Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Realization of Database System for Judgement Documents Based on Natural Language Processing

: In this paper, based on natural language processing technology, we designed and realized a database system of judgement instruments. By analyzing and processing the instruments of crimes, we have used natural language processing technology to classify the instruments, extract keywords and extract information, and realize the rapid retrieval and accurate analysis of the instrument database. In the design process, we adopted a reasonable design method for the instrument database system to ensure the stability and scalability of the system. In the realization scheme, we made full use of the existing technical resources and algorithmic models to ensure the efficiency and accuracy of the system. Through this study, we have come to the conclusion that the database system for job-related crime instruments based on natural language processing can effectively improve the processing efficiency and quality of the instruments, and provide strong support for the research and practice in related fields.
The Role of ZO-2 in Modulating JAM-A and γ-Actin Junctional Recruitment, Apical Membrane and Tight Junction Tension, and Cell Response to Substrate Stiffness and Topography

This work analyzes the role of the tight junction (TJ) protein ZO-2 on mechanosensation. We found that the lack of ZO-2 reduced apical membrane rigidity measured with atomic force microscopy, inhibited the association of γ-actin and JAM-A to the cell border, and instead facilitated p114RhoGEF and afadin accumulation at the junction, leading to an enhanced mechanical tension at the TJ measured by FRET, with a ZO-1 tension probe, and increased tricellular TJ tension. Simultaneously, adherens junction tension measured with an E-cadherin probe was unaltered. The stability of JAM-A and ZO-2 binding was assessed by a collaborative in silico study. The absence of ZO-2 also impacted the cell response to the substrate, as monolayers plated in 20 kPa hydrogels developed holes not seen in parental cultures and displayed a retarded elongation and formation of cell aggregates. The absence of ZO-2 was sufficient to induce YAP and Snail nuclear accumulation in cells cultured over glass, but when ZO-2 KD cells were plated in nanostructured ridge arrays, they displayed an increased abundance of nuclear Snail and conspicuous internalization of claudin-4. These results indicate that the absence of ZO-2 also impairs the response of cells to substrate stiffness and exacerbates transformation triggered by substrate topography.
Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.
Antibiotic-Free Gene Vectors: A 25-Year Journey to Clinical Trials

Until very recently, the major use, for gene therapy, specifically of linear or circular DNA, such as plasmids, was as ancillary products for viral vectors’ production or as a genetic template for mRNA production. Thanks to targeted and more efficient physical or chemical delivery techniques and to the refinement of their structure, non-viral plasmid DNA are now under intensive consideration as pharmaceutical drugs. Plasmids traditionally carry an antibiotic resistance gene for providing the selection pressure necessary for maintenance in a bacterial host. Nearly a dozen different antibiotic-free gene vectors have now been developed and are currently assessed in preclinical assays and phase I/II clinical trials. Their reduced size leads to increased transfection efficiency and prolonged transgene expression. In addition, associating non-viral gene vectors and DNA transposons, which mediate transgene integration into the host genome, circumvents plasmid dilution in dividing eukaryotic cells which generate a loss of the therapeutic gene. Combining these novel molecular tools allowed a significantly higher yield of genetically engineered T and Natural Killer cells for adoptive immunotherapies due to a reduced cytotoxicity and increased transposition rate. This review describes the main progresses accomplished for safer, more efficient and cost-effective gene and cell therapies using non-viral approaches and antibiotic-free gene vectors.
Tropical cyclones characteristic in southern Indonesia and the impact on extreme rainfall event

The southern region of Indonesia is one of the places where tropical cyclones grow in the southern hemisphere. During 1983-2017 there were 51 tropical cyclones occurring in the region. This study aims to understand the characteristic of tropical cyclones in southern Indonesia and their variations, both spatially and temporally, and their effect on extreme rain events in Indonesia. Historical data analysis results show that tropical cyclones in southern Indonesia generally occur in November-April with a lifetime of 7-8 days. The result of data analysis shows that the central pressure value of tropical cyclone in latitude 0°-10°S is more than 960 hPa. The value tends to be higher than the central value pressure of tropical cyclone in latitude 10°S-20°S, which has the range of values about 920-960 hPa. This study also explains that there are 9 tropical cyclones in 35 years back that grow or move closer to the Indonesian archipelago in latitude 0°-10°S. The event of tropical cyclone Dahlia at the end of 2017 also affect the enormously increase of rainfall in Gunungkidul, Yogyakarta region with the increase of rain reaches 750% from the historical average.
Natural 2′,4-Dihydroxy-4′,6′-dimethoxy Chalcone Isolated from Chromolaena tacotana Inhibits Breast Cancer Cell Growth through Autophagy and Mitochondrial Apoptosis

Breast cancer (BC) is one of the most common cancers among women. Effective treatment requires precise tailoring to the genetic makeup of the cancer for improved efficacy. Numerous research studies have concentrated on natural compounds and their anti-breast cancer properties to improve the existing treatment options. Chromolaena tacotana (Klatt) R.M. King and H. Rob (Ch. tacotana) is a notable source of bioactive hydroxy-methylated flavonoids. However, the specific anti-BC mechanisms of these flavonoids, particularly those present in the plant’s inflorescences, remain partly undefined. This study focuses on assessing a chalcone derivative extracted from Ch. tacotana inflorescences for its potential to concurrently activate regulated autophagy and intrinsic apoptosis in luminal A and triple-negative BC cells. We determined the chemical composition of the chalcone using ultraviolet (UV) and nuclear magnetic resonance (NMR) spectroscopy. Its selective cytotoxicity against BC cell lines was assessed using the MTT assay. Flow cytometry and Western blot analysis were employed to examine the modulation of proteins governing autophagy and the intrinsic apoptosis pathway. Additionally, in silico simulations were conducted to predict interactions between chalcone and various anti-apoptotic proteins, including the mTOR protein. Chalcone was identified as 2′,4-dihydroxy-4′,6′-dimethoxy-chalcone (DDC). This compound demonstrated a selective inhibition of BC cell proliferation and triggered autophagy and intrinsic apoptosis. It induced cell cycle arrest in the G0/G1 phase and altered mitochondrial outer membrane potential (∆ψm). The study detected the activation of autophagic LC3-II and mitochondrial pro-apoptotic proteins in both BC cell lines. The regulation of Bcl-XL and Bcl-2 proteins varied according to the BC subtype, yet they showed promising molecular interactions with DDC. Among the examined pro-survival proteins, mTOR and Mcl-1 exhibited the most favorable binding energies and were downregulated in BC cell lines. Further research is needed to fully understand the molecular dynamics involved in the activation and interaction of autophagy and apoptosis pathways in cancer cells in response to potential anticancer agents, like the hydroxy-methylated flavonoids from Ch. tacotana.
Numerical Simulation of the Chemical Reaction on Faraday MHD Accelerator

Magnetohydrodynamic (MHD) is one of the most promising novel propulsion technologies with the advantages of no pollution, high specific impulse, and high acceleration efficiency. As the carrier of this technology, the MHD accelerator has enormous potential for applications in hypersonic wind tunnels, supersonic ramjet engines, and deep space propulsion. In this study, a three-dimensional numerical simulation of an ideal Faraday magnetohydrodynamic (MHD) accelerator is conducted to assess the effect on performance with respect to applied potential and magnetic field intensity. The study is performed by employing a low magnetic Reynolds number MHD model coupled with a 7-component chemical reaction model to simplify the impact of real gas effects. The chemical reaction exhibits an increasing trend with rising applied potential and a decreasing trend with diminishing magnetic field strength. This variation influences the gas conductivity, subsequently affecting the velocity and thrust of the system. Specifically, at a magnetic field intensity of 2.0 T and an applied potential of 600 V, the accelerator exhibits maximum velocity and thrust growth rates of 18.6% and 59.8%, respectively.
Investigating animal activity patterns and temporal niche partitioning using camera‐trap data: challenges and opportunities

Time‐stamped camera data are increasingly used to study temporal patterns in species and community ecology, including species’ activity patterns and niche partitioning. Given the importance of niche partitioning for facilitating coexistence between sympatric species, understanding how emerging environmental stressors – climate and landscape change, biodiversity loss and concomitant changes to community composition – affect temporal niche partitioning is of immediate importance for advancing ecological theory and informing management decisions. A large variety of analytical approaches have been applied to camera‐trap data to ask key questions about species activity patterns and temporal overlap among heterospecifics. Despite the many advances for describing and quantifying these temporal patterns, few studies have explicitly tested how interacting biotic and abiotic variables influence species’ activity and capacity to segregate along the temporal niche axis. To address this gap, we suggest coordinated distributed experiments to capture sufficient camera‐trap data across a range of anthropogenic stressors and community compositions. This will facilitate a standardized approach to assessing the impacts of multiple variables on species’ behaviours and interactions. Ultimately, further integration of spatial and temporal analyses of camera‐trap data is critical for improving our understanding of how anthropogenic activities and landscape changes are altering competitive interactions and the dynamics of animal communities.
The genome sequence of the Straw Underwing, Thalpophila matura (Hufnagel, 1766)

We present a genome assembly from an individual male Thalpophila matura (the Straw Underwing; Arthropoda; Insecta; Lepidoptera; Noctuidae). The genome sequence is 520.4 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.52 kilobases in length. Gene annotation of this assembly on Ensembl identified 19,185 protein coding genes.
A further study on the stability of a baroclinic flow in cyclostrophic balance

Stability of a baroclinic basic flow in gradient wind balance is examined for a very wide range of parameters. When the Rossby number is sufficiently small, it is found that the baroclinic instability occurs not only in the basic flow in geostrophic regime, but also in that in cyclostrophic one. As the Rossby number increases, the baroclinic instability disappears in the nearly geostrophic regime. However, the unstable mode appears again when the dynamic balance changes into the cyclostrophic one; its growth rate is fairly larger than that of the baroclinic instability in the geostrophic regime. It is shown that the baroclinic instability problem in the Venus cloud layer (45–70 km) is not so deviated from that in the terrestrial troposphere, while the cyclostrophic balance is essential in a situation of the Venus lower atmosphere, where the unstable baroclinic wave is predicted to appear.
Parameter-Separable Prox-Lagrangian Method for Convex–Concave Saddle Point Problem

This letter considers the algorithm design and convergence analysis for the non-smooth convex-concave saddle point problem (CCSPP) with a bilinear coupling term. By using a prediction-correction structure and proximal regularizer, we develop a parameter-separable proximal Lagrangian method (Prox-LM) to tackle this problem. Specifically, we obtain a predicted solution from the minimax subproblems that are regularized by a proximal-point term, and then it is further modified in an inertial way to generate a new solution. One distinctive feature of our method is the decoupling of the algorithm parameters from the bilinear term, allowing for independent determination. We demonstrate that our proposed Prox-LM can achieve an $\mathcal {O}(1/{k})$ convergence rate towards the saddle point in an ergodic sense. Numerical experiments on the minimax games and the constrained multiagent problem have verified the effectiveness and performance of our method.
Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling

Background: Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view. Methods: In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation–maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator. Results: The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors. Conclusion: The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.
Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data

Earth system models (ESMs) have been widely used for projecting global vegetation carbon dynamics, yet how well ESMs performed for simulating vegetation carbon density remains untested. We compiled observational data of vegetation carbon density from literature and existing data sets to evaluate nine ESMs at site, biome, latitude, and global scales. Three variables—root (including fine and coarse roots), total vegetation carbon density, and the root:total vegetation carbon ratios (R/T ratios), were chosen for ESM evaluation. ESM models performed well in simulating the spatial distribution of carbon densities in root (r = 0.71) and total vegetation (r = 0.62). However, ESM models had significant biases in simulating absolute carbon densities in root and total vegetation biomass across the majority of land ecosystems, especially in tropical and arctic ecosystems. Particularly, ESMs significantly overestimated carbon density in root (183%) and total vegetation biomass (167%) in climate zones of 10°S–10°N. Substantial discrepancies between modeled and observed R/T ratios were found: the R/T ratios from ESMs were relatively constant, approximately 0.2 across all ecosystems, along latitudinal gradients, and in tropic, temperate, and arctic climatic zones, which was significantly different from the observed large variations in the R/T ratios (0.1–0.8). There were substantial inconsistencies between ESM‐derived carbon density in root and total vegetation biomass and the R/T ratio at multiple scales, indicating urgent needs for model improvements on carbon allocation algorithms and more intensive field campaigns targeting carbon density in all key vegetation components.
Glider data collected during the Algerian Basin Circulation Unmanned Survey

Abstract. We present data collected in the framework of the Algerian BAsin Circulation Unmanned Survey (ABACUS) project. The main objective of ABACUS is the monitoring of the basin circulation and of the surface and intermediate water masses' physical and biological properties in a key region of the Mediterranean Sea circulation. Data presented here have been collected through deep glider cruises in the Western Mediterranean Sea during the autumns of 2014, 2015 and 2016; activities at sea are expected to be repeated during the coming years, so that the dataset will be extended. Glider missions were conducted in the Algerian Basin, between the island of Mallorca and the Algerian coast. Across the three glider missions, eight repeated transects were completed which enabled us to investigate the basin-scale circulation and the presence of mesoscale structures, utilising both the adaptive sampling capabilities of the gliders and the higher resolution of the data. After collection, all data passed a quality control procedure and were then made available through an unrestricted repository host by the SOCIB Data Centre at https://doi.org/10.25704/b200-3vf5. The actual dataset spans three autumn seasons, providing an important contribution to the data collection in the chronically undersampled Algerian Basin. Temperature and salinity data collected in the first 975 m of the water column allowed us to identify the main water masses and describe their characteristics, improving the understanding of the dynamics of the region. On the timescale of the project, data show a large variability in the surface layer and reduced variability in the intermediate and deep layers. Our measurements have been successfully compared to data previously collected in the area from 1909 to 2011. Results showed similar overall distribution, ranges and variability to the historical data, with no outliers in the surface or deep layers.
Efficient Joint Optimization of Sampling Rate Offsets Using Entire Multichannel Signal

In this paper, we propose a joint estimation method for the sampling rate offsets (SROs) of multiple recording devices. In wireless acoustic sensor networks, distributed microphones are connected to different analog-to-digital converters, and thus SROs occur on non-reference channels, which degrades the performance of various array signal processing techniques. To address this problem, we propose to jointly estimate and compensate SROs of all the non-reference channels. Since the proposed method is formulated as a multivariate non-convex optimization problem, we derive an efficient optimization algorithm on the basis of the majorization-minimization and majorization-equalization algorithms. We further propose to update SROs with only low-frequency components in the initial iterations to avoid undesired local optima. Our experimental results validate the effectiveness of the joint estimation of all SROs and demonstrate its advantage in subsequent array signal processing.
Simulation and optimization of cooling water systems with closed wet cooling tower based on reduced order model

This study presents a CFD (computational fluid dynamics)-assistant multi-scale modeling and optimization strategy for synthesizing cooling water systems which incorporates multiple closed wet cooling towers (CWCTs) installed with plain tubes. The utilization of CFD is capable of providing accurate and visual results with respect to complex thermal and fluid flow phenomena that can be used to explore the synergistic mechanism of multi-parameter for CWCTs. Based on CFD results, a systematic reduced order model (ROM) for CWCTs is developed by closely approximating the high-fidelity CWCTs models through integrating principal component analysis (PCA) with Kriging-based surrogate model. This ROM is accessible to the desired result through input data without accounting for the complex geometries and detailed thermal-hydraulics property models. The proposed CFD-assistant ROM for the CWCTs is applied to enhance solution convergence and obtain optimal results of cooling water systems. An industrial case study is used to demonstrate the superiority of the proposed CFD-assistant strategy for the cooling water system synthesis and optimization.