Ideal Model Research Articles

Modulation of Tropical Cyclogenesis by Convectively Coupled Kelvin Waves

Abstract Tropical cyclone numbers can vary from week to week within a hurricane season. Recent studies suggest that convectively coupled Kelvin waves can be partly responsible for such variability. However, the precise physical mechanisms responsible for that modulation remain uncertain partly due to the inability of previous studies to isolate the effects of Kelvin waves from other factors. This study uses an idealized modeling framework—called an aquaplanet—to uniquely isolate the effects of Kelvin waves on tropical cyclogenesis. The framework also captures the convective-scale dynamics of both tropical cyclones and Kelvin waves. Our results confirm an uptick in tropical cyclogenesis after the passage of a Kelvin wave—twice as many tropical cyclones form 2 days after a Kelvin wave peak than at any other time lag from the peak. A detailed composite analysis shows anomalously weak ventilation during and after (or to the west of) the Kelvin wave peak. The weak ventilation stems primarily from anomalously moist conditions, with weaker vertical wind shear playing a secondary role. In contrast to previous studies, our results demonstrate that Kelvin waves modulate both kinematic and thermodynamic synoptic-scale conditions that are necessary for tropical cyclone formation. These results suggest that numerical models must capture the three-dimensional structure of Kelvin waves to produce accurate subseasonal predictions of tropical cyclone activity. Significance Statement Anticipating active tropical cyclone periods several weeks in advance could help mitigate the loss of lives and property due to these phenomena. Recent studies suggest that a type of tropical cloud cluster—known as convectively coupled Kelvin waves—can promote tropical cyclone formation. Kelvin waves travel around the world and can be detected days to weeks in advance. We use a simplified numerical model to isolate the effects of Kelvin waves on tropical cyclone formation. Our unique approach confirms that tropical cyclones are more likely to form 2 days after a Kelvin wave than before the wave. We also demonstrate that—contrary to previous perception—the enhancement of tropical cyclogenesis is due to both more moisture and weaker wind currents following the waves.

Memristive Circuit Implementation of Caenorhabditis Elegans Mechanism for Neuromorphic Computing.

To overcome the energy efficiency bottleneck of the von Neumann architecture and scaling limit of silicon transistors, an emerging but promising solution is neuromorphic computing, a new computing paradigm inspired by how biological neural networks handle the massive amount of information in a parallel and efficient way. Recently, there is a surge of interest in the nematode worm Caenorhabditis elegans (C. elegans), an ideal model organism to probe the mechanisms of biological neural networks. In this article, we propose a neuron model for C. elegans with leaky integrate-and-fire (LIF) dynamics and adjustable integration time. We utilize these neurons to build the C. elegans neural network according to their neural physiology, which comprises: 1) sensory modules; 2) interneuron modules; and 3) motoneuron modules. Leveraging these block designs, we develop a serpentine robot system, which mimics the locomotion behavior of C. elegans upon external stimulus. Moreover, experimental results of C. elegans neurons presented in this article reveals the robustness (1% error w.r.t. 10% random noise) and flexibility of our design in term of parameter setting. The work paves the way for future intelligent systems by mimicking the C. elegans neural system.

3D dynamic landscape simulation of artificial intelligence in environmental landscape design

Three-dimensional (3D) simulations and precise landscape visualizations are crucial for various applications, like landscape management and planning, computer and connection of the landscape, evaluation, and tracking of land use. The consequences of several plans and a large scene cannot be communicated using older methods of comprehensive environmental planning and development in a timely, rational, and coordinated manner. Architects have trouble incorporating ideas into other comprehensive planning implementation processes. Architects did not thoroughly investigate the neighbourhood's demographics and matching behavioural needs and lacked critical thinking. The 3D dynamic landscape simulation is a detailed computerized three-dimensional simulation of the environment that can be dynamically presented. With the aid of Artificial Intelligence (AI) technology, the system possesses a strong sense of reality, a user-friendly interface, and interactive features that can be tailored to the requirements of the contemporary urban environmental landscape. Regarding exterior publicity, domestic assistance, environmental land use planning, and information systems. The novelty of the proposed Interactive Design System based on AI (IDS-AI) is to create a 3D dynamic landscape model based on a real-life environmental scene, utilizing a Geographic Information System (GIS) to optimize landscape vision. Secondly, 3D environmental landscape design simulation was implemented using GIS spatial analysis in conjunction with the Fuzzy Analytical Hierarchical Process (FAHP) to reduce the data overlap rate and help make an accurate decision. Finally, the design incorporates the development of the interactive interface system application of landscape design and environmental resources for viewing the landscape, the factors that affect them, and the area coverage ratio of various land cover types. The experimental outcomes show that the suggested IDS model increases the gradient sensitivity level of 98.3 % and area coverage ratio of 93.4 % compared to other existing models.

An Optimized Uncertainty-Aware Training Framework for Neural Networks.

Uncertainty quantification (UQ) for predictions generated by neural networks (NNs) is of vital importance in safety-critical applications. An ideal model is supposed to generate low uncertainty for correct predictions and high uncertainty for incorrect predictions. The main focus of state-of-the-art training algorithms is to optimize the NN parameters to improve the accuracy-related metrics. Training based on uncertainty metrics has been fully ignored or overlooked in the literature. This article introduces a novel uncertainty-aware training algorithm for classification tasks. A novel predictive uncertainty estimate-based objective function is defined and optimized using the stochastic gradient descent method. This new multiobjective loss function covers both accuracy and uncertainty accuracy (UA) simultaneously during training. The performance of the proposed training framework is compared from different aspects with other UQ techniques for different benchmarks. The obtained results demonstrate the effectiveness of the proposed framework for developing the NN models capable of generating reliable uncertainty estimates.

Cluster-based wireless sensor network framework for denial-of-service attack detection based on variable selection ensemble machine learning algorithms

A Cluster-Based Wireless Sensor Network (CBWSN) is a system designed to remotely control and monitor specific events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. The wide range of applications of technology in almost every field of human activity exposes it to various security threats from cybercriminals. One of the pressing concerns that requires immediate attention is the risk of security breaches, such as intrusions in wireless sensor network traffic. Poor detection of denial-of-service (DoS) attacks, such as Grayhole, Blackhole, Flooding, and Scheduling attacks, can deplete the energy of sensor nodes. This can cause certain sensor nodes to fail, leading to a degradation in network coverage or lifetime. The detection of such attacks has resulted in significant computational complexity in the related works. As new threats arise, security attacks get more sophisticated, focusing on the target system's vulnerabilities. This paper proposed the development of Cluster-Based Wireless Sensor Network and Variable Selection Ensemble Machine Learning Algorithms (CBWSN_VSEMLA) as a security threats detection system framework for DoS attack detection. The CBWSN model is designed using a Fuzzy C-Means (FCM) clustering technique, whereas VSEMLA is a detection system comprised of Principal Component Analysis (PCA) for feature selection and various ensemble machine learning algorithms (Bagging, LogitBoost, and RandomForest) for the detection of grayhole attacks, blackhole attacks, flooding attacks, and scheduling attacks. The experimental results of the model performance and complexity comparison for DoS attack evaluation using the WSN-DS dataset show that the PCA_RandomForest IDS model outperforms with 99.999 % accuracy, followed by the PCA_Bagging IDS model with 99.78 % accuracy and the PCA_LogitBoost model with 98.88 % accuracy. However, the PCA_RandomForest model has a high computational complexity, taking 231.64 s to train, followed by the PCA_LogitBoost model, which takes 57.44 s to train, and the PCA_Bagging model, which takes 0.91 s to train to be the best in terms of model computational complexity. Thus, the models surpassed all baseline models in terms of model detection accuracy on flooding, scheduling, grayhole, and blackhole attacks.

Type Id versus type IId three-level hybrid surgery for the treatment of noncontiguous cervical spondylosis: A finite element analysis

The objective of the research is to simulate different forms of three-level hybrid surgeries, aiming to establish a foundational reference for the selection of suitable treatment strategies for multilevel noncontiguous cervical degenerative disease (CDD). For the development of precise finite element models (FEMs), this study utilized computed tomography (CT) data. Two cross-segment surgical approaches were primarily investigated: C3/4 cervical disc arthroplasty (CDA), C5/6 anterior cervical discectomy and fusion (ACDF), and C6/7 ACDF in the type Id model; C3/4 CDA, C5/6 CDA, and C6/7 ACDF in the type IId model. The follower load technique was employed to apply an initial axial load of 73.6 N at the motion center. Subsequently, a moment of 1.0 Nm was introduced at the center of the C2 vertebra to simulate the overall motion of the model. In contrast to type IId, type Id exhibited lower average intervertebral disc pressure in C4/5 across various motions. The average intervertebral disc pressure in C2/3 was higher in type Id compared to type IId in flexion and axial rotation, whereas the reverse was observed in lateral bending. Type IId exhibited notably lower facet joint contact stresses during extension in C2/3 and C4/5 when compared to type Id. Type Id has a better protective effect on IS, and can significantly reduce the average pressure of the intervertebral disc in IS compared with type IId. Type IId has a significant protective effect on the post-column structure of non-fused segments.

A decentralized multi-authority CP-ABE scheme from LWE

As a cryptographic primitive supporting finer and richer access control, attribute-based encryption can provide more efficient, concise, secure and highly adaptive access control policies for cloud data, which has attracted the attention of many researchers. However, in a standard ABE scheme, the key can only be generated and issued by a central authority. The single authorization center has a heavy load and is vulnerable to attack. Once the center is paralyzed, it will bring serious security consequences. Multi-authorization center attribute encryption (MA-ABE) allows multiple parties to play an authoritative role, which can solve this problem.In this paper, we propose a MA-ABE scheme, which combines a global ID model, a two-stage sampling technique on a lattice and a monotonous linear secret sharing schemes (M-LSSS) to achieve a static security against arbitrary collusion in the random oracle model, in which the access policy of the scheme is expressed by DNF formula. Technically, our scheme is an improvement on the work of Datta et al. (2021), through this improvement, we can get shorter ciphertext and smaller key size.

Real-Time Digital Mapped Method for Sensorless Multitimescale Operation Condition Monitoring of Power Electronics Systems

Operation condition monitoring of power electronics systems can improve analysis visibility for device administrators and researchers. However, the current sensor-based monitoring methods have limitations in accurately monitoring variables at multi-timescales. This article presents a real-time digital mapped (RTDM) method for the sensorless multi-timescale operation condition monitoring in power electronics systems using FPGA simulators. The mathematic models of power converters are deployed in the simulator considering multi-timescale characteristics. Both the digital simulator and the physical power converter are controlled by the same control signals, allowing the simulator to map the accurate operation conditions of the power converters without additional physical sensors. The RTDM method can monitor variables on the switching period timescale to the system transient timescale due to the consideration for the ideal switch modeling of the semiconductor switch. The effectiveness of the proposed method is demonstrated using a 30kW DAB converter as an example, through the integration of an experimental prototype and a simulator to create an RTDM platform. This method offers an easy and sensorless way to monitor multi-timescale variables in power electronics systems, providing a more comprehensive and visual analysis of their operation.

Effect of stator winding chording on the performance of five phase synchronous reluctance motor

The effect of winding chording on five-phase synchronous reluctance motor (SRM) modelled in phase variables is presented. The stator winding configuration is shifted a pole pitch from a full-pitch configuration to a 54 degrees over-full-pitched configuration incorporating the effect of 3rd harmonic of the air-gap magneto-motive force in the inductance equations. The models are monitored on starting, synchronism, loading, faults and loss of synchronism. These are considered simultaneously with vector potential, rotor speed, air-gap flux linkage and winding current. The phase variable (analytical) models have been simulated by using MATLAB/Simulink software while ANSYS Maxwell software has been used to simulate the finite element model (FEM) of the motor for corresponding chording ranges for comparison on direct online starting (DOL). Under all conditions considered in the work, the detailed results are presented and very close similarity is observed between the analytical and the nearly ideal FEM model for all chording ranges and the similarity is enhanced with increase in chording angle.

The use of social information in vulture flight decisions.

Animals rely on a balance of personal and social information to decide when and where to move next in order to access a desired resource. The benefits from cueing on conspecifics to reduce uncertainty about resource availability can be rapidly overcome by the risks of within-group competition, often exacerbated toward low-ranked individuals. Being obligate soarers, relying on thermal updraughts to search for carcasses around which competition can be fierce, vultures represent ideal models to investigate the balance between personal and social information during foraging movements. Linking dominance hierarchy, social affinities and meteorological conditions to movement decisions of eight captive vultures, Gyps spp., released for free flights in natural soaring conditions, we found that they relied on social information (i.e. other vultures using/having used the thermals) to find the next thermal updraught, especially in unfavourable flight conditions. Low-ranked individuals were more likely to disregard social cues when deciding where to go next, possibly to minimize the competitive risk of social aggregation. These results exemplify the architecture of decision-making during flight in social birds. It suggests that the environmental context, the context of risk and the social system as a whole calibrate the balance between personal and social information use.

A 3D tailored monolithic glass chip for stimulating and recording zebrafish neuronal activity with a commercial light sheet microscope

Microfluidic technology is unrivaled in its ability to apply soluble chemical stimuli with high spatiotemporal precision. Analogous, light–sheet microscopy is unmatched in its ability of low phototoxic but fast volumetric in vivo imaging with single cell resolution. Due to their optical translucency during the larval stages, zebrafish (Danio rerio) are an ideal model to combine both techniques; yet, thus far this required light–sheet microscopes, which were in most cases custom–built and adapted to the available softlithographic chip technology. Our aim was to use a commercial light–sheet microscope to illuminate a microfluidic chip from two opposite lateral directions and to record images with the detection objective placed orthogonally above the chip. Deep tissue penetration can be achieved by superimposing beams from opposite directions to form a single light sheet. But a microfluidic chip that allows a) targeted stimulus application in a closed microenvironment, b) interference–free incoupling of excitation light from two directions and c) outcoupling of fluorescence in the perpendicular direction through an optically perfect cover glass was not known until now. Here, we present a monolithic glass chip with the required plane-parallel sidewalls and cover slide closure at the top, constructed by advanced femtosecond laser ablation, thermal bonding and surface smoothing processes. In addition, the 3D shape of a fish fixator unit was tailored to match the body shape of a zebrafish larva to ensure stable positioning during whole–brain recording. With hydrodynamic focusing a targeted partial exposure of the larva’s head to chemical stimuli and fast position switching (in less than 10 s) was possible. With the capabilities of this unique monolithic glass chip and its up–scalable wafer–level fabrication process, the new NeuroExaminer is prone to become an excellent addition to neurobiology laboratories already equipped with high–quality commercial light sheet microscopes.

Geographic distribution of a missense mutation in the KRT38 gene in Chinese indigenous cattle breeds

Context China has a vast area across many temperature zones and a variety of cattle breeds. These cattle resources are ideal models to research their adaptability to the environment. The KRT38 gene is an acidic protein, and its coding product can be used as a component of hair production. Aims The objective of this study was to investigate the diversity of the KRT38 gene in Chinese local cattle and the association of different genotypes with mean temperature, relative humidity and temperature humidity index. Methods A missense mutation g.41650738 A > G in the KRT38 gene was screened from the database of bovine genomic variation (BGVD) and was genotyped in a total of 246 samples from 15 local cattle breeds in China by polymerase chain reaction amplification and sequencing. Finally, the correlation between the locus and the three climatic factors was analysed. Key results We successfully obtained the frequency of this single-nucelotide polymorphism in three groups of cattle in northern, central and southern China. The frequency of allele A gradually declined from north to south, whereas the frequency of allele G showed the opposite trend with a clear geographic distribution. Conclusions Our results indicate that KRT38 variation in Chinese indigenous cattle might be linked to heat tolerance. Implications Our analysis may assist in determining the importance of the variation as a genetic signal for heat tolerance in cattle reproduction and genetics.

The human adenovirus PI3K-Akt activator E4orf1 is targeted by the tumor suppressor p53.

Human adenoviruses (HAdV) are classified as DNA tumor viruses due to their potential to mediate oncogenic transformation in non-permissive mammalian cells and certain human stem cells. To achieve transformation, the viral early proteins of the E1 and E4 regions must block apoptosis and activate proliferation: the former predominantly through modulating the cellular tumor suppressor p53 and the latter by activating cellular pro-survival and pro-metabolism protein cascades, such as the phosphoinositide 3-kinase (PI3K-Akt) pathway, which is activated by HAdV E4orf1. Focusing on HAdV-C5, we show that E4orf1 is necessary and sufficient to stimulate Akt activation through phosphorylation in H1299 cells, which is not only hindered but repressed during HAdV-C5 infection with a loss of E4orf1 function in p53-positive A549 cells. Contrary to other research, E4orf1 localized not only in the common, cytoplasmic PI3K-Akt-containing compartment, but also in distinct nuclear aggregates. We identified a novel inhibitory mechanism, where p53 selectively targeted E4orf1 to destabilize it, also stalling E4orf1-dependent Akt phosphorylation. Co-IP and immunofluorescence studies showed that p53 and E4orf1 interact, and since p53 is bound by the HAdV-C5 E3 ubiquitin ligase complex, we also identified E4orf1 as a novel factor interacting with E1B-55K and E4orf6 during infection; overexpression of E4orf1 led to less-efficient E3 ubiquitin ligase-mediated proteasomal degradation of p53. We hypothesize that p53 specifically subverts the pro-survival function of E4orf1-mediated PI3K-Akt activation to protect the cell from metabolic hyper-activation or even transformation.IMPORTANCEHuman adenoviruses (HAdV) are nearly ubiquitous pathogens comprising numerous subtypes that infect various tissues and organs. Among many encoded proteins that facilitate viral replication and subversion of host cellular processes, the viral E4orf1 protein has emerged as an intriguing yet under-investigated player in the complex interplay between the virus and its host. Nonetheless, E4orf1 has gained attention as a metabolism activator and oncogenic agent, while recent research is showing that E4orf1 may play a more important role in modulating the cellular pathways such as phosphoinositide 3-kinase-Akt-mTOR. Our study reveals a novel and general impact of E4orf1 on host mechanisms, providing a novel basis for innovative antiviral strategies in future therapeutic settings. Ongoing investigations of the cellular pathways modulated by HAdV are of great interest, particularly since adenovirus-based vectors actually serve as vaccine or gene vectors. HAdV constitute an ideal model system to analyze the underlying molecular principles of virus-induced tumorigenesis.

A ROBUST CYBER SECURITY THREAT DETECTION MODEL USING ARTIFICIAL INTELLIGENCE TECHNOLOGY

The difficulty of ensuring cyber-security is steadily growing as a result of the alarming development in computer connectivity and the sizeable number of applications associated to computers in recent years. The system also requires robust defines against the growing number of cyber threats. As a result, a possible role for cyber-security might be performed by developing intrusion detection systems (ids) to detect inconsistencies and threats in computer networks. An effective data-driven intrusion detection system has been created with the use of artificial intelligence, particularly machine learning techniques. This research proposes a novel twin support vector machine (tsvm) based security model which first considers the security features ranking according to their relevance before developing an ids model based on the significant features that have been selected. By lowering the feature dimensions, this approach not only improves predictive performance for unidentified tests but also lowers the model's computational expense. Trials are conducted using four common ml techniques to compare the results to those of the current approaches (decision tree, random decision forest, random tree, and artificial neural network). The experimental findings of this study confirm that the suggested methods may be used as learning-based models for network intrusion detection and demonstrate that, when used in the real world, they outperform conventional ml techniques.

Micro-computed tomographic evaluation of the shaping ability of three nickel-titanium rotary systems in the middle mesial canal of mandibular first molars: an ex vivo study based on 3D printed tooth replicas

BackgroundThe preparation of the middle mesial (MM) canal of mandibular molars represents a challenge because it is often curved, narrow, and close to the root concave. The purpose of this study was to evaluate the ex vivo shaping ability of 3 nickel-titanium (NiTi) rotary systems in the MM canal using 3D printed resin tooth replicas.MethodsA permanent mandibular first molar with a MM canal was acquired from a pool of extracted teeth and reproduced by a 3D printer. The resin tooth replicas (n = 18) were equally assigned to 3 groups for the evaluation of the shaping abilities of 3 NiTi rotary systems (OneShape [OS], Twisted Files [TF], and ProTaper Gold [PTG]) according to the manufacturer’s recommendations. The tooth replicas were scanned by micro-computed tomography (micro-CT) twice before and after instrumentation of the mesiobuccal (MB), mesiolingual (ML), and MM root canals. After 3D reconstruction, the canal straightening, change of root canal volume and surface area, the mesial and distal canal wall thickness and canal transportation at the levels of 1, 2, and 3 mm below furcation were assessed. One-way variance analysis and Turkey’s post hoc test were used for comparisons of the means among different groups, and paired-t test was used to compare the mesial and distal sides of the mesial roots.ResultsAs compared with OS and TF, the use of PTG in preparation of MM canals resulted in significantly more straightening of canal curvature (p < 0.05), greater post-instrumentation canal volume and surface area, and thinner mesial and distal remaining canal wall thickness at 1, 2 and 3 mm below furcation (all p < 0.05). Regarding the root canal transportation in the mesiodistal direction, there was no significant difference among the 3 instruments (all p > 0.05) after the preparation of the MB and ML canals. However, in the MM canal, more pronounced transportation was detected in the PTG group at 2 mm below furcation, and in the TF group at 3 mm below furcation as compared with the other 2 systems (both p < 0.05).Conclusions3D printed tooth replicas have the advantages of consistency and can be an ideal model to evaluate the shaping ability of different instruments in the MM canal. OS and TF files performed similarly and both are appropriate for shaping the MM canal, while PTG may cause excessive and uneven resin removal, especially near the furcation, and may lead to root fragility and procedural errors.

Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm

The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains.

Use the Thermodynamic State Equations to Analyze the Non-ideality of Gas Mixtures

The assessment of gas behavior in chemical engineering systems necessitates a profound understanding of thermodynamic principles that govern the interactions among the components within a given system. To this end, the deviation from ideality in a single gas or gas mixture is associated with the disparity between the actual behavior of the gas or gas mixture and the behavior anticipated by the ideal gas model. This study is aimed at scrutinizing the deviation from ideal behavior in a gas mixture composed of CH4 and CO2. The analysis employs the cubic equations of state: Van Der Waals, Soave-Redlich-Kwong, and generalized Virial equations, truncated to the third term. These equations are widely recognized for their utility in characterizing substance behavior under specific thermodynamic conditions. The investigation involves an evaluation of the mixture's behavior by assessing variations in the compressibility factor concerning pressure, volume, and pressure, using a thermodynamic calculator at 296.15 K and 15 bar. The findings of this study reveal the prevalence of attractive intermolecular forces at higher pressures and repulsive interactions at lower pressures. An analogous examination of the effect of altering the composition of CH4 was undertaken using the Soave-Redlich-Kwong equation, which incorporates parameters allowing for an evaluation of the impact of molecule size and intermolecular interactions within the mixture. Furthermore, experimental data were employed to validate the results obtained in this study. Consequently, it can be inferred that these equations provide insight into the influence of pressure on molecular interaction forces, encompassing repulsive and attractive forces, which in turn can define the new volume of a real system. Thus, based on the corroboration established herein, these equations demonstrate a high degree of consistency and applicability, thereby expanding the realm of thermodynamic inquiry.

A successful and ideal randomized orthogonal additive response model

A successful and ideal randomized orthogonal additive response model

On the sound field of a resilient elliptical disk in an infinite baffle.

A resilient elliptical disk has zero mass and stiffness and is driven by a uniform pressure distribution rather than uniform velocity as would be the case for a rigid disk. It can represent the diffraction of a plane wave through an elliptical aperture in an infinite rigid screen of zero thickness as well as provide an idealized model of a loudspeaker of the electrostatic or planar magnetic type. Using the Fourier (Hankel) Green's function in cylindrical coordinates, together with the monopole Kirchhoff-Helmholtz boundary integral, rigorous analytical formulas are derived for calculating the surface velocity distribution, radiation admittance, on-axis pressure, and directivity pattern of an elliptical resilient disk in an infinite baffle and these are plotted over a range of normalized frequencies. The directivity is compared with that of a resilient circular disk and asymptotic low-frequency approximations are derived for the radiation resistance, reactance, and on-axis pressure.

Modulation of cell-mediated immunity during pregnancy in wild bonobos.

During pregnancy, the mammalian immune system must simultaneously protect against pathogens while being accommodating to the foreign fetal tissues. Our current understanding of this immune modulation derives predominantly from industrialized human populations and laboratory animals. However, their environments differ considerably from the pathogen-rich, resource-scarce environments in which pregnancy and the immune system co-evolved. For a better understanding of immune modulation during pregnancy in challenging environments, we measured urinary neopterin, a biomarker of cell-mediated immune responses, in 10 wild female bonobos (Pan paniscus) before, during and after pregnancy. Bonobos, sharing evolutionary roots and pregnancy characteristics with humans, serve as an ideal model for such investigation. Despite distinct environments, we hypothesized that cell-mediated immune modulation during pregnancy is similar between bonobos and humans. As predicted, neopterin levels were higher during than outside of pregnancy, and highest in the third trimester, with a significant decline post-partum. Our findings suggest shared mechanisms of cell-mediated immune modulation during pregnancy in bonobos and humans that are robust despite distinct environmental conditions. We propose that these patterns indicate shared immunological processes during pregnancy among hominins, and possibly other primates. This finding enhances our understanding of reproductive immunology.