Inadequate Performance Research Articles

Tailoring Network Topology in Mechanically Robust Hydrogels for 3D Printing and Injection.

Tissue engineering and regenerative medicine are confronted with a persistent challenge: the urgent demand for robust, load-bearing, and biocompatible scaffolds that can effectively endure substantial deformation. Given that inadequate mechanical performance is typically rooted in structural deficiencies─specifically, the absence of energy dissipation mechanisms and network uniformity─a crucial step toward solving this problem is generating synthetic approaches that enable exquisite control over network architecture. This work systematically explores structure-property relationships in poly(ethylene glycol)-based hydrogels constructed utilizing thiol-yne chemistry. We systematically vary polymer concentration, constituent molar mass, and cross-linking protocols to understand the impact of architecture on hydrogel mechanical properties. The network architecture was resolved within the molecular model of Rubinstein-Panyukov to obtain the densities of chemical cross-links and entanglements. We employed both nucleophilic and radical pathways, uncovering notable differences in mechanical response, which highlight a remarkable degree of versatility achievable by tuning readily accessible parameters. Our approach yielded hydrogels with good cell viability and remarkably robust tensile and compression profiles. Finally, the hydrogels are shown to be amenable to advanced processing techniques by demonstrating injection- and extrusion-based 3D printing. Tuning the mechanism and network regularity during the cell-compatible formation of hydrogels is an emerging strategy to control the properties and processability of hydrogel biomaterials by making simple and rational design choices.

Supporting sustainability within the Sail Cargo Alliance Ecosystem

ABSTRACT This paper focuses on how transportation and marketing bespoke products as “sail cargo” can promote the common good. The context is the Sail Cargo Alliance’s, UK “Sail Cargo Divisions”, who each promoted trading of sail cargo in their locality. We assess the implementation of this aim in each division on a set of four key sustainable development enabling factors, The results reveal that inadequate performance on this factor set led to the failure, in 2023, of the operating companies for all Divisions except Sail Cargo London and Sail Cargo Kent whose operating companies merged in 2021 thereby gaining the complementary resources that enabled the merged operating Company (Raybel Charters) to bounce forward. We describe a proof-of- value use case initiated within the transformed Division: Sail Cargo Channel in collaboration with Raybel Charters, that can guide the future sustainable development of all the Sail Cargo Alliance’s Divisions for the common good.

How to mine the abnormal information of power transformers: An efficient tool for quantifying the fault characteristics via multi-vibration signals

The new power system puts forward higher requirements for the reliability of power equipment. Mining the intrinsic information of equipment operation from massive data is the key challenge to determining fault early warning. The transformer fault diagnosis through vibration signal analysis has gained increasing prominence. However, the vibration signals produced by power transformers exhibit complex nonlinear and multivariate fluctuations due to the interaction of electromagnetic and mechanical factors. Unfortunately, the traditional fault diagnosis models lack the capability to capture extensive global fault information, resulting in inadequate performance in fault identification. This paper proposes the enhanced hierarchical multivariate multiscale fuzzy entropy (EHMvMFE) as an effective method for quantifying defect information in multivariate transformer signals to overcome the challenge. Firstly, the enhanced hierarchical decomposition method and time series coarse-graining theory are introduced into multivariate fuzzy entropy to develop EHMvMFE, which can comprehensively reflect the feature information of multivariate signals from the full range of frequencies. Then, EHMvMFE is utilized to extract the features via multivariate vibration signals of the transformer. Finally, the extreme learning machine is conducted to achieve effective identification of abnormal conditions. The proposed method is applied to two categories of transformer fault cases, and the diagnostic indicators are at least higher than 99.70 % and 91.82 %, which indicates a strong advantage in comparison with other popular models. The approach could be flexibly extended to the fault diagnosis of other power equipment, which is a potentially effective tool for improving the reliability of power systems.

Research on multi-object detection technology for road scenes based on SDG-YOLOv5

To resolve the challenges of low detection accuracy and inadequate real-time performance in road scene detection, this article introduces the enhanced algorithm SDG-YOLOv5. The algorithm incorporates the SIoU Loss function to accurately predict the angle loss of bounding boxes, ensuring their directionality during regression and improving both regression accuracy and convergence speed. A novel lightweight decoupled heads (DHs) approach is employed to separate the classification and regression tasks, thereby avoiding conflicts between their focus areas. Moreover, the Global Attention Mechanism Group Convolution (GAMGC), a lightweight strategy, is utilized to enhance the network’s capability to process additional contextual information, thereby improving the detection of small targets. Extensive experimental analysis on datasets from Udacity Self Driving Car, BDD100K, and KITTI demonstrates that the proposed algorithm achieves improvements in mAP@.5 of 2.2%, 3.4%, and 1.0% over the original YOLOv5, with a detection speed of 30.3 FPS. These results illustrate that the SDG-YOLOv5 algorithm effectively addresses both detection accuracy and real-time performance in road scene detection.

Microstructural evolution and tensile properties of direct energy deposited Ti-4Al-5Co-0.25Si alloy during heat treatment

Additive manufacturing (AM) stands as an advanced manufacturing process, successfully utilized for the fabrication of high-value titanium components. However, these components have yet to find widespread use in industrial applications due to their inadequate performance when compared to conventionally processed parts. Consequently, there is a pressing demand to develop new alloys and microstructural control strategies to overcome this limitation. Here, we have designed a cost-effective Ti-4Al-5Co-0.25Si alloy for use in laser-based direct energy deposition additive manufacturing. The selection of solute elements has resulted in both solute strengthening and grain refinement. Additionally, the implementation of a heat-treatment schedule has allowed for control over the phase constituents and their stability. The as-fabricated alloy exhibits a prior-β grain size of ∼50 μm, α plate thickness of ∼0.6 μm, and tensile strength of 1145 MPa. Subsequent heat treatment induces the formation of subgrains, discontinuous grain boundary α and metastable β phases, and ultrafine martensitic features ranging from 40 to 100 nm. Moreover, this heat treatment improved the tensile strength of the alloy to 1500 MPa while maintaining an elongation of ∼5%. Importantly, the proposed hierarchical structured alloy offers an economical alternative, outperforming commercially available titanium alloys. This study presents a new direction for further research into alloy and microstructural design, aiming at the development of low-cost, high-performance titanium alloys suitable for additive manufacturing.

Learning Domain Specific Sub-layer Latent Variable for Multi-Domain Adaptation Neural Machine Translation

Domain adaptation proves to be an effective solution for addressing inadequate translation performance within specific domains. However, the straightforward approach of mixing data from multiple domains to obtain the multi-domain neural machine translation (NMT) model can give rise to the parameter interference between domains problem, resulting in a degradation of overall performance. To address this, we introduce a multi-domain adaptive NMT method aimed at learning domain specific sub-layer latent variable and employ the Gumbel-Softmax reparameterization technique to concurrently train both model parameters and domain specific sub-layer latent variable. This approach facilitates the learning of private domain-specific knowledge while sharing common domain-invariant knowledge, effectively mitigating the parameter interference problem. The experimental results show that our proposed method significantly improved by up to 7.68 and 3.71 BLEU compared with the baseline model in English-German and Chinese-English public multi-domain datasets, respectively.

Performance of Random Oversampling, Random Undersampling, and SMOTE-NC Methods in Handling Imbalanced Class in Classification Models

One common challenge in classification modeling is the existence of imbalanced classes within the data. If the analysis continues with imbalanced classes, it is probable that the result will demonstrate inadequate performance when forecasting new data. Various approaches exist to rectify this class imbalance issue, such as random oversampling, random undersampling, and the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC). Each of these methods encompasses distinct techniques aimed at achieving balanced class distribution within the dataset. Comparison of classification performance on imbalanced classes handled by these three methods has never been carried out in previous research. Therefore, this study undertakes an evaluation of classification models (specifically Gradient Boosting, Random Forest, and Extremely Randomized Trees) in the context of imbalanced class data. The results of this research show that the random undersampling method used to balance the class distribution has the best performance on two classification models (Random Forest and Gradient Boosted Tree).

Диагностика сигналов ЭКГ на основе цифровой обработки и оптимизированной модели трансформера

Heartbeat disorders are considered one of the main maladies that cause mortality. Therefore, their precocious diagnosis via ECG signal is critical for introducing prompt therapy. The advanced automatic classification of ECG signals has the potential to save cardiologists a tremendous amount of time while simultaneously decreasing the chance of misdiagnosis. The dilemma of massive parameters is troubling the current methods of ECG signal classification. Most recent methods exhibit inadequate performance for diagnosing ECG signals in the inter-patient mode. In an attempt to deal with the above limitations, this study offers an innovative, efficient, and end-to-end model. The suggested model uses the optimized transformer framework to classify the heartbeats according to the "Association for the Advancement of Medical Instrumentation, AAMI," and obeys the inter-patient setting. We constructed an efficient architecture called the optimized network to substitute the Self Attention Unit (SAU) in the encoder part of the transformer model. The suggested model, which includes an optimized network, outperforms the SAU-based transformer model and requires fewer computations. A robust embedding architecture based on a Convolutional Neural Network (CNN) with a Squeeze and Excitation (SE) network-based attention scheme that has been used for weighting the Local Heartbeat Shape Pattern (LHSP) features is presented. The introduced model exceeds the state-of-the-art. An extensive test has been done to compare the achievements of the suggested model with those of the cardiologists. The results proved the closeness of their performances.

PCNP: A RoCEv2 congestion control using precise CNP

With the development of data centers, the traditional TCP/IP network stacks have become inadequate in addressing the high bandwidth and low latency demands of diverse upper-layer applications. Some cloud providers deploy RoCEv2 (RDMA over Converged Ethernet v2) technology to accelerate network transmission in their data centers, and the reliable network transmission in RoCEv2 relies on congestion control protocols. Currently, traditional congestion control protocols primarily employ heuristic rate adjustment strategies, which often exhibit inadequate performance in terms of convergence, stability, and fairness. Furthermore, the latest congestion control protocols rely on the support of costly switch hardware, which hinders the incremental expansion of data centers. This paper proposes PCNP, a precise congestion control protocol, it extends standard CNP (Congestion Notification Packet) and introduces a bounded rate adjustment strategy that leverages receiver information to achieve precise rate adjustment within existing data centers. Simulation results demonstrate that PCNP effectively decreases the rate fluctuation during the convergence process and enhances fairness. In comparison to DCQCN, HPCC and PCN, PCNP reduces the standard deviation of flow completion time under the incast traffic by 90%, 91% and 91%, respectively. Additionally, it can reduce the tail flow completion time by up to 83%, 40% and 46%.

Dual-parameter combined measurement sensor based on tri-metal structure

Simultaneous measurement of strain and temperature in vibrating environments has always been a key technical issue in the field of structural health monitoring. To address the bottleneck of inadequate dual-parameter measurement performance in existing Fiber Bragg Grating (FBG) strain and temperature sensors, a new type of fiber optic grating sensor with anti-vibration capabilities for the measurement of both strain and temperature is proposed. The sensor utilizes a flexible hinge structure made of 304 steel with high elastic modulus for strain sensitization and a bimetallic structure composed of 7075 aluminum and 4 J36 Invar alloy for temperature sensitization, integrating both types of sensors. Theoretical analysis of the sensor was conducted, followed by the fabrication of a prototype and the establishment of a testing system for performance evaluation. The results show that the designed dual-parameter FBG sensor has a strain sensitivity of approximately 3.06 pm/µε, about 2.62 times that of a bare FBG, and a temperature sensitivity of about 50.2 pm/℃, roughly 4.97 times that of a bare FBG. In anti-vibration performance tests between 5–100 Hz, the wavelength drift of the strain and temperature sections of the FBG in the sensor were both within 1 pm, meeting the requirements for simultaneous measurement of strain and temperature in vibrating environments. These findings provide a reference for the development of similar sensors and further improvement of the measurement performance of FBG strain and temperature sensors.

Impact of virtual problem-based learning of cardiopulmonary resuscitation on fourth-year nursing students’ satisfaction and performance: a quasi-experimental study

BackgroundRegarding competency of nursing students in cardiopulmonary resuscitation (CPR), nursing students frequently exhibit inadequate performance and low satisfaction levels regarding CPR training methods. The problem-based learning (PBL) method, characterized by a constructivist approach, has been underutilized for CPR training, particularly in a virtual format. Hence, this study aims to assess the influence of virtual problem-based learning in cardiopulmonary resuscitation on the satisfaction and performance of fourth-year nursing students.MethodsThis quasi-experimental study, conducted in 2022, involved 80 final-year nursing students from Hamadan University of Medical Sciences, Iran. The participants were randomly assigned to either the experimental group (N = 40) or the control group (N = 40). The experimental group was further divided into six smaller groups on WhatsApp. Both groups initially received routine training sessions, after which the experimental group engaged in four problem-based learning sessions across three different scenarios. Data collection included demographic information, a teaching satisfaction questionnaire, and cardiopulmonary resuscitation checklists administered immediately and one month after the intervention.ResultsThe study was initiated and concluded with 80 participants. The study commenced with no significant disparity in the mean scores of cardiopulmonary resuscitation performance, encompassing chest compressions (P = 0.451) and airway management (P = 0.378), as well as teaching satisfaction (p = 0.115) among the nursing students between the experimental and control groups. However, subsequent to the intervention, both immediately and one month later, the experimental group displayed notable enhancements in mean scores for cardiopulmonary resuscitation performance, comprising chest compressions (p < 0.001) and airway management (p < 0.001), as well as teaching satisfaction (p < 0.001) compared to the control group.ConclusionBased on the study’s findings, it is recommended that nursing educators implement this approach in their teaching practices.

Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models

Latin America represents a growing financial market. The performance of its private sector corporations is critical, as inadequate performance and financial distress can lead to significant losses for many stakeholders. This study assesses the efficacy of Logistic Regression (LR) and Random Forest (RF) techniques in predicting corporate distress up to three years in advance. Additionally, we discuss relevant indicators and compare our findings in two different scenarios (pre versus pandemic period). The results indicate that RF outperforms LR in terms of predictive power and error levels. The most effective predictors remained consistent over the 20-year period but varied between the two models. Importantly, the performance levels remained unaffected by the COVID-19 pandemic.

Visual-guided scene-aware audio generation method based on hierarchical feature codec and rendering decision

Visually guided spatial sound generation (VGSSG) is a well-suited multimodal learning method for dealing with recorded videos. However, existing methods are difficult to be directly applied to spatial sound generation for movie clips. This is mainly due to (1) the existence of Cinematic Audiovisual Language (CAL) in movies, which makes it difficult to construct spatial sound mapping models directly through data-driven based methods. (2) The problem of the inadequate model performance, which is caused by the excessive heterogeneous gap between audiovisual modal information. To solve the aforementioned problems, we propose a VGSSG method based on CAL decision-making and hierarchical feature coding and decoding, which effectively accomplishes spatial sound generation based on the CAL of movies. Specifically, to solve the problem of CAL modeling, a multimodal information-guided movie audio rendering decision maker is established, which can decide the rendering strategy based on the CAL of the current clip. To narrow the heterogeneous gap that hinders the fusion between audiovisual modal data, we propose a codec structure based on hierarchical fusion of audiovisual features and full-scale skip-connections, which improves the efficiency of the comprehensive utilization of audiovisual modal data, and demonstrates the effectiveness of adopting shallow features in VGSSG task. We integrate both 2-channel and 6-channel spatial audio generation into a unified framework. In addition, we establish a movie audiovisual bimodal dataset with hand-crafted CAL annotations. Experimentally, we demonstrate that compared with the existing methods, our method has higher performance in terms of reducing generation distortion.

A novel hybrid ensemble model for mineral prospectivity prediction: A case study in the Malipo W-Sn mineral district, Yunnan Province, China

The utilization of machine learning and data-driven approaches for mineral prospectivity modeling has become increasingly important in supporting regional mineral exploration. However, supervised learning methods have encountered several common challenges, such as overfitting and underfitting, in the process of mineral prospectivity modeling, particularly due to imbalanced sample sizes used for model training. This imbalance could limit the generalization capability of models and result in inadequate predictive performance of individual models. The present study introduced a novel hybrid ensemble model (HEM) that integrates various learner methods, including generative adversarial network (GAN) and transfer learning techniques, to effectively address the issues above. These intelligent prediction techniques were applied to analyze the geological, geochemical, and geophysical dataset in order to delineate potential areas for W-Sn mineralization in Malipo County, Yunnan Province, China. Meanwhile, some well-established supervised learning methods such as Convolutional Neural Networks (CNN), Boosting-based model (e.g., LightGBM), and Bagging-based models, which demonstrated exceptional performance in dealing with classification issues were also integrated into the HEM. The weights for integrating these models were determined automatically using the equidistant-sampling technique. The superior performance of the HEM in identifying known deposits made it an efficient tool for mineral prediction mapping. Additionally, this model also offered high flexibility and scalability by allowing adjustments to the types and quantities of base learners based on data characteristics.

Cubature particle filtering fusion with descent gradient and maximum correntropy for non-Gaussian noise

To address the challenges posed by known heavy-tailed non-Gaussian noise characteristics in multi-sensor fusion, such as complex modeling, reduced estimation accuracy, and inadequate real-time performance, a non-Gaussian cubature particle filter sequential fusion method based on Gradient Descent (GD) and Maximum Correntropy Criterion (MCC) is proposed. Firstly, the proposed approach employs the gradient descent technique to minimize the Pseudo-Huber loss function. This optimization aims to approximate a known heavy-tailed non-Gaussian distribution to a Gaussian distribution, thereby reducing the complexity associated with modeling non-Gaussian noise. Furthermore, by incorporating the MCC into the Cubature Particle Filtering (CPF) process, we achieve real-time optimal estimation of the state, leading to a further improvement in the accuracy of the filter. Finally, a sequential fusion approach is employed for multi-sensor fusion, ensuring real-time filtering and fusion of measurement values. The simulation results demonstrate that in the multi-sensor fusion problem under non-Gaussian noise, the proposed fusion method in this paper can effectively reduce the algorithm’s time complexity while ensuring accuracy.

Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.

Experimental Study on Optimizing Grouting Material Ratio for Rebar Sleeve Connections

In response to the inadequate performance reliability of existing prefabricated sleeve grouting materials, this study used an orthogonal experimental design method to prepare 16 sets of grouting materials based on C52.5 cement and ordinary river sand, with different water-cement ratios and varying amounts of additives including silica fume, defoamers, water reducers, and expansive agents. The effects of silica fume, defoamers, water reducers, expansive agent dosage, and water-cement ratio on the compressive strength, vertical expansion rate, and fluidity of the grouting material were studied. Based on the experimental results and comprehensive analysis, a formulation for high-performance grouting material with high reliability was determined, providing a greater safety margin for practical engineering applications.

Progressive collapse analysis of the corner strut subsystem in a propped excavation with inadequate system safety performance

Frequent collapse accidents in propped excavations stem from inadequate attention to the system safety performance of the retaining structures. This paper examines a propped excavation accident case, where the corner section of the retaining system, with two levels of struts failed, contrasting with the intact section with only one level of strut. The failure causes and the performances of different excavation designs were analyzed and compared by two-dimensional (2D) and three-dimensional (3D) numerical simulations. The actual retaining system design incorporated two levels of struts in the corner section to protect the building outside, presenting a disparity in strut elevation between the corner sections and those with only one level of strut. Despite the 2D analysis deeming the design safe, the 3D analysis reveals a high risk of structural failure due to significant bending moments in the capping beam between different sections. The absence of axial support from the fractured capping beam resulted in the overturning of the corner strut subsystem of the retaining structures into the excavation. This study reveals the progressive collapse mechanism of the corner strut subsystem and highlights the necessity of 3D analysis for excavation design to ensure the system safety performance of the retaining structures. Ensuring the integrity of retaining structures, particularly the connecting structural components between different sections, and preventing local structural failures to enhance system safety performance are critical in complex excavation designs.

Artificial intelligence performance in clinical neurology queries: the ChatGPT model

ABSTRACT Introduction The use of artificial intelligence technology is progressively expanding and advancing in the health and biomedical literature. Since its launch, ChatGPT has rapidly gained popularity and become one of the fastest-growing artificial intelligence applications in history. This study evaluated the accuracy and comprehensiveness of ChatGPT-generated responses to medical queries in clinical neurology. Methods We directed 216 questions from different subspecialties to ChatGPT. The questions were classified into three categories: multiple-choice, descriptive, and binary (yes/no answers). Each question in all categories was subjectively rated as easy, medium, or hard according to its difficulty level. Questions that also tested for intuitive clinical thinking and reasoning ability were evaluated in a separate category. Results ChatGPT correctly answered 141 questions (65.3%). No significant difference was detected in the accuracy and comprehensiveness scale scores or correct answer rates in comparisons made according to the question style or difficulty level. However, a comparative analysis assessing question characteristics revealed significantly lower accuracy and comprehensiveness scale scores and correct answer rates for questions based on interpretations that required critical thinking (p = 0.007, 0.007, and 0.001, respectively). Conclusion ChatGPT had a moderate overall performance in clinical neurology and demonstrated inadequate performance in answering questions that required interpretation and critical thinking. It also displayed limited performance in specific subspecialties. It is essential to acknowledge the limitations of artificial intelligence and diligently verify medical information produced by such models using reliable sources.

Convolutional Channel-Wise Competitive Learning for the Forward-Forward Algorithm

The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. However, its current formulation exhibits limitations such as the generation of negative data, slower convergence, and inadequate performance on complex tasks. In this paper we take the main ideas of FF and improve them by leveraging channel-wise competitive learning in the context of convolutional neural networks for image classification tasks. A layer-wise loss function is introduced that promotes competitive learning and eliminates the need for negative data construction. To enhance both the learning of compositional features and feature space partitioning, a channel-wise feature separator and extractor block is proposed that complements the competitive learning process. Our method outperforms recent FF-based models on image classification tasks, achieving testing errors of 0.58%, 7.69%, 21.89%, and 48.77% on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 respectively. Our approach bridges the performance gap between FF learning and BP methods, indicating the potential of our proposed approach to learn useful representations in a layer-wise modular fashion, enabling more efficient and flexible learning. Our source code and supplementary material are available at https://github.com/andreaspapac/CwComp.