Efficient Technique Research Articles

Text Entry Performance and Situation Awareness of a Joint Optical See-Through Head-Mounted Display and Smartphone System.

Optical see-through head-mounted displays (OST HMDs) are a popular output medium for mobile Augmented Reality (AR) applications. To date, they lack efficient text entry techniques. Smartphones are a major text entry medium in mobile contexts but attentional demands can contribute to accidents while typing on the go. Mobile multi-display ecologies, such as combined OST HMD-smartphone systems, promise performance and situation awareness benefits over single-device use. We study the joint performance of text entry on mobile phones with text output on optical see-through head-mounted displays. A series of five experiments with a total of 86 participants indicate that, as of today, the challenges in such a joint interactive system outweigh the potential benefits.

HGBER: Heterogeneous Graph Neural Network With Bidirectional Encoding Representation.

Heterogeneous graphs with multiple types of nodes and link relationships are ubiquitous in many real-world applications. Heterogeneous graph neural networks (HGNNs) as an efficient technique have shown superior capacity of dealing with heterogeneous graphs. Existing HGNNs usually define multiple meta-paths in a heterogeneous graph to capture the composite relations and guide neighbor selection. However, these models only consider the simple relationships (i.e., concatenation or linear superposition) between different meta-paths, ignoring more general or complex relationships. In this article, we propose a novel unsupervised framework termed Heterogeneous Graph neural network with bidirectional encoding representation (HGBER) to learn comprehensive node representations. Specifically, the contrastive forward encoding is firstly performed to extract node representations on a set of meta-specific graphs corresponding to meta-paths. We then introduce the reversed encoding for the degradation process from the final node representations to each single meta-specific node representations. Moreover, to learn structure-preserving node representations, we further utilize a self-training module to discover the optimal node distribution through iterative optimization. Extensive experiments on five open public datasets show that the proposed HGBER model outperforms the state-of-the-art HGNNs baselines by 0.8%-8.4% in terms of accuracy on most datasets in various downstream tasks.

Identification of Cortex Cercis chinensis Decoction Pieces from Different Growth Origins Using Raman Spectroscopy

The complexity of traditional Chinese medicine (TCM) components and the time-consuming of traditional detection methods make it necessary and meaningful to establish rapid and efficient identification techniques. This study explores the potential of Raman spectroscopy, a non-destructive technique offering details of molecular structure, for rapid and accurate identification. Cortex Cercis chinensis (CCC) decoction pieces from diverse geographical origins, Anhui, Sichuan, Zhejiang, and Hubei, were collected and analyzed using Raman spectroscopy at 785 nm, and the Raman characteristic peaks were analyzed. MATLAB software was employed to analyze the similarity between the spectra of CCC decoction pieces, and the original Raman spectral data were transformed into first and second derivative spectra. The results revealed distinct Raman spectral characteristics of carbohydrates and glycosidic bonds (characteristic peaks at 480, 531, 549, 873, 946 and 1086 cm−1). The correlation coefficients of the all the four samples from different origins ranged from 0.9625 to 0.9912, while the coincidence coefficients ranged from 0.9602 to 0.9934. The first and second derivative demonstrated significantly different peaks within specific ranges, 180–200, 280–380, and 680–740 cm−1 for first derivatives, 160–300, 340–400 and 420–480 cm−1 for second derivatives. These obvious differences in first and second derivative spectra of Raman spectra of CCC decoction pieces demonstrated the different growth origins. In conclusion, the study demonstrated the ability of Raman spectroscopy to accurately differentiate CCC decoction pieces from different geographical growth origin. These findings provided a basis for further application of Raman spectra characteristic fingerprints to be used in quality control for rapid identification of the quality and origin of TCM raw materials.

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

Accelerating Globally Optimal Consensus Maximization in Geometric Vision.

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an n-1 dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to four fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, point set registration, and rotation and focal length estimation. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios.

Current Advances in Production and Drying Technologies of Lactoferrin

Lactoferrin, a multifunctional iron-binding glycoprotein present in milk, exhibits a remarkable array of beneficial properties, including antibacterial, anticancer, antiviral, antioxidant, and anti-inflammatory effects. Its potential as a novel food and nutritional supplement has sparked substantial interest among researchers and the industry. This review summarizes the recent research progress on the physiological functions of lactoferrin, and summarizes various technological advances in the separation and purification of lactoferrin. Furthermore, it conducts a detailed comparative analysis of various production and drying methods, providing fresh insights and a robust theoretical framework for advancing lactoferrin-related applications. The development of cost-effective and efficient lactoferrin production techniques holds paramount importance, as it plays a pivotal role in accelerating industrial lactoferrin production and driving growth in the dairy industry.

Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

Collaborative Knowledge Distillation via Multiknowledge Transfer.

Knowledge distillation (KD), as an efficient and effective model compression technique, has received considerable attention in deep learning. The key to its success is about transferring knowledge from a large teacher network to a small student network. However, most existing KD methods consider only one type of knowledge learned from either instance features or relations via a specific distillation strategy, failing to explore the idea of transferring different types of knowledge with different distillation strategies. Moreover, the widely used offline distillation also suffers from a limited learning capacity due to the fixed large-to-small teacher-student architecture. In this article, we devise a collaborative KD via multiknowledge transfer (CKD-MKT) that prompts both self-learning and collaborative learning in a unified framework. Specifically, CKD-MKT utilizes a multiple knowledge transfer framework that assembles self and online distillation strategies to effectively: 1) fuse different kinds of knowledge, which allows multiple students to learn knowledge from both individual instances and instance relations, and 2) guide each other by learning from themselves using collaborative and self-learning. Experiments and ablation studies on six image datasets demonstrate that the proposed CKD-MKT significantly outperforms recent state-of-the-art methods for KD.

Accurate and Efficient Simulation Models for Power Electronics Transformers Based on Series-Connected Active Unity Power Factor Rectifier Modules

This paper contributes an efficient simulation technique for input-series output-parallel multilevel power electronics transformers based on active unity-power-factor rectifiers as building blocks. The technical motivation of the work comes from the fact that the computational burden for the simulation of these technologies tends to become unaffordable as the number of modules grows when using commercial software blocksets. This paper presents a new method to drastically reduce the simulation complexity by simplifying the switching rules: in a three-phase circuit, each phase zero-crossing condition is implemented by a single and simple algebraic rule, so the complex calculations for determining the conduction state of each single diode are removed from the model. The computational enhancement and accuracy of the proposed technique are proved by experimental verification using a single-phase single-cell prototype together with multiple simulations of full-scale systems of different sizes.

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.

A New Global Gas Dynamics Performance Optimization Method for Refrigeration Centrifugal Compressor Stage Based on the Immune Algorithm

Abstract This study proposes a new global gas dynamics optimization method, which was applied to a multi-objective optimization task of centrifugal compressor performance with the aim of determining the improvement probability for achieving high efficiency across a wide operating range. Initially, the original nondominated neighbor immune algorithm (NNIA) was extended to solve constrained multi-objective optimization problems for the first time, which mainly incorporated a procedure for handling inequality and equality constraints without additional parameters. Subsequently, an adaptive topological back-propagation multilayer feed-forward artificial neural network (BP-MLFANN) was trained using the modified NNIA to quickly evaluate the fitness value of the centrifugal compressor stage performance during the optimization. The feasibility of the method was validated using the first stage of a refrigeration centrifugal compressor. The results indicated a substantial enhancement in the stage efficiency of the optimized impeller at the Near-stall, Design, and Near-choke operating points, with increasement of 1.8%, 1.9%, and 4%, respectively, as compared to the baseline stage. The flow field analysis shows that the impact loss at impeller leading edge and flow separation in the passage reduced greatly, the mixing process between the leakage flow and mainstream in the channel is significantly weakened, thus the flow field becomes more uniform after optimization. The new global gas dynamics optimization method provides a reference for the development of efficient and rapid optimization techniques for centrifugal compressor.

The "Hitch-Stitch" for Preventing High Tracheal Stent Migration: Expanding Indications and Extended Experience.

Airway stenting is a standard treatment option for tracheo-bronchial obstruction and other conditions like tracheo-esophageal fistula (TEF). To prevent stent migration, a percutaneous fixation strategy called the "hitch-stitch" was described in 2016 as an efficient technique. We describe an extended experience of the "hitch-stitch" for silicone stents in high tracheal stenting situations and expanding indications to metal stents. Seventy-four percutaneous stitches were placed in 54 patients (36 males and 18 females). The mean age of the patients was 42 (±19) years. The indication for stenting was tracheal stenosis in 63/74 (85%) cases and TEF in 11/74 (15%) cases. Silicone stents were used in 56/74 (75.5%) cases while self-expanding metal stents (SEMS) were used in 18/74 (24.5%) cases. The majority of the stents (62/74, 85%) were high tracheal stents. The "hitch-stitch" as an anti-migration strategy had a high success rate, with distal stent migration in only 2 cases (2.7%) as the stitch had given way-these were repositioned and hitched with double stitches. Minor complications included delayed skin healing (2.8%) and suture site infection (1.4%), which was successfully managed with oral antibiotics. Stitch removal was easy with no complications. In this large series of percutaneous "hitch-stitch" as a stent migration prevention strategy, long-term data shows it is safe and effective in an expanded cohort of silicone stents. Its extended utility is the successful application to metal stents, especially in TEF, where the consequences can be deleterious. It is simple to do, with minimal extra requirements and not associated with any significant complications.

Big data clustering based on spark chaotic improved particle swarm optimization

In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.

Minimally invasive splenectomy is associated with a low perioperative complication rate and short operative time in cats.

To report the perioperative outcome and complications in cats undergoing minimally invasive splenectomy. 17 client-owned cats. Perioperative data were collected from cats undergoing minimally invasive splenectomy from September 2010 to June 2023. Data included history, signalment, preoperative examination and diagnostic testing results, operative technique and time, perioperative outcomes, complications, hospitalization duration, histopathological diagnosis, and outcome. 13 spayed females and 4 neutered males were included, with a median age of 144 months (48 to 196 months). Seven cats underwent total laparoscopic splenectomy (TLS), with 1 cat requiring conversion from TLS to laparoscopic-assisted splenectomy (LAS) due to splenomegaly and an additional cat requiring conversion from TLS to open splenectomy due to uncontrollable splenic capsular hemorrhage. Ten cats underwent LAS, with 1 cat requiring conversion to open splenectomy due to splenomegaly. Additional procedures were performed in 13 cats, with the most common being liver biopsy in 10 cats. Median operative times were 50 minutes (45 to 90 minutes) for TLS and 35 minutes (25 to 80 minutes) for LAS. An intraoperative complication occurred in 1 cat. All but 1 cat survived to discharge. Median follow-up time was 234 days (18 to 1,761 days), with 15 of 16 cats confirmed alive at 30 days and 9 of 16 cats alive at 180 days postoperatively. Minimally invasive splenectomy in this cohort of cats was associated with short operative times and a low perioperative complication rate. Veterinary surgeons may consider minimally invasive splenectomy as an efficient and feasible technique in the treatment of splenomegaly or modestly sized splenic masses for diagnostic and therapeutic purposes in cats.

A Computationally Efficient Technique Using Characteristic Basis Functions and Large Block Sizes

A Computationally Efficient Technique Using Characteristic Basis Functions and Large Block Sizes

Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm

Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications.

Efficient anti-fibrillation of lyocell fabric by low-formaldehyde resin

Lyocell fiber is a green kind of renewable cellulose fiber derived from plants and has many excellent properties. However, in the process of production and application of lyocell fiber, due to swelling and external force, it is easy to produce a serious fibrillation phenomenon. The phenomenon of fibrillation has a very serious impact on the quality and application of lyocell products. To solve this problem, a simple and efficient anti-fibrillation treatment technique was explored in this work. The treatment of lyocell fabric with etherification modified dimethylol dihydroxy-ethylene-urea (ultra-low formaldehyde 2D resin) can enhance the lateral interaction force between the macromolecular chains of lyocell fiber and enhance the anti-fibrillation performance of lyocell fiber. The reaction mechanism between ultra-low formaldehyde 2D resin and lyocell cellulose chain was determined by measuring the fabric mechanical properties, fiber swelling degree, chemical element content, and Fourier transform infrared spectroscopy (FTIR). When the finishing solution contained 100 g/L resin and 20 g/L catalyst with pH 3, after the fabric was cured under 160 ℃ for 60 s, the fibrillation phenomenon of the fiber was greatly reduced. After the treatment of the fabric, its mechanical properties were basically not affected. There was a slight decrease in strength, and the hydrophilic properties were not affected obviously. Compared with the traditional rolling-drying-baking process, this method not only has a shorter and simpler process flow but also has less influence on fabric properties, which guaranteed the smooth progress of the subsequent production and application.

Study of convergence speed of chaotic particle swarm optimization algorithm

AbstractThis study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.

Exploring the Sources of Academic Stress and Adopted Coping Mechanisms among University Students

The primary objective of this research is to find out the origins of academic stress and the coping strategies employed by university going students. This research seeks to the find out the sources and causes of academic stress and explore the coping mechanisms adopted by students. This study utilizes a qualitative methodology. Thirty university students have undergone in-depth interviews (IDI). The data has undergone thematic analysis. Research findings uncover the various sources and underlying causes of academic stress among university students such as test anxiety, overloads of assignment, standards established by peers, lack of library resources, health issues during class tests and term final exams, virtual group discussions and works, unexpected course difficulty, clashes of deadline, difficulty regarding research proposal, lack of clarity in feedback. The study identifies and analyze coping strategies such as planning and prioritizing, study breaks, mindfulness and deep breathing, setting realistic goals healthy lifestyle choices, utilizing university resources use of time management apps and hobbies. The results are anticipated to provide valuable knowledge to both the academic community and student support services, assisting in the creation of focused interventions to tackle academic stressors and improve students' overall well-being. The primary objective is to establish a nurturing educational setting that promotes resilience and provides students with efficient techniques to effectively handle the difficulties encountered in higher education.

'Marfim pattern’ of wood species in the Southern Brazilian timber market: colorimetry and visible spectra

ABSTRACT Evaluating wood with similar characteristics is a challenging task. Taking into consideration the need for an efficient and rapid technique, the use of visible spectrophotometry to discriminate wood species is proposed. This study aimed to differentiate nine wood species with a similar yellowish color using visible spectrophotometry, associated with two classification models, to verify predictions. Color data were obtained from different anatomical sections of the wood, along with reflectance spectra in the range of 360–740 nm. Two machine learning approaches were applied: artificial neural network (ANN) and k-nearest neighbors (k-NN). In general, the mean spectra of the samples shared similar information, as they are materials of lignocellulosic origin. However, in all tests, the models built using the ANN algorithm demonstrated an accuracy in discriminating species exceeding 97%. Notably, when applying ANN to the spectra obtained solely from the longitudinal surface, superior results were obtained. Discriminative patterns can be obtained by visible spectra independent of the anatomical section. The integration of visible spectra and ANN proved to be a suitable approach to recognize “marfim pattern” wood.