth Numenes albofascia is possibly a dual mimic. The wing color and marking pattern of male and female N. albofascia are completely different, with the male's pattern resembling that of the smoky moth Pidorus atratus, while the female pattern resembles that of the tiger moth Arctia caja. Body size also differs greatly between the sexes of N. albofascia, matching the mimicry model species of each sex. These moths are distributed sympatrically in Japan, and their adult seasons overlap with each other. According to lizard feeding experiments, N. albofascia is palatable, while both male and female model species are unpalatable. Actograms in the laboratory and the light trapping in the field suggest that females of N. albofascia fly actively from sunset to midnight, while males fly during the twilight period around dawn. Therefore, male and female N. albofascia might be Batesian mimics of diurnally active P. atratus and nocturnally active A. caja, respectively, and the great sexual dimorphism of this moth could be caused by dual mimicry.
Optimal Design of Induction Motors Over Driving Cycles for Electric Vehicles

Consideration of the overload (OL) performance of electric machines designed for EVs enables increasing the power density of the propulsion system. This paper aims to show the characteristics and advantages of the optimal IMs which have the capability of handling OL. A subdomain model (SDM) with the capability of the saturation prediction is developed and validated using experimental data. A lumped thermal model is developed to predict the transient temperature variation of the IMs. The thermal model is validated using the Motor-CAD transient thermal analysis. The fast speed and accuracy of the applied SDM allows to select twelve variables in a large search space for the optimization purpose. Initially, an optimization procedure is proposed to design three IMs over three different driving cycles. The optimal designs are validated from the electromagnetic and thermal aspects by the finite element analysis. IMs are then designed optimally with consideration of the OL capability. A transient thermal analysis is carried out to validate the designs. The optimal designs with and without consideration of OL are compared in terms of the machine parameters and geometry to understand how dimensions and equivalent circuit parameters of the IMs vary according to the driving cycles. The comparison allows more intuition about the consideration of OL capability in design.
What have we learned from 15  years of research on cross-situational word learning? A focused review

In 2007 and 2008, Yu and Smith published their seminal studies on cross-situational word learning (CSWL) in adults and infants, showing that word-object-mappings can be acquired from distributed statistics despite in-the-moment uncertainty. Since then, the CSWL paradigm has been used extensively to better understand (statistical) word learning in different language learners and under different learning conditions. The goal of this review is to provide an entry-level overview of findings and themes that have emerged in 15 years of research on CSWL across three topic areas (mechanisms of CSWL, CSWL across different learner and task characteristics) and to highlight the questions that remain to be answered.
Male Army ROTC Cadets Fail to Meet Military Dietary Reference Intakes and Exhibit a High Prevalence of Low Energy Availability and Poor Sleep Quality

The purpose of this study was to assess the dietary habits, prevalence of low energy availability (EA), and sleep quality in a cohort of male army Reserve Officer Training Corps (ROTC) cadets, and to investigate the relationship between EA and sleep quality as well as EA and various body composition variables that are important for tactical readiness. Thirteen male army ROTC cadets (22.2 ± 4.1 yrs; BMI: 26.1 ± 2.3) had their EA and body composition assessed using diet and exercise records alongside bioelectrical impedance analysis. Cadets also completed a validated sleep questionnaire. Sixty-two percent of participants presented with clinically low EA (<30 kcal/kg fat-free mass [FFM]) and none met the optimum EA threshold (≥45 kcal/kg FFM). Dietary analysis indicated that 15%, 23%, 46%, 23%, and 7% of cadets met the Military Dietary Reference Intakes (MDRI) for calories, carbohydrates, protein, fat, and fiber, respectively. Additionally, 85% of cadets exhibited poor sleep quality. Significant associations between EA and fat mass/percent body fat were shown (p < 0.05). There was, however, no statistically significant correlation between EA and sleep quality. The present study found a high prevalence of low EA and sleep disturbance among male army ROTC cadets and that many were unable to meet the MDRIs for energy and macronutrient intake. Further, low EA was associated with higher percent body fat and fat mass but not sleep quality.
A study of new technology of communication system

In this paper, we are going to build a new simulation model of OWC differs from the former studies by considering the performance of the mm-wave and phase modulation. Using a single laser source with 1550 nm wavelength is a cost-effective solution regarding the complexity and processing. The proposed system of OWC has not applied an expensive component such as an optical or electrical amplifier and optical filter to prove that the simulation can give a better at the output of Photo detector
Application of the fusion-fission metaheursitic to document clustering

Following the work of Inderjit S. Dhillon, this paper presents the document clustering problem as a graph partitioning problem. To solve this problem, we use the fusion-fission metaheuristic which has already been applied to several graph partitioning problems. The results obtained with the fusion-fission algorithm are better than those of Graclus, a state of the art graph partitioning software created by Inderjit S. Dhillon. But surprisingly, regarding Inderjit S. Dhillon's objective function, these results are also always better than those of the real partitioning of the documents. This unexpected fact incite us to conclude that Inderjit S. Dhillon's method to convert a document clustering problem into a graph partitioning problem is wanting. Replacing the normalized cut objective function by an another objective function should minimized this problem. However, we do not suggest yet a new objective function more efficient than the normalized cut.
A Deep-Learning-Based Graph Neural Network-Long-Short-Term Memory Model for Reservoir Simulation and Optimization With Varying Well Controls

A new deep-learning-based surrogate model is developed and applied for predicting dynamic oil rate and water rate with different well controls. The surrogate model is based on the graph neural networks (GNNs) and long-short-term memory (LSTM) techniques. The GNN models are used to characterize the connections of injector-producer pairs and producer-producer pairs, while an LSTM structure is developed to simulate the evolution of the constructed GNN models over time. In this way, we use geological attributes at wells and well controls with different times as input data. The oil rates and water rates at different times are generated. In this study, the GNN-LSTM surrogate model is applied to a high dimensional oil-gas-water field case with flow driven by 189 wells (i.e., 96 producers and 93 injectors) operating under time-varying control specifications. A total of 500 high-fidelity training simulations are performed in the offline stage, out of which 450 simulations are used for training the GNN-LSTM surrogate model, which takes about 150 minutes on an RTX2060 GPU. The trained model is then used to provide production forecasts under various well control scenarios, which are shown to be consistent with those obtained from the high-fidelity simulations (e.g., around 4.8% and 4.3% average relative errors for water production rates and oil production rates, respectively). The online computations from our GNN-LSTM model take about 0.3 seconds per run, achieving a speedup of over a factor of 1,000 relative to the high-fidelity simulations, which takes about 363 seconds per run. Overall, this model is shown to provide reliable and fast predictions of oil rates and water rates with a large level of perturbations in the well controls. Finally, the proposed GNN-LSTM model, in conjunction with the particle swarm optimization (PSO) technique, is applied to optimize the field oil production by varying the well control schedule of all injectors. Due to the significant speedup and high accuracy of the proposed surrogate model, the improved well-control strategies can be efficiently obtained.
Electronic Medical Records as Evidence of Therapeutic Transactions

The results of the medical examination are proof of the doctor-patient relationship, which is documented in a record containing an explanation of the patient's health condition based on the results of the examination that has been carried out. Although the recording process is an obligation, there are several cases where medical records are not filled in correctly and some are not even filled in by officers. Therefore, this research is designed to find out the urgency of electronic medical records as evidence of therapeutic transactions. The problems studied in this article are: First, the juridical consequences of health workers and hospitals in making and keeping RM or RME confidential; Second, the position and strength of RM or RME as evidence according to the law of evidence. The results show that health workers or hospitals are obliged to make Medical Records or Electronic Medical Records correctly and responsibly. Regulation of the Minister of Health Number 749a/MENKES/Per/XII/1989 concerning Medical Records is the basis for the obligation to procure Medical Records, therefore it must be adhered to for every health service.Keywords: Medical Records, Electronic Medical Records, Evidence
Combining Piezoelectric Stimulation and Extracellular Vesicles for Cartilage Regeneration

Numerous patients experience articular cartilage defects (ACDs), which are characterized by progressive cartilage degradation and often lead to osteoarthritis (OA). Consequently, 44.7% of OA patients suffer from dyskinesia or disability. Current clinical drug treatments offer limited effectiveness in fully curing the disease. In this study, we propose a collaborative approach that combines physical and biological cues to promote cartilage regeneration. A biodegradable piezoelectric poly (l-lactic acid) (PLLA) nanofiber scaffold facilitates in situ, battery-free electrical stimulation under natural joint loading, while extracellular vesicles (EVs) serve as communication mediators between cells and promote cell proliferation, migration, and secretion of type II collagen. In this combined approach, EVs attached to PLLA are gradually released by localized piezoelectric electrical stimulation and taken up by chondrocytes. This process results in the organization of type II collagen along the PLLA fiber surface, ultimately forming cartilage lacunae that facilitate the residence of new chondrocytes. As an outcome, a significant round cartilage defect (diameter: 3 mm and depth: 1 mm) in the PLLA/EVs group (rat and knee) was rapidly restored within six weeks. In contrast, individual EVs and PLLA groups demonstrated considerably weaker cartilage regeneration capabilities. This research suggests that the synergistic effect of electromechanical stimulation and EVs-based biological cues is a crucial intervention method for treating OA.
Leveraging COBIT 2019 Framework to Implement IT Governance in Business Process Outsourcing Company

The company specializes in delivering information and technology services to businesses operating in the same industry. Recognizing the pivotal role of information technology in achieving its vision, mission, and goals, the company emphasizes the proper implementation of IT governance to drive overall company success. To evaluate the management of IT resources, the study employs the COBIT-2019 framework for measurement. The data collection approach encompasses interviews, questionnaires, observation, and document analysis. The findings indicate that most IT governance processes currently operate at level 2 capability. However, the company aspires to reach level 3 for these processes. Consequently, recommendations are proposed to enhance these processes based on the best practices outlined in COBIT-2019. Key suggestions include implementing performance measurements and facilitating access to knowledge repositories to foster skill and competency development.
Pyrolysis Temperature vs. Application Rate of Biochar Amendments: Impacts on Soil Microbiota and Metribuzin Degradation

Biochar-amended soils influence the degradation of herbicides depending on the pyrolysis temperature, application rate, and feedstock used. The objective of this study was to evaluate the influence of sugarcane straw biochar (BC) produced at different pyrolysis temperatures (350 °C, 550 °C, and 750 °C) and application rates in soil (0, 0.1, 0.5, 1, 1.5, 5, and 10% w/w) on metribuzin degradation and soil microbiota. Detection analysis of metribuzin in the soil to find time for 50% and 90% metribuzin degradation (DT50 and DT90) was performed using high-performance liquid chromatography (HPLC). Soil microbiota was analyzed by respiration rate (C-CO2), microbial biomass carbon (MBC), and metabolic quotient (qCO2). BC350 °C-amended soil at 10% increased the DT50 of metribuzin from 7.35 days to 17.32 days compared to the unamended soil. Lower application rates (0.1% to 1.5%) of BC550 °C and BC750 °C decreased the DT50 of metribuzin to ~4.05 and ~5.41 days, respectively. BC350 °C-amended soil at high application rates (5% and 10%) provided high C-CO2, low MBC fixation, and high qCO2. The addition of low application rates (0.1% to 1.5%) of sugarcane straw biochar produced at high temperatures (BC550 °C and BC750 °C) resulted in increased metribuzin degradation and may influence the residual effect of the herbicide and weed control efficiency.
Propriedades físicas de planetas extrasolares

ROTATION is one the most important aspects to be observed in stellar astrophysics. Here we investigate that particularly in stars with planets. This physical parameter supplies information about the distribution of angular momentum in the planetary system, as well as its role on the control of different phenomena, including coronal and cromospherical emission and on the ones due of tidal effects. In spite of the continuous solid advances made on the study of the characteristics and properties of planet host stars, the main features of their rotational behavior is are not well established yet. In this context, the present work brings an unprecedented study about the rotation and angular momentum of planet-harbouring stars, as well as the correlation between rotation and stellar and planetary physical properties. Our analysis is based on a sample of 232 extrasolar planets, orbiting 196 stars of different luminosity classes and spectral types. In addition to the study of their rotational behavior, the behavior of the physical properties of stars and their orbiting planets was also analyzed, including stellar mass and metallicity, as well as the planetary orbital parameters. As main results we can underline that the rotation of stars with planets present two clear features: stars with Tef lower than about 6000 K have slower rotations, while among stars with Tef > 6000 K we find moderate and fast rotations, though there are a few exceptions. We also show that stars with planets follow mostly the Kraft ́s law, namely < J > ∝ v rot. In this same idea we show
Ultra-short Term Wind Speed Prediction under Multi-model Structure and Uncertainty Analysis

In order to improve the forecasting accuracy and reliability of wind power, the precise prediction of wind speed plays a more and more important role. Thus, the ultra-short-term prediction of wind speed and its uncertainty analysis are studied in the paper. Firstly, based on the daily time series of wind speed and its distribution characteristics, the ADAP (adaptive affinity propagation) method is used to execute the clustering of similar days to extract data samples while the PCA (principal component analysis) algorithm is used for dimensionality reduction. Then, the KELM (kernel extreme learning machine) is used to establish prediction models for different wind speed clusters while the PSO (particle swarm optimization) algorithm is applied to optimize the parameters of KELM. To ensure the generalization ability of prediction model, the k-fold cross validation is brought in to obtain final prediction models with stable performance. Subsequently, the iterative multi-step prediction of wind speed is carried out and its uncertainty is analyzed. The minimum standard error model and the mean-square-root standard error model are achieved to describe the comprehensive uncertainty of the prediction methods. Finally, the operation data of wind turbine is used to evaluate effectiveness of the methods. The results show that the prediction accuracy of wind speed is improved by using the combination model with a certain generalization and stability. In addition, the uncertainty models of prediction error are verified to be effective.
Towards Lower Greenhouse Gas Emissions Agriculture in North Africa through Climate-Smart Agriculture: A Systematic Review

North Africa (NA) is supposed to lower emissions in its agriculture to honor climate action commitments and to impulse sustainable development across Africa. Agriculture in North Africa has many assets and challenges that make it fit to use the tools of Climate-Smart Agriculture (CSA) for mitigation purposes. This study represents a first attempt to understand if CSA practices are sufficiently established in NA to contribute to reducing agriculture emissions. A PRISMA-inspired systematic review was carried out on an initial 147 studies retrieved from Scopus, Google Scholar, and the Web of Science databases, as well as from gray literature. 11 studies were included in the final analysis since they report the mitigation and co-benefits of CSA-based practices within NA. A bias risk was identified around the optimal inclusion of studies produced in French, and a specific plan was set for its minimization. Synthesis results revealed that most studies focused either on improving soil quality (nine studies) or managing enteric fermentation (two studies). The review revealed a poor establishment of the CSA framework in the region, especially in sequestering GHG emissions. A set of recommendations has been formulated to address the identified gaps from research orientations and organizational perspectives and empower the CSA as an ally for mitigation in north African agriculture.