bluuebunny/crossref_metadata_2025 huggingface dataset:
Acidulated Soapstock as a Carbon Source for the Production of Mannosylerythritol Lipids by Pseudozyma antarctica ATCC 2706
Authors: Dombe Sushil S. , Mali Suraj N. , Jadhav Jagruti V. , Kale Sandeep B. , Pratap Amit P. , Cruz Jorddy N.
Published: October 2024

Acid oil produced from the acidulation of soapstock was investigated as a feedstock for producing a glycolipid biosurfactant, manosylerithritol lipid (MEL), by microbial fermentation. Fermentation with the optimized acid oil substrate concentration of 200 g L−1 resulted in 3.89 g L−1 of MEL. To enhance productivity, the titer MEL was produced by a multistage fermentation process with the periodic addition of 20–50 g L−1 substrate after the stationary phase of the culture. The repeated substrate feeding marginally enhanced the MEL titer up to 5.56 g L−1. The MEL from the culture broth was recovered by solvent extraction at different pH levels. Among the solvents tested, ethyl acetate: acetonitrile (9:1) demonstrated a higher partition coefficient value of 3.1 in acidic conditions with 75.6% MEL recovery. The MEL produced by using acid oil as feedstock has significant surface-active properties, measuring 2.8 × 10−6 M in critical micelle concentration with 29.3 mN m−1 surface tension.
Oral Administration of Ulva pertusa Kjellman Improves Intestinal Motility Against Loperamide-Induced Constipation in Mice
Authors: Koh Eun-Jeong , Sunwoo In-Yung , Ryu Yong-Kyun , Lee Won-Kyu , Kim Taeho , Choi Woon-Yong
Published: October 2024

Ulva pertusa Kjellman (U. pertusa) is a seaweed indigenous to the intertidal zone of the Korean coastline. U. pertusa exhibits immune-enhancing and antitumor activities, and its effects on intestinal health have gained attention. However, the mechanisms underlying its beneficial effects on intestinal physiology remain elusive. Here, the effect of U. pertusa intake in ameliorating loperamide-induced constipation in male mice was evaluated. Additionally, cellular levels of proinflammatory cytokines, including nuclear factor-kB and interleukin-1β, were assessed to decipher the intricate interplay between inflammation and improvements in bowel movement. U. pertusa intake increased fecal weight and water content and improved the intestinal transit rate. Moreover, it reduced the levels of proinflammatory cytokines, possibly via short-chain fatty acids implicated in modulating intestinal motility and mucosal inflammation. These findings underscore the efficacy of U. pertusa in improving bowel motility and intestinal functionality, and its potential in ameliorating constipation.
Artificial Intelligence Approach to Business Process Re-Engineering the Information Flow of Warehouse Shipping Orders: An Italian Case Study
Authors: Merli Matteo , Ciarapica Filippo Emanuele , Varghese Kishore Chalakkal , Bevilacqua Maurizio
Published: October 2024

Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase.
Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell
Authors: Gouhail Mohamed , Salhi Issam , El Mazoudi El houssine , Doubabi Said
Published: October 2024

Fuel cell electric vehicles (FCEVs) are among the devices that have emerged in recent years. To provide electricity to the electric motors, they use a proton-exchange membrane fuel cell (PEMFC) as the primary energy source and a secondary source consisting of an energy storage system (battery or supercapacitors). The addition of these sources to the motors and accessories of a vehicle requires the association of static converters to condition the different power sources. In addition, a high-efficiency and enhanced-reliability power converter is essential to connect the PEMFC to the vehicle’s DC bus. This paper proposes a robust feedback controller for a four-phase interleaved boost converter used with PEMFC. The proposed controller has double loops based on a state-feedback controller, and an inner loop which translates the differential equation of the system into a state representation by linearization around its operation points. The reference current is generated by state feedback in the outer loop; the state variable is defined by using a change variable. The strong robustness and highly dynamic characteristics of the proposed controller are demonstrated through its performance in terms of output voltage, source current, and settling time. The findings indicate that the proposed controller achieves a response time of 20 ms, resulting in an over 50% improvement compared to the controllers referenced in the literature. Additionally, it reduces both current and voltage ripple, keeping them each below 10%. Further, the controller gains synthesis is validated using the linear quadratic regulator (LQR) technique as well as boundary conditions, and its robustness is verified, taking into account the uncertainty of various operating conditions and discrepancies in circuit components. A double-loop super-twisting sliding mode controller, a backstepping control algorithm, and a PI controller are selected for comparison and discussion. Subsequently, the effectiveness of the proposed controller is evaluated through simulation with the parameters of a 500 W fuel cell system.
XAI-Based Accurate Anomaly Detector That Is Robust Against Black-Box Evasion Attacks for the Smart Grid
Authors: Elgarhy Islam , Badr Mahmoud M. , Mahmoud Mohamed , Alsabaan Maazen , Alshawi Tariq , Alsaqhan Muteb
Published: October 2024

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benign data and identify deviations from benign patterns as anomalies, but their performance is highly sensitive to the selected threshold values. Additionally, ML detectors are vulnerable to evasion attacks, where attackers make minimal changes to malicious samples to evade detection. To address these limitations, we introduce a hybrid anomaly detector that combines a Deep Auto-Encoder (DAE) with a One-Class Support Vector Machine (OCSVM). This detector not only enhances classification performance but also mitigates the threshold sensitivity of the DAE. Furthermore, we evaluate the vulnerability of this detector to benchmark evasion attacks. Lastly, we propose an accurate and robust cluster-based DAE+OCSVM ET anomaly detector, trained using Explainable Artificial Intelligence (XAI) explanations generated by the Shapley Additive Explanations (SHAP) method on consumption readings. Our experimental results demonstrate that the proposed XAI-based detector achieves superior classification performance and exhibits enhanced robustness against various evasion attacks, including gradient-based and optimization-based methods, under a black-box threat model.
Development and Experimental Study of an Experimental Setup for an Online Vibrating Tube Liquid Densitometer
Authors: Xu Defu , Sun Yaoning , Chen Welin , Mu Jun , Peng Wang
Published: October 2024

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, the real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an online vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density; it also conducts experiments with an online vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant-temperature stirring system, a walk-in constant-temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device’s evaluation is U = 0.08 kg/m3, k = 2. We built a set of pressure-density static test systems, statically testing the online vibrating tube’s liquid-density meter vibration frequency at different pressures; the whole set of systems can be used to assess the specific density, temperature, and pressure range of online vibrating tube liquid density meters in the experimental research to derive the standard temperature. Through the experimental research, we can accurately derive the fitting coefficients under the standard temperature, specific temperature, and pressure of online vibrating tube liquid densitometers, and calculate the fitting error of online vibrating tube liquid densitometers under different temperatures and pressures within the experimental range through fitting equations and coefficients, so as to realize the practical application of online vibrating tube liquid densitometers in engineering by utilizing straight-tube-type and curved-type online vibrating tube densitometers. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system’s vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device’s conformity. The study verified the device’s scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research. Using this device in subsequent experiments can verify the effects of viscosity, inlet, installation, and other factors on the online vibrating tube liquid densitometer’s metrological performance. Further experimental research on the pressure–frequency–density test system and the establishment of a wide range of temperatures and pressures within the pressure standard density test system are needed to achieve a wide range of temperatures and pressures under the standard density test.
Application of the Clark–Wright Method to Improve the Sustainability of the Logistic Chain
Authors: Mašek Jaroslav , Pálková Adriana , Bulková Zdenka
Published: October 2024

The incessant consumption of goods and materials underscores the need to address the growing problem of waste generation and its profound impact on environmental sustainability. The problem of waste removal can be approached in different ways, whether it is the routing of vehicles, the work of drivers, the optimal distribution of waste bins, or other matters in the entire waste process. The aim of this study is to investigate the possibilities of optimizing waste collection processes in the region using a slightly modified Clark–Wright method. Optimal waste collection routes are defined with a focus on cost reduction and overall optimization of logistic chain processes. The established mathematical model for the capacitated vehicle routing problem includes the principles of sustainability and environmental friendliness. The results indicate that the largest messenger of all the newly proposed routes are the routes containing the surrounding settlements. Newly designed routes lead to significant reductions in fuel consumption and vehicle maintenance, which has a positive impact on financial and environmental resources. The conclusion indicates that by applying the Clark–Wright method, we have achieved a reduction in the number of routes of twenty fewer routes. This study provides regions with a detailed plan to improve waste management practices, contributing to a future of increased sustainability and environmental awareness.
Enhancement of Additive Manufacturing Processes for Thin-Walled Part Production Using Gas Metal Arc Welding (GMAW) with Wavelet Transform
Authors: Foorginejad Abolfazl , Khatibi Siamak , Torshizi Hojjat , Emam Sayyed Mohammad , Afshari Hossein
Published: October 2024

Additive manufacturing encompasses technologies that produce three-dimensional computer-aided design (CAD) models through a layer-by-layer production process. Compared to traditional manufacturing methods, additive manufacturing technologies offer significant advantages in producing intricate components with minimal energy consumption, reduced raw material waste, and shortened production timelines. AM methods based on shielded gas welding have recently piqued the interest of researchers due to their high efficiency and cost-effectiveness in manufacturing critical components. However, one of the most formidable challenges in additive manufacturing methods based on shielded gas welding lies in the irregularity of weld bead height at different points, compromising the precision of components produced using these techniques. In this current research, we aimed to achieve uniform weld heights along the welding path by considering the most influential parameters on weld bead geometry and conducting experimental tests. Input parameters of the process, including nozzle angle, welding speed, wire speed, and voltage, were considered. Simultaneously, image processing and wavelet transform were employed to assess the uniformity of weld bead height. These parameters were applied to produce intricate parts after identifying optimal parameters that yielded the smoothest weld lines. According to the results, the appropriate bead for manufacturing the part was extracted. The results show that the smoothest bead line is achieved in 27 V as the highest level of voltage, at a 90° nozzle position and the maximum wire feed rate. Parts manufactured using this method across different layers exhibited no distortions, and the repeatability of production substantiated the high reliability of this approach for component manufacturing.
Using Video-Fluoroscopy and Multibody Modelling to Unveil the Influence of a Gradually Reducing Femoral Radius on Ligament Elongation Patterns Following Posterior Cruciate-Retaining Total Knee Arthroplasty
Authors: Hosseini Nasab Seyyed Hamed , Bänteli Philipp , List Renate
Published: October 2024

Stability in total knee arthroplasties (TKAs) is mainly provided by soft tissue structures and the implant geometry. Paradoxical anterior translation could be decreased with a gradually reducing femoral radius compared to a dual-radii design. However, the influence of the sagittal curvature of the femoral condyles on knee ligaments remains unclear. This study quantified the length change patterns of the medial and lateral collateral ligaments (MCL and LCL) and posterior cruciate ligament (PCL) in 15 subjects with a gradually reducing radius and 15 subjects with a dual-radii TKA. Kinematics obtained from video-fluoroscopy were used to drive personalised multibody knee models. The ligament lengths were analysed throughout complete cycles of level gait, stair descent, and sit-to-stand-to-sit activity. Regardless of the implant design, our results indicated flexion-dependent elongation patterns in all ligament bundles. Importantly, however, subjects with the dual-radii implant design exhibited higher ligament strains during the mid-flexion phase compared to those with gradually reducing designs. Our findings, therefore, emphasise the importance of the impact of subtle changes in implant geometry on the loading patterns of the knee soft tissues, which need to be acknowledged by implant manufacturers and orthopaedic surgeons.
Novel Fluidic Oscillator Evaluation Considering Dimensional Modifications
Authors: Karimzadegan Kavoos , Bergada Josep M.
Published: October 2024

Although flow mixing and cooling can be greatly enhanced when considering the use of fluidic oscillators (FOs), they are more commonly employed in active flow control (AFC) applications where the injected pulsating flow interacts with the boundary layer, usually in order to delay its separation. In fact, prior to any FO implementation in a given application, it is essential to study the range of frequencies and amplitudes it can generate as a function of the incoming mass flow and its dimensions. This is what is being performed in the present manuscript for a rather novel FO configuration. A numerical study of a standard three-dimensional (3D) FO configuration, and also using a two-dimensional (2D) approach, is initially presented. After comparing the 3D and the 2D results and analyzing the main differences, we modified some of the internal dimensions of the FO in order to evaluate the variation in its dynamic performance. The present results clarify which internal dimensional modifications are more effective in generating larger output frequencies and velocity field variations. Care is taken to analyze the origin of self-sustained oscillations. This paper links, for the first time, the origin of the pressure force oscillations at the feedback channel’s outlet, with the interaction of the mixing chamber central jet and the reverse feedback channel flow at the mixing chamber’s converging walls. A novel equation relating the FO outlet mass flow frequency with the time-averaged FC reverse flow is presented and discussed. In fact, the present study needs to be seen as the continuation of a former one, recently published by authors, where the effects of several Reynolds numbers as well as some different internal dimensions were considered.
Towards a Study of Incidental Music Through the Lens of Applied Musicology
Authors: Novaković Monika
Published: October 2024

In this article, applied musicology is discussed in the context of research on incidental music in Serbia—a task which, to my knowledge, has not been undertaken so far. In recent years, the body of publications on applied musicology has notably expanded, resulting in a number of important articles and a landmark collective monograph. This, in turn, prompted me to view my main research interests—applied music and, in particular, incidental music—through the lens of applied musicology and offer my perspective on the possibilities of regarding incidental music as a field that can benefit from applied-musicological interventions. In this article, I draw attention to challenges that arise once a musicologist sets out to analyse incidental music. I undertake this by (a) presenting what I define as a reconstructive-analytical method in approaching incidental music and (b) utilising the narrative “behind the scenes” of my doctoral research.
Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals
Authors: Matrenin Pavel V. , Harlashkin Dmitry A. , Mazunina Marina V. , Khalyasmaa Alexandra I.
Published: October 2024

The accurate prediction of wind power generation, as well as the development of a digital twin of a wind turbine, require estimation of the power curve. Actual measurements of generated power, especially over short-term intervals, show that in many cases the power generated differs from the calculated power, which considers only the wind speed and the technical parameters of the wind turbine. Some of these measurements are erroneous, while others are influenced by additional factors affecting generation beyond wind speed alone. This study presents an investigation of the features influencing the accuracy of calculations of wind turbine power at short-term intervals. The open dataset of SCADA-system measurements from a real wind turbine is used. It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. The root-mean-square error achieved is 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the root-mean-square error to the nominal capacity is 3%.
Making Tax Smart: Feasibility of Distributed Ledger Technology for Building Tax Compliance Functionality to Central Bank Digital Currency
Authors: Louvieris Panos , Ioannou Georgios , White Gareth
Published: October 2024

The latest advancements in distributed ledger technology (DLT) and payment architectures such as the UK’s New Payments Architecture present opportunities for leveraging the hidden informational value and intelligence within payments. In this paper, we present Smart Money, an infrastructure capability for a central bank digital currency (CBDC) which enables real-time value-added tax split payments, oversight, controlled access, and smart policy implementation. This capability is implemented as a prototype called Making Tax Smart (MTS), which utilises the open-source R3 Corda DLT framework. The results presented herein confirm that it is feasible to build an MTS capability which is scalable and co-exists with the current payment systems. Smart Money CBDC has the potential to mobilise payments data, transforming the role of money from a blunt instrument to a government policy sensor and actuator without disrupting the existing money system. DLT, smart contracts, and programmable money have a crucial role to play with benefits for government departments, the economy, and society as a whole.
Spatiotemporal Variations and Driving Factor Analysis of Aerosol Optical Depth in Terrestrial Ecosystems in Northern Xinjiang from 2001 to 2023
Authors: Xiang Zequn , Wu Hongqi , Fan Yanmin , Dang Yu , Bi Yanan , Zhao Jiahao , Song Wenyue , Feng Tianyuan , Zhang Xu
Published: October 2024

Investigating the spatiotemporal variations in Aerosol Optical Depth (AOD) in terrestrial ecosystems and their driving factors is significant for deepening our understanding of the relationship between ecosystem types and aerosols. This study utilized 1 km resolution AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Mann–Kendall (M-K) trend test to analyze the spatiotemporal variations in AOD in seven ecosystems in Northern Xinjiang from 2001 to 2023. The geographic detector model was employed to investigate the effects of driving factors, including gross domestic product, population density, specific humidity, precipitation, temperature, wind speed, soil moisture, and elevation, on the distribution of AOD in the ecosystems. The results indicate that over the past 23 years, wetlands had the highest annual average AOD values, followed by settlements, farmlands, deserts, grasslands, others, and forests, respectively. Furthermore, the AOD values decrease with increasing ecosystem elevation. The annual mean of AOD in Northern Xinjiang generally shows a fluctuating upward trend. The M-K test shows that the proportion of area with an increasing trend in AOD in the settlement ecosystems is the highest (92.17%), while the proportion of area with a decreasing trend in the forest ecosystem is the highest (21.78%). On a seasonal scale, grassland, settlement, farmland, forest, and wetland ecosystems exhibit peak values in spring and winter, whereas desert and other ecosystems only show peaks in spring. Different types of ecosystems show different sensitivities to driving factors. Grassland and forest ecosystems are primarily influenced by temperature and altitude, while desert and settlement ecosystems are most affected by wind speed and humidity. Farmlands are mainly influenced by wind speed and altitude, wetlands are significantly impacted by population density and humidity, and other ecosystems are predominantly affected by humidity and altitude. This paper serves as a reference for targeted air pollution prevention and regional ecological environmental protection.
Impact of Calcium Doping on the Electronic and Optical Characteristics of Strontium Hydride (SrH2): A DFT Study
Authors: Errahoui Hamza , Karouchi Mohamed , Ejjabli Abdelkebir , El haji Aymane , Laassouli Abdelmounaim , Ait El Alia Omar , Chaji Salah , Lachtioui Youssef , Bajjou Omar
Published: October 2024

This study investigates the electronic and optical properties of calcium-doped strontium hydride (SrH2) using first-principles density functional theory (DFT) calculations via the CASTEP code with generalized gradient approximation (GGA). We explore the impact of calcium (Ca) doping on the electronic band structure, density of states (DOS), and optical absorption spectra of SrH2. Our results show that Ca doping significantly alters the electronic properties of SrH2, notably increasing the indirect bandgap from 1.3 eV to 1.6 eV. The DOS analysis reveals new states near the Fermi level, primarily from Ca 3d orbitals. Moreover, the optical absorption spectra display enhanced absorption in the visible range, suggesting the potential for optoelectronic applications. This research highlights the feasibility of tuning the electronic and optical characteristics of SrH2 through Ca doping, thus opening the way for the generation of advanced materials with tailored properties.
Arbitrary Random Variables and Wiman’s Inequality
Authors: Kuryliak Andriy , Skaskiv Oleh , Bandura Andriy
Published: October 2024

We study the class of random entire functions given by power series, in which the coefficients are formed as the product of an arbitrary sequence of complex numbers and two sequences of random variables. One of them is the Rademacher sequence, and the other is an arbitrary complex-valued sequence from the class of sequences of random variables, determined by a certain restriction on the growth of absolute moments of a fixed degree from the maximum of the module of each finite subset of random variables. In the paper we prove sharp Wiman–Valiron’s type inequality for such random entire functions, which for given p∈(0;1) holds with a probability p outside some set of finite logarithmic measure. We also considered another class of random entire functions given by power series with coefficients, which, as above, are pairwise products of the elements of an arbitrary sequence of complex numbers and a sequence of complex-valued random variables described above. In this case, similar new statements about not improvable inequalities are also obtained.
Refined Iterative Method for a Common Variational Inclusion and Common Fixed-Point Problem with Practical Applications
Authors: Thangthong Chaiporn , Suparatulatorn Raweerote , Chaobankoh Tanadon , Chaichana Khuanchanok
Published: October 2024

This paper introduces a novel parallel method for solving common variational inclusion and common fixed-point (CVI-CFP) problems. The proposed algorithm provides a strong convergence theorem established under specific conditions associated with the CVI-CFP problem. Numerical simulations demonstrate the algorithm’s efficacy in the context of signal recovery problems involving various types of blurred filters. The results highlight the algorithm’s potential for practical applications in image processing and other fields.
Necessary and Sufficient Conditions for the Boundedness of Multiple Integral Operators with Super-Homogeneous Kernels in Weighted Lebesgue Space
Authors: Hong Yong , He Bing , Zhang Lijuan
Published: October 2024

Super-homogeneous functions including homogeneous functions, quasi-homogeneous functions, and several non-homogeneous functions are considered. Using the weight function method, the construction conditions of Hilbert-type multiple integral inequalities with super-homogeneous kernels are first discussed. Then, using the obtained results, the construction problem of bounded multiple integral operators with super-homogeneous kernels in weighted Lebesgue space is discussed, and the necessary and sufficient conditions for operator boundedness and the operator norm formula are obtained.
A Hybrid Fuzzy Mathematical Programming Approach for Manufacturing Inventory Models with Partial Trade Credit Policy and Reliability
Authors: Dhandapani Prasantha Bharathi , Kalaichelvan Kalaiarasi , Leiva Víctor , Castro Cecilia , Ramalingam Soundaria
Published: October 2024

This study introduces an inventory model for manufacturing that prioritizes product quality and cost efficiency. Utilizing fuzzy logic and mathematical programming, the model integrates fuzzy numbers to describe uncertainties associated with manufacturing costs and quality control parameters. The model extends beyond conventional inventory systems by incorporating a dynamic mechanism to halt production, employing fuzzy decision variables to optimize the economic order quantity and minimize total costs. Key innovations include the application of approaches related to graded mean integration for defuzzification and the use of Kuhn–Tucker conditions to ensure optimal solutions under complex constraints. These approaches facilitate the precise management of production rates, inventory levels, and cost factors, which are essential in achieving a balance between supply and demand. A computational analysis validates the model’s effectiveness, demonstrating cost reductions while maintaining optimal inventory levels. This underscores the potential of integrating fuzzy arithmetic with traditional optimization techniques to enhance decision making in inventory management. The model’s adaptability and accuracy indicate its broad applicability across various sectors facing similar challenges, offering a valuable tool for operational managers and decision makers to improve efficiency and reduce waste in production cycles.
A Three-Block Inexact Heterogeneous Alternating Direction Method of Multipliers for Elliptic PDE-Constrained Optimization Problems with a Control Gradient Penalty Term
Authors: Chen Xiaotong , Wang Tongtong , Song Xiaoliang
Published: October 2024

Optimization problems with PDE constraints are widely used in engineering and technical fields. In some practical applications, it is necessary to smooth the control variables and suppress their large fluctuations, especially at the boundary. Therefore, we propose an elliptic PDE-constrained optimization model with a control gradient penalty term. However, introducing this penalty term increases the complexity and difficulty of the problems. To solve the problems numerically, we adopt the strategy of “First discretize, then optimize”. First, the finite element method is employed to discretize the optimization problems. Then, a heterogeneous strategy is introduced to formulate the augmented Lagrangian function for the subproblems. Subsequently, we propose a three-block inexact heterogeneous alternating direction method of multipliers (three-block ihADMM). Theoretically, we provide a global convergence analysis of the three-block ihADMM algorithm and discuss the iteration complexity results. Numerical results are provided to demonstrate the efficiency of the proposed algorithm.
Mikhlin-Type Hp Multiplier Theorem on the Heisenberg Group
Authors: Xiao Jinsen , He Jianxun , Wu Yingzhu
Published: October 2024

The classical Fourier multiplier theorem by Mikhlin in Hardy spaces is extended to the Heisenberg group. The proof relies on the theories of atom and molecule functions and the property of special Hermite functions. The main result is a Mikhlin-type Hp multiplier theorem on the Heisenberg group. If an operator-valued function M(λ) satisfies certain conditions, the right-multiplier operator TM is bounded on the Hardy space Hp(Hn), which is defined in terms of maximal functions, and elements can be decomposed into atoms or molecules. The paper also discusses the relationship with other results and open problems.
HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk Factors
Authors: Baharoon Mohammed , Almatar Hessa , Alduhayan Reema , Aldebasi Tariq , Alahmadi Badr , Bokhari Yahya , Alawad Mohammed , Almazroa Ahmed , Aljouie Abdulrhman
Published: October 2024

Study Objectives: This study aimed to develop a multimodal deep learning (MMDL) system called HyMNet, integrating fundus images and cardiometabolic factors (age and sex) to enhance hypertension (HTN) detection. Methods: HyMNet employed RETFound, a model pretrained on 1.6 million retinal images, for the fundus data, in conjunction with a fully connected neural network for age and sex. The two pathways were jointly trained by joining their feature vectors into a fusion network. The system was trained on 5016 retinal images from 1243 individuals provided by the Saudi Ministry of National Guard Health Affairs. The influence of diabetes on HTN detection was also assessed. Results: HyMNet surpassed the unimodal system, achieving an F1 score of 0.771 compared to 0.745 for the unimodal model. For diabetic patients, the F1 score was 0.796, while it was 0.466 for non-diabetic patients. Conclusions: HyMNet exhibited superior performance relative to unimodal approaches, with an F1 score of 0.771 for HyMNet compared to 0.752 for models trained on demographic data alone, underscoring the advantages of MMDL systems in HTN detection. The findings indicate that diabetes significantly impacts HTN prediction, enhancing detection accuracy among diabetic patients. Utilizing MMDL with diverse data sources could improve clinical applicability and generalization.
Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection
Authors: Lee Hyung Rae , Rhee Wounsuk , Chang Sam Yeol , Chang Bong-Soon , Kim Hyoungmin
Published: October 2024

Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique.
Double-Faced Immunological Effects of CDK4/6 Inhibitors on Cancer Treatment: Challenges and Perspectives
Authors: Liu Yongqin , Deng Yiying , Yang Chang , Naranmandura Hua
Published: October 2024

Cyclin-dependent kinases (CDKs) are generally involved in the progression of cell cycle and cell division in normal cells, while abnormal activations of CDKs are deemed to be a driving force for accelerating cell proliferation and tumorigenesis. Therefore, CDKs have become ideal therapeutic targets for cancer treatment. The U.S FDA has approved three CDK4/6 inhibitors (CDK4/6is) for the treatment of patients with hormone receptor-positive (HR+) or human epidermal growth factor receptor 2-negative (HER2−) advanced or metastatic breast cancer, and these drugs showed impressive results in clinics. Besides cell-cycle arrest, there is growing evidence that CDK4/6is exert paradoxical roles on cancer treatment by altering the immune system. Indeed, clinical data showed that CDK4/6is could change the immune system to exert antitumor effects, while these changes also caused tumor resistance to CDK4/6i. However, the molecular mechanism for the regulation of the immune system by CDK4/6is is unclear. In this review, we comprehensively discuss the paradoxical immunological effects of CDK4/6is in cancer treatment, elucidating their anticancer mechanisms through immunomodulatory activity and induction of acquired drug resistance by dysregulating the immune microenvironment. More importantly, we suggest a few strategies including combining CDK4/6is with immunotherapy to overcome drug resistance.
Multiple Omics Analyses Reveal Activation of Nitrogen Metabolism and Flavonoid Glycosylation in Toxicodendron vernicifluum Under High Temperature
Authors: Bai Guoqing , Ding Ruiwen , Su Qizhen , Ge Xiaomin , Li Shasha , Shang Huiying , Zhao Aiguo , Chen Chen
Published: October 2024

Lacquer trees (Toxicodendron vernicifluum), economically vital, face high-temperature stress in summer. Transcriptomic, proteomic, and metabolomic analyses were employed to investigate the mechanisms by which lacquer trees respond to high temperatures. High-temperature treatment led to notable metabolite changes with 224 upregulated and 69 downregulated. Indole-3-acetic acid remained stable while abscisic acid decreased, with increases in jasmonic acid and jasmonoyl-L-isoleucine indicating complex hormonal responses. JAR1 and ABA 8′-hydroxylase encoding genes were upregulated. The rise in JAs boosted the alkaloid content and activated nitrogen transport. High temperatures also increased specific amino acids and upregulated aminotransferase and protease-encoding genes. Metabolomic analysis showed elevated flavonoid glycosides and the upregulation of glycosyltransferase genes. WPCNA found 35 protein modules involved in secondary metabolite biosynthesis, protein phosphorylation, and signal transduction. Protein–protein interaction analysis revealed MYC6’s link with flavonoid biosynthesis, indicating its role in promoting flavonoids.
Development of Simple Sequence Repeat of Monochamus alternatus (Coleoptera: Cerambycidae) Based on Restriction Site-Associated DNA Sequencing
Authors: Lu Jintao , Zhang Senzhe , Liu Jiaxin , Zhang Yuhua , Hu Lijuan , Yang Zhende , Hu Ping
Published: October 2024

Monochamus alternatus, a pest posing a serious threat to coniferous species, such as Pinus massoniana, has had devastating effects on pine forests due to its association with Bursaphelenchus xylophilus. The creation of unique simple sequence repeat (SSR) primers for M. alternatus is crucial, as there has been little study of the species’ phylogeography. The aim of this study was to identify and create polymorphic SSR primers by sequencing samples of M. alternatus obtained from three different sampling points using the restriction site-associated DNA sequencing (Red-seq) approach. Subsequently, supplementary samples were integrated, and genetic typing was performed utilizing the identified polymorphic primers. Through comprehensive analysis, a total of 95,612 SSR loci were identified. Among these, mononucleotide repeats (51.43%), dinucleotide repeats (28.79%), and trinucleotide repeats (16.74%) predominated among the SSR motif types. Ultimately, 18 pairs of SSR primers were screened out, demonstrating stable amplification and high polymorphism. Genetic typing revealed that the mean number of alleles (Na) for these primer pairs ranged from 3 to 8, observed heterozygosity (Ho) ranged from 0.133 to 0.733, polymorphic information content (PIC) ranged from 0.294 and 0.783, and Shannon’s index (I) ranged from 0.590 to 1.802. This study effectively produced 16 pairs of SSR primers that can be applied to different populations of M. alternatus. As a result, important tools for furthering studies on the phylogeography of pine wood nematodes, creating genetic maps, gene mapping, and carrying out in-depth investigations into gene function have been made available.
Evaluation of the Dietary Arginine Supplementation on Yellow Catfish: From a Low-Temperature Farming Perspective
Authors: Cao Quanquan , Kisha Mohamed S. , Gaafar Alkhateib , Younes Abdelgayed Metwaly , Liu Haifeng , Jiang Jun
Published: October 2024

The yellow catfish is an economically significant freshwater fish with increasing importance in aquaculture. However, the low temperature environments prevalent in certain regions pose challenges to its growth, development, and overall health. This study aimed to explore the impact of dietary arginine (Arg) addition on the growth, digestive capacity, and intestinal antioxidant response in fish under low temperature acclimation (18 °C). Total 720 fish were randomly distributed into six groups, each containing 120 fish. Over the course of eight weeks, each group was fed with diets about varying Arg concentrations (1.79–3.26 g/kg). The results indicated that Arg supplementation resulted in an increase in specific growth rate (SGR), feed intake (FI), feed efficiency (FE), as well as pancreatic enzyme activities in both pancreas and intestine. Conversely, malondialdehyde (MDA) and protein carbonyl (PC) contents initially decreased but increased with higher Arg concentrations. Glutathione peroxidase 1a (GPX1a) showed a positive correlation with nuclear factor-erythroid 2-related factor-2 (Nrf2), showing its role in antioxidative capacity. Furthermore, this study revealed that Arg significantly enhanced the activities of anti-superoxide anion, anti-hydroxyl radical, and anti-oxidative enzymes, along with the relative mRNA abundance of Copper-Zinc superoxide dismutase (CuZnSOD), catalase, GPX1a, glutamate-cysteine ligase catalytic subunit (GCLC), and Nrf2 in the intestine. It was determined that yellow catfish weighing between 61.0 g and 89.0 g require an intake of 26.8 g of Arg per kilogram of diet based on polynomial regression analysis of specific growth rate (SGR), which is equivalent to 37.0 g of dietary protein, under sub-low temperature conditions of 18 °C.
Serum from Hypertensive Patients Induces Cancer-Supporting Phenotype of Vascular Endothelium In Vitro
Authors: Uruski Paweł , Mikuła-Pietrasik Justyna , Tykarski Andrzej , Książek Krzysztof
Published: October 2024

Background/Objectives: Large-scale epidemiological studies have established a bidirectional association between hypertension and cancer. However, the underlying mechanisms explaining this connection remain unclear. In our study, we investigated whether serum from patients with hypertension (HT) could enhance the aggressiveness of cancer cells in vitro through alterations in endothelial cell phenotype. Methods: Experiments were performed using EAhy926 endothelial cells and ovarian (SKOV-3), colorectal (SW480), pancreatic (PSN-1), breast (MCF-7), and lung (A549) cancer cell lines. Results: This study showed that conditioned medium (CM) produced by EAhy926 cells, when exposed to serum from patients with untreated hypertension (HT-CM), promotes the proliferation, migration, and invasion of every cancer cell line tested. In addition, endothelial cells subjected to HT serum promote the adhesion of all cancer cell types except PSN-1. An intensified transendothelial invasion of cancer cells was accompanied by decreased expression of junctional proteins (connexin 43, E-cadherin, occluding, desmoglein) in HT serum-treated endothelial cells. Quantitative analysis of the secretome of endothelial cells subjected to HT serum showed that they secrete increased amounts of CCL2, CXCL1, CXCL8, bFGF, HGF, IL-6, PAI-1, and TGF-β1. Moreover, cancer cells exposed to HT-CM display increased mRNA expression for several pro-cancerogenic agents, including CXCL8, tPA, and VEGF. Conclusions: Our report shows that hypertension may potentiate cancer cell aggressiveness by modulating endothelial cell phenotype. Further tests with antihypertensive drugs are required to assess whether effective treatment of hypertension can mitigate its cancer-promoting potential.
Arabidopsis RALF4 Rapidly Halts Pollen Tube Growth by Increasing ROS and Decreasing Calcium Cytoplasmic Tip Levels
Authors: Somoza Sofía C. , Boccardo Noelia A. , Santin Franco , Sede Ana R. , Wengier Diego L. , Boisson-Dernier Aurélien , Muschietti Jorge P.
Published: October 2024

In recent years, the rapid alkalinization factor (RALF) family of cysteine-rich peptides has been reported to be crucial for several plant signaling mechanisms, including cell growth, plant immunity and fertilization. RALF4 and RALF19 (RALF4/19) pollen peptides redundantly regulate the pollen tube integrity and growth through binding to their receptors ANXUR1/2 (ANX1/2) and Buddha’s Paper Seal 1 and 2 (BUPS1/2), members of the Catharanthus roseus RLK1-like (CrRLK1L) family, and, thus, are essential for plant fertilization. However, the signaling mechanisms at the cellular level that follow these binding events remain unclear. In this study, we show that the addition of synthetic peptide RALF4 rapidly halts pollen tube growth along with the excessive deposition of plasma membrane and cell wall material at the tip. The ratiometric imaging of genetically encoded ROS and Ca2+ sensors-expressing pollen tubes shows that RALF4 treatment modulates the cytoplasmic levels of reactive oxygen species (ROS) and calcium (Ca2+) in opposite ways at the tip. Thus, we propose that pollen RALF4/19 peptides bind ANX1/2 and BUPS1/2 to regulate ROS and calcium homeostasis to ensure proper cell wall integrity and control of pollen tube growth.
Exploring the Spectrum of Comorbidities Associated with Primary Aldosteronism: Insights from a Large Real-World Case-Control Study
Authors: Krieg Andreas , Krieg Sarah , Heuser Andreas , Laverenz Ulrich , Istrate Valentin-Alin , Schott Matthias , Kostev Karel
Published: October 2024

Background: Primary aldosteronism (PA) is a common cause of endocrine hypertension, characterized by excessive aldosterone secretion leading to hypertension, hypokalemia, and metabolic alkalosis. While historically diagnosed based on this classic triad of symptoms, current understanding reveals a more nuanced presentation. This study aimed to investigate the prevalence of PA-associated diseases in a large German population. Methods: Medical records from the IQVIATM Disease Analyzer database were analyzed retrospectively. PA patients (n = 860) were matched with non-PA individuals (n = 4300) by age and sex. Associations between PA and predefined chronic diseases were examined using multivariable logistic regression. Results: PA was significantly associated with hypokalemia (7.8% vs. 1.6%, odds ratio (OR): 3.45; 95% confidence intervals (CIs): 2.41–4.96), hypertension (56.1% vs. 28.5%; OR: 2.37; 95% CIs: 2.00–2.81), hepatic steatosis (11.3% vs. 3.0%; OR: 1.85; 95% CIs: 1.34–2.57), gout (8.3% vs. 2.2%; OR: 1.64; 95% CIs: 1.15–2.35), chronic kidney disease (6.3% vs. 2.2%; OR: 1.59; 95% CIs: 1.10–2.31), diabetes mellitus not otherwise specified (7.9% vs. 2.9%; OR: 1.49; 95% CIs: 1.06–2.09), obesity (13.5% vs. 5.1%; OR: 1.38; 95% CIs: 1.05–1.82), and depression (14.8% vs. 6.2%; OR: 1.37; 95% CIs: 1.07–1.77). Conclusions: While the study design had limitations, including reliance on ICD codes for diagnosis, these findings underscore the critical need for early detection and personalized management strategies for PA to reduce associated risks and improve patient outcomes.