Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of nanoparticles in animal models for prostate cancer treatment: a systematic review

ABSTRACT Purpose: To conduct a systematic review of nanoparticles’ use in the treatment of prostate cancer in animals. Methods: A systematic review was conducted in the databases PubMed, Scientific Electronic Library Online (SciELO), Latin American and Caribbean Health Sciences Literature (LILACS), Cochrane Library, and EMBASE, and the descriptors were chosen based on terms indexed in Health Sciences Descriptors (DeCS)/Medical Subject Headings (MESH), which are: nanoparticles, nanomedicine, and prostate cancer. The systematic review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with ID CRD42021271008. Results: A total of 3,897 articles was chosen; after reading the inclusion and exclusion criteria, six scientific articles with themes involving nanoparticles carrying medications were reached. Among the nanoparticles found, there were carboxymethylcellulose polymer, micellar casein nanoparticles, liquid crystal nanoparticles, serum albumin nanoparticles, and poly(ethylene glycol)-block-polylactide (mPEG-PLA) conjugated nanoparticles encapsulating cabazitaxel, docetaxel, and flutamide, which were nanoparticles used to treat prostate cancer in animals. Conclusions: Through using nanoparticles to encapsulate medications for treating prostate cancer in animals, studies show a decrease in weight and tumor reduction, with nanoparticles resulting in greater survival time than free medications. The improved permeability and retention effect of nanoparticles in the bloodstream contribute to their effectiveness.
Assessment of Signal Delay in Digital Twins for Electromechanical and Hydraulic Systems

When making up the mechatronic complexes digital twins (DTs), one should take into account the time delay that occurs during the data exchange between the virtual model of a physical object and the algorithm of the control system being developed. The authors show the diagram of the virtual models configuration on a personal computer (PC) and in the resource of a programmable logic controller (PLC) are given. It is shown that when placing the control program in a PLC, and the DT model in a PC, an additional delay is introduced by the interface between the PLC and the PC. This leads to the distortion of information communication between the systems and disruption of the twinning process. Thus, the assessment of the signal delay in the digital twin structure is a relevant problem. The paper shows an example of the DT implementation in the Matlab Simulink Desktop Real-Time environment and presents a simulation of a conditional object with a proportional controller. The analysis of transients at the different gain coefficients of the controller is provided. It is shown that when the controller coefficients correspond to the optimal process without delays, the delay in signal transmission causes an increase in the system oscillatory properties. The article considers the determination of the delay time by estimating the time shift of the sawtooth signal entering the PLC input and its response after “returning” from the PC. To account for the delay, it is proposed to include additional modeling links in the system structure.
Radial Oxygen Loss from the Roots of Mangrove Seedlings Enhances the Removal of Polycyclic Aromatic Hydrocarbons

The presence of polycyclic aromatic hydrocarbons (PAHs) in soil poses a significant global environmental concern, particularly in coastal wetlands. Mangrove ecosystems exhibit enormous potential in environmental purification; however, the underlying mechanisms involved in the degradation of pollutants (e.g., PAHs) remain ambiguous. In the present investigation, a soil pot experiment was conducted with the addition of pyrene to evaluate the effect of radial oxygen loss (ROL) from roots on PAH degradation using three mangrove seedlings (Rhizophora stylosa, Aegiceras corniculatum, and Avicennia marina). The results showed that mangrove plantation can significantly promote the efficiency of pyrene removal. As for the three mangrove species studied, the greatest removal rate (90.75%) was observed in the soils associated with A. marina, followed by A. corniculatum (83.83%) and R. stylosa (77.15%). The higher PAH removal efficiency of A. marina can be partially attributed to its distinctive root anatomical structure, characterized by a thin exodermis and high porosity, which facilitates ROL from the roots. The results from qPCR further demonstrate that ROL is beneficial for promoting the abundance of PAH-ring hydroxylating dioxygenase gene, leading to a higher removal efficiency. Additionally, Rhizobiales, Defferrisomatales, and Ardenticatenales may also play important roles in the process of pyrene degradation. In summary, this study provides evidence for elucidating the mechanism of PAH removal from the perspective of ROL, thereby contributing valuable insights for species selection during mangrove restoration and remediation.
Scheduling Optimization of Flexible Job Shop Problem

Flexible job shop scheduling (FJSP) is an important problem in manufacturing systems, and many researchers have used the non-dominated sorting genetic algorithm (NSGA-II) to solve this problem. However, NSGA-II has been criticized for its slow convergence speed and low search efficiency. To address these issues, this paper proposes an optimization algorithm that combines automata with NSGA-II. By using automata, the constraints and limitations of the job shop system can be accurately, intuitively described. By using a self-locking algorithm, the generated automata model is screened for better results, which can be the initial population for NSGA-II. Thus, convergence speed and solution quality are improved. The feasibility of the proposed algorithm is verified by simulating the MK04 example in the Brandimarte benchmark dataset.
Computation Energy Efficiency Maximization for NOMA-Based and Wireless-Powered Mobile Edge Computing With Backscatter Communication

—In the Internet of Things (IoT) environment, a wide variety of mobile devices (MDs) have become part of it, leading to a dramatic increase in the amount of task data. However, due to the limited battery capacity and computing resources of MDs, a lot of effort is required to be taken on how to process more data with less energy. In this paper, we take into account the low utilization of spectrum resources and the short battery life of the equipment, and a backscatter communication-mobile edge computing (BC-MEC) network system based on Non-orthogonal multiple access (NOMA) communication mode is proposed. In order to maximize the computation energy efﬁciency (CEE) of the system, we jointly optimize the backscatter coefﬁcient of each MD, the backscatter communication duration, the direct ofﬂoading duration, the MEC server processing time, the local processing time, the direct ofﬂoading power of each MD, the calculation frequency of the MEC server, and the local calculation frequency of each MD. We then formulate it as a joint fractional optimization problem, which is a non-convex optimization problem that is difﬁcult to solve by heuristic algorithms with high computational complexity. To this end, we transform such a problem into a convex problem and apply the Lagrangian dual method to solve it efﬁciently. Furthermore, in order to meet different user requirements, two effective iterative Dinkelbach algorithms based on Backscatter Coefﬁcient Updates (DBCU) are proposed to solve this problem. Extensive simulation results demonstrate the superiority of our proposed approach, which improves the system CEE by at least 10% compared to state-of-the-art methods.
DNA Barcoding Reveals the First Occurrence of Chrysanthemum Leafminer, Chromatomyia syngenesiae Hardy (Diptera: Agromyzidae) in India

: Chrysanthemum is ravaged by numerous agromyzid leafminers. Of which, Chromatomyia syngenesiae Hardy (Diptera: Agromyzidae) is an invasive polyphagous leafminer which causes heavy infestation by mining the green leaf tissue and weakens the plant by reducing the photosynthetic activity that results in unmarketable flowers. Hitherto, no further information is available from India on C. syngenesiae apart from just new record. The present study was undertaken to assess the damage potential and to identify the leafminer species attacking chrysanthemum. In this view, Survey was conducted in chrysanthemum growing polyhouse of Nilgiris, Tamil Nadu, India during 2016 to 2018. The results indicated that the damage potential caused by C. syngenesiae was 75.40 % and 69.00 % in 2016 to 2017 and 2017 to 2018, respectively. The highest parasitization of Diglyphus isaea (Eulophidae: Hymenoptera) Walker on C. syngenesiae was recorded as 18.27 % and 20.80 % in the respective years. In addition, C. syngenesiae and its parasitoid were characterized at molecular level and species specific DNA barcodes using mitochondrial Cytochrome Oxidase I (mtCOI) were developed. BLAST searching of GenBank showed 100 per cent sequence similarity with C. syngenesiae and clearly indicated the presence of C. syngenesiae at Nilgiris, Tamil Nadu, India. The sequences obtained from the present study was submitted to NCBI database as the first entry for species level representation from India and the accession numbers ( C. syngenesiae -MN969922 and D. isaea - MN525179) were obtained.
Childhood Trauma and Exposure to Violence Interventions: The Need for Effective and Feasible Evidence-Based Interventions

Several crimes in South Africa cause physical, economic, and mental problems. Xenophobic attacks, mob justice, and other violent conduct directly traumatise children. Service delivery riots and physical and sexual abuse are examples. This evaluation evaluates childhood trauma and exposure to violence interventions. The review describes the therapeutic methods for traumatised children exposed to violence, the healthcare professionals administering them, and the strategies used to tailor the interventions. The researcher systematically searched PsycINFO, Google Scholar, PubMed, Science Direct, and EBSCOhost. Literature from 2011 to 31 July 2023 was searched, and 19 papers were chosen for further review after the systematic search. The authors conducted an eligibility evaluation according to PRISMA guidelines. A thorough review of article texts identified 19 papers that met eligibility standards. Only nineteen studies have validated trauma and violence therapies for children. An effective multi-phased intervention that is feasible and adaptable to varied socioeconomic backgrounds is needed. Further studies on the mental health benefits of brief trauma intervention treatment are needed.
Research on the deficit irrigation scheduling to winter wheat at critical period based on crop modeling method

Less water at critical period has significant effect on crop yield. The deficit irrigation scheduling is important especially for the drought-prone area. The crop water production function frequently used for deficit irrigation analysis neither has the disadvantage in mechanism explanation. In this study, we use crop model instead of the crop water production function to study deficit irrigation scheduling issue for winter wheat in Jinan City, in North Plain of China. The crop model we employed is APSIM model. The model was calibrated and validated specifically for winter wheat in Jinan using data in 2000-2013 years. Three typical years were selected to represent the normal, light drought and drought conditions. There are 24 irrigation scheduling schemes obtained by combination of three deficit irrigation quotas, the 150 mm, 100 mm and 50 mm, and different irrigation dates from early April to mid-May. By simulating and comparing the ultimate yield of each irrigation scheduling for winter wheat in Jinan, we strongly suggest that: 1) if the antecedent soil moisture is low, perform irrigation at the beginning of critical period, 2) if it under drought stress, perform irrigation once in early May, and 3) if severe drought hits, perform irrigation twice, and allocating more quota for the second time.
Transport Properties in the Adriatic Sea as Deduced from Drifter Data

Abstract The surface transport properties in the Adriatic Sea, a semienclosed subbasin of the Mediterranean Sea, have been studied using a drifter dataset in the period December 1994–March 1996. Three main points have been addressed. First, the exchange between southern and northern regions and between deep and coastal areas have been studied, focusing on the role of topography. A significant cross-topography or cross-shelf exchange has been found, probably due to the direct wind forcing and to the influence of stratification that isolates the surface flow from bottom effects, especially in the open sea. Second, a Lagrangian transport model with parameters derived from the data has been implemented. Simulated particles have been compared with drifter data with positive results. The model is found to be able to reproduce reality with good approximation, except for a specific advective event during the late summer season. Finally, the residence timescale T, that is, the average time spent by a surface parti.
Process of EUR prediction for shale gas wells based on production decline models—a case study on the Changning block

To effectively develop the shale gas in the southern Sichuan Basin, it is essential to accurately predict and evaluate the single-well production and estimated ultimate recovery (EUR). Empirical production decline analysis is most widely used in predicting EUR, since it is simple and can quickly predict the gas well production. However, this method has some disadvantages, such as many parameters of the model, difficulty in fitting and large deviation. This paper presents an efficient process of EUR prediction for gas wells based on production decline models. Application of nine empirical production decline models in more than 200 shale gas wells in the Changning block of the Sichuan Basin was systemically analyzed. According to the diagnosis of flow regime, it was determined that all models are applicable in the prediction of production and EUR in this area, with the fitting degree higher than 80% for gas wells producing for more than 12 months. Based on the fitting and prediction results, the parameter distribution charts of the nine production decline models with initial parameters constrained were plotted for shale gas wells, which greatly improved the prediction accuracy and efficiency. Coupled with the probability method, the EUR was evaluated and predicted effectively, and the average EUR of more than 200 shale gas wells in the Changning block is 1.21 × 108 m3. The EUR of Well CNH1 predicted by the proposed process and charts is believed reliable. The study results provide meaningful guidance for the efficient prediction of gas well production and EUR in the Changning block.
Synthesizing Diabetic Foot Ulcer Images with Diffusion Model

Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care. However, real DFU datasets are limited, hindering clinical training and research activities. In recent years, generative adversarial networks and diffusion models have emerged as powerful tools for generating synthetic images with remarkable realism and diversity in many applications. This paper explores the potential of diffusion models for synthesizing DFU images and evaluates their authenticity through expert clinician assessments. Additionally, evaluation metrics such as Frechet Inception Distance (FID) and Kernel Inception Distance (KID) are examined to assess the quality of the synthetic DFU images. A dataset of 2,000 DFU images is used for training the diffusion model, and the synthetic images are generated by applying diffusion processes. The results indicate that the diffusion model successfully synthesizes visually indistinguishable DFU images. 70% of the time, clinicians marked synthetic DFU images as real DFUs. However, clinicians demonstrate higher unanimous confidence in rating real images than synthetic ones. The study also reveals that FID and KID metrics do not significantly align with clinicians' assessments, suggesting alternative evaluation approaches are needed. The findings highlight the potential of diffusion models for generating synthetic DFU images and their impact on medical training programs and research in wound detection and classification.
Dating the1000-year-old Qilian J uniper in Mountains along the Eastern Margin of the Qaidam Basin

In this paper, the methods for developing the master chr on ologies for 1000-year-old Qilian Juniper (Sabina przewalskii Kom.) are intr oduced in detail. Since the studied sites are located in the arid re gion of northeast Qinghai-Xizang Plateau, the tree-ring cores sampled c ontain a certain number of missing rings and several false rings. To assure the proper placement in time of each growth layer for the 100 0-year-old trees, some improvements were made in the practice of maste r chronologic development. Firstly, trees with a variety of ages were sampl ed besides very old trees. The large sample depth benefited the identification of missing rings for cores from old trees and avoided the possibility that all collected specimens could be missing a ring for any one year. Secondl y, cores from younger trees and from trees that exhibit a little width variation were selected to do the skeleton plot first and cores wit h many missing rings were done last when the ring-width pattern was known. Thirdly, inferring where rings may be missing was made based o n the agreement among the narrow rings as well as among the wide ri ngs. If the agreement of wide rings is not so good and there were some m issing rings, the chronology from nearby sites was used to infer the year of missing ring occurrence. Finally, several methods, such as the comput er program COFECHA and the line plots were used to examine the datin g and the measurements of ring width for a site. The number of core s without missing rings and time span covered by them were also inspected to avoid the possibility that all collected specimens were added a missing ring for any one year. The Gleichlaufigkeit statistics was used to ex amine the dating agreement between master chronologies of different sites.
Relation-driven Query of Multiple Time Series

Querying time series based on their relations is a crucial part of multiple time series analysis. By retrieving and understanding time series relations, analysts can easily detect anomalies and validate hypotheses in complex time series datasets. However, current relation extraction approaches, including knowledge- and data-driven ones, tend to be laborious and do not support heterogeneous relations. By conducting a formative study with 11 experts, we concluded 6 time series relations, including correlation, causality, similarity, lag, arithmetic, and meta, and summarized three pain points in querying time series involving these relations. We proposed RelaQ, an interactive system that supports the time series query via relation specifications. RelaQ allows users to intuitively specify heterogeneous relations when querying multiple time series, understand the query results based on a scalable, multi-level visualization, and explore possible relations beyond the existing queries. RelaQ is evaluated with two use cases and a user study with 12 participants, showing promising effectiveness and usability.
Causes of poor physical health in mental illness

Almost most mental illness, such as schizophrenia, bipolar disorder, and depression are associated with undue medical morbidity and mortality. It represents a major health problem, with 20 to 30 years shorter lifetime mortality are primarily due to premature cardiovascular disease (myocardial infarction, stroke.). The cardiovascular events are strongly linked to non modifiable risk factors such as age, gender, personal and/or family history, but also to crucial modifiable risk factors, such as overweight and obesity, dyslipidemia, diabetes, hypertension and smoking. Although these classical risk factors exist in the general population epidemiological studies suggest that patients with severe mental illness have an increased prevalence of these risk factors. Another point is the causes of increased metabolic and cardiovascular risk in this population are related to poverty, poor diet, sedentary and compared to the general population. The increased morbidity and mortality limited behaviour access to medical care, but also to the use of psychotropic medication. Over recent years it has become apparent that antipsychotic drugs can have a negative impact on some of the modifiable risk factors.
AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction

Argument generation is a challenging task in natural language processing, which requires rigorous reasoning and proper content organization. Inspired by recent chain-of-thought prompting that breaks down a complex task into intermediate steps, we propose Americano, a novel framework with agent interaction for argument generation. Our approach decomposes the generation process into sequential actions grounded on argumentation theory, which first executes actions sequentially to generate argumentative discourse components, and then produces a final argument conditioned on the components. To further mimic the human writing process and improve the left-to-right generation paradigm of current autoregressive language models, we introduce an argument refinement module which automatically evaluates and refines argument drafts based on feedback received. We evaluate our framework on the task of counterargument generation using a subset of Reddit/CMV dataset. The results show that our method outperforms both end-to-end and chain-of-thought prompting methods and can generate more coherent and persuasive arguments with diverse and rich contents.
Log-based Anomaly Detection of Enterprise Software: An Empirical Study

Most enterprise applications use logging as a mechanism to diagnose anomalies, which could help with reducing system downtime. Anomaly detection using software execution logs has been explored in several prior studies, using both classical and deep neural network-based machine learning models. In recent years, the research has largely focused in using variations of sequence-based deep neural networks (e.g., Long-Short Term Memory and Transformer-based models) for log-based anomaly detection on open-source data. However, they have not been applied in industrial datasets, as often. In addition, the studied open-source datasets are typically very large in size with logging statements that do not change much over time, which may not be the case with a dataset from an industrial service that is relatively new. In this paper, we evaluate several state-of-the-art anomaly detection models on an industrial dataset from our research partner, which is much smaller and loosely structured than most large scale open-source benchmark datasets. Results show that while all models are capable of detecting anomalies, certain models are better suited for less-structured datasets. We also see that model effectiveness changes when a common data leak associated with a random train-test split in some prior work is removed. A qualitative study of the defects’ characteristics identified by the developers on the industrial dataset further shows strengths and weaknesses of the models in detecting different types of anomalies. Finally, we explore the effect of limited training data by gradually increasing the training set size, to evaluate if the model effectiveness does depend on the training set size.