Intermediate Model Research Articles

Existential and loсative models of elementary simple sentences in Koryak language

There are three types of spatial models that differ by proposition. Static models refer to the unchanging state of the subject in space. Movement in space from one point to another is denoted by dynamic models. Intermediate models represent motion that cannot be easily categorized into either the first or second group. This article examines the existential-spatial models used in the Koryak language. The first category of static models encompasses existential-spatial models, which utilize verbs of existence to express their predicates. Adverbs, case markers, and suffixes convey the static meaning of the locative component in spatial models. The distinctive feature of the Koryak language lies in its location-indicating suffixes. Utilized in conjunction with the locative cases, these suffixes clarify the location of a subject. According to their semantics, existentialspatial models can be classified into three separate groups. These are as follows: existential models, existential-perceptual models, and locative models. Existential models indicate the presence of the subject in a particular location, while existential-perceptual models indicate the subject’s presence within the observer’s field of vision. Locative models refer to the localization of subjects in a specific point in space. While both the existential and locative models convey the same meaning, their differentiation arises from the placement of the subject and the locative component. An existential model is created when the subject is in the rheme position of the sentence, while a locative model is formed when the subject is in the theme position.

The pre-coated photocatalytic ceramic membrane with Bi2O3–TiO2/powdered activated carbon nanoparticles to alleviate ultrafiltration fouling caused by intracellular organic matter of Microcystis aeruginosa

Algal intracellular organic matter (IOM), released during algae's normal growth phase and water treatment processes, has long been a source of significant membrane fouling, considerably limiting the effective use of ultrafiltration(UF) technology. In this study, a Bi-doped TiO2 composite with powdered activated carbon (Bi2O3–TiO2/PAC) was loaded on ceramic UF membranes by vacuum filtration, and the prepared photocatalytic modified ceramic membranes (MCMs) were investigated for the removal of IOM and the mitigation of membrane fouling at different loading levels and under three operating conditions: Dark, Dark-UV, and UV. The results showed that the photocatalytic MCMs were able to remove the protein and humic acid fluorescent components of IOM and decompose or mineralize organic matter with molecular weights of 10–50 kDa. Under the Dark, Dark-UV, and UV conditions, the final normalized flux of MCM-2.88 was significantly increased by 22.6 %, 82.1 %, and 108.0 %, respectively. The hydrophilicity of MCMs not only transformed the interactions between IOM and the membrane into repulsive forces, but also the composite material on the membrane surface transforms the membrane fouling mechanism from intermediate blocking model to cake layer formation. Furthermore, the application of photocatalytic oxidation under the Dark-UV and UV conditions yielded a reduction in both reversible and irreversible fouling. This action led to an increased hydrophilicity and elevated free energy of adhesion with IOM during the filtration process. Moreover, the main fouling mechanism changed into complete or intermediate blocking. These outcomes collectively underscore the capability of MCMs to alleviate membrane fouling induced by IOM. This advancement holds substantial promise for the advancement and widespread implementation of photocatalytic ceramic membrane filtration techniques.

VERIFICATION OF THE MATHEMATICAL FLIGHT MODEL THE PROJECTILE AS A MODIFIED MODEL OF THE MATERIAL POINT

The analysis of mathematical models of the flight of projectiles has been carried out, it is shown that the nature of their provision varies depending on the degree of reliability of the representation of the real physical process of its flight by a mathematical model, the adequate consideration of certain forces (moments) acting on the projectile, as well as the level of information about external flight conditions. In the simplest mathematical model with three degrees of freedom, the projectile is considered as a material point that moves in the atmosphere under the influence of gravity and full aerodynamic force. The dynamics of projectile flight is most completely described by the mathematical model of the movement of a solid body with six degrees of freedom. In the intermediate model with four degrees of freedom, all aerodynamic forces are taken into account, the orientation of the projectile is characterized by taking into account the angle of nutation, and the kinetic energy of the rotational movement is taken into account through the angular velocity of the projectile around its axis of symmetry. The process of restori ng the coefficients of aerodynamic forces using the material point model does not provide the necessary accuracy and adequacy of the description of the projectile flight process; of the modified material point model is much simpler (the least complex) compared to the model of the motion of a rigid body with six degrees of freedom due to the smaller number of coefficients and differential equations included in its composition. The differential equations of the modified material point model are provided - the equation of motion of the center of mass and the equation of nutational oscillations of the projectile in scalar form; an assessment of its accuracy (adequacy) was carried out on the example of the 155-mm PF projectile M2000, one of the family of projectiles of the company Denel Naschem – Assegai M200X Series 155-mm Projectiles. A comparison of the characteristics of the 155-mm PF of the M2000 projectile calculated according to the 6DoF model and the modified material point model showed their high convergence; the error does not exceed 1%. Keywords: aerodynamic forces (moments), projectile, mathematical models, modified model, aerodynamic coefficients, simulation, accuracy, flight parameters.

Constructive Many-one Reduction from the Halting Problem to Semi-unification (Extended Version)

Semi-unification is the combination of first-order unification and first-order matching. The undecidability of semi-unification has been proven by Kfoury, Tiuryn, and Urzyczyn in the 1990s by Turing reduction from Turing machine immortality (existence of a diverging configuration). The particular Turing reduction is intricate, uses non-computational principles, and involves various intermediate models of computation. The present work gives a constructive many-one reduction from the Turing machine halting problem to semi-unification. This establishes RE-completeness of semi-unification under many-one reductions. Computability of the reduction function, constructivity of the argument, and correctness of the argument is witnessed by an axiom-free mechanization in the Coq proof assistant. Arguably, this serves as comprehensive, precise, and surveyable evidence for the result at hand. The mechanization is incorporated into the existing, well-maintained Coq library of undecidability proofs. Notably, a variant of Hooper's argument for the undecidability of Turing machine immortality is part of the mechanization.

Land use spatial optimization for city clusters under changing climate and socioeconomic conditions: A perspective on the land-water-energy-carbon nexus

Unbalanced land use development has resulted in water scarcity, high energy consumption, and a significant increase in carbon emissions. Complex and changing environments make it more difficult to manage land use sustainably. This study constructed a variety of future change scenarios coupling climate and socioeconomic development and developed a multidimensional land use spatial optimization (LUSO) model linked with water-energy-carbon. The model is solved using a nondominated sorting genetic algorithm-III (NSGA-Ⅲ) coupled with the information feedback model (IFM). The advantages of this framework include: (1) LUSO based on the clarification of the interactions between different land use types and water-energy-carbon; (2) trade-offs between dimensions are considered to achieve coordinated multidimensional development of the economy, resources, environment, and spatial conversion; and (3) a spatial optimization pattern of land use in response to climate change and socioeconomic development changes can be obtained. The model framework is applied to the Mid-Yangtze River City Cluster for empirical analysis. The results show that socioeconomic development can lead to rapid changes in land use patterns, especially in cultivated and construction land. By 2030, the optimized land use pattern under the intermediate route model is the most suitable land use scenario for the region to achieve sustainable development. Under this scenario, the area of construction land increased by 22.09%, the area of cultivated land decreased by 2.2%, the economic output increased by 76,678.31 (1012 yuan), and the carbon emissions increased by only 5677.79 (107 kg), with an overall sustainability level of 0.85. This study can provide decision-makers with sustainable land resource management options that can respond to changing environments and achieve multidimensional synergistic development.

Application of topological constraint theory to alkali borate and silicate glass systems

Principles of Topological Constraint Theory (TCT) were applied to alkali borate and silicate glass systems using intermediate range structural models over wide compositional ranges. The structural model for lithium borate was derived from the Feller, Dell, and Bray model [1] and extended to the terminal composition at R=3 where R is the molar ratio of lithium oxide to borate. The sodium borate structural model was built using both NMR [2] and Raman [3] data, and also included carbonate retention in the glass [4]. This model was extended to R=3 similarly to the lithium borate system. The silicate system models were created from 29Si NMR data [5] and also incorporated carbonate retention where necessary [6].Constraint models considered the effect of intermediate range structures on the system, and also incorporated the effect of “loose” alkali which is not directly associated with a non-bridging oxygen. Constraint models of the alkali borate, silicate, and borosilicate systems were then used to predict properties such as glass transition temperature and fragility.

Multi-scale numerical simulation of fracture behavior for the gadolinia-doped ceria (GDC) under mechano-electrochemical coupling fields at high temperature

The fracture toughness of gadolinia-doped ceria (GDC) solid oxide fuel cells (SOFCs) electrolyte with a central crack is significantly reduced under the mechano-electrochemical coupling fields at high temperature. In this work, an atom-to-continuum (AtC) multi-scale method combining the finite element method (FEM) and the molecular dynamics (MD) simulation is developed. Firstly, the AtC multi-scale method is validated by investigating the uniaxial tensile stress–strain curves of GDC with a central crack at different temperatures. Then, based on the theory of fracture mechanics, the macrostructure of GDC is transformed into a microscopic intermediate transition model, and a detailed computational procedure for analyzing the fracture toughness of the macrostructure of the GDC is given by the AtC multi-scale method. Finally, the fracture toughness of GDC macrostructure under the mechano-electrochemical coupling fields is studied by the proposed approach. The simulation results show that the fracture toughness of 10GDC and 20GDC under the mechano-electrochemical coupling fields is clearly reduced compared to the uniaxial tensile loading. Among them, the fracture toughness of 10GDC under the mechano-electrochemical coupling fields is decreased by 12.28% and 30.67% at 800℃ and 900℃, and the fracture toughness of 20GDC under the mechano-electrochemical coupling fields is decreased by 17.25% and 29.52% at 800℃ and 900℃. These findings are critical in predicting the fracture behavior of GDC electrolyte under real working conditions.

Heterogeneous effect of digital economy on carbon emission reduction

The vigorous development of the digital economy (DIEC) offers a substantial potential for China and global economies to realize the goal of “double carbon”. This study attempts to analyze the heterogeneous effect of DIEC on carbon emission reduction (CERE), in order to extend a valuable reference for effectively achieving the dual-carbon goal. Accordingly, the effect of DIEC on carbon emission scale (CESC) and carbon emission intensity (CEIN) are examined in this paper by using dual fixed effect model and intermediate effect model, respectively. The study findings show that, at first, DIEC significantly supports the CERE while significantly constraining the uplift in CESC and CEIN. Simultaneously, the CERE effect of DIEC contains digital economy dimensional heterogeneity, industry heterogeneity, production and life heterogeneity, and urban-rural heterogeneity. Finally, the DIEC boosts the technological innovation (TEIN) and residents' income (REIN) levels, in order to heighten the CERE. Further, improving invention technology innovation and TEIN is more conducive to catalyzing the reduction of carbon emission in the whole society and the production field. Conversely, the improvement of non-invention technology innovation and rural- and urban residents’ income impedes CERE in the field of life. Thus, the implications of this study are of vital significance for China and the rest of the world to foster CERE and achieve the “dual carbon” goal through DIEC development.

An Overview of the Factors that Influence Purchasing Intentions in Online Reviews

Consumer purchasing behaviour is increasingly influenced by online reviews in the current internet era. Existing models have not fully explained the correlation between online reviews and consumer purchase intention in specific contexts. This study aims to analyse their relationship and propose inspiring viewpoints. The authors summarize current academic research from three perspectives: independent variables, mediating variables and moderating variables. The findings suggest that there are already well-developed studies on the independent variables, but there is still room for further discussion on the mediating variables; for the moderating variables, aspects of service, trust, and personal characteristics are common moderating studies at present. However, there are only a few studies on business service compensation measures and response methods. In addition, most of the existing literature adopts questionnaire survey and data analysis methods, and the content mainly focuses on mediating variables, so there is still room for further development of the study among the remaining variables. Therefore, this paper constructs an intermediate regulation model and proposes one new mediating variable: perceived fit and two new moderating variables: sales volume and business service quality, to investigate how they mediate and moderate the relationship between online reviews and purchase intention. Through an extensive literature review, the authors identified the limitations and shortcomings of previous models. This paper aims to complement and improve the existing research and provides a new research model to provide a basis for further in-depth research.

Microwave-Based Sensor for the Noninvasive and Real-Time Analysis of 3-D Biological Microtissues: Microfluidic Improvement and Sensitivity Study

Microwave dielectric spectroscopy of biological materials is gaining interest as it presents attractive features such as being noninvasive, rapid, label-free, and suitable for intracellular investigations, notably for biological and medical researches. Specific microwave biosensors have consequently been developed to investigate biomolecules, cells in suspension, and also tissues. A type of biosensor is, however, missing for the analysis of 3-D aggregates of cells, also called microtissues or spheroids, which constitute an intermediate biological model between 2-D cell cultures and tissues or organs. Therefore, we present a microwave-based microsensor suitable for the study of such 3-D biological models. It is composed of a coplanar waveguide (CPW) with a central capacitive gap, which allows to measure the capacitive and conductive contrasts between the studied bio-object and a reference liquid, typically the cell culture medium, in the frequency range from 500 MHz to 20 GHz. This sensor also integrates a microfluidic channel to maintain the microtissues in liquid. Two fluidic configurations are investigated, one open and one closed, the latter including a mechanical trap. The advantages of the closed configuration are then highlighted experimentally, enabling accurate and real-time microwave measurements of microtissues maintained in their culture medium and in the vicinity of the sensing area for sensitivity enhancement. In addition, an analytical study on the capacitive gap size performed with both simulations and experiments leads to an optimal gap size for sensitivity improvement. This new sensor consequently offers new noninvasive, nondestructive, and dielectric sensing possibilities for biomedical investigations such as drug screening, personalized medicine, and also fundamental research.

Insights into ENSO Diversity from an Intermediate Coupled Model. Part I: Uniqueness and Sensitivity of the ENSO Mode

Abstract The basic dynamics of the spatiotemporal diversity for El Niño–Southern Oscillation (ENSO) has been the subject of extensive research and, while several hypotheses have been proposed, remains elusive. One promising line of studies suggests that the observed eastern Pacific (EP) and central Pacific (CP) ENSO may originate from two coexisting leading ENSO modes. We show that the coexistence of unstable EP-like and CP-like modes in these studies arises from contaminated linear stability analysis due to unnoticed numerical scheme caveats. In this two-part study, we further investigate the dynamics of ENSO diversity within a Cane–Zebiak-type model. We first revisit the linear stability issue to demonstrate that only one ENSO-like linear leading mode exists under realistic climate conditions. This single leading ENSO mode can be linked to either a coupled recharge-oscillator (RO) mode favored by the thermocline feedback or a wave-oscillator (WO) mode favored by the zonal advective feedback at the weak air–sea coupling end. Strong competition between the RO and WO modes for their prominence in shaping this ENSO mode into a generalized RO mode makes it sensitive to moderate changes in these two key feedbacks. Modulations of climate conditions yield corresponding modulations in spatial pattern, amplitude, and period associated with this ENSO mode. However, the ENSO behavior undergoing this linear climate condition modulations alone does not seem consistent with the observed ENSO diversity, suggesting the inadequacy of linear dynamics in explaining ENSO diversity. A nonlinear mechanism for ENSO diversity will be proposed and discussed in Part II.

Insights into ENSO Diversity from an Intermediate Coupled Model. Part II: Role of Nonlinear Dynamics and Stochastic Forcing

Abstract In this study, we investigate how a single leading linear El Niño–Southern Oscillation (ENSO) mode, as studied in Part I, leads to the irregular coexistence of central Pacific (CP) and eastern Pacific (EP) ENSO, a phenomenon known as ENSO spatiotemporal diversity. This diversity is fundamentally generated by deterministic nonlinear pathways to chaos via the period-doubling route and, more prevailingly, the subharmonic resonance route with the presence of a seasonally varying basic state. When residing in the weakly nonlinear regime, the coupled system sustains a weak periodic oscillation with a mixed CP/EP pattern as captured by the linear ENSO mode. With a stronger nonlinearity effect, the ENSO behavior experiences a period-doubling bifurcation. The single ENSO orbit splits into coexisting CP-like and EP-like ENSO orbits. A sequence of period-doubling bifurcation results in an aperiodic oscillation featuring irregular CP and EP ENSO occurrences. The overlapping of subharmonic resonances between ENSO and the seasonal cycle allows this ENSO irregularity and diversity to be more readily excited. In the strongly nonlinear regime, the coupled system is dominated by regular EP ENSO. The deterministic ENSO spatiotemporal diversity is thus confined to a relatively narrow range corresponding to a moderately unstable ENSO mode. Stochastic forcing broadens this range and allows ENSO diversity to occur when the ENSO mode is weakly subcritical. A close relationship among a weakened mean zonal temperature gradient, stronger ENSO activity, and more (fewer) occurrences of EP (CP) ENSO is noted, indicating that ENSO–mean state interaction may yield ENSO regime modulations on the multidecadal time scale.

Relationships between plant species richness and grazing intensity in a semiarid ecosystem.

Plant species richness is an important property of ecosystems that is altered by grazing. In a semiarid environment, we tested the hypotheses that (1) small-scale herbaceous plant species richness declines linearly with increasing grazing intensity by large ungulates, (2) precipitation and percent sand interact with grazing intensity, and (3) response of herbaceous plant species richness to increasing intensity of ungulate grazing varies with patch productivity. During January-March 2012, we randomly allocated 50, 1.5-m × 1.5-m grazing exclosures within each of six 2500 ha study sites across South Texas, USA. We counted the number of herbaceous plant species and harvested vegetation in 0.25-m2 plots within exclosures (ungrazed control plots) and in the grazed area outside the exclosures (grazed treatment plots) during October-November 2012-2019. We estimated percent use (grazing intensity) based on the difference in herbaceous plant standing crop between control plots and treatment plots. We selected the negative binomial regression model that best explained the relationship between grazing intensity and herbaceous plant species richness using the Schwarz-Bayesian information criterion. After accounting for the positive effect of precipitation and percent sand on herbaceous plant species richness, species richness/0.25 m2 increased slightly from 0% to 30% grazing intensity and then declined with increasing grazing intensity. Linear and quadratic responses of herbaceous plant species richness to increasing grazing intensity were greater for the least productive patches (<15.7 g/0.25 m2) than for productive patches (≥15.7 g/0.25 m2). Our results followed the pattern predicted by the intermediate disturbance hypothesis model for the effect of grazing intensity on small-scale herbaceous plant species richness.

Digital Technology, Factor Allocation and Environmental Efficiency of Dairy Farms in China: Based on Carbon Emission Constraint Perspective

The emission of carbon pollutants stemming from dairy farms has emerged as a significant obstacle in mitigating the effects of global warming. China, being a prominent nation in the field of dairy farming, encounters significant challenges related to excessive component input and elevated environmental pollution. Digital technology presents an opportunity to enhance the factor allocation of dairy farms and thus increase their environmental efficiency. This study utilizes survey data from 278 dairy farms in China to examine the effect of digital technology on the allocation of land, labor, and capital variables in dairy farms. The IV-Probit model, IV-Tobit model, treatment effect model, and two-stage least square technique are employed to empirically analyze these impacts. Simultaneously, the intermediate effect model was employed to examine the mediating function of factor allocation in the effect of digital technology on environmental efficiency. The findings indicate that digital technology has the potential to greatly enhance land transfer and land utilization rates in dairy farms. Additionally, it has been observed that digital technology may lead to a decrease in both the proportion and time of labor input. Furthermore, digital technology has the potential to decrease short-term productive input while simultaneously enhancing long-term productive input within dairy farming operations. Digital technology has been found to have an indirect yet beneficial influence on environmental efficiency. This is mostly achieved through the facilitation of resource allocation, specifically in terms of land, labor, and capital aspects. The article presents a set of policy recommendations, including the promotion of extensive integration of digital technology within dairy farms, the facilitation of optimal allocation of production factors in dairy farms, and the implementation of specialized training programs focused on digital technology.

Intermediate Task Fine-Tuning in Cancer Classification

Reducing the amount of annotated data required to train predictive models is one of the main challenges in applying artificial intelligence to histopathology. In this paper, we propose a method to enhance the performance of deep learning models trained with limited data in the field of digital pathology. The method relies on a two-stage transfer learning process, where an intermediate model serves as a bridge between a pre-trained model on ImageNet and the final cancer classification model. The intermediate model is fine-tuned with a dataset of over 4,000,000 images weakly labeled with clinical data extracted from TCGA program. The model obtained through the proposed method significantly outperforms a model trained with a traditional transfer learning process.

Bridging the evidence gap in spatial planning

The article focuses on how the use of evidence in spatial planning could bridge the gap between vision and reality through the continuous evaluation of the spatial impacts of the proposed interventions. The introduction sets the theoretical, institutional, and practical context on how evidence is used to assess these impacts in relation to both the expected outcomes and the pursued policy priorities. The research section addresses these issues based on the empirical and methodological background derived from a series of successive studies carried out between 1999 and 2014. These studies are related to the establishment and operation of the spatial impact observatory of the Egnatia motorway, a major European transport infrastructure project in northern Greece. The results section introduces a methodological approach, succinctly referred to as the IRIS model in which spatial planning is conceived as an adaptive process, and the use of evidence aims to enhance its flexibility and preparedness in dealing with the uncertainties that arise from dynamic conditions, rather than relying solely on predetermined solutions. It comprises three key components: a theoretical model that simulates the relationship between transport infrastructure and spatial development, an intermediate data model in which raw data were constructed as evidence indicators, and a combination of inductive and deductive paths in which evidence is used to assess the anticipated impact of spatial plans and to evaluate the actual spatial outcomes after their implementation. Finally, the conclusions underline the value added of the IRIS approach as a comprehensive and integrative methodology that aims to improve the efficacy of spatial planning by establishing a link between theoretical models, policy objectives, and evidence-based decision-making.

Recognizing facial emotion leveraging convolutional neural network

Facial expression recognition technology is a technology based on the combination of artificial intelligence technology and biology, and it is interdisciplinary research. The appearance of this technology shows the diversity of the development direction and application fields of computer technology, but it also means that the development of facial expression recognition technology needs not only the support of computer technology but also the exploration and progress of biology. At present, to recognize facial expressions a system usually contains three stages, namely, face detection stage, feature extraction stage, and face expression recognition stage. The facial expression recognition algorithm is transformed into a classification problem, and a convolutional neural network (CNN) is adopted, where features of the facial expressions could be automatically extracted. It takes as input a preprocessed image and can directly produce encoded features and predictions, leveraging the end-to-end strategy. It could discard the complicated intermediate modeling process of traditional machine learning. In this work a CNN is implemented for the recognition of facial expression on Fer2013 dataset. Moreover, the effectiveness of different numbers of CNN layers are testified on this problem. It could be concluded that with deeper architectures, the CNN tends to perform better.

Is Enol Always the Culprit? The Curious Case of High Enantioselectivity in a Chiral Rh(II) Complex Catalyzed Carbene Insertion Reaction.

The mechanism of Rh2 (S-NTTL)4 catalyzed carbene insertion into C(3)-H of indole is investigated using DFT methods. Since the commonly accepted enol mechanism cannot account for enantioinduction, a concerted oxocarbenium pathway was proposed in an earlier work using a model catalyst. However, after considering the full catalytic system, this study finds that akin to other reactions, here, too, the enol pathway is of lower energy, which now naturally raises a conundrum regarding the mode of chiral induction. Herein, a new water promoted mechanistic pathway involving a metal-associated enol intermediate hydrogen bonding and stereochemical model are proposed to solve this puzzle. It is shown how the catalyst bowl-shaped structure along with substrate-catalyst binding is crucial for achieving high levels of enantioselectivity. A stereodetermining water-assisted proton transfer is proposed and confirmed through deuterium-labeling experiments. The water molecules are held together by H-bonding interactions with the carboxylate ligands that is reminiscent of enzyme catalysis. Although several previous studies have aimed at understanding the mechanism of metal catalyzed carbene insertion reactions, the origin of high stereoinduction especially with chiral metal complexes remains unclear, and till date there is no transition state model that can explain the high enantioselectivity with such chiral Rh complexes. The metal-associated enol pathway is currently underrepresented in catalytic cycles and may play a crucial role in catalyst design. Since the enol pathway is commonly adopted in other metal-catalyzed X-H insertion reactions involving a diazoester, the presented results are not specific to the current reaction. Therefore, this study could provide the direction for achieving high levels of enantioselectivity which is otherwise difficult to achieve with a single metal catalyst.

Energy-Efficient Federated Edge Learning in Multi-Tier NOMA-Enabled HetNet

We propose a novel multi-tier (top, intermediate, and bottom tiers) architecture at the edge of a heterogeneous network (HetNet) where non-orthogonal multiple access (NOMA) provides access to user equipment (UE) to participate in federated edge learning (FEL). The HetNet consists of a macro base station (MBS) and several small base stations (SBSs) where each BS is equipped with an edge server (ES). SBSs use the same system bandwidth to increase the system capacity. The top tier consists of the MBS-ES which works as the global model aggregator while ESs of SBSs and UEs connected with MBS reside in the intermediate tier. Similarly, UEs connected with an SBS-ES of the intermediate tier occupy the bottom tier. ESs of SBSs work as the intermediate model aggregators between the ES of the top tier and the UEs of the bottom tier. To minimize the total energy consumption (EC) for local computing (LC) and uplink transmission (UT) of UEs, we formulate a non-linear programming (NLP) optimization problem, present our solution by decomposing the problem into sub-problems, and propose two sequential algorithms to estimate EC for both LC and UT with less complexity. Our extensively simulated results demonstrate the viability of our proposed work.

In silico prediction of maximum perineal muscle strain during vaginal delivery by design of experiment

Background and objectiveThe prevalence of pelvic floor muscle injuries induced by childbirth is higher than 23 % in the general women population. Such injuries can lead to prolapses and other pathologies in future female life. Leveraging computational biomechanics, the study implements an advanced female pelvic floor model for computing the maximum pelvic muscle strain, which serves as an injury risk indicator. The design of experiment method, abbreviated as DoE, is used to compute the maximum strain for boundary values of bony pelvis dimensions, namely the anterior-posterior diameter (abbreviated as APD) and the transverse diameter (abbreviated as TD). This is done in combination with small, medium and large percentiles of fetal head circumference (abbreviated as HC). MethodsWe utilized a previously developed finite element model of a female pelvic floor, as a reference, and enhanced it with new features, including a more detailed tissue geometry and advanced constitutive material models. The APD and TD dimensions were sourced from the set of MRI of 64 nulliparous women. This data was used to estimate the boundary dimensions of the female bony pelvis, combining both small and large values of APD and TD. Together with the 10th and the 95th percentiles for HC, a three-dimensional domain was constructed to assess the maximum pelvic muscle strain. In boundary cases, the maximum pelvic muscle strain was computed across 8 full-factorial design models (each situated at one corner of the domain, thereby combining the minimum and the maximum values of APD, TD and HC). This was done to define a response surface that predicts the maximum pelvic muscle strain within the domain. The accuracy of this response surface prediction was validated using 15 additional intermediate design models. These models were placed at the center of the domain (1 point), the centres of the domain boundary surfaces (6 points), and midway along each domain boundary edge (8 points). ResultsThe maximum strain results for 8 combinations of APD, TD, and HC were employed to construct a linear response surface as a function of APD, TD, and HC. Tests at an additional 19 domain points served to evaluate the efficiency of the response surface prediction. The response surface demonstrated strong predictability, with an absolute average error of 1.52 %, an absolute median error of 1.52 %, and an absolute maximum error of 11.11 %. HC emerged as the most influencing dimension, accounting for 16 % of influence. ConclusionsThe reference finite element pelvic floor model was scaled to 8 full-factorial female-specific pelvic floor models, which represent the combination of boundary values for APD, TD, and HC. The maximum pelvic floor muscle strain from these 8 models was used to design a response surface. When implementing the DoE approach to construct the response, there was consistent predictability for the maximum perineal muscle strain, as validated by the additional 19 intermediate design models. As a result, the response surface methodology can serve as an initial predictor for potential childbirth-induced pelvic floor muscle injury.