bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Surface Topography of Microfinishing Abrasive Films in Relation to Their Machining Capability of Nimonic 80A Superalloy
Authors: Tandecka Katarzyna , Kacalak Wojciech , Szafraniec Filip , Wieczorowski Michał , Mathia Thomas G.
Published: May 2024

This study investigates the surface topography of microfinishing abrasive films and their machining capability on the Nimonic 80A superalloy, a high-performance nickel-based alloy commonly used in aerospace and gas turbine engine applications. Surface analysis was conducted on three abrasive films with nominal grain sizes of 30, 15, and 9 μm, exploring wear patterns, contact frequency, and distribution. To assess the distribution of grain apexes, Voronoi cells were employed. Results revealed distinct wear mechanisms, including torn abrasive grains and cracked bond surfaces, highlighting the importance of efficient chip removal mechanisms in microfinishing processes. Larger grain sizes exhibited fewer contacts with the workpiece but provided more storage space for machining products, while smaller grain sizes facilitated smoother surface finishes. The research demonstrated the effectiveness of microfinishing abrasive films in reducing surface irregularities. Additionally, surface analysis of worn abrasive tools provided insights into wear mechanisms and chip formation, with the segmentation of microchips contributing to efficient chip removal. These findings underscore the significance of selecting appropriate abrasive films and implementing effective chip removal mechanisms to optimize microfinishing processes and improve surface finishing quality in advanced material machining applications. It is worth emphasizing that no prior research has investigated the microfinishing of components crafted from Nimonic 80A utilizing abrasive films, rendering this study truly unique in its contribution to the field.
Exploring the Progress of Hyaluronic Acid Hydrogels: Synthesis, Characteristics, and Wide-Ranging Applications
Authors: Gholamali Iman , Vu Trung Thang , Jo Sung-Han , Park Sang-Hyug , Lim Kwon Taek
Published: May 2024

This comprehensive review delves into the world of hyaluronic acid (HA) hydrogels, exploring their creation, characteristics, research methodologies, and uses. HA hydrogels stand out among natural polysaccharides due to their distinct features. Their exceptional biocompatibility makes them a top choice for diverse biomedical purposes, with a great ability to coexist harmoniously with living cells and tissues. Furthermore, their biodegradability permits their gradual breakdown by bodily enzymes, enabling the creation of temporary frameworks for tissue engineering endeavors. Additionally, since HA is a vital component of the extracellular matrix (ECM) in numerous tissues, HA hydrogels can replicate the ECM’s structure and functions. This mimicry is pivotal in tissue engineering applications by providing an ideal setting for cellular growth and maturation. Various cross-linking techniques like chemical, physical, enzymatic, and hybrid methods impact the mechanical strength, swelling capacity, and degradation speed of the hydrogels. Assessment tools such as rheological analysis, electron microscopy, spectroscopy, swelling tests, and degradation studies are employed to examine their attributes. HA-based hydrogels feature prominently in tissue engineering, drug distribution, wound recovery, ophthalmology, and cartilage mending. Crafting HA hydrogels enables the production of biomaterials with sought-after qualities, offering avenues for advancements in the realm of biomedicine.
Advanced Computational Analysis of Cobalt-Based Superalloys through Crystal Plasticity
Authors: Keshavarz Shahriyar , Campbell Carelyn E. , Reid Andrew C. E.
Published: May 2024

This study introduces an advanced computational method aimed at accelerating continuum-scale processes using crystal plasticity approaches to predict mechanical responses in cobalt-based superalloys. The framework integrates two levels, namely, sub-grain and homogenized, at the meso-scale through crystal plasticity finite element (CPFE) platforms. The model is applicable across a temperature range from room temperature up to 900 °C, accommodating various dislocation mechanisms in the microstructure. The sub-grain level explicitly incorporates precipitates and employs a dislocation density-based constitutive model that is size-dependent. In contrast, the homogenized level utilizes an activation energy-based constitutive model, implicitly representing the γ′ phase for efficiency in computations. This level considers the effects of composition and morphology on mechanical properties, demonstrating the potential for cobalt-based superalloys to rival nickel-based superalloys. The study aims to investigate the impacts of elements including tungsten, tantalum, titanium, and chromium through the homogenized constitutive model. The model accounts for the locking mechanism to address the cross-slip of screw dislocations at lower temperatures as well as the glide and climb mechanism to simulate diffusions at higher temperatures. The model’s validity is established across diverse compositions and morphologies, as well as various temperatures, through comparison with experimental data. This advanced computational framework not only enables accurate predictions of mechanical responses in cobalt-based superalloys across a wide temperature range, but also provides valuable insights into the design and optimization of these materials for high-temperature applications.
The Effects of Calcium Nitrite on the Mechanical Properties and Microstructure of Early-Age Frozen Cement Paste
Authors: Wan Lijun , Yu Maopei , Wu Enze , Zhao Yongqi
Published: May 2024

The objective of this paper is to investigate the effect of calcium nitrite (CN) on improving the mechanical properties and microstructures of early-frozen cement paste. Cement pastes containing 1%, 1.5%, 2%, 2.5%, and 3% CN were prepared. One batch of samples was frozen at −6 °C for 7 days and then cured at 20 °C, and the other batch of samples was directly cured at 20 °C as a control. The compressive strength, ultrasonic pulse velocity, and resistivity of all specimens at different target ages were measured under these two curing conditions. The hydration products and microstructures of typical samples were observed using XRD and scanning SEM. The results showed that the addition of 1.5% CN could promote cement hydration and enhance slurry densification, thereby increasing the compressive strength, ultrasonic pulse velocity, and electrical resistivity of the slurry, and positively affecting the early freezing resistance of the slurry. However, when the CN dosage exceeded 1.5%, the internal structure of the slurry was loose and porous due to the generation of a large amount of nitrite–AFm, which negatively affects the properties of the cement paste. In addition, the effectiveness of CN is only limited to temperature environments above −6 °C. Concrete antifreeze suitable for lower temperatures still requires further research.
Advances in Materials with Self-Healing Properties: A Brief Review
Authors: Dallaev Rashid
Published: May 2024

The development of materials with self-healing capabilities has garnered considerable attention due to their potential to enhance the durability and longevity of various engineering and structural applications. In this review, we provide an overview of recent advances in materials with self-healing properties, encompassing polymers, ceramics, metals, and composites. We outline future research directions and potential applications of self-healing materials (SHMs) in diverse fields. This review aims to provide insights into the current state-of-the-art in SHM research and guide future efforts towards the development of innovative and sustainable materials with enhanced self-repair capabilities. Each material type showcases unique self-repair mechanisms tailored to address specific challenges. Furthermore, this review investigates crack healing processes, shedding light on the latest developments in this critical aspect of self-healing materials. Through an extensive exploration of these topics, this review aims to provide a comprehensive understanding of the current landscape and future directions in self-healing materials research.
Improvements in Wear and Corrosion Resistance of Ti-W-Alloyed Gray Cast Iron by Tailoring Its Microstructural Properties
Authors: Razaq Abdul , Yu Peng , Khan Adnan Raza , Ji Xiao-Yuan , Yin Ya-Jun , Zhou Jian-Xin , Shehabeldeen Taher A.
Published: May 2024

The improved wear and corrosion resistance of gray cast iron (GCI) with enhanced mechanical properties is a proven stepping stone towards the longevity of its versatile industrial applications. In this article, we have tailored the microstructural properties of GCI by alloying it with titanium (Ti) and tungsten (W) additives, which resulted in improved mechanical, wear, and corrosion resistance. The results also show the nucleation of the B-, D-, and E-type graphite flakes with the A-type graphite flake in the alloyed GCI microstructure. Additionally, the alloyed microstructure demonstrated that the ratio of the pearlite volume percentage to the ferrite volume percentage was improved from 67/33 to 87/13, whereas a reduction in the maximum graphite length and average grain size from 356 ± 31 µm to 297 ± 16 µm and 378 ± 18 µm to 349 ± 19 µm was detected. Consequently, it improved the mechanical properties and wear and corrosion resistance of alloyed GCI. A significant improvement in Brinell hardness, yield strength, and tensile strength of the modified microstructure from 213 ± 7 BHN to 272 ± 8 BHN, 260 ± 3 MPa to 310 ± 2 MPa, and 346 ± 12 MPa to 375 ± 7 MPa was achieved, respectively. The substantial reduction in the wear rate of alloyed GCI from 8.49 × 10−3 mm3/N.m to 1.59 × 10−3 mm3/N.m resulted in the upgradation of the surface roughness quality from 297.625 nm to 192.553 nm. Due to the increase in the corrosion potential from −0.5832 V to −0.4813 V, the impedance of the alloyed GCI was increased from 1545 Ohm·cm2 to 2290 Ohm·cm2. On the basis of the achieved experimental results, it is suggested that the reliability of alloyed GCI based on experimentally validated microstructural compositions can be ensured during the operation of plants and components in a severe wear and corrosive environment. It can be predicted that the proposed alloyed GCI components are capable of preventing the premature failure of high-tech components susceptible to a wear and corrosion environment.
Influence of Machine Tool Operating Conditions on the Resulting Circularity and Positioning Accuracy
Authors: Sarvas Matej , Holub Michal , Marek Tomas , Prochazka Jan , Bradac Frantisek , Blecha Petr
Published: May 2024

The operating conditions of the production process significantly influence the resulting dimensional and form accuracy of the workpiece. The operating conditions include the position of the workpiece location, with internal and external heat sources influenced not only by the machine location but also by its operation. In addition, there are the cutting conditions and the feed rate requirements of CNC machine tools. These changes, such as workpiece position, feed rates, and machine heat load, are further reflected in the ability of the machine to run at the position required and interpolate within the given tolerances of circularity. For the accuracy and repeatability of positioning, the machine was set up according to ISO 230-2 and for the circular interpolation tests according to ISO 230-4. The obtained results show the importance of attention to the appropriate setting of the operating conditions of the production process, where the knowledge of the geometric accuracy of the CNC machine tool in its working space can systematically increase the manufacturing accuracy itself or be another tool suitable for predicting the dimensional and form accuracy of workpieces.
Utilizing Artificial Neural Networks for Geometric Bone Model Reconstruction in Mandibular Prognathism Patients
Authors: Mitić Jelena , Vitković Nikola , Trajanović Miroslav , Górski Filip , Păcurar Ancuţa , Borzan Cristina , Sabău Emilia , Păcurar Răzvan
Published: May 2024

Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model’s surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries.
Strategic Queueing Behavior of Two Groups of Patients in a Healthcare System
Authors: Liu Youxin , Liu Liwei , Jiang Tao , Chai Xudong
Published: May 2024

Long waiting times and crowded services are the current medical situation in China. Especially in hierarchic healthcare systems, as high-quality medical resources are mainly concentrated in comprehensive hospitals, patients are too concentrated in these hospitals, which leads to overcrowding. This paper constructs a game-theoretical queueing model to analyze the strategic queueing behavior of patients. In such hospitals, patients are divided into first-visit and referred patients, and the hospitals provide patients with two service phases of “diagnosis” and “treatment”. We first obtain the expected sojourn time. By defining the patience level of patients, the queueing behavior of patients in equilibrium is studied. The results suggest that as long as the patients with low patience levels join the queue, the patients with high patience levels also join the queue. As more patients arrive at the hospitals, the queueing behavior of patients with high patience levels may have a negative effect on that of patients with low patience levels. The numerical results also show that the equilibrium behavior deviates from a socially optimal solution; therefore, to reach maximal social welfare, the social planner should adopt some regulatory policies to control the arrival rates of patients.
Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk
Authors: Zhu Miao , Shia Ben-Chang , Su Meng , Liu Jialin
Published: May 2024

Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent management framework called the consumer default risk portrait (CDRP) to mitigate the default risks associated with online consumer loans. The CDRP framework combines traditional credit information and Internet platform data to depict the portrait of consumer default risks. It consists of four modules: addressing data imbalances, establishing relationships between user characteristics and the default risk, analyzing the influence of different variables on default, and ultimately presenting personalized consumer profiles. Empirical findings reveal that “Repayment Periods”, “Loan Amount”, and “Debt to Income Type” emerge as the three variables with the most significant impact on default. “Re-payment Periods” and “Debt to Income Type” demonstrate a positive correlation with default probability, while a lower “Loan Amount” corresponds to a higher likelihood of default. Additionally, our verification highlights that the significance of variables varies across different samples, thereby presenting a personalized portrait from a single sample. In conclusion, the proposed framework provides valuable suggestions and insights for financial institutions and Internet platform managers to improve the market environment of online consumer credit services.
An Inverse Problem for Estimating Spatially and Temporally Dependent Surface Heat Flux with Thermography Techniques
Authors: Huang Cheng-Hung , Fang Kuan-Chieh
Published: May 2024

In this study, an inverse conjugate heat transfer problem is examined to estimate temporally and spatially the dependent unknown surface heat flux using thermography techniques with a thermal camera in a three-dimensional domain. Thermography techniques encompass a broad set of methods and procedures used for capturing and analyzing thermal data, while thermal cameras are specific tools used within those techniques to capture thermal images. In the present study, the interface conditions of the plate and air domains are obtained using perfect thermal contact conditions, and therefore we define the problem studied as an inverse conjugate heat transfer problem. Achieving the simultaneous solution of the continuity, Navier–Stokes, and energy equations within the air domain, alongside the heat conduction equation in the plate domain, presents a more intricate challenge compared to conventional inverse heat conduction problems. The validity of our inverse solutions was verified through numerical simulations, considering various inlet air velocities and plate thicknesses. Notably, it was found that due to the singularity of the gradient of the cost function at the final time point, the estimated results near the final time must be discarded, and exact measurements consistently produce accurate boundary heat fluxes under thin-plate conditions, with air velocity exhibiting no significant impact on the estimates. Additionally, an analysis of measurement errors and their influence on the inverse solutions was conducted. The numerical results conclusively demonstrated that the maximum error when estimating heat flux consistently remained below 3% and higher measurement noise resulted in the accuracy of the heat flux estimation decreasing. This underscores the inherent challenges associated with inverse problems and highlights the importance of obtaining accurate measurement data in the problem domain.
Abundant New Optical Soliton Solutions to the Biswas–Milovic Equation with Sensitivity Analysis for Optimization
Authors: Hossain Md Nur , Alsharif Faisal , Miah M. Mamun , Kanan Mohammad
Published: May 2024

This study extensively explores the Biswas–Milovic equation (BME) with Kerr and power law nonlinearity to extract the unique characteristics of optical soliton solutions. These optical soliton solutions have different applications in the field of precision in optical switching, applications in waveguide design, exploration of nonlinear optical effects, imaging precision, reduced intensity fluctuations, suitability for optical signal processing in optical physics, etc. Through the powerful (G′/G, 1/G)-expansion analytical method, a variety of soliton solutions are expressed in three distinct forms: trigonometric, hyperbolic, and rational expressions. Rigorous validation using Mathematica software ensures precision, while dynamic visual representations vividly portray various soliton patterns such as kink, anti-kink, singular soliton, hyperbolic, dark soliton, and periodic bright soliton solutions. Indeed, a sensitivity analysis was conducted to assess how changes in parameters affect the exact solutions, aiding in the understanding of system behavior and informing decision-making, especially in accurately designing or analyzing real-world optical phenomena. This investigation reveals the significant influence of parameters λ, τ, c, B, and Κ on the precise solutions in Kerr and power law nonlinearities within the BME. Notably, parameter λ exhibits consistently high sensitivity across all scenarios, while parameters τ and c demonstrate pronounced sensitivity in scenario III. The outcomes derived from this method are distinctive and carry significant implications for the dynamics of optical fibers and wave phenomena across various optical systems.
Micro-Grinding Parameter Control of Hard and Brittle Materials Based on Kinematic Analysis of Material Removal
Authors: Manea Hisham , Lu Hong , Liu Qi , Xiao Junbiao , Yang Kefan
Published: May 2024

This article explores the intricacies of micro-grinding parameter control for hard and brittle materials, with a specific focus on Zirconia ceramics (ZrO2) and Optical Glass (BK7). Given the increasing demand and application of these materials in various high-precision industries, this study aims to provide a comprehensive kinematic analysis of material removal during the micro-grinding process. According to the grinding parameters selected to be analyzed in this study, the ac-max values are between (9.55 nm ~ 67.58 nm). Theoretical modeling of the grinding force considering the brittle and ductile removal phase, frictional effects, the possibility of grit to cut materials, and grinding conditions is very important in order to control and optimize the surface grinding process. This research introduces novel models for predicting and optimizing micro-grinding forces effectively. The primary objective is to establish a micro-grinding force model that facilitates the easy manipulation of micro-grinding parameters, thereby optimizing the machining process for these challenging materials. Through experimental investigations conducted on Zirconia ceramics, the paper evaluates a mathematical model of the grinding force, highlighting its significance in predicting and controlling the forces involved in micro-grinding. The suggested model underwent thorough testing to assess its validity, revealing an accuracy with average variances of 6.616% for the normal force and 5.752% for the tangential force. Additionally, the study delves into the coefficient of friction within the grinding process, suggesting a novel frictional force model. This model is assessed through a series of experiments on Optical Glass BK7, aiming to accurately characterize the frictional forces at play during grinding. The empirical results obtained from both sets of experiments—on Zirconia ceramics and Optical Glass BK7—substantiate the efficacy of the proposed models. These findings confirm the models’ capability to accurately describe the force dynamics in the micro-grinding of hard and brittle materials. The research not only contributes to the theoretical understanding of micro-grinding processes but also offers practical insights for enhancing the efficiency and effectiveness of machining operations involving hard and brittle materials.
Performance Evaluation of Railway Infrastructure Managers: A Novel Hybrid Fuzzy MCDM Model
Authors: Kalem Aida , Tadić Snežana , Krstić Mladen , Čabrić Nermin , Branković Nedžad
Published: May 2024

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.
Uncertainty Analysis of Aircraft Center of Gravity Deviation and Passenger Seat Allocation Optimization
Authors: Zhao Xiangling , Xiao Wenheng
Published: May 2024

The traditional method of allocating passenger seats based on compartments does not effectively manage an aircraft’s center of gravity (CG), resulting in a notable divergence from the desired target CG (TCG). In this work, the Boeing B737-800 aircraft was employed as a case study, and row-based and compartment-based integer programming models for passenger allocation were examined and constructed with the aim of addressing the current situation. The accuracy of CG control was evaluated by comparing the row-based and compartment-based allocation techniques, taking into account different bodyweights and numbers of passengers. The key contribution of this research is to broaden the range of the mobilizable set for the aviation weight and balance (AWB) model, resulting in a significant reduction in the range of deviations in the center of gravity outcomes by a factor of around 6 to 16. The effectiveness of the row-based allocation approach and the impact of passenger weight randomness on the deviation of an airplane’s CG were also investigated in this study. The Monte Carlo method was utilized to quantify the uncertainty associated with passenger weight, resulting in the generation of the posterior distribution of the aircraft’s center of gravity (CG) deviation. The outcome of the row-based model test is the determination of the range of passenger numbers that can be effectively allocated under different TCG conditions.
Global Dynamics of a Social Hierarchy-Stratified Malaria Model: Insight from Fractional Calculus
Authors: Abimbade Sulaimon F. , Chuma Furaha M. , Sangoniyi Sunday O. , Lebelo Ramoshweu S. , Okosun Kazeem O. , Olaniyi Samson
Published: May 2024

In this study, a mathematical model for the transmission dynamics of malaria among different socioeconomic groups in the human population interacting with a susceptible-infectious vector population is presented and analysed using a fractional-order derivative of the Caputo type. The total human population is stratified into two distinguished classes of lower and higher income individuals, with each class further subdivided into susceptible, infectious, and recovered populations. The socio hierachy-structured fractional-order malaria model is analyzed through the application of different dynamical system tools. The theory of positivity and boundedness based on the generalized mean value theorem is employed to investigate the basic properties of solutions of the model, while the Banach fixed point theory approach is used to prove the existence and uniqueness of the solution. Furthermore, unlike the existing related studies, comprehensive global asymptotic dynamics of the fractional-order malaria model around both disease-free and endemic equilibria are explored by generalizing the usual classical methods for establishing global asymptotic stability of the steady states. The asymptotic behavior of the trajectories of the system are graphically illustrated at different values of the fractional (noninteger) order.
A Mathematical Analysis of Competitive Dynamics and Aggressive Treatment in the Evolution of Drug Resistance in Malaria Parasites
Authors: Song Tianqi , Wang Yishi , Li Yang , Fan Guoliang
Published: May 2024

Experimental evidence supports the counterintuitive notion that rapid eradication of pathogens within a host, infected with both drug-sensitive and -resistant malaria parasites, can actually accelerate the evolution of drug-resistant pathogens. This study aims to analyze the competitive dynamics between these two strains through a mathematical model and evaluate the impact of aggressive treatment on the spread of drug resistance. We conducted equilibrium, uncertainty, and sensitivity analyses to assess the model, identifying and measuring the influence of key factors on the outcome variable (the population of drug-resistant parasites). Both equilibrium and local sensitivity analyses concurred that the density of drug-resistant parasites is notably affected by genetic instability, the production rate of red blood cells, the number of merozoites, and competition factors. Conversely, there is a negative relationship between genetic instability and one of the competition coefficients. Global sensitivity analysis offers a comprehensive examination of the impact of each input parameter on the temporal propagation of drug resistance, effectively accounting for the interplay among parameters. Both local and global sensitivity analyses underscore the continuous impact of drug treatment on the progression of drug resistance over time. This paper anticipates exploring the underlying mechanisms of drug resistance and providing theoretical support for developing more effective drug treatment strategies.
Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application
Authors: Ma Yindi , Li Yanhai , Yong Longquan
Published: May 2024

This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the “teaching” and “learning” stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm into three distinct phases: pre-course self-study, classroom blended learning, and post-course consolidation; this structural reorganization and the random crossover strategy in the self-learning phase effectively enhance the global optimization capability of TLBO. To evaluate its performance, the BLTLBO algorithm was tested alongside seven distinguished variants of the TLBO algorithm on thirteen multimodal functions from the CEC2014 suite. Furthermore, two excellent high-dimensional optimization algorithms were added to the comparison algorithm and tested in high-dimensional mode on five scalable multimodal functions from the CEC2008 suite. The empirical results illustrate the BLTLBO algorithm’s superior efficacy in handling high-dimensional multimodal challenges. Finally, a high-dimensional portfolio optimization problem was successfully addressed using the BLTLBO algorithm, thereby validating the practicality and effectiveness of the proposed method.
Secure Active Intelligent Reflecting Surface Communication against Colluding Eavesdroppers
Authors: Xu Jiaxin , Peng Yuyang , Ye Runlong , Gan Wei , AL-Hazemi Fawaz , Mirza Mohammad Meraj
Published: May 2024

An active intelligent reflecting surface (IRS)-assisted, secure, multiple-input–single-output communication method is proposed in this paper. In this proposed scheme, a practical and unfavorable propagation environment is considered by assuming that multiple colluding eavesdroppers (Eves) coexist. In this case, we jointly optimize the beamformers of the base station (BS) and the active IRS for the formulated sum secrecy rate (SSR) maximization problem. Because the formulated problem is not convex, we apply the alternating optimization method to optimize the beamformers for maximizing the SSR. Specifically, we use the semi-definite relaxation method to solve the sub-problem of the beamforming vector of the BS, and we use the successive convex approximation method to solve the sub-problem of the power amplification matrix of the active IRS. Based on the solutions obtained using these stated methods, numerical results show that deploying an active IRS is superior compared to the cases of a passive IRS and a non-IRS for improving the physical layer security of wireless communication with multiple colluding Eves under different settings, such as the numbers of users, Eves, reflecting elements, and BS antennas as well as the maximum transmit power budget at the BS.
Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection
Authors: Aungkulanon Pasura , Atthirawong Walailak , Luangpaiboon Pongchanun , Chanpuypetch Wirachchaya
Published: May 2024

Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.
Assembly Theory of Binary Messages
Authors: Łukaszyk Szymon , Bieniawski Wawrzyniec
Published: May 2024

Using assembly theory, we investigate the assembly pathways of binary strings (bitstrings) of length N formed by joining bits present in the assembly pool and the bitstrings that entered the pool as a result of previous joining operations. We show that the bitstring assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that, for certain N values, it has regularities that can be used to determine the length of the shortest addition chain for N. We show that a bitstring with the smallest assembly index for N can be assembled via a binary program of a length equal to this index if the length of this bitstring is expressible as a product of Fibonacci numbers. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the bitstring so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP since every computable problem and every computable solution can be encoded as a finite bitstring. The lower bound on the bitstring assembly index implies a creative path and an optimization path of the evolution of information, where only the latter is available to Turing machines (artificial intelligence). Furthermore, the upper bound hints at the role of dissipative structures and collective, in particular human, intelligence in this evolution.
FedUB: Federated Learning Algorithm Based on Update Bias
Authors: Zhang Hesheng , Zhang Ping , Hu Mingkai , Liu Muhua , Wang Jiechang
Published: May 2024

Federated learning, as a distributed machine learning framework, aims to protect data privacy while addressing the issue of data silos by collaboratively training models across multiple clients. However, a significant challenge to federated learning arises from the non-independent and identically distributed (non-iid) nature of data across different clients. non-iid data can lead to inconsistencies between the minimal loss experienced by individual clients and the global loss observed after the central server aggregates the local models, affecting the model’s convergence speed and generalization capability. To address this challenge, we propose a novel federated learning algorithm based on update bias (FedUB). Unlike traditional federated learning approaches such as FedAvg and FedProx, which independently update model parameters on each client before direct aggregation to form a global model, the FedUB algorithm incorporates an update bias in the loss function of local models—specifically, the difference between each round’s local model updates and the global model updates. This design aims to reduce discrepancies between local and global updates, thus aligning the parameters of locally updated models more closely with those of the globally aggregated model, thereby mitigating the fundamental conflict between local and global optima. Additionally, during the aggregation phase at the server side, we introduce a metric called the bias metric, which assesses the similarity between each client’s local model and the global model. This metric adaptively sets the weight of each client during aggregation after each training round to achieve a better global model. Extensive experiments conducted on multiple datasets have confirmed the effectiveness of the FedUB algorithm. The results indicate that FedUB generally outperforms methods such as FedDC, FedDyn, and Scaffold, especially in scenarios involving partial client participation and non-iid data distributions. It demonstrates superior performance and faster convergence in tasks such as image classification.
Height Prediction of Water-Conducting Fracture Zone in Jurassic Coalfield of Ordos Basin Based on Improved Radial Movement Optimization Algorithm Back-Propagation Neural Network
Authors: Gao Zhiyong , Jin Liangxing , Liu Pingting , Wei Junjie
Published: May 2024

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.
A Developed Model and Fuzzy Multi-Criteria Decision-Making Method to Evaluate Supply Chain Nervousness Strategies
Authors: Magableh Ghazi M. , Mistarihi Mahmoud Z. , Rababah Taha , Almajwal Ali , Al-Rayyan Numan
Published: May 2024

Nervousness is thought to be a source of confusion, instability, or uncertainty in SC systems due to disruptions and frequent changes in decisions. Nervousness persists even with consistent SCs, which arise from planning flexibility in response to changes, where responsiveness and customer satisfaction balance. Although the term “nervousness” is well known, to our knowledge no prior research has examined and explored supply chain nervousness strategies (SCNSs). This research explores supply chain nervousness strategies, factors, reduction methods, and recent trends in the supply chain’s relationship with nervousness. The main purpose of this research is to determine the comprehensive and relevant nervousness strategies in the supply chains, especially in light of the unprecedented development and change in business, economics, and technology and the fierce competition. SCN strategies are introduced in a developed model to designate SCN measurements and indicators, mitigation strategies and stages, and management strategies. The fuzzy PROMETHEE method is employed to rank the strategies based on their importance and order of implementation. The suggested method for managing nervousness is then presented with a numerical case, along with the results. The research outcomes indicate that the top five strategies for managing nervousness include planning continuity, utilizing technology, managing nervousness, improving the SC cyber system, and managing supplies. The findings assist decision makers, practitioners, and managers in focusing on SC improvement, resilience, and sustainability.
Denigrins H–L: Sulfated Derivatives of Denigrins D and E from a New Zealand Dictyodendrilla c.f. dendyi Marine Sponge
Authors: Gris Lauren , Prinsep Michèle R. , Peters Linda M. , Battershill Christopher N.
Published: May 2024

Five new sulfated arylpyrrole and arylpyrrolone alkaloids, denigrins H–L (1–5), along with two known compounds, dictyodendrin B and denigrin G, were isolated from an extract of a New Zealand Dictyodendrilla c.f. dendyi marine sponge. Denigrins H–L represent the first examples of sulfated denigrins, with denigrins H and I (1–2), as derivatives of denigrin D, containing a pyrrolone core, and denigrins J–L (3–5), as derivatives of denigrin E (6), containing a pyrrole core. Their structures were elucidated by interpretation of 1D and 2D NMR spectroscopic data, ESI, and HR-ESI-MS spectrometric data, as well as comparison with literature data. Compounds 1–5, along with six known compounds previously isolated from the same extract, showed minimal cytotoxicity against the HeLa cervical cancer cell line.
Volatile Organic Compounds Produced by a Deep-Sea Bacterium Efficiently Inhibit the Growth of Pseudomonas aeruginosa PAO1
Authors: Hu Yuanyuan , Liu Ge , Sun Chaomin , Wu Shimei
Published: May 2024

The deep-sea bacterium Spongiibacter nanhainus CSC3.9 has significant inhibitory effects on agricultural pathogenic fungi and human pathogenic bacteria, especially Pseudomonas aeruginosa, the notorious multidrug-resistant pathogen affecting human public health. We demonstrate that the corresponding antibacterial agents against P. aeruginosa PAO1 are volatile organic compounds (VOCs, namely VOC-3.9). Our findings show that VOC-3.9 leads to the abnormal cell division of P. aeruginosa PAO1 by disordering the expression of several essential division proteins associated with septal peptidoglycan synthesis. VOC-3.9 hinders the biofilm formation process and promotes the biofilm dispersion process of P. aeruginosa PAO1 by affecting its quorum sensing systems. VOC-3.9 also weakens the iron uptake capability of P. aeruginosa PAO1, leading to reduced enzymatic activity associated with key metabolic processes, such as reactive oxygen species (ROS) scavenging. Overall, our study paves the way to developing antimicrobial compounds against drug-resistant bacteria by using volatile organic compounds.
Role of Probiotics in Gut Microbiome and Metabolome in Non-Alcoholic Fatty Liver Disease Mouse Model: A Comparative Study
Authors: Wu Tian , Zeng Zheng , Yu Yanyan
Published: May 2024

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver condition worldwide. Numerous studies conducted recently have demonstrated a connection between the dysbiosis of the development of NAFLD and gut microbiota. Rebuilding a healthy gut ecology has been proposed as a strategy involving the use of probiotics. The purpose of this work is to investigate and compare the function of probiotics Akkermansia muciniphila (A. muciniphila) and VSL#3 in NAFLD mice. Rodent NAFLD was modeled using a methionine choline-deficient diet (MCD) with/without oral probiotic delivery. Subsequently, qPCR, histological staining, and liver function tests were conducted. Mass spectrometry-based analysis and 16S rDNA gene sequencing were used to investigate the liver metabolome and gut microbiota. We found that while both A. muciniphila and VSL#3 reduced hepatic fat content, A. muciniphila outperformed VSL#3. Furthermore, probiotic treatment restored the β diversity of the gut flora and A. muciniphila decreased the abundance of pathogenic bacteria such as Ileibacterium valens. These probiotics altered the metabolism in MCD mice, especially the glycerophospholipid metabolism. In conclusion, our findings distinguished the role of A. muciniphila and VSL#3 in NAFLD and indicated that oral-gavage probiotics remodel gut microbiota and improve metabolism, raising the possibility of using probiotics in the cure of NAFLD.
A Review of the Mechanisms of Bacterial Colonization of the Mammal Gut
Authors: Lin Qingjie , Lin Shiying , Fan Zitao , Liu Jing , Ye Dingcheng , Guo Pingting
Published: May 2024

A healthy animal intestine hosts a diverse population of bacteria in a symbiotic relationship. These bacteria utilize nutrients in the host’s intestinal environment for growth and reproduction. In return, they assist the host in digesting and metabolizing nutrients, fortifying the intestinal barrier, defending against potential pathogens, and maintaining gut health. Bacterial colonization is a crucial aspect of this interaction between bacteria and the intestine and involves the attachment of bacteria to intestinal mucus or epithelial cells through nonspecific or specific interactions. This process primarily relies on adhesins. The binding of bacterial adhesins to host receptors is a prerequisite for the long-term colonization of bacteria and serves as the foundation for the pathogenicity of pathogenic bacteria. Intervening in the adhesion and colonization of bacteria in animal intestines may offer an effective approach to treating gastrointestinal diseases and preventing pathogenic infections. Therefore, this paper reviews the situation and mechanisms of bacterial colonization, the colonization characteristics of various bacteria, and the factors influencing bacterial colonization. The aim of this study was to serve as a reference for further research on bacteria–gut interactions and improving animal gut health.