Potential Method Research Articles

Development and mechanical evaluation of induction low Shear Friction Stir Spot rivets (ILSFSR) as crack arrest features in co-consolidated and welded thermoplastic laminates

The technique of Induction Low Shear Friction Stir Riveting (ILSFSR) stands as an alternative riveting process, which utilizes the melting and the resolidifying nature of thermoplastic resins to install metallic rivets into thermoplastic laminates without the need for pre-drilling. In this work, the aspects of the method’s implementation are presented, including its theoretical basis, the technical background, the apparatus, and the rivets’ installation procedure. Moreover, a mechanical evaluation of the resulting joints was held to determine their crack-arresting efficiency along carbon fiber reinforced co-consolidated low-melt PolyArylEtherKetone (LM-PAEK) laminates. A coupon scale testing campaign was employed, including mixed-mode Crack Lap Shear specimens containing the ILSFSR features, which were quasi-statically and fatigue-loaded. The interfacial crack growth results, which were compared to reference specimens, indicated the method's potential to provide significant damage retardation, resulting in an increase of 26% of quasi-static interfacial strength and complete confinement of fatigue crack growth, thus aiding in the damage tolerance design for critical engineering components.

Effects of Erosion Control Works: Case Study–Reservoir Celije, Rasina River Basin, the Zapadna Morava River (Serbia)

The aim of this research was to analyze the impact of implemented erosion control works (ECW) on soil erosion intensity in the watershed of the Ćelije reservoir (Rasina River) in the period between 1968 and 2022. The Erosion Potential Method was used to calculate the annual gross erosion (W), sediment transport (G), and erosion coefficient (Z) in the study area. As a result of the performed ECW there was a general decreasing trend in the intensity of soil erosion processes in the last 54 years. The specific annual gross erosion was 1189.12 m3/km−2/year−1 in 1968, while in 2022 it was 554.20 m3/km−2/year−1. The specific sediment transport was 540.18 m3/km−2/year−1 in 1968 and 253.55 m3/km−2/year−1 in 2022. Due to the changes in the intensity of erosion processes, the specific annual gross erosion decreased by 634.92 m3/km−2/year−1 and the specific sediment transport decreased by 286.63 m3/km−2/year−1. The erosion coefficient was reduced from Z = 0.62 to Z = 0.35. A dependence between the slope of siltation and the natural bed slope was defined. The results show a significant correlation between erosion intensity and performed ECW, providing a basis for future watershed management and defining a strategy for soil erosion control in the Ćelije reservoir watershed.

Validation of ToucHb, a non-invasive haemoglobin estimation: Effective for normal ranges, needs improvement for anaemia detection.

Non-invasive methods for haemoglobin estimation hold enormous potential for early detection and treatment of anaemia, especially in limited resource settings. We sought to validate the diagnostic accuracy of ToucHb, a non-invasive haemoglobin estimation device available in the Indian market. We prospectively evaluated the diagnostic performance of the ToucHb device using the Automated complete blood count (CBC) method as the gold standard. Persons referred for haemoglobin estimation to the central laboratory of the government medical college hospital in Mysore, southern India were included in the study. Out Of 140 people approached, 127 gave consent; 65% (n = 82) were female with median age of 37 (IQR 28-45). ToucHB reported median haemoglobin value of 14 g/dL compared to 13.3 g/dL for CBC. Within 1 g/dL and 2 g/dL of CBC, 55.2% (70/127) and 74% (94/127) of ToucHb haemoglobin observations fell, respectively. The Bland-Altman plot showed a mean difference of 3 g/dL in haemoglobin between ToucHb and CBC among those with anaemia. The ToucHb device showed 22.2% sensitivity and 94.5% specificity for anaemia detection. In rural resource-limited settings, point of care non-invasive devices such as ToucHb can improve access and acceptance for anaemia screening. However, ToucHb has showed low sensitivity for anaemia detection and low accuracy at lower haemoglobin values. The utility of the instrument is especially limited in detecting anaemia, while it can estimate haemoglobin accurately among those with haemoglobin is in the normal range. Based on these findings, ToucHb and devices that work on the core technology deployed in ToucHb may be better suited to monitor known haemoglobin level rather than in anaemia screening or detection in primary/ secondary care and community settings.

First-principles investigation of structural electronic magnetic and thermodynamic properties of CdS alloyed with Co transition metal

This research work investigates the structural, electronic, and magnetic properties of Cd1–xCoxS compounds. The supercells with 16 atoms have been studied by substituting a Cd atom with a Co atom in the cubic unit cell CdS from x = 0 to x = 1 in a Zinc-Blende structure based on the first principles of spin-polarized density functional theory as implemented in the WIEN2k code. The full potential augmented plane wave method FP-LAPW (Full potential linear augmented plane wave) was used in this study to evaluate the structural properties of the compounds, the exchange–correlation term is based on the PBE-GGA (Perdew–Burke–Ernzerhof generalized gradient approximation). For other properties, such as electronic structures, densities of states, and magnetic properties, the TB-mbj GGA (Tight Binding modified Becke–Johnson) exchange potential approximation is utilized. In addition to these properties, the thermodynamic properties were added advantage to clarify their comportment as temperature variation. The analysis of the density of spin-polarized states showed the ferromagnetic character with a from x = 0.125 to x = 0.875. The calculated total magnetic moments of Cd1–xCoxS are addressed concerning the change in lattice parameters. To track the shift brought on by adding Co, we ran our calculationsfrom x = 0.125 to x = 0.875 for Cd1–xCoxS compounds. Hence, the negative energyformation indicates that our compound can be made experimentally. The material may be used for solar cell applications. In summary, our findings represent a guide for future research. The findings of this work might provide useful direction for the usage of this alloy system in a variety of device applications, For example in high-efficiency tandem solar cells, magnetic sensors, and photoconductive detector applications.

Analytical quality by design based on knowledge organization: A case study of developing an ultrahigh-performance liquid chromatography method for the detection of phenolic compounds.

Despite numerous successful cases, there are still some challenges in using analytical quality by design (AQbD) for the development of analytical methods. Knowledge organization helps to enhance the objectivity of risk assessment, reduce the number of preliminary exploratory experiments, identify potential critical method parameters (CMPs) and their scope. In the present study, we aimed to develop a simple, rapid, and robust analytical method for detecting phenolic compounds in Xiaochaihu capsule intermediates utilizing knowledge organization. Knowledge organization and AQbD were combined to obtain the initial analytical conditions through knowledge collection, extraction, reorganization, and analysis. The quantitative relationship between critical method attributes (CMAs) and CMPs was then established by a definitive screening design. The method operable design region was calculated using an exhaustive Monte Carlo approach based on the probability of reaching the standard. Robustness investigation and methodological validation were finally performed. Analytical target profiles, CMAs, potential CMPs, and initial analytical conditions were initially identified, and the optimized ranges of operating parameters were obtained. A UHPLC method was successfully established for the analysis of phenolic compounds in ginger-ginger pinellia percolate, and the method validation outcomes were also satisfactory. The developed method can be a reliable means to detect the phenolic compounds of Xiaochaihu capsule intermediates. Knowledge organization provides a new approach for making better use of prior knowledge, significantly enhancing the efficiency of analytical method development. The approach is versatile and can be similarly applied to the development of other methods.

Hybrid Cellular Nanovesicles Block PD-L1 Signal and Repolarize M2 Macrophages for Cancer Immunotherapy.

The PD1/PD-L1 immune checkpoint blocking is a promising therapy, while immunosuppressive tumor microenvironment (TME) and poor tumor penetration of therapeutic antibodies limit its efficacy. Repolarization of tumor-associated macrophages (TAMs) offers a potential method to ameliorate immunosuppression of TME and further boost T cell antitumor immunity. Herein, hybrid cell membrane biomimetic nanovesicles (hNVs) are developed by fusing M1 macrophage-derived nanovesicles (M1-NVs) and PD1-overexpressed tumor cell-derived nanovesicles (PD1-NVs) to improve cancer immunotherapy. The M1-NVs promote the transformation of M2-like TAMs to M1-like phenotype and further increase the release of pro-inflammatory cytokines, resulting in improved immunosuppressive TME. Concurrently, the PD1-NVs block PD1/PD-L1 pathway, which boosts cancer immunotherapy when combined with M1-NVs. In a breast cancer mouse model, the hNVs efficiently accumulate at the tumor site after intravenous injection and significantly inhibit the tumor growth. Mechanically, the M1 macrophages and CD8+ T lymphocytes in TME increase by twofold after the treatment, indicating effective immune activation. These results suggest the hNVs as a promising strategy to integrate TME improvement with PD1/PD-L1 blockade for cancer immunotherapy.

Numerical Simulation of the Hydrogen-Based Directly Reduced Iron Melting Process

In the context of carbon reduction and emission reduction, the new process of electric arc furnace (EAF) steelmaking based on direct hydrogen reduction is an important potential method for the green and sustainable development of the steel industry. Within an electric furnace for the hydrogen-based direct reduction of iron, after hydrogen-based directly reduced iron (HDRI) is produced through a shaft furnace, HDRI is melted or smelted in an EAF to form final products such as high-purity iron or high-end special steel. As smelting proceeds in the electric furnace, it is easy for pieces of HDRI to bond to each other and become larger pieces; they may even form an “iceberg”, and this phenomenon may then worsen the smelting working conditions. Therefore, the melting of HDRI is the key to affecting the smelting cycle and energy consumption of EAFs. In this study, based on the basic characteristics of HDRI, we established an HDRI melting model using COMSOL Multiphysics 6.0 and studied the HDRI melting process, utilizing pellets with a radius of 8 mm. The results of our simulation show that the HDRI melting process can be divided into three different stages: generating a solidified steel layer, melting the solidified steel layer, and melting HDRI bodies. Moreover, multiple HDRI processes are prone to bonding in the melting process. Increasing the spacing between pieces of HDRI and increasing the preheating temperature used on the HDRI can effectively reduce the aforementioned bonding phenomenon. When the melting pool temperature is 1873 K, increasing the spacing of HDRI to 10 mm and increasing the initial HDRI temperature to 973 K was shown to effectively reduce or eliminate the bonding phenomenon among pieces of HDRI. In addition, with the increase in the melting pool temperature, the time required for melting within the three stages of the HDRI melting process shortened, and the melting speed was accelerated. With the increase in the temperature used to preheat the HDRI, the duration of the solidified steel layer’s existence was also shortened, but this had no significant impact on the time required for the complete melting of HDRI. This study provides a theoretical basis for the optimization of the HDRI process within EAFs.

Social media analytics for academic music library: a case study of CUHK center for Chinese Music Studies

PurposeWith the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.Design/methodology/approachWe conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.FindingsOur results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.Originality/valueFew studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

The Effective Fragment Molecular Orbital Method: Achieving High Scalability and Accuracy for Large Systems.

The effective fragment molecular orbital (EFMO) method has been developed to predict the total energy of a very large molecular system accurately (with respect to the underlying quantum mechanical method) and efficiently by taking advantage of the locality of strong chemical interactions and employing a two-level hierarchical parallelism. The accuracy of the EFMO method is partly attributed to the accurate and robust intermolecular interaction prediction between distant fragments, in particular, the many-body polarization and dispersion effects, which require the generation of static and dynamic polarizability tensors by solving the coupled perturbed Hartree-Fock (CPHF) and time-dependent HF (TDHF) equations, respectively. Solving the CPHF and TDHF equations is the main EFMO computational bottleneck due to the inefficient (serial) and I/O-intensive implementation of the CPHF and TDHF solvers. In this work, the efficiency and scalability of the EFMO method are significantly improved with a new CPU memory-based implementation for solving the CPHF and TDHF equations that are parallelized by either message passing interface (MPI) or hybrid MPI/OpenMP. The accuracy of the EFMO method is demonstrated for both covalently bonded systems and noncovalently bound molecular clusters by systematically examining the effects of basis sets and a key distance-related cutoff parameter, Rcut. Rcut determines whether a fragment pair (dimer) is treated by the chosen ab initio method or calculated using the effective fragment potential (EFP) method (separated dimers). Decreasing the value of Rcut increases the number of separated (EFP) dimers, thereby decreasing the computational effort. It is demonstrated that excellent accuracy (<1 kcal/mol error per fragment) can be achieved when using a sufficiently large basis set with diffuse functions coupled with a small Rcut value. With the new parallel implementation, the total EFMO wall time is substantially reduced, especially with a high number of MPI ranks. Given a sufficient workload, nearly ideal strong scaling is achieved for the CPHF and TDHF parts of the calculation. For the first time, EFMO calculations with the inclusion of long-range polarization and dispersion interactions on a hydrated mesoporous silica nanoparticle with explicit water solvent molecules (more than 15k atoms) are achieved on a massively parallel supercomputer using nearly 1000 physical nodes. In addition, EFMO calculations on the carbinolamine formation step of an amine-catalyzed aldol reaction at the nanoscale with explicit solvent effects are presented.

Progress in Identification of UDP-Glycosyltransferases for Ginsenoside Biosynthesis.

Ginsenosides, the primary pharmacologically active constituents of the Panax genus, have demonstrated a variety of medicinal properties, including anticardiovascular disease, cytotoxic, antiaging, and antidiabetes effects. However, the low concentration of ginsenosides in plants and the challenges associated with their extraction impede the advancement and application of ginsenosides. Heterologous biosynthesis represents a promising strategy for the targeted production of these natural active compounds. As representative triterpenoids, the biosynthetic pathway of the aglycone skeletons of ginsenosides has been successfully decoded. While the sugar moiety is vital for the structural diversity and pharmacological activity of ginsenosides, the mining of uridine diphosphate-dependent glycosyltransferases (UGTs) involved in ginsenoside biosynthesis has attracted a lot of attention and made great progress in recent years. In this paper, we summarize the identification and functional study of UGTs responsible for ginsenoside synthesis in both plants, such as Panax ginseng and Gynostemma pentaphyllum, and microorganisms including Bacillus subtilis and Saccharomyces cerevisiae. The UGT-related microbial cell factories for large-scale ginsenoside production are also mentioned. Additionally, we delve into strategies for UGT mining, particularly potential rapid screening or identification methods, providing insights and prospects. This review provides insights into the study of other unknown glycosyltransferases as candidate genetic elements for the heterologous biosynthesis of rare ginsenosides.

A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

Mental-LLM

Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In this work, we present a comprehensive evaluation of multiple LLMs on various mental health prediction tasks via online text data, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4. We conduct a broad range of experiments, covering zero-shot prompting, few-shot prompting, and instruction fine-tuning. The results indicate a promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for mental health tasks. More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5, outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9% on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%. They further perform on par with the state-of-the-art task-specific language model. We also conduct an exploratory case study on LLMs' capability on mental health reasoning tasks, illustrating the promising capability of certain models such as GPT-4. We summarize our findings into a set of action guidelines for potential methods to enhance LLMs' capability for mental health tasks. Meanwhile, we also emphasize the important limitations before achieving deployability in real-world mental health settings, such as known racial and gender bias. We highlight the important ethical risks accompanying this line of research.

Understanding the biofilm development of Acinetobacter baumannii and novel strategies to combat infection.

Acinetobacter baumannii (A. baumannii) is a Gram-negative, nonmotile, and aerobic bacillus emerged as a superbug, due to increasing the possibility of infection and accelerating rates of antimicrobial agents. It is recognized as a nosocomial pathogen due to its ability to form biofilms. These biofilms serve as a defensive barrier, increase antibiotic resistance, and make treatment more difficult. As a result, the current situation necessitates the rapid emergence of novel therapeutic approaches to ensure successful treatment outcomes. This review explores the intricate relationship between biofilm formation and antibiotic resistance in A. baumannii, emphasizing the role of key virulence factors and quorum sensing (QS) mechanisms that will lead to infections and facilitate insight into developing innovative method to control A. baumannii infections. Furthermore, the review article looks into promising approaches for preventing biofilm formation on medically important surfaces and potential therapeutic methods for eliminating preformed biofilms, which can address biofilm-associated A. baumannii infections. Modern advances in emerging therapeutic options such as antimicrobial peptide (AMPs), nanoparticles (NPs), bacteriophage therapy, photodynamic therapy (PDT), and other biofilm inhibitors can assist readers understand the current landscape and future prospects for effectively treating A. baumannii biofilm infections.

Knowledge fusion method of power grid model based on Seq2seq half pointer and half label method

Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy.

Computational insight into the crystal structures of cubane and azacubanes.

Using quantum chemistry and atom-atom potential methods, the molecular and crystal structures of cubane 1 and all types of unsubstituted azacubanes 2-22 were calculated. Alternative possible polymorphs of cubane 1 have been proposed. The thermochemical properties of azacubanes in the gas and solid phases were assessed. Thermodynamic aspects of stability are considered, and a significant decrease in stability is revealed upon transition from cubane 1 to octaazacubane 22. It has been shown that the density and energetic properties of azacubanes depend nonlinearly on the number of nitrogen atoms in the structure and the density of octaazacubane 22 at room temperature is 1.546gcm-3, which is significantly lower than the previously given estimate. In this work, DFT calculations were conducted through the software Gaussian 09 using B3LYP functional with basis set aug-cc-PVDZ and the Grimme dispersion correction D2. For crystal structure optimization, the atom-atom potential methods with PMC (packing of molecules in crystal) program were used. Charges for molecular electrostatic potential were fitted by FitMEP, and enthalpies of formation in gas phase were assessed by G3B3.

Waste Cooking Oil Biodiesel and Their Nanobiodiesel Blends Infused with Reduced Graphene Oxide (RGO) Nanoparticles for Diesel Engine Applications

This work focusses on the potential application of waste cooking oil (WCO) as alternative to diesel as it is abundantly, locally available. The oil is subsequently converted into its biodiesel called waste cooking biodiesel (WCOBD). GCMS studies of the waste cooking oil and its biodiesel is carried out to determine the fatty acid composition. Further WCOBD is blended with diesel to produce its WCOBD B20 blend. The physio-chemical properties of the WCO, WCOBD, WCOBD B20 and their nano-biodiesel blends were determined and were compared with diesel. These properties of WCOBD B20 are in good agreement with diesel. This B20 blend can successfully provide 20% substitute to fossil diesel and save foreign exchange besides providing energy security to India. The fuel properties of the WCOBD B20 blends are further modified by adding carbon derived nanoparticles of reduced graphene oxide (RGO) to prepare nano-biodiesel blends. The percentage of RGO nanoparticles were varied from 20% to 100 % by volume insteps of 20% using suitable SDS surfactants. The stability of these nano-biodiesel blends is determined by using zeta potential method. The stability of decreased with increase in the nano-particle dosage in base biodiesel blends. Accordingly, the blends with dosage beyond 80% showed lower stability in terms of lower zeta potential values. The nano-biodiesel blends showed improvement in calorific value and self-ignition temperature compared to the B20 biodiesel blend. Among the Nano-biodiesel blends considered WCOME B20 RGO80 showed comparatively higher BTE (11.67%), reduced emissions of smoke opacity (14.63%), HC (21.42%), CO (16.67%), ID (10%), CD (16.67%) and increased NOx (2.5%), and PP (15.38%) respectively when compared to the WCOBD B20 blend.

Heat enhanced bisphenol AF degradation in CoFe2O4@BC activated peroxymonosulfate process: Mechanism and the role of inorganic anions

Carbon-based materials and heat activated peroxymonosulfate (PMS) have emerged as promising technologies for the remediation of contaminated water. This study investigated the feasibility of Bisphenol AF (BPAF) degradation by coupling CoFe2O4@BC catalyst with heat activated PMS technology. Heat significantly enhanced the catalytic performance of CoFe2O4@BC for PMS activation, resulting in 87.0 % removal of BPAF. Meanwhile, the effects of PMS concentration, CoFe2O4@BC catalyst dosage, initial pH, and the inorganic anions on BPAF degradation were investigated. The presence of HCO3− and HPO42− were found to enhance BPAF degradation by promoting PMS decomposition, while humic acid (HA) significantly inhibited BPAF removal. Electron paramagnetic resonance (EPR) and quenching experiments suggested that sulfate radicals (SO4•−) were the dominant reactive oxygen species (ROS) in the system, with hydroxyl radicals (•OH) and singlet oxygen (1O2) also contributing to BPAF degradation. Based on the Density Functional Theory (DFT) and intermediates analysis, two degradation pathways of BPAF were proposed, including hydroxylation and β-scission reactions. This work reveals the underlying mechanism of PMS activation under the coupling of heat with the CoFe2O4@BC system and presents a potential method for contaminants decomposition.

Endogenous AMPKα2 Mediates the Inhibition of Biliary Fibroblasts Proliferation

Background: Although it has been established that activating adenosine monophosphateactivated protein kinase (AMPK) inhibits cell proliferation in several cells, it is unknown whether AMPK is involved in inhibiting biliary fibroblast growth. Objective: The objective of this study is to specifically investigate the influence of AMPK isoforms on proliferation. Methods: To further address its underlying molecular mechanisms, primary cultured rat biliary fibroblasts were transfected with sequence-specific AMPK1 or AMPK2 siRNA. Results: Our findings show that knocking down AMPK2 greatly increased the proliferation of primary cultured biliary fibroblasts, accompanied by the activation of mTOR, an increase in S-phase kinaseassociated protein 2 (Skp2) expression, and a decrease in p27 protein levels. AMPK2 inhibition-triggered Skp2 overexpression and concomitant p27 decrease, as well as biliary fibroblast proliferation, were reversed by rapamycin inhibition or previous silencing of Skp2 production by targeted small interfering RNA (siRNA) transfection. Conclusion: We concluded that AMPK2 regulates the mTOR/Skp2/p27 signaling pathway and causes endogenous suppression of primary cultured biliary fibroblast growth. The reduction of biliary fibroblast proliferation by AMPK2 could be a potential method in treating benign biliary stricture (BBS).

A numerical Poisson solver with improved radial solutions for a self-consistent locally scaled self-interaction correction method

The universal applicability of density functional approximations is limited by self-interaction error made by these functionals. Recently, a novel one-electron self-interaction-correction (SIC) method that uses an iso-orbital indicator to apply the SIC at each point in space by scaling the exchange-correlation and Coulomb energy densities was proposed. The locally scaled SIC (LSIC) method is exact for the one-electron densities, and unlike the well-known Perdew–Zunger SIC (PZSIC) method recovers the uniform electron gas limit of the uncorrected density functional approximation, and reduces to PZSIC method as a special case when isoorbital indicator is set to the unity. Here, we present a numerical scheme that we have adopted to evaluate the Coulomb potential of the electron density scaled by the iso-orbital indicator required for the self-consistent LSIC calculations. After analyzing the behavior of the finite difference method (FDM) and the green function solution to the radial part of the Poisson equation, we adopt a hybrid approach that uses the FDM for the Coulomb potential due to the monopole and the GF for all higher-order terms. The performance of the resultant hybrid method is assessed using a variety of systems. The results show improved accuracy than earlier numerical schemes. We also find that, even with a generic set of radial grid parameters, accurate energy differences can be obtained using a numerical Coulomb solver in standard density functional studies.

Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field

Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for training low-level motion controllers. Unlike traditional potential field methods, our approach modifies the state information received by the motion controllers using the outputs of the APF path planner. Specifically, the assumed target position is pushed away from obstacles, resulting in adjustments to the perceived position errors. Additionally, we address path oscillations by incorporating the target’s velocity information, which is calculated based on the time-derivative of the repulsive force. Experimental results have validated the effectiveness of our proposed framework in avoiding collisions with obstacles and reducing oscillations.