Ternary ReS2(1-x)Se2xalloys of different composition for Q-switched and mode-locked all-fiber laser.

This paper systematically studied the composition-controlled nonlinear optical properties and pulse modulation of ternary ReS2(1-x)Se2xalloys for the first time. The compositionally modulated characteristics of ReS2(1-x)Se2xon the band gap were simulated based on the first principles. We investigated the effect of the band gap on the saturable absorption properties. In addition, we demonstrated the modulation characteristics of different components ReS2(1-x)Se2xon 1.5µm Q-switched pulse performance. The Q-switched threshold, repetition rate, and pulse duration increase as the S(sulfur)-element composition rise. And pulse energy also was affected by the S(sulfur)-element composition. The ReS0.8Se1.2SA was selected to realize a conventional soliton with high energy in the all-fiber mode-locked laser. The pulse was centered at 1562.9 nm with a pulse duration of 2.26 ps, a repetition rate of 3.88 MHz, and maximum pulse energy of 1.95 nJ. This work suggests that ReS2(1-x)Se2xhas great potential in laser technology and nonlinear optics, and widely extends the material applications in ultrafast photonics.

Designing Benzodithiophene-Based Small Molecule Donors for Organic Solar Cells by Regulation of Halogenation Effects.

The donors are key components of organic solar cells (OSCs) and play crucial roles in their photovoltaic performance. Herein, we designed two new donors (BTR-γ-Cl and BTR-γ-F) by finely optimizing small molecule donors (BTR-Cl and BTR-F) with a high performance. The optoelectronic properties of the four donors and their interfacial properties with the well-known acceptor Y6 were studied by density functional theory and time-dependent density functional theory. Our calculations show that the studied four donors have large hole mobility and strong interactions with Y6, where the BTR-γ-Cl/Y6 has the largest binding energy. Importantly, the proportion of charge transfer (CT) states increases at the BTR-γ-Cl/Y6 (50%) and BTR-γ-F/Y6 (45%) interfaces. The newly designed donors are more likely to achieve CT states through intermolecular electric field (IEF) and hot exciton mechanisms than the parent molecules; meanwhile, donors containing Cl atoms are more inclined to produce CT states through the direct excitation mechanism than those containing F atoms. Our results not only provided two promising donors but also shed light on the halogenation effects on donors in OSCs, which might be important to design efficient photovoltaic materials.

Generative Deep Learning-Based Thermographic Inspection of Artwork.

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns.

In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events.

A Silicon Sub-Bandgap Near-Infrared Photodetector with High Detectivity Based on Textured Si/Au Nanoparticle Schottky Junctions Covered with Graphene Film.

We present a straightforward approach to develop a high-detectivity silicon (Si) sub-bandgap near-infrared (NIR) photodetector (PD) based on textured Si/Au nanoparticle (NP) Schottky junctions coated with graphene film. This is a photovoltaic-type PD that operates at 0 V bias. The texturing of Si is to trap light for NIR absorption enhancement, and Schottky junctions facilitate sub-bandgap NIR absorption and internal photoemission. Both Au NPs and the texturing of Si were made in self-organized processes. Graphene offers additional pathways for hot electron transport and to increase photocurrent. Under 1319 nm illumination at room temperature, a responsivity of 3.9 mA/W and detectivity of 7.2 × 1010 cm × (Hz)1/2/W were obtained. Additionally, at -60 °C, the detectivity increased to 1.5 × 1011 cm × (Hz)1/2/W, with the dark current density reduced and responsivity unchanged. The result of this work demonstrates a facile method to create high-performance Si sub-bandgap NIR PDs for promising applications at ambient temperatures.

Okicamelliaside targets the N-terminal chaperone pocket of HSP90 disrupts the chaperone protein interaction of HSP90-CDC37 and exerts antitumor activity.

Heat shock protein 90 (HSP90) has been recognized as a crucial target in cancer cells. However, various toxic reactions targeting the ATP binding site of HSP90 may not be the best choice for HSP90 inhibitors. In this paper, an ellagic acid derivative, namely, okicamelliaside (OCS), with antitumor effects was found. To identify potential anti-cancer mechanisms, an OCS photosensitive probe was applied to target fishing and tracing. Chemical proteomics and protein-drug interaction experiments have shown that HSP90 is a key target for OCS, with a strong binding affinity (KD = 6.45 µM). Mutation analysis of the target protein and molecular dynamics simulation revealed that OCS could competitively act on the key Glu-47 site at the N-terminal chaperone pocket of HSP90, where the co-chaperone CDC37 binds to HSP90, affect its stability and reduce the ∆Gbind of HSP90-CDC37. It was demonstrated that OCS destroys the protein-protein interactions of HSP90-CDC37; selectively affects downstream kinase client proteins of HSP90, including CDK4, P-AKT473, and P-ERK1/2; and exerts antitumor effects on A549 cells. Furthermore, tumor xenograft experiments demonstrated high antitumor activity and low toxicity of OCS in the same way. Our findings identified a novel N-terminal chaperone pocket natural inhibitor of HSP90, that is, OCS, which selectively inhibits the formation of the HSP90-CDC37 protein complex, and provided further insight into HSP90 inhibitors for anti-cancer candidate drugs.

A UCMPs@MIL-100 based thermo-sensitive molecularly imprinted fluorescence sensor for effective detection of ß-lactoglobulin allergen in milk products.

In this study, a thermo-sensitive molecularly imprinted fluorescence sensor was developed for the specific detection of ß-Lactoglobulin (ß-LG) allergen in milk products. The metal-organic frameworks (MIL-100) with a high specific surface area was coated on the surface of upconversion micro-particles (UCMPs). As the core, an imprinted polymer layer allowing for swelling and shrinking with response to temperature was prepared, which exhibited high adsorption and mass transfer capabilities for ß-LG allergen. The fluorescence intensity of UCMPs@MIL-100@MIP decreased linearly with the concentration of ß-LG in the range of 0.1-0.8 mg mL-1, and the limit of detection was 0.043 mg mL-1. The imprinting factor reached 3.415, which indicated that excellent specificity of the UCMPs@MIL-100@MIP for ß-LG allergen. In the analysis of ß-LG allergen in actual milk samples, the proposed UCMPs@MIL-100@MIP fluorescence sensor produced reliable and accurate results (recovery: 86.0-98.4%, RSD: 2.8-6.8%), closely related to the results of standard HPLC method (correlation coefficient: 0.9949), indicating that its feasibility in the detection of ß-LG allergen.

Covariance matrix reconstruction method based on amplitude and phase constraints with application to extend array aperture.

The angular resolution has always been a concern in the underwater direction of arrival (DOA) estimation. The resolution of the uniform linear array will worsen if the array aperture decreases. When the element spacing is determined, increasing the number of array elements (NAE) can improve the resolution. However, the NAE cannot be greatly increased in practical applications. To address this problem, we propose an array aperture extension method. For this method, we design an optimization algorithm to reconstruct the covariance matrix of the extended array by using that of the original array. Moreover, to make the extended array resemble the actual array, the reconstructed covariance matrix is constrained with a pure signal covariance matrix. The solution method of the optimization algorithm is described in detail. The function of this method is to improve the array aperture by increasing the virtual array elements without changing the element spacing. Therefore, when the array elements are insufficient, this method helps to improve the DOA estimation performance, such as the estimation precision and resolution probability of dual targets. Experiments including simulations and real lake experiments are implemented to validate the effectiveness of the proposed method.

The effects of cow-related factors on rectal temperature, respiration rate, and temperature-humidity index thresholds for lactating cows exposed to heat stress.

The objectives of this study were to investigate the effects of the cow-related factors on rectal temperature (RT) and respiration rate (RR) of lactating dairy cows under different heat stress (HS) conditions and establish the temperature-humidity index (THI) thresholds at which RT and RR begin to increase for cows in China. Cow-related factors included body posture (standing and lying), milk yield (<26 kg/d, ≥ 26-39 kg/d, and ≥39 kg/d), days in milk (≤60 d, > 60 and ≤ 150 d, and >150 d), and parity (1, 2, and ≥3). Records of RT, RR, and individual characteristics were collected from July to October 2020 on a commercial dairy farm in Northern China, where 826 Holstein lactating cows were measured. Using the broken-stick regression models and the entire dataset, the THI thresholds for RT and RR were 69.8 and 67.1, respectively. Therefore, the heat stress conditions during this study were classified according to the THI levels as thermoneutrality (TN, 60 < THI ≤ 67), mild (67 < THI ≤ 72), moderate (72 < THI ≤ 80), and severe (80 < THI ≤ 86). Results showed that lying cows exhibited the higher RT and RR but the lower THI threshold for RT (68.8 vs. 70.7) and RR (65.6 vs. 68.4) than standing cows; milk yield is positively associated with the values of RT and RR under TN or HS conditions, and the THI thresholds for RT (70.2 vs. 70.0 vs. 68.0) and RR (68.1 vs. 64.7 vs. 64.7) were progressively lower for low-yielding, middle-yielding, and high-yielding cows; there was a significant increase in RT and RR in early-lactation cows compared to late-lactation cows under TN or HS conditions (P < 0.001), and the lowest THI threshold (67.8 for RT and 64.7 for RR) was observed in early-lactation cows, followed by mid-lactation cows (68.2 for RT and 65.3 for RR) and late-lactation cows (70.0 for RT and 67.3 for RR); the effects of parity were not significant on RT (P > 0.05), but significant on RR (P < 0.001). The THI thresholds for RT (69.2) and RR (66.0) were lowest for cows in 3rd-parity and higher, followed by cows in 2nd-parity (70.0 for RT and 68.9 for RR) and 1st-parity (70.7 for RT and 66.6 for RR). This study highlighted the great significance of considering the cow-related factors in heat stress responses and THI threshold assessment. For dairy cows in China, we suggest that cooling should be initiated when THI reaches 65 to 66.

Micelles self-assembled by 3-O-ß-D-glucopyranosyl latycodigenin enhance cell membrane permeability, promote antibiotic pulmonary targeting and improve anti-infective efficacy.

BACKGROUND: Nanoparticle-based pulmonary drug delivery systems are commonly developed and applied for drug-targeted delivery. They exhibit significant advantages compared to traditional pulmonary drug delivery systems. However, developing the formulation of each drug is a time-consuming and laborious task. RESULTS: In this study, a universal lung-targeting nanoparticle was designed and constructed. The self-assembled micelles were composed of a platycodon secondary saponin, 3-O-ß-D-glucopyranosyl platycodigenin 682 (GP-682), based on its specific amphiphilic structure. The GP-682 micelles exhibited a relatively stable zeta potential with a particle size between 60 and 90 nm, and the critical micelle concentration (CMC) value was approximately 42.3 µg/mL. Preincubation of GP-682 micelles markedly enhanced their cell membrane permeability and improved drug uptake in vitro. The results were visualized using fluorescent dye tracing, transmission electron microscopy (TEM) observations and the lactate dehydrogenase (LDH) release assay. The obtained benefits enhanced the distribution of levofloxacin (Lev) in mouse lung tissue and reduced antibiotics overdosing. The acute lung injury mouse model induced by the Pseudomonas aeruginosa PA 14 strain demonstrated that preinjection of GP-682 micelles before antibiotic administration resulted in a higher survival rate and anti-infective efficacy in vivo. It also caused reductions in pulmonary injury, bacterial invasion and cytokine expression compared with treatment with Lev alone. CONCLUSIONS: GP-682 micelles are another nanoparticle-based pulmonary drug delivery system and provide a new lung-targeting therapy option.

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes.

Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.

Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models.

The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning method, the valuable information hidden in unlabeled data can be explored and absorbed into the prediction model. The application results to an industrial blast furnace show that BLSM has better prediction performance compared with other supervised soft sensors.

Data-driven calibration of decoupling matrix for MIMO precision motion stages.

Decoupling control is a commonly employed technique for achieving high precision in multiple-input multiple-output (MIMO) motion control systems. A static decoupling matrix, which can be determined using geometric construction, is widely used due to its practicality and simplicity. However, inaccurate geometric parameters will lead to a coarse decoupling matrix, and result in interactions among the system axes and performance deterioration. To tackle this challenge, various attempts have been undertaken to calibrate the decoupling matrix. Data-driven on-line approaches have gained considerable attention for their ability to calibrate the decoupling matrix without interrupting the normal operation of the system. This paper presents a data-driven approach to calibrate the decoupling matrix for MIMO and linear time invariant (LTI) systems. Through some reasonable assumptions, a calibrated static decoupling matrix can be derived to improve the performance of the system. Moreover, considering the inevitable presence of measurement noise, the consistency of the proposed method has been analyzed. As a result, the instrument variable is introduced in the improved method to eliminate the impact of the measurement noise. Finally, the effectiveness and practicality of the proposed method are demonstrated through both numerical simulations and experiments carried out on an ultraprecision wafer stage.

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites.

Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasound echoes and noise. In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites. Through 3D convolutional operations, it can synchronously learn the spatiotemporal properties of the data volume. Subsequently, the depth receptive field (RF) of the hidden layer in the autoencoder maps the defect information to the original depth location, thereby mitigating the effects of the defect surface and bottom echoes. In addition, a dual-layer encoder was designed to improve the hidden layer visualization results. Consequently, the size, shape, and depth of the defects can be accurately determined. The feasibility of the method was demonstrated through its application to defect detection in carbon-fiber-reinforced polymers.

Research on directional rock blasting based on different slotted pipe materials of the combined charge structure.

For shaped charge blasting projects in mining, civil engineering, and similar fields, it is proposed to modify the charge structure by combining slotted tubes and shaped charge liners to obtain a new type of charge structure. This aims to achieve directional rock breaking through the focused action of the shaped charge. The influence of different slotted pipe materials on the directional rock-breaking effect of concentrated energy using a new charge structure is explored through theoretical analysis combined with model test study, high-speed camera, stress-strain gauge, and other equipment. A comparison is made between slotted pipes made of aluminum, kraft paper, and PVC, with the cutting width of 2 mm. Based on the characteristics of the cracks formed after blasting, the new charge structure made of aluminum slotted pipe produces a penetrating crack that is almost consistent with the pre-cracking direction. Based on the corresponding characteristics of successively released blasting energy, the guiding and convergence effect of the new charge structure made of aluminum slotted pipe on the explosion energy is greater than that of the new charge structure made of the other two types of slotted pipe material. According to the strain data measured after blasting, the peak arrival time of the strain peak in the direction of the slotted pipe on one side of the shaped hood is shorter than that in the other two directions, and the peak strain is greater than that in the other two directions while having a better energy gathering effect. Based on the findings, the new charge structure with directional energy concentration has a damage reduction effect. Furthermore, the material of aluminum slotted pipe is found to be better than PVC slotted pipe, whereas the material of PVC slotted pipe is better than kraft paper slotted pipe in achieving directional rock breaking.

Bacteriophage therapy for drug-resistant Staphylococcus aureus infections.

Drug-resistant Staphylococcus aureus stands as a prominent pathogen in nosocomial and community-acquired infections, capable of inciting various infections at different sites in patients. This includes Staphylococcus aureus bacteremia (SaB), which exhibits a severe infection frequently associated with significant mortality rate of approximately 25%. In the absence of better alternative therapies, antibiotics is still the main approach for treating infections. However, excessive use of antibiotics has, in turn, led to an increase in antimicrobial resistance. Hence, it is imperative that new strategies are developed to control drug-resistant S. aureus infections. Bacteriophages are viruses with the ability to infect bacteria. Bacteriophages, were used to treat bacterial infections before the advent of antibiotics, but were subsequently replaced by antibiotics due to limited theoretical understanding and inefficient preparation processes at the time. Recently, phages have attracted the attention of many researchers again because of the serious problem of antibiotic resistance. This article provides a comprehensive overview of phage biology, animal models, diverse clinical case treatments, and clinical trials in the context of drug-resistant S. aureus phage therapy. It also assesses the strengths and limitations of phage therapy and outlines the future prospects and research directions. This review is expected to offer valuable insights for researchers engaged in phage-based treatments for drug-resistant S. aureus infections.

The antiprotozoal drug nitazoxanide improves experimental liver fibrosis in mice.

Nitazoxanide is an FDA-approved antiprotozoal drug. Our previous studies find that nitazoxanide and its metabolite tizoxanide affect AMPK, STAT3, and Smad2/3 signals which are involved in the pathogenesis of liver fibrosis, therefore, in the present study, we examined the effect of nitazoxanide on experimental liver fibrosis and elucidated the potential mechanisms. The in vivo experiment results showed that oral nitazoxanide (75, 100 mg·kg-1) significantly improved CCl4- and bile duct ligation-induced liver fibrosis in mice. Oral nitazoxanide activated the inhibited AMPK and inhibited the activated STAT3 in liver tissues from liver fibrosis mice. The in vitro experiment results showed that nitazoxanide and its metabolite tizoxanide activated AMPK and inhibited STAT3 signals in LX-2 cells (human hepatic stellate cells). Nitazoxanide and tizoxanide inhibited cell proliferation and collagen I expression and secretion of LX-2 cells. Nitazoxanide and tizoxanide inhibited transforming growth factor-ß1 (TGF-ß1)- and IL-6-induced increases of cell proliferation, collagen I expression and secretion, inhibited TGF-ß1- and IL-6-induced STAT3 and Smad2/3 activation in LX-2 cells. In mouse primary hepatic stellate cells, nitazoxanide and tizoxanide also activated AMPK, inhibited STAT3 and Smad2/3 activation, inhibited cell proliferation, collagen I expression and secretion. In conclusion, nitazoxanide inhibits liver fibrosis and the underlying mechanisms involve AMPK activation, and STAT3 and Smad2/3 inhibition.

Integrated evaluation of biomechanical and biological properties of the biomimetic structural bone scaffold: Biomechanics, simulation analysis, and osteogenesis.

A porous structure is essential for bone implants because it increases the bone ingrowth space and improves mechanical and biological properties. The biomimetically designed porous Voronoi scaffold can reconstruct the structure and function of cancellous bone; however, its comprehensive properties need to be investigated further. In this study, algorithms based on scaling factors were used to design the Voronoi scaffolds. Classic approaches, such as computer-aided design and the implicit surface method, have been used to design Diamond, Gyroid, and I-WP scaffolds as controls. All scaffolds were prepared by selective laser melting of titanium alloys and three-dimensional printing. Mechanical tests, finite element analysis, and in vitro and in vivo experiments were performed to investigate the biomechanical, cytologic, and osteogenic performance of the scaffolds, while computational fluid dynamics simulations were used to explore the underlying mechanisms. Diamond scaffolds have a better loading capacity, and the mechanical behaviors and fluid flow of Voronoi scaffolds are similar to those of the human trabecular bone. Cells showed more proliferation and distribution on the Diamond and Voronoi scaffolds and exhibited evident differentiation on Gyroid and Voronoi scaffolds. Bone formation was apparent on the inner part of the Gyroid, the outer part of the I-WP, and the entire Diamond and Voronoi scaffolds. The hydrodynamic properties and stimulus response of cells influenced by the porous structure account for the varied biological performance of the scaffolds. The Voronoi scaffolds with bionic mechanical behavior and an appropriate hydrodynamic response exhibit evident cell growth and osteogenesis, making them preferable for porous structural bone implants.

Increased Deep Trap Density in Interfacial Engineered Nanocomposite Revealed by Sequential Kelvin Probe Force Microscopy for High Dielectric Energy Storage.

Nanocomposites combining inorganic nanoparticles with high dielectric constant and polymers with high breakdown strength are promising for the high energy density storage of electricity, and carrier traps can significantly affect the dielectric breakdown process. Nevertheless, there still lacks direct experimental evidence on how nanoparticles affect the trap characteristics of nanocomposites, especially in a spatially resolved manner. Here, a technique is developed to image the trap distribution based on sequential Kelvin probe force microscopy (KPFM) in combination with the isothermal surface potential decay (ISPD) technique, wherein both shallow and deep trap densities and the corresponding energy levels can be mapped with nanoscale resolution. The technique is first validated using the widely-used commercial biaxially oriented polypropylene, yielding consistent results with macroscopic ISPD. The technique is then applied to investigate polyvinylidene fluoride-based nanocomposites filled with barium titanate nanoparticles, revealing higher deep trap density around surface-modified nanoparticles, which correlates well with its increased breakdown strength. This technique thus provides a powerful spatially resolved tool for understanding the microscopic mechanism of dielectric breakdown of nanocomposites.

Effect of Bionic Crab Shell Attitude Parameters on Lift and Drag in a Flow Field.

Underwater bionic-legged robots encounter significant challenges in attitude, velocity, and positional control due to lift and drag in water current environments, making it difficult to balance operational efficiency with motion stability. This study delves into the hydrodynamic properties of a bionic crab robot's shell, drawing inspiration from the sea crab's motion postures. It further refines the robot's underwater locomotion strategy based on these insights. Initially, the research involved collecting attitude data from crabs during underwater movement through biological observation. Subsequently, hydrodynamic simulations and experimental validations of the bionic shell were conducted, examining the impact of attitude parameters on hydrodynamic performance. The findings reveal that the transverse angle predominantly influences lift and drag. Experiments in a test pool with a crab-like robot, altering transverse angles, demonstrated that increased transverse angles enhance the robot's underwater walking efficiency, stability, and overall performance.