bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Applications and Trends in Rabbit Nutraceuticals
Authors: El-Sabrout Karim , Khalifah Ayman , Ciani Francesca
Published: July 2023

To ensure the sustainability of rabbit production and protect the global rabbit industry, cost-effective and practical strategies for improving rabbit production and meat quality must be developed. Recently, rabbit farming, like other animal farming, has faced feed shortages due to the impact of climate change, high competition among livestock species, and war conditions. The continued use of conventional feed additives in rabbit diets, whether derived from plant or animal sources, has become a critical issue. Furthermore, there is a global trend toward finding natural alternatives to synthetic drugs, such as antibiotics, in rabbit farms. Finding readily available and alternative feed additives is therefore critical to protecting the rabbit industry, particularly in subtropical and Mediterranean-developing countries. Nutraceuticals positively influence several physiological and productive traits in animals, as well as enhancing their health and welfare. The present review aims to provide an overview of previous studies on the potential of using some plant and animal products as nutraceutical alternatives and feed additives in rabbit diets, separately or in combination, to act as natural growth promoters, antioxidants, anti-inflammatory, and antimicrobial agents, and immunostimulants in rabbit farms. From the results, some unconventional plant and animal products, such as spirulina, garden cress, milk whey, and bee venom, can be successfully used as dietary supplements and substitutes in rabbit farms to motivate rabbit growth and reproduction, as well as enhance immunity. These products are rich in minerals, vitamins, enzymes, organic acids, flavonoids, and phenolic acids. These active substances benefit the animal’s digestive tract in different ways, including activating the digestive enzymes and maintaining microbial balance, promoting vitamin synthesis. They also improve rabbit production, reproduction, and health.
Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks
Authors: Tsoulos Ioannis G. , Tzallas Alexandros
Published: July 2023

Perhaps one of the best-known machine learning models is the artificial neural network, where a number of parameters must be adjusted to learn a wide range of practical problems from areas such as physics, chemistry, medicine, etc. Such problems can be reduced to pattern recognition problems and then modeled from artificial neural networks, whether these problems are classification problems or regression problems. To achieve the goal of neural networks, they must be trained by appropriately adjusting their parameters using some global optimization methods. In this work, the application of a recent global minimization technique is suggested for the adjustment of neural network parameters. In this technique, an approximation of the objective function to be minimized is created using artificial neural networks and then sampling is performed from the approximation function and not the original one. Therefore, in the present work, learning of the parameters of artificial neural networks is performed using other neural networks. The new training method was tested on a series of well-known problems, a comparative study was conducted against other neural network parameter tuning techniques, and the results were more than promising. From what was seen after performing the experiments and comparing the proposed technique with others that have been used for classification datasets as well as regression datasets, there was a significant difference in the performance of the proposed technique, starting with 30% for classification datasets and reaching 50% for regression problems. However, the proposed technique, because it presupposes the use of global optimization techniques involving artificial neural networks, may require significantly higher execution time than other techniques.
Real-World Data Regarding Dalbavancin Use before and during the COVID-19 Pandemic—A Single-Center Retrospective Study
Authors: Ioannou Petros , Wolff Nora , Mathioudaki Anna , Spanias Christos , Spernovasilis Nikolaos , Kofteridis Diamantis P.
Published: July 2023

Dalbavancin is a semisynthetic lipoglycopeptide, which possesses great potential for bactericidal activity similar to antimicrobials with the same mechanism of action, such as vancomycin and teicoplanin. Due to its very prolonged half-life, it can be used in a single or two-dose regimen to treat infections by Gram-positive microorganisms, even resistant ones, such as methicillin-resistant Staphylococcus aureus (MRSA). Currently, it is approved only for the treatment of acute bacterial skin and skin structure infections (ABSSSIs). The aim of this study was to investigate the clinical and microbiological characteristics of patients to whom dalbavancin was administered at the University Hospital of Heraklion and evaluate its use in regard to the COVID-19 pandemic. In total, 146 patients were included in this retrospective cohort study evaluating the use of dalbavancin from the first time it was used in 2017 until the end of 2022. The median age was 68 years (range: 21–96 years), and 86 (59%) patients were male. The most common indications for dalbavancin use were osteoarticular infections in 43%, followed by ABSSSIs in 37%, and cardiovascular infections in 10%. Dalbavancin was used empirically in one out of three patients, most commonly with the indication of ABSSSIs, and most commonly in the post-COVID-19 era. The most frequently isolated pathogens were coagulase-negative staphylococci in 70%, S. aureus in 27%, Enterococcus spp. in 22%, and Streptococcus spp. in 8%, while one out of three infections were polymicrobial. In 12% of patients, the infection was not cured, but no patients died. For patients with ABSSSIs, endocarditis and vascular infections, and bacteremia, the cure rates were more than 90%, and in osteoarticular infections, the cure rate was 76%. Thus, dalbavancin has great potential for use in complicated and invasive infections that may require prolonged intravenous antimicrobial treatment. However, further studies are required to formally investigate its role in such infections.
Probable Three-Species In Vivo Transfer of blaNDM-1 in a Single Patient in Greece: Occurrence of NDM-1-Producing Klebsiella pneumoniae, Proteus mirabilis, and Morganella morganii
Authors: Meletis Georgios , Malousi Andigoni , Tychala Areti , Kassomenaki Angeliki , Vlachodimou Nikoletta , Mantzana Paraskevi , Metallidis Simeon , Skoura Lemonia , Protonotariou Efthymia
Published: July 2023

NDM carbapenemase-encoding genes disseminate commonly among Enterobacterales through transferable plasmids carrying additional resistance determinants. Apart from the intra-species dissemination, the inter-species exchange of plasmids seems to play an additional important role in the spread of blaNDM. We here present the genetics related to the isolation of three species (Klebsiella pneumoniae, Proteus mirabilis, and Morganella morganii) harboring the blaNDM-1 gene from a single patient in Greece. Bacterial identification and antimicrobial susceptibility testing were performed using the Vitek2. Whole genome sequencing and bioinformatic tools were used to identify resistance genes and plasmids. BlaNDM-1 harboring plasmids were found in all three isolates. Moreover, the plasmid constructs of the respective incomplete or circular contigs showed that the blaNDM-1 and its neighboring genes form a cluster that was found in all isolates. Our microbiological findings, together with the patient’s history, suggest the in vivo transfer of the blaNDM-1-containing cluster through three different species in a single patient.
Apples and Apple by-Products: Antioxidant Properties and Food Applications
Authors: Asma Umme , Morozova Ksenia , Ferrentino Giovanna , Scampicchio Matteo
Published: July 2023

In recent years, there has been a growing interest in utilizing natural antioxidants as alternatives to synthetic additives in food products. Apples and apple by-products have gained attention as a potential source of natural antioxidants due to their rich phenolic content. However, the extraction techniques applied for the recovery of phenolic compounds need to be chosen carefully. Studies show that ultrasound-assisted extraction is the most promising technique. High yields of phenolic compounds with antioxidant properties have been obtained by applying ultrasound on both apples and their by-products. Promising results have also been reported for green technologies such as supercritical fluid extraction, especially when a co-solvent is used. Once extracted, recent studies also indicate the feasibility of using these compounds in food products and packaging materials. The present review aims to provide a comprehensive overview of the antioxidant properties of apples and apple by-products, their extraction techniques, and potential applications in food products because of their antioxidant or nutritional properties. The findings reported here highlight the proper utilization of apples and their by-products in food to reduce the detrimental effect on the environment and provide a positive impact on the economy.
Beneficial Effects of Olive Oil Enriched with Lycopene on the Plasma Antioxidant and Anti-Inflammatory Profile of Hypercholesterolemic Patients
Authors: Martínez Álvarez Jesus Roman , Lopez Jaen Ana Belen , Cavia-Saiz Monica , Muñiz Pilar , Valls-Belles Victoria
Published: July 2023

Olive oil and lycopene are foods that have potent antioxidant activity. The objective was to determine the effects of consumption of olive oil enriched with lycopene on oxidative stress biomarkers in hypercholesterolemic subjects. We examined the effects of oil enriched with lycopene extract daily intake during 1 month on plasma antioxidant capacity, lipids profile (triacylgycerols, total cholesterol, cHDL; cLDL, ox-LDL), biomarkers of oxidative stress, and inflammatory markers related with atherosclerosis risk (C-reactive protein (CRP), IL-6; sDC4L) in subjects hypercholesteremics (cholesterol > 220 mg/dL). In the group consuming olive oil-lycopene, significant increases (p < 0.05) in the levels of plasma lycopene concentration (0.146 ± 0.03 versus 0.202 ± 0.04 (µmol/L)), α-carotene (0.166 ± 0.064 versus 0.238 ± 0.07) and in β-carotene (0.493 ± 0.187 versus 0.713 ± 0.221) were observed. These results are linked with the increases of plasma antioxidants and decreases biomarkers of oxidative stress (carbonyl groups, malondialdehyde and 8-hydroxy-deoxiguanosine) observed in hypercholesterolemic group. In relation to lipid profile, a significant decrease was observed in the levels of ox-LDL (781 ± 302 versus 494 ± 200), remaining unchanged the levels of TG, cholesterol, HDL and LDL-c. Regarding inflammatory biomarkers, the levels of CRP and IL-6 decreased significantly. The positive results obtained in this study support the use of olive oil enriched with lycopene to reduce the risk of coronary disease.
A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
Authors: Hiruta Yuya , Xie Chun , Shishido Hidehiko , Kitahara Itaru
Published: July 2023

As the demand for 3D information continues to grow in various fields, technologies are rapidly being used to acquire such information. Laser-based estimation and multi-view images are popular methods for sensing 3D information, while deep learning techniques are also being developed. However, the former method requires precise sensing equipment or large observation systems, while the latter relies on substantial prior information in the form of extensive learning datasets. Given these limitations, our research aims to develop a method that is independent of learning and makes it possible to capture a wide range of 3D information using a compact device. This paper introduces a novel approach for estimating the 3D information of an observed scene utilizing a monocular image based on a catadioptric imaging system employing an omnidirectional camera and a spherical mirror. By employing a curved mirror, it is possible to capture a large area in a single observation. At the same time, using an omnidirectional camera enables the creation of a simplified imaging system. The proposed method focuses on a spherical or spherical cap-shaped mirror in the scene. It estimates the mirror’s position from the captured images, allowing for the estimation of the scene with great flexibility. Simulation evaluations are conducted to validate the characteristics and effectiveness of our proposed method.
Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
Authors: Peralta-López José-Eleazar , Morales-Viscaya Joel-Artemio , Lázaro-Mata David , Villaseñor-Aguilar Marcos-Jesús , Prado-Olivarez Juan , Pérez-Pinal Francisco-Javier , Padilla-Medina José-Alfredo , Martínez-Nolasco Juan-José , Barranco-Gutiérrez Alejandro-Israel
Published: July 2023

The condition of the roads where cars circulate is of the utmost importance to ensure that each autonomous or manual car can complete its journey satisfactorily. The existence of potholes, speed bumps, and other irregularities in the pavement can cause car wear and fatal traffic accidents. Therefore, detecting and characterizing these anomalies helps reduce the risk of accidents and damage to the vehicle. However, street images are naturally multivariate, with redundant and substantial information, as well as significantly contaminated measurement noise, making the detection of street anomalies more challenging. In this work, an automatic color image analysis using a deep neural network for the detection of potholes on the road using images taken by a ZED camera is proposed. A lightweight architecture was designed to speed up training and usage. This consists of seven properly connected and synchronized layers. All the pixels of the original image are used without resizing. The classic stride and pooling operations were used to obtain as much information as possible. A database was built using a ZED camera seated on the front of a car. The routes where the photographs were taken are located in the city of Celaya in Guanajuato, Mexico. Seven hundred and fourteen images were manually tagged, several of which contain bumps and potholes. The system was trained with 70% of the database and validated with the remaining 30%. In addition, we propose a database that discriminates between potholes and speed bumps. A precision of 98.13% using 37 convolution filters in a 3 × 3 window was obtained, which improves upon recent state-of-the-art work.
VPN: Variation on Prompt Tuning for Named-Entity Recognition
Authors: Hu Niu , Zhou Xuan , Xu Bing , Liu Hanqing , Xie Xiangjin , Zheng Hai-Tao
Published: July 2023

Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity recognition (NER), due to the sophisticated design of entity-related templates. Note that the nature of prompt tuning makes full use of the parameters of the mask language model (MLM) head, while previous methods solely utilized the last hidden layer of language models (LMs) and the power of the MLM head is overlooked. In this work, we discovered the characteristics of semantic feature changes in samples after being processed using MLMs. Based on this characteristic, we designed a prompt-tuning variant for NER tasks. We let the pre-trained model predict the label words derived from the training dataset at each position and fed the generated logits (non-normalized probability) to the CRF layer. We evaluated our method on three popular datasets, and the experiments showed that our proposed method outperforms the state-of-the-art model in all three Chinese datasets.
Approximating Dynamic Elastic Modulus of Concrete for an Old Aqueduct Using Dynamic Tests and BP Neural Network
Authors: Lu Xiaobin , Li Xiulin , Xiao Jun , Li Meng
Published: July 2023

Monitoring the degradation of the dynamic elastic modulus (Ed) of concrete is of great importance to track the durability deterioration for hydraulic concrete structures. For the aqueduct under investigation in this study, the dynamic elastic modulus of bent frames and moment frame supports (Ed-frame), the dynamic elastic modulus of arch trusses (Ed-arch) and the shear stiffnesses of the asphaltic bearings of U-shaped flumes (Kflume) are the main parameters to define the dynamic behavior of the structure, which have direct correlation with its vibrational characteristics and thus practicably can be estimated by a BP (back-propagation) neural network using modal frequencies as inputs. Since it is impossible to obtain sufficient experimental field data to train the network, a full-scale 3D FE model of the entire aqueduct is created, and modal analyses under different combinations of Kflume, Ed-arch and Ed-frame are conducted to generate the analytical dataset for the network. After the network’s architecture is refined by the cross-validation process and its modeling accuracy verified by the test procedure, the primary modal frequencies of the aqueduct obtained from in situ dynamic tests are put into the network to obtain the final approximations for Kflume, Ed-arch and Ed-frame, which sets an evaluation baseline of the general concrete Ed status for the aqueduct and indicates that the makeshift asphaltic bearings of U-shaped flumes basically can be treated as a three-directional hinge in the FE model. It is also found that more inputs of modal frequencies can improve the prediction accuracy of the BP neural network.
Research on Process Quality Prediction and Control of Spindle Housings in Flexible Production Lines
Authors: Huang Bo , Yan Jiawei , Liu Xiang , Xie Jiacheng , Wang Jian , Liu Kang , Xu Yun , Peng Gongli
Published: July 2023

The characteristics of flexible production lines, i.e., “multiple steps and few processes”, increase the complexity of the process and the difficulty of process quality control, but are not conducive for improving the quality and efficiency of a production line. In this study, we use a flexible production line processing spindle box as the research object. Using an extensive data analysis method, we study the key influencing factors of process quality and the prediction and optimization of process quality characteristics, aiming to accurately predict the machining accuracy of flexible production line processes and to achieve efficient quality control. A fuzzy hierarchical analysis-based impact factor model is developed to obtain a process quality impact factor model consistent with the spindle box of a production line. By verifying the prediction accuracy of 24 sets of quality spindle bore data, a prediction model with a relative error of less than 0.01 is obtained, which provides a prediction sample for analyzing potential problems of process quality in a production line. The SPC control principle is used to monitor process quality by using the standard control method, and the change trends between the actual and predicted values of quality characteristics are compared to achieve predictive control of the process quality. The product qualification rate of this control scheme under this monitoring method is 96%.
Identification of Factors Influencing the Operational Effect of the Green Wave on Urban Arterial Roads Based on Association Analysis
Authors: Liang Zijun , Zhan Xuejuan , Wang Ruihan , Li Yuqi , Xiao Yun
Published: July 2023

Green wave control is an important technology that synchronizes traffic signals to improve traffic flow on urban arterial roads. Current research has focused on optimizing and evaluating control schemes; however, their operational effect is easily affected by a variety of traffic and travel factors. This means it is important to study methods to identify the factors influencing the operational effect of the green wave on arterial roads. In this study, we conducted innovative research to identify these factors and made breakthroughs in optimizing and evaluating schemes of green wave control. We use the number of stops, travel time, and delays as representative evaluation indicators to assess the effects of four influencing factors: design speed, signal timing, pedestrian crossing, and heavy vehicles. An association analysis that combines sensitivity analysis and grey relational analysis was used to rank these factors in their degree of correlation. A case study was conducted based on the traffic data on Eshan Road in Wuhu City to verify the proposed method. The results of simulations in Vissim 7.0 showed that pedestrian crossing and heavy vehicles were the more important factors influencing the operational effect of the green wave. Moreover, implementing measures related to traffic management helped improve the effect to an extent greater than by optimizing the scheme for green wave control alone. Additionally, optimizing control schemes in the context of implementing measures related to traffic management significantly improved the operational effect of the green wave.
The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
Authors: Wang Yonggui , Qi Yong , Li Jian , Huan Le , Li Yusen , Xie Bitao , Wang Yongshan
Published: July 2023

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.
Biofilm Adhesion and Micromorphology Analysis after Professional Oral Hygiene Procedures on CAD/CAM Milled and Veneered Zirconia Restorations: In Vitro Study
Authors: Perić Mirjana , Čairović Aleksandra , Špadijer Gostović Aleksandra , Stanimirović Dragan , Kostić Marina , Ivanov Marija , Ćirić Ana , Glamočlija Jasmina , Pavlović Vladimir B.
Published: July 2023

Objective: The purpose of this study was to evaluate the surface texture and biofilm adhesion of veneered or CAD/CAM milled zirconia (partially stabilized with yttrium) after professional oral hygiene procedures. The samples (4 × 4 mm, thickness 2 mm; n  =  72) were separated from zirconia blanks (3Y-TZP-LA). One group was veenered with ceramics, and the other group of samples was CAD/CAM milled. Each group had two subgroups: polished and glazed. The samples were subjected to simulated strokes of professional brushing using abrasive paste and ultrasonic scaling. The parameters of surface micromorphology and receptivity to biofilm were calculated before and after simulating the given methods of the professional maintenance of oral hygiene. Scanning electron microscopy (SEM) was used to evaluate zirconia surface properties. Microbial (bacterial/fungal) species (Staphylococcus aureus, Streptococcus sanguinis and Candida albicans) were used and cultured on respective sterilized zirconia surfaces. Colony-forming unit (CFU) counts were used to quantify the amount of biofilm formation on zirconia samples surfaces. Results: The SEM analysis showed the greatest change in surface microtopography after the use of ultrasonic scaling on glazed zirconia samples. Less formation of colonies on the surfaces of CAD/CAM milled zirconia restorations was observed. Conclusion: Routine methods of oral hygiene professional maintenance can damage the surfaces of glazed zirconia restorations.
Defect Detection in CFRP Concrete Reinforcement Using the Microwave Infrared Thermography (MIRT) Method—A Numerical Modeling and Experimental Approach
Authors: Keo Sam Ang , Szymanik Barbara , Le Roy Claire , Brachelet Franck , Defer Didier
Published: July 2023

This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP.
The Influence of Functional Materials on the Size of the Lipid Vesicles in Beverages
Authors: Yoda Tsuyoshi
Published: July 2023

By investigating the hydrophobic properties and functional components including ethyl caproate (EC), caproic acid (CA), isoamyl acetate (IA), isoamyl alcohol (IAA), isovaleraldehyde (IVA), and procyanidin B2 (PB2) in beverages, one can incorporate them with 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipids to create cell-sized lipid vesicles. The aim of this study was to explore the correlation between the concentration of flavors or functional compounds and the size of the lipid vesicles. It was observed that EC, CA, IA, and IAA decreased the size of lipid vesicles. In contrast, IVA and PB2 increased their size. To comprehend this correlation, both the chemical structure of these compounds in relation to DOPC membranes and the fluidity of the membranes were considered. The size of the lipid vesicles was influenced by the molecular interactions between the compounds and DOPC. Those were caused by, in particular, the balance between hydrophobicity and hydrophilicity. Compounds with higher hydrophobicity tended to decrease the size of the lipid vesicles, whereas compounds with greater hydrophilicity had the opposite effect, leading to an increase in size. These findings suggest that the size of lipid vesicles can serve as a potential indicator for rapidly evaluating the concentration of these components in beverages.
Seasonal Distribution and Source Apportionment of Chemical Compositions in PM2.5 in Nanchang, Inland Area of East China
Authors: Huang Hong , Yin Xin , Tang Yuan , Zou Changwei , Li Jianlong , Yu Chenglong , Zhu Fangxu
Published: July 2023

PM2.5 was sampled in four seasons of 2021 in Nanchang. Organic carbon (OC), elemental carbon (EC), and water-soluble ions were the main chemical compositions in PM2.5. The annual average of OC/PM2.5 and EC/PM2.5 was 17.1% and 2.1%, respectively, while nine water-soluble ions were 56.7%. The order of each ion percentage in PM2.5 was NO3− > SO42− > K+ > Na+ > NH4+ > Cl− > NO2− > Ca2+ > Mg2+. The OC/EC (6.54, 13.17, 8.95, 7.99) and Char-EC/Soot-EC (0.88, 0.64, 1.32, 3.74) indicated that the carbon aerosols mainly originated from coal combustion, biomass combustion, and motor-vehicle emissions. High concentrations of Cl− and Ca2+ in spring were associated with dust sources. A good correlation between Na+, SO42−, and NO3− suggests the formation of Na2SO4 and NaNO3. The results of PM2.5 source apportionment by positive matrix factorisation (PMF) showed five main sources: motor-vehicle sources (18–33%), secondary sources (16–36%), coal combustion sources (16–30%), biomass-combustion sources (10–28%), and dust sources (5–7%). Backward trajectory clustering analysis showed PM2.5 in spring and autumn were more influenced by medium distance and local air but mainly influenced by local sources in winter.
Optimal Decision for Repairable Products Sale and Warranty under Two-Dimensional Deterioration with Consideration of Production Capacity and Customers’ Heterogeneity
Authors: Chen Ming-Nan , Fang Chih-Chiang
Published: July 2023

An effective warranty policy is not only an obligation for the manufacturer or vendor, but it also enhances the willingness of customers to purchase from them in the future. To earn more customers and increase sales, manufacturers or vendors should be inclined to prolong the service life of their products as an effort to gain more customers. Nevertheless, manufacturers or vendors will not provide a boundless warranty in order to dominate the market, since the related warranty costs will eventually exceed the profits in the end. Therefore, it is a question of weighing the advantage of extending the warranty term in order to earn the trust of new customers against the investment. In addition, since deterioration depends on both time and usage, the deterioration estimation for durable products may be incorrect when considering only one factor. For such problems, a two-dimensional deterioration model is suitable, and the failure times are drawn from a non-homogeneous Poisson process (NHPP). Moreover, customers’ heterogeneity, manufacturers’ production capacity, and preventive maintenance services are also considered in this study. A mathematical model with the corresponding solution algorithm is proposed to assist manufacturers in making systematic decisions about pricing, production, and warranty. Finally, managerial implications are also provided for refining related decision-making.
Electrolytes for Aqueous Zn-Ion Batteries Working in Wide-Temperature Range: Progress and Perspective
Authors: Sun Lixia , Song Zhongcheng , Deng Chao , Wang Qiang , Mo Funian , Hu Haibo , Liang Guojin
Published: July 2023

Aqueous Zn-ion Batteries (AZIBs) have garnered significant interest in recent years, owing to their inherent safety, affordability, and eco-friendliness. Recently, substantial research has been conducted to broaden the application scenarios of AZIBs by regulating the electrode and electrolyte materials. In this review, we provide a comprehensive analysis of the challenges and solutions associated with AZIBs to meet extreme conditions, such as low temperatures, high temperatures, and wide temperature ranges. We also discuss electrolyte optimization strategies for each of these conditions. Finally, we outline potential avenues for further advancements and offer insights into the future of this burgeoning field of AZIBs.
Effects of Alterations in Resting-State Neural Networks on the Severity of Neuropathic Pain after Spinal Cord Injury
Authors: Park Eunhee , Park Jang Woo , Kim Eunji , Min Yu-Sun , Lee Hui Joong , Jung Tae-Du , Chang Yongmin
Published: July 2023

Neuropathic pain (NP) following spinal cord injury (SCI) is refractory to pain control strategies, and the underlying neuronal mechanisms remain poorly understood. This study aimed to determine the brain regions engaged in maintaining a spontaneous resting state and the link between those regions and the severity of NP in patients with incomplete SCI. Seventy-three subjects (41 patients and 32 age- and sex-matched healthy controls) participated in this retrospective study. Regarding the neurological level of injury, patients with incomplete SCI experienced at-level or below-level NP. The severity of NP was evaluated using a visual analog scale (VAS), and patients were divided into mild and moderate–severe NP groups based on VAS scores. Graph theory and fractional amplitude of low-frequency fluctuation (fALFF) analyses were performed to compare resting-state functional magnetic resonance imaging (fMRI) analysis results among the three groups. Graph theory analysis was performed through a region of interest (ROI)-to-ROI analysis and then fALFF analysis was performed in the brain regions demonstrating significant differences among the three groups analyzed using the graph theory. We evaluated whether the brain regions showing significant differences using graph theory and fALFF correlated with the VAS scores. Patients with moderate–severe NP showed reduced node degree and fALFF in the left middle frontal gyrus compared with those with mild NP and healthy controls. Furthermore, patients with severe NP demonstrated increased average path lengths and reduced fALFF values in the posterior cingulate gyrus. This study found that changes in intrinsic oscillations of fMRI signals in the middle frontal gyrus and posterior cingulate gyrus were significant considering the severity of NP.
Peripheral Mitochondrial Dysfunction: A Potential Contributor to the Development of Metabolic Disorders and Alzheimer’s Disease
Authors: Sultana Most , Hia Raksa , Akinsiku Oluwatosin , Hegde Vijay
Published: July 2023

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by loss of function and eventual death of neurons in the brain. Multiple studies have highlighted the involvement of mitochondria in the initiation and advancement of neurodegenerative diseases. Mitochondria are essential for ATP generation, bioenergetics processes, the regulation of calcium homeostasis and free radical scavenging. Disrupting any of these processes has been acknowledged as a major contributor to the pathogenesis of common neurodegenerative diseases, especially AD. Several longitudinal studies have demonstrated type 2 diabetes (T2D) as a risk factor for the origin of dementia leading towards AD. Even though emerging research indicates that anti-diabetic intervention is a promising option for AD prevention and therapy, results from clinical trials with anti-diabetic agents have not been effective in AD. Interestingly, defective mitochondrial function has also been reported to contribute towards the onset of metabolic disorders including obesity and T2D. The most prevalent consequences of mitochondrial dysfunction include the generation of inflammatory molecules and reactive oxygen species (ROS), which promote the onset and development of metabolic impairment and neurodegenerative diseases. Current evidence indicates an association of impaired peripheral mitochondrial function with primary AD pathology; however, the mechanisms are still unknown. Therefore, in this review, we discuss if mitochondrial dysfunction-mediated metabolic disorders have a potential connection with AD development, then would addressing peripheral mitochondrial dysfunction have better therapeutic outcomes in preventing metabolic disorder-associated AD pathologies.
TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences
Authors: Kabir Md Wasi Ul , Alawad Duaa Mohammad , Mishra Avdesh , Hoque Md Tamjidul
Published: July 2023

Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. The flexibility is determined by the fluctuations in torsion angles, specifically phi (φ) and psi (ψ), which define the protein backbone. These angle fluctuations are derived from variations in backbone torsion angles observed in different models. By analyzing the fluctuations in Cartesian coordinate space, we can understand the structural flexibility of proteins. Predicting torsion angle fluctuations is valuable for determining protein function and structure when these angles act as constraints. In this study, a machine learning method called TAFPred is developed to predict torsion angle fluctuations using protein sequences directly. The method incorporates various features, such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, and more. TAFPred, employing an optimized Light Gradient Boosting Machine Regressor (LightGBM), achieved high accuracy with correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the φ and ψ angles, respectively. Compared to the state-of-the-art method, TAFPred demonstrated significant improvements of 10.08% in MAE and 24.83% in PCC for the phi angle and 9.93% in MAE, and 22.37% in PCC for the psi angle.
Biological Fitness Cost, Demographic Growth Characteristics, and Resistance Mechanism in Alpha-Cypermethrin-Resistant Musca domestica (Diptera: Muscidae)
Authors: Hafez Abdulwahab M. , Abbas Naeem
Published: July 2023

Musca domestica L., a pest of animals and humans, has developed resistance to alpha-cypermethrin, a pyrethroid insecticide commonly used to control medically important pests in many countries, including Saudi Arabia. We investigated the mechanism underlying the development of alpha-cypermethrin resistance and life history characteristics of alpha-cypermethrin–susceptible (Alpha-SS) and alpha-cypermethrin-resistant (Alpha-RS) M. domestica using the age-stage, two-sex life table theory, which is crucial for developing a future rational management strategy and minimizing the negative effects of alpha-cypermethrin on the environment. Our results showed that Alpha-RS M. domestica had a 405.93-fold increase in resistance to alpha-cypermethrin relative to Alpha-SS M. domestica. This increase in the resistance toward insecticide was attributed to metabolic enzymes, such as glutathione S-transferases, specific esterases, and cytochrome P450 monooxygenases. Furthermore, Alpha-RS M. domestica exhibited lower relative fitness (0.50), longevity, survival rate, life expectancy, reproductive values, intrinsic rate of increase, net reproductive rate, fecundity, maternity, and finite rate of increase, along with shorter larval, female preadult, and adult durations than Alpha-SS M. domestica, indicating fitness costs associated with most parameters. However, no significant differences were found between the strains in the following parameters: egg, pupa, and male preadult durations; adult preoviposition, total preoviposition, and oviposition periods; female ratio; and total generation time. Additionally, Alpha-RS M. domestica had a markedly lower intrinsic rate of increase, net reproductive rate, and finite rate of increase than Alpha-SS M. domestica. The results of this study suggest that alpha-cypermethrin resistance may lead to dominant fitness costs in M. domestica. Overall, these findings will aid in the development of rational control strategies for M. domestica as well as help to reduce pesticide pollution.
The Spatial Precision of Contextual Feedback Signals in Human V1
Authors: Petro Lucy S. , Smith Fraser W. , Abbatecola Clement , Muckli Lars
Published: July 2023

Neurons in the primary visual cortex (V1) receive sensory inputs that describe small, local regions of the visual scene and cortical feedback inputs from higher visual areas processing the global scene context. Investigating the spatial precision of this visual contextual modulation will contribute to our understanding of the functional role of cortical feedback inputs in perceptual computations. We used human functional magnetic resonance imaging (fMRI) to test the spatial precision of contextual feedback inputs to V1 during natural scene processing. We measured brain activity patterns in the stimulated regions of V1 and in regions that we blocked from direct feedforward input, receiving information only from non-feedforward (i.e., feedback and lateral) inputs. We measured the spatial precision of contextual feedback signals by generalising brain activity patterns across parametrically spatially displaced versions of identical images using an MVPA cross-classification approach. We found that fMRI activity patterns in cortical feedback signals predicted our scene-specific features in V1 with a precision of approximately 4 degrees. The stimulated regions of V1 carried more precise scene information than non-stimulated regions; however, these regions also contained information patterns that generalised up to 4 degrees. This result shows that contextual signals relating to the global scene are similarly fed back to V1 when feedforward inputs are either present or absent. Our results are in line with contextual feedback signals from extrastriate areas to V1, describing global scene information and contributing to perceptual computations such as the hierarchical representation of feature boundaries within natural scenes.
Assigning the Absolute Configuration of Inositol Poly- and Pyrophosphates by NMR Using a Single Chiral Solvating Agent
Authors: Ritter Kevin , Jork Nikolaus , Unmüßig Anne-Sophie , Köhn Maja , Jessen Henning J.
Published: July 2023

Inositol phosphates constitute a family of highly charged messenger molecules that play diverse roles in cellular processes. The various phosphorylation patterns they exhibit give rise to a vast array of different compounds. To fully comprehend the biological interconnections, the precise molecular identification of each compound is crucial. Since the myo-inositol scaffold possesses an internal mirror plane, enantiomeric pairs can be formed. Most commonly employed methods for analyzing InsPs have been geared towards resolving regioisomers, but they have not been capable of resolving enantiomers. In this study, we present a general approach for enantiomer assignment using NMR measurements. To achieve this goal, we used 31P-NMR in the presence of L-arginine amide as a chiral solvating agent, which enables the differentiation of enantiomers. Using chemically synthesized standard compounds allows for an unambiguous assignment of the enantiomers. This method was applied to highly phosphorylated inositol pyrophosphates, as well as to lowly phosphorylated inositol phosphates and bisphosphonate analogs. Our method will facilitate the assignment of biologically relevant isomers when isolating naturally occurring compounds from biological specimens.
Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features
Authors: Tian Leqi , Wu Wenbin , Yu Tianwei
Published: July 2023

Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n). Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly connected important features by involving the known biological network when constructing the forest. The algorithm can identify effective features that form highly connected sub-graphs and achieve equivalent classification accuracy to RF. To evaluate the capability of our proposed method, we conducted simulation experiments and applied the method to two real datasets—non-small cell lung cancer RNA-seq data from The Cancer Genome Atlas, and human embryonic stem cell RNA-seq dataset (GSE93593). The resulting high classification accuracy, connectivity of selected sub-graphs, and interpretable feature selection results suggest the method is a helpful addition to graph-based classification models and feature selection procedures.
2-(4-Benzyloxy-3-methoxyphenyl)-5-(carbethoxyethylene)-7-methoxy-benzofuran, a Benzofuran Derivative, Suppresses Metastasis Effects in P53-Mutant Hepatocellular Carcinoma Cells
Authors: Tseng Tsui-Hwa , Shao Yi-Chia , Lee Yean-Jang , Lee Huei-Jane
Published: July 2023

2-(4-Benzyloxy-3-methoxyphenyl)-5-(carbethoxyethylene)-7-methoxy-benzofuran (BMBF), a benzofuran derivative, is an intermediate found in the process of total synthesis of ailanthoidol. Benzofuran derivatives are a class of compounds that possess various biological and pharmacological activities. The present study explored the anti-metastasis effects of BMBF in hepatocellular carcinoma (HCC). Our preliminary findings indicate that BMBF suppresses the proliferation and changes the morphology of Huh7—an HCC cell line with a mutated p53 gene (Y220C). According to a scratching motility assay, non-cytotoxic concentrations of BMBF significantly inhibited the motility and migration in Huh7 cells. BMBF upregulated the expression of E-cadherin and downregulated the expression of vimentin, Slug, and MMP9, which are associated with epithelial–mesenchymal transition (EMT) and metastasis in Huh7 cells. BMBF decreased the expression of integrin α7, deactivated its downstream signal FAK/AKT, and inhibited p53 protein levels. Cell transfection with p53 siRNA resulted in the prevention of cell invasion because of the reduction in integrin α7, Slug, and MMP-9 in Huh7 cells. BMBF had anti-metastatic effects in PLC/PRF/5—an HCC cell line with R249S, a mutated p53 gene. Our findings indicate that BMBF has anti-metastatic effects in downregulating p53 and mediating the suppression of integrin α7, EMT, and MMP-9 in HCC cells with a mutated p53 gene.