Miracle-dz/UniFAR-FTUs huggingface dataset:
2D co-catalytic MoS2 nanosheets embedded with 1D TiO2 nanoparticles for enhancing photocatalytic activity

2D photocatalytic TiO2/MoS2 hybrid nanosheets (HNs) have been prepared via a facile hydrothermal process. X-ray diffraction patterns and Raman spectra are carried out and confirm a well crystalized anatase and 2H-MoS2 hybridization. Additional morphological and microstructural tests verify a distinct MoS2 framework, indicating the relatively stability of the MoS2 nanosheet platform with a high specific surface area. UV–vis spectra and electrochemical impedance spectra exhibit an enhanced light absorption ability and conductivity of TiO2/MoS2 compared to that of just TiO2. Photoelectrochemical (PEC) tests also demonstrate the photocurrent of 20 : 1 TiO2/MoS2 HNs is greatly improved compared to that of as-prepared TiO2. The saturation current density is about 33 µA cm−2 when the applied potential is 0.2 V, which is nearly twice that of pure TiO2 and four times as high as 5 : 1 TiO2/MoS2 HNs and 1 : 1 TiO2/MoS2 HNs. Besides that, the duration test exhibits no detectable distinction after processing 25 cycles. The improved photocatalytic activities are perhaps derived from the high conductivity and the increased active sites for the introduction of co-catalytic MoS2 nanosheets as well as the positive synergetic effect between the TiO2 and MoS2. This work demonstrates that the as-prepared TiO2/MoS2 HNs may have a great potential application in PEC hydrogen production.
Long term variations of actual evapotranspiration over the Tibetan Plateau

Abstract. Terrestrial actual evapotranspiration (ETa) is a key parameter controlling the land-atmosphere interaction processes and the water cycle. However, the spatial distribution and temporal changes of ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Validation against data from six eddy-covariance monitoring sites yielded a root mean square errors ranging from 9.3 to 14.5 mm mo−1, and correlation coefficients exceeding 0.9. The domain mean of annual ETa on the TP decreased slightly (−1.45 mm yr−1, p
Interference Cancellation via D2D CSI Sharing for MU-MISO-NOMA System With Limited Feedback

In downlink non-orthogonal multiple access (NOMA) with multiuser clustering, a single beamforming (BF) vector precodes the superimposed signal of strong and weak users. This precoding results in an imperfect inter-cluster interference (ICI) cancellation at weak users since the BF vectors are designed based on strong users’ channels. Also, the ICI is highly increased with the number of transmit antennas, causing severe degradation in the performance of weak users and total system. In this paper, first, a new cooperative NOMA is introduced, in which weak user beam-matching (WBM) equalizer is proposed to remove the ICI at weak users by matching weak users’ channels with the designed BF vectors. The process of WBM is accomplished with the aid of device-to-device (D2D) channel state information (CSI) sharing between the nearby strong and weak users. Second, based on WBM principle, strong user beam-matching (SBM) equalizer is proposed to eliminate the generated ICI at strong users in the case of limited feedback. Third, a new power allocation strategy is proposed to improve weak users’ performance by considering the gained throughput from interference cancellation. Finally, besides the sum-rate, which is adopted as the performance metric by most of the existing NOMA works, the bit error rate (BER) of NOMA users in cooperative NOMA is calculated and compared with those in other schemes. Simulation results show that our cooperative NOMA with the proposed equalizers achieves significant sum-rate and BER improvements over other non-cooperative schemes with both perfect and limited feedback scenarios.
Increased concentrations of procalcitonin in patients with paracetamol intoxication

Abstract Objectives Paracetamol intoxication is one of the causes of elevated procalcitonin concentrations unrelated to infection. We report a case series of two patients intoxicated with paracetamol whose laboratory data revealed a significant elevation of serum procalcitonin concentrations without clinical, radiological and/or biological evidence of infection. The underlying mechanism by which paracetamol triggers an increase in procalcitonin concentrations is still unclear. Case presentation We report two cases of paracetamol intoxication. Both patients were admitted to the Emergency Department (ED) and subsequently transferred to the Intensive Care Unit (ICU). The patients exhibited elevated procalcitonin levels during the first hours of admission without clinical and/or microbiological evidence of infection that could explain such increase. Notably, only Case 1 developed liver injury, with alterations in alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin and esterified bilirubin concentrations, which were not observed in Case 2. Conclusions The two patients showed elevated procalcitonin concentrations resulting from paracetamol intoxication, although only a patient exhibited signs of liver injury. These findings suggest that increased procalcitonin levels induced by a paracetamol overdose cannot be fully explained by hepatocyte injury alone, but other mechanisms involving other organs and tissues may also be associated. In any case, although this mechanism is not well understood, it is important to be aware of this limitation when using procalcitonin as a biomarker of infection in patients intoxicated with paracetamol.
Nanocarriers for protein and peptide delivery: Recent advances and progress

: Proteins and peptides have been recognized as potential leads for the synthesis of novel therapeutics to treat a variety of human ailments. Unfortunately, therapeutic potential and clinical applications of these biomacromolecules are challenging due to the verge of delivery applications. Nanocarriers offer unique potential to overcome various biological barriers and improve the delivery of therapeutic biomacromolecules like proteins and peptides. A smart nanocarriers-based drug delivery system can be deﬁned as a system that has site-specific drug delivery in a controlled manner against all the physiological barriers and ﬁnally metabolized in the body. This review describes various nanocarriers investigated for the delivery of proteins and peptides to augment their clinical applications. Various biological aspects of protein and peptide delivery have been also focused. We also summarized various patents granted for nanocarrier reported to deliver these biomacromolecules followed by a landscape presentation of marketed nano-formulations for protein and peptide delivery.
Acoustic Panels Made of Paper Sludge and Clay Composites

Studies on recycled materials emerged during recent years. This paper investigates samples’ sound absorption properties for panels fabricated of a mixture of paper sludge (PS) and clay mixture. PS was the core material. The sound absorption was measured. We also consider the influence of an air gap between panels and rigid backing. Different air gaps (50, 100, 150, 200 mm) simulate existing acoustic panel systems. Finally, the PS and clay composite panel sound absorption coefficients are compared to those for a typical commercial absorptive ceiling panel. The average sound absorption coefficient of PS-clay composite panels (αavg. in the frequency range from 250 to 1600 Hz) was up to 0.55. The resulting average sound absorption coefficient of panels made of recycled (but unfinished) materials is even somewhat higher than for the finished commercial (finished) acoustic panel (αavg. = 0.51).
Smart Adaptive Mesh Refinement with NEMoSys

Adaptive mesh refinement (AMR) offers a practical solution to reduce the computational cost and memory requirement of numerical simulations that use computational meshes. In this work, we introduce a novel smart methodology for adaptive mesh refinement. Smart adaptive refinement blends classical AMR with machine learning to address some of the known issues of the conventional approaches. We provide an algorithm for adaptive refinement. Subsequently, we introduce a modular object-oriented structure for our smart AMR algorithm. Then we present procedures used for the training of a smart AMR model. The study follows with a demonstration of preliminary numerical studies indicating the feasibility of performing adaptive mesh refinement on a few demonstrative problems selected from the CFD domain. Finally, we conclude with a few comments about future work.
Disturbance Estimation-Based Robust Model Predictive Position Tracking Control for Magnetic Levitation System

This article presents an improved equivalent input disturbance observer-based model predictive control (IEIDO-based MPC) scheme for position tracking control of a magnetic levitation system. It overcomes the problem of the position tracking performance degradation for the magnetic levitation system in the presence of time-varying disturbances. To enhance the accuracy of estimation, an IEIDO is first proposed to estimate both the system states and the time-varying equivalent input disturbance. The estimations of IEIDO are then integrated into the MPC design for output position prediction. Afterward, an explicit analytical form of the optimal predictive controller is obtained to achieve the optimized tracking performance, as well as the fine robustness against lumped time-varying equivalent input disturbance. Rigorous stability analysis shows that the closed-loop system is stable. Simulation and experimental results demonstrate the effectiveness of the proposed method as compared with the conventional EID and Luenberger observer-based integral MPC approaches.
Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

: Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using Convolutional Neural Network (CNN). The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras. When weapons are identified, the cameras sound an alarm. In the proposed IADC system, CNN was used to detect firearms and ammunition. The CNN model which is a Deep Learning technique consists of neural networks, most commonly applied to analyzing visual imagery has gained popularity for unstructured (images, videos) data classification. Additionally, this system generates an early warning through detection of ammunition before conditions become critical. Hence the faster and earlier the prediction, the lower the response time, loses and potential victims. The proposed IADC system provides better results than earlier published models like VGGNet, OverFeat-1, OverFeat-2, and OverFeat-3.
Improved Procedure for Earth Fault Loop Impedance Measurement in TN Low-Voltage Network

The difficulties and uncertainties related to earth fault loop impedance measurement are addressed in this paper. Based on the presentation of the measurement procedure implemented in the test equipment (diagrams and measured quantities, respectively, interpretation of results), the shortcomings and errors that accompany it are highlighted. The position in the power system, the influence of power transformers, and the use of effective quantities instead of phasors are important sources of errors, but, as will be seen, the switching of loads at the consumer sides and/or the occurrence of fault regimes during measurements can lead to the most serious impairment of the accuracy in the impedance assessment. The clarification of these aspects is achieved, both starting from the equivalent diagrams of the measurement circuits and the analytical interpretation of the phenomena associated with the measurements, as well as based on the modeling and simulation of TN low-voltage electrical distribution networks, in a specialized program, Eaton xSpider, which allows the complete and complex analysis of a large number of scenarios. Thus, essential conclusions were drawn regarding the level of errors and their causes, obviously, with the research coming with solutions to be implemented at the level of the measurement protocols of the devices used.
Level set method with Retinex-corrected saliency embedded for image segmentation

It can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex-corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex-corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efﬁcient, fast, reliable, and robust.
Controlled synthesis of pure Au25(2-Nap)18 and Au36(2-Nap)24 nanoclusters from 2-(diphenylphosphino)pyridine protected Au nanoclusters

The controlled synthesis of pure Au25(2-Nap)18 and Au36(2-Nap)24 nanoclusters were achieved via etching 2-(diphenylphosphino)-pyridine protected polydispersed Au nanoclusters with a mass of 1 to 3 kDa. Au25(2-Nap)18 was obtained at the etching temperature of 80 °C, while Au36(2-Nap)24 was synthesized at the etching temperature of 50 °C. This demonstrates that the simple adjustment of the etching temperatures can realize the controlled synthesis of different atomically precise Au nanoclusters.
Efficient Room-Temperature Phosphorescence from Nitrogen-Doped Carbon Dots in Composite Matrices

Carbon dots (CDs) have various attractive properties and potential applications, but there is much less attention paid to their phosphorescent phenomenon and mechanism. Herein, we prepared a kind of highly efficient CD-based phosphorescent material by a subtle design method that incorporated N-doped CDs (NCDs) into composite matrices (the melting recrystallization urea and biuret from the heating urea) by a one-pot heating treatment for the mixture of urea and NCDs. Through systematic investigation, C═N bonds on the surface of the NCDs can create new energy level structures, and for the first time, evidence that shows they are the origin of phosphorescence is presented. Composite matrices play a dual role to suppress the vibrational dissipation of long-lived triplets by combining the rigidity of the melting recrystallization urea and hydrogen bonding between biuret and NCDs, which have obvious advantages over a single-component matrix. The results show the obtained materials have an ultralong phosphoresce.
Biodegradable poly(amidoamine)s with uniform degradation fragments via sequence-controlled macromonomers

A new and general strategy for the synthesis of high molecular weight, sequence-controlled and selectively degradable poly(amidoamine)s is presented that employs solid-phase synthesis for incorporating degradable linkers at predefined positions within macromonomers. Subsequent molecular weight expansion via Cu(I)-catalyzed azide–alkyne cycloaddition (CuAAC)-mediated addition polymerization yields polymers up to an average Mn of 21 kDa. Control of the number and position of degradable linkers within the polymer backbone thus translates into complete and highly selective enzymatic fragmentation down to uniform degradation products. Hence, the control and selectivity of fragmentation now accessible with our strategy can further promote the development of degradable polymers within diagnostic and therapeutic applications.
Modeling ionic liquids of the 1-alkyl-3-methylimidazolium family using an all-atom force field

A new force field for the molecular modeling of ionic liquids of the dialkylimidazolium cation family based on the OPLS-AA/AMBER framework is presented and discussed. Ab initio calculations were performed to obtain several terms in the force field not yet defined in the literature. These include torsion energy profiles and distributions of atomic charges that blend smoothly with the OPLS-AA specification for alkyl chains. Validation was carried out comparing simulated and experimental data on fourteen different salts, comprising three types of anion and five lengths of alkyl chain, both in the crystalline and liquid phases. The present model can be regarded as a step towards a general force field for ionic liquids of the imidazolium cation family that was built in a systematic way, is easily integrated with OPLS-AA/AMBER and is transferable between different combinations of cation-anion.
Morphology and Structure Controls of Single-Atom Fe–N–C Catalysts Synthesized Using FePc Powders as the Precursor

Understanding the origin of the high electrocatalytic activity of Fe–N–C electrocatalysts for oxygen reduction reaction is critical but still challenging for developing efficient sustainable nonprecious metal catalysts used in fuel cells. Although there are plenty of papers concerning the morphology on the surface Fe–N–C catalysts, there is very little work discussing how temperature and pressure control the growth of nanoparticles. In our lab, a unique organic vapor deposition technology was developed to investigate the effect of the temperature and pressure on catalysts. The results indicated that synthesized catalysts exhibited three kinds of morphology—nanorods, nanofibers, and nanogranules—corresponding to different synthesis processes. The growth of the crystal is the root cause of the difference in the surface morphology of the catalyst, which can reasonably explain the effect of the temperature and pressure. The oxygen reduction reaction current densities of the different catalysts at potential 0.88 V increased in the following order: FePc (1.04 mA/cm2) < Pt/C catalyst (1.54 mA/cm2) ≈ Fe–N–C-f catalyst (1.64 mA/cm2) < Fe–N–C-g catalyst (2.12 mA/cm2) < Fe–N–C-r catalyst (2.35 mA/cm2). By changing the morphology of the catalyst surface, this study proved that the higher performance of the catalysts can be obtained.
Diurnal Variation: Does it Affect Short-term Maximal Performance and Biological Parameters in Police Officers?

Objectives: This study aimed to assess the diurnal variation of short term maximal performance and biological markers of hormonal [cortisol (CRT), testosterone (TST), melatonin (MLT)], biochemical [creatine phosphokinase (CPK), blood glucose (GLC), uric acid (UA)] and hematological [white blood cells (WBC), monocytes (MO), neutrophils (NE), lymphocytes (LY), red blood cells (RBC), hemoglobin (HB), hematocrit (HMT)] parameters in male police officers. Methods: Twenty male police officers performed a running-based anaerobic sprint test (RAST) at two different times of the day (TOD), in the morning (6:00 h - 7:00 h) and evening (19:00 - 20:00) where blood samples were collected from the participants on these two occasions before each exercise session. Results: Short term maximal performance [Peak power (PP), average power (AP) and minimal power (MP)] was higher in the evening as compared to the morning. It was noted that biological markers of hormonal, biochemical and hematological parameters exhibit diurnal fluctuation. Some of them peak in the morning (MLT, CRT, TST, UA, RBC, HB, and HMT) and others peak in the evening (GLC, CPK, WBC, NE, LY, and MO). Conclusions: Short term maximal performance, hormonal, biochemical and hematological parameters were strongly affected by the TOD in police officers. Thus, understanding changes in short term maximal exercice and these circulating mediators during different TOD provide a better understanding of the anaerobic metabolism and could help modulate and quantify training load.
An Online Gyro Scale Factor Error Calibration Method for Laser RINS

The rotational inertial navigation system (RINS) could greatly improve navigation performance by rotating the inertial measurement unit with gimbals. And the self-calibration of error parameters could be achieved conveniently. Among the rate-biased Ring laser gyro (RLG), the input angular velocity can reach tens of degrees per second. Therefore, the attitude errors caused by scale factor is dozens of times that of the other RINS. In this paper, gyro scale factor errors are calibrated through optimal estimation with attitude errors in dual-axis laser RINS. During the movement of vehicle, the dynamic gyro scale factor errors calibration can be performed online by adding parking points. Moreover, the parking points can be accurately determined by the statistical characteristics of accelerometers and gyros output information, without extern sensors. The experiment results indicate that the calibration repeatability scale factor parameters are less than 1ppm, and the attitude errors cause by the scale factor error is reduce from tens of arc seconds to 10 arc seconds, which verifies the effectiveness and accuracy of the method proposed in this paper. Through the compensation of calibration results, the attitude error of RINS is further improved in both the static and dynamic experiment.
Spanning the “Parameter Space” of Chemical Vapor Deposition Graphene Growth with Quantum Chemical Simulations

Graphene is a 2-dimensional allotrope of carbon with remarkable physicochemical properties. Currently, the most promising route for commercial synthesis of graphene for technological application is chemical vapor deposition (CVD). The optimization of this chemical process will potentially enable control over crucial properties, such as graphene quality and domain size. Such optimization requires a detailed atomistic understanding of how graphene nucleation and growth take place during CVD. This mechanism depends on a multitude of synthetic parameters: temperature, CVD pressure, catalyst type, facet and phase, feedstock type, and the presence of chemical etchants, to name only a few. In this feature article, we highlight recent quantum chemical simulations of chemical vapor deposition (CVD) graphene nucleation and growth. These simulations aim to systematically span this complex CVD “parameter space” toward providing the necessary understanding of graphene nucleation, to assist the optimization of CVD grap.