Local Search Engine Research Articles

Solid pseudopapillary neoplasm of the pancreas during pregnancy presenting as gastrointestinal stromal tumor: A case report and review of literature

Solid pseudopapillary neoplasm (SPN) is a rare tumor that can complicate pregnancy. More than its rarity, SPNs are unique neoplasms because of their obscure histogenesis, cytology, immunohistochemical profile, and imaging characteristics. This report describes the case of a 32-year-old gravida 2 para 1 (1001) seen at 24 weeks with an intra-abdominal mass. The patient presented with a long-standing history of abdominal mass with the working impression of gastrointestinal stromal tumor. We employed a multidisciplinary approach to closely monitor tumor growth, ensure maternal and fetal well-being, avert complications, and avoid unnecessary clinical interventions. Histopathological evaluation and immunohistochemistry studies of representative specimens taken at the time of delivery revealed the diagnosis of SPN of the pancreas. Based on a review of local search engine databases, this is the first documented case of SPN complicating pregnancy in the Philippines.

The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems

Recently, the Hypervolume Newton Method (HVN) has been proposed as a fast and precise indicator-based method for solving unconstrained bi-objective optimization problems with objective functions. The HVN is defined on the space of (vectorized) fixed cardinality sets of decision space vectors for a given multi-objective optimization problem (MOP) and seeks to maximize the hypervolume indicator adopting the Newton–Raphson method for deterministic numerical optimization. To extend its scope to non-convex optimization problems, the HVN method was hybridized with a multi-objective evolutionary algorithm (MOEA), which resulted in a competitive solver for continuous unconstrained bi-objective optimization problems. In this paper, we extend the HVN to constrained MOPs with in principle any number of objectives. Similar to the original variant, the first- and second-order derivatives of the involved functions have to be given either analytically or numerically. We demonstrate the applicability of the extended HVN on a set of challenging benchmark problems and show that the new method can be readily applied to solve equality constraints with high precision and to some extent also inequalities. We finally use HVN as a local search engine within an MOEA and show the benefit of this hybrid method on several benchmark problems.

A multi-strategy improved slime mould algorithm for global optimization and engineering design problems

In the application of metaheuristic algorithms (MAs) to complicated optimization problem solving, it is significant to balance the exploitation and exploration to obtain a good near-optimum solution to the problem. Therefore, in this study, to balance the exploitative and explorative features of conventional MAs, a multi-strategy improved slime mould algorithm called MSMA is introduced. In MSMA, a new search equation is developed to achieve a tradeoff between exploitation and exploration. Then the dynamic random search technique is utilized as a local search engine to enhance the search efficiency of the algorithm. Finally, the adaptive mutation probability is designed to avoid premature convergence. MSMA is evaluated using 28 benchmark functions and several practical engineering issues such as welded beam design, pressure vessel design, tension/compression spring design, and UAV path planning. The simulation results based on 30 independent runs demonstrate that it is more efficient and robust than other state-of-the-art techniques from the literature according to the selected performance metrics such as mean values and standard deviations. The source code of MSMA is publicly available at https://github.com/denglingyun123/Multi-strategy-improved-slime-mould-algorithm.

A variable neighborhood search-based algorithm with adaptive local search for the Vehicle Routing Problem with Time Windows and multi-depots aiming for vehicle fleet reduction

This article addresses the Multi-Depot Vehicle Routing Problem with Time Windows with the minimization of the number of used vehicles, denominated as MDVRPTW*. This problem is a variant of the classical MDVRPTW, which only minimizes the total traveled distance. We developed an algorithm named Smart General Variable Neighborhood Search with Adaptive Local Search (SGVNSALS) to solve this problem, and, for comparison purposes, we also implemented a Smart General Variable Neighborhood Search (SGVNS) and a General Variable Neighborhood Search (GVNS) algorithms. The SGVNSALS algorithm alternates the local search engine between two different strategies. In the first strategy, the Randomized Variable Neighborhood Descent method (RVND) performs the local search, and, when applying this strategy, most successful neighborhoods receive a higher score. In the second strategy, the local search method is applied only in a single neighborhood, chosen by a roulette method. Thus, the application of the first local search strategy serves as a learning method for applying the second strategy. To test these algorithms, we use benchmark instances from MDVRPTW involving up to 960 customers, 12 depots, and 120 vehicles. The results show SGVNSALS performance surpassed both SGVNS and GVNS concerning the number of used vehicles and covered distance. As there are no algorithms in the literature dealing with MDVRPTW*, we compared the results from SGVNSALS with those of the best-known solutions concerning these instances for MDVRPTW, where the objective is only to minimize the total distance covered. The results showed that the proposed algorithm reduced the vehicle fleet by 91.18% of the evaluated instances, and the fleet size achieved an average reduction of up to 23.32%. However, there was an average increase of up to 31.48% in total distance traveled in these instances. Finally, the article evaluated the contribution of each neighborhood to the local search and shaking operations of the algorithm, allowing the identification of the neighborhoods that most contribute to a better exploration of the solution space of the problem.

Identification of functional tumor necrosis factor-alpha promoter variants associated with Helicobacter pylori infection in the Sudanese population: Computational approach.

BACKGROUND Helicobacter pylori (H. pylori) is a ubiquitous bacterium that affects nearly half of the world’s population with a high morbidity and mortality rate. Polymorphisms within the tumor necrosis factor-alpha (TNF-A) promoter region are considered a possible genetic basis for this disease.AIMTo functionally characterize the genetic variations in the TNF-A 5’-region (-584 to +107) of Sudanese patients infected with H. pylori using in silico tools.METHODSAn observational study was carried out in major public and private hospitals in Khartoum state. A total of 122 gastric biopsies were taken from patients who had been referred for endoscopy. Genomic DNA was extracted. Genotyping of the TNF-A-1030 polymorphism was performed using PCR with confronting two-pair primer to investigate its association with the susceptibility to H. pylori infection in the Sudanese population. Furthermore, Sanger sequencing was applied to detect single nucleotide polymorphisms in the 5’-region (-584 to +107) of TNF-A in H. pylori-infected patients. Bioinformatics analyses were used to predict whether these mutations would alter transcription factor binding sites or composite regulatory elements in this region. A comparative profiling analysis was conducted in 11 species using the ECR browser and multiple-sequence local alignment and visualization search engine to investigate the possible conservation. Also, a multivariate logistic regression model was constructed to estimate odds ratios and their 95% confidence intervals for the association between TNF-A-1030, sociodemographic characteristics and H. pylori infection. Differences were statistically significant if P < 0.05. Statistical analyses were performed using Stata version 11 software.RESULTSA total of seven single nucleotide polymorphisms were observed in the TNF-A 5’-region of Sudanese patients infected with H. pylori. Only one of them (T > A, -76) was located at the in silico-predicted promoter region (-146 to +10), and it was predicted to alter transcription factor binding sites and composite regulatory elements. A novel mutation (A > T, +27) was detected in the 5’ untranslated region, and it could affect the post-transcriptional regulatory pathways. Genotyping of TNF-A-1030 showed a lack of significant association between -1030T and susceptibility to H. pylori and gastric cancer in the studied population (P = 0.1756) and (P = 0.8116), respectively. However, a significant association was detected between T/C genotype and H. pylori infection (39.34% vs 19.67%, odds ratio = 2.69, 95% confidence interval: 1.17-6.17, P = 0.020). Mammalian conservation was observed for the (-146 to +10) region in chimpanzee (99.4%), rhesus monkey (95.6%), cow (91.8%), domesticated dog (89.3%), mouse (84.3%), rat (82.4%) and opossum (78%).CONCLUSIONComputational analysis was a valuable method for understanding TNF-A gene expression patterns and guiding further in vitro and in vivo experimental validation.

IMPLEMENTASI TEKNIK LOCAL SEARCH ENGINE OPTIMATION (SEO) MENGGUNAKAN GOOGLE MY BUSSINESS (STUDI KASUS DI TB. JAYA SUKSES)

The development of connectivity and better use of data through the Internet has turned the world into a global village. The innovation of digital service products makes consumers easy to reach through digital marketing. To market a product on the Internet, in addition to creating a creative and content-rich website, another very important thing is to get the website to be at the top of the Search Engine Result Pages (SERPS) of the Search Engines or search engines. SEO tools can be used to increase traffic or traffic to websites so that they can increase the income of an online business. One of the tools from Google that can be used to manage the appearance of a business on Google search pages and Google Maps is Google My Business. This research focuses on implementing Local Search Engine Optimization (SEO) using Google my business and also analyzing the influence of keywords. which positively affects digital marketing. The conclusion of this research is that Google My Business is very effective for local SEO optimization so it is very efficient and economical for funneling or sales funnels in digital marketing. Keywords: Google My Business, Local SEO, Digital Marketing

Pandemic-induced fear and stock market returns: Evidence from China

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.

Hematological Implications of Schistosomiasis: Experimental and Clinical Review

This review aimed to explore the deleterious effects of Schistosoma infection on the blood picture of infected hosts. International and local scientific search engines were navigated from till April 2021 for systematic reviews, clinical trials and experimental studies that address the hematological profile of the schistosomiasis-infected patients. Anemia and eosinophilia are the characteristic hematological findings of schistosomiasis but controversy was found regards the effects of schistosomal infection on other blood cells which may be due to discrepancy in infected hosts, Schistosomes strains, various laboratory methodologies or phase of the disease.

Utilization of Google My Business as a Tourism Promotion Media Using Local Search Engine Optimization

The tourism industry is an industry that continues to grow in the world, including in Indonesia. Likewise, Kebumen Regency has various tourism potentials ranging from water tourism, mountain tourism, culinary tours, cave tours, cultural tourism and others. One of them is Curug Sindaro, which is located in the north of Kebumen Regency which is precisely located in Wadasmalang Village, Karangsambung District, Kebumen Regency, Central Java. However, in the process of developing and managing it, the tour, which was inaugurated on March 3, 2019, is increasingly experiencing a decline in visitors. Which is where, the main problem is that the promotion is still not optimal.&#x0D; In the existing tourism development process, of course, requires the media used to carry out promotions with a good marketing strategy. In order to be easily affordable, the right marketing strategy to use is an online marketing strategy. By analyzing promotional needs and analyzing keywords, it can maximize promotion.&#x0D; The purpose of this research is to implement Local SEO using Google My Business as a promotional medium for Wisata Curug Sindaro which is an online marketing strategy. If the Sindaro waterfall tour is promoted through Google my business, tourists around the Sindaro waterfall will find it easy to find Sindaro waterfall tourism through google and it is hoped that visitors will increase. Because, in this Google My Business has sufficient features for promotion. With the initial stage of registering a Google My Business account, then claiming an existing business. Because previously the Sindaro Curug Tour was already on Google Pages. With several stages of verification and changing the cellphone number that stated the Google My Business account for Sindaro Curug Tourism has been successfully created. However, due to the verification method there are obstacles that have been successfully created is the web page for the Sindaro Waterfall Tour.

Proton pump inhibitors in Iranian population: from clinical regimens to pharmacogenomics

Proton pump inhibitors (PPIs) are one of the highly prescribed or over-the-counter available medications among Iranians, mainly to treat conditions such as helicobacter pylori infection, gastroesophageal reflux disease or frequent heartburn. In recent years, several reports have shown potential adverse effects of PPI administration among which cardiovascular adverse events, myocardial infarction and chronic kidney disease are considered as the greatest risks. Recent addition of proton pump inhibitors to the list of medications on Beers Criteria of Potentially Inappropriate Drugs has arisen significant concerns about their safety. This review aims at providing an up-to date overview of PPIs indications and their pharmacogenomics and pharmacokinetics in Iranian population. The focus of this review is on PPIs regimens in Iranian population and then it is compared with the reported studies performed on other ethnic groups around the world. An extensive review of the literature was carried out and data under various sections were identified using a computerized literature search via Pubmed, Web of Science, Google Scholar and some local search engines. All abstracts and full text articles were examined and most relevant papers were selected for inclusion in this review. Also several expert internalists were interviewed for their clinical experiences in this field.

Publication rates of abstracts presented at the World Congress on Pain held by the International Association for the Study of Pain in 2010.

Objective To examine publication rates of the full texts of abstracts presented at the 2010 World Congress on Pain (WCP) of the International Association for the Study of Pain. Methods Poster presentations published in the abstract booklet of the 13th WCP in 2010 were examined. The post-congress status of each abstract was investigated by searching titles, first author, and co-authors, in order, using local search engines. The year of publication, country, index, impact factor (IF) of the publishing journal, and name and title consistencies between the abstract and published text were recorded. Results A total 1907 poster presentations were investigated. Of these, 525 (27.5%) were published in scientific journals. The most poster presentations (402, 37%) were from the United States. The most published presentations appeared in scientific journals in 2011. Science Citation Index (SCI), SCI Expanded, and Emerging Sources Citation Index published 491 abstracts as full texts. The mean IF of journals in which articles were published was 3.90 ± 3.64. Conclusions As with scientific journals, a communication process should be established with authors during assessment of poster presentations at congresses regarding critical progression and rectification of deficiencies, which will increase the likelihood of presentation abstracts being published.

A Study of a Local Search Engine for Desktop PCs using an Enhanced Boyer-Moore Algorithm

The volume of digital information and documents stored on desktop computers and over the Internet has been rapidly increasing in the past few years. Thus, search algorithms, methods, and programs are becoming popular to help in finding the intended information or files. This study proposes a string-matching algorithm to improve the pattern-matching technique of the traditional Boyer-Moore (BM) search algorithm. The proposed enhanced BM algorithm simultaneously scans the text from both sides (left and right) using two windows wherein each window has a size that is equal to the pattern length.

Are you local? The challenges of local search engine optimisation strategies

Local search engine optimisation (SEO) can be a powerful strategy for small to medium-sized enterprises. Limited studies provide information on these strategies. This paper discusses the approaches used by specialist SEO agencies, and the associated challenges they face. Semi-structured interviews with key informants reveal a range of practical approaches. Challenges identified include the influence of recent search engine algorithm updates resulting in fluctuating ranking factors, as well as the importance of mobile when enacting a local SEO strategy. A better understanding of the nuances of local SEO strategies is provided in order to assist and inform scholars and industry practitioners.

On the efficient computation and use of multi-objective descent directions within constrained MOEAs

Abstract Multi-objective evolutionary algorithms (MOEAs) are a widely accepted choice for the numerical treatment of multi-objective optimization problems (MOPs). For constrained problems, however, these methods still have room for improvement to compute satisfactory approximations of the solution sets. A possible remedy is the hybridization of MOEAs with specialized local search mechanisms; which is not a simple task due to their high cost. In this work, we consider the information of the constraints when performing the local search, and propose a new and effective way to compute descent directions for constrained bi-objective optimization problems. Since the directions are computed via neighborhood sampling, the method is perfectly suited for the use within MOEAs or any other population based algorithm as the samples can be taken precisely from the populations. The new method can be used as local search engine within, in principle, any MOEA. As demonstrator, we will consider two particular hybrids. Numerical results on some benchmark problems support the benefits of the novel approach. Though this work focuses on the bi-objective case, this represents an important step to formalize gradient-free multi-objective descent directions and its efficient interleaving into MOEAs.

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.

The Gradient Subspace Approximation as Local Search Engine within Evolutionary Multi-objective Optimization Algorithms

In this paper, we argue that the gradient subspace approximation (GSA) is a powerful local search tool with in memetic algorithms for the treat mentof multi-objective optimization problems. The GSA utilizes the neighborhood information within the current population in order to compute the best approximation of the gradient at a given candidate solution. The computation of the search direction comes hence for free in terms of additional function evaluations within population based search algorithms such as evolutionary algorithms. Its benefits have recently been discussed in the context of scalar optimization. Here, we discuss and adapt the GSA for the case that multiple objectives have to be considered concurrently. We will further on hybridize line searchers that utilize GSA to obtain the search direction with two different multi-objective evolutionary algorithms. Numerical results on selected bench mark problems indicate thes trength of the GSA-based local search within the evolutionary strategies.

POPMUSIC for the travelling salesman problem

POPMUSIC— Partial OPtimization Metaheuristic Under Special Intensification Conditions — is a template for tackling large problem instances. This metaheuristic has been shown to be very efficient for various hard combinatorial problems such as p-median, sum of squares clustering, vehicle routing, map labelling and location routing. A key point for treating large Travelling Salesman Problem (TSP) instances is to consider only a subset of edges connecting the cities. The main goal of this article is to present how to build a list of good candidate edges with a complexity lower than quadratic in the context of TSP instances given by a general function. The candidate edges are found with a technique exploiting tour merging and the POPMUSIC metaheuristic. When these candidate edges are provided to a good local search engine, high quality solutions can be found quite efficiently. The method is tested on TSP instances of up to several million cities with different structures (Euclidean uniform, clustered, 2D to 5D, grids, toroidal distances). Numerical results show that solutions of excellent quality can be obtained with an empirical complexity lower than quadratic without exploiting the geometrical properties of the instances.

Characterizing and Predicting Users’ Behavior on Local Search Queries

The use of queries to find products and services that are located nearby is increasing rapidly due mainly to the ubiquity of internet access and location services provided by smartphone devices. Local search engines help users by matching queries with a predefined geographical connotation (“local queries”) against a database of local business listings. Local search differs from traditional Web search because, to correctly capture users’ click behavior, the estimation of relevance between query and candidate results must be integrated with geographical signals, such as distance. The intuition is that users prefer businesses that are physically closer to them or in a convenient area (e.g., close to their home). However, this notion of closeness depends upon other factors, like the business category, the quality of the service provided, the density of businesses in the area of interest, the hour of the day, or even the day of the week. In this work, we perform an extensive analysis of online users’ interactions with a local search engine, investigating their intent, temporal patterns, and highlighting relationships between distance-to-business and other factors, such as business reputation, Furthermore, we investigate the problem of estimating the click-through rate on local search ( LCTR ) by exploiting the combination of standard retrieval methods with a rich collection of geo-, user-, and business-dependent features. We validate our approach on a large log collected from a real-world local search service. Our evaluation shows that the non-linear combination of business and user information, geo-local and textual relevance features leads to a significant improvements over existing alternative approaches based on a combination of relevance, distance, and business reputation [1].

A Conceptual Model for Evaluating Impact of the Local Search Engine Projects (LOSEPs) on Sustainable Development based on Sustainability Indicators

A Conceptual Model for Evaluating Impact of the Local Search Engine Projects (LOSEPs) on Sustainable Development based on Sustainability Indicators

Agent-based game theoretic model for block motion estimation and its multicore implementation

Abstract Motion estimation (ME) is one of the main tools employed for eliminating temporal redundancies in video coding. It is the most critical and time-consuming tool of the complete encoder and typically requires 60%–80% of the total computational time. Block-matching ME (BME) algorithms divide a frame into macroblocks (MB) and look for the best possible match in the reference frame. This paper introduces a novel parallel framework to speed up the BME process. This is done by introducing a novel level of parallelism within the MB. The problem of BME is cast in a non-cooperative game-theoretic setting and a distributed multi-agent system is employed to solve the problem. First, a given MB is divided into subblocks and an agent is defined for each subblock. Then, the problem is formulated as a Consensus game and our approximation of the global utility function for the MB is defined. Building on this, agents' utilities are derived so that the resulting game is a potential game. To solve the game, distributed sequential and simultaneous algorithms based on game-theoretic Best Response Dynamics (BRD) and particle swarm optimization (PSO) are presented. Each agent uses PSO as its local search engine to autonomously maximize the utility of its subblock and BRD drive the agents with minimum local communication towards the maximum of the global utility function of the whole MB. Experimental results show that these algorithms provide good estimation quality with low computational cost as compared to other techniques. Moreover, in addition to its decentralized and distributed nature, the simultaneous algorithm is also inherently parallel at the agents' level within the MB. A parallel implementation of this algorithm using the MATLAB Parallel Computing Toolbox™ (PCT) on a multicore system shows that speedup is indeed obtained.