Specific Model Research Articles

Insights into the pelletization behaviors of artificial lignocellulosic biomass based on cellulose, hemicellulose and lignin: A simplex lattice mixture design approach

The use of high quality densified pellet as an alternative to traditional fossil fuels is a promising avenue of research due to their interesting higher density, superior heating values, and enhanced combustion performance. However, little is known about the pelletization behaviors from the aspect of lignocellulosic biomass fundamental components (cellulose, hemicellulose and lignin) and their mixtures. This study presents an artificial biomass developed based on cellulose, hemicellulose and lignin using a simplex lattice design approach, aiming to understand the detailed role of major components and its effects on pellet quality. The correlation between the experimental data obtained from the simplex lattice design and the predicted response parameters were effectively explained by Scheffé's special cubic model, with a coefficient of determination beyond 90 %. Parameter estimates showed a significant synergetic effect of each components for density and Meyer hardness. Results revealed that xylan may be softened like lignin and thereby potentially act as binder. The use of a high cellulose concentration resulted in enhanced pellet strength by acting as a supporting skeleton. Xylan and cellulose played a major role in the thermal degradation of densified pellets. Based on the results obtained, the optimal components composition was determined to be 34.5 % lignin, 36.3 % cellulose, and 29.2 % xylan. Under this, the pellet exhibited the best density and Meyer hardness beyond 1500 kg/m3 and 80 N/mm2, respectively. The results provide a foundation for future efforts to be able to predict pellet properties from the perspective of biomass composition.

Modeling viscosity of commercial glass melts: Adam–Gibbs equation and Gaussian process regression

ABSTRACT A statistically designed property database of six commercial glass families is leveraged to develop models relating viscosity to temperature and composition. Specialized models for each glass family were designed based on the Adam–Gibbs equation. We show that the whole range of viscosity data of one glass family from 100 to 1012 Pa s can be accurately modeled using one composition-dependent and two composition-independent parameters when glass transition temperature and viscosity at that temperature are independently determined. Generalization to a broader composition space using the same approach leads to a loss of accuracy compared to specialized models. However, this can be mostly negated when Gaussian process regression is used to estimate the compositional dependence of configuration entropy and fragility exponent parameters of the Adam–Gibbs equation.

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Assessing the Reliability of OpenCap and OpenSim as Open Source Softwares for Biomechanical Analysis in Neurological Rehabilitation: A Case Study

The neurological rehabilitation focuses on enhancing functional recovery and improving the quality of life for people who have experienced injuries or diseases affecting the central or peripheral nervous system. This functional recovery includes a follow up of the kinematics of the patients limbs. The use of a open source software such as OpenSim, has been previously proposed as a tool for kinematic analysis, this software allows for highly specialized 3D musculoskeletal modeling, facilitates kinematic analysis and the assessment of force and angles in the lower and upper limbs of the human body. In this context, the propose of this research was to test the reliability of OpenSim for kinematic analysis during neurological rehabilitation. For this goal, the Motricity Index test was done by a group of three healthy participants, this values were used for comparison to the stroke patient who is currently undergoing neurological rehabilitation process. The results demonstrates all the limitations in the range of motion of the patient in comparison to the healthy group due his motor issues, such as muscle spasticity and weakness. This research shows the advantages and limitations of this software and its application in neurological rehabilitation. The goal is to contribute to the development of effective and personalized therapeutic strategies to improve the recovery process for these patients.

Multi-objective optimization for a composite pressure vessel with unequal polar openings

Multi-objective parametric optimization problem is presented for overwrapped composite pressure vessels under internal pressure for storage and heating water. It is solved using the developed iterative optimization algorithm. Optimal values of design parameters for the vessel are obtained by varying the set of parameters for composite layers, such as the thickness of layers and radii of polar openings, which influence the distribution of fiber angles along the vessel. The suggested optimization methodology is based on the mechanical solution for composite vessels and the satisfaction of the main failure criteria. An innovative approach lies in the possibility of using the developed optimization methodology for designing vessels with non-symmetrical filament winding, which have unequal polar openings on the domes. This became possible due to the development of a special numerical mechanical finite element model of a composite vessel. A specific Python program provides the creation of a model and controls the exchange of data between the modules of the iterative optimization process. The numerical model includes the determination of the distribution of fiber angles on the domes and cylindrical part of the vessel as well as changes in layer thicknesses. The optimization problem solution is provided using a Multi-Island Genetic Algorithm, this type of method showed its efficiency for such applications, by allowing to avoid local solutions. Thus, optimal parameters of a composite vessel were found by minimizing composite mass and thickness and maximizing the strain energy. Test solutions using the developed methodology are presented for three types of composite materials to evaluate their possibility for integration into the vessel design model.

Algebraic Approach to a Special Four-Body Solvable Model

Algebraic Approach to a Special Four-Body Solvable Model

Application of push-ups and pull-ups to develop strength endurance of arm muscles in archery athletes

Background and purpose The lack of a training model in archery causes tremors which will have an impact on a lack of accuracy in archery, this study aims to provide a special training model for archery athletes. Materials and methods With a 2 x 2 factorial design and the population used was archery athletes with a population of 38 taken by purposive sampling with the criteria of being elite archery athletes, aged 16-18 years, then ranked 27% in the upper group and 27% in the lower group. Obtained a research sample of 20 athletes then, from each of these data were divided into two groups by way of ordinal pairing. After carrying out the pretest, the athletes carried out variations of push-up and pull-up exercises which were carried out for 18 meetings, and test instruments for endurance and arm muscle strength tests holding bow digits. Results (1) There is a significant difference in the effect of push-up and pull-up training methods on the arm muscle strength of archery athletes, with an F value of 7,738 and a significance value of p 0.027 <0.05. (2) There is a significant difference in the effect of athletes who have high and low muscle endurance on the arm muscle strength of archery athletes, as evidenced by the F value of 51.203 and a significance value of p 0.000 <0.05. (3) There is a significant interaction between push-up and pull-up training methods and muscle endurance (high and low) on the arm muscle strength of archery athletes, with an F value of 51.203 and a significance value of p 0.000 <0.05. Conclusions There is a significant interaction, namely push-up exercises are suitable for those who have high arm muscle endurance, while pull-up exercises are more suitable for athletes who have low arm muscle endurance.

Effective application of geological process modeling for unravelling carbonate build-up complexity: A case study from the EX-Carbonate build-up in central Luconia Province, Malaysia

In the dynamic field of carbonate reservoir Exploration, the need for specialized models is vital, assisting as a key tool to facilitate interdisciplinary collaboration and enhance strategies for predicting reservoir behavior. This study examines the use of geological process (GPM), a standard tool to simulate forward stratigraphic modelling, particularly focusing on the Central Luconia Province in the South China Sea. Central Luconia Province stands out for its extensive carbonate build-ups and is recognized as a prolific hydrocarbon reservoir. Forward Stratigraphic Modelling (FSM) leads to a paradigmatic change in geological modeling. Unlike geostatistical models controlled by geometrical parameters, FSM relies on mathematical representations of the physical rules determining erosion, transport, and sedimentation in carbonate growth. This tool, supported by various researchers, has proven its ability in quantitative sedimentary system analysis across diverse temporal and spatial scales. This study located in EX Field showcases the ability of stratigraphic forward modeling in replicating sedimentary complexities and resulting stratigraphic architecture of the EX isolated carbonate platform. The methodology links parameters from existing literature with multiscale data. Additionally, carbonate production laws, contingent on water depth, play a pivotal role in the meticulous modeling process. This work illustrates the efficacy of geological process modelling as a transformative approach for unraveling the complexities of carbonate systems, particularly in regions with prolific hydrocarbon reservoirs like Central Luconia. The incorporation of literature-derived parameters and multiscale data serves as evidence of the model's ability to offer nuanced understandings of the dynamic evolution of carbonate platforms, thereby advancing our understanding of reservoir prediction and future management strategies.

The new flexible generalized class of distributions with applications to different fields

We propose a new generator which has been used as a generalized class, and can also be helpful in generating new flexible generalized classes of distributions for continuous random variable. The newly proposed generator-cum-generalized class does not involve any extra parameter, and its functional form plays an important role along with baseline models to develop flexible models. In literature, such classes had been reported as Marshall-Olkin G-class, exponentiated G-class, Transmuted G-class, exponentiated generalized G-class, and Flexible G-class. So, any parent (or baseline) model can be substituted in the proposed class which has no extra burden on the parameters of the class. Some needful characteristics of the newly proposed class are obtained. Furthermore, a special model of the class, that is, the flexible Kumaraswamy distribution is considered and its properties are reported. The parameters estimation is dealt through the method of maximum likelihood, and a simulation is carried out to assess the performance of model’s parameters. Four real-life data sets are analyzed to show usefulness of the proposed model in comparison to some well-established competitive models. It is found that the proposed model yields low values of the goodness-of-fit statistics as compared to the other models, and hence our proposed model performed better as compared to others on these four data sets.

The Intervention and Impact of Digital Society on the Governance of Dual Equity Structure in Enterprises

The digital society has become a prominent feature of today's era. Its highly interconnected, transparent, and intelligent characteristics are profoundly changing the governance mode and operational logic of enterprises. This article aims to explore the intervention and impact of digital society on the governance of dual equity structure in enterprises, and provide practical guidance for the sustainable development of enterprises. The dual equity structure, as a special corporate governance model, is characterized by granting different voting rights to different types of shares, achieving absolute control over the company by the management. However, this structure has also sparked many controversies, such as the possibility of management using control to harm shareholder interests, leading to management dictatorship, and so on. In the context of the digital society, enterprises face new challenges and opportunities in terms of information transparency, decision-making efficiency, and governance mechanisms.

On the Development of an Acoustic Image Dataset for Unexploded Ordnance Classification Using Front-Looking Sonar and Transfer Learning Methods.

This research aimed to develop a dataset of acoustic images recorded by a forward-looking sonar mounted on an underwater vehicle, enabling the classification of unexploded ordnances (UXOs) and objects other than unexploded ordnance (nonUXOs). The dataset was obtained using digital twin simulations performed in the Gazebo environment utilizing plugins developed within the DAVE project. It consists of 69,444 sample images of 512 × 399 resolution organized in two classes annotated as UXO and nonUXO. The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. The research included VGG16, ResNet34, ResNet50, ViT, RegNet, and Swin Transformer. Its goal was to define a base rate for the development of other specialized machine learning models. Neural network experiments comprised two stages-pre-training of only the final layers and pre-training of the entire network. The experiments revealed that to obtain high accuracy, it is required to pre-train the entire network, under which condition, all the models achieved comparable performance, reaching 98% balanced accuracy. Surprisingly, the highest accuracy was obtained by the VGG model.

Advancing building energy modeling with large language models: Exploration and case studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

The characteristics of S-wave velocity and mineralization of the “Double Domes” structure in the eastern Tethys Himalaya

The Tethys Himalayan belt in the southern Qinghai–Tibet Plateau has been intruded by a large amount of leucogranite due to collisional orogeny. In addition, strong tectonic movements since the Cenozoic era have led to the formation of a series of dome structures accompanied by various types of mineralization. The Cuonadong Dome and Yalaxiangbo Dome, forming the “double dome” structure in the eastern part of the Tethys Himalayan Belt, are affected by different geological processes, resulting in differences in their deep structures and affecting the formation of polymetallic minerals. At present, geophysical research on the fine structure of the crust of the “double dome” structure is limited, making it difficult to fully understand the formation of different deep structures in the Tethys Himalayan dome belt. This hinders the progress of research on the genesis of dome structures and large-scale mineralization mechanisms in continental collisional environments. In this study, the ambient noise tomographic method was used to obtain the S-wave velocity structure of the upper crust of the Cuonadong Dome and the Yalaxiangbo Dome, and the following results were found: 1. there is a significant difference in the velocity structures of the two domes, with the core velocity structure of the Yalaxiangbo Dome showing an overall high velocity, extending downward for more than 9 km, while the core of the Cuonadong Dome exhibits low-velocity characteristics, with some high-velocity bodies occurring locally, which may be related to the later intrusion of leucogranite and extensional activity of the Cuona Rift. 2. There are significant differences in the S-wave velocities between the lead–zinc deposits and rare metal deposits in the study area; the lead–zinc deposits occur in basins and graben margins and have large variations in the S-wave velocity, which may be related to the involvement of basin brine in mineralization; below the tungsten–tin–beryllium deposits, the S-wave velocity exhibits high-velocity protrusions, with mineralization occurring at the front ends of the protrusions, which may be caused by crystallization differentiation of leucogranite. 3. The study area has abundant geothermal resources and obvious geothermal structural features. The low-velocity basin in the upper part of the upper crust is a heat storage layer, the low-velocity channel in the middle is a heat-conducting layer, and the lower part is a low-velocity heat source area that continuously supplies heat, forming a special geothermal structural model for the Cuonadong Dome and Yalaxiangbo Dome.

Comparative analysis of saliency map algorithms in capturing visual priorities for building inspections

This study investigates the efficacy of saliency mapping algorithms in capturing the visual priorities of building inspectors for structural damage assessment. Our work established a ground truth dataset by implementing eye-tracking technology to capture the gaze patterns of building inspectors. Further, it enables a detailed evaluation of the saliency models’ ability to reflect experts' visual attention during inspection tasks. Our comparative analysis assesses the performance of three saliency models— EnDec, DeepGaze, and SALICON— against this ground truth data, using conventional saliency metrics such as Area under the Curve, Similarity, Normalized Scanpath Saliency, Correlation Coefficient, and Kullback-Leibler Divergence. Our findings reveal that while the SALICON model demonstrates a marginally better performance and highlights areas where these models fall short, particularly in accurately reflecting the critical visual properties of inspectors, this insight is crucial for advancing the field. By highlighting these limitations, we have drawn attention to the need for developing more specialized saliency models tailored to the unique demands of building inspection tasks. Thus, the study not only fulfills its objectives of comparative analysis but also contributes to the broader discourse on improving automated structural inspection systems. This study highlights the need to develop specialized computer vision models to address specific building inspection challenges. By identifying strengths and improvement areas, this research contributes valuable insights and highlights the potential and current limitations of applying computer vision techniques to real-world building inspection tasks.

Innovative load identification with Res-UNet: Integrating phase space reconstruction and physics-informed deep learning

This study developed a specialized load identification model for a specific ocean platform that integrates advanced deep learning techniques with the unique physical characteristics of marine engineering. Key achievements include high-dimensional mapping of data features using phase space reconstruction (PSR) techniques to enhance the predictive capacity of the model through chaos system theory; optimization of ResNet and UNet deep learning networks with attention mechanisms to boost performance; calculation of stiffness, mass, and damping matrices using finite element software; establishment of corresponding state-space equations; and integration of structural dynamics equations with the deep learning network loss function to significantly improve load identification accuracy. In addition, proprietary structural equations for ocean platform structures were developed, underscoring the uniqueness and applicability of the algorithm. The proposed physics-informed Res-UNet model exhibited excellent performance in identifying both periodic and random loads. These improvements were validated through training with finite element simulated data and subsequent experimental applications, demonstrating the effectiveness of the proposed innovative methods.

Modelling the maximum ceiling temperature with bifurcated plume in tunnel fires

Evaluation of the temperature profile induced by tunnel fire is of great significance for fire detection and structural damage assessment. In this work, a special plume model, namely, the “plume bifurcation” is introduced, and its impact on the temperature distribution is analysed theoretically. Full-scale tunnel fire experiments and numerical simulations are conducted to investigate the plume evolution under various heat release rates and ventilation conditions. Results show that the plume will sperate into two sub-streams under strong ventilation condition, resulting in the multi-dimensional plume movement pattern when rising. The sub-plume impingement point location, quantified by the longitudinal vertical deflection angle θv and horizontal bifurcation angle θh, is correlated with the fire heat release rate and ventilation condition. The plume bifurcation angle is 0 when the dimensionless ventilation velocity V′≤0.33, marking a single-stream plume; and rapidly increase and then slowly decrease with V′ when V′>0.33, representing the sudden occurrence and decay trend of plume bifurcation with the ventilation velocity. Finally, a novel bifurcated plume-based model to estimate the maximum ceiling temperature is proposed. This study reveals the role of multi-dimensional smoke movement in reshaping the temperature field, providing new perspectives for a deeper understanding of the smoke transport behaviour in longitudinally ventilated tunnel fires.

Exhaustive Exploitation of Nature-Inspired Computation for Cancer Screening in an Ensemble Manner.

Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of biomarker genes. While computational methods using nature-inspired algorithms have shown promise in selecting predictive genes, existing techniques are limited by inefficient search and poor generalization across diverse datasets. This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data. The EODE methodology combines an intelligent grey wolf optimization algorithm for selective feature space reduction, guided random injection modeling for ensemble diversity enhancement, and subset model optimization for synergistic classifier combinations. Extensive experiments were conducted across 35 gene expression benchmark datasets encompassing varied cancer types. Results demonstrated that EODE obtained significantly improved screening accuracy over individual and conventionally aggregated models. The integrated optimization of advanced feature selection, directed specialized modeling, and cooperative classifier ensembles helps address key challenges in current nature-inspired approaches. This provides an effective framework for robust and generalized ensemble learning with gene expression biomarkers.

Building a Resilient Society With the Special Economic Zone Concept: A Defense Economic Perspective

This study explores the concept of Special Economic Zones as a strategy for building economic resilience in communities facing contemporary global challenges. Through a systematic literature review, this study analyzes Special Economic Zones’ implementation, contributions, and integration with Defense Economic theory. The study reveals that successful special economic zones are characterized by sector integration, innovation focus, multi-stakeholder involvement, increased productivity, and job creation. Cities adopting this model demonstrate 40% higher economic resilience to external shocks than conventional development approaches. The Special Economic Zone concept contributes to economic resilience through integrated innovation ecosystems, reduction of inequality, strengthening of supply chains, and promotion of green economy transitions. Its integration with Defense Economic theory introduces concepts like functional redundancy and dynamic resilience, enhancing adaptability to market changes and technological advancements. The study also highlights the importance of cybersecurity in maintaining economic resilience in the digital age. While the Special Economic Zone model shows promise in fostering sustainable development and economic resilience, challenges remain, particularly in developing countries, including infrastructure limitations and skills gaps. The research recommends a gradual implementation approach tailored to local contexts, emphasizing digital technology integration and human capital development.

Infrared Regularization of Very Special Relativity Models

We extend the Sim(2) invariant infrared regularization of Very Special Relativity models, that we have proposed recently, to include γ5 Dirac matrix. Then, we solve the Very Special Relativity Schwinger model, find the chiral anomaly, and clarify its meaning in the new context. In addition, we show that the triangle anomaly in four space-time dimensions agrees with the same object in standard quantum electrodynamics. Finally, we apply the infrared regularization to compute the large N limit of the Very Special Relativity Gross–Neveu model.

Service Delivery Models and Outcomes for Students With Disabilities

In this systematic literature review, we examine the corpus of empirical studies in education that use administrative data (i.e., population-level data) to describe and estimate the impacts of service delivery models for specially designed instruction on outcomes for students identified with special education needs. We focus on studies that use quantitative data analysis—either descriptive or causal—to answer questions about the relationship between special education service delivery models and student outcomes. We analyze seven studies, each of which finds a positive relationship between more time spent in general education classrooms and outcomes for students with disabilities (SWDs). In our analysis, we discuss the affordances and limitations of this type of analysis and opportunities for the field to expand data collection and analysis of population-level data in a way that better illuminates the state of special education services, both in the present and longitudinally, for SWDs.