bluuebunny/crossref_metadata_2025 huggingface dataset:
Developmental Aspects of Greek Vowel Reduction in Different Prosodic Positions
Authors: Christodoulidou Polychronia , Nicolaidis Katerina , Stamovlasis Dimitrios
Published: October 2024

This study investigates the development of Greek vowel reduction across different prosodic positions (stressed, pre-stressed, post-stressed), examining normative data from 72 participants aged 3 years to adulthood and balanced for gender. Participants performed a delayed repetition task, producing real trisyllabic words with the vowels [i, ε, ɐ, o, u] examined in the second syllable. Measurements included relative vowel duration, normalized acoustic vowel space areas, and Euclidean distances of vowels from the centroid of the acoustic space. Our findings show that changes in speech motor control, system stiffness, and stress marking with age, along with children’s prosody sensitivity, contributed to several developmental milestones: the completion of the developmental trajectory of relative vowel duration and temporal vowel reduction at early adolescence; the attainment of adult-like spatial vowel characteristics and their reduction at preschool age; and the early acquisition of the prosodic strength of the stress conditions, leading to vowel reduction from the stressed to pre-stressed to post-stressed conditions. The correlation strength between temporal and spatial vowel reduction across ages revealed age-related differences in spatiotemporal speech organization, with significant gender-related differences observed only in vowel space areas, where females exhibited larger areas possibly related to sociophonetic factors. Intrinsic vowel duration appeared from age 3.
Plasma Proteomics of Type 2 Diabetes, Hypertension, and Co-Existing Diabetes/Hypertension in Thai Adults
Authors: Fakfum Puriwat , Chuljerm Hataichanok , Parklak Wason , Roytrakul Sittiruk , Phaonakrop Narumon , Lerttrakarnnon Peerasak , Kulprachakarn Kanokwan
Published: October 2024

The study explored proteomics to better understand the relationship between type 2 diabetes (T2DM) and hypertension (HT) in Thai adults, using shotgun proteomics and bioinformatics analysis. Plasma samples were taken from 61 subjects: 14 healthy subjects (mean age = 40.85 ± 7.12), 13 with T2DM (mean age = 57.38 ± 6.03), 16 with HT (mean age = 66.87 ± 10.09), and 18 with coexisting T2DM/HT (mean age = 58.22 ± 10.65). Proteins were identified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein–protein interactions were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) version 11.5. We identified six unique proteins in T2DM patients, including translationally controlled 1 (TPT1) and nibrin (NBN), which are associated with the DNA damage response. In HT patients, seven unique proteins were identified, among them long-chain fatty acid-CoA ligase (ASCL), which functions in the stimulation of triacylglycerol and cholesterol synthesis, and NADPH oxidase activator 1 (NOXA1), which is involved in high blood pressure via angiotensin II-induced reactive oxygen species (ROS)-generating systems. In coexisting T2DM/HT patients, six unique proteins were identified, of which two—microtubule-associated protein 1A (MAP1A)—might be involved in dementia via RhoB-p53 and diacylglycerol kinase beta (DGKB), associated with lipid metabolism. This study identified new candidate proteins that are possibly involved in the pathology of these diseases.
Temperature-Induced Seasonal Dynamics of Brain Gangliosides in Rainbow Trout (Oncorhynchus mykiss Walbaum) and Common Carp (Cyprinus carpio L.)
Authors: Pavić Valentina , Viljetić Barbara , Blažetić Senka , Labak Irena , Has-Schön Elizabeta , Heffer Marija
Published: October 2024

This study aimed to determine the expression and distribution of gangliosides in specific regions of the brains of rainbow trout (Oncorhynchus mykiss Walbaum) and common carp (Cyprinus carpio L.) with regard to seasonal temperature changes. Seasonal changes in ganglioside expression and distribution within the species were expected. The natural ecosystems of these fishes differ significantly due to their distinct habitat preferences, geographic distributions, and environmental requirements. Based on the fact that the common carp is eurythermic and adapts to a wide range of temperatures, while the rainbow trout is stenothermic and thrives in a narrower temperature range, it was expected that these species would exhibit distinct patterns of ganglioside modification as part of their adaptive response to temperature fluctuations. Immunohistochemistry using specific antibodies for the major brain gangliosides (GM1, GD1a, GD1b, GT1b), along with the Svennerholm method for quantifying sialic acid bound to gangliosides, revealed that cold acclimatization led to an increase in polysialylated gangliosides in the common carp brain and an increase in trisialogangliosides in the rainbow trout brain. Immunohistochemical analysis also identified region-specific changes in ganglioside expression, suggesting specific functional roles in neuronal adaptation. These results supported the hypothesis that the composition and distribution of brain gangliosides change in response to seasonal thermal shifts as part of the adaptive response. The results underscore the importance of gangliosides in neuronal function and adaptation to environmental stimuli, with implications for understanding fish resilience to temperature changes. This study offers valuable insights into species’ temperature adaptation, with implications for physiological and ecological management and improved aquaculture practices. Future research could expand the species scale, study molecular mechanisms and regulatory pathways in ganglioside metabolism, and examine ganglioside interactions with membrane proteins and lipids for a deeper understanding of thermal adaptation.
Assessment of the Position of North Adriatic Terminals in Container Market Based on Different Indices
Authors: Hess Svjetlana , Hess Mirano , Novaselić Marko , Grbić Luka
Published: October 2024

Background: This paper addresses the common question of how service providers compare to their competitors and what their competitive advantages are. It focuses on the North Adriatic ports of Trieste, Koper, Rijeka, Venice, and Ravenna, all members of the North Adriatic Ports Association, analyzing their position, size, competitiveness, and role in the container market. Methods: The research employs three distinct methods: market structure analysis using several concentration indices, the R method for ranking terminals, and aggregation of these indices to create a combined index for port performance. Results: Based on the rankings, indicators, the BCG matrix, and future development plans for each terminal, the ports are ranked as follows: Koper, Rijeka, Trieste, Venice, and Ravenna. Koper emerges as the leader, positioned in the high market share and strong growth category. This advantageous position allows Koper to efficiently attract traffic without requiring substantial investments. Conclusions: The contribution of the work is that the specific measure indices were applied, which were not used in the analysis of the North Adriatic container terminals until now. The research provides both a broad and detailed understanding of the position, role, and condition of each terminal. This insight enables stakeholders to take timely, strategic actions aimed at boosting productivity and traffic, ultimately improving their competitive standing.
Combining Artificial Neural Networks and Mathematical Models for Unbalance Estimation in a Rotating System under the Nonlinear Journal Bearing Approach
Authors: Tselios Ioannis , Nikolakopoulos Pantelis
Published: October 2024

Rotating systems are essential components and play a critical role in many industrial sectors. Unbalance is a very common and serious fault that can cause machine downtime, unplanned maintenance, and potential damage to vital rotating machines. Accurately estimating unbalance in rotor–bearing systems is crucial for ensuring the reliable and efficient operation of machinery. This research paper presents a novel approach utilizing artificial neural networks (ANNs) to estimate the unbalance masses in a multidisk system based on simulation data from a nonlinear rotor–bearing system. Additionally, this study explores the effect of various operating parameters on oil film stability and vibration response through a combination of bifurcation diagrams, spectrum cascades, Poincare maps, and orbit and FFT plots. This study demonstrates the effectiveness of ANNs for unbalance estimation in a fast and accurate way and discusses the potential of ANNs in smart online condition monitoring systems.
Micromechanical Characterization of AlCu Films for MEMS Using Instrumented Indentation Method
Authors: Hou Dongyang , Ouyang Yuhang , Zhou Zhen , Dong Fang , Liu Sheng
Published: October 2024

The micromechanical properties (i.e., hardness, elastic modulus, and stress–strain curve) of AlCu films were determined by an instrumented indentation test in this work. For three AlCu films with different thicknesses (i.e., 1 µm, 1.5 µm, and 2 µm), the same critical ratio (hmax/t) of 0.15 and relative indentation depth range of 0.15–0.5 existed, within which the elastic modulus (i.e., 59 GPa) and nanoindentation hardness (i.e., 0.75 GPa, 0.64 GPa and 0.63 GPa for 1 µm, 1.5 µm and 2 µm films) without pile-up and substrate influence can be determined. The yield strength (i.e., 0.754 GPa, 0.549 GPa and 0.471 GPa for 1 µm, 1.5 µm and 2 µm films) and hardening exponent (i.e., 0.073, 0.131 and 0.150 for 1 µm, 1.5 µm and 2 µm films) of Al-(4 wt.%)Cu films for MEMS were successfully reported for the first time using a nanoindentation reverse method. In dimensional analysis, the ideal representative strain εr was determined to be 0.038. The errors of residual depth hr between the simulations and the nanoindentation experiments was less than 5% when the stress–strain curve obtained by the nanoindentation reverse method was used for simulation.
Valorisation of Aggregate-Washing Sludges in Innovative Applications in Construction
Authors: Villagran-Zaccardi Yury , Carreño Francisca , Granheim Line , Espín de Gea Antonio , Smith Minke Ulf , Butera Stefania , López-Martínez Elena , Peys Arne
Published: October 2024

The valorisation of sludges from aggregate production into construction materials is required for full circularity in mining waste management. This study explores valorisation pathways, relevant regulatory frameworks, and End-of-Waste (EoW) criteria for specific settings in Spain and Norway. The explored valorisation routes involved the production of filler, supplementary cementitious materials (SCMs), and lightweight aggregates (LWAs) for the production of cement-based products, and precursors for 3D printed construction material. The sludge from Norway revealed a non-polluted stream and a stream contaminated with organic phases and clays. Sludge-based filler proved suitable in concrete production with contents of up to 40% of total binder, providing adequate consistency and cohesion. However, clays in the sludge increased the demand for water and superplasticizer. Clay contents were still insufficient for the applications as SCMs, as the calcined sludge demonstrated limited reactivity. The application to produce LWAs was promising, but further microstructure optimization is still required. The clay content was also relevant for the sludge from the site in Spain, as it provided 3D printing mixes with good plasticity. The dosage optimization still required the addition of enzymes, limestone, and natural fibres to improve cohesion, workability, and resistance to the cracking of the 3D printing mix.
Boron Removal in Aqueous Solutions Using Adsorption with Sugarcane Bagasse Biochar and Ammonia Nanobubbles
Authors: Liao Lianying , Chen Hao , He Chunlin , Dodbiba Gjergj , Fujita Toyohisa
Published: October 2024

Boron is a naturally occurring trace chemical element. High concentrations of boron in nature can adversely affect biological systems and cause severe pollution to the ecological environment. We examined a method to effectively remove boron ions from water systems using sugarcane bagasse biochar from agricultural waste with NH3 nanobubbles (10% NH3 and 90% N2). We studied the effects of the boron solution concentration, pH, and adsorption time on the adsorption of boron by the modified biochar. At the same time, the possibility of using magnesium chloride and NH3 nanobubbles to enhance the adsorption capacity of the biochar was explored. The carbonization temperature of sugarcane bagasse was investigated using thermogravimetric analysis. It was characterized using XRD, SEM, and BET analysis. The boron adsorption results showed that, under alkaline conditions above pH 9, the adsorption capacity of the positively charged modified biochar was improved under the double-layer effect of magnesium ions and NH3 nanobubbles, because the boron existed in the form of negatively charged borate B(OH)4− anion groups. Moreover, cations on the NH3 nanobubble could adsorb the boron. When the NH3 nanobubbles with boron and the modified biochar with boron could coagulate each other, the boron was removed to a significant extent. Extended DLVO theory was adopted to model the interaction between the NH3 nanobubble and modified biochar. The boron adsorption capacity was 36 mg/g at room temperature according to a Langmuir adsorption isotherm. The adsorbed boron was investigated using FT-IR and XPS analysis. The ammonia could be removed using zeolite molecular sieves and heating. Boron in an aqueous solution can be removed via adsorption with modified biochar with NH3 nanobubbles and MgCl2 addition.
Optimization Design and SLM Manufacturing of Porous Titanium Alloy Femoral Stem
Authors: Zhao Lisong , Wang Yukang , Wang Qing , Zhang Yongdi , Yang Guang
Published: October 2024

In order to solve the loosening problem caused by stress shielding of femoral stem prostheses in clinical practice, an optimization design method of a personalized porous titanium alloy femoral stem is proposed. According to the stress characteristics of the femur, the porous unit cell structures (TO-C, TO-T, TO-B) under three different loads of compression, torsion, and bending were designed by topology optimization. The mechanical properties and permeability of different structures were studied. Combined with the porous structure optimization, a personalized radial gradient porous titanium alloy femoral stem was designed and manufactured by selective laser melting (SLM) technology. The results show that the TO-B structure has the best comprehensive performance among the three topologically optimized porous types, which is suitable for the porous filling structure of the femoral stem, and the SLM-formed porous femoral stem has good quality. The feasibility of the personalized design and manufacture of porous titanium alloy implants is verified, which can provide a theoretical basis for the optimal design of implants in different parts.
Effect of Er,Cr:YSGG Laser Irradiation on the Surface Modification and Cell Adhesion on Titanium Discs: An In Vitro Study
Authors: Shiba Takahiko , Ho Kailing , Ma Xuehao , Cho Ye Won , Chen Chia-Yu , Kim David M.
Published: October 2024

This study evaluates the potential of erbium, chromium-doped:yttrium, scandium, gallium, and garnet (Er,Cr:YSGG) laser irradiation to modify the titanium surface for optimal seeding of fibroblasts and osteoblasts in the treatment of peri-implantitis. Titanium discs were treated using the Er,Cr:YSGG laser, an ultrasonic device with a stainless tip, or titanium scalers. Changes in surface properties were analyzed by profilometer and scanning electron microscopy (SEM). Murine fibroblast and osteoblast adhesion and proliferation were evaluated qualitatively and quantitatively at 24 and 72 h. Profilometric surface topography and SEM showed that titanium scalers and ultrasonic debridement techniques significantly changed the structure of the machined and rough titanium surfaces. The Er,Cr:YSGG laser irradiation, on the other hand, did not alter titanium microstructures. The Er,Cr:YSGG laser irradiation with the 40 Hz group showed a significantly higher attached fibroblast cell numbers than the titanium scaler group at 72 h after treatment (p = 0.023). Additionally, the number of the attached osteoblasts in the Er,Cr:YSGG laser irradiation with the 40 Hz group was significantly higher than that of the no-treatment groups 24 h after treatment (p = 0.045). The Er,Cr:YSGG laser effectively promoted adherence of fibroblasts and osteoblasts to the titanium surface without significantly altering the titanium surface, suggesting its superiority for treating peri-implantitis.
Mechanical Properties of Iron Tailing Sand Grout Sleeve Joints and Force Analysis
Authors: Guo Fuyin , Wang Jiahao , Zhao Lin , Guo Pan , Wei Dong , Zheng Yuanxun , Zhang Zhe , Deng Enfeng
Published: October 2024

In this paper, the mechanical properties and internal stress condition of the reinforcing bar sleeve connectors with ferro-tailed mineral sand cementitious grout as filler material were analyzed as research objects. Firstly, an experimental study was carried out on the reinforcing bar sleeve connectors of iron tailing sand grout with a 40% substitution rate of mechanism sand to analyze the mechanical properties of different grout types, age, and reinforcement diameters under unidirectional tensile, high stress, and large deformation of repeated tensile and compressive stresses. Next, five groups of sleeve joints with different anchorage lengths were set up for unidirectional tensile tests. The results show that, with the decrease of the diameter of the reinforcement, the grip force and bond strength of the iron tailing sand grout on the internal reinforcement gradually increase. Under conditions of large deformation and high stress due to repeated tensile loading, the residual deformation and total elongation of iron tailing sand grout sleeve joints are satisfactory. Additionally, the restraining anchorage effect of iron tailing sand grout in the end section is small. The utilization rate and integrity of iron tailing sand grout in the initial anchorage section are better.
Automatic Image Analysis Method as a Tool to Evaluate the Anisotropy of Autoclaved Aerated Concrete for Moisture and Heat Transport
Authors: Majerek Dariusz , Sędzielewska Elżbieta , Paśnikowska-Łukaszuk Magdalena , Łazuka Ewa , Suchorab Zbigniew , Łagód Grzegorz
Published: October 2024

In this article, the results of studies testing the anisotropy of autoclaved aerated concrete in terms of water and heat transport are presented. Using image analysis techniques, a study was conducted on four different samples of concrete produced in the same process. To ensure the comparability of results, the pictures were taken from a fixed distance with the same lens settings trimmed to a set size. Cross-sectional profiles of the material were examined and were arranged in two directions: perpendicular and parallel to the growth direction occurring in the autoclave. For each block, approximately 4750 objects were obtained, with an average of 2700 objects along the wall and 2050 across it. As a result of the comparative analysis, metrics concerning pores, significantly distinguishing the profile direction, were identified. These included the pore area (area), the maximum and minimum distance between points on the perimeter (Feret, MinFeret), lengths of the major and minor axes of the fitted ellipse (major, minor), and the ratio of the area of selection to its convex hull (solidity). As a reference, standard investigations were conducted for moisture transport using the time domain reflectometry setup and for thermal conductivity values using the steady-state heat flow plate apparatus.
Mechanisms of Chemically Promoted Material Removal Examined for Molybdenum and Copper CMP in Weakly Alkaline Citrate-Based Slurries
Authors: Gamagedara K. U. , Roy D.
Published: October 2024

Chemical mechanical planarization (CMP) of metal components is an essential step in the fabrication of integrated circuits. Metal CMP is a complex process where strategically activated (electro)chemical reactions serve to structurally weaken the surface layers of the material being processed, and the resulting overburdens are removed under low-force abrasion. Understanding the tribo-electrochemical mechanisms of this process is crucial to successfully designing the consumable materials for advanced CMP slurries that are needed for the new technology nodes. Using a model CMP system involving copper (wiring material in interconnect structures) and molybdenum (a new diffusion barrier material for copper), the present work illustrates a tribo-electroanalytical scheme for studying various mechanistic details of metal CMP. Electroanalytical probes are employed both in the absence and in the presence of surface polishing to quantify the interplay between mechanical abrasion and chemical surface modification. Weakly alkaline slurry formulations are tested with variable concentrations of silica abrasives and a complexing agent, citric acid. The results serve to examine the link between material removal and tribo-corrosion and to identify the functions of the active slurry additives in governing the rates and selectivity of material removal for CMP.
Corrosion Evaluation and Mechanism Research of AISI 8630 Steel in Offshore Oil and Gas Environments
Authors: Zhang Zhao , Wen Liang , Huang Que , Guo Li , Dong Zhizhong , Zhu Lin
Published: October 2024

In this study, we optimized the traditional composition of AISI 8630 steel and evaluated its corrosion resistance through a series of tests. We conducted corrosion tests in a 3.5% NaCl solution and performed a 720 h fixed-load tensile test in accordance with the NACE TM-0177-2016 standard to assess sulfide stress corrosion cracking (SSCC). To analyze the corrosion products and the structure of the corrosion film, we employed X-ray diffraction and transmission electron microscopy. The corrosion rate, characteristics of the corrosion products, structure of the corrosion film, and corrosion resistance mechanism of the material were investigated. The results indicate that the optimized AISI 8630 material demonstrates excellent corrosion resistance. After 720 h of exposure, the primary corrosion products were identified as chromium oxide, copper sulfide, iron oxide, and iron–nickel sulfide. The corrosion film exhibited a three-layer structure: the innermost layer with a thickness of 200–300 nm contained higher concentrations of alloying elements and formed a dense, cohesive rust layer that hindered the diffusion of oxygen and chloride ions, thus enhancing corrosion resistance. The middle layer was thicker and less rich in alloying elements, while the outer layer, approximately 300–400 nm thick, was relatively loose.
Determination and Verification of the Johnson–Cook Constitutive Model Parameters in the Precision Machining of Ti6Al4V Alloy
Authors: Löschner Piotr , Gupta Munish Kumar , Niesłony Piotr , Korkmaz Mehmet Erdi , Jamil Muhammad
Published: October 2024

Numerical simulations of the cutting process play a key role in manufacturing and cost optimization. Inherent in finite element analysis (FEA) simulations is the correct description of material behavior during machining. For this purpose, various material models are used to describe the behavior of the material in the range of high deformation, high temperature values, and high strain rates. Very often the Johnson–Cook (JC) material model is used for this purpose; however, the correct determination of the material constants of this model is a key aspect. Therefore, this paper presents a procedure for determining the material constants of the JC model using an analytical method based on normalized tensile and compression testing of the material for different strain rates over a wide temperature range. After determining the material constants, the authors conducted numerical simulations of the orthogonal turning of Ti6Al4V titanium alloy using the obtained constants. Validation of the obtained results with those obtained in experimental studies was also carried out. The outcomes demonstrated that the difference between FEM simulation and experimental tests did not exceed 0.02 mm (14%) in the case of chip thickness,. Much smaller differences were obtained for the temperature in the cutting zone, where the maximum difference was about 45 °C (4%). Comparing the components of the cutting force, we found that, in the case of the main cutting force, in most cases, the differences did not exceed 70 N (8%). After the verification of the obtained results, it was also found that the determined material constants of the Johnson–Cook model can be successfully used in FEM modeling of the cutting process of Ti6Al4V titanium alloy for the adopted range of values of technological parameters.
High Stability Control of a Magnetic Suspension Flywheel Based on SA-BPNN and CNN+LSTM+ATTENTION
Authors: Zhang Weiyu , Ji Haotian
Published: October 2024

Compared to traditional, static-based flywheel systems, vehicle-mounted magnetic suspension flywheels face more complex operating conditions, and existing control strategies usually regard disturbances in vehicles under different operating conditions to be the same problem. Therefore, it is necessary to determine the interference from complex operating conditions and reasonably distinguish among them under different operating conditions to provide flywheel systems with strong stability (the rotor offset was less than 0.025 mm). Thus, this paper proposes a high-stability control strategy for flywheels based on the classification of vehicle-driving conditions and designs its control strategy by taking the vehicle-mounted magnetic suspension flywheel with a virtual inertia spindle as an example. First, according to the different vehicle working conditions and the varying interference intensities affecting the flywheel system, the working mode is divided into four modes. Considering the obvious differences in each working mode, it is proposed to use BP neural network optimization based on the simulated annealing algorithm (SA-BPNN) to determine the flywheel’s working condition. A relatively simple neural network can improve the response speed of the whole system. It also has a good effect. Secondly, it is proposed to use deep learning models based on convolutional neural networks, long short-term memory networks and attention mechanisms (CNN+LSTM+ATTENTION) to train the corresponding control parameters under each working condition to judge and predict the control parameters under different working conditions. Three evaluation parameters are used to evaluate the training results, and all achieved good results. Finally, the classification of working conditions and performance tests are carried out. The experimental results show the effectiveness and superiority of the proposed control strategy.
Properties of Multiple-Processed Natural Short Fiber Polypropylene and Polylactic Acid Composites: A Comparison
Authors: Liedl Barbara , Höftberger Thomas , Burgstaller Christoph
Published: October 2024

Natural fiber composites have gained increasing attention due to sustainability considerations. One often neglected aspect is the potential for the mechanical recycling of such materials. In this work, we compounded injection-molded polypropylene (PP) and polylactic acid (PLA) short cellulose fiber composites with fiber shares up to 40 percent by weight. Both matrix materials were reinforced by the addition of the fibers. We investigated a trifold full recycling process, where we subjected the materials produced in the first place to compounding, injection molding, testing, and shredding, and then repeated the process. Although the materials’ properties assigned to degradation were found to decrease with progressive recycling, attractive mechanical properties could be preserved even after the third reprocessing cycle.
Projective Spin Adaptation for the Exact Diagonalization of Isotropic Spin Clusters
Authors: Ghassemi Tabrizi Shadan , Kühne Thomas D.
Published: October 2024

Spin Hamiltonians, like the Heisenberg model, are used to describe the magnetic properties of exchange-coupled molecules and solids. For finite clusters, physical quantities, such as heat capacities, magnetic susceptibilities or neutron-scattering spectra, can be calculated based on energies and eigenstates obtained by exact diagonalization (ED). Utilizing spin-rotational symmetry SU(2) to factor the Hamiltonian with respect to total spin S facilitates ED, but the conventional approach to spin-adapting the basis is more intricate than selecting states with a given magnetic quantum number M (the spin z-component), as it relies on irreducible tensor-operator techniques and spin-coupling coefficients. Here, we present a simpler technique based on applying a spin projector to uncoupled basis states. As an alternative to Löwdin’s projection operator, we consider a group-theoretical formulation of the projector, which can be evaluated either exactly or approximately using an integration grid. An important aspect is the choice of uncoupled basis states. We present an extension of Löwdin’s theorem for s=12 to arbitrary local spin quantum numbers s, which allows for the direct selection of configurations that span a complete, linearly independent basis in an S sector upon the spin projection. We illustrate the procedure with a few examples.
Empowering Brain Tumor Diagnosis through Explainable Deep Learning
Authors: Li Zhengkun , Dib Omar
Published: October 2024

Brain tumors are among the most lethal diseases, and early detection is crucial for improving patient outcomes. Currently, magnetic resonance imaging (MRI) is the most effective method for early brain tumor detection due to its superior imaging quality for soft tissues. However, manual analysis of brain MRI scans is prone to errors, largely influenced by the radiologists’ experience and fatigue. To address these challenges, computer-aided diagnosis (CAD) systems are more significant. These advanced computer vision techniques such as deep learning provide accurate predictions based on medical images, enhancing diagnostic precision and reliability. This paper presents a novel CAD framework for multi-class brain tumor classification. The framework employs six pre-trained deep learning models as the base and incorporates comprehensive data preprocessing and augmentation strategies to enhance computational efficiency. To address issues related to transparency and interpretability in deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize the decision-making processes involved in tumor classification from MRI scans. Additionally, a user-friendly Brain Tumor Detection System has been developed using Streamlit, demonstrating its practical applicability in real-world settings and providing a valuable tool for clinicians. All simulation results are derived from a public benchmark dataset, showing that the proposed framework achieves state-of-the-art performance, with accuracy approaching 99% in ResNet-50, Xception, and InceptionV3 models.
Distributed Disturbance Observer-Based Containment Control of Multi-Agent Systems with Event-Triggered Communications
Authors: Hu Lin , Jian Long
Published: October 2024

This article investigates a class of multi-agent systems (MASs) with known dynamics external disturbances, where the communication graph is directed, and the followers have undirected connections. To eliminate the impacts of external disturbance, the technologies of disturbance observer-based control are introduced into the containment control problems. Additionally, to save communication costs and energy consumption, a distributed disturbance observer-based event-triggered controller is employed to achieve containment control and reject disturbance. Furthermore, designing the event-triggered function using an exponential function is beneficial for a time-dependent term while ensuring the exclusion of Zeno behavior. Finally, a numerical simulation is provided to validate the effectiveness of the theoretical analysis.
The Impact of Employee Stock Ownership Plans on Capital Structure Decisions: Evidence from China
Authors: Cheng Fu , Huang Chenyao , Ji Shanshan
Published: October 2024

The determination of the capital structure is a critical component of a company’s financial decision-making process. The question of how to optimize a firm’s capital structure to increase its value has been a significant topic of interest within the financial community. The employee stock ownership plan (ESOP) has developed rapidly in China’s capital market over the past decade, providing a suitable context for studying the impact of employee equity incentives on capital structure decisions. This paper employs cross-sectional ordinary least squares regression models and unbalanced panel fixed effect models to investigate the impact of employee stock ownership plans (ESOPs) on firms’ capital structure decisions. The analysis is conducted on a sample of Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges. The research considers both static capital structure choice and dynamic capital structure adjustment. We find that the implementation of an ESOP reduces the level of corporate debt and accelerates the dynamic adjustment of capital structure, suggesting that employee equity incentives play a role in optimizing firms’ capital structure decisions. We also find that the impact of ESOPs on the dynamic adjustment of capital structure is asymmetric. Specifically, the implementation of ESOPs markedly accelerates the downward adjustment of capital structure, yet has no impact on the upward adjustment of capital structure. Further analysis demonstrates that the impact of ESOPs on capital structure decisions is contingent upon the macroeconomic environment, industry characteristics, corporate governance, and ESOP contract designs. First, the optimization of ESOPs on capital structure decisions is more pronounced in an economic boom environment, in a poor market climate, or in competitive industries. Second, the reduction effect of ESOPs on corporate debt is more pronounced in non-state-owned companies, high-tech companies and those with lower ownership concentration. In contrast, the acceleration effect of ESOPs on capital structure adjustment is more pronounced in state-owned companies, non-high-tech companies and those with higher ownership concentration. Ultimately, ESOPs financed by loans from a firm’s major shareholders—or with a longer lock-up period, smaller shareholding size or executive subscription ratio—demonstrate a more pronounced optimization effect on capital structure decisions. This paper not only contributes to the existing literature on the relationship between equity incentives and capital structure decisions, but also provides guidance for listed companies on the reasonable design of their ESOPs and the optimization of their capital structure decisions.
Understanding Public Opinion towards ESG and Green Finance with the Use of Explainable Artificial Intelligence
Authors: van der Heever Wihan , Satapathy Ranjan , Park Ji Min , Cambria Erik
Published: October 2024

This study leverages explainable artificial intelligence (XAI) techniques to analyze public sentiment towards Environmental, Social, and Governance (ESG) factors, climate change, and green finance. It does so by developing a novel multi-task learning framework combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning to extract nuanced insights from a large corpus of social media data. Our approach integrates state-of-the-art models, including the SenticNet API, for sentiment analysis and implements multiple XAI methods such as LIME, SHAP, and Permutation Importance to enhance interpretability. Results reveal predominantly positive sentiment towards environmental topics, with notable variations across ESG categories. The contrastive learning visualization demonstrates clear sentiment clustering while highlighting areas of uncertainty. This research contributes to the field by providing an interpretable, trustworthy AI system for ESG sentiment analysis, offering valuable insights for policymakers and business stakeholders navigating the complex landscape of sustainable finance and climate action. The methodology proposed in this paper advances the current state of AI in ESG and green finance in several ways. By combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning, our approach provides a more comprehensive understanding of public sentiment towards ESG factors than traditional methods. The integration of multiple XAI techniques (LIME, SHAP, and Permutation Importance) offers a transparent view of the subtlety of the model’s decision-making process, which is crucial for building trust in AI-driven ESG assessments. Our approach enables a more accurate representation of public opinion, essential for informed decision-making in sustainable finance. This paper paves the way for more transparent and explainable AI applications in critical domains like ESG.
Developing a Robust Multi-Skill, Multi-Mode Resource-Constrained Project Scheduling Model with Partial Preemption, Resource Leveling, and Time Windows
Authors: Hatami-Moghaddam Ladan , Khalilzadeh Mohammad , Shahsavari-Pour Nasser , Sajadi Seyed Mojtaba
Published: October 2024

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim’s robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity.
Numerical Study of Shock Wave Interaction with V-Shaped Heavy/Light Interface
Authors: Alsaeed Salman Saud , Singh Satyvir
Published: October 2024

This paper investigates numerically the shock wave interaction with a V-shaped heavy/light interface. For numerical simulations, we choose six distinct vertex angles (θ=40∘,60∘,90∘,120∘,150∘, and 170∘), five distinct shock wave strengths (Ms=1.12,1.22,1.30,1.60, and 2.0), and three different Atwood numbers (At=−0.32,−0.77, and −0.87). A two-dimensional space of compressible two-component Euler equations are solved using a third-order modal discontinuous Galerkin approach for the simulations. The present findings demonstrate that the vertex angle has a crucial influence on the shock wave interaction with the V-shaped heavy/light interface. The vertex angle significantly affects the flow field, interface deformation, wave patterns, spike generation, and vorticity production. As the vertex angle decreases, the vorticity production becomes more dominant. A thorough analysis of the vertex angle effect identifies the factors that propel the creation of vorticity during the interaction phase. Notably, smaller vertex angles lead to stronger vorticity generation due to a steeper density gradient, while larger angles result in weaker, more dispersed vorticity and a less complex interaction. Moreover, kinetic energy and enstrophy both dramatically rise with decreasing vortex angles. A detailed analysis is also carried out to analyze the vertex angle effects on the temporal variations of interface features. Finally, the impacts of different Mach and Atwood numbers on the V-shaped interface are briefly presented.
On Nilpotent Elements and Armendariz Modules
Authors: Ansari Nazeer , Alnefaie Kholood , Ali Shakir , Abbasi Adnan , Singh Kh. Herachandra
Published: October 2024

For a left module MR over a non-commutative ring R, the notion for the class of nilpotent elements (nilR(M)) was first introduced and studied by Sevviiri and Groenewald in 2014 (Commun. Algebra, 42, 571–577). Moreover, Armendariz and semicommutative modules are generalizations of reduced modules and nilR(M)=0 in the case of reduced modules. Thus, the nilpotent class plays a vital role in these modules. Motivated by this, we present the concept of nil-Armendariz modules as a generalization of reduced modules and a refinement of Armendariz modules, focusing on the class of nilpotent elements. Further, we demonstrate that the quotient module M/N is nil-Armendariz if and only if N is within the nilpotent class of MR. Additionally, we establish that the matrix module Mn(M) is nil-Armendariz over Mn(R) and explore conditions under which nilpotent classes form submodules. Finally, we prove that nil-Armendariz modules remain closed under localization.
The Impact of Tariffs on a Transnational Supply Chain under Different Power Structures in China
Authors: Zou Zongbao , Chen Lihao , Liang Yuxin
Published: October 2024

In the trade environment of a globalized economy, tariffs play a crucial role in transnational supply chains. At the same time, the power structure of the supply chain also plays an important role in the decision making and income distribution of a transnational supply chain. Therefore, we construct game-theoretic models to analyze the impacts of tariffs and power structures on the decision making and revenue distribution of transnational supply chains. First, we consider a bilateral monopoly model consisting of a Chinese manufacturer and a U.S. retailer and analyze the effects of tariffs and power structures on decision making and revenue distributions in this supply chain. Then, we extend the model to a duopoly competition model consisting of two Chinese manufacturers and one American retailer, further analyzing the roles of tariffs and power structures. The results indicate that in the bilateral monopoly model, the impact of tariffs on the manufacturer’s profits is always greater than on the retailer’s profits under a manufacturer-led circumstance. However, in a competitive model, when the market size is large, the impact of tariffs on the manufacturer’s profits exceeds that of the retailer’s profits; conversely, when the market size is smaller, the impact of tariffs on the retailer’s profits is greater than on the manufacturer’s profits. Furthermore, we find that in the duopoly competition model, under the manufacturer-led circumstance, both the manufacturer and the retailer earn the highest profits.
The Smirnov Property for Weighted Lebesgue Spaces
Authors: Mayerhofer Eberhard
Published: October 2024

We establish lower norm bounds for multivariate functions within weighted Lebesgue spaces, characterised by a summation of functions whose components solve a system of nonlinear integral equations. This problem originates in portfolio selection theory, where these equations allow one to identify mean-variance optimal portfolios, composed of standard European options on several underlying assets. We elaborate on the Smirnov property—an integrability condition for the weights that guarantees the uniqueness of solutions to the system. Sufficient conditions on weights to satisfy this property are provided, and counterexamples are constructed, where either the Smirnov property does not hold or the uniqueness of solutions fails.
Idempotent-Aided Factorizations of Regular Elements of a Semigroup
Authors: Ćirić Miroslav , Ignjatović Jelena , Stanimirović Predrag S.
Published: October 2024

In the present paper, we introduce the concept of idempotent-aided factorization (I.-A. factorization) of a regular element of a semigroup, which can be understood as a semigroup-theoretical extension of full-rank factorization of matrices over a field. I.-A. factorization of a regular element d is defined by means of an idempotent e from its Green’s D-class as decomposition into the product d=uv, so that the element u belongs to the Green’s R-class of the element d and the Green’s L-class of the idempotent e, while the element v belongs to the Green’s L-class of the element d and the Green’s R-class of the idempotent e. The main result of the paper is a theorem which states that each regular element of a semigroup possesses an I.-A. factorization with respect to each idempotent from its Green’s D-class. In addition, we prove that when one of the factors is given, then the other factor is uniquely determined. I.-A. factorizations are then used to provide new existence conditions and characterizations of group inverses and (b,c)-inverses in a semigroup. In our further research, these factorizations will be applied to matrices with entries in a field, and efficient algorithms for realization of such factorizations will be provided.
Microalgal Phenolics: Systematic Review with a Focus on Methodological Assessment and Meta-Analysis
Authors: Andriopoulos Vasilis , Kornaros Michael
Published: October 2024

A critical review and analysis of the literature relevant to the phenolic content of eucaryotic microalgae was performed. Several issues were identified and discussed. In summary, the main problems with the reporting on the phenolic content of microalgae are the following: (1) despite its usefulness in the determination of phenolic content in plant samples, the Folin–Ciocalteu assay is non-suitable for microalgal research due to the high presence of interfering compounds in microalgal extracts such as chlorophyll and its derivatives in organic extracts and free aromatic amino acids or nucleotides in aqueous extracts; (2) while there is chromatographic evidence for the presence of simple phenolic acids in most microalgal clades, the lack of critical enzymes of phenolic biosynthesis in most microalgae, as well as the high variability of phenolic profiles even in the same genus, require more extensive research before conclusions are drawn; (3) the accumulation and metabolism of external phenolics by microalgae has been almost universally neglected in studies focusing on the phenolic content of microalgae, even when natural seawater or complex organic media are used in the cultivation process. Despite these issues, the literature focusing on the bioremediation of waste streams rich in phenolics through microalgae demonstrates the ability of those organisms to adsorb, internalize, and in many cases oxidize or transform a wide range of phenolic compounds, even at very high concentrations. Simple phenolics found in waste streams, such as olive mill waste, have been shown to enhance the antioxidant activity and various bioactivities of microalgal extracts, while complex biotransformation products of phenolics have also been characterized. In conclusion, the de novo biosynthesis of phenolic compounds via eucaryotic microalgae requires further investigation with better designed experiments and suitable analytical methods, while the response of microalgae to phenolic compounds in their growth medium is of great practical interest, both in terms of waste treatment and for the production of functional foods, cosmetics, and pharmaceuticals.