Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing the quality and technological properties of goose meat during low-temperature storage through the action of biologically active substances from oats and alfalfa

Poultry meat plays a key role as a source of high-quality protein, vitamins, minerals, and unsaturated fatty acids. However, unsaturated fatty acids, especially essential ones, are prone to oxidative reactions during meat storage, which can negatively affect its quality. The object of the study is the technology of obtaining and storing poultry meat. Legart Danish geese were used as the experimental material. Oats (Avena Sativa) and alfalfa (Medicago sativa) contain a large number of biologically active substances (minerals, unsaturated fatty acids, amino acids, antioxidants). Adding vegetative parts of oats and alfalfa to the diet of geese contributes to improving the quality of the obtained meat, especially after prolonged low-temperature storage. The geese were slaughtered on the 63rd day. The meat was stored at a temperature of –18 °C for 90 days. It was found that there was an 11.5 % increase in the live weight of the geese at an early slaughter age, an increase in protein content (by 5 %), better moisture-binding capacity (by 6–7.3 %), and a decrease in the content of lipid peroxidation products, especially on the 67th day of storage (28.3 %). There was a significant increase in the content of ω3 and ω6 polyunsaturated fatty acids (by 24.2 % and 10.8 %, respectively). There was an increase in the content of vitamin E and β-carotene, both before freezing (38.5 % and 19.6 %) and at the end of the storage period (50.9 % and 20 %). A tendency to increase the content of essential amino acids (threonine and methionine) was found. The results can be used in the production of goose meat to improve its nutritional characteristics, which is important for the health of consumers, meat producers, and also in scientific research on the development of technologies in the field of meat production and storage
AN ANALYSIS OF NEIGHBOURHOOD TYPES FOR POINTNET++ IN SEMANTIC SEGMENTATION OF AIRBORNE LASER SCANNING DATA

Abstract. The objective of the study is to conduct a comprehensive examination of how different neighbourhood types, namely spherical, cylindrical, and k-nearest neighbour (kNN), influence the feature extraction capabilities of the PointNet++ architecture in the semantic segmentation of Airborne Laser Scanning (ALS) point clouds. Two datasets are utilized for semantic segmentation analysis: the Dayton Annotated LiDAR Earth Scan (DALES) and the ISPRS 3D Semantic Labelling Benchmark datasets. In the experiments, the kNN method exhibited approximately 1% higher accuracy in weighted mean F1 and intersection over union (IoU) metrics compared to the spherical and cylindrical neighbourhood types on the DALES dataset. However, in the generalization experiment conducted on the ISPRS dataset, the spherical neighbourhood achieved the best results in these metrics, outperforming the cylindrical neighbourhood by a small margin. Notably, the kNN method was the least accurate, with a decrease in accuracy of approximately 1% in both weighted mean IoU and F1 scores. These findings suggest that the features extracted from spherical and cylindrical neighbourhood types are more generalizable compared to those from the kNN method.
The First TESS Self-lensing Pulses: Revisiting KIC 12254688

We report the observations of two self-lensing pulses from KIC 12254688 in Transiting Exoplanet Survey Satellite (TESS) light curves. This system, containing an F2V star and white-dwarf companion, was among the first self-lensing binary systems discovered by the Kepler Space Telescope over the past decade. Each observed pulse occurs when the white dwarf transits in front of its companion star, gravitationally lensing the star’s surface, thus making it appear brighter to a distant observer. These two pulses are the very first self-lensing events discovered in TESS observations. We describe the methods by which the data were acquired and detrended, as well as the best-fit binary parameters deduced from our self-lensing+radial velocity model. We highlight the difficulties of finding new self-lensing systems with TESS, and we discuss the types of self-lensing systems that TESS may be more likely to discover in the future.
Influences of Pressure and Green Supply Chain Management on Sustainable Performance of Green Manufacturing Industry in Thailand

Introduction: Green industries play an important role in economic and social development. The market situation is an important factor for the organizations to be developed with regard to technology, regulations of the government, stakeholders, and the competitors, which all affect the revenue of the companies that are faced with the pressure. Thus, the companies implement the Green Supply Chain Management to obtain advantages over their competition. The Green Supply Chain Management increases the competitiveness and resolves the trade dispute issues. The Green Supply Chain Management is a convergence of the elements of environmental management and supply chain management of the organizations that Thai manufacturers cannot avoid. Objective: This study aims to develop the pressure model and green supply chain management that affect the sustainable performance of green industries in Thailand Method: The study uses a Quantitative Research method, and the data was collected using a survey of 250 factory managers as well as analysis with the structural equation model (SEM). Results: The findings reveal that Pressure has a positive direct effect on sustainability performance and Green Supply Chain Management. And, Green Supply Chain Management (GSCM) has a positive influence on sustainability performance. Conclusion: Pressure and Green Supply Chain Management positively impact the Green Manufacturing Industry's sustainability performance. Furthermore, Market Needs, Regulations, Stakeholders, and Competitors have a significant impact on the improvement of the competitiveness of organizations. They also promote social and community responsibility throughout the supply chain, enhancing the competitiveness and sustainability of the Green Manufacturing Industry in Thailand in order to develop sustainable economic and social environments.
Theoretical Modelling and Experimental Studies of Synchrotron Radiation Induced Molecular Dissociation Processes

In this thesis, synchrotron radiation induced fragmentation of selected small molecules was studied by means of energy-resolved photoelectron photo-ion coincidence technique and computational quantum chemistry methods. The measurements were performed on gas phase molecules acrylonitrile (C3H3N), thymine (C5H6N2O), uracil (C4H4N2O2), 5-Bromouracil (C4H3BrN2O2), D-ribose (C5H10O5), 2-deoxy-D-ribose (C5H10O4), methionine (C5H11NO2S) and selenium (Sen) microclusters. The ab initio methods applied in the research include Hartree-Fock, post Hartree-Fock, density functional theory, time-dependent density functional theory (TDDFT), TDDFT-based Ehrenfest molecular dynamics, Born-Oppenheimer molecular dynamics and CarParrinello molecular dynamics. The principal contribution of the author is in theoretical modelling and experimental data analysis.
Measuring 'Dark Matter' in Asset Pricing Models

We formalize the concept of “dark matter” in asset pricing models by quantifying the additional informativeness of cross‐equation restrictions about fundamental dynamics. The dark‐matter measure captures the degree of fragility for models that are potentially misspecified and unstable: a large dark‐matter measure indicates that the model lacks internal refutability (weak power of optimal specification tests) and external validity (high overfitting tendency and poor out‐of‐sample fit). The measure can be computed at low cost even for complex dynamic structural models. To illustrate its applications, we provide quantitative examples applying the measure to (time‐varying) rare‐disaster risk and long‐run risk models.
Clam Peptides Attenuated Adenine-Induced Chronic Kidney Disease by Improving Inflammation, Oxidative Stress, and Intestinal Flora Composition

Previously, we have prepared clam peptides (RBPs) that exhibited potential antihypertension and kidney protective effects in SHR rats by fermentation of Ruditapes philippinarum with Bacillus natto. In this study, we established an adenine-induced chronic kidney disease (CKD) in SD rats and investigated the improvement effects of RBPs on CKD. The results showed that compared with the model group, 8 weeks gavage of RBPs restored the structure of the kidney and reduced serum levels of CKD-related biochemical indicators, including NGAL, CR, Ca, UA, TG, IP, L-FABP, BUN, CHO, and cystatin C (p<0.05). RBPs treatment also reduced serum levels of MDA, IL-1β, TGF-β1, and TNF-α and increased those of CAT and T-AOC (p<0.05). In addition, the results of intestinal microbiota analysis showed that RBPs improved the intestinal flora composition by downregulating the proportion of Firmicutes and Bacteroidetes, decreasing the relative abundance of Romboutsia, Ruminococcaceae_UCG-005, and Christensenellaceae_R-7_group and increasing that of Lactobacillus and Muribaculaceae (p<0.05). The results of this study indicated that RBPs can also be used as bioactive substances in marine drugs or functional foods to alleviate CKD.
Frequency Stability Control Strategy for Voltage Source Converter-Based Multi-Terminal DC Transmission System

The voltage source converter-based multi-terminal DC transmission (VSC-MTDC) system can use additional frequency control to respond to the frequency change of faulty AC system. However, the control coefficient of traditional additional frequency control is mostly fixed, and the control flexibility is insufficient, so it cannot be adjusted adaptively according to the frequency change of the system. Therefore, a frequency control strategy of the VSC-MTDC system based on fuzzy logic control is proposed. Based on the DC voltage slope controller, this strategy introduces an additional frequency controller based on fuzzy logic control, takes the frequency deviation and frequency change rate as the additional controller input, and dynamically adjusts the control quantity through the fuzzy logic control link to realize the adaptive adjustment of the VSC-MTDC system to the AC system’s frequency. Finally, a three-terminal flexible HVDC system is built on the PSCAD/EMTDC simulation platform for simulation verification. The results show that the proposed control strategy can effectively use the flexible DC system to support the frequency of the AC system and significantly improve the frequency stability of the faulty AC system.
Classification of pedogenic cementation in Podzols by pocket penetrometry

According to the German Soil Taxonomy, the formation of cemented horizons in Podzols is restricted to the precipitation of iron oxides. However, in iron-poor sandy substrates, also illuviation of only organic compounds can form cemented horizons with penetration resistances of up to 14 kg cm−2. We present a reproducible field test for classifying pedogenic cementation in Podzols and suggest considering cemented horizons (Bmh and Bhm) in the upcoming edition of the Guidelines for Soil Mapping.
QPASE: Quantum-Resistant Password-Authenticated Searchable Encryption for Cloud Storage

—Searchable encryption is a powerful tool that 1 enables secure and private searches of encrypted data. It allows 2 users to outsource their data to cloud servers while maintaining 3 the confidentiality and privacy of their data. Password-4 authenticated symmetric searchable encryption (PASE) can help 5 users avoid the complexity and security risks associated with 6 key management while maintaining the advantages of searchable 7 encryption. To the best of our knowledge, none of the existing 8 PASE schemes can resist security threats in the post-quantum era, 9 and there is an urgent need to design quantum-resistant solu-10 tions. However, post-quantum cryptography (e.g., lattice-based 11 cryptography) varies significantly from traditional cryptography, 12 and it is challenging to design a quantum-resistant PASE for 13 cloud storage. In this work, we take the first step towards this 14 challenge by proposing QPASE, a quantum-resistant password-15 authenticated symmetric searchable encryption for cloud storage. 16 We employ lattice-based threshold oblivious pseudorandom func-17 tion to achieve password re-randomization and formally prove 18 that QPASE is authentication secure and indistinguishability 19 against chosen keyword attacks secure under quantum com-20 puters. QPASE can be extended to multi-keyword search and 21 allows servers to update keys without affecting the users. The 22 comparison results show that QPASE outperforms its foremost 23 counterparts in security and computation overhead. 24
Improved Grid System Control Using Chaotic PSO-Based PR Controller and Modified SEPIC Topology

- To meet the increasing demand for electricity, it is necessary to use both conventional and unconventional power sources. One type of Renewable Energy Source that turns solar radiation into electricity is a PV system. However, a combination of low irradiance and bad weather prevents installed Photovoltaic (PV) sources from being used to their maximum capacity. PV systems connected to the utility grid supply power to the grid or receive power from the grid depending on the demand and availability of solar energy. As a significance, this research offers an MPPT technique based on ANN to acquire PV power quickly and to the maximum amount possible with zero oscillation tracking. Moreover, a modified SEPIC converter is used to increase the voltage from PV reduced switch 21 level Multi Level Inverter (MLI) changes the DC output of PV modules into alternating current with an increased number of output levels that is compatible with the grid. A chaotic Particle Swarm Optimization (PSO) Proportional Resonant (PR) controller is used to control MLI efficiently, leading to improved AC supply and efficient grid synchronization. MATLAB validation demonstrates the intended system’s performance, which reveals enhanced converter efficiency with a lower Total Harmonic Distortion (THD) in contrast to state-of-the-art techniques.
Trend Analysis of Changes in Soil Moisture from the Different Depths in the Martew Forestry

The paper presents trends in average annual soil moisture in the area covered by the sixty-year old pine stand. The area of analysis is located in the Tuczno Forest District, the Martew Forestry, in the north-western part of Poland. The calculations were based on the measurements of soil moisture at seven different depths below the ground level. A set of probes was installed for measuring the moisture using TDR method in order to calculate retention changes in the unsaturated zone. Humidity measurements used in this work were carried out at daily intervals throughout the year 2014. Designated trends were analysed using the non-parametric Mann – Kendall test, which is used for detecting trend of hydrological and meteorological parameters. Negative values of the Mann-Kendall statistics point to a declining trend for most – six out of the seven, analysed levels of measurement. The tendency toward dryness was not confirmed by the data set of precipitations. Although precipitations are considered to be the main driver of variations in soil moisture their impact severity seems to be controlled by other characteristics of the catchment area and components of the water balance, such as evapotranspiration.
DDoS Attacks Detection in IoV using ML-based Models with an Enhanced Feature Selection Technique

—The Internet of Vesicles (IoV) is an open and integrated network system with high reliability and security control capabilities. The system consists of vehicles, users, infrastructure, and related networks. Despite the many advantages of IoV, it is also vulnerable to various types of attacks due to the continuous and increasing growth of cyber security attacks. One of the most significant attacks is a Distributed Denial of Service (DDoS) attack, where an intruder or a group of attackers attempts to deny legitimate users access to the service. This attack is performed by many systems, and the attacker uses high-performance processing units. The most common DDoS attacks are User Datagram Protocol (UDP) Lag and, SYN Flood. There are many solutions to deal with these attacks, but DDoS attacks require high-quality solutions. In this research, we explore how these attacks can be addressed through Machine Learning (ML) models. We proposed a method for identifying DDoS attacks using ML models, which we integrate with the CICDDoS2019 dataset that contains instances of such attacks. This approach also provides a good estimate of the model’s performance based on feature extraction strategic, while still being computationally efficient algorithms to divide the dataset into training and testing sets. The best ML models tested in the UDP Lag attack, Decision Tree (DT) and Random Forest (RF) had the best results with a precision, recall, and F1 score of 99.9%. In the SYN Flood attack, the best-tested ML models, including K-Nearest Neighbor (KNN), DT, and RF, demonstrated superior results with 99.9% precision, recall, and F1-score.
Application analysis of financial data mining in investment decision

Amidst the escalating complexities that define the contemporary financial market and the rapid proliferation of information, traditional methods of formulating investment decisions confront increasingly formidable challenges. In response to these intricate dynamics, the realm of financial data mining has emerged as a prominent avenue of scholarly investigation within the investment domain. This paper's fundamental objective is to conduct a comprehensive retrospective analysis of the diverse applications of financial data mining in the context of investment decision-making.This scholarly pursuit entails a meticulous synthesis of existing academic inquiries, concurrently proposing potential avenues for future advancements in this field. By undertaking this academic endeavor, the paper strives to make substantive contributions to the refinement of methodologies essential for adeptly navigating the multifaceted landscape of modern investments. As the financial landscape continues to evolve, this study aspires to offer insights that not only enhance the efficacy of investment strategies but also foster a deeper understanding of the intricate interplay between data mining techniques and decision-making processes. Through the synthesis of empirical findings and theoretical perspectives, this paper seeks to underscore the pertinence of leveraging data-driven approaches in investment practices, thereby promoting a more informed and sophisticated investment landscape.
Feature Block-Aware Correlation Filters for Real-Time UAV Tracking

Recently, by virtue of the high computational efficiency and accuracy, discriminative correlation filter (DCF)-based tracking methods have gained attraction in the field of unmanned aerial vehicle (UAV). However, conventional DCF-based methods merely rely on cyclic shift to produce training samples. As a result, the filter trained by these samples owns limited discriminative ability, ineffectively addressing various challenges in the tracking stage. Here, to promote the filter's discriminative ability, we develop a feature block-aware correlation filter (CF) method. Specifically, the extracted feature is divided into two blocks, i.e., target and background feature blocks. These blocks only contain target and background features, respectively, by using different mask matrixes. Then, two regularization terms are proposed to combine both feature blocks into the DCF framework. In addition, we employ effective channel reliability weights to generate target response for precise positioning. Furthermore, substantial experiments have been accomplished on multiple public UAV benchmarks, proving that our tracker possesses superior tracking capabilities and operates at $\sim$40 frames per second (FPS) on the CPU platform.
Development and Evaluation of Two-Phase Gel Formulations for Enhanced Delivery of Active Ingredients: Sodium Diclofenac and Camphor

The formulation of biphasic gels as potential semi-solid carriers for hydrophilic and lipophilic active substances is promising for the development of pharmaceutical preparations. The aim of this study was to design a stable bigel composition and to determine the influence of the organogel/hydrogel ratio on the gel’s physical-chemical and structural-mechanical properties. The investigated compositions of organogel/hydrogel remained stable at ratios ranging from 5/95 to 40/60. After texture and microstructure analysis, bigels with 20/80 and 25/75 ratios were selected as carriers for the active ingredients, sodium diclofenac and camphor, for use as topical preparations for the treatment of muscle-joint inflammation and pain. Although other researchers have published data on the preparation and evaluation of bigels, there are no scientific results on the development of a two-phase gel with our proposed combination of APIs. Sodium diclofenac release was found to be higher when combined with camphor, which revealed the advantages of the biphasic formulation. The pseudoplastic behavior, thixotropy, and thermal stability of flow of the studied bigel samples was investigated by rheological analysis. Ongoing stability studies confirmed the minimal 6-month period.
Detection of Personal Protective Equipment (PPE) using an Anchor Free-Convolutional Neural Network

—I n industrial environments, the utilization of Personal Protective Equipment (PPE) is paramount for safeguarding workers from potential hazards. While various PPE detection methods have been explored in the literature, deep learning approaches have consistently demonstrated superior accuracy in comparison to other methodologies. However, addressing the pressing research challenge in deep learning-based PPE detection, which pertains to achieving high accuracy rates, non-destructive monitoring, and real-time capabilities, remains a critical need. To address this challenge, this study proposes a deep learning model based on the Yolov8 architecture. This model is specifically designed to meet the rigorous demands of PPE detection, ensuring accurate results. The methodology involves the creation of a custom dataset and encompasses rigorous training, validation, and testing processes. Experimental results and performance evaluations validate the proposed method, illustrating its ability to achieve highly accurate results consistently. This research contributes to the field by offering an effective and robust solution for PPE detection in industrial environments, emphasizing the paramount importance of accuracy, non-destructiveness, and real-time capabilities in ensuring workplace safety.