Desired Results Research Articles

Diagnosis of Dry Eye Syndrome During the Preparing Patients for Cataracts Phacoemulsification. Literature Review

The increasing patient demands for quality of life after surgery necessitate optimization of approaches in the field of surgical intervention, reduction of the frequency of refractive errors and correction of tactics of postoperative interventions. Literature data indicate that refractive errors are possible after surgical treatment for cataracts associated with the presence of dry eye syndrome in the preoperative period. Tear film is used as the first refractive medium, and the size of the refractive change at air­tear film interfaces is the most innovative of any interface in any other optical system. Thus, changes of tear film physiology affect overall vision with higher order aberrations and irregular astigmatism occurring. The presented literature review analyzes data from scientific studies aimed at studying risk factors for the development of dry eye syndrome and diseases associated with it when phacoemulsification of cataracts occurs. Considering the fact that, according to various studies, the incidence of dry eye syndrome after cataract surgery ranges from 9.8 to 96.6% in cataract surgery in collaboration with dry eye surgery, adequate approaches within the framework of preoperative preparation are an urgent development. The lack of clear recommendations for effective preoperative screening and respiratory preparation for surgery, unreliable biometric data, and lack of treatment strategy for postoperative dry eye syndrome are indicators of detectors to obtain the desired results. Adequate diagnosis and preparation of ocular surfaces before biometric measurements can reduce refractive errors after surgery.

Satellite Image Cloud Automatic Annotator with Uncertainty Estimation

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations.

THE PARADOXICAL NATURE OF ORGANIZATIONAL CHANGE COMPANY: A THEORETICAL FOUNDATION AND KEY CONCEPTS

Organizational Change Management (OCM) is a crucial and really important process for companies that seek to adapt and succeed in a modern and dynamic world. However, despite the importance of Organizational Change Management and the numerous models and frameworks available, a significant proportion of change initiatives fail to achieve their desirable results. This problem instantly emphasizes the need for fresh ideas and innovative ways to address the complexities of organizational change in the modern world. First of all, this article explores the paradoxical theory of change as an alternative for better understanding and more efficient management of change that will be useful for various organizations. It reviews the theoretical foundations of this approach, rooted in Gestalt therapy and other relevant literature. Apart from that, the publication defines key concepts such as polarities and tensions, explaining how these seemingly contradictory forces are natural and necessary elements of dynamic systems. By embracing these tensions, organizations can foster adaptability, drive innovation, and enhance resilience. Secondly, the article also considers the concepts of the Real and Ideal Self in the context of organizational change, displaying how these two states' tensions can be a source of resistance or motivation. Understanding and managing these tensions is unarguably crucial for utilizing them as sources of energy and creativity, ultimately leading to more organizational transformations that would be more successful. What is more, this article provides practical insights on how to apply the paradoxical theory to some of the real-life situations. This shows the importance of balancing stability and change, autonomy and control, and exploration and exploitation in business corporations. Taking everything into consideration, by adopting these paradoxical components, organizations can create a holistic and more effective approach to change management, achieving lasting success and sustainability in the modern ever-evolving environment, which is vital for modern and successful business.

On the foundations of the maximum entropy principle using Fenchel duality for Shannon and Tsallis entropies

In this work, we address two main objectives. The first one is to provide a rigorous foundation to the maximum entropy principle in statistical physics, by making use of the Fenchel-Rockafellar duality. The second objective is to discuss the well-foundedness of the so-called escort distributions in the context of non-extensive entropy maximization. The duality treatment of maximum entropy confirms the non-rigorous results obtained via the usual variational calculus, however, the use of escort distributions yields undefined behavior when used consistently, and only leads to the desired results when used in an ad-hoc manner.

Innovative Eco-Friendly Concrete Utilizing Coconut Shell Fibers and Coir Pith Ash for Sustainable Development

Concrete is the most commonly used and essential material in the construction industry, and it is also the most widely utilized product globally. The construction industry is a rapidly expanding industry. To improve the efficiency and strength properties of concrete, researchers from all over the world continue to search for supplementary cementitious materials (SCMs) and industrial by-products that can be incorporated as alternative materials. The current study aimed to determine the effects of partially substituting coir pith ash (CPA) for cement in coconut shell concrete, in addition to utilizing steel and coconut fibers. Various percentages of CPA were used to replace cement in the concrete mixes, ranging from 5% to 20% by cement weight. Steel fibers were utilized in this study at volume ratios of 0.25%, 0.5%, 0.75%, and 1.0%, and coconut fibers were utilized at volume ratios of 0.1% to 0.5% with an increment of 0.1% in the concrete to achieve the desired results. Various properties have been examined, such as workability, mechanical, durability, and morphological tests. The addition of coir pith ash to concrete increased its compressive, flexural, and tensile strengths by 10.36%, 8.75%, and 7.7% at 28 days compared to control concrete. The incorporation of coconut fiber and coconut shell in concrete production improves its performance and strength while also preserving natural resources and offering a solution to the problem of disposing of solid waste.

Battle of the strategies: diet versus drug therapy for gout

The best results in combating gout are achieved through a combination of diet and drug therapy. Urate-lowering therapy, which includes febuxostat, has been shown to be more effective and convenient than diet when it comes to achieving and maintaining target uric acid (UA) levels in gout patients. Febuxostat, a xanthine oxidase inhibitor, helps to reduce UA levels in the blood by blocking its formation. This helps prevent the deposition of urate crystals in joints and tissues and reduces the frequency and severity of gout attacks. At the same time, a diet of low purine foods may also have some effect on UA levels. Diet can improve the results of drug treatment by reducing the need for medications and minimizing the risk of side effects. However, without adequate drug therapy, diet will not produce the desired results. Therefore, febuxostat remains the preferred urate-lowering treatment option for gout, especially given its proven efficacy in these patients.

Utilisation of waste glass from solar thermal collectors for the production of polymer concrete

The article discusses the use of waste tempered glass from damaged or decommissioned thermal solar collectors to produce polymer concretes. The waste underwent mechanical recycling to obtain powders of alternative grain sizes: below 2.0 mm and below 0.5 mm. To produce the polymer concrete, ground glass waste served as the fine fraction, and sandstone gravel as the coarse fraction. An epoxy resin acted as the binder for the resulting composite. The research conducted has demonstrated that glass waste from solar collectors can be successfully utilised as a fine fraction in polymer concrete technologies. It was observed that there are no significant differences between the grains at 0.5 mm and 2.0 mm, and both fractions yield the desired results. The resulting polymer concretes are characterised by high mechanical strength, significantly surpassing the properties of typical cement concretes. The compressive strength is at 110.7 MPa, and the flexural strength is at 41.2 MPa. The proposed recycling method allows for the effective management of waste that is difficult to reuse on an industrial scale.

Image quality enhancement using optimized thresholding and two-level diffusion-based denoising filter

ABSTRACT Image quality enhancement includes the improvement of visual clarity, reduction of noise, and preservation of integral details that produce more realistic and desirable results. However, several existing models in image denoising have problems as they destroy fine details, over smooth them, and suffer complexity in computation with various types of noises besides optimizing parameters. Therefore, to address these challenges, a Teaching Learning Aquila Optimization-based Thresholding and Two Level Diffusion based Denoising Filter (TLAO-TTLDBF) model for image enhancement is developed, in which the Optimized Two-Level Diffusion Denoising Filter is adaptive and effective in noise reduction while preserving the significant details of the image. At the noise level is set to 0.5, the developed model achieves impressive PSNR, SDME, and SSIM values of 53.26 dB, 53.5 dB, and 0.72 respectively for random noise in satellite images and values of 52.23 dB, 48.45 dB, and 0.75 for medical images and outperformed other conventional models.

SH-Alb inhibits phenotype remodeling of pro-fibrotic macrophage to attenuate liver fibrosis through SIRT3-SOD2 axis

Albumin has a variety of biological functions, such as immunomodulatory and antioxidant activity, which depends largely on its thiol activity. However, in clinical trials, the treatment of albumin by injection of commercial human serum albumin (HSA) did not achieve the desired results. Here, we constructed reduced modified albumin (SH-Alb) for in vivo and in vitro experiments to investigate the reasons why HSA did not achieve the expected effects. SH-Alb was found to delay the progression of liver fibrosis in mice by alleviating liver inflammation and oxidative stress. Although R-Alb also has some of the above roles, the effect of SH-Alb is more remarkable. Mechanism studies have shown that SH-Alb reduces the release of pro-inflammatory and pro-fibrotic cytokine through the mitogen-activated protein kinase (MAPK) signaling pathway. In addition, SH-Alb deacetylates SOD2, a key enzyme of mitochondrial reactive oxygen species (ROS) production, by promoting the expression of SIRT3, thereby reducing the accumulation of ROS. Finally, macrophages altered by R-Alb or SH-Alb can inhibit the activation of hepatic stellate cells and endothelial cells, further delaying the progression of liver fibrosis. These results indicate that SH-Alb can remodel the phenotype of macrophages, thereby affecting the intrahepatic microenvironment and delaying the process of liver fibrosis. It provides a good foundation for the application of albumin in clinical treatment.

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

AbstractAutomatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully crafted adversarial interference to the transmitted signal. Based on the requirements of efficient computing and edge deployment, a lightweight automatic modulation classification model is proposed. Considering that the lightweight automatic modulation classification model is more susceptible to interference from adversarial attacks and that adversarial training of the lightweight auto‐modulation classification model fails to achieve the desired results, an adversarial attack defense system for the lightweight automatic modulation classification model is further proposed, which can enhance the robustness when subjected to adversarial attacks. The defense method aims to transfer the adversarial robustness from a trained large automatic modulation classification model to a lightweight model through the technique of adversarial robust distillation. The proposed method exhibits better adversarial robustness than current defense techniques in feature fusion based automatic modulation classification models in white box attack scenarios.

Improving prediction accuracy of laser-induced shock wave velocity prediction using neural networks

The velocity of laser-induced shock waves affects the efficiency and efficacy of laser-based processes. The ability to accurately estimate shock wave velocity is critical for optimizing experimental combinations, creating laser-based systems, and assuring desired results. Traditional approaches to predict shock wave velocity involve empirical equations and analytical models based on simplified assumptions. However, these methods often lack accuracy and fail to capture the complex dynamics of laser-matter interactions. To overcome these limitations, we used a combination of an artificial neural network and a genetic algorithm to predict shock wave velocity. In this method, the neural network structure is dynamically designed. The optimization method does this by modifying the neural network's weights and figuring out the network's structure on our behalf. Based on the findings, our suggested technique worked very well; it surpassed other comparison methods by achieving the lowest average errors in terms of RMSE and MAE, which are 4.38 and 3.74, respectively. Moreover, the analysis has shown that our proposed method has a high level of reliability in predicting impulsive wave velocity using a neural network.

A machine learning based prediction of reaction parameters on reaction kinetics for treatment of industrial wastewater

Industrial wastewater is a major cause of pollution of surface water bodies since it is often discharged into these water bodies without adequate treatment. Past attempts at treating industrial wastewater have led to the emergence of multiple approaches for the removal of impurities. These include conventional techniques like biological, physical, and chemical techniques, and recently advanced oxidation processes that have, under certain specific conditions, produced desired results through decomposing contaminating organic compounds into water, carbon dioxide, or less harmful molecules. This study examines the removal of phenol in industrial wastewater using a Photo-Fenton reagent on account of its cost-effectiveness and the possibility of rapid results. The effectiveness of the removal of contaminants was assessed based on the rate of reaction (k1). Data collected through laboratory experiments was then put through various Machine Learning classifiers including Neural Network, CHAID, Generalized Linear Model, Linear Classifier, and Random Trees using IBM SPSS Modeler and models trained. An accuracy of 97.2 % was observed for the Deep Neural Network. Further, it is observed that for a specific level of impurities in industrial water, the pH of solution, intensity of light, and amount of Hydrogen Peroxide (H2O2) are the most important factors in predicting the rate of reaction with concentration of [Fe3+] ions and catalyst TiO2 playing smaller roles.

Analytical solution of the (2+1)-dimensional Zoomeron equation by rational sine-Gordon Method

The current study is about the solution of the Zoomeron equation, one of the important models of mathematics and physics. In this study, the rational Sine-Gordon expansion method (RSGEM) is used to obtain various analytical solutions of the model. Compared to other methods, this method is quite effective and the desired results were obtained. Although there are many analytical solutions to the model used in the literature, we present rational type solutions for the first time with this method. We obtained rational hyperbolic function solutions, and also classified all soliton solutions (kink-like, kink, singular kink, anti-kink, dark, bright). In addition, geometric representations of the solutions in two-, and three-dimensional space and contour shape are made with the Mathematica software program.

The Effect of Using Technology in Learning on Student Learning Motivation: Analysis of the Effectiveness of Inclusive Education Programs

The objective of this study is to assess the efficacy of technology in inclusive education on enhancing student learning outcomes. The research methodology employed is a comprehensive literature review, encompassing data from various sources pertaining to the integration of technology in educational settings. The findings indicate that the incorporation of technology in inclusive classrooms can markedly enhance student learning outcomes, particularly in developing cognitive and critical thinking abilities. Additionally, technology can facilitate the optimization of the learning process in terms of efficiency and effectiveness. It is also important to note that the use of technology in education presents certain challenges and risks that must be managed effectively in order to achieve optimal outcomes. Therefore, when considering the use of technology in education, it is essential to carefully select and integrate technology in a manner that is conducive to achieving desired results.

External debt and economic growth in selected African economies: A heterogeneous dynamic panel data analysis

The nexus between external debt and economic growth of Ghana, Kenya, Morocco, Nigeria, Rwanda, Tunisia and Zimbabwe from 1981 to 2021 was examined in this study. For analysis, the Pooled Mean Group Heterogeneous Dynamic Panel Data Approach and the Toda Yamamoto Granger causality tests technique were deployed. The results indicated that external debt had a negative effect on economic growth. Moreover, debt service exerted a positive impact on economic growth. The findings of the causality tests showed that there is no causal link between external debt and economic growth. Furthermore, no causal link was established between debt service and economic growth. As a result, the study suggests that the capacity of these economies in terms of revenue generation and debt servicing should be reinforced by their governments through the channelling of external debt into long-term productive investments for the realization of positive economic growth. Moreover, the policies on debt servicing in these economies should be sustained since it is yielding the desired results. Furthermore, thoroughly assessed projects of high significance should be the only yardstick for the contraction of foreign loans in these economies. Also, the policy objectives of external debt, debt service and economic growth can be pursued separately from one another in this group of economies in Africa.

The Effectiveness of Storytelling Method in Developing Children's Receptive and Expressive Language Skills in Raudhatul Athfal Nurul Islam Batam City

This research aims to analyze the application of the game of arranging number blocks in developing children's fine motor skills in RA Nurul Islam, Simpang Sungai Duren Village, Jambi Luar Kota District, Muaro Jambi Regency. The research method uses a descriptive qualitative approach. Data collection was carried out by observation, interviews and documentation. Data analysis uses data reduction, data presentation, data verification and conclusion. The results of this research are (1) The learning process through the activity of arranging number blocks has been carried out effectively. The development of children's fine motor skills implemented through the game of arranging number blocks and delivered by educators has achieved the desired results. Children can arrange the number blocks in an orderly layout, sequence the numbers correctly, differentiate between various colours, arrange the numbers from 1 to 20 properly, and can arrange the blocks vertically. (2) Obstacles that arise during the application of playing with number blocks in developing fine motor skills in group B children at RA Nurul Islam can come from internal factors, such as the child's level of interest, mood and level of willingness to learn. (3) Efforts to develop children's fine motor skills at RA Nurul Islam through playing with number blocks, this approach makes it possible to form several groups of children. Children are invited to participate in various activities, such as competitions in arranging numbers from 1 to 20 or working together to complete tasks given by the teacher.

Prototypical English every days’ Phrases Utilized via College

The researcher , in the current study , studies one of the human categorization theories which is known as ‘ prototype theory’ . He sets a questionnaire to fourth class students at Kirkuk university by using the prototype theory . A good number of every days’ expressions is used as data . The students are required to choose one of the tabulated expressions and give the reason of choosing that expression as well . The reasons ( factors) are the taxonomy of the used model . Two analysis methods namely qualitative and quantitative are followed to arrive at the desired results . The aims of the study are ( 1) showing what can influence the typical member . (2) Showing how people are distinct in creating the prototype. Its hypotheses are (1) each individual has his / her own prototype . (2) The social factors like ( age , religion , one’s background knowledge , etc …) have roles in the formation of the prototype . The study is concluded with points like the context has a well-built relation with the process of creating the prototypical category member .

Study on fluorine vacancy defects in yttrium fluoride coating materials

Abstract Rare-earth fluoride coating materials have attracted wide attention as a substitute for ThF4 low-refractive index materials. However, the coating materials and coated films fail to achieve the desired results due to vacancy defects caused by their fluorine-loss decomposition characteristics. To explore the rare earth fluoride coating materials’ fluorine loss influence law, the melt crystallization method is used to simulate the yttrium fluoride (YF3) pre-melting process through the electronic paramagnetic resonance (EPR) obtained at different temperatures under the fluorine vacancy defects change rule. According to the number of unpaired spintrons, the number of spintrons in each vacancy is calculated. The results show that the fluorine vacancy defects increase with the increase of temperature, the formation of YF3 fluorine vacancy defects does not affect the change of crystal structure, and the addition of the additive ammonium hydrogen fluoride (NH4HF2) can reduce the oxygen content of YF3 and effectively inhibit the formation of fluorine vacancy. The effect is gradually obvious with the amount of additives added.

Indonesia’s Forestry and Other Land Use Net Sink 2030: How Preparedness Central Sulawesi to Start the Program?

Abstract The Government of Indonesia has ratified several rules, including Indonesia’s FoLU Net Sink 2030, to support global policies aimed at reducing the climate disasters that have arisen in recent years. Central Sulawesi is just one of 16 provinces where this initiative is being promoted. It’s crucial that Local decision-makers involved in forest management must have a firm grasp of the details involved in rolling out the program. Identifying ‘who plays what role’ in successfully reducing the rate of climate change can help formulate a plan for executing the policy and thereby clarify the understanding among relevant parties. The public service sector, site-level managers, and supporting parties will all be included in this qualitative study of forest regions. The study found that nearly all parties involved in forest management in Central Sulawesi are dedicated to their work. Stakeholders’ perspectives on the best ways to manage forests have converged to slow the spread of climate change, even if such management isn’t perfect. Nine out of twelve operational plans in the FoLU Net Sink initiative can be realized in Central Sulawesi. To achieve the desired results of the implementation strategy, cooperation and dedication from all parties involved are required, along with equitable budget allocation among the various authorities responsible for the forest region.

Optimization and Improvement of Three-phase Asynchronous Motor Soft Starter Control Circuit

Abstract The soft starters of ABB company are used in a pumped storage power station. Sometimes, a phase absent fault was reported during the process of the motor starting and then led to starting failure. To solve this problem, this article first explained the working principle of the device, and then analyzed the design of the control circuit and the characteristics of the components. Combined with the actual inspection results to find the crux of the problem, It proposed optimization and improvement methods for the soft starter control circuit, and achieved the desired results.