Value Of Function Research Articles

Arti Petuah Matiti Suara dalam Upacara Ngusaba Kedasa di Pura Ulun Danu Batur, Kintamani, Bangli

Matiti Suara is one of the traditions to complete Yadnya carried out in Ulun Danu Batur Temple. Matiti Suara Derived from two words, namely Titi which means Jalan Utawi Bridge, Suara means Sound and gets the prefix -ma, so Matiti Suara is a sound or sound that becomes a road or bridge so that you do good in life. Ulun Danu Batur. The formulations of the research problem are: (1) What is the Order of Matiti Suara at Ngusaba Kedasa at Ulun Danu Batur Temple, (2) What are the benefits of Matiti Suara for the people in Batur Village? (3) What is the Meaning of Matiti Suara that is carried out at Ulun Danu Batur Temple. The theory used to research this research is Structural Functional Theory and Value Theory. The data sources obtained are based on observation methods, interviews, literature and documentation. After the data is obtained, then the data is analyzed or processed based on data reduction techniques, data display, and data verification. The data obtained based on this research is the meaning of the first Matiti Suara's Piteket, namely Baas Barak Baas Putih, Ané Daak Dados Sugih, the second, namely Mula Kliki, Mula Biu, Mula Kidik Mupu Liu, the third, namely Sampunang Nganggé Kriya Usaha Dhana Prestasiné, and the third four, namely giving or giving Uang Képeng.

Acidification induce chemical and microbial variation in tea plantation soils and bacterial degradation of the key acidifying phenolic acids.

Camellia sinensis is an important economic plant grown in southern subtropical hilly areas, especially in China, mainly for the production of tea. Soil acidification is a significant cause of the reduction of yield and quality and continuous cropping obstacles in tea plants. Therefore, chemical and microbial properties of tea growing soils were investigated and phenolic acid-degrading bacteria were isolated from a tea plantation. Chemical and ICP-AES investigations showed that the soils tested were acidic, with pH values of 4.05-5.08, and the pH negatively correlated with K (p < 0.01), Al (p < 0.05), Fe and P. Aluminum was the highest (47-584mg/kg) nonessential element. Based on high-throughput sequencing, a total of 34 phyla and 583 genera were identified in tea plantation soils. Proteobacteria and Acidobacteria were the main dominant phyla and the highest abundance of Acidobacteria was found in three soils, with nearly 22% for the genus Gp2. Based on the functional abundance values, general function predicts the highest abundance, while the abundance of amino acids and carbon transport and metabolism were higher in soils with pH less than 5. According to Biolog Eco Plate™ assay, the soil microorganisms utilized amino acids well, followed by polymers and phenolic acids. Three strains with good phenolic acid degradation rates were obtained, and they were identified as Bacillus thuringiensis B1, Bacillus amyloliquefaciens B2 and Bacillus subtilis B3, respectively. The three strains significantly relieved the inhibition of peanut germination and growth by ferulic acid, p-coumaric acid, p-hydroxybenzoic acid, cinnamic acid, and mixed acids. Combination of the three isolates showed reduced relief of the four phenolic acids due to the antagonist of B2 against B1 and B3. The three phenolic acid degradation strains isolated from acidic soils display potential in improving the acidification and imbalance in soils of C. sinensis.

Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems

This paper introduces a novel meta-heuristic optimization algorithm named Clustering Wavelet Opposition-based Marine Predators Algorithm (CWOMPA) to address some limitations present in the well-established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function-based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition-based learning strategy for maintaining population diversity. Compared with some recent meta-heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real-world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F-Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F-Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real-world applications, including feature selection for medical datasets.

Physiological Response and Comprehensive Resistance Evaluation of East African Endemic Aeollanthus repens under Water and Heat Stress

Aeollanthus repens, native to East Africa, thrives in seasonally dry tropical biomes and boasts qualities ideal for both ornamental and ground cover purposes. However, despite its potential, its current resistance levels remain uncertain. Assessing its adaptability could offer valuable insights for its wider adoption and utilization. In this study, researchers employed 3-month-old cuttings of A. repens, subjecting them to six distinct environments by manipulating the temperature (25/20 °C and 35/30 °C) and soil moisture levels (100%, 20%, and 40%). Their leaf physiological and photosynthetic indices were assessed at intervals of 5, 10, and 15 days following exposure to stress. The findings unveiled that exposure to prolonged moisture, elevated temperatures, or a combination of both led to an increase in osmoregulatory substances in the leaves. This increase was accompanied by heightened enzyme activity and an increased intercellular carbon dioxide concentration, followed by a subsequent decline. Additionally, chlorophyll content, net photosynthetic rate, stomatal conductance, and transpiration rate exhibited a decreasing trend over time. Through a comprehensive assessment of stress tolerance utilizing a composite affiliation function value index, the study concluded that A. repens exhibits optimal growth in a certain high-temperature environments and demonstrates substantial resistance to waterlogging, drought, and simultaneous high-temperature stress. However, the resilience of A. repens appears to diminish under the compounded stresses of high temperature and drought.

以文化振兴促进乡村振兴的理论分析与路径探索

To revitalize the countryside, culture must be revitalized. Based on the basic principles of Marxism on the interaction between culture, economy and society, this paper discusses the core position and functional value of cultural revitalization in rural revitalization strategy. Combined with the specific practice of China’s rural revitalization strategy, this paper first analyzes the key position and mechanism of cultural revitalization in the process of rural revitalization, then discusses the realistic predicament of cultural revitalization in the perspective of rural revitalization, and finally, based on the actual national conditions, proposes targeted path exploration and optimization strategies for the challenges and opportunities faced by China's rural cultural revitalization. By inheriting the excellent traditional Chinese culture, the rural civilization is rebuilt; Dredge the channels of talent introduction, build high-quality cultural revitalization team; Increase financial investment, improve the public cultural service system and other measures, in order to provide valuable reference opinions for the current cultural construction and development of rural areas, and effectively promote the high-quality development of rural revitalization.

Looking into the lipid profile of avocado and byproducts: Using lipidomics to explore value-added compounds.

Consumer priorities in healthy diets and lifestyle boosted the demand for nutritious and functional foods as well as plant-based ingredients. Avocado has become a food trend due to its nutritional and functional values, which in turn is increasing its consumption and production worldwide. Avocado edible portion has a high content of lipids, with the pulp and its oil being rich in monounsaturated fatty acids and essential omega-3 and omega-6 polyunsaturated fatty acids (PUFA). These fatty acids are mainly esterified in triacylglycerides, the major lipids in pulp, but also in minor components such as polar lipids (phospholipids and glycolipids). Polar lipids of avocado have been overlooked despite being recently highlighted with functional properties as well. The growth in the industry of avocado products is generating an increased amount of their byproducts, such as seed and peels (nonedible portions), still undervalued. The few studies on avocado byproducts pointed out that they also contain interesting lipids, with seeds particularly rich in polar lipids bearing PUFA, and thus can be reused as a source of add-value phytochemical. Mass spectrometry-based lipidomics approaches appear as an essential tool to unveil the complex lipid signature of avocado and its byproducts, contributing to the recognition of value-added lipids and opening new avenues for their use in novel biotechnological applications. The present review provides an up-to-date overview of the lipid signature from avocado pulp, peel, seed, and its oils.

Jugong as Traditional Fishermen's Formal Aesthetics in Karangtalun Village, Cilacap

Jugong as Traditional Fishermen's Formal Aesthetics in Karangtalun Village, Cilacap

Comparative evaluation of bone marrow and dental pulp mesenchymal stem cells for motor functional recovery in rat sciatic nerve injury.

This study delves into the impact of mesenchymal stem cells derived from bone marrow (BM-MSCs) and those sourced from dental pulp (DP-MSCs) on the recovery of motor function and morphological aspects of the rat's sciatic nerve after crush injuries. The findings highlight that the groups treated with BM-MSCs, DP-MSCs or a combination of both (BM + DP-MSCs) displayed enhanced sciatic functional index values when juxtaposed with the sham group. This points to bettered motor functionalities. A deeper morphological analysis showed that all the groups had retained perineurium structure and fascicular arrangement. Notably, the sham and BM-MSCs groups had very few inconsistencies. All groups showed standard vascular density. Remarkably, the combined treatment group (BM + DP-MSCs) presented diminished oedema and a lower count of inflammatory cells. Through immunohistochemical methods, the presence of S100 expression was noted in the groups that underwent treatment. In summation, the study suggests that both BM-MSCs and DP-MSCs, whether used singly or in combination, can significantly aid in motor function restoration and morphological enhancements. An interesting observation from our research and earlier studies is that stem cells from dental pulp, which are sourced with less discomfort from milk and wisdom teeth, show a heightened propensity to evolve into nerve cells. This is in contrast to the more uncomfortably acquired BM-MSCs.

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Multilingual information retrieval using graph neural networks offers practical applications in English translation by leveraging advanced computational models to enhance the efficiency and accuracy of cross-lingual search and translation tasks. By representing textual data as graphs and utilizing graph neural networks (GNNs), this approach captures intricate relationships between words and phrases across different languages, enabling more effective language understanding and translation. GNNs can learn complex linguistic structures and semantic similarities from multilingual corpora, facilitating the development of more robust translation systems that are capable of handling diverse language pairs and domains. The paper introduces a novel approach termed the Multilingual Ant Bee Optimization Graph Neural Network (MABO-GNN) for addressing optimization, classification, and multilingual translation tasks. MABO-GNN integrates ant bee optimization algorithms with graph neural networks to provide a versatile framework capable of optimizing objective functions, improving classification accuracy iteratively, and facilitating high-quality translations across multiple languages. Through comprehensive experimentation, the efficacy of MABO-GNN is demonstrated across various tasks, languages, and datasets. in optimization experiments, MABO-GNN achieves objective function values of 0.012, 0.015, 0.011, and 0.013 in Experiment 1, Experiment 2, Experiment 3, and Experiment 4, respectively, with convergence times ranging from 90 to 150 seconds. In classification tasks, the model exhibits notable performance improvements over iterations, with BLEU scores reaching 0.84 and METEOR scores reaching 0.78 in the fifth iteration. The translation results showcase BLEU scores of 0.85 for English, 0.82 for French, 0.79 for German, 0.81 for Spanish, and 0.75 for Chinese, indicating the model's proficiency in generating high-quality translations across diverse languages.

Explaining the model and feature dependencies by decomposition of the Shapley value

Shapley values have become one of the go-to methods to explain complex models to end-users. They provide a model agnostic post-hoc explanation with foundations in game theory: what is the worth of a player (in machine learning, a feature value) in the objective function (the output of the complex machine learning model). One downside is that they always require outputs of the model when some features are missing. These are usually computed by taking the expectation over the missing features. This however introduces a non-trivial choice: do we condition on the unknown features or not? In this paper we examine this question and claim that they represent two different explanations which are valid for different end-users: one that explains the model and one that explains the model combined with the feature dependencies in the data. We propose a new algorithmic approach to combine both explanations, removing the burden of choice and enhancing the explanatory power of Shapley values, and show that it achieves intuitive results on simple problems. We apply our method to two real-world datasets and discuss the explanations. Finally, we demonstrate how our method is either equivalent or superior to state-to-of-art Shapley value implementations while simultaneously allowing for increased insight into the model-data structure.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

A Partition of Unity construction of the stabilization function in Nitsche’s method for variational problems

In this paper we develop a partition-of-unity construction of the stabilization function required in Nitsche’s method, which can be seen as a generalization of the element-wise construction that is widely used in finite element methods. This allows for the use of Nitsche’s method within the Partition of Unity Method with a stabilization function that is not simply a constant over the whole boundary. In addition to that, we introduce a patch-aggregation approach designed to avoid arbitrarily large values of the stabilization function and the associated ill-conditioned systems and deteriorated convergence rates. We present numerical results to validate the proposed methods, covering Dirichlet boundary conditions, interface constraints and higher-order problems. These results clearly show that our approach leads to optimal convergence rates.

Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine operation and maintenance (O&amp;M). Incorporating machine learning in the development of condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Density Based Support Vector Machines (DBSVM) and the evolutionary Fourier spectra of vibrations. This method allows for the smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the DBSVM to be timely determined from the Fourier spectrum through the magnitude–frequency and place of the failure occurring in the wind turbine drivetrains. The trend of the failure was constructed with the maximal values of the optimal frequency function for both yesthe cases of the upwind and downwind parts of the gearbox.

A non-boundary-fitted-grid method, based on compact integrated-RBF approximations, for solving differential problems in multiply-connected domains

This paper presents a new non-boundary-fitted-grid numerical technique for solving partial differential equations (PDEs) in multi-hole domains. A multiply-connected domain is converted into a simply-connected domain of rectangular or non-rectangular shape that is discretised using a Cartesian grid. Compact radial basis function (RBF) stencils, which are constructed through integration rather than the conventional differentiation, are used to discretise the field variables. The imposition of inner boundary conditions is conducted by means of body forces that are derived from satisfying the governing equations and prescribed boundary conditions in small subregions. Salient features of the proposed method include: (i) simple pre-processing (Cartesian grid), (ii) high rates of convergence of the solution accuracy with respect to grid refinement achieved with compact integrated-RBF stencils, where both nodal function and derivative values are included in the approximations, (iii) the system matrix kept unchanged for the case of moving holes, and (iv) no interpolation between Lagrange and Euler meshes required. Several linear and nonlinear problems, including rotating-cylinder flows and buoyancy-driven flows in eccentric and concentric annuli, are simulated to verify the proposed technique.

Establishment of a novel efferocytosis potential index predicts prognosis and immunotherapy response in cancers

The biological function and prognostic value of efferocytosis in cancer remains unclear. In this study, we systematically analysed the expression profiles and genetic variations of 50 efferocytosis-related regulator genes in 33 cancer types. Using data from The Cancer Genome Atlas, we established an efferocytosis potential index (EPI) model to represent the efferocytosis level in each cancer type. The relationship between the EPI and prognosis, immune-related molecules, specific pathways, and drug sensitivity was determined. We found that efferocytosis regulator genes were abnormally expressed in cancer tissue, perhaps owing to copy number variations, gene alterations, and DNA methylation. For the most part, the EPI was higher in tumour vs. normal tissues. In most of the 33 cancer types, it positively correlated with cell death- and immune-related pathway enrichment, the tumour microenvironment, immune infiltration, and drug sensitivity. For specific cancers, a high EPI may be a prognostic risk factor and, in patients treated receiving immune checkpoint therapy, a predictor of poor prognosis. Our study reveals the biological functions of efferocytosis-related regulator genes in distinct cancers and highlights the potential of efferocytosis intervention in cancer therapy.

Efficient Optimization of a Support Vector Regression Model with Natural Logarithm of the Hyperbolic Cosine Loss Function for Broader Noise Distribution

While traditional support vector regression (SVR) models rely on loss functions tailored to specific noise distributions, this research explores an alternative approach: ε-ln SVR, which uses a loss function based on the natural logarithm of the hyperbolic cosine function (lncosh). This function exhibits optimality for a broader family of noise distributions known as power-raised hyperbolic secants (PHSs). We derive the dual formulation of the ε-ln SVR model, which reveals a nonsmooth, nonlinear convex optimization problem. To efficiently overcome these complexities, we propose a novel sequential minimal optimization (SMO)-like algorithm with an innovative working set selection (WSS) procedure. This procedure exploits second-order (SO)-like information by minimizing an upper bound on the second-order Taylor polynomial approximation of consecutive loss function values. Experimental results on benchmark datasets demonstrate the effectiveness of both the ε-ln SVR model with its lncosh loss and the proposed SMO-like algorithm with its computationally efficient WSS procedure. This study provides a promising tool for scenarios with different noise distributions, extending beyond the commonly assumed Gaussian to the broader PHS family.

Achieving 24.6 % efficiency in 2D perovskite solar cells: Bandgap tuning and MXene contact optimization in (BDA)(MA)n−1PbnI3n+1 structures

In the midst of the growing emphasis on renewable energy sources, there is an urgent requirement for materials that are devoid of lead and non-toxic, aligning with the demands of the 21st century. In this regard, the perovskite solar cells present themselves as a compelling option, offering cost-effectiveness and non-toxic nature. Nevertheless, these 3D perovskite materials encounter stability challenges. To mitigate these issues, 2D DJ (Dion Jacobson) perovskite material-based solar cells are increasingly utilized in contemporary applications. In this work, (BDA)(MA)n−1PbnI3n+1-based 2D DJ perovskite solar cells (BDPSC) are investigated. Further, it is crucial to use proper contact with BDPSC in order to eliminate the pinhole effect. Therefore, in this study, MXene contacts have been employed with BDPSC to combine the benefits of both, resulting in enhanced stability, superior charge transport, and tunable energy levels without pinhole effects. Further, the bandgap of the BDA active layer is adjusted by varying the number of inorganic layers (n) from 1 to 6 within the (BDA)(MA)n−1PbnI3n+1. Firstly, the thickness of the BDA active layer along with different MXene are varied for all the possible compositions of the (BDA)(MA)n−1PbnI3n+1. The result shows that the optimized thickness for the BDA layer is 1 µm, and the work function value of the left/right contact MXene is 3.56/5.65 eV. Additionally, this work also investigates the influence of grading on the PV performance of BDPSC. The result shows that the reported BDPSC can deliver a maximum open circuit voltage (VOC) is 1.18 V, short circuit current density (JSC) is 24.58 mA/cm2, Fill Factor (FF) is 84.96 % and efficiency is 24.58 %.

Fractional Star Domination Number of Graphs

Objectives: Consider a graph is the connected, undirected graph. This study creates a new parameter named the Fractional Star Domination Number (FRSDN), which is denoted by to calculate the minimum weight with the star of all vertices of and the function values of all edges. Methods: This study evaluates the of some standard graphs and bounds by generalizing the value that is provided by the function value of an edges. Findings: This study evaluated on some standard graphs, such as paths, cycles, and the rooted product of paths and cycles. Finally, we obtain some bounds on for some general graphs, as well as the exactness value yielded by them. For any graph without isolated vertices, we have . Novelty: The new parameter FRSDN of is created by combining the fractional dominating function and the star dominating set. Keywords: Domination Number, Star Domination Number, Fractional Dominating Function, Fractional Domination Number, Fractional Star Domination Number

Raising Series to A Power

Objective. Let be a formal power series and let . In this note, we consider the function . We find that if has a series expansion at , then its coefficients are polynomials in . The coefficients of these polynomials were found to be a weighted composition sum. Methods. The method to arrive at this representation involves logarithmic derivative and exponential representation. Findings. As a consequence of this, new identities involving partition functions and binomial coefficients were obtained. Further, a particular class of Dirichlet series is found to have the form of an exponential function. Consequently, identities involving Riemann zeta function values were obtained. Novelty. The present work generalizes a class of functions considered by D’Arcais. Divisor-sum identities involving partition functions and exponential representation of Dirichlet series of this article were new to the literature. Keywords: Polynomials, Partitions, Dirichlet Series, Divisor­Sum, Power Series

A revised Liu–Storey conjugate gradient parameter for unconstrained optimization problems with applications

ABSTRACT Conjugate Gradient (CG) methods are renowned for their efficiency and low memory requirements when solving optimization problems. However, certain formulations of CG methods that switch between two or more CG parameters often overlook specific values that could have been integrated. Moreover, the demonstration of the sufficient descent property in these methods usually relies on a strategy that assumes the possible exclusion of certain function values. To alleviate this assumption, this article introduces a structured Liu–Storey spectral CG method. This method extends the formulation of the spectral Fletcher conjugate descent method, enabling it to maintain fast convergence and inherit a good restart property. Therefore, the method ensures that the sufficient descent property holds without additional requirements and converges globally via some standard assumptions. Additionally, the method proves useful in solving robotic and image reconstruction models. Finally, it demonstrates robustness when compared to some standard algorithms.