Model For Induction Research Articles

Intrathecal enalapril reduces adhesion formation in experimentally induced digital flexor tendon sheath injuries in horses.

The objectives of the study were to describe a standing percutaneous adhesion induction model in the digital flexor tendon sheath (DFTS) of horses and to evaluate the effect of intrathecal administration of the angiotensin-converting enzyme (ACE) inhibitor enalapril on tendon healing and adhesion formation. Randomized, blinded, controlled experimental study. Eight healthy horses. A collagenase-induced adhesion model was implemented in the deep digital flexor tendon (DDFT) of both forelimbs under standing ultrasonographic guidance. Daily intrathecal injections of 5 mg enalapril (the treatment condition) were administered to a randomly assigned forelimb for 5 days, with the contralateral limb receiving an equivalent volume of 0.9% NaCl (the control). Lameness and limb circumference were recorded weekly. Horses were euthanized after 8 weeks and evaluated for gross digital flexor tendon sheath (DFTS) adhesions. Tendons were collected for histopathologic scoring of DDFT healing. Paired data were analyzed using a one-sided alternative sign test and longitudinal regression. Multiple DFTS adhesions were formed in control limbs of all horses. The median number of gross DFTS adhesions in treated limbs was less than in control limbs (p = .0039). The average reduction in limb circumference and lameness scores over time occurred faster in treated versus control limbs (p < .025). There were no differences in DDFT histopathologic scores between groups. The standing percutaneous DFTS adhesion induction model demonstrated that intrathecal enalapril reduced DFTS adhesion formation, lameness scores, and limb circumference over time. Intrathecal enalapril administration may reduce morbidity in horses with naturally occurring tendon injuries.

A secure fault detection for digital microfluidic biochips

Abstract Among all the modern technological advances, digital microfluidic biochip has been extending a salient solution to healthcare and bio-laboratories with the pledge of high sensitivity and reconfigurability. Such biochip devices fulfill the requirement of a faster testing kit for the detection of different novel diseases, which is indispensable in the market due to the tremendously disrupted scenario of healthcare systems. To eliminate erroneous testing, the current scope of digital microfluidic biochips is widened as a viable testing method using various bioprotocols with a reduced cost in developing countries. This paper addresses the existing security challenges and operational faults in the identification mechanism of proteins such as severe acute respiratory syndrome coronavirus 2 spike protein in state-of-the-art digital microfluidic biochips. We are the first to propose a safety detection solution along with a fault identification algorithm using an inductive transfer learning model. Experimental results of the proposed model register a threshold accuracy of 98% while applying the own dataset. This work will provide a better security-enabled fault-free safety assurance framework against attack and fault identification with better accuracy in digital microfluidic biochips for the detection of different diseases and many other healthcare diagnoses, without any overhead of completion time for bioprotocols.

Analytical Modeling of Solenoid Inductance in Intermediate-Frequency Electromagnetic Heating System

Analytical Modeling of Solenoid Inductance in Intermediate-Frequency Electromagnetic Heating System

Development of a novel immunocompetent murine tumor model for urothelial carcinoma using in vivo electroporation

A lack of advanced preclinical mouse tumor models impedes the progress in urothelial carcinoma research. We present here a novel fast, robust, reliable, and highly reproducible model for the genetic induction of bladder cancer in immunocompetent mice. Different sets of oncogenic transposons (Cmyc, Kras) and Cre drivers were transfected into the murine bladder wall of two different genetic backgrounds (Trp53fl/fl and BrafV600E, Ptenfl/fl, Ctnnb1exon3-fl/fl). Transfection was carried out using in vivo electroporation of the bladder after surgical exploration and transmural or transurethral intravesical plasmid injection. Up to 100% of animals developed urothelial carcinomas of the bladder. Time to tumor onset ranged from 16 to 97 days with a median of approximately 23 days in the fastest groups. Histological examination identified orthotopic urothelial carcinomas in most cases, in some experimental groups up to 100%. The resulting tumors were highly invasive and often metastatic. Metastases were found in up to 100% of tumor bearing mice per group. Taken together, this study establishes the proof-of-principle that in vivo electroporation can be versatilely employed as a reliable, fast, and robust method for the highly reproducible induction of urothelial carcinomas in the murine bladder wall. This novel murine tumor model could pave the way towards more easily modelling subtype specific urothelial carcinomas in mice.

Construction of a prediction model for induction of labor based on a small sample of clinical indicator data

Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model's generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.

Implementing community policing practices to manage conflict resolution in Galeshewe, South Africa

Community policing has emerged as a vital instrument for combatting crime and enhancing public safety in South Africa. As a result, it has the capacity to go beyond traditional law enforcement functions as a mediator in disputes, fostering improved relationships between the police and the communities where they work. This paper analyses the implementation of community policing strategies by the South African police with the purpose of resolving conflicts. This study aims to address social crime prevention-related concerns through community policing methods in the Galeshewe police area within the Francis Baard policing regions of the Sol Plaatje Municipality, South Africa. The paper examines the tactics that community police employ to enforce the law, avoid social issues, and manage conflict resolution in the communities. A qualitative method and descriptive design were employed. Comprehensive document analysis, semi-structured interviews, and observations were employed as data collection strategies. An inductive reasoning model was used to analysis data. The findings of the study demonstrated that community policing plays an important role in optimizing problem mapping and it increases public knowledge of the importance of upholding security and order in the different police operations that support the community policing program.

A Green's Function Wind Turbine Induction Model That Incorporates Complex Inflow Conditions

ABSTRACTIn this work, we develop a new analytical turbine induction model that can incorporate complex inflow conditions including cases where the wind velocity and temperature profiles can vary as functions of height. This induction model is derived from the linearized Navier–Stokes and leads to a second‐order ODE that can be solved using a Green's function formulation. The corresponding Green's function for several configurations are found including the infinite domain, semi‐infinite domain with ground plane, and a power law velocity inflow profile. The results of this approach are then compared with simulations of the turbine induction field using the AMR‐Wind CFD solver with a uniformly loaded actuator disk model. These comparisons show that the Green's function approach captures the centerline blockage, three‐dimensional blockage flow field, and streamwise velocity slow down, with very good agreement for lower thrust conditions and at larger distances away from rotor disk. The effects of shear on the turbine blockage were also compared using a power law inflow profile, and we show that this approach matches the CFD predictions for the cases considered.

An Overview of Software Sensor Applications in Biosystem Monitoring and Control

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.

Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

Application of the Salp Swarm Algorithm to Optimal Design of Tuned Inductive Choke

The article presents an algorithm and optimization software designed for the optimal configuration of a tuned inductive choke. The optimization software consists of two main parts: an optimization procedure and a mathematical model for the designed electromagnetic devices. A lumped-parameters model of a tuned inductive choke was developed, with the device’s structure described by three design variables. As an optimality criterion, the multi-objective compromise function was adopted. The objective function merges the total inductances of the electromagnetic device under different operation states. The optimized structure was analyzed using the finite element method. The developed lumped-parameters model is characterized by good accuracy and can be successfully applied to optimize tuned inductive chokes for various rated parameters. The optimization procedure was adapted to the tuned inductive choke model by appropriately selecting the characteristic coefficient of the salp swarm algorithm. The reliability of the optimization software was verified through experimental measurements.

A Review of Common Acceleration Models in Accelerated Life Testing

The core of accelerated life testing (ALT) is the development of acceleration models. These models can be classified into three types based on their underlying methodologies: physics-based, empirical inductive, and statistical inference models. This paper primarily examines the acceleration models that are commonly applied in engineering practice and presents the relevant computational formulas to highlight the fundamental research methodologies of ALT. Additionally, the paper underscores that ALT is increasingly recognized as a vital and emerging trend, driven by its role in shortening design cycles, enhancing product reliability, and managing costs.

Gold(I)‐Catalyzed Desymmetrization of Homopropargylic Alcohols via Cycloisomerization: Enantioselective Synthesis of Cyclopentenes Featuring a Quaternary Chiral Center

AbstractCyclopentene rings possessing a chiral quaternary center are important structural motifs found in various natural products. In this work, we disclose expedient and efficient access to this class of synthetically valuable structures via highly enantioselective desymmetrization of prochiral propargylic alcohols. The efficient chirality induction in this asymmetric gold catalysis is achieved via two‐point bindings between a gold catalyst featuring a bifunctional phosphine ligand and the substrate homopropargylic alcohol moiety—an H‐bonding interaction between the substrate HO group and a ligand phosphine oxide moiety and the gold‐alkyne complexation. The propargylic alcohol substrates can be prepared readily via propargylation of enoate and ketone precursors. In addition to monocyclic cyclopentenes, spirocyclic and bicyclic ones are formed with additional neighboring chiral centers of flexible stereochemistry in addition to the quaternary center. This work represents rare gold‐catalyzed highly enantioselective cycloisomerization of 1,5‐enynes. Density functional theory (DFT) calculations support the chirality induction model and suggest that the rate acceleration enabled by the bifunctional ligand can be attributed to a facilitated protodeauration step at the end of the catalysis.

Gold(I)-Catalyzed Desymmetrization of Homopropargylic Alcohols via Cycloisomerization: Enantioselective Synthesis of Cyclopentenes Featuring a Quaternary Chiral Center.

Cyclopentene rings possessing a chiral quaternary center are important structural motifs found in various natural products. In this work, we disclose expedient and efficient access to this class of synthetically valuable structures via highly enantioselective desymmetrization of prochiral propargylic alcohols. The efficient chirality induction in this asymmetric gold catalysis is achieved via two-point bindings between a gold catalyst featuring a bifunctional phosphine ligand and the substrate homopropargylic alcohol moiety-an H-bonding interaction between the substrate HO group and a ligand phosphine oxide moiety and the gold-alkyne complexation. The propargylic alcohol substrates can be prepared readily via propargylation of enoate and ketone precursors. In addition to monocyclic cyclopentenes, spirocyclic and bicyclic ones are formed with additional neighboring chiral centers of flexible stereochemistry in addition to the quaternary center. This work represents rare gold-catalyzed highly enantioselective cycloisomerization of 1,5-enynes. Density functional theory (DFT) calculations support the chirality induction model and suggest that the rate acceleration enabled by the bifunctional ligand can be attributed to a facilitated protodeauration step at the end of the catalysis.

Study on the effect of Lorentz force on the nuclear reactor core melt stratification in electromagnetic cold crucible

To understand the effect of the Lorentz force on core melt stratification during experiments, a multi-physical field coupling model of electromagnetic induction, non-isothermal flow, and two-phase flow is established in this paper. The evolution of the layered core melt in the electromagnetic cold crucible is studied for different relative densities and phase volume fraction ratios between metal and oxide. The calculation results show that the coupling model can accurately describe the structural evolution of core melt. The Lorentz force cannot change the relative positions of the metal and oxide, but it can significantly affect the morphology of the core melt. If the density of the metal is less than that of the oxide, the Lorentz force causes the light metal with a small phase volume fraction to form a hemispherical shape in the top center of the crucible. As the phase volume fraction increases, the Lorentz force become weaker than those of gravity and buoyancy. The light metal is then flat. When the metal density is greater than the oxide density, the Lorentz force causes the heavy metal with a small phase volume fraction to appear as a hemisphere, but causes the heavy metal with a large phase volume fraction to appear as an ellipsoid in the lower part of the crucible.

Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations

Rationale and ObjectivesThe aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions. Materials and MethodsWe analyzed 124 histologically confirmed lesions in 114 patients who underwent breast MR with BI-RADS 4 or 5 assessment. The dataset comprised 10 imaging parameters and one clinical observation. Using the IDTs method (algorithm C5.0) combined with the data balancing method SMOTE (Synthetic Minority Oversampling Technique) and a corresponding new method called LCC (Leveling of Cases per Class), we developed corresponding 3-class classification models (Benign, B3, or Malignant). The training set used for classification model development consists of 112 cases with 11 variables, and the model’s performance was assessed using 10-fold Cross-Validation and Leave One Out methods (utilizing the training set) and the Use Test Set method (testing on an unknown (for the models) dataset of 12 cases with 12 variables). ResultsThis preliminary study demonstrates the potential for IDTs to accurately distinguish between benign, B3 and Malignant lesions based on extracted data from breast MRI exams with a high classification accuracy (88.70%), mean sensitivity of 97.18% and specificity of 98.59% achieved by the optimal classification model, derived from the combination of the IDTs method and the LCC data balancing method. ConclusionThe present study represents a pioneering approach for three-category classification of breast lesions in MRI images using Artificial Intelligence (AI) which can be utilized as second-opinion tool in daily clinical practice by radiologists.

Link Stealing Attacks Against Inductive Graph Neural Networks

A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, including the transductive setting and the inductive setting. In the transductive setting, the trained model can only predict the labels of nodes that were observed at the training time. In the inductive setting, the trained model can be generalized to new nodes/graphs. Due to its flexibility, the inductive setting is the most popular GNN setting at the moment. Previous work has shown that transductive GNNs are vulnerable to a series of privacy attacks. However, a comprehensive privacy analysis of inductive GNN models is still missing. This paper fills the gap by conducting a systematic privacy analysis of inductive GNNs through the lens of link stealing attacks. We propose two types of link stealing attacks, i.e., posterior-only attacks and combined attacks. We define threat models of the posterior-only attacks with respect to node topology and the combined attacks by considering combinations of posteriors, node attributes, and graph features. Extensive evaluation on six real-world datasets demonstrates that inductive GNNs leak rich information that enables link stealing attacks with advantageous properties. Even attacks with no knowledge about graph structures can be effective. We also show that our attacks are robust to different node similarities and different graph features. As a counterpart, we investigate two possible defenses and discover they are ineffective against our attacks, which calls for more effective defenses.

Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

Inductive link prediction (ILP) in knowledge graphs (KGs) aims to predict missing links between entities that were not seen during the training phase. Recent some subgraph-based methods have shown some advancements, but they all overlook the relational semantics between entities during subgraph extraction. To overcome this limitation, we introduce a novel inductive link prediction model named SASILP (Structure and Semantic Inductive Link Prediction), which comprehensively incorporates relational semantics in both subgraph extraction and node initialization processes. The model employs a random walk strategy to calculate the structural scores of neighboring nodes and utilizes an enhanced graph attention network to determine their semantic scores. By integrating both structural and semantic scores, SASILP strategically selects key nodes to form a subgraph. Furthermore, the subgraph is initialized with a node initialization technique that integrates information about neighboring relations. The experiments conducted on benchmark datasets demonstrate that SASILP outperforms state-of-the-art methods on inductive link prediction tasks, and verify the effectiveness of our approach.

Research on parameter deviation modelling of dual LCC wireless power transfer system

Abstract In this paper, parameter deviation modelling analysis is performed based on the dual LCC compensation network of wireless power transfer (WPT) system. Firstly, the mutual inductive coupling model of the dual LCC compensation circuit is derived based on the fundamental wave analysis method; secondly, the parameter deviation is introduced into the circuit model for the errors existing in the inductive and capacitive components of the compensation network circuit of the actual wireless charging system, and the relation between the deviation and the amplitude of the branch currents, the phase angle and the output characteristics of system are obtained through the establishment of derivation model and the resistive deviation correction term is introduced; finally, the parameter deviation is verified through the simulation. The establishment of parameter deviation model increases the accuracy of the whole circuit model and provides the theoretical basis to reduce the negative influence of parameter deviation on transmission characteristics. The value of parameter deviation can be inverted through the phase angle, thus providing a basis for adjusting the resonant state of the compensation circuit.

Three-dimensional modelling of Ganymede’s Chapman–Ferraro magnetic field and its role in subsurface ocean induction

In April 2023, the JUpiter ICy moon Explorer (JUICE) began its journey to orbit Jupiter’s largest and only magnetic moon, Ganymede. Part of the mission’s objectives aim to verify existence of the moon’s subsurface ocean and determine its structure through its induced response to external excitation by periodically varying magnetic field. Known contributions to the excitation are those from Jupiter’s dipole (at synodic period) and quadrupole (at half-synodic period) variations, and Ganymede’s inclined eccentric orbit around Jupiter (at orbital period). We propose that Ganymede’s magnetopause, where the Chapman–Ferraro (C-F) magnetic field arises from local currents, also contributes to subsurface ocean induction. This article introduces the first three-dimensional model of the C-F field and its outputs at Ganymede’s subsurface ocean and larger magnetosphere. The field is shown to be non-uniform - strongest near upstream Ganymede’s subflow region and gradually weakening away from it. Magnetopause asymmetry due to the Jovian guide field results in largely synodic variation of the C-F field, with exceptions near Ganymede’s equator and subflow meridian where asymmetry effects are minimal and the variations are half-synodic. The C-F field amplitude is of general order ∼50 nT, which is significant relative to excitation from the Jovian field. Comparisons to Galileo data and magnetohydrodynamic simulation results suggest the model is useful, therefore the magnetopause effects must be considered in future induction modelling of Ganymede’s subsurface ocean ahead of the JUICE mission.

A PICTURE WORD INDUCTIVE MODEL TO ENHANCING VOCABULARY ACQUISITION AT THE PRIMARY LEVEL

The current research investigates the effect of using the Picture Word Inductive Model (PWIM) Strategy on Vocabulary Acquisition at the Primary Level. The research's null hypotheses have been formulated to be tested in light of the aim. The design of two equivalent groups, namely the experimental and control groups with pre and post-tests, has been adopted. The research sample included 60 female pupils from two primary schools intentionally selected from the center of Nineveh Governorate. They were divided into two groups: the experimental group included 30 female pupils taught according to the PWIM strategy at Al Talai Primary School. The control group included (30) female pupils taught according to the Subscribed method at Al Shaqa'ik Primary School. Both groups were equalized according to a set of variables: pupil's date of birth, pupil's general average of the preceding academic year, pupil's scores in English of the preceding academic year, and parents'. Moreover, the main requirements of this research were prepared, such as determining the teaching materials and preparing the teaching plans as well as; the researcher had prepared an instrument to collect the data based on the dependent variables of this research, namely the vocabulary Acquisition test of a passage followed by five questions. The result reveals a statistically significant difference (0.05) between the experimental and control groups in the Post-Test of Vocabulary Acquisition in favor of The Experimental group. Finally, recommendations and suggestions for further research are presented concerning the necessity of applying the (PWIM) in teaching the English language.