bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Integrative Study on Inhibitory Effects of Pentapeptides on Polymerization and Cell Toxicity of Amyloid-β Peptide (1–42)
Authors: Ye Lianmeng , Ajuyo Nuela Manka’a Che , Wu Zhongyun , Yuan Nan , Xiao Zhengpan , Gu Wenyu , Zhao Jiazheng , Pei Yechun , Min Yi , Wang Dayong
Published: September 2024

Alzheimer’s Disease (AD) is a multifaceted neurodegenerative disease predominantly defined by the extracellular accumulation of amyloid-β (Aβ) peptide. In light of this, in the past decade, several clinical approaches have been used aiming at developing peptides for therapeutic use in AD. The use of cationic arginine-rich peptides (CARPs) in targeting protein aggregations has been on the rise. Also, the process of peptide development employing computational approaches has attracted a lot of attention recently. Using a structure database containing pentapeptides made from 20 L-α amino acids, we employed molecular docking to sort pentapeptides that can bind to Aβ42, then performed molecular dynamics (MD) analyses, including analysis of the binding stability, interaction energy, and binding free energy to screen ligands. Transmission electron microscopy (TEM), circular dichroism (CD), thioflavin T (ThT) fluorescence detection of Aβ42 polymerization, MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay, and the flow cytometry of reactive oxygen species (ROS) were carried out to evaluate the influence of pentapeptides on the aggregation and cell toxicity of Aβ42. Two pentapeptides (TRRRR and ARRGR) were found to have strong effects on inhibiting the aggregation of Aβ42 and reducing the toxicity of Aβ42 secreted by SH-SY5Y cells, including cell death, reactive oxygen species (ROS) production, and apoptosis.
Genetics and Traumatic Brain Injury: Findings from an Exome-Based Study of a 50-Patient Case Series
Authors: Gracheva Alesya S. , Kashatnikova Darya A. , Redkin Ivan V. , Zakharchenko Vladislav E. , Kuzovlev Artem N. , Salnikova Lyubov E.
Published: September 2024

Traumatic brain injury (TBI) is the leading cause of global mortality and morbidity. Because TBI is accident-related, the role of genetics in predisposing to TBI has been largely unexplored. However, the likelihood of injury may not be entirely random and may be associated with certain physical and mental characteristics. In this study, we analyzed the exomes of 50 patients undergoing rehabilitation after TBI. Patients were divided into three groups according to rehabilitation outcome: improvement, no change, and deterioration/death. We focused on rare, potentially functional missense and high-impact variants in genes intolerant to these variants. The concordant results from the three independent groups of patients allowed for the suggestion of the existence of a genetic predisposition to TBI, associated with rare functional variations in intolerant genes, with a prevalent dominant mode of inheritance and neurological manifestations in the genetic phenotypes according to the OMIM database. Forty-four of the 50 patients had one or more rare, potentially deleterious variants in one or more neurological genes. Comparison of these results with those of a 50-sampled matched non-TBI cohort revealed significant differences: P = 2.6 × 10−3, OR = 4.89 (1.77–13.47). There were no differences in the distribution of the genes of interest between the TBI patient groups. Our exploratory study provides new insights into the impact of genetics on TBI risk and is the first to address potential genetic susceptibility to TBI.
Forming Epoxy Coatings on Laser-Engraved Surface of Aluminum Alloy to Reinforce the Bonding Joint with a Carbon Fiber Composite
Authors: Zhu Hongping , Zhang Jinheng , Cheng Fei , Li Jiangzhou , Wu Bo , Zhao Zhijie
Published: September 2024

This study designed laser engraving and resin pre-coating (RPC) treatments on an aluminum alloy (AA) surface to construct through-the-thickness “epoxy pins” for improving the bonding strength with carbon fiber reinforced polymer (CFRP). A laser engraving treatment was used to create a pitted structure on the AA surface; higher wettability was acquired and greater vertical spaces were formed to impregnate epoxy resin, resulting in stronger mechanical interlocking. The RPC technique was further used to guide high-viscosity epoxy resin into pits to form the epoxy coatings and to minimize defects between the resin and the substrate. The bonding strength of the specimen treated with both laser engraving with a unit dimension of 0.3 mm and RPC increased up to 227.1% in comparison with that of the base. The failure modes of the hybrid composites changed from the debonding failure of the AA surface to the delamination-dominated failure of the laminated CFRP composites. It was confirmed that laser engraving is a feasible and effective method when combined with RPC for treating AAs to improve the bonding strength of AA-CFRP composites, which provides a reference for preparing high-performance hybrid composites with metals.
Shelf-Life Extension and Quality Changes of Fresh-Cut Apple via Sago and Soy-Oil-Based Edible Coatings
Authors: Zhang Manmin , Xiao Di , Zhu Congfei , Younis Kaiser , Yousuf Owais
Published: September 2024

In-demand fresh-cut fruits are highly perishable and require shelf stability. Starch, such as sago, is a naturally available polysaccharide with good gas barrier properties. The study aimed to develop an edible coating and examine the effect of its application on the quality parameters of fresh-cut apples. The coating solution was prepared with sago and soy oil in concentrations of 3, 4, and 5% and 0, 0.25, and 0.50%, respectively. Lecithin (0.50%) was used as an emulsifier and glycerol (1.5%) as a plasticizer. Coated fresh-cut apples were evaluated for physicochemical properties (TSS, pH, non-enzymatic browning index, moisture content, weight loss, headspace gas, and color attributes) during a 12-day shelf-life study at 4 °C. Sensory analysis was also performed to assess consumer acceptability, and microbial analysis to investigate its inhibiting effect against yeast and mold. Compared to the control, developed coatings reduced browning, respiration rate, moisture, weight loss, and microbial load in fresh-cut apples. The study indicates that the blend of 5% sago and 0.5% soy oil produced the best coatings which were most effective for retaining the original quality attributes and in extending the shelf life of apple slices for 12 days in storage at 4 °C.
The Synergistic Effect of Cross-Linked and Electrostatic Self-Assembly Si/MXene Composites Anode for Highly Efficient Lithium-Ion Battery
Authors: Kong Songjia , Liu Chenguang , Ren Jiawei , Wang Tianchang , Geng Xianwei , Yuan Yudan , Zhao Chun , Zhao Cezhou , Yang Li
Published: September 2024

Silicon is a promising anode material for high-performance lithium-ion batteries (LIBs), but its rapid capacity degradation has significantly hindered its large-scale application. In this study, we propose an in situ self-assembly polymerization method to fabricate a stable silicon-based anode by leveraging electrostatic self-assembly technology, in situ esterification, and amidation reactions. The incorporation of a cross-linked polymer, combined with the synergistic effects of electrostatic interactions between negatively charged MXene and positively charged silane-coupling-agent-modified silicon, offers a novel strategy for enhancing the electrochemical performance of LIBs. Notably, annealed electrodes with a 65 wt% nmSi-NH2/MXene ratio demonstrate outstanding electrochemical performance, achieving a capacity of 929.5 mAh g⁻¹ at a current density of 1 A g⁻¹ after 100 charge/discharge cycles. These findings suggest that the integration of cross-linked polymers and electrostatic self-assembly can significantly improve the intercalation and overall electrochemical performance of silicon anodes in lithium-ion batteries.
The Aortic Prosthesis and Aortic Valve Bioprosthesis Trombosis as a Late Complication in Patients after the Bentall Procedure Followed by a Valve-in-Valve Transcatheter Aortic Valve Implantation
Authors: Muszyński Paweł , Grunwald Oliwia , Południewski Maciej , Kralisz Paweł , Kocańda Szymon , Hirnle Tomasz , Dobrzycki Sławomir , Kożuch Marcin
Published: September 2024

Background: Valve-in-Valve (ViV) transcatheter aortic valve implantation (TAVI) has emerged as a viable therapeutic option for structural valve degeneration following surgical aortic valve replacement (SAVR) or prior TAVI. However, the understanding of long-term complications and their management remains limited. Case presentation: We present the case of a 69-year-old male with a history of ViV-TAVI, who presented with symptoms of non-ST elevation myocardial infarction (NSTEMI) and transient ischemic attack (TIA). Computed tomography (CT) revealed thrombosis of the ascending aortic graft and aortic valve prosthesis. Transthoracic echocardiography (TTE) further confirmed new valve dysfunction, indicated by an increase in the aortic valve mean gradient. Treatment with low-molecular-weight heparin (LMWH) resulted in partial thrombus resolution. The multidisciplinary Heart Team opted against coronary angiography and recommended the long-term administration of vitamin K antagonists (VKAs). Follow-up CT showed the complete resolution of the thrombus. Conclusions: Thrombosis of the aortic graft and aortic valve following ViV-TAVI may be attributed to alterations in blood flow or mechanical manipulations during the TAVI procedure, yet it can be effectively managed with VKA therapy. CT is a valuable tool in coronary assessment in patients with NSTEMI and aortic valve and/or aortic graft thrombosis.
Combining Signals for EEG-Free Arousal Detection during Home Sleep Testing: A Retrospective Study
Authors: Boudabous Safa , Millet Juliette , Bacry Emmanuel
Published: September 2024

Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. Objectives: The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. Methods: We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. Results: The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. Conclusions: This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection.
The Behavioral Responses of Geoffroy’s Spider Monkeys to Drone Flights
Authors: Pinel-Ramos Eduardo José , Aureli Filippo , Wich Serge , Petersen Merissa F. , Dias Pedro A. D. , Spaan Denise
Published: September 2024

Drones are increasingly used for monitoring wildlife, and it is therefore necessary to evaluate their impact on animal behavior. According to the landscape of fear framework, animals assess and respond to perceived risks in their environment by adjusting their behavior and space use to avoid potential threats. Understanding how drones influence risk perception is thus crucial to avoid generating stress and altering the animal’s natural behavior. Geoffroy’s spider monkeys (Ateles geoffroyi) are endangered arboreal primates, but information on their distribution and abundance is scarce throughout their geographical distribution. Drones can aid to rapidly obtain such information, but data of their impact on the monkeys are needed to design surveys that minimize disturbance (i.e., any interference or modification of the natural behavior of the monkeys caused by the presence and operation of drones). Here, we evaluated whether drone flights influenced the following spider monkey behaviors: agonistic displays, self-scratching, whinny vocalizations, feeding, resting, social interactions, and moving. We also evaluated the effect of three flight parameters, flight height (35, 50 m above ground level), speed (2, 4 m/s), and distance to the drone (“close”, “medium”, and “far”) on spider monkey behavior and examined whether repeated exposure to drones resulted in tolerance (i.e., lack of a behavioral response). We found that drone flights influenced only agonistic displays and resting and that the only flight parameter affecting behaviors was the distance between the monkeys and the drone. We found that spider monkeys developed a tolerance to drone flights only for agonistic displays. Based on our results, we suggest that spider monkeys do not perceive drone flights as major sources of disturbance (such as predators) in the short term, and that drone monitoring can be a viable option to study this species if adequate flight protocols are implemented.
The Optimal Strategies of Maneuver Decision in Air Combat of UCAV Based on the Improved TD3 Algorithm
Authors: Gao Xianzhong , Zhang Yue , Wang Baolai , Leng Zhihui , Hou Zhongxi
Published: September 2024

Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to achieve autonomous air combat for UCAVs. In order to solve the problem of maneuver decision-making, an autonomous model of UCAVs based on the deep reinforcement learning method was proposed in this paper. Firstly, the six-degree-of-freedom (DoF) dynamic model was built in three-dimensional space, and the continuous actions of tangential overload, normal overload, and roll angle were selected as the maneuver inputs. Secondly, to improve the convergence speed for the deep reinforcement learning method, the idea of “scenario-transfer training” was introduced into the twin delayed deep deterministic (TD3) policy gradient algorithm, the results showing that the improved algorithm could cut off about 60% of the training time. Thirdly, for the “nose-to-nose turns”, which is one of the classical maneuvers for experienced pilots, the optimal maneuver generated by the proposed method was analyzed. The results showed that the maneuver strategy obtained by the proposed method was highly consistent with that made by experienced fighter pilots. This is also the first time in a public article that compared the maneuver decisions made by the deep reinforcement learning method with experienced fighter pilots. This research can provide some meaningful references to generate autonomous decision-making strategies for UCAVs.
Digital Construction Preservation Techniques of Endangered Heritage Architecture: A Detailed Reconstruction Process of the Dong Ethnicity Drum Tower (China)
Authors: Huang Wantao , Gao Xiang , Lu Jiaguo
Published: September 2024

This study suggests a pioneering conservation framework that significantly enhances the preservation, renovation, and restoration of heritage architecture through the integration of contemporary digital technologies. Focusing on the endangered drum towers of the Dong ethnic group in Southwestern China, the research employs a meticulous data collection process that combines manual measurements with precise 2D imaging and oblique unmanned aerial vehicle (UAV) photography, enabling comprehensive documentation of tower interiors and exteriors. Collaboration with local experts in drum tower construction not only enriches the data gathered but also provides profound insights into the architectural nuances of these structures. An accurate building information modeling (BIM) simulation illuminates the internal engineering details, deepening the understanding of their complex design. Furthermore, UAV-obtained point cloud data facilitate a 3D reconstruction of the tower’s exterior. This innovative approach to heritage preservation not only advances the documentation and comprehension of heritage structures but also presents a scalable, replicable model for cultural conservation globally, paving the way for future research in the field.
Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach
Authors: Purnell Daren , Etemadi Amir , Kamp John
Published: September 2024

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system.
Exploring Metaphors and Metaphorically Written Narratives in Student Teachers’ Professional Identity Work
Authors: Schellings Gonny , Koopman Maaike , Beijaard Douwe
Published: September 2024

Metaphors can help student teachers to express what is essential in teachers’ work and the process of learning to teach. This study reports on two types of metaphors generated and narrated by student teachers as part of their professional identity development during their internships. One type was about teaching their students (inside-classroom metaphor) and the other was about the student teachers’ (broader) work context (out-of-classroom metaphor). In small groups, supervised by five teacher educators in total, they discussed each other’s metaphors and accompanying narratives in order to gain (deeper) insight into their own and others’ underlying beliefs and experiences. Metaphors and accompanying narratives of different cohorts of student teachers (n = 67 in total) were analyzed. The teacher educators were interviewed about their experiences with discussing the metaphors in small student teacher groups. The findings suggest that the student teachers confidently formulated metaphors and wrote accompanying narratives. They formulated quite a few standard metaphors that seemed very similar at first glance, but their underlying experiences and beliefs, conversely, showed many differences in nuance. Interviews with the teacher educators revealed that the discussions about the metaphors certainly led to new insights among the student teachers, such as thinking about their role as teachers, but the depth of the discussions was often limited. It can be concluded that engaging student teachers in using metaphors is certainly one way to become aware of, reflect on, and talk about one’s (developing) professional identity, but it is important that adequate conditions are met, such as specific competencies teacher educators need for guiding this kind of identity work.
Closing the Gap? The Ability of Adaptive Learning Courseware to Close Outcome Gaps in Principles of Microeconomics
Authors: Gebhardt Karen , Blake Christopher D.
Published: September 2024

Research shows that students who identify as low-income, first-generation, and/or racially diverse disproportionately underperform in college and earn fewer degrees than other students. This study explores the integration of adaptive learning courseware assignments as a tool to help close these outcome gaps and to ensure more equitable learning across diverse student groups. Adaptive learning courseware is an educational technology that requires students to master the same learning objectives but, for each student, the courseware determines the order and timing of content based on how that student interacts with the courseware, thus enabling an individualized learning path for each student. Adaptive learning assignments were implemented in five sections of a highly-enrolled Principles of Microeconomics course at a medium-sized state university in the United States. This study draws from student data (n=581), which includes adaptive learning assignment completion data, detailed exam and final grade data, and institutional demographic data. Descriptive statistics and regression analyses are used to explore if the completion of adaptive learning assignments disproportionately benefited low-income, first-generation, or racially diverse students, thus helping close the gap between students from different backgrounds. Findings include significant evidence that adaptive learning assignment completion was correlated with more exam questions answered correctly by all students, with this correlation being disproportionately stronger for students who identify as being from a minority background and for foundational (easy) exam questions.
Adaptive Multi-Feature Attention Network for Image Dehazing
Authors: Jing Hongyuan , Chen Jiaxing , Zhang Chenyang , Wei Shuang , Chen Aidong , Zhang Mengmeng
Published: September 2024

Currently, deep-learning-based image dehazing methods occupy a dominant position in image dehazing applications. Although many complicated dehazing models have achieved competitive dehazing performance, effective methods for extracting useful features are still under-researched. Thus, an adaptive multi-feature attention network (AMFAN) consisting of the point-weighted attention (PWA) mechanism and the multi-layer feature fusion (AMLFF) is presented in this paper. We start by enhancing pixel-level attention for each feature map. Specifically, we design a PWA block, which aggregates global and local information of the feature map. We also employ PWA to make the model adaptively focus on significant channels/regions. Then, we design a feature fusion block (FFB), which can accomplish feature-level fusion by exploiting a PWA block. The FFB and PWA constitute our AMLFF. We design an AMLFF, which can integrate three different levels of feature maps to effectively balance the weights of the inputs to the encoder and decoder. We also utilize the contrastive loss function to train the dehazing network so that the recovered image is far from the negative sample and close to the positive sample. Experimental results on both synthetic and real-world images demonstrate that this dehazing approach surpasses numerous other advanced techniques, both visually and quantitatively, showcasing its superiority in image dehazing.
A Low-Power, High-Resolution Analog Front-End Circuit for Carbon-Based SWIR Photodetector
Authors: Zhang Yuyan , Chen Zhifeng , Liao Wenli , Xi Weirong , Chen Chengying , Jiang Jianhua
Published: September 2024

Carbon nanotube field-effect transistors (CNT-FETs) have shown great promise in infrared image detection due to their high mobility, low cost, and compatibility with silicon-based technologies. This paper presents the design and simulation of a column-level analog front-end (AFE) circuit tailored for carbon-based short-wave infrared (SWIR) photodetectors. The AFE integrates a Capacitor Trans-impedance Amplifier (CTIA) for current-to-voltage conversion, coupled with Correlated Double Sampling (CDS) for noise reduction and operational amplifier offset suppression. A 10-bit/125 kHz Successive Approximation analog-to-digital converter (SAR ADC) completes the signal processing chain, achieving rail-to-rail input/output with minimized component count. Fabricated using 0.18 μm CMOS technology, the AFE demonstrates a high signal-to-noise ratio (SNR) of 59.27 dB and an Effective Number of Bits (ENOB) of 9.35, with a detectable current range from 500 pA to 100.5 nA and a total power consumption of 7.5 mW. These results confirm the suitability of the proposed AFE for high-precision, low-power SWIR detection systems, with potential applications in medical imaging, night vision, and autonomous driving systems.
A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies
Authors: Xu Zhanyang , Zhao Hong , Xu Chengxi , Shi Hongyan , Xu Jian , Wang Zhe
Published: September 2024

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases.
Coordinated Control of Differential Drive-Assist Steering and Direct Yaw Moment Control for Distributed-Drive Electric Vehicles
Authors: Zhu Shaopeng , Lu Junfei , Zhu Ling , Chen Huipeng , Gao Jian , Xie Wei
Published: September 2024

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility.
Robust Control Scheme for Optimal Power Sharing and Selective Harmonic Compensation in Islanded Microgrids
Authors: Gaeed Seger Al-salloomee Ali , Romero-Cadaval Enrique , Roncero-Clemente Carlos
Published: September 2024

In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in AC-islanded microgrids. The proposed method utilizes selective harmonic order filtering through multiple second-order generalized integrators (MSOGI) to extract negative, positive, and harmonic order components. The distributed generation (DG) unit control mechanism is designed to immediately correct voltage imbalances and harmonic disruptions, distributing the compensatory load evenly to rectify real and reactive power imbalances and harmonic disturbances. The microgrid’s control architecture primarily includes droop controllers for real and reactive power of positive sequences, voltage and current regulation inner control loops, an additional loop for correcting imbalances and harmonics, and secondary controllers to maintain voltage magnitude and frequency at nominal levels, ensuring high-quality voltage at the point of common coupling (PCC). The effectiveness of this approach is demonstrated through simulation results on the MATLAB/Simulink platform, proving its ability to effectively mitigate voltage imbalances and harmonic issues with the total harmonic of voltage reduced to approximately THDv = 0.5% and voltage unbalance factor (VUF) within approximately 0.1%.
TAFENet: A Two-Stage Attention-Based Feature-Enhancement Network for Strip Steel Surface Defect Detection
Authors: Zhang Li , Fu Zhipeng , Guo Huaping , Feng Yan , Sun Yange , Wang Zuofei
Published: September 2024

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method.
Exploring a Dynamic Homotopy Technique to Enhance the Convergence of Classical Power Flow Iterative Solvers in Ill-Conditioned Power System Models
Authors: Lima-Silva Alisson , Freitas Francisco Damasceno
Published: September 2024

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109,272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers.
Application of Torrefaction for Improved Fuel Properties of Sunflower Husks
Authors: Milovanov Oleg , Klimov Dmitry , Kuzmin Sergey , Grigoriev Sergey , Mikhalev Alexander , Isemin Rafail , Brulé Mathieu
Published: September 2024

Sunflower husk (SFH) contributes 45–60% of the total sunflower seed weight and is a by-product of the sunflower oil industry. Among other elements, SFH ash contains K, Na, Ca and Mg. These elements cause rapid growth of ash deposits on convective heating surfaces of the boiler, resulting in reduced efficiency. The aim of this paper is to examine the possibility of producing quality fuel from SFH by its pretreatment with the technique of torrefaction in a fluidized bed in superheated water vapor. Continuous monitoring of the innovative SFH torrefaction process allowed for the determination of optimal process durations. SFH could be converted into a biofuel, having high calorific value and suitable characteristics for co-combustion with coal. Furthermore, the torrefaction in a fluidized bed of superheated water vapor allowed for a 6-fold reduction in the required process duration in comparison with data reported from the literature for the process of torrefaction in a dense bed, along with a 3-fold reduction in the chlorine content in SFH ash. These effects are beneficial to resolve the problem of corrosion on convective heating surfaces of boilers. However, torrefaction in superheated water vapor did not significantly reduce the content of alkaline and alkaline-earth elements in SFH ash. Still, this issue may be alleviated by significantly increasing the duration of SFH pretreatment.
Assessment of Cytotoxicity and Genotoxicity Induced by Diesel Exhaust Particles (DEPs) on Cell Line A549 and the Potential Role of Amide-Functionalized Carbon Nanotubes as Fuel Additive
Authors: Pino Juan Sebastian , Alvarado Pedro Nel , Rojas Winston , Cacua Karen , Gomez-Lopera Natalia
Published: September 2024

Epidemiological studies have consistently linked air pollution to severe health risks. One strategy to reduce the impact of combustion products from engines is adding additives to the fuel. Potential benefits have been observed in terms of performance and emissions, as well as in decreasing fuel consumption. However, the associated emission of particulate matter into the environment may have unforeseen health effects. This study examines the effects of diesel exhaust particles (DEPs) from diesel fuel mixed with amide-functionalized carbon nanotubes (CNTF). The aim is to analyze the properties of DEPs and determine their toxic effects on lung cells. The DEPs were characterized using scanning and transmission electron microscopy, while the polycyclic aromatic hydrocarbons (PAHs) were analyzed through gas chromatography. Various assays were conducted to assess cell viability, apoptosis, oxidative stress, and DNA damage. The addition of CNTF to diesel fuel altered the morphology and size of the particles, as well as the quantity and composition of PAHs. At the cellular level, diesel DEPs induce higher levels of reactive oxygen species (ROS) production, DNA damage, apoptosis, and cytotoxicity compared to both CNTF and diesel–CNTF DEPs. These findings suggest that the nano-additives enhance energy efficiency by reducing pollutants without significantly increasing cell toxicity.
Research on Conduction Delay Time Characteristics of Double-Gap Surface Flashover Triggered Multistage Vacuum Switch
Authors: Zhang Ming , Liao Minfu , Bu Liang , Lu Gang , Yu Longfei , Sun Yifan , Duan Xiongying
Published: September 2024

Due to the insulation saturation effect of a vacuum gap, the series connection is the main method to improve the capability of the triggered vacuum switch (STVS) to withstand voltage. In this paper, a double-gap surface flashover triggered multistage vacuum switch (DMVS) is constructed based on a removable vacuum chamber. The DMVS consists of a surface flashover triggered vacuum gap (STG) and a self-breakdown vacuum gap (SBG) in series. The trend in DMVS conduction delay time under different operating parameters and gap distances is firstly measured through experiments, and then the conduction delay time characteristics of DMVS and single-gap STVS compared. The test results show that increasing the trigger current and operating voltage and decreasing the gap distance of each vacuum gap can optimize the conduction delay time of the DMVS, but the voltage division ratio between the STG and the SBG has no obvious influence on the conduction delay time. The comparison result between the DMVS and the STVS shows that increasing the number of series gaps increases the conduction delay time but, for a certain vacuum gap distance, DMVS exhibits superior conduction delay time characteristics compared to STVS.
Effects of Fertilization and Drip Irrigation on the Growth of Populus × canadensis ‘Zhongliao 1’ Plantation and on Soil Physicochemical Properties and Enzyme Activities
Authors: Zhang Yan , Wang Nairui , Yang Lingyu , Liu Ning , Peng Rusheng , Yu Lei , Liu Fenfen , Wang Shiqi , Gao Chengcheng , Ji Jiabao , Liu Chenggong , Liang Dejun
Published: September 2024

Poplars are crucial for timber supply and ecological protection in China. Enhancing the growth of poplar plantations and improving soil fertility in arid, and semi-arid poor soil regions are key aspects of sustainable forest management. Fertilization (FTL) and drip irrigation (DI) are among the most widely used methods globally for increasing yield and soil productivity. This study conducted field experiments on FTL and DI in a 10-year-old Populus × canadensis ‘Zhongliao 1’ (cultivation varieties of P. canadensis in northern China) plantation. DI limits were set according to soil moisture at 60% (S1), 70% (S2), and 80% (S3) of field capacity; nitrogen FTL rates were set at 100% of the baseline fertilization amount (100% BFA, N 643.20 g·year−1, P 473.37 g·year−1, and K 492.29 g·year−1) (F1), 70% BFA (F2), 130% BFA (F3), and 160% BFA (F4). The treatments of drip irrigation and fertigation (DIF) were H1 (100% BFA, 60% FC), H2 (100% BFA, 80% FC), H3 (160% BFA, 60% FC), and H4 (160% BFA, 80% FC), along with a control group (CK) without any management, totaling 12 experimental combinations. The results showed that the H4 had the most significant promoting effect on the height, DBH, and volume increments. All treatments had little effect on the soil bulk density of the plantation but significantly impacted soil capillary porosity and pH. Compared to DI, soil nutrient and organic matter content were more sensitive to FTL. Appropriate FTL and DI can increase soil sucrase activity. Soil urease activity tended to increase with higher FTL rates, and higher DI levels also positively influenced urease activity. Excessive or insufficient soil moisture and nutrients negatively impacted soil cellulase and catalase activities. Correlation analysis revealed no significant correlation between the growth of P. × canadensis ‘Zhongliao 1’ and soil nutrient content, but significant or highly significant correlations existed between growth and soil porosity and related enzyme activities. Comprehensive evaluation using a membership function indicated that high FTL levels (F4) were more conducive to the simultaneous improvement of the growth and soil fertility of the plantation, followed by H4 and F1, suggesting that high FTL is the key factor affecting the growth of 10-year-old P. × canadensis ‘Zhongliao 1’ plantations and the restoration of stand productivity, with moisture being secondary.
Microbial Fermentation and Therapeutic Potential of p-Cymene: Insights into Biosynthesis and Antimicrobial Bioactivity
Authors: Pyo Yeonhee , Jung Yeon Ja
Published: September 2024

p-Cymene (p-C) [1-methyl-4-(1-methylethyl)-benzene] is a monoterpene found in a variety of plants and has several biological activities, including antioxidant, anti-inflammatory, antimicrobial, and anticancer properties. This paper explores the microbial fermentation pathways involved in the biosynthesis of p-C, with an emphasis on its potential as a therapeutic agent. Through microbial and biochemical processes, p-C can be produced using renewable precursors such as limonene and 1,8-cineole. Recent advances in fermentation technology have enhanced the efficiency of p-C production, highlighting its role in various industries. Additionally, this paper reviews the antimicrobial bioactivity of p-C, focusing on its ability to inhibit pathogens and modulate immune responses. The integration of microbial biosynthesis and fermentation methods offers a sustainable approach to producing p-C for applications in the perfume, cosmetics, food, and pharmaceutical sectors. Understanding these biosynthetic pathways is crucial for advancing the use of p-C as a bio-based chemical with therapeutic potential. In particular, p-C inhibits the expression of cytokine signal 3 in intestinal inflammation and modulates antioxidant and immunomodulatory systems to protect barrier cells and maintain the mucus layer.
Application of Mass Spectrometry for Analysis of Nucleobases, Nucleosides and Nucleotides in Tea and Selected Herbs: A Critical Review of the Mass Spectrometric Data
Authors: Frańska Magdalena , Frański Rafał
Published: September 2024

The main and most commonly known biological function of nucleobases, nucleosides, and nucleotides is usually associated with the fact that they are the building blocks of nucleic acids. However, these compounds also belong to plant secondary metabolites, although in that role they have attracted less attention than the others, e.g., terpenes, phenolics, or alkaloids. The former compounds are also important constituents of the human diet, e.g., as ingredients of tea and herbs, endowing them with specific taste qualities and pharmacological activities. Liquid chromatography–mass spectrometry seems to be the most important analytical method that permits the identification and determination of nucleobases, nucleosides, and nucleotides, along with the other metabolites. The main goal of this review is to discuss in detail the aspects of mass spectrometric detection of nucleobases, nucleosides, and nucleotides in tea and selected herbs. An important conclusion is that the identification of the compounds of interest should be performed not only on the basis of [M + H]+/[M − H]− ions but should also be confirmed by the respective product ions; however, as discussed in detail in this review, it may sometimes be problematic. It also clear that all difficulties that may be encountered when analyzing plant material are caused by the complexity of the analyzed samples and the need to analyze different classes of compounds, and this review absolutely does not debase any of the mentioned papers.
Role of Heat-Shock Proteins in the Determination of Postmortem Metabolism and Meat Quality Development of DFD Meat
Authors: Ijaz Muawuz , Li Xin , Hou Chengli , Hussain Zubair , Zhang Dequan
Published: September 2024

This research explored the potential role of various heat-shock proteins (HSPs) in the determination of postmortem metabolism and the development of meat quality of normal, atypical DFD, and typical DFD beef. Beef longissimus thoracis muscle samples were classified into normal, atypical DFD, and typical DFD beef. The HSP27, HSP70, and HSP90 levels, meat quality parameters, and glycolytic metabolites were tested. The results showed that color coordinates (L*, a*, and b*), glycogen, and lactate contents were lower, whereas water-holding capacity was higher in the typical DFD beef than in the normal and atypical DFD beef (p < 0.05). The expression of HSP27 on day 1 was higher in atypical DFD beef. However, expressions of HSP70 on days 1 and 3 were higher in typical DFD, while the expression of HSP90 on day 1 was higher in atypical and typical DFD compared to the normal beef (p < 0.05). Interestingly, the expression of HSP27 was positively correlated with shear force readings. HSP70 and HSP90 presented a direct correlation with pH and water-holding capacity and an indirect correlation with a* and b*, glycogen and lactate contents (p < 0.05). The study concluded that the heat-shock proteins could influence the formation of DFD beef possibly by regulating the development of postmortem metabolism and meat quality traits.
Changes in Quality Characteristics and Metabolite Composition of Low-Temperature and Nitrogen-Modified Atmosphere in Indica Rice during Storage
Authors: Zhao Yanan , Li Yanfei , Gong Zhigang , Liu Xuguang , Lv Haoxin , Zhao Yan
Published: September 2024

A low temperature (LT) is used to delay grain deterioration effectively. In practical applications, a nitrogen-modified atmosphere (N2) is also an effective way of preventing grain pests and delaying grain deterioration. However, there are few studies on grain quality changes using a combination treatment of an LT and N2 during storage. In this study, the storage quality, processing characteristics, and metabolites of rice under conventional storage (CS), LT (20 °C), N2 (95%), and LT+N2 treatments were analyzed for 180 days, under a controlled humidity of 65% ± 2%. The results showed that compared to the CS, LT, and N2 treatments, the LT+N2 treatment had the best effect in retarding the increase in MDA and electrical conductivity and deferring the decrease in CAT activity. In addition, the LT+N2 treatment maintained the color of the rice better and sustained a better processing quality. Non-targeted metabolomics analysis further confirmed that the LT+N2 treatment maintained the vigor of the rice and retarded its spoilage by activating the metabolisms of amino acids, carbohydrates, and flavonoids. These results suggest a favorable practice for preventing storage deterioration and increasing the processing quality for rice storage. They provided new insights into the mechanisms of rice quality changes using the combination treatment of an LT and N2.