bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Effect of Ionic Liquid Conformation on the Selective CO2 Capture of Supported Ionic Liquid-Phase Adsorbents Based on ZIFs
Authors: Veziri Charitomeni M. , Theodorakopoulos George V. , Beltsios Konstantinos G. , Romanos George E.
Published: September 2024

The CO2 adsorption capacity and the CO2/N2 selectivity of a series of Supported Ionic Liquid-Phase adsorbents (SILPs), including the novel inversely structured SILP “Inverse SILPs”, are thoroughly investigated. ZIF-8, ZIF-69 and ZIF-70 were involved as the solid matrix, while ILs, having tricyanomethanide (TCM) as an anion and alkyl-methylimidazolium of different alkyl chain lengths (C2, C6, C8) as a cation, were used as the liquid constituents of the SILPs. The ultimate target of the work was to ratify a few recently reported cases of enhanced CO2 absorptivity in ILs due to their incorporation in ZIFs and to corroborate phenomena of CO2/N2 selectivity improvements in ZIFs, due to the presence of ILs. This ambiguity originates from the vague assumption that the pores of the ZIF are filled with the IL phase, and the free pore volume of a SILP is almost zero. Yet, through the integration of theoretical predictions with N2 porosimetry analysis of an actual sample, it is suggested that a thin layer of IL covered the exterior surface of a ZIF crystal. This layer could act as an impermeable barrier for N2, inhibiting the gas molecules from reaching the empty cavities laying underneath the liquid film during porosimetry analysis. This consideration is based on the fact that the solubility of N2 in the IL is very low, and the diffusivity at 77 K is negligible. In this context, the observed result reflects an averaged adsorptivity of both the IL phase and the empty pores of the ZIF. Therefore, it is incorrect to attribute the adsorption capacity of the SILP solely to the mass of the IL that ‘hypothetically’ nests inside the pore cavities. In fact, the CO2 adsorption capacity of SILPs is always less than the average adsorptivity of an ideal ZIF/IL mixture, where the two phases do not interact. This reduction occurs because some ZIF pores may become inaccessible, particularly when the IL forms a layer on the pore walls, leaving only a small empty core accessible to CO2 molecules. Additionally, the IL layer masks the active sites on the ZIF’s pore walls. It should also be noted that the CO2/N2 selectivity increases only when the ZIF’s pores are completely filled with the IL phase. This is because ILs have a higher CO2/N2 selectivity compared to the bare ZIF.
Design and Experimental Research of a Non-Destructive Detection Device for High-Precision Cylindrical Roller Dynamic Unbalance
Authors: Zhang Zhuangya , Yang Baorun , Duan Mingde , Gu Ruijie , Liang Shijie , Chen Yang
Published: September 2024

Due to their small size and light mass, small precision cylindrical rollers present challenges in dynamic unbalance detection, including difficulties in measurement and the risk of surface damage. This paper proposes a non-destructive detection device for assessing the dynamic unbalance of small precision cylindrical rollers. The device utilizes an air flotation support method combined with resonance amplification to indirectly measure the dynamic unbalance. A dynamic model of the air flotation tooling-cylindrical roller vibration system was developed to explore the relationship between the vibration parameters of the air flotation tooling and the dynamic unbalance of the cylindrical roller. Modal analysis and harmonic response analysis were performed, revealing that the amplitude of the vibration system at resonance could be detected using the sensor. Additionally, modal testing was conducted to determine the natural frequency of the system. A non-destructive detection platform was constructed for testing the dynamic unbalance of cylindrical rollers. Microscopic observation of the roller surface before and after testing confirmed that the device successfully performs non-destructive detection of dynamic unbalance.
Finite-Time Asynchronous H∞ Control for Non-Homogeneous Hidden Semi-Markov Jump Systems
Authors: Wang Qian , Zhang Xiaojun , Shao Yu , Shi Kaibo
Published: September 2024

This article explores the finite-time control problem associated with a specific category of non-homogeneous hidden semi-Markov jump systems. Firstly, a hidden semi-Markov model is designed to characterize the asynchronous interactions that occur between the true system mode and the controller mode, and emission probabilities are used to establish relationships between system models and controller modes. Secondly, a novel piecewise homogeneous method is introduced to tackle the non-homogeneous issue by taking into account the time-dependent transition rates for the jump rules between different modes of the system. Thirdly, an asynchronous controller is developed by applying Lyapunov theory along with criteria for stochastic finite-time boundedness, ensuring the specified H∞ performance level is maintained. Finally, the effectiveness of this method is verified through two simulation examples.
An Explainable AI-Based Modified YOLOv8 Model for Efficient Fire Detection
Authors: Hasan Md. Waliul , Shanto Shahria , Nayeema Jannatun , Rahman Rashik , Helaly Tanjina , Rahman Ziaur , Mehedi Sk. Tanzir
Published: September 2024

Early fire detection is the key to saving lives and limiting property damage. Advanced technology can detect fires in high-risk zones with minimal human presence before they escalate beyond control. This study focuses on providing a more advanced model structure based on the YOLOv8 architecture to enhance early recognition of fire. Although YOLOv8 is excellent at real-time object detection, it can still be better adjusted to the nuances of fire detection. We achieved this advancement by incorporating an additional context-to-flow layer, enabling the YOLOv8 model to more effectively capture both local and global contextual information. The context-to-flow layer enhances the model’s ability to recognize complex patterns like smoke and flames, leading to more effective feature extraction. This extra layer helps the model better detect fires and smoke by improving its ability to focus on fine-grained details and minor variation, which is crucial in challenging environments with low visibility, dynamic fire behavior, and complex backgrounds. Our proposed model achieved a 2.9% greater precision rate, 4.7% more recall rate, and 4% more F1-score in comparison to the YOLOv8 default model. This study discovered that the architecture modification increases information flow and improves fire detection at all fire sizes, from tiny sparks to massive flames. We also included explainable AI strategies to explain the model’s decision-making, thus adding more transparency and improving trust in its predictions. Ultimately, this enhanced system demonstrates remarkable efficacy and accuracy, which allows additional improvements in autonomous fire detection systems.
The Time Series Classification of Discrete-Time Chaotic Systems Using Deep Learning Approaches
Authors: Akmeşe Ömer Faruk , Emin Berkay , Alaca Yusuf , Karaca Yeliz , Akgül Akif
Published: September 2024

Discrete-time chaotic systems exhibit nonlinear and unpredictable dynamic behavior, making them very difficult to classify. They have dynamic properties such as the stability of equilibrium points, symmetric behaviors, and a transition to chaos. This study aims to classify the time series images of discrete-time chaotic systems by integrating deep learning methods and classification algorithms. The most important innovation of this study is the use of a unique dataset created using the time series of discrete-time chaotic systems. In this context, a large and unique dataset representing various dynamic behaviors was created for nine discrete-time chaotic systems using different initial conditions, control parameters, and iteration numbers. The dataset was based on existing chaotic system solutions in the literature, but the classification of the images representing the different dynamic structures of these systems was much more complex than ordinary image datasets due to their nonlinear and unpredictable nature. Although there are studies in the literature on the classification of continuous-time chaotic systems, no studies have been found on the classification of discrete-time chaotic systems. The obtained time series images were classified with deep learning models such as DenseNet121, VGG16, VGG19, InceptionV3, MobileNetV2, and Xception. In addition, these models were integrated with classification algorithms such as XGBOOST, k-NN, SVM, and RF, providing a methodological innovation. As the best result, a 95.76% accuracy rate was obtained with the DenseNet121 model and XGBOOST algorithm. This study takes the use of deep learning methods with the graphical representations of chaotic time series to an advanced level and provides a powerful tool for the classification of these systems. In this respect, classifying the dynamic structures of chaotic systems offers an important innovation in adapting deep learning models to complex datasets. The findings are thought to provide new perspectives for future research and further advance deep learning and chaotic system studies.
Methodology of Shipboard Spare Parts Requirements Based on Whole Part Repair Strategy
Authors: Wang Houxiang , Liu Haitao , Shao Songshi , Zhang Zhihua
Published: September 2024

This paper introduces an assessment method for shipboard spare parts requirements based on a whole-part repair strategy, aimed at enhancing the availability and combat effectiveness of naval equipment. Addressing the shortcomings of traditional repair strategies, this study innovatively adopts a whole-part rotation repair approach to reduce repair times and improve the rapid response capability of equipment. An evaluation model for support probability and fill rate is established, and Monte Carlo simulation techniques are applied to simulate the impact of different maintenance strategies on spare parts demand and equipment availability. This study also conducts a sensitivity analysis of key parameters, including Mean Time Between Failures (MTBF), repair demand probability, and faulty part repair cycle, to assess their influence on spare parts requirements and equipment availability. The results indicate that the whole-part repair strategy can effectively reduce spare parts demand and enhance equipment availability. In conclusion, the whole-part repair strategy demonstrates a distinct advantage in shipboard spare parts management, optimizing inventory management while ensuring combat readiness. This research provides a novel analytical approach for naval logistics and maintenance planning.
Solving Transport Infrastructure Investment Project Selection and Scheduling Using Genetic Algorithms
Authors: Ječmen Karel , Mocková Denisa , Teichmann Dušan
Published: September 2024

The development of transport infrastructure is crucial for economic growth, social connectivity, and sustainable development. Many countries have historically underinvested in transport infrastructure, necessitating more efficient strategic planning in the implementation of transport infrastructure investment projects. This article addresses the selection and scheduling of transport infrastructure projects, specifically within the context of utilizing pre-allocated funds within a multi-annual budget investment program. The current decision-making process relies heavily on expert judgment and lacks quantitative decision support methods. We propose a genetic algorithm as a decision-support tool, framing the problem as an NP-hard 0–1 multiple knapsack problem. The proposed genetic algorithm (GA) is unique for its matrix-encoded chromosomes, specially designed genetic operators, and a customized repair operator to address the large number of invalid chromosomes generated during the GA computation. In computational experiments, the proposed GA is compared to an exact solution and proves to be efficient in terms of quality of obtained solutions and computational time, with an average computational time of 108 s and the quality of obtained solutions typically ranging between 85% and 95% of the optimal solution. These results highlight the potential of the proposed GA to enhance strategic decision-making in transport infrastructure development.
Reinforcement Learning with Value Function Decomposition for Hierarchical Multi-Agent Consensus Control
Authors: Zhu Xiaoxia
Published: September 2024

A hierarchical consensus control algorithm based on value function decomposition is proposed for hierarchical multi-agent systems. To implement the consensus control algorithm, the reward function of the multi-agent systems can be decomposed, and two value functions can be obtained by analyzing the communication content and the corresponding control objective of each layer in the hierarchical multi-agent systems. Therefore, for each agent in the systems, a dual-critic network and a single-actor network structure are applied to realize the objective of each layer. In addition, the target network is introduced to prevent overfitting in the critic network and improve the stability of the online learning process. During the updating of network parameters, a soft updating mechanism and experience replay buffer are introduced to slow down the update rate of the network and improve the utilization rate of training data. The convergence and stability of the consensus control algorithm with the soft updating mechanism are analyzed theoretically. Finally, the correctness of the theoretical analysis and the effectiveness of the algorithm were verified by two experiments.
Scaling-Invariant Serrin Criterion via One Row of the Strain Tensor for the Navier–Stokes Equations
Authors: Du Juan , Wu Fan
Published: September 2024

Miller (Arch. Rational Mech. Anal., 2020) posed the question of whether it is possible to prove the Navier–Stokes regularity criterion using only one entry of the strain tensor Sij. Although this paper does not fully address this question, we do establish several scaling-invariant Serrin-type criteria based on one row of the strain tensor.
Attribute-Based Designated Combiner Transitive Signature Scheme
Authors: Hou Shaonan , Yang Shaojun , Lin Chengjun
Published: September 2024

Transitive signatures allow any entity to obtain a valid signature of (i,k) by combining signatures of (i,j) and (j,k). However, the traditional transitive signature scheme does not offer fine-grained control over the combiner. To address this issue, we propose a formal definition of the attribute-based designated combiner transitive signature (ABDCTS) and its security model, where only entities whose inherent attributes meet the access policy can combine signatures. By introducing the fine-grained access control structure, control over the combiner is achieved. To demonstrate the feasibility of our primitive, this paper presents the first attribute-based designated combiner transitive signature scheme. Under an adaptive chosen-message attack, we prove its security based on the one-more CDH problem and the co-CDH problem, and that its algorithms have robustness.
Assessing the Bridge Structure’s System Reliability Utilizing the Generalized Unit Half Logistic Geometric Distribution
Authors: Tolba Ahlam H. , Alamri Osama Abdulaziz , Baaqeel Hanan
Published: September 2024

Reliability is now widely recognized across various industries, including manufacturing. This study investigates a system composed of five components, one of which is a bridge network. The components are assumed to follow the generalized unit half logistic geometric distribution (GUHLGD) with equal failure rates over time. The following three improvement methods are considered: reduction, cold duplication, and hot duplication. The reliability function and mean time to failure (MTTF) are employers liability equivalence factors (REFs). Additionally, the λ fractiles of both the original and enhanced systems are obtained. Numerical results illustrate the effectiveness of these techniques, with cold duplication shown to be the most effective, offering higher reliability and MTTF compared to hot duplication. The enhanced system outperforms the original system overall.
Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence
Authors: Susanu Carolina , Hărăbor Anamaria , Vasilache Ingrid-Andrada , Harabor Valeriu , Călin Alina-Mihaela
Published: September 2024

Background and Objectives: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the prediction of postpartum and intrapartum hemorrhage. Materials and Methods: A prospective multicenter study was conducted, involving 203 patients with or without intra/postpartum hemorrhage within the initial 24 h postpartum. The participants were categorized into two groups: those with intra/postpartum hemorrhage (PPH) and those without PPH (control group). The PPH group was further stratified into four classes following the Advanced Trauma Life Support guidelines. Clinical data collected from these patients was included in four machine learning-based algorithms whose predictive performance was assessed. Results: The Naïve Bayes (NB) algorithm exhibited the highest accuracy in predicting PPH, boasting a sensitivity of 96.3% and an accuracy of 98.6%, with a false negative rate of 3.7%. Following closely were the Decision Tree (DT) and Random Forest (RF) algorithms, each achieving sensitivities exceeding 94% with a false negative rate of 5.9%. Regarding severity classification I, the NB and Support Vector Machine (SVM) algorithms demonstrated superior predictive capabilities, achieving a sensitivity of 96.4%, an accuracy of 92.1%, and a false negative rate of 3.6%. The most severe manifestations of HPP were most accurately predicted by the NB algorithm, with a sensitivity of 89.3%, an accuracy of 82.4%, and a false negative rate of 10.7%. Conclusions: The NB algorithm demonstrated the highest accuracy in predicting PPH. A notable discrepancy in algorithm performance was observed between mild and severe forms, with the NB and SVM algorithms displaying superior sensitivity and lower rates of false negatives, particularly for mild forms.
Multienzyme Immobilization on PVDF Membrane via One-Step Mussel-Inspired Method: Enhancing Fouling Resistance and Self-Cleaning Efficiency
Authors: Mulinari Jéssica , Rigo Diane , Demaman Oro Carolina Elisa , Meneses Alessandra Cristina de , Zin Guilherme , Eleutério Rafael Vidal , Tres Marcus Vinícius , Dallago Rogério Marcos
Published: September 2024

Immobilizing different enzymes on membranes can result in biocatalytic active membranes with a self-cleaning capacity toward a complex mixture of foulants. The membrane modification can reduce fouling and enhance filtration performance. Protease, lipase, and amylase were immobilized on poly(vinylidene fluoride) (PVDF) microfiltration membranes using a polydopamine coating in a one-step method. The concentrations of polydopamine precursor and enzymes were optimized during the immobilization. The higher hydrolytic activities were obtained using 0.2 mg/mL of dopamine hydrochloride and 4 mg/mL of enzymes: 0.90 mgstarch/min·cm2 for amylase, 10.16 nmoltyrosine/min·cm2 for protease, and 20.48 µmolp-nitrophenol/min·cm2 for lipase. Filtration tests using a protein, lipid, and carbohydrate mixture showed that the modified membrane retained 41%, 29%, and 28% of its initial water permeance (1808 ± 39 L/m2·h·bar) after three consecutive filtration cycles, respectively. In contrast, the pristine membrane (initial water permeance of 2016 ± 40 L/m2·h·bar) retained only 23%, 12%, and 8%. Filtrations of milk powder solution were also performed to simulate dairy industry wastewater: the modified membrane maintained 28%, 26%, and 26% of its initial water permeance after three consecutive filtration cycles, respectively, and the pristine membrane retained 34%, 21%, and 7%. The modified membrane showed increased fouling resistance against a mixture of foulants and presented a similar water permeance after three cycles of simulated dairy wastewater filtration. Membrane fouling is reduced by the immobilized enzymes through two mechanisms: increased membrane hydrophilicity (evidenced by the reduced water contact angle after modification) and the enzymatic hydrolysis of foulants as they accumulate on the membrane surface.
Comparative Analysis of Mechanical Properties and Microstructure of 7B52 Aluminum Alloy Laser-MIG Hybrid Welding and MIG Welding Joints
Authors: Lu Yu , Wang Dafeng , Cao Lijun , Ma Liangchao , Zeng Haolin
Published: September 2024

Laser-MIG hybrid welding and MIG welding were carried out on 7B52 aluminum alloy, and the microstructure and mechanical properties of the joints were compared and analyzed. The results show that the average grain sizes of laser-MIG hybrid welding, MIG welding, and the laser weld zone are 18.38 μm, 24.16 μm, and 15.96 μm, respectively. The width of the HAZ of the laser zone is lower than that of the laser-MIG hybrid welding zone and MIG welding zone. The hardness is higher than that of the hybrid welding zone and MIG welding zone. The tensile strength of each laser-MIG hybrid welding joint zone is better than that of the MIG welding zone. The tensile strength of the laser zone is the worst. When stress ratio R = 0.1 and fatigue cycle Nf = 106, the fatigue strength of the laminated zone of the laser-MIG hybrid welding joint is better than that of the MIG welding joint, and the welding defect is the leading cause of fatigue cracks.
Enhanced DC and AC Soft Magnetic Properties of Fe-Co-Ni-Al-Si High-Entropy Alloys via Texture and Iron Segregation
Authors: Tan Xiaohua , Li Junyi , Zhang Shiqi , Xu Hui
Published: September 2024

The microstructure and soft magnetic properties under direct current (DC) mode and alternating current (AC) mode of FeCoNiAl1−xSix (x = 0.2, 0.4, 0.6) high-entropy alloys (HEAs) are investigated. All the studied HEAs show body-centered cubic (BCC) structures, and the [100] texture is formed in the x = 0.4 HEA. The iron (Fe) segregation at the grain boundaries is helpful in increasing the soft magnetic properties under DC. The FeCoNiAl0.6Si0.4 (x = 0.4) HEA exhibits optimal DC and AC soft magnetic properties, primarily due to the formation of the texture along the easy magnetization axis. The x = 0.4 HEA shows the highest permeability (μi = 344 and μm = 1334) and the smallest coercivity (Hc = 51 A/m), remanence (Br = 132 mT), and hysteresis loss (Pu = 205 J/m3). In comparison to the x = 0.2 HEA and x = 0.6 HEA, the total loss (AC Ps) at 50 Hz of the x = 0.4 HEA is decreased by 15% and 18%, and it is reduced at 950 Hz by 13% and 7%. Our findings can provide a useful approach for developing novel HEAs with increased soft magnetic properties by tuning ferromagnetic elemental segregation and forming the texture along the easy magnetization axis.
Revisiting Rare Earth Permanent Magnetic Alloys of Nd-Fe-C
Authors: Fan Jianing , Zhou Bang , Yu Hongya , Liu Zhongwu
Published: September 2024

Nd-Fe-C alloys have been reported as hard magnetic materials with a potential higher coercivity than Nd-Fe-B alloys. However, it has been seldom studied since its intrinsic properties were investigated in the last century. Here, we revisited the structure, phase precipitation and magnetic properties of rapidly quenched ternary Nd-Fe-C alloys for further understanding their composition-microstructure-property relationships. The Nd10+xFe84−xC6 (x = −2, 0, 2, 3, 4, 5) alloys with various compositions were prepared by melt spinning. The results show that the hard magnetic Nd2Fe14C phase can be hardly formed in the as-spun alloys. Instead, the alloys are composed of soft magnetic α-Fe phase and planar anisotropic Nd2Fe17Cx phase. After annealing above 650 °C, the Nd2Fe14C phase is precipitated by the peritectoid reaction. All optimally annealed alloys contain Nd2Fe14C and Nd2Fe17Cx phases, while the presence and content of α-Fe phase are determined by the alloy composition. The crystallization degree of the as-spun alloys has an effect on their magnetic properties after annealing. After the annealing treatment, partly crystallized as-spun alloys exhibit better magnetic properties than the amorphous alloys. The intrinsic coercivity Hcj = 847 kA/m, remanence Jr = 0.69 T, and maximum energy product (BH)max = 64.3 kJ/m3 were obtained in the Nd14Fe80C6 alloy annealed at 725 °C. The formation of the Nd2Fe14C and Nd2Fe17Cx phases with the Nd2O3 phase precipitated at the triangular grain boundaries is responsible for its relatively good properties. Although the magnetic properties of Nd-Fe-C alloys obtained in this work are inferior to those of Nd-Fe-B, the present results help us to further understand the magnetic behavior of Nd-Fe-C alloys.
Peripheral Brain-Derived Neurotrophic Factor (BDNF) and Its Regulatory miRNAs as Biological Correlates of Impulsivity in Young Adults
Authors: Zakowicz Przemyslaw , Narozna Beata , Kozlowski Tomasz , Bargiel Weronika , Grabarczyk Maksymilian , Terczynska Maria , Pilecka Julia , Wasicka-Przewozna Karolina , Pawlak Joanna , Skibinska Maria
Published: September 2024

Background: Impulsivity assessment may serve as a valuable clinical tool in the stratification of suicide risk. Acting without forethought is a crucial feature in the psychopathology of many psychiatric disturbances and corresponds with suicidal ideations, behaviors, and attempts. Methods: We present data on biological and psychological correlates of impulsivity among young adults (n = 47). Psychological analysis included both the self-description questionnaire—Barratt Impulsiveness Scale (BIS-11)—and neuropsychological behavioral tests, including the Iowa Gambling Task (IGT), the Simple Response Time task (SRT), and the Continuous Performance Test (CPT). mRNA and micro-RNA were isolated from peripheral blood mononuclear cells (PBMC). Expression levels of Brain-Derived Neurotrophic Factor (BDNF) mRNA and its regulatory micro RNAs, mir-1-3p, mir-15a-5p, mir-26a-5p, mir-26b-5p, and mir-195-5p, were analyzed using the quantitative reverse transcription polymerase chain reaction (RT-qPCR) method. proBDNF and BDNF plasma protein levels were quantified using enzyme-linked immunosorbent assay (ELISA). Results: Significant correlations between BDNF mRNA and mir-15a-5p as well as proBDNF levels and mir-1-3p were detected. proBDNF protein levels correlated with motor and perseverance, while mir-26b correlated with cognitive complexity subdimensions of the BIS-11 scale. Correlations between BDNF, miRNAs, and the results of neuropsychological tests were also detected. Conclusions: The BDNF pathway shows a clinical potential in searching for biomarkers of impulse-control impairment. BDNF-regulatory micro-RNAs are detectable and related to clinical parameters in the studied population, which needs further research.
Research Progress on Precision Tool Alignment Technology in Machining
Authors: Liu Qimeng , Jiang Junxiang , Xiu Wencui , Ming Zhe , Cui Bo , Zheng Liang , Wang Jian , Qi Liyan
Published: September 2024

In the field of numerical control machining, tool alignment technology is a key link to ensure machining accuracy and quality. Tool alignment refers to determining the correct position of the tool relative to the workpiece, and its accuracy directly affects the precision of part machining. With the development of precision machining technology, the research and application of cutting technology are increasingly valued. Tool alignment methods are mainly divided into two categories: contact and non-contact. The contact type tool alignment method relies on direct contact between the tool and the workpiece or tool alignment instrument to measure the position. Among them, the trial cutting method is a traditional contact type tool alignment method that determines the tool position through actual cutting, which is intuitive but inefficient. The contact type tool presetter uses specialized equipment to improve the accuracy and efficiency of tool presetting through contact measurement. The non-contact tool alignment method does not rely on physical contact, while the image method uses image recognition technology to determine the tool position, making it suitable for high-precision applications. The laser diffraction method and the laser direct method use laser technology for non-contact measurement. The laser diffraction method determines the position of the tool by analyzing the diffraction mode of the laser beam, while the laser direct method directly measures the distance between the laser and the tool. This article mainly introduces the classification of tool alignment, commonly used knife alignment methods and common tool alignment devices, as well as the development status of international tool alignment instrument products.
Development of a Compact NDIR CO2 Gas Sensor for a Portable Gas Analyzer
Authors: Xu Maosen , Tian Wei , Lin Yuzhe , Xu Yan , Tao Jifang
Published: September 2024

A carbon dioxide (CO2) gas sensor based on non-dispersive infrared (NDIR) technology has been developed and is suitable for use in portable devices for high-precision CO2 detection. The NDIR gas sensor comprises a MEMS infrared emitter, a MEMS thermopile detector with an integrated optical filter, and a compact gas cell with high optical coupling efficiency. A dual-ellipsoid mirror optical system was designed, and based on optical simulation analysis, the structure of the dual-ellipsoid reflective gas chamber was designed and optimized, achieving a coupling efficiency of up to 54%. Optical and thermal simulations were conducted to design the sensor structure, considering thermal management and light analysis. By optimizing the gas cell structure and conditioning circuit, we effectively reduced the sensor’s baseline noise, enhancing the overall reliability and stability of the system. The sensor’s dimensions were 20 mm × 10 mm × 4 mm (L × W × H), only 15% of the size of traditional NDIR gas sensors with equivalent detection resolution. The developed sensor offers high sensitivity and low noise, with a sensitivity of 15 μV/ppm, a detection limit of 90 ppm, and a resolution of 30 ppm. The total power consumption of the whole sensor system is 6.5 mW, with a maximum power consumption of only 90 mW.
Research on Spatial Localization Method of Magnetic Nanoparticle Samples Based on Second Harmonic Waves
Authors: Wang Zheyan , Huang Ping , Zheng Fuyin , Yu Hongli , Li Yue , Qiu Zhichuan , Gai Lingke , Liu Zhiyao , Bai Shi
Published: September 2024

Existing magnetic tracer detection systems primarily rely on fundamental wave signal acquisition using non-differential sensor configurations. These sensors are highly susceptible to external interference and lack tomographic localization capabilities, hindering their clinical application. To address these limitations, this paper presents a novel method for achieving the deep spatial localization of tracers. The method exploits second harmonic signal detection at non-zero field points. By considering the combined nonlinear characteristics of the coil’s axial spatial magnetic field distribution and the Langevin function, a correlation model linking the signal peak and bias field is established. This model enables the determination of the tracer’s precise spatial location. Building on this framework, a handheld device for localizing magnetic nanoparticle tracers was developed. The device harnesses the second harmonic response generated by coupling an AC excitation field with a DC bias field. Our findings demonstrate that under conditions of reduced coil turns and weak excitation fields, the DC bias field exhibits exclusive dependence on the axial distance of the detection point, independent of particle concentration. This implies that the saturated DC bias field corresponding to the second harmonic signal can be used to determine the magnetic nanoparticle sample detection depth. The experimental results validated the method’s high accuracy, with axial detection distance and concentration reduction errors of only 4.8% and 4.1%, respectively. This research paves the way for handheld probes capable of tomographic tracer detection, offering a novel approach for advancing magnetically sensitive biomedical detection technologies.
Research Advances in Electrospun Nanofiber Membranes for Non-Invasive Medical Applications
Authors: Wang Junhua , You Chongyang , Xu Yanwei , Xie Tancheng , Wang Yi
Published: September 2024

Non-invasive medical nanofiber technology, characterized by its high specific surface area, biocompatibility, and porosity, holds significant potential in various medical domains, including tissue repair and biosensing. It is increasingly becoming central to healthcare by offering safer and more efficient treatment options for contemporary medicine. Numerous studies have explored non-invasive medical nanofibers in recent years, yet a comprehensive overview of the field remains lacking. In this paper, we provide a comprehensive summary of the applications of electrospun nanofibers in non-invasive medical fields, considering multiple aspects and perspectives. Initially, we introduce electrospinning nanofibers. Subsequently, we detail their applications in non-invasive health, including health monitoring, personal protection, thermal regulation, and wound care, highlighting their critical role in improving human health. Lastly, this paper discusses the current challenges associated with electrospun nanofibers and offers insights into potential future development trajectories.
Laser Metal Deposition of Rene 80—Microstructure and Solidification Behavior Modelling
Authors: Srinivasan Krishnanand , Gumenyuk Andrey , Rethmeier Michael
Published: September 2024

New developments in nickel-based superalloys and production methods, such as the use of additive manufacturing (AM), can result in innovative designs for turbines. It is crucial to understand how the material behaves during the AM process to advance the industrial use of these techniques. An analytical model based on reaction–diffusion formalism is developed to better explain the solidification behavior of the material during laser metal deposition (LMD). The well-known Scheil–Gulliver theory has some drawbacks, such as the assumption of equilibrium at the solid–liquid interface, which is addressed by this method. The solidified fractions under the Scheil model and the pure equilibrium model are calculated using CALPHAD simulations. A differential scanning calorimeter is used to measure the heat flow during the solid–liquid phase transformation, the result of which is further converted to solidified fractions. The analytical model is compared with all the other models for validation.
A Review of Bacterial Biofilm Components and Formation, Detection Methods, and Their Prevention and Control on Food Contact Surfaces
Authors: Olaimat Amin N. , Ababneh Ahmad Mohammad , Al-Holy Murad , Al-Nabulsi Anas , Osaili Tareq , Abughoush Mahmoud , Ayyash Mutamed , Holley Richard A.
Published: September 2024

The microbial biofilms are a community of microorganisms that adhere to each other and to surfaces, typically in a mucilaginous or gel-like matrix composed of extracellular polymeric substances, including polysaccharides, proteins, lipids, and DNA. In the food industry, the bacterial biofilms may be formed on different surfaces and cause post-processing contamination or cross-contamination from the food contact surfaces to food products. Conventional cleaning and sanitizing methods are often ineffective at removing bacterial biofilms. Among more recent alternative methods proposed to address this problem are the use of hydrolytic enzymes, essential oils, and bacteriocins. These methods show promise since their antibacterial and antibiofilm actions involve degradation of the extracellular polymeric matrix of the biofilm and lead to inhibition of the foodborne pathogens present. Understanding the limitations and mechanisms of action of enzymes, bacteriocins, and essential oils in controlling bacterial biofilms on foods and food contact surfaces is essential for developing solutions to prevent and control biofilm formation. This review critically summarizes the current knowledge of bacterial biofilm components, their formation, detection methods, prevention, and removal from food contact surfaces.
Performance of Geopolymer Insulation Bricks Synthesized from Industrial Waste
Authors: El Naggar Kamilia A. M. , Abd El-Razik Mahmoud M. , Kuku Mohammed , Arishi Mohammad , Maafa Ibrahim M. , Yousef Ayman , Abdel Hamid Eman M.
Published: September 2024

In the current work, aluminum dross from the aluminum industry and fired-clay brick waste (Homra) were combined with alkaline activators (Na2SiO3/NaOH) with varying molarities of NaOH to create insulating geopolymer bricks. An alkaline activator with an (S/L) ratio of 3/1 was combined with Homra to replace it partly. The mixture was then molded into 50 × 50 × 50 mm3 stainless steel molds. Water absorption, compressive strength, bulk density, and apparent porosity of the produced geopolymer were measured at various curing durations and NaOH molarities. The findings showed that adding more aluminum dross waste replacement leads to a decrease in bulk density and compressive strength and an increase in the creation of pores that improve the produced bricks’ ability to insulate against heat. Using 8 M of NaOH after 28 days, the findings revealed that 3% aluminum dross waste replacement has a compressive strength of about 8.6 MPa, water absorption of 14.6%, bulk density of 1.307 g/cm3, and thermal conductivity of 0.32 W/m·K. The Egyptian standard, ASTM C62, satisfies these outcomes. Using DesignBuilder energy simulation software version 6.1.06, the constructed insulating brick under ideal circumstances was utilized to calculate its contribution performance in energy consumption. The annual energy consumption was reduced by about 21% compared to traditional hollow cement bricks.
Mineralogy and Major Element Geochemistry of the Oligocene Barail Group Sandstones from the Sylhet Trough, Bengal Basin: Provenance and Tectonic Implications
Authors: Mohinta Sunjuckta , Sayem Abu Sadat Md , Rahman M. Julleh Jalalur , Alam Md Sha , Abdullah Rashed
Published: September 2024

The origin of Oligocene sediments in the Bengal Basin and associated tectonic setting remain poorly understood. This study investigates the framework mineralogy and major element geochemistry of the Barail Group sandstones from the Sylhet Trough within the Bengal Basin to clarify the provenance and tectonic history of the Oligocene. Modal analysis (Q83F7L10) and geochemical data support a classification of sublitharenite to subarkose, some with Fe enrichment. The heavy mineral assemblage is dominated by opaque minerals, followed by ultrastable minerals with zircon > tourmaline > rutile. The sub-angular to sub-rounded sand grains with a compositionally moderate mature nature suggest that the sediments were deposited close to the source area. The mineralogical and geochemical provenance discrimination diagram suggests contributions from felsic igneous, sedimentary/metasedimentary, and low-grade metamorphic sources, with detritus derived from the Indian craton and proto-Himalaya region. Data suggest moderate to intense chemical weathering, indicative of low relief and a sub-humid to humid climate in the source area. The tectonic analyses indicate that the Bengal Basin transitioned from a predominantly passive margin to an active tectonic margin setting during the Oligocene.
Biological Potential and Essential Oil Profile of Two Wild Apiaceae Species from Algeria (Daucus carota L. and Foeniculum vulgare Mill.): Larvicidal and Antibacterial Effects
Authors: Khemili Aicha , Bensizerara Djamel , Chenchouni Haroun , Chaibi Rachid , Aissani Nadjwa , Tegegne Desiye Tesfaye , El-Sayed El-Sayed R. , Szumny Antoni
Published: September 2024

Infectious diseases, including vector-borne and antibiotic-resistant infections, present significant global health challenges, necessitating the exploration of natural alternatives for disease control. In this study, we investigated the essential oil (EO) profile as well as larvicidal and antibacterial properties of two wild Apiaceae species used in Algeria: Daucus carota L. (DCEO) and Foeniculum vulgare Mill. (FVEO). EO was extracted from the aerial parts by steam distillation and analyzed using Gas Chromatography Mass Spectrometry (GC/MS). Major constituents identified in DCEO were geranyl acetate (50.07%) and elemicin (10.77%), while FVEO contained estragole (24.93%), fenchone (20.20%), and α-phellandrene (17.96%). Both EOs were highly effective towards Culex pipiens larvae, with low LC50 values of 30.6 ± 1.06 ppm for DCEO and 34.7 ± 1.06 ppm for FVEO, indicating their potential as bioinsecticides due to their bioactivity and bioavailability. Additionally, the two Eos demonstrated moderate antibacterial efficacy against gram-positive bacteria, Staphylococcus aureus ATCC 25923 and Staphylococcus aureus MRSA ATCC 43300, respectively, with DCEO showing MIC values of 10 and 20 mg/mL, respectively, and FVEO exhibiting MIC values > 20 mg/mL. However, both EOs showed limited effectiveness against gram-negative bacteria, Escherichia coli ATCC 25922 and Klebsiella pneumonia ATCC 700603. These results highlight the potential applications of DCEO and FVEO as natural bioinsecticides and antibacterial agents, offering promising avenues for further research and development in pest control and food preservation.
Secondary Metabolites from Australian Lichens Ramalina celastri and Stereocaulon ramulosum Affect Growth and Metabolism of Photobiont Asterochloris erici through Allelopathy
Authors: Bačkor Martin , Kecsey Dajana , Drábová Blažena , Urminská Dana , Šemeláková Martina , Goga Michal
Published: September 2024

In the present work, the phytotoxic effects of secondary metabolites extracted from lichen Ramalina celastri (usnic acid) and lichen Stereocaulon ramulosum (a naturally occurring mixture of atranorin and perlatolic acid, approx. 3:1) on cultures of the aposymbiotically grown lichen photobiont Asterochloris erici were evaluated. Algae were cultivated on the surface of glass microfiber disks with applied crystals of lichen extracts for 14 days. The toxicity of each extract was tested at the two selected doses in quantities of 0.01 mg/disk and 0.1 mg/disk. Cytotoxicity of lichen extracts was assessed using selected physiological parameters, such as growth (biomass production) of photobiont cultures, content of soluble proteins, chlorophyll a fluorescence, chlorophyll a integrity, contents of chlorophylls and total carotenoids, hydrogen peroxide, superoxide anion, TBARS, ascorbic acid (AsA), reduced (GSH) and oxidized (GSSG) glutathione, and composition of selected organic acids of the Krebs cycle. The application of both tested metabolic extracts decreased the growth of photobiont cells in a dose-dependent manner; however, a mixture of atranorin and perlatolic acid was more effective when compared to usnic acid at the same dose tested. A higher degree of cytotoxicity of extracts from lichen S. ramulosum when compared to identical doses of extracts from lichen R. celastri was also confirmed by a more pronounced decrease in chlorophyll a fluorescence and chlorophyll a integrity, decreased content of chlorophylls and total carotenoids, increased production of hydrogen peroxide and superoxide anion, peroxidation of membrane lipids (assessed as TBARS), and a strong decrease in non-enzymatic antioxidants such as AsA, GSH, and GSSG. The cytotoxicity of lichen compounds was confirmed by a strong alteration in the composition of selected organic acids included in the Krebs cycle. The increased ratio between pyruvic acid and citric acid was a very sensitive parameter of phytotoxicity of lichen secondary metabolites to the algal partner of symbiosis. Secondary metabolites of lichens are potent allelochemicals and play significant roles in maintaining the balance between mycobionts and photobionts, forming lichen thallus.
Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence
Authors: Son Ahrum , Park Jongham , Kim Woojin , Yoon Yoonki , Lee Sangwoon , Park Yongho , Kim Hyunsoo
Published: September 2024

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.
Development and Evaluation of a Cost-Effective, Carbon-Based, Extended-Release Febuxostat Tablet
Authors: Al-Ani Israa Hamid , Hailat Mohammad , Mohammed Dina J. , Matalqah Sina Mahmoud , Abu Dayah Alaa Azeez , Majeed Bashar J. M. , Awad Riad , Filip Lorena , Abu Dayyih Wael
Published: September 2024

This study outlines the development of a cost-effective, extended-release febuxostat (FEB) tablet using activated charcoal as an adsorbent to enhance drug release. FEB, a BCS Class II drug, presents formulation challenges due to low solubility and high lipophilicity. We evaluated eight formulations with varying FEB-to-charcoal ratios using FTIR and DSC for physical interactions and followed USP standards for overall assessment. The optimal 1:0.25 FEB-to-charcoal ratio demonstrated a consistent 12 h zero-order release pattern. In vivo studies indicated a significantly extended plasma profile compared to immediate-release tablets. The optimal tablets demonstrated acceptable hardness and disintegration times. This innovative approach enhances patient compliance, improves bioavailability, and reduces production costs, offering a promising solution for controlled FEB delivery.
Curcumin-Based Molecularly Imprinted Polymer Electropolymerized on Single-Use Graphite Electrode for Dipyridamole Analysis
Authors: Preda Daniel , Radu Gabriel Lucian , Iorgulescu Emilia-Elena , Cheregi Mihaela-Carmen , David Iulia Gabriela
Published: September 2024

A new molecularly imprinted polymer (MIP)-based disposable electrochemical sensor for dipyridamole (DIP) determination was obtained. The sensor was rapidly prepared by potentiodynamic electrochemical polymerization on a pencil graphite electrode (PGE) using curcumin (CUR) as a functional monomer and DIP as a template molecule. After the optimization of the conditions (pH, monomer–template ratio, scan rate, number of cyclic voltammetric cycles applied in the electro-polymerization process and extraction time of the template molecule) for MIP formation, DIP voltammetric behavior at the modified electrode (MIP_PGE) was investigated. DIP oxidation took place in a pH-dependent, irreversible mixed diffusion-adsorption controlled process. Differential pulse voltammetry (DPV) and adsorptive stripping differential pulse voltammetry (AdSDPV) were used to quantify DIP from pharmaceutical and tap water samples. Under optimized conditions (Britton–Robinson buffer at pH = 3.29), the obtained linear ranges were 5.00 × 10−8–1.00 × 10−5 mol/L and 5.00 × 10−9–1.00 × 10−7 mol/L DIP for DPV and AdSDPV, respectively. The limits of detection of the methods were 1.47 × 10−8 mol/L for DPV and 3.96 × 10−9 mol/L DIP for AdSDPV.