Miracle-dz/UniFAR-FTUs huggingface dataset:
A study on the inﬂuence of inorganic ions, organic carbon and microstructure on the hygroscopic property of soot

Soot is a crucial component of aerosols in the atmosphere. Understanding the hygroscopicity of soot particles is important for studying their role as cloud condensation nuclei (CCN) as well as their chemical behavior and atmospheric lifetime. However, there is still a lack of comprehensive understanding regarding the factors that determine the hygroscopic properties of soot. In this work, the hygroscopic behavior of soot particles generated from different types of fuel combustion and aged with SO 2 for varying durations was measured by a vapor sorption analyzer. Various characterizations of soot were conducted to understand the key factors that inﬂuence the hygroscopic properties of soot. It was found that water-soluble substances in soot facilitate the completion of monolayer water adsorption at low relative humidity and increase the number of
Financial Risk Evaluation of Real Estate Enterprises under Supply-Side Reform

: In recent years, driven by supply-side reform and deleveraging and other macro policies, real estate enterprises are faced with great financial risks due to their low capital turnover, significant debt ratio and unbalanced cash flow. This paper selects 65 A-share listed real estate companies as samples, constructs A comprehensive evaluation model of real estate enterprises' financial risks based on factor analysis method, and expects to evaluate and prevent the financial risks of real estate enterprises from both theoretical and practical perspectives, so as to provide some guidance and suggestions for the healthy and orderly development of real estate enterprises.
Evaluation of ﬁve models for constructing forest NPP–age relationships in China based on 3121 ﬁeld survey samples

Forest net primary productivity (NPP), representing the net carbon gain from the atmosphere, varies signiﬁ-cantly with forest age. Reliable forest NPP–age relationships are essential for forest carbon cycle modeling and prediction. These relationships can be derived from forest inventory or ﬁeld survey data, but it is unclear which model is the most effective in simulating forest NPP variation with age. Here, we aim to establish NPP–age relationships for China’s forests based on 3121 ﬁeld survey samples. Five models, including the semi-empirical mathematical (SEM) function, the second-degree polynomial (SDP) function, the logarithmic ( L ) function, the Michaelis–Menten ( M ) function, and the (cid:48) function, were compared against ﬁeld data. Results of the comparison showed that the SEM and (cid:48) functions performed much better than the other three models, but due to the limited ﬁeld survey samples at old ages, the (cid:48) function showed a sharp decrease in NPP (decreased to almost zero) at old ages when building some forest NPP–age curves, while SEM could capture the variations in forest NPP at old ages reasonably well. Considering the overall performance
VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks

Neural network repair aims to fix the ‘bugs’ 1 of neural networks by modifying the model’s architecture or parameters. However, due to the data-driven nature of neural networks, it is difficult to explain the relationship between the internal neurons and erroneous behaviors, making further repair challenging. While several work exists to identify responsible neurons based on gradient or causality analysis, their effectiveness heavily rely on the quality of available ‘bugged’ data and multiple heuristics in layer or neuron selection. In this work, we address the issue utilizing the power of formal verification (in particular for neural networks). Specifically, we propose VeRe, a verification-guided neural network repair framework that performs fault localization based on linear relaxation to symbolically calculate the repair significance of neurons and furthermore optimize the parameters of problematic neurons to repair erroneous behaviors. We evaluated VeRe on various repair tasks, and our experimental results show that VeRe can efficiently and effectively repair all neural networks without degrading the model’s performance. For the task of removing backdoors, VeRe successfully reduces attack success rate from 98.47% to 0.38% on average, while causing an average performance drop of 0.9%. For the task of repairing safety properties, VeRe successfully repairs all the 36 tasks and achieves 99.87% generalization on average.
Determining Marketing Strategy for Coffee Shops with Conjoint Analysis

Coffee has become an important part of any individual nowadays. The purpose of this study is to determine the marketing strategy for the coffee shops. This study uses several coffee attributes such as coffee type, sugar level, temperature, coffee beans, and frappe were analyzed by utilizing a conjoint approach. A questionnaire was utilized for this research which collected 500 respondents. Conjoint analysis showed that sugar level was the most important coffee attribute (43.013%), followed by temperature (37.75%), coffee type (14.701%), coffee beans (2.601%), and frappe (1.936%). Interestingly, cold cappuccino with Arabica coffee bean, less sugar, and with frappe was found to be the best combination for marketing strategy. This study can be utilized for developing the marketing strategy of coffee shops. By Understanding the relationship between the factors, coffee shops can find out which factors influence customers the most.
Research on Power Equipment Detection Method Based on Improved YOLOv5s

The traditional inspection method of electric power equipment mainly relies on manpower, which needs to consume a lot of manpower and material resources. With the rapid development of deep learning, the use of target detection algorithm to detect power towers, poles, nails, power engineering cars and insulator equipment has become the main way of inspection. Aiming at the problems of low accuracy and poor real-time performance of traditional target detection algorithms for small targets, an improved algorithm model based on YOLOv5s is proposed in this paper. First, the backbone layer of YOLOv5s is improved, and the original Spatial Pyramid pool (SPP) module of YOLOv5s is adjusted to Spatial Pyramid Pool-Plus (SPPP) module, which has better feature extraction while reducing parameters. Then, the original C3 module of YOLOv5s backbone layer is adjusted to C2f module to obtain more abundant gradient flow information. Finally, a small target detection layer of 160×160 is added to improve the detection performance of the model. The experimental results show that the average accuracy of validation set and test set is increased by 2.25% and 3.1% respectively compared with the original algorithm, and the detection frame rate reaches 120.94 fps.
Opleiding Bioinformatica 3D mesh optimization strategy for images acquired from Bioimaging

The analysis of biological structures on a macroscopic and microscopic scale can lead to new insights in the biology research field. By gathering bioimages from microscope systems, extracting the point cloud and converting it to a surface representation, computerized three-dimensional (3D) models can be generated for measurement and visualisation. In this paper, a pipeline is proposed to improve the quality of the 3D mesh derived from 3D bioimages. This will in turn increase the accuracy of the 3D representation. For this research, a zebrafish embryo model and a mouse mammary gland model are used. The contour stack derived from the raw image data is first aligned and then converted to an oriented point cloud. Three surface reconstruction algorithms (Screened Poisson, Alpha Shapes and Ball Pivoting) are then used to generate the 3D models. The results are compared to determine which algorithm can be best used for the pipeline. Looking at the zebrafish embryo it can be concluded that the model generated by the Screened Poisson method adheres to the evaluation criteria the most. Aside from creating a smooth watertight surface, the model contains the most preserved details. Due to inconsistent orientation of the point cloud normals, the mammary gland model could not be constructed optimally for two of the three algorithms. This is a point of improvement that can be worked on in future development of the pipeline.
Mapping the spatial distribution of tropospheric ozone and exploring its association with elevation and land cover over North Jordan

Abstract This study aims to map the spatial distribution of tropospheric ozone (O3) in the northern parts of Jordan and explore the types of the relationships between the observed spatial patterns and elevation and land cover. Summer and daytime tropospheric O3 data were procured following a stratified random sampling strategy. The collected data were corrected for temporal variations using piecewise linear regression. Empirical Bayesian Kriging showed that tropospheric O3 concentrations are high in the west, where agricultural and urban areas dominate, relative to the east, where rangelands and barren lands dominate. One-way analysis of variance followed by means comparison using Tukey–Kramer HSD. test emphasized the previous observation suggesting a proportional relationship between agricultural and urbanization activities and high tropospheric O3 concentrations. However, simple linear regression, polynomial regression, and geographically weighted regression showed that the relationship between elevation and tropospheric O3 is complex and not clear and not amenable to direct explanation.
FEASE: Fast and Expressive Asymmetric Searchable Encryption

Asymmetric Searchable Encryption (ASE) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform keyword searches over encrypted data for users. To be useful, an ASE scheme must support expressive search queries, which are expressed as conjunction, disjunction, or any Boolean formulas. In this paper, we propose a fast and expressive ASE scheme that is adaptively secure, called FEASE. It requires only 3 pairing operations for searching any conjunctive set of keywords independent of the set size and has linear complexity for encryption and trapdoor algorithms in the number of keywords. FEASE is based on a new fast Anonymous Key-Policy Attribute-Based Encryption (A-KP-ABE) scheme as our first proposal, which is of independent interest. To address optional protection against keyword guessing attacks, we extend FEASE into the first expressive Public-Key Authenticated Encryption with Keyword Search (PAEKS) scheme. We provide implementations and evaluate the performance of all three schemes, while also comparing them with the state of the art. We observe that FEASE outperforms all existing expressive ASE constructions and that our A-KP-ABE scheme offers anonymity with efficiency comparable to the currently fastest yet non-anonymous KP-ABE schemes FAME (ACM CCS 2017) and FABEO (ACM CCS 2022).
Camera Autocalibration Methods Using Modern Approaches in Deep Learning

Single image calibration is the problem of predicting the camera parameters from one image. This problem is of importance when dealing with images collected in uncontrolled conditions by non-calibrated cameras, such as crowd-sourced applications. [2] It is also a fundamental step for numerous applications, such as 3D reconstruction, image undistortion, augmented reality and camera motion estimation. Existing calibration methods require multiple images of a calibration pattern (typically a checkerboard), assume the presence of lines, require manual interaction and/or need an image sequence. [1] Our networks automatically estimate the intrinsic parameters of the camera (focal length and distortion parameter) from a single input image. For training the CNN, we leverage a relatively small amount of images available on the Internet to automatically generate a dataset. Experiments successfully demonstrated the quality of our results, both quantitatively and qualitatively. In this project we evaluate several proposed automatic deep learning-based approaches that overcome all these limitations and work with a single image of general scenes. [2]
Yield and Water Use Efﬁciency of Drip Irrigation of Pepper

: Drip irrigation is gaining importance in mitigating the consequences of water scarcity even in regions with abundant rainfall. The transition from surface to subsurface drip irrigation is accompanied by numerous problems. To overcome these issues, shallow subsurface drip irrigation can be potentially used as an effective drought control tool that brings additional beneﬁts compared to conventional surface drip irrigation techniques. This research investigated the effects of different calculations of daily crop water requirements, reference evapotranspiration (ETo), and pan evaporation (Eo) on the yield and water use efﬁciency of pepper irrigated with a surface and shallow subsurface drip irrigation system. The experiment was conducted in ﬁeld conditions in the Vojvodina region, the northern part of Serbia. The irrigation scheduling was based on the water balance approach. The calculated evapotranspiration rate was about 400 mm for the pepper growing period, regardless of the calculation method. The highest yield of pepper and evapotranspiration water use efﬁciency was obtained on the Eo variant with surface drip irrigation. However, irrigation water use efﬁciency showed no statistical signiﬁcance concerning the calculation of evapotranspiration and irrigation type. The results indicated that both calculation methods and irrigation types can be used in pepper production, but priority should be given to pan-evaporation-based calculation.
CORPORATE GOVERNANCE MECHANISM AND EARNING MANAGEMENT: MODERATING ROLE OF CAPITAL STRUCTURE

This study conducted an analysis of the impact corporate governance has on earnings management in the context of manufacturing companies operating in Saudi Arabia. Furthermore, the research investigated potential moderating effects within the correlation between corporate governance and earnings management. Employing a quantitative research approach, data were gathered from the annual reports of 50 manufacturing firms listed on the Saudi Arabia Stock Exchange during the period spanning 2014-2022, utilizing a cross-sectional research design. Panel data analysis techniques, including pooled, random, and fixed effects, were applied using STATA software. The empirical findings of the work presents a positive and statistically significant correlation between corporate governance practices—specifically, board independence, meetings, composition, managerial ownership, and gender diversity—and earning management, aligning with theoretical expectations derived from agency theory. In contrast, institutional ownership and board size exhibited a negative association with earning management. Moreover, the study brought attention to the noteworthy moderating impact of capital structure, where elements such as board size, board meetings, board composition, independence, and managerial ownership interacted synergistically with capital structure, thereby enhancing its efficacy in mitigating earning management. However, certain governance factors, such as gender diversity and institutional
The Role of Shallow Convection over the Tibetan Plateau

AbstractCumulus (Cu) from shallow convection is one of the dominant cloud types over the Tibetan Plateau (TP) in the summer according to CloudSat–CALIPSO observations. Its thermodynamic effects on the atmospheric environment and impacts on the large-scale atmospheric circulation are studied in this paper using the Community Atmospheric Model, version 5.3 (CAM5.3). It is found that the model can reasonably simulate the unique distribution of diabatic heating and Cu over the TP. Shallow convection provides the dominant diabatic heating and drying to the lower and middle atmosphere over the TP. A sensitivity experiment indicates that without Cu over the TP, large-scale condensation and stratiform clouds would increase dramatically, which induces enhanced low-level wind and moisture convergence toward the TP, resulting in significantly enhanced monsoon circulation with remote impact on the areas far beyond the TP. Cu therefore acts as a safety valve to modulate the atmospheric environment that prevents the fo.
A model for the attenuation of peak ground acceleration in New Zealand earthquakes based on seismograph and accelerograph data

A combination of weak-motion velocity data from seismographs and strong-motion acceleration data from accelerographs has been used to model the attenuation of peak ground acceleration (PGA) in New Zealand earthquakes. The resulting model extends the PGA attenuation model of Zhao, Dowrick and McVerry [30] to include the variability of rock strength, and also describes the unusually high attenuation in the volcanic zone of the North Island of New Zealand. Strong-rock sites were found to experience lower PGAs than either weak rock or soil sites for magnitudes below M W 7, and the apparent degree of amplification on going from strong rock to weak rock or soil decreased as the magnitude increased from M W 5 to M W 7. At magnitude 7 the PGAs were very similar for all site classes for source distances up to 100 km. When extrapolated to magnitudes beyond the maximum of the data, M W 7.4, the model predicted that PGAs for strong rock sites were greater than for weak rock or soil sites. The so-called whole Taupo Volcanic Zone was found to provide a good boundary for the zone of high attenuation in the volcanic region of the North Island. The high attenuation was successfully modelled as a simple function of the length of travel path through the zone of high attenuation. Over the effective maximum volcanic path length of about 70 km the extra attenuation resulted in a factor of ten reduction in PGA compared with non-volcanic paths of the same length.
Fast and Simple Au3+ Colorimetric Detection Using AgNPs and Investigating Its Reaction Mechanism

One of the precious metals that has numerous applications is gold. Although it is non-toxic and biocompatible, the oxidized form, Au3+, is toxic and can cause damage to human organs. Detection of Au3+ becomes a necessary and interesting topic to be conducted. Colorimetric analysis using metal nanoparticles such as silver nanoparticles (AgNPs) can analyze metal ions more simply, sensitively, and selectively than traditional methods. In this research, AgNPs were synthesized using polyvinyl alcohol (PVA) and ascorbic acid as stabilizers and reducing agents. The interaction between Au3+ and AgNPs selectively decreased the absorbance intensity of AgNPs and altered the color of colloidal AgNPs from yellow to colorless. These two phenomena indicated a redox reaction between Au3+ and AgNPs, leading to the decomposition of AgNPs. The decomposition of AgNPs (the proposed mechanism) was confirmed by TEM images and UV-vis spectra. The decrease in AgNPs’ absorbance intensity correlated linearly with the increase in added Au3+ concentration. The calibration curve of ∆A versus Au3+ ion concentration yielded LOD and LOQ of 0.404 and 1.347 μg/mL, respectively.
Design, Synthesis of New Imidazolium-1,2,3-triazole Hybrid Derivatives as Antimicrobial Agents

New imidazolium salts with 1,2,3-triazole rings (rm1-rm5) were prepared in the current work using a design-driven synthetic procedure scheme. By using analytical techniques such as NMR, IR and spectral information, the chemical structures of target synthesized products were identified using results that were discovered in perfect accord with their assigned structures. The microorganism used in the current study were bacterial strains of Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Bacillus subtilis and Micrococcus luteus, as well as the fungal strains of Aspergillus niger and Candida albicans. All the end products were estimated for their antibacterial activity. The values for the minimum inhibitory concentration (MIC) were confirmed by comparison to the widely used antibiotics fluconazole and ciprofloxacin as control drugs. Particularly, the compounds (rm5) and (rm4) showed observable antibacterial activity. These compounds might offer a brand-new place to start when looking for new antibacterial medications.
AIMS65 Scoring System for Predicting Clinical Outcomes Among Emergency Department Patients with Upper Gastrointestinal Bleeding

Introduction: Several scoring systems were developed for early risk stratification in Upper Gastrointestinal Bleeding (UGIB) patients. AIMS65 score is a scoring system that only consists of five parameters, it might be used in daily clinical practice because of rapid and easy to calculate within 12 hours of admission. Objective: To evaluate the AIMS65 scoring system as a predictor of mortality, rebleeding events, need for endoscopic therapy, blood transfusion, and ICU admission for all causes of UGIB. Methods: We conducted a systematic review on PubMed, ScienceDirect, ProQuest, and Cochrane Library databases from the 2012 to 2022 publication period. We included either prospective or retrospective cohort studies that reported UGIB with all kinds of aetiologies who presented in the emergency department (ED), reported discriminative performance for each outcome, and reported the optimal cut-off of AIMS65. The primary measurement of discriminative performance for clinical outcomes includes mortality, rebleeding incidents, need for endoscopic therapy, blood transfusion, and ICU admission. Results: We identified 351 published studies, of which 20 were included in this study. Most of the studies reported discriminative performance for predicting mortality, which amounts to about 18 out of 20 studies. Rebleeding prediction was reported in 11 studies, need for endoscopic therapy in 5 studies, blood transfusion in 7 studies, and ICU admission in 2 studies. Most of the studies reported fair to excellent discriminative performance for predicting mortality, but in contrast for predicting rebleeding, the need for endoscopic therapy, blood transfusion, and ICU admission. Cut-off values≥ 2 are frequently reported to distinguish between high-risk and low-risk patients in mortality. Conclusion: AIMS65 can be applied to patients with UGIB in ED for predicting mortality, but not applicable for predicting rebleeding events, the need for endoscopic therapy, blood transfusion, and ICU admission. It enhances early decision-making and triage for UGIB patients.
Re-collection of Dermeaprunus in China, with a description of D.chinensis sp. nov.

Abstract Dermea was protected against its synonym, Foveostroma, due to its well-circumscribed generic concept and more frequent use. We describe and illustrate Dermeachinensissp. nov. based on its morphological characteristics and a molecular analysis of the internal transcribed spacer (ITS) and large subunit (LSU) sequence data. Dermeachinensis is isolated from Betulaalbosinensis with sexual and asexual morphs and can be distinguished from D.molliuscula on Betula trees by its aseptate and wider ascospores. The connection between the two morphs is proved based on sequence data. Here, we describe the asexual morph of D.pruni for the first time based on morphological and molecular data from the same host and country of origin, and compare it with other species of Prunus.
A Simple Procedure of Epidural Electrode Lead Replacement through a Tissue Sheath in Spinal Cord Stimulation

Spinal cord stimulation (SCS) is widely performed to treat several types of intractable chronic pain. To maintain lasting SCS, epidural electrode leads must be replaced sometimes due to problems like lead breakage. However, in lead replacement, guiding the new lead to the original position may be difficult because granulation tissue sheath forms around the lead. We encountered a surgical case where we inserted new leads through tissue sheaths forming around the old leads from the epidural space to the thoracolumbar fascia; the lead was smoothly introduced to the original place. This procedure is simpler than previously reported techniques. Here, we report the detailed surgical procedure and review the relevant literature.