bluuebunny/crossref_metadata_2025 huggingface dataset:
Cuticular Hydrocarbon Profiling of Australian Gonipterini Weevils
Authors: Johnson Joel B.
Published: August 2023

Cuticular hydrocarbon (CHC) profiling shows promise as a chemotaxonomic tool for identifying and discriminating between closely related insect species. However, there have been limited studies using CHC profiling to differentiate between weevil species (Coleoptera: Curculionidae). This proof-of-concept study investigated the use of CHC and volatile profiling to discriminate between five weevil species from three genera in the Gonipterini tribe. A total of 56 CHCs and 41 other volatile compounds were found across the five species, with 83 of the compounds being identified through their mass fragmentation patterns. The number of CHCs from each species ranged from 20 to 43, while the proportion of CHCs unique to each species varied between 0% and 19%. The most abundant CHCs were nonacosane, 7-methylheptacosane, heptacosane, and hexacosane. Principal component analysis of the centred log-ratio transformed data revealed broad differences in CHC profiles between the two Oxyops species, with Bryachus squamicollis demonstrating the greatest divergence from the other Gonipterini species. The results suggest that CHC analysis could be used to support established taxonomic methods, including morphological features and genetic sequencing results.
Application of Fuzzy Simple Additive Weighting Method in Group Decision-Making for Capital Investment
Authors: Aliyeva Kamala , Aliyeva Aida , Aliyev Rashad , Özdeşer Mustafa
Published: August 2023

Investment management is a common process and practice used for achieving a desirable investment goal or outcome. Unfortunately, the systematic variation of economic situations in the marketplace effects the continuous and frequent change of investment conditions and environment in which the investor should act and operate. Hence, the rules required for providing a reasonable quality of investment projects can be based only on the investor’s management strategy, intuition and practice. There exists various decision-making approaches to investment management, and simple additive weighting (SAW) is one of the most well-known multicriteria decision-making (MCDM) methods aiming to provide an optimal decision for the decision-maker when solving various real-life problems, particularly investment problems. In this paper, the fuzzy simple additive weighting (FSAW) method is applied in group decision-making to undertake capital investment expenditure for purchasing cars with the purpose of renting them out to the public. A numerical example illustrates the importance and effectiveness of the suggested approach with the aim of ranking alternatives, and, hence, determining the preferred alternative in the MCDM problem.
Evolution of Safety Behavior of High-Power and High-Energy Commercial Li-Ion Cells after Electric Vehicle Aging
Authors: Kuntz Pierre , Lonardoni Loïc , Genies Sylvie , Raccurt Olivier , Azaïs Philippe
Published: August 2023

The Li-ion battery is one of the key components in electric car development due to its performance in terms of energy density, power density and cyclability. However, this technology is likely to present safety problems with the appearance of cell thermal runaway, which can cause a car fire in the case of propagation in the battery pack. Today, standards describing safety compliance tests, which are a prerequisite for marketing Li-ion cells, are carried out on fresh cells only. It is therefore important to carry out research into the impact of cell aging on battery safety behavior in order to ensure security throughout the life of the battery, from manufacturing to recycling. In this article, the impact of Li-ion cell aging on safety is studied. Three commercial 18,650 cells with high-power and high-energy designs were aged using a Battery Electric Vehicle (BEV) aging profile in accordance with the International Electrotechnical Commission standard IEC 62-660. Several thermal (Accelerating Rate Calorimetry—ARC) and standardized safety (short-circuit, overcharge) tests were performed on fresh and aged cells. This study highlights the impact of aging on safety by comparing the safety behavior of fresh and aged cells with their aging conditions and the degradation mechanisms involved.
Preclinical Studies on the Safety and Toxicity of Photoditazine in the Antibacterial Photodynamic Therapy of Uropathogenic Bacteria
Authors: Streltsova Olga , Antonyan Artem , Ignatova Nadezhda , Yunusova Katerina , Elagin Vadim , Kamensky Vladislav
Published: August 2023

The ‘dusting’ technique of lithotripsy for the removal of infected urinary calculi and the wide use of drainage after endoscopic surgery may stimulate spreading of multidrug-resistant bacterial strains. Antibacterial photodynamic therapy (PDT) is one promising method for the elimination these strains. The purpose of our study was to evaluate alterations of renal pelvis morphology and renal function in laboratory animals after bactericidal regimens of PDT. Renal pelvises of pigs were filled with Photoditazine and then assessed either by examining the accumulation of Photoditazine in the urothelium or by illumination with a laser at a wavelength of 662 nm. A renal test and a complete blood count was performed to assess a negative effect of the treatment on health. Structural alterations of the kidney tissues were analyzed by histological examination. No photosensitizer fluorescence was detected in the urothelium of the pelvis. Histological study showed that PDT caused minor changes to the urothelium of the renal pelvis but did not affect the underlying connective tissue. No renal function abnormalities were found after PDT. Thus, the study indicates that antibacterial PDT is a safety technique that can complement common antibiotic therapy in the surgical treatment of urolithiasis.
Racial Disparity in Anthracycline-induced Cardiotoxicity in Breast Cancer Patients
Authors: Balaji Swetha , Antony Antu K. , Tonchev Harry , Scichilone Giorgia , Morsy Mohammed , Deen Hania , Mirza Imaduddin , Ali Mohamed M. , Mahmoud Abeer M.
Published: August 2023

Breast cancer has become the most common cancer in the US and worldwide. While advances in early detection and treatment have resulted in a 40% reduction in breast cancer mortality, this reduction has not been achieved uniformly among racial groups. A large percentage of non-metastatic breast cancer mortality is related to the cardiovascular effects of breast cancer therapies. These effects appear to be more prevalent among patients from historically marginalized racial/ethnic backgrounds, such as African American and Hispanic individuals. Anthracyclines, particularly doxorubicin and daunorubicin, are the first-line treatments for breast cancer patients. However, their use is limited by their dose-dependent and cumulative cardiotoxicity, manifested by cardiomyopathy, ischemic heart disease, arrhythmias, hypertension, thromboembolic disorders, and heart failure. Cardiotoxicity risk factors, such as genetic predisposition and preexisting obesity, diabetes, hypertension, and heart diseases, are more prevalent in racial/ethnic minorities and undoubtedly contribute to the risk. Yet, beyond these risk factors, racial/ethnic minorities also face unique challenges that contribute to disparities in the emerging field of cardio-oncology, including socioeconomic factors, food insecurity, and the inability to access healthcare providers, among others. The current review will address genetic, clinical, and social determinants that potentially contribute to this disparity.
In Vitro Evaluation of the Antimicrobial Properties of Nanoparticles as New Agents Used in Teat Sealants for Mastitis Prevention in Dry Cows
Authors: Radzikowski Daniel , Kalińska Aleksandra , Kot Magdalena , Jaworski Sławomir , Wierzbicki Mateusz , Gołębiewski Marcin
Published: August 2023

Mastitis prevention and treatment in dry cows are complex issues with limited solutions. The most common is intramammary antibiotic treatment. However, the effectiveness of this treatment varies among countries and even within herds in the same region. Therefore, it is necessary to develop new strategies for dry cow therapy. Metal nanoparticles (NPs), which have strong biocidal properties for treating diseases caused by bacteria, fungi, and algae, are increasingly used to reduce antibiotic use. In this study, AuNPs, CuNPs, AgNPs, PtNPs, NP-FeCs, and their triple complexes were used at different concentrations to evaluate their practical use in treating cows during their dry period. The nanoparticles were in hydrocolloid form and were added separately to form a mixture with beeswax, a mixture with oil, or a mixture based on vegetable glycerin and propylene glycol. The NPs’ concentrations were 0.5, 1, 2, and 5 ppm. Gram-positive and Gram-negative bacteria, and fungi isolated from cows diagnosed with mastitis were used to determine pathogen viability. The results indicated that AuNPs, CuNPs, AgNPs, and their complexes show biocidal properties against mastitis pathogens. AgNPs at 5 ppm had the strongest biocidal properties and reduced Streptococcus agalactiae’s survival rate by 50%; however, the nanoparticle complexes showed poor synergism. The strongest biocidal properties of NPs in wax and in glycerin mixed with glycol were shown against Escherichia coli. Additionally, low nanoparticle concentrations showed no cytotoxicity for BME-UV1 bovine cells, suggesting that these mixtures might be used for further in vivo testing.
A Robust Semi-Direct 3D SLAM for Mobile Robot Based on Dense Optical Flow in Dynamic Scenes
Authors: Hu Bo , Luo Jingwen
Published: August 2023

Dynamic objects bring about a large number of error accumulations in pose estimation of mobile robots in dynamic scenes, and result in the failure to build a map that is consistent with the surrounding environment. Along these lines, this paper presents a robust semi-direct 3D simultaneous localization and mapping (SLAM) algorithm for mobile robots based on dense optical flow. First, a preliminary estimation of the robot’s pose is conducted using the sparse direct method and the homography matrix is utilized to compensate for the current frame image to reduce the image deformation caused by rotation during the robot’s motion. Then, by calculating the dense optical flow field of two adjacent frames and segmenting the dynamic region in the scene based on the dynamic threshold, the local map points projected within the dynamic regions are eliminated. On this basis, the robot’s pose is optimized by minimizing the reprojection error. Moreover, a high-performance keyframe selection strategy is developed, and keyframes are inserted when the robot’s pose is successfully tracked. Meanwhile, feature points are extracted and matched to the keyframes for subsequent optimization and mapping. Considering that the direct method is subject to tracking failure in practical application scenarios, the feature points and map points of keyframes are employed in robot relocation. Finally, all keyframes and map points are used as optimization variables for global bundle adjustment (BA) optimization, so as to construct a globally consistent 3D dense octree map. A series of simulations and experiments demonstrate the superior performance of the proposed algorithm.
Mitochondrial Dysfunction in Lung Resident Mesenchymal Stem Cells from Idiopathic Pulmonary Fibrosis Patients
Authors: Mercader-Barceló Josep , Martín-Medina Aina , Truyols-Vives Joan , Escarrer-Garau Gabriel , Elowsson Linda , Montes-Worboys Ana , Río-Bocos Carlos , Muncunill-Farreny Josep , Velasco-Roca Julio , Cederberg Anna , Kadefors Måns , Molina-Molina Maria , Westergren-Thorsson Gunilla , Sala-Llinàs Ernest
Published: August 2023

Idiopathic pulmonary fibrosis (IPF) is characterized by an aberrant repair response with uncontrolled turnover of extracellular matrix involving mesenchymal cell phenotypes, where lung resident mesenchymal stem cells (LRMSC) have been supposed to have an important role. However, the contribution of LRMSC in lung fibrosis is not fully understood, and the role of LRMSC in IPF remains to be elucidated. Here, we performed transcriptomic and functional analyses on LRMSC isolated from IPF and control patients (CON). Both over-representation and gene set enrichment analyses indicated that oxidative phosphorylation is the major dysregulated pathway in IPF LRMSC. The most relevant differences in biological processes included complement activation, mesenchyme development, and aerobic electron transport chain. Compared to CON LRMSC, IPF cells displayed impaired mitochondrial respiration, lower expression of genes involved in mitochondrial dynamics, and dysmorphic mitochondria. These changes were linked to an impaired autophagic response and a lower mRNA expression of pro-apoptotic genes. In addition, IPF TGFβ-exposed LRMSC presented different expression profiles of mitochondrial-related genes compared to CON TGFβ-treated cells, suggesting that TGFβ reinforces mitochondrial dysfunction. In conclusion, these results suggest that mitochondrial dysfunction is a major event in LRMSC and that their occurrence might limit LRMSC function, thereby contributing to IPF development.
Remote Measurements of Tear Electrolyte Concentrations on Both Sides of an Inserted Contact Lens
Authors: Lakowicz Joseph R. , Badugu Ramachandram , Sivashanmugan Kundan , Reece Albert
Published: August 2023

In this paper, a method is described to perform ion concentration measurements on both sides of an inserted contact lens, without physical contact with the eye or the contact lens. The outer surface of an eye is covered with a tear film that has multiple layers. The central aqueous layer contains electrolytes and proteins. When a contact lens is inserted, it becomes localized in the central layer, which creates two layers known as the pre-lens tear film (PLTF) and the post-lens tear film (PoLTF). The PoLTF is in direct contact with the sensitive corneal epithelial cells which control electrolyte concentrations in tears. It is difficult to measure the overall electrolyte concentration in tears because of the small 7 μL volume of bulk tears. No methods are known, and no method has been proposed, to selectively measure the concentrations of electrolytes in the smaller volumes of the PLTF and the PoLTF. In this paper, we demonstrate the ability to localize fluorophores on each side of a contact lens without probe mixing or diffusion across the lens. We measured the concentration of sodium in the region of the PoLTF using a sodium-sensitive fluorophore positioned on the inner surface of a contact lens. The fluorescence measurements do not require physical contact and are mostly independent of eye motion and fluorophore concentration. The method is generic and can be combined with ion-sensitive fluorophores for the other electrolytes in tears. Instrumentation for non-contact measurements is likely to be inexpensive with modern opto-electronic devices. We expect these lenses to be used for measurements of other ions in the PLTF and the PoLTF, and thus become useful for both research and in the diagnosis of infections, keratitis and biomarkers for diseases.
Filler Migration after Facial Injection—A Narrative Review
Authors: Wollina Uwe , Goldman Alberto
Published: August 2023

Background: The injection of dermal fillers for facial esthetics has become a very popular procedure. Although usually safe in the hands of the experienced user, filler injections may bear a risk of unwanted side effects. Material and Methods: This is a narrative review of dermal filler migration after facial injections. We performed research on the literature on Pubmed and Google Scholar. Inclusion criteria were observational studies, case reports, and clinical trials which investigated the association of facial filler injections to filler migration. Animal studies have not been considered. Intravascular injections were excluded. Results: We identified 28 reports that met the inclusion criteria. The age range of affected patients was 21 to 86 years (mean ± standard deviation: 47 ± 14.8 years). Women were 25 times more reported than males. Hyaluronic acid and polyalkylimide were the most commonly encountered filler substances. Injections into the nose, lips, nasolabial folds, and forehead (including glabella) are more often reported for filler migration than injections into the cheeks. Tear-trough correction bears a risk for orbital migration. The delay from injection to presentation of filler migration was highly variable. Very late filler migration was more commonly seen with permanent fillers than non-permanent products. Conclusions: Filler migration distant from the injection site can occur even several years after the primary treatment. All filler types can be involved. Permanent fillers bear a higher risk of very late filler migration. Migration of permanent fillers needs surgical treatment, while HA fillers respond to hyaluronidase injections. Detailed knowledge of facial anatomy, safer injection techniques, and filler qualities are preventive measures.
Ecology and Diversity of Weed Communities in the Northern Andes under Different Anthropogenic Pressures
Authors: Duque Yessica P. , Giraldo-Sánchez Carlos E. , Quijano-Abril Mario A. , Rojas Jose M.
Published: August 2023

Weeds can have both positive and negative effects on agricultural environments. However, despite the growing interest in the ecology of weed communities in agricultural areas, a few studies have been carried out in the northern region of the Andes of Colombia, where urban and agricultural expansion have generated highly disturbed scenarios. The aim of this study was to analyze the diversity of vegetation and weed seed banks in three agricultural production systems and a forest ecosystem in the northern Andes of Colombia. Hill numbers were used to compare diversity, Beta diversity to assess changes in composition, and range—abundance–dominance curves at different sites. Likewise, indicator species were analyzed to find species associations to each system. The results revealed differences in the composition of weeds between the forest ecosystem and the agricultural production systems, with higher equitability in the forest ecosystem and higher dominance in agricultural systems. Significant differentiation was observed among the dominant species within each agricultural system, particularly highlighting those species considered pests due to their unique life history traits. These traits confer them with a greater advantage in the face of various anthropogenic selection pressures. These findings highlight the impact of anthropogenic disturbances on the ecological dynamics of weed communities in different ecosystems, which should be considered when planning integrated weed management techniques.
VR Traffic Dataset on Broad Range of End-User Activities
Authors: Polupanova Marina
Published: August 2023

With the emergence of new internet traffic types in modern transport networks, it has become critical for service providers to understand the structure of that traffic and predict peaks of that load for planning infrastructure expansion. Several studies have investigated traffic parameters for Virtual Reality (VR) applications. Still, most of them test only a partial range of user activities during a limited time interval. This work creates a dataset of captures from a broader spectrum of VR activities performed with a Meta Quest 2 headset, with the duration of each real residential user session recorded for at least half an hour. Newly collected data helped show that some gaming VR traffic activities have a high share of uplink traffic and require symmetric user links. Also, we have figured out that the gaming phase of the overall gameplay is more sensitive to the channel resources reduction than the higher bitrate game launch phase. Hence, we recommend it as a source of traffic distribution for channel sizing model creation. From the gaming phase, capture intervals of more than 100 s contain the most representative information for modeling activity.
Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation
Authors: Bagheri Farid , Reforgiato Recupero Diego , Sirnes Espen
Published: August 2023

Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (d) according to different metrics (i) to their respective variants that included stock return forecast information of d and stock return data of the days before d and (ii) to a GARCH model that included return prediction information of d and stock return data of the days before d. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces.
Simultaneous Momentum and Position Measurement and the Instrumental Weyl-Heisenberg Group
Authors: Jackson Christopher S. , Caves Carlton M.
Published: August 2023

The canonical commutation relation, [Q,P]=iℏ, stands at the foundation of quantum theory and the original Hilbert space. The interpretation of P and Q as observables has always relied on the analogies that exist between the unitary transformations of Hilbert space and the canonical (also known as contact) transformations of classical phase space. Now that the theory of quantum measurement is essentially complete (this took a while), it is possible to revisit the canonical commutation relation in a way that sets the foundation of quantum theory not on unitary transformations but on positive transformations. This paper shows how the concept of simultaneous measurement leads to a fundamental differential geometric problem whose solution shows us the following. The simultaneous P and Q measurement (SPQM) defines a universal measuring instrument, which takes the shape of a seven-dimensional manifold, a universal covering group we call the instrumental Weyl-Heisenberg (IWH) group. The group IWH connects the identity to classical phase space in unexpected ways that are significant enough that the positive-operator-valued measure (POVM) offers a complete alternative to energy quantization. Five of the dimensions define processes that can be easily recognized and understood. The other two dimensions, the normalization and phase in the center of the IWH group, are less familiar. The normalization, in particular, requires special handling in order to describe and understand the SPQM instrument.
Joint Probabilities Approach to Quantum Games with Noise
Authors: Legón Alexis R. , Medina Ernesto
Published: August 2023

A joint probability formalism for quantum games with noise is proposed, inspired by the formalism of non-factorizable probabilities that connects the joint probabilities to quantum games with noise. Using this connection, we show that the joint probabilities are non-factorizable; thus, noise does not generically destroy entanglement. This formalism was applied to the Prisoner’s Dilemma, the Chicken Game, and the Battle of the Sexes, where noise is coupled through a single parameter μ. We find that for all the games except for the Battle of the Sexes, the Nash inequalities are maintained up to a threshold value of the noise. Beyond the threshold value, the inequalities no longer hold for quantum and classical strategies. For the Battle of the sexes, the Nash inequalities always hold, no matter the noise strength. This is due to the symmetry and anti-symmetry of the parameters that determine the joint probabilities for that game. Finally, we propose a new correlation measure for the games with classical and quantum strategies, where we obtain that the incorporation of noise, when we have quantum strategies, does not affect entanglement, but classical strategies result in behavior that approximates quantum games with quantum strategies without the need to saturate the system with the maximum value of noise. In this manner, these correlations can be understood as entanglement for our game approach.
Critical Thinking in Teacher Education: Course Design and Teaching Practicum
Authors: Lithoxoidou Angeliki , Georgiadou Triantafyllia
Published: August 2023

Critical Thinking is considered a key component of Higher Education that supports graduates’ preparation for the labor market. However, in the field of teacher education more research is needed to support student-teachers with regard to the complexity of schools. It is considered that a teaching practicum can set the stage for the cultivation of Critical Thinking skills and dispositions, as student-teachers have to face real problems and be led to decision-making. The paper presented here describes the curriculum transformation of courses offering theoretical lectures and teaching practicum in the Department of Primary Education, University of Western Macedonia. After focus group discussion with in-service teachers, the Critical Thinking skills and dispositions considered necessary for teacher education were thoroughly described and utilized for the course design. The courses were modified to include a variety of teaching strategies following the direct infusion approach for training in Critical Thinking with the aim of promoting reflective practices for fostering student-teachers’ critical reflection. In this way, they can be empowered regarding their professional development.
Climate Competencies of Finnish Gifted and Average-Ability High School Students
Authors: Tolppanen Sakari , Kang Jingoo , Tirri Kirsi
Published: August 2023

In the face of global issues such as climate change, the world needs action competent, transformationally gifted citizens, who are willing to step up and take responsibility for a better future. However, empirical evidence on what supports the development of transformational giftedness is limited. Furthermore, the relationship between academic giftedness and transformational giftedness has not been clearly pronounced. The purpose of this study is to address this research gap by examining students’ climate competencies. A total of 1703 students from five Finnish high schools (grades 10–12) participated in this study. Using a questionnaire, students’ climate change knowledge, values, willingness to take action, sense of responsibility, environmental concern, and perceptions on how climate change issues are dealt with in school were examined. Four of the schools were general education high schools, while one was for students formally identified as gifted students. The findings indicate that academically gifted students in both general education schools and the gifted school show more climate competencies than average-ability students. Furthermore, gifted students that attended the school for gifted students show more climate competencies than the gifted students from general education schools. Based on the findings, the paper discusses how the development of transformational giftedness can be better supported in education.
An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method
Authors: Liu Huilin , Wei Huazhang , Yang Gaoming , Xia Chenxing , Zhao Shenghui
Published: August 2023

Foreground detection is the main way to identify regions of interest. The detection effectiveness determines the accuracy of subsequent behavior analysis. In order to enhance the detection effect and optimize the problems of low accuracy, this paper proposes an improved Vibe algorithm combining the frame difference method and adaptive thresholding. First, we adopt a shallow convolutional layer of VGG16 to extract the lower-level features of the image. Features images with high correlation are fused into a new image. Second, adaptive factors based on the spatio-temporal domain are introduced to divide the foreground and background. Finally, we construct an inter-frame average speed value to measure the moving speed of the foreground, which solves the mismatch problem between background change rate and model update rate. Experimental results show that our algorithm can effectively solve the drawback of the traditional method and prevent the background model from being contaminated. It suppresses the generation of ghosting, significantly improves detection accuracy, and reduces the false detection rate.
Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models
Authors: Hsieh Cheng-Hsiung , Chen Ze-Yu
Published: August 2023

Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain hazy ground truth (GT) images. This can lead to supervised deep image dehazing (SDID) models learning inappropriate mapping between hazy images and GT images, which negatively affects the dehazing performance. To address this problem, two difficulties must be solved. One is to estimate the haze level in an image, and the other is to develop a haze level indicator to discriminate clear and hazy images. To this end, we proposed a haze level estimation (HLE) scheme based on dark channel prior and a haze level indicator accordingly for training data cleaning, i.e., to exclude image pairs with hazy GT images in the data set. With the data cleaning by the HLE, we introduced an SDID framework to avoid inappropriate learning and thus improve the dehazing performance. To verify the framework, using the RESIDE data set, experiments were conducted with three types of SDID models, i.e., GCAN, REFN and cGAN. The results show that our method can significantly improve the dehazing performance of the three SDID models. Subjectively, the proposed method generally provides better visual quality. Objectively, our method, using fewer training image pairs, was capable of improving PSNR in the GCAN, REFN, and cGAN models by 3.10 dB, 5.74 dB, and 6.44 dB, respectively. Furthermore, our method was evaluated using a real-world data set, KeDeMa. The results indicate that the better visual quality of the dehazed images is generally for models with the proposed data cleaning scheme. The results demonstrate that the proposed method effectively and efficiently enhances the dehazing performance in the given examples. The practical significance of this research is to provide an easy but effective way, that is, the proposed data cleaning scheme, to improve the performance of SDID models.
An Intelligent Risk Forewarning Method for Operation of Power System Considering Multi-Region Extreme Weather Correlation
Authors: Yao Degui , Han Ji , Li Qionglin , Wang Qihang , Li Chenghao , Zhang Di , Li Muyuan , Tian Chunsun
Published: August 2023

Extreme weather events pose significant risks to power systems, necessitating effective risk forewarning and management strategies. A few existing researches have concerned the correlation of the extreme weather in different regions of power system, and traditional operation risk assessment methods gradually cannot satisfy real-time requirements. This motivates us to present an intelligent risk forewarning method for the operation of power systems considering multi-region extreme weather correlation. Firstly, a novel multi-region extreme weather correlation model based on vine copula is developed. Then, a risk level classification method for power system operations is introduced. Further, an intelligent risk forewarning model for power system operations is proposed. This model effectively integrates the multi-region extreme weather correlation and the risk level classification of the system. By employing the summation wavelet extreme learning machine, real-time monitoring and risk forewarning of the system’s operational status are achieved. Simulation results show that the proposed method can rapidly identify potential risks and provides timely risk forewarning information, helping enhance the resilience of power system operations.
Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology
Authors: Liu Xin , Wang Ruxue , Luo Dan , Xu Gang , Chen Xiubo , Xiong Neal , Liu Xiaomeng
Published: August 2023

With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.
Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations
Authors: Zielinski Sebastian , Nüßlein Jonas , Stein Jonas , Gabor Thomas , Linnhoff-Popien Claudia , Feld Sebastian
Published: August 2023

One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation. Recently, it has been shown that the choice of the 3sat-to-QUBO transformation can significantly impact the solution quality of quantum annealing. It has been shown that the solution quality can vary up to an order of magnitude difference in the number of correct solutions received, depending solely on the 3sat-to-QUBO transformation. An open question is: what causes these differences in the solution quality when solving 3sat-instances with different 3sat-to-QUBO transformations? To be able to conduct meaningful studies that assess the reasons for the differences in the performance, a larger number of different 3sat-to-QUBO transformations would be needed. However, currently, there are only a few known 3sat-to-QUBO transformations, and all of them were created manually by experts, who used time and clever reasoning to create these transformations. In this paper, we will solve this problem by proposing an algorithmic method that is able to create thousands of new and different 3sat-to-QUBO transformations, and thus enables researchers to systematically study the reasons for the significant difference in the performance of different 3sat-to-QUBO transformations. Our algorithmic method is an exhaustive search procedure that exploits properties of 4×4 dimensional pattern QUBOs, a concept which has been used implicitly in the creation of 3sat-to-QUBO transformations before, but was never described explicitly. We will thus also formally and explicitly introduce the concept of pattern QUBOs in this paper.
Liquid Water Characteristics in the Compressed Gradient Porosity Gas Diffusion Layer of Proton Exchange Membrane Fuel Cells Using the Lattice Boltzmann Method
Authors: Yan Song , Yang Mingyang , Sun Chuanyu , Xu Sichuan
Published: August 2023

The mitigation of water flooding in the gas diffusion layer (GDL) at relatively high current densities is indispensable for enhancing the performance of proton exchange membrane fuel cells (PEMFCs). In this paper, a 2D multicomponent LBM model is developed to investigate the effects of porosity distribution and compression on the liquid water dynamic behaviors and distribution. The results suggest that adopting the gradient GDL structure with increasing porosity along the thickness direction significantly reduces the breakthrough time and steady–state total water saturation inside the GDL. Moreover, the positive gradient structure reaches the highest breakthrough time and water saturation at 10% compression ratio (CR) when the GDL is compressed, and the corresponding values decrease with further increase of the CR. Considering the breakthrough time, total water saturation and water distribution at the entrance of the GDL at the same time, the gradient structure with continuously increasing porosity can perform better water management capacity at 30% CR. This paper is useful for understanding the two–phase process in a gradient GDL structure and provides guidance for future design and manufacturing.
Oat as a Potential Source of Energy
Authors: Tobiasz-Salach Renata , Stadnik Barbara , Bajcar Marcin
Published: August 2023

Oat (Avena sativa L.) is one of the agricultural crops that can be grown in marginal areas. Grain and straw are used mainly for food and fodder purposes. However, due to the high-fat content in the grain and the small amount of ash, it can be an attractive raw material for energy production. The biomass can be straw and oat grain. Grain should be intended for food purposes, but if it does not meet the quality requirements, it can be used for energy purposes. The aim of the experiment was to evaluate the energy usefulness of four oat cultivars depending on the applied level of nitrogen fertilization. The research results show that oat grain and straw can be used as fuel for energy purposes. The average calorific value of grain was 18.7 MJ·kg−1, and the ash content in dry matter was 2.03%. With the increase in the dose of nitrogen fertilization, a decrease in the calorific value of oat and straw grains and a decrease in ash content were noted. The findings show significant varietal differences. Oat straw had a lower calorific value and a higher ash content, which indicates its lower usefulness for energy purposes compared to grain.
Graph Complexity Reduction of Exergy-Based FDI—A Tennessee Eastman Process Case Study
Authors: Styger Rikus , Uren Kenneth R. , van Schoor George
Published: August 2023

When applying graph-based fault detection and isolation (FDI) methods to the attributed graph data of large and complex industrial processes, the computational abilities and speed of these methods are adversely affected by the increased complexity. This paper proposes and evaluates five reduction techniques for the exergy-graph-based FDI method. Unlike the graph reduction techniques available in literature, the reduction techniques proposed in this paper can easily be applied to the type of attributed graph used by graph-based FDI methods. The attributed graph data of the Tennessee Eastman process are used in this paper since it is a popular process to use for the evaluation of fault diagnostic methods and is both large and complex. To evaluate the proposed reduction techniques, three FDI methods are applied to the original attributed graph data of the process and the performance of these FDI methods used as control data. Each proposed reduction technique is applied to the attributed graph data of the process, after which all three FDI methods are applied to the reduced graph data to evaluate their performance. The FDI performance obtained with reduced graph data is compared to the FDI performance using the control data. This paper shows that, using the proposed graph reduction techniques, it is possible to significantly reduce the size and complexity of the attributed graph of a system while maintaining a level of FDI performance similar to that achieved prior to any graph reduction.
Advanced Diagnostic Approach for High-Voltage Insulators: Analyzing Partial Discharges through Zero-Crossing Rate and Fundamental Frequency Estimation of Acoustic Raw Data
Authors: Maresch Kaynan , Freitas-Gutierres Luiz F. , Oliveira Aécio L. , Borin Aquiles S. , Cardoso Ghendy , Damiani Juliano S. , Morais André M. , Correa Cristian H. , Martins Erick F.
Published: August 2023

Acoustic inspection is a valuable technique that can detect early stage defects in equipment, thereby facilitating predictive maintenance. In recent times, ultrasonic sensors have made detecting partial discharges through acoustic sensing increasingly feasible. However, interpreting the acoustic signals can pose challenges, as it requires extensive expertise and knowledge of equipment configuration. To address this issue, a technique based on zero-crossing rate and fundamental frequency estimation has been proposed to standardize insulator diagnosis. In an experiment involving a database of 72 raw acoustic signals with frequencies ranging from 0 to 128 kHz, various types of pollution and defects were introduced to a chain of insulators. By employing the proposed technique, the occurrence of partial discharges can be detected and classified according to type, such as corona or surface discharges. This advanced approach to diagnosis simplifies the process while providing valuable insights into the severity of observed phenomena in the field.
Benefits of Judo Training for Brain Functions Related to Physical and Cognitive Performance in Older Adults
Authors: Yamasaki Takao
Published: August 2023

Judo is a well-known Japanese martial art that also features in Olympic games. Recently, an increasing interest has been observed in the application of judo as a multicomponent exercise, with a growing body of evidence showing that this feature improves physical and cognitive performance in older adults. Therefore, this review highlights the benefits of judo training in preventing physical and cognitive decline in older adults. Specifically, first, this review outlines the basic characteristics of judo (philosophy, match, and training). Subsequently, prior research examining the impacts of judo training on the physical and cognitive aspects of elderly individuals is reviewed. Thereafter, the brain mechanisms underlying the effects of judo training in improving physical and cognitive performance are discussed. Throughout this review, judo training demonstrated some positive effects on physical (gait and balance, among others) and cognitive (memory and executive function) function in older adults. These positive effects are attributed to a variety of changes in the brain (e.g., increased neurotrophic factor expression and increased cerebral blood flow, among others) that affect different brain regions and networks both functionally and structurally. From these findings, this review concludes that judo training can be an effective way to maintain and prevent physical and cognitive decline in older adults.
Advanced Fermentation Techniques for Lactic Acid Production from Agricultural Waste
Authors: Huang Jiaqi , Wang Jianfei , Liu Shijie
Published: August 2023

Lactic acid plays an important role in industrial applications ranging from the food industry to life sciences. The growing demand for lactic acid creates an urgent need to find economical and sustainable substrates for lactic acid production. Agricultural waste is rich in nutrients needed for microbial growth. Fermentative production of lactic acid from non-food-competing agricultural waste could reduce the cost of lactic acid production while addressing environmental concerns. This work provided an overview of lactic acid fermentation from different agricultural wastes. Although conventional fermentation approaches have been widely applied for decades, there are ongoing efforts toward enhanced lactic acid fermentation to meet the requirements of industrial productions and applications. In addition, agricultural waste contains a large proportion of pentose sugars. Most lactic-acid-producing microorganisms cannot utilize such reducing sugars. Therefore, advanced fermentation techniques are also discussed specifically for using agricultural waste feedstocks. This review provides valuable references and technical supports for the industrialization of lactic acid production from renewable materials.
Recognition and Measurement of Crypto-Assets from the Perspective of Retail Holders
Authors: Dragomir Voicu D. , Dumitru Valentin Florentin
Published: August 2023

The Markets in Crypto-Assets (MiCa) Regulation of the European Union is the first comprehensive piece of legislation that seeks to protect the interests of investors in the crypto-assets sector. Although the market value of crypto-assets is significant at world level, there is a lack of clear regulatory guidelines regarding the recognition, measurement, and presentation of crypto-assets in the financial statements of investors. Considering that not all digital assets are the same, retail holders need to take into account the characteristics, rights, and obligations associated with the crypto-assets they purchase to determine the appropriate accounting method. Therefore, the research question of the present article is: Which are the main types of crypto-assets and how should they be recognized and measured in the financial statements of investors and holders? We perform a review of the accounting policies and options, relying on relevant regulations, standards, regulatory drafts, legal and academic papers, recommendations of market regulators, crypto-asset white papers, industry opinions, and media articles. There are different accounting treatments that can be applied, depending on the legal and technological aspects of each class of crypto-assets. Based on a critical discussion of accounting policies and options, our research has implications for accounting professionals, but also for standard setters, who are urged to provide clear guidelines. Identifying the key economic characteristics of each asset and determining the most appropriate way to recognize these characteristics in the financial statements are crucial for the development of a functional and trustworthy market in crypto-assets.