Efficient Technique Research Articles

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.

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.

Four-Layer Wound Closure Technique with Barbed Sutures for Inframammary Fold in Mastopexy Augmentation.

The inframammary fold (IMF) is an important landmark in breast aesthetic surgery. In augmentation mastopexy procedures, secure and accurate placement of the IMF is essential to aesthetic outcomes and to allow the new IMF to heal in the correct position without displacement. The authors present a simple and efficient four-layer wound closure technique using barbed sutures for closure of the repositioned IMF in augmentation mastopexy procedures. This method was previously described by the first author for reset of the IMF in breast augmentation surgery and has been adapted to the longer IMF incision in augmentation mastopexy procedures. A retrospective review was undertaken of 335 patients who underwent bilateral breast augmentation mastopexy procedures with a Wise pattern technique at a single unit. The newly reset IMF was closed using barbed sutures and a four-layer closure technique. There were no cases of complications related to wound healing or wound dehiscence. One patient required explantation for an infected implant. The four-layer wound closure technique with barbed sutures provides a quick and efficient method for accurate closure of the newly reset IMF, with positive outcomes related to wound healing. Secure and accurate placement of the inframammary fold is crucial in augmentation mastopexy procedures. The use of a four-layer wound closure technique with barbed sutures provides an efficient method for accurate closure of the newly reset inframammary fold. This method was previously described by the first author for reset of the IMF in breast augmentation surgery and has been adapted to the longer IMF incision in augmentation mastopexy procedures. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Performance analysis of distillate quality in solar still using culinary discharge

ABSTRACT The scarcity of clean water is a pressing global problem resulting from limited freshwater resources and substantial sewage discharge. It is essential to develop cost-effective and straightforward methods for converting wastewater into potable water to ensure the long-term sustainability of the human population. In this study, we introduce a simple and efficient technique that combines a single slope solar still (SSSS) with a sand bed filtration system. We evaluated the effectiveness of this technique by examining various water quality parameters, including pH, total dissolved solids, electrical conductivity, turbidity, alkalinity, hardness, biochemical oxygen demand, chemical oxygen demand, phosphate, nitrate, and E. coli, for kitchen wastewater (KWW), settled kitchen wastewater (SKWW), and borewell water (BW). Our results showed that the SSSS process successfully removed impurities and bacteria via solar evaporation, resulting in significant improvements in all water quality parameters. The SKWW demonstrated the highest efficiency of 40%, with a water depth of 1 cm and a daily freshwater yield of 3.1 Lm-2. The SSSS process offers several advantages over conventional wastewater treatment methods, including high performance, ease of use, portability, and low cost, making it a promising solution for obtaining clean water in remote areas.

Simulation and FPGA based Implementation of PI and Fuzzy Controllers for Cascaded Asymmetric Multilevel Inverter

The main objective of this work is to improve the performance of Multilevel Inverter by using the novel LS-PD- PWM and controlling technique instead of the existing approach. The proposed work focus on closed loop controller such as existing PI and Fuzzy Logic Controller for resistive load with Multilevel Inverter. The proposed Asymmetric fifteen level Inverter offers less THD and high frequency than the Asymmetric nine level inverter. Asymmetric multilevel inverter is used to reduce the switching losses and THD. In this work is the generation and implementation of Pulse Width Modulation (PWM) techniques using Field Programmable Gate Array (FPGA) for Multilevel Inverter. PWM can be generated via Microprocessors, Microcontroller, FPGA and DSP Processors. Higher density Programmable Logic Devices (PLD) such as FPGAs can be used to amalgamate large amounts of logic into a single Integrated. Pulse width modulation (PWM) is generally used in power converter control. To generate LS-PD- PWM by using FPGA, this occupies less memory capacity and accurate than conventional one. An efficient PWM generation technique is performed using FPGA for lower frequency applications. Simulation and synthesis is done by ModelSim6.3c and Xilinx10.1. Further, the Implementation is carried out by FPGA Spartan3 Xc3s50. Key Word: PWM, FPGA, PLD (Programmable Logic Devices), LS-PD-PWM

Postharvest Chemical Treatment of Physiologically Induced Stem End Blockage Improves Vase Life and Water Relation of Cut Flowers

Wound-induced xylem occlusion significantly affects the vase life of cut flowers, as oxidative stress and the polymerization of phenolic compounds lead to the deposition of phenolic compounds/secondary metabolites in the stem ends of cut flowers to heal open tissues of freshly cut stems and prevent microbial invasion. However, this deposition causes blockage of vessels, reduced water uptake, and shortened vase life. The physiological plugging of vessels is linked with various oxidative enzymes’ (PAL, PPOs, LACs, and COs) actions taken to increase the synthesis of different compounds, e.g., lignin, suberin, tyloses, gel, and latex, in wounded areas. The use of chemical preservatives/enzyme inhibitors is one of the safest and most efficient techniques employed to minimize vascular blockage and inhibit phenolic compounds deposition and exudation. This review mainly discusses the types of oxidative enzymes, their pathways and biochemistry along with production of secondary metabolites, their biosynthesis, and their modes of action involved in vascular blockage. It also summarizes the different types of preservatives used in postharvest treatments to improve relative water uptake, flower fresh weight, petal protein content, and hydraulic conductance and prolong the vase life of cut flowers during storage. It is hoped that this elaborate study will help researchers in designing new studies concerning occlusion caused by the accumulation of phenolic compounds in vessels.

Topological Invariants of Hexagonal Cage Network by Using Algebraic Polynomials

In this article, we go beyond earlier limits in the analysis by using algebraic polynomials to compute the topological indices for hexagonal cage networks. We investigate hexagonal cage networks of different orders and copies by introducing M-Polynomials and Forgotten polynomials, and we derive novel closed formulae and conclusions for a broad range of topological indices. We also create an efficient technique to compute , a pivotal polynomial, as part of our work. In addition to computing, we apply algebraic polynomials to the analysis of these indices, providing more insights into their structural significance in hexagonal cage networks. This work illuminates the algebraic underpinnings of these intricate networks and broadens the scope of topological index computation, with implications for a variety of scientific domains. These polynomials allow us to calculate several features of the network, such as the first and second Zagreb, modified Zagreb, General Randić, inverse General Randić, harmonic, symmetric division, inverse sum, and so on. There are now new closed formulae and outcomes available. Additionally, we offer a technique for calculating the polynomial . We also use algebraic polynomials to analyses all of the aforementioned indices.

The application of laser‑induced fluorescence in oil spill detection.

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

SCT: Summary Caption Technique for Retrieving Relevant Images in Alignment with Multimodal Abstractive Summary

This work proposes an efficient Summary Caption Technique that considers the multimodal summary and image captions as input to retrieve the correspondence images from the captions that are highly influential to the multimodal summary. Matching a multimodal summary with an appropriate image is a challenging task in computer vision and natural language processing. Merging in these fields is tedious, though the research community has steadily focused on cross-modal retrieval. These issues include the visual question-answering, matching queries with the images, and semantic relationship matching between two modalities for retrieving the corresponding image. Relevant works consider questions to match the relationship of visual information and object detection and to match the text with visual information and employing structural-level representation to align the images with the text. However, these techniques are primarily focused on retrieving the images to text or for image captioning. But less effort has been spent on retrieving relevant images for the multimodal summary. Hence, our proposed technique extracts and merge features in the Hybrid Image Text layer and captions in the semantic embeddings with word2vec where the contextual features and semantic relationships are compared and matched with each vector between the modalities, with cosine semantic similarity. In cross-modal retrieval, we achieve top five related images and align the relevant images to the multimodal summary that achieves the highest cosine score among the retrieved images. The model has been trained with seq-to-seq modal with 100 epochs, besides reducing the information loss by the sparse categorical cross entropy. Further, experimenting with the multimodal summarization with multimodal output dataset, in cross-modal retrieval, helps to evaluate the quality of image alignment with an image-precision metric that demonstrate the best results.

Efficiency of Desiccation, Decomposition and Release of Nutrients in the Biomass of Forage Plants of the Genus Brachiaria After Intercropping with Sorghum in Integrated Systems for Soybean Productivity

ABSTRACT The implementation of more sustainable soil management strategies, such as the use of cover crop for no-till system with crop rotation, can help increase nutrient use efficiency and consequently decrease the use of mineral fertilizers for agriculture. In this context, the objective was to evaluate the efficiency of desiccation, decomposition and release of nutrients in the biomass of forage plants of the genus Brachiaria after intercropping with sorghum in integrated systems for soybean productivity. The experiment was conducted in the field in two agricultural seasons in randomized block design with four repetitions. The treatments consisted of biomass obtained after sorghum intercropping with forage plants of the Brachiaria genus (Brachiaria ruziziensis, Brachiaria brizantha cv. Marandu, Brachiaria brizantha cv. Xaraés, Brachiaria brizantha cv. BRS Piatã, Brachiaria brizantha cv. BRS Paiaguás, Brachiaria cv. BRS Ipyporã), sorghum biomass in monoculture, which after cutting for grain production, remained fallow for one year and an additional soybean treatment without soil cover biomass. The results showed that Cropping systems on sorghum and forage biomass resulted in a 25.64% increase in soybean yields compared to soybeans grown without soil cover. The use of tropical forage as a cover crop in integrated systems proved to be an efficient technique for biomass production, in order to minimize the effect of water deficit during soybean development, and nutrient cycling, which resulted in the replacement of mineral potassium fertilization for the no-till soybean system, by making better use of soil nutrients and contributing to greater sustainability.

Approximate Solution of PHI-Four and Allen–Cahn Equations Using Non-Polynomial Spline Technique

The aim of this work is to use an efficient and accurate numerical technique based on non-polynomial spline for the solution of the PHI-Four and Allen–Cahn equations. A recent discovery suggests that the PHI-Four equation focuses on its implications for particle physics and the behavior of scalar fields in the quantum realm. In materials science, ongoing research involves using the Allen–Cahn equation to understand and predict the evolution of microstructures in various materials as well as in biophysics. It depicts pattern formation in biological systems and the dynamics of spatial organization in tissues. To obtain an approximate solution of both equations, this technique uses forward differences for the time and cubic non-polynomial spline function for spatial descretization. The stability of the suggested technique is addressed using the von Neumann technique. Convergence test is carried out theoretically to show the order of convergence of the scheme. Some numerical tests are carried out to confirm accuracy and efficiency in terms of absolute error LR. Convergence rates for different test problems are also computed numerically. Numerical results and simulations obtained are compared with the existing methods.

Efficient single image-based dehazing technique using convolutional neural networks

Efficient single image-based dehazing technique using convolutional neural networks

PVDF piezoelectric sensor based on solution blow spinning fibers for structural stress/strain health monitoring

Strain monitoring is of great significance to the maintenance and safe operation of engineering structures. To address the shortcomings of piezoelectric ceramics such as high inflexibility and brittleness, in this study, polyvinylidene fluoride (PVDF) fibrous films were prepared by a safe and efficient solution blow spinning technique. In the case of similar β-phase content, the preparing efficiency of PVDF fibers by the solution blow spinning method increased by about 8–10 times compared to that of electrospinning method in our previous work. The fibers with average diameter of 0.79 μm had the highest β-phase content, around 83%. A flexible piezoelectric fiber sensor with simple design for structural strain monitoring was prepared based on the PVDF fibers. The sensor responded well to strains as low as 23 μϵ, better than commercial strain gauges, with a high response accuracy of 1.11 mV/μϵ. And the piezoelectric properties of the sensors could charge a 4.7 μF capacitor to 3 V in 40 s, offering the potential for low-cost, self-powered structural health monitoring.